ICAROB2017PROCEEDINGS OF THE 2017
INTERNATIONAL CONFERENCE
ON ARTIFICIAL LIFE AND ROBOTICS
January 19-22, 2017
Seagaia Convention Center, Miyazaki, JAPANInternational Meeting Series
Editor-in-Chief
Masanori Sugisaka
Editors: Yingmin Jia, Takao Ito, Ju-Jang Lee
ISBN 978-4-9908350-2-6

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

Proceedings of The 2017 International Conference on

ARTIFICIAL LIFE AND ROBOTICS(ICAROB2017)

January 19-22, 2017
Seagaia Convention Center, Miyazaki, JAPANInternational Meeting Series
Editor-in-Chief
Masanori Sugisaka
Editors: Yingmin Jia, Takao Ito, Ju-Jang Lee
ISBN 978-4-9908350-2-6©ICAROB 2017 ALife Robotics Corp. Ltd.

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

Contents
1

Organization, etc.

1

2

Messages

93Time Table

14

4

Opening Ceremony

17

5Technical paper index

18

6Abstracts
PS abstracts
IS abstracts
OS abstracts
GS abstracts

40
41
43
83

7

Authors index

1028Conference room

113

6-1
6-2
6-3
6-4

©ICAROB 2017 ALife Robotics Corp. Ltd.

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

SPONSEREDAROB (ALife Robotics Corporation Ltd.)ORGANIZED BYInternational Steering Committee of International Conference on Artificial Life and Robotics(ICAROB)TECHNICAL Co-SPONSORED BYIEEE Fukuoka Section (Japan)

IEEE Robotics and Automation Society (USA)

CO-ORGANIZED BY
University of Miyazaki, Japan©ICAROB 2017 ALife Robotics Corp. Ltd.

-1-The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

Chinese Association for Artificial Intelligence (CAAI, P. R. China)

SUPPORTRD BY
Miyazaki Prefectural Government

ADVISORY COMMITTEE CHAIR
Moshe Kam (New Jersy Institute of Technology, Former IEEE President, USA)ADVISORY COMMITTEE
Adam Grzech (Wroclaw University of Technology, Poland)
Bruce Eisenstein (Drexel University, Former IEEE President, USA)
Fumio Harashima (Former The University of Tokyo, Japan)
Guang-Ren Duan (Harbin Institute of Technology, P.R.China)
Hidenori Kimura (JST, Japan)
Jeffrey Johnson (The Open University, UK)
Jerzy Świątek (Wroclaw University of Technology, Poland)
Joshua M. Epstein (The Johns Hopkins University, USA)
Kai-Tai Song (National Chiao Tung University, Taiwan)
Kazuhiko Terashima (Toyohashi University of Technology, Japan)
Kazuo Kyuma (Mitsubishi Electric Corporation, Japan)
Masayoshi Tomizuka (University of California Berkeley, USA)
Moshe Kam (New Jersey Institute of Technology, Former IEEE President, USA)Paul Kalata (Drexel University, USA)Paul Oh (Drexel University, USA)
Robert Fischl (Drexel University, USA)
Steen Rasmussen (University of Southern Denmark, Denmark)Toshio Fukuda (Meijyo University, Japan)
ZengqiSun (Tsighua University, P.R. China)©ICAROB 2017 ALife Robotics Corp. Ltd.

-2-

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

GENERAL CHAIR
Masanori Sugisaka (Alife Robotics Corporation Ltd., Japan)(Visiting Professor, Open University, UK)
CO-GENERAL CHAIRS
Yingmin Jia (Beihang Univesty, P. R. China)
Takao Ito (Hiroshima University, Japan)
Ju-Jang Lee (KAIST, Korea)
VICE GENERAL CHAIR
Changshui. Zhang (Tinghua University, P. R. China)
Henrik. H. Lund (Technical University of Denmark, Denmark)John. L. Casti (International Institute for Applied Systems Analysis, Austria)
Jangmyung Lee (Pusan National University, Korea)
Luigi Pagliarini (Technical University of Denmark, Denmark)(Academy of Fine Arts of Macerata, Italy)
Mohd Rizon bin Mohamed Juhari (National Culture Arts and Heritage Academy, Malaysia)
Yongguang Zhang (Academia Sinica, P. R. China)
PROGRAM CHAIRMAN
Makoto Sakamoto (University of Miyazaki, Japan)
CO-PROGRAM CHAIR
Marion Oswald (The Vienna University of Technology, Austria)
INTERNATIONAL ORGANIZING COMMITTEE
Akira Fukuda (Kyushu University, Japan)
Eiji Hayashi (Kyushu Institute of Technology, Japan)
Evgeni Magid (Kazan Federal University, Russia)
Hazry Desa (University of Malaysia, Perlis, Malaysia)
Hidehiko Yamamoto (Gifu University, Japan)
Hideyuki Suzuki (The University of Tokyo, Japan)
Hiroki Tamura (The University of Miyazaki, Japan)
Hiroshi Kage ((Mitsubishi Electric Corporation, Japan)
Hiroshi Matsuno (Yamaguchi University, Japan)
Jiwu Wang (Beijing Jiaotong University, P. R. China)©ICAROB 2017 ALife Robotics Corp. Ltd.

-3-

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

Jovana Jovic (CNRS-AIST JRL, Japan, France)
Katsunori Shimohara (Doshisha University, Japan)
Kenichi Tanaka (Meii University, Japan)
Kenji Hashimoto (Waseda University, Japan)
Kevin Voges (Canterbury University, New Zealand)
Kohei Ohtsu (Tokyo University of Marine Science and Technology, Japan)
Kuo-Hsien Hsia (Far East University, Taiwan)
Kuo-Lan Su (National Yunlin University of Science and Technology, Taiwan)
Kyungho Park (Director for Micro-System Programs, RDECOM, ITC-PAC, U.S. ARMY, Japan and USA)
Masao Kubo (National Defense Academy of Japan, Japan)
Masanao Obayashi (Yamaguchi University, Japan)
Mehta Rajiv (New Jersey Institute of Technology, USA)Peter Sapaty (Ukrainian Academy of Science, Ukraine)
Qu Yanbin (Harbin Institute of Technology, P. R. China)
Takashi Kohno (LIMMS/CNRS-IIS, Institute of Industrial Science, The University of Tokyo, Japan)
Takashi Ogata (Iwate Prefectural Univerity)
Teruhisa Hochin (Kyoto Prefectural University, Japan)
Tetsuro Hattori (Kagawa University, Japan)
Thi Thi Zin (University of Miyazaki, Japan)
Thomas S. Ray (University of Oklahoma, USA)
Toru Yamamoto (Hiroshima University, Japan)
Victor Berdonosov (Komsomolsk-on-Amur State University of Technology, Russia)
Yasunari Yoshitomi (Kyoto Prefectural University, Japan)
Yo Horikawa (Kagawa University, Japan)
Yoshifumi Morita (Nagoya Institute of Technology, Japan)
Yoshiro Imai (Kagawa University, Japan)
Yuichi Tanji (Kagawa University, Japan)
INTERNATIONAL PROGRAM COMMITTEE
Akira Nakamura (AIST, Japan)
Ali Selamat (University of Technology of Malaysia (UTM), Malaysia)
Arsit Boonyaprapasorn (Chulachomkloa Royal Military Academy, Thailand)
Bin Fu (Shanghai Jiaotong University, P. R. China)
Dongmei Ai (University of Science and Technology Beijing, P. R. China)
Endra Joelianto (Bandung Institute of Technology, Indonesia)
Fengzhi Dai (Tianjin University of Science & Technology, P. R. China)
Haruna Matsushita (Kagawa University)
Hidetsugu Suto (Muroran Institute of Technology, Japan)
Hiroyuki Iizuka (Osaka University, Japan)
Huailin Zhao (Shanghai Institute of Technology, P. R. China)
Hussein Abbass (University of New South Wales and ADFA, Australia)
Istvan Harmati (Budapest Institute of Technology and Economics, Hungary)©ICAROB 2017 ALife Robotics Corp. Ltd.

-4-

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

Ivan Tanev (Doshisha University, Japan)
Jiandong Zhao (Beijing Jiaotong University, P. R. China)
Jinglu Hu (Waseda University, Japan)
Joono Cheong (Korea University, Korea)
Kathryn Elizabeth Merrick (University of New South Wales and ADFA, Australia)
Kunikazu Kobayashi (Aichi Prefectural University, Japan)
Manabu Yamada (Nagoya Institute of Technology, Japan)
Masahide Ito (Aichi Prefectural University, Japan)
Malachy Eaton (University of Limerick, Ireland)
Masayoshi Kano (Chukyou University, Japan)
Masayoshi Tabuse (Kyoto Prefectural University, Japan
Masaomi Hatakeyama (University of Zurich, Switzerland)
Norrima Mokhtar (University of Malaya, Malaysia)
Noritaka Sato(Nagoya Institute of Technology, Japan)
Palakorn Tantrakool (King Mongkut’s Institute of Technology Thonburi, Thailand)
Satoshi Ikeda (The University of Miyazaki, Japan)
Sanjay S. Joshi (College of Engineering, University of California, USA)
Seong-Ik Han (Pusan National University, Korea)
Shingo Mabu (Yamaguchi University, Japan)
Shyi-Ming Chen (National Taichung University of Education, Taiwan)
Takashi Kuremoto (Yamaguchi University, Japan)
Takayoshi Yamada (Gifu University, Japan)
Taishiro Kishimoto (Keio University, Japan)
Tao Zhang (Tsinghua University, P. R. China)
Takashi Iwamoto (Mitsubishi Electric Corporation, Advanced Technology
R&D Center, Japan)
Tetsuro Katayama (The University of Miyazaki, Japan)
Thunyaseth Sethaput (Thammasat University, Thailand)
Toshinori Nawata (Kumamoto National College of Technology, Japan)
Tsunehiro Yoshinaga (Tokuyama National College of Technology, Japan)
Weicun Zhang (University of Science and Technology Beijing, P. R. China)
Yuanyuan Shang (Capital Normal University, P. R. China)
Yueyue Fan (University of California-Davis, USA)
Young Im Cho (The University of Suwon, Korea)
LOCAL ARRANGEMENT COMMITTEE
Makoto Sakamoto (University of Miyazaki,
Japan) Masanori Sugisaka (ALife Robotics Corporation Ltd., Japan)(Visiting Professor, Open University, UK)
Satoshi Ikeda (University of Miyazaki, Japan)
Takao Ito (Hiroshima University, Japan)
Tetsuro Katayama (University of Miyazaki, Japan)©ICAROB 2017 ALife Robotics Corp. Ltd.

-5-

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

HISTORY
The International Conference on Artificial Life and Robotics (ICAROB) resulted from the AROBsymposium (International Symposium on Artificial Life and Robotics) whose first edition was held in1996 and the eighteenth and last edition in 2013. The AROB symposium was annually organized by
Oita University, Nippon Bunri University (NBU), and ALife Robotics Corporation Ltd., under thesponsorship of the Science and Technology Policy Bureau, the Ministry of Education, Science, Sports,
and Culture (Monbusho), presently, the Ministry of Education, Culture, Sports, Science, andTechnology (Monkasho), Japanese Government, Japan Society for the Promotion of Science (JSPS),
the Commemorative Organization for the Japan World Exposition ('70), Air Force Office of Scientific
Research, Asian Office of Aerospace Research and Development (AFOSR/AOARD), USA. I would liketo express my sincere thanks to not only Monkasho (annually fund support from 1996 to 2013) but
also JSPS, the Commemorative Organization for the Japan World Exposition ('70), and various otherJapanese companies for their repeated support. The old symposium (this symposium has been held
every year at B-Con Plaza, Beppu, Oita, Japan except in Oita, Japan (AROB 5th ’00) and in Tokyo, Japan(AROB 6th ’01).) was organized by the International Organizing Committee of AROB and was cooperated by the Santa Fe Institute (USA), RSJ, IEEJ, ICASE (Now ICROS) (Korea), CAAI (P. R. China),
ISCIE, IEICE, IEEE (Japan Council), JARA, and SICE. The old AROB-symposium expanded much byabsorbing much new knowledge and technologies into it. This history and character of the former
AROB symposiums are passed on the current ICAROB conference and to this journal, International
Journal of Robotics, Networking and Artificial Life (JRNAL). From now on, ALife Robotics Corporation
Ltd. is in charge of management of both the conference and the journal. The future of the ICAROB isbrilliant from a point of view of yielding new technologies to human society in the 21st century. Thisconference invites you all.
AIMS AND SCOPE
The objective of this conference is the development of new technologies for artificial life and robotics
which have been recently born in Japan and are expected to be applied in various fields. Thisconference presents original technical papers and authoritative state-of-the-art reviews on thedevelopment of new technologies concerning robotics, networking and artificial life and, especiallycomputer-based simulation and hardware for the twenty-first century. This conference covers abroad multidisciplinary field, including areas such as:
Artificial intelligence & complexity
Artificial living
Artificial mind research
Artificial nervous systems for robots
Artificial sciences
Bipedal robot
Brain science and computing
Chaos
Cognitive science©ICAROB 2017 ALife Robotics Corp. Ltd.

-6-

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

Computational Molecular biology
Computer graphics
Data mining
Disasters robotics
DNA computing
Empirical research on network and MOT
Environment navigation and localization
Evolutionary computations
Facial expression analysis, music recommendation and augmented reality
Foundation of computation and its application
Fuzzy control
Genetic algorithms
Human-welfare robotics
Image processing
Insect-like aero vehicles
Intelligence in biological systems
Intelligent control
Management of technology
Medical surgical robot
Micro-machines
Multi-agent systems
Nano-biology
Nano-robotics
Networking
Neural circuits
Neuro-computer
Neuromorphic Systems
Neuroscience
Pattern recognition
Quantum computing
Reinforcement learning system & genetic programing
Robotics
Software development support method
System cybernetics
Unmanned underwater vehicles
Unmanned Aerial Systems Technologies
Unmanned Aerial Systems designing, controls and navigation
Unmanned Aero vehicles
Virtual reality
Visualization
Hardware-oriented submissions are particularly welcome. This conference will discuss new
results in the field of artificial life and robotics©ICAROB 2017 ALife Robotics Corp. Ltd.

-7-

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

COPYRIGHTS
Accepted papers will be published in the proceeding of The 2017 International Conference on
Artificial Life and Robotics (ICAROB 2017) by ALife Robotics Corp. Ltd. Copyright belongs to ALife
Robotics Corp. Ltd. Some of high quality papers in the proceeding will be requested to re-submit theirpapers for the consideration of publication in an international journal ROBOTICS, NETWORKING AND
ARTIFICIAL LIFE under agreement of both Editor-in- Chief Dr. Masanori Sugisaka and 3 reviewers. All
correspondence related to the conference should be addressed to ICAROB Office.

ICAROB Office
ALife Robotics Corporation Ltd.
3661-8 Oaza Shimohanda, Oita 870-1112, JAPANTEL/FAX：+81-97-597-7760
E-MAIL：icarob@alife-robotics.co.jp
Home Page：http://alife-robotics.co.jp/©ICAROB 2017 ALife Robotics Corp. Ltd.

-8-

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

MESSAGES
Masanori Sugisaka
General Chair of ICAROB

Masanori Sugisaka
General Char
(Professors, Open University(UK), University of Malaysia-Peris(Malaysia) and President of Alife
Robotics Co., Ltd. (Japan))

It is my great honor to invite you all to The 2017 International Conferenceon Artiﬁcial Life and Robotics (ICAROB 2017).
This Conference is changed as the old symposium from the ﬁrst (1996) tothe Eighteenth (2013) annually which were organized by Oita University,
Nippon Bunri University(NBU), and ALife Robotics Corporation Ltd. under
the sponsorship of the Science and Technology Policy Bureau, the Ministryof Education, Science, Sports, and Culture (Monbusho), presently, theMinistry of Education, Culture, Sports, Science, and Technology(Monkasho), Japanese Government, Japan Society for the Promotion of
Science (JSPS), The Commemorative Organization for the Japan World
Exposition (’70), Air Force Oﬃce of Scientiﬁc Research, Asian Oﬃce ofAerospace Research and Development (AFOSR/AOARD), USA. I would liketo express my sincere thanks to not only Monkasho (annually fund support
from 1996 to 2013) but also JSPS, the Commemorative Organization for theJapan World Exposition (’70) , Japanese companies for their repeatedsupport.
The old symposium was organized by International Organizing Committeeof AROB and was co-operated by the Santa Fe Institute (USA), RSJ, IEEJ,ICASE (Now ICROS) (Korea), CAAI (P. R. China), ISCIE, IEICE, IEEE (Japan
Council), JARA, and SICE. The old AROB symposium was growing up by
absorbing many new knowledge and technologies into it.
This history and character was inherited also from ICAROB 2014(The 2014
International Conference on Artiﬁcial Life and Robotics, included a series of
ICAROB proceedings in SCOPUS and CPCI now. From now on, ALife
Robotics Corporation Ltd. is in charge of management. This year we have
The 2017 International Conference on Artiﬁcial Life and Robotics(ICAROB2017) (22nd AROB Anniversary). The future of The ICAROB isbrilliant from a point of view of yielding new technologies to human society
in 21st century.
I hope that fruitful discussions and exchange of ideas between researchers
during Conference (ICAROB2017) will yield new merged technologies forhappiness of human beings and, hence, will facilitate the establishment of
an international joint research institute on Artiﬁcial Life and Robotics infuture.

©ICAROB 2017 ALife Robotics Corp. Ltd.

-9-

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017
Yingmin Jia
Co-General Chair of ICAROB

Yingmin Jia
Co-General Chair(Professor, Beihang University,
R .P. China)

It is my great pleasure to invite you to The 2017 International Conferenceon Artiﬁcial Life and Robotics (ICAROB 2017), in Miyazaki, Japan from Jan.
19th to 22nd, 2017.
ICAROB develops from the AROB that was created in 1996 by Prof.
Masanori Sugisaka and will celebrate her birthday of 22nd years old in2017. Doubtless, new mission and big challenges in the ﬁeld of artiﬁcial lifeand robotics will promote ICAROB to start a new stage and attract wide
interests among scientist, researchers, and engineers around the world.
For a successful meeting, many people have contributed their great eﬀorts
to ICAROB. Here, I would like to express my special thanks to all authors
and speakers, and the meeting organizing team for their excellent works.
Looking forward to meeting you at ICAROB in Miyazaki and wishing you
enjoy your stay in Japan.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 10 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017
Takao Ito
Co General Chair of ICAROB

Takao Ito
Co-General Chair(Professor Hiroshima
University, Japan)

It is my great honor to invite you all to The 2017 International Conferenceon Artiﬁcial Life and Robotics (ICAROB 2017). This Conference is changed
as the old symposium from the ﬁrst (1996) to the Eighteenth. I am pleased
to welcome you to The 2017 International Conference on Artiﬁcial Life andRobotics in the wonderful city of Miyazaki City, Miyazaki Prefecture, Japan.
The ICAROB has its long history. The former organization of the ICAROB wasdeveloped under the strong leadership of the President, famous
Professor Masanori Sugisaka, the father of AROB. We gathered many
researchers, faculty members, graduate students from all over the world,and published many high-quality proceedings and journals every year.
Over the years, dramatic improvements have been made in the ﬁeld of
artiﬁcial life and its applications. The ICAROB has becoming the unifying theexchange of scientiﬁc information on the study of man-made systems thatexhibit the behavioral characteristic of natural living systems including
software, hardware and/or wetware. Our conference shapes thedevelopment of artiﬁcial life, extending our empirical research beyond theterritory circumscribed by life-as-we-know-it and into the domain of lifeas-it-could–be. It will provide us a good place to present our new research
results, excellent ideas, and valuable information about artiﬁcial
intelligence, complex systems theories, robotics, management oftechnology, etc.
The conference site is Seagaia Convention Center, one of the most famous
resort hotels in Japan. You can ﬁnd many fantastic scenic spots andsplendid historical places in Miyazaki city. Enjoy your stay and take yourtime to visit the city of Miyazaki.
I am looking forward to meeting you in Miyazaki during the ICAROB 2017
and to sharing a most pleasant, interesting and fruitful conference.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 11 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017
Ju-Jang Lee
Co-General Chair of ICAROB

Ju-Jang Lee
Co-General Chair(Professor, KAIST)The First International Conference on Artiﬁcial Life and Robotics (ICAROB)
was held in Oita City, Oita, Japan from Jan. 11th to 13th, 2014. This
year’s Conference will be held amidst the high expectation of theincreasingly important role of the new interdisciplinary paradigm ofscience and engineering represented by the ﬁeld of artiﬁcial life androbotics that continuously attracts wide interests among scientist,
researchers, and engineers around the globe.
Distinguished researchers and technologists from around the world arelooking forward to attending and meeting at ICAROB. ICAROB is becoming
the annual excellent forum that represents a unique opportunity for theacademic and industrial communities to meet and assess the latest
developments in this fast growing artiﬁcial life and robotics ﬁeld. ICAROB
enables them to address new challenges, share solutions, discussresearch directions for the future, exchange views and ideas, view theresults of applied research, present and discuss the latest developmentof new technologies and relevant applications.
In addition, ICAROB oﬀers the opportunity of hearing the opinions ofwell-known leading experts in the ﬁeld through the keynote sessions,provides the bases for regional and international collaborative research,and enables to foresee the future evolution of new scientiﬁc paradigms
and theories contributed by the ﬁeld of artiﬁcial life and robotics andassociated research area. The twenty-ﬁrst century will become thecentury of artiﬁcial life and intelligent machines in support of humankind
and ICAROB is contributing through wide technical topics of interest that
support this direction.
It is a great honor for me as a Co-General Chair of the 4th ICAROB 2017
to welcome everyone to this important event. Also, I would like to extend
my special thanks to all authors and speakers for contributing theirresearch works, the participants, and the organizing team of the 4th
ICAROB.
I’m looking forward to meeting you at the 4th ICAROB in Miyazaki City,
Miyazaki Prefecture and wishing you all the best.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 12 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

GENERAL SESSION TOPICS
GS1 Artificial Neural Network & Bio-Signal Controlled
Robotics (3)GS3 Automated Guided Vehicles II (3)GS5 Filtering & Control Systems (2)GS7 Micro-Machines & Robotics I (6)GS9 Neuromorphic Robotic Systems (5)GS11 Others (12)GS2 Automated Guided Vehicles I (6)GS4 Biological Systems (4)GS6 Human-Welfare Robotic System & Medical
Application (5)GS8 Micro-Machines & Robotics II (3)GS10 Reinforcement & Evolutionary Computations (3)ORGANIZED SESSION TOPICS
OS1 Informational Narratology and AutomatedContent Generation (5)OS2 Intelligence Control Systems and Applications I (6)OS3 Intelligence Control Systems and Applications II(6)
OS5 Human Interface and Content Security (5)OS7 Advances in Marine Robotics and Applications (6)OS9 Theory and Implementation of Neuromorphic
Systems (5)OS11 Robot Control and Localization (4)OS13 Intelligent Control (6)OS15 Recognition and Control (8)OS17 Natural Computing and Biology (4)OS19 Kansei Engineering and Applications (4)OS21 Computer Science and Information Processing(5)

OS4 Human-In-The-Loop (HITL) Systems (4)©ICAROB 2017 ALife Robotics Corp. Ltd.

OS6 Software Development Support Method (6)OS8 Multiagent systems and Reality Mining (4)OS10 Biological Signal Sensing Technology, Device and
Its Applications (5)OS12 Machine Learning and Its Applications (4)OS14 Advanced Control (5)OS16 Image Recognition and Chaotic Systems (8)OS18 Advanced Management and Technology (4)OS20 Image Processing and Computer Graphics (6)OS22 Robotic Technology for Competition (4)- 13 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

1/19(Thu.) 17:30-19:30

Welcome Party (Conference Site: Gibraltar)

1/19(Thu.) - 1/22(Sun.)ICAROB Secretariat

1/22(Sun.) 16:45-17:15

Farewell Party (Conference Site: Gibraltar)

TIME TABLE (1/20)
1/20(Fri.)

Fountain Room (A1) 2nd F

8:409:00-10:15

OS14 Advanced Control (5)Orchard Room (A2) 2nd F, N
Registration
GS6 Human-Welfare
Robotic System & Medical
Application (5)Orchard Room (A3) 2nd F, S
OS15 Recognition andControl (8) & OS16 Image
Recognition and Chaotic
Systems (8)10:15-10:30

Coffee break

10:30-11:00

Opening Ceremony (Fountain Room (A1) 2nd F)

11:10-12:10

Chair: Takao Ito
Invited session IS-2, IS-4 (Fountain Room (A1) 2nd F)
Henrik Hautop Lund

12:10-13:10

Lunch

13:10-13:50

Chair: Jangmyung Lee
Invited session IS-1 (Fountain Room (A1) 2nd F)
Luigi Pagliarini

13:50-14:10

Invited session IS-3 (Fountain Room (A1) 2nd F)
Ismael Baira Ojeda

14:10-14:30
14:30-16:00

OS3 IntelligenceControl Systems andApplications (II) (6)16:00-16:20
16:20-17:50

OS2 IntelligenceControl Systems andApplications (I) (6)©ICAROB 2017 ALife Robotics Corp. Ltd.

Coffee break
OS5 Human Interface andContent Security (5)Coffee break
OS9 Theory and
Implementation of
Neuromorphic Systems (5)- 14 -

GS7 Micro-Machines &Robotics I (6)OS7 Advances in Marine
Robotics and Applications (6)The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

TIME TEBLE (1/21)
1/21(Sat.)

Fountain Room (A1) 2nd F

8:409:00-11:00

Registration
GS2 Automated Guided
Vehicles I (6)OS6 Software
Development Support
Method (6)11:00-11:15
11:15-12:00

Orchard Room (A2) 2nd F, N

Orchard Room (A3) 2nd F, S
OS4 Human-In-The-Loop(HITL) Systems (4) & OS11
Robot Control andLocalization (4)Coffee break
GS5 Filtering & Control

GS8 Micro-Machines &GS1 Artificial Neural

Systems (2)Robotics II (3)Network and Bio-Signal
Controlled Robotics (3)12:00-13:00

Lunch

13:00-14:00

Chair: Yingmin Jia
Plenary Speech PS1(Fountain Room (A1) 2nd F)
Kazuo Ishii

14:00-14:20
14:20-15:50

Coffee breakOS20 Image Processing
and Computer Graphics (6)15:50-16:10
16:10-17:25

OS13 Intelligent Control (6)OS8 Multiagent systems andReality Mining (4)Coffee break
GS9 Neuromorphic Robotic
Systems (5)18:00-20:00©ICAROB 2017 ALife Robotics Corp. Ltd.

OS22 Robotic Technology forCompetition (4)Banquet: Tenzui (4th F)- 15 -

GS4 Biological Systems (4)The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

TIME TABLE (1/22)
1/22(Sun.)

Fountain Room (A1) 2nd F

8:509:10-10:25

OS10 Biological Signal
Sensing Technology, Device
and Its Applications (5)Orchard Room (A2) 2nd F, N
Registration
OS19 Kansei Engineeringand Applications (4)10:25-10:40

Coffee break

10:40-11:40

Chair: Makoto Sakamoto

Orchard Room (A3) 2nd F, S
OS1 Informational
Narratology and AutomatedContent Generation (5)Plenary Speech PS2 (Fountain Room (A1) 2nd F)
Tomoyuki Nishita
11:40-13:00

Lunch

13:00-13:50

Chair: Takao Ito
Invited session IS-5 (Fountain Room (A1) 2nd F)Peter Sapaty

13:50-14:10
14:10-15:10

Coffee break
OS17 Natural Computing and
Biology (4)OS18 Advanced
Management andTechnology (4)15:10-15:30
15:30-16:45

OS12 Machine Learning
and Its Applications (4)Coffee break
GS10 Reinforcement &Evolutionary Computations(3)

GS3 Automated Guided
Vehicles II (3)Farewell Party (16:45-17:15)©ICAROB 2017 ALife Robotics Corp. Ltd.

- 16 -

OS21 Computer Scienceand Information Processing(5)The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

The 2017 International Conference on
ARTIFICIAL LIFE AND ROBOTICS(ICAROB2017)
January 19 (Thursday)
17:30-19:30

Welcome Party (Conference Site: Gibraltar 2nd F)January 20 (Friday)
10:30-11:00

Opening Ceremony (Fountain Room (A1) 2nd F)

Chair: Marion Oswald (The Vienna University of Technology, Austria)
Welcome Addresses
1. General Chairman of ICAROB

Masanori Sugisaka (ALife Robotics Corporation Ltd.
Japan)
2. Co-General Chairman of ICAROB Yingmin Jia (Beihang University, China)
3. Co-General Chairman of ICAROB TaKao Ito (Hiroshima University, Japan)
4. Vice General Chair of ICAROB
Henrik Hautop Lund (Technical University of Denmark,Denmark)
5. Vice General Chair of ICAROB
Jangmyung Lee (Pusan National University, South
Korea)

January 21 (Saturday)
Banquet: Tenzui (4th F)

18:00-20:00
Chair: T. Ito (Hiroshima University, Japan)
Welcome Addresses
Prof. Yingmin Jia (Beihang University, P.R. China.)
Prof. Jang-Myung Lee (Pusan National University, South Korea)
Prof. Marion Oswald (The Vienna University of Technology, Austria)
Prof. Saori Iwanaga (Japan Coast Guard Academy, Japan)©ICAROB 2017 ALife Robotics Corp. Ltd.

- 17 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

TECHNICAL PAPER INDEXJanuary 20 (Friday)
08:40-Registration
Fountain Room (A1) 2nd F
9:00-10:15 OS14 Advanced Control (5)Chair: Yingmin Jia (Beihang University, P.R. China)
Co-Chair: Weicun Zhang (University of Sciences and Technology Beijing, P.R. China)
OS14-1

Targeting Chaos System via Minimum Principle Control
Yunzhong Song, Ziyi Fu, Fuzhong Wang (Henan Polytechnic University, P.R.China,)

OS 14-2

Three-dimensional Leader-Follower Formation Flocking of Multi-Agent System
Yongnan Jia, Weicun Zhang (University of Sciences and Technology Beijing, China)

OS14-3

Leader-follower Formation Control of Mobile Robots with Sliding Mode
Wenhao Zheng and Yingmin Jia ( Beihang University, P.R.China)

OS14-4

H  Containment Control for Nonlinear Multi-agent Systems with Parameter
Uncertainties and Communication Delays
Ping Wang1, Yingmin Jia2 (1 North China Electric Power University, 2Beihang University, P.R.China)

OS14-5

Stochastic Resonance in an Array of Dynamical Saturating Nonlinearity with Second-Order
Yumei Ma, Lin Zhao, Zhenkuan Pan and Jinpeng Yu*(Qingdao University , P.R.China)

10:30-11:00 Opening Ceremony
Chair: Marion Oswald (The Vienna University of Technology, Austria)
11:10-12:10
Invited session IS-2, IS-4
Chair: Takao Ito (Hiroshima University, Japan)
IS-2 Playware ABC: Engineering Play for Everybody
Henrik Hautop Lund (Technical University of Denmark, Denmark)IS-4 Playware ABC2: a Disruptive Technology for Global Development
Henrik Hautop Lund (Technical University of Denmark, Denmark)©ICAROB 2017 ALife Robotics Corp. Ltd.

- 18 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 201713:10-13:50
Invited session IS-1
Chair: Jang-Myung Lee (Pusan Na

nal University, South Korea)

IS-1 The future of Robo cs Technology.
Luigi Pagliarini 1,2, Henrik Hautop Lund 1 (1 Technical University of Denmark, Denmark, 2 Academy ofFine Arts of Macerata, Italy)
13:50-14:10
Invited session IS-3
Chair: Jang-Myung Lee (Pusan Na

nal University, South Korea)

IS-3 A combina on of Machine Learning and Cerebellar models for the Motor Control andLearning of a Modular Robot
Ismael Baira Ojeda, Silvia Tolu, Moisés Pachecho, David Johan Christensen and Henrik Hautop Lund
(Technical University of Denmark, Denmark)14:30-16:00 OS3 Intelligence Control Systems and Applica ons II (6)Chair: Chian C. Ho (Na onal Yunlin University of Science & Technology, Taiwan)
Co-Chair: Chia-Nan Ko (Nan Kai University of Technology, Taiwan)
OS3-1

Image Compression Using Hybrid Evolu n Based Takagi-Sugeno Fuzzy Neural Network
1
Chia-Nan Ko and 2Ching-I Lee (1,2 Nan Kai University of Technology, Taiwan)

OS3-2

MQPSO Algorithm Based Fuzzy PID Control for a Pendubot System
1
Li-Chun Lai, 2Yu-Yi Fu, and 3Chia-Nan Ko (1Na onal Pingtung University, 2,3Nan Kai University ofTechnology, Taiwan)

OS3-3

A Sensorless Ultra-High Speed Motor Driver
Chung-Wen Hung, Yan-Ting Yu, Bo-Kai Huang, Wei-Lung Mao (Na onal Yunlin University of
Science and Technology, Taiwan)

OS3-4

Android-Based Patrol Robot Featuring Autoc Vehicle Patrolling and Aut
Recogn on
Chian C. Ho, Shih-Jui Yang, Jian-Yuan Chen, Chang-Yun Chiang, and Hsin-Fu Chen
(Na onal Yunlin University of Science and Technology, Taiwan)

OS3-5

Adap

CMAC Filter for Cha

c Time Series Predi on

Technology, Taiwan)©ICAROB 2017 ALife Robotics Corp. Ltd.

- 19 -c PlateThe 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS3-6

Surface Defect Detection for Anodized Aluminum Tube Based on Automatic Optical Inspection
Hsien-Huang P. Wu and Hsuan-Min Sun(National Yunlin University of Science and Technology, Taiwan)

16:20-17:50
OS2 Intelligence Control Systems and Applications I (6)Chair: Kuo-Hsien Hsia (Far East University, Taiwan)
Co-Chair: Kuo-Lan Su (National Yunlin University of Science & Technology, Taiwan)
OS2-1

Develop Low Cost IoT Module with Multi-Agent Method
Jr-Hung Guo, Kuo-Hsien Hsia, Kuo-Lan Su(National Yunlin University of Science and Technology, Taiwan)

OS2-2

Based on Short Motion Paths and Artificial Intelligence Method for Chinese Chess Game
Chien-Ming Hung, Jr-Hung Guo, Kuo-Lan Su(National Yunlin University of Science and Technology, Taiwan)

OS2-3

Design and Implementation of the SCARA Robot Arm
Jian-Fu Weng, Bo-Yi Li, Kuo-Lan Su(National Yunlin University of Science and Technology, Taiwan)

OS2-4Transmission Power Control for Wireless Sensor Network
Kuo-Hsien Hsia1, Chung-Wen Hung2, Hsuan T. Chang, Yuan-Hao Lai2
(1Far East University, Taiwan, 2National Yunlin University of Science and Technology, Taiwan)

OS2-5

Mechanism of Autonomous Mowing Robot for Long Grass
Kuo-Hsien Hsia1, Yao-Shing Huang2, Kuo-Lan Su2 and Jr-Hung Guo2
(1Far East University, Taiwan, 2National Yunlin University of Science and Technology, Taiwan)

OS2-6

Design of Optimal Position Controller for Three-Phase Brushless DC Motor Applying Adaptive
Sliding Mode Control
Tai-Huan Tsai, Mei-Yung Chen (National Taiwan Normal University, Taiwan)

Orchard Room (A2) 2nd F, N
9:00-10:15 GS6 Human-Welfare Robotic System & Medical Application (5)Chair:
GS6-1

Virtual surgery system with realistic visual effects and haptic interaction
Vlada Kugurakova, Murad Khafizov, Ruslan Akhmetsharipov, Alexei Lushnikov, Diana Galimova,
Vitaly Abramov (Kazan Federal University, Russia),
Omar Correa Madrigal (University of Informatic Sciences, Cuba)©ICAROB 2017 ALife Robotics Corp. Ltd.

- 20 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

GS6-2

A Human Reaching Movement Model for Myoelectric Prosthesis Control
Go Nakamura*1, 5, Taro Shibanoki*2, Yuichiro Honda*1, Futoshi Mizobe*3, Akito Masuda*4,
Takaaki Chin*1, Toshio Tsuji*5, *1(Robot Rehabilitation Center in The Hyogo institute of Assistive
Technology, Japan), *2(Ibaraki University, Japan), *3(Hyogo Rehabilitation Center, Japan)*4(Kinki Gishi Corporation, Japan), *5(Hiroshima University, Japan)

GS6-3

Re-creation of a membrane puncture’s sense of an object constituted of liquid and an outermembrane by a haptic device and a deformation simulation of the virtual objects
Takahiro Okada, Eiji Hayashi (Kyusyu Institute of Technology, Japan)

GS6-4

Exercise classiﬁcation using CNN with image frames produced from time-series motion data
Hajime Itoh, Naohiko Hanajima, (Muroran Institute of Technology, Japan), Yohei Muraoka,
Makoto Ohata, (Steel Memorial Muroran Hospital, Japan), Masato Mizukami, Yoshinori Fujihira,(Muroran Institute of Technology, Japan)

GS6-5

Proposal and Evaluation of the Gait Classiﬁcation Method using Arm Acceleration Data andDecision Tree
Kodai Kitagawa, Yu Taguchi, Nobuyuki Toya
(National Institute of Technology, Kushiro Collage, Japan)

14:30-16:00 OS5 Human Interface and Content Security (5)Chair: Yasunari Yoshitomi (Kyoto Prefectural University, Japan)
Co-Chair: Masayoshi Tabuse (Kyoto Prefectural University, Japan)
OS5-1

A Method for Secure Communication Using a Discrete Wavelet Transform for Audio Dataand Improvement of Speaker Authentication
Kouhei Nishimura, Yasunari Yoshitomi, Taro Asada, and Masayoshi Tabuse(Kyoto Prefectural University, Japan)

OS5-2

A Recipe Decision Support System Using Knowledge Information and Agent
Keita Saito, Taro Asada, Yasunari Yoshitomi, Ryota Kato, and Masayoshi Tabuse(Kyoto Prefectural University, Japan)

OS5-3

A System for Analyzing Facial Expression and Verbal Response of a Person While Answering
Interview Questions on Video
Taro Asada, Yasunari Yoshitomi, and Masayoshi Tabuse (Kyoto Prefectural University, Japan)

OS5-4

Real-Time System for Horizontal Asymmetry Analysis on Facial Expression and Its
Visualization
Ryoichi Shimada, Taro Asada, Yasunari Yoshitomi, and Masayoshi Tabuse(Kyoto Prefectural University, Japan)©ICAROB 2017 ALife Robotics Corp. Ltd.

- 21 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS5-5

Development of Mouse System for Physically Disabled Person by Face Movement Using
KinectJunpei Miyachi, Masayoshi Tabuse (Kyoto Prefectural University, Japan)

16:20-17:35 OS9 Theory and Implementation of Neuromorphic Systems (5)Chair: Takashi Kohno (The University of Tokyo, Japan)
Co-Chair: Takuya Nanami (The University of Tokyo, Japan)
OS9-1

Implementation of Multi-FPGA Communication using Pulse-Coupled Phase Oscillators
Dinda Pramanta, Takashi Morie, Hakaru Tamukoh (Kyushu Institute of Technology, Japan)

OS9-2

Multi-Valued Quantization Convolutional Neural Networks toward Hardware Implementation
Yoshiya Aratani, Yoeng Jye Yeoh, Daisuke Shuto, Takashi Morie, Hakaru Tamukoh(Kyushu Institute of Technology, Japan)

OS9-3

An Improved Parameter Value Optimization Technique for the Reflectionless Transmission-Line
Model of the Cochlea
Takemori Orima and Yoshihiko Horio (Tohoku University, Japan)

OS9-4

A parameter optimization method for Digital Spiking Silicon Neuron model
Takuya Nanami and Takashi Kohno (The University of Tokyo, Japan)

OS9-5

A Multistage Heuristic Tuning Algorithm for an Analog Silicon Neuron Circuit
Ethan Green and Takashi Kohno (The University of Tokyo, Japan)

Orchard Room (A3) 2nd F, S
9:00-10:15 OS15 Recognition and Control (8)Chair: Fengzhi Dai (Tianjin University of Science and Technology, China)
Co-Chair: Hongtao Zhang (Tianjin University of Science and Technology, China)
OS15-1

Integral Design of Intelligent Home Equipment
Yuxing Ouyang 1, Fengzhi Dai 1*, Yiqiao Qin 1, Ce Bian 1, Bo Liu 2, Hongwei Jiao 3 (1 Tianjin Universityof Science & Technology, 2 Inner Mongolia University, 3 Tianjin Technology School of Printing and
Decoration, China)

OS15-2

Research on Underwater Robot Recognition
Binhu Song1, Fengzhi Dai1*, Qijia Kang1, Haifang Man1, Hongtao Zhang1, Long Li2, Hongwei Jiao3(1 Tianjin University of Science and Technology, 2 Tianjin Electric Locomotive Co., Ltd, 3 Tianjin
Technology School of Printing & Decoration, China)©ICAROB 2017 ALife Robotics Corp. Ltd.

- 22 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS15-3

Design of Intellectual Vehicles with Path Memorizing Function
Yiqiao Qin 1, Fengzhi Dai 1*, Yuxing Ouyang 1, Baochang Wei 1, Simini Chen 2, Hongwei Jiao 3(1 Tianjin University of Science & Technology, 2 Palace Museum, 3 Tianjin Technology School ofPrinting & Decoration, China)

OS15-4

Action Recognition based on Binocular Vision
Yiwei Ru 1,2,*, Hongyue Du 1, Shuxiao Li 2, Hongxing Chang 2 (1 Harbin University of Science andTechnology, China, 2 Institute of Automation Chinese Academy of Sciences, China)

OS15-5

Analysis and Control of a Novel 4D Chaotic System
Hong Niu (Tianjin University of Science and Technology, China)

OS15-6

Analysis of a three-dimensional chaotic system and its FPGA implementation
Hefei Li *, Xianghui Hu (Tianjin University of Science and Technology, China)

OS15-7

Image Encryption Based on Fractional-order Chaotic Model of PMSM
Wei Xue *, Mei Zhang, Shilong Liu, Xue Li (Tianjin University of Science and Technology, China)

OS15-8The Application of a Novel Fractional Hyper-chaotic in Image Encryption
Wei Xue *, Shilong Liu, Mei Zhang, Xue Li (Tianjin University of Science and Technology, China)

OS16 Image Recognition and Chaotic Systems (8)Chair: Huailin Zhao (Shanghai Institute of Technology, China)
Co-Chair: Shunzhou Wang (Shanghai Institute of Technology, China)
OS16-1

A Method of Detecting Abnormal Crowd Behavior Events Applied in Patrol Robot
Huailin Zhao, Shunzhou Wang, Shifang Xu, Yani Zhang (Shanghai Institute of Technology, China),
Masanori Sugisaka (ALife Robotics Corp. Ltd, Japan)

OS16-2

Design of the Multi-Car Collaboration System
Huailin Zhao, Yangguang Guo (Shanghai Institute of Technology, China)
Masanori Sugisaka (ALife Robotics Corp. Ltd, Japan)

OS16-3

Research on an Algorithm of the Character Recognition with Self-learning the Recognition Errors
Huailin Zhao, Yawei Hou, Shifang Xu, Congdao Han (Shanghai Institute of Technology, China),
Masanori Sugisaka (ALife Robotics Corp. Ltd, Japan)

OS16-4

An Improved Method of the Power System Short Term Load Forecasting Based on the Neural
Network
Shunzhou Wang, Huailin Zhao, Yani Zhang, Peng Bai (Shanghai Institute of Technology, China)

OS16-5

Improvement on LEACH Agreement of Mine Wireless Communication Network
Liu Yun-xiang and Zhang Wei (Shanghai Institute of Technology, China)©ICAROB 2017 ALife Robotics Corp. Ltd.

- 23 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS16-6

A New Four-Wing Chaotic System Generated by Sign Function
Hongyan Jia, Shanfeng Wang, Yongjun Wu (Tianjin University of Science and Technology, China)

OS16-7A Three-Dimensional Chaotic System Generating Single-wing or Two-Wing Chaotic Attractors
Hongyan Jia, Yongjun Wu, Shanfeng Wang (Tianjin University of Science and Technology, China)

OS16-8Circuit implementation of a new fractional-order hyperchaotic system
Xuyang Wu, Hongyan Jia, Ning Bai, Weibo Jia(Tianjin University of Science and Technology, China)

14:30-16:00 GS7 Micro-Machine & Robotics I (6)Chair:
GS7-1

Estimation and Categorization of Errors in Error Recovery Using Task Stratiﬁcation and Error
Classiﬁcation
Akira Nakamura*1, Kazuyuki Nagata*1, Kensuke Harada*2 and Natsuki Yamanobe*1(*1 National Institute of Advanced Industrial Science and Technology (AIST),*2Osaka University, Japan)

GS7-2

The Suitable Timing of Visual Sensing in Error Recovery Using Task Stratiﬁcation and Error

GS7-3

Hexapod Type MEMS Microrobot Equipped with an Artiﬁcial Neural Networks IC
Kazuki Sugita, Taisuke Tanaka, Yuya Nakata, Minami Takato, Ken Saito, Fumio Uchikoba(Nihon University, Japan)

GS7-4

Heat Distribution of Current Output Type Artiﬁcial Neural Networks IC for the MEMS
Microrobot
Taisuke Tanaka, Yuya Nakata, Kazuki Sugita, Minami Takato, Ken Saito, Fumio Uchikoba(Nihon University, Japan)

GS7-5

AGV with Mind and its production simulations for autonomous decentralized FMSs
Masato Chikamatsu, Hidehiko Yamamoto, Takayoshi Yamada (Gifu University, Japan)

GS7-6

UNARM System to Decide Units Locations of Cell-type Assembly Machines with Robots
Arms
Hirotaka Moribe, Hidehiko Yamamoto and Takayoshi Yamada (Gifu University, Japan)

Classiﬁcation
Akira Nakamura*1, Kazuyuki Nagata*1, Kensuke Harada*2 and Natsuki Yamanobe*1(*1 National Institute of Advanced Industrial Science and Technology (AIST),*2Osaka University, Japan)©ICAROB 2017 ALife Robotics Corp. Ltd.

- 24 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 201716:20-17:50 OS7 Advances in Marine Robotics and Applications (6)Chair: Kazuo Ishii (Kyushu Institute of Technology, Japan)
Co-Chair: Keisuke Watanabe (Tokai University, Japan)
OS7-1

Development of a Hydraulic Underwater Manipulator for Deep-Sea Survey AUV
Kazuo Ishii, Amir Ali Forough Nassiraei, Ivan Godler, Takashi Sonoda, Tharindu Weerakoon(Kyushu Institute of Technology, Japan)

OS7-2

Experiments of ﬂoatable UAV drone for wave dissipating block inspection(I)
Keisuke Watanabe, Kazuho Mitsumura, Koshi Utsunomiya, Shiyun Takasaki(Tokai University, Japan)

OS7-3

Experiments of ﬂoatable UAV drone for wave dissipating block inspection(II)Keisuke Watanabe, Kazuho Mitsumura, Koshi Utsunomiya, Shiyun Takasaki(Tokai University, Japan)

OS7-4

Development of self-diagnostic system of an autonomous underwater vehicle Tuna-Sand 2
Naoya Fujii, Yuya Nishida, Kazuo Ishii (Kyushu Institute of Technology, Japan)

OS7-5

Development of Underwater Wireless Power Supply System Using resonant energy transfer
Kazuo Ishii, Hidaka Shota, Keisuke Watanabe(Kyushu Institute of Technology, Tokai University, Japan)

OS7-6

Development of End-eﬀector for Sampling-AUV “TUNA-SAND2”
Kazuo Ishii, Takashi Sonoda, Atsushi Nishjima, Keisuke Watanabe(Kyushu Institute of Technology, Tokai University, Japan)

January 21 ( Saturday)
08:40-Registration
Fountain Room (A1) 2nd F
9:00-10:30 OS6 Software Development Support Method (6)Chair: Tetsuro Katayama (University of Miyazaki, Japan)
Co-Chair: Tomohiko Takagi (Kagawa University, Japan
OS6-1

Development of a Tool for Extended Place/transition Net-Based Mutation Testing
Tomohiko Takagi1, Shogo Morimoto1, Tetsuro Katayama2(1Kagawa University, 2University of Miyazaki, Japan)©ICAROB 2017 ALife Robotics Corp. Ltd.

- 25 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS6-2

Improvement of Decision Table Automatic Generation Tool VDTable for let inStatement
Yinuo Huang*, Tetsuro Katayama* Yoshihiro Kita†, Hisaaki Yamaba*, and Naonobu Okazaki*(*University of Miyazaki, Japan, †Tokyo University of Technology, Japan)

OS6-3

Prototype of Test Cases Automatic Generation Tool BWDM Based on Boundary Value
Analysis with VDM++
Hiroki Tachiyama*, Tetsuro Katayama* Yoshihiro Kita†, Hisaaki Yamaba*, and Naonobu Okazaki*(*University of Miyazaki, Japan,†Tokyo University of Technology, Japan)

OS6-4

Prototype of Refactoring Support Tool MCC Focusing on the Naming of Variables
Satoshi Tanoue*, Tetsuro Katayama* Yoshihiro Kita†, Hisaaki Yamaba*, and Naonobu Okazaki*(*University of Miyazaki, Japan,†Tokyo University of Technology, Japan)

OS6-5

Improvement of Transitions and Flow Visualization TFVIS for Exception Handling.
Takuya Sato*, Tetsuro Katayama*, Yoshihiro Kita†, Hisaaki Yamaba*, and Naonobu Okazaki*(*University of Miyazaki, Japan,†Tokyo University of Technology, Japan)

OS6-6

Automatically Business Decision Making System for Software Development by using
CMMI
Hnin Thandar Tun†, Tetsuro Katayama*, Kunihito Yamamori*, and Khine Khine Oo†(*University of Miyazaki, Japan, †University of Computer Studies, Myanmar)

11:15-11:45 GS5 Filtering & Control Systems (2)Chair:
GS5-1

An Application of Collaborative Filtering in Student Grade Prediction
Chaloemphon Sirikayon and Panita Thusaranon (Dhurakij Pundit University, Thailand)

GS5-2

An improved detection method for railway fasteners
Jiwu Wang1, Yan Long1, Masanori Sugisaka 2 (Beijing Jiaotong University, China1, Alife Robotics
Corporation Ltd, Japan and Open University, United Kingdom2)

13:00-14:00
Plenary Speech PS1
Chair: Yingmin Jia (Beihang University, P.R. China)
PS1 A New Tool to Access Deep-Sea Floor “Sampling-AUV”
Kazuo Ishii, Takashi Sonoda, Yuya Nishida, Shinsuke Yasukawa and Tamaki Ura (Kyushu Institute ofTechnology, Japan)©ICAROB 2017 ALife Robotics Corp. Ltd.

- 26 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 201714:20-15:50 OS20: Image Processing and Computer Graphics (6)Chair: Thi Thi Zin (University of Miyazaki, Japan)
Co-Chair: Masayuki Mukunoki (University of Miyazaki, Japan)
OS20-1

An effective method for detecting snatch thieves in video surveillance
Hiroaki Tsushita1, Thi Thi Zin2 ( 1, 2 University of Miyazaki, Japan)

OS20-2

Color and Shape based Method for Detecting and Classifying Card Images
Cho Nilar Phyo1, Thi Thi Zin2, Hiroshi Kamada3, Takashi Toriu4 (1, 2 University of Miyazaki, Japan,
3
Kanazawa Institute of Technology, Japan 4 Osaka City University, Japan)

OS20-3

Automatic Assessing Body Condition Score from Digital Images by Active Shape Modeland Multiple Regression Technique
Nay Chi Lynn, Thi Thi Zin, Ikuo Kobayashi (University of Miyazaki, Japan)

OS20-4General Image Categorization Using Collaborative Mean Attraction
Hiroki Ogihara, Masayuki Mukunoki (University of Miyazaki, Japan)

OS20-5

Consideration on the Photo-Realistic Rendering of Fruits by 3DCG
Haruka Tsuboi1, Makoto Sakamoto1, Sho Yamada1, Kensuke Ando1, Chongyang Sun1, Makoto
Nagatomo1, Koshiro Mitsuhashi2, Yukari Kodama2 (1University of Miyazaki, Japan, 2Miyazaki
Multimedia Academy, Japan)

OS20-6

Consideration for the Possibility to the Tourism by the AR Technology
Masamichi Hori1, Makoto Sakamoto1, Koshiro Mitsuhashi2, Yukari Kodama2, Takeshi Tanaka1,
Mihoko Fukushima1, Chikashi Deguchi1, Masahiro Yokomichi1, Masayuki Mukunoki1, Kunihito
Yamamori1, Atsushi Iiboshi3 ( 1University of Miyazaki, Japan, 2Miyazaki Multimedia Academy,
Japan, 3Takachiho Muratabi Co., Ltd., Japan)

16:10-17:25 GS9 Neuromorphic Robotic Systems (5)Chair:
GS9-1

Development of Cloud Actions for Seamless Robot Using Backpropagation Neural Network
Wisanu Jitviriya1, Jiraphan Inthiam2 and Eiji Hayashi3 (1King Mongkut’s University of Technology
North Bangkok, Thailand, 2,3Kyushu Institute of Technology, Japan)

GS9-2

Mathematical modelling of human fear and disgust emotional reactions based on skin surface
electric potential changes
Gaisina Kristina and Gaisin Ruslan (Kazan Federal University, Russia)©ICAROB 2017 ALife Robotics Corp. Ltd.

- 27 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

GS9-3

Neuromorphic Robot Dream: A Spike Based Reasoning System
Alexander Toschev, Max Talanov (Kazan Federal University, Russia),
Alexander Tchitchigin (Innopolis University and Kazan Federal University, Russia),
Salvatore Distefano (Kazan Federal University, Russia)

GS9-4

Development of Behavioral Robot using Imitated Multiplex Neurotransmitters System
Saji Keita1, Wisanu Jitviriya2 and Eiji Hayashi3 (1,3 Kyushu Institute of Technology, Japan)(2 King Mongkut’s University of Technology North Bangkok, Thailand)

GS9-5

Nonlinear Estimation Strategies Applied on an RRR Robotic Manipulator
Jacob Goodman1, Jinho Kim1, Andrew S. Lee1, S. Andrew Gadsden1,2(1University of Maryland, USA) (2University of Guelph, Canada)

Orchard Room (A2) 2nd F, N
9:00-10:30 GS2 Automated Guided Vehicles I (6)Chair:
GS2-1

Adaptive Negotiation-rules Acquisition Methods in Decentralized AGV Transportation
Systems by Reinforcement Learning with a State Space Filter
Masato Nagayoshi, Simon Elderton (Niigata College of Nursing)
Kazutoshi Sakakibara (Toyama Prefectural Univ.), Hisashi Tamaki (Kobe Univ.)

GS2-2

Modeling and Control of a Quadrotor Vehicle Subject to Disturbance Load
Jun Wang, Song Xin*, Yuxi Zhang (Beihang University, P. R. China)

GS2-3

A multithreaded algorithm of UAV visual localization based on a 3D model of environment:
implementation with CUDA technology and CNN filtering of minor importance objects
Alexander Buyval, Mikhail Gavrilenkov (Bryansk State Technical University, Russia)
Evgeni Magid (Kazan Federal University, Russia)

GS2-4

Modelling a crawler-type UGV for urban search and rescue in Gazebo environment
Maxim Sokolov, Aidar Gabdullin, Roman Lavrenov, Ilya Afanasyev (Innopolis University,
Russia), Leysan Sabirova, Evgeni Magid (Kazan Federal University, Russia)

GS2-5

Development of Autonomous Robot for Laborsaving of the Forestry - Positioning of the
Robot using IMU, GPS, and Encoder Sho Yamana, and Eiji Hayashi (Kyushu Institute of Technology, JAPAN)

GS2-6

Development of Autonomous Robot for Laborsaving of the Forestry - Detection of youngplants by RGB and Depth Sensor Nobuo Miyakawa and Eiji Hayashi (Kyushu Institute of Technology, Japan)©ICAROB 2017 ALife Robotics Corp. Ltd.

- 28 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 201711:15-12:00 GS8 Micro-Machine & Robo cs II (3)Chair:
GS8-1

Development of arm trajectory planning of Seamless Robot
Teedanai Pramanpol, Eiji Hayashi (Kyushu Ins te of Technology, Japan)

GS8 -2

Localiza n Method of Autonomous Moving Robot for Forest Industry
Ayumu Tominaga, Eiji Hayashi (Kyushu Ins te of Technology, Japan), Tsutomu Sasao (Meiji
University, Japan)

GS8-3

Dynamic Modeling and Mo n Control of an RRR Rob c Manipulator
Jinho Kim1, Kevin Chang1, Brian Schwarz1, Andrew S. Lee1, S. Andrew Gadsden1,2(1University of Maryland, USA), (2University of Guelph, Canada)

14:20-15:50 OS13: Intelligent Control (6)Chair: Yingmin Jia (Beihang University (BUAA), P.R.China)
Co-Chair: Fuzhong Wang (Henan Polytechnic University, P.R.China)
OS13-1Modeling and Simula n for a Quadrotor Vehicle with Adap
Wing
Qunpo Liu ,Fuzhong Wang, Hongqi Wang, Jikai Si (Henan Polytechnic University, P.R.China)
e of Technology, Japan)
Hanajima Naohiko (Muroran In

OS13-2

Fuzzy self-tuning PID control algorithm for belt conveyor driven by-motorCaixia Gao, Fuzhong Wang, Ziyi Fu (Henan Polytechnic University, P.R. China)

OS13-3Con uous Non-singular Fast Terminal Sliding Mode Control for an Ac e Gravity Field
Simulator
Jiao Jia, Yingmin Jia and Shihao Sun( Beihang University (BUAA), P.R.China)

OS13-4Weighted Mu ple Model Adap
ntrol for a Category of Systems with Colored Noise
Yuzhen Zhang, Qing Li and Weicun Zhang(University of Science and Technology Beijing, P. R. China)

OS13-5

Neutral Networks-Based Adap
xed-Time Consensus Tracking Control for Uncertain
Mu ple AUVs
Lin Zhao1,*, Yingmin Jia2 and Jinpeng Yu1(1Qingdao University, P.R.China; 2 Beihang University (BUAA), P.R. China)

OS13-6

Conducted electromagn c interference predi on of the Buck Converter via Neural Networks
Sumin Han, Fuzhong Wang (Henan Polytechnic University, P.R. China)©ICAROB 2017 ALife Robotics Corp. Ltd.

- 29 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 201716:10-17:10 OS22 Robotic Technology for Competition (4)Chair: Kazuo Ishii (Kyushu Institute of Technology, Japan)
Co-Chair: Keisuke Watanabe (Tokai University, Japan)
OS22-1Development of a Tomato Harvesting Robot
Bingh Li, Shinsuke Yasukawa, Takashi Sonoda, Kazuo Ishii(Kyushu Institute of Technology, Japan)

OS22-2Development of SOM algorithm for Relationship between Roles and Individual’s Role in
Rugby.
2nd Reports: University Rugby teams analysis using Physical and Psychological data
Yasunori Takemura (Nishi Nippon Institute of Technology, Japan)

OS22-3

Ball Dribbling Control for RoboCup Soccer Robot
Shota Chikushi, Kenji Kimura, Kazuo Ishii(Nippon-Bunri University, Kyushu Institute of Technology, Japan)

OS22-4

Strategy Analysis of RoboCup Soccer Teams Using Self-Organizing Map
Moeko Tominaga, Yasunori Takemura, Kazuo Ishii(Kyushu Institute of Technology, NishiNippon Institute of Technology, Japan)

Orchard Room (A3) 2nd F, S
9:00-11:00 OS4 Human-In-The-Loop (HITL) Systems (4)Chair: Toru Yamamoto (Hiroshima University, Japan)
OS4-1

Feature Extraction for Digging Operation of Excavator
Kazushige Koiwai*, Toru Yamamoto (Hiroshima University, Japan)
Takao Nanjo, Yoichiro Yamazaki, Yoshiaki Fujimoto (KOBELCO Construction Machinery CO.,LTD. , Japan)

OS4-2

Design of a Data-Oriented Kansei Feedback System
Takuya Kinoshita and Toru Yamamoto (Hiroshima University, Japan)

OS4-3

Parameter Estimation of Skill Evaluation Model
Kazuo Kawada, Toru Yamamoto (Hiroshima University, Japan)

OS4-4

Human Skill Quantiﬁcation for Excavator Operation using Random Forest
Hiromu Imaji, Kazushige Koiwai*, Toru Yamamoto (Hiroshima University, Japan)
Koji Ueda, Yoichiro Yamazaki, Yoshiaki Fujimoto (KOBELCO Construction Machinery CO., LTD. ,Japan)©ICAROB 2017 ALife Robotics Corp. Ltd.

- 30 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS11 Robot Control and Localization (4)Chair: Jang-Myung Lee (Pusan National University, South Korea)
Co-Chair: Min-Cheol Lee (Pusan National University, South Korea)
OS11-1Marker Recognition System for Localization of the Rover on the Lunar Space
Na-Hyun Lee, Jang-Myung Lee (Pusan National University, South Korea)

OS11-2

Dynamic Model and Finite-Time SMC and Backstepping Control of a Mabile Manipulator
SystemSeongik Han, Hyunuk Ha, and Jangmyung Lee (Pusan National University, Korea)

OS11-3

Bilateral Control of Hydraulic Servo System for 1DOF Master Slave Manipulator
Jie Wang, Karam Dad and Min Cheol Lee (Pusan National University, Korea)

OS11-4

Advanced impedance control of haptic joystick for eﬀective mobile robot handling
Gyung-I Choi, Jang-Myung Lee (Pusan National University, South Korea)

11:15-12:00 GS1 Artiﬁcial Neural Network and Bio-Signal Controlled Robotics (3)Chair:
GS1-1

Obstacle Avoidance Method for Electric Wheelchairs Based on a Multi-Layered Non-Contact
Impedance Model
Haruna Kokubo, Taro Shibanoki (Ibaraki University, Japan), Takaaki Chin (Hyogo Rehabilitation
Center, Japan), and Toshio Tsuji (Hiroshima University, Japan)

GS1-2

A Voice Signal-Based Manipulation Method for the Bio-Remote Environment Control System
Based on Candidate Word Discriminations
Taro Shibanoki (Ibaraki University, Japan), Go Nakamura, Takaki Chin (Hyogo Rehabilitation
Center, Japan), and Toshio Tsuji (Hiroshima University)

GS1-3

Experiments on classiﬁcation of electroencephalography (EEG) signals in imagination of
direction using Stacked Autoencoder
Kenta Tomonaga, Takuya Hayakawa, Jun Kobayashi (Kyushu Institute of Technology, Japan)

14:20-15:20 OS8 Multiagent systems and Reality Mining (4)Chair: Masao Kubo (National Defense Academy, Japan)
Co-Chair: Saori Iwanaga (Japan Coast Guard Academy, Japan)
OS8-1

Inﬂuence of Partner Selection on Functional Diﬀerentiation:
Emergence of Diversity by Isolated Interaction and Preference Change
Saori Iwanaga (Japan Coast Guard Academy, Japan),
Akira Namatame (National Defense Academy, Japan)©ICAROB 2017 ALife Robotics Corp. Ltd.

- 31 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS8-2

Beacon-based tourist information system to identify visiting trends of tourists
Akihiro Yamaguchi1, Masashi Hashimoto1, Kiyohiro Urata1, Yu Tanigawa1, Tetsuya Nagaie1,
Toshitaka Maki1, Toshihiko Wakahara1, Akihisa Kodate2, Toru Kobayashi3 and Noboru Sonehara4(1 Fukuoka Institute of Technology, Japan) (2 Tsuda College, Japan) (3 Nagasaki University,
Japan) (4 National Institute of Informatics, Japan)

OS8-3

Analysis of Survey on Employment Trends
Masao Kubo, Hiroshi Sato (National Defense Academy, Japan),Akihiro Yamaguchi (Fukuoka Institute of Technology, Japan),Yuji Aruka (Chuo University, Japan)

OS8-4

Direction switch behavior to enclose a target
Masao Kubo, Nhuhai Phung, Hiroshi Sato (National Defense Academy, Japan),

16:10-17:10 GS4 Biological Systems (4)Chair:
GS4-1

Force and Motion Analysis of larval zebraﬁsh (Danio rerio) using a body dynamics model
Naohisa Mukaidani1, Zu Soh1, Shinichi Higashijima2, Toshio Tsuji1 (1Hiroshima University,
Japan, 2National Institutes of Natural Sciences, Okazaki Institute for Integrative Bioscience,
National Institute for Physiological Sciences, Japan)

GS4-2

Behavior Analysis on Boolean and ODE models for Extension of Genetic Toggle Switch from
Bi-Stable to Tri-stable.
Masashi Kubota, Manabu Sugii, Hiroshi Matsuno ( Yamaguchi University, Japan)

GS4-3

Boolean modeling of mammalian cell cycle and cancer pathways
Hideaki Tanaka, Hiroshi Matsuno, Adrien Fauré(Yamaguchi University, Japan)

GS4-4

An Estimation Method for Environmental Friction Based on Body Dynamic Model of
Caenorhabditis elegans
Zu Soh (Hiroshima University, Japan), Michiyo Suzuki (National Institutes for Quantum andRadiological Science and Technology, Japan), Toshio Tsuji (Hiroshima University, Japan)©ICAROB 2017 ALife Robotics Corp. Ltd.

- 32 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

January 22 (Sunday)
08:50-Registration

Fountain Room (A1) 2nd F
9:10-10:25 OS10 Biological Signal Sensing Technology, Device and Its Applications (5)Chair: Hiroki Tamura (University of Miyazaki, Japan)
Co-Chair: Koichi Tanno (University of Miyazaki, Japan)
OS10-1

Mouse Cursor-like Control System in Consideration of the DC-EOG Signals using EOG-sEMG
Human Interface
Mingmin Yan, Yu Cheng, Keiko Sakurai, Hiroki Tamura, Koichi Tanno(University of Miyazaki, Japan)

OS10-2

A Study on Eyes Tracking Method using Analysis of Electrooculogram Signals
Keiko Sakurai, Mingmin Yan, Hiroki Tamura, Koichi Tanno (University of Miyazaki, Japan)

OS10-3Development of Diagnosis Evaluation System of Facial Nerve Paralysis Using sEMG
Shogo Okazaki, Misaki Syoichizono, Hiroki Tamura, Takahiro Nakashima, Eiji Kato,
Tetsuya Tono (University of Miyazaki)

OS10-4High CMRR and Wideband Current Feedback Instrumentation Amplifier Using Current
Conveyors
Shota Mago, Hiroki Tamura, Koichi Tanno (University of Miyazaki, Japan)

OS10-5

Voltage Rectifier Circuit with Voltage Doubler Using New Active Diode
Masayuki Uchihara, Hiroki Tamura, Kochi Tanno (University of Miyazaki, Japan)

10:40-11:40 Plenary Speech PS2
Chair: Makoto Sakamoto (University of Miyazaki, Japan)
PS2 Realistic Visualization of Complex Systems such as Natural Phenomena based on Particle Behaver
Tomoyuki Nishita (Hiroshima Shudo University/UEI research)
13:00-13:50 Invited Session IS-5
Chair: Takao Ito (Hiroshima University, Japan)
IS-5 Towards Unified Human-Robotic Societies
Peter Sapaty (Academy of Sciences, Ukraine), Masanori Sugisaka (ALife Robotics, Japan)
Takao Ito (Hiroshima University, Japan)©ICAROB 2017 ALife Robotics Corp. Ltd.

- 33 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 201714:10-15:10 OS18 Advanced Management and Technology (4)Chair: Kensuke Ogata (University of Nagasaki, Japan)
Co-Chair: Takao Ito (Hiroshima University, Japan)
OS18-1Development of English Text for the engineers to preserve the environment of North-East
Asia
Yuji Minami, Kenji Fukuchi, Shinya Tagawa (National Institute of Technology, Ube, Japan)

OS18-2

Measuring Fragility and its Implications in Networked Systems
Tsutomu Ito*, Katsuhiko Takahashi, Katsumi Morikawa, Takao Ito (Hiroshima University,
Japan), Rajiv Mehta (New Jersey Institute of Technology, USA), Seigo Matsuno (Ube National
College of Technology, Japan), Makoto Sakamoto (University of Miyazaki, Japan)

OS18-3

A Formation of Standard Setter to Transplant Global Standards into Domestic Institution
Kensuke Ogata (University of Nagasaki, Japan)

OS18-4

A Comparison Study on the Vertical Integration and Horizontal Specialization of Chinese
ICT Companies
Yunju Chen (Shiga University, Japan), Yousin Park (Prefectural University of Hiroshima, Japan)
Iori Nakaoka (National Institute of Technology, Ube College, Japan)

15:30-16:15 GS10 Reinforcement & Evolutionary Computations (3)Chair:
GS10-1The Optimized Function Selection Using Wolf Algorithm for Classification
Duangjai Jitkongchuen, Worapat Paireekreng (Dhurakij Pundit University, Thailand)

GS10-2

Tell Agent Where to Go: Human Coaching for Accelerating Reinforcement Learning
Nakarin Suppakun1, Suriya Natsupakpong2, and Thavida Maneewarn3(Mongkut's University of Technology Thonburi, Thailand)

GS10-3

Fall Risk Reduction for Elderly Using Mobile Robots Based on the Deep Reinforcement
Learning
Takaaki Namba (Nagoya University, Japan)

Orchard Room (A2) 2nd F, N
9:10-10:10 OS19 Kansei Engineering and Applications (4)Chair: Tetsuo Hattori (Kagawa University)
Co-Chair: Hiromich Kawano (NTT AT)©ICAROB 2017 ALife Robotics Corp. Ltd.

- 34 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS19-1

Histogram Matching Based on Gaussian Distribution Using Regression Analysis Variance
Estimation
Yusuke Kawakami (DynaxT Co., Ltd., Japan), Tetsuo Hattori, Yoshiro Imai, Kazuaki Ando,
Yo Horikawa (Kagawa University, Japan)
R. P. C. Janaka Rajapakse (Tainan National University of the Arts, Taiwan)

OS19-2

Histogram Matching Based on Gaussian Distribution Using Variance Estimation -- Comparison
between Curvature Computation and Regression Analysis –Yusuke Kawakami (DynaxT Co., Ltd., Japan), Tetsuo Hattori, Yoshiro Imai, Kazuaki Ando,
Yo Horikawa (Kagawa University, Japan)
R. P. C. Janaka Rajapakse (Tainan National University of the Arts, Taiwan)

OS19-3

An Extended Optimal Stopping Method for Structural Change Point Detection Problem
Yoshihide Koyama, Tetsuo Hattori, Yoshiro Imai, Yo Horikawa, Hiromichi Kawano*,
Yusuke Kawakami** ( Kagawa University, Kagawa, Japan, *NTT advanced technology
Company Ltd, Japan, ** DynaxT Co., Ltd., Japan)

OS19-4

A Study of Sentimental Value Analysis for Tweeting Message
Shunsuke Doi*, Shinya Hara*, Yoshiro Imai*, Yusuke Kawakami** and Tetsuo Hattori*(* Kagawa University, Japan, **DynaxT Co., Ltd., Japan)

14:10-15:10 OS17 Natural Computing and Biology (4)Chair: Marion Oswald (The Vienna University of Technology, Austria)
Co-Chair: Yasuhiro Suzuki (Graduate School of Information Science, Nagoya University, Japan)
OS17-1

Molecular Artificial Intelligence by using DNA reactions
Yasuhiro Suzuki, Rie Taniguchi (Nagoya University, Japan)

OS17-2

Neural Networks by using Self-Reinforcement Reactions
Yasuhiro Suzuki (Nagoya University, Japan)

OS17-3

Artificial Chemistry by Sound Waves
Yasuhiro Suzuki (Nagoya University, Japan)

OS17-4

Variants of spiking neural P systems with energy control
Rudolf Freund, Marion Oswald (TU Wien, Vienna, Austria)

15:30-16:15 GS3 Automated Guided Vehicles II (3)Chair:
GS3-1

Motion Improvement of Four-Wheeled Omnidirectional Mobile Robots for Indoor Terrain
Amornphun Phunopas1 and, Shinichi Inoue2 (1King Mongkut’s University of Technology North
Bangkok, Thailand, 2Kyushu Institute of Technology, Japan)©ICAROB 2017 ALife Robotics Corp. Ltd.

- 35 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

GS3-2

An Improved Algorithm for Obstacle Avoidance by Follow the gap method combined
Potential Field
Chakhrit La-orworrakhun, Suriya Natsupakpong (King Mongkut's University Technology
Thonburi, Thailand)

GS3-3

Study on the target positioning for an Omni-directional 3 DOF mobile manipulator basedon machine vision
Jiwu Wang, Yao Du, Wensheng Xu (Beijing Jiaotong University, China)
Masanori Sugisaka (ALife Robotics Corporation Ltd., Japan and Open University,
United Kingdom)

Orchard Room (A3) 2nd F, S
9:10-10:25 OS1 Informational Narratology and Automated Content Generation (5)Chair: Takashi Ogata (Iwate Prefectural University, Japan)
Co-Chair: Yoji Kawamura (Kindai University, Japan)
OS1-1

Informational Narratology and Automated Content Generation
Takashi Ogata (Iwate Prefectural University, Japan), Yoji Kawamura (Kindai University, Japan)
Akihito Kanai (Hosei University, Japan)

OS1-2

Analyzing Multiple Narrative Structures of Kabuki based on the Frameworks of Narrative
Generation Systems
Takashi Ogata (Iwate Prefectural University, Japan)

OS1-3

Rhetoric of Commercial Film and the Response of Viewers
Yoji Kawamura (Kindai University, Japan)

OS1-4

Comparison Between Variational Autoencoder and Encodder-Decoder Models for Short
Conversation
Shin Asakawa (Tokyo Women's Christian University, Japan)
Takashi Ogata (Iwate Prefectural University, Japan)

OS1-5

Changing and Transforming a Story for a Framework of an Automatic Narrative
Generation Game
Jumpei Ono and Takashi Ogata (Iwate Prefectural University, Japan)

14:10-15:10 OS12 Machine Learning and Its Applications (4)Chair: Kunikazu Kobayashi (Aichi Prefectural University, Japan)
Co-Chair: Shingo Mabu (Yamaguchi University, Japan)
OS12-1

A Rule-Based Classiﬁcation System Enhanced by Multi-Objective Genetic Algorithm
Kenzoh Azakami, Shingo Mabu, Masanao Obayashi, Takashi Kuremoto(Yamaguchi University, Japan)©ICAROB 2017 ALife Robotics Corp. Ltd.

- 36 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS12-2

A Method of Feature Extraction for EEG Signals Recognition using by ROC curve
Takashi Kuremoto, Yuki Baba, Masanao Obayashi, Shingo Mabu
(Yamaguchi University, Japan)
Kunikazu Kobayashi (Aichi Prefectural University, Japan)

OS12-3

Forecasting Real Time Series Data using Deep Belief Net and Reinforcement Learning
Takaomi Hirata, Takashi Kuremoto, Masanao Obayashi, Shingo Mabu
(Yamaguchi University, Japan), Kunikazu Kobayashi (Aichi Prefectural University, Japan)

OS12-4

Improvement of Robot's Self-localization by Using Observer View Positional Information
Yo Aizawa, Takuo Suzuki, and Kunikazu Kobayashi (Aichi Prefectural University, Japan)

15:30-16:45 OS21 Computer Science and Information Processing (5)Chair: Makoto Sakamoto (University of Miyazaki, Japan)
Co-Chair: Yu-an Zhang (Qinghai University, China)
OS21-1

Consideration on the Recognizability of Three-Dimensional Patterns
Chongyang Sun1, Makoto Sakamoto2, Makoto Nagatomo3, Yu-an Zhang2, Shinnosuke Yano1,
Satoshi Ikeda2, Takao Ito4, Tsutomu Ito4, Yasuo Uchida5, Tsunehiro Yoshinaga6, (1, 2, 3 Universityof Miyazaki, Japan), (4 Hiroshima University, Japan), (5 Ube National College of Technology,
Japan), (6 Tokuyama college of Technology, Japan)

OS21-2

Some Properties of Four-Dimensional Homogeneous Systolic Pyramid Automata
Makoto Nagatomo1, Makoto Sakamoto2, Yu-an Zhang2, Chongyang Sun3, Shinnosuke Yano3,
Satoshi Ikeda2, Takao Ito4, Tsutomu Ito4, Yasuo Uchida5, Tsunehiro Yoshinaga6 (1, 2, 3 University ofMiyazaki, Japan) (4 Hiroshima University, Japan) (5Ube National College of Technology, Japan)(6 Tokuyama college of Technology, Japan)

OS21-3

Reduction of the search space to find perfect play of 6×6 board Othello
Yuki Takeshita1, Makoto Sakamoto1, Takao Ito2, Tsutomu Ito2, Satoshi Ikeda1(1University of Miyazaki, 2Hiroshima University, Japan)

OS21-4

A comparative study on the delisting ratings of firms from the UN Global Compact in theinternational management environment
Kanako Negishi (National Institute of Technology, Ube college, Japan)

OS21-5

Application of ViSC to the Natural Grazing in Qinghai Tibet Plateau
Cheng-shui Niu1, Bing-fen Li1, Yu-an Zhang1, Makoto Sakamoto2 (1Qinghai University, P.R. China,
2
University of Miyazaki, Japan)
Farewell Party©ICAROB 2017 ALife Robotics Corp. Ltd.

- 37 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

GS11 Others (12)GS11-1

Clinical Evaluation of UR-System-PARKO for Recovery of Motor Function of Severe Plegic Hand
after Stroke
Hirofumi Tanabe (Shonan University of Medical Sciences, Japan)
Yoshifumi Morita (Nagoya Institute of Technology, Japan)

GS11-2

A Piston Finger Device for Restoring the Motor Function of Chronic Plegic Fingers: Analysis of
the Piston Finger TechniqueMengsu Wang (Nagoya Institute of Technology: Nitech, Japan)
Hirofumi Tanabe (Shonan University of Medical Sciences, Japan)
Kenji Ooka (Nitech, Japan), Yoshifumi Morita (Nitech, Japan)

GS11-3

Verifying the Sleep-Inducing Effect of a Mother's Rocking Motion in Adults
Hiroaki Shibagaki, Keishi Ashida, Yoshifumi Morita (Nagoya Institute of Technology, Japan),
Ryojun Ikeura (Mie University, Japan) and Kiyoko Yokoyama (Nagoya City University, Japan)

GS11-4

Design of Automated Real-Time BCI Application Using EEG Signals
Chong Yeh Sai, Norrima Mokhtar, Hamzah Arof, Masahiro Iwahashi(University of Malaya, Malaysia / Nagaoka University of Technology, Japan)

GS11-5

A Hybrid Simulated Kalman Filter - Gravitational Search Algorithm (SKF-GSA)
Badaruddin Muhammad, Zuwairie Ibrahim, Mohd Falfazli Mat Jusof (Universiti Malaysia
Pahang, Malaysia), Nor Hidayati Abdul Aziz, Nor Azlina Ab. Aziz (Multimedia University,
Malaysia), Norrima Mokhtar (University of Malaya, Malaysia)

GS11-6

Simulated Kalman Filter with Randomized Q and R Parameters
Nor Hidayati Abdul Aziz, Nor Azlina Ab. Aziz (Multimedia University, Malaysia)
Zuwairie Ibrahim, Saifudin Razali, Mohd Falfazli Mat Jusof (Universiti Malaysia Pahang,
Malaysia), Khairul Hamimah Abas, Mohd Saberi Mohamad (Universiti Teknologi Malaysia,
Malaysia), Norrima Mokhtar (University of Malaya, Malaysia)

GS11-7

Real Detection of 3D Human Hand Orientation Based Morphology
Abadal-Salam T. Hussain, Hazry D., Waleed A. Oraibi, M.S Jawad, Zuradzman M. Razlan,
A. Wesam Al-Mufti, S. Faiz Ahmed, Taha A. Taha, Khairunizam WAN, Shahriman A.B(Universiti Malaysia Perlis, Malaysia)

GS11-8Multilevel Non-Inverting Inverter Based Smart Green Charger System
Abadal-Salam T. Hussain, Waleed A. Oraibi, Hazry D, Zuradzman M. Razlan, S. Faiz Ahmed,
Taha A. Taha, Khairunizam WAN & Shahriman AB (Universiti Malaysia Perlis, Malaysia)

GS11-9Design A New Model of Unmanned Aerial Vehicle Quadrotor Using The Variation in The Lengthof The Arm
Yasameen Kamil, D. Hazry, Khairunizam Wan, Zuradzman M. Razlan, Shahriman AB(Universiti Malaysia Perlis, Malaysia)©ICAROB 2017 ALife Robotics Corp. Ltd.

- 38 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

GS11-10Development of Automatic Take Off and Smooth Landing Control System for Quadrotor UAV
Syed. F. Ahmed (Universiti Kuala Lumpur, British Malaysian Institute, Malaysia, Universiti
Malaysia Perlis, Malaysia), D. Hazry (Universiti Malaysia Perlis, Malaysia), Kushsairy Kadir(University Kuala Lumpur, British Malaysian Institute, Malaysia), Abadal Salam T. Hussain,
Zuradzman M. Razlan, Shahriman AB (Universiti Malaysia Perlis, Malaysia)

GS11-11

Auto Pilot Ship Heading Angle Control Using Adaptive Control Algorithm
Abadal-Salam T. Hussain, Hazry D., S. Faiz Ahmed, Wail A. A. Alward, Zuradzman M. Razlan& Taha A. Taha (Universiti Malaysia Perlis, Malaysia)

GS11-12Classification of Hippocampal Region using Extreme Learning Machine
Muhammad Hafiz Md Zaini, Mohd Ibrahim Shapiai (Universiti Teknologi Malaysia)
Ahmad Rithauddin Mohamed (Hospital Kuala Lumpur)
Norrima Mokhtar (University of Malaya), Zuwairie Ibrahim (Universiti Malaysia Pahang)©ICAROB 2017 ALife Robotics Corp. Ltd.

- 39 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

PS abstracts
PS1 A New Tool to Access Deep-Sea Floor “Sampling-AUV”

Kazuo Ishii, Takashi Sonoda, Yuya Nishida, Shinsuke Yasukawa and Tamaki Ura (Kyushu Institute ofTechnology, Japan)
Underwater robot is one of the important research tools to explore deep-sea.
Especially, Autonomous Underwater Vehicles (AUVs) attract attentions as
new tools because they are un-tethered and suitable for wide area
observation. As the next generation AUV, we have been developing a
Sampling-AUV that can dive into deep-sea and bring back marine creaturesusing a mounted manipulator. In the mission, the AUV transmits the deepsea floor images to the support ship using acoustic communication, and theoperator selects the marine creatures to sample and bring back them. In thistalk, we introduce the new AUV, the underwater manipulator and image
processing techniques for sampling.

PS2 Realistic Visualization of Complex Systems such as Natural Phenomena based on Particle
Behaver
Tomoyuki Nishita (Hiroshima Shudo University/UEI research)

Computer graphics (CG) is very useful tool for visualization of scientificsimulation, computer aided design, arts, education, and games.
Researches on complex systems and AI (acritical intelligence) including
artificial life also become popular recently. Complex systems is a field of
science studying how parts of a system give rise to the collective behaviorsof the system, and how the system interacts with its environment. Examples
are Earth's global climate, organisms, social organization, a living cell, andultimately the entire universe. In complex systems, neural network, cellar
automation, fractal, chaos dynamics, and GA are useful techniques. These
are also used in CG. The purpose of CG is reality with interactive speed. In
recent years, point primitives have received growing attention in CG. The
particle system refers to CG technique to simulate certain fuzzy phenomena.
My talk also includes pioneers of CG, my research history, and CG
application related to AI.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 40 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

IS abstracts
IS-1 The future of Robotics Technology.

Luigi Pagliarini1,2, Henrik Hautop Lund1( Technical University of Denmark, Denmark, 2Academy of Fine Arts of Macerata, Italy)
1

In the last decade the robotics industry has created millions of additionaljobs led by consumer electronics and the electric vehicle industry, andby 2020, robotics will be a $100 billion worth industry, as big as thetourism industry.. For example, the rehabilitation robot market has
grown 10 times between 2010 and 2016, thanks to advancements inrehab/therapy robots, active prostheses, exoskeletons, and wearable
robotics. In short, the very next decade robotics will become vital
components in a number of applications and robots paired with AI will
be able to perform complex actions that are capable of learning fromhumans, driving the intelligent automation phenomenon. Therefore, inthis paper we try to depict the direction and the fields of application ofsuch important sector of future markets, and scientific research.

IS-2 Playware ABC: Engineering Play for Everybody

Henrik Hautop Lund (Technical University of Denmark, Denmark)This paper describes the Playware ABC concept, and how it allows
anybody, anywhere, anytime to be building bodies and brains, whichfacilitates users to construct, combine and create. The Playware ABC
concept focuses engineering and IT system development on creating
solutions that are usable by all kinds of users and contexts. The result
becomes solutions, often based on modular technologies that are highly
flexible and adaptable to different contexts, users, and applications.

IS-3 A combination of Machine Learning and Cerebellar models for the Motor Control andLearning of a Modular Robot
Ismael Baira Ojeda, Silvia Tolu, Moisés Pachecho, David Johan Christensen, Henrik Hautop Lund
(Technical University of Denmark, Denmark)We scaled up a bio-inspired control architecture for the motor control andmotor learning of a real modular robot. In our approach, the Locally
Weighted Projection Regression algorithm (LWPR) and a cerebellar
microcircuit coexist, forming a Unit Learning Machine. The LWPR
optimizes the input space and learns the internal model of a single robot
module to command the robot to follow a desired trajectory with its endeffector. The cerebellar microcircuit refines the LWPR output deliveringcorrective commands. We contrasted distinct cerebellar circuits including
analytical models and spiking models implemented on the SpiNNaker
platform, showing promising performance and robustness results.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 41 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

IS-4 Playware ABC 2: as a Disruptive Technology for Global Development

Henrik Hautop Lund (Technical University of Denmark, Denmark)This paper describes the Playware ABC concept, and how it allows
anybody, anywhere, anytime to be building bodies and brains, whichfacilitates users to construct, combine and create. The Playware ABC
concept focuses engineering and IT system development on creating
solutions that are usable by all kinds of users and contexts in our globalizedsociety. The result becomes solutions that are highly flexible and adaptable
to different contexts, users, and applications. The paper gives examples of
how playware becomes a disruptive technology for global development, forinstance in the health sector.

IS-5 Towards Unified Human-Robotic Societies

Peter Sapaty (Academy of Sciences, Ukraine), Masanori Sugisaka (ALife Robotics, Japan)
Takao Ito (Hiroshima University, Japan)

Large numbers of robotic facilities are accumulated worldwide, but existing
robots still remain specialized devices rather than intelligent collaboratorsfor humans. To efficiently integrate massive robotics into human societies,
more general and universal approaches are needed. The presentation will
reveal higher-level, semantic, model supported by Spatial Grasp Language(SGL) in which top operations and decisions in distributed spaces are
expressed in extremely compact form, with traditional system management
shifted to automatic SGL interpretation. Communicating SGL interpreters
associated with humans and robots allow us to organize goal driven teams
up to entire societies operating under unified control. Presentation will
exhibit numerous scenarios in SGL from most critical applications.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 42 -.

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS abstracts
OS1 Informational Narratology and Automated Content Generation (5)OS1-1 Informational Narratology and Automated Content Generation

Takashi Ogata (Iwate Prefectural University, Japan) Yoji Kawamura (Kindai University, Japan)
Akihito Kanai (Hosei University, Japan)
Authors have been proposing a new concept called “informational narratology”
or “information narratology”. In this concept, we integrate narratologicalstudies including various literary theories into information technologies, suchas AI and cognitive science to mainly design and develop systems that
automatically generate, create or produce digital narrative contents including avariety of existing or future genres. Under this idea’s framework, the threeauthors respectively design and develop narrative generation systems (Ogata),
advertising image processing systems (Kawamura), and narrative film
processing systems (Kanai). In this paper, we propose the concept or idea of
informational narratology and introduce the above three researches as concert
approaches to the informational narratology.

OS1-2 Analyzing Multiple Narrative Structures of Kabuki based on the Frameworks ofNarrative Generation Systems
Takashi Ogata (Iwate Prefectural University, Japan)
Although kabuki is a genre of, so to speak, traditional performing arts, it has
many characteristics, as a contemporary genre including various elements suchas narrative, drama, dance, music, and so on, that can contribute to the designand development of digital narrative generation studies. Previously, we have
surveyed and analyzed kabuki’s narrative structures, the generation or
production and reception or consumption process from the viewpoint of themultiple narrative structures model. In the multiple narrative structures modelof kabuki, we mean that the entire structure of kabuki is constructed throughmultiple usages of relating information. For example, the element of a “person”
in kabuki is divided into a “character” in a narrative work, the existence of anactor with a historical flow and a name as the actor, and a real human with atrue name. And this multiplicity, for instance, give a kind of multiple and deep
characteristic to a person. Based on this previous studies, in this presentation,
we show a method that bridges this kabuki analysis to system design throughour two narrative generation systems: “Integrated Narrative Generation System(INGS)” and “Geino Information System (GIS)”.

OS1-3 Rhetoric of Commercial Film and the Response of Viewers
Yoji Kawamura (Kindai University, Japan)
In this paper, firstly the way of thinking of commercial film techniques andrhetoric is shown. Next, based on the various investigations, the relationships
between rhetoric of commercial film and the response of viewers are
summarized. Then, by using the summarized relationship as a rule, theexpression know-how of commercial film is extracted, and how to makeinformation system is considered. By introducing a framework called "overall
rhetorical type" as a combination of commercial film techniques and using thefact that there is a correlation between the overall rhetorical type and theviewer's response (favorable factor, acceptance image), it is possible to build an
information system to promote the interest and purchase willingness of theviewers.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 43 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS1-4 Comparison Between Variational Autoencoder and Encodder-Decoder Models for Short
Conversation
Shin Asakawa (Tokyo Women's Christian University, Japan)
Takashi Ogata (Iwate Prefectural University, Japan)

Conversational modeling is an important task in language understanding andgeneration. Recent models in this area have improved their performances basedon recurrent neural network model (RNN). RNN models can generate sentenses
and conversation. It is not always restricted to any specified handcrafted rules.
The variational autoencoder and encoder-decoder models might be considered
as a promissing model among them. They were able to extract knowledge fromthe training dataset. Samples from the prior over these sentence representations
produced diverse and well-formed sentences through simple deterministic
decoding. We tried to compare them with several different data set and to reveal
underlying mechanism between them. By examining paths through latent space,
we are able to generate coherent novel sentences that interpolate between
known sentences. Our preliminary results suggest that optimizing the wrong
objective function might play a role producing some kinds of convsersation.

.

OS1-5 Changing and Transforming a Story for a Framework of an Automatic Narrative
Generation Game
Jumpei Ono and Takashi Ogata (Iwate Prefectural University, Japan)

Authors has proposed an idea of a game system including an automatic story
generation function based on tabletop-top role playing game (TRPG). In thisidea, stories are generated or transformed through the communication between
a “game manager (GM)” who controls story generation and “Players (PLs)”
who change and transform the content of a story proposed by a GM. This paper
focuses on the mechanism for changing and transforming a proposed story by a
GM. In particular, this mechanism has various techniques from macro leveltechniques related to the entire structures of a story to micro level techniques
related to the partial structures of a story. For instance, when a GM is a realhuman, these techniques in PLs who are computer agents can produce changed
and transformed stories which the GM has not imagined originally.

OS2 Intelligence Control Systems and Applications I (6)OS2-1 Develop Low Cost IoT Module with Multi-Agent Method

Outside of a story
Inside of a story
Game
MasterJr-Hung Guo, Kuo-Hsien Hsia, Kuo-Lan Su(National Yunlin University of Science and Technology, Taiwan)

IOT (Internet of Thing) is a very hot topic recently, many of the important issues
in the field of research. But to the conventional apparatus or systems andnetwork connections, sometimes we need complex modifications or expensive
costs. Therefore, this paper using the MCS-51 series single-chip integration of
analog, digital signal input, and ETHERNET, WIFI and other communicationinterface, to develop general-purpose IoT modules. This module can be used toreplace existing equipment or devices, or combination use with the original
device. We also use the module in a Multi-Agent Method, we can ensure thatthe module in signal capture or communication failure can be replaced or
assisted by other modules.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 44 -Non Player
Character

PlayerCharacter

Player

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS2-2 Based on Short Motion Paths and Artificial Intelligence Method for Chinese Chess Game
Chien-Ming Hung, Jr-Hung Guo, Kuo-Lan Su(National Yunlin University of Science and Technology, Taiwan)

The article develops the decision rules to win each set of the Chinese chessgame using evaluation algorithm and artificial intelligence method, and uses themobile robot to be instead of the chess, and presents the movement scenarios
using the shortest motion paths for mobile robots. User can play the Chinesechess game according to the game rules with the supervised computer. The
supervised computer decides the optimal motion path to win the set using
artificial intelligence method, and controls mobile robots according to theprogrammed motion paths of the chesses moving on the platform via wirelessRF interface. We use enhanced A* searching algorithm to solve the shortest
path problem of the assigned chess, and solve the collision problems of the
motion paths for two mobile robots moving on the platform simultaneously. We
implement a famous set to be called “wild horses run in farm” using the
proposed method. First we use simulation method to display the motion pathsof the assigned chesses for the player and the supervised computer. Then the
supervised computer implements the simulation results on the chessboard
platform using mobile robots. Mobile robots move on the chessboard platform
according to the programmed motion paths and is guided to move on the centre
line of the corridor, and avoid the obstacles (chesses), and detect the cross pointof the platform using three reflective IR modules.

OS2-3 Design and Implementation of the SCARA Robot Arm
Jian-Fu Weng, Bo-Yi Li, Kuo-Lan Su (National Yunlin University of Science and Technology, Taiwan)
The article designs a four-joint robot arm using PLC-based control system. The
control system is all in one device to be produced by the DELTA Company, andcontain four axis controllers and drivers. The robot arm contains four AC
servomotors, four driver devices and a vision system. The PLC-based controller
also programs motion commands of the gripper to finish the assigned tasks
using Ladder Diagram (LG), Function Block Diagram (FBD), Sequential
Function Chart (SFC), Instruction List (LL) and Structure Test (ST). Eachdriver has been tuned the parameters of the PID controller for the robot arm.
The human machine interface (HMI) is a touch panel to be used for the robot
arm. Users can control the motion path of any joint on the user interface. In theexperimental results, users can program English or Chinese words or plot
assigned graphs on the human machine interface. The SCARA robot arm
catches the pencil, and put down to touch the assigned position repeat on theplatform, and identifies the precious of the robot arm, and catches various
objects to put down the assigned positions.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 45 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS2-4 Transmission Power Control for Wireless Sensor Network

Kuo-Hsien Hsia1, Chung-Wen Hung2, Hsuan T. Chang, and Yuan-Hao Lai2
(1Far East University, Taiwan) (2National Yunlin University of Science and Technology, Taiwan)

Wireless sensor networks can be widely applied for a security system or a smart
home system. Since some of the wireless remote sensor nodes may be poweredby energy storage devices such as batteries, it is a very important issue totransmit signals at lower power with the consideration of the communication
effectiveness. In this paper, we will provide a fuzzy controller with two inputs
and one output for received signal strength indicator (RSSI) and link quality
indicator (LQI) to adjust transmission power suitably in order to maintaining acertain communication level with a reduced energy consumption. And we will
divide the sampling period of a sensor node into four intervals so that the sensor
node radio device does not in receiving or transmission status all the time.
Hence the sensor node can adjust transmission power automatically and reduce
sensor node power consumption. Experimental results show that the battery life
can be extended to about 10 times for the designed sensor node comparing to anormal node.

OS2-5 Mechanism of Autonomous Mowing Robot for Long Grass

Kuo-Hsien Hsia1, Yao-Shing Huang2, Kuo-Lan Su2 and Jr-Hung Guo2
(1Far East University, Taiwan)
2( National Yunlin University of Science and Technology, Taiwan)

A land full of grass can be easily seen all over the world. For example, a golfcourse, a large playground, garden and waste plowed farmland and wild
wasteland, all of above are full of grass. Almost all mowing work are operated
by manpower, especially the long grass section. There have been someautonomous mowing robots for short grass up to now. However, there is almost
no commodity for long grass mowing in the market. The main possible
consideration may be the issue of safety. It is highly possible that a sharp mower
blade may cause harm under the condition that the mower is not operated
directly by a skilled person. In this paper, we will focus on the mechanism
design for an autonomous mowing robot that can cut long grass safely. We will
also outline the safety requirements for an autonomous mowing robot for longgrass.

OS2-6 Design of Optimal Position Controller for Three-Phase Brushless DC Motor Applying
Adaptive Sliding Mode Control
Tai-Huan Tsai, Mei-Yung Chen(National Taiwan Normal University, Taiwan)

In this paper, we design an adaptive sliding mode controller which is applied on
optimal position tracking of three-phase brushless DC motor. Consider theuncertainties and external disturbances of a three-phase brushless DC motor,
we choose the sliding mode control (SMC) to be major one. Usually we can’t
figure out the motor’s uncertainty, so we propose an adaptive control to tune themotor’s uncertainty, that would be able to handle the unknown uncertainties anddisturbances. Then we proof the stability of system by Lyapunov function, andthe simulation results show excellent performance of this controller.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 46 -

u

Lu

iu
R

v

iv
R

w

iw
R

Luw

+ eu -

Lv

Luv

Lw

Lvw

+ ev + ew -nThe 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS3 Intelligence Control Systems and Applications II (6)OS3-1 Image Compression Using Hybrid Evolution Based Takagi-Sugeno Fuzzy Neural
Network
Chia-Nan Ko and 2Ching-I Lee
Nan Kai University of Technology)

1

(

1,2

This article proposes a model of hybrid evolution-based Takagi-Sugeno fuzzy
neural networks (TSFNNs). The model is to research and analyze how to
improve the efficiency of image compression. In the proposed model, the hybrid
evolution method integrates the advantages of improved quantum-behaved
particle swarm optimization, adaptive annealing learning, and mutation
operation to train Takagi-Sugeno fuzzy neural networks. The proposed hybrid
evolution-based quantum-behaved particle swarm optimization Takagi-Sugeno
fuzzy neural networks (HEQPSO-TSFNNs) can improve the coding result
around boundaries to enhance the coding efficiency of the lossless compressionof images.

OS3-2 MQPSO Algorithm Based Fuzzy PID Control for a Pendubot System
Li-Chun Lai, 2Yu-Yi Fu, and 3Chia-Nan Ko(1 National Pingtung University, Taiwan)
2,3( Nan Kai University of Technology, Taiwan)
1

In this study, a modified quantum-behaved particle swarm optimization(MQPSO) approach is proposed to design an optimal fuzzy PID controller forasymptotical stabilization of a pendubot system. In the fuzzy PID controller,
parameters are determined by using MQPSO algorithm. The MQPSO method
and other PSO methods are then applied to design an optimal fuzzy PID
controller in a pendubot system. Comparing the simulation results, thefeasibility of the MQPSO are verified.

OS3-3 A Sensorless Ultra-High Speed Motor Driver

Chung-Wen Hung, Yan-Ting Yu, Bo-Kai Huang and Wei-Lung Mao(National Yunlin University of Science and Technology, Taiwan)

An ultra-high speed motor is important in machine tools. A speed sensor-lessultra-high speed motor driver is proposed in this paper. Due to cost, only few
drivers build up a closed-loop control, which could improve driver’s
performance. Therefore, a sensor-less speed estimation for ultra-high speed
motors is developed. The design and implementation of the driver and feedback
circuits are detailed in this paper. And experimental results show that the
proposed system is workable.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 47 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS3-4 Android-Based Patrol Robot Featuring Automatic Vehicle Patrolling and Automatic Plate
Recognition
Chian C. Ho, Shih-Jui Yang, Jian-Yuan Chen, Chang-Yun Chiang, and Hsin-Fu Chen(National Yunlin University of Science and Technology, Taiwan)

This work develops an Android-based patrol robot featuring Automatic Vehicle
Patrolling (AVP) and Automatic Plate Recognition (APR). The AVP feature
integrates 3 novel methods, wheel-wheelcover-based AdaBoost wheel
detection, contour-wheel-oriented vehicle approaching, and Ad-Hoc-based
remote motion control. The APR feature integrates 4 novel methods, Wienerdeconvolution vertical edge enhancement, AdaBoost plus vertical-edge plate
detection, vertical-edge horizontal-projection histogram-segmentation stain
removal, and customized optical character recognition. Implementation results
show the vehicle detection rate and plate recognition rate of the Android-based
patrol robot are over 96% and over 94%, respectively, under various scene
conditions. On the other hand, the average execution time of AVP and APR of
the Android-based patrol robot takes at most 8 second per round and at most
0.8 second per frame, respectively.

OS3-5 Adaptive CMAC Filter for Chaotic Time Series Prediction

Wei-Lung Mao, Suprapto, and Chung-Wen Hung
(National Yunlin University of Science and Technology, Taiwan)

Chaotic signal is a natural phenomenon exhibiting in every condition of
dynamical system. Chaotic signals are almost unpredictable, noise-like,
uncertain and irregular behavior, yet they are very useful in numerous
applications of signal processing. Due to their behaviors, the quest of a good
method to model and analyze of the chaotic signal is very crucial. This paper
present a novel strategy to analyze chaotic signal using the cerebellar model
articulation controller (CMAC) network combined with evolutionary
algorithms (EAs) such as biogeography-based optimization (BBO), genetic
algorithm (GA) and particle swarm optimization (PSO). Mackey-glass chaotic
signal time series is tested and demonstrated by the conventional and the
proposed algorithms. They are compared with each other to determine theoptimal filtering and prediction. The result demonstrated that the CMAC
combined with EAs could filter, predict and estimate chaotic signal time series
well rather than the conventional methods. The best result of the algorithms
tested for chaotic signal time series is the CMAC combined with BBO
algorithm.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 48 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS3-6 Surface Defect Detection for Anodized Aluminum Tube Based on Automatic Optical
Inspection

Hsien-Huang P. Wu and Hsuan-Min Sun (National Yunlin University of Science and Technology, Taiwan)
This paper proposed using the automated optical inspection (AOI) technology todevelop a system which can automatically detect and classify defects for theshock absorber tube (SAT) made with steel. It is a high economic product which
requires high-quality even under mass production. Nevertheless, the current
manual quality-inspection is not only error-prone but also very manpower
demanding. Due to the strong reflective property of the surface, as well as its
various sizes and subtle flaws, it is very difficult to take good quality image for
automatic inspection. However, based on the surface properties and shape of the
SAT, lighting and proper structure combined with line scan camera have been
designed to acquire image with good quality. Methods were proposed to detectvarious kinds of defects, and experimental results show that all the defects can bedetected in real time. We believe the proposed system can greatly increase theefficiency and accuracy of defect detection and decrease the cost of manual labor.

OS4 Human-In-The-Loop (HITL) Systems (4)OS4-1 Feature Extraction for Digging Operation of Excavator

Kazushige Koiwai, Toru Yamamoto (Hiroshima University, Japan), Takao Nanjo, Yoichiro Yamazaki,
Yoshiaki Fujimoto (KOBELCO Construction Machinery CO., LTD. , Japan)

Japanese Ministry of Land, Infrastructure, Transport and Tourism pushes apolicy of “i-construction”. The technologies of Information andCommunication Technology (ICT), Internet of Things (IoT), Big Data, and AI
are applied to some construction equipments based on the policy. As the result,manpower-saving are achieved. However, the manpower is still required
continuously in the construction field because skilled operators are needed forcomplicated conditions. Those skilled operators who have the unique techniqueare reduced in these days. In this study, the skill difference of digging operations
between the skilled operator and the novice operator are evaluated based on theview of the control engineering. The database driven human skill based PID
controller is applied to the skill evaluation for the digging operation. In
considering a human to be a controller, the skill can be evaluated numerically.
Moreover, it would be possible to apply the skill improvement for the operation.

OS4-2 Design of a Data-Oriented Kansei Feedback System

Takuya Kinoshita, Toru Yamamoto (Hiroshima University, Japan)

In the development of the aging society, it is important for patients withhemiplegia to introduce the adaptive welfare equipment. However, it is difficult
to support them by using general welfare equipment because there are a lot of
individual disabilities. Therefore, an adaptive welfare equipment is needed innear future. However, it is very difficult to determine the suitable reference
signal for each person. In this study, the design of a data-oriented cascade
control system based on Kansei is proposed. In the proposed control system,
there are two controllers which are a data-driven controller and a fixed
controller. In particular, a data-driven controller is for a human and it cancalculate the suitable reference signal of a welfare equipment based on Kansei.
The effectiveness of the proposed scheme is verified by simulation results.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 49 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS4-3 Parameter Estimation of Skill Evaluation Model

Kazuo Kawada, Toru Yamamoto (Hiroshima University, Japan)

In this study, the aim is to construct "teacher-student model" for optimal skill
acquisition. To support the optimal skill acquisition, modeling and estimationof individual learning process are very important. The first-order system withdead time (system gain K, time constant T, and dead time L) is introduced as
individual learning process model based the control engineering approach.
These system parameters included in the learning process model are estimatedby using a real-coded genetic algorithm (GA). In order to evaluate theeffectiveness of the proposed scheme, it is employed for the classification taskof fastener components.

OS4-4 Human Skill Quantification for Excavator Operation using Random Forest

Hiromu Imaji, Kazushige Koiwai, Toru Yamamoto (Hiroshima University, Japan)
Koji Ueda, Yoichiro Yamazaki, Yoshiaki Fujimoto (KOBELCO Construction Machinery CO., LTD. , Japan)
In the construction field, the improvement of the work efficiency is one ofimportant problems. However, the work efficiency using construction
equipments is depend on their operation skills. Thus, in order to increase thework efficiency, operation skill is required to be quantitatively evaluated. In
this study, the Random Forest (RF), one of machine learning method, isadopted as the quantitatively evaluation for the operation skill of construction
equipment. Evaluated target is the operation on excavation to load onto a truck
for a hydraulic excavator. The RF learns to classify some states by the pilot ofexpert’s operation. States are defined as ‘excavate’, ‘lift’, ‘dump’, ‘return’ and‘rest’. The RF with the learning result of expert is applied to other operator’soperation. It is revealed that the rate of ‘rest’ is related to their skill.

OS5 Human Interface and Content Security (5)OS5-1 A Method for Secure Communication Using a Discrete Wavelet Transform for Audio
Data and Improvement of Speaker Authentication

Kouhei Nishimura, Yasunari Yoshitomi, Taro Asada, Masayoshi Tabuse (Kyoto Prefectural University, Japan)
We developed a secure communication method using a discrete wavelet
transform. Two users must both have a copy of the same piece of music to beable to communicate with each other. The music and the sender's message are
encoded using the scaling coefficients obtained from a discrete wavelet
transformation. The message receiver can produce the audio data similar tothe sending user's speech using an inverse discrete wavelet transform, together
with information on the difference between these two codes. For improving
the speaker authentication accuracy, the quantization level for the scalingcoefficients is increased. Furthermore, the amount of data sent to the message
receiver is remarkably reduced exploiting the characteristic of those data.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 50 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS5-2 A Recipe Decision Support System Using Knowledge Information and Agent
Keita Saito, Taro Asada, Yasunari Yoshitomi, Ryota Kato, Masayoshi Tabuse(Kyoto Prefectural University, Japan)

We have developed a system for recipe recommendation using collaborative
filtering and impression words. As a human interface, we have adopted anagent named as MMDAgent. In the proposed system, the firstrecommendation process using collaborative filtering is terminated when nousers in the reference list have the same preference of recommended recipe asthat of a new user. Then, the second recommendation process finds the mostsimilar recipe, by exploiting the scores for impression words, to thosesuccessfully recommended among recipes not recommended up to themoment. The recommendation accuracy of the proposed system was muchbetter than that of random recommendation. As a final step, one recipe isselected from all successfully recommended recipes for the user by using the
reputation for them in a database and interaction between the user and theagent.

OS5-3 A System for Analyzing Facial Expression and Verbal Response of a Person While
Answering Interview Questions on Video
Taro Asada, Yasunari Yoshitomi, and Masayoshi Tabuse(Kyoto Prefectural University, Japan)

We have developed a system for analyzing facial expressions of a person
while answering interview questions on video. A video captured an answererat an interview is analyzed using image processing software (OpenCV) andthe previously proposed feature parameter (facial expression intensity), whichis measured for an eye-part area in addition to the mouth-part area focused inour reported research. Moreover, the time to utterance of the answerer justafter an interview question is measured for analyzing the mental state of theanswerer. In our previously reported system for analyzing facial expression ofa person during conversation with another person, facial expression intensity
could be affected by (1) a conversation topic, (2) a partner and (3)the facial
expression of the partner. In this study, all of (1), (2) and (3) are fixed by using
an interview video. The experimental result shows the usefulness of the
proposed system.

OS5-4 Real-Time System for Horizontal Asymmetry Analysis on Facial Expression and Its
Visualization
Ryoichi Shimada, Taro Asada, Yasunari Yoshitomi, and Masayoshi Tabuse(Kyoto Prefectural University, Japan)

We have been developing a real-time system for horizontal asymmetry
analysis on facial expression and its visualization. The image signal input
from web camera is analyzed by our real-time system using image processing
software (OpenCV) and the feature parameter (facial expression intensity),
which is separately measured for the left and right half regions in the mouthpart area. Our real-time system draws the graph expressing the facial
expression intensity change using OpenCV. The experimental result suggests
the usability of this system for a test where a real-time reaction of the facial
expressions is one of important factors.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 51 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS5-5 Development of Mouse System for Physically Disabled Person by Face Movement Using
KinectJunpei Miyachi, Masayoshi Tabuse (Kyoto Prefectural University, Japan)
It is necessary to support of computer operation for a physically disabled
person. One of the possible physical movements of the physically disabled
person is facial movement. Recognition of facial movement of a person makes
it possible to operate a computer. Furthermore without the adjustment for auser and adjustment for the distance from a user, it is possible to reduce theburden on a user. We developed a system to resolve these problems. In oursystem, Kinect is used to obtain the face direction and extract feature pointsof the face. Changing the face direction, we can move a mouse cursor.
Recognizing an open mouth or closed eye, we can carry out an operation ofmouse click. In this paper, we evaluated the effect on operability due to theface direction and recognition rate due to distance.

OS6 Software Development Support Method (6)OS6-1 Development of a Tool for Extended Place/transition Net-Based Mutation Testing
Tomohiko Takagi1, Shogo Morimoto1, Tetsuro Katayama2(1Kagawa University, 2University of Miyazaki, Japan)

This paper shows a tool for EPNBMT (Extended Place/transition Net-Based
Mutation Testing) to evaluate and improve the quality of test cases forconcurrent software. The tool consists of the following four functions: (1) an
original EPN can be created to define the expected behavior of concurrent
software under test, (2) mutant EPNs including intended failures can becreated by applying mutation operators to the original EPN, (3) arbitrary test
cases to be evaluated can be converted to execution paths on the original EPN,
and (4) each mutant EPN can be executed by the converted test cases in orderto calculate a mutation score. If the mutation score is not good, the test cases
are improved based on mutant EPNs whose failures have not been detected.

OS6-2 Improvement of Decision Table Automatic Generation Tool VDTable for let in Statement
Yinuo Huang*, Tetsuro Katayama* Yoshihiro Kita†, Hisaaki Yamaba*, Naonobu Okazaki*(*University of Miyazaki, Japan, †Tokyo University of Technology, Japan)

Formal methods, in software development, is one of the means to describe thespecification strictly. On the other hand, the decision table is one of the testing
techniques to exhaustively represent the logical combination included insoftware. The VDTable can automatically generate the decision table, fromthe specification written in specification description language VDM++(VDM++ specification). But, the VDTable, since many VDM++ syntax is notcorresponded, its usefulness is limited. To improve the usefulness, this paper
expands VDTable. Specifically, VDTable is improved to be able to correspondto the statement “let in”, which is one of the VDM++ syntax, that the existing
VDTable does not support.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 52 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS6-3 Prototype of Test Cases Automatic Generation Tool BWDM Based on Boundary Value
Analysis with VDM++
Hiroki Tachiyama*, Tetsuro Katayama* Yoshihiro Kita†, Hisaaki Yamaba*, Naonobu Okazaki*(*University of Miyazaki, Japan,†Tokyo University of Technology, Japan)

As a method to eliminate the ambiguity and to write strictly specification,
VDM++ (one of the Formal Methods) is proposed. However, it takes the timeand effort of testers to design test cases and to carry out software testing, from
specification written in VDM++.Therefore, to optimize testing process, wedeveloped BWDM; the tool of automatic generation of test cases from
VDM++ specification. BWDM analyzes boundary value of if-expressions
and argument types in definition of functions. And, it generates test caseautomatically from a result of the analysis. This tool aims to optimize software
testing process by both of automatically generation and designing test cases
based on a boundary value analysis, which can detect statements that bugs
seem to be included in.

OS6-4 Prototype of Refactoring Support Tool MCC Focusing on the Naming of Variables
Satoshi Tanoue*, Tetsuro Katayama* Yoshihiro Kita†, Hisaaki Yamaba*, Naonobu Okazaki*(*University of Miyazaki, Japan,†Tokyo University of Technology, Japan)

This research has implemented a prototype of refactoring support tool
MCC(Make Clean Coder) which focuses on the naming of variables. This
prototype helps to describe a clean code by static analysis for the source code
written in C language. And, it can help to reduce factors that prevent
programmers understanding the source code when they modify it by pointing
out improper variable names. We applied some source codes written in C
language to the prototype, and confirmed that it works properly. By using thisprototype, because it can reduce reduction of time to understand the source
code, programmers can shorten the coding time, lower the possibility ofembedded bugs, and decrease in the time required to add functions.

OS6-5 Improvement of Transitions and Flow Visualization TFVIS for Exception Handling.
Takuya Sato*, Tetsuro Katayama*, Yoshihiro Kita†, Hisaaki Yamaba*, Naonobu Okazaki*(*University of Miyazaki, Japan,†Tokyo University of Technology, Japan)

In software development, it takes much time in debugging process. To findbugs in a program effectively, it’s important to understand the dynamic
behavior of the program. But it’s difficult since the dynamic behavior of the
program is generally invisible. So we developed TFVIS(transitions and flow
visualization), which can visualize the dynamic behavior of Java programs. It
provides visualization of data transitions in each method and flow of thewhole program. But it’s not useful because it visualizes only some control
structures and expressions. We aim at the improvement of the usefulness bycorresponding an unsupported structure. This paper focuses on Exceptionhandling, which is one of the feature of Java program, and especiallycorresponds to Try-Catch syntax.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 53 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS6-6 Automatically Business Decision Making System for Software Developmentby using
CMMI
Hnin Thandar Tun†, Tetsuro Katayama*, Kunihito Yamamori*, Khine Khine Oo†(*University of Miyazaki, Japan, †University of Computer Studies, Myanmar)

In the current software development, business decision making system isimportant process to support the suggestive business goals automatically in a
specified area or project. In this paper, we proposed the CMMI model that isused to define the business goals for monitoring and control measurements
applied by GQM approach. This paper suggests the key processes of CMMI
which is necessarily for support on business effective process improvement,
needs design implementation, and needs techniques and tools for achieving
the future targets of goals systematically. For this illustration purpose, weneed to define the business data which will be defined goals by senior level
management to come out business goals aligned with resources functionality.

Excel
DatasetSenior Management

Define Phase

Project Monitoring
and Control

Goal Question Metric Approach
Graph & Chart Tool

Monitoring
ReportOS7: Advances in Marine Robotics and Applications (6)OS7-1 Development of a Hydraulic Underwater Manipulator for Deep-Sea Survey AUV
Kazuo Ishii, Amir Ali Forough Nassiraei, Ivan Godler, Takashi Sonoda, Tharindu Weerakoon(Kyushu Institute of Technology, Japan)

We are developing a group of AUVs whose mission is to capture underwater
creatures or acquire in-situ biological samples in the deep sea. The AUV underdeveloping has a hydraulic manipulator to capture the target creature, andsends underwater images that provide biologists (operators) with information
to determine which creatures should be targeted and captured during theoperation. In this paper, the concept and design of hydraulic actuatorfor 5-axis manipulator, and the mechanics of manipulator is
proposed, and the experimental results of manipulator control at 10
and 20 M Pa pressure are shown.

OS7-2 Experiments of floatable UAV drone for wave dissipating block inspection(I)

Watanabe, Kazuho Mitsumura, Koshi Utsunomiya, Shiyun Takasaki (Tokai University, Japan)

Determination of underwater coordinates is important in such activities likeunderwater vehicle operation, installation of underwater structure and so on.
The determination methods are using LBL, SSBL or an inertia navigation
sensor, which are all very expensive. In his paper, a floating LBL concept ispresented. The LBL system consists of at least three floating buoys each ofwhich has a GPS, a pair of ultrasound transmitter and receiver, wirelesscommunication device and a microcomputer board which controls the total
procedure. We will introduce and explain about each module of the buoysystem, ultrasound transmitter/receiver measuring distance experimental
results as the first step of this research©ICAROB 2017 ALife Robotics Corp. Ltd.

- 54 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS7-3 Experiments of floatable UAV drone for wave dissipating block inspection(II)Keisuke Watanabe, Kazuho Mitsumura, Koshi Utsunomiya, Shiyun Takasaki (Tokai University, Japan)
UAV drones are an effective low cost photographing apparatus for ocean
environment monitoring. Taking images is essential for these environment
monitoring such as coastal line monitoring where beach erosion is severe,
inspection of offshore structures like wind power stations or coral reef
observation etc. In this presentation, we present our modification experienceof making a drone watertight and floatable. Then we present several
experiments on taking pictures considering ocean monitoring in the sea near
our campus. The waterproof experiment is done from our school’s pier. In thisexperiment, we repeated to start flying the drone from the sea surface and fallit down into the water several times. We can see the shoreline very clearly
from more than 200m high as a macro image and also we can inspect theblocks looking around its stacks as well as approaching near the sea surface
where waves break and wave splash washes the drone.

OS7-4 Development of self-diagnostic system of an autonomous underwater vehicle
Tuna-Sand 2
Naoya Fujii, Yuya Nishida, Kazuo Ishii (Kyushu Institute of Technology, Japan)
For autonomous underwater vehicle (AUV), high autonomy is required inorder to accomplish mission such as inspection, observation, manipulation
under extreme environments, deep-ocean. One of necessary function for
AUV is the self-diagnosis system to detect the abnormality can be said to be
an important feature . In this paper, we propose a self-diagnostic system using
the dynamical model of Sampling-AUV “TUNA-SAND2”, where the fault
device detection is carried out using the model, and evaluated through tanktests.

OS7-5 Development of Underwater Wireless Power Supply System Using resonant energy
transfer
Kazuo Ishii, Hidaka Shota, 1Keisuke Watanabe(Kyushu Institute of Technology, 1Tokai University, Japan)

Autonomous Underwater Vehicles (AUV) is one of the key technologies fordeep ocean research. However, AUV cannot work for long time due to thelimitation of battery capacity. To solve this problem, a noncontact wirelesspower supply is required for AUV to conduct observations for long period. In
this study, we developed a resonant energy transfer system, which is mounted
into a small AUV “DaryaBird” and the simulations and evaluation tests arecarried out in the test tank.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 55 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS7-6 Development of End-effector for Sampling-AUV “TUNA-SAND2”

Kazuo Ishii, Takashi Sonoda Atsushi Nishjima, 1Keisuke Watanabe(Kyushu Institute of Technology, 1Tokai University, Japan)

AUV requires high autonomy to accomplish various deep-ocean observation
missions, and the next AUV is expected to have sampling function of marine
benthos, that is, the end-effector to catch and bring back marine creatures.
However, sampling is not easy task for AUV as the shape, size, motion oftargets differs. One of the effective end-effectors is the slurp-gun type device
to absorb the target with water. In this research, we had developed a sampling
device for deep-ocean observation and sampling AUV, Sampling-AUV
TUNA-SAND2 in order to inspection of marine ecosystem.

OS8 Multiagent systems and Reality Mining (4)OS8-1 Influence of Partner Selection on Functional Differentiation: Emergence of Diversity by
Isolated Interaction and Preference Change
Saori Iwanaga (Japan Coast Guard Academy, Japan), Akira Namatame (National Defense Academy, Japan)
Functional differentiation is important in nature. For example, in an ant
colony, there are ants which work and does not work. I focused on how theheterogeneity emerged. I modeled that each agent can self- reinforce own
preference in homogeneous population. There, the behavior reinforce theproperty and the property reinforce the behavior of an agent. Then, I foundthat interaction with steady agents enforce the properties. But, interaction withsteady partners doesn't lead optimal collective behavior. We found that thepartner change is good method for optimal collective behavior for both eachagent and population.

OS8-2 Beacon-based tourist information system to identify visiting trends of tourists

Akihiro Yamaguchi1, Masashi Hashimoto1, Kiyohiro Urata1, Yu Tanigawa1, Tetsuya Nagaie1, Toshitaka Maki1,
Toshihiko Wakahara1, Akihisa Kodate2, Toru Kobayashi3, Noboru Sonehara4(1
Fukuoka Institute of Technology, Japan) (2 Tsuda College, Japan) (3 Nagasaki University, Japan)(4 National Institute of Informatics, Japan)
In this study, we propose a system that provides tourist information andobtains trends of visiting tourists using beacons and cloud service. As part ofour research, we are working on the promotion of local area tourism incooperation with a local community. A low energy Bluetooth device is used
as a beacon to transmit a universally unique identifier. In addition, beacons
are placed at sightseeing spots and tourist facilities. Our proposed systemcomprises two application programs; one is a client-side application programthat provides area-specific tourist information corresponding to the detected
beacon. The other is a server-side application to record time and location
information of the detected beacons. In this paper, we describe the scheme ofour system, and present the results of experiments conducted using theprototype system in the local tourist area. In addition, we discuss an open
platform for information collection services using beacons.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 56 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS8-3 Analysis of Survey on Employment Trends
Masao Kubo (National Defense Academy, Japan), Hiroshi Sato (National Defense Academy, Japan), Akihiro
Yamaguchi (Fukuoka Institute of Technology, Japan), Yuji Aruka (Chuo University, Japan)
Employment is a major important issue of economics and it has been difficult
for the public to collect authentic information about employment because thisis a sort of information should be kept secret on the both side, employer andemployee. Therefore we try to reveal a trend of recent employment of Japan
from Survey on Employment Trends of Japan which is a report publishedfrom Health, Labor and Welfare Ministry. This questionnaire data is mainly
categorized by industrial sectors so we need extra works to know the relations
and correlations over sectors. In this paper, we show results of traditional
machine learning methods to this issue.

OS8-4 Direction switch behavior to enclose a target

Masao Kubo, Nhuhai Phung, Hiroshi Sato (National Defense Academy, Japan),This paper discusses a target enclosing behavior when a part of the orbit isblocked. Usually, an orbit to enclose a target supposes to be clear. However,this situation often takes place in realistic environment. For this issue we
propose an online adaptive method to enclose a target. The group of robots
communicate their direction until they resolve their difficulties.

OS9 Theory and Implementation of Neuromorphic Systems (5)OS9-1 Implementation of Multi-FPGA Communication using Pulse-Coupled Phase Oscillators
Dinda Pramanta, Takashi Morie, Hakaru Tamukoh (Kyushu Institute of Technology, Japan)

This paper proposes an implementation of multi-Field Programmable Gate
Array (FPGA) communication using pulse-coupled phase oscillators. First,
serial communication between two boards of FPGAs architecture aredeveloped. First Input First Outputs (FIFOs) are used as an interface of data
buffer. Second, we construct a network over two FPGAs for evaluation asshown in Fig. 1. By using a multi-FPGA as a hardware processor through the
Gigabit Transceiver (GTX) clock domain, pulse-coupled phase oscillators of
Winfree's model are utilized as a spike generator and we expect the ideal ofmodel circuit will synchronize. We employ two Virtex6-Ml605 FPGA boards
to construct a mult-FPGA and implement four oscillators on a hardware level.
Experimental results show that each board (two oscillators) obtains therequired number of resources of 16 % IOB, 434.972 MHz maximum
frequency, and first spike synchronizing over two FPGAs takes 12.47 μsec
with data bit speed stream 3.2 Gbps.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 57 -

Fig.1 Communication overtwo FPGA boards using
pulse-coupled phaseoscillators

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS9-2 Multi-Valued Quantization Convolutional Neural Networks toward Hardware
Implementation
Yoshiya Aratani, Yoeng Jye Yeoh, Daisuke Shuto, Takashi Morie, Hakaru Tamukoh(Kyushu Institute of Technology, Japan)

This paper proposes a Multi-Valued Quantization (MVQ) of connecting
weight for efficient hardware implementation of Convolutional Neural
Network (CNN). The proposed method multiplies an input value by a multivalued quantization weight during the forward and backward propagations,while retaining precision of the stored weights for the update process. In theboth propagation processes, multipliers can be replaced with adders andshifters by setting appropriate quantized weights. We train two- to six-valued
quantization CNNs with MNIST and CIFAR-10 dataset to compare theperformance of them with a 32-bit floating point CNN. In the four-valued
quantization, random noise is added to the quantized weight to improve theperformance of generalization ability. In addition, the robustness of MVQ
CNN to noise is evaluated. Experimental results show that the MVQ CNNs
achieve better learning accuracy than the floating point CNN and the fourvalued CNN is highly robust to the noise.

Fig.1 Comparison among
floating point,
BinaryConnect, MVQ-4 and 6.

OS9-3 An Improved Parameter Value Optimization Technique for the ReflectionlessTransmission-Line Model of the Cochlea
Takemori Orima, Yoshihiko Horio (Tohoku University, Japan)
The reflectionless transmission-line model of the cochlea can reproduce
physiological cochlear properties by adjusting its parameter values. We have
employed an optimization technique to obtain good parameter values that givea desired cochlear property. However, we found that, in some case, it isdifficult to find the sub-optimal parameter values because of complex
dependencies between parameters. In this paper, we explicitly formulate thecochlear properties in the reflectionless transmission-line model to understandthe dependencies. Furthermore, based on the results, we propose an improved
optimization technique for determining the parameter values.

OS9-4 A parameter optimization method for Digital Spiking Silicon Neuron model
Takuya Nanami, Takashi Kohno (The University of Tokyo, Japan)
DSSN model is a qualitative neuronal model designed for efficient
implementation in a digital arithmetic circuit. In our previous studies, we
extended this model to support a wide variety of neuronal classes. Parametersof the DSSN model were hand-fitted to reproduce neuronal activity precisely.
In this work, we studied automatic parameter fitting procedure for the DSSN
model. We optimized parameters of the model by a GPU-based
implementation of the differential evolution algorithm in order to reproduce
waveforms of the ionic-conductance models and reduce necessary circuit
resources for the implementation.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 58 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS9-5 A Multistage Heuristic Tuning Algorithm for an Analog Silicon Neuron Circuit
Ethan Green and Takashi Kohno (The University of Tokyo, Japan)

Analog silicon neurons, electronic circuits that mimic theelectrophysiological characteristics of neuronal cells, may in the future beused as fundamental building blocks of neuromorphic technologies likebrain-mimetic computers. This research looks at an ultra-low power
subthreshold-operated silicon neuron circuit designed with qualitative
neuronal modeling. One technical challenge to future implementation ofsuch circuits is parameter tuning—a problem stemming from temperaturesensitivity of subthreshold-operated MOSFETs and the uniqueness of
individual circuits in a neuronal network due to transistor variation. This
research proposes a fully automated parameter tuning algorithm thatcombines two heuristic approaches to search for appropriate circuit
parameters over a range of temperatures. The algorithm only requires theuser to input the benchmark circuit characteristics and can tune the circuit
to behave as a Class I or Class II neuron.

OS10 Biological Signal Sensing Technology, Device and Its Applications (5)OS10-1 Mouse Cursor-like Control System in Consideration of the DC-EOG Signals
using EOG-sEMG Human Interface
Mingmin Yan, Yu Cheng, Keiko Sakurai, Hiroki Tamura, Koichi Tanno(University of Miyazaki, Japan)

Patients who suffered with the limb disorders cannot take care of themselves
by communication barrier. However, it is possible to improve thecommunication abilities of the patients by using biological signal such as
ocular potential and muscle potential which is caused by moving eyes or
facial muscle. Therefore, we have developed the human interface using theelectrooculogram (EOG) and the facial surface electromyogram (sEMG)
signals which can control PC. However, this system could not control themouse cursor in accordance with the direction and magnitude of the
movement of eyes to control PC smoothly and intuitively. Thus, we proposeda new mouse cursor-like control system in consideration of the DC elementsof EOG signals using the EOG-sEMG human interface. Our proposed
method has both drift and blink countermeasures which had better
performance in mouse cursor control, and all subjects could control themouse cursor for both moving and clicking flexibility.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 59 -

EOG-sEMG Human Interface

PC Control

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS10-2 A Study on Eyes Tracking Method using Analysis of Electrooculogram Signals

Keiko Sakurai, Mingmin Yan, Hiroki Tamura, Koichi Tanno (University of Miyazaki, Japan)

Gaze estimation has been an active research in the past years and it is an
important technique for transmitting the intention for seriously physicallyhandicapped person who cannot move their body. In this paper, we proposed
eye tracking method using electrooculogram signals which is widely appliedin medical field. The proposed method has the merit to have low burden of
the patient, but there is the problem not to be so high in resolution. Therefore,
we carried out the experiments by electrooculogram method to make a study
the calculation method of the electrooculogram element with strong
correlation for the change of the eyeball movements. Specifically, we
examined by comparing next values; alternating current signals, direct
current (DC) signals, integral value of DC signals and DC difference values.
From these experimental results, we show the possibility of eyes tracking
method using the analysis of electrooculogram signals.

OS10-3 Development of Diagnosis Evaluation System of Facial Nerve Paralysis Using sEMG
Shogo Okazaki, Misaki Syoichizono Hiroki Tamura, Takahiro Nakashima, Eiji Kato, Tetsuya Tono
(University of Miyazaki, Japan)
In Japan, diagnostic of facial nerve paralysis is mainly used theelectroneuronography (ENoG) method. However, this method has thepossibility that there is variability of the diagnosis results by doctors.
Therefore, it is necessary the quantitative diagnostic methodology. Previous
our research showed the certain correlation between diagnosis results of
ENoG and surface-electromyogram (sEMG) data. In this paper, wedeveloped the diagnosis evaluation system of facial nerve paralysis usingsEMG by software. We compared with our developed software and manualby experimental person. From simulation results, our developed software
showed the correlation coefficient (R2) between ENoG is 0.68. In our future
works, we evaluate our system by the opinion of otolaryngologist.

OS10-4 High CMRR and Wideband Current Feedback Instrumentation Amplifier Using
Current Conveyors
Shota Mago, Hiroki Tamura, Koichi Tanno (University of Miyazaki, Japan)
This paper presents a high CMRR and wideband current feedback
Instrumentation Amplifier (IA). The proposed IA architecture consist of
Fully Balanced Differential Difference Amplifier (FBDDA) andDifferential Difference Amplifier (DDA) based on 2 nd generation current
conveyor (CCII)
with a buffer. The proposed IA was evaluated by using
HSPICE simulation with 1P 3M 0.6-um CMOS process. From thesimulation results, the proposed IA exhibits average CMRR was 109.3 dB
higher than the conventional one. Furthermore, since the current feedback
based on CCII is adopted, the proposed IA can get higher closed-loop gain
over a larger bandwidth than corresponding voltage feedback.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 60 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS10-5 Voltage Rectifier Circuit with Voltage Doubler Using New Active Diode

Masayuki Uchihara, Hiroki Tamura, Kochi Tanno (University of Miyazaki, Japan)

In this paper, voltage rectifier circuit with voltage doubler using new Active
Diode (AD) is proposed for battery less biological signal measurement
system using smartphone. Firstly, we propose the new AD which consistsof AD core circuit and Bulk Regulation Transistor (BRT). The advantage of
proposed AD is insensitive to the threshold voltage of MOSFETs and very
useful for low power and low voltage operation. Next, the voltage rectifier
circuit with voltage doubler using the proposed ADs is presented. Thanks to
the good performance of the proposed AD, the designed rectifier could
operate as theory by only the sin waves from the earphone terminal ofsmartphone. The proposed circuits were fabricated actually using 1-P 2-M
0.6-um CMOS process. The detailed simulation and measurement results
are reported in this paper.

OS11: Robot Control and Localization (4)OS11-1 Marker Recognition System for Localization of the Rover on the Lunar Space
Na-Hyun Lee, Jang-Myung Lee (Pusan National University, South Korea)

This paper proposes a marker realization system for rover localization inlunar environment. CPU used in the space environment is not as sensitive
to changes of the surrounding environment differently from normal CPU,but it has a lower performance. So it is necessary to use an algorithm
reduced amount of computation. In order to reduce the amount ofcomputation, we propose a marker-based algorithm. It has a smaller quantityof computation than map-based localization algorithm commonly used in
the space environment. For constructing this algorithm, we performed amarker detection test, and then extracted the center point coordinate of themarker.

image

Gaussian
Filtering

Canny Edge
detection

Hough circle
transform

Circlecenterpoint

OS11-2 Dynamic Model and Finite-Time SMC and Backstepping Control of a MabileManipulator System
Seongik Han, Hyunuk Ha, and Jangmyung Lee (Pusan National University, Korea)

A mobile manipulator was designed by combining three-wheeled mobile
robot equipped with the DC motor and three-links manipulator equippedwith dynamixel motor. The kinematic relation and dynamic model werebuilt via nonholonomic constraint and Euler-Lagrange equation. For thedecoupled model of this system, adaptive finite-time controllers sliding
mode controller (SMC) and backstepping controller were designed
respectively to obtain fast tracking response. Simulation and experimental
results show the efficacy of the proposed control scheme.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 61 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS11-3 Bilateral Control of Hydraulic Servo System for 1DOF Master Slave Manipulator
Jie Wang, Karam Dad and Min Cheol Lee (Pusan National University ,Korea)

In this research reaction force estimation method based on Sliding mode
control with sliding perturbation observer (SMCSPO) of a bilateral control
in hydraulic servo system for one degree of freedom (DOF) master-slave
manipulators is proposed. 1DOF hydraulic servo system used in dismantling
nuclear power plant. The reaction force at the end effector of slaver isestimated by sliding perturbation observer (SPO) without using any sensor.
This research verifies through experiment that slaver can follow the
trajectory of the master device using the proposed bilateral control strategy
and reaction force estimation method.

OS11-4 Advanced impedance control of haptic joystick for effective mobile robot handling
Gyung-I Choi, Jang-Myung Lee (Pusan National University, South Korea)

This paper proposes optimized structure of 6 D.O.F. haptic joystick and teleoperated mobile robot system based on haptic interfaces. Kinematic analysisof specially designed 6 D.O.F. haptic joystick derives the coordinate of end
effect. The designed haptic joystick transfers operator’s moving command
to mobile platform with force controlled coordinate information. Attachedultra-sonic sensors detect nearby obstacles including walls and retransfer
the distance between mobile platform and obstacles to the haptic joystick.
Attained real-time displacement information controls DC motors between
links supporting the handle for effective user handling. Practical simulations
and real experiments verified proposed impedance control and its teleoperation system of intelligent haptic joystick.

OS12 Machine Learning and Its Applications (4)OS12-1 A Rule-Based Classification System Enhanced by Multi-Objective Genetic Algorithm
Kenzoh Azakami, Shingo Mabu, Masanao Obayashi, Takashi Kuremoto (Yamaguchi University, Japan)
Recent years, information is diversified, and the enormous amount of datais left unorganized. Data mining is a technique of extracting rules from big
data. In this paper, we aim to realize a classification system using a rule
extraction method named genetic network programming (GNP). However,there are some problems to be considered, that is, a large number of rules
are extracted, so it is difficult to judge important rules; and even in simple
classification problems, e.g., two-class problems, a variety of patterns
potentially exist in each class. To select important rules and make ruleclusters corresponding to the potential patterns, a rule clustering algorithm
using multi-objective genetic algorithm (MOGA) is proposed to enhance
our conventional classification system.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 62 -

Rule clustering

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS12-2 A Method of Feature Extraction for EEG Signals Recognition using by ROC curve
Takashi Kuremoto, Yuki Baba, Masanao Obayashi, Shingo Mabu (Yamaguchi University, Japan)
Kunikazu Kobayashi (Aichi Prefectural University, Japan)

The feature extraction for EEG signal Recognition plays an important role
for all kinds of classifiers. However, it is difficult to recognize the activities
and mental tasks in the brain using EEG signals because the difference
between individuals and the change of mental states. In this study, we
propose a method of extract EEG signals using by FFT and receiver
operating characteristic (ROC) curve. At first, the raw EEG data areprocessed by discrete Fourier transform (DFT). After that, data of
frequency-spectrum of different mental tasks were compared by the valueof area under the curve (AUC), and the data with higher AUC (near 1.0)
were chosen as feature vectors. Experiment results using benchmark data of
EEG signals showed the effectiveness of the proposed feature extraction
method in the cases of self-organizing map (SOM), support vector machine(SVM) , and multi-layer perceptron (MLP).

Table 1 Mental tasks in a benchmark database.
Mental Task
Baseline
Multiplication
Lettercomposing
Rotation
Counting

Contents
Relaxing as much as possible
Calculating multiplication mentally.
Considering the contents of theletterImagining rotation of a 3-D object
Imagining writing a number in order

OS12-3 Forecasting Real Time Series Data using Deep Belief Net and Reinforcement Learning
Takaomi Hirata, Takashi Kuremoto, Masanao Obayashi, Shingo Mabu (Yamaguchi University, Japan);
Kunikazu Kobayashi (Aichi Prefectural University, Japan)
Artificial neural networks (ANNs) typified by deep learning (DL) is one of
the artificial intelligence technology which is attracting the most attentionof researchers recently. However, the learning algorithm used in DL isusually with the famous error-back propagation (BP) method. BP is a very
powerful parameter optimization method, however, it also learns theunpredictable noises of the data. In this paper, we adopt a reinforcement
learning (RL) algorithm “Stochastic Gradient Ascent (SGA)” into a Deep
Belief Nets (DBN) with multiple restricted Boltzmann machines (RBM).
Prediction experiments used a benchmark of time series forecasting
competition and realistic data verified the effectiveness of the proposed DL
method.

Comparison of prediction meansquared errors between different
learning methods

OS12-4 Improvement of Robot's Self-localization by Using Observer View Positional
Information

Yo Aizawa, Takuo Suzuki, and Kunikazu Kobayashi (Aichi Prefectural University, Japan)

This study aimed to improve the precision of the robot’s self-position
estimation in the standard platform league of RoboCup, i.e. a robotic soccer
competition. For the precision improvement of the self-position estimation,
we suggest a new technique that uses a camera out of the field for assistance.
Robots in the field use the unscented particle filter that estimates theirposition from landmark. When a robot which is equipped with the filter
cannot recognize landmarks exactly, particles spread and the estimation
precision of the self-position decreases. Therefore, the overlooking camera
out of the field observes each robot's position. When particles spread, thecamera out of the field estimates the foot of a robot, and then the robot
sprinkles particles on the neighborhood again. In this way, even if a robot
cannot recognize landmarks exactly, assists of camera out of the field revise
the position of particles and improve the precision.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 63 -

Resampled position of particles

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS13: Intelligent Control (6)OS13-1 Modeling and Simulation for a Quadrotor Vehicle with Adaptive Wing

Qunpo Liu , Fuzhong Wang, Hongqi Wang, Jikai Si (Henan Polytechnic University, P.R.China)
Hanajima Naohiko (Muroran Institute of Technology, Japan)

This study intends to address the questions of low effective and high cost in
the process of artificial sorting and packing of fruit. In this paper, a kind of
automatic packaging and plastic foam net packing robot based on neuralnetwork plastic foam netting is presented. The round fruit can be filteredand classified by neural network controller based on fruit’s size, skinroughness and color which are based on machine vision. And the expansion
degree of plastic foam nets is adjusted adaptively according to fruit size.
The system was simulated by V-REP simulation software. The simulation
results showed that the system could complete the classification andpacking with plastic foam mesh of round fruit.

OS13-2 Fuzzy self-tuning PID control algorithm for belt conveyor driven by multi-motor
Caixia Gao, Fuzhong Wang, Ziyi Fu (Henan Polytechnic University, P.R. China)

Aiming at the problems of low efficiency when long distance and highinclination belt conveyor driven by multi-motor is in light or idle run as wellas electrical power unbalance. Control strategy for belt conveyor driven by
four-motor via fuzzy self-tuning PID control algorithm was proposed. The
Speed setting algorithm of belt conveyor and fuzzy self-tuning PID
algorithm for speed control and power balance control are introduced. The
application shows that, according to the coal flow changes of the belt
conveyor in real-time, the speed can be automatically adjusted through thisalgorithm, making the power of the four motors convergence ,whose speed
control error and the power control error both lower than 2.6%.

OS13-3 Continuous Non-singular Fast Terminal Sliding Mode Control
for an Active Gravity Field Simulator

Jiao Jia, Yingmin Jia and Shihao Sun(Beihang University (BUAA), P.R.China)

In this study, a non-singular fast terminal sliding mode (FTSM) controller
is designed for an active gravity field simulator (AGFS). Some sufficientconditions are established to guarantee the stability by using the Lyapunov
theory. It is shown that the proposed controller can eliminate the chattering
effect without losing the robustness property. Simulation results show that
faster and high-precision tracking performance can be obtained comparedwith the conventional continuous sliding mode control method.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 64 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS13-4 Weighted Multiple Model Adaptive Control for a Category of Systems with Colored
Noise
Yuzhen Zhang, Qing Li and Weicun Zhang (University of Science and Technology Beijing, P. R. China)
The multiple model adaptive control (MMAC) of discrete-time stochastic
system with colored noise is considered in this paper. Model set consists ofa finite number of fixed and one adaptive identification models. After that,based on the output errors of the models, a simple weighting algorithm isadopted with convergence guaranteed. The proofs of global stability and theconvergence of the overall system are presented. Besides, the influence ofinitial value selection of adaptive model on system performance is
described. Finally, computer simulation results are shown to verify thetheoretical results.

OS13-5 Neutral Networks-Based Adaptive Fixed-Time Consensus Tracking Control for
Uncertain Multiple AUVs
Lin Zhao1, Yingmin Jia2 and Jinpeng Yu1(1Qingdao University, P.R.China; 2BeihangUniversity (BUAA), P.R. China)

This paper is concerned with the fixed-time consensus tracking problem for
multi-AUV (autonomous underwater vehicle) systems with uncertain
parameters and external disturbances. Firstly, a fixed-time terminal sliding
mode is proposed, which can avoid the singularity problem. Then, acontinuous distributed consensus tracking control law is designed based on
Neutral Networks approximation technique, which can guarantee theconsensus tracking errors converge to the desired regions in fixed time. A
simulation example is given to show the effectiveness of proposed methods.

OS13-6 Conducted electromagnetic interference prediction of the Buck Converter via Neural
Networks
Sumin Han, Fuzhong Wang (Henan Polytechnic University, P.R. China)

This paper proposes an approach to predict conducted electromagnetic
interference (EMI) on the power supply side in the buck converter. The
experimental scheme is designed to collect the input and output target
samples, whose correlativity is analyzed to verify their correlation degree.
The paper establishes a three-layer network including one hidden layer,
whose activation function is of the hyperbolic tangent type to ensure therapidity of convergence .The conducted EMI predicting model is establishedby sample training, the differential mode(DM) and common mode (CM)interference prediction waveforms is obtained and analyzed . The results
demonstrate that the forecasted and measured waveform is in decent
consistency of time domain and power spectrum.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 65 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS14 Advanced Control (5)OS14-1 Targeting Chaos System via Minimum Principle Control

Yunzhong Song, Ziyi Fu, Fuzhong Wang (Henan Polytechnic University, P, .R.China,)

Chaos targeting via Minimum Principle Control (MPC) was suggested here,
where like the already existing results, the targeting via MPC can stabilize
all the non-stable equilibrium when the extra added control was introducedin the first or the second equation of the chaos system, however, unlike thealready existing results, when the extra added control was introduced in thethird equation of the chaos system, the non-stable equilibrium can not bestabilized at all with the already existing methods, to be unique, the MPC
can still be effective when the added extra control was introduced in thethird equation, albeit with the cost of switching control direction for aduration.

OS14-2 Three-dimensional Leader-Follower Formation Flocking of Multi-Agent
SystemYongnan Jia, Weicun Zhang (University of Sciences and Technology, Beijing, China)

This paper aims to investigate the three-dimensional formation flocking
problem of multi-agent system with leader-follower structure. We consider
each agent as an extended unicycle. Based on the nearest neighborinteraction rules, a kind of distributed control algorithm is proposed withthe combination of consensus algorithm and attractive/repulsive functions.
The proposed algorithm enables all unmanned aerial vehicles
asymptotically converge to fly with the same velocity and approach the
expected formation with their neighbors, provided that the initial interaction
network of the system is leader-follower connected. Numerical simulations
are carried on the multi-agent system with large-scale to validate thefunctionality of the proposed algorithm.

OS14-3 Leader-follower Formation Control of Mobile Robots with Sliding Mode
Wenhao Zheng and Yingmin Jia (Beihang University, P.R.China)

This paper considers the formation control of nonholonomic mobile robots.
The formation problem is converted to the tracking error model based on
the leader-follower structure. A sliding mode controller, which is proved tobe globally finite time stable by Lyapunov stability theory, is presented inthis study. In addition, a continuous reaching law is designed to reduce thechattering which caused by the computation time delays and limitations ofcontrol. The results of simulation and experiment verifies the feasibility andeffectiveness of the control strategy.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 66 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS14-4 H Containment Control for Nonlinear Multi-agent Systems with Parameter
Uncertainties and Communication Delays

Ping Wang1, Yingmin Jia2 (1 North China Electric Power University, 2Beihang University, P.R.China)
This paper considers robust containment control problem for uncertain
multi-agent systems with inherent nonlinear dynamics. A distributed
protocol is proposed using local delayed state information, and then theoriginal problem is converted into an H control problem by defining anappropriate controlled output function. Based on robust H control theory,
sufficient conditions in terms of linear matrix inequalities (LMIs) are
derived to ensure the prescribed H containment. A numerical example isprovided to demonstrate the effectiveness of our theoretical results.

OS14-5 Stochastic Resonance in an Array of Dynamical Saturating Nonlinearity with SecondOrder
Yumei Ma, Lin Zhao, Zhenkuan Pan and Jinpeng Yu (Qingdao University P.R.China)

We study the Stochastic Resonance (SR) in parallel array of dynamical
saturating nonlinearities with second-order via the measure of output signalto-noise ratio (SNR). Firstly, the numerical results demonstrate that theoutput SNR can be enhanced by parallel array of dynamical saturating
nonlinearities with second-order by tuning the internal noise and the selfcoupling coefficient. Then, the SR effects of the dynamical nonlinearity
with second-order are superior to the dynamical nonlinearity with firstorder.

OS15 Recognition and Control (8)OS15-1 Integral Design of Intelligent Home Equipment

Yuxing Ouyang 1, Fengzhi Dai 1*, Yiqiao Qin 1, Ce Bian 1, Bo Liu 2, Hongwei Jiao 3(1 Tianjin University of Science & Technology, 2 Inner Mongolia University,
3
Tianjin Technology School of Printing and Decoration, China)

The set of equipment is designed to all household equipment network
monitoring and control. Using WIFI module for wireless networking
purpose is to upload information of all connected smart home devices tothe server through the network, and it can be remotely controlled by theexternal network and monitoring of household equipment information.
For simple household equipment, the control is directly taken to the builtin MCU’s WIFI module, and for the complex household equipment, it isthrough the external MCU and WIFI module for communication control.
Take the Airkiss distribution mode to connect the WIFI master module tothe network, and the other WIFI module devices to adopt the ad hoc
network mode for wireless networking.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 67 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS15-2 Research on Underwater Robot Recognition

Binhu Song1, Fengzhi Dai1*, Qijia Kang1, Haifang Man1, Hongtao Zhang1, Long Li2, Hongwei Jiao3 (1 Tianjin
University of Science and Technology, 2 Tianjin Electric Locomotive Co., Ltd, 3 Tianjin Technology School ofPrinting & Decoration, China)
Due to the characteristics of the underwater robot, the computer vision
technology for pedestrian detection and object detection is not adapt better toa variety of underwater robots. The recognition algorithm must be more robustand extensive that can adapt to the changing environment. Support vector
machine (SVM) method is a kind of good classification algorithm that is usedin many aspects. Its kernel function analysis is very advanced. After the
optimization, the kernel function can be adapted to a large number ofunderwater robot images that are difficult to be recognized by other methods.

OS15-3 Design of Intellectual Vehicles with Path Memorizing Function

Yiqiao Qin 1, Fengzhi Dai 1*, Yuxing Ouyang 1, Baochang Wei 1, Simini Chen 2, Hongwei Jiao 3 (1 Tianjin
University of Science & Technology, 2 Palace Museum, 3 Tianjin Technology School of Printing &Decoration, China)
In this paper, after analyzed the deficiencies of current intelligent
transportation vehicles, the design of a new transport vehicle system that has
the function of path memorization (a new algorithm to recognize the positionand path of the vehicle) is proposed. It neither relies on external positioning
methods such as GPS, wifi, etc, nor on the electromagnetic or photoelectricrails. This algorithm sets the starting point as the coordinate origin torecognize the position and the path, and compares its current position with theideal path, enabling the vehicle to travel on an established path. On the basisof this algorithm, we design a kind of intelligent transportation system by
NXP ARM-M4 K60 Singlechip. This system is not affected by the groundcondition, so there is no need for any ground signs. Still, it can transport goods
in factories scattered with water and iron scrap, and on open-air dirt roads orrainy outdoor environments.

OS15-4 Action Recognition based on Binocular Vision

Yiwei Ru 1,2,*, Hongyue Du 1, Shuxiao Li 2, Hongxing Chang 2 (1 Harbin University of Science & Technology,
2Chinese Academy of Sciences, China)
Aimed at the problem that the recognition accuracy of the monocularcamera is low, a binocular vision recognition algorithm based on CNN(convolutional neural network) is proposed. Firstly, the left and rightviews obtained by the binocular camera are matched to obtain the depth
map of the human body under the camera coordinate system (O-XYZ).
Then the depth information is projected onto the three planes of O-XY, OYZ and O-ZX respectively in the camera coordinate system to overcome
the problem that the human body motion detection is prone to error in theabsence of the overhead view and the side view of the monocular camera.
In order to reflect the motion of the timing information, the projection
images of three directions were used to construct MHI (motion history
image), and then the three MHIs were constructed as the three channels of
the image to construct a new image. Using CNN to train the classifier.
Experimental results show that the binocular recognition algorithm is 12%
more accurate than the monocular recognition algorithm, which meets therequirements of practical application .

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 68 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS15-5 Analysis and Control of a Novel 4D Chaotic System

Hong Niu (Tianjin University of Science and Technology, China)

In this paper, a novel four-dimensional(4D) autonomous chaotic system
is presented. For chaos control of the 4D system, a linear feedback
controller only with one variable is designed via matching the variable
coefficients of the Lyapunov function, so that the system is no longer
chaotic or periodic but globally asymptotically converges to theequilibrium point at the origin. The numerical simulation results are
given to illustrate the feasibility and effectiveness of the method.

OS15-6 Analysis of a three-dimensional chaotic system and its FPGA implementation
Hefei Li, Xianghui Hu(Tianjin University of Science and Technology, China)

In this paper, a three-dimensional chaotic system is implemented basedon the Field Programmable Gate Array (FPGA). The 3-D chaotic system
has a very complex chaotic characteristic with its real four-wing chaotic
attractor. By means of numerical simulation, phase orbits, bifurcation
diagram and Lyapunov exponents are given and analyzed to observe
dynamic characteristics of the three-dimensional chaotic system.
Numerical simulation and the results of implementation in FPGA showthat this chaotic system really has many obvious characteristics of chaos.

OS15-7 Image Encryption Based on Fractional-order Chaotic Model of PMSM
Wei Xue *, Mei Zhang, Shilong Liu, Xue Li(Tianjin University of Science and Technology, China)

The permanent magnet synchronous motor (PMSM) is a nonlinear
system with multi-variable and significant coupling. When PMSM
works in certain conditions, the chaotic behavior will occur. In thispaper, a application of image encryption based on fractional-order
PMSM chaotic model is investigated. By the mean of drawing
histogram, adjacent pixels correlation, key sensitivity of the ciphertext
were analyzed. The results show that the image encryption based on
fractional-order chaotic model of PMSM have a large key space and highsecurity.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 69 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS15-8 The Application of a Novel Fractional Hyper-chaotic in Image Encryption
Wei Xue *, Shilong Liu, Mei Zhang, Xue Li.
(Tianjin University of Science and Technology, China)

70
60
50
40

z

In this paper, a novel fractional-order hyper-chaotic system is proposed,
and its dynamic characteristics are analyzed by drawing the phasetrajectory and Lyapunov exponent spectrum. The simulation results
show that the fractional-order chaotic system has hyper-chaotic
characteristic. Then, image encryption implementation based on thefractional-order hyper-chaotic system is investigated, a three-color
separation of color pictures and scrambling the image pixel location, thehistogram, key space, pixel distribution, correlation coefficient and key
sensitivity of ciphertext are tested and analyzed. The results show thatthe algorithm has good security and practicability.

30
20
10
0
-10 -8

-6

-4

-2

0

x

2

4

6

8

10

OS16 Image Recognition and Chaotic Systems (8)OS16-1 A Method of Detecting Abnormal Crowd Behavior Events Applied in Patrol Robot
Huailin Zhao, Shunzhou Wang, Shifang Xu,Yani Zhang ,(Shanghai Institute of Technology, China) Masanori Sugisaka (ALife Robotics Corp.Ltd.)When the ground or air patrol robot monitors a certain area, one of theimportant intelligent functions is to estimate the crowd density of themonitored area. This paper analyzes the typical crowd density estimation
algorithm, and proposes a convolution neural network model for crowd
density estimation. Combining the crowd density estimation and the crowd
speed calculated by the optical flow algorithm, we can predict and detect
abnormal behavior events of the crowd. The method can not only estimate
the population density of the specified area, but also analyze and detect theabnormal behavior events of the crowd. This application provides animportant technical support for enhancing the patrol robot monitoring effect.

OS16-2 Design of the Multi-Car Collaboration System

The flowchart of the wholealgorithm

Huailin Zhao,Yangguang Guo (Shanghai Institute of Technology, China)
Masanori Sugisaka (ALife Robotics Corp.Ltd., Japan)

With the development of the computer and electronical technology, it is anirresistable trend to make the multiple intelligent agents cooperate with eachother to complete a specific complex task. Due to the high cost of realvehicles, most of the researches are based on simulation softwares. In our
project, a few real small smart cars are used, and the design of multi-car
cooperation system is completed. A system platform of the small smart cars
is established, which can simulate the behaver such as positioning,formation and so on. In the experiment field, a global camera is used toachieve global synchronical positioning. The simulation results show thatthe multiple smart cars can get collaborating.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 70 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS16-3 Research on an Algorithm of the Character Recognition with Self-learning the
Recognition Errors
Huailin Zhao, Yawei Hou, Shifang Xu, Congdao Han (Shanghai Institute of Technology, China)
Masanori Sugisaka (ALife Robotics Corp.Ltd., Japan)

With the development of science and technology, the character recognition
technology has been widely applied in people’s daily life. The related
researchers are studying on it in order to achieve the technology with higher
recognition rate. In this paper, a new recognition algorithm is proposed
based on self-learning the recognition errors. The method is able to identify
whether the learning result is correct or not and then recognize thecorrectness of the learning result by itself, correct the recognition result of
the error and simultaneously memorize it. When the same kind of error isrecognized again, it is automatically corrected. This article has carried on
the simulation verification on the Matlab platform to prove the effectivenessof this method. The simulation results show that the method can improvethe character recognition rate.

OS16-4 An Improved Method of the Power System Short Term Load Forecasting Based on the
Neural Network
Shunzhou Wang, Huailin Zhao, Yani Zhang, Peng Bai(Shanghai Institute of Technology, China)

Load forecasting is an important content of planning and operating powersystem. It is the prerequisite to ensure the reliable power supply andeconomic operation. In this paper, an improved method of short-term load
forecasting for load data of two different regions is proposed. Firstly,
multiple linear regression and quadratic regression are used to analyze theregression relationship between weather factors and load, and then thegreatest impact on load of weather factors are selected. The Elman neuralnetwork is used to predict unknown one-week load data taking into accountwithout whether factors situation and whether factors situation. In thepredicting situation of considering whether factors, the multi-weather
factors are integrated with the temperature and humidity index , which areused as the neural network input training samples. The prediction result isgood.

OS16-5 Improvement on LEACH Agreement of Mine Wireless Communication Network
Liu Yun-xiang and Zhang Wei (Shanghai Institute of Technology, China)

According to the features of mine communication with wireless sensor
network, a routing algorithm called LEACH-KPPE based on K-means++
was proposed. The clustering was optimized. According to the energy anddistance factors, the selection of cluster head node was optimized. The
problem of uneven distribution of cluster head node, network stability andenergy inequality on the LEACH algorithm was improved effectively. The
simulation results show that the LEACH-KPPE algorithm could better
improve the energy consumption of the whole network, improve theutilization of energy and effectively prolong network life cycle.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 71 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS16-6 A New Four-Wing Chaotic System Generated by Sign Function
Hongyan Jia, Shanfeng Wang,Yongjun Wu(Tianjin University of Science and Technology, China)

In the paper, a new chaotic system is obtained by adding a Sign Function toa three-dimensional chaotic system. Its some basic characteristics including
the equilibrium point, phase trajectory, bifurcation diagram, Lyapunov
exponent and so on, are subsequently calculated. Moreover, the dynamic
characteristics of the new chaotic system is also analyzed with the variationof the system parameters. In the end, the paper design an analog circuit to
implement the chaotic system, the results from the circuit are consistent withthose from the numerical analysis, and thus the chaotic characteristics of the
new system is verified physically. The new chaotic system can provide a
new model for engineering applications.

150

100

50

z

0

-50

-100

-150
-150

-100

-50

0
x

50

100

150

OS16-7 A Three-Dimensional Chaotic System Generating Single-wing or Two-Wing Chaotic
Attractors
Hongyan Jia, Yongjun Wu, Shanfeng Wang.
(Tianjin University of Science and Technology, China)

25

20

15
z

In this paper, a three-dimensional chaotic system is proposed based on asimple 3-D autonomous system by adding a linear piecewise function. It isvery interesting is that the new three-dimensional chaotic system cangenerate single-wing or two-wing chaotic attractors with variation ofparameter. Several basic characteristics of the system, such as bifurcation
diagram, phase orbits, and Lyapunov exponents are given to investigate
different chaotic motions for the new system. The new system is found to
be chaotic in a wide parameter range, and to show many complex dynamical
behaviors. That is, the results obviously show the system is chaotic and its
dynamics are very complex.

10

5

0
-8

-6

-4

-2

0
y

2

4

6

50

100

150

OS16-8 Circuit implementation of a new fractional-order hyperchaotic system
Xuyang Wu, Hongyan Jia, Ning Bai, Weibo Jia(Tianjin University of Science and Technology, China)©ICAROB 2017 ALife Robotics Corp. Ltd.

- 72 -

150

100

50

z

In the paper, some basic dynamic properties of a new fractional-order hyperchaotic system are firstly investigated, such as the equilibrium point, phasetrajectory, bifurcation diagram, Lyapunov exponent, and so on. Then thepaper design an analog circuit to implement the fractional-order hyperchaotic system, the results from the circuit are consistent with those fromthe numerical analysis, and thus the hyper-chaotic characteristics of the new
fractional-order system is verified physically. It is very important toimplement fractional-order hyper-chaotic system with more complicateddynamics for theoretical research and practical application. The new hyperchaotic system can provide a new model for engineering applications.

0

-50

-100

-150
-150

-100

-50

0
x

8

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS17 Natural Computing and Biology (4)OS17-1 Molecular Artificial Intelligence by using DNA reactions

Yasuhiro Suzuki, Rie Taniguchi (Nagoya University, Japan)

We have developed molecular Artificial Intelligence system by using DNA
molecules, where "intelligence" means that the reaction system can "select"
DNA molecules to sustain their reactions. We have bio-chemically
implemented the reaction system by using the DNA strand-displacement
reaction and have obtained several mutated DNA sequences that can sustain
the reactions and we have investigated behaviors of reactions when there areseveral mutated DNA sequences.

OS17-2 Neural Networks by using Self-Reinforcement Reactions
Yasuhiro Suzuki (Nagoya University, Japan)

We consider a chemical reaction network model in which selections ofreaction are stochastic and depend on past history. In this chemical reaction
network, we found the emergence of Auto-Catalytic Sets (ACS) andcomplex dynamics in which ACS are repeatedly created and destroyed; wehave called this reaction system as the Self-Reinforcement Reactions, SRR.
We developed a neural-networks system by using SRR and confirm theneural network of SRR can solve a linear classification problem.

OS17-3 Artificial Chemistry by Sound Waves

Yasuhiro Suzuki (Nagoya University, Japan)

We have proposed an Artificial Chemistry based on a rewriting system on
multisets and developed various models such as computational protocells in
Artificial Life. However, such an Artificial Chemistries are implemented insilico and cannot interact with its environment out of the silico (e.g. acomputer system). Hence we have transformed and Artificial Chemistry,
Abstract Rewiring System on Multisets, ARMS into sound waves
interactions systems; where every abstract chemical is a sound waves andartificial chemical reaction is interaction with sound waves. By using thissystem, the artificial chemical reactions inside silico is able to interact with
its environment through sound waves.

OS17-4 Variants of spiking neural P systems with energy control

Rudolf Freund, Marion Oswald (TU Wien, Vienna, Austria)

We consider several variants of spiking neural P systems (SNPSs),
theoretical frameworks for brain modeling. Whereas the original variant
only allows for one kind of spikes, in this paper special emphasis is laid on
SNPSs with different colors or SNPSs with anti-spikes. Moreover, insteadof choosing the rules in the neurons based on the current contents being in a
regular set, we (also) consider the way of choosing the rule to be applied ineach neuron consuming the maximal energy. This choice can beaccomplished by assigning energy values to each rule or by assigning(different) energy directly to the colored spikes.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 73 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS18 Advanced Management and Technology (4)OS18-1 Development of English Text for the engineers to preserve the environment of NorthEast Asia
Yuji Minami, Kenji Fukuchi, Shinya Tagawa (National Institute of Technology, Ube, Japan)

Our research began in 2007 as a part of the activities of ‘Good Practice’ for
Education of Engineers in cooperation with the UMICNEA, that is, “Unionof Machinery Industrial Cities in North-East Asia”. Through our study, we
found it necessary to acquire the knowledge of ‘Fish Breeding Forest’ whendeveloping and preserving the area of North-East Asia, where the river
Amur plays an important role for sustaining the environment of this region.
We made the research of the origin of the river Amur, named ‘the riverOnon’, located in the area of ‘Strictly Protected Area’ in Mongolia by theagreement of Mongolia, Russia, and China in 2005. We plan to make it
clear that future engineers should find a good way of cooperation throughEnglish as a communication tool for the purpose of playing an active part
together.

OS18-2 Measuring Fragility and its Implications in Networked Systems

Tsutomu Ito*, Katsuhiko Takahashi, Katsumi Morikawa, Takao Ito (Hiroshima University, Japan), Rajiv
Mehta (New Jersey Institute of Technology, USA), Seigo Matsuno (Ube National College of Technology,
Japan), Makoto Sakamoto (University of Miyazaki, Japan)
A survey the literature reveals that a plethora of network analytic facets,
such as degree, eigenvalue, density, block, cluster, have been developedand employed to further our understanding of network structures. To
extend our understanding of network systems, additional dimensions need
to be identified that shed light on the dynamic processes among individual
member nodes within network structures. Specific to the context of
networked systems, this paper proposes a new concept of fragility.
Specifically, it develops and empirically tests a mathematical model offragility from the standpoint of how ties among network members
significantly influence corporate performance. Using data drawn from twowell-known network organizations, Mazda and Toyota, this researchattempts to calculate the relationship between degree and fragility, thus
confirming the validity of the new concept. Furthermore, the relationshipbetween fragility and corporate performance is also assessed. This paper
makes a contribution to extant thought by: 1) Defining fragility, 2)
Discussing the nature of the relationship between fragility and corporate
performance, and 3) Empirically testing the dimensional differencesamong fragility and corporate performance, thus enabling a contrast
between Mazda and Toyota. Based on the findings, the managerial
implications are discussed, the study limitations are identified anddirections for further research are suggested.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 74 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS18-3 A Formation of Standard Setter to Transplant Global Standards into Domestic
Institution
Kensuke Ogata (University of Nagasaki, Japan)

Japanese accounting standard setter, the ASBJ, developed lots of domestic
accounting regulations to converge with the global standards in the period of
2005-2008, which substantially differs from the period of 2001-2004. A
general organizations theory states that organizations make a change in theirstrategy and structure aimed at changing their performance. Based on thistheory, we analyzed the change in organizational structure of the ASBJ usingsocial network analysis. According to our result, the ASBJ formed a networkin which accounting professions played a central role. The reason could bethat the ASBJ made use of knowledge and wisdoms on the global standards
Japanese big accounting firms has through their global networks.

OS18-4 A Comparison Study on the Vertical Integration and Horizontal Specialization ofChinese ICT Companies

Yunju Chen (Shiga University, Japan), Yousin Park (Prefectural University of Hiroshima, Japan)
Iori Nakaoka (National Institute of Technology, Ube College, Japan)

The debate between vertical integration vs. horizontal specialization
appears to be reinvigorated. The decision of vertical integration or
horizontal specialization affects a firm’s profit and competitive
advantage, especially in the ICT industry which is difficult to create
added value due to the commoditization of digital products. The
ongoing commoditization of smartphone brings the arising of Chinese
ICT companies but these companies adopt different operation systems
to create their own competitive advantages. In this paper, we focus on
top-shared Chinese companies in global smartphone industry, Huawei
and Xiaomi, to examine how they design their operation systems in
R&D and gain competitive advantages, also to compare their systems
with each other. The patent information of these two companies is used
to visualize their technical orientations and operation systems in R&D
by text mining©ICAROB 2017 ALife Robotics Corp. Ltd.

- 75 -The patent (inventors)
network of Xiaomi

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS19 Kansei Engineering and Applications (4)OS19-1 Histogram Matching Based on Gaussian Distribution Using Regression Analysis
Variance Estimation
Yusuke Kawakami (DynaxT Co., Ltd., Japan)
Tetsuo Hattori, Yoshiro Imai, Kazuaki Ando, Yo Horikawa (Kagawa University, Japan)
R. P. C. Janaka Rajapakse (Tainan National University of the Arts, Taiwan)

This paper describes an improved method for variance estimation which isused in Histogram Matching based on Gaussian Distribution (HMGD). In
the previous papers, based on curvature computation, we have describedthat how to estimate the variance of reference histogram, which is used in
HMGD processing. However, we have considered that the histogram oforiginal image is not always ideal shape. And the variance estimation
method based on curvature computation might not have high reliability. In
this paper, we propose improvement variance estimation method using
regression analysis. As for the method, first, we detect the histogram peakof original image by using curvature computation; next, we perform
regression analysis using approximation formula of curvature. Then, we
illustrate processing results through some experimentation.

OS19-2 Histogram Matching Based on Gaussian Distribution Using Variance Estimation-- Comparison between Curvature Computation and Regression Analysis –Yusuke Kawakami (DynaxT Co., Ltd., Japan)
Tetsuo Hattori, Yoshiro Imai, Kazuaki Ando, Yo Horikawa (Kagawa University, Japan)
R. P. C. Janaka Rajapakse (Tainan National University of the Arts, Taiwan)

This paper describes variance estimation method comparing between
regression analysis and curvature computation which is used in Histogram
Matching based on Gaussian Distribution (HMGD). In the previous paper,
we have described and illustrated that the variance estimation method havebeen considered of value for HMGD processing results. Though we haveconsidered that histogram of original image is not always ideal. So, in thispaper we propose improvement variance estimation method using
regression analysis. First of all, we describe the principle of varianceestimation methods using curvature computation, and regression analysis.
Then, through some HMGD processing experiment, we compare between
curvature computation results and regression analysis.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 76 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS19-3 An Extended Optimal Stopping Method for Structural Change Point Detection Problem
Yoshihide Koyama, Tetsuo Hattori, Yoshiro Imai, Yo Horikawa (Kagawa University, Japan)
Hiromichi Kawano (NTT advanced technology Company Ltd,, Japan)
Yusuke Kawakami (DynaxT Co., Ltd., Japan)

Previously, we have proposed and formulated the SCPD (Structural Change
Point Detection) problem in time series data as an Optimal Stopping one
using the concept of DP (Dynamic Programming). And also we have shown
the solution theorem in the form of inequality. In this paper, based on therelation between the solution of Optimal Stopping and SPRT (Sequential
Probability Ratio Test), we present the extended Optimal Stopping Method
in order to obtain more practical one, considering a loss cost and an action
cost involved by failure prediction.

OS19-4 A Study of Sentimental Value Analysis for Tweeting Message

Shunsuke Doi, Shinya Hara, Yoshiro Imai, Tetsuo Hattor (Kagawa University, Japan)
Yusuke Kawakami (DynaxT Co., Ltd., Japan)

This paper focuses on Twitter used by students of universities, and analyzes
tweeting data (messages) by students from Twitter, and calculates
sentimental values from the according data. It also investigates existence ofsome relations between calculated sentimental values and practical thoughtof students. Moreover, this paper discusses whether the above procedure andanalysis can visualize conventionally hidden relationship between contentsof tweeting messages and characteristic behavior of students in categorized
universities.

OS20: Image Processing and Computer Graphics (6)OS20-1 An effective method for detecting snatch thieves in video surveillance
Hiroaki Tsushita, Thi Thi Zin (University of Miyazaki, Japan)

Nowadays, a tremendous amount of accidents and terrorisms has been
occurred all over the world no exception Japan. Thus, detection of
suspicious activities in public areas such as railway stations, shopping malls
and many other areas using video surveillance becomes important.
However, very little has been achieved regarding real-time event
recognition of two person interactions such as snatch theft events. Moreover
the way of snatch theft has been increasing like using a motorcycle and soon. In this paper, we propose an effective method for detecting the snatch
theft event between two persons. Specifically, the proposed method consistsof several steps: pedestrian tracking, feature computation and snatch theft
detection. For feature computation, shape and motion features are used. To
confirm the validity of proposed method some experimental results in avarious situations are shown by using some collected video sequences.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 77 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS20-2 Color and Shape based Method for Detecting and Classifying Card Images

Cho Nilar Phyo, Thi Thi Zin (University of Miyazaki, Japan) Hiroshi Kamada (Kanazawa Institute ofTechnology, Japan) Takashi Toriu (Osaka City University, Japan)
In this paper we propose an effective method for detecting and classifying
card images by using color and shape features. Generally, color card images
have been widely used for teaching and learning programing languages ininteractive classrooms lessons. For this purpose, we first extract the area ofcard color by squeezing from the entire image by using the color
information. Then we remove the regions of low possibility with the aids ofshape features particularly aspect ratios. Moreover, by taking the classroom
size and the distance from the camera, we classify the image according to
the sizes. Thus our method can extract even quite small cards in theclassroom activities. In order to confirm the proposed method, we conduct
a sequence of experiments by taking our own video sequences. Our
experimental results show that the proposed method is very promising
compared to some existing methods.

OS20-3 Automatic Assessing Body Condition Score from Digital Images by Active Shape Modeland Multiple Regression Technique
Nay Chi Lynn, Thi Thi Zin, Ikuo Kobayashi (University of Miyazaki, Japan)

Body Condition Scoring is a magnificent indicator for determining
nutritional status, energy reserves, fatness or thinness of cows. The purposeof this study is to assess body condition scores (BCS) of dairy cattle byexploiting digital images of back view of cows. To determine back shape ofthe cows and its tail head area, anatomical landmark points labelling is
performed. Next, active shape model is used to extract the angle featuresfrom these cow’s tail head contours. In this study, we choose the hook andtail head depression angle from the cow’s tail head area. Those angle
features with their respective BCS are used to estimate body condition
scores by employing multiple regression analysis. Using threshold value on
the residuals between the actual BCS and predicted BCS is determined toconfirm the proposed method. Moreover, the experimental results show thatthe proposed system is promising compared to some existing methods.

BCS?

OS20-4 General Image Categorization Using Collaborative Mean Attraction

Hiroki Ogihara, Masayuki Mukunoki (University of Miyazaki, Japan)

In this paper, we apply Collaborative Mean Attraction (CMA) method,which has been developed for person re-identification problem with multicamera, to general image categorization problem. CMA is a generic
identification method, so once training
images are given, CMA canidentify test images into a class of the training images. It means that CMA
can be used for image categorization. Experimental results using thecaltech101 dataset reveal that CMA shows better categorization accuracy
than standard SVM method, particularly in the case when the size of training
data is relatively small. We also apply CMA to caltech256 dataset, which ismore difficult to categorize than caltech101. Even for caltech256, CMA
shows better results than other methods, especially for small training data.
Furthermore, we discuss the parameter settings for CMA through several
experiments.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 78 -

100

80

C
M…

60

40
0

15

30

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS20-5 Consideration on the Photo-Realistic Rendering of Fruits by 3DCG

Haruka Tsuboi, Makoto Sakamoto, Sho Yamada, Kensuke Ando, Chongyang Sun,
Makoto Nagatomo (University of Miyazaki, Japan)
Koshiro Mitsuhashi, Yukari Kodama (Miyazaki Multimedia Academy, Japan)

Three-dimensional computer graphics (3DCG) is the technique of creating
an image having a stereoscopic effect by converting an object in the virtual
three-dimensional space into two-dimensional information. It is also
possible to produce an image that cannot be distinguished from the real thingby using the 3DCG. 3DCG is expected for the application to visual
representations very much. However, it is difficult to express the "freshness"of fresh food by 3DCG.Therefore, expressiveness of 3DCG is considered to
be improved by overcoming this weakness. In this paper, we consider thephoto-realistic rendering of fruits by 3DCG. The algorithm is implemented
by using the Visual C++ 2010 and C on a personal computer. Through thistrial, we hope to improve the representation in photo-realistic rendering by
3DCG.

OS20-6 Consideration for the Possibility to the Tourism by the AR Technology

Masamichi Hori, Makoto Sakamoto, Takeshi Tanaka, Mihoko Fukushima, Chikashi Deguchi,
Masahiro Yokomichi, Masayuki Mukunoki, Kunihito Yamamori (University of Miyazaki, Japan)
Yukari Kodama, Koshiro Mitsuhashi (Miyazaki Multimedia Academy, Japan)
Atsushi Iiboshi, Takachiho (Muratabi Co., Ltd., Japan)

The promotion of the tourism nation became the important problem of ourcountry, and we expect increase of the number of the foreign tourists visiting
Japan. Therefore, we have to make sightseeing spots of our country more
attractive them, so we must contribute plain guidance information to theforeigners who cannot understand Japanese, guidance markers, and so on.
On the other hand, AR (augmented reality) is becoming the boom recently,
but this is information technology to let the reality world compose the virtualworld. In this paper, when we use the AR technology for tourism andsightseeing, we inspect what kind of possibilities we have through someexperiments.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 79 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS21 Computer Science and Information Processing (5)OS21-1 Consideration on the Recognizability of Three-Dimensional Patterns

Chongyang Sun, Makoto Sakamoto, Makoto Nagatomo, Yu-an Zhang, Shinnosuke Yano, Satoshi Ikeda(University of Miyazaki, Japan) Takao Ito, Tsutomu Ito (Hiroshima University, Japan) Yasuo Uchida (UbeNational College of Technology, Japan) Tsunehiro Yoshinaga (Tokuyama college of Technology, Japan)
Due to the advances in computer vision, robotics, and so forth, it has become
increasingly apparent that the study of three-dimensional pattern processing
should be very important. Thus, the study of three-dimensional automata as
the computational model of three-dimensional information processing has
been signiﬁcant. During the past about thirty years, automata on a threedimensional tape have been obtained. On the other hand, it is well-known
that whether or not the pattern on a two- or three-dimensional rectangulartape is connected can be decided by a deterministic one-marker ﬁnite
automata. As far as we know, however, it is unknown whether a similar
result holds for recognition of the connectedness of patterns on threedimensional arbitrarily shaped tape. In this paper, we consider whether or
not the pattern on a three-dimensional arbitrarily shaped tape is connected
can be decided by a deterministic multi-marker ﬁnite automaton.

OS21-2 Some Properties of Four-Dimensional Homogeneous Systolic Pyramid Automata

Makoto Nagatomo, Makoto Sakamoto, Yu-an Zhang, Chongyang Sun, Shinnosuke Yano, Satoshi Ikeda(University of Miyazaki, Japan) Takao Ito, Tsutomu Ito (Hiroshima University, Japan) Yasuo Uchida (UbeNational College of Technology, Japan) Tsunehiro Yoshinaga (Tokuyama college of Technology, Japan)
The question of whether processing four-dimensional digital patterns ismuch more difficult than three-dimensional ones is of great interest fromthe theoretical and practical standpoints. Thus, the study of fourdimensional automata as a computational model of four-dimensional patternprocessing has been meaningful. Cellular automata were investigated not
only in the viewpoint of formal language theory, but also in the viewpointof pattern recognition. Cellular automata can be classified into some types.
A systolic pyramid automaton is also one parallel model of various cellular
automata. In this paper, we propose a homogeneous systolic pyramid
automaton with four-dimensional layers (4-HSPA), and investigate someproperties of real-time 4-HSPA.

OS21-3 Reduction of the search space to find perfect play of 6×6 board Othello

Yuki Takeshita1, Makoto Sakamoto1, Takao Ito2, Tsutomu Ito2, Satoshi Ikeda1(1University of Miyazaki, 2Hiroshima University, Japan)

In 1993, mathematician Feinstein found out perfect play on 6×6 board of
Othello gives 16-20 loss for the first player by using computer. He reported
on the Web that it took two weeks to search forty billion positions in orderto obtain the result. In our previous papers, we confirmed the perfect play
he found is correct. And we also found another perfect play different fromthe one he found to search 884 billion positions. In order to searchefficiently, we attempted to reduce the search space to find some perfectplay. In this paper, we introduce some techniques to solve 6×6 Othello bysearching about fifteen billion positions.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 80 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS21-4 A comparative study on the delisting ratings of firms from the UN Global Compact in
the international management environment
Kanako Negishi (National Institute of Technology, Ube college, Japan)

This paper explores the international management environment for Japanesemanufacturing firms’ sustainability strategies. In particular, it clarifies firms’
positioning and characteristics in the environment. Firms have adopted
voluntary standards for environmental protection provided by NGOs andinternational institutions like the GRI and United Nations. Although there
are various standards without legal binding, there is a recent trend toward
integration to achieve comparability for firms’ international management ofsustainability. It then bound firms’ management as a de facto standard. This
paper discovers Japanese firms’ unique characteristics related to theenvironment though the comparison.

OS21-5 Application of ViSC to the Natural Grazing in Qinghai Tibet Plateau
Cheng-shui Niu1, Bing-fen Li1, Yu-an Zhang1, Makoto Sakamoto2(1Qinghai University, P.R. China, 2University of Miyazaki, Japan)“Sanjiangyuan Region” is a complex ecosystem with typical characteristics
of alpine grassland, which the livestock production mainly relies on the yak
grazing. In recent years, artificial destruction were due to grassland
degradation，the reduction of herdsmen income and on-going deteriorationof the ecological environment. It is necessary to establish a grazing mode
which ecological animal husbandry and grassland sustainable development.
In this study applying the ViSC that combined with optimization theory, weestablish sustainable grazing system in alpine grassland yak, which provides
a theoretical basis for scientific grazing.

OS22 Robotic Technology for Competition (4)OS22-1 Development of a Tomato Harvesting Robot

Bingh Li, Shinsuke Yasukawa, Takashi Sonoda, Kazuo Ishii (Kyushu Institute of Technology, Japan)
Most of commercialized robots are for industry, and the robots for
agriculture, forestry and fisheries are under developing and notcommercialized yet. The reasons for the difficulty are caused by costefficiency of the robotization, safety ensuring of the works using robots,
outdoor operations, and knowledge transfer problem from farmers tocomputer so on. Tomato is one of important fruit vegetables and most
tomatoes are produced in the greenhouses, or large-scale farms, where thehigh temperature and humidity, and long harvest age force the farmers heavy
works. With an aim to promote the automation of tomato harvesting, we haveorganized the tomato harvesting robot competition and developed a tomato
harvesting robot. In this paper, we propose the system of tomato harvesting
robot.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 81 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

OS22-2 Development of SOM algorithm for Relationship between Roles and Individual’s Rolein Rugby 2nd Reports: University Rugby teams analysis using Physical andPsychological data
Yasunori Takemura (NishiNippon Institute of Technology, Japan)

Victory or defeat in team sports depends on each player’s technique,physical strength, and psychological condition. It follows that teamperformance depends on the player’s adaptation to (suitability for) a certain
role (position in the team) and the relationships between different roles. We
assume that team performance is related to physical and psychologicalfeatures. Many researchers have proposed that physical features determine
a player’s suitability for a position. Psychological features have also been
researched as factors of position adaptation. However, each feature has been
investigated independently. The present research aims to develop aclustering method that considers both physical and psychological featuresin judging an individual’s role and adaptation in the game. This paper
reports the concept of the algorithm and result of analysis using both
physical data and psychological data[O1].

OS22-3 Ball Dribbling Control for RoboCup Soccer Robot

Shota Chikushi, Kenji Kimura, Kazuo Ishii (Nippon-Bunri University, Kyushu Institute of Technology, Japan)
RoboCup is a platform designed to promote the research fields such as
Artificial Intelligent (AI) and robotics. We also organize a RoboCup
team “Hibikino-Musashi”, and working on co-operated behavior control
system using multiple autonomous mobile vehicles. In this paper,
motion analysis and control of a ball operated by two active wheels
mounted on upper side of the ball is discussed. Based on the analysis
and simulation, a ball dribbling mechanism is developed. The kinetics
of ball and two-active wheels is derived and evaluated using thedeveloped ball dribbling mechanism.

OS22-4 Strategy Analysis of RoboCup Soccer Teams Using Self-Organizing Map

Moeko Tominaga, Yasunori Takemura, Kazuo Ishii(Kyushu Institute of Technology, NishiNippon Institute of Technology, Japan)

In the soccer games, the player’s behavior changes depending on thegame situation such as wining or losing, score gap, remaining time. The
players act more offensive when their team is losing, or more defensive
when their team is winning with minimum score difference. Currently
most of robot teams keep the same strategy during the game so that theresult of game depends on the ability of each player like speed of robot,
quality of localization, obstacle avoidance and ball handling. Next issue
for the robot intelligence is collaborated team behavior and strategy. In
this paper, the team strategy is analyzed based on parameters such as the
positions of robots, the number of robots in play, scores, time andactions of robots using Self-Organizing Map.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 82 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

GS abstracts
GS1 Artificial Neural Network & Bio-Signal Controlled Robotics (3)GS1-1 Obstacle Avoidance Method for Electric Wheelchairs Based on a Multi-Layered NonContact Impedance Model
Haruna Kokubo, Taro Shibanoki (Ibaraki University, Japan),
Takaaki Chin (Hyogo Rehabilitation Center, Japan), and Toshio Tsuji (Hiroshima University, Japan)
This paper proposes an obstacle avoidance method based on a multi-layered
non-contact impedance model for the biosignal-controlled electric wheelchair.
The proposed system can calculate a virtual repulsive force before the collisionby multi-layered impedance fields, which have different impedance parameters,
covered around the robot. In this way, this system regulates desired path to
avoid obstacles in a variety of situations such as a natural evasion and an
emergency avoidance. In the simulation and practical experiments, the mobile
robot moved to a desired position, and some obstacles were appeared on their
moving paths. The results showed that the robot passed through obstacles
smoothly, and could stop emergently to avoid the obstacle in front of the robot
owing to virtual forces calculated by the proposed modelGS1-2 A Voice Signal-Based Manipulation Method for the Bio-Remote Environment Control
System Based on Candidate Word Discriminations
Taro Shibanoki (Ibaraki University, Japan), Go Nakamura, Takaki Chin
(Hyogo Rehabilitation Center, Japan), and Toshio Tsuji (Hiroshima University)

This paper proposes a voice signal-based manipulation method for the BioRemote environment control system. The proposed system learns
relationships between multiple candidate words’ phonemes extracted by alarge-vocabulary speaker-independent model and control commands fordomestic appliances based on a self-learning look-up table (self-learning
LUT). This allows the user to control various devices even if false
recognition results are extracted because of slurred speech. The efficacy ofthe proposed system was demonstrated through speech recognition forslurred words and domestic appliance control experiments conducted withhealthy male participants. The results showed that the method could be used
to accurately discriminate seven slurred words (average discrimination rate:
93.9 ± 2.2 [%]), and that the subject was able to voluntarily control domestic
appliances as intended.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 83 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

GS1-3 Experiments on classification of electroencephalography (EEG) signals in imagination of
direction using Stacked Autoencoder
Kenta Tomonaga, Takuya Hayakawa, Jun Kobayashi (Kyushu Institute of Technology, Japan)

This study presents experimental results of classification methods for brain
activity in the imagination of direction. We used a wireless portableelectroencephalography (EEG) headset to collect EEG data from subjects inexperiments, during which the subjects imagined arrows indicating one of thefour direction: up, down, right, left. The classification methods estimated the
direction that the subjects imagined on the basis of their brain wave signals
measured by an electrode on the portable EEG headset. The classification
methods implemented in our previous studies consisted of a band-pass filter,
fast Fourier transformation, principal component analysis (PCA), and neuralnetwork. The PCA works for feature extraction and selection. In this study, we
have implemented Stacked Autoencoder (SAE) for EEG signal classification.
The SAE carries out not only feature extraction but also classification in theform of multi-layered neural network. Experimental results showed that the
SAE outperformed the previous classifier with PCA.

GS2 Automated Guided Vehicles I (6)GS2-1 Adaptive Negotiation-rules Acquisition Methods in Decentralized AGV Transportation
Systems by Reinforcement Learning with a State Space Filter
Masato Nagayoshi, Simon Elderton (Niigata College of Nursing),
Kazutoshi Sakakibara (Toyama Prefectural Univ.), Hisashi Tamaki (Kobe Univ.)

In this paper, we introduce an autonomous decentralized method for multiple
Automated Guided Vehicles (AGVs). Transportation route plans of AGVs are
expected to minimize the transportation time without collisions between the
AGVs in the systems. In our proposed system, each AGV as an agent computes
its transportation route by referring to the static path information. route. Once
potential collisions are detected, one of the two agents chosen by a negotiation
rule modifies its route plan. The rules are improved by reinforcement learningwith a state space filter. Then, the performance is confirmed with regard to theadaptive negotiation rules.

GS2-2 Modeling and Control of a Quadrotor Vehicle Subject to Disturbance Load
Jun Wang, Song Xin, Yuxi Zhang (Beihang University, P. R. China)

In this paper, a dynamic model of quadrotor, an unmanned aerial vehicle (UAV),
is derived for theoretic and practical evaluation. Four transfer functions indifferent channels are converted from the state equations. To study the behaviorof quadrotor subject to the external disturbance load, a fuzzy logic controller(FLC) is designed to compare with the PID (proportional-integral-derivative)
control method. Subsequently, Liapounov function is applied for stability
analysis. Finally, simulation results are presented to illustrate the performance
between FLC and PID. Considering model error, the evaluation simulations are
divided into two parts, which describe the ability for rejecting external
disturbance, setpoint tracking and disturbance rejection respectively. The
simulation scheme demonstrates the FLC method outperforms the PID control
scheme.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 84 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

GS2-3 A multithreaded algorithm of UAV visual localization based on a 3D model ofenvironment: implementation with CUDA technology and CNN filtering of minor
importance objects
Alexander Buyval, Mikhail Gavrilenkov (Bryansk State Technical University, Russia)
Evgeni Magid (Kazan Federal University, Russia)

Visual based navigation plays an important role in localization and path
planning, especially in GPS-denied environments. This paper presents a visualbased localization algorithm for a UAV within an indoor environment. The
algorithm uses multithreaded computing CUDA technology and CNNpreprocessing filtering, which is responsible for filtering out minor importanceobjects. We assume that while an initial 3D model of environment is available,
the scene may undergo minor dynamical changes, e.g., new objects may appear
in the scene as the time passes. The localization is performed in two steps.
Initially, a neural filtering module detects new objects in the scene and filtersthem out. Next, the filtered data is passed to a multithreaded edge-computing
module, which compares it with the initial 3D model. The algorithm issimulated in ROS/Gazebo environment with two different approaches – one
uses CPU only and the other uses CPU and GPU - and their performance iscompared.

GS2-4 Modelling a crawler-type UGV for urban search and rescue in Gazebo environment
Maxim Sokolov, Aidar Gabdullin, Roman Lavrenov, Ilya Afanasyev (Innopolis University, Russia)
Leysan Sabirova, Evgeni Magid (Kazan Federal University, Russia)
A long-standing goal of robotics is to substitute humans in unreachable or
dangerous environments. One of the most dangerous and human unfriendly
environments is urban search and rescue (USAR) domain, where a rescue robot
is used for victims search and environment monitoring purposes. To deal with
USAR tasks we have selected a novel Russian crawler robot “Engineer”, andthis paper presents our first successful steps toward modelling the robot in
ROS/Gazebo environment. We convert the provided by “Engineer” developers
CAD models into workable ROS-based 3D simulation and incorporate physicalparameters of the mechanisms into the model. Robot motion and relativeinterplay of its visible mechanical parts is visualized in RViz software. The
proposed model is integrated into a ready-to-use ROS navigation stack and themodel’s behavior is thoroughly investigated while navigating through static
obstacles populated scene in Gazebo environment.

GS2-5 Development of Autonomous Robot for Laborsaving of the Forestry - Positioning of the
Robot using IMU, GPS, and Encoder Sho Yamana, and Eiji Hayashi (Kyushu Institute of Technology, JAPAN)

This paper presents about the positioning of the autonomous robot using IMU,
GPS, and encoder devices. Using acceleration and orientation from IMU sensor
that is used to calculate the position of the robot. To locate the position, need toknow the distance when the robot moved. Distance is calculated by integratingof velocity and also the velocity is considered by integrating of acceleration.
Results showed distance measured using only IMU. It shows the difficulty oflocating using only IMU. Therefore, GPS and encoder devices need to be
combined into the system for position measurements.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 85 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

GS2-6 Development of Autonomous Robot for Laborsaving of the Forestry - Detection of youngplants by RGB and Depth Sensor Nobuo Miyakawa, Eiji Hayashi(Kyushu Institute of Technology, Japan)

Nowadays In Japan, the forest industry have problem about safety and labor
shorting. This research is about development of the autonomous moving robot
in the forest focused on solving the problem. As the first phase of automationof forest industry, autonomous movement control system must be developed.
Because, the environment surrounding the robot changes every moment inforest. For example, the tree and weed in forest is growing every day. So thisdynamic environment has to be considered for the robot to continue moving in
the forest. Therefore we aim at the development of autonomous system thatmovement in the forest in this study. This paper proposes to detect plants ofautonomous robot system in the forest.

GS3 Automated Guided Vehicles II (3)GS3-1 Motion Improvement of Four-Wheeled Omnidirectional Mobile Robots for Indoor
TerrainAmornphun Phunopas (King Mongkut’s University of Technology, Thailand)
Shinichi Inoue (Kyushu Institute of Technology, Japan)

The four-wheeled omnidirectional platform is great to use for an indoor mobile
robot. It can increasingly move and change heading direction. However, The
robot is easy to slip when it is moving. As well as, one or more wheels are
sometimes not touching the ground. Therefore the robot may miss the planned
path, or in the worst case, the robot possibly gets stuck due to unrecognizedcircumstances. This paper approaches to solving the problems by computational
simulation in locomotion. Mathematical models simulate the robot movement.
The robot struggles to go to the target with randomly simulated slip and inactive
wheel circumstances. The robot can estimate the positions using the Kalmanfilter and readjust itself to the planned path. Consequently, this paper
demonstrates the motion improvement and compare the results of decreasing
error.

GS3-2 An Improved Algorithm for Obstacle Avoidance by Follow the gap method combined
Potential Field

Chakhrit La-orworrakhun, Suriya Natsupakpong (Institute of Field Robotics, King Mongkut's University
Technology Thonburi, Thailand)This paper proposes a novel obstacle avoidance algorithm for boat survey. The
proposed algorithm, “Follow the Gap Plus”, for boat navigation is presented by
combining the “Follow the Gap” method and adapting the “Potential Field”
method with an attractive field of obstacles. Then the simulation environment
for testing system and algorithm are generated in various situations and similar
real-world environment. The results show that the survey boat simulation withproposed algorithm can go to the desired destination with better performance
than “Follow the Gap” method in the environment with obstacles about 4.47
percentage of total distance, about 7.63 percentage of total time used, and about
54.33 percentage of average change of direction.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 86 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

GS3-3 Study on the target positioning for an Omni-directional 3 DOF mobile manipulator
based on machine vision
Jiwu Wang, Yao Du, Wensheng Xu (Beijing Jiaotong University, China)
Masanori Sugisaka (ALife Robotics Corporation Ltd, Japan)

The omni-directional mobile robot with multi DOF, because the operation
posture and operation accuracy of the manipulator can be better controlled in anarrow or crowded workplace compared with the general manipulator, is getting
more interested in practical applications. The present problem is to improve its
flexibility for operating multiple different targets. Target recognition with image
processing is an effective solution. Based on the image processing, the positionand posture of the target can be determined. Then the signal will be sent to thearm control system. In this paper, the illumination conditions, distortion, etc.
are studied in the target recognition. The target position with image processing,is verified with real coordinates. The experiments show target recognition withimage processing can effectively improve the flexibility of our robot

GS4 Biological Systems (4)GS4-1 Force and Motion Analysis of larval zebrafish (Danio rerio) using a body dynamics model
Naohisa Mukaidani, Zu Soh, Toshio Tsuji (1Hiroshima University, Japan)
Shinichi Higashijima (National Institutes of Natural Sciences, Japan.)

Larval zebrafish are often employed as a vertebrate model to study thephenomena during the stages of development. This study proposes a method ofbody dynamics analysis for larval zebrafish incorporating a viscoelastic bodymodel and a fluidic environment model to support the study of development
mechanisms in motion generation. An algorithm for estimation of the externaldrag forces acting on larval zebrafish enabled calculation of thrust forces
resulting from body motion. The results showed that the estimated fluid dragcoefficients enabled the body dynamics model to approximate the paths ofactual larvae with a high accuracy level of 0.76±0.74 [%] of the total length.
Based on the analysis results, we found that the fins generate dominant forcesat the initiation of swimming, but have little role during swimming.

GS4-2 Behavior Analysis on Boolean and ODE models for Extension of Genetic Toggle Switchfrom Bi-Stable to Tri-stable.
Masashi Kubota, Manabu Sugii, Hiroshi Matsuno (Yamaguchi University, Japan)

The artificial genetic circuit (AGC) is a gene network in which its expression
timing, period and function are designed by computational and biological
technique. The AGCs for the functions of electronic circuits like a toggle switch
and an oscillation circuit were realized in E.coli by Gardner and Elowitz in
2000, respectively. We visualized and verified the structure and behavior of
Boolean network (BN) and ordinary differential equation (ODE) models for 2variable genetic toggle switch in the phase plane by using GINsim and Scilab.
ODE has been the most widely used formalism for biological processes, but BN
model allows us to easily understand mathematical expression of biologicalprocesses. Then, we extended the models for 3-variable genetic toggle switchfrom the 2-variable ones, and we demonstrate a mathematical formalization by
showing correspondence between state transitions of BN and trajectory pathsof ODEs in the phase space.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 87 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

GS4-3 Boolean modeling of mammalian cell cycle and cancer pathways

Hideaki Tanaka, Hiroshi Matsuno, Adrien Fauré (Yamaguchi University, Japan)

The cell division cycle is controlled by a complex molecular network: a recent
model of the cell cycle and cancer pathways includes close to a hundred genes[Fumiã and Martins, 2013]. To cope with such complexity, different
approaches have been used by modelers. Recently, Deritei et al [2016] haveemphasized modularity as a key organizational principle. Their model however,includes only 22 components. This raises the question how the approach would
fare on larger models such as the one published by Fumiã and Martins. To
explore that question we first convert these two models to a common modelingframework [Naldi et al., 2009]. Preliminary results show that there is only
limited overlap between the two models, with shared variables being controlled
by different regulators. This suggests that at this stage module definition may
still depend on the modeler and thus may not yet reflect actual biologicalorganization.

GS4-4 An Estimation Method for Environmental Friction Based on Body Dynamic Model of
Caenorhabditis elegans

Zu Soh (Hiroshima University, Japan), Michiyo Suzuki (National Institutes for Quantum and Radiological
Science and Technology, Japan), Toshio Tsuji (Hiroshima University, Japan)
Caenorhabditis elegans is a small worm which is approximately 1.3 mm inlength. For the reason of technological limits, it has been difficult to measure
the friction between the body and environments. The present study proposes anestimation method for frictional force using locomotion information obtained
from video analysis of actual worms. The results indicate that the body model
driven by the estimated frictional force can trace the locomotion of the worm
within 4% of the body length. The proposed method may be able to be applied
to analyze the relationship between friction and gait control.

GS5 Filtering & Control Systems (2)GS5-1 An Application of Collaborative Filtering in Student Grade Prediction

Chaloemphon Sirikayon and Panita Thusaranon (Dhurakij Pundit University, Thailand)This research proposed the process of student performance prediction by using
the collaborative filtering (CF) technique. The benefit of this research includes
assist instructor to identify student performance, personalized advising, andstudent degree planning. The CF technique composes of similarity calculationand prediction. In our experiments, different techniques are compared to
calculate students’ similarity. We also found that a prior course clustering withheuristic knowledge can be used to enhance predictability. The data used for thisstudy obtained from Dhurakij Pundit University with enrollments of 200
undergraduate students between 2012 and 2016 from the Faculty of Information
Technology. The performance of each student has been predicted since semester
2, 2014 by using existing grades available at that time. The A-F letter grades
were converted to the 4–0 scale, which is actually discrete. In additional, we
also conducted the experiment by using actual score from each courses.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 88 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

GS5-2 An improved detection method for railway fasteners

Jiwu Wang, Yan Long (Beijing Jiaotong University, China)
Sugisaka Masanori (ALife Robotics Corporation Ltd, Japan)

Aiming at the disadvantages of low efficiency and poor stability of the existing
methods of fastener detection, this paper proposes a method of fastener detection
based on template matching. Firstly, the image is processed by noise reduction,
this method does not need to be based on histogram can match template directly.
We choose a standard fastener image as template, read template firstly, then slide
the image blocks on the input image to match the template and the input image.
Experimental results show that this method can effectively detect the flaw,
deformation and obstacles of railway fastener occlusion. This method has a fast
matching speed and a good robustness, it’s detection accuracy is up to 96%

GS6 Human-Welfare Robotic System & Medical Application (5)GS6-1 Virtual surgery system with realistic visual effects and haptic interaction.

Vlada Kugurakova, Murad Khafizov, Ruslan Akhmetsharipov, Alexei Lushnikov, Diana Galimova,
Vitaly Abramov (Kazan Federal University, Russia)
Omar Correa Madrigal (University of Informatic Sciences, Cuba)
Currently, educational processes in medical surgery field involve not only
theoretical in-class studies that are immediately followed by practical lessons inmortuaries and hospitals, but also involve simulations of various levels of reality.
This paper describes our current progress in Virtual Surgery System development,
which is targeted to support educational processes in medical surgery. With thissystem, all surgery operations are performed using a virtual reality headset andhaptic manipulators with force feedback. The main feature of our approach isapplying a voxel data structure of a human body that provides an opportunity tosimulate realistic behavior of the body. Thus, cutting, sewing, and welding of
human tissues processes become realistic. Together with a realistically simulated
virtual surgery scene, these will significantly speed up educational processes.

GS6-2 A Human Reaching Movement Model for Myoelectric Prosthesis Control

Go Nakamura*1, 5, Taro Shibanoki*2, Yuichiro Honda*1, Futoshi Mizobe*3, Akito Masuda*4, Takaaki Chin*1,
Toshio Tsuji*5*1(Robot Rehabilitation Center in The Hyogo institute of Assistive Technology, Japan)*2(Ibaraki University, Japan) *3(Hyogo Rehabilitation Center, Japan)*4(Kinki Gishi Corporation, Japan) *5(Hiroshima University, Japan)
This paper proposes a human reaching movement model for myoelectric
prosthesis control. The main purpose of the proposed model is to generate desired
trajectories provided in a reaching training system for myoelectric prosthesis
control. First, an experiment was performed to observe reaching movements witha healthy subject and a myoelectric prosthesis user (upper limb amputee). Then,
we found several distinctive features shown in reaching movements for the
myoelectric prosthesis user, and constructed a model considering the observed
characteristics by using a logistic function. The proposed model can generate
three different types of hand trajectory such as straight line paths, circular arc
paths and S-shaped paths with bell-shaped speed profile by adjusting only a few
parameters. It was indicated that the proposed model could generate both hand
trajectories of the healthy subject and the myoelectric prosthesis user from thecomparison results between observed and simulated trajectories.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 89 -

Human hand trajectory model
for reaching traning system
HMD

VR environment
Hand trajectoryt ) f(

Amputee

Bell-shaped speed profile

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

GS6-3 Re-creation of a membrane puncture’s sense of an object constituted of liquid and anouter membrane by a haptic device and a deformation simulation of the virtual objects
Takahiro Okada, Eiji Hayashi (Kyusyu Institute of Technology, Japan)

Technologies that can accurately perform minute work are now being sought for
medical treatment and bio-technology field. Such minute work is improved byusing micromanipulators, but their operation is difficult because the operator has
no sense of force. The operator relies only on sight through a microscope. As aresult, a person skilled in the use of this technology is needed for all minute work.
For the efficiency of minute work, we used a haptic device and amplified theforce feedback from a minute sample. The purpose of this research is to develop

a combined sense system that uses both force feedback and visual feedback
on a deformation simulation. Especially, I focused a way to recreate a membranepuncture’s sense of salmon rows assumed a cell.

GS6-4 Exercise classification using CNN with image frames produced from time-series motion
data
Hajime Itoh, Naohiko Hanajima (Muroran Institute of Technology, Japan)
Yohei Muraoka, Makoto Ohata (Steel Memorial Muroran Hospital, Japan)
Masato Mizukami, Yoshinori Fujihira (Muroran Institute of Technology, Japan)

To enhance the strength of the elderly is important for the care prevention.
Exercise support systems for the elderly have been developed and some were
equipped with a motion sensor. Normally it provides three-dimensional timeseries data of over 20 joints. It is important to integrate such large data andevaluate the motion of the users. In this study, we propose to apply Convolutional
Neural Network (CNN) methodology to the motion evaluation. The deep learning
has a success in integrating big data, for example, image classification. Our mainidea is to convert the motion data of one exercise interval into one gray scale
image by taking joint types for the row direction and time for the column direction
and by changing the data into the gray scale value. From simulation results, theCNN was possible to classify the images into specified motions. It can potentiallybe used for exercises classification.

GS6-5 Proposal and Evaluation of the Gait Classification Method using Arm Acceleration Data
and Decision Tree
Kodai Kitagawa, Yu Taguchi, Nobuyuki Toya
(National Institute of Technology, Kushiro Collage, Japan)

We have been developed a system to classify gait patterns based on stride lengthand foot clearance by acceleration sensors of Smartphone as simple sensors forfall prevention. This system places a Smartphone around an arm as a convenient
location. In this paper, we propose gait classification method using arm
acceleration data and decision tree. Also, we evaluate whether decision tree using
three-axis accelerations as feature quantities can classify three gait patterns.
(Three gait patterns are "Normal", "High step" and "Long step".) The result
showed that this method can classify three gait patterns of some subjects. Besides,
it was found that x-direction accelerations and composite accelerations were
important feature quantities for gait classification by decision tree.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 90 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

GS7 Micro-Machines & Robotics I (6)GS7-1 Estimation and Categorization of Errors in Error Recovery Using Task Stratificationand Error Classification

Akira Nakamura*1, Kazuyuki Nagata*1, Kensuke Harada*2 and Natsuki Yamanobe*1( National Institute of Advanced Industrial Science and Technology (AIST), *2 Osaka University, Japan)
*1

task2

error

taskk

error

Class 2

Class 4

task1 error

Class 1

Process flow with error recovery

Class 3In manipulation tasks of plant maintenance and industrial production, error
recovery is an important research theme for robots. However, systematicalmethods of error recovery have not been appeared yet. We have proposed error
recovery using the concepts of both task stratification and error classification. In
the error recovery, the judgment of the error is carried out on a process of thepractice of the system. In our method, classification of errors that occurred iscarried out based on an estimated cause. Specifically, errors are classified into
several categories such as sensing errors, modeling errors, planning errors andexecution errors. When an error is classified correctly, a possibility that the mostsuitable recovery is performed becomes high. In this paper, a procedure of thecategorization of the error that we have considered is described..

Classification of error

Class 1 → Execution error

Class 2 → Planning error
Class 3 → Modeling error
Class 4 → Sensing error

GS7-2 The Suitable Timing of Visual Sensing in Error Recovery Using Task Stratification andError Classification
Akira Nakamura*1, Kazuyuki Nagata*1, Kensuke Harada*2 and Natsuki Yamanobe*1( National Institute of Advanced Industrial Science and Technology (AIST), *2 Osaka University, Japan)*1

In manipulation tasks of industrial production and housework, the techniques oferror recovery become important since robots need to perform complicated tasks.
We have proposed error recovery using the concepts of task stratification anderror classification. In the error recovery, the judgment of the error is performed
in processes of the practice of the system. Ideally, it is desirable for the judgmentof the error to be carried out in many timings. However, in that case, many sensors
are needed and it leads to disturb a workflow. Therefore it becomes important tobe judged by the most suitable timing in a little number of times. In this paper, anefficient timing of visual sensing in an error recovery is described.

Vision system
Manipulator

Manipulator

Objects

GS7-3 Hexapod Type MEMS Microrobot Equipped with an Artificial Neural Networks
IC
Kazuki Sugita, Taisuke Tanaka, Yuya Nakata, Minami Takato, Ken Saito, Fumio Uchikoba(Nihon University, Japan)
This paper proposes the hexapod type microrobot controlled by the artificial
neural networks. The silicon based structural component of microrobot is
produced by micro electro mechanical systems (MEMS). The rotary actuator isconstructed from the artificial muscle wire of shape memory alloy (SMA)
material. The actuator generates the locomotion of the microrobot by supplying
the electrical current to artificial muscle wires. The microrobot was controlled byusing artificial neural networks IC. The IC includes cell body models, inhibitory
synaptic models and current mirror circuits. In addition, the artificial neuralnetworks IC outputs the electric current whose pulse waveform. We installed thelead wires in the microrobot’s interior. When the driving circuit is equipped on
the MEMS microrobot, the internal placements of the lead wires reduced thewiring length, and achieved excellent weight balance and structurally stableperformance of the MEMS microrobot.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 91 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

GS7-4 Heat Distribution of Current Output Type Artificial Neural Networks IC for the
MEMS Microrobot
Taisuke Tanaka, Yuya Nakata, Kazuki Sugita, Minami Takato, Ken Saito, Humio Uchikoba(Nihon University, Japan)
Heat distribution of the artificial neural networks IC developed to driving circuitof microrobot were described in this paper. We previously constructed themicrorobot imitating the insects with micro electro mechanical systems (MEMS)
technology. The rotary actuator of MEMS microrobot is composed of shapememory alloy (SMA) based artificial muscle wires. The rotary motion is
generated by passing an electric current through artificial muscle wires. We
constructed artificial neural networks on IC chip as a driving circuit for the
MEMS microrobot. The current mirror circuits also constructed, for the actuator
is electric current driven type. The design rule of the IC was 4 metal 2 poly CMOS
0.35μm. We measured the heat distribution in the two types ICs having different
stage number of the current mirror circuits by thermography. In addition, we
examined heat generation mechanisms.

GS7-5 AGV with Mind and its production simulations for autonomous decentralized
FMSs
Masato Chikamatsu, Hidehiko Yamamoto, Takayoshi Yamada (Gifu University, Japan)

The autonomous decentralized FMS does not have a management mechanism
to integrate the entire system and each agent like Automated Guided Vehicles(AGVs) autonomously determine the acts by recognizing other agents. This
study controls AGVs moving by using a mind model in order to avoid AGVs’interference in an autonomous decentralized FMS. The mind model can avoid
the AGVs’ interference by repeating the two types of mind changes, thearrogant mind and modest mind. AGV with the arrogant mind takes the action
to approach forcibly at the destination. AGV with the modest mind takes theaction to make way for other AGVs. By applying the mind to several FMSs, theproduction simulations were carried out. As a result, AGVs can avoid the pathinterference flexibly even if the shape of the production floor is changed and it
is ascertained that the mind model is able to control the AGV actions ofautonomous decentralized FMSs.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 92 -

AGV

Machining Center

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

GS7-6 UNARM System to Decide Units Locations of Cell-type Assembly Machines with Robots
Arms
Hirotaka Moribe, Hidehiko Yamamoto and Takayoshi Yamada (Gifu University, Japan)

This study develops the system called Units-layout Nomination for Assemblywith Robot Mechanism (UNARM).UNARM assists a production engineers when
they design an assembly machine with a both arms robot. To design the assembly
machine, the engineers must decide the locations of parts and assembly jig (wecall them as units) which become the objects assembled in the assembly machine.
By using UNARM, every unit location can be automatically decided. To develop
UNARM, we adopt Reinforcement Learning as a key technology. UNARM’scharacteristics are not only to decide the units locations but also to incorporate
the both arms waiting time concept so that the both arms collisions do not occur.
We applied UNARM to the assembly machine for the inline motor of an electricvehicle. The machine has 8 units in total. After carrying out UNARM
simulations, the assembly time was decreased and the usefulness of UNARM wasascertained.

GS8 Micro-Machines & Robotics II (3)GS8-1 Development of arm trajectory planning of Seamless Robot

Teedanai Pramanpol, Eiji Hayashi (Kyushu Institute of Technology, Japan)

This paper presents arm trajectory planning of Seamless Robot. The Seamless
Robot is a Human Support Robot (HSR) which is the ongoing developed projectat Hayashi Laboratory, this robot is consist of three main parts base, body andarms, this paper will focus on controlling arm movement. Each arm has been
designed with four degrees of freedom with a limitation of each joint is set tovalues close to a human arm. In order to solve for each joint angle the Levenberg
Marquardt method is used for solving the inverse kinematic problem forcalculating point to create a trajectory planning of robot`s arms. The result oftrajectory planning has been proven in simulation and compare with theexperimental result from the robot.

GS8-2 Localization Method of Autonomous Moving Robot for Forest Industry

Ayumu Tominaga, Eiji Hayashi (Kyushu Institute of Technology, Japan), Tsutomu Sasao (Meiji University.,
Japan)In the past, the working time of labor was spent too much for the forest
management and maintenance. Particularly, survey of a forest and eliminationof weed plants place a huge burden on the workers. We would like to propose anautonomous moving robot, which has the weeding mechanism. The robot canachieve the large labor-saving of forest industry. In this study, we aredeveloping an autonomous moving robot for labor-saving of forest industry.
Traditional forest management and maintenance consumes excessive labor time.
Particularly, the survey and weeding in the forest place large labor burden on
the workers. Therefore, the robot which has the weeding mechanism canachieve to reduce the workload. We installed a depth sensor into an all-terrain
vehicle. The trajectory path of the robot is determined from the obtained depth
information. In addition, we are using the Monte Carlo Localization for localizeof the robot in the forest. Experimental results that show the feasibility of anautonomous moving in the real environment.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 93 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

GS8-3 Dynamic Modeling and Motion Control of an RRR Robotic Manipulator

Jinho Kim1, Kevin Chang1, Brian Schwarz1, Andrew S. Lee1, S. Andrew Gadsden1,2(1University of Maryland, USA) (2University of Guelph, Canada)This paper presents the dynamic modeling and motion control of a three-linkrobotic manipulator, also known as the RRR robot. The Kinect motion capturesystem by Microsoft is used in conjunction with the manipulator. Kinect is used
to capture the motion of a user’s arm and tracks certain angles made by parts of
the arm. We consider a pinhole camera model to generate reference angles as per
the Kinect model in our simulations. These desired angles are fed into thecontroller and are used by the RRR robot in an effort to copy the movement of
the user. A proportional-integral-divider (PID) controller is developed andapplied to the manipulator for improved trajectory tracking. The RRR robot
manipulator is dynamically modeled and the results of the proposed control
strategy demonstrate good trajectory following.

Fig 1. Configurationof the RRR robotic
manipulator.

GS9 Neuromorphic Robotic Systems (5)GS9-1 Development of Cloud Actions for Seamless Robot Using Backpropagation Neural
Network
Wisanu Jitviriya (King Mongkut’s University of Technology, Thailand)
Jiraphan Inthiam, Eiji Hayashi (Kyushu Institute of Technology, Japan)

This paper presents the cloud actions for a five DOF Seamless robotic arm using
the inverse kinematics solution based on artificial neural network (ANN).
Levenberg-Marquardt method is used in training algorithm. The desired positionand orientation of the end effector is defined as the input pattern of neuralnetwork. In addition, we propose the cloud actions which are the movement
patterns of the robotic arm. The cloud actions platform is created in order toperform the basic behaviors of the Seamless robot such as “Catch”, “Approach”,“Interest”, “Look around”, “Alert” and “Avoid” actions. Experimental results
show the suitable structure of artificial neural network used for solving theinverse kinematics equation, and the testing points in the robot’s workspace wereverified with the robotic arm.

GS9-2 Mathematical modelling of human fear and disgust emotional reactions based on skinsurface electric potential changes
Gaisina Kristina, Gaisin Ruslan (Kazan Federal University, Russia)

An appropriate numerical representation of neurotransmitters’ level allows
applying numerical methods for a study of psycho-emotional human reaction tovarious external stimuli, which could be further integrated into artificial life androbotic systems to reproduce human emotions and reactions. This paper deals
with skin surface electric potential data registration of a human while the humanis watching specially selected videos that are supposed to activate basic emotionsof fear and disgust. We describe the selected video materials and data logging
process and present human emotional reactions model, which was implemented
with numerical methods. We demonstrate experimental results for fear anddisgust psycho-emotional states that were obtained from 100 respondents. We
believe that our findings might be applied for conditional measuring of
neurotransmitters’ levels.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 94 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

GS9-3 Neuromorphic Robot Dream: A Spike Based Reasoning System

Alexander Toschev, Max Talanov, Salvatore Distefano (Kazan Federal University, Russia)
Alexander Tchitchigin (Innopolis University & Kazan Federal University, Russia)

Re-implementation of bio plausible processes in a real-time robotic
system is one of challenging problems. We propose a novel approach
for an integration of biologically plausible simulation of a mammalianbrain with a real-time/semi-real-time autonomous robotic systemusing metaphor of “wake” and ”dream” phases of a mammalian life.
During the “wake” phase the robotic system makes its decisions
applying a light-weight rule-based system and stores inbound sensory
information in a form of pseudo-neuronal activities. During the“dream” phase the “sleeping brain” plays-back the stored pseudoneural activities over a simulated spiking neural network and later via
the “reverse translation” translates the updated neuronal structuresin the form of light-weight rules of the robotic system. We call the
proposed architecture the “Robot dream”

GS9-4 Development of Behavioral Robot using Imitated Multiplex Neurotransmitters System
Saji Keita1, Wisanu Jitviriya2 and Eiji Hayashi1(1Kyushu Institute of Technology, Japan)
2( King Mongkut’s University of Technology, Thailand)This paper presents the design of robot’s behavior by using the Imitated Multiplex
Neurotransmitter system. The major neurotransmitter that consists of Dopamine,
Noradrenaline and Serotonin, which called monoamine neurotransmitter and it isrelated to motivation for behavior and feeling. The proposed system is based on
Wundt‘s three-dimensional theory of feeling. So, Dopamine is applied as acomfort dimension, Noradrenaline is defined as a tense dimension, Serotonin isconsidered as an energetic dimension. In our robot, three neurotransmitters are
generated in each of different factors and the motivation is calculated. Next, therobot’s motivation and some external information are classified into behavior andemotion by Self-Organizing Map learning. Finally, robot’s behavior and emotion
are decided by Markov’s stochastic model.

GS9-5 Nonlinear Estimation Strategies Applied on an RRR Robotic Manipulator
Jacob Goodman1, Jinho Kim1, Andrew S. Lee1, S. Andrew Gadsden1,2( 1University of Maryland, USA) ( 2 University of Guelph, Canada)

Nonlinear estimation strategies are important for accurate and reliable control ofrobotic manipulators. This paper studies and compares two strategies known as
the extended Kalman filter (EKF) and the smooth variable structure filter (SVSF).
An overview of the two strategies, including their advantages and disadvantages,
are provided. The algorithms and pseudocode are also included. The EKF and
SVSF are applied to a dynamically modelled three-link (RRR) robotic
manipulator. The results of the paper demonstrate that the EKF provides goodstate estimates, however lacks robustness to modeling uncertainties anddisturbances. The SVSF provides sub-optimal estimates, yet is very robust to
uncertainties and disturbances. Suggestions for future estimation and robotics
research is also provided.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 95 -

Fig 2. Configurationof the RRR robotic
manipulator.

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

GS10 Reinforcement & Evolutionary Computations (3)GS10-1 The Optimized Function Selection Using Wolf Algorithm for Classification

Duangjai Jitkongchuen, Worapat Paireekreng (Dhurakij Pundit University, Thailand)

Several classification techniques have been widely explored during the past
decade. One of the novel approaches is Nature Based Algorithm. The focus of
Nature Based Algorithm mostly is related to selection the optimized functions for
self-learning. However, Some Nature Based Algorithms are suitable for generalsituation, some may be suitable for customized situation. This research proposes
the Featured-Wolf (F-Wolf) algorithm to optimize the function selection problem
in classification. The proposed algorithm applies the movement of a wolf andcharacteristics of wolves’ leaders which can be more than one leader in a pack.
Therefore, the pack can have more than one dominant leader which can help toselect the most optimized functions to selection the most relevant features in thedataset. The experiment shows the comparison among other popular Nature
Based Algorithms such as Ant Colony Optimization. The preliminary results
show that F-Wolf performs comparable results in terms of F-measure.

GS10-2 Tell Agent Where to Go: Human Coaching for Accelerating Reinforcement Learning
Nakarin Suppakun, Suriya Natsupakpon, Thavida Maneewarn(King Mongkut's University of Technology Thonburi, Thailand)

In this work, we proposed a method to accelerate learning by allowing a human
to coach a robot behavior by inserting an intermediate goal at the early phase of
the reinforcement learning. By using an intermediate goal, the different pair ofpolicy and reward function was temporarily used to select an action that most
likely to drive the robot toward the intermediate location, while the global reward
function is still used for updating the state-action value. Q learning algorithm wasused to test with the proposed method on three learning tasks: ball following,
obstacle avoidance, and mountain car. The proposed technique resulted in lessnumber of accumulated failed episodes than the traditional RL

GS10-3 Fall Risk Reduction for Elderly Using Mobile Robots Based on the Deep Reinforcement
Learning
Takaaki Namba (Nagoya University, Japan)
Slip-induced fall is one of the main factors causing serious fracture among theelderly. This paper proposes a deep learning based fall risk reduction
measurement by the mobile assistant robot for the elderly. First, we collectpreparatory data regarding past incidents and accidents data as input data to
analyze fall risks and to learn examples of risk reduction measurement. Second,
we use a deep convolutional neural network to analyze fall risks of elderly. Third,
we apply a deep reinforcement learning to control mobile robots and reduce slipinduced fall risks of elderly. Moreover, we evaluate the effect of risk reduction.
The results suggest that the applicability of our method to other cases of the falland other cases of accidents.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 96 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

GS11 Others (12)GS11-1 Clinical Evaluation of UR-System-PARKO for Recovery of Motor Function of Severe
Plegic Hand after Stroke
Hirofumi Tanabe (Shonan University of Medical Sciences, Japan)
Yoshifumi Morita (Nagoya Institute of Technology, Japan)

The first author has developed TANABE therapy for severe hemiplegic stroke
patients. In the therapy, he performed repeated facilitation training using his
hands on patients to help them recover their motor function and achieved a goodtreatment outcome. In this paper we developed a training system (UR-SystemPARKO) on the basis of TANABE therapy. The clinical test of the therapeutic
effect of the UR-System-PARKO was performed in severe plegic hand. As aresult, the active ranges of motion of finger extension were improved, andElectromyogram ignition increased after the training. Moreover, the Modified
Ashworth Scale scores of finger extension were increased. These results show theeffectiveness of the training by the UR-System-PARKO for recovery of motor
function for finger extension of the severe plegic hand.

GS11-2 A Piston Finger Device for Restoring the Motor Function of Chronic Plegic Fingers:
Analysis of the Piston Finger TechniqueMengsu Wang (Nagoya Institute of Technology: Nitech, Japan), Hirofumi Tanabe (Shonan University of
Medical Sciences, Japan), Kenji Ooka (Nitech, Japan), Yoshifumi Morita (Nitech, Japan)
The second author has developed Piston Finger Technique (PFT) for restoring
motor function of chronic plegic fingers and has achieved a good treatment
outcome, namely reduction of spasticity and improvement of muscle shortening.
In this paper, before developing a piston finger device (PFD) simulating the PFT,
we analyzed the PFT by the second author by using a motion capture system. The
patent's finger motion of a chronic plegic index finger was investigated when thepatient received two types of treatment based on the PFT. As a result, the rangesof motion of the proximal interphalangeal joint and the metacarpophalangeal jointof the index finger as well as the vibration width and frequency during treatment
were revealed. These results will be useful for developing a PFD in the near
future.

GS11-3 Verifying the Sleep-Inducing Effect of a Mother's Rocking Motion in Adults

Hiroaki Shibagaki, Keishi Ashida, Yoshifumi Morita (Nagoya Institute of Technology, Japan),
Ryojun Ikeura (Mie University, Japan) and Kiyoko Yokoyama (Nagoya City University, Japan)

In our previous study, from subjective experimental results, we have found that,of the ten types of rocking motions examined, the linear motion component of amother's rocking motion was the most effective rocking motion for inducing sleepin adults. This motion was referred to as the candidate rocking motion. In thisstudy, we confirmed that the candidate rocking motion was effective for inducing
sleep by using electroencephalogram analysis in comparison with the casewithout rocking motion. Moreover, we also compared the candidate rocking
motion and aromatherapy. As a result, in the case of aromatherapy theeffectiveness for inducing sleep varied between individuals. In the case of thecandidate rocking motion individual variation in the effectiveness was small. We
concluded that the candidate rocking motion was effective.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 97 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

GS11-4 Design of Automated Real-Time BCI Application Using EEG Signals

Chong Yeh Sai, Norrima Mokhtar, Hamzah Arof, Masahiro Iwahashi(University of Malaya, Malaysia / Nagaoka University of Technology, Japan)

This study proposed a design of real time BCI application using EEG recording,pre-processing, feature extraction and classification of EEG signals. Recorded
EEG signals are highly contaminated by noises and artifacts that originate fromoutside of cerebral origin. In this study, pre-processing of EEG signals using
wavelet multiresolution analysis and independent component analysis is applied
to automatically remove the noises and artifacts. Consequently, features ofinterest are extracted as descriptive properties of the EEG signals. Finally,classification algorithms using artificial neural network is used to distinguish thestate of EEG signals for real time BCI application.

GS11-5 A Hybrid Simulated Kalman Filter - Gravitational Search Algorithm (SKF-GSA)

Badaruddin Muhammad, Zuwairie Ibrahim, Mohd Falfazli Mat Jusof (Universiti Malaysia Pahang, Malaysia)
Nor Hidayati Abdul Aziz, Nor Azlina Ab. Aziz (Multimedia University, Malaysia)
Norrima Mokhtar (University of Malaya, Malaysia)
In this paper, simulated Kalman filter (SKF) and gravitational search algorithm(GSA) are hybridized in such a way that GSA is employed as prediction operator
in SKF. The performance is compared using CEC2014 benchmark dataset. The
proposed hybrid SKF-GSA shown to perform better than individual SKF and
GSA algorithm.

Evaluate Fitness of Each Agent
Update Xtrue, Xbest, worse,

Calculate Mass, Force and

Yes
No

No

Yes
(Xtrue)GS11-6 Simulated Kalman Filter with Randomized Q and R Parameters

Nor Hidayati Abdul Aziz, Nor Azlina Ab. Aziz (Multimedia University, Malaysia)
Zuwairie Ibrahim, Saifudin Razali, Mohd Falfazli Mat Jusof (Universiti Malaysia Pahang, Malaysia)
Khairul Hamimah Abas, Mohd Saberi Mohamad (Universiti Teknologi Malaysia, Malaysia)
Norrima Mokhtar (University of Malaya, Malaysia)
Inspired by Kalman filtering, simulated Kalman filter (SKF) has been introduced
as a new population-based optimization algorithm. The SKF is not a parameterless algorithm. Three parameter values should be assigned to P, Q, and R, which
denotes error covariance, process noise, and measurement noise, respectively.
While analysis of P has been studied, this paper emphasizes on Q and R
parameters. Instead of using constant values for Q and R, random values are usedin this study. Experimental result shows that the use of randomized Q and R
values did not degrade the performance of SKF and hence, one step closer to therealization of a parameter-less SKF.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 98 -

Function 5

521.4

Original SKF
Random QR SKF

521.2
521
520.8
520.6
520.4
520.2
520

0

100

200

300

400

500

600

700

800

900

1000

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

GS11-7 Real Detection of 3D Human Hand Orientation Based Morphology

Abadal-Salam T. Hussain, Hazry D., Waleed A. Oraibi, M.S Jawad, Zuradzman M. Razlan, A. Wesam AlMufti, S. Faiz Ahmed, Taha A. Taha, Khairunizam WAN, Shahriman A.B(Universiti Malaysia Perlis, Malaysia)
This paper describes a new methodology to track a human hand movements in
3D space and estimate its orientation and position in real time. The objective of
this algorithm development is to ultimately using it in robotic spherical wrist
system application. This method involves image processing and morphology
techniques in conjunction with various mathematical formulae to calculate thehand position and orientation. The advantage of this technique is that, there is noneed for continuous camera calibration which is required in other conventional
methods in similar applications. The result of proposed method shows correctly
identifying a large number of hand movements and its orientations. The proposed
method could therefore be used with different types of tele operated robotic
manipulators or in other human-computer interaction applications. This method
is more robust and less computationally expensive, unlike other approaches thatuse costly leaning functions. The high performance is achieved duringexperiments testing because of its accurate hand movement identification and the
low computational load.

GS11-8 Multilevel Non-Inverting Inverter Based Smart Green Charger System©ICAROB 2017 ALife Robotics Corp. Ltd.

- 99 -

M/標準

M/標準

B

This paper discusses about the development of low cost efficient battery charging
標準
system using PIC Microcontroller. The demand of efficient battery charging
system are going increase day by day, because of its usage in various applications
標準such as it use in Hybrid electric cars, PV solar electric generation system, other標準
energy storage systems and many more. In this paper an efficient and cost
標準
effective battery charging system is presented that use buck-boost converter
topology. First, the software (PIC Programming) simulation is performed using
Micro C software and then hardware is developed and tested to check the
performance of the developed battery charger system. The efficiency of circuit is85.66% and it can be use in any battery charging applicationry voltage (V)Abadal-Salam T. Hussain, Waleed A. Oraibi, Hazry D, Zuradzman M. Razlan, S. Faiz Ahmed, Taha A. Taha,
Khairunizam WAN & Shahriman AB (Universiti Malaysia Perlis, Malaysia)

Charging current (A)The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

GS11-9 Design A New Model of Unmanned Aerial Vehicle Quadrotor Using The Variation in The
Length of The Arm
Yasameen Kamil, D. Hazry, Khairunizam Wan, Zuradzman M. Razlan, Shahriman AB(Universiti Malaysia Perlis, Malaysia)

The direction of technological advancement toward autonomous aerial vehicle isincreased. As a consequence of this evolution, the quad rotor used to accomplish
a complex task in several fields. This paper presents the dynamic model of thisminiature aerial vehicle in altitude and new dynamic model for yaw attitude
movement, based on varying the arm length of quadrotor instead of varying the
speed of motors to obtain a rotation around z-axis. This achieved by fixed a
stepper motor in the arm of quadrotor to increase or decrease the length of thearm according to controller command for yawing movement. The controller
command achieved by design PID controller with specific parameters to maintainthe stability of the quadrotor in the flight path. The equipped energy to the motors
during flight and maneuvering is reduced by selecting the motors’ speed. The
MATLAB software code used to evaluate and presents the comparison between
the proposed and conventional quadrotor dynamic model. Simulation results
show the robustness performance of the proposed model due to the movement
around the z-axis with high system stability.

GS11-10 Development of Automatic Take Off and Smooth Landing Control System for
Quadrotor UAV
Syed. F. Ahmed (Universiti Kuala Lumpur, British Malaysian Institute, Malaysia
Universiti Malaysia Perlis, Malaysia)
D. Hazry (Universiti Malaysia Perlis, Malaysia)
Kushsairy Kadir (University Kuala Lumpur, British Malaysian Institute, Malaysia)
Abadal Salam T. Hussain, Zuradzman M. Razlan, Shahriman AB(Universiti Malaysia Perlis, Malaysia)

This paper covers the development of automatic takeoff and smooth landing
control system for UAV Quadrotor. This paper includes development, simulation,mathematical modeling and experimental results of flight test. In the developed
UAV model has a system to stabilize the quadrotor. Altitude stabilization inquadrotor make the quadrotor can perform take off and smooth landing perfectly
and can avoid crash during landing which was proved during experiment. Yaw,
pitch, and roll in quadrotor body is detected by gyro sensor when flying isbalanced by gyro sensor. Gyro sensor act as input that detect stabilization problem
and input data will be sent to microcontroller to make new output for quadrotor.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 100 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

GS11-11 Auto Pilot Ship Heading Angle Control Using Adaptive Control Algorithm

Abadal-Salam T. Hussain Hazry D., S. Faiz Ahmed, Wail A. A. Alward, Zuradzman M. Razlan & Taha A.
Taha (Universiti Malaysia Perlis, Malaysia)
In this paper discussed about development of an auto pilot system for ships
using adaptive filter. Adaptive filter in the application of auto pilots for ships
is presented for controlling the ship such that it follows its predeterminedtrajectory. Due to random environmental effects such as wind speed or
direction and sea current, the path of the ship may alter. The objective of thisresearch is to investigate that whether proposed system will adapts to therandom changes and maintain the desired ship trajectory. The proposed auto
pilot system is developed using Least Mean Square algorithm (LMS)
adaptive filter. The performances of the system are analyzed based on
accuracy and computational times. MATLAB Simulink model tool is used
for execute the simulations of the auto pilot system for ships.

GS11-12 Classification of Hippocampal Region using Extreme Learning Machine

Muhammad Hafiz Md Zaini, Mohd Ibrahim Shapiai (Universiti Teknologi Malaysia)
Ahmad Rithauddin Mohamed (Hospital Kuala Lumpur)
Norrima Mokhtar (University of Malaya)
Zuwairie Ibrahim (Universiti Malaysia Pahang)

Important brain parts like hippocampal usually being manually segmented
by doctors. But with the introduction of hybrid between machine learning
along with neuroimaging technique, it has proved to shows some promising
results regarding on segmenting subcortical structures. However, it is known
that Extreme Learning Machine (ELM) is to be superior machine learningtechnique. This study will investigate on the usage of ELM to segment
hippocampal by using various hidden nodes configuration. This study also
will address on the usage of full image and region of interest (ROI) using
ELM. Bag of features is used as a feature extractor where it will segment
the hippocampal of the MRI in order to get its visual words. ELM will used
it to learn its feature. Results shows that with suitable hidden nodes, it could
achieve up to 100% performance on both cases for full image and ROI in
hippocampal segmentation.

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 101 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

AUTHORS INDEX
Notation of session name
PS: Plenary Session IS: Invited Session, OS: Organized Session, GS: General Session,
Note: 33/90 = (page no. in Technical Paper Index) / (page no. in Abstracts)[A]
AB.

Shahriman.

AshidaKeishiGS11-3

38/97

GS11-7

38/99

AzakamiKenzoh

OS12-1

36/62

GS11-8

38/99

Aziz

Nor

GS11-5

38/98

GS11-938/100

GS11-10

39/100

GS11-6

38/98

GS11-5

38/98

GS11-6

38/98

Abramov

Vitaly

GS6-1

20/89

Abas

KhairulGS11-6

38/98

AzlinaAb.
Aziz

Nor Hidayati
Abdul

HamimahAfanasyev

Ilya

GS2-4

28/85

Ahmed

S. Faiz

GS11-7

38/99

GS11-8

38/99

Baba

Yuki

OS12-2

37/63

GS11-10

39/100Bai

Ning

OS16-8

24/72

GS11-11

39/101Bai

Peng

OS16-4

23/71[B]

AizawaYo

OS12-4

37/63

Bian

Ce

OS15-1

22/67

Akhmetsharipov

Ruslan

GS6-1

20/89

BuyvalAlexander

GS2-3

28/85

Al-Mufti

A. WesamGS11-7

38/99

AlwardWail A. A.

GS11-11

39/101

Ando

Kazuaki

OS19-1

35/76

Chang

Hongxing

OS15-4

23/68

OS19-2

35/76

Chang

Hsuan T.

OS2-4

20/46

[C]

Ando

Kensuke

OS20-5

27/79

Chang

Kevin

GS8-3

29/94

Aratani

Yoshiya

OS9-2

22/58

Chen

Hsin-Fu

OS3-4

19/48

Arof

Hamzah

GS11-4

38/98

Chen

Jian-Yuan

OS3-4

19/48

Aruka

Yuji

OS8-3

32/57

Chen

Mei-Yung

OS2-6

20/46

Asada

Taro

OS5-1

21/50

Chen

SiminiOS15-3

23/68

OS5-2

21/51

Chen

Yunju

OS18-4

34/75

OS5-3

21/51

Cheng

Yu

OS10-1

33/59

OS5-4

21/51

ChiangChang-Yun

OS3-4

19/48

OS1-4

36/44

Chikamatsu

Masato

GS7-5

24/92

Asakawa

Shin©ICAROB 2017 ALife Robotics Corp. Ltd.

- 102 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017
ChikushiShota

OS22-3

30/82

Chin

Takaaki

GS1-1

31/83

Fukuchi

GS1-2

31/83

Fukushima

GS6-2

21/89

OS4-4

30/50

Kenji

OS18-1

34/74

MihokoOS20-6

27/79

Choi

Gyung-I

OS11-431/62[G]Christensen

David Johan

IS-3

19/41

Gabdullin,

Aidar

GS2-4

28/85

GadsdenS. Andrew

GS8-3

29/94

GS9-5

28/95

[D]
D.

Hazry

GS11-7

38/99

GaisinRuslan

GS9-2

27/94

GS11-838/99

Gaisina

Kristina

GS9-2

27/94

GS11-938/100

Galimova

Diana

GS6-1

20/89

GS11-10

39/100Gao

CaixiaOS13-2

29/64

GS11-11

39/101Gavrilenkov

Mikhail

GS2-3

28/85

Dad

Karam

OS11-331/62

Godler

Ivan

OS7-1

25/54

Dai

FengzhiOS15-1

22/67

Goodman

Jacob

GS9-5

28/95

OS15-2

22/68

Green

Ethan

OS9-5

22/59

OS15-3

23/68

Guo

Jr Hung

OS2-1

20/44

DeguchiChikashiOS20-6

27/79

OS2-2

20/45

Distefano

Salvatore

GS9-3

28/95

OS2-5

20/46

Doi

ShunsukeOS19-4

35/77

Yangguang

OS16-2

23/70

Du

Hongyue

OS15-4

23/68

Du

Yao

GS3-3

36/87
Ha

Hyunuk

OS11-2

31/61

Han

CongdaoOS16-3

23/71

HanSeong Ik

OS11-2

31/61

Han

Sumin

OS13-6

29/65

Hanajima

Naohiko

GS6-4

21/90

OS13-1

29/64

Guo
[H]

[E]
Elderton

Simon

GS2-1

28/84

[F]
Fauré

Adrien

GS4-3

32/88

FreundRudolf

OS17-4

35/73

Hara

ShinyaOS19-4

35/77

Fu

Yu-Yi

OS3-2

19/47

HaradaKensuke

GS7-1

24/91

Fu

Ziyi

OS13-2

29/64

GS7-2

24/91

OS14-1

18/66

Hashimoto

Masashi

OS8-2

32/56

Hattori

TetsuoOS19-1

35/76

Fujihira

Yoshinori

GS6-4

21/90

Fujii

Naoya

OS7-4

25/55

OS19-2

35/76

Fujimoto

Yoshiaki

OS4-1

30/49

OS19-3

35/77©ICAROB 2017 ALife Robotics Corp. Ltd.

- 103 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017
OS19-4

35/77

GS11-11

39/101

GS11-538/98

Hayakawa

Takuya

GS1-3

31/84

Hayashi

Eiji

GS2-5

28/85

[I]

GS2-6

28/86

Ibrahim

GS6-3

21/90

GS11-6

38/98

GS8-1

29/93

GS11-1239/101

GS8-2

29/93

IiboshiAtsushi

OS20-6

27/79

GS9-1

27/94

Ikeda

SatoshiOS21-1

37/80

GS9-4

28/95

OS21-2

37/80

OS21-3

37/80

Zuwairie

Hidaka

Shota

OS7-5

25/55

Higashijima

Shinichi

GS4-1

32/87

IkeuraRyojunGS11-3

38/97

HirataTakaomiOS12-3

37/63

Imai

YoshiroOS19-135/76

Ho

Chian C.

OS3-4

19/48

OS19-2

35/76

Honda

Yuichiro

GS6-2

21/89

OS19-3

35/77

Hori

Masamichi

OS20-6

27/79

OS19-4

35/77

Horikawa

Yo

OS19-1

35/76

Imaji

Hiromu

OS4-4

30/50

OS19-2

35/76

Inoue

Shinichi

GS3-1

35/86

OS19-335/77

InthiamJiraphan

GS9-1

27/94

Ishii

Kazuo

PS1

26/40

Horio

Yoshihiko

OS9-3

22/58

Hou

Yawei

OS16-3

23/71

OS7-1

25/54

Hsia

Kuo-Hsien

OS2-1

20/44

OS7-4

25/55

OS2-4

20/46

OS7-5

25/55

OS2-5

20/46

OS7-6

25/56

Hu

XianghuiOS15-6

23/69

OS22-1

30/81

Huang

Bo-Kai

OS3-3

19/47

OS22-3

30/82

Huang

Yao-Shing

OS2-5

20/46

OS22-4

30/82

Huang

Yinuo

OS6-2

26/52

IS-5

33/42

Hung

Chien-Ming

OS2-2

20/45

OS18-2

34/74

Hung

Chung-Wen

OS2-4

20/46

OS21-1

37/80

OS3-3

19/47

OS21-2

37/80

OS3-5

19/48

OS21-337/80

GS11-7

38/99

OS18-2

34/74

OS21-1

37/80

HussainAbadal Salam

Ito

Ito

T.

Takao

Tsutomu

GS11-838/99

OS21-2

37/80

GS11-10

39/100OS21-337/80©ICAROB 2017 ALife Robotics Corp. Ltd.

- 104 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017
ItohHajime

GS6-4

21/90

OS6-4

26/53

Iwahashi

Masahiro

GS11-4

38/98

OS6-5

26/53

Iwanaga

Saori

OS8-1

31/56

OS6-6

26/54

[J]

Kato

Eiji

OS10-3

33/60

Kato

Ryota

OS5-2

21/51

Jawad

M.S

GS11-7

38/99

Kawada

Kazuo

OS4-3

30/50

Jia

HongyanOS16-6

24/72

Kawakami

YusukeOS19-1

35/76

OS16-7

24/72

OS19-2

35/76

OS16-824/72

OS19-3

35/77

OS19-4

35/77

OS1-1

36/43

OS1-3

36/43

Jia

Jiao

OS13-3

29/64

Jia

Weibo

OS16-8

24/72

Jia

YingminOS13-329/64

OS13-529/65

KawanoHiromichi

OS19-3

35/77

OS14-3

18/66

Khafizov

Murad

GS6-1

20/89

OS14-4

18/67

Kim

Jinho

GS8-3

29/94

GS9-5

28/95

Kawamura

Yoji

Jia

Yongnan

OS14-2

18/66

Jiao

Hongwei

OS15-1

22/67

KimuraKenji

OS22-330/82

OS15-2

22/68

Kinoshita

Takuya

OS4-2

30/49

OS15-3

23/68

Kita

Yoshihiro

OS6-2

26/52

Jitkongchuen

Duangjai

GS10-1

34/96

OS6-3

26/53

Jitviriya

Wisanu

GS9-1

27/94

OS6-4

26/53

GS9-4

28/95

OS6-5

26/53

GS11-5

38/98

Jusof

Mohd FalfazliMatGS11-6

Kitagawa

Kodai

GS6-5

21/90

Ko

Chia-Nan

OS3-1

19/47

OS3-2

19/47

38/98

[K]

Kobayashi

Jun

GS1-3

31/84

Kobayashi

Kunikazu

OS12-2

37/63

Kadir

Kushsairy

GS11-10

39/100OS12-3

37/63

KamadaHiroshi

OS20-2

27/78

OS12-4

37/63

Kamil

Yasameen

GS11-938/100

Kobayashi

Ikuo

OS20-3

27/78

Kanai

Akihito

OS1-1

36/43

Kobayashi

Toru

OS8-2

32/56

Kang

Qijia

OS15-2

22/68

KodamaYukariOS20-5

27/79

Katayama

Tetsuro

OS6-1

25/52

OS20-6

27/79

OS6-2

26/52

KodateAkihisa

OS8-2

32/56

OS6-3

26/53

Kohno

Takashi

OS9-4

22/58

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 105 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

Koiwai

Kazushige

OS15-823/70

Bo

OS15-1

22/67

Liu

Qunpo

OS13-1

29/64

Liu

Shilong

OS15-7

23/69

OS15-8

23/70

OS9-5

22/59

OS4-1

30/49

Liu

OS4-4

30/50

KokuboHaruna

GS1-1

31/83

KoyamaYoshihide

OS19-3

35/77

Kubo

Masao

OS8-3

32/57

Liu

Yun-xiang

OS16-5

23/71

OS8-4

32/57

Long

Yan

GS5-2

26/89

Lund

Henrik

IS-1

19/41

KubotaMasashi

GS4-2

32/87

Kugurakova

Vlada

GS6-1

20/89

Kuremoto

TakashiOS12-1

36/62

IS-2

18/41

OS12-2

37/63

IS-3

19/41

OS12-3

37/63

IS-4

18/42

Hautop[L]
La-

Chakhrit

GS3-2

Lushnikov

Alexei

GS6-1

20/89

Lynn

Nay Chi

OS20-3

27/78

36/86
[M]

orworrakhun
Lai

Li-Chun

OS3-2

19/47

Ma

Yumei

OS14-5

18/67

Lai

Yuan-Hao

OS2-4

20/46

Mabu

ShingoOS12-1

36/62

Lavrenov

Roman

GS2-4

28/85

OS12-2

37/63

LeeAndrew S.

GS8-3

29/94

OS12-3

37/63

GS9-5

28/95

Madrigal

Omar Correa

GS6-1

20/89

Magid

Evgeni

GS2-3

28/85

GS2-4

28/85

Lee

Ching-I

OS3-1

19/47

Lee

JangmyungOS11-1

31/61

OS11-2

31/61

Mago

Shota

OS10-433/60

OS11-4

31/62

Maki

Toshitaka

OS8-2

32/56

Lee

Min CheolOS11-3

31/62

Man

Haifang

OS15-2

22/68

Lee

Na-HyunOS11-1

31/61

Maneewarn

ThavidaGS10-2

34/96

Li

Bing-fen

OS21-537/81

Mao

Wei-Lung

OS3-3

19/47

Li

Bingh

OS22-1

30/81

OS3-5

19/48

Li

Bo-Yi

OS2-3

20/45

Masuda

Akito

GS6-2

21/89

Li

Hefei

OS15-6

23/69

MatsunoHiroshi

GS4-2

32/87

Li

Long

OS15-2

22/68

GS4-3

32/88

Li

Qing

OS13-429/65

MatsunoSeigo

OS18-2

34/74

Li

ShuxiaoOS15-4

23/68

Mehta

Rajiv

OS18-2

34/74

Li

Xue

OS15-7

23/69

MinamiYuji

OS18-1

34/74©ICAROB 2017 ALife Robotics Corp. Ltd.

- 106 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017
Mitsuhashi
Mitsumura

KoshiroKazuhoOS20-5

27/79

NagaieTetsuya

OS8-2

32/56

OS20-6

27/79

NagataKazuyuki

GS7-1

24/91

OS7-2

25/54

GS7-2

24/91

OS7-3

25/55

OS20-5

27/79

Nagatomo

MakotoMiyachiJunpei

OS5-5

22/52

OS21-1

37/80

Miyakawa

Nobuo

GS2-6

28/86

OS21-237/80

MizobeFutoshi

GS6-2

21/89

Nagayoshi

Masato

GS2-1

28/84

Mizukami

Masato

GS6-4

21/90

Nakamura

Akira

GS7-1

24/91

MohamedAhmad

GS11-1239/101

GS7-2

24/91

GS1-2

31/83

GS6-2

21/89

Nakamura

Rithauddin

Go

MohamadMohd Saberi

GS11-6

38/98

MokhtarNorrima

GS11-4

38/98

NakaokaIori

OS18-4

34/75

GS11-538/98

Nakashima

TakahiroOS10-3

33/60

GS11-6

38/98

Nakata

Yuya

GS7-3

24/91

GS11-1239/101

GS7-4

24/92

MoribeHirotaka

GS7-6

24/93

Namatame

Akira

OS8-1

31/56

Morie

Takashi

OS9-1

22/57

Namba

Takaaki

GS10-3

34/96

OS9-2

22/58

NanamiTakuya

OS9-4

22/58

Morikawa

Katsumi

OS18-2

34/74

Nanjo

Takao

OS4-1

30/49

Morimoto

Shogo

OS6-1

25/52

Nassiraei

Amir Ali

OS7-1

25/54

MoritaYoshifumi

GS11-1

38/97

GS11-2

38/97

GS3-2

36/86

GS11-3

38/97

GS10-2

34/96

GS11-7

38/99

Negishi

KanakoOS21-4

37/81

GS11-838/99

Nishida

Yuya

PS1

26/40

GS11-938/100

OS7-4

25/55

GS11-10

39/100

Nishjima

Atsushi

OS7-6

25/56

GS11-11

39/101

Nishimura

Kouhei

OS5-1

21/50

M. Razlan

Zuradzman

ForoughNatsupakpong

SuriyaMuhammadBadaruddin

GS11-5

38/98

Nishita

Tomoyuki

PS2

33/40

Mukaidani

Naohisa

GS4-1

32/87

Niu

Cheng-shui

OS21-5

37/81

Mukunoki

MasayukiOS20-4

27/78

Niu

Hong

OS15-5

23/69

OS20-6

27/79

GS6-4

21/90

MasanaoOS12-1

36/62

OS12-2

37/63

Muraoka

Yohei[O]
Obayashi[N]©ICAROB 2017 ALife Robotics Corp. Ltd.

- 107 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017
OS12-3

37/63

Pramanpol

Teedanai

GS8-1

29/93

Pramanta

Dinda

OS9-1

22/57

YiqiaoOS15-1

22/67

OS15-3

23/68

OS19-1

35/76

OS19-2

35/76

Ogata

Kensuke

OS18-3

34/75

Ogata

Takashi

OS1-1

36/43

OS1-2

36/43

OS1-4

36/44

OS1-5

36/44

OgiharaHirokiOS20-4

27/78

Ohata

Makoto

GS6-4

21/90

[Q]
Qin

[R]
RajapakseR. P. C.
JanakaOkada

Takahiro

GS6-3

21/90

OkazakiNaonobu

OS6-2

26/52

OS6-3

26/53

Razali

SaifudinGS11-6

38/98

OS6-4

26/53

Ru

Yiwei

OS15-4

23/68

OS6-5

26/53
[S]

Okazaki

Shogo

OS10-3

33/60

Ojeda

Ismael Baira

IS-3

19/41

Sai

Chong Yeh

GS11-438/98

OnoJumpei

OS1-5

36/44

Sabirova

Leysan

GS2-4

28/85

Oo

Khine Khine

OS6-6

26/54

Saito

Keita

OS5-2

21/51

Ooka

Kenji

GS11-2

38/97

Saito

Ken

GS7-3

24/91

OraibiWaleed A.

GS11-7

38/99

GS7-4

24/92

GS11-8

38/99

Saji

Keita

GS9-4

28/95

Orima

Takemori

OS9-3

22/58

Sakakibara

Kazutoshi

GS2-1

28/84

OswaldMarionOS17-4

35/73

Sakamoto

MakotoOS18-2

34/74

OuyangYuxing

OS15-1

22/67

OS20-5

27/79

OS15-3

23/68

OS20-6

27/79

OS21-137/80

OS21-237/80[P]PachechoMoisés

IS-3

19/41

OS21-337/80

Pagliarini

Luigi

IS-1

19/41

OS21-5

37/81

Paireekreng

Worapat

GS10-1

34/96

OS10-1

33/59

Pan

ZhenkuanOS14-5

18/67

OS10-2

33/60

Park

YousinOS18-434/75

Sapaty

Peter

IS-5

33/42

Phung

Nhuhai

OS8-4

32/57

Sasao

T.

GS8-2

29/93

Phunopas

Amornphun

GS3-1

35/86

Sato

Hiroshi

OS8-3

32/57

Phyo

Cho Nilar

OS20-2

27/78

OS8-4

32/57

©ICAROB 2017 ALife Robotics Corp. Ltd.

Sakurai- 108 -

Keiko

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017
Sato

Takuya

OS6-5

26/53

OS21-1

37/80

Schwarz

Brian

GS8-3

29/94

OS21-237/80

Shapiai

Mohd

GS11-1239/101IbrahimSun

Hsuan-Min

OS3-6

20/49

Sun

ShihaoOS13-329/64

Shibagaki

Hiroaki

GS11-3

38/97

Suppakun

Nakarin

GS10-2

34/96

Shibanoki

Taro

GS1-1

31/83

Shuto

Daisuke

OS9-2

22/58

GS1-2

31/83

Suprapto

OS3-5

19/48

GS6-2

21/89

Suzuki

Michiyo

GS4-4

32/88

Suzuki

YasuhiroOS17-1

35/73

ShimadaRyoichi

OS5-4

21/51

Si

Jikai

OS13-1

29/64

OS17-2

35/73

Sirikayon

Chaloemphon

GS5-1

26/88

OS17-3

35/73

Soh

Zu

GS4-1

32/87

SuzukiTakuo

OS12-4

37/63

GS4-4

32/88

Syoichizono

MisakiOS10-333/60

Masayoshi

OS5-1

21/50

Sokolov

Maxim

GS2-4

28/85

Sonehara

Noboru

OS8-2

32/56

Song

Binhu

OS15-2

22/68

Song

Yunzhong

OS14-1

18/66

OS5-2

21/51

SonodaTakashi

PS1

26/40

OS5-3

21/51

OS7-1

25/54

OS5-4

21/51

OS7-6

25/56

OS5-5

22/52

OS22-1

30/81

Tachiyama

Hiroki

OS6-3

26/53

OS2-1

20/44

TagawaShinya

OS18-1

34/74

OS2-2

20/45

Taguchi

Yu

GS6-5

21/90

OS2-3

20/45

Taha

Taha A

GS11-7

38/99

OS2-5

20/46

GS11-8

38/99.

GS11-11

39/101Su

Kuo-Lan[T]
Tabuse

Sugii

Manabu

GS4-2

32/87

Sugisaka

Masanori

IS-5

33/42

TakagiTomohiko

OS6-1

25/52

GS3-3

36/87

Takahashi

KatsuhikoOS18-2

34/74

GS5-2

26/89

Takasaki

Shiyun

OS7-2

25/54

OS16-1

23/70

OS7-3

25/55

OS16-2

23/70

GS7-3

24/91

OS16-3

23/71

GS7-4

24/92

GS7-3

24/91

OS22-2

30/82

GS7-4

24/92

OS22-430/82

OS20-5

27/79

OS21-337/80

SugitaSun

KazukiChongyang©ICAROB 2017 ALife Robotics Corp. Ltd.

TakatoTakemura
Takeshita- 109 -

MinamiYasunori
Yuki

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017
Talanov

Max

GS9-3

28/95

GS1-2

31/83

Tamaki

Hisashi

GS2-1

28/84

GS4-1

32/87

Tamukoh

Hakaru

OS9-1

22/57

GS4-4

32/88

OS9-2

22/58

GS6-2

21/89

OS10-1

33/59

Tsushita

Hiroaki

OS20-1

27/77

OS10-2

33/60

Tun

Hnin Thandar

OS6-6

26/54

OS10-3

33/60

OS10-433/60

OS10-5

33/61

Yasuo

OS21-1

37/80

GS11-1

38/97

OS21-2

37/80

GS11-2

38/97

Uchihara

MasayukiOS10-5

33/61

Uchikoba

Fumio

GS7-3

24/91

GS7-4

24/92

TamuraTanabeHiroki

Hirofumi[U]
UchidaTanakaHideaki

GS4-3

32/88

TanakaTaisuke

GS7-3

24/91

GS7-4

24/92

Ueda

Koji

OS4-4

30/50

TanakaTakeshi

OS20-6

27/79

Ura

TamakiPS1

26/40

Tanigawa

Yu

OS8-2

32/56

Urata

Kiyohiro

OS8-2

32/56

Taniguchi

Rie

OS17-1

35/73

Utsunomiya

Koshi

OS7-2

25/54

Tanno

KoichiOS10-1

33/59

OS7-3

25/55

OS10-2

33/60

OS10-433/60[W]OS10-5

33/61

Wakahara

Toshihiko

OS8-2

32/56

Wan

Khairunizam

GS11-7

38/99

TanoueSatoshi

OS6-4

26/53

Tchitchigin

Alexander

GS9-3

28/95

GS11-838/99

Thusaranon

Panita

GS5-1

26/88

GS11-938/100Tolu

Silvia

IS-3

19/41

OS13-1

29/64

Tominaga

Moeko

OS22-430/82

OS13-2

29/64

Tominaga

Ayumu

GS8-2

29/93

OS13-6

29/65

Tomonaga

Kenta

GS1-3

31/84

OS14-1

18/66

Tono

TetsuyaOS10-3

33/60

Wang

HongqiOS13-1

29/64

Toriu

Takashi

OS20-2

27/78

Wang

Jie

OS11-331/62

Toschev

Alexander

GS9-3

28/95

Wang

Jiwu

GS3-3

36/87

Toya

Nobuyuki

GS6-5

21/90

GS5-2

26/89

Tsai

Tai-Huan

OS2-6

20/46

Wang

Jun

GS2-2

28/84

TsuboiHarukaOS20-5

27/79

Wang

MengsuGS11-2

38/97

Tsuji

Toshio

GS1-1

31/83

Wang

Ping

OS14-4

18/67©ICAROB 2017 ALife Robotics Corp. Ltd.

Wang

- 110 -

FuzhongThe 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017
Wang

Shanfeng

Wang

Shunzhou

Watanabe

Keisuke

OS6-6

26/54

OS20-6

27/79

GS7-5

24/92

GS7-6

24/93

OS4-1

30/49

25/55

OS4-2

30/49

OS7-5

25/55

OS4-3

30/50

OS7-6

25/56

OS4-4

30/50

OS16-6

24/72

OS16-7

24/72

OS16-1

23/70

OS16-4

23/71

OS7-2

25/54

OS7-3

Yamamori
Yamamoto
Yamamoto

Kunihito
Hidehiko
Toru

Weerakoon

Tharindu

OS7-1

25/54

Yamana

Sho

GS2-5

28/85

Wei

Baochang

OS15-3

23/68

Yamanobe

Natsuki

GS7-1

24/91

Weng

Jian-Fu

OS2-3

20/45

GS7-2

24/91

Wu

Hsien-Huang

OS3-6

20/49

OS4-1

30/49

OS4-4

30/50

OS10-1

33/59

OS10-2

33/60

Yamazaki

YoichiroP
Wu

XuyangOS16-8

24/72

Wu

YongjunOS16-6

24/72

OS16-7

24/72

Yan

MingminYang

Shih-Jui

OS3-4

19/48

Yano

Shinnosuke

OS21-137/80

OS21-2

37/80

PS1

26/40

OS22-1

30/81[X]
Yasukawa

Shinsuke

Xin

Song

GS2-2

28/84

Xu

Shifang

OS16-1

23/70

OS16-3

23/71

Yeoh

Yoeng Jye

OS9-2

22/58

Xu

Wensheng

GS3-3

36/87

Yokomichi

MasahiroOS20-6

27/79

Xue

Wei

OS15-7

23/69

Yokoyama

KiyokoGS11-3

38/97

OS15-823/70

Yoshinaga

TsunehiroOS21-137/80

OS21-2

37/80

OS5-1

21/50

Yoshitomi[Y]
YamabaHisaaki

OS6-2

26/52

OS5-2

21/51

OS6-3

26/53

OS5-3

21/51

OS6-4

26/53

OS5-4

21/51

OS6-5

26/53

OS13-529/65

OS14-5

18/67

Yan-Ting

OS3-3

19/47

MuhammadGS11-1239/101

YamadaSho

OS20-5

27/79

YamadaTakayoshi

GS7-5

24/92

GS7-6

24/93

OS8-2

32/56

OS8-3

32/57

Yamaguchi

AkihiroYasunari©ICAROB 2017 ALife Robotics Corp. Ltd.

Yu

Jinpeng

Yu
[Z]
Zaini

- 111 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017
Hafiz Md
Zhang

HongtaoOS15-2

22/68

Zhang

Mei

OS15-7

23/69

OS15-8

23/70

Zhang

Wei

OS16-5

23/71

Zhang

WeicunOS13-429/65

OS14-2

18/66

OS16-1

23/70

OS16-4

23/71

OS21-137/80

OS21-2

37/80

OS21-5

37/81

Zhang
Zhang

Yani
Yu-an

Zhang

Yuxi

GS2-2

28/84

Zhang

YuzhenOS13-4

29/65

Zhao

Huailin

OS16-1

23/70

OS16-2

23/70

OS16-3

23/71

OS16-4

23/71

OS13-529/65

OS14-5

18/67

Zhao

Lin

Zheng

WenhaoOS14-3

18/66

Zin

Thi Thi

OS20-1

27/77

OS20-2

27/78

OS20-3

27/78©ICAROB 2017 ALife Robotics Corp. Ltd.

- 112 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 113 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 114 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 115 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

シーガイアコンベンションセンター ２Ｆ 「ファウンテン」【
様 学会会場】
スクール形式 名＋コーヒーブレイク用
丸テーブル 卓■ファウンテン■面積………331㎡■寸法 天井高…4.50m
間口……15.61m
奥行……21.19m

ハンガーラック

コーヒーブレイク用テーブル

音響卓

プロジェクター（常設 天井部）ＰＣ
演台（有線マイク）

常設スクリーン
ｲﾝﾁＨＤ
×©ICAROB 2017 ALife Robotics Corp. Ltd.

- 116 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

シーガイアコンベンションセンター ２Ｆ 「オーチャード北」【
様 分科会会場１】 スクール形式 名■オーチャード北■面積………150㎡■寸法 天井高…3.60m
間口……10.70m
奥行……13.99m
音響卓
ＰＣ

ＣＬＯＳＥ
演台
（有線マイク）

ＣＬＯＳＥ

プロジェクター（仮設 下打ち）

常設スクリーン
ｲﾝﾁＨＤ
×

音響卓

ＰＣ

演台
■オーチャード南（有線マイク）■面積………150㎡■寸法 天井高…3.60m
間口……10.70m
奥行……13.99m「オーチャード南」【
様 分科会会場２】
スクール形式 名©ICAROB 2017 ALife Robotics Corp. Ltd.

プロジェクター（常設 天井部）

常設スクリーンｲﾝﾁＨＤ×- 117 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

シーガイアコンベンションセンター ２Ｆ 「プライベートレストラン」【
様 事務局】 椅子 席

■プライベートレストラン■面積………59㎡■寸法 天井高…3.30m
間口……5.60m
奥行……10.00m©ICAROB 2017 ALife Robotics Corp. Ltd.

- 118 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

シーガイアコンベンションセンター ２Ｆ 「ジブラルタル」【
様 ウェルカムパーティー会場（）】■ジブラルタル■面積………140㎡■寸法 天井高…4.00m
間口……9.30m
奥行……15.00m

ー ー

料
理
コ
ナ

司会台

有線マイク（スタンド）

音響卓©ICAROB 2017 ALife Robotics Corp. Ltd.

- 119 -

名

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

シーガイアコンベンションセンター ４Ｆ 「天瑞」【
様 バンケット会場（）】 着席ブッフェ形式
名掛× 卓 名掛× 卓 合計
席

■天瑞（
）
■面積………915㎡■寸法 天井高…9.00m
間口……21.60m
奥行……42.38m

音響卓常設せりｽﾃｰｼﾞ

司会台

ー

料
理
コ

ー

ナ

ー

料
理
コ

ー

ナ

ドリンクカウンター

ハンガーラック
ＣＬＯＳＥ

ＣＬＯＳＥ

ＣＬＯＳＥ

©ICAROB 2017 ALife Robotics Corp. Ltd.

ＣＬＯＳＥ

- 120 -

The 2017 International Conference on Artificial Life and Robotics (ICAROB2017), Seagaia Convention Center, Miyazaki,
Japan, January 19-22, 2017

©ICAROB 2017 ALife Robotics Corp. Ltd.

- 121 -

Targeting Chaos System via Minimum Principle Control
Yunzhong Song, Ziyi Fu, Fuzhong Wang

College of Electrical Engineering and Automation, Henan Polytechnic University, 2001 Century Avenue,
Jiaozuo, 454003, P.R.China
E-mail: songhpu@126.com, fuzy@hpu.edu.cn, wangfzh@hpu.edu.cn
www.hpu.edu.cn

Abstract
Chaos targeting via Minimum Principle Control (MPC) was suggested here, to be unique, the targeting via MPC can
not only stabilize all the non-stable equilibrium when the control was introduced in the first or the second equationof the chaos system, but also does the extra added control was introduced in the third equation, with the cost ofswitching control direction for a duration.
Keywords: chaos system, Minimum Principle Control (MPC), Hamiltonian function, switching control

second equation, we will consider the case, where control
action added on the third equation of the Chen chaossystem. Thanks to the uniqueness of the nonlinear system,
different systems may express different behavior, so do
the Chen chaos system; and even further, the switchingsurface resulted from the Hamiltonian function and the
Minimum Principle Control (MPC) will also be distinct
from each other, without saying the parameters selectionof the controller. And the most striking point is with thecompletion of Chen chaos system control via MPC
strategy, the belief in Lorenz family chaos system control
can come into mature naturally, and more importantly,
the wait to go style of chaos system control is rerecognized to be the first candidate when the otherstrategies fail.
The rest of the paper is as follows. First, at the second
part, we will give some preliminaries about Chen chaossystem and MPC with constraints; and then the main
results will be introduced in the third part; the conclusionand some prospects will be provided in the end.

1. Introduction
An additive, scalar, and inequality constrained control
method has been introduced to the well known chaotic
system Lorenz system by Yu-Chu Tian, Mose O Tade,David Levy which appeared in Physics Letters
A296(2002)87-90, please referring to Ref. 1. The
resulting control law is bang-bang one and can stabilize
the unstable equilibrium, two cases such that control
action added at the first and second equations of theLorenz system are reported. And Professor Song,
together with the other others, has addressed the toughobstacle that when the control action was added in thethird equation, whereas the Tian’s method was out ofcontrol, please referring to Reference 2. Since then,seldom results of constrained control to chaos systemwere reported and extended. In this paper, we will extend
the suggested strategy to another chaos system, i.e theChen chaos system, please referring to Reference 3,
besides the control action being added in the first or the©The 2017International Conference on Artificial Life and Robotics (ICAROB 2017),Jan. 19-22,Seagaia Convention Center, Miyazaki, Japan

P-1

Yunzhong Song, Ziyi Fu, Fuzhong Wang

In order to identify the optimal control action u, it isnecessary for us to come to the helps MPC, thenH ( x* , u * , * , t ) H ( x* , u *u, * , t )(7)
To arbitrary u , constraint defined by (7) can stand.
And
is deployed to represent variation, and thesuperscript “＊”is assigned to represent the optimalvalue. To Hamiltonian function (5), under constraint of(7), it is easy for us to get( * )T Bu * ( * )T Bu(8)

2. Preliminaries
2.1. Glimpse of chen chaos system
For Chen chaos system
x1

f1 ( x1 , x2 , x3 )a ( x2x1 ),x2

f 2 ( x1 , x2 , x3 )(c a ) x1x3

f 3 ( x1 , x2 , x3 )

x1 x2

x1 x3

cx2 ,

(1)

bx3 .

when the parameters and their values are deployed asa 35, b 3, c 28 , the Chen chaos system will
demonstrate chaotic behavior and in that case has threedifferent equilibriums, and all of them are not stable.
xe(1) [0, 0, 0]T ,xe(2,3)[(2c a )b ,(2c a )b , 2c a ][ 7.9373, 7.9373, 21].

And the optimal control action comes naturally

u*

(9)

where sgn demonstrates the sign function mathematically.
And in this case, the optimal control is bang-bang format,
and the corresponding switching curve is defined as( * )T B 0
(10)

(2)

2.2. Glimpse of constrained control via MPC
Consider continuous and nonlinear chaotic systemx f ( x), x(t0 ) x0 , 0 xe f ( xe ),M sgn[( * )T B]

To linear time invariant system (4), Hamiltonian
function (5) will keep constant in locus of (10). And we(3)

only consider the free terminal time constant case here,

R n , f R n , t0 is the initial time constant. And
xe demonstrates the equilibrium of the system, theintroduction of control action aims to drive the unstableequilibrium into stable states. Assumed we introduce one
constrained scalar control action to system (3), thesystem under control will turn intox f ( x) Bu, x(t0 ) x0 , u M , M 0,
(4)
The column vector B only has one non-zero cellamong its n cells, i.e. bk 1, k 1, 2, , n , and M isconstant, which clamps the amplitude of the control
action. The constrained control problem is find thecontrol action, which meets the constraint defined in Eq.
(3) to drive the chaos system of (1) from the arbitrary
initial state x(t0 ) to the desired non-stable equilibrium
where xthen0

H ( x, u , , t )T[ f ( x) Bu ](11)

Results and Analysis
Control here aims at stabilizing xe(2) or xe(3) via added
outside action u for Equation (4). Here, M is assigned as
35, and x0[0.1, 0, 0]T , in case of completeness, control

action added in the first sub-equation f1 , through thesecond sub-equation f 2 and across the third subequation f3 are covered.
3.1. Control action added in the first sub-equationx(T ) xe . And T is the free terminal time. The further
requirement of chaos system control is that when thechaos system is driven to the desired non-stableequilibrium, system state was required to stick to thatstates forever.
Assume the Hamiltonian function of system (4) asT
H ( x, u , , t )[ f ( x ) Bu ](5)First of all, the control action added in the first subequation is touched upon. And in this case B [1, 0, 0]T ,from Equ. (10), we have

1

0, 1

0 . If we assumed[ 2 , 3 ]T has non-zero solution, then from (6), we can
have
f2
f3
0
(12)
2
3
x1
x1
Connected with (11) and (4), we can have
0
(13)
2 f2
3 f3
It is easy to find that the following stands if (12) and(13) are joined verified.

where
R n is the unknown adjoint variable, which canbe defined by the following equation
H
f
T
(6)
x
x

©The 2017International Conference on Artificial Life and Robotics (ICAROB 2017),Jan. 19-22,Seagaia Convention Center, Miyazaki, Japan

P-2

Targeting Chaos System via

S ( x)

f2

f3
x1

f3
(2)
e

Obviously, xf2
x1

cx22 bx32
(3)
e

and x

(c a )bx3

often when state comes to the xe(2) nearby. Similarly, the0 (14)

result of u is illustrated in Figure 2.

meets the constraints of Eq.

In case of the space limitation, we omit the comments to
Figure (2).

(14), so the two feasible control law can be listed asu
M sgn[ S ( x)], u M sgn[ S ( x)]; where u , u are

3.2. Control action added in the second subequation

assigned to xe(3) , xe(2) , separately. The result of u is
illustrated in Figure 1.

And in this case, the switching curve is transformed intoS ( x)

f1

f3
x2

f3

f1
x2

ax12

abx3

(15)

Similarly, the results of u- , u+ can be successfully
demonstrated for their capability in stabilizing theunstable equilibriums. In case of space limitation, theillustration of the resulted curves is omitted here.
3.3. Control action added in the third subequation
And in this case, the switching curve is transformed intoS ( x)
Fig.1. System states and control action transient curves with uadded in the first equation.

f1

f2
x3

f2

f1
x3

ax1 x2ax12(16)

To be curious, u can not stabilize xe(2) , however, u
can still hold its capability in stabilizing xe(3) , the results
about u is illustrated in figure (3), and the result of u isalso omitted here.

Fig.2. System states and control action transient curves with u+
added in the first equation.

And for initial state x0 , after duration of the transient
process, state will be driven to and stabilized on xe(2) , andcontrol action switches between -35 and +35, and further
more, because the satisfaction of xe(2) with the switchingFig.3. System states and control action transient curves with uadded in the third equation

curve, the switching frequency will become much more
©The 2017International Conference on Artificial Life and Robotics (ICAROB 2017),Jan. 19-22,Seagaia Convention Center, Miyazaki, Japan

P-3

Yunzhong Song, Ziyi Fu, Fuzhong Wangassumed as 2, and after this kind of reform, the result ofu is illustrated as figure (4)And by this wait to go style, the sub-equation based
control can fully functions for all the non-stableequilibrium.
3. Conclusion
In this paper, inspired by the initial value sensitive
peculiarity of the chaos system, in helps of the MPC, thewait to go style sub-control action is extra introduced tomake it possible to realize the full scale stabilization of
the non-stable equilibrium of Chen chaos system, and thesuggested strategy deserves its further exploration in the
future.
Acknowledgements

Fig.4. System states and control action transient curves withimproved u- added in the third equation

This work is partially supported by NSFC Grant(61340041 and 61374079) and the Project-sponsored by
SRF for ROCS, SEM to Yunzhong Song.

In order to stabilize xe(2) , inspired by the initial
sensitive peculiarity of chaos system, the sub-control
action u is segmented further intouu ; x1 c0; x1 c.

(17)

where x1 is the first sub-state variable of chaos system,
while c anditself,are constants, and c and be chosen as xe1(2)References
1. Y. Tian, M. Tade and D.Levy, Constrained control of chaos,Physics Letters A, 296 (2002) 87-90.
2. Y. Song, G. Zhao and D. Qi, Some Comments on constrained
control of chaos, Physics Letters A, 359 (2006) 624-626.
3. G. Chen and T.Ueta, Yet another chaotic attractor, Int. J. of
Bifurcation and Chaos, 9 (1999) 1465-1466.

can be viewed as the threshold value, here we©The 2017International Conference on Artificial Life and Robotics (ICAROB 2017),Jan. 19-22,Seagaia Convention Center, Miyazaki, Japan

P-4

Three-Dimensional Leader-Follower Formation Flocking of Multi-Agent System
Yongnan Jia,Weicun Zhang

School of Automation and Electrical Engineeringt, University of Sciences and Technology Beijing
30 Xueyuan Road, Haidian District, Beijing, 100083, China
E-mail: ynjia@pku.edu.cn, weicunzhang@263.net
www.ustb.edu.cn

Abstract
This paper aims to investigate the three-dimensional formation flocking of multi-agent system with leader-follower
structure. Based on the nearest neighbor interaction rules, a kind of distributed control algorithm is proposed, whichenables all the agents asymptotically converge to fly with the same velocity and approach the expected formation
with their neighbors, provided that the initial interaction network of the system is leader-follower connected.
Numerical simulations are carried on the multi-agent system to validate the functionality of the proposed algorithm.
Keywords: Formation control, flocking cooperation, leader-follower structure, multi-agent system.

leader-follower formation flocking problem of multiagent system.
We consider the multi-agent system consists of one
leader and several followers. Each agent is modeled as
an extended second-order unicycle with limited
interaction capability. Graph theory is used to depict theinteraction network of the multi-agent system. A
distributed algorithm is proposed with the combinationof consensus algorithm and artificial potential field
method. The numerical simulation based on MATLAB
further validates the functionality of the proposed
algorithm for a heterogenous multi-agent system with
one leader and nine followers.
The rest of this paper is organized as follows. The
model of each agent is given and the formation flocking
problem is formulated in section II. Section III presents
a distributed control algorithm to solve the formation
flocking problem. In section IV, the simulation results
are shown by six agents. Finally, the conclusions are
drawn in section V.

1. Introduction
Formation control of multi-agent system has received
more and more attention from the scholars all over theworld, due to its widespread applications on militaryand civilian areas. Except for these traditional methods,
such as leader-follower [1]-[2], behavior-based [3]-[4],virtual structure [5]-[7], new algorithms are investigatedto give some new results on formation control problem[8]-[9]. In this paper, we try to apply the flocking
strategy to solve the formation control problem of multiagent system in this paper.
Flocking is such a collective phenomenon that canbe widely observed from the social animals in nature,and it is also a kind of typical distributed behavior worth
to be investigated in further [10]-[13]. For example, thewild gooses fly as a herringbone three-dimensional
formation when they migrate from one place to another.
Therein, the leading wild goose plays an important role.
We introduce leader-follower structure to the multiagent system for the purpose of simulating theformation behavior of the wild gooses. Thus, in thispaper, we aim to investigate the three-dimensional© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, Japan

P-5

Yongnan Jia, Weicun Zhang

2. Model and Problem Statement

L {1} and follower set be F {2,..., N } . If an agent isdominated by external control input, we call it a leader;otherwise, we call it a follower. Let Ni t denote theFirstly, we model each agent by the following kinematic
equation.

xi (t )i(t ) cos i (t ) cos i (t )i(t ) sin i (t ).

yi (t ).

zi (t )
.

(t )

i

i

(t )

ai (t ).
i.
ineighbor set of the follower i F at time t , and theinitial neighbor set of the follower i is defined as(t )li cos i (t ) sin i (t )N i (0) { j |‖pi (0)i (t ) cos i (t ) sin i (t )

i
.

i

(t )

(t ) bi (t ) / li(t ) ci (t )(1)
R 3 is the position[0, 2 ) is the yaw
iwhere pi (t ) [ xi (t ), yi (t ), zi (t )]T
vector of the agent i at time t ,angle of the agent i at time t ,, N, ji}graph consisting of a vertex set{1, , N} indexed byagentsandatime-varyingedge
set(t ) {(i, j ) | (i, j ) F , j Ni (t )} . Therein, thefollowers' interaction network Gˆ (t ) with vertex set F
and edge set ˆ (t ) {(i, j ) | (i, j ) F F, j N i (t )} is an, ) is the pitch
2 2
angle
of
the
agent
i at time t ,qi (t ) [ i (t ), i li (t )]T [ i (t ), i (t )]T is the velocity vector of
the agent i at time t , i R is the thrusting speed ofip j (0)‖ D, j 1,

(4)
where D 0 is a constant satisfying D 2R , and ‖‖•is the Euclidean norm. We suppose that for a follower,interconnection with the leader is unidirectional, that is
to say, the information of the leader may be obtained by
the follower if the follower has a leader neighbor.
Meanwhile, we suppose that the interconnection
between any two followers is bidirectional.
The interaction network G t is a dynamic directed

i (t )li cos i (t ) cos i (t )(R is the rotational speed

undirected graph. In order to clarify the neighbor
relationship, we introduce the adjacency matrix AN tof the agent i at time t , li is the distance between theof the graph G t and the Laplacian matrix LN 1 t of

the agent i at time t ,i (t )

the graph Gˆ (t ) . AN (t ) [ wij (t )]N N is defined asgeometrical center Ci and the mass center M i of theagent i ,(t )R is the tangential speed of thei (t )li
agent i at time t , ai (t ) R is the thrusting acceleration

1, i 1, j 1
1, i F, j Ni (t )iwij (t )0, otherwiseof the agent i at time t , bi (t ) R is the rotational
acceleration of the agent i at time t , and ci (t ) R is
the pitching acceleration of the agent i at time t , li isa positive constant, i 1, , N .
Withpi (t ) [ xi (t ), yi (t ), zi (t )]Tlij (t )Given that G 0

H iT (t )qi (t )jwik (t ),ij.

is a leader-follower connected

graph.
G t is a fixed graph in each nonempty, bounded, and.

qi (t ) ui (t )i

(6)
As Gˆ (t ) is an undirected graph, the Laplacian matrix
LN 1 t is symmetric and positive semi-definite.

andmerged as the following matrix form.

wij (t ),N
k 2, k iqi (t ) [ i (t ), i li (t )]T , some equations in (1) can bepi (t )

(5)and LN 1 (t ) [lij (t )]( N 1) ( N 1) is given by

(2)

where

contiguous time-interval tr , tr 1 , where r

H i (t ) [ti , ni ]T
cos i (t ) cos i (t ) sin i (t ) cos i (t ) sin i (t )cos i (t ) sin i (t )0cos i (t ) cos i (t )

0,1,

.

3. Control Algorithm Design(3)
and ui (t ) [ai (t ), bi (t )]T . Besides, uui (t ) [ai (t ), bi (t ), ci (t )]T is
taken as the control input of the agent i .
The multi-agent system under consideration consistsof one leader and N 1 followers. Let leader set be

For the external input of the leader is zero, that is,
uu1 (t )a1 (t )
b1 (t )
c1 (t )

0,

(7)

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, Japan

P-6

Three-Dimensional Leader-Follower Formation Flocking

provided that the leader does a curve motion with aconstant thrusting speed 1 , a constant rotational speedi F j Ni0 ), and a constant pitch angle 1 ( 1 0 ).
1
Note that q1 [ 1 , l1 1 ] is a constant vector. Here, 0

wheredenotes the zero vector.
Unless specified otherwise, all variables in thispaper are time-variant. Thus, in the following sections,
we may use pi instead of pi t for example. Let
ˆ
w ,
w , ˆ
w , ˆ
w , ˆ
i

i

i1 1

i

i

i1 1

i

i

i1

i

1

i

point of‖pij (t ‖) 1.
So far, we are able to state our main result on theleader-follower formation flocking problem. LaSalleKrasovskii invariance principle and graph theory areused to prove that the agents (1) applying the control
protocol (9) will be asymptotically stable.
Consider a system of N agents with kinematics (1).
The leader and followers are respectively steered bycontrol protocols (7) and (9). Suppose that the initial

i1 1

wi1q1 , i 1, , N . Coupled with (2), we

qi

have

pˆ

pi*

pi

H iT qi wi1H iT q1

H iT qˆi(8)Given a desired geometric patternthat
has N vertices pid [ xid , yid ]T , i 1,..., N . Then, thecontrol protocol for follower i ( i 2,..., N ) is designed
by
. T

ai

( pi
j Ni ( t )bij Ni (t )pij

V‖( pij‖)T ti. Tp j ) nipij

V‖
( pij‖)T ni
ˆ (t )).
j(9)

j Ni ( t )

where
ti

pˆ ij

follower connected graph. Then the following
statements hold:No collision happens between neighbors for two
authors: D. Ruan, T. Li.
The connectivity of the interaction topology ispreserved at all times.
The thrusting speed, the rotational speed, the yaw
angle, and the pitch angle of each follower
asymptotically become the same as those of theleader.
The system approaches a desired geometricconfigurationthat minimizes the total potential.

j Ni ( t )( ˆi (t )

ci

( pi

j

j Ni ( t )j Ni ( t )

. T

ˆ)

( ˆi

li

interaction topology G 0 of the system is a leader-. Tp j )ti

pˆ i

pˆ j

,

pij

‖pˆ ij (t ‖),T[cos i cos i ,sin i , cos i sin i ],‖pijd‖

V‖( pˆ1 ‖
j )
j Nl

(10)
N l {i | wi1 1, i F} denotes the set offollowers who have one leader neighbor. The potentialfunction V ‖( pij (t ‖) ) reaches its minimal value at the(1and qˆiV‖( pˆ ij‖)V

4. Simulation Results

andThe initial interaction network of the leader-follower
multi-agent system is a leader-follower connected graph.
The initial attitudes of six agents are generate randomly.
The simulation results are shown in Fig. 1.

ni [ cos i sin i , 0, cos i cos i ] . ti and ni are twounit vectors orthogonal to each other, and pˆ V ‖( pˆ ij‖) isTij

the gradient of an artificial potential function V ‖( pˆ ij‖) .

( pˆ ij‖)
Definition 1 (Potential Function): Potential V ‖is a differentiable, nonnegative, radially unboundedˆ ij‖ between agent i and j ,function of the distance‖ psuch that(1) V ‖( pˆ ij‖)( pˆ ij‖)(2) V ‖

ˆ ij‖
as‖p
ˆ ij‖
as‖p

2 R* ( R

R*

D );

(a)

trajectory(b) yaw angle versus time

2D ;( pˆ ij‖) attains its unique minimum when‖pˆ ij‖(3) V ‖*

equals to a desired value between 2R and 2D .
Besides, the total potential of the system is(c) pitch angle versus time (d)thrusting speed versus time
© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, Japan

P-7

Yongnan Jia, Weicun Zhang(e) rotational speed versus time

2. A. Loria and J. Dasdemir and N. Alvarez Jarquin,
Leader-Follower Formation and Tracking Control of
Mobile Robots Along Straight Paths, IEEE Transactions
on Control Systems Technology. 24(2016)727-732.
3. T. Balch and R. C. Arkin, Behavior-based formation
control for multirobot teams(1998).
4. S. Monteiro and E. Bicho, A dynamical systems approach
to behavior-based formation control, in Proceedings ofIEEE International Conference on Robotics andAutomation. 3(2002)2606-2611.
5. W. Ren and R. W. Beard, Formation feedback control for
multiple spacecraft via virtual structures, IEEE
Proceedings of Control Theory and Applications.
151(2004)357-368.
6. C. B. Low, A dynamic virtual structure formation control
for fixed-wing UAVs, in 9th IEEE International
Conference on Control and Automation. (2011) 627-632.
7. L. Chen and M. Baoli, A nonlinear formation control ofwheeled mobile robots with virtual structure approach, in
34th Chinese Control Conference. (2015)1080-1085.
8. M. Defoort and T. Floquet and A. Kokosy and W.
Perruquetti, Sliding-Mode Formation Control for
Cooperative Autonomous Mobile Robots, IEEE
Transactions on Industrial Electronics. 55(2008)39443953.
9. H. Xiao and Z. Li and C. L. Philip Chen, Formation
Control of Leader-Follower Mobile Robots' Systems
Using Model Predictive Control Based on NeuralDynamic Optimization, IEEE Transactions on Industrial
Electronics. 63(2016)5752-5762.
10. C. W. Reynolds, Flocks, herds, and schools: a distributed
behavioral model, Comput. Graph.. 21(1987)25-34.
11. R. Olfati-Saber, Flocking for multi-agent dynamic
systems: algorithms and theory, IEEE Trans. Autom.
Control. 51(2006)401-420.
12. F. Cuker and S. Smale, Emergent behavior in flocks,
IEEE Trans. Autom. Control. 52(2007)852-862.
13. E. Ferrante and A. E. Turgut and C. Huepe and A.
Stranieri and C. Pinciroli and M. Dorigo, Self-organizedflocking with a mobile robot swarm: a novel motion
control method, Adaptive Behavior. 20(2012)460–477.

(f) distances between any two
agents versus time

Fig. 1 Numerical simulation results for leader-follower
formation flocking cooperation of six agents.
5. Conclusion
We have investigated the three-dimensional formation
flocking coordination problem of multi-agent system
consisting of one leader and several followers. Eachagent is modeled as an extended second-order unicyclewith limited communication capability. According to
LaSalle-Krasovskii invariance principle and the graphtheory, the followers asymptotically track the leader'svelocity and form a table geometrical configuration with
their neighbors. The numerical simulations are carriedon six agents to further validate the functionality of the
proposed algorithm for multi-agent system.
Acknowledgements
The authors would like to thank the support from
National Natural Science Foundation ofChina (No. 61603362) and Long Wang of Peking
University for his helpful discussions and valuableassistance.
References
1. G. L. Mariottini and F. Morbidi and D. Prattichizzo and
N. Vander Valk and N. Michael and G. Pappas and K.
Daniilidis, Vision-Based Localization for LeaderFollower Formation Control, IEEE Transactions on
Robotics. 25(2009) 1431-1438.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, Japan

P-8

Leader-follower Formation Control of Mobile Robots with Sliding Mode
Wenhao Zheng , Yingmin Jia

The Seventh Research Division and the Center for Information and Control,
School of Automation Science and Electrical Engineering,
Beihang University (BUAA), 37 Xueyuan Road, Haidian District,
Beijing 100191, China
E-mail: zhengwenhao@163.com, ymjia@buaa.edu.cn
www.buaa.edu.cn

Abstract
This paper considers the formation control of nonholonomic mobile robots. The formation problem is converted tothe error model based on the leader-follower structure. A sliding mode controller, which is proved to be globally
finite-time stable by Lyapunov stability theory, is presented in this study. In addition, a continuous reaching law isdesigned to reduce the chattering which caused by the computation time delays and limitations of control. Simulation
results verify the feasibility and effectiveness of the control strategy.
Keywords: nonholonomic mobile robot, leader-follower, formation control, sliding mode, continuous reaching law.

parameters which were designed to limited control inputs,
was presented by Lyapunov theory, and geometric analysis contributed to the designment of optimal feedback
functions which made the controller more effective.
This paper mainly focuses on the formation control
of nonholonomic mobile robots with sliding mode. The
formation problem is proposed with leader-follower
setup, which is used to deduce the error model. Then, asimple sliding mode surface is chose to design control
law. And a continuous reaching law is presented toreduce the chattering. Moreover, the formation error
model is proved to be globally finite-time stable. Comparing with existing results, this paper primarily contributes
to the novel solution of the formation control with slidingmode.
The structure of the rest paper is organized as follows.
The section 2 introduces leader-follower system and theformation error model. In the section 3, the sliding mode
controller is designed with a continuous reaching law.
Simulation results are presented to show the validity of

1. Introduction
Formation control of differential-drive mobile robots
have been widely researched in recent years. The mobile
robots are supposed to maintain the desired geometricconfiguration in lots of different situations including
personnel rescue, logistics transportation, military affairs
and environmental exploration. Various formation control strategies have been proposed, such as feedback linearization1, backstepping2, sliding-mode control3 and sorts
of intelligent control method. In Ref.3, the kinematic
model and the dynamic model were both considered, thena sliding mode controller was proposed to deal withmodel uncertainties and disturbances, furthermore, thevoltage inputs of driving motors were taken as control
inputs, which are more realistic than the velocity. Basic
controller based on backstepping method was presented
in Ref.4, a bioinspired neurodynamics based approach
was further developed to solve the impractical velocity
jumps problem. In Ref.5, a controller with time-varying© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP-9

Wenhao Zheng, Yingmin Jia

the control law in the section 4. In the end, the section 5summarizes the whole paper and draws the conclusion.

distance between the point O f ( x of , y of ,The relation can be presented as (4)2. Problem Statement
2.1. Kinematic model of nonholonomic mobile
robot

x of

xf

d cos

f

y of

yf

d sin

f

) and C l is d .

f(4)

Differentiating the above equations leads toIn this paper, the differential-drive mobile robot is
considered as the research object, which is subject tononholonomic constraint (1).
As shown in figure 1, let v L and v R be thevelocities of the left and right driving wheel. The control
inputs are usually taken as the linear speed v and therotational angular velocity. The relation between
control inputs and driving wheels can be expressed as (2).

xofv f cos f

d

f

sin f

yof

v f sin f

d

f

cos f

(5)

The desired geometric shape can be denoted by

Fig. 2. Leader-follower formation structure
Fig. 1. The configuration of a two-wheeled mobile robot.

coordinate representation. The expected position of thefollower should be expressed as ( x d , y d ) in thecoordinate system of the leader. And the real position
Cd ( xc , yc ) in world frame can be deduced as equation (6).

In practical systems, control inputs of mobile robots arebounded.
y cos
v

( vL

x sin

v R ) / 2,

x = v cos ,y(vRv sin ,

0(1)

vL ) / 2

(2)

xc
yc

(3)

xl
yl

cos l
sin l

sin l
cos l

xd
yd

(6)

( x d , y d ) are considered as constants, and differentiating
both side of (6) will refer towhere ( x, y) are the robot position and is the robot
orientation. Moreover, the kinematic of the mobile robot
can be expressed as equation (3).

xc
yc

cos l
sin l

sin l
cos l

vl

yd
xd

l

(7)

l

We can define the formation errors as the Eq. (8).

2.2. Leader-follower systemxe

In the figure 2, leader-follower formation structure ispresented in Cartesian coordinates. The nonholonomic
mobile robot R l is treated as the leader. The center ofthe leader Cl ( xl , yl , l ) describes the position and the orientation of the leader in the world frame. And (vl , l ) arecontrol inputs. The follower is the robot R f . The centerof the robot C f ( x f , y f , f ) and inputs (v f , f ) have theanalogous definitions like the leader. Assume that the

ye

cos f
sin f

sin f
cos f

xc xofyc yof(8)

Moreover, the angular difference between the leader
and the follower is remarked as (9).
l

f

,

l

f

(9)

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 10Leader-follower Formation Control of

Combining the equations from (4) to (7) anddifferentiating both sides of (8), formation error model
can be expressed asxevf

yd l )cos
f ye (vlxd l sinye(d xe ) f (vl yd l )sin

xd l coschattering which caused by the computation time delays
and limitations of control. The reaching law is as follow:sHere, f ( s ) is given as (16) and sgn( s ) is a sign function.
Besides,( 1 , 2 )T , k (k1 , k2 )T ,( 1 , 2 )T ,0
and 1 , 2 , k1 , k 2 0, 0< 1 , 2 1 .

(10)

The destination of control law design is to find theappropriate control inputs (v f , f ) to meet the desired
outcome:lim xet0, lim ye

0

t

0,

0

(

0)

(11)

vf

s1

xe cosye sins2

xe sinye cos(vlf

s2

v f sin

d

f

sin

(12)

f cos

vl

yd

xd

s1 l

l

l

s2 l

xd l sinye l s1 coss2 sin[(vl yd l )sin

xd l cosxe l s1 sin

s2 cos ]/ dfsi / siyd l )cos

(16)

if si

xd l sinye l( 2 s2 k2 f2 ( x))sin1[(vl yd l )sind
( 1s1 k1 f1 ( x))sinVi

xd l cos

(17)

xe l

( 2 s2 k2 f2 ( x))cos ]

2
i i

si si

s

(18)

ki f i ( si ) si

Case1: if si, the equation (18) will be converted toVi
2 iVi 2(1 i )/ 2 kiVi (1 i )/ 2 . Define V Vi (0) at t 0 .
By solving differential equation, we can deduce formula
1

(13)

i

Vi 2

Furthermore, the formation controller can be simply
written as:vf (vl yd l )cos

if si

Theorem 1. Consider the formation error system (10).
Using the control law (17), the sliding surface (12) will
converge to zero in finite time Tr , and xe0, ye0
as t Tr .
Proof. Choose the Lyapunov function as Vi si2 / 2(i 1, 2) . It is easy to refer to

Let V 0.5sT s , thus we can deduce V 0.5( s12 s22 )0.5( xe2 ye2 ) from equation (12), which indicates that
if s1
0, s2
0 , the xe0, ye0 will hold. Hence,
when the sliding surface converges to zero, the geometricshapes of the robot formation will be established.
Differentiating the formula (12) will conclude (13)dsgn( si )( 1s1 k1 f1 ( x))cos

The most significant process of sliding mode control is tochoose the suitable sliding surface. Here, we design thesliding surface which ensures that the formation error
will converge to zero when the system trajectory lies on
the sliding surface (12).

v f cosi

Thus, we can get the sliding mode controller (17)from equation (12), (14) and (15).

3.1. Sliding surface designs1

sifi (s)

3. Sliding-Mode Controller Design

s

(15)

s kf ( s )Let Vi

i

2 2

1

k

1

i

k

(2 2

i

i)

t

(19)

0 ; the reaching time can be expressed as
i

Tri

(14)

1

Vi 2 (0))e (1

1
(1

1

2 2
i

)

1

k

ln(

1

i

2

i

Vi 2 (0)

)

(20)

k

2

Therefore, si (i 1, 2) will converge to zero at t Tr
max(Tri ) (i 1, 2) and xe0, ye
0 as t Tr .
Case 2: if si, the equation (18) can be presented as
the formula (21).

3.2. Controller design with the reaching law
In this paper, a continuous reaching law is adopted tomake each state reaching the sliding surface. Comparing
with the switching function of general reaching law, asaturation function is taken in order to reduce theVi

2 i si2

ki si

i

0

(21)

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 11

Wenhao Zheng, Yingmin Jia

Consequently, there must be a Tr which makes si.

Thus, formation error model will be globally finite timestable.
Theorem 2. Consider the equation (9) with the control
law (17).
will be bounded, if the velocity of the leader
0 vl const , 0
at t 0 and the boundedness of
the angular velocity l hold.
Proof. Combining the formula (9) with the control law,
we can obtain the equation (22).

(vl sin ) / d
(t )
( 1s1

Fig. 4. The sliding surface and inputs of followers.

control inputs are limited as v f 3m / s, f
/ 2rad / s .
The formation error is denoted as E
x e2 y e2 , . The
simulation results are presented in Fig.3 and Fig.4.

(t )1[ yd l sin
xd l cos
xe l
(22)
l
d
k1 f1 (s1 ))cos( 2 s2 k2 f 2 (s2 ) 2 )sin ]5. Conclusion
In this paper, the formation control problem of thenonholonomic mobile robot has been solved. A controller
with sliding mode is proposed using a continuous
reaching law. The simulation results show that thecontrol law can guarantee great formation performanceand the specified geometrical shapes of multi-mobile
robots will be achieved in finite time.

The nominal system can be given as(vl sin ) / d at
0 . Select the Lyapunov function as V 1 cos .
So we can get V(vl sin 2 ) / d 0 and V is nonincreasing. 0
at t 0 , there must exist a positive
constant which meets 0 [,] . Then, V[0, 2), (t ) [,] and V(vl / d ) V .
2
Besides, V
,
it
is
easy
to
obtain2 sin ( / 2)
2 2/ 22V/ 2 at( , ) . Thus, the nominal system
is exponentially stable using the Lyapunov method. Obviously, l , xe , s1 , s2 ,sin and cos are bounded, which
can be used to deduce the conclusion that (t ) is bounded.
Therefore, in the system (21), is bounded by using thestability theory of perturbed systems.
4.

Acknowledgements
This work was supported by the NSFC (61327807,
61520106010, 61521091, 61134005) and the National
Basic Research Program of China (973 Program:
2012CB821200, 2012CB821201)
References
1. J. P. Desai, J. P. Ostrowski, and V. Kumar, “Modeling andcontrol of formations of nonholonomic mobile robots,”
IEEE Trans. Robot. Autom, vol. 17, no. 6, pp. 905–908,
Dec. 2001.
2. X. Li, J. Xiao, and Z. Cai, “Backstepping based multiple
mobile robots formation control,” in Proc. Conf. Intell.
Robots Syst., 2005, pp. 887–892.
3. Park, Bong Seok, B. P. Jin, and Y. H. Choi. "Robust
formation control of electrically driven nonholonomic
mobile robots via sliding mode technique." International
Journal of Control, Automation and Systems 9.5(2011):
888-894.
4. Peng, Zhaoxia, et al. "Leader–follower formation control
of nonholonomic mobile robots based on a bioinspired
neurodynamic based approach." Robotics & Autonomous
Systems 61.9(2013):988-996.
5. Chen, X., and Y. Jia. "Input-constrained formation control
of differential-drive mobile robots: geometric analysis andoptimization." IET Control Theory & Applications 8.7(2014):522-533.

Simulation Results

Fig. 4. Formation process and formation errors.

Three robots are expected to keep a triangle formationand the expected formation shapes are designed as xd1
xd 2
1m, yd 1 0.5m, yd 3
0.5m . The movement of
the leader robot is set as the circular motion withvl 1m / s, l
/ 12rad / s , and the initial poses ofthree robots are (0, 0, π/2), (−3, −2, 0) and (2.5, −3, −π/2).
Moreover, Controller (16) is used with 1 = 2 5,
k1 = k 2 7, 1
1 / 3,
5, d 2 d 3 0.25m. The
2© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 12

H Containment Control for Nonlinear Multi-agent Systems with Parameter Uncertainties
and Communication Delays
Ping Wang

The Department of Mathematics and Physics, North China Electric Power University, 619 Yonghua North Street
Baoding, 100073, China
Yingmin Jia

The Seventh Research Division and the Center for Information and Control, School of Automation Science andElectrical Engineering, Beihang University (BUAA), 37 Xueyuan Road, Haidian District
Beijing, 100191, China
E-mail: wangpinglh2014@126.com; ymjia@buaa.edu.cn
www.ncepu.edu.cn

Abstract
This paper considers robust containment control problem for uncertain multi-agent systems with inherent nonlinear
dynamics. A distributed protocol is proposed using local delayed state information, and then the original problem isconverted into an H control problem by defining an appropriate controlled output function. Based on robust H
theory, sufficient conditions in terms of linear matrix inequalities (LMIs) are derived to ensure the H containment.
A numerical example is provided to demonstrate the effectiveness of our theoretical results.
Keywords: multi-agent systems, containment control, robust H

control, inherent nonlinear dynamics.

considered in Refs. 3 and 4, respectively. Whereas, theeffects of communication delays on H containment
control are not considered in the existing works.
Motivated by the above analysis, we employ H
control approach to investigate the containment control
problem of nonlinear multi-agent systems withparameter uncertainties and communication delays.
Based on the local delayed state information, a
distributed protocol is proposed, and then the original
problem is reformulated as an H control problem bydefining an appropriate controlled output function.
Sufficient conditions in terms of LMIs are derived toensure the containment performance with a desired H
disturbance attenuation level. Moreover, the proposed
protocol can be determined by solving only two LMIs
with the same dimensions as a single agent.

1. Introduction
Containment control of multi-agent systems hasreceived considerable attention due to its potential
applications, such as earth monitoring, stellar
observation for satellite formation1, removing hazardous
materials for autonomous robots2, and so on.
Containment control problem arises in the presence ofmultiple leaders, where its objective is to drive thefollowers into the convex hull spanned by the leaders.
In engineering practice, multi-agent systems areoften subject to parameter uncertainties and external
disturbances, which might degrade the system
performance and even cause the network system todiverge or oscillate. For such cases, H containment
control problems for second-order and uncertain multiagent systems with inherent nonlinear dynamics were

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 13

Ping Wang, Yingmin Jia

2. Preliminaries and Problem Formulationx, yi R m , i 1, 2, N M , t 0.
When system matrices A, B1 , B2 , B3 are uncertain,
they are assumed to be of the following form
A A0
A(t ),B1 B10B1 (t ),
(5)
B2 B20B2 (t ), B3 B30
B3 (t )

Preliminaries
Directed graphs5 are used to model the interaction
topologies among agents. Suppose that a multi-agent
system consists of M followers and N M leaders. An
agent is called a leader if the agent has no neighbor, anda follower if the agent has at least one neighbor. Due tothe fact that the leaders have no neighbor, the Laplacianmatrix L associated with graph G can be partitioned as
L1
L2
L
(1)
0( N M ) M 0( N M ) ( N M )where L1R M M and L2

where A0 , B10 , B20 , B30 are known real constant matrices,A(t ), B1 (t ), B2 (t ), B3 (t ) are unknown real normbounded matrix functions satisfyingA(t ), B1 (t ), B2 (t ), B3 (t ) E (t ) F1 , F2 , F3 , F4 (6)with E , F1 , F2 , F3 and F4 being known constant matrices,and (t ) being an unknown real time-varying matrix
that satisfies T (t ) (t ) I . It is also assumed that

RM (N M ).

( A0 , B10 ) is stabilized.

Assumption 1. Suppose that for each follower, there
exists at least one leader that has a directed path to that
follower. Moreover, the interaction topology among the
M followers is undirected.
Lemma 1.6 Under Assumption 1, L1 is positive definite,

B3 f (t , xi (t )), ii (t )

where

F (2)

|| (t ) ||22

N M
i
i 1

outputfunctionwithN

x | i R, i 0,

i M 1 i iNi M 1 i1}. Obviously, if

R

,

,

N M

i 1

i

f (t , yi (t )) |||| x (t )

N M
i
i 1

T

T
1

(t), , MT (t) , || z (t ) ||22T(t ) (t ) dt , Tz ( s ) represents the closed-00z T (t )z (t ) dt ,3. Main Results
Protocol design and problem reformulation
A distributed protocol is proposed using local delayedstate information.
ui (t ) K
aij [ x j (t (t )) xi (t (t ))], i F
(9)

(3)
N M

with

(t)

loop transfer function matrix from the external
disturbance (t ) to the controlled output z (t ).

1

j F

R

where K is a feedback gain matrix to be determined later,(t ) is the time-varying communication delay which(t ) h , t 0.
satisfies 0
Denotex f (t ) [ x1T (t ), , xMT (t )]T , xl (t ) [ xMT 1 (t ), , xTN (t )]T ,and i 0, i 1, , N M . There exists a nonnegative
constantsuch that nonlinear function f (t , x(t ))satisfies|| f (t , x(t )), z (t )T

multi-agent system (2)-(3) can be solved. Thus, theattenuating ability of multi-agent system against
external disturbances can be quantitatively measured by|| z (t ) ||2|| Tz ( s ) ||sup(8)(t ) ||20 ( t ) L2 [0, ) ||R m is the state of the leader i.
1

T
M

t

Axi (t ) B3 f (t , xi (t )), i

Assumption 2. Given

controlled

lim z(t ) 0, the containment control problem of thewhere xi (t ) R m , ui (t ) R m1 are the state and thecontrol input (or protocol), respectively, i (t ) R m2 isthe external disturbance that belongs to L2 [0, ), andf : R Rm
R m is the inherent nonlinear dynamics of
the ith follower.
The leaders of the concerned multi-agent system aredescribed bywhere xi (t )z (t ), z (t ),(t) {Consider the multi-agent system consisting of M
followers and N M leaders. We use F {1, , M } andR {M 1, , N} to denote the follower set and theleader set, respectively. Each follower with nonlinear
dynamics is represented asxi (t )T2z i (t ) xi (t ) yi (t ), i F , yi (t )(t )(7)to quantitatively analyze the effect of external
disturbances on the containment control problem, where,

Problem formulation

Axi (t ) B1ui (t ) B2aT1z (t )

L1 1 L2 is nonnegative, and the sum of the entries ineach row equals 1.

xi (t )

Defineyi (t ) || (4)© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 14

H
F (t , x f (t )) [ f T (t , x1 (t )),definite matrix X R m m , a matrix W R m1 m andpositive scalars c and such that the following LMI, f T (t , xM (t ))]T ,F (t , xl (t )) [ f T (t , xM 1 (t )),, f T (t , xN (t ))]T .

i
11

Let x (t ) x f (t ) ( L1 1 L2 I m ) xl (t ), the closed-loopsystem (2) and (3) under protocol (9) can be written asx (t ) ( I M A) x (t ) ( L1 B1 K ) x (t (t ))(IMwhere F (t )B2 ) (t ) ( I M

F (t , x f (t )) ( L1 1 L2i
12z (t )

B2 ) (t ) ( I M

2

B3 ) F (t )PB2
0 m m2

PB3
0m

Im
0m

2

0 m2 m0 m2 mhB2T RIm

0m

hB3T R

Im
2

1

H

i
12
i
26

i

PB1 K ,

i
i
16

0m2 m

F3T

Im

0m
Im

hcB30T

0m

F4T

0m

0m

0m

cX

hcE
Im

0m
0m
Im

( PB1 KK T B1T P),

h( AT

i

A0 X

i
12

i

XA0T
i
16

B10W ,X T F1T

[ F1

i

i
11

i

WF

i

0,

( B10W W T B10T ),hc( XA0T

W T F2T ,ii
26

W T B10T ),

(13)

i

hc iW T B10T

F2 K , i F2 K , F3 , F4 , 0m , 0m ], i
i
12

R
i

F.

PB20
0m m2

PB30
0m

Im
0m

i
16
i
26

2

0m2 m

0m2 m

T
hB20
R

Im

0m
Im

T
hB30
R

I m2

2

0

1

0m
R

0m

i
11

PA0

i
12

i

A0T P

PB10 K ,ii
16( PB10 K

K T B10T P ),h( A0Ti

K T B10T ) R,i
26

h i K T B10T R.
Substituting (5) and (6) into (12) results in the following
inequalityi(14)
F (t ) H i H iT T (t ) F T 0.

(12)

PA AT P

hcB

1

i
18
T T
2

is simultaneously satisfied for i 1 and M. Moreover,when the above two LMIs are feasible, the feedback
matrix of the protocol (9) is designed as K WX 1 .
Proof. Denote
F [ E T PT , 0m , 0m m2 , 0m , 0m , hcE T PT ]T ,

R
i
11

0m2 m

i
11

i
18

(11)

i
16
i
26

I m2

Im2 0m2 mT
20

2Conditions for H containment performance
Lemma 2. Suppose that Assumptions 1 and 2 hold.
System (11) is asymptotically stable with || Tz ( s ) ||satisfied, if there exist positive definite matrices
P , R R m m such that the inequalityRE0m

I m ) F (t , xl (t )).

Therefore, if the system (11) is asymptotically stablewith || Tz ( s ) ||satisfied, then H containment
control for the multi-agent system (2)-(3) can be solved.

i
12

i
16
i
26

B30
0m

B3 ) F (t )x ( t ).

i
11

X
0m

B20
0m m2

cX

By Lemma 1, ( L1 1 L2 I m ) xl (t ) is in the convex
hull spanned by the leaders. In this paper, the controlled
output function z (t ) is defined as(10)z (t ) x ( t ).
Until now, the containment control problem can bereformulated as the following H control problem
x (t ) ( I M A) x (t ) ( L1 B1 K ) x (t (t ))(IMContainment Control for

K T B1T ) R,

h i K T B1T R

Due to the fact that T (t ) (t ) I , inequality (14)holds if and only if there exists a scalar
0 such that
(15)
i
1
FF T
H iT H i 0.

is simultaneously satisfied for all i F , where i is theith eigenvalue of the matrix L1 .
Theorem 1. Suppose that Assumptions 1 and 2 hold.
Protocol (9) solves the containment control problem
with || Tz ( s ) ||satisfied, if there exist a positive

By Schur Complement, inequality (15) can betransformed to a matrix inequality. Let R cP , c 0.
Pre- and post-multiplying the above inequality with© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 15

Ping Wang, Yingmin Jia

diag { P 1 , P 1 , I m2 , I m , I m , P 1 , I m , I m } and T ,respectively, and then denoting X P 1 , KP 1 W , we
can obtain an equivalent inequality (13), which is an
LMI with respect to variable W and X . Due to the factthat 0in view of its convex
1
2
M ,property, when LMI (13) holds for 1 and M
simultaneously, it holds for all i , i F. By Lemma 2,
the containment control problem can be solved withsatisfied.
|| Tz ( s ) ||energy relationship between z(t) and (t ), respectively.
Clearly, the containment control problem is indeed
solved with || Tz || 1 satisfied.

Fig.2. Left: trajectories of the network. Right: trajectories ofz (t ) and

4. Simulation
Consider a network consisting of four followers and two
leaders, whose dynamics are described by (2) and (3)respectively, with
A0

0
2

1
, B10
1

E

0
0.2

0
, (t )
0.2

F2
xi ( t )

0.2

0

0

0.2

, F3

1
0

0
, B20
2

0
, B30
1

0
0

sin(t )
0
, F1
0
1 e t
0.3
0.3

xi 1 ( t ), f (t , xi (t ))xi 2 ( t )

, F2

0.4

0

0

0.4

(t ).

5. Conclusions
In this paper, we have employed H control approach
to solve the containment control problem for nonlinear
multi-agent systems with parameter uncertainties andcommunication delays. Sufficient conditions in terms of
LMIs have been derived to ensure the H containment
performance. Moreover, the proposed protocol has been
designed by solving only two LMIs with the same
dimensions as a single agent.

0
,
1

0.1 0
,
0 0.1
,

0.5 sin(t ) xi1 (t ).
0.5 cos(t ) xi 2 (t )Acknowledgements
This work was supported by the National Basic
Research Program of China (973 Program:
2012CB821200, 2012CB821201), the NSFC (61134005,
61521091, 61327807) and the Fundamental ResearchFunds for the Central Universities 2016MS129.

The external disturbance is supposed to be(t ) [ (t ),1.5 (t ), 2 (t ), (t )]T , where (t ) is the
band-limited white noise with noise power 0.5, whose
action time interval is [0, 2].
Fig.1. shows the communication graph with 0-1
weights, where nodes 5 and 6 are two leaders and theothers are followers. The smallest and largest
eigenvalues of the matrix L1 are 1 0.3820 and
3.6180 , respectively.
4

References
1. D.V. Dimarogonas, P. Tsiotras and K.J. Kyriakopoulos,
Leader-follower cooperative attitude control of multiple
rigid bodies, Syst.Control Lett., 58(6) (2009) 429–435.
2. G. Notarstefano, M. Egerstedt and M. Haque,Containment in leader-follower networks with switchingcommunication topologies, Automatica, 47(5) (2011)
1035–1040.
3. P. Wang and Y. Jia, Robust H containment control forsecond-order multi-agent systems with nonlinear
dynamics in directed networks, Neurocomputing, 153(2015) 235–241.
4. P. Wang and Y. Jia, Robust H containment control for
uncertain multi-agent systems with inherent nonlinear
dynamics, Int. J. Syst. Sci., 47(5) (2016) 1073–1083.
5. C. Godsil and G. Royle, Algebraic Graph Theory,(Springer-Verlag, New York, 2001).
6. W. Ren and Y. Cao, Distributed Coordination of Multiagent Networks: Emergent Problems, Models and Issues,(Springer-Verlag, London, 2011).

Fig.1. The communication graphTake
0.5, c 30, and
1. Solving LMI (13)related to 1 and 4 , we obtain the maximal allowabledelay h 0.042. Here we take the communication
delay in networks as (t ) 0.04sin 2 (t ).
When there are no external disturbances, the initialstates of the followers are chosen randomly from thebox [10, 30] [ 5,5], and the leaders' initial states are
given as x5 (0) [15, 1]T and x6 (0) [20,1]T , respectively.
Fig.2. give the state trajectories of the network and the© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 16

Stochastic Resonance in an Array of Dynamical Saturating Nonlinearity with Second-Order
Yumei Ma

College of Computer Science & Technology, Qingdao University, 308 Ningxia Road, Shinan District
Qingdao, 266071, China
Lin ZhaoCollege of Automation and Electrical Engineering, Qingdao University, 308 Ningxia Road, Shinan District
Qingdao, 266071, China
Zhenkuan Pan

College of Computer Science & Technology, Qingdao University, 308 Ningxia Road, Shinan District
Qingdao, 266071,China
Jinpeng Yu

College of Automation and Electrical Engineering, Qingdao University, 308 Ningxia Road, Shinan District
Qingdao, 266071, China
E-mail: mayumei_qdu@163.com; zhaolin1585@163.com;zkpan@qdu.edu.cn; yjp1109@hotmail.com
www.qdu.edu.cn

Abstract
The transmission of weak noisy signal byparallel array of dynamical saturating nonlinearities with second-orderis studied. Firstly, the numerical results demonstrate that the output SNR can be enhanced by parallel array ofdynamical saturating nonlinearities with second-order by tuning the internal noise. Secondly, the SR effects can be
optimized by the self-coupling coefficient of the dynamical nonlinearity.Then, the SR effects when the nonGaussian noise acts as the external noise are superior to that with external Gaussian noise.
Keywords: stochastic resonance, dynamical nonlinearity, second-order, signal-to-noise ratio

Collins designed the neuron network model, summing
nonlinear units intoa parallel array, the system
performance can be enhanced by adjustingthe couplingstrength
andthearraynoise
intensity[3].
Subsequently,various SReffects as types of array SR are
investigated, for instance, the Supra threshold SR[4].The positive role of the array noise has been foundin some complex networks, e.g. stochastic pooling
networks [5], scale-free networks [6] and small-world

1. Introduction
Stochastic resonance (SR) establishes a phenomenon
where the additive noise can enhance the performanceof some certain nonlinear systems [1-18]. Benziinitiallyobserved the SR effect in climate modelseveral decades
ago [1]. Then, the existence of SR is proved withexperiments by McNamara [2].Later, some new types of
SR models are applied in many research fields. When

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22,Seagaia Convention Center, Miyazaki, JapanP - 17

YumeiMa, Lin Zhao, Zhenkuan Pan, Jinpeng Yu

networks [7].Recently, the SR in some physical system
focuses on the noisy bi stable system, such as the bi
stable fractrional-order system, asymmetric bi stablesystem and fractional harmonic oscillator and so on [810]. The SR effects can be measured by signal-to-noise
ratio, the fisher information [11], etc.
In this paper,the SR effect in parallel array ofdynamical saturating nonlinearities with second-orderis
firstly studied.It is demonstrated that, the SR effectoccurs in arrays of dynamical saturating nonlinearities
with second-order as increasing the array size and thearray noise intensity. And diverse forms of the output
SNR appear which against the array noise intensity andthe array scale.When the Gaussian noise acts as theexternal noise, the self-coupling coefficient has a greater
impact on the output SNR. While the self-coupling
coefficient takes smaller value, the SR effect isobviously visible. With larger value of the self-coupling
coefficient, the bell-shape behavior of the outputis notclearly.As the array size N, the output of thenonlinear systemswith the external Laplacian noise
precedes that with the external Gaussian noise.

100

output SNR

80

0noisebackground in a small frequency bin
1 / T , that is2| E[Z(t)]exp( i 2 t / T ) |Routvar[Z(t)] H (1/ Ts ) B

E [x 2 ]x 2 f (x) dx . The maximal

amplitude of s (t) is A ( | s (t) | A ) and the period is T .
Next, the input mixture (t)s (t)(t) is applied toeach identical subsystem of an uncoupled parallel
array.

i(t) plays a role of array noise, independentof (t) , with the same PDF and variance

2.Then theoutput of a subsystem as
y i (t)k[ (t)i(t)]

5

B around(3)

Similarly,the input SNR is given by2| s (t) exp( i 2 t / T ) |
(4)
Rin
2
t B
When a sinusoidal signal buried in white noise as
the input signal, the input SNR is written as
A2
(5)
Rin
4 2 t B
When the array size N, the array output SNR canbe defined as| E[yi (t)]exp( i 2 t / T ) |2
Rout
(6)
E[yi (t) y j (t)] E 2 [yi (t)] H (1/ Ts ) B(t) , which is independent ofs (t) with a probability density function (PDF) f and
2

1
2
3
4
noise RMS amplitudeFig. 1. Output SNR as a function of the RMS amplitudeof the array noise i (t) .

We considera weak periodic sinusoid signal s (t) added

variance40
20

2. Theoretical Modelto a white noise

60

(1)

Then the system output Z (t) is written as
1 N
Z (t)
yi (t)(2)
N i1
The output SNR can be a measure of the system
performance, which is defined as the power contained in
the outputspectral line at fundamental frequency
1 / T divided by the power contained in the

3. Experiment Results
In thissection, we consider a dynamical saturating
nonlinearity with second-order as Eq.(7)2dxdxxi (t) C tanh[ xi (t)] s(t) (t) i (t)
dt 2
dt
(7)

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22,Seagaia Convention Center, Miyazaki, JapanP - 18

Stochastic Resonance in an

120

350

(a)

300

80

250

60

output SNR

output SNR

100

40
20
0

1
2
3
4
noise RMS amplitude

5

200
150
100
50

120

output SNR

0
0

(b)

100

1

C

80

3

Fig. 3.Output SNR as a function of the self-coupling
coefficient C of Eq.(7).

60
40

The array noise terms

20
0

2

distributed over [1
2
3
4
noise RMS amplitude

RMSamplitude5

where C is the self-coupling coefficient andas aslope parameter. Here, s (t) 0.2sin(2 t/ T) is adeterministic sinusoid with period T. The component(t) is the external noise and it is the zero-mean
generalizedGaussian noise,and some general cases such
as Gaussian noiseare contained in the class. The PDF of
the generalized Gaussian noise (t) is defined asc1

xexp( c 2 |

| )

(8)

where c1 and c 2 is defined as Eq.(9) and Eq.(10)

c1

c2

1
2

2

1

(3

[ (3

1

)/

)/ (

3
2

(

1

1

)] 2

)

(t) are zero-mean uniformly, 3] with.

Figure 1 and Figure 2 demonstrate the output SNR of
Eq.(3)as a function of the root-mean-square (RMS)
amplitude.The slope parameterin Eq.(7) is set
5 . The self-coupling coefficient of the subsystem
of Eq.(7) takes C=0.5 in Fig.1. While C=1 in Fig.2(a)and C=2 in Fig.2(b).In Fig.1 and Fig.2, the parameter
2 , i.e., the Gaussian noise actsof Eq.(8) is set
as the external noise. The external noise level
0.3 .
The output SNR Rout is revealed for N=1, 5, 10, 50
and from the bottom up in Fig.1 and Fig.2.The SR
effect occurs upon increasing the array noise level
and the array size N . It is shown that the bell-shape
behavior is obviously as the array size N 1 in Fig.1.
But the SReffect appears lightly in Fig.2 with a certain
small rangeof the array noise intensity when N 1 inFig.2. The SR effect is more unstable while increasing
the self-coupling coefficient C in Fig.2. By comparison,
it is shown that the SR effect can be obtained by tuning
the self-coupling coefficient C of the dynamical
saturating nonlinearity with second-order.

Fig. 2. Output SNR as a function of the RMS amplitudeof the array noise i (t) .The self-coupling coefficient C =1 in(a) and C =2 in (b).

f (x)

3

i

(9)
(10)

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22,Seagaia Convention Center, Miyazaki, JapanP - 19YumeiMa, Lin Zhao, Zhenkuan Pan, Jinpeng Yu

Figure 3 illustrates the output SNR as a function of
the self-coupling coefficient as the array sizeNwith different external noises. The output SNR
is represented by triangles when Gaussian noise acts as
the external noise, while that is represented bypentagrams with external Laplaciannoise. The RMS
amplitude of the external noise
0.3 and the array1/ 3 . The system output
noise intensity is set
with external Laplacian noise is obviously superior tothat of the nonlinear array when Gaussiannoise is theexternal noise.
Discussion
In this paper,SR effect is investigated firstly in an
uncoupled array of dynamical saturating nonlinearities
with second-order for transmitting weak noisy signal.
Firstly, the SR effect occurs in parallel array ofdynamical saturating nonlinearities with second-order as
increasing the array size and the array noise intensity.
Then, the numerical results demonstrate that higher
output SNR can be obtained with smaller selfcoefficient. The output SNR with the Laplace noise as
the array noise is superior to that with the Gaussian
noise as the internal noise.The dynamical saturating
nonlinearity with second-order can be applied in thefield of physical systems actual signal processing.The
results from the dynamical saturating nonlinearity withsecond-order may be beneficial to theoretical researchand signal processing.
Acknowledgements
This work is partially supported by the Natural Science
Foundationof China (61501276, 61573204), the China
Postdoctoral Science Foundation(2016M592139), theQingdao
Postdoctoral
Application
Research
Project(2015120).
References
1. R. Benzi, G. Parisi, A. Sutera and A. Vulpiani, Stochastic
resonance in climate changes, Tellus.34 (1981) 10-16.
2. McNamara B, Wiensenfeld K, Theory of stochastic
resonance. Physical Review A.39 (1989) 4854-4869.
3. J.J.Collins, C. C. Chow, T.T.Imhoff, Stochastic resonance inneuron models, Physical Review E. 1995, 52: 3321-3324.

4. N. G.Stocks, Suprathreshold stochastic resonance inmultilevel threshold systems, Physical Review Letters, 84(2000) 2310–2313.
5. M.D. Mcdonnell, P. O. Amblard, N. G. Stocks, Stochastic
Pooling Networks, Journal of Statistical Mechanics Theory& Experiment. 1 (2009) P01012.
6. M. Perc, Stochastic resonance on weakly paced scale-free
networks via a pacemaker, Physicsl Review E. E 76 (2007)
066203.
7. P. Matja, stochastic resonance on excitable small-world
network via a pacemaker, Physical Review E. 76 (2007)
066203.
8. Z. Lai and Y. Leng, Weak-signal detection based on thestochastic resonance of bistable Duffing oscillator and its
application in incipient fault diagnosis, Mechanical System
and Signal Processing. 81 (2016) 60-74.
9. F. Guo, C. Zhu, X. Cheng and H. Li, Stochastic resonance ina fractional harmonic oscillator subject to random mass andsignal-modulated noise, Physica A. 459 (2016) 86-91.
10. P. Shi, Q. Li and D. Han, Stochastic resonance in a new
asymmetric bi stable system driven by unrelated
multiplicative and additive noise, Chinese Journal of Physics.
54 (2016) 526-532.
11. Y. Ma,F. Duan, F. Chapeau-Blondeau and D. Abbott, WeakPeriodic Stochastic Resonance in a Parallel Array of Static
Nonlinearities, PLoS ONE. 8(3) 2013 e58507.
12. B. Lindner, J. Garcia and A. Neiman, Schimansky-Geier L.
Effects of noise in excitable systems, Physics Reports. 392(2004) 321-424.
13. F. Chapeau-Blondeau, Noise improvements in stochastic
resonance: from signal amplification to optimal detection,
Fluctuation and Noise Letters, 2(3)(2002) L221.
14. L. Gammaitoni, P. Hänggi, P. Jung and F. Marchesoni,
Stochastic resonance, Reviews of ModernPhysics. 70(1)(1998) 233-287.
15. F. Chapeau-Blondeau and X. Godivier, Theory of stochastic
resonance in signal transimission bystaticnonlinear systems,
Physical Review E. 55 (1997) 1478-1495.
16. B. Chen, Y. Zhu, J. Hu and M. Zhang, Weak signal
detection: Condition for noise induced enhancement,Digital
Signal Processing. 23(2013)1585-1591.
17. H. Droogendijk, M. J. de Boer, R. G. p. Sanders and G. J. M.
Krijnen,
Stochastic
Resonance
inaVoltageControlledMicromechanical Slider,Microelectromechanical
Systems. 24(3) (2015)651-660.
18. T. Djurhuus and V. Krozer, A Generalized Model of Noise
Driven Circuits with Application toStochasticResonance,
Circuits and Systems I: Regular Papers, IEEE Transactions
on. 62(8) (2015)1981-1990.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22,Seagaia Convention Center, Miyazaki, JapanP - 20Playware ABC: Engineering Play for Everybody
Henrik Hautop Lund

Center for Playware, Technical University of Denmark, Building 326, 2800 Kgs. Lyngby, DenmarkE-mail: hhl@playware.dtu.dk
www.playware.elektro.dtu.dk

Abstract
This paper describes the Playware ABC concept, and how it allows anybody, anywhere, anytime to be building bodies
and brains, which facilitates users to construct, combine and create. The Playware ABC concept focuses engineering
and IT system development on creating solutions that are usable by all kinds of users and contexts. The result becomessolutions, often based on modular technologies that are highly flexible and adaptable to different contexts, users, andapplications.
Keywords: Playware, user-friendly, modular robots, playful robotics and intelligent systems.

Play is for everyone, as described in work on play for theelderly [3]. Though society has often viewed play as achildish and frivolous activity, we all engage in play over
our entire lifespan, and engage in such play activities inwhich we forget about time and place just for theenjoyment and pleasure of play itself. Games, sport, art,sex, and scientific research activities can in many cases
be described as playful activities in which the subject
performs an ontological shift forgetting about time andplace, and in which the activity has its own course andmeaning. The play activity provides life fulfillingenjoyment and meaning to the player. The player can beof all ages. It appears limiting and exclusive to definesuch life fulfilling enjoyment as an activity for children
and youth alone. Play is, for everybody, a fundamental
activity submitted to free will. In the act of playing, wemanage our lives at our own choice, as we create thespecial form of lived life outside the “regular” life where(lust for) life and happiness as the essence of play rules.
When we play we become, in the words of thephilosopher Friedrich Schiller, “a whole and complete
human being” [4].

1. Introduction
Play is a free and voluntary activity that we do for noother purpose than play itself. When we play, we
experience a joyful feeling and being free to express
ourselves. There is no product or end-goal with play, but
it holds a life-fulfilling sensation of freedom andexpression of yourself when in play. As is described in
the UN Convention on the Rights of the Child, article 31[1], every child has the right to play and leisure. Even,
there exists organisations such as UNICEF, Right To
Play and Playright Children’s Play Association, whichworldwide promote children’s right to play. For instance,
Right To Play work with the idea that “play is a powerfultool to change the world. It can inspire individuals andbring together entire communities. A game of footballcan educate children about tolerance and peace, and agame of tag can teach about malaria. When children play,they develop skills like cooperation, confidence andleadership–all important life lessons. Play provides aretreat from everyday hardships and brings joy andlaughter, allowing kids to be kids.” [2]© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 21

Henrik Hautop Lund

Further, scientific studies [3, 5] have shown the collateraleffect of play. We may observe certain effects of play.
For the one who plays, these effects are not the primaryreason to engage in play. Therefore, we term such effects
the collateral effects of play. The collateral effects of playcan be educational achievements, motor skill
enhancement, cognitive and physical rehabilitation, etc.
These collateral effects of play can be significant andimportant [5].

interaction and physical movement. It has been outlined
how the playware may act as a play force that pushes theuser into play dynamics [3]. The playware is a mediator,
which acts like a play force. The user is drawn into
making interaction with the (playware) technology, andby acting as a play force, the playware pushes the user
into a play dynamics. When you are in such play
dynamics, you may feel transformed from the normal
state of being and feel as if forgetting about time andplace. Sometimes we may feel being able to perform
more or better when in play, which is interesting if such
performance may have a desired collateral effect. Indeed,
Vygotsky puts it like “Play creates a zone of proximal
development in the child. In play, the child always
behaves beyond his average age, above his daily
behavior; in play it is as though he were a head taller thanhimself. As in the focus of a magnifying glass, playcontains all developmental tendencies in a condensed
form and is itself a major source of development.” [8].

If we agree that everybody has the right to play, we also
have the responsibility to ensure that we facilitate suchplay for everyone in the development of our society. It
seems valid to ask if play happens only when you havethe economical freedom to play and time to spend on
play? If that is so (even if it is only so to some degree),then such wealth requirements may limit the possibilitiesof play, including the derived creative possibilities, forpart of the global population. Further, since thedevelopment of modern technology to a large degree is a
development, which defines our global society, we needto ask if the technology development allow everybody toparticipate, or does the technology development actuallyprovide a further division in our global society between
the technology savvy who have been brought up withtechnology as part of their daily life, and the non-tech
population who have not had such a tech-filled
upbringing? With the penetration of technology into allkinds of aspects of our lives and society (including play),
there is a risk that opportunities are further polarized
between the experts and non-experts of such technology,
unless technology developers are consciously taking thetask upon themselves of making technology, which canbe easily implemented, understood and used in all kindsof contexts globally.

We believe that this quality of play may translate to manydifferent groups of people. The vision of providing thisquality of play and its collateral effects on many different
groups of people imposes a design challenge on the
playware to facilitate interaction by anybody. The
Playware ABC concept addresses this issue. The
Playware ABC concept if formed by:
A: Anybody, Anywhere, Anytime
B: Building Bodies and Brains
C: Construct, Combine, Create
The Playware ABC concepts works for creating
technology solutions for anybody, anywhere, anytime byusing embodied artificial intelligence building bodies
and brains, which facilitates that users can themselves
manipulate with the technology solutions to construct,combine and create their own solutions.

Therefore, in this work, we outline a concept for how it
is possible to develop playful technology, which can beused creatively by anybody, anywhere, anytime. By
concrete examples, we will describe the collateral effectsof play with such playful technology.

Often, engineering and IT system design takes it’s
starting point from solving a given challenge with any
technology solution available in order to achieve optimal
performance under a given circumstance. This often
results in systems with high performance, but with acomplex installation process, which demands a large
infrastructure. The Playware ABC concept suggest thatsuch infrastructure and installation demands may be
avoided to a large degree when the starting point of the

2. Playware ABC
Playware is defined as intelligent hardware and software,
which creates play and playful experiences for users of
all ages [6, 7]. Often, it is believed that such playware canmediate other actions, for instance actions such as social© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 22

Playware ABC: Engineeringengineering and IT system design is transformed from theoptimal system performance to become a focus on
creating a solution that can be used by anybody,
anywhere, anytime. A demand for anybody, anywhere,anytime on the technology design should be the starting
point, which forces us to find flexible solutions that canbe used easily and in a flexible manner in all kinds ofcontexts. Fortunately, high performance and robustnessof the engineered IT solution also derives from this focus
on anybody, anywhere, anytime, since such performanceand robustness is critically important to allow anybody tounderstand interacting with the solution, and to apply it
anywhere.

By designing, engineering and testing the modules in theright manner, so that they facilitate that anybody,
anywhere, anytime can be building bodies and brains, we
facilitate that these users can essentially construct,combine, and create. The users will construct and deconstruct with the modules by making different
combinations and physical structures with the modules(which results in different bodies and brains). By
constructing and combining with the modules, the users
create their different solutions. Different modules
combinations will give different results in terms ofphysical layout and processing result.
By playing with modules, the users should easily
understand the content and the construction possibilities.
Therefore, the engineering design must ensure a seamless
interaction and understanding for anybody. This
demands focusing on such seamless interaction andunderstanding for anybody in the engineering design ofthe modules, their content, their affordance andtransparency, etc. The Playware ABC offers this focus in
the engineering design process.

A solution for allowing anybody, anywhere, anytime tounderstand and interact with the technology may be touse a modular approach derived from modern artificial
intelligence, namely from embodied artificial
intelligence [9]. In embodied artificial intelligence, focus
is on understanding intelligence by building complete
agents (robotic systems) interacting in the realenvironment. As opposed to good old-fashioned artificial
intelligence, which to a large degree imposed a division
between the body (hardware) and the brain (AI software)
for instance with symbol-processing systems and expert
systems, embodied artificial intelligence states that there
is an intimate relationship between the body and the brain.
A modular system of physical modules with processing
power allows us to be building bodies and brains [10].
As the physical structure (body) is being built, theprocessing structure (brain) is made with those samebuilding actions.

3. Playware ABC implementation
Based Utilising the Playware ABC concept, we candesign modules that allow anybody, anywhere, anytime
to be building bodies and brains, which facilitate thatanybody can construct, combine, and create. The form,
content, and interaction modalities will be defined by theapplication area, and will vary dependent on this, as
exemplified in Fig. 1. Fig. 1 shows examples of modular
playware systems, which we designed according to the
Playware ABC. They include (a) ATRON selfreconfigurable modular robot, (b) I-Blocks in LEGO
Duplo e.g. used in primary schools in Tanzania, (c)Light&Sound Cylinders and Rolling Pins for elderly
dementia patient therapy in multi-sensory room, (d)modular interactive tiles for rehabilitation of stroke andcardiac patients, (e) modular interactive tiles forrehabilitation of mentally and physically handicappedchildren in Africa, (f) Fable user-configurable modular
robot, (g) Fatherboard modular robotic wearable, (h)modular interactive tiles for soccer and playgrounds, (i)Music I-Blocks, (j) MusicTiles magic matchboxes, (k)MusicTiles magic cubes for the Roskilde Festival.

By using this concept of building bodies and brains, theabstract cognitive challenge of programming istransformed for the user to become physical actions ofbuilding. Essentially, the representation for a cognitive
challenging problem is transformed to a physicalrepresentation. In a sense, this is similar to instructing
small children to count numbers and make addition with
their fingers: instead of using the cognitive challenging
abstract representation of the numbers and addition
operator, they are instructed to make a physicalrepresentation, which transforms the cognitive challenge.
In this manner, engineering systems of modules may
allow anybody to construct technology solutions bybuilding bodies and brains.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 23Henrik Hautop Lund

4. Discussion and Conclusion
We developed the Playware ABC concept to focus
engineering and IT system development on creating
solutions that are usable by all kinds of users and contexts
in our globalized society. The Playware ABC should
result in systems that allow anybody, anywhere, anytime
to be building bodies and brains, which facilitates users
to construct, combine and create. The concept was
exemplified with numerous examples of systems basedon the Playware ABC concept, which allow use bymentally disabled children, users in developing countries,
elderly stroke patients and dementia patients, etc., alongwith professional musicians, footballers, etc. In future,
we will investigate further the adaptation,
contextualisation and implementation of such Playware
ABC derived technology solutions in different contexts.

References
1. http://www.unicef.org/crc/files/Rights_overview.pdf
2. http://www.righttoplay.com/Learn/ourstory/Pages/default.aspx
3. H. H. Lund, “Play for the Elderly - Effect Studies ofPlayful Technology,” in Human Aspects of IT for the
Aged Population. Design for Everyday Life. (LNCS Vol.
9194, pp 500-511, Springer-Verlag, 2015)
4. F. Schiller, Letters Upon The Aesthetic Education of Man,
1795.
5. H. H. Lund, and J. D. Jessen, “Effects of short-term
training of community-dwelling elderly with modular
interactive tiles,” GAMES FOR HEALTH: Research,
Development, and Clinical Applications, 3(5), 277-283,
2014.
6. H. H. Lund, and C. Jessen, “Playware - Intelligent
technology for children’s play,” Technical Report TR2005-1, June, Maersk Institute, University of Southern
Denmark, 2005.
7. H.H. Lund, T. Klitbo, and C. Jessen, “Playware
Technology for Physically Activating Play”, Artificial Lifeand Robotics Journal, 9:4, 165-174, 2005
8. L. S. Vygotsky, Mind in society: The development ofhigher psychological processes. Harvard university press,
1980.
9. R. Pfeifer, and C. Scheier. Understanding intelligence.
MIT press, 2001.
10. H. H. Lund, “Building bodies and brains,” Adaptive
Behavior, vol 22, no. 6, pp. 392-395, 2014.

Fig 1. Examples of modular playware systems with userinteraction: (a) ATRON self-reconfigurable modular robot, (b)I-Blocks in LEGO Duplo, (c) Light&Sound Cylinders andRolling Pins for elderly dementia patient therapy in multisensory room, (d) modular interactive tiles for rehabilitation of
stroke and cardiac patients, (e) modular interactive tiles forrehabilitation of mentally and physically handicapped children
in Africa, (f) Fable user-configurable modular robot, (g)Fatherboard modular robotic wearable, (h) modular interactive
tiles for soccer and playgrounds, (i) Music I-Blocks, (j)MusicTiles magic matchboxes, (k) MusicTiles magic cubes.

In the shown examples, the modules were designed basedon the Playware ABC concept, so there is no need for any
complex infrastructure or installation. Instead, themodules can easily be used anywhere on any continent of
the world within minutes. Users ranging from smallchildren with mental retardation in Africa, overprofessional football players in Europe, to older dementia
patients in Asia can use the technology immediately.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 24

Playware ABC 2: a Disruptive Technology for Global Development
Henrik Hautop Lund

Center for Playware, Technical University of Denmark, Building 326, 2800 Kgs. Lyngby, DenmarkE-mail: hhl@playware.dtu.dk
www.playware.elektro.dtu.dk

Abstract
The Playware ABC concept is used to create solutions that are usable by all kinds of users and contexts in our
globalized society. In this paper, the Playware ABC can be exemplified with the development of the modular
interactive tiles for health prevention and rehabilitation of anybody, anywhere, anytime. The paper gives examples
of how playware becomes a disruptive technology for global development, for instance in the health sector. For
instance, in Tanzania doctors and community-based rehabilitation workers are constructing and combining modular
playware tiles to easily create the right kind of intervention for their patients in both urban and deep rural areas in
Tanzania.
Keywords: Playware, user-friendly, modularity, development, playful robotics and intelligent systems.

1. Introduction

Indeed, the Playware ABC concepts works for creating
technology solutions for anybody, anywhere, anytime byusing embodied artificial intelligence building bodies
and brains, which facilitates that users can themselves
manipulate with the technology solutions to construct,combine and create their own solutions.

We outlined in [1] that when people get into playdynamics, there can be certain effects of play which wetermed the collateral effects of play. The collateral effectsof play can be educational achievements, motor skill
enhancement, cognitive and physical rehabilitation, etc.
In the following, we will exemplify such collateraleffects of play in the area of health [2].

2. Moto tiles for HealthIf it is true that the collateral effects of play on playware
may lead to significant and important healthimprovements, then it also becomes mandatory toinvestigate the engineering principles, which may
facilitate a distribution of playware to everybody
regardless of their skills, societal context, etc. We
address this issue with the Playware ABC concept, which
is based on:
A: Anybody, Anywhere, Anytime
B: Building Bodies and Brains
C: Construct, Combine, Create

The Playware ABC can be exemplified with thedevelopment of the modular interactive tiles for healthprevention and rehabilitation of anybody, anywhere,anytime. In this context, play as a free and voluntary
activity, which is performed for the personal enjoyment,
may result in highly motivated patients, if the technology
can mediate such playful engagement. At the same time,
it may be possible to observe the collateral effects of playin the form of quantifiable health effects that exceeds theeffects measured with traditional health intervention
methods [2, 3].

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 25

Henrik Hautop Lund

Therefore, based on the Playware ABC, we developed
modular playware in the form of the modular interactive
tiles, now called Moto tiles (www.mototiles.com). The
Moto tiles activates the user to perform playfulinteraction with play and games on the tiles that light up
in different colors and registers when users step on them.

Qualitative studies suggest that the high health effectfrom playing with the Moto tiles arises since the Mototiles act as a play force, which pushes the participants intoa play-dynamics, in which they forget about time andplace, and thereby perform more than they would
normally do. In play, they may forget about their fears offalling and forget about their perceived physical andcognitive limitations.

These Moto tiles are based on various generations ofprototypes of modular interactive tiles, which wedeveloped over the last decade [4, 5]. Each tile has a ringof 8 RGB LEDs, a sensory system to register step on the
top surface, its own processor and rechargeable lithium
battery. As a consequence of the design based on the
Playware ABC concept, these Moto tiles can be easilytransported to be set-up and used anywhere within aminute.

With such quantifiable health effect results of play,playware technology may disrupt certain areas of thehealth sector. Already, the Moto tiles are used in thehealth sector for the benefit of cardiac patients, stroke
patients, elderly citizens at risk of falling, dementia
patients, children with cerebral palsy, and in special
schools in three continents. This disruptive technology
for the health sector is a result of the Playware ABC withthe possibility for the end-user to easily adapt to different
tasks and practices, e.g. for different kinds of patients.

The new Moto tiles are connected to a tablet with the
ANT+ radio protocol. On the tablet, the user can selectbetween numerous games that challenge both thephysical and cognitive abilities of the user. Further, thetablet shows the score in each games, shows statistics forthe user, and make automatic documentation of effect.

Fig 2. Modular interactive tiles for rehabilitation of mentallyand physically handicapped children in Tanzania.
Fig. 1. Moto tiles and app interface (www.mototiles.com)

Indeed, the modular interactive tiles can be used byanybody, anywhere, anytime, as it does not demand any
particular knowledge of the user, and it does not demand
any infrastructure. Hence, it is used as a playful
technology solutions for rehabilitation of children withcerebral palsy, mental retardation, etc. in deep rural subSaharan Africa [6, 7]. This derives from the Playware
ABC, which here results in a highly mobile, modular andenergy efficient technology, which can be set up and usedanywhere and anytime. In Tanzania, we are forming anational partnership for sustainable implementation,

Scientific studies of effect among community-dwellingelderly who perform group play on the modular tiles once
per week show highly statistical significant effect on
functional abilities of the elderly. Especially, thebalancing abilities of the elderly (avg. 83 years of age)increase by more than 60% after merely 13 training
sessions [3]. Also, all other measured functional abilities(strength, mobility, agility, and endurance of the elderly)
improved with statistical significant effect.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 26

Playware ABC 2:

comprising a governmental representative, national
hospital, national health university department, regional
hospitals, Living Labs and NGOs performing
community-based rehabilitation.

Further, due to the relatively low amount of healthspendings and of health workers e.g. in sub-SaharanAfrica compared to high-income countries, the individual
health worker will typically need to treat a vast array ofhealth problems compared to the high degree of
specialization to single health problems amongst healthworkers in high-income countries. Hence, to be effective
in low-income regions such as sub-Saharan Africa, there
is a need for health technology solutions, which facilitate
treatment of people with various diagnoses.
Through the Playware ABC, we address this challengewith the modular interactive tiles, which aim at
motivating patients to perform rehabilitative actions. By
taking advantage of the flexibility and easy approach topersonalization inherent in the modularity, the proposed
modular approach possesses the potential of becoming
particularly helpful in addressing both the technologicalchallenges and health challenges in low-income
communities. With the high prevalence of disability,
stroke and coronary heart cases in sub-Saharan Africa
countries, it is of paramount importance to investigate
how this kind of an approach can provide an eHealthsolution flexible enough to adapt to the needs of patients.
Based on the Playware ABC, we therefore aim at refining
and contextualising modular interactive tiles to allow a
local community to have one modular solution that any
community rehabilitation worker can easily adapt to theindividual patient and his/her needs.

Fig. 3. Playful rehab of meningitis affected child in Iringa
Regional Hospital, Tanzania.

The users such as doctors in the hospitals or healthworkers in community-based rehabilitation programs areconstructing and combining the modular tiles to easily
create the right kind of intervention for their patients. The
battery-powered tiles present a vast number ofopportunities for games and can be used for manydifferent purposes. Due to the modularity, it is very easy
to co-design and adapt the modular tiles system todifferent contexts, different intervention areas, anddifferent levels of competencies.
This can have an important impact in rehabilitation of
disabled, stroke, and cardiac patients in parts of the world
where a large part of the population lives in remote andrural areas. Here, potential health technology solutions
often face problems of scarce availability of electricity,of technology professionals, and of health personnel.

Fig. 4. Modular interactive tiles for rehabilitation at Muhimbili
National Hospital, Dar Es-Salaam, Tanzania.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 27

Henrik Hautop Lund

3. Conclusion

7. H. H. Lund, L.S.D. Jensen, Y. Ssessanga, R.
Abdalahman. ”Implementing Modular Interactive Tiles
for Rehabilitation in Tanzania – a Pilot Study”, in ISTAfrica 2014 Conference Proceedings, P Cunningham andM Cunningham (Eds), IIMC International Information
Management Corporation, 2014, ISBN: 978-1-905824-434

We developed the modular interactive tiles as an
exemplification of Playware ABC to allow anybody,
anywhere, anytime to be building bodies and brains,which facilitates users to construct, combine and create.
Indeed, in health institutions in Sub-Saharan Africa,
doctors and health care workers are now utilizing themodular interactive tiles for a variety of interventions formentally disabled children, elderly stroke patients, etc.
In future, we will investigate further the adaptation,
contextualisation and implementation of such Playware
ABC derived technology solutions in different contexts,
including in areas of development. Especially for thehealth area, we will investigate with different
rehabilitation methods and centres, including hospitals
both in a city centres and in a rural area, NGO’s
performing community based rehabilitation, andrehabilitation centres [6].

Acknowledgements
The author would like to thank colleagues at the Center
for Playware, Technical University of Denmark.
References
1. H. H. Lund, “Playware ABC: Engineering Play for
Everybody” ibid.
2. H. H. Lund, “Play for the Elderly - Effect Studies ofPlayful Technology,” in Human Aspects of IT for the
Aged Population. Design for Everyday Life. (LNCS Vol.
9194, pp 500-511, Springer-Verlag, 2015)
3. H. H. Lund, and J. D. Jessen, “Effects of short-term
training of community-dwelling elderly with modular
interactive tiles,” GAMES FOR HEALTH: Research,
Development, and Clinical Applications, 3(5), 277-283,
2014.
4. H.H. Lund, T. Klitbo, and C. Jessen, “Playware
Technology for Physically Activating Play”, Artificial Lifeand Robotics Journal, 9:4, 165-174, 2005
5. H. H. Lund “Modular Robotics for Playful Physiotherapy”,
in Proceedings of IEEE International Conference on
Rehabilitation Robotics (IEEE Press, 571-575, 2009)
6. H. H. Lund, L. S. D. Jensen, Y. Ssessanga, S. Cataldo &K. I. Yahya-Malima, “An Approach for a National eHealth
Implementation – the Case of Modular Interactive Tiles
for Rehabilitation,” in P Cunningham & M Cunningham(eds), IST-Africa 2015 Conference Proceedings,
10.1109/istafrica.2015.7190552© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 28

The future of Robotics Technology
Luigi Pagliarini 1,21Henrik Hautop Lund 1

Centre for Playware, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark2
Academy of Fine Arts of Macerata, Via Berardi, 6, 405111 Macerata, Italy
luigipagliarini@gmail.com

Abstract
In the last decade the robotics industry has created millions of additional jobs led by consumer electronics and theelectric vehicle industry, and by 2020, robotics will be a $100 billion worth industry, as big as the tourism industry..
For example, the rehabilitation robot market has grown 10 times between 2010 and 2016, thanks to advancements
in rehab/therapy robots, active prostheses, exoskeletons, and wearable robotics. In short, the very next decade
robotics will become vital components in a number of applications and robots paired with AI will be able toperform complex actions that are capable of learning from humans, driving the intelligent automation phenomenon.
Therefore, in this paper we try to depict the direction and the fields of application of such important sector of future
markets, and scientific research.
Keywords: Playware, user-friendly, modular robots, playful robotics and intelligent systems.

largely excluding the dream of a “polyfunctional” robot.
Such limits are (1) Mechanical, (2) Energetic and (3)Computational. Indeed, robots’ mechanics tends toreach an unsustainable degree of complexity movingfrom a limited to a larger set of outputs. In the verysame way, the energy consumption rises in parallel tothe increasing of degree of freedom of any mechanics,
making the energetic battery life span of a machinequite harder, up to the a critical level of inadequacy.
Finally, the Computational resources of AI have largely
demonstrated to be very good at solving a single task
while, on the contrary, getting proportionally lessefficient when handling a larger number of possible
outputs. Therefore, the goal of multi-purpose robot is tobe considered unreal and, oppositely, researchers andbusinessmen are going for a Robotics that is pretty
specialized in a single task, if not a single subtask.

1. Introduction
Most certainly, in near future, and as for biologicalsystems, Robotics will be submitted to a selective
pressure under which most of its branches and authors
will change. All of that will happen because of several
factors: the enormous costs of production andmaintenance of such machines; because of theecosystemic and energetic costs of the robots, which aresimilar if not higher than any other machine; likely due
to the saturation of an already seemingly fragile market.
Because of that, it is quite important to try to predict the
future of intelligent machines in order to focus one’s
efforts on the appropriate field.
In the beginning of the millennium indeed, scientists
and enterprises started to apply the binomial Robot/AI
to almost all of the possible domains with the “naïve”
conception that the emerging technologies could havedealt with any task. Which is, in part, true. Nevertheless,on the other side, it slowly becomes self evident that
there is a set of bottlenecks which are quite hard to pass.
To put it in a practical way, there are at least three setsof problems, which relegate both efficiency, andfunctionality of such machines to a limited domain,

Given that, it happens that the number of artefacts - that
we call Robots - has been exploited to such a numberthat both the scientific and the business markets cannot
actually absorb and, as a consequence, we are about toface a phase in which selection will become necessary.
Therefore, let’s now see which are the largest andmaybe more promising fields, trying to distinguish their© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 29

Luigi Pagliarini, Henrik Hautop Lunddomain of competences with their domains ofapplication, since the two things that are not necessarily
the same.

Collaborative Robots. Recently the market
has been opened to Domotics and other Homeand Public Spaces (e.g. Shops) Automation
coordinators (12).
Military Robotics. Drones, Navigators,
Researchers, Warriors and all of the possible
robotics extensions which are to be applied inspying operations and battle fields (13).
Underwater, Flying and Self -Driving
Machines. All the Robotics that deals with
self-piloting in all circumstances, on earth, airand water (13).
Space Robots. All of the Robotics used inSpace missions, therefore highly resistant,expert in exploration and material datacollection (14).
Entertainment. Toys, Games and Interactive
Robotics for children (15).
Art. Most creative robotics, which don’t aim at
a specific functionality but follows criteria ofbeauty and conceptual inspiration (16).
Environmental and Alternately poweredrobots use sources like solar, wind and wave
energy to be powered indefinitely and open up
applications in areas that are off-grid (17).
Swarm and Microbots allow emergency
responders to explore environments that are too
small or too dangerous for humans or larger
robots; deploying them in “swarms”
compensates for their relatively limited
computational ability (18).
Robotic networks emerge and allow robots toaccess databases, share information and learn
from one another’s experience.
Modular Robotics. Robots that can arrange
themselves in pre-set patterns to accomplish
specific tasks (19).

2. Robotics Fields
At the moment being, the number of robotics fields isnearly uncatchable, since robot technology is being
applied in so many domains that nobody is able to know
how many and which they are. Such an exponential
growth cannot be fully tracked and we will try to
identify and discuss upon the most evident fields ofapplication, which, as far as we, comprehend are:
Healthcare Robotics; Robotics used in thecontext of patient monitoring/evaluation,
medical supplies delivery, and assisting
healthcare professionals in unique capacities aswell as, Collaborative robots and robotics used
for Prevention (1,2,3).
Medical and Surgery Robotics; Devices usedin hospitals mostly for assisting surgery since
they allow great precision and minimal
invasive procedures (4,5).
Body-machine interfaces help amputees tofeed-forward controls that detect their will tomove and also receive sensorial feedback thatconverts digital readings to feelings (6).
Telepresence Robotics; Act as your stand-in
at remote locations it is meant to be used in
hospitals and for business travellers, with theidea of saving both time and money (7).
Cyborgs, Exoskeletons, and Wearable
Robotics. Allow users to augment their
physical strength, helping those with physicaldisabilities to walk and climb (8).
Humanoids. Combine artificial intelligenceand machine learning technologies to give
robots human-like expressions and reactions(9).
Industrial. Arms, grippers and all of thewarehouse robotics used for automation ofindustrial processes. They are used both forsaving manoey and speed up the productions(10).
Housekeeping. Floors, Gardens Pools and all
the Robot Cleaners (11).

3. Analysis
Let us now look at all of these branches distinguishingthem on two different scales.
Robotics that mainly belongs to the research domainfrom those that aim at application, therefore, market
oriented. Fields that are proficient and uprising from© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 30

The future of Robotics

those that seem declining or apparently stuck in a bottle
neck.

The Upcoming.
There are few promising fields of robotics for the early
future. They are Exoskeletons, Wearable Robotics,
Healthcare and Collaborative Robotics. There is, indeed,a flow of investments both from Medical and Fashion
Houses that are trying to sustain the research in thesefields. Investments are motivated by marketing reasons
and, sometime, by a real intent of generating a new
trend in their consumers market.

The Market.
Looking at the market, the richest applications are thosethat belong to industrial robotics, where automation ofprocesses is requiring more and more clever and fast
robots for assembling any kind of product. No
comparison, this is and apparently will be the mostimportant robotics field for quite a while, at least.
Following that, there are at least few very promising
fields,Entertainment,
Medical,Surgery
andHousekeeping Robotics. With a completely different
philosophy – few expensive vs. lots of cheap sells - they
established themselves into the market in a quitepersistent way. Nevertheless, even if those market slices
are not saturated yet, we would say they are getting
close to it, leaving little space for future improvement.
We cannot say the same for Industrial Robotics.

Out of trend
In its short history, Robotics is already producing
leftovers. Indeed, few branches of past pretty popular
Robotics are getting out of trend. They are Humanoids,
Geminoids, Cyborgs, etc. Their appeal and impact on
the public (and on a big slice of researchers) seems to befading away, as if both the experimentation and the
public imaginary had been saturated.
Innovations.
At the moment, the most innovative branch isEnvironmental and Alternately Powered Robotics that is
attracting interests from different Institutes andIndustries.
Anyway,few
aretheexisting
application (i.e.: toys, navigation, etc.) and they do not
represent a valuable set by which we could evaluate
their effective potentialities. Nevertheless, they
theoretically represent a quite important goal since a big
obstacle in this machine-driven society is around theenergy costs, and renewable energies in Robotics, as inall other applications seems to be the unique answer.

The Research.
At the moment, researchers are keen on a set of fields
that are very interesting under the scientific point ofview but, oppositely, pretty poor as market chances.
They are Humanoids, Telepresence, Swarm, Microbots,
Robotic Networks, Modular Robotics, and BodyMachine Interfaces. Such fields are crucial for basicresearch and, probably, something exceptional will
come out of all these investigations, but for the moment
being, we can’t foresee a single reasonably profitableapplication from all of them. On the contrary,
Underwater, Self -Driving Machines and, in particular,
Flying Robots (e.g. Amazon’s Drones, etc.) are moving
towards a promising number of vends and who’s impact
can be more than consistent in the next decade.

An important answer to the energy consumption
question may also be found in the branch of
Neurorobotics (20) which exploits Neuromorphic
computing. Here, Research is investigating the use ofprocesses similar to (human) brain processes, which
offer large scale computation at a much lower energy
consumption than is currently known in any
computational/robotic device.

The Exceptions.
An odd man out seems to be both Space and Military
Robotics. For these two quite rich fields - where theconcept of research and application tend to melt – there
is still a large margin of exploration and exploitation. Of
course, besides any further consideration, both Spaceand Military application can be considered, under the
economical point of view, self-sustaining since theyboth produce and consume for their own market.

4. Conclusions
In the last two decades Robotics has literally exploded,
both in terms of research and applications. It has© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 31

Luigi Pagliarini, Henrik Hautop Lund

9. S. Kajita, H. Hirukawa, K. Harada, K. Yokoi,
Introduction to Humanoid Robotics. Springer (2014).
10. S. Y. Nof, Springer Handbook of Automation. SpringerVerlag (2008).
11. R. Siegwart, I. R. Nourbakhsh, D. Scaramuzza.
Introduction to Autonomous Mobile Robots.The MIT
Press, Cambridge Massachusetts, London England (2004).
12. J. Gerhart, Home Automation and Wiring, McGraw Hill
Professional (1999).
13. P. J. Springer, Military Robots and Drones: A Reference
Handbook. ABC-CLIO Editor (2013).
14. R. D. Launius, H. E. McCurdy. Robots in Space:
Technology, Evolution, and Interplanetary Travel. The
Johns Hopkins University Press, Baltimore. (2008).
15. R. Malone. Ultimate Robot. DK Pub., 2004.
16. L. Pagliarini, H. H. Lund. The Development of Robot Art.
AROB, 13th International Symposium on Artificial Lifeand Robotics, Oita, Japan, January 31–February 2, 2008.
17. R. Hanson. The Age of Em: Work, Love, and Life when
Robots Rule the Earth. Oxford University Press. (2016).
18. S. Kernbach. Handbook of Collective Robotics:
Fundamentals and Challenges. Pan Stanford Publishing.
(2013).
19. K. Stay, D. Brandt, D. J. Christensen. Self-reconfigurable
Robots. An Introduction. MIT Press, (2010).
20. P. Artemiadis. Neuro-Robotics: From Brain MachineInterfaces to Rehabilitation Robotics. Springer (2013).

invaded the people’s imaginary and almost all of theexisting markets, up to the point that, on one side, we
can spot at robotics news each single day and, on theother, Robotics is about to reach a market slice of 100
Billions dollars. In this paper we tried to summarize andanalyse which are the most profitable and promisingbranches and where to look for new horizons. It appears
that Industries’ Automation is the leader of such a world
while a number of applications are consolidating
themselves or about to emerge and to play a consistentrole in Robotics research and production. They are
Healthcare, Surgery, Housekeeping, Autonomous
Vehicles and, in part, Entertainment.
We alsounderlined which are the branches that seem to lose
affection of markets and researches, as for example
Humanoids, and those who are gaining interests, as forexample Alternately Powered Robotics. It is our belief
that our analysis can provide a wider view on the worldof Robotics and how to approach it in the early future.

References
1. H. H. Lund, “Play for the Elderly - Effect Studies ofPlayful Technology,” in Human Aspects of IT for the
Aged Population. Design for Everyday Life. (LNCS Vol.
9194, pp 500-511, Springer-Verlag, 2015)
2. H. H. Lund, and J. D. Jessen, “Effects of short-term
training of community-dwelling elderly with modular
interactive tiles,” GAMES FOR HEALTH: Research,
Development, and Clinical Applications, 3(5), 277-283,
2014.
3. A. Okamura, M. Mataric, & H. Christensen Panels.
CCC/CRA, Roadmapping for Robotics
Workshop: A Research Roadmap for Medical and
Healthcare
Robotics.
[Online]Available
at: http://www.us-robotics.us/medical-ws.html (2008).
4. M. Sood, S. W. Leichtle. Essentials of Robotic Surgery,
Spry Publishing LLC, Mar 1, 2013
5. T. Lendvay, (2008). Robotic Surgery Simulation: An
Unintuitive Reflection. Medical Robotics Magazine.
[Online]Availableat:
http://medicalrobotics.blogspot.com/2008/10/roboticsurgery-simulation-unintuitive.html .
6. T. D. Coates, Neural Interfacing, Forging the HumanMachine Connection. Morgan & Clayton Publishers.
John D. Enderle Series Editor, (2008).
7. I. R. Nourbakhsh, Robot Futures. The MIT Press,
Cambridge Massachusetts, London England (2013).
8. J. L. Pons. Wearable Robots: Biomechatronic
Exoskeletons. John Wiley & Sons Ltd. (2008).

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 32

A Combination of Machine Learning and Cerebellar Models for the Motor Control andLearning of a Modular Robot
Ismael Baira Ojeda

Center for Playware, Department of Electrical Engineering, Technical University of Denmark, Elektrovej Building 326
Kongens Lyngby, 2800, Denmark
Silvia Tolu, Moisés Pacheco, David Johan Christensen, and Henrik Hautop Lund

Center for Playware, Department of Electrical Engineering, Technical University of Denmark, Elektrovej Building 326
Kongens Lyngby, 2800, DenmarkE-mail: iboj@elektro.dtu.dk, {stolu, mpac, djchr, hhl}@elektro.dtu.dk
www.dtu.dk

Abstract
We scaled up a bio-inspired control architecture for the motor control and motor learning of a real modular robot. In
our approach, the Locally Weighted Projection Regression algorithm (LWPR) and a cerebellar microcircuit coexist,
forming a Unit Learning Machine. The LWPR optimizes the input space and learns the internal model of a single
robot module to command the robot to follow a desired trajectory with its end-effector. The cerebellar microcircuit
refines the LWPR output delivering corrective commands. We contrasted distinct cerebellar circuits including
analytical models and spiking models implemented on the SpiNNaker platform, showing promising performance androbustness results.
Keywords: Motor control, cerebellum, machine learning, modular robot, internal model, adaptive behavior.

bodies in uncertain or unknown environments, how wecoordinate smooth movements and the mechanisms of
motor control and motor learning of the central nervous
system (CNS) has become of interest towards thedevelopment of bio-inspired autonomous robotic systems.

1. Introduction
Understanding the human brain is one of the greatest andmost appealing challenges facing scientific research.
Therefore, initiatives such as the Human Brain Project1(HBP) were conceived to encourage the delivery of
beneficial breakthroughs for society and industry. The
HBP unites the effort of numerous research centers anduniversities involving multiple disciplines and goals in
the form of 12 subprojects. In particular, our group isframed within the subproject 10 focused onneurorobotics.
Robots lack the adaptability and precision of humanbeings towards uncertain or unknown environments. In
contrast, the brain accomplishes tasks in an admirable
way allowing smooth movements with a low power
consumption. As a result, studying how we control our

1.1. The cerebellum
Among the distinct parts of the brain, the cerebellum
stands out due to its key role in modulating accurate,complex and coordinated movements, acting as auniversal learning machine2. Its contributions include theneural control of bodily functions, such as postural
positioning, balance or coordination of movements overtime3. Thus, understanding and mimicking the cerebellar
mechanisms through bio-inspired architectures areinteresting processes in the development of innovative© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 33

Ismael Baira Ojeda, Moisés Pacheco, David Johan Christensen, Henrik Hautop Lund

unit and show a uniform almost crystalline
microcircuitry10. Thus, we decided to use its structure tocontrol a robot module in a generic manner.
Two microcomplexes were used to command the jointsof a 2-DoF module. Each cerebellar output was linked to
one joint as it happens in our body, where one motor cellcommands one motor unit.

robotic systems capable of carrying out complex andaccurate tasks in varying situations (See Refs. 4-6).
Following this motivation, we took inspiration from thecerebellum for the motor control and learning of a real
modular robot. For this purpose, we used a bio-inspired
control architecture which combines machine learningand a cerebellar microcircuit. The cerebellar models were
simplifications of the real biological microcircuit
including only the Purkinje, granule and deep cerebellar
nuclei cells, and the parallel, mossy and climbing fibers.
Moreover, we considered three cerebellar models,
including spiking and non-spiking approaches, aiming atenlightening when to use which model and establishing
the grounding for our future research. The result was acompliant robot module that by means of a bio-inspired
approach was able to learn how to trace out a circular
trajectory with its end-effector.
2. Material and Methods

Fig. 2. Modular scheme of the connections between thecomputer, the modular robot Fable and the neuromorphic
SpiNNaker platform, which was used for the implementation of
the spiking cerebellar model.

In this section, we address the modular robot, ourmodular control approach and the Machine Learningtechnique utilized.
2.1. The modular robot: Fable
The target robotic platform is the modular robot called
Fable7. Fable is based on the combination of selfcontained modules which can work independently or
collaborate in modular configurations. Due to a low lag
radio communication link to the modules the user canprogram the distributed robot modules at different levelsof abstraction as if they were centralized and connected
directly to the computer. To do so, the communication isdone via a radio dongle addressing each module with an
ID and a radio channel.

2.3. The bio-inspired modular control architecture
In order to perform the motor control and learning of a
Fable module, we chose the Adaptive Feedback Error
Learning (AFEL) architecture4 shown in Fig. 3.
The trajectory generation block generates the desired
joint angles and velocities (Qd, Q̇d) by inverse kinematics.
On the one hand, the AFEL scheme guarantees thestability of the system by means of a control loop in
which a Learning Feedback (LF) controller4 is
implemented. The LF overcomes the lack of a precise
robot morphology dynamic model ensuring stability andadjusting its output torque through a learning rule after
consecutive iterations of the same task. Its gains were
heuristically tunes to Kp = 7.5, Kv = 6.4 and Ki = 1 for
the Fable module.
On the other hand, the AFEL architecture is endowedwith a ULM, comprised by the LWPR algorithm and acerebellar circuit. The ULM performs a feedforward
control of the robot module. The LWPR is in charge ofabstracting the internal model of the robot, while thecerebellar microcircuit refines the output deliveringcorrective torques.

Fig. 1. The robot Fable. An example of a modular configuration
comprising four actuated modules and a head module endowedwith ultrasound sensors.

2.2. The modular approach
Scientific research studies on the cerebellum such as Refs.
8-9, describe it as a set of adaptive modules, calledmicrocomplexes, which represent the minimal functional© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 34

A Combination of Machine

The weight measures how often an item xi of the data falls
into the validity region of each linear model,
characterized by a positive definite matrix Dk, called
distance matrix.
The LWPR conveys the weights to the cerebellar circuit
and at the same time, it delivers a torque output computed
as the weighted mean average of the linear local model'scontributions.
We chose the LWPR algorithm for three reasons: tooptimize the input space to enhance learning speed andaccuracy; since it can substitute and optimize the role ofa certain group of cells of the cerebellum, called granulecells; and due to its capability of learning incrementally
on-line.
3. Results

Fig. 3. The AFEL control architecture. The AFEL control
architecture embeds a ULM which acts as a feed-forward
controller while it abstracts the internal model of the robot
module.

We tested the control architecture using the threecerebellar model cases described in Section 2.3 bycommanding the robot to trace out a circular trajectorywith its end-effector. The tests considered the normalized
mean square error (nMSE) of the position of the joints
with respect to the desired positions. First, theperformance test consisted in following a circular
trajectory with constant amplitude and spin frequency(see Fig. 4).

The cerebellar microcircuitry considered three cases.
Case 1, based on Ref. 4, included only the Purkinje cells
whose learning rule is based on the heterosynaptic
covariance learning rule11 in the continuous form andadjusted by an error signal τfb. In Case 2 we included the
Deep Cerebellar Nuclei (DCN) cells, adding two extra
synaptic plasticities whose learning rules were inspired
by Ref. 12.
In case 3 we implemented a simplified spiking cerebellar
model using the neuromorphic platform SpiNNaker13,
consisting of Purkinje cells and DCN cells but without
considering recurrent or inhibitory synapses.
2.4. The LWPR algorithm
The LWPR algorithm14 creates and combines N linear
local models and feeds the sensorimotor inputs (Qd, Q̇d,
Q, Q̇) of the robot including desired and real values tothem. Thereafter, the LWPR incrementally divides thissensorimotor input space into a set of receptive fields(RFs) performing an optimal function approximation.
The RFs are represented by a Gaussian weighting kernel(Eq. 1) which computes a weight in each k-th local unit,
for each xi data point according to its distance to the ck
center of the kernel.

=

(

)

(

)

Fig. 4. Performance test: Circular trajectory with constant
amplitude and spin frequency.

Secondly, we carried out two robustness tests (see Figs.
5 and 6) where we considered trajectories that varied they
amplitude keeping the spin frequency constant, andthereafter, trajectories that kept the amplitude constantbut varied their spin frequency (0.5-1Hz).

(1)© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 35

Ismael Baira Ojeda, Moisés Pacheco, David Johan Christensen, Henrik Hautop Lund

References
1. Ailamaki, A., A. Alvandpour, K. Amunts, W. Andreoni, J.
Ashburner, M. Axer, M. Baaden, and R. Badia. "TheHuman Brain Project: A Report to the European
Commission”. Lausanne: The HBP-PS Consortium (2012).
2. Ito, Masao. “Control of mental activities by internal
models in the cerebellum”. Nature Reviews Neuroscience9, no. 4 (2008): 304-313.
3. Ito, Masao. “Cerebellar circuitry as a neuronalmachine.” Progress in neurobiology 78, no. 3 (2006):
272-303.
4. Tolu, Silvia, Mauricio Vanegas, Niceto R. Luque, Jesús A.
Garrido, and Eduardo Ros. “Bio-inspired adaptive
feedback error learning architecture for motor control.”
Biological cybernetics 106, no. 8-9 (2012): 507-522.
5. Lepora, Nathan F., Paul Verschure, and Tony J. Prescott.
“The state of the art in biomimetics.” Bioinspiration &biomimetics 8, no. 1 (2013): 013001.
6. Bologna, L. L., J. Pinoteau, J. B. Passot, J. A. Garrido, Jörn
Vogel, E. Ros Vidal, and A. Arleo. “A closed-loop
neurobotic system for fine touch sensing.” Journal ofneural engineering 10, no. 4 (2013): 046019.
7. Pacheco, Moises, Rune Fogh, Henrik Hautop Lund, andDavid Johan Christensen. “Fable: A modular robot for
students, makers and researchers.” In Proceedings of the
IROS workshop on Modular and Swarm Systems: fromNature to Robotics. 2014.
8. Dean, Paul, John Porrill, Carl-Fredrik Ekerot, and Henrik
Jörntell. “The cerebellar microcircuit as an adaptive filter:
experimental and computational evidence.” Nature
Reviews Neuroscience 11, no. 1 (2010): 30-43.
9. Verduzco-Flores, Sergio O., and Randall C. O'Reilly.
“How the credit assignment problems in motor control
could be solved after the cerebellum predicts increases inerror.” Frontiers in computational neuroscience 9 (2015).
10. Ruigrok, Tom JH. “Ins and outs of cerebellar modules.”The cerebellum 10, no. 3 (2011): 464-474.
11. Sejnowski, Terrence J. “Storing covariance withnonlinearly interacting neurons.” Journal of mathematicalbiology 4, no. 4(1997): 303-321.
12. D’Angelo, Egidio, Lisa Mapelli, Claudia Casellato, Jesus
A. Garrido, Niceto Luque, Jessica Monaco, Francesca
Prestori, Alessandra Pedrocchi, and Eduardo Ros.
“Distributed circuit plasticity: new clues for the cerebellar
mechanisms of learning.” The Cerebellum 15, no. 2(2016): 139-151.
13. Furber, Steve B., Francesco Galluppi, Steve Temple, andLuis A. Plana. “The spinnaker project.” Proceedings of theIEEE 102, no. 5 (2014): 652-665.
14. Vijayakumar, Sethu, Aaron D’souza, and Stefan Schaal.
“Incremental online learning in high dimensions.” Neuralcomputation 17, no. 12 (2005): 2602-2634.

Fig. 5. Robustness test: Using three distinct amplitude values.

Fig. 6. Robustness test: Varying spin frequency (0.5-1Hz).

4. Conclusions
We combined the LWPR algorithm and a cerebellar
circuit for the motor control and learning of a real robot
module. Furthermore, we implemented three distinct
cerebellar models: two non-spiking and one spiking
using the neuromorphic SpiNNaker platform.
Compared to Case 1, Case 2 showed better results in theperformance test while keeping similar results in bothrobustness tests. Case 3 did not show improvements withrespect to the non-spiking models, but since its circuitry
was quite simple there is much room for promisingfurther research. Future research will exploit the potentialof more detailed spiking models where inhibitory andrecurrent synapses will take place and explore the control
of several robot modules using SpiNNaker.
Acknowledgements
This scientific work has received funding from theEuropean Union Horizon 2020 Programme under the
grant n. 720270 (Human Brain Project SGA1). A special
thank is expressed to the University of Manchester for the
SpiNNaker board.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 36

Image Compression Using Hybrid Evolution Based Takagi-Sugeno
Fuzzy Neural Networks
Chia-Nan Ko

Department of Automation Engineering, Nan Kai University of Technology
t105@nkut.edu.tw
Ching-I Lee
Department of Automation Engineering, Nan Kai University of Technology
lcy@nkut.edu.tw

Abstract
This article proposes a model of hybrid evolution-based Takagi-Sugeno fuzzy neural networks (TSFNNs). The
model is to research and analyze how to improve the efficiency of image compression. In the proposed model, thehybrid evolution method integrates the advantages of improved quantum-behaved particle swarm optimization,
adaptive annealing learning, and mutation operation to train Takagi-Sugeno fuzzy neural networks. The proposed
hybrid evolution-based quantum-behaved particle swarm optimization Takagi-Sugeno fuzzy neural networks(HEQPSO-TSFNNs) can improve the coding result around boundaries to enhance the coding efficiency of thelossless compression of images.
Keywords: Takagi-Sugeno fuzzy neural networks, Quantum-behaved particle swarm optimization, Imagecompression, adaptive annealing learning

1.

Introduction

Image is usually the source of data, via different
transmitting and compressing procedure, data receive
different compressed results. Recently, some image
compression techniques have already been proposed.1,2
Artificial intelligence methods adopted to compress
image are issued successively.
How to improve compress efficiency to reach the topic
that the high-quality image stores and transmits is theresearch issue. Recently, some methods about losing theimage code and compress have already been proposed
successively.3,4 However, it is computation consuming
work. To overcome this problem, some authors utilize
fuzzy logic concept and neural networks to design the
non-linear predictor.5,6Authors proposed Takagi-Sugeno fuzzy neuralnetworks (TSFNNs) to carry out the non-linear predictor.
The neural network of Takagi-Sugeno fuzzy type has
parallel computing ability.7 In this study, we adopt the
hybrid evolution approach integrating quantum-behaved
particle swarm optimization (HEQPSO), adaptive
learning algorithm, and mutation operation to search
optimal TSFNNs (HEQPSO-TSFNNs). The HEQPSOTSFNNs are adopted to perform image compression.
2.

Takagi-Sugeno Fuzzy Neural Networks

This study built an image compression system using
TSFNNs. The appropriate Takagi-Sugeno fuzzy neuralnetworks can improve the efficiency of image
compression.
2.1. Frame of TSFNNs© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 37

Chia-Nan Ko, Ching-I Lee

In this article, we adopted TSFNNs to construct thepredictor of image codes. The TSFNNs can perform thenon-linear model which describes high complexity withless fuzzy rules.8,9 The Ri fuzzy rule can be expressed asfollows:
Ri : If z1 F1i and z2 F2i , then(1)yiBi u), i 1,2, ,9i ( Ai xF2 kdenoted fuzzy sets. yi R is the output of the ith fuzzy
rule and Ai R1 6 , x R 6 , Bi R1 2 , u R 2 . The frameof TSFNNs is described as follows:

weighting coefficients associated with the ith rule for thevector u .
The output, y , of the predictor is a linear combinationof the consequent part. The weighted summation of
output y is given by
9

Layer 2 (membership function layer): In this layer, themembership values specifying the degree to which an
input value belongs to a fuzzy set is calculated. To
describe the linguistic terms, Gaussian function
representing fuzzy membership functions is adopted as( zi cij ) 2
(
z
)
exp
for i 1,2, j 1,2,3 , (3)iji
2 sij2where cij and sij denoted the center and width for the ith
input and the jth membership function.
Layer 3 (inference rule layer): Product inference method
is used to determine the firing strength of each node.
Each inference rule can be expressed as(4)
1,2,3, q 1,2,3 ,i
1p
2 q for p
Layer 4 (output layer): Each node represents an
individual output of the TSFNNs by using a simple
weighted summation. The connection weight wi of the ith
neuron in inference rule layer is defined as follows:
wi Ai x Bi u
(5)Tx x1 , x2 ,..., x6
denotedcodingpixel.
1
2
6 T
Ai ai , ai ,..., ai
can be regarded as predictor
TT
coefficients. u u1 , u2
x1 x3 , x2 x3 is used forThorizontal and vertical edge detection. Bi bi1 , bi2 is the
where

wi

i

( Ai x Bi u )

(6)

i 1

From (6), the output of the proposed TSFNNs-based1where z1 and z2 are represented horizontal offset andvertical offset, respectively.

ii 1where z1 and z2 denoted input variables. F1k andLayer 1 (input layer): This layer includes two inputs z1and z2 which are transmitted to layer 2. z1 and z2 aredefined as (2).
z [ z1 , z2 ]T [ x1 x3 , x2 x3 ]T
(2)

9

yi

predictor is evaluated in an un-normalized manner.
2.2. Optimization of TSFNNs
In the evolution procedure of designing optimal TSFNNs,a hybrid evolution technique is adopted to findappropriate parameters combinations. Each particlei (k ) can be denoted as (7).
i(k )Ai , Bi , cij , sij(7)

To adapt the parameters of the proposed TSFNNs
during the coding process, a fitness function is defined as
(8).
n

I

d

y

2

(8)

j 1

where d is the desired output and y is output of
TSFNNs.
3.

Hybrid Evolution-Based QPSO

In the PSO algorithm, each particle keeps trajectory of its
coordinates in the problem space. The coordinate of eachparticle is related to its own best position (local best
position) and the global best position achieved so far.
From the view of classical dynamics, to avoid
explosion and guarantee convergence, particles must bebounded and fly in an attractive potential field. Clerc andKennedy10 have proved that if these coefficients areproperly defined, the particle’s position pi will converge
to the center of potential field, pf c

[ pf1c , pf 2c ,and is defined as:( c1 r1 pil c2 r2 p g )pf i c, i 1,2,(c1 r1 c2 r2 ), pf nc ] ,, n . (9)where pil and p g are the best position of the ith particle
and the global best position© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 38

Image Compression Using Hybrid

Inspired by the behavior that particles move in abounded state and preserve the global search ability, Sunet al.11,12 proposed the QPSO algorithm. In the QPSO
model, particles move in a quantum multi-dimensional
space, the state of particles is usually depicted by anormalized wave function. That is, a single particle withm mass is subjected to the influence of a potential field inquantum space and the wave function of this particle isgoverned by the Schrödinger equation.12 The solution oftime-independent Schrödinger equation for this system in
one dimensional space can be expressed as:12
L1
pi pf i c
ln
,
(10)
2
where
is a random number uniformly distributed on[0, 1] and L is the characteristic length of delta potential
well (called "Creativity" of particles) which specifies thesearch scope of a particle. In order to improve
performance, Sun et al.13 employ a mainstream thought
point to evaluate the parameter. The mainstream thought
point and can be expressed as the following forms:
n

mbest

n

n

pi ,1
pi ,2
,
,
n i1 n
i 1

,

L

mbest

2

pi , n, i 1,2,
n
i 1

pi ,

reconstructed images, respectively.
Two typical 512 512 images named “LENA” and“BASKET” with pixel amplitude resolution of 8 bits are
given to evaluate the HEQPSO-TSFNNs. Different
codebooks with size 512 are implemented. We compare
HEQPSO with TVACPSO (particle swarm optimization
with time-varying acceleration coefficients).14
In experiments, the two algorithms are implemented andthen executed 10 times. The image compression andreconstruction are shown in Figs. 1-2. Meanwhile, Table
1 showed the average PSNR and CPU-T values using
HEQPSO-TSFNNs and TVACPSO-TSFNNs. From theTable 1, the superiority of the proposed HEQPSOTSFNNs for image compression was confirmed.
Table1 The average values of PSNR and CPU-T using
HEQPSO-TSFNNs and TVACPSO-TSFNNs.
Codbook
size

image
LENA
BASKET
LENA
BASKET, n, (11)
(12)

256
512

TVACPSOTSFNNs
PSNR
28.82
24.29
30.02
25.34

CPUT(sec)
72.69
67.06
122.66110.31HEQPSOTSFNNs
PSNR
29.02
24.77
30.06
25.79

CPUT(sec)
64.80
66.20
120.89105.35The creative coefficientwith adaptive annealinglearning mechanism according to the change rate of
optimal estimation has the form:
fit ,
(13)
max

fit
where
is step length of
optimal estimation so far.
4.

pg

pil ,
,

(14)

fit is the change rate ofExperimental Results

In this article, the encoded image quality of HEQPSOTSFNNs was evaluated using the peak signal-to-noise(PSNR) and computational time (CPU-T). The definitionof PSNR is expressed as follows:
255
PSNR 20 log
(15)
RMSE

RMSE

where

N

MSE
and f ( m, n ) andMSE

1f ( m, n )N 2 m ,n 1

Fig. 1. LENA image: (a) Original image, (b) Low
frequency image, (c) Vector quantization image,(d) Reconstructed image.

(16)fˆ ( m, n )

2

(17)

fˆ ( m, n ) denote the original and© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 39Chia-Nan Ko, Ching-I Lee

Fig. 2. BASKET image: (a) Original image, (b) Low
frequency image, (c) Vector quantization image,(d) Reconstructed image.

Acknowledgements
This work was supported in part by the Ministry of
Science and Technology, R.O.C., under grants MOST
105-2221-E-252-002.
References
1. H. Veisi and M. Jamzad, A complexity-based approach inimage compression using neural networks, Signal Processing,5(2009) 82–92.
2. D. Salomon and A Concise, Introduction to Data Compression,(Springer, New York, 2008).
3. K. Sayood, Introduction to Data Compression, 4th edn.
(Morgan-Kaufmann, New York, 2012).
4. L. J. Kau and Y. P. Lin, Least squares-based switching
structure for lossless image coding, IEEE Transactions on
Circuits and Systems I, 54(7)(2007) 1529–1541.

5. S. Sahami and M. G. Shayesteh, Bi-level image compression
technique using neural networks, IET Image Processing,
6(5)(2012) 496–506.
6. B. Aiazzi, L. Alparone and S. Baronti, Fuzzy logic-basedmatching pursuits for lossless predictive coding of still images,
IEEE Transactions on Fuzzy Systems, 10(4)(2002) 473–483.
7. C. H. Lee and M. H. Chiu, Adaptive nonlinear control using
TSK-type recurrent fuzzy neural network system, Lecture
Notes in Computer Science, 4491(2007) 38–44.
8. T. Takagi and M. Sugeno, Fuzzy identification of systems andits applications to modeling and control, IEEE Transactions on
System Man and Cybernetics, 15(1985) 116–132.
9. K. Tanaka and H. O. Wang, Fuzzy Control Systems Design andAnalysis: A Linear matrix inequality approach, (John Wiley &Sons, Inc., New York, 2001).
10. M. Clerc and J. Kennedy, The particle swarm: explosion,
stability, and convergence in a multidimensional complex
space, IEEE Transactions on Evolutionary Computation 1(2002) 68–73.
11. S. M. Mikki and A. A. Kishk, Quantum Particle Swarm
Optimization for Electromagnetics, IEEE Transactions on
Antennas and Propagation, 54 (2006) 2764–2775.
12. Y. Fu, M. Ding, and C. Zhou, Phase angle-encoded andquantum-behaved particle swarm optimization applied to threedimensional route planning for UAV, IEEE Transactions on
Systems, Man and Cybernetics, Part A: Systems and Humans
42 (2012) 511–526.
13. J. Sun, B. Feng and W.B. Xu, Particle swarm optimization withparticles having quantum behavior, In: Proceedings of IEEE
Congress on Evolutionary Computation, (2004) 325–331.
14. A. Ratnaweera, S. K. Halgamuge and H. C. Watson, Selforganizing hierarchical particle swarm optimizer with timevarying acceleration coefficients, IEEE Transactions on
Evolutionary Computation, 8(2004) 240–255.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 40MQPSO Algorithm Based Fuzzy PID Control for a Pendubot System
Li-Chun Lai

Computer and Intelligent Robot Program for Bachelor Degree, National Pingtung University
Yu-Yi Fu
Department of Automation Engineering, Nan Kai University of Technology

t098@nkut.edu.tw
Chia-Nan Ko

Department of Automation Engineering, Nan Kai University of Technology
t105@nkut.edu.tw

Abstract
In this study, a modified quantum-behaved particle swarm optimization (MQPSO) approach is proposed to design
an optimal fuzzy PID controller for asymptotical stabilization of a pendubot system. In the fuzzy PID controller,
parameters are determined by using MQPSO algorithm. The MQPSO method and other PSO methods are thenapplied to design an optimal fuzzy PID controller in a pendubot system. Comparing the simulation results, thefeasibility of the MQPSO is verified.
Keywords: modified quantum-behaved particle swarm optimization, fuzzy PID controller, pendubot system

1.

Introduction

Most industrial processes nowadays are still controlled by
PID controllers. However, a conventional PID controller
may have poor control performance for nonlinear and/or
complex systems that have no precise mathematicalmodels. In Keel et al1 and Cervantes et al2, how to tune the
PID controller based on mathematical models is proposed,
but complex mathematical computation is generally
required in tuning procedures. For the methods in
Whidborne and Istepanian3 and Lin et al,4 since the PID
gains are fixed, the main disadvantage is that they usuallylack in flexibility and capability.
Fuzzy controllers provide reasonable and effective
alternatives for conventional controllers. Many researchersattempted to combine conventional PID controllers withfuzzy logic.5 Despite the significant improvement of thesefuzzy PID controllers over their classical counterparts, it
should be noted that they still have disadvantages.
The PSO algorithm possesses the ability of highconvergent speed, easily falling in some local optima is its
fatal defect. Many researchers have presented revised PSO
algorithms and obtained good results.6 Anotherimprovement on traditional PSO algorithm is quantumbehaved particle swarm optimization (QPSO).7 However,in QPSO, particles fall into local optimal state inmultimode optimization problems and cannot find any
better state. To overcome the premature phenomenon in
QPSO, a modified quantum-behaved particle swarm
optimization (MQPSO) is proposed to find an optimal PID
fuzzy controller. To show the flexibility and capability ofthe proposed method, an underactuated pendubot system
is adopted as an illustrative example.
2.

Fuzzy PID Controllers

In the proposed fuzzy PID controller, the input variablesof the fuzzy rules are the error signals and their derivatives,
while the output variables are the PID gains. Asymmetric
Gaussian functions are used as the antecedent fuzzy sets inthis paper. This means that input membership functions
are represented as© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 41

Li-Chun La, Yu-Yi Fu, Chia-Nan Ko

X

mk
k

exp

xk

exp

xk

mk
k

2

mk
kl

( xk )

mk
k

if xk

mk
k

if xk

mk
k

2

mk
kr

for

k

1

m1

n1 , 1m2

n2 , 1m3

n3 , 1

m4

1,2,

n4

where xk represents the input linguistic variables,
mk
kl

, and

mk
kr

,4,

(1)
mk
k

,

denote the values of the centers, the left

widths, and the right widths of the input membershipfunctions, respectively. For the output membershipfunctions, singleton sets are adopted. In the defuzzification
process, Wang used the center of gravity method todetermine the output crisp values.8 Then, if the PID
control law is used, the control signal is determined asu(t )k p1e1 (t ) k I 1 e1 (t )dt k D1e1 (t )The creative coefficientwith adaptive annealinglearning mechanism according to the change rate of
optimal estimation has the form:
fit ,
(7)
max

fit

(2)

k p 2 e2 (t ) k I 2 e2 (t )dt k D 2 e2 (t )From the above description, one can find that the gainsof the fuzzy PID controller are adaptive such that thecontroller should have more flexibility and capability thanthe conventional ones. The MQPSO method is proposed tosearch for the optimal values of these parameterssimultaneously.
3.

the solution of time-independent Schrödinger equation forthis system in one dimensional space can be expressed
as:10
L
1
pi pf i c
ln
,
(4)
2
where
is a random number uniformly distributed on [0,1] and L is the characteristic length of delta potential well(called "Creativity" of particles) which specifies the searchscope of a particle. The mainstream thought point and canbe expressed as the following forms:8
n p
n p
n p
i ,1
i,2
i ,n
mbest
,
, ,
, i 1,2, , n, (5)
i 1 n
i 1 n
i 1 n
L 2 mbest pi ,(6)

Modified Quantum Particle Swarm
Optimization

From the view of classical dynamics, to avoid explosion
and guarantee convergence, particles must be bounded and

pg

pil ,

where
is step length of , fit is the change rate of
optimal estimation so far. The mechanism of adaptive
annealing learning can overcome the stagnation problem
to accelerate the convergent speed.
4.

An Simulation Example

4.1. The Pendubot System
The general dynamic model of underactuated mechanisms
with m actuated joints from a total of n joint can be
expressed as follows (Spong and Vidyasagar) :11
M ( q ) q C ( q , q ) q G (q )(9)nfly in an attractive potential field. Clerc and Kennedy6

where qhave proved that if these coefficients are properly defined,

M (q) denotes the inertia matrix, C (q, q )qthe particle’s position pi will converge to the center ofpotential field, pf c

[ pf1c , pf 2c ,, pf nc ] , and is defined(8)

R is the position vector indicating link angles,n

R is thevector of damping, coriolis, and centrifugal torques,nnc2 r2 p g )G (q ) R represents the gravitational term andR isthe vector of control torque which has ( n m) zerocomponents.
For the pendubot system in Fig. 1, let m1 and m2

and the global best position; c1 and c2 are cognitive anddenote the distributed mass of the actuated link (called
link 1) and the unactuated link (called link 2), respectively.
Meanwhile, let q1 and q2 denote the angles of the twoas:pf i c( c1 r1 pil, i 1,2, , n .
(3)(c1 r1 c2 r2 )where pil and p g are the best position of the ith particlesocial constriction coefficients, respectively; r1 and r2 are
random numbers between 0 and 1.
Inspired by the behavior that particles move in abounded state and preserve the global search ability, Sunet al.9 proposed the QPSO algorithm. In the QPSO model,

links, l1 and l2 denote the lengths of the two links, andl1c and l2c denote the distances to the center of masses,
respectively.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 42MQPSO Algorithm Based Fuzzyypendubot system are [ q1 , q1 , q2 , q2 ]and [ q1 , q1 , q2 , q2 ]

q2

l2

q1

x

1

Fig. 1. Dynamics of the pendubot system
When the configuration is at equilibrium state; that is,pendubot balances at a state q 0 and q 0, thefollowing can be derived from (10).
( m1 glc1 m2 gl1 ) cos q1 m2 glc 2 cos( q1 q2 )
(10)
1

q2 )

0

(11)

4.2. MQPSO tuning Fuzzy PID Controller
In the pendubot system, the desired value of q1 (t ) andq2 (t ) are denoted by q1d and q2 d . If the PID control law
is employed, then the input-output relation of the pendubot
system is expressed as

(t )

where

e1 ( t )q1d

k P 1e1 (t ) k I 1 e1 (t ) dt

k D1e1 (t )k P 2 e2 (t ) k I 2 e2 (t ) dt

k D 2 e2 (t )e1 ( t )e2 ( t )q1d

q1 ( t ),q1 ( t ), and e2 ( t )q2 d2 , 0, 0, 0]

2 , 0, 0, 0]. Meanwhile, the input

torque (t ) of the motor is assumed to be within the range

l1

m2 glc 2 cos( q1

[

[

(12)

q2 d

q2 ( t ),q2 ( t ).

[ 10 Nm, 10 Nm] .
In the proposed algorithm, the population size, themaximal iteration number, the crossover rate, andmutation rate are chosen to be 20, 10000, 0.8, and 0.2,
respectively. Moreover, it is assumed that the values of thesingletons of the output linguistic variables are all chosen
as real numbers in the range [ 10, 10].
In PID tuning techniques, we proposed MQPSO method
to design a fuzzy PID controller to asymptotically stabilize
the pendubot and maintain the equilibrium state over all
control processes. From Figs. 2 and 3, the illustration
results demonstrate the proposed MQPSO fuzzy PID
controller has good performance for asymptoticalstabilization of the pendubot system.

6.

Conclusions

In PID tuning techniques, the PID gains are difficult toobtain the optimal values for stabilizing a pendubot
system. In this paper, we proposed MQPSO to design afuzzy PID controller to asymptotically stabilize thependubot and maintain the equilibrium state over all
control processes. From the simulation results, onedemonstrates the proposed fuzzy PID controller has good
performance for asymptotical stabilization of the pendubot
system.
Acknowledgements

4.3. Fitness

This work was supported in part by the Ministry of
In designing the fuzzy PID controller, the primary goal is Science and Technology, R.O.C., under grants MOST
to drive a pendubot system from the given initial state to 105-2221-E-252-002.
the desired final state. The performance criteria can be
included in the fitness as follows:
Referencesft e12 (t ) e22 (t ) dt
(13)
1. L. H. Keel, J. I. Rego and S. P. Bhattacharyya, A new
approach to digital PID controller design, IEEE Transactions
From the definition (14), the fitness value can beon Automatic Control 48(4)(2003) 687-692.
calculated to evaluate the performance of the fuzzy PID
2. I. Cervantes, R. Garrido and A. R. Jose, et al, Vision-based
controller and a lower fitness value denotes a better
PID control of planar robots, IEEE Transactions on
performance.
Mechatronics 9(1)(2004) 132-136.
3. J. F. Whidborne and R. S. H. Istepanian, Genetic algorithm
5. Simulation Results
approach to designing finite-precision controller structures,
IEE Processing of Control Theory Applications 148(5)(2001)
The parameters of the pendubot system shown in Fig. 1
377-382.
4. L. Lin, H. Y. Jan and N. C. Shieh, GA-based multiobjective
are chosen as m1 2.0 kg, m2 1.5 kg,
l1 0.3m,
PID control for a linear brushless dc motor, IEEE2
l2 0.5m, l1c 0.15m, l2 c 0.25m, and g 9.8 m/s .
Transactions on Mechatronics 8(1)(2003) 56-65.

The initial state and the desired final state of the pendubot© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 43

Li-Chun La, Yu-Yi Fu, Chia-Nan Ko

5. C. W. Tao and J. S. Taur, Robust fuzzy control for a plantwith fuzzy linear model, IEEE Transactions on Fuzzy
Systems 13(1)(2005) 30-41.
6. A. Ratnaweera, S. K. Halgamuge, and H. C. Watson, Selforganizing hierarchical particle swarm optimizer with timevarying acceleration coefficients, IEEE Transactions on
Evolutionary Computation 8 (2004) 240-255.
7. M. Clerc and J. Kennedy, The particle swarm: explosion,
stability, and convergence in a multidimensional complex
space, IEEE Transactions on Evolutionary Computation 1(2002) 68-73.
8. L. X. Wang, A Course in Fuzzy Systems and Control,(Prentice-Hall, New Jersey, 1997).
9. J. Sun, B. Feng and W.B. Xu, Particle swarm optimization
with particles having quantum behavior, In: Proceedings ofIEEE Congress on Evolutionary Computation, 2004. pp.
325-331.
10. C. N. Ko, Y. M. Jau and J. T. Jeng, Parameter Estimation of
Chaotic Dynamical Systems Using Quantum-behaved
Particle Swarm Optimization Based on Hybrid Evolution,
Journal of Information Science and Engineering 31(2)(2015) 675-689.
11. M. W. Spong and M. Vidyasagar, Robot dynamics andcontrol, (Wiley, New York, 1989.)

Fig. 2. Plot of angle q1 (t ) of the pendubot system.

Fig. 3. Plot of angle q 2 (t ) of the pendubot system.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 44

A Sensor-less Ultra-high Speed Motor Driver
Chung-Wen Hung*, Yan-Ting Yu, Bo-Kai Huang and Wei-Lung Mao
Department of Electrical, National Yunlin University of Science and Technology
123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan, R.O.C.
*wenhung@yuntech.edu.tw
www.yuntech.edu.tw

Abstract
An ultra-high speed motor is important in machine tools. A speed sensor-less ultra-high speed motor driver is
proposed in this paper. Due to cost, only few drivers build up a closed-loop control, which could improve driver’s
performance. Therefore, a sensor-less speed estimation for ultra-high speed motors is developed. The design andimplementation of the driver and feedback circuits are detailed in this paper. And experimental results show that the
proposed system is workable.
Keywords: Ultra-high Speed Motor, Speed sensor-less Control, Motor Driver, VVVF Control[3] used fuzzy rules to determine the VVVF commandand increased efficiency. [4] proposed a slip frequency
compensation method to decrease the speed error. [5]discussed two vector control methods: field orientation
control, FOC, and direct torque control, DTC. The torqueresponds of both controls are better than scalar control.
Complex calculations are necessary in above controls, sothey are not suitable for ultra-high speed motor. For
speed estimation, [6] described two methods. Model
Reference Adaptive System, MRAS, could be used wider
speed range but too sensitive to motor parameters.
Another method, Least Square Method, LSM, calculated
speed by iteration, but only suitable for narrow speed
range. In this paper, an easier speed estimation method isused: the motor slip speed is calculated with the motor
model, and then motor speed is estimated by taking
induction magnetic field speed to minus the slip angle.

Introduction
Ultra-high speed motors are important in machine tools,
and most motors are induction motor, IM, because of its
high reliability and low cost. There are some studies
discussed about driver method, such as variable voltage
variable frequency, VVVF, and vector control; and some
focused on control method including proportionalintegrated (PI) control, fuzzy control and neural network.
Due to cost, only few drivers build up a closed-loop
control, which could improve driver’s performance.
Therefore, a sensor-less speed estimation for ultra-highspeed motors is developed.
However, only few literatures discussed about ultra-highspeed motors. [1] optimized the best driver efficiency
based on the total harmonic distortion (THD) of motor
current with auto-adjust driver voltage. In [2], an ultrahigh speed soft switch inverter was developed to improve
driver efficiency. Both proposed new driver methods.
Conventional IM driver methods are open-loop scalar
control, VVVF, due to easy implement. However, some
papers tried to use closed-loop control.

Slip speed estimation
The calculation of slip speed is based on rotor flux fieldorientation control. It can simplify the motor model byreferring to rotor flux filed. The derivation is described© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 45

Chung-Wen Hung, Yan-Ting Yu, Bo-Kai Huang, Wei-Lung Mao

as (1) - (5). The equation of calculating the slip, (4), canbe derivate by substituting that flux on the q-axis is zero
into (3). In (4), there is a variable flux on d-axis. It can becalculate by (5) using the current on d-axis.
rrr
 r  Lm i s  Lr i r .
1rrr
 ir 
( r  Lm i s ).
Lrdefined as (7). In Fig 1, (4) and (5) are performed toestimate slip speed and the rotor flux on d-axis. The
inputs of these two blocks are stator current referring therotor flux.
e

R r
r
r
Rr ids - r  dr   sl qr .
dt
Lr
Lr
L
d r
R r
r
r
 qr  m Rr iqs - r  qr   sl dr .
dt
Lr
Lr
r

 dr 

in Fig 1. The r is calculated by (6). And the  is

} (1)

d r
r
r
r
Vr  0  Rr ir   r  j sl  r .
dt
R
Lm
d r
r

r 
Rr i rs  ( r  j sl ) r .
dt
Lr
L
r

d

The block diagram of estimator the slip speed is shown

Vs
Is

Ia
Ib

Lm ：

rotor flux referrotor fieldrotor flux on the
q-axis refer rotor
field
mutualinductance sl ：

slip speedr qr ：

r
is ：

r
ids ：

stator current on
rotor flux
stator current on
d-axis refer rotor
field flux

(3)

r
i ds

Calculate the rotor
flux on d-axis

(6)

2

}

L
  1  ( m ).

(7)

Ls Lr

where
r ：

(4)

is ：

rotor flux
stator current

Vs ：
Ls ：

stator voltage
stator inductance

Scalar control}

Scalar control drives motor by suitable waveform. In Fig
2, the curve of speed to voltage for a 200Krpm motor isprovided by the motor manufactory. To drive motor in
30Krpm, the inverter should provide a three-phasesinusoidal 57 volts and 500Hz wave. However, the realrotor speed is different to the driving speed due to slip
speed. Therefore, a PI controller is used in the proposedsystem to compensate the speed error caused by slip
speed. As shown in Fig 3, the input of controller is the
speed error, and the output is the index of the VVVF table
to set inverter.

(5)

}

r

flux on the d-axis
refer rotor fieldRr ：rotor resistor

Lr ：rotor inductances

r
Vr ：

rotor
voltagerefer rotor fieldrotor current referrotor fieldstator current on
q-axis refer rotor
field flux

r
iqs ：

r

±

Fig 1. Block of slip speed estimator dr ：

r
ir ：

slip

r
dr

L
d
r   r (Vs  Rs is  Ls is )dt.
Lm
dt

where
r

a-b-c axis tod-q axis

Lm

L
R r
r
r
r
 qr  m Rr iqs - r  qr   sl dr .
dt
Lr
Lr
Lm
r
r
0
Rr iqs   sl dr .
Lrr
Lm Rr iqs
  sl r .
Lr  dr
L
d r
R r
r
r
 dr  m Rr ids  r  dr    qr .
sl
dt
Lr
Lr
Lm
d r
R r
r
  dr 
Rr ids  r  dr .
dt
Lr
Lr
r
 dr
Lm
 r 
.
Lr
ids
s 1
Rr

Slip speed
calculating

r

} (2)

d

r ：

Calculate therotor flux angleri qs

Motor parameter measurement
The IM’s electric parameters are a key in slip speed
estimation. Three parameter measuring tests are used inthis paper. First, DC test is used to measure the stator
resistor. Second, stall test could measure the sum of statorand rotor resistor, and the sum of stator and rotor leakage
inductance. Then the rotor resistor can be got, and thestator and rotor leakage inductances, 𝐿𝑙𝑠 and 𝐿𝑙𝑟 , areassumed equal. Third, no load test is adopted for thestator inductance, and then the mutual inductance could
be derived. A virtual stall test is adopted, because a
traditional stall test is not convenient due to extra stalling
equivalent. Fig 4 is the wiring diagram of virtual stall test.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, Japa

P - 46

A Sensor-less Ultra-high Speed

generate driving signals for the inverter circuit. The
inverter circuit converts DC voltage to AC driving
waveform.
 r*
e

 r'

Speed cntroller±

V & freq

VVVF tablerM

Driver
Switch-State±

Slip speed
calculatingslip

Angle of rotor
magnetic fieldcalculating

Voltage andcurrent
feedback

Bandpass
filter

Is

 r

r
dr

Current tomagnetic field

Vdc

Vs

r
I qs

Ia Ib

a-b-c to d-qr
I ds

Fig 5. Control block diagram of system
Fig 2. Speed-Voltage characteristic curve of 200Krpm motor
 r*
±

Speed
controller

 r'

7

VVVF
table

V

Terminal

Inverter

freq

r

SCI

ePWM

SwitchdriverMCU

DC BUS4

ADC

Fig 3. Block of scalar controlACsourceM

Six
Switches

2

M

A

InverterFeedback
circuitFig 6. System architecture diagram

P1

Fig 7 is the photo of the system proposed in this paper.
At upper left corner is TMS320F28335, lower left corner
is an isolated power board, middle is inverter, lower
middle is the current feedback circuit and lower rightcorner is the DC BUS feedback circuit.

M

Fig 4. Wiring diagram of virtual stall test

In this paper, two ultra-high speed motors are installed inexperiments. The parameters of 200Krpm and 300Krpm
motors are measured and shown in table 1.
Table 1. Parameters of 200Krpm and 300Krpm motor
Motor
𝑅𝑆
𝐿𝑙𝑠
𝑅𝑟
𝐿𝑙𝑟
𝐿𝑚
200K
1Ω
0.43mH 2.84Ω 0.43mH 5.22mH
300K
1.78Ω 0.21mH 4.33Ω 0.21mH 2.99mH

System architecture and hardware implementation
The control block diagram of system is shown in Fig 5.
Scalar control is used in the speed controller. A PI
controller is adopted to create the speed command whichis the index of the voltage-frequency curve. Here, lookup
table and interpolation method is used to index thesuitable voltage and frequency commands for the inverter.
And, the feedback speed is calculated by speed ofsynchronization field and the slip speed computed fromcurrent and voltage.
Fig 6 presents the system architecture diagram. The
feedback circuit is used to sense the motor current andDC BUS states for speed estimation. The microcontroller
is applied to speed estimation and speed control, andFig 7. System photo

Experimental results
Two experiments are shown in this paper: step and ramp
acceleration. Drilling function is performed when the
motor achieves the rated speed, but no more speed
control, due to the fast working moment. Controller ismainly used to accelerate. The experimental power
supply is limited, so step range and ramp acceleration of

Table 2. Step and ramp acceleration of 200Krpm motor
Speed command(rpm)Improved speed error(rpm)Percentage error(%)
Improved stability time(s)

40000
44
0.11
0.281

Step
50000
94
0.18
0.228

60000
132
0.22
X

100000
940
0.94
0.34

Ramp acceleration
120000
1600
1.33
0.9

160000
4900
3.06
1.43

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 47

Chung-Wen Hung, Yan-Ting Yu, Bo-Kai Huang, Wei-Lung Mao

speed are limited. Fig 8 shows the experimental results
under 100Krpm ramp command.

Fig 9. The error between estimated and measurement speed of
200Krpm motor
Fig 8. The experimental results of 100Krpm ramp

The experimental results are shown in table 2, and theimproved numbers of the proposed controller arecompared with an open-loop driver. The speed error is
reduced and proportional to speed command, and thenstability time is improved in 0.2 to 1 seconds. Note,
proposed controller experimental results of the 60Krpm
step are not shown in this table, because the required
inrush current is over the capability of power supply.
In order to verify the proposed method is suitable fordifferent motors, the other motor, rated 300Krpm, isadopted in the experiment. The speed errors of different
commands in a 200Krpm motor are shown in Fig 9 and300Krpm motor in Fig 10. Though the errors of 300Krpm
motor are bigger than 200Krpm’motors, but the trends in
two figures are the same. And error rate of 200Krpm
motor is low than 1.5%, the same as 300Krpm motor is
low than 3.5%.

Fig 10. The error between estimated and measurement speedof 300Krpm motor

Reference[1]

C. H. Yang, “The automatically switching hybrid
modulation driver for an ultra-high speed motor”,
National Digital Library of Theses and Dissertations in
Taiwan, 2013.

[2]

B. F. Li, ”The Research and Implementation of an Ultrahigh Speed Soft Switch Inverter”, National Digital
Library of Theses and Dissertations in Taiwan, 2014.
J. Li and Y. R. Zhong, “Efficiency Optimization ofInduction Machines Based on Fuzzy Search Controller”,
IEEE International Conference on Machine Learning
and Cybernetics, Vol. 4, pp. 2518 – 2522, Aug. 2005.
M. Tsuji, S. Chen, S. Hamasaki, X. D. Zhao and E.
Yamada, “A Novel V/f Control of Induction Motors for
Wide and Precise Speed Operation”, SPEEDAM 2008
International Symposium on Power Electronics,
Electrical Drives, Automation and Motion, pp. 1130 1135, Jun. 2008.
J. A. Santisteban and R. M. Stephan, “Vector Control
Methods for Induction Machines: An Overview”, IEEE
Transactions on Education, Vol. 44, pp. 170 – 175, Aug.
2002
M. Bodson and J. Chiasson, “A Comparison of
Sensorless Speed Estimation Methods for Induction
Motor Control”, American Control Conference on
Proceedings, Vol. 4, pp. 3046 - 3081, May 2002

Conclusion[3]

As shown in experimental results, the motor with theproposed ultra-high speed motor driver can fast run
stability. Though speed error is proportional to speedcommand, speed estimator can effectively estimate
motor speed and react load fluctuation, and speed error
rate is low than 1.5%. Closed-loop controller actually
improves system responding time by low cost feedback
circuit. The controller proposed in this paper is also
suitable for different motors.

[4]

[5]

Acknowledgment:This work is partially supported by the Ministry of
Science and Technology, ROC, under contract No.
MOST 105-2221-E-224-024 and 104-2622-E-224-016 CC3.

[6]

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, Japa

P - 48

Android-Based Patrol Robot Featuring
Automatic Vehicle Patrolling and Automatic Plate Recognition
Chian C. Ho*, Shih-Jui Yang, Jian-Yuan Chen, Chang-Yun Chiang, and Hsin-Fu Chen

Department of Electrical Engineering, National Yunlin University of Science and Technology
Douliou, Yunlin 64002, Taiwan
E-mail: futureho@yuntech.edu.tw*
www.yuntech.edu.tw

Abstract
This work develops an Android-based patrol robot featuring Automatic Vehicle Patrolling (AVP) and Automatic
Plate Recognition (APR). The AVP feature integrates 3 novel methods, wheel-wheelcover-based AdaBoost wheel
detection, contour-wheel-oriented vehicle approaching, and Ad-Hoc-based remote motion control. The APR feature
integrates 4 novel methods, Wiener-deconvolution vertical edge enhancement, AdaBoost plus vertical-edge plate
detection, vertical-edge horizontal-projection histogram-segmentation stain removal, and customized opticalcharacter recognition. Implementation results show the vehicle detection rate and plate recognition rate of theAndroid-based patrol robot are over 96% and over 94%, respectively, under various scene conditions. On the other
hand, the average execution time of AVP and APR of the Android-based patrol robot takes at most 8 second perround and at most 0.8 second per frame, respectively.
Keywords: Android, robot, automatic vehicle patrolling, automatic plate recognition.

1. Introduction
With the ever-increasing demand of anti-terrorism andpublic security worldwide, the global law enforcement
has severely been fighting against stolen vehicles orvehicles hung with stolen vehicle plates so far. Because
these vehicles, both automobiles and motorcycles, aremost likely used for terrorist activities or criminal
vehicles. In addition, these vehicles are always parking inor running from unimaginable corners. It is necessary forthe global law enforcement to carry out the vehicle plate
investigation anywhere, anytime, even under exhausted
manpower condition. Therefore, the automatic patrolrobot equipped with Automatic Plate Recognition (APR)
functionality can satisfy the growing demand and gain
the expanding attention.
On the other hand, global digital video surveillance
manufacturers are unexceptionally interested in themobile robotic APR technology so as to evolve theexisting fixed APR systems to portable embedded ones

Fig. 1. Mechanism of Android-based patrol robot featuring
AVP and APR.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 49Chian C. Ho, Shih-Jui Yang, Jian-Yuan Chen, Chang-Yun Chiang, and Hsin-Fu Chenor mobile robotic ones. Thus this work develops theAndroid-based patrol robot offering accurate APR andreliable Automatic Vehicle Patrolling (AVP) motion
control for this emerging demand. It is composed of anAndroid-based smartphone platform and an Ad-Hocbased mobile robot as shown in Fig. 1, and the Androidbased smartphone platform as the host is steadily
mounted onto the Ad-Hoc-based mobile robot.
In this work, the motion control of the Ad-Hoc-based
mobile robot is actually steered by the Android-based
smartphone platform through peer-to-peer Ad Hoc
networking, while the AVP feature is routinely began by
the Ad-Hoc-based mobile robot. As soon as the Androidbased smartphone platform detects the vehicle contour,
vehicle wheel, or vehicle plate, the Ad-Hoc-based mobile
robot will be remotely controlled to approach the vehicle
and further approach the plate. As for the APR feature, it
is still performed by the Android-based smartphoneplatform, especially by the built-in auto-focus camera
and low-power embedded processor, for long-endurance
and cost-effective requirements. Besides, it is also a
critical consideration that plenty of open-source
computer vision libraries, like OpenCV Tesseract OCR,
etc., are increasingly ported and supported on Android
operating system [1].
This work can meet both specifications of portableembedded and mobile robotic APR systems, and it canbe further transformed into any ubiquitous and diverse
devices. This work not only can be applied to stolen
vehicle plate tracking and roadside inspection, but also
can be extensively applied to parking lot patrolling,
container logistic investigation, or automotive
manufacturing management.
Fig. 2 illustrates the overall algorithm flowchart ofthe developed Android-based patrol robot featuring AVP
and APR. The flowchart in Fig. 2 is made up of 2 mainstages. 1) Automatic Vehicle Patrolling (AVP) and 2)
Automatic Plate Recognition (APR). Referring to Fig. 2,
the organization of this paper is as follows. Sections 2
and 3 describe AVP feature and APR feature,
respectively, in detail. Here, Section 2 is divided into 3
subsections: wheel-wheelcover-based AdaBoost wheel
detection, contour-wheel-oriented vehicle approaching,and Ad-Hoc-based remote motion control. Section 3 isdivided into 4 subsections: Wiener-deconvolution
vertical edge enhancement, AdaBoost plus vertical-edge
plate detection, vertical-edge horizontal-projection
histogram-segmentation stain removal, and customized

Fig. 2. Algorithm flowchart of Android-based patrol robot
featuring AVP and APR.

Optical Character Recognition (OCR). Implementation
results about the vehicle detection rate and the plate
recognition rate improved by these proposed methods in
Sections 2 and 3 are also exhibited, and so is theexecution time of the overall implementation. Finally,
Section 4 draws conclusions and future work.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 50Android-Based Patrol Robot Featuring

Fig. 3. Algorithm flowchart of developed AVP feature.
closely enough, as shown in Fig. 3, whether the Androidbased patrol robot is located on the frontal/rear side of thevehicle or lateral side of the vehicle. When the robot ison the frontal/rear side of the vehicle, the plate detection
or frontal/rear vehicle detection is easy andstraightforward. However, when the robot is on thelateral side of the vehicle, no plate can be detected, andlateral vehicle detection depends upon nothing but
ambiguous features of wheel or lateral vehicle contour.
Thus, this work proposes Wheel-wheelcover-based
AdaBoost wheel detection based on wheel feature andunique wheelcover feature, and verifies the proposed
wheel detection achieves better detection rate thanconventional wheel detection methods, like Hough
Transform wheel detection [3]-[5], Histogram ofOriented Gradients (HOG) wheel detection [6], [7], and
AdaBoost wheel detection [8], [9].

2. Automatic Vehicle Patrolling Feature
As shown in upper half part of Fig. 2, in the stage of AVP,
autonomous patrol is initially launched by the mobile
robot according to the predefined patrolling point andpath planning. Next, the Android-based smartphoneplatform keeps working on the vehicle/plate detection to
search the vehicle/plate candidates. Once one or more
vehicle/plate candidates are found, the Android-based
smartphone platform will evaluate the relative distanceand the visual pose angle of the nearest vehicle/platecandidate, and will drive the mobile robot through Ad
Hoc networking to approach the nearest vehicle/plate
until the plate is close enough, that is, the character on theplate is clear enough to be recognized.
Because reliable AVP is the fundamental step toward
the developed Android-based patrol robot, especiallywhen vehicle/plate patrolling, searching, andapproaching are proceeding under various conditions ofillumination, scene, perspective, etc. This work proposes
3 novel methods for vehicle/plate detection andapproaching, and implements peer-to-peer Ad Hoc
networking mode for remote motion control [2]. The
proposed 3 novel methods are summarized as follows.

2.2. Contour-wheel-oriented vehicle approaching
Once the Android-based patrol robot can smoothly detectvehicle plate or frontal/rear vehicle contour, the robot
simply moves ahead to approach the vehicle/plate. But,
if the Android-based patrol robot detects the vehicle
wheel/wheelcover or lateral vehicle contour, it mustmove ahead and turn around to face the frontal/rear
vehicle side, and then must go through theaforementioned steps of the frontal/rear vehicle side

2.1. Wheel-wheelcover-based AdaBoost wheel
detection
This work develops an accurate and reliable AVP toapproach the vehicle and further approach the plate© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 51

Chian C. Ho, Shih-Jui Yang, Jian-Yuan Chen, Chang-Yun Chiang, and Hsin-Fu Chen

again for approaching the vehicle and further
approaching the plate, illustrated as shown Fig. 3.

those of the background, like radiator grilles, aresimultaneously weakened.

2.3. Ad-Hoc-based remote motion control

3.2. AdaBoost plus vertical-edge plate detection

At first, the WiFi driver of the Android-based
smartphone platform is elaborately switched from Access
Point mode to Ad Hoc mode. Then, the WiFi mode of themobile robot is also switched from Access Point mode to
Ad Hoc mode, and makes the mobile robot log into the
Ad Hoc network whose Service Set IDentifier (SSID) is
broadcasted by the Android-based smartphone platform.
Finally, after the mobile robot registers an IP address to
the Ad Hoc networking gateway on the Android-based
smartphone platform successfully, the Android-based
smartphone platform can request any motion control
commands to steer the Ad-Hoc-based mobile robot
remotely.

Although AdaBoost cascaded classifier with proper andadequate training of vehicle plate patterns is almost
feasible for exact plate detection, this work proposes two
auxiliary methods to make plate-based AdaBoost plate
detection more accurate and more reliable under various
scene conditions. One method is character-based
AdaBoost plate verification to filter out the false positive
outcomes of plate-based AdaBoost plate detection by
polling, and the other method is vertical-edge-basedalternative plate detection which is activated when noplate candidate can be found by plate-based AdaBoost
plate detection and character-based AdaBoost plate
verification.

3. Automatic Plate Recognition Feature

3.3. Vertical-edge horizontal-projection
histogram-segmentation stain removal

After approaching some nearest vehicle and plate quite
closely, APR feature is engaged in detecting andrecognition the vehicle plate, as shown in lower half partof Fig. 2. this work proposes 4 novel methods, Wienerdeconvolution vertical edge enhancement, AdaBoost
plus vertical-edge plate detection, vertical-edge
horizontal-projection histogram-segmentation stain
removal, and customized OCR, for better plate detection
rate and character recognition rate. These 4 novel
methods are clarified in order as below.

The localized vehicle plate candidate is inevitably
accompanying with some border stains, so thesubsequent OCR procedure tends to misunderstandborder stains as characters and is prone to makerecognition mistakes. It is easier to remove interior stains
on the vehicle plate because interior stains are similar tosalt, and pepper noise can be easily eliminated bymathematical morphology. But it is quite harder toremove trapezoid border stains residing at the right side
and left side, and interlinking border stains at the upsideand downside. In this work, vertical-edge horizontalprojection histogram-segmentation stain removal basedon fill-in and connected component labeling is proposed
to effectively remove trapezoid border stains at the rightside and left side, and interlinking border stains at theupside and downside.

3.1. Wiener-deconvolution vertical edge
enhancement
In general, most vehicle plate detection systems make useof the vertical edge density feature to discriminate thevehicle plate candidate region from the background of thescene image, because most characters on the vehicle plate
have higher vertical edge density than the background. Ifsome vertical edge enhancement preprocessing method is
put on the original scene image in advance, vehicle plate
detection will work better. Because Wiener convolution
is originally used for image blurring, this work proposes
to deconvolute the scene image with a horizontaldirection Wiener filter for vertical edge enhancement.
The experimental result shows the vertical edge density
and intensity of the vehicle plate are strengthened by ahorizontal-direction Wiener deconvolution method, and3.4. Customized optical character recognition
Because Tesseract OCR open-source library is originallyused for OCR of general documents, not for OCR ofvehicle plates, this work elaborately retrains the fontcharacteristics of each character on real Taiwanese
vehicle plate into the character dictionary of Tesseract
OCR [10]. Then, through Java Native Interface (JNI)
technique, this work ports vehicle-plate-customized© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 52

Android-Based Patrol Robot Featuring

4. Conclusions and Future WorkIn this work, the Android-based patrol robot featuring
mobile AVP and real-time APR already can work well.
In the near future, more smart AVP features and efficient
APR features will continuously be developed andintegrated into the Android-based patrol robot.
References
1. Google, Android Developers, [Online]. Available:
http://developer.android.com/index.html
2. WowWee, API Specification for Rovio Version 1.ˇ3,[Online].
Available:
http://breckon.eu/toby/teaching/dip/rovio/Rovio_API_Sp
ecifications_v1.3.pdf
3. F. Tan, L. Li, B. Cai and D. Zhang, Shape Template Based
Side-View Car Detection Algorithm, Proceedings ofInternational Workshop on Intelligent Systems andApplications (ISA), pp. 1-4, 2011.
4. V. Vinoharan, A. Ramanan and S. R. Kodituwakku, A
wheel-based side-view car detection using snake
algorithm, Proceedings of IEEE International Conferenceon Information and Automation for Sustainability, pp.
185-189, 2012.
5. G. Xu, J. Su, H. Pan and D. Zhang, A Novel Method forWheel Rim Recognition, Proceedings of International
Symposium on Electronic Commerce and Security, pp.
1051-1054, 2008.
6. R. Brehar, S. Nedevschi and L. Dăian, Pillars detection forside viewed vehicles, Proceedings of IEEE International
Conference on Intelligent Computer Communication andProcessing, pp. 247-250, 2010
7. L. Liu, F. Zhou and Y. He, Vision-based fault inspectionof small mechanical components for train safety, IET
Intelligent Transport Systems, vol. 10, no. 2, pp. 130-139,
Feb. 2016.
8. D. Dooley, B. McGinley, C. Hughes, L. Kilmartin, E.
Jones and M. Glavin, A Blind-Zone Detection Method
Using a Rear-Mounted Fisheye Camera With
Combination of Vehicle Detection Methods, IEEE
Transactions on Intelligent Transportation Systems, vol.
17, no. 1, pp. 264-278, Jan. 2016.
9. L. Liu, F. Zhou and Y. He, Automated Visual Inspection
System for Bogie Block Key Under Complex Freight
Train
Environment,
IEEE
TransactionsonInstrumentation and Measurement, vol. 65, no. 1, pp. 2-14,
Jan. 2016.
10. Tesseract OCR, Core Developers, [Online]. Available:
http://code.google.com/p/tesseract-ocr/

Fig. 4. field trial about OCR results of the developed Androidbased patrol robot featuring AVP and APR.

Tesseract OCR onto the Android-based smartphoneplatform as a plug-in module of Android operating
system. In addition, this work also proposes to adjust
hybrid-pitch character segmentation of Tesseract OCR
for the font characteristics of Taiwanese vehicle plate.
Fig. 4 demonstrates the field trial about OCR resultsof the developed Android-based patrol robot featuring
AVP and APR. In Fig. 4, the upper row demonstrates
AVP and APR results of automobile cases, and the lower
row demonstrates those of motorcycle cases. The fieldtrial result with the document recognition rate of over
99% and the plate recognition rate of over 98% verifies
that Tesseract OCR can perform well on APR in Taiwan
or elsewhere by customizing its character dictionary
elaborately. Besides, Fig. 4 also demonstrates the vehicle
detection rate and plate recognition rate of the Androidbased patrol robot are over 96% and over 94%,
respectively, under various scene conditions. On theother hand, the average execution time of AVP and APRof the Android-based patrol robot takes at most 8 second
per round and at most 0.8 second per frame, respectively.
Here, the clock frequency of the processor on Androidbased smartphone platform is simply 1 GHz.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 53

Adaptive CMAC Filter for Chaotic Time Series Prediction

Wei-Lung Mao, Suprapto, Chung-Wen Hung

Department of Electrical Engineering and Graduate School of Engineering Science and Technology, National Yunlin
University of Science and Technology, No. 123, University Road, Section 3, Douliou, Yunlin 64002, Taiwan, ROC.

E-mail: wlmao@yuntech.edu.tw, d10210035@yuntech.edu.tw, wenhung@yuntech.edu.tw
www.yuntech.edu.tw

Abstract
Chaotic signal is a natural phenomenon exhibiting in every condition of dynamical system. Chaotic signals are almost
unpredictable, noise-like, uncertain and irregular behavior, yet they are very useful in numerous applications of signal
processing. Due to their behaviors, the quest of a good method to model and analyze of the chaotic signal is very
crucial. This paper present a novel strategy to analyze chaotic signal using the cerebellar model articulation controller(CMAC) network combined with evolutionary algorithms (EAs) such as biogeography-based optimization (BBO),
genetic algorithm (GA) and particle swarm optimization (PSO). Mackey-glass chaotic signal time series is tested anddemonstrated by the conventional and the proposed algorithms. They are compared with each other to determine theoptimal filtering and prediction. The result demonstrated that the CMAC combined with EAs could filter, predict andestimate chaotic signal time series well rather than the conventional methods. The best result of the algorithms tested
for chaotic signal time series is the CMAC combined with BBO algorithm.
Keywords: Chaotic signal, Mackey-glass time series, signal prediction, evolutionary algorithm (EA), biogeographybased optimization (BBO), cerebellar model articulation controller (CMAC).

implementation [2]. Due to some advantages, the CMAC
can be applied as a filter to predict the chaotic signal
series. A time windowing strategy called Time-division
CMAC or CMAC filter is an appropriate to tackle theprediction problem. The CMAC filter is a modifiedclassical CMAC networks for predicting chaotic signal
series [3].
To achieve a good prediction result, training process is an
important part in the neural network. Some evolutionary
algorithms (EAs) are employed to enhance the learning
system capability. There are biogeography-based
optimization (BBO), genetic algorithm (GA) and particle
swarm optimization (PSO).

1. Introduction
A chaotic signal is a natural phenomenon exhibiting inevery condition of a dynamical system. The chaotic
signal has become an attractive issue for many
researchers in recent year. Some methods have beendeveloped to obtain the meaning of the chaotic signal.
One of the common techniques to predict the value of thechaotic signal is time delays and an embedding
dimension [1]. To realize this method, a predictor design
involves a neural network to obtain a better result.
A kind of neural networks imitating the humancerebellum is cerebellar articulation model controller(CMAC). The CMAC has advantages, such as low
computation, good generalization capability and easy

2. CMAC Filter© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 54Wei-Lung Mao, Suprapto, Chung-Wen Hung

The structure of the CMAC, comprises two input states,
association cells (ai), memory cells, and an output state(y). In Figure 1(a), the simple example shows a memory
structure of the CMAC, which each layer is mapped into
three blocks. Each layer has nine areas labeled as
hypercube Aa, Ab, Ac, …, and Cc. The hypercubes
would be used as memory cell, which the active cell isaddressed by an association cell. The actual CMAC
output comes from the summation of the weights stored
in the memory of each layer.
The structure of the CMAC filter design involves somebasic CMACs. The prediction output can be obtained by
the equation (1):y(i)

a iT wd Md
i 1j 1x( i )

xi

xi 2
y(i)

(b)
Figure 1. (a) memory structure of the CMAC, (b)memory structure of CMAC filter
3. Evolutionary Algorithms (EAs)
3.1. Biogeography-based Optimization (BBO)
Biogeography is the study of biology as it relates to thegeographical distribution of species over time and space.
The emigration rate (μ) and the immigration rate (λ)
determine the BBO algorithm. In Figure 2(a), the BBO
depicts emigration and immigration curves by straightlines. The equilibrium point of species S0 occurs when
the immigration and emigration rates are equal. It occurs
temporarily, as conditions continually vary. The
equilibrium point of migration can be written by equation(3):

where Md is the memory size, w is the weight vector, andd is the number of delays. The equation for the learning
phase can be written as follows:
wi

wi 1

d * Nea i y desy(i)x(i 1)

xi d(1)

a ij w ij

1(2)

where ydes is the desired output, y(i) is the actual output,
w(i) is the weight vector, β is the learning rate, and Ne is
the number of activated memories.

λ k μk .

(3)
The immigration and emigration rates can be written byequation (4) and (5):

μk

Ek
a
(4)

λk I 1

(a)

k
a
(5)

where I is the maximum immigration rate, E is the
maximum emigration rate, k is the number of species, anda is the largest number of species. The mutation rate canbe expressed as follows:© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 55

Adaptive CMAC Filter forms

mmax 1

Ps
Pmax

I

E

(6)
where mmax is the value defined by the user, Ps is themutation probability, and Pmax = argmax(Pn) for
n=1,2,…,N. A species living in a habitat can becalculated using Ps , which changes time t to time (t+∆t),and is given as follows:
Ps t
Ps 1

t

S0

Ps t 1 λ s Δtt λs 1Δt

Smax

μ s ΔtPs 1

t μs 1

(a)

Δt

(7)
where Ps(t) is the probability that a habitat contains S
species, S is the species at time t without migration, S-1
is the species at time t with one species immigrating, and
S+1 is the species at time t with one species emigrating.

3.2. Genetic Algorithms (GAs)
Genetic algorithms are population-based modeled bybiological evolution based on Charles Darwin’s theory ofnatural selection. The principle of the GA is the
movement from one population of chromosomes to a new
population using crossover, mutation, and selection [4].
To find a good solution, GA involves the chromosomes
represented by strings of bits, the manipulation operation
string, and the selection to their fitness. In one evolution
cycle of the genetic algorithm, there are some steps suchas evaluating the fitness of all the individuals, creating a
new population, replacing the old population anditerating again using the new population.
The crossover operation is swapping one segment of one
chromosome with the segment on another chromosome
at a random position. The mutation can be obtained byflipping the selected bits randomly and usually has small
probability. The objective of mutation is to increase thevarious populations. Fitness reproduction or elitism isachieved by the selection of an individual in a population.
Diagram of crossover and mutation can be depicted in
Figure 2(b) and 2(c).

(b)

(c)

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 56

Wei-Lung Mao, Suprapto, Chung-Wen Hung4.

In this paper, the parameter values for the CMAC filter
have two inputs, s1 and s2, in which the number of layers
is five, the width is ten, and the number of delays is three.
The learning parameter values are configured as follows:
the same parameters of BBO, GA and PSO have
population: 15, chromosome: 10, mutation: 1%, andelitism: 2. The parameters for each EAs can be
configured as follows: habitat probability of BBO is 1,
upper is 1 andupper is 0; crossover probability andcrossover type of GA is one; and initial w, c1, and c2 of
PSO are 0.03, 1, and 1, respectively. The Mackey-glassis generated by a time-delay differential equation, suchas that shown in Equation (10):x*iParticle i

Possible Directiong*(d)Figure 2. (a).Migration curve of species of a single
habitat; (b).Diagram of crossover; c).Diagram of
mutation; (d).Representation of the motion of particle i.

x(t)3.3 Particle Swarm Optimization (PSO)
The concept of PSO algorithm is the movement to search
new space of swarm behavior toward an objectivefunction by adjusting the trajectories of particles [5].
Each particle moves to a new location determined by avelocity term, which represents the attraction of global
best (g*) and its best location xi* in the history of theparticle and random coefficient. If the location of theparticle is better than any previously found location,
updates such location as new current best for particle i.
The purpose of this algorithm looks for the global best
among all the current best solution for particles. A
description of the movement of the particle is represented
in Figure 2(d). The global best comes from the minimum
value of function f(xi) for i=1,2,…,n. Let xi=(xi1,
xi2,…,xid) be the position vector and vi=(vi1,vi2,…,vid) be
the velocity for the particle i. Particle updating to obtainthe new velocity vector is specified by Equation (8)vi(t 1) w vi(t) c1 1(g* xi(t) ) c2

*(t )
2 (xg

Simulation Resultsx(t τ)
1 x (t τ)

(10)

x(t)

where α = 0.2, β = 0.1, γ = 10, and = 17. In this paper,
400 and 100 data points are employed for training andtesting. Figure 3(a) shows the prediction curves of theoutput. In Figures 3(b) and 3(c), the curves show themean square error (MSE) and mean absolute percentage
error (MAPE) values.

(a)x(it) )

(8)x (i t 1 )xi (t)v i( t 1 )

(9)
where

1 and
2 are two random vectors, c 1 and c 2
are the learning parameter, w is the inertia weight, andvi(t+1) is the new position.

(b)© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 57

Adaptive CMAC Filter for

filtering and prediction. Based on the result, the CMAC
filter combined with EAs could filter, predict andestimate chaotic signal time series well. The best resultof the algorithms tested for chaotic signal time series is
the CMAC filter combined with BBO algorithm.
Acknowledgements
The authors would like to thank the Ministry of Scienceand Technology of the Republic of China, Taiwan, forfinancially supporting this research under Contract No.
MOST 105-2622-E-224 -010 –CC, MOST 105-2221-E224-024, and MOST 105-2218-E-150 -004 -.

(c)
Figure 3. (a) prediction outputs, (b) MSE (dB) prediction
indexes, (c) Absolute prediction errors

References

In Figure 3, the predictor employs the chaotic data series.
The CMAC filter using BBO, GA or PSO learning
system is employed to predict the chaotic signal data
series. The comparative of Mackey-glass prediction
results are presented in Table 1.

1. P. Zhao, L. Xing, J. Yu, Chaotic time series prediction:
From one to another, Physics Letters, A373, (2009) 21742177.
2. S. H. Lane, D.A. Handelman, J. J. Gelfand, Theory anddevelopment of higher-order CMAC neural networks,
IEEE Control Systems Magazine, 12, (1992) 23-30.
3. M. Abbaspour, A. M. Rahmani, M. Teshnehlab, Carbon
monoxide prediction using novel intelligent network,
International Journal of Environmental Science &Technology, 1(4) (2005) 257-264
4. M. Mitchell, An Introduction to genetic algorithms (MITPress, 1999).
5. Xin-She Yang, Nature-inspired optimization algorithms
1st edition (Elsevier 2014).

Table 1. The results of MSE, MAPE, and ARV
Methodology
PSO
GA
BBO
MSE
3.9647e-04 9.6950e-05 4.2610e-05
MAPE 4.3233e-02 2.1219e-02 1.4106e-02
ARV
9.6198e-03 3.5797e-03 2.2484e-03
The MSE, MAPE, and ARV evaluate the output of the
CMAC filter. The prediction results are compared withthose of algorithms to identify the algorithm with the best
performance. Based on the error results listed in Table 1,
the learning process using BBO algorithm improves theperformance of the CMAC filter. The CMAC filter using
BBO learning process has the smallest MSE, MAPE, and
ARV. They are 4.2610e-05 for MSE, 1.4106e-02 for
MAPE, and 2.2484e-03 for ARV. Based on these errors
and curves, the best prediction was made by the BBO
algorithm.
Error

5. Conclusion
This paper presents a novel filter using CMAC network
to predict chaotic data time series, in which generated byMackey-glass chaotic signal time series equations. The
proposed CMAC filter is employed using EAs learning
system to obtain a good performance. The EAs are BBO,
GA, and PSO algorithm. The prediction results arecompared with each other to determine the optimal© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 58

Surface defect detection for anodized aluminum tube based on automatic optical inspection
Hsien-Huang P. Wu and Hsuan-Min Sun

Department of Electrical Engineering, National Yunlin University of Science and Technology
Douliou, Yunlin, Taiwan R.O.C.
Runzi Zhao

Straits College of Engineering, FuJian University of Technology#3 Xueyuan Rd, Gulou, Fuzhou Shi, Fujian Sheng, China

E-mail: wuhp@yuntech.edu.tw, m10312026@yuntech.edu.tw
http://el404.ee.yuntech.edu.tw/

Abstract
This paper proposed using the automated optical inspection (AOI) technology to develop a system which can
automatically detect and classify defects for the shock absorber tube (SAT) made with steel. It is a high economic
product which requires high-quality even under mass production. Nevertheless, the current manual quality-inspection
is not only error-prone but also very manpower demanding. Due to the strong reflective property of the surface, as
well as its various sizes and subtle flaws, it is very difficult to take good quality image for automatic inspection.
However, based on the surface properties and shape of the SAT, lighting and proper structure combined with line
scan camera have been designed to acquire image with good quality. Methods were proposed to detect various kindsof defects, and experimental results show that all the defects can be detected in real time. We believe the proposedsystem can greatly increase the efficiency and accuracy of defect detection and decrease the cost of manual labor.
Keywords: Automated Optical Inspection, surface defect detection, shock absorber tube (SAT), strong reflectivity
surfacecostly. AOI technology has been successfully applied invarious fields for product inspection. For examples,several methods have proposed using AOI for theinspection of cylindrical objects .1,2 It is also used insome of the traditional industrial line for inspecting thedefects for the bearing column3 and iron bars.4,5 For theinspection of metal surface, a variety of different
methods of defect identification have been used.6,7 In Ref.
8, iterative approximation threshold method was used todetect defective areas on metal surfaces. These literatures
show that for different material of metal surface, one canutilize various method to detect the defects. However, theother approach is to properly design the lighting andselect correct camera9,10 to let the defects emerge fromthe background and simplify the process of defectdetection. In this paper, lighting, camera and imaging
structure have been properly designed to generate good

1. Introduction
In recent years, due to the ups and downs of oil prices,
the idea of energy saving and carbon reduction prevails
and the concept of healthy living gradually rises.
Therefore, it becomes more common for people to ridebicycles. Furthermore, the bike lane planning and designis also gaining more attention, which makes the bike
become not only a tool for travel but also an instrument
for recreation. The shock absorber is the key component
for the comfort of riding a bike, where the shock absorbertube (SAT) is the main body and is the most importantpart inside the absorber. When the surface of the SAT is
defective, it can cause air leakage, oil leakage and loss ofthe suspension function. Thus, the finished SAT products
must be inspected to detect any defects that might exist
in it before shipping. This is currently done by humanvisual inspection which is error-prone, inefficient and© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 59Hsien-Huang P. Wu and Hsuan-Min Sun, Runzi Zhao

quality image which makes the detection process become
very easy.

must be carefully setup in order to obtain useful images
for inspection, and this needs experiments and experience.
2.3. Rotatory mechanism

2. Hardware for AOI System

The line scan camera demands a very stable rotation
which depends on the stability of the rotary mechanism
and the straightness of the SAT itself. This is because theline-scan camera only focuses on one thin line area of theobject, and slight deviation of the rotation could miss or

To inspect a cylindrical object like SAT (200mm oflength), as shown in Fig. 1, we need equipment with anaxis to rotate it in order to inspect the whole surface.
Given its round shape, a line scan camera and a line lightfor illumination is the suitable to acquire the image. The
overall system setup is illustrated in Fig.2 and in thefollowing subsections, we will describe these systemcomponents respectively.
2.1. Line scan camera
Line-scan camera accumulates many lines to form thelength of the image and has the advantage of fast speed
and wide FOV (field of view). The camera we used is
SUFi74 (7.4k pixels/line) with F-mount lens (F1.8 andFig. 2. Top: Sketch of the AOI system setup. Bottom: theimplementation of the rotary mechanism.
Fig. 1. Left: The shock absorber tubes under inspection by AOI.
Right: SAT inside the absorber chamber.

degrade image acquisition of the line. The rotatory
mechanism we built is shown in bottom of Fig.2, where
A is the servo motor and encoder for controlling theposition and speed of rotation (180rpm), B is the couplingfor connecting axes of two different rotating objects toachieve concentric rotation, C is the measuring fixture to
hold the SAT for rotation, and D is a slide bushing
structure which can be adjusted to fit different length of
the SAT.

50mm focal length).
2.2. Illumination
The lighting for the line-scan camera are mostly based online light source, which is very bright and provides very
narrow stripe of illumination. Not like the golf club [10],the body of SAT is straight enough to provide stablerotation while it is rotated for image taking. The lighting
we used is IDBB-LSR300W (300mm of length)
manufactured by KKIMAC. The angle of illumination© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 60Surface defect detection for

3. Methods of Defect Detection

greatly simplify the follow-up treatment of thresholding,morphological operation and classification. The flaw
detection results are shown in Fig. 4. As we can see fromthe figure (SAT-3), this method can detect long-thinflaws with much less broken defect feature.

When the SAT is placed on the rotating mechanism torotate with proper side lighting, the bright field is the area
to be acquired for image. After the camera and motor
parameters are set, the camera is controlled via the trigger
signal and synchronized to the speed of the rotarymechanism to capture the full-circle image. After thewhole SAT surface is acquired, the image is ready fordefect detection process. Two simple methods are used
to verify the suitability of the proposed approach.

3.1. Filtering method
Because texture of the tube can cause obvious horizontal
stripes in the image, it will interfere with the judgment of
the defect. It can be eliminated by ROI filtering to remove
the high-frequency periodic pattern. The filtered image isfirst binarized to find the darker gray-scale region, andthen expanded by morphological close operation to pieceup the broken defect. Finally, each candidate region is
discriminated as defect if its size is larger than apredefined threshold. Some of the detection results areshown in Fig.3. The disadvantage of this approach is thatwhen the defect appears as horizontal line, it will befiltered out with the background texture, which is shownas missed detection SAT-3 in Fig. 3.

Fig. 4. The defect detection results based on image difference
method. Horizontal defect (in SAT-3) can now be detected.

4.

Experimental Results and Discussion

Some of the flaws on the SATs under test can berecognized by the naked eye, but some cannot. These
subtle defect needs to be inspected visually using
magnifying glasses or to be imaged by the proposedsystem. The acquired image by the system has very highresolution such that the defects can then be visually
identified on the computer screen or automaticallydetected by machine vision method. In this evaluation
phase, we put our proposed system and detection method
for an on-line test to automatically identify the defects
using 13 SATs provided by the manufacturer. The
software is developed under C#/emgu environment, andthe results are shown in Table 1. The smaller size image(1000×1000) was used for verifying detection method
which only processed the region around defects. The
larger size image (7392×2500) covers the actual FOV of
the camera and acquired the region inside the FOV. One
example is illustrated in Fig. 5 to show the detection
results. Note that the darker regions on the left and rightboundaries of the image might be caused by the lightsource or rotatory mechanism, but it does not interfere
the detection. Given processing time of 550~600ms byusing multithread (image difference method), it canachieve real-time to inspect the whole tube on line.

Fig. 3. The defect detection results based on filtering method.
Top: acquired image, Bottom: defect detection (defect in red).

3.2. Image difference method
This method is to put the original image through bothGaussian filtering and mean filtering first. The two
output images are then subtracted each other to obtain animage with much less texture and noise, which can© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 61

Hsien-Huang P. Wu and Hsuan-Min Sun, Runzi Zhao

References

Table 1. The evaluation results conducted for 13 SATs.

Image size
(pixels)

Processing
time (ms)

Detection
rate

Image
Difference

1000×1000

60 ~ 80100%

7392×2500

550 ~ 600

100%

Filtering
Method1000×1000

110 ~ 130100%

7392×2500

1700 ~ 2000

80%

Method

1. X. C. Luo, A hybrid SVM-QPSO model based ceramic
tube surface defect detection algorithm. IEEE 5th Int. Conf.
on Intelligent Systems Design and Engineering
Applications (ISDEA) (Zhangjiajie, China, 2014), pp. 28–
31
2. Y. C. Chiou, and W. C. Li, Flaw detection of cylindricalsurfaces in PU-packing by using machine vision technique,
Measurement, 42(7) (2009) 989–1000.
3. F. Pernkopf and P. O'Leary, Visual inspection ofmachined metallic high-precision surfaces, EURASIP
Journal on Applied Signal Processing, 7(2002) 667–678.
4. J. P. Yun, et al., Real-time vision-based defect inspection
for high-speed steel products, Optical Engineering, 47(7)(2008) 077204-077204-8.
5. W. B. Li, C. H. Lu, and J. C. Zhang, A local annular
contrast based real-time inspection algorithm for steel bar
surface defects, Applied Surface Science, 258(16) (2012)
6080-6086.
6. M. Mansano, et al. Inspection of metallic surfaces using
local binary patterns. IECON 2011- 37th annual conf. ofIEEE Industrial Electronics Society (Melbourne, Australia,2011) pp. 2227–2231
7. X. Q. Huang, and X. B. Luo. A real-time algorithm foraluminum surface defect extraction on non-uniform image
from CCD camera, IEEE Int. Conf. on Machine Learning
and Cybernetics (Lanzou, China, 2014), pp. 556–561
8. M. Senthikumar, V. Palanisamy, and J. Jaya, Metal surface
defect detection using iterative thresholding technique,IEEE 2nd Int. Conf. on Current Trends in Engineering andTechnology (ICCTET), pp. 561-564
9. H. H. Wu, J. W. Su, and C. L. Chen, Automatic opticalinspection system design for golf ball, Proc. SPIE 9971,
Applications of Digital Image Processing XXXIX (San
Diego, California, USA, 2016).
10. H. P. Wu and H. Y. Guo, Automatic Optical Inspection for
Steel Golf Club, 12th Int. Conf. on Fuzzy Systems andKnowledge Discovery (FSKD'15), (Zhangjiajie, China,2015), pp.2378–2382.

Fig. 5. The defect detection results based on image difference
method with image size 7392×2500.

5. Conclusions
In this study, we used the oblique light with a proper
angle to highlight the defects, so as to simplify the defectdetection process and improve the detection rate. One
detection method can successfully detect most of thedefects on the tube surface and the other method can
detect all the defective tubes with 100% detection rate.
Because the manufacturer-defined defect-free SAT tubes
also have been identified as defective (false alarm), there
is still room for discussion between human and machineinspection. Manually visual inspection of SAT defects is
not only less efficient but also less stable, we anticipate
the AOI technology will soon replace the manual
inspection to improve the efficiency and stability andreduce the cost.
Acknowledgements
The authors wish to express their appreciation for thefinancial support of the Ministry of Science andTechnology under project MOST 105-2221-E-224-064.
The SATs used in the experiments were kindly supplied
by Jinyun Cheng Enterprise Co., Ltd., Taiwan. Theirgenerous support is greatly appreciated.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 62

Develop low cost IoT module with multi-agent method

Jr-Hung Guo *Department of Electrical Engineering, National Yunlin University of Science & Technology, 123 University Road,
Section 3,Douliou, Yunlin 64002, Taiwan
Kuo-Hsien Hsia

Department of Electrical Engineering, Far East University, No.49, Zhonghua Rd.,
Xinshi Dist., Tainan City 74448, Taiwan
Kuo-Lan Su

Department of Electrical Engineering, National Yunlin University of Science & Technology, 123 University Road,
Section 3,Douliou, Yunlin 64002, Taiwan

E-mail: g9710801@yuntech.edu.tw, khhsia@cc.feu.edu.tw, sukl@yuntech.edu.tw

Abstract
Internet of Thing (IOT) is a very hot topic recently, many of the important issues in the field of research. But to theconventional apparatus or systems and network connections, sometimes we need complex modifications or expensive
costs. Therefore, this paper using the MCS-51 series single-chip integration of analog, digital signal input, andETHERNET, WIFI and other communication interface, to develop general-purpose IOT modules. This module canbe used to replace existing equipment or devices, or combination use with the original device. We also use the modulein a Multi-Agent Method, we can ensure that the module in signal capture or communication failure can be replacedor assisted by other modules. Finally, we can use these modules to build the sensor network.
Keywords: Internet of Thing (IOT), single-chip, Multi-Agent Method, sensor network.

applications related to people's daily lives, IOT is also thebasis of industrial 4.0 and intelligent factories [4].
From the above example we can know that the IOT
technology for future life have a great impact. But IOT
technology still has many problems to be solved. For
example, many devices simultaneously communication
problems, different devices connected, and IOT module
costs higher etc. In this paper, we use the lower cost
single chip as the core, integrating many communicationinterfaces. As well as multi-channel A / D and digital I /O, to development of a low-cost IOT module. This
module can operate independently, and can be operated
directly with the browser. In addition, modules can be

1. Introduction
Internet of things technology (IOT) is a very popularresearch topic recently, because this technology canmake people's life more convenient. For example, Gubbi,
Jayavardhana, et al. [1] put forward many examples andvisions for the application and future of this technology.
In addition, He, Wu et al. [2] proposed an intelligent
parking cloud service and a vehicular data mining cloudservice. From this example we can know that the IOT
technology is also the basis of cloud computing and big
data. And [3] Yang, Geng, et al. Put forward theapplication of IOT in home care. In addition to these© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 63

Jr-Hung Guo, Kuo-Hsien Hsia, Kuo-Lan Su

grouped according to the communication interface orfunction. Grouping mechanism can be manual; we canalso use multi-agent method to automatically create agroup module. Finally, we have also developed amonitoring system for the management of all modules
and data collection. This constitutes a complete system,
and let the paper developed by the module in the use of
more flexible.
2. System Architecture
The IOT module developed in this paper is based on
MCS-51 series of single-chip-based controller. Because
this module is IOT applications as the main purpose, soin the communications we have integrated Ethernet and
WIFI interface. In addition the module also has UART
(Universal Asynchronous Receiver / Transmitter), thisinterface can be converted into RS232 / 422/485 interface.
So that modules can be connected with many traditional
devices or devices. Finally, we also provide a433/868/915 MHz triple-frequency, 170-channel RFinterface. This module allows communication even more
complete and powerful.
The IOT module in addition to powerful communications
features also has a digital and analog interface. In theanalog interface section, the module has eight 10-bit A /D channels, and in the digital I / O section, the modulehas 10 channels. When necessary or when the application
is not used to the A / D, it can also be changed to digital
I / O, so this module can have a maximum number of 18bit I / O channels. In addition the IOT module also has a
1280Byte eeprom, which can be used to record the A / D
data. This allows IOT module can calculate preliminary
data, so that IOT module has certain decision-making
analysis capability. In addition, this function can also be
used in case of communication failure, as temporarystorage of data. This IOT module structure is shown in
Figure 1.

Fig. 1. IOT module Architecture

In order to make the communication module more
efficient, we designed a communication protocol, thecommunication protocol structure shown in Table 1.
Table 1. Communication Protocol.
Byte 0 Byte 1
35H

ID

Byte 2Byte 3

Byte 4

Sensor Kind Module

State

4N(N=1~16)

Checksum

Data/Parameter

SUM( all byte)

3. Multi-agent Method
We designed the IOT module can operate independently,
and from our design of the communication protocol, oursystem can have up to 65,536 devices. If using IPV4
protocol, so much the device will need 256 Class C
network segment. So the device for the communicationand management will cause great distress. Therefore, we
use the structure of the group, so that modules can begrouped in accordance with the function or task group
approach. Group communication, is handled by the groupmanager, the group has two ways.
(1) Based on Sensor Kind: This approach is based on themodule preset Sensor Kind grouped, which is basedon modules grouped categories.
(2) Grouped by task: This approach is by the user,
depending on the task, such as fire detection, gas
detection, and greenhouse control and so on. The© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 64

Develop low cost IoT

user can freely set the task type and the task type towhich the module belongs.
The group manager in the group is generated in thefollowing way. Each communication interface is set withdifferent weights as shown in Table 2. Usually we firstmanner of communication create a group, and we havethree modules while communication interface work. So
the weights are the sum of the weights of the threecommunication interfaces. If one of the interfaces fails,
the weight of that interface will be set to 99. So all thenormal network communication module, the probabilityof a group manager will be higher. The number ofconnected modules of the module manager will also
affect the weight, the impact of which is shown in Table
3.
Table 2. Interface Weight

When using a task to create a group, Sensor Kind changes
to the task number set by the user. These same task
modules to quickly create groups.
Because of our communication interface, and the numberof modules connected modules are set weights so that the
total weight was W:
W=w1+w2
Therefore, we can rewrite Equation 1 as.
F(n)=G(n)+(H(n)+W)

4. Experimental Results

Table3. Conn. Weightw1

We use MCS-51 series of single-chip, combined with
Ethernet, WIFI, RF and other communication interface tocomplete a low-cost IOT module. This module ismodular design, all the communication interface, can beinstalled in accordance with demand, and can have threecommunication interface work at the same time. And all
the A / D and I / O can allow users to determine their ownfunctions, so that the use of the entire IOT module more
flexible. Each IOT module can operate independently,
when necessary, between the modules can also use multiagent method to form groups. Module between groups
can function in accordance with the communication
priority or function priority setting, when the groupmanager failure, the group well automatically re-generate
the manager module. The use of such a mechanism, toensure that data communication between the transfer
modules will not be interrupted. In this way, when use of
Ethernet or WIFI communication, it also to reduce IP use
and collision, improve the efficiency of the entire system.
Figure 2 is some example of a group of modules. We also
designed a management software as shown in Figure 3,
this system can immediately manage and monitor all
modules of the situation and information, so that thewhole IOT system more convenient. Finally, in order tomake it easier to use, we make all the IOT module has
WEB server function, so all of the IOT module canoperation directly with a browser. Do not need to throughother management software or WEB server, the results

w2
NumberEthernet

1

1

1

WIFI

1

2

2

RF

2

…

…

UART

3

15

15

Finally, the module is wired to the module manager's
algorithm. We use the A* algorithm, because thealgorithm is simpler, so the IOT module can calculate
itself, without the need to pass through the monitoring
system. In addition, if the IOT module has the ability tolocate or set location information, this algorithm can also
be used to find the shortest secure path. This algorithm isshown in Equation 1.
F(n)=G(n)+H(n)(3)

This allows the module to quickly create groups, andwhen the group is abnormal, the module can quickly reestablish the group, so that the entire system management
communication more efficient.

Conn.
Interface

(2)

(1)

F(n) is the movement cost of any path point. G(n) is thecost of movement from the starting point to any point on
the path. H(n) is predicts the cost of moving from any
point on the path to the end.
We are based on the IOT module Sensor Kind andModule as coordinates. Therefore, when we create agroup in mode 1, the modules of the same type canquickly become a group. And group Module smallest
value becomes higher probability of a group of managers.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 65

Jr-Hung Guo, Kuo-Hsien Hsia, Kuo-Lan Su

shown in Figure 4. This makes our IOT module system
more complete.

(a) Group 1(c) Group3

In the future we will continue to improve the function of
our development module. For example, let the number ofconnections for each module is capable of more, let IOT
module can support IPV6, and let the volume IOT
module smaller, more power saving. Finally be able todevelop into a wearable device, so this paper developed
modules can be applied in more fields.

(b) Group 2

References
1. Gubbi, Jayavardhana, et al. "Internet of Things (IoT): A
vision, architectural elements, and future directions."
Future Generation Computer Systems 29.7 (2013): 16451660.
2. He, Wu, Gongjun Yan, and Li Da Xu. "Developing
vehicular data cloud services in the IoT environment."
IEEE Transactions on Industrial Informatics 10.2 (2014):
1587-1595.
3. Yang, Geng, et al. "A health-IoT platform based on the
integration of intelligent packaging, unobtrusive biosensor, and intelligent medicine box." IEEE transactions
on industrial informatics 10.4 (2014): 2180-2191.
4. 4. Wang, Shiyong, et al. "Implementing smart factory ofindustrie 4.0: an outlook." International Journal ofDistributed Sensor Networks 2016 (2016): 7.

(d) Group4

Fig. 2. IOT Module Group Sample.

Fig. 3. User Interface.

(a) IOT Web server 1(b)IOT Web server 2

Fig. 4. IOT Module Web Server example.

5. Conclusion Tables
We have developed a MCS-51 single-chip-based IOT
module, the module has Ethernet, WIFI, RF and othercommunication interface. This module can operate
directly in the browser, but the overall cost only about US$ 30. Therefore, the IOT module developed in this thesis
is powerful and inexpensive, which will be very helpful
for the application and development of IOT system.
© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 66Based on Short Motion Paths and Artificial Intelligence Method for Chinese Chess Game
Chien-Ming Hung

Department of Power Mechanical Engineering, Army Academy R. O. C., Jhongli, City, Taoyuan 32092, Taiwan
Jr-Hung Guo

Department of Electrical Engineering, National Yunlin University of Science & Technology, 123 Sec. 3, University Road
Douliou, Yunlin 64002, Taiwan
Kuo-Lan Su*

Department of Electrical Engineering, National Yunlin University of Science & Technology, 123 Sec. 3, University Road
Douliou, Yunlin 64002, Taiwan
E-mail: tpemail77@yahoo.com.tw, g9710801@yuntech.edu.tw, sukl@yuntech.edu.tw
Abstract
The article develops the decision rules to win each set of the Chinese chess game using evaluation algorithm andartificial intelligence method, and uses the mobile robot to be instead of the chess, and presents the movement
scenarios using the shortest motion paths for mobile robots. User can play the Chinese chess game according to thegame rules with the supervised computer. The supervised computer decides the optimal motion path to win the set
using artificial intelligence method, and controls mobile robots according to the programmed motion paths of thechesses moving on the platform via wireless RF interface. We use enhanced A* searching algorithm to solve theshortest path problem of the assigned chess, and solve the collision problems of the motion paths for two mobile
robots moving on the platform simultaneously. We implement a famous set to be called “wild horses run in farm”
using the proposed method. First we use simulation method to display the motion paths of the assigned chesses forthe player and the supervised computer. Then the supervised computer implements the simulation results on thechessboard platform using mobile robots. Mobile robots move on the chessboard platform according to theprogrammed motion paths and is guided to move on the centre line of the corridor, and avoid the obstacles(chesses), and detect the cross point of the platform using three reflective IR modules.
Keywords: Evaluation algorithm, artificial intelligence method, wireless RF interface, enhance A* searchingalgorithm

shortest path for mobile robots (chesses) moving to thetarget points. Player moves the chess to the assigned
location or takes the chess of the supervised computer.
Then there are two chesses (robots) moving in theplatform simultaneously. The assigned two robots may
collide on the programmed motion paths. The proposed
algorithm can solve the collision condition of two
mobile robots and improve the shortest motion pathusing enhance A* searching algorithm [2, 3].
In some condition, the mobile robot must programs
the shortest path and avoids the other chess moving to
the next position. A* heuristic function is introduced to
improve local searching ability and to estimate theforgotten value [4]. Flavio et al. presented a multi-robot

1. Introduction
Chinese chess game is one of the most popular games,
and is similar to Western chess to be a two-player gamewith a complexity level, and is classified red side andblack side. In the recent, the Chinese chess game has
gradually attracted many researchers’ attention, andmany evolutionary algorithms to be proposed. Darwen
et al. proposed the co-evolutionary algorithm to solve
problems where an object measure to guide the search
process is extremely difficult to device [1].
In the paper, we use the multi-robot system topresent the scenario of the Chinese chess game, anduses enhance A* searching algorithm to program the© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 67

Chien-Ming Hung, Jr-Hung Guo, Kuo-Lan Su

exploration algorithm that aims at reducing the
exploration time and to minimize the overall traverse
distance of the robots by coordinating the movement of
the robots performing the exploration [5].

time of each step. Players don’t obey the game rules tomove chess using the mouse. The supervised computer
can’t permit and display the movement status on theuser interface.

2. System Architecture

3. Motion Planning

The system architecture of the Chinese chess gamesystem is shown in Fig. 1. The system contains asupervised computer, some wireless RF modules, a gridbased platform, thirty-two mobile robots and somewireless modules. The game is classified red side (User)and black side (The supervised computer), and belongs
to two players. Each side includes sixteen chesses.
Player moves chess using the mouse on the userinterface, or takes chess of the other side. The chessgame will be ending until the king of each side to betaken by another side.
We want to increase the entertainment function using
mobile robots to present the movement scenarios of thechesses. The supervised computer is a player to competewith the user, and programs the shortest motion pathusing enhance A* searching algorithm and transmits the
ID code and motion command to the assigned mobile
robots. The assigned mobile robots receive the self-ID
code and the target positions via wireless RF interface,and move to the assigned positions to avoid the collision
paths according to the programmed motion paths.

The mobile robot has the shape of cylinder, and it’sequipped with a microchip (STC12C5A60S2) as thecontroller, two DC servomotors and two driver devices,
some sensor circuits (contain compass circuit), a voice
module, three Li batteries, a wireless RF interface(2.4GHz) and three reflect IR sensor modules (Onemodule contains two reflective IR sensors). Meanwhile,
the mobile robot has four wheels to provide thecapability of autonomous mobility. The structure of themobile robot is shown in Fig. 1.
The mobile robot uses three reflective IR modules todetect the wall of each grid and obstacles. Two
reflective IR sensors are fixed on the both sides of themobile robot. The detection distance of the front side isshorter than the behind side shown in Fig. 2(a). In theboth side’s modules, two reflective IR sensors of thefront side can detect distance to control the mobile robot
moving in the centre line of the corridor shown in Fig.
2(b). Two reflective IR sensors can detect the maximum
distance to be equal to the width of the corridor minuses
the width of the mobile robot to be fixed on the behindside of the module shown in Fig. 2(c). The otherreflective IR module is fixed on the front side, anddetects the object or the mobile robot on the motion path.

(a)Fig. 1. System architecture
In the collision problem, player moves chess to takechess of the other side. The user interface programs two
motion paths for the assigned chesses. Two mobile
robots avoid collision points and paths according to theprogrammed motion paths moving on the platform.
Players can set game time of each set and limit moving(b)(c)
Fig. 2. The detection range for reflective IR modules© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 68Based on Short Motion

A* searching algorithm solves the path planningproblem of multiple nodes travel system. The formulaof A* searching algorithm is following

f n

gn

hn

The movement status of the mobile robot is instead of
the assigned chess shown in Fig. 4(a). Then thecomputer moves the black advisor to protect the kingfrom (5, 2) to (6, 1). The movement scenario of theassigned chess is shown in Fig. 4(b).

(1)

The core part of an intelligent searching algorithm isthe definition of a proper heuristic function f n .
g n is the exact cost at sample time n from start point
to the next point. h n is the minimum cost. In thisstudy, n is reschedules as n to generate an
approximate minimum cost schedule. The equation (1)can be rewritten as follows:

f n

gn

hn

(2)

The A* searching algorithm can program local
minimum motion path. We improve A* searchingalgorithm that is called enhance A* searching algorithm,
and searches the shortest motion path for mobile robots[7]. In the Chinese chess game, the chesses of red side
must face to the black side. In the same way, the chessesof black side must face to red side, too. The enhance A*
searching algorithm can delete the turn numbers todecrease the total motion distance, and select all crosspoints from the programmed motion path to cut down
redundant motion path in the rectangle region.
4.

Fig. 3 A famous set “Wild horses run in farm”
The player moves the red horse to take the blackking from the position (7, 5) to the position (6, 3), andthe computer moves the black king from the position (5,1) to (5, 2) in the second step. The movement process ofthe grid based platform is shown in Fig. 4(c) and (d).
Each step of the computer must calculate the score of
the moveable chesses shown in Table 1. Then thecomputer decides the optimal motion path of theselected chess. In the third step, the player moves the
red rook from (8, 1) to (8, 2), and the computer moves
the black elephant from (3, 1) to (1, 3) shown in Fig. 4(e)and (f). Then the black king will be taken by the redrook in the fourth step of the red side, and the game will
be ending. The movement scenarios of two chesses (twomobile robots) are shown in Fig. 4(g). The motion pathsof two mobile robots have collision problem in the case.
The supervised computer must re-program the new
motion paths to avoid the collision path. The black kingis taken by the red rook, and moves to the assigned
position 28. The set will be ending. The final
arrangement positions of the remainder chesses shownin Fig. 4(h).

Experimental Result

We make an example to explain how to implement in
Chinese chess game, use evaluation algorithm andartificial intelligence method to decide the moving chesswith the highest evaluation score, and use enhance A*
searching algorithm to program the shortest motion
paths of the assigned chesses from the start positions to
the target positions. There is a famous set to be called“wild horse run in farm” shown in Fig. 3.
There are eight chesses (one king, two elephants,
two rooks, one horse, two pawns) in red side and ninechesses (one king, two advisors, two elephants, one
rook, and three pawns) in the black side. Movement
process of the set is belonging to the player and thesupervised computer shown in Table.1 to be classified
four steps. In the first step, the player moves red rookform (8, 5) to (8, 1). The motion path of the chess “redrook” is programmed using enhance A* searchingalgorithm. The movement scenario of the assigned chessis shown in left-up side of Fig. 4(a) on the user interface.

Table 1 Movement process of the assigned set
Computer (black side, using
evaluation algorithm)
Player (red side)
Movement process
Score
Rook
Advisor (5,2)
First step-295(8,5)→(8,1)→(6,1)© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 69

Chien-Ming Hung, Jr-Hung Guo, Kuo-Lan Su

Second
step
Third
step
Fourthstep
ResultHorse (7,5)→(6,3)
Rook (8,1)→(8,2)
Rook (8,2)→(5,2)
Winner(a)The first step (player)

King (5,1) →(5,2)-18888Elephant (3,1)→(1,3)-19991for multiple mobile robots moving on the chessboard
platform simultaneously. We implemented a famous set
to be called “wild horses run in farm” using the
proposed method. The paper used the set to implement
the evaluation algorithm, artificial intelligence method
and enhance A* searching algorithm. The proposed
methods are not only used in the Chinese chess game,
but also applied in various game, and entertainment
field, and manufacture process and production
management. Further the moving process of mobile
robots can be used in the simultaneous war using the
proposed algorithms.

(b)The first step(computer)

Acknowledgements
This work was supported by the Ministry of Science andTechnology in Taiwan, (MOST 105-2221-E-224-022).
References
1. P. Darwen and X. Yao, Coevolution in Iterated Prisoner’s
Dilemma with Intermediate Levels of Cooperation:
Application to Missile Defense, International Journal ofComputational Intelligence Applications, Vol. 2, No. 1,
pp.83-107, 2002.
2. K. L. Su, B. Y. Li and C. Y. Chung, “Enhance A*
Searching Algorithm Applying in Multiple Robot
System,” Applied Mechanics and Materials, Vol.479-480,
pp.773-777, 2014.
3. K. L. Su, S. T. Su, K. H. Hsia and B. Y. Li,
Implementation of the Chinese Chess Game with theComputer Using Artificial Intelligence, International
Journal of Research and Surverys, ICICExpress Letters,
Part B: Applications, Vol.7, No.6, pp.1353-1358, 2016.
4. T. Fu and H. Yin, Designing a hybrid position evaluation
function for Chinese-chess computer game, International
Conference on Software Engineering and Service Science,
Beijing, China, pp.75-78, 2012.
5. Y. Saber and T. Senjyu, Memory-bounded ant colony
optimization with dynamic programming and A* local
search for generator planning, IEEE Trans. on Power
System, Vol.22, No. 4, pp.1965-1973, 2007.
6. K. L. Su, B. Y. Li, J. H. Guo and K. H. Hsia,“Implementation of The Chess Game Artificial
Intelligent Using Mobile Robot,” International
Conference on Soft Computing and Intelligent System,
Kitakyushu, Japan, Dec. 3- 6, pp.169-174, 2014.
7. K. L. Su, B. Y. Li and C. Y. Chung, Enhance A*
Searching Algorithm Applying in Multiple Robot System,
Applied Mechanics and Materials, Vol.479-480, 2014,
pp.773-777.

(c)The second step (player) (d)The second step (computer)(e)The third step (player) (f)The third step (computer)(i)The final step (player) (j)The final arrangement
Fig. 4. Moving process for the set
5. Conclusion
The Chinese chess game system contained a supervised
computer with the user interface, some wireless RF
modules, a chessboard platform and thirty-two mobile
robots. The computer can select moveable method of
the assigned chess using evaluation algorithm andartificial intelligence method. We program the shortest
motion paths of assigned chesses using enhance A*
searching algorithm to obey the rule of Chinese chessgame. The proposed methods can program two motion
paths for two chesses, and solve the collision problem© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 70Design and Implementation of the SCARA Robot Arm
Jian-Fu Weng

Graduate school Engineering Science and Technology, National Yunlin University of Science & Technology, 123 Sec. 3,
University Road, Douliou, Yunlin 64002, Taiwan
Bo-Yi Li

Department of Electrical Engineering, National Yunlin University of Science & Technology, 123 Sec. 3, University Road
Douliou, Yunlin 64002, Taiwan
Kuo-Lan Su*

Department of Electrical Engineering, National Yunlin University of Science & Technology, 123 Sec. 3, University Road
Douliou, Yunlin 64002, Taiwan
E-mail: tpemail77@yahoo.com.tw, g9910813@yuntech.edu.tw, sukl@yuntech.edu.tw
Abstract
The article designs a four-joint SCARA robot arm using PLC-based control system. The control system (ASDASM) is all in one device to be produced by the DELTA Company, and contains four axis controllers and drivers.
The robot arm contains four AC servomotors, four driver devices and a vision system. The PLC-based controller
also programs motion commands of the gripper to finish the assigned tasks using Ladder Diagram (LG), Function
Block Diagram (FBD), Sequential Function Chart (SFC), Instruction List (LL) and Structure Test (ST). Each driver
has been tuned the parameters of the PID controller. The human machine interface (HMI) is a touch panel to be
used for the robot arm. Users can control the motion path of any joint, and uses the DOPSoft language to design thehuman machine interface. In the experimental results, The SCARA robot arm catches a seal, and falls to stamp theassigned positions step by step, and identifies the precious of the robot arm, and moves eight objects to the assigned
positions.
Keywords: SCARA Robot arm, PLC-based controller, AC servomotors, PID controller.

and robot guidance applications [1]. Homayounzade etal. developed an observer-based impedance controller
for robot arm during a constrained motion. The
proposed controller required the measurements of link
position and interaction force [2]. Sim et al. presented abinocular stereo vision to decide the desired location of
the SCARA robot arm [3]. Kenmochi et al. proposed a
motion control method based on environmental mode
for a dual arm robot. By controlling mode information,
particular features or trends can be given to the robot’s
motion. Then a distinctive complex motion can berealized [4].
In some conditions, the robot arm catches theassigned object using the feedback signal of the image
system. Karthikeyan et al. presented a simple active
tracking system, using a laser diode, a steering gear box
setup and a photo-resistor, which is capable of acquiring

1. Introduction
How to find a fast and effective way to program the
motion trajectory of the robot arm becomes animportant problem. A robot arm is a mechanical device
driven by some electronic motors, pneumatic devices or
hydraulic actuators. A well-trained robot arm can help
human to complete assigned tasks automatically. The
purpose of the paper is to design and implement a fourdegree-of-freedom SCARA robot arm. The robot arm iscomposed of four AC servomotors. In the control aspect,
a PLC-based (ASDA-MS system) controller is used tocontrol the robot arm.
There are some researches regarding the robot armin the past. For example, Shafik et al. presented an
innovative 3D piezoelectric ultrasonic actuator using
flexural vibration ring transducer for machine vision© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 71Jian-Fu Weng, Bo-Yi Li, Kuo-Lan Su

two dimensional coordinate in real time without theneed of any image processing technique [5]. Cao et al.
designed a 5-DOF SCARA robot arm for welding, andbuilt the model and the kinematic equations using D-H
method [6].

each joint. “F” part connect with the encoder of each
motor as feedback signal and measure the real-timerotation angle. The power input is the “G” part. The
arrangement method of the controller is shown in Fig. 4
with AC servomotors. The connection pin of the powersystem is shown in Fig. 5.

2. System Architecture
The system architecture of the SCARA robot arm
system is shown in Fig. 1. The system contains acomputer, a PLC-based controller (ASDA-SM), a image
system (Open CV), four AC servomotors, a solenoid
and a gripper. ASDA-SM and four AC servomotors anda solenoid and a gripper integrate the SCARA robot arm.
The solenoid drives the gripper to catch the assigned
object.

Fig. 2. Prototype of the SCARA robot arm
Table 1. Specifications of the robot arm
Functions

Joint

Range

Length of the robot
armFirst joint

205mm

Second joint

205mm

First joint

157 0Second joint

1420

Third joint

150mm

Fourth joint

1800

Fig. 1. System Architecture of the SCARA robot arm
The SCARA robot arm has four DOFs, (Degree ofFreedom) to be shown in Fig. 1. The first and second
joints rotate along the Z axis. The rotation radius of two
joints is the same to be 205mm. the rotation angle of the00
first joint is 157 , and the second joint is 142 .
The movement displacement of the third joint is 150mm.
0
the rotation angle of the fourth joint is 180 . The
specifications of the SCARA robot arm are shown in thetable1.

Rotation anddisplacement range

The prototype of the controller (ASDA-SM) shows in
the Fig. 3. We explain each function of the controller.
“A” part is the communication port. The controller canuse MODBUS, RS485 or RS232 interface to connect
with the computer. “B” part can display the operation
status and error codes. Four AC servomotors willconnect with the part “C” of the controller. “D” part isthe standard input and output terminal with digital
signals. The limit positions of each servomotor connect
with the part “E”, and decide the moveable range of

Fig. 3. The PLC-based controller(ASDA-SM)© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 72

Design and implementation of

In the first experiment, the robot arm executes catchinga seal and stamps the seal on eight positions. The
positions of the working space are shown in the rightside of Fig. 6, and the relation distance of each working
position is shown in the left side of Fig. 6. The robot
arm must control the seal to stamp in the circle. The
radius of the circle is 12mm. The robot arm programs aseries trajectories using point to point control
technology. First the robot arm moves to the initial
position shown in Fig. 7(A), and catches the seal
moving to the assign position “A”, and falls to stamp
the seal on the position shown in Fig. 7(B)-(D). Thenthe robot arm rises up and moves to the second position“B”, and falls to stamp the seal on the assigned position
shown in Fig. 7(E)-(H). The robot arm finishes theothers step by step shown in Fig. 7(I)-(O). Finally the
robot arm moves to the eight position, and falls to stamp
the seal on the assigned position. Then the SCARA
robot arm moves to the initial position and puts down
the seal on the original position to stop shown in Fig.
7(P).

Fig. 4. Arrangement method of four servomotors

Fig. 6. The working space of the first experimentFig. 5. The power connection method
In the assigned task, the SCARA robot arm cancomplete various assigned tasks such as coming andgoing on two points, moving multiple objects to theassigned positions and working on the multiple
positions. Finally the robot arm can catch the color
object moving to the assigned position according to thedetection result of the image system..
3.

Experimental Result

We implement the functions of the SCARA robot armin two aspects. The robot arm catches a seal to stamp
the seal on eight positions in the first experiment, andmoves eight objects to the assigned positions in thesecond experiment.

Fig. 7. The first experimental result© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 73

Jian-Fu Weng, Bo-Yi Li, Kuo-Lan Su

4. Conclusion

In the second experiment, the robot arm catches eight
objects with the same size on the left side of Fig. 8, andmoving to the right side with the same relation position.
The size of each object is cube to be 1cm in length,
width and height respectively. The robot arm programs
a series trajectory using point to point control
technologies, too. First the robot arm moves to theinitial position shown in Fig. 9(A), and catches the firstobject on the right-up side. The object moves to thesame position on the right side show in Fig. 9(B) and(C). Then the robot arm rises up and moves to the leftup position shown in Fig. 9(D), and catches the object
moving to the assigned position shown in Fig. 9(E) and(F). And then the robot arm catches the others step bystep, and moves and falls to the assigned position show
in Fig. 9(M)-(Q). Finally the robot arm catches the last
object on the left-down side and moves and falls on theassigned position show in Fig. 9(R) Then the robot arm
moves to the initial position and stop.

The papers designed and implemented a four joints
SCARA robot arm, and controlled the robot arm using
PLC based system. The PLC based system is ASDAMS that is produced by DELTA Company in Taiwan.
We calculated motion displacement and rotation angleof each joint from the inverse kinematic equations. In
the experimental results, first the SCARA robot arm
catches a seal, and falls to stamp the on eight positions
one by one. Then the robot arm catches the eight objects
moving to the assigned locations according to theprogrammed motion paths..
Acknowledgements
This work was supported by the Ministry of Science andTechnology in Taiwan, (MOST 105-2221-E-224-022).
References
1. M. Shafik, B. Nyathi and S. Fekkai: An innovative 3D
ultrasonic actuator with multidegree of freedom formachine vision and robot guidance industrial applications
using a single vibration ring transducer, International
Journal of Engineering and Technology Innovation, Vol.
3, No. 3(2013), pp.168-179.
2. M. Homayounzade and M. Keshmiri: Observer-based
impedance control of robot manipulators, International
Conference on Robotics and Mechatronics (2013),
pp.230-235,
3. H. S. Sim, Y. M Koo, S. H. Jeong, D. K. Ahn and B. N.
Cha: A study on visual feedback control of SCARA robot
arm, International Conference on Control, Automationand System (2015), pp.1268-1270.
4. T. Kenmochi, N. Motoi, T. Shimono and A. Kawamura:
A motion control method of dual arm robot based on
environmental modes, IEEE International Conference on
Mechatronics (2013), pp.458-463.
5. R. Karthikeyan, P. Mahalakshmi and N. GowriShankar:
Smart laser based tracking system for robotic arm,
International Conference on Communication and Signal
Processing (2013), pp.903-907.
6. F. Cao, J. Chen, C. Zhou, Y. Z. Zhao, Z. Fu and W. X.
Yan: A novel 5-DOF welding robot based on SCARA,
International Conference on Industrial Electronics andApplications (2015), pp.2016-2019.

Fig. 8. The working space of the second experimentFig. 9. The second experimental result© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 74

Transmission Power Control for Wireless Sensor Network
Kuo-Hsien Hsia

Department of Electrical Engineering, Far East University
No. 49, Zhonghua Road, Xinshi,
Tainan 74448, Taiwan (R.O.C.)
Chung-Wen Hung †, Hsuan T. Chang, Yuan-Hao Lai

Department of Electrical Engineering, National Yunlin University of Science & Technology
No. 123, Section 3, University Road,
Douliou, Yunlin, 64002, Taiwan (R.O.C.)
E-mail: khhsia@cc.feu.edu.tw, {† wenhung; htchang, m10312040}@yuntech.edu.tw
www.yuntech.edu.tw

Abstract
Wireless sensor networks can be widely applied for a security system or a smart home system. Since some of thewireless remote sensor nodes may be powered by energy storage devices such as batteries, it is a very important issue
to transmit signals at lower power with the consideration of the communication effectiveness. In this paper, we will
provide a fuzzy controller with two inputs and one output for received signal strength indicator (RSSI) and linkquality indicator (LQI) to adjust transmission power suitably in order to maintaining a certain communication levelwith a reduced energy consumption. And we will divide the sampling period of a sensor node into four intervals sothat the sensor node radio device does not in receiving or transmission status all the time. Hence the sensor node canadjust transmission power automatically and reduce sensor node power consumption. Experimental results show thatthe battery life can be extended to about 10 times for the designed sensor node comparing to a normal node.
Keywords: Transmission power control, fuzzy control, wireless sensor network, internet of thing.

1.

networks become the key technology for the IoT (internetof things) [1]. Since some of the wireless remote sensor
nodes may be powered by energy-storage devices such asbatteries, it is a very important issue to transmit signals
at lower power with the consideration of thecommunication effectiveness. Researchers used TPC(transmission power control) to achieve the goal of
maintaining communication quality with reduced node
power consumption. Ikram et al used SINR (signal tointerference plus noise ratio) and SNR (signal to noise
ratio) to determine a golden receive power range of RSSI
(received signal strength indicator) to ensure the successof signal transmission [2]. Ramakrishnan and Krishnadeveloped a fuzzy logic based TPC for the consideration

Introduction

Sensors play an important role in a variety of intelligent
applications or control systems. If a sensor cannot
transmit the sensed message to the control panel, thecontrol panel will never know what control signal to send.
Thus one may say “No sensor, no control.” As thedevelopment of wireless communication, a sensor
module, which containing sensor unit and wirelesstransmission unit, can transmit the sensed information to
the information center in wireless way. Such sensing
modules can be distributed in different locations to forma wireless sensor network and transmit the sensed
information back to the control center. Wireless sensor© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 75

Kuo-Hsien Hsia, Chung-Wen Hung , Hsuan T. Chang, Yuan-Hao Lai

of energy efficiency [3]. However, only RSSI cannot
guarantee a good communication in an environment withinterference signals. Kim et al used LQI (link quality
indicator), which is determined by signal strength anddetected SNR, to determine if there is interference in theenvironment [4]. PER (packet error rate) is usually used
as a performance index in determining an acceptable
range of wireless signal transmission. Many documents
showed that a system with PER over 1% may not retrieve
the complete transmitted data [5-7]. So we set theallowable PER to be within 1% in our research.

Fig. 1. Architecture of transmission power control.

121 transmission power parameters can be set. To
prevent large variation of the parameters, only thetransmission powers in the range of 10dBm to -34dBm
are used. There are 45 emission power parameters withintervals about 1dBm in this range.
The transmission power is determined by the central
controller with the RSSI and LQI at the end of a packet
and then transferred to the nodes via bridge to adjust thetransmission power of the nodes.

2. Transmission Power Control
Maximizing the transmission power of each node in awireless sensor network system can minimize the PER
and make the transmission distance to the farthest. But
this will lead to unnecessary power consumption andshorten the battery life of a wireless sensor node. Henceto control the transmission power with a certain level ofcommunication being guaranteed is an important topic oftransmit power control.
In general, the steps for controlling transmission power
is as follows. At first the information about thetransmission status, such as RSSI, LQI or SNR, of thenode is gathered. Based on the transmission state
information, the transmission power of the node is judged
whether in an appropriate range or not. This range isdetermined by a large of experiments. If it is out of therange, then the transmission power has to be adjusted forsuccessful communication.
The architecture of the TPC is shown in Fig. 1. It is
implemented by TI CC430F6137 development board inthis study. A Sub-1GHz transmission packet with theinformation about RSSI, LQI and CRC checking results
attached will be received by the development board.
RSSI, in dBm, can be used for determining whether thenode transmission power is appropriately set or not. LQI
represents the quality of connection in the current
environment. In CC430F6137, LQI is expressed as a 7bit positive integer. The lower the value is, the better thecommunication quality is. In this study, LQI will be used
as a supplement. If the communication quality is too bad,then the transmission power will be added immediately
to reduce the PER.
For CC430F6137, the transmission power of the Sub1GHz is in the range of 10.7dBm to -69.2dBm. A total of

3. Fuzzy Transmission Power Controller
In order to establish the RSSI standard receiving range
and the LQI threshold, a large number of experiments areneeded to find out the relationship between RSSI/LQI
and PER. The scenario of the experiments are describedas follows:
1. There are a central controller, a bridge, and a node in
our experiment. The central controller can command
the node to transmitting data with a designatedtransmission power.
2. The initial emission power is set to 0dBm. This is thedefault set of CC430F6137. The distance between thenode and the bridge (N-B distance) is 12m.
3. Since the RSSI value is easily disturbed by theenvironment and this may cause the output of thecentral controller variated frequently, a Kalman filter
is used to eliminate the RSSI disturbance.
For the issue of RSSI, the central controller command thenode to transmit a 32-byte data and extract the
information of RSSI, LQI, and CRC checking result. The
N-B distance varies from 1m to 12m stepping by 1m. At
each distance, the node transmits 1000 data to the central
controller through the bridge. The PER is defined by (1).
And the resulting relationship of PER and RSSI is shownin Fig. 2.
PER = (#(lost package) + #(error package))/1000© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 76(1)

Transmission Power Control for

For the issue of LQI, an extra node transmitting noise isadded to the experiments for RSSI. The N-B distancevaries from 1m to 12m stepping by 1m. At each distance,
the node transmits 1000 data to the central controller
through the bridge in the noised environment. The PER
is also defined by (1). The resulting relationship of PER
and RSSI is shown in Fig. 3.
Refer to Fig. 2, the PER at RSSI equaling -81dBm is
1.1%. Hence we define the PER value as the lower
threshold. Fig. 4 shows the RSSI of 500 data transmitted
in 0dBm. The drift range is 9dBm. Hence the higher
threshold can be set as -72dBm. The LOI threshold is set
as 47 since the PER rising rapidly when LQI over 47 inFig. 3. If the RSSI and LQI are in these ranges, thetransmission power is maintained. If any one of them isout of the range, the transmission power will be adjusted
by the fuzzy transmission power controller.
The membership functions of RSSI and LQI are definedin Figs. 5 and 6. The output of the controller, PAP, is anadjustment parameter. 7 fuzzy numbers of PAP are set
for the controller. For the case that no power adjustment
is needed, the output is AZ which is set as 0dBm. To set
appropriate PAPs for the controller, we design anexperiment to get the adjustment amount of nodetransmission power to tune the RSSI to -72dBm when
RSSI is -100, -94, and -81dBm. The experimental resultis shown in Fig. 7. The values marked in red are intervals
from PL, PM, PS to high threshold. So we set the coresof PL, PM, and PS as 30, 22, and 7dBm, respectively.
The cores of NL, NM, and NS are obtained in a similar
way. The resulting membership functions are defined inFig. 8. The control laws are as follows:
IF (LQI is PM) or (RSSI is NL) then (PAP is PL).
IF (LQI is PS) and (RSSI is not NL) then (PAP is PM).
IF (LQI is AZ) and (RSSI is not NM) then (PAP is PM).
IF (LQI is AZ) and (RSSI is not NS) then (PAP is PS).
IF (LQI is AZ) and (RSSI is not AZ) then (PAP is AZ).
IF (LQI is AZ) and (RSSI is not PS) then (PAP is NS).
IF (LQI is AZ) and (RSSI is not PM) then (PAP is NM).
IF (LQI is AZ) and (RSSI is not PL) then (PAP is NL).
Experiments show that the designed fuzzy controller cancontrol the transmission power efficiently, and the PER
can be reduced to under 1%. However, the efficiency ofthe battery is of no significant improvement.
4.

Fig. 2. Relationship of PER and RSSI.

Fig. 3. Relationship of PER and LQI.

Fig. 4. Drift range of RSSI.

Fig. 5. Membership functions of RSSI.

Fig. 6. Membership functions of LQI.

Time division communication is technique of preventing
package collision in communication by shifting thetransmission time at which each node transmitting theArchitecture of Time Division© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 77

Kuo-Hsien Hsia, Chung-Wen Hung , Hsuan T. Chang, Yuan-Hao Lai

package. The architecture of time division of this paper
is shown in Fig. 9. Ttransform is the time of a node changing
status from sleep to data transmission. Twaiting is thewaiting time which is the sum the time of sensing
neighboring sensors around the nodes and Ttransform. TgetN
is the data getting time for Node N. Tacq is the acquisition
time of receiving information from all nodes.
The experimental results with battery of 1000mAh aresummarized in Table 1. From Table 1, whether in shortdistance or long-distance, the designed TPC controller
can make PER less than 1%. If coupled with TDMA, thebattery life can be increased by about 10 times.

Fig. 7. Intervals of transmission power.

Table 1. Experimental results of TDMA
N-B dist.

1M

12M

5.

Type
NormalTPC
TDMA
TDMA+TPC#-Pack.

#-Err-Pack. PER (%)
0.013
23707232
0.024
23747656
0.009
2030361185
0.015
2030361311

Normal
TPC

235292234662

8651
2087

3.677
0.889

TDMA
TDMA+TPC

2183322
1977369

52236
11172

2.393
0.565

Fig. 8. Membership functions of PAP.

Conclusion

We have successfully designed an energy-savingarchitecture of transmission power control for wirelesssensor network. Battery life is important for a wirelessremote sensing node. With our design, the battery life canbe extend to about 10 times and also the reliability of thedata transmission can be improved. It is suitable for smart
home or home-security system design.

Fig. 9. Time division architecture.
3. S. Ramakrishnan and B. T. Krishna, Closed loop fuzzy
logic based transmission power control for energy
efficiency in wireless sensor networks, in Proc. 2014 Int.
Conf. Computer Communication and Systems, (IEEE,
2014), pp.195-200.
4. S. K. Kim and S. H. Kim, Doo-Seop Eom, RSSI/LQIbased transmission power control for body area networks
in healthcare environment, IEEE J. Biomedical and Health
Informatics, 17(3) (2013) 561-571.
5. S. Thodupunoori, Reverse Link Performance of an 1xEVDO Revision A System, Master Thesis (University ofMissouri-Kansas City, 2008).
6. S. Myneni, Application Note -- MRF89XA Radio Utility
Driver Program (Microchip Technology Inc., 2010).
7. M. Petrova, J. Riihijarvi, P. Mähönen and S. Labella,
Performance Study of IEEE 802.15.4 Using
Measurements and Simulations, in Proc. 2006 IEEE
Wireless Communications and Networking Conf. WCNC2006 (IEEE, 2006), pp. 487 – 492.

Acknowledgment:This work is partially supported by the Ministry of
Science and Technology, ROC, under contract No.
MOST 105-2221-E-224-024.
References
1. International Electrotechnical Commission, Internet ofThings: Wireless Sensor Networks (International
Electrotechnical Commission, Switzerland, 2014).
2. W. Ikram, S. Petersen, P. Orten and N. F. Thornhill,
Adaptive multi-channel transmission power control forindustrial wireless instrumentation, IEEE Trans. on
Industrial Informatics, 10(2) (2014) 978-990.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 78

Mechanism of Autonomous Mowing Robot for Long Grass
Kuo-Hsien Hsia*

Department of Electrical Engineering, Far East University
No. 49, Zhonghua Road, Xinshi,
Tainan 74448, Taiwan (R.O.C.)
Yao-Shing Huang, Kuo-Lan Su and Jr-Hung Guo

Department of Electrical Engineering, National Yunlin University of Science & Technology
No. 123, Section 3, University Road,
Douliou, Yunlin, 64002, Taiwan (R.O.C.)
E-mail: khhsia@cc.feu.edu.tw, ahriman159@gmail.com, {sukl; g9710801}@yuntech.edu.tw
www.feu.edu.tw

Abstract
A land full of grass can be easily seen all over the world. For example, a golf course, a large playground, garden,
waste plowed farmland and wild wasteland, all of above are full of grass. Almost all mowing work are operated bymanpower, especially the long grass section. There have been some autonomous mowing robots for short grass up to
now. However, there is almost no commodity for long grass mowing in the market. The main possible consideration
may be the issue of safety. It is highly possible that a sharp mower blade may cause harm under the condition thatthe mower is not operated directly by a skilled person. In this paper, we will focus on the mechanism design for anautonomous mowing robot that can cut long grass safely. We will also outline the safety requirements for anautonomous mowing robot for long grass.
Keywords: Long grass, mowing robot, autonomous, agricultural machinery.

1.

to different sizes of the work area, we believe that itshould be considerable development and market size inthe future.
Mowing machine is a kind of agricultural machinery. The
trend of agricultural machinery industry are: (1) needs ofautomation since the aging of agricultural manpower andrural manpower reduction; (2) miniaturization,functional diversification and low prices; (3)environmental awareness resulting the regulatory
requirements or need on the reduction of the relevant
emissions, noise and energy consumption [1]. Therefore,
it is important to co-ordinate products that meet market
needs in response to this trend. Since autonomous robots
must be able to operate autonomously in unattended

Introduction

A land full of grass can be easily seen all over the world.
For example, a golf course, a large playground, courtyard,
garden, waste plowed farmland and wild wasteland, allof above are full of grass and a mowing machine isneeded for trimming. Almost all mowing work areoperated by manpower, either carrying or driving,
especially the long grass section. There have been someautonomous mowing robots for short grass up to now.
However, it is now common in countries that the rural
population is declining, while the cost of labor isincreasing year by year. Therefore, if the mower can beworking automated and unmanned, and allowed to adapt© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 79Kuo-Hsien Hsia, Yao-Shing Huang, Kuo-Lan Su, and Jr-Hung Guo

situations, the primary consideration for autonomous
mowing robots is the issue of safety. In this paper, we
will focus on the mechanism design for an autonomous
mowing robot that can cut long grass safely.
(a)

2. Overview of Mowing Machine
Generally, there are four kinds of mowing machine.
(1) Portable lawn mower, as shown in Fig. 1(a) [1]: Thisis the most popular kind of mowing machine. It is
carried by a person and operated manually and cuts
the grass by rotating the blade at the front end. It is
suitable for a non-smooth terrain or a hillside. But it
is dangerous since the blade is exposed and themowing efficiency is poor. And it usually has the
drawbacks of noise and air pollution because of fuelengine driven. Hence some manufacturer replace theblade by a tendon rope or the fuel-engine by a motor,
as shown in Fig. 1(b) [2].
(2) Self-propelled lawn mower, or push-type lawn
mower, as shown in Fig. 1(c) [3]: This lawnmower
is usually pushed by hand and cuts the weeds withthe blades below the fuselage. The blade is mounted
below the lawn mower, thus providing better safety.
But for longer grass, there will be problems due tothe mechanism. Most of its power source is by fuelengines, hence the noise and exhaust are still
inevitable.
(3) Driving lawnmower, as shown in Fig. 1(d) [4]: Thislawn mower is of a large body with a rotating blade
below the fuselage. The operator can sit on it andcontrol it like driving a car. This type of mower ismore suitable for large and flat land, usually park,
stadium, or large squares.
(4) Automated lawnmower, as shown in Fig. 1(e) [5]:The aforementioned lawn mowers need to be
operated by people. This lawnmower basically uses
the concept of sweeping robots, with the cleaning
component replaced by a blade for mowing weed. It
can be regarded as an autonomously moving robot.
However, due to safety considerations, the blade
must be completely hidden in the body, so it can only
be used to cut the short grass. In addition, due to thecharacteristics of its institutions, it can only beapplied to flat grass.

(c)

(b)

(d)

(e)
Fig. 1. Different types of mowing machine.

For the 4 types of mowing machine, only the portablelawn mower is suitable for long grass. However, it must
be carried by a staff to operate, and it is very dangerous
due to the exposed blade.
In a wasteland covered with long grass, it is likely to lurksome danger. Operating a lawn mower to finish a large
grassy field of long grass requires a lot of manpower andtime. In these cases, if one can have the help ofautonomous mower, he will be able to avoid thedangerous and save much time.
3. Safety Requirement for Autonomous Robot
Machines or robots are working for people to save timeand manpower, and to avoid dangerous. However, thesafety should be the first and most importantconsideration on designing the manpower workings by
robot workings. Almost all automatic or autonomous
facilities (including systems) have to meet some safety
requirements. For example, ISO 13482 is an international
standard on the safety of personal care robots (life© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 80Mechanism of Autonomous Mowing

support robots) [6]. Hence all personal care robots haveto meet the requirements of ISO 13482. Ni and Leungsuggested some safety and liability of autonomousvehicle technologies [7]. ISO 26262 [8] is aninternational standard of safety-related features ofelectrical and electronic systems for the cars with total
weight less than 3.5 tons and less than 8 passenger-seats.
In our opinion, a safe autonomous long-grass mowing
robot should meet the following regulations:
1. It will not make people hurt, especially by the blade.
2. The blade will not be broken by hard objects.
3. It can cut the long grass successfully.
4. It does not need to be driven by people.
5. It can move autonomously.
6. It can be commanded.
7. It can be stopped in emergency.
8. It has the ability of obstacle avoidance.
9. It can avoid falling into a hole or falling down due toterrain.
10. It can be programmed on moving.
The first regulation, which is the most important
regulation, is to prevent people working or movingaround the mowing robot to get injury in accident. The
third regulation is the main function of the robot.
4.

Fig. 2. Parts of the designed autonomous mowing robot.

Fig. 3. Grasping and cutting parts work in cooperation.

Fig. 4. Our design of the autonomous mowing robot.

Possible Mechanism of Autonomous Mowing
Robot

The simplest design of a mowing robot is to cut the grasswith a high-speed rotating blade or tendon, just as people
cutting the grass with a sickle in hand. But this will havea high chance of accidental injury to people nearby. In
addition, because of the softness and flexibility of thestems and leaves of grass, if the blade rotation speed is
not fast enough, the long grass cannot be cut smoothly.
Our design of the autonomous mowing robot contains
four parts: (1) grasping part, (2) cutting part, (3) motion
part, and (4) sensing part, as shown in Fig. 2. All parts
are controlled by a central control unit. The motion partcan be wheels or tracks. The sensing part may containinfrared, ultra-sonic, or laser distance sensors, forcefeedback sensors, limit sensors, thermal sensors, cameras,or other sensors.
The grasping part and the cutting part work in close
cooperation. The grasping part is responsible for the longgrass caught up, and then the cutting part cuts the grasped

Fig. 5. Our design of the grasping part.

grass, as shown in Fig. 3. This mowing method is similar
to the way people cut long grass. The cutting part can bea sickle, a gear saw, a chain saw, or clipping blades.
Fig. 4 is our design of autonomous mowing robot. It has
four wheels for moving. The cutting part is a gear saw
below the fuselage with a fence-type security mechanism.
Anything touch the fence while the robot moving forward
will cause the robot stop the gear saw immediately forsafety. The grasping part is composed of two 3-arm
rotating plate like shuriken in opposite direction and with© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 81

Kuo-Hsien Hsia, Yao-Shing Huang, Kuo-Lan Su, and Jr-Hung Guo

https://www.iso.org/obp/ui/#iso:std:iso:26262:-1:ed1:v1:en.

phase angle difference 60°, as shown in Fig. 5. This phaseangle difference is to ensure that the arms of these two
grasping blades never collide with each other during
rotation. Hence this mechanism can grasp the grass 6times in one period. The grasped grass will be cut by thegear saw below the fuselage.
5.

Discussion and Conclusion

A general self-propelled mower can only mowing shortgrass. For long grass cutting, only the portable lawn
mower can deal with. Mowing with this type of mowing
machine is not only hard but also of some potential
dangers. In this paper, we have designed a new way forcutting long grass like the way of human cutting. Also thedesigned mowing robot can cut long grass in a safe way.
This mowing robot without sensing part has been verified
that it can successfully cut long grass. We will integrate
software design and sensing systems to enable the designof the robot for autonomous moving with programmedroute. We believe that an autonomous mowing robot forlong grass will be commercially available in the market
in the near future.
Acknowledgment:This work is partially supported by the Ministry of
Science and Technology, ROC, under contract No.
MOST 105-2622-E-269-004-CC3.
References
1. http://www.jorco.com.tw/webc/html/product/02.aspx?kin
d=456
2. Energy - saving lithium battery mower，
https://www.facebook.com/comlink.com.tw
3. http://www.jorco.com.tw/webc/html/product/02.aspx?kin
d=270
4. http://www.jorco.com.tw/webc/html/product/show.aspx?
num=141&kind=117&page=1
5. http://detail.1688.com/offer/524607039492.html?spm=a2
61b.2187593.1998088710.109.xx
6. T. Jacobs and G. Singh Virk, ISO 13482 - The new safety
standard for personal care robots, in Proceedings of 41stInternational Symposium on Robotics (ISR/Robotik 2014),(IEEE, 2014), pp. 1-6.
7. R. Ni and J. Leung, Safety and Liability of Autonomous
Vehicle Technologies, Massachusetts Institute ofTechnology (MIT): Boston, MA (2014).
8. ISO 26262-1:2011(en) Road vehicles — Functionalsafety —Part 1:
Vocabulary,
available
at:© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 82

Design of Optimal Position Controller for Three-Phase Brushless DC Motor Applying
Adaptive Sliding Mode Control
Tai-Huan, Tsai

Department of Mechatronic Engineering, National Taiwan Normal University,
162 Section 1 Heping E. Rd., Taipei City 106, Taiwan
Mei-Yung, Chen

Department of Mechatronic Engineering, National Taiwan Normal University,
162 Section 1 Heping E. Rd., Taipei City 106, Taiwan
E-mail: storyq29804@gmail.com, cmy@ntnu.edu.com.tw
www3.ntnu.edu.tw

Abstract
In this paper, we design an adaptive sliding mode controller which is applied to optimal position tracking of threephase brushless DC motor. Considering the uncertainties and external disturbances of a three-phase brushless DC
motor, we choose the sliding mode control (SMC) to be the major one. Normally we can’t figure out the motor’s
uncertainty, so we propose an adaptive control to tune the motor’s uncertainty. The adaptive control could be able tohandle the unknown uncertainties and disturbances. Then we proved the stability of system by Lyapunov function,and the simulate results showed that it has excellent performance.

Keywords: adaptive control, sliding mode control, three-phase brushless DC motor.

controllability. In this study, we design a position
controller with three-phase DC motor to let the motor get
an optimal position and contribute in the automationindustry.

1. Introduction
As we all know, the motor plays an important role in
the automation industry. The motor can drive a lot ofdifference external load of mechanical structure. And thethree-phase DC motor is better than the traditional motor.
Because the operation of three-phase DC motor has nofriction between the brush and the shaft, it has highdurability and it is quiet. And because the three-phrase
DC has no friction this characteristic, it can work on highspeed and avoid to maintain. The three-phase brushless
DC motor has gradually replaced DC motor in theautomation industry. The three-phase brushless DC
motor is a very good drive device, it can be used in manydifferent mechanical structure and has good stability and2. The Mathematical Model of Three-Phase
Brushless DC Motor
Brushless motors can be constructed in several
different physical configurations: In the conventional
configuration, the permanent magnets are part of therotor. Three stator windings surround the rotor. In
the outrunner configuration, the radial-relationshipbetween the coils and magnets is reversed; the stator coils
form the center of the motor, while the permanent© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Ginowan, JapanP - 83Tai-Huan Tsai, Mei-Yung Chen

3.2. Adaptive Control

magnets spin within an overhanging rotor whichsurrounds the core.
Three-phase DC motor’s equivalent circuit diagram:
The equation is follow as:vuvv

R 0 0 iu
0 R 0 iv

vw

0

0 R iw

L M M iud
M L M iv
dt
M M L iw

In the system, the load and the disturbance of the
motor is unknown. In this study, to let the controller havemore robustness, the load of the motor is adjusted byadaptive method.
So, we define the load estimation as T̂L , and thecurrent load as T L . And we can get the equation and theadaptive control law as:

eu
ev
ew

(1)

iu

~
TˆL - TL TL

(4)

TˆL

(5)

u adapiv

The adaptive sliding mode control law is follow as:

u
iw

B r

J

J e k sgn( ) uadap (6)In steady state, this meets the design of the original
parameter.
Fig. 1. Equivalent circuit diagram

The electromagnet torquemechanical equation is follow as:

Te

and

the

4.
dynamic

Stability Analysis

To prove the controller is stability, we need to define
the Lyapunov function as:(eu iu ev iv ewiw )
r

d r
dt

(Te

TL B
J

1
2

~
TL

1 ~ ~
TL TL
J

(2)
r

)

V
(3)

J

~

And we define TL
Lyapunov function V as:

3. Design Adaptive Sliding Mode Control
In this study, we design a sliding mode control to be themain controller because it has robustness. And we add
the adaptive controller to estimates the unknown term.

V

J

2

-

k sgn( )

1 ~2
TL
2J
J

(7)

k sgn( )(8), so we can get the

k
J

0

(9)

5. Conclusion

3.1. Sliding mode control

This study proposed an adaptive sliding mode
controller, and successfully used on the three-phase DC
brushless motor. It has been clearly shown that a goodtransient response.

An upper bound should be designed in a SMC andthe parameter should be greater than other unknown
parameters. First, we define switching function ase e . And the error defined as ed . ismotor’s current position, and d is motor reference. The
control law of SMC is shown asu B r J

V

References
1. Zhi-Xiang Yang and Mei-Yung Chen, Using Adaptive
Sliding Mode Control with an Exponential Reaching Law
to Improve the Response Time of Positioning of Robot Arm(Taipai, Taiwan, 2014).

J e k sgn( )(4)© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Ginowan, JapanP - 84

Design of Optimal Position

2. Bing-Hua Chou, Tian-Hua Liu and Cheng-Kai Lin,
Design and Implementation of Adaptive Sliding-ModeController for Synchronous Reluctance Motor Drive
System, (Tainan, Taiwan, 2010).
3. J. L. Shi, T. H. Liu, and Y. C. Chang, Adaptive controller
design for a sensorless IPMSM drive system with amaximum torque control (Taipei, Taiwan, 2006), pp. 823–
833.
4. L. K. Wong, H. F. Leung and Peter K. S. Tam, A Fuzzy
Sliding Controller for Nonlinear System(Kowloon, Hong
Kong, 2001)© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Ginowan, JapanP - 85

Virtual surgery system with realistic visual effects and haptic interaction
Vlada Kugurakova

Digital Media Lab, Kazan Federal University, Kazan, Russia
Murad Khafizov
Ruslan Akhmetsharipov
Alexei Lushnikov
Diana Galimova
Vitaly Abramov

Digital Media Lab, Kazan Federal University, Kazan, Russia
Omar Correa Madrigal

University of Informatic Sciences, Havana, Cuba
E-mail: vlada.kugurakova@gmail.com, murkorp@gmail.com, ahmetsharipov.ruslan@gmail.com,
alexkennyy@gmail.com, diana.lokiana@gmail.com, ivitazour@gmail.com, ocorreamadrig@gmail.com
www.kpfu.ru

Abstract
Currently, educational processes in medical surgery field involve not only theoretical in-class studies that are
immediately followed by practical lessons in mortuaries and hospitals, but also involve simulations of various levelsof reality. This paper describes our current progress in Virtual Surgery System development, which is targeted tosupport educational processes in medical surgery. With this system all surgery operations are performed using avirtual reality headset and haptic manipulators with force feedback. The main feature of our approach is applying avoxel data structure of a human body that provides an opportunity to simulate realistic behavior of the body. Thus,
cutting, sewing, and welding of human tissues processes become realistic and, together with providing a realistic
surgery scene, these will significantly speed up educational processes.
Keywords: Virtual surgery, virtual simulation, soft tissue mechanics

spending1. This comes with a price of possible lack ofproper experience with real-life operations and reduced
realism of the simulation, which in surgery case can very
well be downright lethal. We can, therefore, conclude thatthe field of virtual surgery has requirements much stricter
than any other application of VR. What are those
restrictions exactly?

1. Use of VR in surgery simulations
As the presence of virtual reality in our daily lives
increases, the educational system -- from thekindergarten and all the way to the college -- must
respond to this new challenge. Naturally, medical
surgical education is one of the top candidates for virtual
reality technologies usage, as it can save many reagentsand laboratory supplies, and overall reduce monetary© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 86

Vlada Kugurakova, Murad Khafizov, Ruslan Akhmetsharipov, Alexei Lushnikov, Diana Galimova, Vitaly Abramov, Omar Correa Madrigal

Highly-quality graphics, which can give theoperating person a good idea of how the realthing looks;
Realistic physics of human tissues and fluids,
their proper response to various surgicalprocedures;
Haptic and force feedback for user's hands
according to situations in all processes;
High level of user immersion.
As sad it might be to admit, none of currently existing
virtual surgery solutions are capable of matching thesecriteria. This leads us to the point of this paper: we
propose a complex of technological solutions for virtualsurgery simulators. Among these technologies are:
Implementation of soft tissue physics via
triangulated mesh;
Destruction and deformation of said mesh;
Information collecting;
Cloud storage of points necessary for renderingof multi-polygonal net;
UV-less texturing;
Manual interaction and haptic feedback using
Glove One.
Following is description of how each of thesetechnologies was implemented.

Step 1. At the beginning of the process, we put a newvertex at the starting point of the cut line within the first
triangle. From this vertex, we draw new edges to every
other apex of this triangle, creating three new triangles asa result.
Step 2. Now, starting from this point, we start moving
in the direction of the cutting tool, creating new vertices
on every edge we cross. Old edges are replaced with a
new one that are connected by the new apex, and extra
edges are added to avoid creating quads.
Step 3. At the end of the cut, we repeat the process ofthe first step: splitting the last triangle into three parts.
We should note, that in this algorithm we limit the
movement of the cutting tool to the straight line between
two points on the triangle's edges. Because of this, theprecision of the cut is in inverse relationship with theaverage size of triangle, and some of the more complex
curves cannot be implemented into the cuts. One
workaround for this is to add new vertices withpredefined time intervals.
2.2 Tissue tension
We can implement the tissue tension by moving somevertices of the mesh, with movement gradually
decreasing for vertices located further away from theinitial point in which the force applies. The are several
possible solutions for this.
The simplest one is to move all of the vertices within
the given area. The amount of the movement would beinversely related to the distance between the moving
vertex and the initial point to which force was applied.
One critical flaw of this solution is, of course, the factthat it may end up moving the vertices not directly
connected to the ones we were meaning to move.
The way the transformation affects an edge in themesh depends on the angle between the edge and the
direction of movement -- the acuter the angle, the greater
would be the movement of the vertices adjacent to theedge. The spread of vertices movement further continues
recursively, each one passing less movement to anotherone. This method allows us to simulate physics of tissue
tension with satisfying accuracy. It is also advised to takethe distance between vertices into account to reduce thenumber of visual artifacts.

2. Soft tissue implementation with triangulated
mesh
Proper simulation and rendering of physically realistic
destructible soft tissue are costly both in resources
needed to implement them and in computing power
required for satisfactory performance. Hence, the choiceof methods to use for this purpose is not to be taken
lightly. After some deliberation of existing solutions withthe functionality required for our project in mind, wesettled on the triangulated mesh approach.
Following algorithms were used to implement
functionality and physics of soft tissue with polygonalmesh.
2.1 Tissue cutting
To cut the mesh is to split the polygons located on the
cutting line in half. This is accomplished by adding moreedges and vertices.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 87

Virtual surgery system with

2.3 Tissue stitching

4. Digitalizing the human body

To cut the mesh is to split the polygons located on the
cutting line in half. This is accomplished by adding moreedges and vertices.
There are two possible ways of implementing tissue
stitching.
а. "Stapler-like" binding, in which specified vertices
are bounded on the transformation level, passing eachother information about their movement and acting in
unison. In this case, we need to visibly mark the binding
so it would be visible to player.
b. Stitching of the mesh. This method involves
connecting two different meshes together with new edges
and vertices, with triangles next to the seam being
replaced to absorb the edges that are both adjacent andbeing stitched together.

The soft tissue is merely one of the many building blocksof the human body, and of course, those blocks do notjust float in the vacuum, separated from any otherinfluences. In fact, the opposite is true -- human body iscomplex with so many systems of organs tightlyintertwined it is almost impossible to seclude just a partof it for surgery processes least we want to sacrifice therealism of simulated operation. And yet, this is what wehave to do. Our prime task, therefore, is to limit theconsequences of separating the part of the body.
This is, however, not the only one issue weencounter. Recreation of even a separate part of thehuman body is a tedious process in itself. It's not only thephysical properties of tissues and their biological
purposes that are uneasy to properly implement -- thesheer size of the information we need to store is alarmingby itself.

3. Implementation of blood
Many mathematical blood flow models had been
proposed by the researchers around the world, such as theones described in Ref. 2, and we shall notice that the
choice of the model is very strictly dependent on the
purposes for which those model will be used. In thevirtual surgery, the choice is affected by the specificoperation, as blood may behave differently in different
conditions. For now, we have settled on the general andall-purpose models for demonstration purposes.
While it is possible to implement blood as a systemof particles each with its own form and textures, it isusually not recommended for most projects, as thecomputational complexity increases sharply. Physicalinteractions between each particle and surrounding tissue
as well as surgery tools are very costly to compute and,as a result, lead to frame rate dropping. Visual artifacts inwhich particles "seep" through supposedly solid mesh arealso not unheard of.
In our project, we used the Screen Space Fluids
system. This system somewhat resembles the one wehave described above but demands significantly lesscomputational power. There is no need for a separatemesh or any other game objects in SSF implementation
since all of the computing is done inside of the shaders.
This greatly speeds up the whole process.

4.1 Storing the information
The data structure we choose directly affects the speed of
information retrieval, recording and more. A good data
structure can also spare us a lot of time and computational
power, as we won't need to check every triangle
sequentially to see if a collision has occurred. True,
collision detection is one of the most essential things inany virtual simulation, and in a project where precision isliterally a matter of life and death, extra effort should be
put to ensure optimal performance.
A Bounding Volume Hierarchy may be used for thispurpose -- a data structure which allows fast collisioncheck, essentially a tree structure for sets of vertices3, 4.

5. Conclusion
This concludes our report on the implementation of soft
tissue physics. Naturally, this is the only beginning of theextensive research of the matter of virtual surgery and its
possible applications for education. After the basics areproperly implemented, we shall now focus on thecreation of the realistic surgery simulation, which willconsist of thoroughly recreated surgical operation -appendix removal.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 88Vlada Kugurakova, Murad Khafizov, Ruslan Akhmetsharipov, Alexei Lushnikov, Diana Galimova, Vitaly Abramov, Omar Correa Madrigal

References

Fig. 1. Exampled of tissue cut rendered in real time1.

V. Abramov, V. Kugurakova, A. Rizvanov, M.
Abramskiy, N. Manakhov, M. Evstafiev and D.
Ivanov. Virtual Biotechnological Lab Development.
BioNanoScience 2016

2.

O. Voropaeva, Y. Shockin. Numerical modeling in
the medicine: goal setting and the results of thecomputations. Computational technologies (2012) pp
29-55

3.

Samet, H. (1989, July). Hierarchical spatial data
structures. In Symposium on Large Databases (pp.
191-212). Springer Berlin Heidelberg.

A.

Baerentzen. Octree-based volume sculpting. In IEEE
Visualization, 1998

Acknowledgements
This work was funded by the subsidy of the Russian
Government to support the Program of competitive
growth of Kazan Federal University among worldclass academic centers and universities.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 89

A Human Reaching Movement Model for Myoelectric Prosthesis Control
Go Nakamura*, Taro Shibanoki†, Yuichiro Honda*, Akito Masuda‡, Futoshi Mizobe§, Takaaki Chin* and Toshio Tsuji***

Robot Rehabilitation Center in The Hyogo institute of Assistive Technology, †Ibaraki University,‡
Kinki Gishi Corporation, §Hyogo Rehabilitation Center Hospital, **Hiroshima University
E-mail: g_nakamura@assistech.hwc.or.jp

Abstract
This paper proposes a reaching movement model for the generation of desired trajectories within a myoelectric
prosthesis training system. First, an experiment was performed to observe reaching movements with a non-impaired
subject and a myoelectric prosthesis user. Reaching movements made by the prosthesis user were then adopted toconstruct a model based on a logistic function. The proposed model can be used to generate three trajectory types
with a bell-shaped speed profile with the adjustment of only a few parameters.
Keywords: Myoelectric prostheses, Motion planning, Logistic functionof an evaluation method and such a system requires theconstruction of a model capable of generating humanreaching movement trajectories. A clinically applicabletraining system with such a model requires thefollowing features: (1) high adaptability for generationof optimized and other trajectories (as myoelectric
prosthesis users do not have the reaching movement of
non-impaired people due to wrist-related limitations);(2) adjustability of hand velocity (movement time) in
consideration of individual differences and variations in
trainees’ operational ability; (3) easy and intuitive
trajectory adjustment for use in clinically applicabletraining; and (4) real-time trajectory generation for use
in training. A number of studies on human reaching
movement have been conducted [2] – [4] to clarify thehuman motor planning mechanism. By way of example,

1. Introduction
The prescription of myoelectric prostheses to upperlimb amputees supports everyday living and opens up
new possibilities for rehabilitation. However, months oftraining is needed until such prescription can be made.
Before being able to use a myoelectric prosthesis,
patients must learn the skills necessary to perform tasks
with natural posture (e.g., the direction of grasping
approach and the manner of forearm usage) in addition
to smooth hand opening/closing [1]. However, there iscurrently no quantitative evaluation method or training
system for prosthesis control with natural posture.
Against such a background, the authors sought tosupport myoelectric prosthesis prescription by
developing a training system (Fig. 1). The establishment© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 90Go Nakamura, Taro Shibanoki, Yuichiro Honda, Akito Masuda, Futoshi Mizobe, Takaaki Chin, Toshio Tsuji

the minimum-jerk model [2], the minimum torquechange model [3] and the minimum-variance model [4]have all been proposed and found to be capable ofgenerating human trajectories accurately. However, thetrajectories in these studies were determined via
optimization calculation; it is difficult to generate
trajectories in real time. For control of machinery suchas manipulators and vehicles, real-time trajectory
generation models have been proposed [5] – [7]. As an
example, Tsuji et al. proposed a real-time trajectory
generation model based on an artificial potential field
and a time base generator (TBG) involving a scalarsignal with a controllable finite duration and a bellshaped velocity profile. However, these models areaccompanied by problems such as the difficulty ofadjusting the many related parameters, which are
determined by trial and error.
This paper proposes a human reaching movement
model for myoelectric prosthesis control training. The
model has high adaptability and can be used to generate
three hand trajectory types (straight-line paths, circulararc paths and S-shaped paths) with a bell-shaped speed
profile in near-real time with the adjustment of only afew parameters.

HMD

VR environment
Hand trajectoryf (t )

Amputee

Bell-shaped speed profile

Fig. 1. Concept of the training system for myoelectric
prosthesis control using the prosthetic arm trajectory model

was repeated five times. A 3D position sensor (Isotrak II,
Polhemus Corp.) was attached to the right cubital fossa
of each subject, and their elbow positions(accuracy: ± 2.4 mm) andpostures (Eulerian angle)(accuracy: ± 0.75 ) were measured at 60 [Hz]. The
hand trajectories and hand velocity were evaluated by
calculating the hand position
from themeasured elbow position and posture. The hand position
is defined as follows:(1)

2. Human Reaching Movement for Myoelectric
Prostheses

Here, is the forearm length as determined in advance.
However, the two subjects had different degrees of
freedom in the wrist because the wrist joint of a
myoelectric prosthesis is fixed, making evaluation on alevel playing field difficult. Accordingly, the location of
the wrist joint was defined as the hand position. Hand
velocity was estimated using a second order
Butterworth low-pass filter (cut-off frequency: 10 [Hz]),and was then calculated using a low-pass differential
filter. As the study focused on reaching for and picking
up a cup, a two-dimensional trajectory model wasconstructed as a first step.

2.1. Experimental conditions
A monitoring experiment was conducted with a nonimpaired subject (25 years old, male) and an
experienced myoelectric prosthesis user (49 years old,male; MyoBock hand) toward the development of areaching movement model for myoelectric prosthesis
control. The prosthesis user was a right upper-limb
amputee (amputation site: 14 cm below the elbow), andhad used a myoelectric prosthesis for 14 years. His
average score for the Box and Block Test [8] wasapproximately 40 blocks, which exceeds the average of
normal-level users. His performance was more efficientthan that of most myoelectric prosthesis users.
The subjects were asked to sit on a chair and pick up acup from one of nine locations on a table, lift it to theirmouth and return it to its original location. Each task

2.2. Results
Figures 2 shows scenes of reaching tasks for the nonimpaired subject and the myoelectric prosthesis user,
respectively. Figure 3 shows trajectories observed inreaching for the cup, with origin coordinates based on
the initial hand position. The trajectories represent the
average of five trials for each subject. The dashed lines
represent the standard deviation for five sessions. The© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 91

A Human Reaching Movement

figure shows linear trajectories for both subjects. The
trajectories of the skilled myoelectric prosthesis user
exhibit characteristics similar to those of the nonimpaired subject, but several distinctive differencessuch as different end points are also observed. The
myoelectric prosthesis user also approached the cup
placed in the nearest row (lower right, bottom and lowerleft) after pulling back his elbow, and the trajectories
show downward movement. This can be attributed to
the fixed wrist joint of the myoelectric prosthesis, whichmeant that the end points and trajectories for the cup in
the nearest row were affected by wrist flexion/extension
limitations. The two subjects exhibited different wrist
postures at the time of grasping (Fig. 2).
Figure 4 shows hand velocity results based on averagesof five trials for each subject. Velocity for the
myoelectric prosthesis user was lower than that for the
non-impaired subject, but both exhibit bell-shaped
velocity profiles. This is because the myoelectric
prosthesis user was able to approach the target while
opening his hand within the series of motions, andperformed the tasks without reducing hand velocity.
Figure 2 shows that the myoelectric prosthesis user
opened the hand at the start of the approach. These
results indicate that the myoelectric prosthesis user
performed the tasks with smooth control and handvelocity similar to that of the non-impaired subject.
The hand position and posture data are characterized
by a monotonously increasing/decreasing function or a
combination thereof, and a similar tendency for all data
was observed. The next step in the study was toconstruct a human reaching movement model using alogistic function.

0.0 [s]0.5 [s]1.2 [s](a) A non-impaired subject
Myoelectric
prosthesis

0.0 [s]0.5 [s]

1.8 [s](b) A prosthesis user

Fig. 2. Subjects approaching the cup with upper-rightplacement

200y [cm]

20

0

20

20

60
40
20
0 x [cm]
0 20 20

0

20

60
40
20
0 x [cm]
0 20 20

20

60
40
20
0 x [cm]
0 20 200y [cm]

20

60
40
20
0 x [cm]
0 20 200y [cm]

20

60
40
20
0 x [cm]
0 20 206040
200 x [cm]
020
20

y [cm]

0y [cm]

060
40
20
0 x [cm]
0 20 206040
200 x [cm]
020
20

y [cm]

20

60
40
20
0 x [cm]
020
20

y [cm]

0y [cm]y [cm]A prosthesis user
A non-impaired subject

0Fig. 3. Measured hand trajectories for the non-impaired
subject and myoelectric prosthesis user0

Velocity [cm/s]

200
150
100
50
0

0

Velocity [cm/s]

200
150
100
50
0

0

200
150
100
50
0
0
200
150
100
50
0
0

Velocity [cm/s]

0.5 1.0 1.5 2.0
Time [s]Velocity [cm/s]

200
150
100
50
0
0

0.5 1.0 1.5 2.0
Time [s]Velocity [cm/s]

200
150
100
50
0
0

0.5 1.0 1.5 2.0
Time [s]

200
150
100
50
0

Velocity [cm/s]

3.1. Logistic function

Velocity [cm/s]

The proposed model is designed to highlight theappropriate trajectory to a target in a training system. As
this study focused on accurately reproducing measured
hand trajectory and velocity, the model was not basedon the human movement mechanism.

Velocity [cm/s]

3. Trajectory Generation Model for Myoelectric
Prostheses

200
150
100
50
0
0

Velocity [cm/s]

A prosthesis user
A non-impaired subject

200
150
100
50
0
0

0.5 1.0 1.5 2.0
Time [s]0.5 1.0 1.5 2.0
Time [s]0.5 1.0 1.5 2.0
Time [s]0.5 1.0 1.5 2.0
Time [s]0.5 1.0 1.5 2.0
Time [s]0.5 1.0 1.5 2.0
Time [s]Fig. 4. Measured hand velocities for the non-impaired
subject and myoelectric prosthesis user

The study involved the proposal of a human reaching
movement model using a logistic function.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 92

Go Nakamura, Taro Shibanoki, Yuichiro Honda, Akito Masuda, Futoshi Mizobe, Takaaki Chin, Toshio Tsuji

Here,
and

(2)

y [cm]
40(3)

30

is the start point
is defined as
.

20

,

is the end point.

,andbehavior.

10

are parameters determining function

x [cm]

0

0

20

0

0
0

Fig. 5. Straight-line path dependence on the parametersand3.2. Spatial trajectory modely [cm]
40

The study involved the proposal of a human reaching
movement model using a logistic function.
andare defined as

30
20
10

(4)

x [cm]

(5)

0

0

20

0

0

0

Fig. 6. Circular-arc path dependence on the parameters

To derive an equation for trajectory on the XY plane,
the parameterfor (4) and (5) is eliminated. The
trajectory on the XY plane can be represented as

y [cm]
40
30

(6)

20
10

Here, is defined as.
To prevent the denominator from being 0, (6) can betransformed by multiplying the numerator anddenominator by:x [cm]

0

0

20

0

0

0

Fig. 7. S-shaped path dependence on the parameters

For S-shaped paths, trajectories are generated under
the conditions ofand. These paths
can be controlled by adjusting only the ratio ofwithmaintained. Figure 7 shows time historiesof spatial trajectory based on the parameters. The
curvature gradually increases the farther the ratio ofis from 1.0.

(7)Here, the condition is given by.
The proposed model can be used to generate three
hand trajectory types (straight-line paths, circular-arc
paths and S-shaped paths) featuring a bell-shaped speed
profile with the adjustment of only the two ratios ofand.
Straight line paths can be generated by setting
andunder the conditions ofand.
Figure 5 shows time histories of spatial trajectory basedon the parametersand .
For circular arc paths, trajectories are generated under
the conditions ofand. These paths
can be controlled by adjusting only the ratio ofwithmaintained. Figure 6 shows time
histories of spatial trajectory based on the parameters. The curvature gradually increases the farther theratio ofis from 1.0.

3.3. Bell-shaped velocity profiles
The proposed model can be used to generate bellshaped velocity profiles. Single-peaked (unimodal)
profiles, double-peaked (bimodal) profiles and othertypes can be expressed only with adjustment ofand.
Unimodal profiles can be generated under theconditions of. These profiles can be controlled
by adjusting only the ratio ofwithmaintained. Figure 8 shows time histories of spatial
trajectory based on the parameters. Kurtosis
gradually increases the farther the ratio ofisfrom 1.0.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 93

A Human Reaching Movement

4. Simulation Experiment

Velocity [cm/s]

120
100

A simulation experiment was conducted to evaluate
the proposed model’s propensity for hand trajectory
generation.
andwere set as the times at which theobservation values of
andwere nearest toand, respectively. The parameter
was themeasured movement time, andandwere given
using (9). was set as = 0.01.
Figures 10 and 11 show reaching hand trajectories andhand velocity for each subject, respectively. Solid lines
represent hand velocity based on the averages of five
trials for observation and generation using the proposedmodel, and dashed lines represent the standard deviation
for five sessions. Although approximation errors areseen with the reaching hand trajectories and handvelocity of the myoelectric prosthesis user for the cup
placed in the nearest row, the simulated hand
trajectories and hand velocity both correspond to theobservations, and the simulated and observed lines arealmost identical. Approximation error for the nearest
row arises because that the observed time-series data are
a combination of an increasing/decreasing function, andthe proposed model based on a monotonously
increasing/decreasing function was not capable ofaccurate replication. With this in mind, the authors plan
to introduce methods for appropriate combination withthe proposed model. From these results, it can beconcluded that the proposed model is capable ofgenerating hand trajectories and hand velocity data bothfor non-impaired subjects and for myoelectric prosthesis
users.

80
60
40
20
0
0

0.5

1.0

1.5

time [s]

2.0

2.5

3.0

Fig. 8. Unimodal profile dependence on the parameters

Velocity [cm/s]

120
100
80
60
40
20
0

0

0.5

1.0

1.5

time [s]

2.0

2.5

3.0

Fig. 9. Bimodal profile dependence on the parameters

Bimodal profiles can be generated under the conditionsof. These profiles can be controlled byadjusting only the ratio of. Figure 9 shows time
histories of spatial trajectory based on the parameters. The inter-peak distance gradually increases thefarther the ratio ofis from 1.0.
3.4. Adjustment of movement time
This subsection outlines how movement time for the
proposed model is adjusted. For the logistic function, it
is difficult to specify the convergence time because it is
not necessarily a finite period. To address this problem,a small arbitrary positive constant
was set. The
movement time is defined as, and the logistic

5. Conclusion
This paper proposes a human reaching model for
myoelectric prosthesis control. In the study, the authors
performed an experiment to observe reaching
movements with a non-impaired subject and amyoelectric
prosthesis
user.
The
distinctive
characteristics of reaching movements made by theprosthesis user were identified and adopted to support
the construction of a model. A simulation experiment
was also performed, with results indicating that the
proposed model was capable of successfully generating
hand trajectories for both subjects.
In future work, the authors plan to introduce methods
for combination with the proposed model and expand it
model will also be applied for use with a training system.

(8)functionsatcan be expressed as
Solving (8) gives the parametersand, whichspecify the movement time. The issue of their mutual
dependence is solved by fixing as the time at which
the function is.
can then be easily estimatedfrom measurement values. In addition, a rational value
for can be obtained because =even when it
is difficult to obtain a measurement value. Accordingly,(9)
is fixed in advance andis calculated as
Here, the condition is given byand.
Specifying
allows setting of the desired movement
time.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 94

Go Nakamura, Taro Shibanoki, Yuichiro Honda, Akito Masuda, Futoshi Mizobe, Takaaki Chin, Toshio Tsuji

0

20

0.85)

0.08)
0.09)

0

20

y [cm]

(0.68
(0.62

60
40
20
0 x [cm]
0 20 20

0.06)

60
40
20
0 x [cm]
0
20
20

60
(0.97 0.05)(0.98 0.04)
4020
0 x [cm]
0 20 200 20(0.95 0.07)
60(0.96 0.06)
40
20
0 x [cm]
0 20 20
0 20

60
40
20
0 x [cm]
0 20 20

(0.89

0.15)

60
(0.89 0.13)4020
0 x [cm]
0 20 200 20(a): A prosthesis user

(1.82

0.55)

60
(1.31 0.09)4020
0 x [cm]
0 20 200 20

y [cm]

20y [cm]

0y [cm]y [cm]

0.07)
0.05)

(1.12

y [cm]

(2.31

(0.96
(0.97

60
(1.12 0.08)4020
0 x [cm]
0
20
0 20
20

y [cm]

0.91)

60
40
20
0 x [cm]
0 20 200.03)

y [cm]

(1.46

60
60
(1.11 0.52)(1.54 0.11)
40
40
20
20
0 x [cm]
0 x [cm]
0 20 20
0 20 0 20 20
0 20

20

y [cm]y [cm]

20

0

(1.03

60
(1.04 0.03)4020
0 x [cm]
0
20
0 20
20

y [cm]

0.17)
0.10)

0

(1.11 0.10)
60(1.08 0.08)4020
0 x [cm]
0 20 20
0 20

0.05)
0.04)

60
40
20
0 x [cm]
020
20

y [cm]

(1.32
(1.17

20

0.07)
0.06)

(1.03
(1.02

y [cm]

060
40
20
0 x [cm]
0 20 20

(1.23
(1.21

y [cm]

20
0.06)
0.07)

y [cm]

0
(1.13
(1.13

Generated hand trajectories by the proposed model
Mesured hand trajectories(1.05 0.02)(1.11 0.13)
6060(1.06 0.03)(1.13 0.18)
40
40
20
20
0 x [cm]
0 x [cm]
020
0 20 0
20
0 20
20
20

y [cm]

0.06)
0.05)

y [cm]

(1.08
(1.07

y [cm]

Generated hand trajectories by the proposed model
Mesured hand trajectories

60
40
20
0 x [cm]
0 20 20(b): A non-impaired subject

Fig. 10. Observed and simulated hand trajectories for the myoelectric prosthesis user and the non-impaired subject

0.91)
0.52)

0.5 1.0 1.5 2.0
Time [s]

200
150
100
50
0
0

0.10)
0.08)

0.5 1.0 1.5 2.0
Time [s]
(2.31
(1.54

0.85)
0.11)

0.5 1.0 1.5 2.0
Time [s]

200
150
100
50
0
0

(a): A prosthesis user

0.5 1.0 1.5 2.0
Time [s]
(0.96
(0.97

0.07)
0.05)

0.5 1.0 1.5 2.0
Time [s]
(0.68
(0.62

0.08)
0.09)

0.5 1.0 1.5 2.0
Time [s]Velocity [cm/s]

Velocity [cm/s]

Velocity [cm/s]

200
150
100
50
0
0

0.05)
0.04)

200
150
100
50
0
0
200
150
100
50
0
0
200
150
100
50
0
0

(1.03
(1.04

0.03)
0.03)

0.5 1.0 1.5 2.0
Time [s]
(0.97
(0.98

0.05)
0.04)

0.5 1.0 1.5 2.0
Time [s]
(0.89
(0.89

0.15)
0.13)

0.5 1.0 1.5 2.0
Time [s]Velocity [cm/s]

(1.46
(1.11

(1.11
(1.08

(1.03
(1.02

Velocity [cm/s]

0.5 1.0 1.5 2.0
Time [s]

200
150
100
50
0
0

0.5 1.0 1.5 2.0
Time [s]

200
150
100
50
0
0

200
150
100
50
0
0

Velocity [cm/s]

0.5 1.0 1.5 2.0
Time [s]

200
150
100
50
0
0

0.07)
0.06)

0.13)
0.18)

200
150
100
50
0
0

Velocity [cm/s]

0.17)
0.10)

(1.23
(1.21

(1.11
(1.13

Velocity [cm/s]

(1.32
(1.17

0.5 1.0 1.5 2.0
Time [s]

200
150
100
50
0
0

Velocity [cm/s]

0.5 1.0 1.5 2.0
Time [s]

200
150
100
50
0
0

0.02)
0.03)

Generated hand trajectories by the proposed model
Mesured hand trajectories

Velocity [cm/s]

0.06)
0.07)

(1.05
(1.06

Velocity [cm/s]

(1.13
(1.13

Velocity [cm/s]

0.5 1.0 1.5 2.0
Time [s]

200
150
100
50
0
0

Velocity [cm/s]

200
150
100
50
0
0

0.06)
0.05)

Velocity [cm/s]

200
150
100
50
0
0

(1.08
(1.07

Velocity [cm/s]

Velocity [cm/s]

200
150
100
50
0
0

Velocity [cm/s]

Velocity [cm/s]

Generated hand trajectories by the proposed model
Mesured hand trajectories

200
150
100
50
0
0

(1.12
(1.12

0.06)
0.08)

0.5 1.0 1.5 2.0
Time [s]
(0.95
(0.96

0.07)
0.06)

0.5 1.0 1.5 2.0
Time [s]
(1.82
(1.31

0.55)
0.09)

0.5 1.0 1.5 2.0
Time [s](b): A non-impaired subject

Fig. 11. Observed and simulated hand velocity for the myoelectric prosthesis user and the non-impaired subject
4. C. M. Harris and D. M. Wolpert, Signal-dependent Noise
Determines Motor Planning, Nature 394 (1998) 780–784.
5. P. Morasso, V. Sanguineti and T. Tsuji, A Dynamical
Model for the Generator of Curved Trajectories,
Proceedings of the International Conference on Artificial
Neural Networks (1993) 115–118.
6. T. Tsuji, Y. Tanaka P. Morasso, V. Sanguineti and M.
Kaneko, Bio-mimetic Trajectory Generation of Artificial
Potential Field with Base Generator, IEEE Transaction
on Systems, Man and Cybernetics 32 (4) (2002) 426–439.
7. T. Tanaka and T. Tsuji, Bio-mimetic Trajectory
Generation Using a Neural Time-base Generator, The
Journal of Robotic Systems 22 (1) (2005) 625–637.
8. V. Mathiowetz, G. Volland, N. Kashman and K. Weber,
Adult Norms for the Box and Block Test of Manual
Dexterity, The American Journal Occupational Therapy.
39 (6) (1985) 386–391.

Acknowledgements
This work was partially supported by a Grant-in-Aid forYoung Scientists B Number 26730111.
References
1. T. Chin, Kinden Gishiyu Kunren Maniyuaru, All-Japan
Hospital Publication Association Ltd. (2006), p. 83. (inJapanese).
2. T. Flash and N. Hogan, The Coordination of Arm
Movements:
An
Experimentally
Confirmed
Mathematical Model, Journal of Neuroscience 5 (7)(1985) 1683–1703.
3. Y. Uno, K. Kwato and R. Suzuki, Formation and Control
of Optimal Trajectory in Human Multi-joint Arm
Movement, Biol. Cybern. 61 (1989) 89–101.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 95

Re-creation of a membrane puncture’s sense of an object constituted of liquid and an outermembrane by a haptic device and a deformation simulation of the virtual objects
Takahiro Okada

Graduate School of Computer Scienceand System Engineering
Kyushu Institute of Technology
680-4, Iizuka City, Fukuoka, Japan, 820-8502
okada@mmcs.mse.kyutech.ac.jp
Eiji Hayashi

Department of Mechanical Information
Science and Technology
Kyushu Institute of Technology
680-4, Iizuka City, Fukuoka, Japan, 820-8502
haya@mmcs.mse.kyutech.ac.jp

Abstract
The purpose of this research is to develop a combined sense system that uses both force feedback and visual feedback
on a deformation simulation. We constructed a haptic device that gives a sense of that force to the operator whentouching the sample on a computer. We focused on a way to recreate a membrane puncture’s sense of salmon roes.
Keywords: force feedback, haptic interface, simulation

microscope but also a sense of force from the
manipulator. For this research, focus on to recreate
reaction force of puncture against an object whichconstituted of liquid and an outer membrane. Amongthem, salmon rows were handled in this research.

1. Introduction
Technologies that can accurately perform minute work
are now being sought for medical treatment and bio
technology field. Such minute work is improved by using
micromanipulators, but their operation is difficult
because the operator has no sense of force. He or she
relies only on sight through a microscope. As a result, aperson skilled in the use of this technology is needed forall minute work. The efficiency of minute work would be
improved if the operator is able to have a sense of force
while using a manipulator.
Here we describe the development of a more efficientsystem for minute operations. Our aim is to develop asystem using not only the sense of sight through a© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 96

Takahiro Okada, Eiji Hayashi.

1. System Structure
The system structure is shown in Fig. 1. This system
consists of the haptic device for generating reaction force,a microcomputer for controlling the haptic device,simulation for showing virtual objects and microscopefor measuring dynamic characteristics.
Fig. 2 provides a diagram of the haptic device. It
consists primarily of a rotor, a laser, and a positionsensitive detector (PSD). The angle of the rotor can bemeasured by the laser and the PSD. The haptic device
has a coil on the rotor with a polarity magnet. Whencurrent flows through the coil, the coil experiences aforce (Lorentz force) perpendicular both external
magnetic field and to the direction of the current flow.
The rotor is able to stable follow an input waveform lessthan the frequency 36 Hz.
Fig. 3 shows the connection between the haptic device
and a microcomputer. The haptic device is controlled bycommand signals from the microcomputer process.

Fig.3. The connection between the haptic device
and a microcomputer.
2. Force modeling for sensing a membranepuncture.
This section describes the modeling of a membranepuncture’s sense of an object constituted of liquid and anouter membrane. The object is assumed salmon rows.
Because salmon rows are easy to get and tractable.
Fig.4 shows the relation between the pushing amountof needle and the reaction force. The reaction force by
deformation before puncture is modeled by the Voigt’s
model. When the pushing amount exceeds the puncture
point, Transition to Maxwell’s model that is expressed as
a viscous resistance of a liquid. At this moment, operators
can feel the puncture sense by caused the sudden fall of
the reaction force. Fig.5 shows each model, and Table.1
shows characteristics of each model.

Fig.1. The system structure.

Fig.4. The relation between the pushing amount ofneedle and the reaction force.
Fig.5. Voigt’s model and Maxwell’s model.

Fig.2. The structure of the haptic device.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 97

Re-creation of a membrane

Table.1. Characteristics of each model.

3. Deforming the sample in simulation
Fig.8. Placing an arbitrary object on a bitmap.

In this study, we attempted to build a working systemusing a microscope, a haptic device, and a simulation. A
fundamental element was simulating the deformation ofa minute object. Fig.6 shows the graphical user interface(GUI) of the simulator. A graphic tool was created using
OpenGL to draw the object and to choose the shape ofthe sample, for instance, a cube or sphere. The dynamic
model of the sample consisted of a spring-mass array ofmass points in both the vertical and horizontal directions.
An example of the arrangement of mass points is shownin Fig. 7 When a force was applied at a mass point, thesimulation calculated the speed of all mass points that
had been affected. The image was renewed after every
ten calculations.
We defined a spring as having a size but no weight,
and a mass point as having a size, a weight, and a rigid
body. An arbitrary mass object can be placed on a spring
on a bitmap (Fig. 8). In addition, a sample can be seenfrom various viewpoints, and the deformation of thesample, which is impossible to observe by microscope,
can be checked. The shape of this object can be either acube or a sphere, and any point may be selected as a fixed
point or an operating point.

4. Characteristic measurements and areappearance experiment for an object
Dynamic characteristics of the object were measured
and then recreated using the haptic device as reaction
force before puncture. The object used this time wassalmon roe. We measured the response of salmon roe andshowed the displacement of its surface a figure. Fig. 9shows the response of the salmon roe.
Salmon roe is 6 millimeters in diameter and we cannot
precisely feel the sense of touch. Hence values in the
figure are multiplied by 68 so that human can feel thesense of salmon roe’s touch. Fig. 10 shows that responseof the salmon roe and haptic device. Table.2 shows thefrequency, constant of spring, and damping coefficient ineach ectopic focus.
In this experiment, there was a tendency to create atouch feeling that resembles a real and big salmon roe
when characteristic was recreated.

0.1

0.0

0.2

Time [s]
0.4
0.6

0.8

Displacement [mm]

0.0
-0.1
-0.2
-0.3
-0.4

Salmon roe

-0.5
-0.6

Fig.6.Simulator.

Fig.9. Time response of Salmon roe

Fig.7.Arrangement of masspoints© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 981.0

Takahiro Okada, Eiji Hayashi.

Table.2. Haptic device parameter of dynamic characteristic

0.0

Displacement [mm]

5.0
0.0
-5.0
-10.0
-15.0
-20.0
-25.0
-30.0
-35.0
-40.0

Frequency[Hz]

Spring constant[N/mm]

Damping ratio

6
6

0.072
0.072

2.6
1.0

0.2

Time [s]
0.4
0.6

0.8

0.3

First

0.25

Second

0.2

Third

0.15

approximation
straight line

Difference of damping ratio

Displacement
[mm]
0～6
6～35

1.0

0.1

y = 0.0187x
R² = 0.7133

0.05
0
0

5

10

15

Lateral area of under the liquid[mm2]Fig.12. the relation between the difference of damping
ratio and lateral area of under the liquid.

Salmon roe (seventy
times)(68 times)

5. Conclusion

HapticThe reaction force from puncturing an object which
consists of the liquid and an outer membrane wasrecreated by Voigt model and Maxwell model. Operators
can feel the puncture sense by caused the sudden fall of
the reaction force when changing the controlling model.
Future research should focus on building a system thatallows a reaction force to be detected and shown more
precisely. Such a system would make it possible to test
smaller samples.

Fig.10. Time response of Salmon roe and haptic device
Next, a viscosity was measured in order to recreate theliquid viscosity of salmon rows. Fig.11 shows theconditions of an experiment. The moving part of thehaptic device was sunk and then the moving part was
vibrated constant parameters. Then, the vibration wasdamped to compare the case of not touched liquidbecause of viscosity. The damping difference between
touched liquid and not.
In this experiment, Sodium alginate aqueous solution
1% weight ratio was used. Because the viscosity of liquid
is similar to salmon roes and also use in artificial salmonroes.
Fig.12 shows the relation between the difference of
damping ratio and lateral area of under the liquid. As aresult, the bigger the lateral area of under the liquid ofmoving part, result in the bigger the damping ratio.

6. Acknowledgement
This research was partially supported by the Ministryof Education, Science, Sports and Culture, Grant-in-Aid
for Scientific Research, 2012.
7. References
1.

2.

T.DOMOTO (2012) Construction of a sense of force
feedback and vision for micro-objects: Recreate theresponse and a sense of force of objects. Kyushu Instituteof Technology. Proceedings of the 15th International
Symposium on Artificial Life and Robotics, Beppu, Oita,
Japan, 2012
Y.ISHII (2016) Construction of a sense of force feedback
and vision for micro-objects: Development of a haptic
device. Kyushu Institute of Technology. International
conference on artificial life and robotics (ICAROB2016),
Jan 29-31, Okinawa, Japan, 2016

Fig.11. Experiment of measurement of viscosity.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 99

Exercise classification using CNN with image frames
produced from time-series motion data
Hajime Itoh

Division of Production Systems Engineering, Muroran Institute of Technology, 27-1 Mizumoto-cho
Muroran, 050-8585, Japan
Naohiko Hanajima

College of Design Manufacturing Technology, Muroran Institute of Technology, 27-1 Mizumoto-cho
Muroran, 050-8585, Japan
Yohei Muraoka, Makoto Ohata

Steel Memorial Muroran Hospital, 1-45 Chiribetsu-cho
Muroran, 050-0076, Japan
Masato Mizukami, and Yoshinori Fujihira

College of Design Manufacturing Technology, Muroran Institute of Technology, 27-1 Mizumoto-cho
Muroran, 050-8585, Japan
E-mail: 15042009@mmm.muroran-it.ac.jp, hana@mondo.mech.muroran-it.ac.jp

Abstract
Exercise support systems for the elderly have been developed and some were equipped with a motion sensor toevaluate their exercise motion. Normally, it provides three-dimensional time-series data of over 20 joints. In thisstudy, we propose to apply Convolutional Neural Network (CNN) methodology to the motion evaluation. The method
converts the motion data of one exercise interval into one gray scale image. From simulation results, the CNN was
possible to classify the images into specified motions.
Keywords: CNN, Gray scale image, Exercises classification, Time-series data.

Normally, the motion sensor provides three-dimensional
time-series data of over 20 joints. In previous studies, themotion was judged by human-induced conditions, suchas the height of a hand position or the angle of the arm
motion5. However, the human-induced conditions tended
to be experiential and intuition in some case. Sometimes,
the judgement did not fit the human judgement6.
In this study, we propose applying Convolutional NeuralNetwork (CNN) methodology to the motion evaluation.
The deep learning has succeeded in integrating big data.
CNNs is the oldest deep neural-network architecture andis widely used as a means of handwriting recognition7. In

1. Introduction
Population of the elderly is increasing compared with thatof the young in Japan1. It is estimated that the number of
elderly care staff will be short of approximately 680,000
in 20352, 3. In order to contribute for solving this problem,
exercise support systems has been developed4. It is
expected that the elderly can become healthy bystrengthening their muscles with gymnastics, and lesselderly care staffs will be necessary.
Some exercise support systems have been equipped witha motion sensor to evaluate the elderly’ exercise.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 100

Hajime Itoh, Naohiko Hanajima, Yohei Muraoka, Makoto Ohata, Masato Mizukami, Yoshinori Fujihira

recent years, image classification has been highly
succeeded with CNN8. However, there are few examples
that are applied to time-series data, and approaches tospeech recognition have been proposed9. In this paper,
we propose to apply CNN methodology to the motion
evaluation. The method converts all joints of the motion
data into one gray scale image. Then, CNN learns andclassifies the converted images. We first explain amethod to create motion data for CNN learning in
Section 2. Section 3 proposes a conversion method fromthe motion data to the gray scale image. After that, somecase studies are explained. Finally, we show the result oflearning using CNN and discuss its ability ofclassification.

Fig. 1. The image converted from a motion data. One of the
stripes represents time-series of the X-Y-Z change of eachjoint and it is represented in rows in order

2. Creation of motion data for training data

column. The origin of the X-Y-Z coordinate system isplaced 1200 mm downward from the joint at the centerof the body, the luminance value changes every 10 mm
and can express up to 2550 mm.

Learning with CNN requires a lot of training data.
However, exercise by humans cannot always do the rightmotion every time. Therefore, we created motion data by
simulating human exercises using simple formulas. They
were prepared with respect to what was recorded by amotion sensor in advance4. The motion sensor canacquire three-dimensional position data of 20 joints. To
imitate the position coordinate, we utilized sinusoidal
functions like Eq. (1). Eq. (1) is an example of thecoordinate in the vertical direction of a hand position P.
=+ ×sin( ) + rand

(0 <3.2. Arrangement of order of rows
We can define several arrangements of order of the row.
In this paper, we try two kinds of arrangement. First one
is a joint oriented order as shown in Fig. 1. Second one isa coordinate oriented order, that is , , , …, , ,, …, , , , …, where subscript is the number ofthe joints.

< 2 ) (1)

Where R is the coordinate of the rotating joint, l is thelength of the arm from the rotating joint to the hand. The
function rand is a uniform random number generator witha certain value.

4. Case study for learning with CNN
We chose five kinds of motion for learning experiment.
They are shown in Fig.2 and their descriptions are shownas follows4.

3. Conversion of motion data to image
CNN cannot process time-series data because there is no
input unit related to producing image frame from the time.
In this section, we propose the conversion method fromthe time-series motion data to the gray scale image. Thenthey are learned with CNN.

i. Raise and drop arms from sideways to top.
ii. No actioniii. Raise and drop your arms from front to top.
iv. Raise the left leg 90 degrees forward.
v. Bend trunk 90 degrees forward
We used Caffe to learn CNN. It is a deep learning
framework developed by BVCL. The number of layersof CNN is six, the first layer has kernel size 5 and output
is 32, the second layer has kernel size 5 and output is 32,
the third layer has kernel size 5 and output is 64. Also,
each pooling layer has kernel size 3. The number of

3.1. Representation of pixels
Fig. 1 shows the image converted from a time-series
motion data. Each row expresses X-Y-Z coordinate value
for each joint, and the value is converted thereby to 255
tone gray scale. Time is ordered from left to right for the© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 101

Exercise classification using CNN

Table 1.
Results when processing 100 images of the motion i. As the image being processed approaches 1, the possibility ishigher, and as it is closer to 0, it indicates that there is a high possibility that the image is different.
Comparative item
Confirmation of Average
exercise image Standard deviation
Learning for big Average
motionStandard deviation
Learning for
Averagesmall changeStandard deviation

i.

ii.

iii.

iv.

v.

vi.

vii.

1
2.58E-09
0
1.06E-11
1
1.87E-11 2.94E-19 1.39E-23 4.2E-15
01.65E-13 2.80E-21 1.00E-25 3.3E-17
0.999781 5.54E-17
01.88E-29 2.76E-44 0.000219 2.34E-22
7.07E-06 1.66E-18
05.75E-31 8.49E-46 7.07E-06 6.9E-24

output units varies depending on learning problem setting.
We prepared 100 images for each kind of motion using
Eq. (1). The random number contributes the variety ofdata set. The number of training iterations was set to 4000.
We performed three kinds of case study to confirm theability of classification of the motions using proposed
method.
4.1. Detection of exercise
In order to confirm whether the exercise movement canbe detected correctly, two kinds motions, i and ii werelearned with CNN. Their images are greatly different.
There are two inputs / outputs in the CNN composition.
4.2. Classifying for large difference
There is much difference over the prepared exercises. In
order to confirm the ability to classify completely
different actions, 5 kinds of exercises, i to v were learned

viii.

ix.

x.

0
0

1.1E-14
2.98E-1600with CNN for the classification of the exercises. There
are five inputs / outputs in the CNN composition.
4.3. Classifying for small difference
There is a possibility that the arm does not rise to the top
because of a disorder of the body. It may cause a verysmall difference in the actions. To investigate ability ofclassification for such small difference, we prepare five
additional motions, vi to x with respect to the motions, ito v. The motions, vi. and viii raise the arms to the lower
position from the top by 10 degrees than the motions, i
and iii. The motions, ix and x raise the legs up to 80
degrees other than the montions, iv and v. Also, themotion vii raises the arms lower by 10 degrees than the
motion i. There are ten inputs / outputs in total. The smallchange of 10 degrees set here is an exercise that cannot
actually be judged by the exercise support system.
According to the exercise supervisor, the small change of
10 degrees is judged to be a bad practice, so we willverify whether it can be judged or not in this study.
5. Experimental result and discussion
After learning, classification test was performed. We
prepare data set for the classification separately from thelearning data, and the number of data is 100. Table 1
shows the probability and standard deviation ofclassification for result of each motion with test data. At
first, the row was arranged for the joint oriented order.
5.1. Detection of exercise
It can be seen from first 2 rows in Table 1 that they arecorrectly classified, that is average of i is 1, and ii isalmost zero. The average value of 1 shows possibility ofFig. 2. Exercise to learn at CNN. The direction of the arrow
indicates the movement direction, the top is the front view andthe bottom is the horizontal.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 102

Hajime Itoh, Naohiko Hanajima, Yohei Muraoka, Makoto Ohata, Masato Mizukami, Yoshinori Fujihira

excessive learning. The reason is considered that the kindof the motions is just two and the images used forlearning are very similar.

larger than that in Table 1. It indicates that the possibilityof obtaining the feature is higher for the motion i.
6. Conclusions

5.2. Classification for large difference

In this paper, we proposed a method to apply time-series
motion data to CNN. We showed that learning andclassification by CNN are possible by converting the
motion data of one exercise interval into one gray scale
image. It was found that not only large difference but also
small difference in the motion data was correctlyclassified. Arrangement for rows did not affect the resultsof classification in this case. We will apply the proposed
method to analyze the human exercise data in our future
work.

From middle 2 rows in Table 1, the motion i is detected
correctly. An average value of 1 was output for the
motion i. It is considered that the reason is same as in
Section 5.1. And, since other average values are verysmall, it is considered that the correct classification isachieved.
5.3. Classification for small difference
In classification including the small change, it isobserved from bottom 2 rows Table 1 that the averagevalue of the motion i is almost 1 and the others are quite
small values. Therefore, we should say that theclassification was achieved. The average of the motion i
is smaller than the previous section. The average value ofthe motion vi is second largest in the motions. It suggeststhat there is some correlation between the motion i andvi. Actually, the motion vi is a variant of the motion i.
These values seem to represent their relationship.
Because of this, small changes in luminance values canalso be regarded as feature amounts, therefore there is ahigh possibility of judging the difference in small change
accurately.

References
1. Cabinet office, government of Japan, Heisei 28th edition
Aged Society White Paper (Japan, 2016), pp. 2-8.
2. Ministry of Economy, Trade and Industry, "Study Groupon Providing Long-Term Care Service According toFuture Nursing Care Demand" Report (Japan, 2016), pp.
22-39.
3. M. Kasahara et al., Relationship between One Leg
Standing Time and Knee Extension Strength in Elderly
Patients, Tairyoku Kagaku. 50(3) (2001), pp. 369–373.
4. Y. Takahashi, Study on exercise support robots providing
individualized instruction, Presentation of Graduation
Theses. (2014).
5. M. Hirano et al., Development of an Exercise Support
System for the Elderly Which Use a small Humanoid
Robot, Journal of Robotics and Mechatronics . 25(6)(2013) 939–948.
6. K. Urita et al., Precision Analysis of Center-Of-Gravity
Points Estimation Based on a Motion Sensor -Comparison
with a Force Plate, SICE annual conference 2014conference proceedings, In Ternational Conference on
Instrumentation, Information Technology and System
Integration (Japan, Sapporo, 2014), pp. 2020–2024.
7. D. Ciresan et al., Multi-column Deep Neural Networks for
Image Classification, In Computer Vision and Pattern
Recognition (CVPR), 2012 IEEE Conference on (2012),
pp. 3642-3649.
8. A. Krizhevsky et al., ImageNet Classification with Deep
Convolutional Neural Networks, In Advances in Neural
Information Processing Systems 25 (2012).
9. O. A. Hamid et al., Convolutional Neural Networks for
Speech Recognition, IEEE/ACM Transactions on Audio,
Speech, and Language Processing. 22(10) (2014) 1533–
1545.

5.4. Rearrangement for rows in the coordinate
oriented order
Next, we show the result when the row was arranged in
the coordinate oriented order. Table 2 shows a part of theresult of the rearrangement for the rows order andlearning with CNN.
Table 2. Result of rearranging rows of pixels.
Except for the motions, i and vi, since it was lessthan E - 24, it is excluded in this table.
AverageStandard deviationi.
0.999892
3.15E-06vi.
0.000108
3.15E-06

It is observed that there is no significant difference
between two kinds of row orders. It was found that the
average value of the motion i in Table 2 was slightly© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 103

Proposal and Evaluation of the Gait Classification Method
Using Arm Acceleration Data and Decision Tree
Kodai Kitagawa

National Institute of Technology, Kushiro Collage,
2−32−1,Otanoshike-nishi, Kushiro, Hokkaido, 084−0916, Japankitagawakitagawa156@gmail.com

Yu Taguchi

National Institute of Technology, Kushiro Collage,
2−32−1,Otanoshike-nishi, Kushiro, Hokkaido, 084−0916, Japantgcyu56@gmail.com

Nobuyuki Toya

National Institute of Technology, Kushiro Collage,
2−32−1,Otanoshike-nishi, Kushiro, Hokkaido, 084−0916, Japan
toya@kushiro-ct.ac.jp

Abstract
We have been developing a system for falling prevention to classify gait patterns based on stride length and footclearance by arm accelerations. In this paper, we propose gait classification method using arm acceleration data anddecision tree. Also, we evaluate whether decision tree using three-axis accelerations as feature quantities could
classify three gait patterns. (Three gait patterns are "Normal", "High step" and "Long step".) The result showed that
this method can classify three gait patterns of some subjects.
Keywords: Gait, Falling prevention, Decision tree, Arm, Acceleration, Smartphone

accidents of elderly people cause not only damages but
also serious effects for daily life after accidents.
It is known that tripping is the most frequent causeof falling [1-2]. Due to decreased perceptual ability,
elderly people cannot recognize that foot clearance andstride length are insufficient in gait [3]. Therefore, wehave been developing a system which urges elderly
people to improve of gait for falling prevention byclassifying gait patterns based on stride length and footclearance using acceleration data from sensors of a

1. Introduction
Lately, Japan has a lot of falling accidents of elderly
people. Falling accidents often lead to hospitalization on
serious injuries. Hospitalization adversely affects
exercise capacity and mental state. According to asurvey, 86.2% of elderly people who have experienced
falling accidents are afraid of falling, and 44% of them
have restricted outdoor activities due to the fear of
falling [1]. From these reports, it can be said that falling© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan.19-22, Seagaia Convention Center, Miyazaki, JapanP - 104

Kodai Kitagawa, Yu Taguchi, Nobuyuki Toya

3. Experimentsmartphone [4]. In this system, the smartphone is wornon an arm as a part at which it is easy to attach on adaily basis for users, and gait patterns are classified byarm acceleration data. In previous studies, it has been
pointed out that movement of the arm is influenced by
the gait in terms of dynamic stability [5].
It has been found that time waveforms of arm
accelerations change by stride length and foot clearance.
However, we also have found that it is difficult toclassify gait patterns by only peak value and averagevalue of time waveforms [4]. In this paper, we propose
the gait patterns classification method using machinelearning and arm acceleration data as feature quantities.
2.

3.1. Data Measurement
We conducted the gait experiment on the treadmill
by young people as basic examination of the proposed
method. In this experiment, we instructed subjects (10young people) three gait patterns ("Normal", "Long step", "High step") on the treadmill. As for "Long step", weconfirmed that stride lengths based on steps and gait
speeds were the largest in three gait patterns duringwhole analysis. Also, as for "High step", we confirmedthat foot clearance was rising higher than 15 cm byinfrared sensors. Besides, we set gait speed which iseasy to walk for each subject by treadmill.
In this experiment, as feature quantities, three-axis arm
acceleration data for 60 steps per each gait pattern were
measured by the smartphone. In addition, composite
direction acceleration data also were calculated fromthree-axis acceleration data as feature quantities.

MethodIn proposed method, gait patterns are classified bydecision tree with three-axis acceleration and composite
direction acceleration data on an arm as feature
quantities. As an advantage of the decision tree, it is
known that if-then rules in a tree structure forclassification are readable. In addition, some algorithmsof the decision tree can use continuous variables.
Gait parameters are often measured in time periods.
Because elderly people are unstable in their gait cycle,
there is a possibility that astandard deviation may
increase in measured data. Therefore, in the proposed
method, one set of arm acceleration data is measured
per step by detecting the timing at which the forearmcrosses the body by a proximity sensor of thesmartphone.

Fig. 2. Experiment system

3.2. Data Analysis
As a basic study of the proposed method, we
evaluated whether decision trees using arm acceleration
data as feature quantities could classify gait patterns.
We used Weka and J48 algorithm for evaluation ofclassification performance. In the data set used forFig. 1. Method for gait classification© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan.19-22, Seagaia Convention Center, Miyazaki, JapanP - 105

Proposal and Evaluation of

Table 1. Accuracy and threshold variable of root nodeclassification, arm acceleration data ("x-direction", "ydirection", "z-direction", and "composite direction")measured by the experiment were used as feature
quantities, and the information of the gait patterns("Normal", "Long step", or "High step") was added toeach data as a correct answer. We calculated accuracy,
precision, recall, and F-measure as evaluation index ofclassification performance for each subject on 5-foldcross-validation. We also analyzed if-then rules of a tree
structure for each subject.
4. Results and discussions
It was found that gait patterns can be classified withaccuracy more than 0.65 in 7 subjects (Table 1). This
result shows the possibility that the proposed method isusable for gait classification. Comparing F-measure ineach gait pattern, it was confirmed that F-measure of"Normal" was larger than other gait patterns in 6
subjects (Fig. 3). In addition, it was found that precisionand recall of "Normal" was larger than other gait
patterns too (Fig. 4). As the reason for these, it isassumed that "Normal" was not required an arm swing
for dynamic stability, so difference of arm acceleration
data appeared between "Normal" and other gait patterns.
Results of the analysis for if-then rules in the tree
structures of each subject showed that threshold valuesof "y-direction” are larger than threshold values of "xdirection" and "z-direction" (Fig. 5). It is conceivablefrom these results that "y-acceleration" is dominant in"composite direction".
In addition, we examined the root node in the tree
structure of each subject, and found that arm
acceleration data of "x-direction" and "composite
direction" are selected more frequently as the thresholdvariable for root node in the tree structure of highaccuracy subjects (Table 1). In the decision tree, sinceroot node is arranged so that decrease of entropy gets a
maximum, root node is considered to be important for
gait classification. Therefore, "x-direction" and"composite direction" which are selected more
frequently as the threshold variable for root node, and"y-direction" which is dominant in "composite
direction" are considered to be important in gaitclassification.

Fig. 3. F-measure of proposed method

Fig. 4. Precision and recall of proposed method© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan.19-22, Seagaia Convention Center, Miyazaki, JapanP - 106

Kodai Kitagawa, Yu Taguchi, Nobuyuki Toya

Fig. 5. Tree structure subject G

5. Conclusions

References

In this paper, we proposed a method to classify gait
patterns by decision tree using arm acceleration data("x-direction","y-direction","z-direction",and"composite
direction")asfeaturequantities.
Experimental results revealed that the proposed method
could classify three gait patterns ("Normal", "Longstep", and "High step") of some subjects.
Moreover, from tree structure of decision tree, we
found that arm acceleration data of "x-direction" and"composite direction" were selected more frequently as
the threshold variable for root node in the tree structureof high accuracy subjects. In addition, we found that "yacceleration" is dominant in "composite direction".
From these results, tree structure of decision tree
showed that "x-direction", "composite direction" and"y-direction" are considered to be usable in gaitclassification.
In order to improve classification performance, it isconsidered that analysis of "two-axis" acceleration datais needed in further studies. From now on, we will
examine the gait classification method using "two-axis"
composite direction acceleration data from "x-direction"and "y-direction" which are considered to be importantin gait classification.

1. K. Hunkyung, et al. , The Relationship between Fallrelated Activity Restriction and Functional Fitness inElderly Women, Japanese Journal of Geriatrics,
38(6)(2001), pp. 805-811.
2. P. W. Overstall, A. N. Exton-Smith, F. J. Imms, & A.
L.Johnson, Falls in the elderly related to postural
imbalance, Br. Med. J. , 29（1）(1977), pp. 261-264.
3. S. Saito, S. Muraki, Study on Tracks and Sense of Feet
Position while Stepping Over an Obstacle in the Elderly,
The Japanese journal of ergonomics, 46(2)(2010), pp.
172-179.
4. K. Kitagawa, K. Go, N. Toya, Some Discussions ofWalking State Estimation Using Miniaturized Sensors for
Fall Prevention, Research Reports: Kushiro National
College of Technology, 49(2016), pp.27-30.
5. S. Tanaka, S. Murata, T. Kodama, Effect of Upper Limbs
on Walking, Rigakuryoho Kagaku, 25(2) (2010), pp. 177180.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan.19-22, Seagaia Convention Center, Miyazaki, JapanP - 107

A Method for Secure Communication Using a Discrete Wavelet Transform for Audio Dataand Improvement of Speaker Authentication
Kouhei Nishimura, Yasunari Yoshitomi, Taro Asada, and Masayoshi Tabuse

Graduate School of Life and Environmental Sciences, Kyoto Prefectural University,
Nakaragi-cho, Shimogamo, Sakyo-ku, Kyoto 606-8522, Japan
E-mail: {yoshitomi, tabuse}@kpu.ac.jp, t_asada@mei.kpu.ac.jp}
http://www2.kpu.ac.jp/ningen/infsys/English_index.html

Abstract
We developed a secure communication method using a discrete wavelet transform. Two users must each have a
copy of the same piece of music to be able to communicate with each other. The message receiver can produce
audio data similar to the sending user's speech by using our previously proposed method and the given recording ofmusic. To improve the accuracy of speaker authentication, the quantization level for the scaling coefficients isincreased. Furthermore, the amount of data sent to the message receiver can be remarkably reduced by exploiting
the characteristics of this data.
Keywords: Secure communication, Audio data processing, Wavelet transform, Encoding

1.

data. Exploiting this phenomenon, we have developed asecure communication method using audio data.1

Introduction

The elderly are often targets of telephone fraud. The
fraudster pretends to be a grandchild of the elderly person
while talking on the phone, and appeals to the elderly
person to send money, for example, through a banktransfer. In the present study, we propose a method for
secure communication using a discrete wavelet transform(DWT) and thus improve speaker authentication; this isan enhancement of our previously proposed method.1 It
can be used with Internet protocol (IP) telephones, and it
has the potential to help prevent telephone fraud.
2.

2.1.2. Use of five quantization levels for scalingcoefficients(1) Parameter setting
In our reported study,1 we set the following coding
parameters.
The values of Th ( minus ) and Th(plus) in Fig. 1
are chosen such that the nonpositive scaling coefficients( Sm in total frequency) are equally divided into twogroups by Th(minus) , and the positive scaling

Proposed Method

coefficients ( S p in total frequency) are equally divided
into two groups by Th(plus) . Next, the values of T1,
T2, T3, and T4, which are the parameters for controllingthe authentication precision, are chosen to satisfy thefollowing conditions:
1) T1 Th(minus) T 2 0 T 3 Th(plus) T 4 .
2) The value of S T 1 , which is the number of scalingcoefficients in (T 1, Th(minus)) , is equal to ST 2 ,2.1. Encoding
2.1.1. Phenomenon exploited for the coding
algorithm for audio data
In the course of our research,1 we found that thehistogram of the scaling coefficients for each domain of amultiresolution analysis (MRA) sequence is centered at
approximately zero when a DWT is performed on audio© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 108

Kouhei Nishimura, Yasunari Yoshitomi, Taro Asada, Masayoshi Tabuse

which is the number of scaling coefficients in[Th(minus), T 2) , i.e., ST 1 ST 2 .

2.1.3. Use of eight quantization levels for scalingcoefficients

3) The value of ST 3 , the number of scaling coefficients in

Here, we define eight sets of G8,0, to G8,7, as follows:• G 8 , 0 {V | V V SC , V T 1} ,• G 8 ,1 {V | V V SC , T 1 V Th (minus) } ,(T 3, Th(plus)] , is equal to ST 4 , the number of scalingcoefficients in (Th ( plus), T 4) , i.e., ST 3 ST 4 .
4) ST 1 / S m ST 3 / S p .

SC, Th (minus) V T 2} ,• G8,2 {V | V V
SC• G 8 , 3 {V | V V
,T 2
V
0} ,
SC
• G 8 , 4 {V | V V , 0 V T 3} ,• G 8 , 5 {V | V V SC , T 3 V Th (plus) } ,• G 8 , 6 { V | V V SC , Th (plus)
VT 4} ,• G 8 , 7 { V | V V SC , T 4 V } .
Again, we let the representative value for each set, G8,i,be its average, m8,i. For the formation of audio data, we
use the code C8 , which is the sequence of c 8 , i definedfor eight quantization levels for scaling coefficients inthe similar manner as c i described in Section 2.1.2, andm 8 , j as defined above.

In the present study, the values of both ST 1 / Sm andST 3 / S p

are set to 0.3, which was determined

experimentally.

2.1.4. Use of 16 quantization levels for scalingcoefficients(1) Parameter setting
The values of T1m, T1p, T2m, T2p, T3m, T3p, T4m, andT4p, which are the parameters for controlling theauthentication precision, are chosen to satisfy thefollowing conditions:
1) T 1m T 1 T 1 p Th(minus) T 2m T 2 T 2 p 0

Fig. 1. Schematic diagram for demonstrating the selection of
the scaling coefficients for encoding the audio data.1(2) Encoding
In the preprocessing of the audio data prior to encoding,the scaling coefficients V of the MRA sequence are
separated into five sets (G0 to G4), as shown in Fig.1,
under the following criteria:• G 0 {V | V V SC , V T 1} ,T 3m T 3 T 3 p Th(plus) T 4m T 4 T 4 p
2) The value of T1m is defined so that it equally divides
the number of scaling coefficients in [V min, T 1] . T1p,
T2m,…, T4p are defined similarly to T1m.
(2) Encoding
Sixteen sets of G16,0 to G16,15. are defined as follows:• G 16 , 0 { V | V V SC , V T 1 m } ,• G16 ,1 {V | V V SC , T 1 m V T 1} ,• G 16 , 2 { V | V V SC , T 1 V T 1 p } ,• G 16 , 3 { V | V V SC , T 1 p V Th (minus) } ,• G 16 , 4 {V | V V SC , Th (minus) V T 2 m } ,• G 16 , 5 { V | V V SC , T 2 m V T 2 } ,• G 16 , 6 { V | V V SC , T 2 V T 2 p } ,• G 16 , 7 { V | V V SC , T 2 p V 0 } ,• G 16 , 8 { V | V V SC , 0 V T 3 m } ,• G16 , 9 {V | V V SC , T 3 m V T 3 } ,• G16 ,10 {V | V V SC , T 3 V T 3 p} ,• G16 ,11 {V | V V SC , T 3 p V Th (plus) } ,• G 1 {V | V V SC , T 1 V T 2 } ,• G 2 {V | V V SC , T 2 V T 3} ,• G 3 {V | V V SC , T 3 V T 4 } ,• G 4 {V | V
V SC , T 4V } ,SC
where V is the set of scaling coefficients in the audio
data file.
The scaling coefficients for the MRA sequence are
encoded according to the following rules, where V idenotes scaling coefficient i: Vi G0 , c i = 0; when
Vi G1 , c i = 1; when Vi G2 , c i = 2; when Vi G3 ,c i = 3; and when Vi G4 , c i = 4. We represent thescaling coefficient for each set, Gj, by its average value,
mj. For the formation of audio data, we use a code C ,which is the sequence of c i and m j defined above.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 109

A Method for Secure• G 16 ,12 {V | V V SC , Th (plus) V T 4 m } ,• G16 ,13 {V | V V SC , T 4 m V T 4 } ,• G 16 ,14 {V | V V SC , T 4 V T 4 p } ,• G 16 ,15 {V | V V SC , T 4 p V } .
As before, the value for each set, G16,i, is represented by
its average value, m16,i. For the formation of audio data,

2.3 Data for communication

we use the code C16 , which is the sequence of c 16 ,isignifies the maximum integer that is not greater than x.
Then, a sequence D2(BA )n is defined as

A sequence D1( B A ) n is defined as D1( B A ) n

where nis the total number of cases where Yi

zp

2.2. Audio data formation using code replacement
In this subsection, the formation of sound data is explained;
for this example, we use five quantization levels for thescaling coefficient.1 The scaling coefficient sequence foraudio data A is expressed as S ( A ) k { x1 , x 2 , x 3 , , x k } ,Section 2.4 and is expected to be similar to A .

of scaling coefficients of B at this level . Then, asequence C ( B ) k {Y1 , Y2 , Y3 , , Yk } is determined,
where Yi {0,1,2,3,4} is the element index, which indicatesis the average of the scaling

to which of the five sets of scaling coefficients y i of B
belongs. S ( B ) k is defined as S ( B ) k {b1 , b2 , b3 , , b k } ,coefficients of A in the range denoted by X i {0,1,2,3,4} andis obtained from A . Then, the audio data B A is defined ashaving the scaling coefficient sequence S(BA )k and a valueof zero for all wavelet coefficient values at every level.
S ( B A ) k is defined as S (BA )k {bA,1, bA,2 , bA,3 , , bA, k } ,

where

bi

m0B , m1B , m2B , m3B , m4B

is the average of thescaling coefficients of B at the range denoted by
Yi {0,1,2,3,4} and is obtained from B .
A sequence D3(B)k is defined as D3( B ) k z B ,1 , z B , 2 , , z B , k ,is the average of the scaling

where k is the total number of scaling coefficients of B

coefficients of B in the range denoted by X i {0,1,2,3,4}

at this level, and zB, qobtained from A . S (BA )k is obtained by replacing Y iwith X i when Yi

In communications between two users, the message
sender and the receiver each have the secret key B, andthe sender sends D1( B A ) n and D2(BA )n to the receiver.1
Then, the receiver composes B A , which is defined in

In this subsection, the processing of sound data formationis also explained using the case of five quantization levels,
as an example, for the scaling coefficient.1 The scalingcoefficient sequence for audio data B is expressed as
S ( B ) k { y1 , y 2 , y 3 , , y k } , where k is the total number

is determined, where X i {0,1,2,3,4} is the element index,
which indicates to which of the five sets of scalingcoefficients x i of A belongs. Next, the audio data A isdefined as having the scaling coefficient sequence S ( A ) k
and a value of zero for all wavelet coefficient values at every
level. S ( A ) k is defined as S ( A )k {a1, a2 , a3 , , ak } ,m0B, m1B, m2B, m3B, m4B

X i .1 Here, x

2.4. Audio data composition

where k is the total number of scaling coefficients of A at
this level. Then, the sequence C ( A) k {X 1 , X 2 , X 3 , , X k }where bA,iXi ,D 2( B A ) n
Z 1 , Z 2 , , Z n , where n is the total
number of cases for which Yi X i and Z p X i .1

Section 2.1.2, and m 16 , j defined above.

m0A, m1A, m2A, m3A, m4A, zn ,

yi mod256 , and the integer p is increased from

1 to n, in steps of size 1, when Yi

defined for 16 quantization levels for scalingcoefficients in the similar manner as c i described inwhere aiz1 , z 2 ,X i , and then replacing b i with b A , i ,where b i is the average of the scaling coefficients of B in
the range denoted by Y i . Therefore, C ( BA ) k C A k . As ayq mod256 . B A is determined

as follows: S(BA )k is calculated from S ( B ) k by replacingb q with mZ p when zB,qB

z p , for p 1 ,…, n, thenthe audio data B A is composed using the inverse DWT(IDWT) of the scaling coefficient sequence S(BA )k andthe value of zero for all wavelet coefficients at every level.
The receiver composes B A from D1( B A ) n and D2(BA )n ,result, B A is expected to be similar to A .

which are determined by both A and B and are sentby the sender, and B , which the receiver has obtained© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 110

Kouhei Nishimura, Yasunari Yoshitomi, Taro Asada, Masayoshi Tabuseprior to the conversation. B A is expected to be similar toA.
2.5. Data reduction
2.5.1. Processing for D1
Because z p

yi mod256 , z p is in the range from 0 to

255, and thus it can be expressed using 8 bits. In our
computer, an integer is represented by 32 bits. Therefore,
four values for z p , each expressed using 8 bits, can be
integrated into a single value expressed by 32 bits. For
D1( B A ) n, z n , z j is defined as

z1 , z 2 ,
z' j

z4i 3

z4i 2 256

taken from a copyright-free database.2 In all cases, all of the
produced B A were audible and sounded similar to A ; each
B A was made with five, eight, or 16 quantization levels. An
increase in the quantization level improved the sound quality
because a waveform made from B A with a higher
quantization level was more similar to the original waveformthan was one made with a lower quantization level, as
shown in Fig. 2. For (1), (2), and (3) in Section 2.5.2, thedata reduction for one minute of audio data at 44.1 kHz, 16
bits, a single channel, and volume of 87 KB was as follows:(1) D1(75 KB )D1' ( 48 KB ), D 2( 49 KB )D 2' (9 KB )(2) D1(86 KB)(3) D1(92 KB)

D1' (55 KB), D2(57 KB)
D1' (59 KB), D2(65 KB)

D2 (21 KB)
D2 (29 KB)

z4 i 256 3 ,z4 i 1 256 2

where i, j are natural numbers. As a result, we obtain asequence for D1' ( B A )m

m
When

n/4
n/4

n mod 4 0 ,, z 'm , wherez '1 , z '2 ,1n mod 4 0
n mod 4 0 .

z4 m k 20 (k0,, n mod 43).

Here, x is defined as in Section 2.3. In the first case of
the above formula on m , the total amount of data,

D1' , stored in a computer is thus one quarter of that
stored for D1. However, the total amount of data sent to

Fig. 2. Examples of waveforma receiver depends on the way in which the data areexpressed.
2.5.2. Processing for D 2(1) Case of five quantization levels
D2(BA )n Z1, Z2 , , Zn and D2' (BA )l2

Z' j Z13i 12 Z13i 11 5 Z13i 10 5

Z1, Z2 , , Zl , whereZ13i 512 ,aredefined as described in Section 2.5.1.
(2) Case of eight quantization levels
D2(BA )n Z1, Z2 , , Zn and D2 (BA )r Z1, Z2 , , Zr ,where Z j Z 10 i 9 Z 10 i 8 8 Z 10 i 7 8 2
Z 10 i 8 9 ,are defined as described in Section 2.5.1.
(3) Case of 16 quantization levels
D2(BA )n Z1, Z2 , , Zn and D2 (BA )s Z1, Z2 , , Zs ,where Z j Z 8 i 7 Z 8 i 6 16 Z 8 i 5 16 2
Z 8 i 16 7 ,References
1. Y. Tsuda, K. Nishimura, H. Oyaizu, Y. Yoshitomi, T.Asada,and M.Tabuse, A method for secure communication using a
discrete wavelet transform for audio data, J. Robotics,
Networking and Artif. Life to appear.
2. M. Goto, H. Hashiguchi, T. Nishimura and R. Oka, RWC
music database: database of copyright-cleared musicalpieces and instrument sounds for research purposes,
Trans. IPSJ, 45(3) (2004) 728-738.

are defined as described in Section 2.5.1.
3.

4. Conclusion
We developed a secure communication method using a
discrete wavelet transform for audio data; we used anincreased number of quantization levels for the scalingcoefficients along with a data reduction technique. The
waveform produced by the proposed method was moresimilar to the original one than that produced by ourpreviously proposed method.1

Numerical Experiment

We applied the proposed method, using several voicerecordings for A , and for B , we used two recordings ofmusic, one classical and the other hip-hop. The music was© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 111

A Recipe Decision Support System Using Knowledge Information and Agent
Keita Saito, Taro Asada, Yasunari, Yoshitomi, Ryota Kato, Masayoshi Tabuse

Graduate School of Life and Environmental Sciences, Kyoto Prefectural University,
1-5 Nakaragi-cho, Shimogamo, Sakyo-ku,Kyoto 606-8522, Japan
E-mail: {yoshitomi, tabuse}@kpu.ac.jp, {t_asada, r_kato}@mei.kpu.ac.jp}
http://www2.kpu.ac.jp/ningen/infsys/English_index.html

Abstract
Abstract – We report on the development of a recipe recommendation system using collaborative filtering andimpression words. As a user interface, we adopted MMDAgent. In our system, the first recommendation processusing collaborative filtering is terminated based on the previously decided condition, after which the second
recommendation process identifies the recipe that is most similar to past successful recommendations from recipesthat have not been recommended thus far. As a final step, one recipe is selected from all successfully recommended
recipes.
Keywords: Recipe recommendation, Collaborative filtering, Impression word, MMDAgent.

being the least appealing. We then transformed thesubjective scores of “1” to “3” and “4” to “5” into “0”and “1”, respectively. Those scores (“0” or “1”) are
expressed as "evaluation scores" below.

1. Introduction
It is sometimes bothersome to decide upon a recipe formeals because there are frequently numerous choices
available. Therefore, a support system that recommends a
new recipe every day could be beneficial. In this study, wedeveloped and evaluated a new system that can be used tonarrow down recipe choices to a final recommendation. In
the current study, we have adopted MikuMikuDanceAgent(MMDAgent)2 as a user interface (UI).

3. Recipe recommendation method using
impression words
Our recipe recommendation method also uses
impression words.1 In this study, 10 impression words pairs(see Table 1) extracted from “Sizzle Word Report 2014”
were used. The method explained briefly below is also basedon our previously reported music recommendation method.3
Each recipe prepared in advance was assigned oneof seven score levels ranging from “-3” to “+3” byparticipating test subjects who used those impression
word pairs. We then transformed these subjective scoresto a three-level scale (“-3” to “-2” as “-1”", “-1” to “+1”as “0” and “+2” to “+3” as “+1”). These scores are
expressed as “impression evaluation scores” below.

2. Recipe recommendation method using
collaborative filtering
The recipe recommendation method1 proposed in our current
study is based on collaborative filtering, and is, in turn, basedon our previously reported music recommendation method.3
That method is explained briefly below.
We began by preparing a database composed ofrecipes that had been already been subjectively scored at
five-levels with “5” being the most appealing to “1”© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 112

Keita Saito, Taro Asada, Yasunari, Yoshitomi, Ryota Kato, Masayoshi Tabuse

When a recipe not recommended to a user has thesame values except “0” as that for at least one
recommended recipe having a high evaluation by theuser on the three-level score for at least five impression
words, the recipe is treated as having a positive
evaluation by the user. In contrast, when a recipe notrecommended to the user has the same scores(disregarding “0”) as that for another recipe justrecommended to the user and having a negativeevaluation by the user on the three-level score for atleast seven impression words, the recipe is treated ashaving a negative evaluation by the user.
Table 1. Impression word pairs.
Good flavor －Refreshing flavor
Mellow － Spicy
Sweet － Gentle
Addicted － not Addicted
Rich － Crispy
Juicy － Scratchy
Warm －Cool
Melty － Chewy
Seasonal Limited－ not Seasonal Limited
Authentic － not Authentic

4.

Reciperecommendation
using
the
collaborative filtering and impression words

Fig. 1 shows a flowchart of the proposed system, which
combines collaborative filtering and impression words based on
the authors’ recipe recommendation method.1 The systemrecommends recipes stored in the database to the user. Both therecommendation process using collaborative filtering andimpression words are terminated when the number ofrecommended recipes reaches the pre-set upper limit (K). In our
proposed system, the recommendation process using
collaborative filtering is terminated when there no users in theuser reference list that show the exact same evaluation for therecommended recipe as the user up to that point. The
recommendation process then continues by identifying the mostsimilar recipe, from the viewpoints of three-level scores(disregarding “0”) based on impression words, to thatsuccessfully recommended among recipes not yet
recommended. Just before concluding the recommendation
process, the user database is updated with subjective evaluationsof the recipe by adding the viewpoints of users the recipe byadding the viewpoints of users recommending the recipe.

Fig. 1. Recipe recommendation using collaborative filtering andimpression words.

that considers yesterday’s dinner recipe in addition to therecommendation method discussed in Section 4. First,
recipes in recipe groups that are similar to the recipe
that user selected the day before are recommended byusing the method described in Section 4. Next, recipes
in recipe groups that differ from previous day’s

5. Proposed system
In this section, we propose a system that narrows down
recipe choices to a final recommendation using a method© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 113

A Recipe Decision Support

selection are also recommended by using the method
described in Section 4. As in the impression word
recommendation, the judgment of a recipe whose
impressions are similar to yesterday’s dinner recipe isbased whether the "impression evaluation score"
coincides with five or more of the impression wordpairs of 10 items. Fig. 2 shows a recipe recommendation
flowchart that considers yesterday's dinner.
6. Performance evaluations
6.1. Conditions
From a total of 49 recipes that exclude side dishes, soups,
and arranged dishes, a main course dish that one or moreuser designated as “I have never tried” were extracted fromthe 293 entries in the "Rakuten recipe"4 category for use in
our evaluation. The impression words selected were four
pairs of “taste expression”, four pairs of “texture
expression”, and two pairs of “situation expression”
extracted from the “Sizzle Word Report 2014”.5 Synonyms
and antonyms were judged in the Weblio dictionary.6
Similar expressions in the “expression” rankings aregrouped together and chosen via the following procedure:
Step 1: The top 10 words satisfying the condition that
their antonyms are within the top 25 words arechosen in each ranking.
Step 2: The words that ranked within the top 10 and theirantonyms within the same ranking are chosen.
Step 3: The words ranked within the top 10 are chosen under
the restriction that their antonyms are out of ranking.
Step 4: Processing repeats for words ranking below the top 10.
The proposed system recommends a recipe to 10 new
participating users who were not among the 12 users
who entered evaluations into the database. K was set to15, and experiments were conducted under thefollowing two conditions:
Recommendation accuracy = Number of recipes towhich the user replied, “I want to try” / Number ofrecommended recipes.
Final recommendation accuracy = Number of users thatrecommended the recipe that the user most wants to try /Number of users.
Our experiment was performed on a Dell Inspiron 7559
personal computer (PC) equipped with Intel Core i7-6700HQ
2.60 GHz central processing unit (CPU) and 8.0 GB of
random access memory (RAM). The Microsoft Windows 10
Professional operating system (OS) was installed on the PC

Fig. 2. Recipe recommendation considering yesterday’s dinner.

and Microsoft Visual C++ 6.0 and Microsoft Visual C++
2010 were used as the development languages.
6.2. Results and discussions
Fig. 3 shows a sample recommendation for a new user,
while Fig. 4 shows the recommendation accuracy of 10
new users. The mean value of the proposed system’s
recommendation accuracy was 33.0%, while that of therandom recommendation was 21.4%. The value of the
final recommendation accuracy of the proposed system© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 114

Keita Saito, Taro Asada, Yasunari, Yoshitomi, Ryota Kato, Masayoshi Tabuse

was 50.0%, while that of the random recommendation
was estimated as lower than 21.4%.
The mean value (33.0%) of the proposed system’s
recommendation accuracy was lower than that (71.1%)
by our previously reported system.1 From this result, itis thought that a user's favorite recipes focused in ourpreviously reported system1 were not necessarily thosethat he or she wants to try, so it will be necessary tochange the subjective recipe scoring system used in thethis study to consider recipes the user wants toexperience in the future. Since the accuracy value of our
proposed system’s final recommendation exceeds thevalue of random selection, it can be said that our
proposed system has proven to be effective.
We then conducted a questionnaire survey of 10
user participants regarding the recommendations
produced by our system. The four categories below
were evaluated:
Speaking ease of user to UITime needed to produce recommendation
Order ofrecommendation

RecipeNo.

Recommendation
resultEvaluation matching user

1

19

○

1,2,3,4,5,7,8,9,10

2

34

○

1,3,4,5,7,8,9,10

3

18

×

φ

4

8

○

5

33

○

6

14

○

7

21

○

8

9

○

9

4

×

10

20

○

11

38

○

12

40

○

13

42

○

14

2

×

15

7

○

Naturalness of UI output as utterance
Dish display necessity
For the category results, one participant reported
that the UI was “very easy to talk to”, three participants
said it was “somewhat easy to talk to”, five said that“talking was normal”, and one said it was “slightly hardto talk to”. It should be noted here that no participant
said the UI was “Hard to talk to”. For the category
results, no participants reported that time required to
output a recommendation was “too short”, “somewhat
short”, or “too long”. Instead, seven participants said thetime was “as expected,” and three participants said it
was “slightly excessive”. For the category
results,
one participant said the UI’s utterances were “verynatural”, five said they were “somewhat natural”, one
called them “appropriate”, three called them “slightly
unnatural”. It should be noted here that no participant
said the UI utterances were “too unnatural”. For the
category
result, seven participants said visualdisplays were “necessary”, two participants said they
were “somewhat necessary”, and one was “undecided”.
No participant said the displays were “slightly
unnecessary” or “unnecessary”.
7. Conclusion
In this paper, we proposed a recipe recommendation
system that combines collaborative filtering andimpression words and is based on our previously reported
system.1 We showed that our proposed system is more
effective than random recommendations and that it has theability to narrow down potential recipes to a single choice.

Recommendation
using impression
word

References
1. K. Saito, T. Asada, Y. Yoshitomi, R. Kato, and M. Tabuse,
A Recipe recommendation System Using Knowledge
Information and Agent (in Japanese), in Proc. of Human
Interface Symposium 2016 (Japan, Tokyo, 2016), pp.51 -54.
2. MMDAgent, http://www.mmdagent.jp/ Accessed 25 July
2016.
3. Yoshizaki S, Yoshitomi Y, Koro C, Asada T (2013), Music
recommendation hybrid system for improving recognition
ability using collaborative filtering and impression words. J.
Artif. Life and Robotics 18(1-2) (2013) 109-116.
4. Rakuten recipe, http://recipe.rakuten.co.jp/category/
5. Sizzle Word Report2014 Accessed 25 July 2016.
http://www.bmft.jp/pdf/services/ kotoba14.pdf
6. Weblio dictionary, http://thesaurus.weblio.jp/ Accessed
25 July 2016.

Fig. 3. Sample new user recommendation.

Fig. 4. Accuracy recommendations of 10 new users.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 115

A System for Analyzing Facial Expression and Verbal Response of a Person While Answering
Interview Questions on Video
Taro Asada, Yasunari Yoshitomi, and Masayoshi Tabuse

Graduate School of Life and Environmental Sciences, Kyoto Prefectural University,
1-5 Nakaragi-cho, Shimogamo, Sakyo-ku, Kyoto 606-8522, Japan
E-mail: t_asada@mei.kpu.ac.jp, {yoshitomi, tabuse}@kpu.ac.jp
http://www2.kpu.ac.jp/ningen/infsys/English_index.html

Abstract
We have developed a system for analyzing facial expressions of a person while answering interview questions on avideo. A video capturing the answerer is analyzed by OpenCV and the feature parameter for an eye area in addition
to the mouth area focused in our reported research. Moreover, the time to utterance of the person answering justafter an interview question and the fundamental frequencies of his or her voice are measured for analyzing his or
her mental state.
Keywords: Facial expression analysis, Movement analysis, Mouth area, Eye area, Interview on video, OpenCV.

video. The video capturing the “answerer” in aninterview is analyzed by image processing software(OpenCV3) and the previously proposed featureparameter (facial expression intensity), which ismeasured for the eye area in addition to the mouth area.
The mouth area was the focus of our previously
reported research. Moreover, the time to utterance of theanswerer just after an interview question and thefundamental frequencies of his or her voice aremeasured for analyzing his or her mental state.

1. Introduction
In Japan, the average age of the population has been
increasing, and this trend is expected to continue. Alongwith this, the number of older people with dementia
and/or depression has been increasing very rapidly. To
improve the quality of life (QOL) of elderly people
living in a care facility or at home, we have developed amethod for analyzing the facial expressions of a person
who is having a conversation with another person on avideophone.1,2 However, in our previously reported
system1,2 for analyzing the facial expression of a person
during a conversation with another person, facial
expression intensity was assumed to be affected by (1)the conversation topic, (2) the partner, and (3) the facial
expression of the partner. In the present study, (1), (2),and (3) are fixed by using an interview video. We
developed a system for analyzing the facial expressionsof a person while answering the interview questions on

2. Proposed System and Method
2.1. System overview and outline of the method
We constructed three modules in our system based on
our reported research.4 The first is a module forreplaying an interviewer’s video for the answerer, thesecond is a module for recording the answerer’s© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 116

Taro Asada, Yasunari Yoshitomi, Masayoshi Tabuse

response on video, and the third is a module forprocessing the recorded video of the answerer. The firstmodule is a pseudo-interview. In the recorded data, thesizes of the faces are standardized, and the data are
analyzed by using OpenCV for some feature parameters,
as outlined below.
The Y component obtained from each frame in the
dynamic image is used for analyzing the facial
expressions. The proposed method is as follows: (1) thesize of the lower part and the upper part of the face arestandardized; (2) the mouth area in the lower part andthe eye area in the upper part of the face are extracted;(3) the facial expression intensities for these two areas
are measured; (4) the reference frame is selected; (5) thebest positions for the mouth and the eye areas in theframe are determined; (6) the time to utterance of theanswerer just after a given interview question ismeasured for analyzing the mental state of the answerer;(7) the feature parameters for the facial expression
strengths of these two areas are calculated; and (8) the
fundamental frequencies for voices of the answerer aremeasured by a conventional method. In the following
subsections, (2) and (3) are explained in detail. For
details on (1), (4), and the judgment of utterance in (6),see Refs. 1 and 2. For details on (5) of the mouth area,
see Ref. 2. For details on (7) of the mouth area, see Ref.
1. The best positions for the eye areas in the frame aredetermined in a way similar to that for the mouth area.
In addition, the feature parameters for facial expression
strengths of the eye areas are calculated in a way similar
way to that for the mouth area.

2.2. Extraction of the mouth and eye areas
Next, by using OpenCV, the mouth and eye areas are
extracted as rectangular shapes. An example of a face
image and the extracted images of the mouth and theeye areas is shown in Fig. 1.
2.3. Measurement of facial expression intensity
For the Y component of the selected frame, the feature
vector for facial expression was extracted for the mouth
and the eye areas. The extraction was performed byusing a discrete cosine transform (2D-DCT) for eachsection of 8×8 pixels.
As the feature parameters for expressing facial
expression, we selected 15 low-frequency components
from the 2D-DCT coefficients, but excluded the direct
current component. Next, we obtained the mean of theabsolute value of each of these components in the areasof the mouth-part and the eye-part. In total, we obtained
15 values as elements of the feature vector for the areasof the mouth-part and the eye-part. The facial
expression intensity is defined as the norm of thedifference vector, which is a vector of the difference
between the feature vector for the expressionless face
and that for an observed expression. The candidate of
facial expression intensity, defined as the norm of thedifference vector between two feature vectors, was used
for selection of the reference frame.2
3. Experiments
3.1. Conditions
The interviewer’s video was made from a video
recording a scene in which a psychiatrist interviewed asubject. Only the psychiatrist’s voice remains in theinterviewer’s video. In the video, the psychiatrist asks
the subject three fundamental questions generally used
for a patient with potential depression. Based on apreliminary experiment for deciding the appropriate
interval time, the interval time between two questions
was edited by using video editing software. The
experiment was performed in the following
computational environment: the PC set in front ofsubject A was a Dell Precision T1600; CPU: Intel Xeon
E31225 3.1 GHz; 8.0 GB memory; OS: Microsoft
Windows 7 Professional. The development language
was Microsoft Visual C++ 2008 Express Edition.

Fig. 1. Whole-face image (left), extracted images of the eye
area (upper right) and the mouth area (lower right).

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, JapanP - 117

A System for Analyzing

Table 2. Average of fundamental frequencies of voice of thesubjects for each intentional emotion in Experiments 1-3.

Two males (subject A in his 30s, subject B in his 20s)
participated in the experiments. The subjects wereinterviewed by the interviewer video, which was about 40
seconds in length. As an initial condition in the experiment,
the subjects were instructed to keep a neutral facial
expression without utterance for about five seconds just after
starting the playback of the interview video. After the initialstate of the neutral facial expression was terminated, thesubjects were requested to intentionally respond with three
types of emotions (Experiment 1: relaxed, Experiment 2:
excited, Experiment 3: depressed). The visual and audio
information of the subjects in the experiments were saved as
AVI files having 640×480 pixels for each frame. The AVI
files were used for measuring the feature parameters of the
facial expressions, the time to utterance of the subject justafter an interview question was given, and the fundamental
frequencies of the subject.

1(relaxed)2(excited)3(depressed)

Subject A

127.97144.61107.03

Subject B

93.33

137.3899.55(Hz)

Table 3. Time to utterance of the answerer just after aninterview question was terminated.

Subject A

Subject B

Exp.

Q.1

Q.2

Q.3

Ave.

1(relaxed)

2.3

1.3

0.8

1.4

2(excited)

0.6

1.0

0.0

0.5

3(depressed)

2.5

2.6

2.1

2.4

1(relaxed)

1.7

1.5

1.1

1.4

2(excited)

0.8

0.7

0.3

0.6

3(depressed)

1.7

2.0

1.8

1.8
(s)

3.2. Results and discussion

4. Conclusion

The facial expression intensity for the mouth area wasmore sensitive than that for the eye area (Figs. 2-5). The
facial expression intensity for the mouth area becamehigh during the response to a question. On the other hand,the facial expression intensity for the eye area fluctuatedwidely and was almost independent of the utterance. Notethat the feature parameters for facial expressions for themouth area tended to be greater in Experiment 2 than inboth Experiments 1 and 3, while those for the eye area
tended to be smaller in Experiment 1 than in bothExperiments 2 and 3 (Table 1). The average of thefundamental frequencies of the voice of the subjects was
greater in Experiment 2 than in both Experiments 1 and 3(Table 2). The order of increasing time to utterance of theanswerer just after an interview question was terminatedwas Experiments 2, 1, and 3 (Table 3). The experimental
results described in this subsection show the usefulness ofthe proposed system.

We developed a system for analyzing facial expressionsof a person while answering interview questions in avideo. Moreover, the time to utterance of the answerer
just after an interview question and the fundamental
frequencies of his or her voice were measured in orderto analyze his or her mental state. The experimental
results show the usefulness of the proposed system.
Acknowledgements
The authors would like to thank Professor J. Narumotoof Kyoto Prefectural University of Medicine for his
valuable support and helpful advice in the course of thisresearch. We would also like to thank Mr. K.
Nishimura, a student of the Graduate School of Kyoto
Prefectural University, for his cooperation in theexperiments. This research was supported by COI
STREAM of the Ministry of Education, Culture, Sports,
Science, and Technology of Japan.

Table 1. Feature parameters for facial expressions.
Exp.

Exp.

1(relaxed)2(excited)3(depressed)

Subject

Mouth area

1.5

1.8

1.7

A

Eye area

2.4

3.6

4.4

Subject

Mouth area

2.2

3.5

1.4

B

Eye area

3.4

4.3

3.6

References
1. T. Asada, Y. Yoshitomi, R. Kato, M. Tabuse, and J.
Narumoto, Quantitative evaluation of facial expressions
and movements of persons while using video phone, J.
Robotics, Networking and Artif. Life 2(2) (2015) 111-114.
2. T. Asada, Y. Yoshitomi, R. Kato, M. Tabuse, and J.
Narumoto, A System for Facial Expression Analysis of a

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, JapanP - 118

Taro Asada, Yasunari Yoshitomi, Masayoshi Tabuse

Exp.1(relax)
Exp.1(relaxed)

15

Facial expression intensity

Facial expression intensity

Person While Using Video Phone, J. Robotics,
Networking and Artif. Life 3(1) (2016) 37-40.
3. Open CV. http://opencv.org/ Accessed 18 November 2016.
4. T. Asada, Y. Yoshitomi, M. Tabuse and J. Narumoto, A
System for Facial Expression Analysis of a Person While
Answering Interview Questions on Video (in Japanese),
in Proc. of Human Interface Symposium 2016 (Japan,
Tokyo, 2016), pp.679-682.

Exp.2(excited)
Exp.2(excited)

B2

15

B1

10
5
0

Exp.2(excited)
Exp.2(excited)0

Exp.3(depressed)
Exp.3(depressed)

10

20Time (s)

30

A2

A1

10

2

5

B1
B2
Fig. 4. Changes in facial expression intensity of mouth
area for subject B (upper graph). Whole-face images andmouth images are shown for two moments during
Experiment 2 (B1 and B2), as indicated on the graph(lower images).

0
0

10

20
Time (s)30Exp.1(relaxed)
Exp.1(relax)

15

Facial expression intensity

A1
A2
Fig. 2. Changes in facial expression intensity of mouth
area for subject A (upper graph). Whole-face images andmouth images are shown for two moments during
Experiment 2 (A1 and A2), as indicated on the graph(lower images).
Facial expression intensity

Exp.1(relaxed)
Exp.1(relax)
Exp.3(depressed)
Exp.3(depressed)

A4

A3

10

B3

Exp.3(depressed)
Exp.3(depressed)

Exp.2(excited)
Exp.2(excited)

B4

10

Exp.2(excited)
Exp.2(excited)

Exp.3(depressed)
Exp.3(depressed)

Exp.1(relaxed)
Exp.1(relax)

15

5
0
0

2

10

20
Time (s)

30

5
0
0

10

Time (s)

20

30

B3
B4
Fig. 5. Changes in facial expression intensity of eye area
for subject B (upper graph). Whole-face images and eyepart images are shown for two moments during Experiment
2 (B3 and B4), as indicated on the graph (lower images).

A3
A4
Fig. 3. Changes in facial expression intensity of eye area
for subject A (upper graph). Whole-face images and eyepart images are shown for two moments during Experiment
2 (A3 and A4), as indicated on the graph (lower images).
© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, JapanP - 119

Real-Time System for Horizontal Asymmetry Analysis on Facial Expression and Its
Visualization
Ryoichi Shimada, Taro Asada, Yasunari Yoshitomi, Masayoshi Tabuse

Graduate School of Life and Environmental Sciences, Kyoto Prefectural University,
1-5 Nakaragi-cho, Shimogamo, Sakyo-ku,Kyoto 606-8522, Japan
E-mail: {yoshitomi, tabuse}@kpu.ac.jp, t_asada@mei.kpu.ac.jp}
http://www2.kpu.ac.jp/ningen/infsys/English_index.html

Abstract
We report on the development of a real-time system for facial expression horizontal asymmetry analysis andvisualization. In our system, the image signal input from a webcam is analyzed using OpenCV, and featureparameters (facial expression intensity) are measured separately for the left and right half regions in the mouth area.
Our real-time system then draws a graph expressing facial expression intensity changes. The experimental results
obtained thus far suggest that this system could be useful.
Keywords: Facial expression analysis, Real-time system, Moving image, Mouth area, Visualization, OpenCV

on the mouth area. The visualization method proposedin this paper is based on our reported method4 which
involves creating a graph from the OpenCV image data
and then analyzing the relationship between the facial
expression intensity and time. This visualization is
performed concurrently with the facial expression
intensity measurement.

1. Introduction
In Japan, the average age of the population has been
increasing, and this trend is expected to continue into
the future. We have therefore been studying ways ofapplying information technology (IT) to improving the
medical treatment provided to elderly people withpsychiatric disorders.
One of our current studies is aimed at analyzing thefacial expressions of persons in a moving image
captured by webcams. In this paper, we propose a realtime facial expression horizontal asymmetry analysis
and visualization system. Unlike current and previously
used methods1,2 in which the moving image data are
first saved in a personal computer (PC) and thenanalyzed, the moving image in our method is captured
by a webcam and analyzed in real-time via Open Source
Computer
Vision(OpenCV)
image-processing
3
software along with a previously proposed featureparameter (facial expression intensity) 1,2 that is based

2. Proposed System and Method
2.1. System overview and outline of the method
In this system, webcam moving imagery captured inreal-time is analyzed via the following process. We
constructed the modules in our system based on ourreported research.4 First the sizes of faces in thecaptured image data are standardized, and then analyzed
using OpenCV and our proposed facial expression
feature parameters.
The frames of the moving images are then changed
from RGB image data into YCbCr image data, after
which the Y component obtained from each frame in the© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 120

Ryoichi Shimada, Taro Asada, Yasunari Yoshitomi, Masayoshi Tabuse

dynamic image is used for facial expression analysis.
The proposed method consists of (1) the extracting themouth area, (2) measuring the facial expressions feature
vectors, and (3) measuring the facial expression
intensity. In (1), the mouth area is extracted from theframes by using OpenCV. In (2), for the Y componentof the selected frame, facial expressions feature vectors
are extracted for each 8 8-pixel section. In (3), thefacial expression intensity, which is defined as the norm
difference between the facial expression feature vectorof the reference and target frames, is measured. These
details are explained in the following subsections.

DCT) for each 8 8-pixel section. To measure thefeature parameters of the facial expressions, we selected
low-frequency components from the 2D-DCT
coefficients as the facial expression feature vector
elements. However, the direct current component is notincluded. In total, 15 feature vector elements are
obtained. As mentioned above, facial expression
intensity is defined the norm difference between the
facial expression feature vector of the reference andtarget frames. The reference frame selection method is
explained in next subsection.

2.2. Mouth area extraction

In this subsection, we propose a new method forautomatically selecting reference frames. This method
was adopted because, in our new system, the moving
image captured by webcam is analyzed in real-time.
This means we cannot use our previous method,1,2
which was designed for the non-real-time analysis.
In this method, the first 10 continuous frames ofmouth area data successfully extracted after the webcam
recording begins are treated as reference frame candidates.
Then, beginning with the first reference frame candidate,
the facial expression intensity of all candidate frames ismeasured, and the sum of all facial expression intensities
is calculated. The candidate frame with the minimum
value is selected as the reference frame.

2.4. Reference frame selection

First, moving image data are changed from RGB toYCbCr image data, after which the face area is extracted
from the changed image as a rectangular shape, and thelower 40% portion of the face area is standardized. Next,
the mouth area is extracted from that area. The reason
the mouth area was selected for facial expression
analysis is because it is where the differences between
neutral and happy facial expressions appear mostdistinctly. An example of the moving image frame data
and the extracted mouth area image is shown in Fig. 1.

2.5. Facial expression intensity visualization
Facial expression intensity is measured using ourpreviously discussed method,4 in which a straight line isdrawn on a graph image prepared using OpenCV.

Fig. 1. Moving image frame and extracted mouth area.

In this paper, the mouth area is separated into left
and right half regions, and the facial expression
intensity of each region is separately measured. An
example of a mouth area separated into the left and righthalf regions is shown in Fig. 2.

3. ExperimentFig. 2. The mouth area separated in the left and right half regions

2.3. Facial expression intensity measurement
For the Y component of the selected frame, the facial
expression feature vector is extracted for the mouth area
using a two-dimensional discrete cosine transform (2D-The experiment was performed on a Dell OPTIPLEX
780 PC equipped with Intel Core 2 Duo E8400 3.0 GHz
central processing units (CPUs) and 4.0 GB of random
access memory (RAM). The Microsoft Windows 7
Professional operating system (OS) was installed on the
PC and Microsoft Visual C++ 2008 Express Edition
was used as the development language.
The newly proposed system display is shown in Fig.
3. We performed evaluation experiments for two test
subjects (hereafter Subjects A and B) under the three
conditions listed below. Furthermore, the message“SMILE, PLEASE!” was flashed on the display screen
at points 20 and 40 s from the system start.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 121

Real-Time System for Horizontal

40

Average: 9.90

28.60
20 40

20

40
Average: 16.71

40

Fig. 3. Display while new system is in operation.

1.
2.
3.

Subjects maintained neutral faces for about 60 s.
Subjects smiled when the above message was displayed.
Subjects raised the right side of their mouth when
the above message was first displayed, and raised
the left side of their mouth when the above
message was displayed a second time.
The experimental results are shown in the following
figures (Figs. 4 to 16). Arrows in each graph indicate
the max point of the facial expression intensity.
10

10

20

20

Time(s)40

Average: 8.71

26.18
20

Average: 3.15

8.70

20
Average: 5.12

8.46

40

Average: 4.01

8.82

20

Average: 14.90

40.28

Time(s)20
40
Fig. 9. Facial expression intensity of the right half of the mouth
under experimental condition 2 (upper: Subject A, lower: Subject
B).
40

Average: 14.11

29.21

20

40

20

Average: 5.48

11.62

40

20

Fig. 5. Reference frame under experimental condition 1 (left:
Subject A, right: Subject B).
1040

Fig. 8. Facial expression intensity of the whole mouth underexperimental condition 2 (upper: Subject A, lower: Subject B).

40
Time(s)20
Fig. 4. Facial expression intensity of the whole mouth underexperimental condition 1 (upper: Subject A, lower: Subject B).

10

44.85

40
Average: 16.06

20
Time(s)40
Fig. 6. Facial expression intensity of the right half region of the mouth
under experimental condition 1 (upper: Subject A, lower: Subject B).

40

43.56

20

Average: 7.94

13.48
10

20
20
16.51

40

40

Time(s)Fig. 10. Facial expression intensity of the left half region underexperimental condition 2 (upper: Subject A, lower: Subject B).

Average: 7.78

10

20
40
Time(s)Fig. 7. Facial expression intensity of the left half region underexperimental condition 1 (upper: Subject A, lower: Subject B).
© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 122

Ryoichi Shimada, Taro Asada, Yasunari Yoshitomi, Masayoshi Tabuse
40
Average: 6.68

A1: 15.29

20

Fig. 16. Left half mouth area of point A3 (left) and point B3 (right).

The experimental results obtained thus far suggest that
this system could be useful for situations where realtime reaction to facial expressions is one of theimportant factors. Upon examining the experimental
results, some problems were discovered. Facial
expression intensities on the left half of the subject’s
faces during neutral face recording were always higher
than those of the right half, and the reason for thisdiscrepancy is unclear.

40

20

Average: 10.04

40
B1: 36.12

20

20

40

Time(s)Fig. 11. Facial expression intensity of the whole mouth underexperimental condition 3 (upper: Subject A, lower: Subject B).

4. Conclusion
Herein, we proposed a real-time system for facial
expression horizontal asymmetry analysis and visualizationand performed a number of evaluation experiments. It isbelieved that this system could prove useful in thetreatment of psychiatric ailments such as depressive
disorder and dementia, in addition to providing treatment
options for persons with half side body paralysis.

Fig. 12. Mouth area of point A1 (left) and point B1 (right).
40
20

Average: 6.20

A2: 22.69

20

40
Average: 13.23

40

Acknowledgements

B2: 46.25

The authors would like to thank Professor J. Narumotoof Kyoto Prefectural University of Medicine for his
valuable support and helpful advice in the course of thisresearch. We would also like to thank Mr. K. Nishimura,a student of the Graduate School of Kyoto Prefectural
University, for his cooperation in the experiments. This
research was supported by COI STREAM of the
Ministry of Education, Culture, Sports, Science, andTechnology of Japan.

20

Time(s)40
20
Fig. 13. Facial expression intensity of the right half of the mouth underexperimental condition 3 (upper: Subject A, lower: Subject B).

Fig. 14. Right half mouth area of point A2 (left) and point B2 (right).
40

References

Average: 11.48

A3: 30.92

1. T. Asada, Y. Yoshitomi, R. Kato, M. Tabuse, and J.
Narumoto, Quantitative evaluation of facial expressions
and movements of persons while using video phone, J.
Robotics, Networking and Artif. Life 2(2) (2015) 111-114.
2. T. Asada, Y. Yoshitomi, R. Kato, M. Tabuse and J.
Narumoto, Analysis of facial expressions robust against
small imperfections in mouth-part area extraction fromface-images of persons while using video phone (inJapanese), in Proc. of Human Interface Symposium 2015
(Japan, Hakodate, 2015), pp.187-190.
3. Open CV. http://opencv.org/ Accessed 1 December 2015.
4. R. Shimada, T. Asada, Y. Yoshitomi, M. Tabuse, and J.
Narumoto, A real-time system for facial expression analysis
and its visualization (in Japanese), in Proc. of Human Interface
Symposium 2016 (Japan, Tokyo, 2016), pp.643-646.

20

20

40

20

40

40

Average: 11.69

B3: 36.44
20

Time(s)Fig. 15. Facial expression intensity of the left half region underexperimental condition 3 (upper: Subject A, lower: Subject B).

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 123

Development of Mouse System for Physically Disabled Person by Face Movement Using Kinect
Junpei Miyachi, Masayoshi Tabuse

Graduate School of Life and Environmental Sciences, Kyoto Prefectural University,
1-5 Nakaragi-cho, Shimogamo, Sakyo-ku, Kyoto 606-8522, Japan
E-mail:j_miyachi@mei.kpu.ac.jp, tabuse@kpu.ac.jp

Abstract
We developed a system which operates a computer by a facial movement using Kinect. Recognition of a facial
movement of a person makes it possible to operate a computer. We can move a mouse cursor by changing the face
direction, and we can carry out an operation of mouse click by recognizing an open mouth or a closed eye. In thispaper, we evaluated the effect on operability due to the face direction and recognition rate due to distance.
Keywords: Mouse system, Kinect, Physically disabled people, Recognition of face movements.

of the face. We can move a mouse cursor by changing
the face direction. We can carry out an operation ofmouse click by recognizing an open mouth or closed
eye. This reduces the burden on the user.

1. Introduction
Physically disabled people cannot move their limbs
freely. It is difficult for a physically disabled person touse a computer. The importance of the computer with
the information society increases as a current social
background. Therefore, support of the computer
operation is required. The interface for them has beendeveloped so that they can use a computer recently.
There are two types of the interface. One is the contacttype which operates while attaching the device to a
body. Another is the noncontact-type which operates byrecognizing the movement of a body. Although thecontact-type has the advantage of easy detection of body
movement, the user must attach it directly during use.
On the other hand, noncontact-type must adjust
parameters for a user. To resolve these problems reducea burden for the user.
One of the possible physical movements of aphysically disabled person is facial movement (Thismeans the face direction and open and closed mouth oreyes). If a computer operates with recognizing the facial
movement, the physically disabled person can use it.
We developed a system which operates a computer by
the facial movement. Our system uses Kinect for
obtaining the face direction and extracting feature points

2. Kinect
Kinect is a motion capture device developed byMicrosoft Corporation. RGB camera, depth sensor, andmicrophone array is mounted in Kinect, it can obtain
RGB images, depth images, and audio information.
Moreover, joint positions of the whole body can be
estimated from the obtained depth images.
Kinect for Windows SDK is a software development
kit for using Kinect. Face Tracking is one of thefunctions in Kinect for Windows SDK. It canperform facial recognition and face tracking using
RGB images and depth images which Kinect
acquired. Furthermore, it can acquire feature pointsof face parts (eyes, nose, and mouth) and face
direction. Face tracking algorithm is based for Active
Appearance Model (AAM) with depth fitting1. It
allows accurate and real time tracking of human faces.
High definition face tracking (HD face) is also one of
the functions in Kinect for Windows SDK. It is possible©The 2017International Conference on Artificial Life and Robotics (ICAROB 2017),Jan. 19-22,Seagaia Convention Center, Miyazaki, JapanP - 124

Junpei Miyachi, Masayoshi Tabuse

to extract detailed facial feature points, and there are1347 feature points that it can extract.
3. Proposed System

direction as the vector. The moving speed of a mouse
cursor is changed according to the size of the vector.
When |a| < 10, we define the face is turning to the front.
Accordingly, a mouse cursor isn't moved.

3.1. System overview

3.3. Recognition of open mouth

Our system includes Kinect for operating a computer. A
user moves his face towards Kinect. The movement isrecognized on the basis of information acquired using
Kinect. It is possible to operate a computer. Our system
has three functions for a physically disabled person.
Firstly, it can control a mouse cursor along face
direction. Secondly, it can perform a mouse click byrecognizing an open mouth. Finally, it can carry out amouse click by judging the open or closed eyes. In thefollowing subsections, these functions are explained indetail.

We extract four feature points of mouth (top, bottom,
left and right) using HD face of Kinect for Windows
SDK 2.0, as shown in Fig. 2. The vertical length of themouth is found by feature point coordinates of the top
and bottom. The horizontal length of the mouth is foundby feature point coordinates of the left and right. The
rate of the open mouth Rm is expressed as Rm = hm / wm
where hm and wm denote the vertical and horizontal
length of the mouth. Let Thm be the threshold forjudgment of opened or closed mouth. When Rm > Thm, it
is recognized that the mouth is opened. In the present
study, the value of Thm is set to 0.4, which was
determined experimentally.

3.2. Control of a mouse cursor by face direction
We acquire angles of face direction using Face Trackingof Kinect for Windows SDK 2.0. The angles to use areℎ andwhich express a value of the angle(degree) for vertical and horizontal face direction. If theuser turn his face toward Kinect, the values of the face
direction are respectively, pitch = 0, yaw = 0. However,the user watches a monitor while using a computer.
Therefore, we change the origin of face direction
coordinate to the center of the monitor. We consider thedifference between the angle on watching a monitor andthe angle on watching Kinect. As shown in Fig. 1, thedifference of the angles is expressed using the following
equation:yθ = arctan ,zwhere y denotes a length between the center of monitor
and Kinect, and z denotes a distance of the depth
between a face of the user and Kinect, which is obtainedusing Kinect. We define the face direction vector a as:
a = ( yaw, pitch - θ ) .
The control of a mouse cursor is operated following
the face direction vector a. It is moved in the same

Fig. 2. Feature points of mouth extracted using Face
Tracking and the vertical and horizontal length.

3.4. Judgment of opened and closed eyes
We extract four feature points of eyes (top, bottom, left
and right) using HD face of Kinect for Windows SDK
2.0, as shown in Fig. 3. The range of X-axis is thedifference of x-coordinate between the left and right,
and the range of Y-axis is the difference of y-coordinate
between the top and bottom. We define these ranges as
eye region.
The judgment of opened or closed eye uses binary
images. For the binarization, at first, the detection of theeye region is performed in a RGB image. It is converted
from the RGB data to the luminance value. Next, we
make the histogram of luminance values in the eyeregion. The threshold for judgment of opened or closed
eye is decided using discriminant analysis method2 fromthe histogram. Our system applies the threshold in thefirst frame, and it is used for the judgment after the next
frame. It makes the luminance value a two level withthe threshold. The binary image is made by the above
procedure.
The vertical and horizontal lengths of the eye aresettled from the binary image. We make the histogramz

MonitoryKinectθ

User

Fig. 1. Positional relation of the user and Kinect, which is
looked from the side.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22,Seagaia Convention Center, Miyazaki, JapanP - 125

Development of Mouse Systemof black pixels. The vertical length of the eye is the
maximum on the histogram, and the horizontal length ofthe eye is the range of x-coordinate in the eye region.
The rate of the open eye Re is given by Re = he / wewhere he and we denote the vertical and horizontal lengthof the eye. Let The be the threshold for judgment ofopened or closed eye. When Re > The , the eye is judged
to be opening state. When Re < The , the eye is judged to
be closing state. In the present study, the value of The is
set to 0.3, which was determined experimentally.

was produced by Xbox One Kinect sensor which wasplaced in front of the PC, as shown in Fig. 4.
4.2. Method
We conducted three kinds of experiments which ware
performed by five subjects, respectively. For the firstexperiment, we measured times that a mouse cursor
moved by face direction from a circle drawn on screen
to another circle drawn in the distance of 20 cm. The
subject moved to eight directions (vertical, horizontal,
and diagonal), respectively. This experiment was
conducted to assess the operability by difference of
direction. For the second experiment, we examined therecognition rate of mouse click by open mouth. The
distance between Kinect and the subjects was conducted
at 0.6m, 0.8m, 1.0m, 1.5m, and 2.0m for evaluating
influence to the recognition rate by the difference indistance. The subjects perform three times of opening
movement for one second. The recognition rate is the
proportion that click processing is carried out in fact.
For the last experiment, we conducted experiment tojudge opened or closed eyes. This experiment was
conducted at distance of 0.6m, 0.8m, 1.0m, 1.5m, and2.0m between Kinect and subjects for evaluating
influence to the judgment accuracy by the difference indistance. We evaluate the accuracy of judgment with Fmeasure. F-measure is expressed the following
equation:
2PR
F=
,
P+R
where P, R and F denote the rate of frames which isreally closing eye among frames judged as closed state,
the rate of frames judged as closing state among frames
which is closing eye ,and the harmonic mean of
precision rate P and recall rate R. We performed
judgment of closed eye by visual observation.

Fig. 3. Feature points of eye extracted using Face
Tracking (left), and the binary image state and the verticaland horizontal length of opened eye (center) and closedeye (right).

3.5. Mouse click processing
Mouse click processing is performed by recognizing theintentional movement of the user. The movement is thefollowing conditions:
When the duration time of opening mouth statereach for t1,
When the duration time of closing eye state reachfor t2,where t1 and t2are arbitrary times. In the present study,
the value of t1 and t2 are set to 1.0 s and 0.8 s,
respectively. Mouse click processing is carried out onlyin the case of |a| < 10.
4. Experiments
4.1. Conditions
The experiment was performed in the following
computational environment: the PC was a HP ENVY
700-560jp (CPU: Intel(R) Core(TM) i7-4790 CPU
3.60GHz, memory: 8.00GB); the OS was Microsoft
Windows 8.1 Pro; the development language wasMicrosoft Visual C++ 2013 Express Edition. The image

4.3. Results and discussion
4.3.1. Movement of mouse cursor by face direction
Fig.5 shows the time that took to move from a circle toanother circle. From averaging of the moving time of 5
subjects, it was the movement of bottom direction totake the longest time. The second and third were themovement of bottom right and bottom left direction,

Fig. 4. Position of PC and Kinect during experiments.
© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22,Seagaia Convention Center, Miyazaki, JapanP - 126

Junpei Miyachi, Masayoshi Tabuse

Time (s)respectively. It is probably due to the frame of thesubject's glasses which interrupt his operation.
7
6
5
4
3
2
1
0

out of position was not correctly judged. Moreover,there were that the shadow around a closed eye wasdescribed black in the binary image. Therefore, it was
the reason that the rate of eye Re didn't fall below The.
1
0.8

TL

T

TR

R

BR

B

BL

L

0.6

P

0.4

R

0.2

F

Face direction
0

Fig. 5. Average moving time of five subjects by face
direction. TL, T, TR, R, BR, B, BL, and L denote top left,
top, top right, right, bottom right, bottom, bottom left, andleft direction, respectively.

0.6

1.0
1.5
Distance (m)2.0

Fig. 7. Precision rate, recall rate, and F-measure in theexperiment about judging opening and closing eyes.

4.3.2. Mouse click recognition by open mouth

5. Conclusion

Fig.6 shows recognition rate of mouse click by open
mouth. The recognition rate at 0.8m was 86.7%, which
was the highest rate. In contrast, the recognition rate at
1.5m was 73.3%, which was the lowest rate. The causeof the recognition failure was that the rate of open
mouth felt below the threshold momentarily because thesubject didn't open his mouth of adequate size.
Therefore, mouse click processing was carried out. It
was carried out by opening mouth of adequate size forone second again.

We developed a mouse system for physically disabled
people using Kinect. In this system, the user can move amouse cursor by face direction and perform a mouseclick processing by opening the user's mouth or closing
the user's eye. As the results, there was not theconspicuous influence to recognition of mouse clickprocessing by open mouth. F-measure of eye judgment
decreased greatly at 1.0m over. However, because
computer operation is usually performed at 1.0m or less,it can be operated by facial movement. As an area of
future work, we intend to develop additional functions
by recognizing other facial movements.

100
Recognition rate (%)

0.8

80

References60

1. Nikolai Smolyanskiy, Christian Huitema, Lin Liang, andSean Eron Anderson, Real-time 3D face tracking basedon active appearance model constrained by depth data,
Image and Vision Computing, vol.32 (2014) 860-869.
2. Nobuyuki Otsu, An Automatic Threshold Selection
Method Based on Discriminant and Least Squares
Criteria, The IEICE Transactions, J63-D (1980), 349-356.

40
20
0
0.6

0.8

1.0

1.5

2.0

Distance (m)Fig. 6. Recognition rate of mouse click by open mouth .

4.3.3.Judgment accuracy of opened and closed eyes
Fig.7 shows precision rate, recall rate, and F-measure of
judging opened and closed eyes. The F-measure at 0.8m
was the highest value. Conversely, the F-measure at
1.5m and over greatly decreases. One of the failurecases were that the feature points extracted at 1.5m andover were out of alignment. The eye region which got© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22,Seagaia Convention Center, Miyazaki, JapanP - 127

Implementation of Multi-FPGA Communication using Pulse-Coupled Phase Oscillators
Dinda Pramanta, Takashi Morie, Hakaru Tamukoh
Graduate School of Life science and systems engineering, Kyushu Institute of Technology
2-4 Hibikino, Wakamatsu-ku, Kitakyushu 808-0196, Japan
E-mail: dinda-pramanta@edu.brain.kyutech.ac.jp, morie@brain.kyutech.ac.jp, tamukoh@brain.kyutech.ac.jp

Abstract
This paper proposes an implementation of multi-Field Programmable Gate Array (FPGA) communication using
pulse-coupled phase oscillators. At first, we construct a digital phase oscillator circuit with adjustable in-phaseparameters. While performing the synchronization task, the oscillators are connected into the first input and first
output (FIFO) interface. The communication in-between of FPGAs will occur by enabling the following inputs andoutputs on the FIFO through the Gigabit Transceiver (GTX) clock domain. Pulse-coupled phase oscillators of
Winfree's model are utilized as a spike generator and we expect the ideal of model circuit will synchronize. We
employ two FPGA boards of Virtex6 ML605 and implement four oscillators on a hardware level. Experimental results
show that first spike synchronizing over two FPGAs takes 12.47 μsec with data bit speed stream 3.2Gbps.
Keywords: pulse-coupled oscillators, synchronization, Winfree’s model, FIFO, GTX, multi-FPGA,In this paper, we propose an implementation of multiFPGA communication using pulse-coupled phaseoscillators. First, we design a digital circuit of pulsecoupled oscillators3 based on the Winfree model.2 Second,
we propse a communication method between two FPGAs
using the designed digital oscillator, the first input andfirst output (FIFO) interface and the serial connection of
Gigabit Transceiver (GTX). As the multi-FPGA platform,
we employ two FPGA boards and connect them by theserial connection of GTX. Experimental results show thatthe four oscillators network reaches to synchronizationstate and first spike synchronizing over two FPGAs takes
12.47 μsec with data bit speed stream 3.2Gbps.

1. Introduction
Pulse-coupled phase oscillator is one of the spiking
neural networks. It consists of individual oscillators anda network of mutual interactions, which represented by
the phase sensitivity function. 1, 2 Coupling between
oscillators is determined by the timing of the spike pulse
output from each oscillator. The oscillators are often
assumed to be identical or nearly identical. In this case,the network reaches the synchronization-state where all
the oscillators have the same phase. Based on the
updating principles, one-bit signal line is required
between oscillators for spiking the pulse. This feature isefficient for hardware implementation.3, 4A field programmable gate array (FPGA) is areconfigurable integrated circuit (IC) to perform complex
interface and logic processing in a small footprint.5 In
addition, a multi-FPGA based design5 overcomes thelimitation of single FPGA resources and enables toimplement large scale pulse coupled phase oscillator
networks. However, communication delay between
FPGAs may affect synchronizing-state. Therefore, highspeed communication is required to implement
oscillators over the multi-FPGA and synchronizing-stateshould be verified.

2. Pulse Coupled Oscillator model
In order to design the hardware implementation ofpulse-coupled oscillator, Winfree model2 provides anefficient ways, the fundamental of coupled phaseoscillators is expressed as follows:
∅

=

+ ( )

( ),

(1)

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 128

Dinda Pramanta, Takashi Morie, Hakaru Tamukoh

where i is the i-th phase variable with 2π periodicity,is the i-th natural angular frequency, ( ) is aphase sensitivity function, which gives the response of
the i-th oscillator. Inputs from other oscillators, Spk(t),are assumed here pulse input as follows:
( )=

where

∑

∑

−

,

(2)

is the coupling strength, N is the number

oscillators, andis the firing time. Mathematically, δ

is a Dirac‘s delta function that represents the input spike
pulse has a defined width ∆t during which
is updated
according to the value of ( ).

Fig. 1. Pulse-coupled phase oscillator: (a) Coupled network
schematic, (b) positive update, and (c) negative update

2.1. Discretized model.

In order to implement Eqs. (1) and (2) into digital
hardware, oscillator is regularized into following
expressions:( + 1) =
( )=

( )+

(4)

K0
N

0,

∑Nj=1 Z(ϕi )Spk(t)

1,

ℎ

( )=

(3)

The phase value of the next time step iscalculated by summing the current phase value,
natural angular frequency, and a product of the phasesensitivity function and input
pulses.
Naturalangular frequency
is set at a constantvalue.
The oscillator outputs will spike the pulse
when the phase variable reaches a threshold value.
By the above discretization, the proposed model can be
implemented with simple logic circuit.3

2.2. How the oscillator works

Figure 1 shows a two pulse-coupled phase oscillators andtheir updating manner. Synchronization is occured by
updating the pulse timing. There are two phases of
updating the from each oscillator, which are positive
updating and negative updating. Positive update willconducts whenever spike input () to anotheroscillator, the leading condition triggers thefunction Z( ), which gives effect to the pulse timing forreaching the maximum value become earlier. Negative
update conducts whenever Spike input () to anotheroscillator, the lagging condition triggers
thefunction Z( ), which gives effect to the pulse timing
for reaching the maximum value become later.
3. Hardware architecture design
Hardware architecture design of a pulse-coupledphase oscillator is shown in Fig.2.(a). It consists of an
oscillator circuit, a function generator circuit and an
update circuit.3 The oscillator circuit consists an n-bits
counter (CNT), a spike generator (SPKGEN) andcombinational circuits. The n-bit counter represents aphase variable

Basically the concept of oscillator works is based onneighbor connection between each oscillator. The model
explains the local interaction only, but can generatevarious phenomena in the large oscillator network (inarray), based on a coupling function formula as shown in
Eqs. (1) and (2).

and counts the clock inputs forrealizingin Eq.(4). In this design, time step t in thediscretized model corresponds to a clock cycle.

P - 129

Implementation of Multi-FPGA Communication

4.1. In-phase three oscillators of pulse coupledspiking oscillator inside one FPGA(a)Fig. 3. In-phase three oscillators of pulse coupled spiking
oscillator: (a) Three oscillator with rotary connection, and (b)Spikes results from chipscope.
Fig. 2. Design of pulse-coupled phase oscillator circuit: (a)Properties of pulse-coupled oscillator circuit, and (b) FPGA’s
for each board overview.

(b)

The oscillator circuit also outputs intermediatesignals cMSB, cMid0 and cMid1 which determine theshape of the function Z( ) and are used in the function
generator circuit. The function generator circuit
combines signals cMSB, cMid0 and cMid1 into Zp and
Zn. The update circuit receives Zp, Zn and spike pulses
Spkj received from other oscillators and outputs signal
update.
In order to write data into the FIFO,
spike output (oSpike) goes into the DataIn (iDS) and thenstrobe the write enable signal input WrEn high for one
clock cycle as shown in Fig.2.(b). WrEn is output fromoToFIFO into the FIFO’s internal memory. If writing inbulk the WrEn signal can be left high while changing thedata on the DataIn (iDS) for each clock cycle. When the
Full flag goes high, this means that the FIFO’s memory
is full and will not accept any more writes (iEnable = 0)
until data is read using the RdEn.

Real-time implementation results of simple rotary
three oscillator connection are shown on Fig.3.(a). Eachof oscillator has two outputs represents as label and unit.
Figure 3(b) shows the condition of FPGA when runningfrom the beginning time zero (0μsec), ∆clock between
sychronzing was achieved 132 clock cycles. Testingarchitecture was using 16-bit of data and internal max
clock (200MHz) from FPGA Virtex-6 ML605. Inside thesimulation part, for one second could trigger and countthe counter 200 Mega cycles, in this case first spike wasoccurred on 3.08 μsec and reached the synchronizationstate on 20.01 μsec. The total of data rate is equal to 16bit

4. Experimental results
Evaluation of the proposed model of hardware design
was realized. The pulse-coupled phase oscillator
networks over two FPGAs through serial communicationof Gigabit Transceiver (GTX) explains throughsubsection parts. In all experiments, respectively basic
synchronizing phenomena could be observed (in-phasemode).

Fig. 4. Four oscillators over tow FPGAs using serial
communication.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 130

Dinda Pramanta, Takashi Morie, Hakaru Tamukoh* 200 Mega counter/sec which reaches the maximum
value of 3200bps or 3.2Gbps.

results, we verified that the pulse-coupled phaseoscillator synchronized over two FPGAs via the highTable 1.
Slice Register
Look-Up Tables
IOBs

Fig. 5. Realtime result of four pulse-coupled phaseoscillators over two FPGAs using chipscope. Signals 1, 2, 3
are corresponds to the numbers in Fig. 4.

Used
416
348
101

Device utlization
Available
301440150720600

speed serial communication and the FIFO interface.
In future work, we will increase the number of
FPGAs and implement large scale pulse coupled phaseoscillator networks, then apply it to engineering
application such as image processing.

4.2. Four oscillators over two FPGAs using serial
communication
After we achieved with simple oscillator inside one
FPGA, we employ two FPGA boards as the multi-FPGA
platform. By using Virtex-6 XC6VLX240T ML605
Xilinx board design, it provides a free and open highspeed protocol for multi-FPGA communication called
Aurora.7 It is intended for serial communication between
FPGAs with speeds up to and above 10 Gbps.8
Figure 4 shows the experimental setup using two
FPGA boards of Virtex6 ML605 and implementation offour oscillators on a hardware level. Figure 5 shows theobservation result of synchronization in FPGA. Here, we
employed chipscope which is in-circuit debugger
provided by Xilinx to observe spike pulses. From theresult, the spike synchronization in-phase was achieved
and a total time consumed around 12.47 μsec.

Acknowledgements
This research was supported by JSPS KAKENHI
Grand Numbers 26330279 and 15H01706.
References
1. Y. Kuramoto, Chemical Oscillation, Waves, andTurbulence, (Springer, Berlin, 1984).
2. A. T. Winfree, The Geometry of Biological Time,(Springer, NewYork, 1980).
3. Y. Suedomi, H. Tamukoh, K. Matsuzaka, M. Tanaka, andT. Morie, Parameterized Digital Hardware Design ofPulse-coupled
Phase
Oscillator
Networks,
Neurocomputing, 165 (2015), 54–62.
4. Kenji Matsuzaka et al, VLSI Implementation of Coupled
MRF model using pulse-coupled phase oscillators,
Electronics Letters. (2015), 46-48.
5. J. Li, Y. Atsumari, H. Kubo, Y. Ogishima, S. Yokota, H.
Tamukoh, and M. Sekine, A Multidimensional
Configurable Processor Array - Vocalise, IEICE Trans. on
Information and Systems, E98-D(2) (2015), 313-324.
6. X. Y. Wang, A. Apsel, Pulse coupled oscillator
synchronization for low power UWB wirelesstransceivers, in: Proc. of 50th Midwest Symposium on
Circuits and Systems (2007) 1524–1527.
7. Xilinx Inc, Aurora 8B/10B Protocol Specification,(2010),http://www.xilinx.com/support/documentation/ipd
ocumentation/au-rora8b10bprotocolspecsp002.pdf.
8. Abhijit Athavale and Carl Christensen, High-Speed Serial
I/O Made Simple. Xilinx,Inc., 1.0 edition,(2005),
http://www.xilinx.com/publications/archives/books/serialio.pdf.

4.3. FPGA design summary
Table 1 shows the results of total usage resources ofthe multi-FPGA, communication using pulse-coupledphase oscillators. Four oscillators were implemented andcarried out onto the hardware level with the FPGA
synthesizer of Xilinx Tools. By using ISE Design Suite
software information, we obtained the report of the
proposed model circuit implemented with total
maximum frequency 434.972MHz.
5. Conclusion
In this paper, we proposed an implementation ofmulti-FPGA communication using pulse-coupled phaseoscillators. In the experiment, two FPGA boards wereused to implement four oscillators network, and first
spike synchronization over two FPGAs took 12.47 μsec
with data bit speed stream 3.2 Gbps. From experimental

P - 131Multi-Valued Quantization Neural Networks toward Hardware Implementation

Yoshiya Aratani , Yeoh Yoeng Jye, Akihiro Suzuki, Daisuke Shuto, Takashi Morie, Hakaru Tamukoh
Graduate School of Life science and systems engineering, Kyushu Institute of Technology
2-4 Hibikino, Wakamatsu-ku, Kitakyushu 808-0196, Japan
E-mail: aratani-yoshiya@edu.brain.kyutech.ac.jp, yeoh-yoeng-jye@edu.brain.kyutech.ac.jp,
tamukoh@brain.kyutech.ac.jp

Abstract
This paper proposes a Multi-Valued Quantization (MVQ) method of connecting weight for efficient hardware
implementation of Convolutional Neural Networks (CNNs). The proposed method multiplies an input value by amulti-valued quantized weight during the forward and backward propagations, while retaining precision of the stored
weights for the update process. In the both propagation processes, multipliers can be replaced with adders and shifters
by setting appropriate quantized weights. We train two- to six-valued quantization CNNs with MNIST and CIFAR10 dataset to compare the performance of them with a 32-bit floating point CNN. In the four-valued quantization,
random noise is added to the quantized weight to improve the performance of generalization ability. In addition, therobustness of MVQ CNN to noise is evaluated. Experimental results show that the MVQ CNNs achieve better
learning accuracy than the floating point CNN and the four-valued CNN is highly robust to the noise.
Keywords: Deep Learning, CNN, MVQ, BinaryConnect, hardware, noise

simplify neural networks by binarizing the weights.2 By
the quantization, hardware resource can be reduced byreplacing multiplier with adder and subtractor. However,
the recognition accuracy may drop by the quantization.
Therefore, the verification of the relationship between thequantized bit rate and the accuracy of CNN is necessary
in order to ensure the performance of quantization.
In this paper, we propose a Multi-Valued Quantization(MVQ) of weights for efficient hardware implementationof CNN. The recognition accuracy of CNN is compared
between the proposed method with five different types(two-six quantized weights) and the conventional CNN
with 32-bits floating point, using MNIST and CIFAR-10
dataset. In addition, we desire to be robust against noisein hardware implementation. Therefore, noise is applied
to the MVQ-4, which shows the highest recognition
accuracy among MVQs, and the change in result against
noise is observed.

1. Introduction
Convolutional Neural Network (CNN)1 is a kind ofneural network that have proven significant improvement
in image recognition and computer vision fields. Thus,
the implementation of CNN in embedded system raises
high expectation for real world application.
Real time processing and low power consumption are
significant criteria for embedded systems. However, it isdifficult to achieve them by using only software
implementation. In contrast, a special purpose hardware
architecture of CNN enables parallel and pipeline
processing that improves processing speed and reduces
power consumption. However, the repetition of matrix
computation, convolution of input vectors and weights,
need a lot of multipliers that require huge hardware
resources. Therefore, a simplification method is desiredas the limitation of hardware resources.
One of the method that simplify the processing isquantization.2, 3 BinaryConnect is an approach to© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 132

Yoshiya Aratani, Yeoh Yoeng Jye, Akihiro Suzuki, Daisuke Shuto, Takashi Morie, Hakaru Tamukoh

2. Related research
The operations of neural networks can be divided into
forward propagation, backward propagation, and updateprocess of the weight value. BinaryConnect binarizes theweight value to (+1,-1) at the forward and backward
propagations while continuous-value are stored. During
the update process, the update parameter is calculated
through the stored continues-value. Via the binarizationof the weight, the multipliers are simplified to the adderand subtraction, thus it is efficient in reducing the circuit
size. Especially during propagation, the multiplier iscompletely removed. Moreover, the generalization
performance of binarized CNN is improved andexceeded the recognition accuracy of the regular CNNs.

=

+ _
⎧
⎪+ _
⎨− _
⎪
⎩− _

−

≥

0≤

<

_

ℎ

<

_

_

(1)

<0

The other MVQs follow similar quantization manner asshown in Eq. (1). In this paper, we employ stochastic
gradient decent (SGD) with MVQ for training CNNs andit is shown in Algorithm 1.
Algorithm 1
with MVQ.

Stochastic Gradient Descent (SGD) training

1. Forward propagation:

3. Proposed method

For

In this paper, we proposed the MVQ method, which
extends binarization to multi value.

←= 1 to , compute

2. Backward propagation:()
knowing,

Initialize output layer’s activations gradient3.1. MVQFor

MVQ is a method that quantizes the weights into
smaller division than BinaryConnect. When thequantized bit rate is larger, the quantized weight is closer
to the real valued weights, but losing the merit ofsimplification of calculation. It is important to balance
between the quantized bit number and the computationalcost. In this paper, the quantized value is changed fromthree-value to six-value, and the recognition accuracy
results are observed.

=

to 2, computeknowing

3. Parameter update:Computeand==knowing−−: Dot product in ℎ layer ，:The number oflayers ， Mini batch scale:10,,:previous
parameters weights and biases， : learning rate, C:costfunction

3.2. Quantization method
With MVQ-4, the algorithm of the quantization is
explained in detail. The quantization can be done byeither stochastic or deterministic approach.2 In this paper,the deterministic approach is employed. The original
weights W are clipped to the clipping point w_range toquantize the weights into new quantized values before
forward and backward propagations. The multi-valued
weightis formulated as in equation below.

3.3. Addition of noise
To observe for the stability and robustness to the noise,
the addition of noise are also done during the experiments.
The noise is generated using the uniform random number
generator and insert into layers during forward
propagation, backward propagation and update process,as shown below.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 133

Multi-Valued Quantization Neural Networks

50,000 train image data and 10,000 test image data.
Figure 3 shows the CNN architecture for CIFAR-10
dataset. Figure 4 shows the result of learning.
Similar to the experimental results of MNIST dataset,the MVQ-4 also achieved the highest accuracy.

Noise additional position

Forward propagation’s weight
=

→

=

( +

= ′( )→= ′( )

( +

= −
， :

( +
ℎ，

)
:

Backward propagation’s error signal :)4.3. Addition of noise

Experiments are carried out by modifying Tiny_CNN(now known as Tiny_DNN)4 from the CNN header
library. This library uses 32-bit floating point in usual.
Here, w_range is set to 0.7 for all MVQs and Algorithm
1 is applied for the training.

Noise addition experiments are only carried out with
the MVQ-4, since it achieved the best result for both of
MNIST and Cifar-10. Figure 5 shows the results of every
position of noise addition. From the result, adding noise
to the error during backward propagation was the best
result.
To measure the noise tolerance, we increased the noise
range throughout the experiment to observe the effect. As
the result, the proposed method is robust to the noise up
to the range around 1.0 in backward propagation. The
largest value of weights in MVQ-4 is 0.999643, thus the
proposed CNN seems to withstand the noise equal to the
maximum of weight value.

4.1. MNIST dataset

5. Conclusion

MNIST dataset contains grayscale images with the sizeof 28x28 pixels, which has 10 different classes ofhandwritten number zero to nine.5 It includes 60,000 train
image data and 10,000 test image data.
The architecture of CNN is based on LeNet-55 as shownin Fig. 1. The result of learning is shown in Fig. 2. Fromthe experimental results, the MVQ-4 achieved the best
result of all.

This paper proposed the MVQ method, and investigate
the relationship of the degree of multi-value to the CNN
recognition accuracy. From the experimental results, theproposed method showed higher accuracy than theconventional CNN with 32-bit floating point operation,
and among the different multi quantized value, MVQ-4
possessed the best result in accuracy. Furthermore, theexperiment of noise addition into MVQ-4 was carried out
to observe the relationship between noise position andrecognition accuracy. The addition of noise during

Weight update ::4. Experimental results)

4.2. CIFAR-10 dataset
CIFAR-10 dataset contains color images with the sizeof 32x32 pixels, which has 10 different classes of objects
such as animals and vehicles. CIFAR-10 dataset includes

Figure 1 Construction of CNN in MNIST dataset

Figure 2 Results of MNIST dataset© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 134

Yoshiya Aratani, Yeoh Yoeng Jye, Akihiro Suzuki, Daisuke Shuto, Takashi Morie, Hakaru Tamukoh

Figure 3 Construction of Tiny-cnn in CIFAR-10 dataset

Figure 4 Results of CIFAR-10 dataset

(a)

(b)

Addition of noise
noise No.1……in forward propagation only
noise No.2……in backward propagation only
noise No.3……in update only
noise No.4……noise No.1 & noise No.2
Figure 5. Results of noise addition. (a) Effect with noise range = 0.0001, (b) Noise tolerance in backward propagation.

backward propagation in MVQ-4 showed the best resultcompare to insertion in other positions. In addition, therobustness of proposed method to noise was verified.
In future work, we will design a special purpose
hardware architecture based on the proposed method forembedded systems.

2.

3.

Acknowledgement
4.

This research was supported by JSPS KAKENHI
Grand Numbers 26330279 and 15H01706.

5.
Reference
1.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet
classification with deep convolutional neural networks,
Advances in Neural Information Processing Systems, 25(2012), 1106–111.

M. Courbariaux, Y. Bengio, J.P. David, Binaryconnect:
Training deep neural networks with binary weights
during propagations, Advances in Neural Information
Processing Systems, 28 (2015), 3105–3113.
M. Courbariaux, I. Hubara, D. Soudry, R. El-Yaniv, Y.
Bengio, “Binarized Neural Networks: Training Deep
Neural Networks with Weights and Activations
Constrained to +1 or -1, arXiv:1602.02830, (2016).
Header only, dependency-free deep learning framework
in C++11, http://github.com/tiny-dnn/tiny-dnn
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner,
Gradient-Based Learning Applied to Document
Recognition, Proceedings of the IEEE, 86(11) (1998),
2278-2324.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 135

An Improved Parameter Value Optimization Technique
for the Reflectionless Transmission-Line Model of the Cochlea
Takemori Orima and Yoshihiko Horio

Research Institute of Electrical Communication, Tohoku University
2-1-1 Katahira, Aoba-ku, Sendai, Miyagi, 980-8577, Japan
E-mail: takemori.orima.q2@dc.tohoku.ac.jp

Abstract
The passive reflectionless transmission-line model can reproduce physiological cochlear properties. We have
employed an optimization technique to obtain good parameter values that give a desired cochlear property. However,
we found that, in some case, it is difficult to find the sub-optimal parameter values because of complex dependencies
among parameters. In this paper, we explicitly formulate some of the cochlear properties in the reflectionlesstransmission-line model, and improve the optimization technique using these formulae.
Keywords: cochlea, passive model, reflectionless transmission-line model, optimization technique.

1. Introduction
The physiological transmission-line model of the cochlea
can reproduce the mechanical vibration of the basilar
membrane well. To approximate individual humancochlear characteristics to experimental data, it isnecessary to tune parameter values of the model.
However, because of the large number of parameters, it
is difficult to determine these values.
The passive reflectionless transmission-line model1(passive model) has been proposed to alleviate thisproblem. Although this model has less number ofparameters, it can reproduce the characteristics of thecochlea well. Therefore, we proposed a design technique
for this model using parameter optimization2. However,this method requires a long calculation time to design.
In this paper, we explicitly give approximate
formulae for some key characteristics of the passivemodel to accelerate the calculation time. In addition, an
optimization method is employed in the design techniquewith proposed formulae. We compare three optimization
methods, and chose the best one for this design technique.

Finally, we demonstrate some design examples toconfirm the validity and efficiency of the proposed
method.
2. The passive reflectionless transmission-line
model
A minute part between x and x + dx of the passivereflectionless transmission-line model1 is shown in Fig.
1, where x is a distance from the input terminal. The
parallel impedance Zp(x, ) in Fig. 1 is given by

Z p x,
where

j Lp x

Rp x

1,
j Cp xis an angular frequency of the input signal.

Fig. 1. A minute part between x and x + dx of the passivereflectionless transmission-line model1.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 136(1)

Takemori Orima, Yoshihiko Horio

Assuming that the values of each circuit elements inZp(x, ) changes exponentially with distance, they can begiven as

Rp x

R0 e ax , Lp ( x)L0 e ax , C p x

C0 e ax , (2)where R0, L0, and C0 are values at x = 0, and a is a constant.
In Fig. 1, we further assume that the characteristic
impedance Z0(x, ) is independent of the distance, so thatZ 0 x,(3)r,

where r is a constant. As a result, the series impedanceZs(x, ) in Fig. 1 can be defined asr2
Z p x,

Z s x,

Fig. 2. The solid line: the frequency vs. gain characteristics ofthe original passive model obtained from Eq. (7). The dashedline: the approximation with Eqs. (13) and (14).

(4).

On the other hand, the resonance angular frequency
res(x) is given by1(5)
e ax .
res x
L0C 0

where
g max x1(x)and 2(x) are the angular frequencies at
10 given by1

Taking the integral of the propagation constant withrespect to x yields
x,

r
0 Z
p x,

dx

1

j R0C0

L0C0 e ax

1

j R0C0L0 C0 e ax

x

ln

ln

1

x

10

1

j R0C0

L0C0

1

j R0C0

L0C0

Ax

exp

c

F x,

exp

x,

(6)

gz x

(7)
z

3. Explicit formulation of key characteristics ofthe passive modelvz x

We explicitly formulate some of the key characteristicsof the passive model, the center angular frequency c(x),the maximum gain gmax(x), and the quality factor Q(x)shown in Fig. 2 as follows:
x
c

x
c

x,
res

(8)

g max x

F x,

c

x ,

(9)

,

(10)

Qx

c
1

x

x
2

x

g max x10

In addition,z

ln g max x
x
c

z

.

x,

res

10

x ln g z xx
cBx

Finally, the transfer function is given by1
Z p x,

1

1

(11)
(12)

,

where

j R0 L0

.

x

1
1
ln
Ax
Bx

x

2

jr
2a L0 C0

c

x
C0
L0

F x,

x

z

vz x

R0 e

ax 2

x

x ln g max x, (13)xz

ln g z x
,
x
z

(14)

z

x ,

(15)

res

x,

(16)

vz x
2

2

10
g max x

4

,

(17)

2

2.

(18)

c(x) is given by

x
c

vc x

vc x

2

4

2

,

(19)

where

vc x

C0 r
L0 a

R0 e ax R0 e ax .

(20)

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 137

An Improved Parameter Value

Comparisons between the original characteristics
calculated numerically from Eq. (7) and approximatedvalues from the above formulae are shown in Figs. 3 to 5,
where a = 0.288, R0 = 1.5, L0 = 2.385 10-7, C0 = 2.132
10-7, and r = 5. Equation (7) took about 0.26 sec. forcalculation, in contrast, the formulae about 2.5 sec.
From these results, we can confirm validities andefficiency of the proposed formulae.
4. Design method using parameter optimization
We design the passive model using the proposed
formulae to reproduce the human cochlear characteristics.
We employ an optimization technique to obtain themodel parameters.
First, we define the object functions as follows:
E1 x

x cf x
,
cf x

(23)

g max x mg x
,
mg x

(24)

Qx qx
,
qx

(25)

c

E2 x
E3 x

where cf(x), mg(x), and q(x) are the target values for thecenter frequency, maximum gain, and quality factor at a
distance x, respectively. Then, we optimize the weighted
sum of these object functions E(x).
3

E xFig. 5. The original (circles) and approximated (solid line)
quality factors Q(x) according to the distance x obtained from
Eq. (7) and Eq. (10), respectively. The crosses: the percent
errors between them.

3

wi Ei x and
i 1

wi

1,

Fig. 4. The original (circles) and approximated (solid line)
maximum gains gmax(x) according to the distance x obtained
from Eq. (7) and Eq. (9), respectively. The crosses: the percent
errors between them.

(21)

i 1

where wi 0 is a weight. In the following section, we
design the passive model with the target parameter values

shown in Table 1, where cf(x), mg(x), and q(x) were
estimated based on the physiological experiments3,4.
4.1. Optimization techniques
We compare the downhill simplex (DHS), the genetic
algorithm (GA), and the particle swarm optimization(PSO), using the design target at x = 25 mm in Table 1.
The conditions of each optimization methods are
Table 1. The target design specifications.

Fig. 3. The original (circles) and approximated (solid line)
center frequencies c(x) according to the distance x obtained
from Eq. (7) and Eq. (8), respectively. The crosses: the percent
errors between them.

x

15 mm

20 mm

25 mm

cf(x) [Hz]

2500

1200

500

mg(x) [A/V]

9.4

9.4

6.1

q(x) [Hz/Hz]

3.6

2.6

1.8

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 138

Takemori Orima, Yoshihiko Horiosummarized in Table 2. In the table, N is the number ofsimplexes, individuals, and particles in DHS, GA, and
PSO, respectively. Note that, in the case of PSO, one
iteration is counted each time as the global best is updated.
The results of the comparisons are shown in Table 3.
In the table, DHS gives the smallest optimization
function value.
Therefore, we use DHS as the
optimization method in our design.
4.2. Design examples

Table 3. The obtained parameters and object function values
with DHS, GA, and PSO for x = 25 mm.
DHS
a
Rp
Lp
Cp
r
E (Eq. (21))

We designed the passive model specified in Table 1
using DHS. The results are shown in Table 4 and Fig. 7.
From Table 4, we confirm that the designed values arealmost the same as the target ones, although some errors
remain due to approximation errors in the proposed
formulae, in particular, Eq. (10).

GA

PSO

0.19490.22990.21412.315 10-3 2.596 10-3 2.816 10-3
4.963 10-9 8.227 10-10 2.122 10-9
5.875 10-8 1.068 10-8 2.071 10-8
2.5
2.5
2.5
1.47

10-6

1.34

10-4

6.38

10-2

Table 4. The obtained center frequencies c(x) maximum
gains gmax(x), and quality factors Q(x) by the proposed design
technique with DHS.

5. Conclusion
We have explicitly formulated some key characteristicsof the passive reflectionless transmission-line model ofthe cochlea to alleviate the complex calculation loads.
These expressions efficiently reproduced the original
characteristics.
In addition, we defined the object functions forparameter optimizations. We compared DHS, GA, and
PSO as the optimization methods. As a result, we foundthat DHS is the best method.
We finally designed three cochlear characteristics at
different distances successfully using the proposed
formulae with DHS.
As a future problem, we will improve the formulaefor Q(x) for more precise designs.

x

15 mm

20 mm

25 mm

c(x) [Hz]

2496

1196

495

gmax(x) [A/V]

9.408

9.417

6.133

Q(x) [Hz/Hz]

3.858

2.733

1.834

Table 2. The simulation conditions.

N
Trials
Iterations

DHS

GA

PSO

30
30
5000

30
30
5000

30
30
5000

Fig. 7. The obtained frequency vs. gain characteristics at x = 15mm, 20 mm, and 25 mm through the proposed optimization
technique using downhill simplex method.

Referencesline model of the cochlea,” in Proc. of Int. Symp. on
NOLTA2016, pp. 503–506, 2015.
3. G. V. Bekesy, “Experiments in Hearing,” McGraw-Hill
Book Co., New York, 1960.
4. M. A. Ruggero and A. N. Temchin, “Unexceptional
sharpness of frequency tuning in the human cochlea,”
PNAS, vol. 102, no. 51, pp. 18614–18619, 2005; DOI:10.
1073/PNAS.0509323102.

1. T. Kohda, T. Une, and K. Aihara, “An active,reflectionless transmission-line model of the cochlea:
Revisited,” in AIP Conf. Proc., vol. 1403, no. 1, pp. 578–
583, 2011; DOI:10.1063/1.3658152.
2. T. Orima, Y. Horio, and T. Kohda, “A parameter value
optimization technique for a reflectionless transmission-© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 139

A parameter optimization method for Digital Spiking Silicon Neuron model
Takuya Nanami

The University of Tokyo, Institute of industrial Science
Tokyo, Japan
Takashi Kohno

The University of Tokyo, Institute of industrial Science
Tokyo, Japannanami@sat.t.u-tokyo.ac.jp, kohno@sat.t.u-tokyo.ac.jp

Abstract
DSSN model is a qualitative neuronal model designed for efficient implementation in a digital arithmetic circuit. In
our previous studies, we extended this model to support a wide variety of neuronal classes. Parameters of the DSSN
model were hand-fitted to reproduce neuronal activity precisely. In this work, we studied automatic parameter
fitting procedure for the DSSN model. We optimized parameters of the model by a GPU-based implementation of
the differential evolution algorithm in order to reproduce waveforms of the ionic-conductance models and reduce
necessary circuit resources for the implementation.
Keywords: Silicon neuronal network, Spiking neuron model, Differential evolution, FPGA

To reproduce a variety of spiking properties in neurons,
appropriate parameter sets for neuronal models have to
be found. The relationship between the values of theparameters and the behavior of the model is generally
complex, and it is difficult and time-consuming to find
an appropriate parameter set. Automatic parameter
fitting procedures have been studied as a solution to thisproblem. Parameters for Hodgkin-Huxley-type models
have been tuned automatically in [6] and [7]. Pospischil
et al [8] reproduced regular and fast spiking neuron
classes by simulated annealing based optimization
method using a cost function composed of the firing rate
and the adaptation time. Buhry et al. [9] developed
automatic parameter estimation using a differential
evolution algorithm for a Hodgkin-Huxley type modeland a neuromimetic analog integrated circuit.
In this work, we studied automatic parameter fitting
procedure for the DSSN model. We reproduced regularand fast spiking classes by a GPU-based
implementation of the differential evolution algorithm.
Regular-spiking (RS) is a most typical classes ofcortical neurons and is characterized by spike-frequency
adaptation; the spike frequency decreases over time inresponse to a constant stimulus input. Conversely, Fastspiking (FS) neurons maintain firing at a constant

1. Introduction
A wide variety of neuronal models have been used insilicon neuronal networks because of the trade-off
between reproducibility of neuronal activities andcomputational efficiency. For example, ionicconductance models can reproduce a neuronal activity
accurately but demands enormous computational
resources in large-scale implementations. In contrast,
integrate-and-fire based models such as the LIF and
Izhikevich models [1] can be implemented with lessresource, because they approximate a spiking process by
the resetting of the state variables. However, they have
reduced reproducibility of complex neuronal activities.
For example, these models assume fixed maximum
membrane potentials during the spike process, whereas
the spike intensity is nonuniform in the nervous system[2]. The DSSN model [3] is a qualitative neuronalmodel designed for efficient implementation in a digitalarithmetic circuit. In our previous studies [4][5], we
extended the DSSN models to support various neuronal
activities; regular spiking, fast spiking, intrinsicallybursting, low-threshold spiking, elliptic bursting, andparabolic bursting.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 140

Takuya Nanami, Takashi Kohno

frequency. Parameters were optimized to reproduce
waveforms of the ionic-conductance models in [8] andreduce the circuit resource requirements for
implementation.
The remainder of this paper is organized as follows.
Section 2 introduces our neuron model, differential
evolutional algorithm, details of our parameter
optimization procedure, and GPU-based implementation.
The result is shown in Section 3. Section 4 summarizes
the work and suggests ideas for future.
2. Method
2.1. Digital Spiking Silicon Neuron model
The 3-variable DSSN model is a qualitative neuronmodel that can simulate several classes of neuronal
activities by Euler's method with fixed point operation
including RS and FS [4]. Equations for regular and fast
spiking are given by∅),= ( ( )− − + +
=

( )≡

( )≡

1

( ( ) − ),= (ℎ( ) − ),
−

−

−

−
( −

+

( < 0)( ≥ 0),

+
) +c

≥

+

+c

<

,

( < )
( ≥ ),−+
where corresponds to the membrane potential, andand are the fast and slow variables, respectively, that
abstractly describe the activity of the ion channels.
Parameter is a bias constant andrepresents the
input stimulus. Parameters ∅, ε, and τ control the time
constants of the variables. Parameters, , , and ,where is , , , , ℎ , or ℎ , are constants that
adjust the nullclines of the variables. All of the variables
and constants in this qualitative model are purely withno physical units. A cubic shaped -nullcline is a key toreplicate the spiking dynamics in qualitative models.
Because multipliers are resource-consuming in a digitalarithmetic circuit, the DSSN model adopts a piecewise
quadratic function for the-nullcline so that its
numerical integration step requires only one
multiplication between variables.
ℎ( ) ≡

2.2. Differential evolution algorithm
Differential evolution (DE) algorithm [10] is a popularheuristic method to solve optimization problems using areal number function. It is characterized by its mutation
process that uses geometrical location of whole
population. In the following, we explain each step in the
DE algorithm. We define ( ) as the gene j of the i th
individual of the k th generation.
Initialization
We generate M-dimensional parameter vectors( =0, … , N − 1), where N and M are the number of theindividuals and parameters of the DSSN model.
Parameter vectors are chosen randomly within theboundary constraints of the DSSN model.
Mutation
We generate new parameter vector
as follows:),∀ = 1, . . . , N,=
+ ∙(−where r1, r2, and r3 are integer randomly chosen from[0, N − 1]. F is usually set to 0.5.
CrossoverWe generate new parameter vectorsthat inherit
genes ofwith a probability CR.
∀ = 1, … , N,
∀ = 1, . . . , M,
() (
≤
)
( )=
()
ℎ
,
where CR is usually set to 0.5.
Selection
A selection is conducted by comparing the cost function
values ofand, respectively, as follows:∀ = 1, . . . , N,()()≤ ( )=ℎ,where ( ) returns the value of the cost for parameter
vector .
A set of procedures including mutation, crossover, andselection is repeated for 10000 times, and the parameter
vector that produces the lowest value in the costfunction is finally adopted.
2.1. Parameter optimization procedure
The DSSN model for RS and FS classes has 24
parameters, exploring whose spanning space is notrealistic with average computing power available tothose who are not specialized in high performancecomputing (HPC). To reproduce the dimension of theparameter space to be explored by the DE algorithm, wefirst explored the parameters that control the dynamics© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 141

A parameter optimization methodof the fast state variables and to fit the spike
generation mechanism. Then, the parameters related tothe slow state variable were determined to fit theadaptation of the spike-frequency.
In the DSSN model’s equation, some parameters are acoefficient of a variable. In its circuit implementation, amultiplication between a coefficient and a state variable
was realized by shifters and adders, and we need alarger number of couples of an adder and a shifter as thenumber of digits with value 1 in the fixed point
representation of the coefficient increases. Parameters
were optimized for not only reproducing waveforms of
the ionic-conductance model but also reducing thecircuit size.
In the procedure to determine the parameters that
control the dynamics of the two fast variables, we fixed-nullcline to facilitate parameter exploration whileconsidering the dynamics of the spike generation
process. It does not severely restrict the dynamical
property of the model, because the relation between the- and - nullclines is one of the major factors that rulethe dynamical structures in our neuronal model. We
defined the cost function as follows:( ),( )= ( )+where ( ) is a square error between the waveforms of
the DSSN model with parameter vector and a target
waveform. The target waveform was calculated by afast subsystem of the DSSN model in our previous
study [4], which is tuned to reproduce mathematicalstructure of the ionic conductance model in [8]. Notethat minimum and maximum values of waveforms arenormalized to 0 and 1 for comparison. Function
is anumber of digits with value 1 in the binary fixed-point
expression of the coefficients in . Constantis tobalance this couple of cost functions. Parameters that
control the dynamics of the fast variables were
determined to minimize the cost function by the DE
algorithm.
The remaining parameters related to slow variable
were determined to reproduce the spike frequency
adaptation. The cost function is( )= ( )+( ),where ( ) is a square error between spike timings ofthe DSSN model with a parameter vector and that of
the target ionic-conductance model in response to a step
stimulus. Function
is a number of digits with value 1

Figure 1: Waveforms of the ionic-conductance model (red)and the DSSN model (blue) in response to a weak (left) andstrong (right) step stimulus input. (a)-(c) corresponds toregular spiking neuron cells. (d) and (e) corresponds to fast
spiking neuron cells.

in the binary fixed-point expression of the coefficientsin , andis a constant to balance this couple of costfunctions.
The DE algorithm is known to be compatible withparallel computation schemes because of its intrinsic
parallel structure. We accelerated the calculation by theGraphic processing unit (GPU) to obtain betterparameter solution by executing a larger number of DE
steps in a time period. The calculation of the costfunction, in which the differential equations are solvedby the Euler’s method for thousands of times, wasparallelly processed on a GPU.
3. Result
We found five parameter sets with which the DSSN
model reproduces the activities of the ionic-conductancemodels for three RS and two FS cells whosecharacteristics are slightly different from each other (Fig.
1).
We evaluated the similarity of the spiking patterns
between the ionic-conductance model and the DSSN
model by calculating
and[11] that are statistics
for the spike sequence while changing the stimulus
intensity (Fig. 2). They are defined as follows:

=
=

1
−1

1
−1

( − ) /

3( −
( +

)
)

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 142

Takuya Nanami, Takashi Kohno

Acknowledgement
This work was partially supported by JSPS SAKURA
Program and JST PRESTO and CREST.

References
1. Eugene M. Izhikevich, Simple model of spiking neurons,
IEEE Transactions on neural networks, 14(6), pp. 15691572, 2003
2. Andrew A.V. et al., A Model of a Segmental Oscillator in
the Leech Heartbeat Neuronal Network., Journal ofComputational Neuroscience, Vol. 10, pp. 281-302, 2001.
3. T. Kohno and K. Aihara. Digital spiking silicon neuron:
Concept and behaviors in gj-coupled network.
Proceedings of International Symposium on Artificial
Life and Robotics 2007, 2007.
4. T. Nanami and T. Kohno. Simple cortical and thalamic
neuron models for digital arithmetic circuit
implementation, Frontiers in Neuroscience, Vol. 10,
No.181, pp. 1—12, 2016
5. T. Nanami, K. Aihara, and T. Kohno, Elliptic andparabolic bursting in a digital silicon neuron model, 2016
International Symposium on NOLTA, Japan, November
27th–30th, 2016
6. Tawfik B, Durand DM, Nonlinear parameter-estimation
by linear association : Application to a 5-parameter
passive neuron model. IEEE Trans Biomed Eng 41:461–
469, 1994.
7. Haufler D, Morinc F, Lacaille JC, Skinner FK, Parameter
estimation in single-compartment neuron models using
asynchronization-based
method.
Neurocomputing
70:1605–1610, 2007.
8. M. Pospischil et al.. Minimal Hodgkin-Huxley typemodels for different classes of cortical and thalamic
neurons. Biological Cybernetics, 99(4-5):427–441, 2008.
9. L. Buhry et al., Automated parameter estimation of the
Hodgkin-Huxley model using the differential evolution
algorithm: application to neuromimetic analog integrated
circuits, Neural computation 23 (10), 2599-2625, 2011.
10. R. Storn, K. Price, A Simple and Efficient Heuristic for
global Optimization over Continuous Spaces, Journal of
Global Optimization, Vol. 11, pp. 341-359, 1997.
11. Shinomoto, S., Shima, K., and Tanji, J. Differences inspiking patterns among cortical neurons. Neural Comput.
15, 2823–2842. 2003

Figure 2:−
characteristics of the ionic-conductancemodel (red) and the DSSN model (blue).
Table. 1 : Device Utilization. The labels (a)-(e) correspond to
those in Fig.1.

wheredenotes the th interspike interval,is theaverage of , and n is the number of spikes in thesequence. Both models have same characteristics on−plane in each cell. The results in Figs. 1 and 2
were obtained by numerical integration with the Euler's
method ( dt = 2s) where each variable wasexpressed by 18-bit fixed point with 14-bit fraction part.
The same results are expected to be observed in thefollowing FPGA implementation, because this conditionis the same as our VHDL codes.
We compiled the DSSN models for Virtex-7
XC7VX690T FPGA using Xilinx Vivado Design Suite.
Device utilization is listed in Table 1. In the column
labeled (f) and (g), the resource usage for RS and FS
settings in [4] is shown. The requirement for LUTs was
reduced down to less than half in all the settings.
4. Conclusion
In this work, we developed a parameter optimization
method for the DSSN model on the basis of the DE
algorithm. By splitting the optimization process in 24dimentional parameter space into two steps, we could
find parameter sets with which the DSSN model
reproduces the characteristics activities of RS and FS
cells without using special HPC systems. Cost functions( = 1, 2) were introduced so that the circuit size is
reduced. We also confirmed qualitative similarity
between the ionic-conductance model and the DSSN
model by measuring statistics for the spike timing. In
our future work, we will improve this optimization
method to cover above other neuron classes.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 143

A Multistage Heuristic Tuning Algorithm for an Analog Silicon Neuron Circuit
Ethan Green

Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba,
Meguro-ku, Tokyo, 153-8505, Japan
Takashi Kohno

Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba,
Meguro-ku, Tokyo, 153-8505, Japan
E-mail: green@sat.t.u-tokyo.ac.jp, kohno@sat.t.u-tokyo.ac.jp
www.u-tokyo.ac.jp

Abstract
This research looks at an ultra-low power subthreshold-operated silicon neuron circuit designed with qualitative
neuronal modeling. One technical challenge to future implementation of such circuits is parameter tuning—a problem
stemming from temperature sensitivity of subthreshold-operated MOSFETs and the uniqueness of individual circuits
in a neuronal network due to transistor variation. This research proposes a fully automated parameter tuning algorithm
that combines two heuristic approaches to search for appropriate circuit parameters over a range of temperatures. The
algorithm can tune the circuit to behave as a Class I or Class II neuron.
Keywords: neuromorphic engineering, analog VLSI, silicon neurons

effect transistors (MOSFETs) in their subthreshold
regime4.
The large number of circuit parameter voltages, thetemperature sensitivity of subthreshold-operated
MOSFETs, and the problem of transistor variation
among circuits of equivalent design means that effective
parameter tuning to achieve consistent neuron-likedynamics is a significant challenge. A parameter tuningalgorithm that addresses these issues will be necessary
for future implementation of these circuits in large scale
networks.
This research is inspired by positive results in [5] inwhich the Differential Evolution (DE) algorithm wasused to tune the parameter voltages of a conductancebased silicon neuron circuit. The DE algorithm is anevolutionary algorithm which begins with random
solution vectors, in this case vectors of circuit parameter
voltages, evaluates their performance with a cost function,and passes on elements of vectors with good performanceto the next generation6.

1. Introduction
Analog silicon neurons, electronic circuits that mimic theelectrophysiological characteristics of neuronal cells,
may in the future be used as fundamental building blocksof neuromorphic technologies like brain-mimetic
computers. These circuits operate in continuous time,
consume low power, and are expected to be implemented
in massively parallel networks that are fundamentallydifferent from digital transistor logic circuits1. The circuit
used in this research is an ultra-low power analog siliconneuron designed with the techniques of qualitativemodeling—an approach which seeks to reduce thecomplexity of ionic conductance models by describing
the same dynamics with fewer variables2. Such a modelallows for replication of a wide variety of spiking
dynamics in neuronal cells, including Class I and Class IIin Hodgkin’s classification3, with a less complex low
power circuit. Power consumption is further reduced byoperating the circuit’s metal-oxide-semiconductor field-© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 144Ethan Green, Takashi Kohno

Table 1. Spectre Circuit Parameters (x=v,n)Circuit Spectre Parameter
Function
fx(v)
gx(v)
r(n)
Iax

=

In [7], we presented a script which pairs the Spectre
circuit simulator with optimization algorithms toautomatically search for optimum circuit parameter
voltages over a range of temperatures. Two heuristic
approaches were used to improve upon a previous trialand-error based tuning method. In this paper, we present
a multistage tuning algorithm which combines those twoheuristic methods with consideration given to their merits
and drawbacks, and adds an additional pretuning stage tomake the algorithm completely automated. Our new
algorithm eliminates the need for trial-and-error, only
requires input of benchmark characteristics, and can tune
the circuit to behave as a Class I or Class II neuron overa range of temperatures.

− ( )+− ( )(2)

Our algorithm requires the user to input the benchmark
circuit characteristics: the nullcline structure, circuit
behavior detailed in Sec. 3.3, and current-voltage curvesof the Iax and r(n) circuits (x=v,n) at 27°C. The user thenselects the target temperature that circuit parameter
voltages are generated for.
Circuit simulations were conducted in the Cadence
software environment with the Spectre circuit simulator
on two X5570 processors (2.9 GHz, 8 threads).

The silicon neuron circuit is divided into a v-block andan n-block which each integrate the currents fromtransconductance circuit components over a capacitor.
Variables v and n represent membrane potential andabstracted ionic activity and are coded by subtracting thevoltage over their respective capacitors from Vdd. The
system equations are as follows:
( )+

( )+

3. Multistage Parameter Tuning Algorithm

2. Circuit Description

( )−

( )−

scaling
offsetscaling
scaling
scaling
adjustment

The current-voltage relationships for fx(v), gx(v) (x=v,n),
and r(n) are sigmoidal curves. A detailed description ofthese circuits is given in [2]. Iax (x=v,n) aretransconductance amplifiers used as constant current
sources and Istim is an externally applied stimulus current.
Transconductance amplifiers TAV and TAN use a voltage
clamp method to draw the nullclines—curves on which
the system equations equal zero—which are used todescribe the dynamics of the silicon neuron8. Alltransistors in the silicon neuron circuit are operated intheir subthreshold regime, yielding desirable exponential
current-voltage characteristics and power consumption
as low as 3 nW.

Fig. 1. Block diagram of silicon neuron circuit, reprinted from
[7]

=

fx_Vb
fx_Vdlt
gx_Vm
rn_Vm
Iax_Vb
Iax_Vin3.1. Stage I: Pretuning
As listed in Table 1, parameter voltages Iax_Vb and
rn_Vm scale the current-voltage characteristics of the Iax
and r(n) circuits (x=v,n). The first stage of the algorithm
uses Differential Evolution to find the optimum value ofthese parameters at the target temperature. The costfunction is the mean absolute difference of the currentvoltage curve resulting from a given parameter to the(1)© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 145

A Multistage Heuristic Tuning

3.3. Stage III: Tuning transient behavior

current-voltage curve of the circuit with benchmark
settings at 27°C.
( )≜ ∑| ( )−|

The final stage of the algorithm uses rn_Vm from StageI and gv_Vm, fn_Vb, and Iax_Vb (x=v,n) from Stage II
as the center of a 5 × 5 search space by adding +/– 0.5
and +/–1 mV to each of these five parameter values. A
brute force approach is then used to evaluate each of the
55 = 3125 combinations of parameters in this search
space by running transient simulations of the circuit. For
Class I mode, the simulation first approximates thesilicon neuron’s threshold current to generate a neuronal
spike by sending successively stronger 500 µs pulse
stimuli in intervals of 8.9 pA, 5% of the benchmark
threshold current of 178.5 pA. The time v rises above 450
mV is recorded. This value roughly corresponds to thethreshold current. The circuit is then subjected to 5 and
10 pA sustained stimuli and the spiking frequency isevaluated using data from an 800 ms time period.
The cost function evaluates each parameter set bycalculating the magnitude of the difference vector
between these three simulated circuit behaviors j(x) andtheir benchmark values b, a vector calculated fromsimulation results with a parameter set which yields
typical Class I behavior.

(3)

x is the parameter value for a given simulation, Ii(x)represents each point on the current-voltage curve
yielded by a given parameter value, bi represents eachpoint on the benchmark curve, and v = 0 V when k = 1
and v = 1 V when k = 101. The DE algorithm is used once
for each of these three circuits, requiring about 1.5
minutes and 130 iterations.
3.2. Stage II: Nullcline tuning
The second stage of the algorithm uses the DE algorithm
to tune the nullclines to match the benchmark nullclines
at 27°C in the range of 300–450 mV. Spectre simulationsof the silicon neuron circuit’s nullcline mode are run with
the input being a vector of circuit parameters gv_Vm,
fn_Vb, and Iax_Vin (x=v,n). The functions of theseparameters are listed in Table 1. fv_Vb, gn_Vm, and thescaling factors for the Iax circuits from the pretuning stage
and fv_Vb are kept constant. The r(n) circuit is set to zeroin the nullcline mode and is not directly evaluated.
The cost function is the magnitude of the vector of themean absolute differences of the v and n nullclines fromtheir respective benchmark nullclines.
( )≜∑| ( )−|+∑| ( )− |threshold index( ) = 5 pA stimulus response ,10 pA stimulus responseℎ( ) ≜ ‖ ( ) − ‖1.21= 15.87
37.18(5)5 pA stimulus response
7.5 pA stimulus response( )=,10 pA stimulus response
12.5 pA stimulus response

0
22.08
=
30.21
35.3
(6)

Class II neurons do not show a clear threshold togenerate a neuronal spike and instead are characterized
by the sudden onset of periodic spiking when subject toan adequately strong sustained stimulus. The costfunction in Class II mode evaluates a vector of thecircuit’s frequency response to 5, 7.5, 10, and 12.5 pA
sustained stimuli. b again was calculated from simulation
results with a parameter set which results in typical Class
II behavior.

(4)vi(x) and ni(x) represent the points on the output v and nnullcline curves for parameter settings x, bi and ci
represent the points on the benchmark v and n nullclines
at 27°C, and v = 300 V when k = 1 and v = 450 V whenk = 151. Differential Evolution for this stage of thealgorithm typically requires 7 thousand nullcline mode
simulations and 4 hours of calculation time.
As reported in [7], the DE algorithm accurately
replicated the benchmark nullclines at a variety oftemperatures, but did not yield accurate transient
behavior, suggesting the need for an additional tuning
stage.

Stage III concludes with a polishing step whichinterpolates the optimum parameter set between the© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 146

Ethan Green, Takashi Kohno

Table 2. Algorithm results with benchmark behavior highlighted in grey. “X” indicates non-neuron-like behavior
and “D” indicates decaying response.

Class IClass II

Temperature (°C)
Ith(pA)
5 pA response (Hz)10 pA response (Hz)5 pA response (Hz)
7.5 pA response (Hz)10 pA response (Hz)12.5 pA response (Hz)

2
X
X
X
0
22.0
29.5
35.1

7
X
0
22.2
0
D
30.3
35.3

12
241.0
13.5
32.4
0
D
30.6
35.4

members of the original search space. This stage requires
about 4 hours of calculation time to run all the transientcircuit simulations. Increasing the search space
exponentially increases calculation time, thus illustrating
the need for nullcline tuning in Stage II.
4.

17
220.5
12.5
36.8
0
D
30.8
35.5

22
186.0
16.3
35.5
0
D
30.6
35.4

27
178.5
15.9
37.2
0
22.1
30.2
35.3

32
153.0
18.4
37.3
0
22.1
30.1
35.4

37
140.0
19.5
37.3
0
22.1
30.2
35.3

42
123.0
21.0
37.1
0
0
0
0

47
X
X
X
0
0
0
0

neurons, since it can find unique parameter sets which
may yield similar nullcline structure among individual
members of a network. Future silicon neuron circuits
may be designed with on-chip feedback mechanisms
which automatically adjust parameter voltages based on
the results of a tuning algorithm.

Results
Acknowledgements

The algorithm was run in Class I and Class II modes for
2° to 47°C in 5° steps and the results are listed in Table
2. In Class I mode, the circuit displayed behavior closest
to the benchmark in the range of 12° to 42°C. 2°, 7°, and
47° did not yield neuron-like behavior. The thresholdcurrent descended from 241 to 123 pA as temperature
increased.
In Class II mode, transient behavior nearly identicalto the benchmark was observable for 32° and 37°C. At
7°, 12°, and 22°C the 10 pA and 12.5 pA sustained
stimuli induced spiking at a similar frequency to thebenchmark, but the 7.5 pA response decayed after a few
iterations. A simplified version of the algorithm which
does not contain the Iax_Vb tuning step in Stage I yieldedbehavior nearly identical to the benchmark at 22°,
suggesting that slight modifications to the algorithm may
improve the decaying problem for 7–22°C.

This study was supported by JST PRESTO and CREST,
and VDEC, the University of Tokyo in collaboration with
Cadence Design Systems, Inc.
References
1. S. Brink, et al., “A learning-enabled neuron array IC based
upon transistor channel models of biological phenomenon,”IEEE Transaction Biomedical Circuits and Systems, vol.
7, no. 1, pp. 71-81, Feb. 2013.
2. T. Kohno and K. Aihara, “A Qualitative-Modeling-Based
Low-Power Silicon Nerve Membrane,” Electronics,
Circuits, and Systems (ICECS), IEEE, pp. 199-202,
December, 2014.
3. A. Hodgkin, “The local electric changes associated withrepetitive action in a non-medullated axon,” The Journalof Physiology, 107, 2, pp. 165-181, March, 1948.
4. S. Liu, et al., Analog VLSI: Circuits and Principles, MIT
Press, 2002.
5. F. Grassia, et al., “Tunable neuromimetic integrated
system for emulating cortical neuron models,” Frontiersin Neuroscience, vol. 5, article 134, December, 2011.
6. K. Price and R. Storn, Differential Evolution: A Practical
Approach to Global Optimization, Springer, 2005.
7. E. Green and T. Kohno, “Two Heuristic Approaches to
Parameter Tuning for an Analog Silicon Neuron Circuit,”2016 International Symposium on Nonlinear Theory and
Its Applications, pp. 194-197, November, 2016.
8. J. Rinzel and B. Ermentrout, “Analysis of Neural
Excitability and Oscillations,” Methods in Neuronal
Modeling, Massachusetts Institute of Technology, pp.
251-291, 1998.

5. Discussion
The multistage heuristic tuning algorithm considers the
merits and drawbacks of the DE algorithm-based
nullcline tuning and brute force transient tuning
approaches as noted in [7] and combines them into a
hybrid approach which in simulation can effectively tune
the silicon neuron circuit to behave as a Class I or Class
II neuron over a range of temperatures. The next step will
be to implement a version of this algorithm with theactual silicon neuron circuit using LabVIEW or Python.
This algorithm could also be effective in dealing withissues of transistor variation in a network of silicon© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 147

Integral Design of Intelligent Home Equipment
Yuxing Ouyang 1, Fengzhi Dai 1*, Yiqiao Qin 1, Ce Bian 1, Bo Liu 2, Hongwei Jiao 3
1
College of Electronic Information and Automation,
Tianjin University of Science and Technology, China;
2 Inner Mongolia University, China;
3 Tianjin Technology School of Printing and Decoration, China

E-mail: * daifz@tust.edu.cn
www.tust.edu.cn

Abstract

The equipment is designed to household network monitoring and control. The WIFI module is used for
wireless network communication to upload information of all connected smart home devices to the server,and it can be remotely controlled by the external network and monitoring of household equipment
information. For simple household equipment, the control is directly taken by the built-in MCU’s WIFI
module, and for the complex household equipment, it is through the external MCU and WIFI module forcommunication control. The Airkiss distribution mode is used to connect the WIFI master module to the
network, and the other WIFI module devices to adopt the ad hoc network mode for wireless networking.
Keywords: WIFI, intelligent home, ad hoc network, remote control and monitoring, Airkiss distribution network

1. Introduction

intelligent home living environment, so that our smart
home life can be further optimized and improved3. This
design adopts the wireless ad hoc network mode of WIFI
module. So that the local WIFI module can set up the
local LAN network, and achieve the purpose ofconnecting all the smart home equipment4. The WIFI
master module connects to the server through the MQTT
protocol, which achieved the purpose of we control
intelligent household device and monitor the informationof intelligent household device. In the intelligent home
life monitoring and control, the design has a very highpractical value.

With the rapid development of the times and the rise of
the Internet of things, many of the traditional home
appliances have gradually developed into smart homedevices, such as the intelligent sockets, intelligent clock,intelligent closestool and so on1.
Compared to the traditional home appliances,intelligent home equipment added the function ofmonitoring and control equipment2. But each device
needs to be controlled in different ways, so it is very
difficult to realize the integrated control of the wholehousehold equipment. Also, most of today's smart homeequipment adopt the Bluetooth and other devices tocontrol, or manually set the time to time control, and it
cannot be achieved using the network remote control andmonitoring.
Based on the above discussion, this design focuses on
the integration of smart home equipment. The purpose ofthis design is to create a comfortable, fast and convenient

2. The hardware structure designIntelligent devices are mainly responsible for the control
of intelligent home appliances and data collection, so thesize is better to be small. Therefore, in the design we haveadopted a 10*10 PCB board to place the external MCU,

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 148

Yuxing Ouyang, Fengzhi Dai, Yiqiao Qin, Ce Bian, Bo Liu, Hongwei Jiao

2.2. WIFI module

WIFI main module and other modules, the rest of the
WIFI submodule is directly placed inside the smart homedevices.
First, the schematic is plotted using the Altium
Designer software platform. Second, the PCB file isgenerated and the reasonable layout is designed. And
finally, PCB is copper clad and sent to the factory to
produce the system board5.
PCB board is designed to make the circuit mini,
intuitive, and play an important role in the optimizationof electrical layout. The design of the PCB board isshown in Fig.1.

The design adopts the WIFI module of ESP8266, and itcomes with a core MCU. The MCU has the built-in RAM,
ROM, support RTOS, and reaches the maximum clock
speed of 80 MHz. The WIFI module has the following
characteristics:
Built-in 10-bit high-precision ADC, with a complete
TCP / IP protocol stack.
Supports Cloud Server Development / Firmware and
SDK for fast on-chip programming.
Wide temperature range: -40 oC ~ 125 oC.
Operating Voltage 3.0V ~ 3.6V.
The WIFI module is shown in Fig.3.

Fig.1 The design of the PCB board

Fig.3. WIFI module

2.1. ILI9341 with resistance touch screen

2.3. Main control chip

ILI9341 is a 320*240 resolution TFT screen, with a fullcolor display function. It can use the 8080 timing and it
can also work during -40oC to 80oC.
This design adopts the STM32 and ILI9341 tocommunicate through SPI timing. A series of device data
information are displayed on the resistive screen and thesmart home device can be manipulated by touch. ILI9341
touch screen is shown in the Fig.2.

In this design, the external MCU is the
STM32F103ZET6, which is the Cortex-M3 ARM coreand has a 144-pin. The chip has 512KB flash memory
and 64KB RAM6. It can through SWD or JTAG with
KEIL 4 to download the program and online debugging.
And it has more than 80 general-purpose IO ports,
multiple timers and serial ports, which fully meet thedesign of the required pin and memory requirements. It
has the following characteristics:
On-chip dual RC crystal, providing 8M and 32K
frequency.
Supported peripherals: Timer, ADC, DAC, SDIO,
I2S, SPI, I2C and USART.
Operating Voltage 2.0V ~ 3.6V.
Operating temperature range: -40 oC ~ 105 oC.
The design of the main control chip is shown in Fig.4.

Fig.2. ILI9341 touch screen© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 149

Integral Design of Intelligent

eight general-purpose IO ports, a serial port and a power
amplifier interface.
The device receives the digital audio data from the
STM32 micro-controller by the SPI interface, andconverts the data into an analog audio signal through the
chip's built-in DAC. And finally, through the amplifierinterface and external speaker connection, the analog
audio signal playback function is achieved.
Fig.4. STM32F103ZET6 chip

4. Introduction of functional module
3. System circuit module design

With the functional modules, we can easily control andmonitor the smart home equipment.

In the circuit design, this design adopts a voltage
stabilizing module, temperature and humidity dataacquisition module and voice module. These modules
greatly improve the function of the device.

4.1. Wireless Ad Hoc Network Design
This design uses a wireless Mesh network for ad hoc
network communication. The Mesh network has a meshnetwork structure, and the number of it can be determinedby the number of nodes. The relationship between thenumber (L) and node (N) can be expressed as:

3.1. Voltage stabilizing circuit design
The design adopts the AMS1117-5.0 voltage stabilizing
chip for stable 5V design. It is a linear voltage stabilizing
chip, and the input voltage is (at least) greater than the
output voltage 1.3V. It has four pins, two of which are
output pins, one is the input pin, and the remaining pin is
the ground (GND)7. In the voltage stabilizing circuit, the10uf and 100nf capacitors are added as filter capacitors.

L C N2N

N 1 /2(1)

By this way, each node can transmit data quickly andefficiently. But it is actually very difficult to achieve
because it requires a large number of links to connect,
which is not possible in reality. Therefore, most of the
network layout is part of the Mesh network layout. For
example, the internet is a part of the Mesh network layout.
Therefore, most of the network layout adopt the partial
Mesh network Layout mode. The paper also adopt partial
Mesh network Layout mode.
This design adopts two kinds of modes for equipment
connections. One is that the device node and the router isconnected, and is not in the cloud server activation. The
device of this connection is called the local device.
The other mode is in the cloud server activate, which
is known as the network equipment. The WIFI mastermodule is activated in the cloud server, and the othermodules are locally connected through the router.
This design uses the automatic networking mode. As
soon as the firmware is downloaded to the module, theWIFI master module will connect to the router, and thenthe routing is connected to the WAN. Other sub-modules
will automatically scan around the WIFI access point(AP). The WIFI master module has the capability toreceive, transmit and forward data packets, while the3.2. Design of connection circuit for temperatureand humidity module
The design adopts the temperature and humidity
compound sensor DHT11. It has humidity measurement
elements to measure humidity and NTC temperature
measurement elements to measure temperature, and its
correction factor is stored in OTP memory by program.
DHT11 has four pins, one pin is the temperature andhumidity data output pin, the other three pins areconnected to VCC, GND and floating8. The DHT11 is atemperature and humidity sensor, and its measurement
accuracy is very high. The accuracy of temperature
measurement is about 2oC, and the accuracy of humidity
measurement is about 5%. Its signal transmission
distance can reach to 20 meters.
3.3. Hard-decoding circuit for speech chip
The VS1053 chip that used in this design is the Ogg
Vorbis audio decoder. The chip contains not only a lowpower high-performance DSP processing core, it also has© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 150

Yuxing Ouyang, Fengzhi Dai, Yiqiao Qin, Ce Bian, Bo Liu, Hongwei Jiao

other sub-modules only have the capability to receive andsend data packets.

The test includes the sensitivity of the touch screen, thestability of the data transfer between the WIFI modules,
and the speed and stability of the Airkiss distribution
network. The test results show that the device canbasically meet the user's control and monitoring ofintelligent home equipment.

4.2. Airkiss distribution network
With the mobile-phone APP WeChat, the WIFI moduleof this design can be carried out Airkiss distribution
network, which makes WIFI module access to theInternet.
The principle of the Airkiss distribution network is
through the LAN discovery and promiscuous mode tocapture the way to achieve the purpose of smart
configuration. Since the SDK programming and thefirmware is burned to the WIFI module, the WIFI module
can access to the network through the phone.
The Airkiss smart config network steps are as follows:
Scan the two-dimensional code by WeChat.
Fill in the SSID and password according to the popup window.
WIFI master module receives the SSID andpassword, automatically connects to the router.

Acknowledgements
The research is partly supported by the Research Fundfor the Doctoral Program of Higher Education of China(20131208110005). It is also supported by the Science &Technology Fund Planning Project of Tianjin Higher
Schools (20120831, 20140710-1400020005).
References
1. Santing Zheng, Yanyan Bai, Xiaoxia Hu, The design andapplication of digital electronic clock, Electronic
Production, 2015, 5: P90 - P91.
2. Yingang Fu, Design and implementation of information
appliance control platform (Shangdong, Shandong
University of Science and Technology, 2007)
3. Xiong Sun, Smart home system based on Internet of things(Tianjin, Tianjin University of Technology, 2013)
4. Jinrui Zhang, Research and implementation of intelligent
transportation system gateway based on Internet of things(JlLin, Changchun University of Science and Technology,
2014)
5. Bing Xia, Design and implementation of data acquisition
and identification algorithms in an online logic chipanalysis tool (Henan, PLA Information Engineering
University, 2006)
6. Wei Qin, Based on the AM2301 temperature and humidity
meter design, Hunan Agricultural Machinery, 2012, 9:
P59 - P60.
7. Qingxia Li, Wenhong Wei, Study on the structure of an
extended OTIS-Mesh network, Computer Science, 2012,
z3: P90 - P92.
8. Jean-Christophe Cuilliere, Vincent Francois, Remi
LacroisLi, A new approach to automatic and a priori mesh
adaptation around circular holes for finite element analysis,
Computer-Aided Design, 2016.

5. Testing and conclusion
By the test of the smart home devices, the following data
are obtained: The Mesh network allows up to 5 hops,
each mesh-non-leaf node can allow up to 4 direct childnodes to access the network, the transmission distance
between nodes is best within 100 meters, the router can
mount a Mesh network with a network size of 80 nodes.
The above data shows that the device can basically meetthe needs of smart home equipment integration.
By using DHT11 module, the indoor temperature andhumidity data can be real-time transmitted to the remoteclient.
By using voice modules, the voice is used as the failure
alarm when the touch screen is operated.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 151

Research on Underwater Robot Recognition
Binhu Song1, Fengzhi Dai1*, Qijia Kang1, Haifang Man1, Hongtao Zhang1, Long Li2, Hongwei Jiao3
1

College of Electronic Information and Automation, Tianjin University of Science and Technology, China;
2

3

Tianjin Electric Locomotive Co., Ltd, China)

Tianjin Technology School of Printing and Decoration, China* daifz@tust.edu.cn

Abstract
The underwater robot fish needs to keep shaking the tail fin to move, but the wave of the water is very difficult to bepredicted, in which the robot fish will show different shape. This paper proposes to use of Histogram of Oriented
Gradient method for image feature extraction, and then use the genetic algorithm method to optimize the parametersof the support vector machine. We use the parameters optimized for the classification training of experimental images
to recognition the robot fish.
Keywords: support vector machine, genetic algorithm, underwater robot fish, histogram of oriented gradient

1. Introduction
2.2. Calculation of the gradient of the target image
Support vector machine (SVM) belongs to the machinelearning. It is a classifier for finding the maximum
interval classification. It has a good effect in solving nonlinear and high-dimensional pattern recognition
problems1. Over the past few years, its importance is
growing. In the use of the classifier, its performance
depends on the choice of the kernel function and its
parameters. In this paper, the genetic algorithm is used to
optimize the parameters.

Calculating the gradient of the target needs to be applied
to the first-order differential theory, because thisprocessing can be applied to the grayscale graphics toform a more satisfactory effect. The calculation is asfollows:= = ( + 1) − ( )(1)= = ( + 1) − ( )In order to calculate the image gradient and gradientdirection better, we need to use the gradient template tocalculate both the gradient of the vertical component andhorizontal component2. The gradient template is [-1, 0, 1].
The horizontal and vertical components of the gradientcan be obtained as follows.
G ( , ) = f( , + 1) − f( , − 1)
(2)
G ( , ) = f( + 1, ) − f( − 1, )2. Histogram of oriented gradient feature extraction
2.1. Image normalization
The purpose of the normalization to image is to give thedetector more robustness to illumination as the primaryfactor affecting the image.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 152

Binhu Song, Fengzhi Dai, Qijia Kang, Haifang Man, Hongtao Zhang, Long Li, Hongwei Jiao

Gradient direction histogram is a very representative
feature extraction method, since the emergence of thismethod has been shown a very powerful function in avariety of areas. Fig.1 is the basic process of HOG.

By the horizontal gradient component and the verticalgradient component as shown in the above expression,
we can get the size of the gradient (that is, the gradientFig.1 Basic process of HOG

2.3. Using genetic algorithms to select and optimize
SVM parameters
The genetic algorithm based on model theory, applied byProfessor Holland, is used most commonly so far. The
general formula for the model theory is as follows3:

intensity), as well as the direction of the gradient, that is,
the above-mentioned amplitude (magnitude) and angle(theta) values.
magnitude = xGradient + yGradient(3)
theta = tan (yGradient/xGradient)As shown in the above equation, the edge strength isaffected by the sum of the gradients of the x-direction andthe y-direction, and the angle is derived from tan-1 of its
ratio. According to the simplification rule proposed
above, the gradient intensity formula above-mentioned
can be simplified as follows
magnitude ≈ | ( , )| + | ( , )|(4)m( , + 1) ≥ m( , )

( )
(1 −
̅

( )

4

− ( )

(7)

Where H is the code of the pattern , t is chromosome
algebra, m(H, t) is the t-generation chromosome numberof strings, pc is the exchange probability, ( ) is theaverage fitness of the H-mode in t generation, ̅ is theoverall fitness,at last is the probability of mutation.
Fig.2 gives the flow chart of GA optimization to the SVM
parameters.

The angle of the gradient is given by thefollowing equation:
theta(x, y) =theta( , ) + π
theta(x, y) < 0
theta( , )others(5)
A weighted projection is used in the direction of thegradient.
The goal of this step is to project the values of the
pixels in the block in the gradient direction of the image.
After the projection, a horizontal axis is obtained, and thehistograms of our cells are aligned along this axis. With
the horizontal axis, we can compute the Histogram ofOriented Gradient of all the cells in each block, and thencreate a histogram at each gradient angle, placing several
gradient angles above the horizontal axis. There are avariety of ways to project in a gradient, not just one. But
in general, the user uses a weight projection function tosolve this problem. The eigenvectors of the directional
gradient histograms are normalized. At the last of thisstep, we need to normalize each block.
L2 − normv ← v/ ‖ ‖ +(6)

Fig.2 Flow chart of GA to optimize SVM parameters2.4. Support vector machine
The main idea of machine learning is: there is a certain
unknown dependency between the variable y and input x,
which can also be understood as the variable y and input
x follows an unknown joint probability F(x, y). Machinelearning is based on n independent identically distributed
observation samples (x1, y1), (x2, y2), … , (xn, yn) .
Then an optimal function f (x, ω∗ ) is obtained from a
set of functions {f (x, ω)}. This optimal function canmake the expected risk function
R( ) = ∫ ( , ( , )) ( , )(8)© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 153

Research on Underwater Robot

That is, it does not use any optimization means to predict
and classify.
Prepare the appropriate training samples anddetermine the classification of labels after the start oftraining, the support vector machine parameters c and gwere selected for the two groups to comparative
experiment.
After obtaining the training result, we use several
pictures to call the recognition function to classify andpredict. After the prediction, we count the number ofsuccessful training samples and the number of failed
samples to calculate the correctness of recognition as
follows Table 1:

have the minimum value. Here, {f ( , )} is a set ofprediction function. Since ω ∈ Ω is a generalized
function parameter, theoretically {f ( , )} can refer toany set of functions. And L ( , f ( , )) is the loss to
predict variable y by using the set of predicting functions{f (x, ω)}.
The output of the function is the codename of thesorted category. The binary output function of the
problem can be simply defined as a binary function y =(−1,1). With the output function, we need the loss offunction output function of the forecast will result in theloss function.
0if= ( , )
L , f( , ) =(9)1if≠ ( , )

Table1 Standard SVM classification results

The support vector machine training in thisexperiment will be completed by the SVM toolbox in
MATLAB. For feature extraction of training image, theimage itself can provide accurate pixel. However, theamount of training pixel data will cause delay theexperimental speed, so the application of the direction of
the image gradient as a feature extraction.

SVM parameter

c=1, g=10

c=0.5, g=0.6

accuracy

50%

52%

It can be seen that the parameters of SVM in theexperiment are determined manually, the result is limited,
and the result of recognition accuracy is not very high.
But this kind of training method has got certain results.

2.5 Opencv for reflective compensation

3.2 Using optimized parameters to the sample training

In this part, we use Opencv to optimize the image of theexperimental site. Experiments were taken from different
periods of the experimental site of the reflective picture5,
and the obtained picture is subjected to hue saturation
adjustment, get a more pure color and exposure of theimage. Subsequently, the image is subjected to agrayscale operation, after all the Opencv functions havebeen processed, the compensation results are shown in
the following Fig.3 are obtained6.

In order to improve the accuracy of the previous SVM
predictive classification experiments, the parameters of
SVM are optimized by the genetic algorithm mentioned
above.
First, the corresponding sample set and classification
information are prepared, which includes the sample set
and classification information used in the training of
SVM. The parameters of SVM are optimized by using
the genetic algorithm parameter optimization provided
by the libSVM toolbox described above. The optimized
support vector machine parameters are c = 3.4 and g =5.7. The experimental result is shown in Table2.
Table2 SVM classification results with GA optimization

Fig.3 Before (left) and after (right) reflective compensation

3. Recognition of the underwater robot fish

best parameterc=0.1,g=0.4

accuracy

85%

These experiments can let us clearly see that, with thegenetic algorithm to optimize participation, support
vector machine training out of the classification

3.1 Default parameters to recognize the robot fish
The recognition experiment of the underwater robot fish
begins with the method of the basic support vector set.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 154

Binhu Song, Fengzhi Dai, Qijia Kang, Haifang Man, Hongtao Zhang, Long Li, Hongwei Jiao

algorithm for the recognition of the robot fish in the water
is significantly more successful.

References
1. Williams C K I. Learning With Kernels, Support Vector
Machines, Regularization, Optimization, and Beyond.
Journal of the American Statistical Association, 2003,
16(462):781-781.
2. Dalal N, Triggs B, Histograms of oriented gradients forhuman detection. IEEE Conference on Computer Vision &Pattern Recognition. 2005:886-893.
3. Holland J H. Adaptation in natural and artificial systems: anintroductory analysis with applications to biology, control,
and artificial intelligence. 2nd ed. Cambridge: MIT Press,
1992.
4. Tang K Z, Sun T K, Yang J Y. An improved genetic algorithm
based on a novel selection strategy for nonlinear
programming problems. Computers & Chemical Engineering,
2011, 35(4):615-621.
5. Inamura M, Toyota H, Fujimura S. Influence of the Light
Reflection on Water Surface in Quantitative Remote Sensingof Water Quality and Its Removal. Journal of the Remote
Sensing Society of Japan, 1984, 4:39-50_4.
6. Rudolph G., Convergence analysis of canonical genetic
algorithms. IEEE Trans on Neural Networks,1994,5(1):96101.

4. Conclusion
The results of the SVM-trained algorithm show that the
proposed algorithm outperforms the default support
vector machine, in the case of hardware and experimental
environment has withstood the test.
In the future, the hardware and software will be a great
development prospects, a large number of the input of
training sample sets will make the training of thealgorithm be more comprehensive, and the classification
prediction will be more accurate.
Acknowledgements
The research is partly supported by the Research Fundfor the Doctoral Program of Higher Education of China(20131208110005). It is also supported by the Science &Technology Fund Planning Project of Tianjin Higher
Schools (20120831, 20140710-1400020005).

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 155

Design of Intellectual Vehicles with Path Memorizing Function
Yiqiao Qin 1, Fengzhi Dai 1*, Yuxing Ouyang 1, Baochang Wei 1, Simini Chen 2, Hongwei Jiao 3
1

Tianjin University of Science and Technology,
1038 Dagunanlu Road, Hexi District, Tianjin, 300222, China;
2 Palace Museum, China;
3 Tianjin Technology School of Printing and Decoration, China

E-mail: * daifz@tust.edu.cn
www.tust.edu.cn

Abstract
Based on the deficiencies of current intelligent vehicles, we design a new vehicle system that has a function ofstoring path data. It has a kind of algorithms to recognize the position and path of the vehicle that neither relies on
the external positioning methods such as GPS, WIFI, etc, nor the electromagnetic rails or photoelectric rails. Thissystem is rarely affected by the ground condition, so there is no need for any ground signs.
Keywords: intellectual vehicles, path-recognition, path-correction, MCU

1. Introduction

guided vehicles, but both GPS and WIFI have someobvious defects that are hard to overcome. GPS isvulnerable to the interference of buildings and theaccuracy is poor in indoor positioning. Similarly, WIFI
positioning is usually applicable to indoor space.
Moreover, before positioning the vehicles, WIFI
transceiver must be installed in some key locations in
the room and we have to set the location information to
the position system. This process generally takes a longtime and is very cumbersome2. Also, when the indoor
space changes, such as installing a new large
mechanical equipment in the plant, the location
information needs to be reset.
Based on the above points, it is necessary to design atransportation system that is not limited by the groundcondition, and it does not rely on the GPS, WIFI or
markers on the ground and can accurately transport the
goods from one place to another.
Therefore, we designed a kind of intelligent vehicle
with path memory function, which can record path
between two locations, and quickly and accuratelytransport the goods from the starting point to the end
point without human operation.

Nowadays there are two kinds of guided vehicles thathave found an increasingly wide utilization in the shortdistance transport of goods.
Vehicles are guided by electromagnetic track.
Vehicles are guided by photoelectronic track.
Both of them need to rely on the track on the floor,
and require a good ground condition. Their limitations
are as follows:
In the factory or warehouse, the ground condition
may not be optimistic. Debris, water, dust and otherissues often make it difficult for photoelectric
sensors to accurately detect the lane. Similarly, theelectromagnetic guidance is also limited by thewater, metal debris and other adverse impact.
Before applying these two methods of guidance,
workers must lay the black wire or electromagnetic
wire on the ground as a "track". This is notconvenient for factory to changes the product line,
and it is difficult to lay "tracks" on unrepaired
ground such as dirt roads and brick roads1.
In addition to the ground track, we also could adopt
wireless location technology, such as GPS and WIFI, to© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 156

Yiqiao Qin, Fengzhi Dai, Yuxing Ouyang, Baochang Wei, Simin Chen, Hongwei Jiao

2. The hardware structural design

marker in its line of sight, and determine the relative
angle to the end point. According to the relative angle
and relative distance, the MCU could adjust the vehicle
to reach the end point more accurately.

The whole hardware design consists of 9 modules:
MCU, ultrasonic ranging module, communication
module, speed measuring module, motor drive module,
electronic compass, CCD camera, SD card and LCD
module.3 The hardware structure is shown in Fig.1.

2.4. Vehicle body structure
As shown in Fig.2, the vehicle body has three wheels,"A" and "B" are drive wheels, "C" is a mecanum wheel.

Fig.1 The hardware structural design of vehicles
Fig.2. vehicle body structure

The structural design of the search and rescue robots
is shown in Fig.1. And the followings are some of theimportant details about the hardware structural design.

This kind of body has greater advantage in operation,such as the followings:
Compared with the four-wheeled vehicle, thetricycle does not require steering gear, and its costis lower than the four-wheeled vehicle.
The MUC can control "A" and "B" with different
speed to achieve pivot steering, while the fourwheel vehicle cannot do this. Therefore, in theadjustment of moving error, the three-wheeled bodyhas a greater advantage4.

2.1. Ultrasonic ranging module
The ultrasonic ranging module can measure the distance
between the vehicle and the surrounding obstacles toprevent the vehicle from hitting them.
Moreover, when the vehicle moves near to the end
point of the transport, cooperating with the camera, theultrasonic sensor can measures the distance between thevehicle and the marker, which is set at the end point, toeliminate the position deviation caused in the transport
process, so that the vehicle can reach the destination
more accurately.

3. Vehicle forward path identification algorithm

2.2. Optical encoder and electronic compass
The photoelectric encoder records the vehicle traveling
distance, electronic compass detects the current
direction of the vehicle. With the data from both sensors,
we can determine the relative position between thecurrent vehicle and the starting point, as well as thedeviation between the current vehicle and the preset
travel path.
2.3. CCD camera
The camera can detect the terminal markers. With thedate from optical encoder and electronic compass, if the
MCU identifies that the vehicle is about to reach theterminal point, the camera will look for the terminal

The vehicle designed in this paper can travel along theestablished route that is stored in the SD card. There are
two sources of the established route. One is the manual
input specific data, such as the distance, steering angle,
etc. The other is that the vehicle is remotely controlled
to move forward. At the same time, the vehicle
automatically records the path in real time5.
Therefore, it requires the vehicle to have the ability todetect the path in real time, the detailed methods are as
follows:
The MCU reads the speed data from photoelectric
encoders every 500ms. As we know, it is impossiblethat the speeds of the two drive wheels are exactly thesame in the meantime. If we just integrate the average
speed as vehicle’s path, it will lead a large deviation.
Therefore, it can be considered that in this 500ms, the© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 157

Design of Intellectual Vehicles

vehicle moves an arc M as shown in Fig.3. We need tofigure out the line distance L in the 500ms.

same manner. Finally, the straight line distance anddirection of path AM can be obtained.6
The direction and the distance of a path can be stored
in the SD card as ideal path to guide the vehicle moving.
In addition, by calculating the path which the vehicle
has traveled in real time, error between authentic pathand the ideal path can be figured out.
4. Vehicle routing correction algorithm
When the vehicle runs autonomously without operator,
its real-time position and direction can be obtained by
the path identification algorithm, and the ideal forward
route can be obtained from the information stored in the
SD card.
As shown in Fig.5, the straight line CD is the ideal
forward route of the vehicle, the line AB is the distancedeviation between the vehicle and the ideal forward
route, and the ANG is the angle deviation between thevehicle and the ideal traveling direction.7 The overall
deviation is obtained by Formula 8.

Fig.3. The calculation of line distance L

L

r2

M

r a

L

r 2 2 r r cos aM

2 (1 cos a ) / a

(1)
(2)
(3)

Where, "a" is the direction difference during the
500ms, which is measured by the electronic compass.
Formula 3 can be derived from the formula 1 and 2, andthe line distance L can be figured out.
Assuming that the path is shown in Fig.4(a), andFig.4(b) is the vehicle's actual travel path. The following
is the algorithm to calculating the A-M straight-line
distance and relate angle.

Fig.5. Calculation of distance and angle deviation

Deviat = AB m + ANG (1 - m)

Fig.4. The algorithm to calculating the travel distance

Where, "Deviat" is the overall deviation value and is
the error input of the PID algorithm. In this correction
algorithm, "m" is the ratio adjustment factor of thelinear deviation and the angle deviation. That is to say,when the value of m increases, the control algorithm
focus much more on reducing the distance deviation.
When the m decreases, the angle deviation has more
affection to the control algorithm.
According to the above information, it is easily to add
the PID in control algorithm.8

As shown in Fig.4(a), AB, AC and Ang1, Ang2 are
obtained by Formula 3 and electronic compass.

angle3 180
AC

AB

2

ang 5 arccos[( AB 2

ang 4

BC

2

(ang1 ang2)
2 AC BC cos(angle3)

AC 2

BC 2 ) /( 2 AB BC )]ang1 ang 5

(8)

(4)
(5)
(6)
(7)

From Formula 4 to 7, the direction (ang4) and the
distance of the path AC can be obtained. And then use
the above formula again, we can get the direction andthe distance of the path AD. The rest can be done in the© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 158

Yiqiao Qin, Fengzhi Dai, Yuxing Ouyang, Baochang Wei, Simin Chen, Hongwei Jiao

The path recognition algorithm of the design
performs better on the ground and on the downhill path.
The error is in the acceptable range when on dry dirt
roads. However, in the potholes, or on muddy roads theperformance is still not ideal. So we will continue to
optimize the vehicle recognition algorithm and focus on
improving the adaptability of the vehicle to thepavement with poor road conditions.

5. Testing and conclusion
We obtained the following data by testing the intelligent
vehicle (the test environment is cement ground).
In the 10 times of the random tests (human remotecontrol), the vehicle travels 10m in straight line. The
maximum location error for the end point is 2.7cm andthe probability of error within 2 cm is 80%.
There is a probability of 90% that the steering error ofturning 90 angle is in the range of 0.3.
As shown of the routes in Fig.6, there is a probabilityof 90% that the error of vehicle's position in end point isless than 8cm. If adding the recognition algorithm ofend point, the probability that the error that is less than
4cm will be 95%, and the probability that the vehicle
does not recognize the end point is about 1.5%. It canmeet the demand basically.

Acknowledgements
The research is partly supported by the Research Fundfor the Doctoral Program of Higher Education of China(20131208110005). It is also supported by the Science& Technology Fund Planning Project of Tianjin Higher
Schools (20120831, 20140710-1400020005).
References
1. HP Wang, Y Yang, JT Liu, Research and Development
Trend of High-speed Mobile Robot. Automation &Instrumentation, 26 (12) (2011) 1-4.
2. HM Khoury, VR Kamat, Evaluation of position tracking
technologies for user localization in indoor construction
environments, Automation in Construction, (4) (2009)444-457.
3. YX Wu, H Zhang, XS Wang, The Design of Intelligent
Vehicle Path Recognition Based on Monochrome
Camera, Information Technology & Informatization, 13(2) (2009) 42-45.
4. Yonggang Wei, Yunping Fan, Guoying Meng,
Comparison of Steering Performance between two
wheels and four wheel Car, Coal Mine Machinery, 30(5)(2009) 78-80.
5. MY Fu, ZH Deng, Intelligent vehicle navigation
technology (Beijing, Science Press, 2009)
6. Shiyong Li, The theory of fuzzy control and intelligent
control (Harbin, Harbin Institute of Technology Press,
1999)
7. Xiong Bo, QU Shi-ru, Intelligent Vehicle's Path Tracking
Based on Fuzzy Control, Journal of Transportation
Systems Engineering and Information Technology, 10 (2)(2010) 70-75.
8. Md.Nazmul Hasan, S.M Didar-Al-Alam, Intelligent Car
Control for a Smart Car, International Journal ofComputer Applications, 14(3) (2011) 15-16.

Fig.6. The test line

The functions and system advantages of theintellectual vehicle with path-memorizing function
introduced in this paper are as follows:
It can realize the transportation of goods without
any signs or tracks.
It can be applied to a variety of indoor and outdoor
occasions. Even if it transports goods in the rainydays or on dirt roads, the transportation could still
be proceed very well.
It can quickly change the transport path. Thus suchfeature is convenient for the change of plant
production line.
However, there are still some defects andshortcomings in this design, which mainly focus on thetransport accuracy. Next, we will continue to improvethe design on this aspect.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 159

Action recognition based on binocular vision
Yiwei Ru1,2*, Hongyue Du1,Shuxiao Li2, Hongxing Chang2
1.Harbin University of Science and Technology, Harbin, China;.Institute of Automation Chinese Academy of Sciences ,Beijing, China

2E-mail: *ruyiwei2014@ia.ac.cn

Abstract
Aimed at the problem that the recognition accuracy of the monocular camera is low, we propose a binocular vision
recognition algorithm for action recognition based on HART-Net(Human action recognition networks).Firstly, the left andright views obtained by the binocular camera are matched to obtain the depth map of the human body .Then, the depth
information is projected onto the three planes, the projection images of three directions are used to construct MHI (motion
history image), and are combined into a new image. Finally, we use HART-Net to train a classifier for action recognition.
Experimental results show that the binocular recognition algorithm is 18% more accurate than the monocular recognition
algorithm.
Keywords: action recognition; binocular version; convolutional neural networks; motion history image;

1. INTRODUCTION
Since 1980s, the concept of search and rescue robot has
been presented. Recognizing human activity is one of theimportant areas of computer vision research today1.Its
applications include video surveillance, virtual reality,
human-computer interaction and others. In recent years,
many human motion recognition research is based on
monocular vision. Although the recognition algorithm
based on monocular vision has achieved good results, but
due to the monocular camera's own limitations, thisapproach is very sensitive to complex backgrounds, sowhen the background changes significantly, the accuracyof recognition will decline. In order to improve thedetection accuracy of human action recognition algorithm
in complex background, people use time-of-flight or
structured light technology to obtain the depth map of theobject. However, for some applications such active sensors
are not suitable. For example, in outdoor setup or in a
scenario with multiple autonomous robots whose active
sensors would interfere to each other2.
In order to solve the problem that the human action
recognition algorithm is sensitive to the background underthe monocular visual and cannot obtain the object depth
map based on the structured light or time-of-flighttechnology in the outdoor environment3, we propose anaction detection algorithm based on binocular vision. In
order to improve the accuracy of motion recognition inbinocular vision, a novel convolutional neural networks
architecture named HART-Net (human action recognitionnet) is presented. Firstly, the left and right views obtainedby the binocular camera are matched to obtain the depth
map of the human body under the camera coordinate
system (O-XYZ). Then, the depth information is projected
onto the three planes of O-XY, O-YZ and O-ZX
respectively in the camera coordinate system4. In order toreflect the motion of the timing information, the projection
images of three directions are used to construct MHI(motion history image), and then the three MHIs are
regarded as the three channels of the image to construct a
new image. We finally use CNN to train the classifier foraction recognition.
2. RELATED WORK
2.1. Stereo Matching Algorithm in Binocular
Binocular stereo matching algorithm can be divided into:
local matching algorithm and global matching algorithm.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 160

Yiwei Ru, Hongyue Du,Shuxiao Li, Hongxing Chang(a) left image(b) right image(c) depth map

Fig1. Left-right image after line alignment, and depth map(a)Outline of the human body(b) MHI in O-XY(c) MHI in O-YZ(d)MHI in O-ZX

Fig2. Outline and MHI image(a)Outline of the human body(b) MHI in O-XY(c) MHI in O-YZ(d)MHI in O-ZX

Fig2. Outline and MHI image

Local matching algorithm mainly compare the matching
point within a certain range of loca characteristics to match.
Bigone5 et al. Used the edge information of the image as amatching feature. Marr6 et al. Proposed to use zerocrossing as the basis, plus and continuous constraint
iterations obtained after the disparity map. Nevatia andMedina7 use line segments as matching primitives.
Global matching algorithm is generally used to scan theline or the overall consideration of the image information
to be matched to solve the disparity. Boykov8 et al. first
introduced the graph-cut theory into the stereo matchingalgorithm. For the first time, Kolmogorov9 incorporated
the solution of the occlusion problem into the constructionof the energy function. Confidence propagation was
proposed by Sun Jian10 of Microsoft Research Asia. Ohta11
proposed an edge-characterized DP algorithm.
2.2.Action Recognition Algorithm Based on RGB andRGBD

As the RGB image is different from the RGBD image16
which can be directly segmented out of the body region
using depth information, RGB-based action recognition
generally requires the use of segmentation methods toextract the contours of the human body. Ali andAggarwal12 define an action boundary by using a feature
vector that contains three angles to the main part of thehuman body. Hanjalic13 et al. used logical tale units (eachof which is represented by one or several events not related
to timing) to detect the motion bounds. Zhai and Shah14
used the Markov chain Monte Carlo technique to segment
the temporal scenes in various videos. Shi et al. 15 usedsemi-Markov model to achieve the action segmentation.
For the RGBD method, the depth of the human body canbe obtained by face detection or head and shoulder
detection, and then the human body position can beextracted using the depth information. After the outline of
the human body is obtained, the MHI17 is used to reflect
the time when the human action takes place and the time
to change. The obtained MHI is used as the input pictureof the classifier.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 161Action recognition based on

7
128

7

64

64

3
128

3
3 32

3
3

3
64

64

3
convolutional
ReLU

convolutional
ReLU
32 Max Pooling

128

64

64
128

16

64

64
128

16

3

convolutional
ReLU
Max Poolingconvolutional
ReLU

full-connected
ReLU

512
full-connected
ReLU

Fig3. HART-Net architecture

3.

PROPOSED METHOD

3.1.Overall framework
The proposed method mainly include the following four
modules:(1) Human body region extraction. After using thecamera's internal and external parameters for linealignment between the left and right images, we can get
the depth image by seed pixel propagation matchingalgorithm, as shown in Fig 1. In order to get the locationof the human body, we first use the seetaface detection
algorithm to detect the face position. Then, the average
depth value of the face is computed in the depth map,
denoted as D(mm). Finally, [D-300, D+300] is used to
extract the contours of the human body.
(2) Human body region refinement. As the ground andthe human body is connected, the acquired human body
region will contain part of the ground information. In orderto remove the ground, we use RANSAC algorithm todetect plane in the picture, and then remove it from thebody outline image, as shown in Fig2 (a).
(3) Get MHI images. After the human body depth map
is obtained, the depth map is cropped and cut into 480 *
480. According to the position information of the humanface, the human body profile is located at the middle
position of the image, and the depth map is projected onto
the O-XY, O-YZ, O-ZX three planes, and then the threeplanes were accumulated to get MHI images, as shown in
Fig 2. In order to meet the different speed of movement ofdifferent people, we selected 15,20, 25, 30 images in thesame sequence of motion for each action to construct MHI.
Finally, the MHI of the three projection planes is merged
into one image as an RGB image as shown in Fig 4.

(4) Action recognition by HART-Net. By using the MHI
image as the input image of the classifier A novel
convolutional neural networks architecture named HARTNet (human action recognition net) is presented in thefollowing subsections.
3.2.HART-Net architecture
In order to meet the training needs, we propose our own
convolution neural network training framework: HARTNet, as shown in Fig3. This convolutional neural network
has four convolutions and two fully-connected layers. The
input of the first convolution layer is 64 * 64 * 3, this layer
contains 32 convolution kernel, and the convolution kernel
size is 7 * 7 * 3. The second layer contains a pooling layer
with max pooling. The output of the first convolution layer
is the input of the pooling layer. The pooling layer contains
64 convolution kernels. The size of the convolution kernel
is 3 * 3 * 32. The third layer and the fourth layer are similar
to the first layer and the second layer, and the specificparameters are shown in Fig3. The output of the last fullyconnected layer is fed to a soft-max layer, and the outputof the soft-max layer is used as the confidence of theclassifier for each class to be categorized.
Notice that, the ReLU non-linearity is applied to the
output of every convolutional and fully-connected layer.
3.3.Training process
Our classification task has 8 kinds of action: Drinking,eating, making phone call, reading book, walking, waving,hand clapping, boxing. For each category, there are 1000
MHI images. For these images, we will first of all resize
them to 130 * 130.For each resized picture, we randomly
cut out 128 × 128 windows on these images as training
samples, and then horizontally rotated all the training© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 162

Yiwei Ru, Hongyue Du,Shuxiao Li, Hongxing Changsamples. After these transformations, our training sample
is 50 times the original. Although the samples obtained
from these transformations are highly dependent, training
with these samples still greatly improves the training
results. Because our training sample size is relatively large,
limited to GPU performance, so in the course of training
batch_size is general set to 16.Because the difference
between the samples is relatively small, we set base_lr to
0.01 and lr_policy to "inv" where gamma is set to 0.0001
and power is set to 0.75.
4. EXPERIMENT

classifier performance trained by the RGBD sample ishigher than the classifier trained by RGB by about 8%. But
for actions of walking, waving, hand clapping and boxing,
the classifier performance trained by RGBD issignificantly better than the classifier trained by the RGB,
because these three-dimensional characteristics of theaction are more obvious. For RGBD, classifier training
based on RGBD can get more information, so theclassification performance will be better. Overall, theclassifier performance training by the RGBD classification
is higher than the classifier training by RGB classification
by about 18%.

Fig4. MHI with three channels

In order to evaluate the performance of the classifieraccurately, we use the self-made set, using crossvalidation to test the classification of the results. The MHI
is divided into five parts, one fifth is test set, three fifth istraining set, and the remaining one fifth is verification set.
The verification set and training set are mainly used to test
the classification performance of HART-Net during
training. The test set is used to test the performance of aclassifier trained on RGB and RGBD samples in a real
sample. For comparison, we use the projection of RGBD
in the O-XY direction as a contour in the RGB.

Fig7. Confusion matrix - RGBD
Fig6. Confusion matrix - RGB

5.
Fig5.outline of the human body in RGB image

From the classification result of HART-Net, we can get
the corresponding classification confusion matrix.
Through the confusion matrix, we can see: For actions ofdrinking, eating, making phone call and reading book, the

CONCLUSION

In this wok, we propose a human motion detection fork
based on binocular vision. In speed, we can easily estimate
the location of the human body according to face positionor head and shoulder position .However, based on
monocular human action recognition algorithm need to use
motion information and graph cut algorithm to get theoutline of human motion, so compared with monocular© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 163

Action recognition based on

vision algorithm our algorithm has certain advantages in
speed. In terms of applicability, our algorithm can be used
outdoors, while algorithms based on structured light andtime of flight can only be used indoors, and there isinterference between devices, so our algorithm has thesame advantage in the applicability.
REFERENCES
1. S. Birchfield. Elliptical head tracking using intensity
gradients and color histograms. In CVPR’98, pages 232–237,
Santa Barbara, CA, June 23-25, 1998.
2. M. Blank, L. Gorelick, E. Shechtman, M. Irani, and R. Basri.
Actions as space-time shapes. In ICCV’05, volume 2, pages
1395–1402, Beijing, China, Oct. 17-21, 2005.
3. A. F. Bobick and J. W. Davis. The recognition of human
movement using temporal templates. IEEE Trans. PatternAnal. Machine Intell., 23(3):257–267, Mar. 2001.
4. P. Dollar, V. Rabaud, G. Cottrell, and S. Belongie. Behavior
recognition via sparse spatio-temporal features. In VS-PETS’
05, pages 65–72, Beijing, Oct. 15-16, 2005
5. Frank Bignone, Olof Henricsson, Pascal Fua, et al.
Automatic extraction of generichouse roofs fromhighresolution aerial imagery. Proceedings of the 4th European
Conference on Computer Vision: Cambridge, United
Kingdom, Springer Verlag, 1996: 85-96
6. D. Marr, T. Poggio. Cooperative Computation of Stereo
Disparity. Science，1976, 194: 209-236
7. Cordelia Schmid, Andrew Zisserman. The geometry andmatching of curves in multiple views. Proceedings of the
5th European Conference on Computer Vision: Freiburg,
Germany, Springer Verlag, 1998: 104-118
8. Boykov Y, Veksler O, Zabih R. Fast approximate energy
minimization via graph cuts. Pattern Analysis and MachineIntelligence, IEEE Transactions on, 2001, 23(11): 12221239

9. Kolmogorov V, Zabih R. Computing visual correspondencewith occlusions using graph cuts. Computer Vision, 2001
ICCV 2001 Proceedings Eighth IEEE International
Conference on. IEEE, 2001, 2: 508-515
10. X. Sun, X. Mei, S.Jiao, et al. Stereo matching with reliabledisparity propagation. In Proc: 3DIMPVT, 2011:132-139
11. Ohta Y, Kanade T. Stereo by intra-and inter-scan line searchusing dynamic programming. Pattern Analysis andMachine Intelligence, IEEE Transactions on, 1985, (2):139-154
12. Ali A，Aggarwal J K． Segmentation and Recognition ofContinuousHuman Activity / / Proc of the IEEE Workshop
on Detection and Recognition of Events inVideo． Vancouver，Canada，2001: 28－35
13. Hanjalic A，Lagendijk RL，Biemond J. Automated HighLevelMovie Segmentation for Advanced Video-Retrieval
Systems． IEEE Trans on Circuits and Systems for Video
Technology， 1999， 9( 4) : 580－588
14. Wang Jinjun，Xiao Jing． Human Behavior Segmentation
and Recognition Using Continuous Linear Dynamic
Systems / / Proc of the IEEE Workshop on Application ofComputer Vision． Tampa， USA，2013: 61－67
15. Qinfeng Shi, Li Wang, Li Cheng，et al. Discriminative
Human Action Segmentation and Recognition Using SemiMarkov Model. 2013 － 3 － 17. http: / /ieeexplore.
ieee.org/stamp/ stamp.jsp? tp=＆arnumber=4587557
16. Gagalowicz A，Quah C K. 3D Model-Based Marker-Less
Human Motion Tracking in Cluttered Environment / / Procof the IEEE 12th International Conference on Computer
Vision Workshops． Kyoto, Japan, 2009: 1042－1049
17. Bobick A F, Wilson A D. A State-Based Approach to theRepresentation and Recognition of Gesture. IEEE Trans on
Pattern Analysis and Machine Intelligence, 1997, 19( 12) :1325－1337© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 164

Analysis and Control of a Novel 4D Chaotic System
Hong Niu
College of Electronic Information and Automation, Tianjin University of Science & Technology
80 Mailbox, Tianjin University of Science & Technology, No. 1038 Dagu Nanlu, Hexi District, Tianjin, China 300222
E-mail: spots@163.com
www.tust.edu.cn

Abstract
In this paper, a novel four-dimensional (4D) autonomous chaotic system is presented. For chaos control of the 4D
system, a linear feedback controller only with one variable is designed via matching the variable coefficients of the
Lyapunov function, so that the system is no longer chaotic or periodic but globally asymptotically converges to theequilibrium point at the origin. The numerical simulation results are given to illustrate the feasibility and effectivenessof the method.
Keywords: the novel 4D chaotic system; chaos control; Lyapunov function with variable coefficients; global
asymptotic stability

to design a linear feedback controller only with one
feedback variable and prove the global asymptotic
stability of the controlled 4D system. Some simulation
results are given to demonstrate the validity of the linear
feedback controller. The conclusions are drawn in
Section 4.

1. Introduction
In 1963, Edward Lorenz developed a simplified
mathematical model for atmospheric convection,1 which
is a three-dimensional(3D) autonomous system and wellknown as the Lorenz system. Since then, many 3D
chaotic systems have been proposed, such as the unified
system2, the Qi system3 and so on. With the wide
application of chaotic characteristics in securecommunication and other fields, the complexity ofchaotic systems is increasing. Four and higher
dimensional chaotic systems have been investigated.4-6
In this paper, a novel 4D chaotic system is presented.
Chaos control is one of the major subjects in chaos
study, and the feedback control is a common method inchaos control.7-10 In this paper, the aim of chaos control
is to design a linear feedback controller and make thenovel 4D chaotic system no longer chaotic or periodic but
globally asymptotically stable at the origin.
The rest of this paper is organized as follows. In
Section 2, the model of the novel 4D chaotic system andits simulation phase portraits are given. In Section 3, a
Lyapunov function with variable coefficients is selected

2. The Novel 4D Chaotic System
The dynamic equations of the novel 4D chaotic system
are formulated asx a y x ,y c x yz xw ,z mx y hz ,w xy bw,

1

where x , y , z , w
are state variables, and a = 25, b= 3, c = 18, m = 19 and h = 14.
Let the initial values of the 4D system (1) be (x0, y0,
z0, w0) = (1, 1, 1, 1), then the Lyapunov exponents
respectivelyare1

2.6686 0© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 165,

Hong Niu

24

3. Chaos Control of the 4D Chaotic System

0.0003 0 ,11.7885 0 and
3
14.8804 0 . It indicates that the system (1) is

3.1. Formulation of the controlled systemchaotic. The phase portraits of the 4D system (1) are
shown in Fig. 1(a1)-(a3).

X

Let

X

O

x

y

z

w

controlled state vector, where X = x
the

O

state

vector

of

the

T

be

the

y z w

system

T

(1),

is

and

T

0 0 0 0 is the origin. Then, the controlled

system can be represented as

where
uc
u c1

x

a y x

uc1 ,

y

c x y

z xw uc2 ,z

mx y hz uc3 ,w

xy bw uc4 ,

uc2

uc3

uc4

k2 y

k3 z

k1 x

2

T
T

k4 w

, k1 ,, k4

0 3

is the linear feedback controller to be designed.
3.2. Design of the controller
Convert Eq. (2) to a state-space model which is expressed
as

X

AX

g X ,

c

0
1

4

where

k1 a
c

A

a
k2

m

1

k3

0

0

0

xw

T

and g X

0

0

0
0
h

,

0
k4

b

xy .

Take a positive definite function

1 T
X PX
2

V X

as a Lyapunov function candidate for the system (4),
where

P

Fig. 1. Phase portraits of the novel 4D chaotic system:(a1) x-y (a2) y-z (a3) z-w

n1

0

0

0

0

n2

0

0

0

0

n3

0

0

0

0

n4

Then, the derivative V X, n1 , n2 , n3 , n40.

is given by© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 166

Analysis and Control of

V X

1 T T
X A P PA X
2
1
+ g T X PX X T P g X
2
1 T
X QX + n4 n2 xyw,
2

k2

Q

q12

q13

0

q21
q31

q22
q32

q23
q33

0
,
0

0

0

0

q44

q11

2n1 k1 a ,q22

2n2 k2 c ,q33

2n3 k3 h ,q44

2n4 k4 b ,

q12

q21

an1 cn2 ,

q13

q31

mn3 ,

q23

q32

n2 n3 .

5

2mn3 an1 cn2 n2 n3
2n2 m2 n32 k2 c

3

2

c,

12

so that Eq. (9) is satisfied as long as k4 > -b. Hence, let k4= 0.
For V X

to be negative definite, k2 should satisfy

both Eq. (10) and Eq.(12). It means that
2

k2

h a c
4ah m 2

c

a c
4a

2

c,

to be negative definite, theresult, because the derivative V X

is negative

definite and the Lyapunov function V X

is positive

0,

6

definite and radially unbounded, it can be confirmed thatthe controlled system (2) is globally asymptoticallystable at the origin.
Substituting k2 = 45 and k1 = k3 = k4 = 0 into Eq. (3)yieldsTuc uc1 uc2 uc3 uc 4

4n1n2 k1 a k2 c
2

0,

7

8n1n2 n3 k1 a k2 c k3 h

0

2 2
3

2n2 m n k2 c
2n3 k3 h an1 cn2
2n1 k1 a n2 n3
2n2 k4 bh a c
4ah m2k2

2mn3 an1 cn2 n2 n3

4

11

when n1 = n2 = n3 = n4. Consequently, let k2 = 45. As a2n1 k1 a
an1 cn20.

2

Let n1 = n2 = n3. From Eq. (11), k2 should satisfy

leading principal minors of the matrix Q must satisfy

22

2an1 n2 n3

2hn3 an1 cn2

1 T
X QX .
2

For the derivative V X1

108ahn1n2 n3 k2 c

3Let n4 = n2, then Eq. (5) is simplified as

V X

c.

Assume that the minimum number of the feedback
variables might be equal to the number of the positive
Lyapunov exponents.11 The 4D chaotic system (1) only
has one positive Lyapunov exponent and k2 ≠ 0 as shownin Eq. (10), so let k3 = 0 and substitute k1 = k3 = 0 into Eq.
(8). It is obtained that

where

q11

2

an1 cn 2
4 an1 n 2

3

0.

2

T

45 y 0 0 .

133.3. Numerical simulation2

Substitute Eq. (13) into Eq. (2) and let the initial values

0,x0 , y0 , z0 , w0

1,1,1,1

8

still

9

Lyapunov exponents of the controlled system (2)berespectively are

From Eq. (6), k1 should satisfy k1 > -a. Let k1 = 0 andsubstitute it into Eq. (7), then k2 should satisfy

c3

c1

15.3788 and

,

then

the

3.0397 , c2 3.7495 ,46.8316 , which are allc4© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 167

Hong Niu

negative. It implies that the controlled system (2) is nolonger chaotic or periodic but stable at the origin.

make the system globally asymptotically converge to any
point or even some specified states via center translation.
Thus, this method could be applied to chaos
synchronization of the novel 4D chaotic systems. It
would be discussed in another paper. The study in thispaper has some engineering significance.
References
1. E. N. Lorenz, Deterministic nonperiodic flow, Journal of
the atmospheric sciences 20(2) (1963) 130–141.
2. J. H. Lü, G. R. Chen, D. Z. Cheng and S. Celikovsky,
Bridge the gap between the Lorenz system and the Chensystem, International Journal of Bifurcation and Chaos
12(12) (2002) 2917–2926.
3. G. Y. Qi, G. R. Chen, S. Z. Du, Z. Q. Chen and Z. Z. Yuan,
Analysis of a new chaotic system, Physica A 352(2)(2005) 295–308.
4. Y. Lin, C. H. Wang, H. Z. He and L. L. Zhou, A novel
four-wing non-equilibrium chaotic system and its circuit
implementation, Pramana–Journal of Physics 86(4)(2016) 801–807.
5. F. R. Tahir, R. S. Ali, V. -T. Pham, A. Buscarino, M.
Frasca and L. Fortuna, A novel 4D autonomous 2nbutterfly wing chaotic attractor, Nonlinear Dynamics
85(4) (2016) 2665–2671.
6. C. H. Wang, H. Xu and F. Yu, A novel approach forconstructing high-order Chua's circuit with multidirectional multi-scroll chaotic attractors, International
Journal of Bifurcation and Chaos 23(2) (2013) 1350022–
1–10.
7. J. X. Zhang and W. S. Tang, Analysis and control for a
new chaotic system via piecewise linear feedback, Chaos,
Solitons & Fractals 42(4) (2009) 2181–2190.
8. T. Sangpet and S. Kuntanapreeda, Output feedback control
of unified chaotic systems based on feedback passivity,
International Journal of Bifurcation and Chaos 20(5)(2010) 1519–1525.
9. C. J. Xu and Q. M. Zhang, On the chaos control of the Qi
system, Journal of Engineering Mathematics 90(1) (2015)
67–81.
10. H. Niu, Stability control and circuit implementation of anovel 3D chaotic system with variable coefficient,
Dynamical Systems and Control 5(1) (2016) 31–40.
11. B. Z. Liu and J. H. Peng, Nonlinear Dynamics (Higher
Education Press, Beijing, 2007).

Fig. 2. Curves of the state variables of the controlled system

The curves of the state variables of the controlled
system (2) are shown in Fig. 2. The horizontal axis t
expresses the solution interval of differential equations,so t is a dimensionless quantity. From Fig. 2, it can beseen that the state variables x, y , z and w converge
to zero asymptotically and rapidly. It illustrates that thecontroller (13) is feasible and effective for chaos controlof the novel 4D chaotic system (1).
4. Conclusions
In this paper, a novel 4D chaotic system is presented. The
model of the 4D system and its chaotic attractor arecomplex and could be applied to secure communication.
For chaos control of the 4D chaotic system, different
groups of the coefficients of the Lyapunov function yield
different linear feedback controllers. It makes the form
and parameters of the linear feedback controller flexible
to select. The linear feedback controller designed in thispaper only has one feedback variable, so that it is easy to
implement via circuit. Furthermore, the centers of thestate variables of the 4D chaotic system have beentranslated to the origin before the controlled system isformulated. It means that this method can be used to© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 168

Analysis of a three-dimensional chaotic system and its FPGA implementation
Hefei Li1, Xianghui Hu1
1

Tianjin University of Science and Technology,

1038 Dagunanlu Road, Hexi District, Tianjin, 300222, China
E-mail: 1027755628 @qq.com
www.tust.edu.cn

Abstract
In this letter a three-dimensional chaotic system is implemented based on Field Programmable Gate Array (FPGA).
The 3-D chaotic system has a very complex chaotic characteristic with its a real four-wing chaotic attractor. By meansof numerical simulation, phase orbits, bifurcation diagram and Lyapunov exponents are given and analyzed to observe
dynamic characteristics of the three-dimensional chaotic system. Numerical simulation and the results ofimplementation in FPGA show that this chaotic system really has many obvious characteristics of chaos.
Keywords: three-dimensional chaotic system, bifurcation diagram Lyapunov exponents, FPGA

1. Introduction
Lorenz system1 was proposed as the first chaotic system inyear 1963, which created a precedent for the study ofchaotic systems. Later, scholars and researchers began to
do a lot of research on the chaotic system and found andput forward many new chaotic systems. Such as Chensystem2, Lü system3, Chua system4 and so on. The researchon chaotic characteristics can be used in many application
areas, such secure communication5, image encryption6 andetc.
Lü system was proposed in year 2002 by Lü et.al to be anovel chaotic system with its unique chaotic characteristics
different from other chaotic systems. After then, many
research began to be done on Lü system. A hybrid TS fuzzymodeling was proposed approach for the newly coined
chaotic Lü system in7. A new hyper chaotic attractor coinedfrom the chaotic Lü system was reported by using a state
feedback controller, and theoretical analyses andsimulation experiments are conducted to investigate thedynamical behavior of the proposed hyper chaotic system
in8. A new three-dimensional chaotic system, named as

generalized augmented Lü system, was proposed in9 byusing the method of anti-control chaos.
In this paper, the chaotic characteristics of the integer
order generalized augmented Lü system is analyzed bymeans of phase orbits, bifurcation diagram and Lyapunov
exponents. The uniqueness of this chaotic system is that it
implements a real four-wing attractor with compound
structure and contains two mirrored symmetrical subsystem. A circuit is designed based on DSP Builder in
FPGA to implement and observe the real four-wing chaotic
attractor. Numerical simulation and the results ofimplementation in FPGA show that this chaotic systemreally has many obvious characteristics of chaos.
2. Analysis of the generalized augmented LüsystemThe dynamical system of the generalized augmented Lüsystem is described as following,̇ = −[ ⁄( + )] −
̇=
+
̇=
+
+

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 169(1)

Hefei Li, Xianghui Hu

Where the system parameters a, b are negative realconstants, and c is a real constant.
2.1 Chaotic attractor of the system
For the system (1), when the parameters a = −10, b =−4, c = 1 and the initial state [1, 2, 3] , it is chaotic andhas a real three dimensional four-wing chaotic attractor as
shown in Fig.1.(1)and Fig.1.(2).
30

40

30

20

20

10

z

y

10

0

0

-10
-10

-20

-20

-30
-30

-20

-10

0
x

10

20

30

-30
-30

Fig.1. (1) x-y plane

-20

-10

0
x

10

20

30

Fig.1. (2) x-z plane

Fig.1 a real four-wing chaotic attractor of system (1): theparameters a = −10 , b = −4 , c = 1 and the initialstate [1, 2, 3] .

2.2 Dynamics analysis of the system
Bifurcation diagram and Lyapunov exponents can wellreflect the dynamic characteristics of the system. So we
mainly observe the Bifurcation diagram and Lyapunov
exponents of the system to analyze the dynamic behaviorof the system. As shown in the following Figs, theBifurcation diagram and Lyapunov exponents are given
varying the parameters a or b.
80
2

60

0

40

-4

20
YY

Lyapunov exponents

-2

-6

0
-8

-20
-10

-40

-12
-14
-8

-7

-6

-5

-4
b

-3

-2

-1

-60
-8

0

Fig.2(1)Lyapunov exponents

-7

-6

-5

-4
b

-3

-2

-1

0

Fig.2(2)Bifurcation diagram

Fig.3 Lyapunov exponents and Bifurcation diagram ofsystem (1): parameters value a = −10 , b = −4 , c ∈[−4, 4] and the initial state [1, 2, 3] .
From the Lyapunov exponents shown in Fig.2(1) andFig.3(1), there is only a single positive Lyapunov within acertain interval respectively, which shows that the system
is chaotic. The corresponding Bifurcation diagram also
indicates the system exists chaos. Especially, as shown in
Fig. 2(1), the Lyapunov exponents is almost symmetricalon the origin. In fact, when we set a = −10 , b = −4 ,c = −1, a four-wing chaotic attractor can also be obtained
and it has the same topological structure with the chaotic
attractor shown in Fig. 1. But the difference is that it
mirrored changes in the direction of the corresponding
plane through the chaotic trajectory.
3. FPGA realization of chaotic attractor
In this paper, the method of Field Programmable Gate
Array is applied to implement the chaotic attractor ofsystem (1). This is a method similar to analog circuits thatthe chaotic system can be shown in oscilloscope. A chaotic
circuit is designed by applying the DSP Builder module
based on the system (1) as shown in Fig. 4. And then, thechaotic circuit is derived as V-HDL language that will be
download to FPGA development board. Here, the main
chip of the FPGA development board is EP3C25Q240C8N.
The adders, delays, multipliers, amplifiers and data
selectors in circuit come from the DSP Builder module andthe digital integrator is designed in Fig.4(1) . As shown in
Fig.4(2), the obtained discrete digital signal is converted toanalog signal by high speed D/A, and two of the three
singles are inputted in the oscilloscope. By this method, we
can obtain the chaotic attractor shown in Fig.5(1)and
Fig.5(2) which are the same as the chaotic attractor shownin Fig.1(1) and . Fig.1(2).

Fig.2 Lyapunov exponents and Bifurcation diagram ofsystem (1): parameters value = −10, = 1, ∈ [−8,0] and the initial state [1, 2, 3] .
40

2

30

0

20
10

-4

0
YY

Lyapunov exponents

-2

-6

-10

-8

-20

-10

-30
-40

-12
-14
-4

-3

-2

-1

0
c

1

2

3

4

Fig.3(1)Lyapunov exponents

-50
-4

-3

-2

-1

0
c

1

2

3

4

Fig.3 (2) Bifurcation diagram© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 170

Analysis of a three-dimensional

Fig.4(1) circuit of the digital integrator

Fig.4(2) circuit of the system(1)4. Conclusion

Fig.5(1)x-y plane

Fig.5 (2) x-z plane

Fig.5 attractor obtained by the circuit

In this paper, the chaotic characteristics and dynamic
behaviors of a three-dimensional chaotic system isanalyzed by the method of bifurcation diagram andLyapunov exponents. The oretical analysis and numericalsimulation are applied to analysis the characteristics of thegeneralized augmented Lü system. In the last section, amethod is applied based on FPGA and EDA tool to realize
the chaotic attractor of the generalized augmented Lüsystem.The numerical simulation results and experimental
results are completely consistent, showing that this method
is practical and can be used in other chaotic system.

References© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 171

Hefei Li, Xianghui Hu

1.Lorenz E N, Deterministic non-periodic follow, Journal of
Atmospheric Sciences. 20(2) (1963) 130-141.
2.Chen G R, Ueta T, Yet another chaotic attractor,
International Journal of Bifurcation and Chaos. 9 (1999)
1465-1466.
3.Jinhu Lu, Guanrong Chen, A new chaotic attractor coined,
International Journal of Bifurcation and Chaos. 12 (2002)
659-661.
4.Guanrong Chen, Yaobin Mao, Charles K. Chui, A symmetricimage encryption scheme based on 3D chaotic cat maps,
Chaos, Solitons and Fractals, 21 (2004) 749–761.
5.Dequan Li, TS fuzzy realization of chaotic Lü system,
Physics Letters A, 356 (2006) 51–58.

6.Bocheng Bao, Zhong Liu, A Hyperchaotic Attractor Coinedfrom Chaotic Lü System, Chinese Physics Letters, 7(25)(2008) 2396-2399.
7.Qiao XiaoHua, BaoBoCheng, Three-dimensional four-wing
generalized augmented Lu system, ACTA PHYSICA
SINICA, 12(58) (2009) 8152-8159.
8.Itoh Makoto, Chua, Leon O,MEMRISTOR OSCILLATORS,INTERNATIONAL JOURNAL OF BIFURCATION AND
CHAOS, 11(18) (2008) 3183-3206.
9.Xiangjun Wu, Hui Wang, Hongtao Lu, Modified generalized
projective synchronization of a new fractional-order hyper
chaotic system and its application to secure communication.
Nonlinear Analysis: Real World Applications, 13 (2012)
1441–1450.

- 172 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, Japan© The 2017 International Conference on Artificial Life and RoboticsP (ICAROBImage Encryption Based on Fractional-order Chaotic Model of PMSM
Wei Xue*, Mei Zhang#, Shilong Liu, Xue Li

Department of Automation, Tianjin University of Science and Technology,
1038 Dagunanlu Road, Hexi District, Tianjin 300222, PR China
E-mail:* xuewei@tust.edu.cn; # zhangmeifuwei@163.com
www.tust.edu.cn

Abstract
The permanent magnet synchronous motor (PMSM) is a nonlinear system with multi-variable and significant
coupling. When PMSM works in certain conditions, the chaotic behavior will occur. In this paper, the applicationof image encryption based on fractional-order chaotic model of PMSM is investigated. By the mean of drawing
histogram, adjacent pixels correlation, key sensitivity of the cipher-text were analyzed. The results show that image
encryption based on fractional-order chaotic model of PMSM have a large key space and high security.
Keywords: Fractional-order chaotic model; Permanent magnet synchronous motor（PMSM）; Image encryption;
Algorithm.

model of PMSM.13-14 As the fractional-order increases,
the PMSM system enters the chaotic motion through thefixed point and quasi-periodic state.15 In general, theexistence of chaos is harmful in the actual systems, it iscontrolled to eliminate its chaotic behavior. But the
chaotic characteristics have good effects in some
aspects, we can use chaotic characteristics to achieve
secure communication and image encryption.
The basic theoretical research of chaotic dynamics
has been great development in the field of confidential
communication and image encryption. Fractional-orderchaotic system provides more key parameters for theencryption algorithm and increases the key space, inorder to improve the security of the system.16 Digital
image pixel location scrambling is an important
encryption method, the characteristics of the histogram
are not changed, it also reflects the information system
and memory the information. In addition, thenonlinearity and complexity are enhanced by thefractional-order chaotic system, which increases the key
space. By the mean value of histogram, the correlationof adjacent pixels and the key sensitivity of cipher-text,
the image encryption effect based on the fractionalorder chaotic model of PMSM is analyzed in this paper.

1. Introduction
In the nonlinear system described by the equation ofstate, when motion of the system is far from theequilibrium state, with the changing of the systemparameters, the dynamic behavior of the system enter
into the dissipative structure state with time-space orderand symmetry through bifurcation or chaotic state moredisorder.1 In recent years, many new chaotic systems
have been proposed. 2-8 In 1999, the Chen system was
successfully constructed by Chen et al.3 In 2002, the Lüsystem was further discovered by Lü et al.4 In 2003,
Liu et al. discovered a three-dimensional continuous
autonomous chaotic system of four-helical chaotic
attractors, named as Liu system.5 In 2005, a new fourwing autonomous chaotic system, the Qi system was
proposed by Qi et al.6-8 Those researches have madegreat progress in chaotic dynamics analysis. When the
chaotic system is described by fractional-order model,
the system can still show the chaotic state, it is foundthat the fractional-order chaotic model can accurately
describe the real physical phenomena.9-10 Some
practical nonlinear systems are chaotic in the process ofoperation sometime. For example, there has chaotic
behavior in permanent magnet synchronous motor(PMSM) when it works under certain condition.11-12
Some scholars have studied the fractional-order chaotic© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 173

Wei Xue, Mei Zhang, Shilong Liu, Xue Li

performing XOR operation. Encryption flow chart isshown in Fig.3, decryption flow chart is shown in Fig.4.

2. Numerical analysis of fractional–order chaotic
model of PMSM
The fractional-order PMSM chaotic model is as follow:

Input the plain text image

17-18

D x

x

zy

D y

y

xz

D z

(y

z)

Fractional-orderChaotic
Model
of
PMSM

(1)

z

Where the dimensionless state variables x, y, z
represent the stator current, the rotator current and themechanical angular velocity respectively;
and is thesystem parameters related to the motor damping andpermanent flux; α ∈ (0,1] is the fractional order.
When α = 0.95,σ = 20, and γ = 300, phase portraits
are shown in Fig.1. Lyapunov exponent spectrum isshown in Fig.2.
It is showed clearly that system (1) has complex
dynamic characteristics.

y chaotic sequence

Three-color separation
Three-color matrix
pixel value position
scrambling

x, y, zchaotic sequences

x, y, z sequence
respectively R, G, B
matrix pixel XOR
operation
output the encrypted image
Fig.3 Encryption flow chart

80

50
60
40

y

x, y, zchaotic sequences

0

z

20
0
-20

50
-60
-80
220

400
350

0

300

-50
240

260

280

300
x

320

340

360

380

y

250
-100

200

x

(a)
(b)
Fig.1 Phase portraits of system (1) with α = 0.95,σ = 20, andγ = 300: (a) x y ;(b) x y z .

x,y,z
sequence
respectively R, G, B matrix
pixel XOR operationy chaotic sequence

5

LEThree-color
separation

Fractional-order
Chaotic
Model
of
PMSM

-50
100

-40

Input the encrypted
image

0

-5
0

5

10

15

20

25

30

Three color
pixel value
scrambling
Output the plain
text image

35

Fig.2 Lyapunov exponent spectrum of system
Fig.4 Decryption flow chart

3. Application of fractional-order chaotic model of
PMSM in image encryption

3.1. Encryption process

A message is called a plaintext, in order to hide content,
the process of concealing messages in some way iscalled encryption. Encrypted messages are called
cipher-text, and the process of converting cipher-text toplaintext is called decryption. Digital image pixel
location scrambling change is an important document
encryption method. Color images are described by R, G
and B primary color, and it stores in a matrix format ofm n 3 in computer. Fractional-order chaotic modelof PMSM is a system with three state variables, the ychaotic sequence is used to perform one-dimensional
time-domain scrambling sequence on the digital image.
And then the three-dimensional sequence matrix is used
to realize the spatial encryption algorithm by

The system (1) parameters are still chosen as α =0.95 , σ = 20 , and γ = 300 ,the initial states are
assumed as x0 0 .1 , y0 0 .2 , z 0 0 .3 , then the digital
image is encrypted. The encryption process steps are as
follows:
1）The plain text is divided into three primary colors: R
component image, G component image and B
component image. The results are shown in Fig.5.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 174

Image encryption based on

Original RGB image

R component image

G component image

B component image

Fig.9 is the gray histogram of G component. It can beseen from the figure: Before encryption, the pixel
distribution is obvious in each gray level, the difference
between the highest point and the lowest point is verylarge; after encryption, the number of pixels is almost
the same, and the frequency of the pixels is basically thesame in each gray level. The pixel distribution of theoriginal image is hidden in a large degree, which can bea good resistance to the statistic.
600

700
600

Fig.5 Separate images

500

500

400

400
300

300

2) The y sequence is sorted to obtain a sequence y_change, and then transform R, G, B corresponding
components. After that, the R, G and B components ofcolor image are calculated by using chaotic sequences.
Due to space constraints, here only the encryption resultof the G component is given. The result of theencryption of G component is shown in Fig.6.

The original G component image

200

200
100

100

0

0

利 用 混 沌 序 列 y进 行 空 域 图 像 的 直 方 图 结 果

G分 量 的 原 始 图 像 的 直 方 图
0

50
100
150
200
Histogram of G component before encryption

250

0

50

100
150
200
Histogram of G component after encryption

250

Fig.9 Each color component histogram

3.3.2. Adjacent pixel correlation analysis
One row and one column of pixels are selected from theoriginal image and the encrypted image. The correlation
coefficients of its adjacent pixels are calculated in Tab.1.
The encryption algorithm of this method has good
performance of diffusion and aliasing, and theencryption algorithm of fractional-order chaotic modelof PMSM has better security for statistical analysis
attacks.

Out of order G component image

Fig.6 Spatial domain XOR results3) The encrypted three-color matrix is used tosynthesize the encrypted image. The result is shownin Fig.7.

Tab.1 The correlation of adjacent pixels
Different
directions
Horizontal
VerticalDifferent
direction
direction
imagesThe original image
R pixel matrix
0.97303
0.97327
Encrypted image R
pixel matrix
0.0025357
0.00088599
The original image
G pixel matrix
0.97530.97538
Encrypted image G
pixel matrix
0.00036788
0.00060489
The original image
B pixel matrix
0.988070.98861
Encrypted image B
pixel matrix
0.00068514
0.00061357

Fig.7 Encryption results of the original image

3.2. Decryption process
The image decryption process based on system (1) is
the reverse operation of its encryption. And theplaintext will not repeat. Decryption result is shownin Fig.8.

3.3.3. Key sensitivity analysis
Given the system (1) parameter: α = 0.95,σ = 20, andγ = 300 . When the initial states are assumed asx0 0 .1 , y0 0 .2 , z 0 0 .3 to the system key, theimage is encrypted. When the correct key is input, thedecrypted image can be obtained successfully. If wechange the initial value as x0 0 .100000000001 andthe other parameter values are unchanged, the image

Fig.8 Decryption results of the original image

3.3. Algorithm security analysis
3.3.1. Histogram analysis© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 175

Wei Xue, Mei Zhang, Shilong Liu, Xue Li

decryptions fail. Unsuccessful decryption results areshown in Fig.10. As long as the key changes, theoriginal figure can not be decrypted. This shows that thesensitivity of the encryption system on the key
requirements is high.

7.
8.
9.
10.

Fig.10 Decryption results of the wrong key
11.

4. Conclusion
In this paper, the fractional-order chaotic model of
PMSM is used as the key to encrypt the digital image.
The phase trajectories and Lyapunov exponent spectrumof fractional-order chaotic model of PMSM are drawn.
Furthermore, the chaotic model is applied to image
encryption, and the flow chart of image encryption anddecryption process are given. By means of histogram,adjacent pixel correlation and key sensitivity, thecipher-text is analyzed. The results show that the digital
image encryption algorithm based on the fractionalorder chaotic model of PMSM has the characteristics oflarge key space and high security, and indicates that it isfeasible and effective to use the model for image
encryption.

12.

13.
14.
15.
16.

5. Acknowledgement
17.

This work is supported by the Young Scientists Fund of
the National Natural Science Foundation of China(Grant No.11202148).

18.

6. References
1.
2.
3.
4.
5.
6.

Wang Lidong, Xing Xiuying, Chu Zhenyan. On
definitions of chaos in discrete dynamical system.
Hunan: IEEE computer society, 2008:2874-2878.
Chen G, Lai D. Feedback control of Lyapunov
exponents from discrete-time dynamical systems. Int. J.
Bifurcation and Chaos, 1996, 6(7):1341-1349.
Chen G R and Ueta T. Yet another chaotic attractor. Int.
J. Bifurcation and Chaos, 1999,9 (7):1465-1466.
Lü J G. Chaotic dynamics of the fractional order Lüsystem and its synchronization. Physics Letters A. 2006,
59(4): 305-311.
Lü Jinhu, Chen Guangrong. A new chaotic attractor
coined. International journal of bifurcation & Chaos,
2002, 12(3):659-661.
Qi G, Du S, Chen G, et al. On a 4-dimensional chaotic
system. Chaos Solitons & Fractals, 2005, 23(5): 16711682.

19.

20.

21.

22.

Qi G, Du S, Chen G, Zhang Y. Analysis and circuit
implementation of a new 4-D chaotic system. Physic
Letters A, 2006, 352(4):386-397.
Qi G, Chen G, Li S, Zhang Y. Four-wing attractors:
from pseudo to real. International Journal of
Bifurcation & Chaos, 2006, 16(4): 859-885.
Lü J H, Chen G R. A new chaotic attractor coined.
International Journal of Bifurcation and Chaos, 2002,
12(3):659-661.
Liu C X, Liu L, Liu T et al. A new butterfly-shaped
attractor of Lorenz-like system. Chaos, Solitons andFractals, 2006, 28(5):1196-1203.
Zhang Bo, Li Zhong, Mao Zongyun, Pang Minxi. A
primary study on an erratic behavior and chaotic
phenomena of electric machine drive systems,
Proceedings of the CSEE, 2001, 21(7):40-45.
Zhang Bo, Li Zhong, Mao Zongyun, Pang Minxi. The
chaotic model and hope bifurcation of a type of
permanent magnent synchronous motor, Proceedings of
the CSEE, 2001, 21(9):13-17.
Chen J H, Chau K T, Chan C C, Chaos in voltage-mode
controlled Dc drive system. Int J Electr, 1999, 86 (7):857-874.
Zhu J J, Chang Y, Chen G R. Complex dynamics inpermanent-magnet synchronous motors model. Chaos
Solitons & Fractals, 2004, 22(2):831-848.
Z. Li, et al. Bifurcations and chaos in a permanentmagnet synchronous motor, IEEE Transaction on
circuits system. 2002, 49(3):383-387.
Wang Yaqing, Zhou Shangbo. Image encryption
algorithm based on fractional-order Chen chaotic
system, Journal of Computer Applications, 2013,
33(4):1043-1046.
Xue Wei, Li Xue. Synchronization of the Fractionalorder Permanent Magnet Synchronous Motor,
International Conference on Artificial Life and Robotics
2016, 264-267.
Xue Wei, Li Yongli, Cang Shijian, Jia Hongyan, Wang
Zenghui. Chaotic behavior and cirxuit implementationof fractional-order permanent magnet synchronous
motor model. Journal of the Franklin Institute, 2015,
352(7):8149-8151.
Wei Xue, Hui Xiao, Xu Jinkang, Jia Hongyan. A
Fractional-order Hyper-chaotic System and its
Application in Image Encryption, Journal of Tianjin
University of Science & Technology, 2015, 30(5):67-71.
Y. K. Chin, J. Soulard, Modeling of iron losses inpermanent magnet synchronous motors with fieldweakening capability for electric vehicles, International
Journal of Automotive Technology, 2003, 4(2):87-94.
Gao Yuan, Fan Jianwen, Luo Wenguang, Li Zhongfu.
Chaos in the fractional order permanent magnet
synchronous motor and its control. Journal of Wuhan
University of Technology, 2012.
Li Chunlai, Yu Simin, Luo Xiaoshu. Fractional-orderpermanent magnet synchronous motor and its adaptive
chaotic control. Chinese Physics B, 2012(10):168-173.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 176

The Application of a Novel Fractional-order Hyper-chaotic System in Image Encryption
Wei Xue, Shilong Liu, Mei Zhang, Xue Li

Department of Automation, Tianjin University of Science and Technology,
1038 Dagunanlu Road, Hexi District, Tianjin 300222, PR China
E-mail: xuewei@tust.edu.cn, 16851911@mail.tust.edu.cn
www.tust.edu.cn

Abstract
In this paper, a novel fractional-order hyper-chaotic system is proposed. By drawing the phase trajectory andLyapunov exponent spectrum, its dynamic characteristics are analyzed. The simulation results show that thefractional-order hyper-chaotic system has hyper-chaotic characteristic. Then, image encryption implementation
based on the fractional-order hyper-chaotic system is investigated. And, a three-color separation and scrambling theimage pixel location, the histogram, key space, pixel distribution, correlation coefficient and key sensitivity of
cipher-text in color pictures are tested and analyzed. The results show that the algorithm has good security andpracticability.
Keywords: Fractional-order; hyper-chaotic system; Numerical simulation; Image encryption; algorithm.

makes the cipher-text image disorder to cover theplaintext information and to achieve the effect of image
encryption.10 In early 1990, Matthews applied thechaotic system to the image encryption algorithm. 11 In
the early stage, the low dimensional chaotic system isapplied to image encryption. Although the lowdimensional chaotic system was designed easily, therandom sequence acquired was fast. Because of low
security and low complexity, it is easy to be deciphered.
The hyper-chaotic system has four state variables so thatthe system complexity is relatively high, the key spaceis larger than chaotic system. The initial conditions are
extremely sensitive, and the overall system security ishigh. With further studies of fractional-order chaotic
systems, it is found that fractional-order chaotic system
not only has characteristics of sensitivity to initialvalues and pseudo-randomness, but also can reflect thehistorical information of the system, which has thestrong historical memory. 12 In addition, the fractionalorder chaotic system enhances nonlinearity andcomplexity of system researched, which increases key
space of encryption algorithm.13 The existing integerorder chaotic system can not predict and enhance thesecurity of communication. Based on the literature,14 anovel fractional-order hyper-chaotic system is proposedin this paper, and the application of this fractional-orderchaotic system in digital image encryption is studied bynumerical simulation.

1. Introduction
In 1963, Lorenz E N delivered a paper on "deterministica periodic flow", which reveals a series of properties ofchaotic motion, such as deterministic aperiodicity,
extreme sensitivity to initial values, long-term behavior
unpredictability, and so on.1 His work guides the
direction for chaos theory research and development inchaotic systems, such as Chen system, 2 Lü system, 3 Liu
system, 4 Qi system, 5 and so on, which make chaotic
dynamics theory and applied research get rapid
development. In recent years, because of theunpredictability of the chaotic system and the extreme
sensitivity of the system parameters, 6 coupled with therapid development of fractional calculus theory, thefractional-order calculus and chaotic system are
integrated by scholars. 7 The calculus operator which
added to the chaotic system is studied.8 It is found thatthe fractional-order system can still exhibit the chaotic
state sometime, and the application of the fractionalorder chaotic system has a better practical value. 9
Therefore, the fractional-order chaotic systems havearoused great enthusiasm of scholars.
With the rapid development of Internet technology,
much of information is released and transmitted quickly
through internet. Therefore, information security issues
become a focus of people’s attention. Image encryption
based on chaotic system processes digital image, which© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 177

Wei Xue, Shilong Liu, Mei Zhang, Xue Li

2. Numerical Analysis of a Novel Fractionalorder Hyper-chaotic System

3. The application of fractional-order hyperchaotic system in image encryption

In this paper, a novel fractional-order hyper-chaotic
system is constructed on the basis of literature. 14 The
mathematical model as follows:

3.1. Image encryption and decryption process

q

d x
（
a x y） yz
dt q
dq y
bx y xz
dt q
dq z
dz cy 2
q
dt
dq w
ex
dt q

(1)

Each color image is composed of R, G, B, which
belongs to three primary colors. Different tricolor
distributions make up different images. Image
encryption is to scramble each color dot. The novel
fractional-order hyper-chaotic system proposed in thispaper adopts one-dimensional time-domain out-of-orderat first, and then the other three-dimensional sequencematrix is used to realize the spatial encryption algorithm
by performing XOR operation, to achieve the purposeof disorder. Decryption process is the inverse of theencryption process, the specific flow chart is shown in
Fig.3:The system parameters are a = 33.2, b = 10, c = 18, d= 15, e = 26. The calculous order of the system (1) is q ,and q ( 0 ,1) , where q = 0.95. The phase trajectory of
the novel fractional-order hyper-chaotic system isshown in Fig. 1:
100

z

90
100

70

80

60

60
z

80

50

40

40

20

30

0
20

20

10

10

10
0
-15

5

0

0

-10
-10

-5

0
y

5

10

15

-5
-20

y

-10

x

Fig.1 Phase portraits of system (1)The Lyapunov exponent can characterize the motion
characteristics of the system. Its positive and negativevalues and size in one direction, denotes the degree ofdivergence or convergence of the adjacent orbits in theattractors for a long time. 15 For the hyper-chaotic
system, there must be two or more positive Lyapunov
exponents. The Lyapunov exponent spectrum of system(1) is shown in Fig.2. When a =33.2, they areL1 =4.1476, L2 =0.7313, L3 =-10.8891, L4 =-36.1858,
respectively, the system (1) is hyper-chaotic.
50-5

Fig.3 Flow chart of encryption and decryption
The specific steps of the encryption scheme as follows:
1) The original image of R, G, B three primary colors
is separated from the corresponding gray-scale
image, the results are shown in Fig.4:

-10

LE

-15
-20
-25
-30
-35
-40
32.2

32.4

32.6

32.8

33

33.2
a

33.4

33.6

33.8

34

34.2

Fig.2 Lyapunov exponent spectrum of system (1)© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 178

The application of a

The original RGB image

R component

Fig.7 Decryption results of the original image
3.2. Algorithm security performance analysis

G component

3.2.1 Key sensitivity analysis

B component

Given system parameters a = 33.2, b = 10, c = 18, d =15, e = 26, q = 0.95. When set the system initial value x0= y0 = z0 = w0 = 0.1 as the system key, the image isencrypted. When you enter the correct key, you cansuccessfully decrypt to get the correct image. If wechange the initial value as x0 = 0.100000000001, otherinitial values and parameter values are unchanged, theimage decryption fail. The result is shown in Fig.8. It
can be seen that slightly small key changes cannot
decrypt the cipher-text image. System (1) has higher
key sensitivity.

Fig.4 The image of original and each component
2) Firstly, the chaotic sequences of w series is used toscramble the components. And then use x, y, z
sequence respectively on the R, G, B matrix toperform XOR operation to achieve spatial domain
encryption. The results of the encryption of eachcomponent are shown in Fig.5:

The original R component image

Out of order R component image

The original G component image

Out of order G component image

Fig.8 Decryption result of the wrong key
3.2.2 Histogram analysis

4)

Fig.5 Spatial domain XOR results
After scrambling, the three-primary color matrix
are synthesized for encryption, the original image
encryption results are shown in Fig.6:

600

900

Fig.6 Encryption results of the original image
Decrypting the ciphertext image is the inverse
process of the encryption operation. First of all, thethree primary colors of the image are separated.
Then, use R, G, B matrix components that wereseparated out to perform XOR inverse according tox, y, z sequences. And then in accordance with thew chaotic sequence, each pixel is anti-scrambled.
And finally, the original image is obtained.
Decryption results are shown in Fig.7:

800

500

700
N um be r of pix els

3)

Out of order B component image

N um be r of pix els

The original B component image

Fig.9 is a gray histogram of the B component before andafter encryption, which describes the number of pixels
in each gray level. It can be seen from Fig.9 that the
number of pixels on each gray level before encryption is
not uniform and the fluctuation is relatively large. The
difference in the number of pixels between wave crests
and troughs is relatively large, and the image
distribution is obvious. After the encryption, the numberof pixels in each gray level is relatively uniform, almost
no fluctuations, a large degree of hiding the pixel
distribution of the original image, which can be a good
resistance to the statistical analysis of the decipher.

600
500
400

400

300

200

300
200

100

100
0

0
Gray level
0

50

100

利 用 混 沌 序 列 w进 行 空 域 图 像 结 果

150Gray level

200

250

0

50

100

150

200

250

Gray level

Fig.9 Histograms before and after image encryption
3.2.3 Adjacent pixel correlation analysis
One row and one column of pixels are selected from theoriginal image and the encrypted image. The correlationP - 179

Wei Xue, Shilong Liu, Mei Zhang, Xue Li

coefficients of its adjacent pixels are calculated in Tab.
1. Observation of the data in Tab.1 can be drawn: thecorrelation coefficients of the original image adjacentpixels are close to 1, the correlative performance isrelatively high. In addition, the correlation coefficientsof the adjacent pixels of the encrypted image is close tozero, and the adjacent pixels are almost irrelevant.
Therefore, the encryption algorithm based on system (1)has higher resistance to attack.

5. Acknowledgement
This work is supported by the Young Scientists Fund of
the National Natural Science Foundation of China(Grant No.11202148).
6.

References

1.

Lorenz E N. Deterministic non-period follow. Atoms Sci.
1963, 20(2): 130-141.
Chen G R, Ueta T. Yet another chaotic attractor.
International Journal of Bifurcation and Chaos. 1999,
9(7): 1465-1466.
Lü J H, Chen G R. A new chaotic attractor coined.
International Journal of Bifurcation and Chaos. 2002,
12(3): 659-661.
Liu C X, Liu L, Liu T etal. A new butterfly-shaped
attractor of Lorenz-like system. Chaos, Solitons andFractal. 2006, 28(5): 1196-1203.
Qi G Y, Chen G R, van Wyk M A, et al. A four-wingchaotic attractor generated from a new 3-D quadratic
autonomous system. Chaos, Solitons and Fractals. 2006,
28(5): 1196-1203.
Jia H Y, Chen Z Q, Xue W. Analysis and circuit
implementation for the fractional-order Lorenz system.
Acta Physica Sinica. 2013, 62(14): 14503-1–140503-7.
Huang L L, Xin F, Wang L Y. Circuit implementationand control of a new fractional-order hyperchaotic
system. Acta Physica Sinica. 2011, 60(1): 010505-1–
010505-9.
Grigorenko I, Grigorenko E. Chaotic dynamics of thefractional Lorenz system. Physical Review Letters. 2003,
91(3): 034101-1–034101-4.
Li C P, Peng G J. Chaos in Chen’s system with afractional order. Chaos, Solitons and Fractals. 2004,
22(2): 443–450.
Zhu Cong Xu, Sun Ke Hui. Cryptanalysis andimprovement of a class of hyperchaos based image
encryption algorithms. Chinese Journal of Theoretical
Physics. 2012, 61(12): 120503-1–120503-12.
Matthews. On the Derivation of a “Chaotic” encryption
algorithm. Cryptologia. 1989, 13(1): 29-42.
Wang Q Y, Zhou S B. Image encryption algorithm
based on fractional-order Chen chaotic system. Journalof Computer Applications. 2013, 33(4): 1043-1406.
Wu X, Wang H, Lu H. Modified generalized projective
synchronization of a new fractional-order hyperchaotic
system and its application in secure communication.
Nonlinear Analysis: Real World Applications. 2012,13(3): 1441–1450.
Li Xue, Xue Wei. Analysis and unidirectionally coupledsynchronization of a novel chaotic system. The 2016
International Conference on Artificial Life and Robotics.
2016, 412-415.
Xue Wei, Xiao Hui, Xu Jinkang, Jia Hongyan. A
fractional-order hyper-chaotic system and its applicationin image encrption. Journal of Tianjin University of
Science & Technology. 2015, 30: 01-05.

2.

4. Conclusion
In this paper, a novel fractional-order hyper-chaotic
system is proposed and its dynamical properties areanalyzed. Firstly, the phase trajectories and Lyapunov
exponent spectrum are gained, which verify that thefractional-order system has hyper-chaotic attractors.
Then, this system is applied to the digital image
encryption. Algorithm based on the one-dimensional
time-domain chaotic sequence, and three-dimensional
spatial re-encryption is used to encrypt and decrypt thecolor image. Finally, the key sensitivity, histogram
characteristics and correlation coefficients of adjacentpixels are analyzed. The analysis results show that theencryption algorithm based on a novel fractional-order
hyper-chaotic system proposed has advantages of highsensitivity and reliability.

3.
4.
5.

6.
7.

8.

Tab.1 The correlation of adjacent pixels
Different
directions Horizontal
VerticalDifferent
direction
direction
imagesThe original image
R pixel matrix

0.97757

0.9555

Encrypted image
R pixel matrix

0.0011284

0.00056923

The original image
G pixel matrix

0.97161

Encrypted image
G pixel matrix

0.0016781

0.0027984

The original image
B pixel matrix

0.95435

0.92538

Encrypted image
B pixel matrix

0.0036514

0.0070632

9.
10.

11.
12.

0.94387
13.

14.

15.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 180

A Method of Detecting Abnormal Crowd Behavior Events Applied in Air Patrol Robot
Huailin Zhao

School of Electrical and Electronic Engineering ,Shanghai Institute of Technology, Shanghai, China
Shunzhou Wang

School of Electrical and Electronic Engineering ,Shanghai Institute of Technology, Shanghai, China
Shifang Xu

School of Electrical and Electronic Engineering ,Shanghai Institute of Technology, Shanghai, China
Yani Zhang

School of Computer Science and Information Engineering ,Shanghai Institute of Technology, Shanghai, China
Masanori Sugisaka

Alife Robotics Corporation LTD, Oita, Japanzhao_huailin@yahoo.com

Abstract
When the ground or air patrol robot monitors a certain area, one of the important intelligent functions is to estimate
the crowd density of the monitored area. This paper analyzes the crowd density estimation algorithm, and use a
Gaussian process regression model for crowd density estimation. Through the crowd density estimation andchanges, we can detect abnormal behavior events of the crowd. The method can not only estimate the population
density of the specified area, but also analyze and detect the abnormal behavior events of the crowd. Thisapplication provides an important technical support for enhancing the patrol robot monitoring effect.
Keywords: Air Patrol robot, Abnormal event detection, Gaussian process regression

segmentation 2) feature extraction 3) crowd regression.
The method of counting the number of people in asingle image generally split the image into patches, andthen extract the feature of each patch, and sum thenumber of each patch to get the total number of thepicture. The crowd counting work based on the deeplearning usually use the population density map of thepicture as the supervisory information, and design the
convolutional neural network to return the density map.
The prediction of density map can be integrated to get
the number of people in the frame.

1. Introduction
It is an important research topic to monitor the emergent
events in large public places. With the development ofrobotics technology in recent years[1,2,3,4], unmanned
aerial vehicles (UAVs) play a more and more importantrole in the field of intelligent monitoring. In this paper,an unmanned aerial vehicle (UAV) is used to monitor
the abnormal event of public area. By monitoring thenumber of people in the specific area, it can alarm in ashort time when abnormal number change happens.

Considering the requirements of the performance ofreal-time and computational resource constraints, we
adopt the video-based crowd counting algorithm ofRef.4, and its prediction results are used as the criteriafor UAV monitoring abnormal time.

A lot of works on the crowd counting algorithm
have been studied. The crowd counting algorithm iscurrently divided into three categories: counting thenumber of people in the video[5,6,7,8], counting thenumber of people in a single image[9], and counting thenumber of people based on the deep learning[10,11]. The
method of counting the number of people in the video isgenerally divided into three steps: 1) foreground© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 181

Huailin Zhao, Shunzhou Wang, Shifang Xu, Yani Zhang, Masanori Sugisaka

2. Design of the Whole System

3.1 Foreground segmentation

The flowchart of air patrol robot monitoring abnormal
event in crowded public area is shown in Fig.1.
Unmanned aerial vehicles monitor the large public
places, the collected images are real-time transmit toremote monitoring terminal. The monitoring terminal
use the crowd counting algorithm to count the images
from unmanned aircraft real-time, and record thenumber of changes over time. When the crowd number
changes dramatically in a short term, the monitoring
terminal remind the management that the region haveabnormal immediately.

The purpose of segmentation is to segment the crowd
people from the image to facilitate the subsequent step
feature extraction. The performance of the segmentation
is directly related to the final count precision, so it is an
important factor limiting the performance of crowd
counting algorithms. Commonly used segmentation
algorithms are Optical Flow, Mixture of Dynamic
Textures[6], Wavelets and so on. The disadvantage ofthis motion-based foreground segmentation algorithm isobvious. If the person does not move in the video, thestationary person will be divided into the background,which affects the performance of the crowd counting. In
this paper, we use the Mixture of Dynamic Textures[6] toprocess the foreground segmentation on the UCSD
dataset and PETS2009 dataset respectively. The
concrete process is shown in Fig.2.

Fig.1. The flowchart of air patrol robot monitoring abnormal
event in crowded public area

3. Crowd Counting Algorithm Based on Video
Segmentation and Traditional Machine
Learning
Video-based population counting algorithm is generally
divided into three steps: 1) foreground segmentation; 2)feature extraction; 3) regression. In this paper, themethod in Ref.4 is used, the whole algorithm is shownin Fig.1.

(a)(b)
Fig. 2. Foreground segmentation based on mixture of
dynamic textures(a)UCSD dataset (b)PETS2009 dataset

Fig.1. Monitoring video crowd counting system[5]© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 182

An Method of Detecting

coordinates (230,123) correspond to the reference
person on the right side of the sidewalk. Figure 3(b) is aperspective view with the contour line (red) which isindicated that the pixel weight at that location is {1, ...,
5}.

3.2 Perspective normalization
Due to the perspective of view, people who are close to
the camera take more pixels in the image than those
who are away from the camera. We use two different
perspective normalization method to two different
datasets. The perspective normalization is shown in
Fig.3.

The mean absolute error (MAE) and the meansquare error (MSE) are commonly used to measure theperformance of the algorithm.
1
𝑀𝐴𝐸 = ∑𝑁
(1)
1 |𝑧𝑖 − 𝑧̂𝑖 |𝑁

1

2
𝑀𝑆𝐸 = √ ∑𝑁
1 (𝑧𝑖 − 𝑧̂𝑖 )𝑁(2)

Where N is the number of pictures to be tested(number of video frames). 𝑧𝑖 is the number of the i-thframe. 𝑧̂𝑖 is the estimated number of the algorithm.
3.3 Feature extraction(a)After completion of the foreground segmentation, avariety of low-level features are extracted from theforeground (population) obtained from the segmentation.
Common features are: Area and Perimeter of Crowd
Mask, Edge Count, Edge Orientation, Texture Features,
Minkowski Dimension, and so on. In this project, twodatasets are tested respectively, and the feature of each
image is saved as a feature vector. There are 29 featuresin each feature vector of the UCSD dataset[5]. Eachfeature vector of the PETS2009 dataset has 30
features[7].

(b)Fig.3.
Perspective
dataset(b)PETS2009dataset

normalization(a)UCSD

4. Experiments
In this part, we repeat the reference papers Ref.4
and Ref.6 experiments. We use the regression model toregress the feature extracted in the previous step to thenumber of people in the image. The regression can be asimple linear regression, or a complex nonlinear
regression. Commonly regression methods are Linear
Regression, Piecewise Linear Regression, Ridge
Regression, Gaussian Process Regression, and so on.
We use Gaussian process regression to predict the
number of people in the image of UCSD dataset andPETS2009 dataset. The prediction result is shown as in
Fig.4.

On the UCSD dataset, we make a ground plane[5], whichis scaled to measure the height ℎ1 of the person at 𝑎𝑏and the height ℎ2 of the person on the 𝑐𝑑 .We can see
the ground plane as in Figure 3a. The weight of the
middle pixel is obtained by multiplying the pixels on 𝑎𝑏and 𝑐𝑑 by the weights 1 and̅̅̅̅ |ℎ1 |𝑎𝑏respectively, and̅̅̅̅ |ℎ2 |𝑐𝑑the middle pixel weight is obtained by the linear
interpolation between the two lines.
On the PETS2009 dataset, the perspective map isapproximating a person moving in a 3-D scene to acylinder with a height of 1.75 m and a radius of 0.25
m.[6] For each pixel (x, y) in the 2-D camera view, thecylinder is positioned in the 3D scene so that the centerof the cylinder is projected to (x, y) in the 2-D view asshown in Figure 3(b), which is shown on the left. The
total number of pixels used to fill the cylinder isexpressed as c (x, y). The perspective is then calculatedas 𝑀(𝑥, 𝑦) = 𝑐(230,123)/𝑐(𝑥, 𝑦) , where the© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 183

Huailin Zhao, Shunzhou Wang, Shifang Xu, Yani Zhang, Masanori Sugisaka

set scene S1.L1 count results shown in Figure 4b), theMSE of the two groups of people counting are 0.6063
and 9.873, the accuracy of the crowd counting algorithm
basically meet the design requirements.
5. Conclusion
In this paper, we design a monitoring function of patrolrobot to detect the abnormal crowd behavior events
applied in the monitoring area with the changes of
crowd counting results. We analyze the crowd counting
algorithm and select the typically machine learning
method rather than convolution neural network to apply
in this specific filed due to the real-time performanceand the computation of the algorithm. We repeat thereference papers experiment and the prediction result of
the crowd counting algorithm is not the state of the art,
but can basically meet the design purposes. In the future,
we will design a new crowd counting algorithm toincrease the accuracy and attempt to apply deep learningin the patrol robot monitoring function.

(a)6. References
1.

(b）

2.

Fig.4. Crowd counting method employed in (a)UCSD dataset
and (b)PETS2009 dataset
3.

For the UCSD dataset, 800 pictures are used as thetraining set, and the remaining 1200 pictures are used as
test set. The output of the Gaussian process regression isrounded to the nearest integer to generate a population
count and record the mean square error (MSE) and themean absolute error between the estimate and the true
value. The results of the crowd counting algorithm forcounting the UCSD dataset are shown in Figure 4(a)4.

5.

The MSE of the two groups were 6.015 and 4.529,
respectively. The accuracy of the algorithm basicallymeet the design requirements.
The whole area of scene S1.L1 of PETS2009
dataset was tested by the crowd counting algorithm, and1308 images are used as training set and the video 13-57,
13-59F, 13-59F, 14-03,14-03F, 221 images are used as atest set. The output of the Gaussian process regression isrounded to the nearest integer to generate a population
count and record the mean square error (MSE) and theabsolute error between the estimate and the true value.
The population count algorithm for the PETS2009 data

6.

7.

Shunzhou Wang, Huailin Zhao, Xuyao Hao. Design of
An Intelligent Housekeeping Robot Based on IOT.
Proceedings of 2015 International Conference Intelligent
Informatics and BioMedical Sciences (ICIIBMS2015),
2015.11, p197-200.
Huailin Zhao, Lin Wang, Bei Wang, Masanori Sugisaka,“System development of an artificial assistant suit”,
Artificial Life and Robotics (ISSN1433-5298), Vol.17,
2013, No.3-4, p331-335.
Huailin Zhao, Bei Wang, “Configuration of the
Mckibben Muscles and Action Intention Detection for an
Artificial Assistant Suit”, International Journal ofAdvanced Robotic Systems (ISSN 1729-8806), Vol. 9,
2012, p1-7.
Huailin Zhao, Masanori Sugisaka, “Simulation study ofCMAC control for the robot joint actuated by McKibben
muscles”, Applied Mathematics and Computation(ISSN0096-3003), Vol.203, 2008, p457-462
Chan A B, Liang Z S J, Vasconcelos N. Privacy
preserving crowd monitoring: Counting people without
people models or tracking[C]. Computer Vision and
Pattern Recognition, 2008. CVPR 2008. IEEE
Conference on. IEEE, 2008: 1-7.
Chan A B, Vasconcelos N. Modeling, clustering, andsegmenting video with mixtures of dynamic textures[J].
IEEE Transactions on Pattern Analysis and MachineIntelligence, 2008, 30(5): 909-926.
Chan A B, Morrow M, Vasconcelos N. Analysis of
crowded scenes using holistic properties[C].Performance
Evaluation of Tracking and Surveillance workshop at
CVPR. 2009: 101-108.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 184

An Method of Detecting

8.

Fengzhi Dai, Huailin Zhao, Resonance algorithm forimage segmentation, Computer Engineering and Design(ISSN1000-7024), Vol.28 (No.23), 2007, p5657-5660.
9. Idrees H, Saleemi I, Seibert C, et al. Multi-source Multiscale Counting in Extremely Dense Crowd Images[C].
IEEE Conference on Computer Vision and Pattern
Recognition. IEEE Computer Society, 2013:2547-2554.
10. Zhang C, Li H, Wang X, et al. Cross-scene crowd
counting via deep convolutional neural networks[C].
Proceedings of the IEEE Conference on Computer Vision
and Pattern Recognition. 2015: 833-841.
11. Zhang Y, Zhou D, Chen S, et al. Single-image crowd
counting via multi-column convolutional neuralnetwork[C]. Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition. 2016: 589597.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 185

Design of the Multi-Car Collaboration System
Huailin Zhao1, Yangguang Guo1, Masanori Sugisaka2

1. Schol of Electrical and Electronic Engineering,
Shanghai Institute of Technology, Shanghai , China
2. Alife Robotics Corperation LTD, Oita, Japanzhao_huailin@yahoo.com
www.sit.edu.cn

Abstract
With the development of the computer and electronical technology, it is an irresistible trend to make the multiple
intelligent agents cooperate with each other to complete a specific complex task. Due to the high cost of realvehicles, most of the researches are based on simulation softwares. In our project, a few real small smart cars areused, and the design of multi-car cooperation system is completed. A system platform of the small smart cars isestablished, which can simulate the behavior such as positioning, formation and so on. In the experiment field, a
global camera is used to achieve global synchronical positioning. The simulation results show that the multiple
smart cars can get collaborating.
Keywords: Multi agent; Simultaneous localization; Robot formation;formation algorithm. Firstly, according to the structure,function and characteristic of the real intelligent vehicle,
the intelligent vehicle which can imitate the real vehicle
is designed. Secondly, the software system is designed
to realize the control function of the intelligent vehicle.
Finally, aiming at the problem of cooperative control ofmulti intelligent vehicles in the experimental
environment, this paper designs a control algorithm offormation control based on the travel mode.

1. Introduction
For multi agent cooperative system, some universities
and research institutions in developed countries havestudied the related aspects of multi-agent collaboration
system. For example, the European Regional intelligent
transportation CyberCars-2 project, the Collective
Robotics experimental system was developed by the
research team in Alberta university of Canada, theresearch team which led by Professor T.Fukuda in
Nagoya University of Japan develop the CEBOT(Cellular Robotic System). In recent years, Shanghai
Institute of Technology has designed some special
robots[1,2,3]. They have developed some useful control
algorithms[4,5,6], and going to establish the collaboration
system based on them[7,8,9]. Because of the cost of theabove research, this paper designed a low-cost platform
to facilitate the research. The main contents of thispaper are the overall design of the car hardware, thesoftware design of the car and the design of the car

2. Integrated design of multi vehicle cooperative
systemThe integrated scheme of multi vehicle cooperative
system is shown in Fig.1.
The whole system includes a PC machine, a global
camera and three independent design of the car. The car
to get the location information as shown in Figure 2 andFigure 3, PC machine running global positioning
algorithm, real-time access to three cars by the global© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 186

Huailin Zhao, Yangguang Guo, Masanori Sugisakacamera position, so as to establish the corresponding
coordinate positioning, and every car smart car range.
The decision-making process of each intelligent vehicle
is shown in Figure 4. Position data is transmitted to eachcar via a host. Three car can read the location of theother car information, and through the car's own
processor to make decisions, and then send the results tothe car around the motor, real-time control of the car's
work.

modules. Respectively is the motor drive module, speed
sensor module and communication module. Smart car
by driving the processor module to send PWM [1] to themotor wave to realize the motor control; speed sensor isused in integrated photoelectric encoder on the motor,
the encoder interface will speed by the speed of the data
passed to the small processor; wireless communication
module can realize communication workshop
installation trolley car.

Fig.1 The overall system design

Fig.5 Intelligent Vehicle Hardware Architecture

4. The design of software system
As shown in Figure 6, on the basis of the softwaresystem for the realization of intelligent vehicle control
functions, the system will be divided into 3 groups. The
first time, should be in the detection of measuring
magnetic sensor and speed sensor; second, software
project management system should also be in this pieceof the signal as the navigation and rate control; third, thesoftware system should also be in the management of
the implementation of communication signal block
management and control command analysis project.

Fig.2 Global Positioning camera car diagram (front view)Fig.3 Global positioning camera car schematic (platform)

Fig.4

Single car control flow chart

Fig.6

3. The hardware design of control system

The basic functions of the software system

The software should not only ensure timeliness, but also
to complete a wealth of difficult projects. Several levels

The hardware system structure of the intelligent car isshown in Figure 5, there are three main function© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 187

Design of the Multi-Car

measurements are weighted equally to form one error
measurement
1
Zi 
(4)
 zij
J i jJ i

of software architecture for intelligent vehicle control
system is shown in Figure 7.

Where | Ji | is the cardinality of the set Ji. The choice of
weighting does not impact the results, as long as theweights for a given vehicle sum to one. We also define adecentralized control law K which maps yi to zi and hasinternal states ui, represented in state-space form byvi  K A vi  K B1 yi  K B 2 z i(5)u i  K C vi  K D1 yi  K D 2 z iThis is 2nd system for xi. It is based on the observationsof the other agents and is used to determine the input toagent i.
Dynamic model design of intelligent vehicle:(6)xi  Aveh xi  Bvehui i  1,...,N

Fig.7 Software architecture

5. The Formation control algorithm of multivehicle cooperation
Our approach relies on two key ideas[10,11,12]. The first is
the use of dynamical systems as a paradigm forunderstanding information exchange between vehicles,
and the design of a dynamical system which enables thevehicles to achieve consensus on the formation center.
The second is the use of feed-forward compensation torender the sensed and transmitted information timely.
5.1 The equation of state for the control system of asingle vehicle is expressed as:x  AX  Bu(1)y  Cx1 
x p 
00 
Aveh  , Bveh   , xi   x 
a
a
1
22 
 
 21
 v
State variable of N vehicle:
x x i   v , xa  a 21 x p  a 22 xv  ui
 xa 

For the information from other agents, we set ji, set of
agents that can be observed by agent i.
z i   xi  hi   x j  h j







j J iLG  D  Q(8)

L  LG  I 2

A single control system (one agent) becomes multiple
agents:xi  Axi  Bui(2)z  L x  h 

Where, xi∈Rn，ui∈Rm are the vehicle states and controls,
and i is the index for the vehicles in the flock. Eachvehicle receives the following measurements:yi  C1 xi(3)z ij  C 2 xi  x j , j  J i
Where the set(7)

5.1. Feedback Equation
By using feedforward compensation, the sensing andtransmission of information are obtained in a timely
manner, and the feedback equations are as follows:J i  1, N  \ i represents the set ofx  Ax  BFL( x  h)x  I N  Aveh x  LG  BvehFveh ( x  h)vehicles which vehicle can sense. Thus, yi∈R
represents internal state measurements, and zij∈Rl
represents external state measurements relative to othervehicles. We assume that J i   , meaning each vehicle
can sense at least one other vehicle. Note that a single
vehicle cannot drive all the zij∈Rl terms to zerosimultaneously; the errors must be synthesized into asingle signal. For simplicity, and without loss ofgenerality, we assume that all relative statekDimensionsx : 2 N  2, Fveh : 2  1, Bveh : 2  1, LG : 2 N  2
I N  Aveh : 2 N  : 2 N , LG  Bveh Fveh : 2 N  : 2 N

6. Experiment simulation
Problem :-Get three agents to move into a triangular formation© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 188(9)

Huailin Zhao, Yangguang Guo, Masanori Sugisaka-Each agent starts with a certain position and
Velocity.

often a more natural model for problems, such as
Autonomous highway driving, Swarm robotics and soon.
Reference

Assumptions:- Same dynamics for each agent-The internal system of each agent is decoupled fromthe others–Each agent can observe the positions of the other two
and then adjust its own velocity accordingly–No agent can directly change the velocity of another1.

Shunzhou Wang, Huailin Zhao, Xuyao Hao. Design of
An Intelligent Housekeeping Robot Based on IOT.
Proceedings of 2015 International Conference Intelligent
Informatics and BioMedical Sciences (ICIIBMS2015),
2015.11, p197-200.
2. Huailin Zhao, Lin Wang, Bei Wang, Masanori Sugisaka,“System development of an artificial assistant suit”,
Artificial Life and Robotics (ISSN1433-5298), Vol.17,
2013, No.3-4, p331-335.
3. Huailin Zhao, Bei Wang, “Configuration of the
Mckibben Muscles and Action Intention Detection for an
Artificial Assistant Suit”, International Journal ofAdvanced Robotic Systems (ISSN 1729-8806), Vol. 9,
2012, p1-7.
4. Huailin Zhao, Wei Ren, Consensus Problem ofDistributed Multi-agent Systems, Proceedings of the2015 International Conference on Artificial Life andRobotics (ICAROB 2015), 2015.01, p201-206.
5. Huailin Zhao, Masanori Sugisaka, “Simulation study ofCMAC control for the robot joint actuated by McKibben
muscles”, Applied Mathematics and Computation(ISSN0096-3003), Vol.203, 2008, p457-462.
6. Huailin Zhao, Masanori Sugisaka, “A Model of the
Mckibben muscle actuator based on experiment”,
Systems Science (ISSN0137-1223), Vol.34, No.2, 2008,
p83-88.
7. Huailin Zhao, Masanori Sugisaka, Leilei Cui, Guanglin
Shi, “Research on the intelligent control algorithm for asoft joint actuated by Mckibben muscles”, Artificial Lifeand Robotics (ISSN1433-5298), Vol.14, No.1, 2009,p85-88.
8. Huailin Zhao, Xiaoqing Jia, Masanori Sugisaka, “Somethought for the Mckibben muscle robots”, Proceedings ofthe 16th International Symposium on Artificial Life andRobotics (AROB 16th ’2011), p533-536.
9. Huailin Zhao, Yulong Xia and Yi Liu, “A Jacket Robotand Its Human-Robot Interacting Technology”, Lecture
Notes in Electrical Engineering (ISSN18761100),Vol.255(2013), Chapter 92, p1-7
10. Lin R, Du Z, Sun L. Moving object tracking based on
mobile robot vision[C]// International Conference on
Mechatronics and Automation. 2009:3625-3630.
11. Fax J A, Murray R M. Information flow and cooperative
control of vehicle formations[J]. IEEE Transactions on
Automatic Control, 2002, 35(1):115-120.
12. Huailin Zhao, Datai Yu, Guo Li, A humanoid joint
actuated by Mckibben muscles, Journal of University of
Science and Technology Beijing (ISSN1001-053X),
Vol.28 (No.11), 2006, p1096-1100.

Simulation result

Simulation results are shown in Figure8

Fig.8 Three cars brought together a little

The red line represents the moving track of the trolley,
and the green represents the track of the trolley. The
blue represents the track of the car. The initial locationof the three vehicles (5,0), (2,2), (0,3), the convergencepoint is (0,0). This experiment through the global
real-time camera reads three car position, so as toestablish the corresponding coordinate positioning, andevery car intelligent vehicle location and location
information will be sent to the car, the car intelligent
formation algorithm makes the threeutual cooperation, from the initial position of therespective converge at a point.
7. Conclusion
This experiment through the global real-time camera
reads three car position, so as to establish thecorresponding coordinate positioning, and every car
intelligent vehicle location and location information will
be sent to the car, the car intelligent formation algorithm
makes the three mutual cooperation, from the initial
position of the respective converge at a point.
Multi-Agent Systems: multiple agents working together
to achieve a common goal. Multi Agent Systems are© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 189Research on a Method of Character Recognition for Self-learning Errors
Huailin Zhao1，Yawei Hou1，Shifang Xu1, Congdao Han1, Masanori Sugisaka2
1
Schol of Electrical and Electronic Engineering
Shanghai Institute of Technology, Shanghai , China
2
ALife Robotics Corperation LTD, Oita, Japan
E-mail:hyw_2016@163.com,zhao_huailin@yahoo.com
www.sit.edu.cn

Abstract
Due to the different writing habits, the handwritten numeral is difficult to identify. No matter what kind of network,
the computer can not judge the output of the network. This reduces the recognition rate of the network. In order toimprove the recognition rate, this paper proposes a method of character recognition for self-learning errors. Finally,on the matlab simulation platform, it is proved that the method proposed in this paper can improve the recognition
accuracy.
Keywords: probabilistic neural network; handwritten numeral recognition; self-learning; matlab;

1. Introduction
The optical character recognition technology includes
the handwritten character recognition and printed
characters recognition. As a part of handwritten
character recognition, handwritten numeral recognition
is a very important research direction. In recent years,
with the development of computer and patternrecognition
technology,
characterrecognition
technology has been widely used in postal code,
financial amount and robot[1,2], artificial assistant suit[3,4].
Although the classification of the classifier has beenfurther enriched, but the researchers still can not find analgorithm to achieve the perfect effect.
The artificial neural network with strong self-learning
ability,
self-adaptability,classification
ability,fault-tolerance and fast recognition has attracted muchattention, and it has been widely used in character
recognition[5]. Common neural networks have BP neuralnetwork, CMAC neural network[6]. Neural networks canbe combined with other intelligent controls, such as

fuzzy neural networks[7]. In this paper, the probabilisticneural network is selected in the research of handwritten
numeral recognition, and the recognition rate ofcharacters is improved by using the method ofre-recognition. By using the data of MNIST database, itis proved that the recognition rate can be improved
when the self-learning error recognition method is used.
2. Fundamentals
Networks

ofProbabilistic

NeuralThe theoretical basis of probabilistic neural networks is
the Bayesian minimum risk criterion. The theoreticalbasis of probabilistic neural networks is the Bayesianminimum risk criterion. The basic principle of Bayesianclassifier: Under the condition of prior probability,
according to the prior probability of an object, theBayesian formula can get its posterior probability.
Finally, the class with the largest posterior probability is
chosen as The class to which the object belongs [8].
For ease of analysis, it is assumed that the classification
to be made as c = c1 or c = c2. The prior probability is© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 190

Huailin Zhao, Yawei Hou, Shifang Xu, Congdao Han, Masanori Sugisakah1

p c1 , h2

p c 2 , h1 h2

1.

3.1. Pretreatment(1)

x=[x1,x2,...,xn]as the input vector and is classified
according to Eq.(2)cc1 , p c1 x

p c2 xc2 , otherwise.

Pretreatment can improve the final recognition rate, so
preprocessing is very important. Mainly for denoising,filtering and other operations. The digital image which
be used in digital identification needs to be binarized.
For image analysis, image segmentation is necessary[10].
The data used in this article from the MNIST databasewhich is not pretreatment, but directly using the datafrom the database experiments.

(2)p(c1|x)is the posteriori probability of class c1 in the caseof x. According to the Bayesian formula, the posterior
probability is equal top c1 p x c1
p c1 x.
(3)
px

3.2. Feature extraction

But the actual situation should also consider the loss andrisk issues. The samples belonging to c1 may be
assigned to c2, the samples belonging to c2 may be
assigned to c1, this will cause losses, so theclassification rules should be adjusted.
The adjusted Bayes decision rule becomes:cc1 , R c1 x

p c2 xc2 , otherwise.

The importance of feature extraction[11] in image
recognition is self-evident. Commonly used handwritten
digital features are: structural features and statisticalcharacteristics. In order to get better results, this paper
uses the combination of the two characteristics of theimage feature extraction, and obtain the 14-dimensionalfeature. There are eight structural features and six
statistical characteristics in it. Constitute a feature vector
with 14 values, each digital image is represented by thefeature vector.

(4)

Where R(c1|x) is the expected risk that the input isclassified as c1.
It is found that the training of probabilistic neuralnetwork is simple and fast convergence, which can be
fully realized in real-time processing, and the network
has better performance.

3.3. Implementation of handwritten numeral
recognitionx1
1

3. Implementation ofrecognition system

handwritten

x2

numeral2yx3

The handwritten numeral is generally provided in theform of picture in its practical application. The original
image can be obtained by the input device, and thenhandwritten numeral recognition is started after
pre-processing,charactersegmentation,
featureextraction and classifier selection [9].

3x14Fig. 2.

60000

The probabilistic neural network in experiments

It can be seen from Fig. 2 that the probabilistic neuralnetwork used in the experiment consists of 14 inputs.
Since the training data used MNIST's data set, it
contains 60000 training samples and 10000 test samples.
In this paper, we don’t select the 60000 training samples.
All the training samples are directly used. Therefore,
there are 60000 neurons in the hidden layer. As to
identify the number zero to nine, so the Summation
layer have ten neurons. The final result of classificationis only one, the network output layer is a neuron.

Fig. 1. The process of handwritten numeral recognition© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 191Research on a Method

4. The experimental results and improvements
The selected MNIST datasets[12] consisted of 60000
training samples and 10000 test samples. All thetraining samples were used to train the network, andthen 10000 test samples were tested. Table 1 shows therecognition results at different network diffusion rates.
Table 1. the recognition rate of the system at
different diffusion rates
Network diffusion speed
0.1
0.2
0.3
0.4
0.5

Sample number
60000
60000
60000
60000
60000

Recognition rate
80.51%
80.33%
73.86%
68.29%
64.16%In order to improve the recognition rate of the network,
this paper proposes a recognition method ofself-learning errors.

shown in Table 1. When the diffusion speed of the
network is 0.1, the recognition effect of the network isthe best. In order to improve the recognition rate, thenetwork can make up for the wrong knowledge bylearning errors. Compare the results of the network withthe expected output of the test samples, we can obtain acomparison result. If the comparison results are thesame, the network output the results of recognition. If
the results are different, the network is misidentification.
According to the desired output, we should establish amatrix which contains the number of false images andthe corresponding expected output. Through statistical
data, it is find that there are altogether 1949 images
which are not recognized correctly. There are some ofthe values shown in Table 3.
Table 3. A partial misrecognition of the image
number and the corresponding expected output
resultImage ID
Expected output

30
1

38
1

...
...

9994
10

10000
10

Select the eigenvectors of the error image and thecorresponding expected output to train the error. The
final recognition rate of the network is the sum of thetwo recognition results. Through the MATLAB
simulation platform, we can get the final recognition
rate which is shown in Table 4.
Table 4. The improved network recognition
rates at different diffusion speed

Fig. 3.

Network diffusion speed
0.1
0.2
0.3
0.4
0.5

The block diagram of improved system

The number of individual digits in the selected test
samples is known, so we can create a matrix which has
1 row and 10000 columns. If the test samples is number
0, then it is marked as 1, and so on. As shown in Table
2.
Table 2.

1 2 3 ...
1 1 1 ...

9998
10

9999
10

Recognition rate
100%
99.57%
94.35%
87.46%
81.90%

By analyzing Table 1 and Table 4, it can be seen thatthe method of using self-learning error can improve therecognition rate of the network. When the network
diffusion speed is 0.1, the final recognition rate is 100%,
which is nearly 20% higher than before.
In practice, the computer can’t establish the desired
output corresponding to the image to be recognized inadvance. In order to solve the problem, this paper
designs a method to set up the expected output
corresponding to the image to be recognized by the

The expected output of test samples

Image ID
Expected output

Sample number
60000
60000
60000
60000
60000

10000
10

In the experiment, the recognition rate obtained throughthe network training at different diffusion speed is© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 192

Huailin Zhao, Yawei Hou, Shifang Xu, Congdao Han, Masanori Sugisaka

computer. In order to enable the computer to quickly
compare the test data with the training data, the contrast
data used in this paper are the feature vectors obtainedby the feature extraction which proposed in thepreceding paper. The Euclidean distance between thetwo sets of vectors is compared by distance-based
method to judge the similarity.
For the two n-dimensional sample points a（x11,x12,...,x1n）and b（x21,x22,...,x2n）, the Euclidean
distance is
n

d12

x1k

x2k

2

.

network can increased by 6%. The establishment of the
expected output still need to conduct in-depth study.
References
1. Huailin Zhao, Xiaoqing Jia, Masanori Sugisaka, “Somethought for the Mckibben muscle robots”, Proceedings ofthe 16th International Symposium on Artificial Life andRobotics (AROB 16th ’2011), p533-536.
2. Huailin Zhao, Masanori Sugisaka, “A Model of the
Mckibben muscle actuator based on experiment”,
Systems Science (ISSN0137-1223), Vol.34, No.2, 2008,
p83-88.
3. Huailin Zhao, Lin Wang, Bei Wang, Masanori Sugisaka,“System development of an artificial assistant suit”,
Artificial Life and Robotics (ISSN1433-5298), Vol.17,
2013, No.3-4, p331-335.
4. Huailin Zhao, Bei Wang, “Configuration of the
Mckibben Muscles and Action Intention Detection for an
Artificial Assistant Suit”, International Journal ofAdvanced Robotic Systems (ISSN 1729-8806), Vol. 9,
2012, p1-7.
5. Impedovo S，Pirlo G，Modugno R，et al．Zoning Methodsfor Hand-Written Character Recognition ： An
Overview[C]．International Conference on Frontiers in
Handwriting Recognition ． IEEE Computer Society ，2010：329-334.
6. Huailin Zhao, Masanori Sugisaka, “Simulation study ofCMAC control for the robot joint actuated by McKibben
muscles ” , Applied Mathematics and Computation(ISSN0096-3003), Vol.203, 2008, p457-462.
7. Huailin Zhao, Masanori Sugisaka, Leilei Cui, Guanglin
Shi, “Research on the intelligent control algorithm for asoft joint actuated by Mckibben muscles”, Artificial Lifeand Robotics (ISSN1433-5298), Vol.14, No.1, 2009,p85-88.
8. Jianju Xing, Jun Li, Zanfu Xie. Application of PNNin
handwritten numeral recognition[J]. Modern Computer,2016(08):20-23.
9. Sanping Li, Zhenjun Yue. Realization of Handwritten
Numeral Recognition System Based on Probabilistic
Neural Network [J]. Military Communication Technology,
2005,26 (1): 54-57.
10. Fengzhi Dai, Huailin Zhao, Resonance algorithm forimage segmentation, Computer Engineering and Design(ISSN1000-7024), Vol.28 (No.23), 2007, p5657-5660.
11. N. Das, S. Basu, R. Sarkar, M. Kundu, M. Nasipuri, D.
kumar Basu. An Improved Feature Descriptor for
Recognition of Handwritten Bangla Alphabet, Jan, 2015.
12. Qiong Li, Li Chen, Weihu Wang. Study on handwriteen
numeral rapid identification based on SVM[J]. Journal ofComputer Technology and Development, 2014, 24(2):205-208.

(5)

k 1

It can also be expressed in the form of vector
operations:d12Ta b a b .

(6)

Where a and b represent two vectors.
Through the analysis, we can seen that the Euclidean
distance between the two vectors is smaller, thesimilarity between the two vectors is larger. Therefore,
the characteristic vectors of each test samples arecalculated by Euclidean distance with the characteristic
vectors of the 60000 training samples, and the numberof the training samples with the smallest Euclidean
distance from the test samples is calculated.
Using Euclidean distance as a condition of similarity
judgment has some misconceptions. In order to reduce
the error, we use the network recognition result tomodify the expected output to get the final expected
output of the test samples.
According to the above description, the network issimulated and the recognition rate of the network is
86.32%. The recognition rate is improved by 6%
compared with the network before the improvement.
5. Conclusion
In this paper, the probabilistic neural network is deeply
analyzed. The feasibility and validity of handwritten
numeral recognition of probabilistic neural network are
explored. An automatic learning error recognition
method is designed to improve the probabilistic neuralnetwork. If the expected output of the test samples canbe obtained, the recognition rate of the network canreach 100%. But the expected output of the test samples
is not easy to obtain. The recognition rate of the© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 193An Improved Method of Power System Short Term Load Forecasting Based on Neural
Network
Shunzhou Wang

School of Electrical and Electronic Engineering
Shanghai Institute of Technology, Shanghai, China
Huailin Zhao

School of Electrical and Electronic Engineering
Shanghai Institute of Technology, Shanghai, China
Yani Zhang

School of Computer Science and Information Engineering
Shanghai Institute of Technology, Shanghai, China
Peng Bai

School of Electrical and Electronic Engineering
Shanghai Institute of Technology, Shanghai, China
E-mail: wangszvision@qq.com

Abstract
Load forecasting is an important content of planning and operating power system. It is the prerequisite to ensure thereliable power supply and economic operation. In this paper, an improved method of short-term load forecasting forload data of two different regions is proposed. Firstly, we analyze the relationship between weather factors and load,
and then the greatest impact on load of weather factors are selected. The Elman neural network is used to predict
unknown one-week load data taking into account without whether factors situation and whether factors situation. In
the predicting situation of considering whether factors, the multi-weather factors are integrated with the temperatureand humidity index , which are used as the neural network input training samples. The prediction result is good.
Keywords: Load forecasting , Elman neural network, Temperature and humidity index

method[8], wavelet analysis[9], genetic algorithm[10] andsupport vector machine[11]and so on
Short-term load forecasting is becoming more andmore important. In recent years, the maximum load ofpower supply increased year by year, which constitutethe peak load of electricity. The load is very sensitive tometeorological changes, and the impact of
meteorological factors is growing[12]. Although manymodels have taken into account the effect of weather on
power load, most of these models only consider
temperature as a single meteorological factor[13,14,15].
However, relative humidity, air pressure, wind speed,radiation and other meteorological factors will have agreat impact on power load.

1. Introduction
Short-term load forecasting is an important part of load
forecasting, which is of great significance for economic
dispatch, electrical market transactions and so on.
In the load forecast theory and methods field,
academia and industry have done a lot of research andhave made fruitful progress. In recent years, experts andscholars have proposed a variety of methods. Thesemethods include: single consumption method, elasticity
coefficient method, partitioned load density method[1],time series method[2], trend extrapolation method[3],regression analysis method[4] and gray model method[5],artificial neural network method[6,7], expert system© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 194

Shunzhou Wang, Huailin Zhao, Yani Zhang, Peng Bai

Most of the neural network for short-term power
load forecasting requires a large number of data samples,and only forecast the power load of one day, and there islittle work to predict the unknown one-week load[16,17,18].
In this paper, an improved short-term load forecastingalgorithm based on Elman neural network is proposed,
which can predict unknown power load data with asmall amount of known load data, and achieves goodresults by combining the characteristics of temperatureand humidity meteorological factors.

Commonly the normalization means [0,1] is used, theformula is as follows:
Normalization：(1)=

Anti-normalization：
=(
−
(2)

)

+

2.2 The framework of the neural network
2. Short Term Load ForecastingThis paper aims to predict the two regions total of 7
unknown days load data from January 11, 2015 toJanuary 17, 2015, based on the historical load data. We
select the load data from December 25, 2014 to January
2015 10 , a load data of 17 days to be as a training
sample, excluding large fluctuations changes inrelatively data: the region 1 December 28, December 31,
January 1 data and the region 2 December 30,
December 31 Day, January 1 data. After removing theabnormal data, a total of 8 days of data from December
25, 2014 to January 4, 2015 were selected as thetraining samples of the network. The load is used as the
input vector every 3 days and the load on the 4th day as
the target vector. Thus, five sets of training samples canbe obtained. A total of six days of data ,January 5, 2015
to January 10, 2015 can be as a network of test samples
to verify the network reasonably predict the day's load
data or not. As the daily load data are 96 points, threedays of load data is as a set of input, the day's load data
is as the output, so the input variable is 288 variables,
the output variable is 96 variables.
The number of hidden layers is two layers, thenumber of the first layer nodes is 11, the number of thesecond layer nodes is 5, the practice effect is better.

The feedback neural network is suitable for powersystem load forecasting because of input delay.
According to the historical data of load, the input andoutput nodes of feedback neural network are selected toreflect the inherent law of power system load operation,
so as to forecast the load of future time. Therefore, theprimary problem of the artificial neural network for
power system load forecasting is to determine the neuralnetwork input and output nodes, so that it can reflect thepower load operation law.
2.1Data sample preprocessing
2.1.1. Processing of abnormal data
The power load has a clear weekly change
characteristics, working days Monday to Friday high,weekends low. This is due to weekend industrial
electricity load reduction reasons. In addition, someholidays load, such as International Labor Day, National
Day, the New Year, the Spring Festival, compared withusual day is significantly lower, which is due to theholiday industry caused by a substantial reduction inelectricity. Therefore, the weekday and holiday
electricity load is removed from the data sample, andonly the electricity load of the working day is chosen as
the historical data to improve the accuracy of theprediction result.

Fig. 1. The framework of the neural network for short termload forecasting

2.1.2. Data sample normalization
If the neural network directly use the original data as
input, it will make the neuron training saturation.
Therefore, before the network training, the data must be
normalized to the same number level, so that the neuralnetwork can be converged. Finally, we can get the realload through the anti-normalization process[19].

3. Short Term Load Forecasting Based on
Weather Factors
The relationship between temperature, humidity andrainfall is analyzed[19]. The influence of humidity factor© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 195

An Improved Method ofon the load is more significant. Based on the above, theshort-term load model based on Elman neural network
is established in this section considering the effect of
meteorological factors on load forecasting.

Considering the correlation of meteorologicalfactors and short-term load forecasting model based on
historical load data, In this paper, the neural network
input is mainly selected temperature and humidity
meteorological factors, and then on the basis of theprevious model this paper build a meteorological factors
in the load forecasting model.

3.1 Comprehensive meteorological factors
It has been pointed out that the change of the electricload is affected by various meteorological factors, andthe meteorological factors also have some coupling
effect. Therefore, it can not be evaluated on the basis ofsingle temperature or other meteorological factors. It is
an important content of short-term load forecasting how
to use all meteorological factors reasonably andeffectively to reduce the input judgment amount andimprove the prediction precision. Fig.2. shows theinfluence of various meteorological factors on the
power load.

3.2 The load prediction algorithm flowchart
In this paper, the temperature and humidity index is
added to each input sample of the Elman neural network(the temperature and humidity index has beennormalized). Firstly, the neural network model is trainedwith the known date load data and the temperaturehumidity index. Then, when the load data of January 11,2015 is forecasted, the actual temperature humidity
index THI (1) replaces the neural network prediction
THI (1), which is added to the training and iteration of
the neural network. The actual temperature humidity
index THI (2) of January 12 is instead of the neuralnetwork predictive THI (2) to train and iterate the neuralnetwork. Until the data of January 17 is predicted. The
flow chart of the whole algorithm is shown in Fig.3.

Fig. 2. The influence of various meteorological factors on the
power load daily prediction

In practice, although the rainfall has a certain impact on
the load forecast. However, since the rainfall mainly
concentrates at a certain time of the day, the study ofrainfall should be carried out for the time period duringwhen rainfall occurs, and should not be added to eachpoint throughout the day in a general manner. In thisway, a comprehensive temperature and humidity index(Temperature and Humidity Index) is derived[19], which
is derived from the effective temperature formulaestablished by the well-known Russian scholar, and canbe well described the effect of temperature and humidity
on the power load. The formula is as follows.
=− (0.55 − 0.55

)∗(

− 58)

Fig. 3. Flow chart of load forecasting algorithm withmeteorological factors(3)

where，
is Fahrenheit，it is necessary to convert
the given temperature data firstly.
© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 196

Shunzhou Wang, Huailin Zhao, Yani Zhang, Peng Bai

3.3 Experiment results
In order to test the accuracy of the forecasting model,
the paper first calculates the power load of the area 2
from January 5, 2015 to January 10, 2015 (576points) ,by taking account of the meteorological factors.
The predicted results are shown in Fig. 4, and theresulting prediction errors are shown in Fig. 5.
(b)
Fig.5. The prediction error of area 2 load from January 5,2015
to January 10,2015 (a)the prediction without consideration the
Meteorological Factors (b) the prediction error withconsideration the Meteorological Factors

In Figure 4, the difference between the predicted
results in the two cases is not obvious, but it is clear
from Fig. 5 that the error of the prediction result of theregion 1 is greatly reduced by meteorological factors,
and controlled to half of the error without considering
meteorological factors, the precision of the prediction isgreatly improved. In order to show the superiority of theforecasting with the meteorological factors, the power
load from January 11 to 17, 2015 (672 points) is
forecasted without meteorological factors. The predicted
results for region 2 are shown in Fig.6.

(a)

(b)
Fig.4. The forecasting result of area 2 load from from January
5, 2015 to January 10, 2015 (a) the prediction result without
consideration the Meteorological Factors (b) the prediction
result with consideration the Meteorological Factors

(a)

(a)
(b)
© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 197

An Improved Method of

Fig.6. The forecasting result of area 2 load from January 11,2015 to January 17, 2015 a)the prediction result without
consideration the Meteorological Factors (b) the prediction
result with consideration the Meteorological Factors

6.

Based on the comparison of forecasting error andmeteorological factors between meteorological factors
and meteorological factors in the above two regions, it
is clear that the prediction accuracy has beensignificantly improved with the meteorological factors.
In this way, the result, which prediction model based on
the Elman neural network and meteorological factors isused to forecast the load of the two regions in the period
from January 1 to 17, 2015, is valuable.

7.

8.

9.

4. Conclusion
In this paper, we have proposed an improved powersystem short term load forecasting method. We analyze
the factors affect the power load and use thetemperature and humidity index to be as an input
sample of Elman neural network. To validate themethod effectively, we use the neural network to predict
two different area load data .We only use a few history
load data to predict an unknown week load. The
prediction result is reasonable and prediction error issmall. Our method will help the manager to analyze andplan weekly load data.

10.

11.

12.

13.

5. References
1.

2.

3.

4.

5.

Li Yu-mei, “The Utility of Combination Forecasting
Method in Mid-long Term Load Forecasting ”, M.S.
Thesis, Sichuan University, Sichuan province, SW,
China, 2006
Soliman S A, Persaud S, El-Nagar K, et al.
Application of least absolute value parameter
estimation technique based on linear programming toshort-term load forecasting. Canadian Conference on
Electrical & Computer Engineering, 1996, 2(3):529 533.
Jinying Li, Dongxiao Niu. Study On Gray Combined
Forecasting of Nonlinear Seasonal. Power Load
Power System Technology, 2003, 27(5):26-28.
Chongqing Kang, Qing Xia, Mei Liu. Special
Problems in Regression Analysis Applied to Load
Forecasting Automation of Electric Power Systems,
1998(10):38-41.
Dahai Zhang, Yanqiu Bi, Yanxia Bi.etc. Power Load
Forecasting Method Based on Series Gray Neural
Network. System Engineering Theory and Practice.
2004, 24(12):128-132.

14.

15.

16.

17.

18.

Huailin Zhao, Masanori Sugisaka, Leilei Cui,
Guanglin Shi, “Research on the intelligent control
algorithm for a soft joint actuated by Mckibben
muscles”, Artificial Life and Robotics (ISSN14335298), Vol.14, No.1, 2009, p85-88.
Huailin Zhao, Masanori Sugisaka, “Simulation studyof CMAC control for the robot joint actuated by
McKibben muscles”, Applied Mathematics andComputation (ISSN0096-3003), Vol.203, 2008, p457462.
Wei Wei, Dongxiao Niu, Zheng Chang. New
Development of Load Forecast Technology. Journalof North China Electric Power University, 2002,
29(1):10-15.
Bi Y, Zhao J, Zhang D. Power Load Forecasting
Algorithm Based on Wavelet Packet Analysis.
International Conference on Power System
Technology, 2004. Powercon. 2004:987-990 Vol.1.
Zhengyuan Jia, Dongxiao Niu. Genetic Neural
Network Model Research for Power Load Forecasting.
Operations Research and Management Science,
2000(2):31-36.
Xuegong Zhang. Introduction to Statistical Learning
Theory and Support Vector Machines. Acta
Automatica Sinica, 2000, 26(1):32-42.
Xiping Zhao, Dai Song, Guoqing Zhang, XinyangHan, Zhaoguang Hu. Research on Correlativity
Among Air Temperature, Maximum Load and Power
Consumption in Shandong Power Grid. Power System
Technology, 2004(17).
LI Ran, LIU Yu, LI Jing-hua.etc.Study on the Daily
Characteristic Load Forecasting Based on the
Optimizied Algorithm of Decision Tree[J] Proceedingof the CSEE, 2005, 25(23):36-41.
LI Yang, WANG Zhi-hua, LU Yi.etc. Characteristic
Analysis of Summer Air Temperature Daily PeakLoad in Nanjing. Power System Technology,
2001,25(7):63-66.
WANG Zhi-hua, LI Yang, ZHAO Cui-yu,etc. Studyon Summer Temperature Sensitive Load in Nanjing.
Automation of Electric Power Systems, 2002,
26(3):60-63.
Guo Jiao-jiao. “The Short-term Power Load
Forecasting Based on Improved Elman Neural
Network”, M.S. Thesis, Liaoning University ofTechnology, NE, China,2015
REN Lina. “Research on Medium-Term Electrical
Load Forecasting Model Based on Elman Neural
Network”, M.S. Thesis, Lanzhou University ofTechnology, NW,China,2007
HU Chang-hong, “Research on Short-term Load
Forecasting Based on Hourly Weather Factors”, M.S.
Thesis, Zhejiang University, Zhejiang province, EC,
China, 2010

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 198

Shunzhou Wang, Huailin Zhao, Yani Zhang, Peng Bai

19. Rong Liu. “Short Term Load Forecasting Based on
Elman Neural Network”, M.S. Thesis, Zhejiang
University, Zhejiang province, EC, China, 2013

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 199

Improvement on LEACH Agreement of Mine Wireless Sensor Network
Liu Yun-xiang

Computer and Information Engineering College
Shanghai Institute of Technology, Shanghai, China
Zhang Wei

Computer and Information Engineering College
Shanghai Institute of Technology, Shanghai, China
Zhou Lan-feng

Computer and Information Engineering College
Shanghai Institute of Technology, Shanghai, China
E-mail: yxliu@sit.edu.cn

Abstract
Based on the characteristics of wireless sensor network communication in mine, LEACH protocol clustering is
optimized, and the factors of energy and distance are considered fully. The selection of cluster head nodes is optimized,
and a routing algorithm based on K-means ++ clustering is proposed. The problem of uneven distribution of cluster
head nodes, uneven energy consumption and network stability in LEACH algorithm is improved effectively.
Simulation results show that the proposed algorithm can improve the energy consumption of the whole network andimprove the energy utilization rate, extending the network life cycle effectively.
Keywords: Wireless sensor networks, LEACH agreement, K-means++ algorithm, Mine

According to the characteristics of the coal mine tunnel,
the network topology is banded, and the communication
environment is relatively poor. Same as the general
wireless communications networks, the nodes work insome of the unmanned monitoring area, not easy tomaintain and are limited and not renewable energy, so
efficient, stable, energy-efficient routing protocol for theentire wireless sensor network is very important. Low
Energy Adaptive Clustering Hierarchy (LEACH)
protocol is widely used at present hierarchical routing,which can be applied to the general working environment.
But for the special conditions in the mine under, LEACH
protocol can’t meet their requirements, therefore in thispaper, we propose a route-based K-means++ clustering
algorithm named LEACH-KPPE, to enable it efficientand stable to work in the mine environment. The
clustering algorithm is optimized by K-means clustering
algorithm, and fully consider the factor of energy anddistance to control the selection of cluster head nodes.
The problem of uneven distribution of cluster head nodes,
uneven energy consumption and network stability in
LEACH algorithm is effectively improved.

1. Introduction
Wireless Sensor Network is composed of a large numberof data acquisition, data processing and communication
capabilities of Sensor nodes to be self-organization
formed a dynamic Network topology 1-3. Which has astrong anti-destruction, self-adaptable and capable of
rapid deployment and other advantages, it is widely usedin important areas of industrial control, environmental
monitoring, traffic control, health care, military and other4-5. Coal mining operations mainly in downhole andbefore the wireless sensor network has not been popularin the mine the wireline communication device is still inuse. Wired communication there are some drawbacks,
unable to pinpoint the underground site of the incident
and the staff position when a security incident, coalground personnel could not get the downhole specificinformation timely and accurately, it is not conducive tothe underground work and security management. By
using wireless sensor networks to achieve downholecommunications and wireless security system to improvethe mine mobile communication system, and tostrengthen the safety management of the mine.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 200

Liu Yun-xiang, Zhang Wei, Zhou Lan-feng

2. LEACH Protocol
Considering the correlation of meteorologicalfactors and short-term load forecasting model based on
historical load data, In this paper, the neural network
input is mainly selected temperature and humidity
meteorological factors, and then on the basis of theprevious model this paper build a meteorological factors
in the load forecasting model.

LEACH protocol is the wider application of ahierarchical routing protocol in wireless sensor networks,
the workflow is divided into two steps: generation andtransmission of data clusters. The whole process will be
repeated periodically, so that the process can be called aperiod of "round" . In each round several more suitablenode will be selected as a cluster head node from the
network nodes randomly, before selecting cluster head,
LEACH algorithm is given a pre-defined threshold value
T(n)，the threshold value is calculated as:,∈∗T( ) =(1)0,
others
Wherein is the networks cluster head node proportion
accounted for all the expectations, is the current cycleof the number of rounds selected cluster head, is acollection of which is not a cluster head node.
After completing the clustering work, the wirelesssensor network begins to work. The nodes in the cluster
begin to collect the required information, and then send
the collected data to the head node within their cluster.

3.2 LEACH-KPPE algorithm implementation
3.2.1 The optimal number of cluster nodes
In this paper, by analyzing the traffic and energy
consumption in the network, the optimal number ofcluster head nodes can be obtained. There are M nodes inthe erected wireless area, and they all be distributed in the
N×N area. The number of cluster nodes is k, the wholenetwork is divided into k clusters, each cluster has M/k1 nodes, so the number of common nodes in each cluster
is M/k-1. Multipath fading is used here, so the energy
consumption of each cluster head nodes including
receiving the rest of the nodes within cluster data
information of power consumption, energy consumptionof the data fusion and sends the data to base station:

3. LEACH-KPPE Protocol
3.1 LEACH-KPPE algorithm basic idea=

In this paper, a new LEACH-KPPE protocol is proposed
based on the LEACH protocol for some defects oftraditional LEACH applications in coal mines. LEACHKPPE agreement in the main work process is same withthe original LEACH basically, they are also cyclicalround of reconstruction, and each round also includes
two stages of the establishment of clusters and stable datatransmission. In the cluster stage, the nodes in the wholenetwork send their own position information and residualenergy information to the base station. After the basestation integrates the data, the K-means ++ algorithm isused to cluster the nodes within the network area. Afterthe K-means ++ algorithm clustering processing, theclustering is complete. Next according to the quantity of
the node residual energy and location information todecide the optimal cluster head nodes. After thecompletion of the establishment of clusters and the
choice of cluster head there will be going on stable datatransmission phase. In the LEACH-KPPE protocol not
only retain the original single-hop communication, alsojoined the multi-hop communication.

−1

+

+

(2)

Where
is the energy consumption of transmitting or
receiving 1bit data, is the number of transmitted data,is constant and associated with hardware,is theenergy consumption of cluster head node fusion data,is the distance between cluster head node and thebase station.
When the communication between a member node
in a cluster and cluster head node, the member node issimilar to the cluster head node, then use the free spacemodel, then the energy consumption with a member ofthe cluster node in a round of work is:=+(3)
Where:、 andas above,denotes the
distance between the member node and the cluster headnode.
Assuming that the whole × area are circular anddivided into clusters, then the radius R = /√ .
Assuming that the probability density of nodes in acluster is ρ (x, y), then:= ∬( + ) ( , )(4)is the expectation of the square of the distance© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 201

Improvement on LEACH Agreement

between the cluster member node and the cluster headnode, by the radius R = /√ available:
=∫

∫

⁄√

Start
Sending node location andresidual energy information(5)=

Number of clustering
centers k

By the above calculation, in each round of energy
consumption of each cluster are as follows:=+ ( / − 1)
(6)
At this point the whole network has a total of kclusters, and the energy consumption in each round of E
as follows:==+( − )
= 2

+

−2

+

−

+

Select a node as the cluster center
randomly m = 1⁄

Yes

Calculation of each node and its recent clustering
center distance D(x),the Sum(x) with the sum of
D(x), and take a random value

The nodes are classified according to k
cluster centers
Calculate the criterion function

Random -= D(x)Random<=0No

The threshold issatisfied

No

Yes

Yes

End

Fig. 1. This is the caption for the figure. If the caption is lessthan one line then it is centered. Long captions are justified
manually.

(7)

Because,andis constant andindependent of the , so derivative the type (7) can get
the extreme value of with
as follows:
=

No

m<k
m=m+1
The node will tobe the newclustering centerat this time

3.2.3 Stable data transmission phase
After the cluster head node selection and cluster
work of the whole area is completed, the datatransmission is started. Single-hop communication
cannot meet the requirements of communications in coalmines, this paper based on single-hop communicationand then use multi-hop mode for data transmission.
Figure 2 and Figure 3 compare the LEACH and LEACHKPPE inter-cluster communication.

(8)

In this way, the optimal number of cluster heads
in the × region is obtained, that is the numberof cluster centers to be used by the K-means ++algorithm.
3.2.2 Establish cluster stage

Base station

When building cluster, the information of location andresidual energy of all the nodes in the area will be sent
to the base station first, then use the K-means++algorithm to cluster the nodes within the network. Its
main working process is as follows:①The number of
optimal cluster centers is obtained by the above; ②For
each node x within the region, to calculate the distanceD(x) with the nearest cluster center;③Choose a newnode as a new cluster center,;④Repeat ② and ③ until
the election of the k cluster centers;⑤Calculate thedissimilarity of k cluster centers in the rest of the nodes;⑥Re-update the k cluster centers by calculating
dimension of all the nodes in each cluster;⑦Repeat
steps ⑤ and ⑥ until the criterion function began toconvergence. Establish the cluster of flow chart shownin figure 1

Cluster head nodeFig.2. LEACH inter-cluster communication
Base station

Cluster head nodeFig.3. LEACH-KPPE inter-cluster communication

4. Simulation and Result Analysis
In this paper, the traditional LEACH protocol and theimproved LEACH-KPPE protocol are simulated andanalyzed in MATLAB platform. In this paper,=
100 sensor nodes are distributed in the 100×© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 202

Liu Yun-xiang, Zhang Wei, Zhou Lan-feng(a) LEACH-KPPE clustering results

100 network area. The parameters in the simulation
are shown in Table 1.
Table 1 Simulation parameters
Parameter Name
Parameter Value
Base station location(50,120)
Node initial energy
1JTransmit/Receive circuit
50 nJ·
energyPower amplification)
0.0013 pJ·( ·factorData fusion energy
5 nJ·
consumption
4000
Packet length(b) LEACH clustering result
Fig.5. The clustering simulation results of the two algorithms

Figure 6 shows the relationship between the total energy
consumption of the entire network and its operating time,
which is represented by the number of rounds.

From the above parameters, we can see that the
distance between the cluster head node and the basestation ofis in the range of 50− 130, then we canknow from formula (8) that the optimal cluster headnumber is related to the value of. The number ofvalues ranges from 1 ≤≤ 4 . According to theparameters in Table 1, the relationship between the
average energy consumption and the number of cluster
head nodes k in the network is shown in Figure 4.

Fig.6. Network energy consumption curve

Figure 7 shows the relationship between the number ofremaining nodes and their working time.

Fig.4. Relationship between cluster head node number andnetwork energy consumption

Figure 5 shows the clustering simulation results of
LEACH and LEACH-KPPE in one round. In Figure 5 (a)and (b), x represents the cluster center or thecorresponding candidate node.

Fig.7. Network remaining node relationship diagram

5. Conclusion
In this paper, the K-means ++ clustering algorithm isused to improve the traditional clustering method of
LEACH protocol, and the cluster head selection is
optimized based on the energy of node and multi-hop
mode is used between some cluster heads and basestations. LEACH-KPPE routing protocol, making it more

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 203

2.

suitable for wireless communications in coal mines. The
simulation results show that the improved protocol has asignificant improvement in terms of clustering results,
energy consumption and node survivability comparedwith the traditional LEACH protocol, thus improving the
network life cycle effectively and stability.

3.

4.

6. References1.

Sun liming, Li jianzhong. Wireless Sensor Networks [M].
Beijing: Tsinghua University Press, 2005.

5.

Wang Shu, Yang Yujie, Hu Fuping et. Theory andapplication of wireless sensor networks [M]. Beijing:
Beijing University of Aeronautics and Astronautics,
2007.
Zhang Haiyan, Liu Hong. Energy consumption balanced
routing algorithm for WSN based on K-means clustering[J]. Chinese Journal of Sensors and Actuators,2011，24(11):1639-1643.
Lin nan, Shi weihang. Optimization and Simulation ofWireless Sensor LEACH Algorithm [J] .Computer
Simulation, 2011,28 (1): 178-182.
Zhao xiulan, Xu xiulan, Li keqing. Different Clustering
Routing Algorithm Based on LEACH [J]. Application
Research of Computers, 2013,30 (3): 866-868.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 204

A New Four-Wing Chaotic System Generated by Sign Function
Hongyan Jia, Shanfeng Wang
Department of Automation, Tianjin University of Science and Technology,
1038 Dagunanlu Road, Hexi District, Tianjin300222, PR China
E-mail: jiahy@tust.edu.cn
www.tust.edu.cn

Abstract
In the paper, a new chaotic system is obtained by adding a Sign Function to a three-dimensional chaotic system.
Its some basic characteristics including the equilibrium point, phase trajectory, bifurcation diagram, Lyapunov
exponents and so on, are subsequently calculated. Moreover, the dynamic characteristics of the new chaotic
system is also analyzed with the variation of the system parameters. In the end, the paper design an analog
circuit to implement the chaotic system, the results from the circuit are consistent with those from the numericalanalysis, and thus the chaotic characteristics of the new system is verified physically. The new chaotic system
can provide a new model for engineering applications.
Keywords: four-wing chaotic system; sign function; dynamic characteristics

1. Introduction
Chaos is a new discipline developed over the past halfa century, the phenomenon of chaos is ubiquitous in our
nature. In 1963, Lorenz, a famous meteorologist, found aphenomenon of chaos when he studied meteorologicalchanges. Then, he published a paper named"Deterministic nonperiodic flow" [1]. And the curtain of
the chaotic phenomenon slowly began to open. After
more than 10 years of development, many landmark
papers have been published. For example, the concept of
strange attractors was proposed by French physicist Ruell
and Dutch mathematician Takens[2]. The theory of"Period three implies chaos" has also been proposed by
the Chinese-American Tianyan Li and Americanmathematician Yorke[3]. The scalability and universal
constants in the phenomenon of double-periodic
bifurcations are found by Feigenbaum[4]. All theseimportant discoveries have made important contributions
to the development of chaos research.
With the deepening of the research on chaotic theory, it
is gradually realized that the characteristics of themulti-wing chaotic systems are more complicated thanones of the single-wing or two-wing chaotic systems, andmulti-wing chaotic systems have a wide range ofapplications. So, there are many different research which
dedicate to construct some multi-wing chaotic systems,
and they present some different methods such as theabsolute value [5], the triangular wave sequence [6] andso on.
The multi-wing chaotic systems may be obtained byadding the multi-pulse function with the sign function tosome one-wing or two-wing chaotic systems. In the paper,a new multi-wing chaotic system is firstly constructed byadding the multi-pulse function to a three-dimensional
chaotic system. Then chaotic characteristics of the new
system is showed by analyzing phase trajectory diagram,bifurcation diagram, equilibrium point and Lyapunov
exponent approach. Finally, the new chaotic system isstudied by the real analog circuit. All the results verify
that the chaotic characteristics exist in the new system.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 205

Hongyan Jia, Shanfeng Wang

2 A New Four-Wing Chaotic System

3 The Dynamics Of The New Four-Wing Chaotic
System

2.1 Sign Function
3.1 Equilibrium point analysis

Generally, sign function can be described by=1f(x) = = 0
= −1

>0
=0
<0

(1)

where ∈ .
The sign function is a continuous function except forthe only jump break point x = 0[7] . Becausesgn(−x) = −sgn(x) , the sign function is an oddfunction.
The multi-pulse function p(x) adopted in the paper is(x) = m ∗ [sgn(x + ) − sgn(x − ) + sgn(x +) − sgn(x −) − 4](2)
where m, B = 0.8 and= 1.17 is positive
parameter. It will be used to generate multi-wing chaotic
attractors in this paper.

The multi-pulse function (2) with sign function issegmental function. So, the method of segment
calculation is used to analyze the equilibrium point of the
new chaotic system. The interval of x is as follows:
D = {( , , )| < −1.17}
D = {( , , )| − 1.17 <D = {( , , )| − 0.8 <D = {( , , )|0.8 <

̇=
̇=
̇=

+
+
+

3)

=

+
̇=
̇=
+

⎧
⎪

−4

⎨
⎪
⎩−4

−2
0
−2

X

+

< −1.17− 1.17 ≤ < −0.8
− 0.8 ≤ < 0.8
0.8 ≤ < 1.17
1.17 ≤

Equilibrium point(4)

where a, b, c, d, e and k are system parameters, x,y and z are variables, and (x) is the multi-pulse
function(2). the new system exhibits the characteristics ofthe four-wing chaotic attractor, when the initial value is(1,1,1) and a = 8, b = −1, c = −40, d = 1, e = 1 , k =−14.
The corresponding changes of multi-pulse function
p(x) will happen when selecting different free variables
m. So, in the paper, some characteristics of the new
chaotic system will be discussed when free variable mchanges.

+ sgn(x + 1.17) − sgn(x − 1.17) − 4]

p(x)

（

+ ( )

< 1.17}(5)
And thus equilibrium points of the system (4) are shownin Table 1.
Table 1 The equilibrium point of equation (4)And then add the multi-pulse function p(x) in thesecond term of system (3), and finally a new chaotic
system with sign functions is obtained. As follows:̇=< 0.8}

D = {( , , )|1.17 < }The values of multi-pulse function p(x) in every
interval of x is follow:(x) = m ∗ [sgn(x + 0.8) − sgn(x − 0.8)

2.2 A New Chaotic System
The fourth term of the 4D hyper-chaos system in Ref[8] is firstly removed to constitute a 3D chaotic system.
As follows:< −0.8}

D1

-4m

（

（

（

D2

-2m

7

8√7
7

140

16

（

7

+ 320

8√7
7

7 320 −

16

140

（

0, −

4√7
7

280

+ 320

8√7
7

320 −

8

8√7
7

+ 320

4√7
7

−

−

140

−

140

−

−

+ 320

7

8√7
7

8√7
7

16

16

0, −

−

7

+ 320

8√7
7

8√7
7
20

4√7
7

4√7
7

,0

+ 320

320 −

7 320 −

280

10

8

,0

8√7
7

+ 320）, 4√7, −8√77+ 320）, −4√7, 320 −8√77, −4√7, − 320 −

+ 320

+ 320

, 4√7,

8√7
7

）

）

, 4√7,

4√7
7+ 320）, 4√7, −4√77+ 320）© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 206

A New Four-Wing Chaotic（（

7 320 −
8

280

320 −

(0,0,0) ,

D3

0

D4

-2m

D5

-4m

4√7
7
4√7
7

−

280

−

320 −

7 320 −
8

4√7
7

4√7
7

（4√35, 4√7, 8√5 ） ,（4√35, −4√7, −8√5）,, −4√7, 320 −4√77, −4√7, − 320 −4√7
7

Fig 3 The bifurcation diagram of equation (4)In the Fig 3, the bifurcation diagram start to enter intomuch sparse condition when m = 45 . That is, thebifurcation gradually varies from dense points to several
lines, from several lines to one line, respectively.
Generally, the system will be chaotic when points inbifurcation diagram are dense. To the contrary, the system
is quasi periodicity or cycle when points in bifurcation
diagram is several lines or one line.

））（ − 4√35, −4√7, 8√5 ） ,（ − 4√35, 4√7, −8√5）

The same as D2
The same as D1

3.4 Lyapunov exponent

3.2 hase trajectory
Here, When a = 8, b = −1, c = −40, d = 1, e = 1
and k = −14. The phase trajectory of equation (3) isshown in Fig 1.
phase of x-z

250phase of y-z

250

150

150

z

200

z

200

The characteristic of chaotic motion is always analyzed
by calculating lyapunov exponent. Therefore, The
laypunov exponents of equation (4) is also computed andshown in Fig 4.

100

100

50

0
-200

50

-150

-100

-50

0

50

100

150

200

0
-200

250

-150

-100

-50

x

0

50

100

150

200

y

Fig 1 The phase trajectory of equation (3)When a = 8, b = −1, c = −40, d = 1, e = 1 , k =−14 and m = 8, the phase trajectory of equation (4) isshown in Fig 2.
phase of x-z

phase of y-z

150

100

100

50

50

z

z

150

0

-50

0

Fig 4 Lyapunov exponent of equation (4)If the biggest one of lyapunov exponents is larger thanzero, the equation will show the character of chaos. So, in
Fig 4, chaotic dynamics of system (4) can be observed
when m ∈ (0,44) , and periodic dynamics of system (4)can be obtained when m > 44. The phenomenon canalso be described by phase trajectory, as shown in Fig
5-6.

-50

-100

phase of x-z

-100
150

-100

-50

0

50

x

100

150

-150
-120

-100

-80

-60

-40

-20

0

20

40

60

80

y

z

Fig 2 The phase trajectory of equation (4)Comparing chaotic attractors in Fig 1 with ones in Fig
2, a conclusion will be obtained that multi-wing chaos
can be generated by adding multi-pulse function withsign function to equation (3).

phase of y-z

150

100

100

50

50

z

-150
-150

0

-50

0

-50

-100

-100

-150
-150

-100

-50

0

50

100

-150
-150

150

-100

-50

0

x

50

100

150

y

Fig 5 Chaotic dynamics of equation (4), m = 25
phase of x-z

0

-10

-10

Bifurcation diagram can indicate the performance of
chaotic system with the variation of parameter. The
bifurcation diagram of equation (4) is shown in Fig 3.

z

-5

z

3.3 Bifurcation diagram

phase of y-z

0

-5

-15

-15

-20

-25

-20

0

5

10

15

x

20

25

30

-25
-18

-16

-14

-12

-10

-8

-6

-4

y

Fig 6 Periodic dynamics of equation (4), m = 55

4 Analog Circuit for The New Chaotic System

The analog circuit for the new chaotic systems isdesigned by operational amplifier, multiplier, resistor and© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 207

Hongyan Jia, Shanfeng Wang

capacitance, as showed in Fig 7. The analog circuit for
multi-pulse function is consisted of operational amplifier
and resistor, as showed in Fig 8. The experiment results is
obtained, as showed in Fig 9. The results are inagreement with the results of the numerical analysis,
which physically proves the system (4).

5 Conclusion

Fig 7 The analog circuit for system (4)References

Fig 9 Experimental results by using the Multisim.

In the paper. Firstly, the basic properties of the signfunction is introduced. Secondly, a multi-pulse function
p(x) is made up of sign function and the new chaotic
system with multi-pulse function is designed. Third, by
analyzing the dynamic characteristics of the system suchas phase trajectory, bifurcation diagram and Lyapunov
exponent, the chaotic characteristics of the new system
are verified. Finally, it can be concluded that themulti-pulse function can produce four-wing chaotic
attractor in the chaotic system. Because the chaotic
system with sign function can generate complex characterof chaos, it is meaningful for the chaotic system with signfunction to be used in encryption communication.

[1]
[2]

[3]
[4]

[5]

[6]

Fig 8 The analog circuit for multi-pulse function
[7]

[8]

(a)

Lorenz E N. Deterministic nonperiodic flow. Journalof the Atmospheric Sciences, 1963, 20(2): 130-141.
Ruell D, Takens F. On the nature of turbulence. Com
munications in Mathematical Physics, 197 1, 20(3): 1
67-192.
Li T Y, Yorke J A, Period three implies chaos. American Mathematical Monthly, 1975, 82(10): 985-992.
Feigenbaum M J. Quantitative universality for a classof nonlinear transformations. Journal of Statical Phy
sics, 1978, 19(1): 25-52.
Liu Jin-mei1, Qiu Shui-sheng2. A New Three-Dimens
ion Chaotic System with an Absolute Item. Journal of South China University of Technology, 2013, 04(4):137-141.
Yu Si-Min. Circuit implementation for generating three-dimensional multi-scroll chaotic attractors via triang
ular wave series. ACTA PHYSICA SINICA, 2005, 04(4): 1500-1509.
Lü Ensheng, Sun caiyun. Design of Chua’s circuit ba
sed on sign function and application thereof. Chena s
cience paper, 2014, 1, 9(1): 37-39.
Zhan Kai, Jiang Wengang. A novel four-wing hyper-chaos system and its application in image encryption.
Computer Engineering and Applications, 2016, 3.

(b)(a) x − z phase plane, (b) y − z phase plane.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 208

A three-dimensional chaotic system generating single-wing
and two-wing chaotic attractors
Hongyan Jia, Yongjun Wu

Department of Automation, Tianjin University of Science and Technology,
1038 Dagunanlu Road, Hexi District, Tianjin300222, PR China
E-mail: jiahy@tust.edu.cn
www.tust.edu.cn

Abstract
In this paper, a three-dimensional chaotic system is proposed based on a simple 3-D autonomous system by adding alinear piecewise function. It is very interesting that the new three-dimensional chaotic system can generate
single-wing or two-wing chaotic attractor with variation of parameter. Several basic characteristics of the system,
such as bifurcation diagram, phase orbits, and Lyapunov exponents are given to investigate different chaotic motions
for the new system. The new system is found to be chaotic in a wide parameter range, and to show many complex
dynamical behaviors. That is, the results obviously show the system is chaotic and its dynamics are very complex.
Keywords: autonomous system; single-wing attractor; two-wing attractor; Lyapunov exponents; bifurcation diagram.

1. Introduction
In recent decades, many chaotic systems have been
proposed since Lorenz discovered a simple
three-dimensional smooth autonomous chaotic system in
1963 [1]. It is well known that chaotic systems can showcomplex dynamic characteristics that can be used inmany application fields such as engineering [2], image
encryption [3], secure communications [4] etc. Therefore,
many scholars and researchers proposed and studied
some chaotic systems, such as Chen system [5], Lüsystem [6], Qi system [7], and so on [8,9].
Generally, the structure and characteristics of thechaotic systems can be changed when introducing linear
piecewise function. Therefore, by the method of
introducing linear piecewise function, researchers havedesigned many complex chaotic systems. For example, in[10], the paper introduced the method of designing
chaotic system by using piecewise linear function, anddiscussed the computer simulation and circuit
implementation in detail. In addition, the application of
the nondominated sorting genetic algorithm to optimize

two characteristics of multi-scroll chaotic oscillators wasintroduced in [11]. Where the linear piecewise function isused to generate the two multi-scroll chaotic oscillators.
In this paper, a new chaotic system is designed byadding a linear piecewise function to the second equationof a simple 3-D autonomous system [12]. This system
can show some very complex dynamic characteristics
generating single-wing or two-wing chaotic attractor
when varying the system parameters. In the following
sections, bifurcation diagram, phase orbits, and Lyapunov
exponents of the system are calculated to analyze thedynamic characteristics of the system. The numericalresults analysis clearly show that this system is chaotic
and its dynamics are very complex.
2. The 3-D chaotic system
Considering the simple three-dimensional autonomous
system [12]:̇ =− + +
̇=
− +
̇=
−ℎ

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 209(1)

Hongyan Jia, Yongjun Wu

Where the parameters a, b, c, d and h are real constants.
Here, a linear piecewise function is introduced to thethree-dimensional autonomous system above. This
piecewise function will substitute for the linear term z ofthe second equation, and the new chaotic system isobtained as following.

|
|

= 1+2

(

−

)(| +

|−| −

|
|

|)

(3)

(4)

3. Dynamical characteristics of the chaotic system

For the chaotic system (2), when the parameters a, b, c, d,h andare taken different values, the system canshow different kinds of chaotic states. By analyzing, thesystem is found to show a single-wing chaotic attractor
when the parameters = 37, = 25, = 13, = 6,
ℎ = 8,= 2 and two-wing chaotic attractor when theparameters= 37 ,= 25 , = 16 ,= 6, ℎ = 8
and= 0.5, respectively, as shown in Fig.1 and Fig.2.
In the following section, the Lyapunov exponents and thecorresponding bifurcation diagram of system (2) are
given to analysis the complex chaotic dynamics.
5
4
15
3
10

y

z

2
1

5
0
0
5

-1

30

-2

20

0

10
0

-5

0

5

10
x

15

20

20

2

15
z

0
y

10

-2

5

-4

0
10

-6

25

y

-5

-10

x

(a) x-y plane(b) x-y-z plane
Fig.1 Single-wing attractor of system (2)

40
20

0

-10
-40

(2)

Where
=−
,
= 0, 1, 2, ⋯ , = 0, 1, 2, ⋯ ,= 1, 2, ⋯, equation (4) is the turning points formula ofthe linear piecewise function. Here, we set= 1, =
1, = 0.5 , so the linear piecewise function can be
simplified as equation (5).
( )=− (| + 0.5| − | − 0.5|)
(5)

-3
-10

25

4

-8

Where (z) is the linear piecewise function describedas formula (3):
+ 0.5

6

5

̇ =− + +̇=− + ( )
̇=
−ℎ

( )=

8

0

-5
-30

-20

-10

0
x

10

20

30

40

y

-20
-10

-40

x

(a) x-y plane(b) x-y-z plane
Fig.2 Two-wing attractor of system (2)4. Lyapunov exponents and bifurcation diagram
analysis
Now let = 37, = 25, = 16, = 6 , ℎ = 8,
and select= 2, and= 0.5, the chaotic dynamics
of this system are investigated by means of Lyapunov
exponents analysis and bifurcation diagram analysis,
respectively.
4.1 Lyapunov exponents and bifurcation diagram
analysis when=

Here, dynamics analysis of the system (2) is firstly done
when the parameters = 37, = 25,= 2, = 6,ℎ = 8 and vary .
The Lyapunov exponents and the corresponding
bifurcation diagram of the system with variation ofparameter c are shown in Fig.3 (a) and Fig.3 (b), where= 37 ,= 25 ,=6, ℎ=8,= 2 and∈[0, 30]. The basic dynamics of the chaotic system (2)can be summarized by the Lyapunov exponents andbifurcation diagrams. By analyzing Lyapunov exponents
in the Fig.3 (a), when ∈ [1.8, 9.3), the system (2)exhibits periodic dynamics with the biggest Lyapunov
exponent of the system (2) being zero. when ∈ [9.3,25.2), the biggest Lyapunov exponent of the system (2) isfound to positive, implying that the system is chaotic.
In addition, by analyzing the corresponding bifurcation
diagram in Fig.3 (b), the system (2) also exhibits periodic
dynamics when ∈ [1.8, 9.3), here, a period-2 orbit isgiven to show its periodic dynamics when = 8, asshown in Fig.4 (a) and Fig.4 (b). And this system also
becomes chaotic when ∈ [9.3, 25.2), and its dynamics
very complex. A single-wing chaotic attractor is foundwhen the parameter = 16, as shown in Fig.4 (c) and
Fig.4 (d). And a double-wing chaotic attractor is observed
when the parameter = 20, as shown in Fig.4 (e) and© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 210

A three-dimensional chaotic System

Fig.4 (f). Moreover, when increasingin the range of∈ [25.2, 30), there are just several or two lines in thebifurcation diagram. The system (2) shows period-2 orbit
when = 27, as shown in Fig.4 (g) and Fig.4 (h).
5

4

3

YY

Lyapunov exponents

0

-25
-20

-40

-40
-45

0

5

10

15
c

20

25

-60

30

0

5

10

15
c

20

25

30

(a) Lyapunov exponents(b) bifurcation diagrams
Fig.3 Lyapunov exponents and bifurcation diagrams ofsystem (2) with = 37, = 25, = 6, ℎ = 8,=
2 and ∈ [0, 30].
4
3.5
3

8

y

2
z

6
4

1

2

0.5

0
4

0

15

2

-0.5

10
5

0
-1
-4

-2

0

2

4

6
x

8

10

12

14

16

0
-2

y

(a) x-y plane-5x(b) x-y-z plane

5
4
15
3
10

y

z

2
1

5
0
0
5

-1

30

-2

20

0

10
0

-3
-10

-5

0

5

10

15

20

25

-5

y

x

(c) x-y plane-10x(d) x-y-z plane

15

30

10

25
20
15

y

z

5

10
0

5
0
20

-5

50

10
0

0
-10
-50

-40

-30

-20

-10

0
x

10

20

30

(e) x-y plane

40

50

z
-5

0

5
x

y

-10

-50

x

(f) x-y-z plane

10

15

20

y

0
-4

-10

x

(g) x-y plane(h) x-y-z plane
Fig.4 The orbital modes of system (2) with = 37, =25, = 6, ℎ = 8,
= 2. (a) period-2 orbit when =8, (b) period-2 orbit when = 8, (c) single-wing chaotic
attractor when = 16, (d) single-wing chaotic attractor
when = 16, (e) double wing chaotic attractor when
= 20, (f) double wing chaotic attractor when = 20,(g) period-2 orbit when = 27, (h) period-2 orbit when
= 27.
4.2 Lyapunov exponents and bifurcation diagram

10

1.5

20
10
-2

analysis when

12

2.5

2
0

-3
-10

20

-35

6
4

-2

-15

-30

8

-1

40

-20

10

y
0

0

-10

12

1

60

-5

14

2

= .

Next, the dynamics behavior of the system (2) is analyzed
when the parameters = 37, = 25,= 0.5, =6, ℎ = 8 and vary .
When varying parameter c, the Lyapunov exponents
and the corresponding bifurcation diagram of the system(2) are shown in Fig.5(a) and Fig.5(b), where = 37,= 25, = 6, ℎ = 8,
= 0.5 and ∈ [0, 30]. By
analyzing the Lyapunov exponents in Fig.5(a), it is foundthat the system shows periodic dynamics when ∈[0, 4.5). And in the regions ∈ [4.5, 21.8) and ∈[22.2, 25.5), the system (2) exhibits chaos with just one
positive Lyapunov exponent. When ∈ [25.5, 30], thesystem shows periodic motion again with the biggest
Lyapunov exponent of the system (2) being zero.
Considering the bifurcation diagram in Fig.5(b), in theregion ∈ [0, 4.5), the system shows the characteristicof period orbit and it shows a period-2 orbit when =28 , as shown in Fig.6(a) and Fig.6(b). When ∈ [4.5,21.8) and ∈ [22.2, 25.5) , the system (2) exhibits
complex chaotic characteristics. In this case, a two-wingchaotic attractor is found when = 16 and it is given in
Fig.6(c) and Fig.6 (d). As c increases in the region ∈[25.5, 30], the system enters into periodic dynamics andit exhibits period-1 orbit when = 29 , as shown in
Fig.6(e) and (f).

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 211

Hongyan Jia, Yongjun Wu

10found that the system can generate a single-wing chaotic
attractor or two-wing chaotic attractor by varying
parameter. Some basic properties of the chaotic
system were analyzed by means of Lyapunov exponents
and bifurcation diagrams analysis in detail. The
numerical results confirm that this chaotic system ischaotic and it can show complex dynamic characteristics.

50
40
30
20

-10

10
-20

0

YY

Lyapunov exponents

0

-10
-30

-20
-30

-40

-40
-50

0

5

10

15
c

20

25

-50

30

0

5

10

15
c

20

25

30

(a) Lyapunov exponents(b) bifurcation diagrams
Fig.5 Lyapunov exponents and bifurcation diagrams ofsystem (2) with = 37, = 25, = 6, ℎ = 8，=
0.5 and ∈ [0, 30].
4
3

10

2

8
1
6
z

0
y

4
-1
2
-2
0
5

-3
-4

0

-5
-15

-10

-5

0

5

10

-5

y

x

(a) x-y plane

-15

-10

5

0

-5

10

x

(b) x-y-z plane

8
6

25

4

20

2

15
z

0
y

10

-2

5

-4

0
10

-6

5
-8
-10
-40

40
20

0

0

-5
-30

-20

-10

0
x

10

20

30

40

-10

y

(c) x-y plane

-20
-40

x

(d) x-y-z plane

3

2

12
11

y

1

z

10

0

9
8
7

-1

6
4
-2

2

20
10

0
-3
-15

0

-2
-10

-5

0
x

5

10

15

y

-10
-4

-20

x

(e) x-y plane(f) x-y-z plane
Fig.6 The orbital modes of system (2) with = 37, =25, = 6, ℎ = 8,
= 0.5. (a) period-2 orbit when
= 28, (b) period-2 orbit when = 28, (c) two-wingchaotic attractor when = 16 , (d) two-wing chaotic
attractor when = 16, (e) periodic orbit plane when
= 29, (f) period-1 orbit when = 29.
5. Conclusion

References
1.

Lorenz E N, Deterministic non-periodic follow, Journal of
Atmospheric Sciences. 20(2) (1963) 130-141.
2. Ihsan Pehlivan, Akif Akgul, Irene Moroz, Sundarapandian
Vaidyanathan, A new four-scroll chaotic attractor and its
engineering applications. Optik, 127 (2016) 5491–5499.
3. Leyuan Wang, Hongjun Song, Ping Liu, A novel hybrid
color image encryption algorithm using two complex
chaotic systems. Optics and Lasers in Engineering, 77(2016) 118–125.
4. Xiangjun Wu, Hui Wang, Hongtao Lu, Modified
generalized projective synchronization of a new
fractional-order hyperchaotic system and its application tosecure communication. Nonlinear Analysis: Real World
Applications, 13 (2012) 1441–1450.
5. Chen G R, Ueta T, Yet another chaotic attractor,
International Journal of Bifurcation and Chaos. 9 (1999)
1465-1466.
6. Jinhu Lu, Guanrong Chen, A new chaotic attractor coined,
International Journal of Bifurcation and Chaos. 12 (2002)
659-661.
7. Guoyuan Qi, Guanrong Chen, M. A. van Wyk, B. J. van
Wyk, A four-wing chaotic attractor generated from a new
3-D quadratic autonomous system. Chaos, Solitons andFractals. 38 (2008) 705-721.
8. Itoh, Makoto, Chua, Leon O, Meristor oscillators,
International journal of bifurcation and chaos, 11(18)(2008) 3183-3206.
9. Guoyuan Qi, Barend Jacobus van Wyk, Michael Antonie
van Wyk, A four-wing attractor and its analysis, Chaos,
Solitons and Fractals, 40 (2009) 2016–2030.
10. Jinhu Lu, Guanrong Chen, Generating multiscroll chaotic
attractors: Theories, methods and applications.
International Journal of Bifurcation and Chaos, 4(16)(2006) 775-858.
11. Luis Gerardo de la Fraga · Esteban Tlelo-Cuautle,
Optimizing the maximum Lyapunov exponent and phasespace portraits in multi-scroll chaotic oscillators,
Nonlinear Dyn, 76 (2014) 1503–1515.
12. Sara Dadras, Hamid Reza Momeni, A novel
three-dimensional autonomous chaotic system generating
two, three and four-scroll attractors. Physics Letters A,
373 (2009) 3637-3642.

In this paper, a three-dimensional chaotic system isdesigned by introducing a linear piecewise function. It is© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 212Circuit Implementation of a New Fractional-Order Hyperchaotic System
Xuyang Wu, Hongyan Jia, Ning Bai, Weibo Jia

Department of Automation, Tianjin University of Science and Technology,
1038 Dagunanlu Road, Hexi District, Tianjin 300222, PR China
E-mail: jiahy@tust.edu.cn
www.tust.edu.cn

Abstract
In the paper, some basic dynamic properties of a new fractional-order hyperchaotic system were firstly investigated, suchas the phase trajectory, bifurcation diagram, Lyapunov exponent etc. Then the paper designed an analog circuit to implement the fractional-order hyperchaotic system. The results from the circuit were consistent with those from the numericalanalysis, and thus the hyperchaotic characteristics of the new fractional-order system are verified physically. It is very
important to implement fractional-order hyperchaotic systems with more complicated dynamics for theoretical researchand practical application. The new hyperchaotic system will provide a new model for engineering applications.
Keywords: fractional-order; hyperchaotic; circuit implementation; dynamical characteristics

1. Introduction
In the last century, Rӧssler proposed the first hyperchaotic system [1]. Since then, the hyperchaotic system has
attracted more and more attention owning to its abundantand complex dynamic characteristics. Extensive andin-depth researches have been carried out, such as themodified hyperchaotic system, circuit implementation of
hyperchaotic systems [2-4], especially in synchronous
control [5-7].
In 1995, Hartley studied the fractional-order Chua system in which the chaotic attractor was found [8]. Soonchaotic attractors in the fractional-order Lorenz systemwere also confirmed [9] and then fractional-order systems
have been investigated and developed in more and more
fields [10-13].
At present, most researchers focus on the
low-dimensional chaotic system, while there are few researches on the higher-dimensional and hyperchaotic
system. However, many real systems in nature and soci-ety often are higher-dimensional and hyperchaotic.
Therefore, it is very interesting to study fractional-order
hyperchaotic systems with more complicated dynamics
for theoretical research and practical application.
For the fractional-order differential equation, numerical methods used commonly are the frequency domain
approximation or Adams-Bash-Moulton algorithm. In
this paper, with the frequency domain approximation, westudy some basic dynamical characteristics of afour-dimensional hyperchaotic system, such as the phasetrajectory, Lyapunov exponent and bifurcation diagram.
And then we design a circuit to implement the
four-dimensional fractional-order hyperchaotic system.
These will provide technical support for engineering applications.
2. Four-Dimensional Hyperchaotic System
Recently, a new four-dimension integral-order hyper-chaotic system [14] based on the Dadras-Momeni（D-M）system [15] was proposed by Nik, Van Gorder et© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 213

Xuyang Wu, Hongyan Jia, Ning Bai, Weibo Jia

al. which is given by𝑥̇ = 𝑦 − 𝑎𝑥 + 𝑏𝑦𝑧,𝑦̇ = 𝑐𝑦 − 𝑥𝑧 + 𝑧 − 𝜔,{𝑧̇ = 𝑑𝑥𝑦 − ℎ𝑧,𝜔̇ = 𝑝𝑥 + 𝑘,

𝑑 𝛼 𝑓(𝑡)𝐿[(1)where 𝑥, 𝑦, 𝑧, 𝜔 are state variables and 𝑎, 𝑏, 𝑐, 𝑑, ℎ, 𝑝, 𝑘
are system parameters.
When the parameters are chosen as:
𝑎 = 36,𝑏 = 0.4,
𝑑 = 1,ℎ = 5.07,
𝑝 = 5,{ 𝑘 = 0.4,
the system is hyperchaotic and the hyperchaotic strange
attractor is shown in Fig.1.

] = 𝑠 𝛼 𝐿[𝑓(𝑡)].

𝑑𝑡 𝛼

(4)

Reference [1] provides an approximation of 1/𝑠 0.9
with an error of about 2 dB as follows:12.2675(𝑠+1.292)(𝑠+215.4)≈ (𝑠+0.01292)(𝑠+2.154)(𝑠+359.4) .
𝑠0.9

(5)

We use the approximation to analyze the system (2) byusing the Matlab. Fig.2 shows the fractional-order hyperchaotic phase diagram with𝑎 = 36,𝑏 = 0.4,
𝑑 = 1,ℎ = 5.07,
𝑝 = 5,{ 𝑘 = 0.4,
which indicates clearly that hyperchaotic behaviors exist
in the above fractional-order system.

180
160
140
120

100

110

80

100

z

100

90

60

80

80

40

60

70
z

y

20

40

0

20

-20

60
50

0
-80

40

-40

-60

-40

-20

0
x

20

40

60

80

30

-60
-80
-40

Figure 1 Hyperchaotic attractor of the system (1).

20

-30

-20

-10

0

10

20

30

40

10
-40

50

-30

-20

-10

0

x

10

20

30

40

50

x

Figure 2 Attractors in fractional-order system (2). (a)x − y phase plane, (b) x − z phase plane.

3. Fractional-Order Four-Dimensional
Hyperchaotic System

𝑑𝛼𝑥
𝑑𝑡
𝑑𝛼𝑦
𝑑𝑡
𝑑𝛼𝑧
𝑑𝑡
𝑑𝛼𝜔

= 𝑦 − 𝑎𝑥 + 𝑏𝑦𝑧,= 𝑐𝑦 − 𝑥𝑧 + 𝑧 − 𝜔,
= 𝑑𝑥𝑦 − ℎ𝑧,(2){ 𝑑𝑡 = 𝑝𝑥 + 𝑘,

In order to further study the chaotic characteristics ofthe system (2), we obtain a bifurcation diagram by altering parameter ℎ, which is shown in Fig.3. It can be seenfrom the figure that the system discretization is most obvious when ℎ is in the range of 5-12.
In addition, we further consider the Lyapunov exponents of the system (2), which is shown in Fig.4. The
result shows that the biggest two Lyapunov exponents are
positive, which satisfies the necessary conditions for thehyperchaotic system.

where 𝑥, 𝑦, 𝑧, 𝜔 are state variables and 𝑎, 𝑏, 𝑐, 𝑑, ℎ, 𝑝, 𝑘
are system parameters.
We utilize the Riemann-Liouville definition of fractional-order differentiation:𝑑 𝛼 𝑓(𝑡)𝑑𝑡=𝑑𝑛𝑡 𝑓(𝜏)1𝑑𝜏,∫Γ(𝑛−𝛼) 𝑑𝑡 𝑛 0 (𝑡−𝜏)

80
60
40
20

YY

Basing on the integer-order system mentioned above,
we introduce a fractional-order hyperchaotic H-M system:

0
-20

(3)

-40
-60
-80

its Laplace transform is:

0

2

4

6

8

10
h

12

14

16

18

20

Figure 3 Bifurcation diagram of the system (2).
© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 214Circuit Implementation of aagreement with the numerical analysis, which further
confirms the existence of the four-dimensional fractional-order hyperchaotic system. The work in this paper may
offer a model for engineering applications.

20

10

0

LE

-10

-20

-30

-40

-50

0

2

4

6

8

10
h

12

14

16

18

20

Figure 4 Lyapunov exponents of the system (2)4.

Circuit Design of the Fractional-Order System

In this section, utilizing the frequency domain approximation, we design the circuit of the system (2) by using
the resistance, capacitance, analog operational amplifier
LF347BN and multiplier AD633JN. The amplitude ofvoltage operational amplifier is ±13.5V that is significantly less than the range of the system. Therefore, we
make the voltage to be one tenth of the value that is anormal value in the system. We choose 𝑐1 = 𝑐4 = 𝑐7 =𝑐10 = 0.52𝜇𝐹 , 𝑐2 = 𝑐6 = 𝑐9 = 𝑐12 = 0.73𝜇𝐹 , 𝑐3 =𝑐5 = 𝑐8 = 𝑐11 = 1.1𝜇𝐹2 , 𝑅2 = 𝑅5 = 𝑅6 = 𝑅14 =
𝑅15 = 𝑅16 = 𝑅17 = 𝑅22 = 𝑅23 = 𝑅25 = 𝑅26 =𝑅31 = 𝑅32 = 𝑅37 = 𝑅38 = 10𝐾Ω ,𝑅18 = 𝑅24 =
𝑅30 = 𝑅36 = 20𝐾Ω , 𝑅19 = 𝑅27 = 𝑅33 = 𝑅39 =1.5𝑀Ω, 𝑅20 = 𝑅28 = 𝑅34 = 𝑅40 = 61.54𝐾Ω, 𝑅21 =𝑅29 = 𝑅35 = 𝑅41 = 2.5𝐾Ω , 𝑅1 = 0.278𝐾Ω , 𝑅3 =0.25𝐾Ω , 𝑅4 = 0.5𝐾Ω , 𝑅7 = 𝑅9 = 0.1𝐾Ω , 𝑅8 =1.9𝐾Ω , 𝑅10 = 2𝐾Ω , 𝑅11 = 250𝐾Ω , 𝑅12 = 14𝐾Ω ,𝑅13 = 1𝐾Ω. The circuit design of the system is shown in

Figure 5 Circuit implementation of the system (2)Fig. 5.
This circuit is designed to implement the system (2),and the experimental results obtained by the Multisim are(a)shown in Fig.6. Obviously, the experimental results are
consistent with the results of the numerical analysis,
which physically proves the system (2).

5.

(b)

Conclusions

In this paper, we proposed a fractional-order hyperchaotic
H-M system based on the integer-order H-M system.
Some basic dynamical characteristics of the proposedsystem, such as the phase trajectory, Lyapunov exponent,
and bifurcation diagram, were analyzed respectively byusing the Matlab. The results show that the system has
more complicated dynamic characteristics, which will
enrich the research content of the fractional-order hyperchaotic system. What’s more, an analog circuit was designed to realize the four-dimensional fractional-order
hyperchaotic system, and the experimental results are in

Figure 6 Experimental results by using the Multisim. (a)x − y phase plane, (b) x − z phase plane.

Acknowledgements
This work was supported by the Undergraduate Training Program for Innovation and Entrepreneurship ofTianjin (201610057058).

References
1.

Rӧssler, An equation for hyperchaos, Phys. Lett. A. 71(2-3)© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 215

Xuyang Wu, Hongyan Jia, Ning Bai, Weibo Jia

2.
3.

4.

5.

6.

7.

8.

9.

(1979) 155-157.
G Wang, X Zhang, Y Zheng, Y Li, A new modified hyperchaotic Lȕ system, Phys. A. 371(2) (2006) 260-273.
G Y Qi, M A V Wyk, B J V. Wyk, G R Chen, A new hyperchaotic system and its circuit implementation, Chaos
Soliton. Fract. 40(5) (2009) 2544-2549.
Zarei A, Tavakoli S, Hopf bifurcation analysis and ultimate bound estimation of a new 4-D quadratic autonomous hyper-chaotic system, Appl. Math. Comput. 291(2016) 323-339.
S Zheng, G G Dong, Q S Bi, A new hyperchaotic system
and its synchronization, Appl. Math. Comput. 215(9)(2010) 3192-3200.
S Q Pang, Y J Li, A new hyperchaotic system from the Lȕ
system and its control, I. ComDut. ADDl. Math. 235(8)(2011) 2775-2789.
Luo RZ, Zeng YH, The control and synchronization of aclass of chaotic systems with output variable and external
disturbance, J.Comput. Nonlin. Dyn. 11(5) (2016) 051011.
Hartley T T, Lorenzo C F, Qammer H K, Chaos in a fractional order Chua’s system, IEEE Trans. Circuits Syst. I:
Fundamental Theory and Applications. 42(8) (1995)
485-490.
Grigorenko I, Grigorenko E, Chaotic dynamics of the

10.

11.

12.

13.

14.

15.

fractional Lorenz system, Phys. Rev. Lett. 91(3) (2003)
034101/1-034101/4.
Yang Q G, Zeng C B, Chaos in fractional conjugate Lorenz system and its scaling attractors, Commun. Nonlinear.
Sci. 15(12) (2010) 4041-4051.
Jia H Y, Chen Z Q, Xue W, Analysis and circuit implementation for the fractional-order Lorenz system, Acta
Phys. Sin. 62(14) (2013) 140503/1-140503/7 (in Chinese)
Yang JH, Sanjuan, MAF , Liu HG, Cheng G, Bifurcationtransition and nonlinear response in a fractional-ordersystem, J. Comput. Nonlin. Dyn. 10(6) (2015) 061017.
Boulkroune A, Bouzeriba A , Bouden T, Fuzzy generalized projective synchronization of incommensurate fractional-order chaotic systems, Neurocomputing. 173 (2016)606-614.
Dadras S, Momeni HR, A novel three-dimensional autonomous chaotic system generating two, three andfour-scroll attractors, Phys. Lett. A. 373(40) (2009)3637-3642.
Nik HS, Van Gorder RA, Gambino G et al. The chaotic
Dadras-Momeni system: control and hyperchaotification,
Ima. J. Math. Control I. 33(2) (2016) 497-518.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 216

Estimation and Categorization of Errors in Error Recovery
Using Task Stratification and Error Classification
Akira Nakamura, Kazuyuki Nagata

Intelligent Systems Research InstituteNational Institute of Advanced Industrial Science and Technology (AIST)
Central 2, 1-1-1 Umezono, Tsukuba, Ibaraki, 305-8568 Japan
Kensuke Harada

Robotic Manipulation Research Group
Systems Innovation Department
Graduate School of Engineering Science, Osaka University
1-3 Machikaneyama, Toyonaka 560-8531, Japan
Natsuki Yamanobe

Intelligent Systems Research InstituteNational Institute of Advanced Industrial Science and Technology (AIST)
Central 2, 1-1-1 Umezono, Tsukuba, Ibaraki, 305-8568 Japan
E-mail: a-nakamura@aist.go.jp, k-nagata@aist.go.jp,
harada@sys.es.osaka-u.ac.jp, n-yamanobe@aist.go.jp
www.aist.go.jp

Abstract
We have proposed an error recovery method using the concepts of task stratification and error classification in which
errors are classified based on an estimated cause into several categories such as modeling errors and planning errors.
When an error is classified correctly, the possibility increases that the most suitable recovery will be performed. This
paper describes our procedure for the categorization of errors.
Keywords: error recovery, task stratification, error classification, manipulation, planningIn error recovery, judgment of an error is performed
during the execution of a process by the system. In our
method, classification of errors that have occurred is
performed based on an estimated cause. Specifically,
errors are classified into several categories such as
sensing errors, modeling errors, planning errors andexecution errors. When an error is classified correctly,
the possibility increases that the most suitable recovery
will be performed. In this paper, we describe a procedure
for the categorization of errors.
A concept of error recovery is described in Section 2.
A method of error classification and the recovery process

1. Introduction
Error recovery is an important research theme for the
manipulation tasks of plant maintenance and industrial
production robots.1-4 However, systematical methods oferror recovery have not yet appeared. We have proposed
an error recovery method using the concepts of task
stratification and error classification.5, 6 A highly reliable
system expression is derived by an analysis of an error
and an estimate of the cause in our error recovery
technique, and the task is continued based on theexpressions of the corrected system.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 217

Akira Nakamura, Kazuyuki Nagata, Kensuke Harada, Natsuki Yamanobe

in which the classification is used are proposed in Section
3 and 4, respectively.

If a failure occurs in this main part, the processadvances to the recovery part (Fig. 1). The basic flow inthis recovery part is as follows: the cause of the error isestimated, the error is classified, the system is corrected,
and re-execution is performed using the corrected systemwith improved reliability.

2. Concept of Error Recovery
The basis of the error recovery technique we use isdescribed in this section. The target tasks are robot
applications in industrial factories and power generation
facilities that mainly involve assembly and parts
replacement. In recent years, as robots have been playing
increasingly active roles in the daily life of humans, suchtasks as cleaning, washing and cooking have also becomea target of our error recovery system.
In this paper, our technique of error recovery with theconcepts of both task stratification and error
classification as shown in Ref. 5 is used. We proposed a
new type of error recovery using both forward andbackward pass in Ref. 6.
Figure 1 shows the basic concept of our error
recovery method. The main part comprises tasks
performed by the robot that are basically elements withsequences of sensing, modeling, planning and execution.
This main part is hierarchized from the main work
objective to a bottom layer in our method, but iscomposed of process flows of sensing, modeling,planning and execution for each hierarchy.

3. Cause of Error
We have explained that the tasks of the robot consistof the process flows of sensing, modeling, planning andexecution. Therefore, the following four types of errors
are considered and the Class number is assigned
respectively. For simplification, the cause of the error isset to only one in this paper.
(Class 1) Execution Error
This error occurs from a problem with the machine
and the task can be achieved without failing usually if thesame execution is repeated many times. When a problem
with a mechanism is indicated specifically, the cause of
the most typical error is a backlash of a gear. The
clearance is altered by abrasion. There are many causessuch as changes in temperature and humidity, vibration,
bending, aged deterioration, and decreased lubricant. It is
desirable to identify the cause through experience andartificial intelligence.
(Class 2) Planning Error
This error occurs when the expression of the systemused in the computer software does not express a realsystem strictly. The cause of the error is mostly a setting
mistake of a threshold and a parameter in planning, andwhen that mistake is corrected, the task should succeed.
A correct system expression is also derived by specifying
the part to be corrected from an analysis of the error.
(Class 3) Modeling Error
This error occurs when the geometry model used in
the computer software does not express the real object
exactly. Therefore, the task may succeed if the model iscorrected and the task is performed again. When themodel used is a simplification of the real object, thenecessary part is changed to express the model moreexactly. A model appropriate for performing a moreaccurate task should be derived by performing it
repeatedly.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 218

Estimation and Categorization of(Class 4) Sensing Error

4.

This error occurs due to an error in sensing andinsufficient calibration, and when that is corrected andthe task is re-executed, the task should succeed. A camera
parameter will be generally corrected. In many cases, it
becomes necessary to stop the execution task and makethe correction.

In this section, we will consider how to take passes
based on the possibility of the error classification.
(Case 1) Estimation of the cause of the error is notpossible.
The error recovery is performed by completely
regarding the error cause as a virtual one. In this case, as
shown in the process of the recovery in the flow chart in
Fig. 3, the cause is first regarded as an execution error of(Class 1) and the task is merely repeated. When the error
status continues even after repeating more than one time,the error is then regarded as a planning error of (Class 2).
When numerical values for various parameters arechanged, it is possible that something to improve may befound. When that is found and corrected, the task is
performed and the error status is checked. When the error
status is removed, it is possible to assume that a planningerror was the cause. If the error recovery does not succeed,
the cause is estimated as (Class 3) and (Class 4) and asimilar procedure is performed successively.

The correction is performed based on the cause of theerror specified by the classification, and the task is
performed again using the corrected system withimproved reliability as shown in the flow chart (Fig. 2)
depicting a task of loosening a screw using screwdriveras an example.

Sensing
Modeling
Planning
Execution(Case 2) Estimation of the cause of the error ispossible to some extent.

Move-to-initialize skillerror

In this case, the cause is narrowed down to someextent from experience and it can be chosen from thosecorrect

Class 4error

Class 3

Sensing

correct･ Execution error→ Class 1(0)task(3)

Rota te-to-bite skill
Sensing
Modeling

Confirmation

error

Execution

Confirmation

Planning

task1･ Sensing error→ Class 4

Rotate-toloosen/extra ct
skill (without
interruption)
Sensing
Modeling･ Planning error→ Class 2･ Modeling error→ Class 3

correct(0)task(4)Modeling

task2

error
error

Class 4

Execution

Class 3

Sensing
Modeling

Confirmation

Class 1Minimum Traceable Unit

Move-to-touch skill

Class 2

Execution(0)task(2)

Class 2

Sensing
Modeling

Confirmation

Class 1

task (0)(1)

Process of Error Recovery

Change of planningparameters
Change of modelingparameters
Change of sensing
parameters

Classification of error
task k error

error

correct

Process flow with error recovery in skill sequenceFig. 2 Process flow with error recovery
of loosening a screw using a screwdriverClass 1 → Execution error
Class 2 → Planning error
Class 3 → Modeling error
Class 4 → Sensing error

Fig. 3 Fundamental process flow
with error recovery

in skill sequence of looseninga screw using a screwdriverin a list. The error cause is sorted by the order in which it© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 219

Akira Nakamura, Kazuyuki Nagata, Kensuke Harada, Natsuki Yamanobe

tends to occur and the recovery flow chart (Fig. 3) ischanged. For example, when the priority of the error
cause is the following,

ways: with experience and without experience. In
addition, we described the route that approximated thetrue cause in each category of the error cause. When thecause can be identified correctly, the expression of thesystem becomes correct. Therefore, the probability thatthe error will occur in the execution of a later task will
decrease. Our future studies will include the applicabilityof our method to an actual system and a search for amethod that can derive the cause more accurately evenwhen there is little experience.

(i) Error 1 (Class 1)(ii) Error 2 (Class 3)(iii) Error 3 (Class 2)(iv) Error 4 (Class 3)(v) Error 5 (Class 2)(vi) Error 6 (Class 4)
the flow chart of the recovery becomes as shown in Fig.
4. The cause is specified from the error conditions, thetask when the error occurred, and the process up to thatpoint. The system is corrected and the task is re-executedby the system with improved reliability.

Acknowledgement
This work was supported by JSPS KAKENHI Grant
Number 15K00370.

5. Conclusion

References

We have shown a method for classifying the cause
when an error occurs and how to process the recovery forthe case in question. The accuracy of the estimated cause

1. B. R. Donald, Planning multi-step error detection andrecovery strategies, Int. J. Robot. Res., 9(1) (1990) 3-60.
2. E. Z. Evans and C. S. George Lee, Automatic generationof error recovery knowledge through learned reactivity, inProc. IEEE Int. Conf. Robot. Autom., (San Diego, USA,1994), 2915-2920.
3. C. M. Baydar and K. Saitou, Off-Line error prediction,
diagnosis and error recovery using virtual assembly
systems, in Proc. IEEE Int. Conf. Robot. Autom., (Seoul,
Korea, 2001), 818-823.
4. C. W. Moon and B. H. Lee, A model-based error recovery
scheme for a multi-robot system, Robotica, Cambridge
Univ. Press, 19(4) (2001) 371-380.
5. A. Nakamura and T. Kotoku, Systematization of error
recovery in skill-based manipulation, Artificial Life andRobotics, Springer, 14(2) (2009) 203-208.
6. A. Nakamura, K. Nagata, K. Harada, N. Yamanobe, T.
Tsuji, T. Foissotte and Y. Kawai, Error recovery using task
stratification and error classification for manipula-tion
robots in various fields, in Proc IEEE/RSJ Int. Conf. onIntell. Robots Syst., (Tokyo, Japan, 2013), 3535-3542.

Priority of type of error① Error 1 （Class 1）② Error 2 （Class 3）③ Error 3 （Class 2）④ Error 4 （Class 3）⑤ Error 5 （Class 2）⑥ Error 6 （Class 4）

Change ofplanning
parameters
Change ofmodeling
parameters

Sensing
Modeling

Change ofsensing
parameters

task2

③

④

⑤

Class 4②

Class 2①

Class 3error

Class 3error

Class 2

task1

Class 1

Planning⑥

Classification of error
task k error

Class 1 → Execution error
Class 2 → Planning error
Class 3 → Modeling error
Class 4 → Sensing error

Fig. 4 Corrected process flow
with error recoveryof the error depends on the degree of experience with thetask. We presented and described the estimation in two© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 220

The Suitable Timing of Visual Sensing in Error Recovery
Using Task Stratification and Error Classification
Akira Nakamura, Kazuyuki Nagata

Intelligent Systems Research InstituteNational Institute of Advanced Industrial Science and Technology (AIST)
Central 2, 1-1-1 Umezono, Tsukuba, Ibaraki, 305-8568 Japan
Kensuke Harada

Robotic Manipulation Research Group
Systems Innovation Department
Graduate School of Engineering Science, Osaka University
1-3 Machikaneyama, Toyonaka 560-8531, Japan
Natsuki Yamanobe

Intelligent Systems Research InstituteNational Institute of Advanced Industrial Science and Technology (AIST)
Central 2, 1-1-1 Umezono, Tsukuba, Ibaraki, 305-8568 Japan
E-mail: a-nakamura@aist.go.jp, k-nagata@aist.go.jp,
harada@sys.es.osaka-u.ac.jp, n-yamanobe@aist.go.jp
www.aist.go.jp

Abstract
Judgment of errors for recovery is performed during execution of the system. Ideally, it is desirable for the judgment
to be performed at several times. However, in that case, many sensors would be needed and it would lead to disturbing
the workflow. Therefore, it is important to be able to judge an error efficiently in the most suitable timing and withina few attempts. This paper describes a method for efficient timing of visual sensing for error recovery.
Keywords: error recovery, task stratification, error classification, manipulation, sensing timing

desirable to perform visual sensing for error judgments atseveral times. However, it is difficult practically since alarge number of sensors would be necessary for the
hardware and execution system, and the interruption of
the workflow and time limitations are also problems. In
this paper, a method to derive efficient timing for visual
sensing during error recovery is proposed.
Section 2 shows the significance of visual sensing inerror recovery. Classification of visual sensing is
described in Section 3. Efficient timing for visual sensing
is shown in Section 4.

1. Introduction
In the manipulation tasks performed by robots
involved in industrial production and household chores,
error recovery techniques are important since robots needto perform complicated tasks.1-6 We have proposed anerror recovery method using the concepts of task
stratification and error classification.7, 8
In a robot system with error recovery capability,
sensing is performed by using vision systems for the
judgment of errors mainly at important points. For the
purpose of evading errors as much as possible, it is© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 221

Akira Nakamura, Kazuyuki Nagata, Kensuke Harada, Natsuki Yamanobe

2. Significance of Visual Sensing in Error
Recovery
The robot tasks targeted for this paper are mainly
assembly, disassembly, inspection, and parts
replacement in industrial factories and power plants.
Tasks such as bed-making, cooking and washing
performed by robots assisting humans in daily life arealso our target.
We proposed a method of error recovery for amanipulation robot system in Ref. 7 and proposed a new
method of error recovery in Ref. 8. These methods are
based on the concepts of task stratification and error
classification.
The robot tasks that we consider basically consist of
sequences of sensing, modeling, planning and execution.
We consider the sequences to achieve the completion of
the main task using a hierarchy, but the robot system iscomposed of sequences basically for each layer. After
execution of the task, the error judgment is performed.
That is to say, after execution of the sequence of sensing,modeling, planning and execution for each task, sensing
is performed once again to check for an error. A check ofwhether the objective of the given task has beenaccomplished is performed in the top layer. In addition, acheck of whether the goals of the sub-tasks have beenachieved is performed in each layer. Usually, visual
sensing is used for judgment of the error status in alllayers. Sensing based on vision is performed for the mostpart in manufacturing processes and in tasks providing
human life assistance in particular. Therefore, we also
considered the checking of errors by visual sensing in thispaper.
There are various methods of checking errors using
data obtained by sensing. For example, there are methods
using judgment based on raw data, using geometricmodels derived from the original data, and so on.

Sensing

Common data

Taskk

Sensing
Comfirmation

error

Sensing
Taskk+1

Sensing
Comfirmation

error

Fig. 1 Common data in successive
sub-tasks in (Case 1)

This is a high-speed three-dimensional dataacquisition device such as high-speed stereovisionequipment. However, this is not applicable for this paper
because such equipment is expensive and unrealistic forgeneral-purpose applications.
(b) Vision sensor for stationary object use onlyThis is a three-dimensional data acquisition device ofthe normal-speed stereovision that is inexpensive passive
stereovision equipment. The laser range finder of active
stereovision is this type, too. For this paper, this is thetype of sensor we considered. In addition, a stationary
object is ideal for data acquisition. When an object is notstationary, the acquired data may include errors.
4.

Timing of Efficient Visual Sensing

In this section, we will consider a case in which datais acquired in a stationary state such as (b) of Section 3.
In our method, sequences composed of sensing,modeling, planning and execution are performed in eachlayer derived by stratification, but it is difficult toperform sensing to obtain visual data at all sensing points.
It is the cause that it takes time, with computational
complexity becoming extensive and task motion
interruptions increasing. Therefore, it is desirable to keep
the timing of data acquisition to a minimum. For thisreason, we will explain desirable sensing timing in detail.

3. Classification of Visual Sensing
There are many kinds of visual sensors. In our
method, visual sensors from which three-dimensional
data can be obtained are needed. Depending on the kindof visual sensor used, the visual data obtained will differ
as follows.
(a) Visual sensor from which three-dimensional data
can be obtained for moving objects© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 222

Task 1

Task 1

Task2

Task 2

Task kSensing

MTU2

Task k+1
Task k+2

Minimum Traceable Unit (MTU)MTU1

The Suitable Timing of

Task k-1
Task kDifficultsub-task

Sensing
Task k+1

Task lTask lSensing

Fig. 4 Sensing point in(Case 4)

Fig. 2 Sensing pointsin (Case 2)(Case 1) Use of sensing data for an error check in thenext sub-task(Case 4) With consideration for the difficulty ofaccomplishing a sub-task

It is sometimes possible that the sensing data acquired
for the error check in a sub-task can be used for the dataneeded first in the next sub-task. If the necessary
conditions of both data are the same in the range andprecision of the three-dimensional data, they can be used
as common data (Fig. 1).

When the place where a sub-task tends not to achieve
the sub-goal or where a failure tends to occur is known inadvance, a check of whether or not an error has occurredis performed at that point (Fig. 4). When the emphasis ison the achievement of the sub-task, this method iseffective.

(Case 2) Use of sensing on the basis of a minimum
traceable unit

(Case 5) With consideration for a change in the range
or accuracy of sensing

Next, we will show visual sensing on the basis of aminimum traceable unit (MTU), which means thesmallest unit in which it is necessary to return to the first
node of a motion primitive sequence if an error occurs.
Visual sensing is performed just after an MTU, and acheck of the error status is performed (Fig. 2).

When the range of sensing varies from the local
domain to a wider domain (or the reverse) or when theaccuracy of sensing varies from precise to rough (or thereverse), a check of whether or not an error has occurredis performed at that point. For example, local range
sensing is performed for manipulator operations andglobal range sensing is performed for transfer of objects
by manipulator (Fig. 5). When a camera parameterchanges due to the place or the area of the space, thismethod is effective.

(Case 3) With consideration for the ease of performinga recovery task
When the place where a recovery task is easy to do is
known in advance, a check of whether or not an error has
occurred is performed at that point (Fig. 3). When theemphasis is on the ease of the task, this method iseffective.

We have shown guidelines where the sensor timings
can be placed to reduce the number of sensing executions.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 223

Global range

Akira Nakamura, Kazuyuki Nagata, Kensuke Harada, Natsuki Yamanobe

3. K. Yamazaki, M. Tomono, T. Tsubouchi and S. Yuta,
Motion planning for a mobile manipulator based on joint
motions for error recovery, in Proc. IEEE/RSJ Int. Conf.
on Intell. Robots Syst., (Beijing, China, 2006), 7-12.
4. M. Scheutz and J. Kramer, Reflection and reasoning
mechanisms for failure detection and recovery in a
distributed robotic architecture for complex robots, in
Proc. IEEE Int. Conf. Robot. Autom., (Roma, Italy, 2007),
3699-3704.
5. J. C. Himmelstein, E. Ferre and J.-P. Laumond,
Teleportation'-Based Motion Planner for Design Error
Analysis, in Proc. IEEE Int. Conf. Robot. Autom., (Kobe,
Japan, 2009), 914-920.
6. P. Pastor, M. Kalakrishnan, S. Chitta, E. Theodorou and S.
Schaal, Skill learning and task outcome prediction for
manipulation, in Proc. IEEE Int. Conf. Robot. Autom.,(Shanghai, China, 2011), 3828-3834.
7. A. Nakamura and T. Kotoku, Systematization of error
recovery in skill-based manipulation, Artificial Life andRobotics, Springer, 14(2) (2009) 203-208.
8. A. Nakamura, K. Nagata, K. Harada, N. Yamanobe, T.
Tsuji, T. Foissotte and Y. Kawai, Error recovery using task
stratification and error classification for manipula-tion
robots in various fields, in Proc IEEE/RSJ Int. Conf. onIntell. Robots Syst., (Tokyo, Japan, 2013), 3535-3542.

Task 1
Task 2

Task kLocal range

Sensing
Task k+1
Task k+2

Task lFig. 5 Sensing pointin (Case 5)

5. Conclusion
We have shown guidelines for effective timing forvisual sensing in a manipulation system with error
recovery capability. It is possible to reduce the number ofvision sensors and the calculation workload of thesoftware according to the guidelines we suggested. Our
future studies will include applicability of this techniqueto an actual system, derivation of effective sensing timing
for a moving manipulation system, error estimation using
non-vision sensors, and so on.
Acknowledgement
This work was supported by JSPS KAKENHI Grant
Number 15K00370.
References
1. L. Seabra Lopes and L. M. Camarinha-Matos, A machinelearning approach to error detection and recovery inassembly, in Proc. IEEE/RSJ Int. Conf. on Intell. Robots
Syst., (Pennsylvania, USA, 1995), vol. 3, 197-203.
2. T. Fukuda, M. Nakaoka, T. Ueyama and Y. Hasegawa,Direct teaching and error recovery method for assemblytask based on a transition process of a constraint condition,
in Proc. IEEE Int. Conf. Robot. Autom., (Seoul, Korea,
2001), 1518-1523.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 224

Hexapod Type MEMS Microrobot Equipped with an Artificial Neural Networks IC
Kazuki Sugita

Graduate School of Precision Machinery Engineering College of Science and Technology, Nihon University,
7-24-1, Narashinodai, Funabashi, Chiba, 274-8501, Japan
Taisuke Tanaka, Yuya Nakata, Minami Takato, Ken Saito, Fumio Uchikoba

Department of Precision Machinery Engineering College of Science and Technology, Nihon University,
7-24-1, Narashinodai, Funabashi, Chiba, 274-8501, Japan
E-mail: cska15018@g.nihon-u.ac.jp, takato@eme.cst.nihon-u.ac.jp, kensaito@eme.cst.nihon-u.ac.jp,
uchikoba@eme.cst.nihon-u.ac.jp

Abstract
This paper proposes a hexapod type microrobot controlled by an artificial neural networks IC. The structuralcomponent of the microrobot is produced by micro electro mechanical systems (MEMS) process. The rotary actuatorinside the robot is composed of the artificial muscle wire of shape memory alloy (SMA) material. The artificial neuralnetworks IC includes cell body models, inhibitory synaptic models and current mirror circuits. By reducing the heat
capacity of the actuator and the length of electrical wire to the actuator, the walking speed achieved 12 mm/min.
Keywords: Microrobot, MEMS, Artificial neural networks, SMA, Artificial muscle wirebased on the integrated circuit (IC) production process,which applies photolithography on a silicon wafer.
Some studies have considered artificial neuralnetworks as a new alternative control method for robots4.
The study of the artificial neural networks is roughly
divided into software models and hardware models. The
software model is a numerical simulation method using acomputer system. To achieve the fabrication of largescale neural networks using software models, processing
in continuous time is difficult. The hardware model isimitating the neural networks using electronic circuits.
The nonlinear operation of an electronic circuit can be
performed at a high speed and processed in continuous
time. Therefore, the hardware neural networks can obtainan output in real time, even if the circuits scale increases.
Previously, we developed the microrobot that
imitates the hexapod walking motion of insects using
MEMS technology. We fabricated small walking
systems equipped with the actuator using artificial

1. Introduction
Several studies have reported the miniaturization ofrobots1-2. Microrobots are suggesting possibility ofactivities in a small space. However, the miniaturizationand high functionalization of microrobots have not been
sufficiently achieved yet.
In heading toward the implementation of moresophisticated microrobots, biomimetics, a technologythat imitates living organisms, is attracting attention3.
Insects have the compact walking mechanisms and theexcellent flexible control systems using biological neuralnetworks. Hence, the realization of small walking
systems with the size of an insect is valuable. However,the processing of the miniature component as small as theinsect is difficult only by the conventional mechanicalmachining. One of the solutions is micro electro
mechanical systems (MEMS). The MEMS process is© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 225

Kazuki Sugita, Taisuke Tanaka, Yuya Nakata, Minami Takato, Ken Saito, Fumio Uchikoba

muscle wires based on shape memory alloy (SMA). The
lead wires electrically connecting the microrobot andartificial neural networks IC were exposed outside themicrorobot. This wire connection was harmful for the
robot balance. This microrobot walked at a locomotion
speed of 2.8 mm/min.
In this study, we report that the faster walking motionof the hexapod-type MEMS microrobot. The
miniaturization of rotor parts was performed to reduce its
heat capacity. The electrical wires were leaded inside themicrorobot. It improved the weight balance of the
microrobot. As a result, the microrobot could achieve thewalking speed at 12 mm/min.

because new design can suppress the heat capacity. Also,
we installed the lead wire inside the microrobot to reduce
the harmful element of the weight balance.

2.5 mm
4.0 mm
2.7 mmFig. 1. Hexapod type microrobot.

2. Mechanism of the MEMS Microrobot
Rotor

The microrobot that we manufactured is shown in Fig. 1.
The microrobot is a hexapod walking robot with a total
length of 5 mm or less. The components of the
microrobot are the frame, the rotary actuator, the shaft,and six legs. The each part is fabricated from siliconwafers using MEMS process. We used link mechanism
and the artificial muscle wire to achieve the further
advanced miniaturization required for a small walking
system. As the material of the artificial muscle wire itself
has the function of generating the displacement, this issuitable for the miniaturization of the microrobot.
Artificial muscle wires and a ground (GND) wire areelectrically and mechanically connected to the rotor partthrough the silver conductive paste. The displacement of
the artificial muscle wires changed by successively
applying an electrical current to generate joule heat.
Previously, we applied a circle-type rotor for the rotaryactuator as shown in Fig. 2 (a). This circle-type rotor wascompletely over-coated with the silver conductive paste.
On the contrary, the two-hole-type rotor in this paper can
fix the artificial muscle wire firmly by the hole. So, theamount reduction of the silver conductive paste wasachieved (see Fig. 2 (b)).
We compared the heat capacity calculated based on
the weight of two-type rotors. The weight of silicon partsof the circle-type rotor and two-hole-type rotor were 52.4
μg and 18.1 μg, respectively. The weight of silver
conductive paste were 1.18 mg and 139 μg, respectively.
In terms of the two-type-rotors, the calculated heat
capacity is shown in Table 1. As a result, the two-holetype rotor is expected to shorten the revolving period

Lead wires

200 µmFrames0.22 mm0.61 mm0.94 mm 0.3 mm
1.2 mm(a)(b)

Fig. 2. Structure of the microrobot.
Table 1. Heat capacity of two-type rotors
Circle-typerotor

Two-hole-type
rotor

38.8

13.4

277

32.7

Heat capacity ofrotor part [μJ/K]
Heat capacity of
conductive paste[μJ/K]

2.1. Walking mechanism
We employed a link mechanism that converts the rotary
motion of the actuator to walking motion. The linkmechanism connects the middle leg to the front and rear

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 226

Hexapod Type MEMS Microrobot

legs. The locomotion pattern generates a 180° phase shifton each side. Therefore, the microrobot performs ahexapod walking by grounding the three legs like an
insect (see Fig. 3).

the IC was 4 metal 2 poly CMOS 0.35 μm. The chip size
was 2.45 mm square.
iS

CG

VC

MC2

MC3

CM

MC1

MC4

VA

Fig. 4. Circuit diagram of the cell body model.
CIS1
MIS12

Fig. 3. Locomotion diagrams of the microrobot.

3.

Artificial Neural Networks

VC1

The neural networks present in their brains consist of cellbodies, dendrites, axons and synapses. The cell body fires
electrical pulses by changing the membrane potential.
The signals for electrical pulses are transmitted to thedendrites of another neuron through the axon. The
synapses change signals for electrical pulses by inhibit orpromote transmitting of electrical pulses.
In this study, we modeled the cell body and theinhibitory synapse as shown in the Fig. 4 and Fig. 5. The
cell body model has a refractory period, an analog
characteristic for the output pulse, and time varying
negative resistance characteristics. The circuit
parameters for the cell body model were VA = 2.8 V, MC1,
MC2: W/L = 10, MC3: W/L = 0.1, MC4: W/L = 0.3, CG =4.7μF, CM = 1.0μF. A cell body model connected by an
inhibitory
synaptic
model
causesanti-phasesynchronization. The circuit parameters for the inhibitory
synaptic model were VISDD = 2.8 V, CIS = 1 pF, MIS1, MIS2,
MIS3, MIS11, MIS12: W/L = 1.
We used the four cell body models that coupledinhibitory mutually using 12 inhibitory synaptic models
as shown in the Fig. 6. The artificial neural networks andthe current mirror circuit for obtaining a driving currentof the actuator were constructed on an IC. The layout
pattern of the IC is shown in Fig. 7. The design rule of

VDD
MIS13

MIS11

MIS2

MIS3

iS

Fig. 5. Circuit diagram of the inhibitory synaptic model.
Output ports

Input ports: Cell body model: Synaptic modelFig. 6. Connection diagram of the artificial neural networks.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 227

Kazuki Sugita, Taisuke Tanaka, Yuya Nakata, Minami Takato, Ken Saito, Fumio Uchikoba

Artificial neural networks

Current mirror circuit

2.45 mm

0s

3s

6s

9s

2.45 mmFig. 7. Layout pattern of the artificial neural networks IC.

Fig. 9. Walking locomotion of the microrobot.

4. Results

5. Conclusion

Fig. 8(a), (b) shows the microrobots equipped theartificial neural networks IC. The side, the end, andheight dimensions of the microrobot include the wholecircuit were 4.0, 2.7, and 5.0 mm, respectively. Only
power sources were connected externally. While theprevious microrobot had lead wires outside themicrorobot, in this case, we installed the lead wire inside
the microrobot. As a result, the internal wiring achieved
excellent weight balance and structurally stableperformance of the microrobot.
The hexapod walking locomotion of the microrobot
was achieved as shown in fig. 9. Previously, the input
pulse duration was a 0.5 s, and cycle period was 2.0 s.
The new two-hole-type rotor was possible to accelerate
the revolving frequency of the actuator. Therefore, we
could shorten the input pulse width and cycle period to
0.3 s and 1.2 s, respectively, and consequently increased
the rotation speed of the rotary actuator. The locomotion
speed of the microrobot accelerated from 2.8 mm/min to
12 mm/min.

In this study, we proposed the hexapod type MEMS
microrobot controlled by the artificial neural networks IC.
We designed two type actuators using artificial muscle
wires. The new two-hole-type rotor was possible toreduce the thermal capacity of the rotor by reducing thesize and the amount of the conductive paste. The new
microrobot had lead wires inside of the microrobot, andachieved the better weight balance. It was possible tomount a driving circuit on the microrobot by using a barechip IC. As a result, the speed of the hexapod typemicrorobot increased to 12 mm/min. The microrobot
equipped all of constituent elements except the powersource.
References
1. T. Shibata, Y. Aoki, M. Otsuka, T. Idogaki, and T. Hattori,
Microwave Energy Transmission System for Micro-robot,
IEICE Trans. ELECTRON. Vol. E80-C,NO.2, pp. 303–
308, Feb. 1997.
2. M. K. Habib, Innovations and robotics, International
Journal of Mechatronics andManufacturing Systems,
4(2),1993, pp. 123–135.
3. A. T. Baisch, P. S. Sreetharan and R. J. Wood, E. H. Miller,
Biologically-inspired locomotion of a 2g hexapod robot,
in Proc. Int. Conf. on Intelligent Robots and systems,
Taipei, Republic of China, 2010, pp. 5360–5365
4. S.A. Vukosavljev, D. Kukolj, I. Papp, and B. Markoski,
Mobile robot control using combined neural-fuzzy andneural network, in Proc. 12th IEEE Int. Symposium on
Computational Intelligence and Informatics (CINTI 2011),
Budapest, 2011, pp. 351–356.

5.0 mm(a) Previous robot
with outside wire

4.0 mm
2.7 mm(b) Present robot
with inside wire

Fig. 8. Fabricated microrobot equipped driving circuit.
© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 228

Heat Distribution of Current Output Type Artificial Neural Networks IC for the
MEMS Microrobot
Taisuke Tanaka, Yuya Nakata, Kazuki Sugita, Minami Takato, Ken Saito, Fumio Uchikoba

Department of Precision Machinery Engineering College of Science and Technology, Nihon University,
7-24-1, Narashinodai, Funabashi, Chiba, 274-8501, Japan
E-mail: csta13083@g.nihon-u.ac.jp, takato@eme.cst.nihon-u.ac.jp, kensaito@eme.cst.nihon-u.ac.jp,
uchikoba@eme.cst.nihon-u.ac.jp

Abstract
Heat distribution of the artificial neural networks IC developed for controlling microrobots is described in this paper.
We measured the temperature distribution of the designed IC using thermography. As a result, it is found that the
major heat source is the current mirror part on the IC. However, it was observed that the part of the artificial neuralnetworks also generated the heat. The heat contribution of the artificial neural network part was evaluated. As a result,
the temperature rise of the artificial neural networks part on the IC was calculated to be 0.0535°C per second.
Keywords: Microrobot, Artificial neural networks, MEMS, IC

1. Introduction
Insects inhabit the earth from about 300 million years ago.
During that process, insects have experienced manyenvironmental changes and have developed biological
functions1. The insect has excellent form and function.
Therefore, if it is possible to produce micro robots having
functions equivalent to those of the insect, it is considered
to be possible that the robot deal with various situations
and application in various fields2. For example, in the
medical field, the microrobot that mimics the peristaltic
movement of the insect has been studied3.
The Insect has a flexible control system with acompact moving mechanism and neural networks of thebrain. In order to imitate this, we are applying microelectro mechanical systems (MEMS) technology tocreate the movement mechanism of the microrobot.
MEMS technology is based on the integrated circuit (IC)production process. Also, the conventional program
control used as a robot control makes it difficult to deal
with unknown situations. Therefore, the control method
using the artificial neural networks has been expected as

an alternative method of the program control4. Neuronsof the brain in the living organisms are connected to eachother and form networks. By carrying out informationtransmission using those, the flexible information
processing can be performed. By modeling the neuron of
the living organism and constructing the artificial neuralnetworks, the robot can judge and execute the required
process by themselves like the living organism. We haveintegrated simple artificial neural networks into a IC andused it as a controlling circuit of the MEMS microrobot5.
We designed a current output type artificial neuralnetworks IC to drive the MEMS microrobot using shapememory alloy (SMA) for the actuator. In this paper,
focusing on heat generation of the artificial neuralnetworks IC, its heat distribution was measured bythermography. We considered the source of the heatgeneration from the results.

2. Walking control of microrobot© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 229

Taisuke Tanaka, Yuya Nakata, Kazuki Sugita, Minami Takato, Ken Saito, Fumio Uchikoba

Artificial muscle wires based on SMA are used in theactuator of the robot. The wire shrinks by joule heat ofcurrent flow and extends by cooling. Fig. 1 shows thewalking mechanism of the microrobot. The walking
motion of the microrobot is generated by the rotationalmotion of the rotor attached with 4 helical artificial
muscle wires. The rotational action of the actuator is
obtained from current flows through the artificial muscle
wire in the order of A to D. Therefore, the rotor rotates
and transmits to the link mechanism of the leg portion.
Fig. 2. Circuit diagram of the cell body model.

Artificial muscle
wire

3.2. Synaptic modelFig. 3 shows a circuit diagram of an inhibitory synaptic
model. When a multiple of cell body models areconnected by the synapse model, a synchronization
phenomenon occurs at the oscillation timing of the cellbody model. The inhibitory synaptic model, particularly,
causes anti-phase synchronization. We used theinhibitory mutual coupling to generate driving pulses ofthe microrobot. The circuit parameters for the inhibitory
synaptic model were VDD = 3V, CIS1 = 1 pF, MIS1, MIS2,
MIS3, MIS4: W/L = 1.

Link mechanism

Rotor
Fig. 1. Walking mechanism of the microrobot.

3. Artificial neural networks
An artificial neural circuit mimics the function of a neuralcircuit of the living organism with analog electronic
circuits. The neural circuits of the living organism ismainly composed of cell bodies, dendrites, axons andsynapses. We modeled the cell body and the synapse.
3.1. Cell body modelFig. 2 shows the circuit diagram of the cell body model.
VC in Fig. 2 is the output voltage. The cell body model
consists of a capacitor CG and a membrane potential
capacitor CM, MOSFETs MC1, MC2, MC3 and MC4. The
cell body changes the membrane potential using external
stimulation and fires electrical pulses. The cell bodymodel has a refractory period, an analog characteristic forthe output pulse, and time varying negative resistancecharacteristics. The circuit parameters for the cell bodymodel were VA = 3V, MC1, MC2: W/L = 10, MC3: W/L =0.1, MC4: W/L = 0.3, CG = 4.7μF, CM = 1.0μF.

Fig. 3. Circuit diagram of the inhibitory synaptic model.

3.3. Pulse- type hardware neural networks
Fig. 4 shows the pulse-type hardware neural networks
mimic a central pattern generator of the living organism.
The central pattern generator is known as basic rhythm
generator of the living organism. As shown in Fig. 4, fourcell body models are inhibitory mutually coupled using
12 inhibitory synaptic models. As a result, a four-phaseanti-phase synchronous waveform is generated.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 230

Heat Distribution of Current

Output port
Current mirror

Input port

Cell body
model

2.45
mm

Artificial neuralnetworks

Inhibitory synaptic
model
2.45mmFig. 4. Pulse-type hardware neural networks.

Fig. 6. Layout pattern of Artificial neural networks IC.

3.4. Current mirror circuit
Fig. 5 shows a circuit diagram of the current mirror
circuit. The shape memory alloy actuator requires theelectrical current to generate movement. Therefore, thecurrent mirror circuit is connected to the pulse typehardware neural networks in order to convert the voltage
into the current. The circuit parameters for the current
mirror circuit were VDD = 4V, MS1: W/L = 40, MS2: W/L =1, MON: W/L = 66. The number of stages of the current
mirror varied by the designed IC.

Fig. 7. ICs mounted on the circuit board.

Amplitude [V]Amplitude [V]

4.0
2.0
4.0
2.0
4.0
2.0
4.0
2.0
0

Time [0.5s/div](a)Output Waveform ofartificial neural networks

Fig. 5. Circuit diagram of the current mirror circuit.

4.0
2.0
4.0
2.0
4.0
2.0
4.0
2.0
0

Time [0.5s/div](b)Output waveform of
current mirror

Fig. 8. Example of the output waveform of the pulse typehardware neural networks IC

3.5. Artificial neural networks IC
We designed an IC that constructed pulse-type hardware
neural networks. Fig. 6 shows the layout pattern of thedesigned IC. The design rule of the IC was 4 metal 2 poly
CMOS 0.35μm. The sizes of the capacitors CG, CM of thecell body model were too large to pattern on the IC.
Therefore, it was added on the peripheral circuit. Fig. 7
shows an IC mounted on the circuit board. Fig. 8 shows
an example of output waveform of the pulse-type
hardware neural networks IC.

4. Measurement of heat generation
4.1. Measurement condition
3V was applied to the artificial neural networks using a
power source and 4V was applied to the current mirror.
A 12Ω load resistance was connected as output instead ofSMA. The measurement was carried out for about 30
seconds after the voltage input. The room temperature
was 20°C.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 231

Taisuke Tanaka, Yuya Nakata, Kazuki Sugita, Minami Takato, Ken Saito, Fumio Uchikoba

4.2. Measurement result

The measurement was performed without applying thepower source of the current mirror. In the IC shown in
Fig. 11, it was confirmed that the maximum temperaturein the artificial neural networks was 29.2°C. Fig. 12shows the measurement result of the temperature rise at
the representative points of the artificial neural networks.
The temperature rise in the artificial neural network was
confirmed.

Fig. 9 shows measurement results. Mainly two hightemperature areas are confirmed. The two areas are theartificial neural networks and the current mirror. In the
IC shown in Fig. 9, it was confirmed that the maximum
temperature in the artificial neural network was 100.4°C
and the maximum temperature in the current mirror was
100.1°C.
110.0
96.3

30.0
26.3

82.5

22.5

68.8

18.8

55.0

15.0

41.3

11.3

27.5
13.8

7.5

0.0° C

0.0° C

3.8

Fig. 9. Temperature distribution measurement on ICs with 12
load.

Fig. 11. Measurement of heat distribution of IC without inputof current mirror.
Temperature [°C]4.3. Measurement result with reduced influence of
current mirror
We focused on the heat generation in the artificial neuralnetworks. In order to confirm that the artificial neuralnetwork was generating heat, the load resistance wasincreased from 12Ω to 110Ω. Fig. 10 shows themeasurement result. Load resistance of 110Ω was thehighest resistance the IC output the stable pulse. In the IC
shown in Fig. 10, it was confirmed that the maximum
temperature in the artificial neural networks was 39.1°C
and the maximum temperature in the current mirror was
39.3°C.

28.6
28.4
28.2
28.0
0

20

10

30

Time[s]Fig. 12. Measurement result of temperature rise of artificial
neural networks.

5. Discussion
5.1. Heat generation in artificial neural networks
IC

45.0

From these results, it is found that the major heat source
is current mirror part because the temperature decreased
with increasing of the load resistance. However, the hightemperature areas of the IC are corresponding to the not
only the current mirror but also the artificial neuralnetworks. The temperature of the artificial neuralnetworks portion was higher than that of the neighborarea. Also, we confirmed the heat generation of theartificial neural network even when the power supply of
the current mirror is stopped. The temperature rise almost
corresponded to the pulse timing. So, it is suggested the

39.4
33.8
28.5
22.5
16.9
11.3
5.6
0.0° C

Fig. 10. Temperature distribution measurement on ICs with110 load.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 232

Heat Distribution of Current

6. Conclusion

artificial neural networks also generates the heat to someextends.

The heat distribution of the artificial neural networks IC
developed to controlling the microrobot was described inthis paper. As a result, heat generation was observed withthe artificial neural networks and the current mirror parts.
It was found that major heat source was the current mirror
part. Focusing on the heat generation of artificial neuralnetworks, we thought that heat generated by charges
extracted from CG and CM of the cell body model. The
temperature rise per second was evaluated using thepower consumption of CG and CM. As a result, it wascalculated that the temperature rises of 0.0535°C per
second for the IC.

5.2. Evaluation of temperature rise per second of
artificial neural networks
In the cell body model, when a voltage of VA is applied,the charge accumulates in CG, and the potential
difference between MOSFETs MC1 and MC2 disappears.
Then, MC2 conducts, the charges of CG and CM are
extracted, applied to the MIS3 of the inhibitory synapse
model, and flow to GND. It is thought that the heat is
generated by the charges extracted from CG, CM. We
evaluated the calculated value of the temperature rise persecond from the power consumption of the capacitors CG,
CM, the mass of the heat generation part, and the specificheat of silicon. The power consumption due to charges
extracted from CG and CM can be obtained by equation(1).

1
CV 2 [ J ]2W

7. References
1. H. Hugo, 2008, People learn-Insect wisdom, Tokyo:
Tokyo Agricultural University Press Publications
2. A. T. Baisch, P. S. Sreetharan and R. J. Wood, E. H. Miller,
Biologically-inspired locomotion of a 2g hexapod robot,
in Proc. Int. Conf. on Intelligent Robots and systems,(Taipei, Republic of China, 2010), pp. 5360–5365
3. Y. Sonobe, Y. Arai, Y. Nakazato, Development of In-pipe
Micro Robot Moving in the Blood Vessel, Welfare
Engineering Symposium Presentation Papers 2007,(japan,2007), pp. 231-232
4. A. Iwata, Y. Amemiya, 1995, Neural network LSI, Tokyo:
Institute of Electronics, Information and Communication
Engineers
5. K. Saito, Y. Ishihara, K. Okane, H. Oku, Y. Asano, K.
Iwata, M. Tatani, M. Takato, Y. Sekine and F. Uchikoba,
Artificial Neural Circuit Integration for MEMS
Microrobot System, in Proc. 2015 Int. Conf. AdvancedIntelligent Mechatronics, (Busan, Korea, 2015), pp.10551060.

(1)

CG and CM are 4.7μF and 1.0μF respectively. It is
assumed that 0.5 V is applied to CG and 1.5 V is applied
to CM. Therefore, power consumption of 0.587μJ and1.13μJ are obtained on each capacitor. Because theartificial neural networks is composed of four cell bodymodels, then, the one cycle power consumption of thewhole artificial neural networks is calculated as 6.87μJ.
The duration of one cycle is 1.1 seconds. Therefore, thetotal power consumption per second is 6.25μW. The
temperature rise of the artificial neural networks per
second can be obtained by equation (2).

TP[° C ]VCV(2)

CV is the specific heat of silicon, 0.73J/gK. V is thevolume of heated area. Those are derived from thetemperature distribution of the ICs, 6.88×10-5cm3. isthe density of the silicon, 2.33g/cm3. From these, it iscalculated that an artificial neural networks increases
0.0535°C per second. Compared with the calculation
result and the measurement result of Fig. 12, thecalculated value follows the tendency of the measured
value.
© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 233

AGV with Mind and its production simulation for autonomous decentralized FMS
Masato Chikamatsu

Human and Information System Division, Gifu University,
1-1, Yanagido, GifuCity, GifuPref 501-1193, Japan
Hidehiko Yamamoto

Department of Mechanical Engineering,
1-1, Yanagido, GifuCity, GifuPref 501-1193, Japan
Takayoshi Yamada

Department of Mechanical Engineering,
1-1, Yanagido, GifuCity, GifuPref 501-1193, Japan
E-mail:u3128017@edu.gifu-u.ac.jp, yam-h@gifu-u.ac.jp, yamat@gifu-u.ac.jp
www1.gifu-u.ac.jp/~yamlab/

Abstract
This study controls Automated Guided Vehicle (AGV) moving by using a mind model in order to avoid AGVs’ interference.
The mind model can avoid the interference by repeating the two types of mind changes. By applying the mind to several
FMSs, the production simulations were carried out. As a result, AGV could avoid the interference flexibly even if the shapeof the production floor was changed, and it is ascertained that the mind model could control the AGV actions.
Keywords: Autonomous Decentralized FMS, AGV control, AGV mind, Production simulation

1. Introduction
We have developed an autonomous decentralized FMS
factory. We need to avoid collisions among AGVs when we
use the autonomous decentralized FMS. Although somerules to avoid collisions are considered, these rules are notenough to control when many AGVs are used. Therefore, we
try to control the behavior of AGVs by using a mind model.
In this study, we apply AGV with the mind model to plural
FMSs and examine whether AGVs can avoid the pathinterferences flexibly even if the shape of the production
floor is changed. Moreover we examine the influence on
production efficiency.

Grid patterns

Machining
Centers

AGV
Fig.1. Example of autonomous decentralized FMS

2. Mechanism of autonomous decentralized FMS

2.2. Model of mind and behavior of AGVs

2.1. Overview of autonomous decentralized FMS

In autonomous decentralized FMS, each AGV determines
the behavior autonomously. Therefore, AGVs often cause
path interferences. In our previous studies, we used control
rules to avoid path interferences. However, in the control
rules, it was not able flexibly to adapt the environmental
changes including the changes of the number of AGVs andthe changes of the shape of production floor.
Therefore, in this study, we apply the AGV mind tosolve the problem and realize the autonomous movement
control not to produce path interferences of AGVs.
AGVs’ mind is expressed two kinds of an arrogant mind

Fig.1 illustrates an autonomous decentralized FMS factory
scheme. As Fig.1 shows, the factory floor is divided into grid
patterns and AGVs are moving along these lines to carryparts to the warehouse and machining centers. The
autonomous decentralized FMS does not have a management
mechanism to integrate the entire system, such as machining
centers, AGV, product warehouse and parts warehouse. Eachagent that configures the system autonomously determines
the act by recognizing the purpose of the system bycooperating and negotiating to other agents© The 2017 International Conference on Artificial Life and Robotics
P (ICAROB- 234 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, Japan

Masato Chikamatsu, Hidehiko Yamamoto, Takayoshi Yamada

and a modest mind. Specifically, (1) AGV with an arrogant
mind takes the action forcibly to approach the destination.
(2) AGV with a modest mind takes the action to make way
for other AGVs.
AGVs take three actions by changing the two kinds ofmind.
If AGV with an arrogant mind and AGV with a modest
mind run on the same line at the same time, AGV witha modest mind takes the action to give way andAGV with an arrogant mind takes the action toapproach the destination forcibly.
If the two AGVs with an arrogant mind run on
the same line at the same time, one of the AGVschanges its mind to a modest mind and gives
way for the other AGV.
If the two AGVs with modest mind run on thesame line at the same time, one of the AGVschanges its mind to an arrogant mind and theother gives way for the AGV just changed to anarrogant mind.
We use the mind model as shown in Fig.2 to express themind. In the study, we call it Minimum Unit of Mind (MUM).
In Fig.2, A1 and A2 is a unit, X is a load, and an arrow is a
stimulation vector. The threshold is determined by the unit.
When the internal value reaches the threshold, we call themind situation as excited and if it does not reach, normal.
The functions of each component of MUM are as
follows.

① When the AGV with an arrogant mind encounters a pathinterference, the value of A1 is increased by 1. Keeping
the situation of the interference, the value of A1 will
increase and soon becomes the threshold value. As soon
as the value becomes the threshold value, the AGV mind
is changed to a modest mind.
② When AGV with a modest mind keeps giving way, thevalue of A2 is increased by 1. When the situation isrepeated an optional time and A2 becomes exited, thesignal is sent to a load.
③ The load received the signal from A2 decreases thevalue of units A1 and A2 by an optional value. Becauseof this, A1 and A2 return to normal and AGV with amodest mind is changed to AGV with an arrogant mind.
In this way, by changing minds from arrogant to modest
or from modest to arrogant repeating ① to ③, the mindchange is realized.
3. Target production line
We performed the production simulations of autonomous
decentralized FMS using the production floors of two
different shapes.
One is the integrated production floor that parts
warehouse and products warehouse are next to each other asshown in Fig.3 and are contiguous with one production floor.
The other is the split production floor shown Fig.4 thatthe floor of Fig.3 is split into the production floor connectedparts warehouse and production floor connected products
warehouse. AGVs come and go between the two floors.
Partswarehouse
Products
warehouse

・Unit: When a signal is sent to the unit, it sends a signal tothe direction of the arrow when excited, and it is not sent
when normal.
・Load: Load has the function to change the internal valueof the unit. When the signal is sent to the load, the value of
unit is decreased by the value of X.
・Stimulation vector: Stimulation vector is a line connecting
the load and the unit. It gives a signal to the load or unit when
the signal comes.
When A1 keeps exited, we call a modest mind, andwhen normal, we call an arrogant mind.

Next, we describe the internal functions of MUM by thefollowing ① to ③.

Fig.3 Integrated production floor

Fig.2. Model of mind (MUM)© The 2017 International Conference on Artificial Life and Robotics
P (ICAROB- 235 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, Japan

AGV with Mind andPartswarehouse

Integrated
production floor
Split production
floor

Production
Outputs

Number of pathinterference

238

1507.6165.4

2507.6

Products
warehouse

Fig.4 Split production floor

We carried out the production simulation by using thesetwo kinds of production floors and compared the production
outputs and the number of path interferences of AGVs.
The initial internal value of MUM unit A1, A2 is 0 andthe threshold values are chosen as random. Therefore, all
AGVs keep arrogant minds for the first time.

Fig.5 Comparison of Production Outputs

4. Simulation results
We investigated parameters changing number of AGVs andsimulation times in each integrated production floor and split
production floor.
Condition 1(number of AGV: 5 units, simulation time: 8hours)
Condition 2(number of AGV: 5 units, simulation time: 40
hours)
Condition 3(number of AGV: 8 units, simulation time: 8hours)
Table.1, 2, 3 and Fig.5, 6 are the acquired number of
production outputs and the acquired number of pathinterferences in each condition.
Table.1. Simulation result of condition 1

Integrated
production floor
Split production
floor

Production
Outputs

Number of pathinterference

186

769.6

134.6

911.8

Table.2. Simulation result of condition 2

Integrated
production floor
Split production
floor

Production
Outputs

Number of pathinterference

971

3190.8

712.2

3752

Fig.6 Comparison of Number of path interference

Fig.5 shows that the integrated production floor
acquired more production outputs than the split production
floor in any conditions. In condition 1 and 2, the production
outputs are proportional at simulation time. In condition 1
and 3, condition 3 manufactured more production outputs
than condition 1. Therefore, the more number of AGVs
becomes, the more production outputs.
Fig.6 shows that the split production floor gets more
path interferences than the integrated production floor in any
conditions. Comparing condition1 and condition 2, 3, it isproved that the more simulation time and the number ofAGVs, the more the path interferences. The split production
floor gets much more path interferences than the integrated
production floor in condition 3.
Table.4, 5, 6 indicate the percentage of each places(periphery of parts warehouse, products warehouse and theroute connecting two floors) in the total places caused pathinterference in each condition. Fig.7, 8, 9 are the plots of pathinterference places of the split production floor.

Table.3. Simulation result of condition 3© The 2017 International Conference on Artificial Life and Robotics
P (ICAROB- 236 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, Japan

Masato Chikamatsu, Hidehiko Yamamoto, Takayoshi Yamada

Table.4. Path interference place of condition 1
Parts
warehouse
Integrated
production
floor
Split
production
floor

21%
12.7%

Products
warehouse
8.4%
3%

Route
connecting two
floor

Table.6. Path interference place of condition 3

37%

Integrated
production
floor
Split
production
floor

Parts
warehouse

Products
warehouse

Routeconnecting two
floor

19.3%

5.6%

-

11.2%

1.7%

56%

Fig.7 Path interference place of condition 1
Fig.9 Path interference place of condition 3
Table.5. Path interference place of condition 2

Integrated
production
floor
Split
production
floor

Parts
warehouse

Products
warehouse

Routeconnecting two
floor

17.2%

10%

-

8%

4.5%

43.3%

Table.4, 5 and 6 shows that the split production floor
has much more path interferences on route connecting two
floors. For this reason, it is assumed that the pathinterferences increase in the split production floor. As a result,
it is assumed that the integrated production floor has moreproduction outputs than the split production floor.
Comparing condition 1 and condition 2, 3, the pathinterference percentage increased a little in condition 2 andthe path interference percentage much increased in condition
3 on the route connecting two floors. From Table.4, 5 and 6,
it is assumed that the reason for condition 3 acquired much
path interferences on the route connecting two floors thancondition 1 or 2. From this, the number of AGVs have a great
influence on congestion of the route connecting two floors insplit production floor.
5. Conclusions

Fig.8 Path interference place of condition 2

With AGV minds, even if a different shape production floor
is adopted, AGV was able to avoid path interferences
flexibly in autonomous decentralized FMS. In the split
production floor, AGV was crowded very much near theroute connecting two floor. However, it was able to finish thesimulation without freezing and it is ascertained that theresults of the production simulation becomes proper.
Therefore, we were able to control actions of AGVs by using
AGV mind.

© The 2017 International Conference on Artificial Life and Robotics
P (ICAROB- 237 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, Japan

UNARM System to Decide Units Locations of Cell-type
Assembly Machines with Robots Arms
Hirotaka Moribe

Department of Human Information Systems, Gifu University, Yanagido 1-1,
Gifu, Gifu 501-1194, JAPAN
Hidehiko Yamamoto

Department of Mechanical Engineering, Gifu University, Yanagido 1-1
Gifu, Gifu 501-1194, JAPANTakayoshi Yamada

Department of Mechanical Engineering, Gifu University, Yanagido 1-1
Gifu, Gifu 501-1194, JAPANE-mail: u3128029@edu.gifu-u.ac.jp, yam-h@gifu-u.ac.jp, yamat@gifu-u.ac.jp

Abstract
This study develops the system called UNARM which assists the unit placement decision of the automatic
assembling equipments to assemble efficiently. The unit is a part supply or a robot hand constituting assembling
equipment. It is the system which decides these units placement where of the assembling equipment you place theinterference of the both arms of the robot using reinforcement learning in spite of being a guard.
Keywords: Reinforcement Learning, Unit placement decision, Double arm robot, Assembly machine

1. Introduction

2. UNARM

In recent years, automations of the assembly works withrobots have been performed in the production area. As
for the automatic assembly machine with robots, theproduction efficiency changes by the placement of each
unit. However, because the placement of units isdetermined by the experience of engineers in manyfactories, it is questionable whether the determinedplacement is good or not. To solve the problem, wedevelop the system, Units-layout Nomination for
Assembly with Robot Mechanism (UNARM), to assist
the unit placement decision of the assembly machine withrobots. In addition, we apply UNARM to a cell typeassembly machine with robot both arms and ascertain theusefulness of UNARM.

UNARM is the system to decide each unit placement of
the assembly machine using reinforcement learning. For
the placement place of each unit chosen by every learning,
UNARM calculates the assembly time and improves theunit placement using machine learning and the calculate
time.
2.1. Structure of UNARM
UNARM consists of two modules, (ⅰ)the conditions
making module and (ⅱ)the learning module as shown in
Fig.1.
After the condition making module reads the work
data and the possible area of unit placements, the module
gets ready to learn in a learning module based on it. The© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 238

Hirotaka Moribe, Hidehiko Yamamoto, Takayoshi Yamada

work data means the time between chuck and unchuck,the coordinates that both arms move and the work
sequence. The learning module uses reinforcement
learning based on values and repeats the four processes,①to choose the tentative placements, ②to calculate theworking time, ③to evaluate the acquired cycle time, and④ to repeat the four reward distribution operations.
Finally, the most suitable placement of the units can bedecided.
Fig. 2. Divided placements.

10

10

10

10

10

10

10

10

10

10

10

10

10

10

10

10

Fig. 3. Distributions of values.

Step1. Make an initial values of the values.
Step2. Decide the placement of the unit by a rouletteselection.
Step3. Calculate the coordinates of each part.
Step4. Calculate the moving distances of the robot arms.
Step5. Calculate the working time of the robot arms.
Step6. Compare the calculated working times with thestandard value.
Step7. If the time is smaller, give the reward.
Step8. If the working time is smaller, update the standard
value.
Step9. Repeats Step2 from Step8 till a decided reputation
times.
We adopt the concept of waiting time not to produce
arm interferences because the two arms move at the sametime in Step5. In step6, the concept chooses the longer
working time as the next evaluation time.

Fig. 1. System construction of UNARM.

2.2. Condition making module
The condition making module carries out the following
processes.
Step1. Read each work data of the robot two arms.
Step2. Read the possible area of each unit placement.
As shown in Fig.2, the possible area of each unit
placement is divided as a lattice and the center of eachlattice becomes the center of the candidate place.
Step3. Give initial values to the candidate place createdin Step2.

3. Moving method of robot arms
2.3. Learning module

To calculate the working time of the robot arms in Step5,
the robot arm moving speed is necessary to calculate.

The learning module carries out the following processes.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 239

UNARM System to Decide

Therefore we define the arm moving locations as follows,
Apprpach-1: the upper point of the part that a robot will
take, Approach-2: the upper point closer to Approach-1
and Chuck point: the point that the arms chuck, unchuckor put a part. We also define the arm moving speed
among the upper three points. The speed between
Approach-2 and Approach-2 is defined a feet speed. The
speed between Approach-2 and Approach-1 as approach
speed I, the speed between Approach-1 and chuck point
as approach speed II. In addition, as these three kinds ofspeed have different speeds whether the arm grasp a partor not, the six kinds of speed value definitions in totalmust be chosen. In this study, we adopt that the robot
arms always move by regular linear motion.

confirm the completion of assembly work X. In otherwords, the assembly work A must start after thecompletion of assembly work X. We define this time lagas the waiting time.

Fig. 5. Both arms of robot.

The following shows the waiting time.We define thetwo flags, Lp and P.
Lp(Look at Point): Job that starts to need the waiting time.
P(Point): Work corresponding to the waiting time of Lp.
In order not to make arm interferences, the works
that need the waiting time are given Lp and thecorresponding works are given P.
For example, as shown in Fig.6, when the left arm
starts Lp, the left arm can continue the work as the rightarm still finished P. This is because waiting time is not
given to the left arm.

Fig. 4. Robot arm movements.

4. Concept of waiting time
The robot of this study has both arms. When we use theboth arms, the most difficult point is to interfer both arms
each other. In other words, it is necessary to decide thework sequence not to make the interference of the rightarm and the left. Therefore we adopt the concept, thewaiting time, to prevent arm interferences in calculating
working times of one cycle in Step5. in the learning
module. For example, the assembly sequence exists thatthe assembly work A must be set after the completion of
the assembly work X. In this case, when one of the twoarms performs assembly work A, the other arm mustFig. 6. No waiting time.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 240

Hirotaka Moribe, Hidehiko Yamamoto, Takayoshi Yamada

Acquired placements of the minimum evaluation
time are shown in Fig.8. The placements acquired byrandom selections are shown in Fig.9.

On the contrary, in the case of Fig.7, when the left
arm starts Lp, the work of P of the right arm is notfinished. This is because the left hand has to wait sevenseconds to start Lp. The seven seconds correspond to
waiting time.

Fig. 8. Placements of the minimum.

Fig. 7. Waiting time.

5. Simulation applications
We applied UNARM to the cell type assembly machinewith the both arms of the robot to ascertain the usefulness
of UNARM. The application example adopted eight units(two of the eight are trays that many parts were put), 10
initial values, 10 reward levels, 600,000 times learning
number, 36 left arm assembly processes and 40 right arm
assembly processes. Table 1 shows the moving speed of
the robot arms. In addition, in order to compare thelearning results, the simulations randomly to choose unit
placements were also carried out.
Table 1. Moving patterns of arms.
Approach-2―Approach-2
Approach-2―Approach-1
Approach1―Chuck point

5.1.

Without part
770mm/s
400mm/s
25mm/sFig. 9. Placements by a random method.

Comparing the results of Table2, UNARM’sevaluation times are better than the random ones.
Especially, the mean evaluation time is shorter six
seconds. It is said that UNARM can continuously acquire
the better assembly cycle time corresponding to theefficient units placement.

With part
380mm/s
250mm/s
50mm/sSimulation results

We carried out ten times simulations for each conditionand showed the mean and the minimum of two kinds
simulations in Table2.

6.

In this study, we developed UNARM system which finds
the better unit placements and prevents the interferencesof both arms. To do this, UNARM adopted the conceptof waiting time. By applying UNARM to the cell-type
assembly machine that includes the both arms robot,
UNARM acquired the good unit placements. I t is
ascertained that UNARM is useful.

Table 2. Simulation results.

Average timeThesmallest
time

UNARM(s)Random(s)237.4228
237.2925

243.9498
241.9623

Conclusions© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 241Development of a Hydraulic Underwater Manipulator for Deep-Sea Survey AUV
Takashi Sonoda

Dept. of Human Intelligence Systems, Kyushu Institute of Technology,
2-4 Hibikino, Wakamatsu, Kitakyushu, Fukuoka, Japan
E-mail: t-sonoda@brain.kyutech.ac.jp
Amir Ali Forough Nassiraei

Dept. of Human Intelligence Systems, Kyushu Institute of Technology,
2-4 Hibikino, Wakamatsu, Kitakyushu, Fukuoka, Japan
E-mail: nassiraei@brain.kyutech.ac.jp
Ivan Godler

Twist Drive Technologies, Inc.,
967-3 Ueki, Nogata, Fukuoka, Japan
E-mail: ivan.godler@twistdrive.co.jp
Tharindu Weerakoon

Dept. of Human Intelligence Systems, Kyushu Institute of Technology
2-4 Hibikino, Wakamatsu, Kitakyushu, 808-0196, Japan
E-mail: weerakoontharindu@brain.kyutech.ac.jp
Kazuo Ishii

Dept. of Human Intelligence Systems, Kyushu Institute of Technology
2-4 Hibikino, Wakamatsu, Kitakyushu, 808-0196, Japan
E-mail: ishii@brain.kyutech.ac.jp

Abstract
We are developing a group of AUVs whose mission is to capture underwater creatures or acquire in-situ biologicalsamples in the deep sea. The AUV under developing has a hydraulic manipulator to capture the target creature, andsends underwater images that provide biologists (operators) with information to determine which creatures should
be targeted and captured during the operation. In this paper, the concept and design of hydraulic actuator for 5-axis
manipulator, and the mechanics of manipulator is proposed, and the experimental results of manipulator control at
10 and 20 M Pa pressure are shown.
Keywords: Underwater Manipulator, Hydraulic Actuator, AUV

we are still unable to get detailed information about themechanism of ecological system of dep-sea because of
special features, high pressure, darkness and radio
attenuation. Recently, underwater robots are employed
to observe deep-sea floor as useful tools1-5, and they are

1. Introduction
New discoveries of mineral resources, energy
resources and marine lives in the deep-sea have big
potential to give deep impacts on our society. However,© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 242Kazuo Ishii, Amir Ali Forough Nassiraei, Ivan Godler, Takashi Sonoda, Tharindu Weerakoon.

mainly classified as Human Occupied Vehicle (HOV),
Remotely Operated Vehicle (ROV) and Autonomous
Underwater Vehicle (AUV) depending on the extent ofautonomy and mounted facilities. We have beendeveloping a group of AUV systems whose mission is
to capture underwater creatures or acquire in-situ
biological samples like DNA from various types ofcreatures in the deep sea. Although the final goal is to
get the sample of wide variety of fish and marine
creatures, considering the state of the arts of AUVs, ourfirst target is to catch a creature, which moves slowly
like a small crab or marine benthos.
The AUV under developing6 has a hydraulicmanipulator to capture the target creature, and sends
underwater images that provide biologists (operators)
with information to determine which creatures should
be targeted and captured during the operation. These
images are to be sent from the AUV to the surface
support vessel where the biologist are checking
monitors to determine his/her desirable target creatureand the transmission of image information data iscarried out through an ultrasound communication.
The operation is illustrated in Fig.1. There exist a lotof issues to realize underwater sampling AUV such as
navigation, control, image processing, actuators,
sensory systems. In this paper, we introduce anunderwater manipulator for a deep-sea survey andsampling AUV. Firstly, we introduce our project thataims to develop an observation and capturing system ofmarine creatures using multiple underwater robots
composed of cruising and manipulator mounted AUVs.
Next, we introduce the concept and design of hydraulicactuator for 5-axis manipulator, and the mechanics ofmanipulator to change the linear motion to rotationalmotion using cams, and the experimental results ofmanipulator control at 10 and 20 M Pa pressure.

Operator：
Image I0 is
obtained here.

Operator:
Point out the target
sample in Image I0.

Time

t0
t0+dT
Robot:
Image I0 is
obtained

tC0

tT0

Robot：
Recognize the target
sample in I0.

ReturnImage
ID = I0tR0

Robot：
Return to theposition at t0, start
tracking the target.

Fig.1 Concept of AUV sampling system
2. Hydraulic Underwater Manipulator
The manipulator is supposed to have minimum 3
degrees of freedom (DOF) in the same plane forpositioning (2 DOF) and orientation (1 DOF) of the arm
tip, and 1 DOF for open-close of a gripper.
Basic requirements are:− arm length ~ 600 mm− payload ~ 0.5 kg (max)− external pressure 20 MPa (max)The manipulator should be able to operate in thesalty water at neutral buoyancy, and is actuated by a setof stepping motors, ball screws and hydraulic cylinders
that generates hydraulic pressure to move cylinders on
the manipulator links. In order to convert the linear
motion of cylinder into rotation, a special cam
mechanism was introduced. The variation of linear
motion is 50 mm and converted to 180 degree rotation.
The criterion for the cam design is to achieve minimal,
possibly small lateral force on the piston rod during the
motion.
The problem to minimize lateral force on the piston
rod is the same as to orient the hydraulic cylinder in thenormal direction to the cam shape. We assume that thecylinders are aligned with the direction of the roboticarm length; therefore, the problem is translated into
finding a cam shape r (φ), which satisfies the cylinder
direction to be normal to the cam. For this we need toderive the equations that consider the offset angle
between the cam shape angle parameter φ and the arm
rotation angle θ, which is cause by the initial offset
angle θ0 of the cylinder towards the cam radial direction.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 243

Development of a Hydraulic

The constraint to the solution here is linear motion of
the piston Kθ that is proportional to the rotational angleof the arm θ. The parameters and obtained cam shapeare shown in Fig. 2.

The developed manipulator is shown in Fig.4. The
structure is made of MC-nylon and the pressure hull ismade aluminum with coating.

(a) Cam design parameters

Fig.4 Underwater manipulator for deep-sea

3. Experiments in the Pressure Tank
The experimental results at pressure 10 to 20 MPa in
test tank are shown in Fig.4. Pressure change (increase,decrease) was performed in 12 minutes, and thepressure was kept constant for 12 minutes to do themeasurements. During the measurements, each joint
was moved separately. Joint angles were measured by
RLS 12bit absolute angle magnetic sensor RMD08. A
video was also recorded during each measurement. Eachmain piston of each joint was moved for 4 mm: that is
joint angles 23.59 degrees for DOF1, 36.86 degrees for
DOF2, and 73.72 degrees for DOF3.
Although hysteresis of about 3 degrees (from 3 to
3.5 degrees) in DOF1, about 3 degrees (2.7 to 3
degrees) in DOF2, and about 9 degrees (8 to 9.5
degrees) in DOF3 can be observed in the results
independently of the pressure, the manipulator showed
good performance in deep-sea pressure.

(b) Results of Cam calculation

Fig.2 Parameters for cam design and obtained cam
shape.
The hydraulic underwater manipulator is composedof dual-oil system as shown in Fig. 3. The oil in themain cylinder is pushed to the arm cylinder whose shaft
pushes the cam, then the arm rotates. The other side ofarm cylinder is connected to the back side of main
cylinder.

4. Conclusion
In this paper, the concept and design of hydraulicactuator for 5-axis manipulator, and the mechanics ofmanipulator is proposed, and the experimental results ofmanipulator control at 10 and 20 MPa pressure are
evaluated. It is shown that the developed manipulator isFig. 3 Concept of manipulator system.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 244Kazuo Ishii, Amir Ali Forough Nassiraei, Ivan Godler, Takashi Sonoda, Tharindu Weerakoon.

enough small to be mounted on AUV, and can be
actuated under the deep-sea pressure.

DOF1
25

20

15
Joint angle (deg)Acknowledgement
This research is supported by JST CREST“Establishment of core technology for the preservation
and regeneration of marine biodiversity andecosystems”.

0MPa-01
10Mpa-01
20Mpa-01
10Mpa-02

100MPa-02
20Mpa-02
0MPa-03

5

20Mpa-03
0MPa-04

0

-5
0

0.5

References

1

1.5

2
2.5
Main piston motion (mm)

3

3.5

4

4.5

(a) First Joint Angle

1. Y. Junku, T. Ura , G. Bekey, Underwater robots,
Springer Science & Business Media, (2012)
2. Whitcomb, Underwater robotics: Out of the researchlaboratory and into the field, Proc. of IEEE/ICRA'00,(2000)
3. Y. Nishida, T. Ura, T. Sakamaki, J. Kojima, Y. Ito , K.
Kim, “Hovering Type AUV “Tuna-Sand” and Its Surveys
on Smith Caldera in Izu-Ogasawara Ocean Area, Proc. ofIEEE/OCEANS2013, (2013)
4. T. Maki, Y. Sato, T. Matsuda, R. T. Shiroku , T.
Sakamaki, “AUV Tri-TON 2: An intelligent platform fordetailed survey of hydrothermal vent fields,” Proc. ofIEEE/AUV2014, (2014)
5. Y. Nishida, K. Nagahashi, T. Sato, A. Bodenmann, B.
Thornton, A. Asada, T. Ura, Development of anautonomous underwater vehicle for survey of cobalt-richmanganese crust, Proc. of IEEE/OCEANS2015, (2015)
6. Y. Nishida, T. Ura, T. Nakatani, T. Sakamaki, J. Kojima,
Y. Itoh and K. Kim, Autonomous Underwater Vehicle“Tuna-Sand” for Image Observation of the Seafloor at a
Low Altitude, Journal of Robotics and Mechatronics,
Vol.26, No.4, pp.519-521, 2014

DOF2
35

30

Joint angle (deg)25200MPa-01
10Mpa-01

15

20Mpa-01
10Mpa-02
0MPa-0210

20Mpa-02
0MPa-03

5

20Mpa-03
0MPa-04

0

-5
0

0.5

1

1.5

2
2.5
Main piston motion (mm)

3

3.5

4

4.5

(b) Second Joint Angle
DOF3
90
80
70

Joint angle (deg)60500MPa-01
10Mpa-01
20Mpa-01
10Mpa-02
0MPa-02
20Mpa-02
0MPa-03
20Mpa-03
0MPa-04

40
30
20
10
0
-10
0

0.5

1

1.5

2
2.5
Main piston motion (mm)

3

3.5

4

4.5

(c) Third Joint Angle
Fig.4 Experimental results in the pressure tank.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 245

Floating LBL concept for underwater position detection
Keisuke Watanabe

Dept. of Navigation and Ocean Engineering, Tokai University,
3-20-1 ORido Shimizuku, Shizuoka, Shizuoka, Japan
E-mail: keisukejapan@gmail.com
Kazuo Ishii

Dept. of Human Intelligence Systems, Kyushu Institute of Technology
2-4 Hibikino, Wakamatsu, Kitakyushu, 808-0196, Japan
E-mail: ishii@brain.kyutech.ac.jp

Abstract
Determination of underwater coordinates is important in such activities like underwater vehicle operation,
installation of underwater structure and so on. The determination methods are using LBL, SSBL or an inertia
navigation sensor, which are all very expensive. In some scientific or academic operations, the cost of position
determination of an underwater structure or a vehicle is too expensive to realize in the sea experiments and a low
cost position detection system should be developed. In this paper, a floating LBL concept is presented. The LBL
system consists of at least three floating buoys each of which has a GPS, a pair of ultrasound transmitter andreceiver, wireless communication device and a microcomputer board which controls the total procedure. We will
introduce and explain about each module of the buoy system, ultrasound transmitter/receiver.
Keywords: Underwater position detection, floating LBL, subsea installation

map of the total reef condition in the supposed area. The
price of ROVs or underwater cameras is becoming
cheaper and it seems ridiculous to use SSBL whose
price is almost from five to ten times of ROV itself.
Another supposed operation is installation ofunderwater devices or structures. To put the structure
down on the desired position of the seabed, we need tomonitor the coordinate of the structure during its
installation operation. If the project is related to oil/gas
development, this coordinate detection cost is relatively
small compared to the total project investment.
However, if the project is related to an academic
research like settling a seismic sensor down on theseafloor, the cost of position detection must be muchcheaper.
In this paper, a floating LBL concept is presented.
The LBL system consists of at least three floating buoys

1. Introduction
Determination of underwater coordinates isimportant in such activities like underwater vehicle
operation, installation of underwater structure and so on.
The determination methods are using LBL, SSBL or aninertia navigation sensor, which are all very expensive.
In some scientific or academic operations, the cost of
position determination of an underwater structure or avehicle is too expensive to realize in the sea
experiments and a low cost position detection systemshould be developed. One example supposed in ourlaboratory is position detection of a ROV in the coral
reef monitoring operation. In this operation, we need totake underwater pictures of coral reefs to record andmonitor the condition of the reefs, where we needunderwater coordinates of the picture taken to make a© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 246

Keisuke Watanabe, Kazuo Ishii

each of which has a GPS, a pair of ultrasoundtransmitter and receiver, wireless communication device
and a microcomputer board which controls the total
procedure. We will introduce and explain about each
moduleofthebuoysystem,ultrasoundtransmitter/receiver.

(4) The buoys receive the ultrasound signal from theunderwater structure. Each buoy stops the timer andrecords the GPS (N,E) coordinate at the same time.
(5) If the master buoy receives the returned signal fromthe underwater structure, it stops its timer and records
the time as well as its GPS position values.
(6) The master buoy calls to the slave buoy No.1
through Xbee and the slave No.1 calls back its timer
count value and GPS coordinate through Xbee
communication to the master buoy.
(6) The slave buoy No.2 calls back according to therequest from the master buoy.
(7) The master buoy calculates the distance between themaster and the slave respectively using the GPS
coordinate values gotten from slaves.

2. Floating LBL system
The floating LBL system consists of at least 3 buoys
each of that has a pair of ultrasound receiver andtransmitter. A buoy also has a set of Xbee wirelesscommunication device to make a serial communication
link with a master computer.

(8) The master calculates the distance between eachbuoy and the underwater structure using the timer
values gotten from slaves and its own.
(9) Using the distances mentioned above, the master cancalculate the underwater coordinate of the structure.
(10) This procedure will be repeated periodically in thegiven interval time.

3. An example of underwater structure motion
Fig.1 The concept of floating LBL system
A GPS unit is attached on each buoy. The masterbuoy is the origin of the coordinate and controls thetotal system. The procedure of the coordinate
calculation of underwater structure is as follows.
(1) The master buoy transmits the start signal to slave
buoys using Xbee module. At the same time, the masterbuoy transmits the ultrasound signal to underwater
structure. The timer of the master buoy is cleared andset zero.
(2) As soon as a slave buoy receives the start signal, it
clears its own clock and set zero. At the same time, theultrasound receiver of the slave buoy goes waiting mode.
(3) The underwater structure receives the transmitted
signal from the master buoy and responds back theultrasound signal toward water surface where each buoy
is floating.

As an example of the application of the floating
LBL system, we considered a hanged underwater
structure from a crane on the vessel. The offset angle
from the vertical axis is a parameter of the nonlinear
pendulum motion of the structure as shown in Fig.2.
It is assumed that the crane cable is paid out fromthe initial length l_0 with v m/s speed. The initial length
corresponds to the height from the initial submerged
point of the structure to the hanging point of the top of
the crane arm.
Considering the added mass and fluid resistanceforce, the equation of motion of the pendulum system is,, where, f is control force, Cm is added mas
coefficient, m is the mass of the structure, theta is theangle of the cable, l is the length of the cable, v is the
speed of the cable pay out, Cd is the drag coefficient, A
is the effective area of the structure.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 247

Floating LBL concept for

The simulation is executed using next parameters.
The structure size is 5m wide 5m long 4m height. The
mass is assumed 50tf. The water depth is assumed as2500m.The initial offset angle is 20 deg. The paid out
rate of the cable are 2 cases, which is 1m/s and 0.5 m/s.
Added mass coefficient is assumed 1.0, whereas dragforce coefficient is assumed as 3.0.
The simulation result of the paid out rate is 1.0m/s isshown in Fig.3. The horizontal displacement whichmeans the fluctuation from the desired installation pointis shown in Fig.4. As Fig.3 shows, the offset angle isdecreasing according to time, which is due to damping
effect of fluid resistance force. However, as the crane
cable length is increasing, the horizontal offset from thedesired point is developing as shown in Fig.4. I assumed
the drag coefficient higher assuming that some damping
device is attached to the structure. But from the result
shown in Fig.4, it shows very difficult to reduce theoffset vibration amplitude only using passive fluid
damping force. So the active control method should be
employed to reduce the vibration of the structure. For
the active control of the structure, we need to detect theunderwater position, so the floating LBL system should
be developed.

Fig.3 Vibration angles of the cable of the structure

Fig. 4. The horizontal displacement of the structure

4. Ultrasound transmitter/receiver
Ultrasound receiver/transmitter is expensive in
general. So we are developing a set of ultrasoundreceiver/transmitter for this experiments. Fig.5 shows
the sea trial of our ultrasound receiver/transmitter. We
succeeded more than 20m distance measurement,
however, we found that the directivity of the sensor
probe is too narrow so we need to improve it to realize
the sea experiments. Through this experiments werealized the delivery of the master and slave buoys on
the sea surface is very hard task. So we modified a UAV
drone to be floatable and watertight to bring the buoys
from the support vessel to the desired sea surface.

Fig. 2 Sand captured by the blocks© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 248

Keisuke Watanabe, Kazuo Ishii

Acknowledgement
This research is supported by KAKANHI, Grant-inAid for Challenging Exploratory Research, JSPS
JP26630230.

5. Conclusion
In this paper, we presented our floating LBL
concept for the detection of the coordinate of theunderwater
structures.
Using
Xbee
wireless
communication and ultrasound ranging system, we canconstitute a set of LBL much cheaper than the
traditional LBL system that needs very expensive setup
for seafloor baseline installation. To put the buoys on
the desired surface position from the support vessel, we
also modified a UAV drone to deliver the buoy systemfrom the vessel to the sea.

References
1. K. Watanabe, “A Possibility of Active Controlled
Installation for Deep Water Subsea Production Modules”,
Proceedings of ICETET2012, IEEE
2. Y. Watanabe, K. Watanabe, F. Yuasa, “ A self-walking
vertical mining system concept using DTH for seafloor
mining and its preliminary design”, OTC27003-MS, 2016
3. K. Watanabe, Y. Watanabe, F. Yuasa, “A vertical mining
system concept using DTH for seafloor mining”,
OTC25825-MS, 2015

Fig.5 Distance detection experiments at sea© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 249

Experiments of Floatable UAV Drone for Wave Dissipating Block Inspection
Keisuke Watanabe

Dept. of Navigation and Ocean Engineering, Tokai University,
3-20-1 ORido Shimizuku, Shizuoka, Shizuoka, Japan
E-mail: keisukejapan@gmail.com
Koshi Utsunomiya

Dept. of Navigation and Ocean Engineering, Tokai University,
3-20-1 ORido Shimizuku, Shizuoka, Shizuoka, Japan
E-mail: bebop0911@gmail.com
Kazuho Mitsumura

Dept. of Navigation and Ocean Engineering, Tokai University,
3-20-1 ORido Shimizuku, Shizuoka, Shizuoka, Japan
E-mail: kazuho.rc@gmail.com
Shiyun Takasaki

D Dept. of Navigation and Ocean Engineering, Tokai University,
3-20-1 ORido Shimizuku, Shizuoka, Shizuoka, Japan
E-mail: 4bkw1105@tokai-u.ac.jp

Abstract
In this paper, we present our modification experience of making a UAV drone watertight and floatable. Then we
present several experiments on taking pictures considering ocean monitoring in the sea near our campus. The
watertight experiments are done from our school’s pier. In these experiments, we repeated to start flying the drone
from the sea surface and fall it down into the water several times. Using this UAV drone, we tested several
experiments to take pictures of wave dissipating blocks at MIHO Island where beach line erosion is critical andmany blocks are arranged to prevent waves from washing the shore. We can see the shoreline very clearly from
more than 200m high as a macro image and also we can inspect the blocks looking around its stacks as well as
approaching near the sea surface where waves break and wave splash washes the drone.
Keywords: UAV drone, environmental monitoring, coastal erosion, offshore structure monitoring

structures like wind power stations or coral reef
observation etc. Usually the UAV drone flies highabove the water surface, however, it is possible that thedrone happens to fall down into the water due tooperator’s control mistake or malfunction of itself. In
addition, in some operations, it is desirable for the drone
to be able to float on the water surface to save energy

1. Introduction
A UAV drone is an effective low costphotographing apparatus for ocean environment
monitoring. Taking images is essential for theseenvironment monitoring such as coastal line monitoring
where beach erosion is severe, inspection of offshore© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 250

Keisuke Watanabe, Koshi Utsunomiya, Kazuho Mitsumura, Jiyun Takasaki

consumption. In those cases, we need to make the drone
watertight and floatable.
As shown in Fig.1, while the MIHO Island in
Shizuoka prefecture is designated as a World Heritage
Site, rapid and massive beach erosion of the island is
one of the critical issues. It is said that UNESCO is
warning if the wave dissipating blocks will not be
removed, they will cancel the WHS designation because
the blocks damage the beauty of the landscape of thecoastline of the island. However, the wave dissipating
blocks are essential to keep shore sand from being
washed away by waves or currents. There is a very
important road Route 150 that faces the shore and themost important role of these blocks is protecting theroad from caving or depression due to road foundation
washing away.
So it is very important for us to monitor the
shoreline as well as the condition of those blocks widely
viewed for some long term constantly. Using watertight
UAV drones are one of the possible solutions for thisproblem.

Fig.2 Electrical component in a watertight container

2. UAV drone modification for watertightFig.3 UAV watertight experiment in the sea

To realize the UAV drone monitoring system, wefirstly need to examine how to make it watertight andfloatable. The electrical components that are vulnerable
to water are GPS module, wireless communication
system, motor drivers, batteries and motors forpropellers. We packed those systems inside a watertightcontainer as shown in Fig.2.

We tested in our tank to check whether this method
is available or not. At first, we experienced sometroubles, for example, one of the wire connectors of
motors are detached by the impact of falling down to thewater surface, which capsized the drone due to theflying force unbalance. Through these experiences wemodified the drone to be able to use on the water
surface.
As the motors for the propeller are brushless motors
and if we wash those motors with fresh water soon after
we use them, they can be used even if we put the drone
into seawater as shown in Fig.3 and Fig.4.

3. Monitoring of wave dissipating blocks
After we confirmed our watertight method isavailable, we took several experimental pictures of the
wave dissipating blocks at MIHO Island.
Fig.5 shows one area of those blocks from the heightof around 100m. From this picture, we can see theFig.1 Coastal shore erosion and wave dissipating blocks© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 251

Experiments of Floatable UAV

blocks captured some sand that is put by using many
dumper trucks to fill the eroded shore to recover.
However, it also shows at the edge of the lined blocks,
there seems another erosion started as shown in theupper left of Fig.5. Fig. 6 shows that shore erosion
proceeds in between two block areas. As we can see
from the picture, the shore line vanished clearly
between the two block areas. From the different colorsof the water, we can see the sand flow in the sea. As thelower part of the Fig.6 shows we can notice a kind ofvortex flow which surrounds the edge of blocks wherewater color is rather green than blue compared to theleft side of the picture. This implies a mountain ofblocks can keep some sand around its area, however, theeffect of accumulation is quite limited. For more than 20
years, hundreds of dumper trucks transported sand to fill
the shore and put tons of sand into water, the shore line
has not recovered yet. From the pictures taken this time,
we confirmed our feeling that sand put into water neverrecover shoreline, instead, waves wash away sand. This
picture indicates if we monitor the same point for a longterm and trace changes of the landscape, we canestimate the amount of washed away sand as well as
find more effective arrangements of the blocks tocapture sand put by the dumper trucks everyday.
Fig.7 shows the closer look of the blocks where theisland of blocks are sagged. From the picture, we findthe sagging downward of the blocks are where thoseblocks are not old but newly put into the shore. The
reason why this area is sagged downward is not clear,
however, several blocks underwater are destroyed. To
find the reason, we inspected the blocks further bytaking many pictures.
As shown in Fig.8, we found a broken block thatlooks newly put judging from its surface condition that
there are few seaweeds on the surface of the block. We
can find several broken blocks like this, and the reasonof sagging downward shown in the Fig.5 seems due tothis kind of collapse. We need to investigate further tofind the exact reason of this kind of collapse of a block.

Fig.4 UAV floating experiment on the seasurface

Fig. 5 Sand captured by the blocks

Fig.6 Erosion proceeds between block areas
Furthermore, we found the evidence of wave abrasionof the block as shown in Fig.9. This is because the wateraround the mountain of blocks contain sand as I
mentioned in Fig.6. From Fig.9, we guess sand mixed
water abrades the block faster compared to normal sea
water, though we need to investigate further from thisview point.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 252

Keisuke Watanabe, Koshi Utsunomiya, Kazuho Mitsumura, Jiyun Takasaki

4. Conclusion
In this paper, we modified the UAV drone to be able
to use on the sea. We packed the electrical components
into a watertight container and checked the brushless
motors of propeller can be used with no special
fabrication if we maintenance them correctly. Using thiswatertight and floatable UAV drone, we triedmonitoring of wave dissipating blocks at MIHO shore.
We found out several new things related to collapse ofblocks and shore line erosion from observation using
this UAV drone.

Fig.8 Broken blocks which are relatively new

Acknowledgement
This research is supported by KAKANHI, Grant-inAid for Challenging Exploratory Research, JSPS
JP26630230.
References
1. K. Nihiguchi, S. Sato, T. Sakashita, Y. Tajima, Y.
Sekiguchi, K. Tsuchihashi, “Beach Erosion Mechanism
by a Structure Protruding from Shoreline and the
Performance of a Sand-packed Jetty against Beach
Erosion”, Journal of JSCE, Vol.68, No.2 (2012)
2. K. Kimura, K. Nemoto, Y. Nagahama, O. Nishimura,“Characteristics of Beach Erosion and Effect of the
Beach Erosion of the Habitat of Macrobenthos in MIHO
Beach”, Journal of JSCE, Vol.21, pp.229-234, (2005)

Fig.9 Wave sand abraded block

Fig.7 Downward sagging area of the blocks© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 253

Development of Self-Diagnosis System of an Autonomous Underwater Vehicle Tuna-Sand 2
Takashi Sonoda

Dept. of Human Intelligence Systems, Kyushu Institute of Technology
2-4 Hibikino, Wakamatsu, Kitakyushu, Fukuoka, 808-0196, Japan
E-mail: t-sonoda@edu.brain.kyutech.ac.jp
Naoya Fujii

Dept. of Human Intelligence Systems, Kyushu Institute of Technology
2-4 Hibikino, Wakamatsu, Kitakyushu, Fukuoka, 808-0196, Japan
E-mail: t-sonoda@edu.brain.kyutech.ac.jp
Yuya Nishida

Dept. of Human Intelligence Systems, Kyushu Institute of Technology
2-4 Hibikino, Wakamatsu, Kitakyushu, Fukuoka, 808-0196, Japan
E-mail: ynishida@lsse.kyutech.ac.jp
Kazuo Ishii

Dept. of Intelligence Systems, Kyushu Institute of Technology,
2-4 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka, Japan
E-mail: ishii@brain.kyutech.ac.jp

Abstract

For autonomous underwater vehicle (AUV), high autonomy is required in order to accomplish mission suchas inspection, observation, manipulation under extreme environments, deep-ocean. One of necessaryfunction for AUV is the self-diagnosis system to detect the abnormality can be said to be an important feature.
In this paper, we propose a self-diagnostic system using the dynamical model of Sampling-AUV “TUNASAND2”, where the fault device detection is carried out using the model, and evaluated through tank tests.
Keywords: AUV, Self-Diagnosis System .

1.

self-diagnosis system to detect the abnormality can besaid to be an important feature.
As a previous study, Takai7 et al. proposed a selfdiagnosis system utilizing a dynamic model by a neuralnetwork (NN) However, NN cannot prove the stability ofthe model theoretically.
In this paper, we propose a self-diagnostic system using
the dynamical model of Sampling-AUV “TUNA-

Introduction

Underwater robots have attracted attention as usefultools1-6 to observe deep-sea floor. Especially,
Autonomous Underwater Vehicle (AUV) can move wide
area freely not having tethered cable with support vessel.
On the other hand, for AUV, high autonomy is requiredin order to accomplish mission such as inspection,observation, manipulation under extreme environments,
deep-ocean. One of necessary function for AUV is the© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 254

Takashi Sonoda, Naoya Fujii, Yuya Nishida, Kazuo Ishii

SAND28”, where the fault device detection is carried out

Based on the above condition setting, we define theequation of motion of AUV9 in eq. (1) and the current
consumption of AUV in eq.(2).
= −1{− ( −1 − )| −1 − | + ( − ) −1} (1)
= 0+
Here,

∑(1 − )
y

= [
=[

AUV,

1

2

3×3

3

Z ]
4

(2)

−1

means the velocity vector of] is the thruster control signal

vector,∈ R
is the mass and inertia moment
3×1
including additional mass,∈ R is the hydraulic
3×4

coefficient, ∈ R is the inverse kinematics transfer
thruster output to the force to the center of gravity of
AUV. Regarding eq. (2),is the total consumption
current of AUV, 0 is the steady consumption current of
CPU and others excluding thrusters, e τ is the conversion
coefficient from command thrust to thruster consumption
current. Also, μ = [μ x μ 0] , γ ={γ 1, γ 2, γ 3, γ 4}
are the ambient power flow rate to be diagnosed, thepower reduction rate of each thruster (0 to 100 [%]) .
State vector , observation vector , manipulation
vector , abnormal variable β to be diagnosed as inequations (3) - (6).

using the model, and evaluated through tank tests.
Fig.1 Newly Developed AUV “TUNA-SAND 2”

=

=
= [
=

Fig. 2 Thruster Arrangement in Horizontal Plane.

̇

̇

̇

(3)
]

(4)
(5)
(6)

, the translational speed, angular velocity, current
consumption, can be observed from the velocity sensor,
the inertial navigation device and the current sensor.
Using eqs. (1) to (6), the state equation motion is in (7)and observation equation is in (8) .

2. Dynamic Model of AUV
Depending on the mission objectives, AUV has
mounted different sensors and thrusters, so themathematical model also changes. Therefore, a hovering
type AUV “TUNA-SAND2” jointly developed by the
University of Tokyo and Kyushu Institute of Technology
was set as target robot. We assume two abnormalities tobe detected are (1) tidal disturbance and (2) low thruster
output.
TUNA-SAND 2 has six thrusters, as shown in Fig. 2,
it has a redundant drive system using four thrusters forthe horizontal movement of the three DOF of Surge,
Sway and Yaw. In order to develop an algorithm to solve
this redundancy, AUV models the horizontal planemovement in diagnosing the failure of the thruster.

= ( −1,
=
=[
3.

−1,
4×3

)
]∙

(7)
(8)

Self-Diagnosis System

The basic concept of self-diagnosis system is shownin Fig. 2. Based on the time series data of the manipulated
variableand observation variable y of AUV, theobserver to estimate βis constructed.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 255Development of Self-Diagnosis System

Table 1 Simulation Condition for Observer
Evaluation

Fig. 3 The concept of self-diagnosis system

1-10 - 25
25 - 50Thruster 1 stop
Normal=1-

2
3

50 - 75
75 - 100Thruster 1&3 50% down
All thruster 50% down

4100-125

Current

,

= 0.5
= 0.5

x=0.2

④

③

②

①

④

( )−

Parameters-③(

Condition
Normal②∑

Time[s]0-5①First, the diagnostic data is the time series of data intime [ , - ]. The initial state in the diagnosis at time t is
- and the manipulated variable are- ,…,-1. Bysubstituting these into the eqs. (7) and (8), theobservations( ) to( ) are estimated. Here,the estimation error RSS( ) is calculated in eq. (9).
( )=∑

ID
-

( )) (9)

a

④

③

②

①

(d) 4

④
④
③
③
②
②

①
①

④

③

②

①

3

④

②

①
c

(b) 2

1

③

Here, M is the dimension number of the observation
vector, ε is the weighting parameter, and(β) showsthe error between the proposed model and the measured
value. By finding the optimal solution β that minimizes
this function(β), the model estimates the current
defaults. The steepest descent method was used forsearching for the optimal solution β (see Fig.4).

Fig.4 Parameter search by changing .
4.

Evaluation of Proposed System

4.1. Simulation

(e x
(f) y
Fig.5 Performance Evaluation by Simulations.

The simulations are carried out supposing that
defaults and disturbances happen during the cursing at
0.2 m/s in forward direction as shown in Table 1.

We evaluated the diagnostic performance bysimulation as shown in Fig.5, assuming the case where© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 256

Takashi Sonoda, Naoya Fujii, Yuya Nishida, Kazuo Ishii

four kinds of defaults occurred. The solid line means theestimated defaults and dot line true value. In thesimulations, the observer can estimate the defaults anddisturbances.
4.2. Experiments
We had evaluation experiments using AUV TUNASAND2, where the AUV is cruising at 0.2 m/s in surge
direction and all thrusters outputs become half after 10
seconds passed. The evaluation results are shown in Fig.6.
The estimation of reduction percentage is over-estimatedthan commanded values, thrusters’ defaults are detected.

Low Altitude, Journal of Robotics and Mechatronics,
Vol.26, No.4, pp.519-521, 2014M.
7. Takai, T. Ura, Development of a system to diagnoseautonomous underwater vehicles, International Journal of
Systems Science, 30(9), (1999) 981-988.
*2
8. Y. Nishida, T. Sonoda, S. Yasukawa, J. Ahn, K. Nagano ,K. Ishii and T. Ura, Development of an Autonomous
Underwater Vehicle with Human-aware Robot Navigation,
Proc. IEEE/OCEANS2016, (2016) 4 pages.
9. Thor I. Fossen, Guidance and Control of Ocean Vehicle ,John wiley & sons, (1995)

5. Conclusions
In this research, we proposed a model based faults
detection system for diagnosis of AUV. The system is
evaluated by simulations and experiments using AUV
TUNA-SAND2, and the results show good performanceand detect the faults.

(a

1

(b) 2

(c

3

(d) 4

Acknowledgement
This research is supported by JST CREST“Establishment of core technology for the preservation
and regeneration of marine biodiversity and ecosystems”.
References
1. Y. Junku, T. Ura , G. Bekey, Underwater robots, Springer
Science & Business Media, (2012)
2. Whitcomb, Underwater robotics: Out of the researchlaboratory and into the field, Proc. of IEEE/ICRA'00,(2000)
3. Y. Nishida, T. Ura, T. Sakamaki, J. Kojima, Y. Ito , K.
Kim, “Hovering Type AUV “Tuna-Sand” and Its Surveys
on Smith Caldera in Izu-Ogasawara Ocean Area, Proc. ofIEEE/OCEANS2013, (2013)
4. T. Maki, Y. Sato, T. Matsuda, R. T. Shiroku, T. Sakamaki,“AUV Tri-TON 2: An intelligent platform for detailed
survey of hydrothermal vent fields,” Proc. ofIEEE/AUV2014, (2014)
5. Y. Nishida, K. Nagahashi, T. Sato, A. Bodenmann, B.
Thornton, A. Asada, T. Ura, Development of anautonomous underwater vehicle for survey of cobalt-richmanganese crust, Proc. of IEEE/OCEANS2015, (2015)
6. Y. Nishida, T. Ura, T. Nakatani, T. Sakamaki, J. Kojima,
Y. Itoh and K. Kim, Autonomous Underwater Vehicle“Tuna-Sand” for Image Observation of the Seafloor at a

(e x
(f) y
Fig. 6 Evaluation Using TUNA-SAND2.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 257

Development of Underwater Wireless Power Supply System Using Resonant Energy Transfer
Shota Hidaka

Dept. of Human Intelligence Systems, Kyushu Institute of Technology
2-4 Hibikino, Wakamatsu, Kitakyushu, Fukuoka, 808-0196, Japan
Kazuo Ishii

Dept. of Intelligence Systems, Kyushu Institute of Technology,
2-4 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka, Japan
E-mail: ishii@brain.kyutech.ac.jp
Keisuke Watanabe

Dept. of Navigation and Ocean Engineering, Tokai University,
3-20-1 ORido Shimizuku, Shizuoka, Shizuoka, Japan
E-mail: keisukejapan@gmail.com

Abstract
Autonomous Underwater Vehicles (AUV) is one of the key technologies for deep ocean research. However AUV cannot
work for long time due to the limitation of battery capacity. To solve this problem, a noncontact wireless power supply
is required for AUV to conduct observations for long period. In this study, we developed a resonant energy transfer
system, which is mounted into a small AUV “DaryaBird” and the simulations and evaluation tests are carried out in thetest tank.
Keywords: AUV, Wireless Power Supply .

between power supply and receive coil should be short.
Thus, AUVs are required to have highly accurate docking
with the underwater stations. Ura7 et. al developed awireless power supply system using Electromagnetic
induction system, and accomplish 3-days operation.
The purpose of this research is to perform underwater
wireless power supply using magnetic resonance method.
It is possible to feed in a wide range stronger than theconventional methods even if the large gap of coils andthe coil center axis exit. Measurements of thetransmission efficiency in the underwater environment
using the magnetic field resonance method are carried out,
and the changes in the transmission efficiency due to thesize and position deviation of the coil are evaluated.
Transmission range do the actual experiment using the
AUV "DaryaBird". The comparison of electromagnetic

1. Introduction
Japan possesses a vast ocean area, seabed resources
such as abundant energy resources and minerals havebeen found in the deep-sea. However, the deep-sea is one
of extreme environments that human cannot accessdirectly. Therefore, Underwater robots have attracted
attention as useful tools1-6 to observe deep-sea floor.
Especially, Autonomous Underwater Vehicle (AUV) canmove wide area freely not having tethered cable withsupport vessel. However, AUV has the disadvantage of ashort operation time. In order to realize long-term
operation of the AUV, a wireless battery charging systemon the underwater observation station is necessary.
In the conventional electromagnetic induction system
for wireless power supply, the transmission distance© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 258

Shota Hidaka, Kazuo Ishii, Keisuke Watanabe

induction method and magnetic resonance method isshown in Table 1. The advantage of electromagnetic
induction method is high efficiency, however the needs
accurate coil setting. The magnetic resonance method cantransfer longer distance, and the disadvantage is thedifficulty of the design of resonance circuit. The conceptof magnetic resonance method is shown in Fig.1.
In this study, we developed a resonant energy transfer
system, which is mounted into a small AUV “DaryaBird”
and the simulations and evaluation tests are carried out inthe test tank.

Next, the changes in transmission efficiency caused
by winding number of the coil is evaluated. The radius ofcoils are 30, 40 and 50 mm, respectively (see Fig.2). The
specification of each coil is in Table 2.
Table 2. The specifications of coils.
r [mm]
Outer Diameter [mm]

30
64

40
83

50
112

Winding Number
Inductance [ H]

9
38.3

13
51.2

16
78.1

Table 1. The comparison of wireless supply methods.
Electromagnetic
Magnetic
Induction
Resonance~ 10 cm~1m

100 kHz order~ MHz

Efficiency
Size

70 ~ 90 %~ 3 cm40~ 60 %~ 30 cm

Transmitter

r= 3 0r= 4 0r= 5 0

Fig.2 The coils for performance evaluation.
The results are shown in Fig. 3. The bigger coil ismore efficient that small one, and small gap distanceshows the better performance.
Efficiency

Transfer
Distance
Frequency

RecieverFig. 1 The concept of magnetic resonance method.
Gap Dist [cm]2. Basic Property of Magnetic Resonance(a) Results of Simulations

Efficiency

As the performance evaluation experiments in themagnetic field resonance method, transmission
efficiency by changing environment (air, pure water andsalty water) is compared, and the result is shown in Fig.
2. In all environments, we could not find the distinguishdifferences.

Efficiency

Gap Dist [cm]Air(b) Results of Experiments
Fig. 3 The comparison of efficiency by changing thesize of coil and gap distance.

Pure
Pure
Pure
Water
Water
Water
Salty
Water
Gap Dist. [cm]Fig.2 Comparison by Operation Environments© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 259

Development of Underwater Wireless

Then, the influence of position alignment of coils
centers of transmitters and receivers are evaluated. It isshown that the big diameter coil is more robust than thesmall coil in center difference.

3. Development of Wireless Power Transfer
System for AUV
The wireless power supply system is developed under
the following conditions from the specification of thetarget AUV.
(1) mounting position of the receiving coil is with thebottom surface of the AUV to adopt the landing system.
(2) the maximum diameter of the coil as long as it can beinstalled in AUV considering the axial misalignment
between coils.
(3) the optimal position for maximum powertransmission efficiency exits, so it is necessary to adjust
the gap length between the charging station receiving coil.

(a) Experimental setup. X is the gap of two coils.

Under the above conditions, the wireless powertransmission system was developed, whose maximum
efficiency was 81% at the time of the gap length 8.5cm.
Also regarding positional displacement of the coils has
resulted in power can be supplied in a range of -6cm ~7.5cm.
The wireless power supply system was mounted forcharging experiment to AUV "DaryaBird". Figure 5
shows the block diagram of the system, and Figure 6
shows the arrangement of AUV and charging system.
The voltage of the battery is monitored while charging
the battery, and when the voltage comes to charging
termination voltage, the system cut off the power supply
automatically. The experiments were carried out with thepositional gaps of coils 0 cm, 3 cm, at 6 cm, and eachcharging time were 40.8 min., 61 min. and 137.5 min.
Figure 7 shows the results at the time of 0cm.

(b) Efficiency of r = 30 mm

Fig. 5 The Block diagram of wireless power supply
system.

(c) Efficiency of r = 50 mm
Fig.4 The comparison by position alignment of coilcenters.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 260

Shota Hidaka, Kazuo Ishii, Keisuke Watanabe

and charging time is 1 hour, the positional gap of 3.5 [cm]to 4.5 [cm] is acceptable.
References
1. Y. Junku, T. Ura , G. Bekey, Underwater robots, Springer
Science & Business Media, (2012)
2. Whitcomb, Underwater robotics: Out of the researchlaboratory and into the field, Proc. of IEEE/ICRA'00,(2000)
3. Y. Nishida, T. Ura, T. Sakamaki, J. Kojima, Y. Ito , K.
Kim, “Hovering Type AUV “Tuna-Sand” and Its Surveys
on Smith Caldera in Izu-Ogasawara Ocean Area, Proc. ofIEEE/OCEANS2013, (2013)
4. T. Maki, Y. Sato, T. Matsuda, R. T. Shiroku, T. Sakamaki,“AUV Tri-TON 2: An intelligent platform for detailed
survey of hydrothermal vent fields,” Proc. ofIEEE/AUV2014, (2014)
5. Y. Nishida, K. Nagahashi, T. Sato, A. Bodenmann, B.
Thornton, A. Asada, T. Ura, Development of anautonomous underwater vehicle for survey of cobalt-richmanganese crust, Proc. of IEEE/OCEANS2015, (2015)
6. Y. Nishida, T. Ura, T. Nakatani, T. Sakamaki, J. Kojima,
Y. Itoh and K. Kim, Autonomous Underwater Vehicle“Tuna-Sand” for Image Observation of the Seafloor at a
Low Altitude, Journal of Robotics and Mechatronics,
Vol.26, No.4, pp.519-521, 2014M.
7. J. Han, A. Asada, T. Ura, Noncontact power supply forseafloor geodetic observing robot system, J. Marine
Science and Technology, (2007) 12:183-189

Fig. 6 The arrangement of power transmission
system and AUV.

Fig. 7 The results of power supply.

Fig. 8 The relation between charging time and gapof coils’ center.

4. Conclusions
In this research, we proposed a wireless power supply
system of AUV. The system is evaluated by simulations
and experiments using AUV, and the results show good
performance and detect the faults. Using magnetic
resonance method, the chargeable range and the expectedcharging time of the battery in the underwater wirelesspower supply system created are shown in Fig.8.
Assuming that the charging efficiency is more than 60%© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 261Development of End-effector for Sampling-AUV “TUNA-SAND2”
Kazuo Ishii

Dept. of Intelligence Systems, Kyushu Institute of Technology,
2-4 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka, Japan
E-mail: ishii@brain.kyutech.ac.jp
Atsushi Nishijima

Dept. of Human Intelligence Systems, Kyushu Institute of Technology
2-4 Hibikino, Wakamatsu, Kitakyushu, Fukuoka, 808-0196, Japan
Takashi Sonoda

Dept. of Intelligence Systems, Kyushu Institute of Technology,
2-4 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka, Japan
E-mail: t-sonoda@brain.kyutech.ac.jp
Keisuke Watanabe
Dept. of Navigation and Ocean Engineering, Tokai University,
3-20-1 ORido Shimizuku, Shizuoka, Shizuoka, Japan
E-mail: keisukejapan@gmail.com

Abstract
UV requires high autonomy to accomplish various deep-ocean observation missions, and the next AUV is expected tohave sampling function of marine benthos, that is, the end-effector to catch and bring back marine creatures. However,sampling is not easy task for AUV as the shape, size, motion of targets differs. One of the effective end-effectors is theslurp-gun type device to absorb the target with water.
In this research, we had developed a sampling device for deep-ocean observation and sampling AUV, Sampling-AUV
TUNA-SAND2 in order to inspection of marine ecosystem.
Keywords: AUV, Sampling, End-Effector .

predicting ecological changes after marine resource
mining, biological strains, and life history. The ocean
around Japan is the one of most biodiversity area in theworld, which is an irreplaceable resource of Japan.
In this research, we will develop a sampling device ofunderwater robot for collecting living organisms for the
purpose of technical contribution to investigation of
ecosystem in the deep ocean floor. In order to elucidate
the diversity of marine life, biological sampling isplanned using an autonomous underwater robot1-6(hereafter AUV). However, sampling by AUV is

1. Introduction
In recent years, marine survey and development has
been carried out in various fields for the purpose ofmineral resources, sea floor biological sampling etc. A
wide variety of organisms live in the vast ocean and it canbe said that the ocean is the largest biosphere on Earthwith diverse organisms ranging from microorganisms towhales and huge algae. Investigation of the deep oceanand ecosystems has big contributions for elucidation of
biological diversity such as ensuring marine life,© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 262

Kazuo Ishii, Atsushi Nishijima, Takashi Sonoda, Keisuke Watanabe

different from the remote-operated underwater robot(hereafter ROV), fine operation by human and marine
surveys without instruction, and the collection of a
precise path plan is required. Figure 1 is the histogram toshow which kind methods are used for underwater
sampling based on JAMSTEC database7. As a first step,
we studied the suction type capturing method like a slurp
gun, and developed a sampling device that can capturesmall crabs or shells, and propose the combination ofslurp gun and gripper for performance evaluation.

equipment. In the experiment, it was investigatedwhether the target moves to the canister while leaving thenozzle tip a certain distance from the suction target. Then
aspirated and up to 10cm while away the distance fromthe center of the nozzle for each 2cm, to measure thesuction force. It was a ping-pong ball (in water weight 10~ 100g) as a suction target. Prototype maximum suction
amount was 100g. The specifications are follows: Pump
power is 90 W, the diameter of nozzle is 9.4 cm, thelength of pipe is 1 m, water flow is about 350 litter/sec.
Fig.2 The slurp gun prototype for performance
NozzleSlurp gun
Robot hand
Water sampler
Plankton net
Scoop Sample
OthersPump

Canisterevaluation.
The experimental variables are shown in Fig. 3, andthe experimental result is in Fig. 4.

Fig. 1 Classification of sampling method based on
JAMSTEC biological database7.

Flow
Nozzule2. Development of Sampling Device

Suctiontarget c[g]2.1. Concept of Device
The slurp gun developed in this research is a device
that brings the nozzle closer to the target object to be
aspirated, sucks the sea floor organisms together with theseawater, and preserves the organisms in a storage
container called a canister after collection. Figure 2
shows the prototype of Slurp gun.
One of our target benthos is Shinkaia crosnieri, which
is a species of squat lobster in a monotypic genus8 in thefamily Munidopsidae9. The target weight in water is 100g,
and size is below 10 cm.

Height from plate: b[cm]Flat plate

Distance from the center :a[cm]Fig.3 Experimental condition and parameters.

2.2. Suction Force Evaluation
The suction force experiment was conducted as
performance evaluation of developed sampling© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 263Development of End-effector for0

× × × ×1

× × × ×2

× × × ×3b[cm] 4× × × ×

A

5
6
7

0

2

4 6 8
a[cm]

10

Fig.6 The combination of slurp gun and gripper.
Fig.4 Results of suction force evaluations.

2.3. Slurp gun with Gripper
Biological capture around the nozzle is important toimprove the capture rate of organisms. Therefore, wedeveloped a suction gripper that attracts organisms
around the nozzle into the nozzle diameter. Also, it wasplaced streamlined to suppress the fluid resistanceportions attract. Fig. 5 shows the state of the opening andclosing of the gripper in, Fig.6 shows the combination ofslurp gun and gripper.

0× × × ×1

× × × ×2

× × × ×3

× × × ×b
[cm] 4

5
6
7

0

2

4 6 8
a[cm]

10

Fig.7 Results of suction force evaluations with slurp
gun and gripper.
The combination of slurp gun and gripper has beensuccessful in suction pulls the suction target of 100g at a
distance of a=10 cm from the center of the nozzle, which
could not be suction with only slurp gun. The developed
end-effector is shown in Fig. 8.

Fig. 5 The concept of the gripper.

Fig. 8 The developed end-effector.
© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 264

Kazuo Ishii, Atsushi Nishijima, Takashi Sonoda, Keisuke Watanabe(Crustacea: Decapoda: Anomura), Zootaxa. (2010), 2676:
57–68

3. Conclusions
In this research, we developed the sampling device
for seabed biological sampling and evaluated its
performance. It is assumed that the undersea organism isassumed to be close to the neutral buoyancy, therefore
the developed end-effector has sufficient suction power
for undersea biological collection. Aspiration around thenozzle which was difficult with conventional slap gun
was also possible by attaching gripper. As a result,capture of living organisms is improved and harvesting is
expected.

Acknowledgement
This research is supported by JST CREST“Establishment of core technology for the preservation
and regeneration of marine biodiversity and ecosystems”.
References
1. Y. Junku, T. Ura , G. Bekey, Underwater robots, Springer
Science & Business Media, (2012)
2. Whitcomb, Underwater robotics: Out of the researchlaboratory and into the field, Proc. of IEEE/ICRA'00,(2000)
3. Y. Nishida, T. Ura, T. Sakamaki, J. Kojima, Y. Ito , K.
Kim, “Hovering Type AUV “Tuna-Sand” and Its Surveys
on Smith Caldera in Izu-Ogasawara Ocean Area, Proc. ofIEEE/OCEANS2013, (2013)
4. T. Maki, Y. Sato, T. Matsuda, R. T. Shiroku, T. Sakamaki,“AUV Tri-TON 2: An intelligent platform for detailed
survey of hydrothermal vent fields,” Proc. ofIEEE/AUV2014, (2014)
5. Y. Nishida, K. Nagahashi, T. Sato, A. Bodenmann, B.
Thornton, A. Asada, T. Ura, Development of anautonomous underwater vehicle for survey of cobalt-richmanganese crust, Proc. of IEEE/OCEANS2015, (2015)
6. Y. Nishida, T. Ura, T. Nakatani, T. Sakamaki, J. Kojima,
Y. Itoh and K. Kim, Autonomous Underwater Vehicle“Tuna-Sand” for Image Observation of the Seafloor at a
Low Altitude, Journal of Robotics and Mechatronics,
Vol.26, No.4, pp.519-521, 2014.
7. http://www.jamstec.go.jp/e/database/
8. K. Baba, E. Macpherson, G. C. B. Poore, Shane T. A., A.
Bermudez, Patricia C., Chia-Wei Lin, M Nizinski, Celso
Rodrigues and K. E. Schnabel, Catalogue of squat lobstersof the world (Crustacea: Decapoda: Anomura – families
Chirostylidae, Galatheidae and Kiwaidae) Zootaxa. 1905:(2008),1–220.
9. S. T. Ahyong, K. Baba, E. Macpherson & Gary C. B.
Poore, A new classification of the Galatheoidea© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 265

Development of a Tool for Extended Place/transition Net-Based Mutation Testing
Tomohiko Takagi

Faculty of Engineering, Kagawa University
2217-20 Hayashi-cho, Takamatsu-shi, Kagawa 761-0396, Japan
Shogo Morimoto

Faculty of Engineering, Kagawa University
2217-20 Hayashi-cho, Takamatsu-shi, Kagawa 761-0396, Japan
Tetsuro Katayama

Faculty of Engineering, University of Miyazaki
1-1 Gakuen-kibanadai nishi, Miyazaki-shi, Miyazaki 889-2192, Japan
E-mail: takagi@eng.kagawa-u.ac.jp, s13t270@stmail.eng.kagawa-u.ac.jp, kat@cs.miyazaki-u.ac.jp

Abstract
This paper shows a tool for EPN (Extended Place/transition Net)-based mutation testing to evaluate and improve thequality of a test suite for concurrent software. The tool includes functions for (1) construction of an original EPN that
represents the expected behavior of concurrent software under test, (2) construction of mutant EPNs by applying
mutation operators to the original EPN, (3) execution of a test suite on each mutant EPN in order to calculate its
mutation score, and so on.
Keywords: Software Testing, Mutation Testing, Model-Based Testing, Vienna Development Method

calculated to evaluate the quality of the test suite. The
ratio is called a mutation score, and it can be improvedby modifying the test suite based on mutant models thathave not been killed.
For example, some studies showed MBMT using
directed graphs or automata as their formal model.2 On
the other hand, we have been discussing PNBMT (PNBased Mutation Testing), that is, MBMT using a PN
(Place/transition Net) that can represent concurrent
behavior of SUT.3-5 However, a PN cannot represent
actions (data processing that should be executed withstate transitions of SUT) and guards (conditions to gettransitions fireable). The actions and guards play animportant part especially when MBMT (also MBT) is
applied to SUT including complex data processing.
In this paper, we propose an EPN (Extended
Place/transition Net), that is, a PN that is extended by

1. Introduction
The techniques to create a test suite (a set of test cases)
based on formal models that represent specifications of
SUT (Software Under Test) is called MBT (Model-Based
Testing),1 and they can be introduced into a software
testing process in order to systematically evaluate andimprove the reliability of large SUT. The effects of MBT
depend on the quality of a test suite, that is, the capacityof a test suite to find possible failures in SUT. MBMT
(Model-Based Mutation Testing) was proposed as atechnique to evaluate and improve the quality of a test
suite of MBT.
In MBMT, mutant models are created by inserting an
intentional failure into a formal model of SUT, and thenthe ratio of killed mutant models (that is, mutant models
whose failures have been found by a test suite) is© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 266

Tomohiko Takagi, Shogo Morimoto, Tetsuro Katayama

introducing VDM++ (a formal specification description
language for VDM (Vienna Development Method))6 inorder to define actions and guards, and then, we show atool that we have been developing to support EPNBMT(EPN-Based Mutation Testing). The tool is supposed to
be used by test engineers. This study is based on the ideathat the effectiveness of PNBMT will be improved byachieving (a) the higher representation power of a PN by
actions and guards, and (b) the semi-automation by ourtool.
The rest of this paper is organized as follows. In section
2, we propose an EPN. Section 3 shows the principal
functions of our tool according to the procedure of
EPNBMT. Section 4 gives a conclusion and future work.

class EPN

t1 [v mod 2=0] / v:=v+1;

Legendplacetransition
arc
token

p1

instance variables
public v : nat := 0;
operations
public t1: () ==> ()t1() ==
v := v + 1
pre v mod 2 = 0;p2t2 [ ] / v:=v+1;

p3

public t2: () ==> ()t2() ==
v := v + 1;
public t3: () ==> ()t3() ==
v := v + 1
pre v mod 2 = 1;p4
t3 [v mod 2=1] / v:=v+1;

end EPN

Complete VDM specifications

Fig. 1. Simple example of an EPN (initial state).

is expressed as {[0,1,1,0], v=0}. The action "v:=v+1;" isattached to the transitions t1, t2, t3, and it is executed by
the firing of each transition. Additionally, the guards "v
mod 2=0" and "v mod 2=1" are attached to t1 and t3,
respectively. t1 and t3 can be fired, when there are tokens
for their ingoing arcs and their guards are satisfied. For
example, only t1 is fireable in the initial state of the EPNof Fig. 1.

2. EPN (Extended Place/transition Net)In this section, we propose an EPN, that is, a PN that is
extended by introducing VDM++ (a formal specification
description language for VDM) in order to define actions
and guards.
An EPN consists of a PN and VDM specifications. The
PN is a traditional formal model suitable for representing
expected state transitions of SUT including concurrent
behavior. It consists of places to represent the states ofcomponents of SUT, transitions and arcs to represent thechange of states of SUT, and tokens to represent current
states or resources of the components. The VDM
specifications are a set of VDM++ codes to represent
actions and guards that are given to transitions of the PN
if necessary. The actions can represent data processingthat should be executed with state transitions of SUT, andthe guards can represent conditions to get transitions
fireable.
In an EPN, a state of SUT corresponds to a set thatconsists of a marking (that is, a distribution of tokens ona PN) and values of instance variables (that is, variables
that are defined in VDM specifications and are used inactions and guards), and a state transition of SUT
corresponds to the change of a marking and values of
instance variables that results from the firing of atransition of a PN.
A simple example of an EPN is shown in Fig. 1. The
initial marking of the EPN is expressed as [0,1,1,0], since
the numbers of tokens in the places p1, p2, p3, p4 are 0, 1,
1, 0, respectively. Also, the instance variable v is
initialized to 0, and therefore the initial state of the EPN

3. Tool for EPNBMT (Extended Place/transition
Net-Based Mutation Testing)This section shows the functions of the tool that we havebeen developing to support EPNBMT, according to theprocedure of EPNBMT. The functions (the procedure)
consist of (1) construction of an original EPN, (2)construction of mutant EPNs, (3) conversion of a test
suite, and (4) execution of a test suite.
3.1. Construction of an original EPN
First, test engineers need to construct an original EPN
based on specifications of SUT. The original EPN is an
EPN that represents the expected behavior of SUT; it iscalled "original" in order to distinguish it from mutant
EPNs that will be constructed on the next step. The EPN
has been discussed in the previous section.
Our tool provides the function to construct an original
EPN. Fig. 2 gives the screen image in the execution of
the function. The GUI (Graphical User Interface) for thefunction consists of (A) a pane to edit a PN and (B) a pane
to edit VDM specifications for actions and guards.
Additionally, (B) consists of five code editors to edit
values, types, instance variables, operations, andfunctions of the VDM specifications, and one of them can© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 267

Development of a Tool

Fig. 2. Screen image of the tool in constructing an original EPN.

Fig. 3. Screen image of the tool in constructing mutant EPNs.

be selected by a tab. Test engineers can define not only
the PN of an original EPN but also the actions and guardsof an original EPN.

(A) enables test engineers to select one of existing mutant
EPNs and show it on (B) and (C). Additionally, it enables
to add and remove a mutant EPN. (B) highlights a partthat a mutation operator has been applied to in order thattest engineers can easily confirm a result of applicationof a mutation operator. (B) also gives test engineers theway of manual and automatic construction of a mutant
EPN. When performing manual construction, test
engineers select a model-based mutation operator fromthe menu, and then specify a part of a PN that it should
be applied to. When executing automatic construction, amodel-based mutation operator is randomly selected, andthen a part that it will be applied to is randomly
determined by our tool. (C) consists of five code editors
to edit values, types, instance variables, operations, andfunctions of VDM specifications, and one of them can beselected by a tab. These code editors enable test engineers
to manually apply a code-based mutation operator.

3.2. Construction of mutant EPNs
After completing constructing an original EPN, test
engineers need to construct mutant EPNs. The mutant
EPN is an EPN that intentional failure(s) has been
inserted into by the use of mutation operators. In thisstudy, the following mutation operators are supposed tobe utilized.
Model-based mutation operators: the addition anddeletion of an element of a PN (that is, a place, atransition, an arc, or a token) can be performed inorder to insert an intentional failure. They were
proposed in our previous study.3
Code-based mutation operators: the grammar of
VDM++ is somewhat similar to those of generalprocedural programming languages. Therefore, inorder to insert an intentional failure, traditional codebased mutation operators (for example, replacementof operators, variables, and constants) can be applied
to actions and guards that have been implemented as
VDM specifications.
Our tool provides the function to construct mutant EPNs.
Fig. 3 shows the screen image in the execution of thefunction. The GUI for the function consists of (A) a listof mutant EPNs that have been constructed, (B) a pane toedit a PN of a mutant EPN by the use of model-based
mutation operators, and (C) a pane to edit VDM
specifications for actions and guards by the use of codebased mutation operators.

3.3. Conversion of a test suite
After the construction of an original EPN, a test suite tobe evaluated needs to be converted to execution paths(that is, sequences of successive markings, transitions,and values of instance variables) on the original EPN. It
is a preparation for automatic execution of the test suitein the next step. Our EPNBMT accepts an arbitraryformat and design technique of the test suite.
Our tool provides the function to manually convert anarbitrary test suite to execution paths on an original EPN.
Fig. 4 shows the screen image in the execution of thefunction. The GUI for the function consists of (A) a listof test cases that a test suite consists of, (B) a list to edit

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 268

Tomohiko Takagi, Shogo Morimoto, Tetsuro Katayama

Fig. 4. Screen image of the tool in converting a test suite.
Fig. 5. Screen image of the tool in executing a test suite.

transitions of a test case selected on (A), and (C) a pane
to show an original EPN and edit a test case.
(A) enables test engineers to select one of existing(already converted) test cases and show its execution path
representation on (B) and (C). (C) highlights a transition
that is selected on (B), and shows a marking and valuesof instance variables just after the firing of the transition.
Additionally, (A) enables to add and remove a test case.
When adding a test case (converting a test case andentering it into our tool), test engineers input required
information (successive markings, transitions, and valuesof instance variables) via (B) and (C). (B) enables to add
and remove a transition of a test case selected on (A).
When a new transition is added, (C) can highlight fireable
transitions based on a current marking and values of
instance variables, and then selecting a highlighted
transition brings the automatic updating of the current
marking and values of instance variables, which will save
test engineers' labor.

(B), (D) a list of transitions of a test case selected on (C),(E) a pane to show markings and values of instancevariables of an original EPN (the upper part) and theselected mutant EPN (the lower part) just after the firingof the selected transition in the selected test case, and (F)a pane to show a detailed log of test suite execution,
including a mutation score. (B)-(F) enable test engineers
to easily confirm the result of test suite execution invokedby (A).
Each test case is executed as follows:(i) A mutant EPN is initialized based on an initial
marking and initial values of instance variables of atest case.
(ii) In the mutant EPN, transitions are fired from the topof the test case. Each of firings is performed by thefollowing (a)-(c).
(a) The pre-conditions to fire a transition, that is,
the existence of tokens for ingoing arcs of thetransition, and the guard of the transition arechecked. When they are not satisfied, it isfound that the mutant EPN is killed by the test
case.
(b) The transition is fired, and the action attachedto the transition is executed. As a result, acurrent marking and values of instancevariables are updated.
(c) If the transition includes post-conditions, andthey are not satisfied, it is found that the
mutant EPN is killed by the test case.
(iii) When the firing of the final transition of the test casehas been completed, the test result (that is, a final
marking and values of instance variables in the
mutant EPN) is compared with the expected result

3.4. Execution of a test suite
Finally, test engineers need to apply the converted test
suite to all of the constructed mutant EPNs, and thencalculate a mutation score as the ratio of killed mutant
EPNs to all of the constructed mutant EPNs. If themutation score is not good, the test suite can be improved
based on mutant EPNs that have not been killed.
Our tool provides the function to execute a test suite. Fig.
5 shows the screen image in the execution of the function.
The GUI for the function consists of (A) a control panel
to start executing a test suite, (B) a list of mutant EPNs
with the results of testing, (C) a list of test cases with theresults of their application to a mutant EPN selected on© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 269

Development of a Tool

Acknowledgements

included in the test case (that is, a final marking andvalues of instance variables in the original EPN). If
there are differences between the test result and the
expected result, it is found that the mutant EPN is
killed by the test case.

This work was supported by JSPS KAKENHI Grant
Number 26730038.
References

4. Conclusion and Future Work

1. M. Utting, A. Pretschner, and B. Legeard, A taxonomy ofmodel-based testing approaches, Software Testing,
Verification and Reliability, Vol.22 (2012), pp.297-312.
2. F. Belli, C.J. Budnik, E. Wong, Basic Operations for
Generating Behavioral Mutants, in Proc. 2nd Workshop
on Mutation Analysis in conjunction with ISSRE'06 (Nov.
2006), pp.9.
3. T. Takagi, R. Takata, Z. Furukawa, F. Belli, M. Beyazıt,
Metrics for Model-Based Mutation Testing Based on
Place/Transition Nets, in Proc. of Joint Conf. of 21st Int.
Workshop on Software Measurement and 6th Int. Conf. on
Software Process and Product Measurement (IWSMMENSURA) (Nov. 2011), pp.7-10.
4. T. Takagi, T. Arao, "Overview of a Place/Transition NetBased Mutation Testing Framework to Obtain Test Cases
Effective for Concurrent Software", in Proc. of 16th Int.
Conf. on Software Engineering, Artificial Intelligence,
Networking and Parallel/Distributed Computing (June2015), 3 pages.
5. T. Takagi, T. Teramoto, Extended Mutation Score Basedon Weighted Place/Transition Nets to Evaluate Test Suites,in Proc. of 15th Int. Conf. on Computer and Information
Science (ICIS) (June 2016), pp.959-961.
6. J. Fitzgerald, P.G. Larsen, P. Mukherjee, N. Plat, M.
Verhoef, Validated Designs for Object-Oriented Systems,(Springer-Verlag London, 2005).

This paper has shown a tool for EPNBMT (Extended
Place/transition Net-Based Mutation Testing) to evaluate
and improve the quality of a test suite for software
including concurrent behavior. The EPN proposed in thispaper is a PN extended by introducing a formal
specification description language for VDM, in order torepresent actions and guards. The tool consists of thefollowing four functions: (1) an original EPN can becreated to define the expected behavior of SUT, (2)mutant EPNs including intended failures can be createdby applying mutation operators to the original EPN, (3)an arbitrary test suite to be evaluated can be converted to
execution paths on the original EPN, and (4) a mutation
score can be calculated by applying the converted test
suite to all the mutant EPNs. If the mutation score is not
good, the test suite can be improved based on mutant
EPNs that have not been killed.
In the tool, model-based mutation operators areautomatically applicable, but code-based mutation
operators are only for manual application. The latter will
be automated in order to save test engineers' labor in our
future work. Additionally, if the tool can generate mutant
EPNs intensively including failures that are possible inoperational environments, a degree of precision of amutation score would be improved. Thus we will plan todevelop the techniques for modeling of actual failures
and for strategical application of mutation operators
based on the model.
Another approach to improve the degree of precision ofa mutation score is to utilize fault-proneness informationthat can be derived from software metrics, in order toadjust a mutation score. In the previous study, we
proposed a technique in which the weights based on
fault-proneness information and usage distributions are
given to a PN in order to calculate an adjusted mutation
score5. Extending this technique for EPNs is also
included in our future work.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 270

Improvement of Decision Table Automatic Generation Tool VDTable for let in Statement
Yinuo Huang*, Teturou Katayama* Yoshihiro Kita†,
Hisaaki Yamaba*, and Naonobu Okazaki*

University of Miyazaki, 1-1 Gakuen-kibanadai nishi, Miyazaki, 889-2192 Japan†Tokyo Institute of Technology, 1404-1 katakura, Hachioji, 192-0914 Japan
E-mail: kouitsudaku@earth.cs.miyazaki-u.ac.jp, kat@cs.miyazaki-u.ac.jp,
kitayshr@stf.teu.ac.jp, yamaba@cs.miyazaki-u.ac.jp, oka@cs.miyazaki-u.ac.jp

Abstract
This research has expanded the applicable range of VDTable. VDTable is a tool of decision table automatic
generation tool using VDM++ specification which is the lightweight formal methods VDM. We have improved
VDTable to be able to correspond to let in statement, which is one of the VDM++ syntax, that the existing VDTable
doesn’t support. We applied an application example to improved VDTable. From the result of the application example,
the applicable range of VDTable has been expanded.

Keywords: VDM++, automatic generation, decision table, formal specification, VDTable, let in statement.

to correspond to let in statement, which is one of theVDM++ syntax, that the existing VDTable does notsupport.

1. Introduction
Formal methods1, in software development, is one of themeans to describe the specification strictly. On the other
hand, the decision table2 is one of the testing techniques
to exhaustively represent the logical combination
included in software. But, for generating a decision table,
it is necessary to understand the description content of thetarget system or specification. In extracting conditions
and actions manually from specifications, it takes timeand errors are prone to occur.
Therefore, our laboratory developed a VDTable3(VDM Decision Table) to reduce this labor and time.
VDTable can automatically generate the decision table,from the specification written in specification description
language VDM++ (VDM++ specification). But, the
VDTable, since many VDM++ syntax is notcorresponded, its usefulness is limited.
To improve the usefulness, this paper expands
VDTable. Specifically, VDTable is improved to be able

2. Introduction of existing VDTable
The flow of processing of VDTable is shown in Fig.1.
VDTable consists of three parts: Parser, Converter, andDT-Generator.
Parser uses VDMJ4 to parse the VDM++ specificationand outputs a parsing data. The parsing data has an
abstract syntax tree. The abstract syntax tree holds
analysis information such as the definition block type,argument, definition name and expression for each classdefinition. The parsing data to be used as input in theConverter.
Converter converts the parsing data outputted byParser into internal expression data for analysis in orderto facilitate extraction of conditions and actions andcreation of truth values, which are necessary ingenerating a decision table. The internal expression data

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 271

Yinuo Huang, Teturou Katayama, Yoshihiro Kita, Hisaaki Yamaba, Naonobu Okazaki

Table 1. Condition extraction rules and action
extraction rules of let in statement.
Condition extraction pattern

Action extraction pattern

Let condition 1 in condition 2

Name action

Table 2. Example of creating a String typetwo-dimensional array that stores truth-values

Fig. 1. Process flow of VDTable

Condition 1--

Condition 2--

Action--

Proposal of truth table generation rules of let instatement

for analysis is data obtained by dividing the parsing data
in module units and expressing the expression of thedefinition main for each token as the minimum unit of thesyntax.
DT-Generator defines extraction rules of conditions
and actions. Firstly, it extracts conditions and actions,when its matching the condition extraction pattern or
actions extraction pattern from the analysis internal
expression data, and extracts String type array (condition
array and action array), respectively. Next, DT-Generator
stores conditions and actions in an array of String type.
DT-Generator makes CA-Table (Condition Action
Table), when DT-Generator extracts conditions andactions. CA-Table is a table which is correspondence ofconditions and actions. CA-Table is three columns ofcondition index, token and action index. And then, DTGenerator generates truth-values based on this CA-Table.
Finally, DT-Generator generates a decision table fromthe created condition and action, and the truth-values of
the condition and action.

3.1. Proposal of data conversion rules forconverting parsing data of let in statement tointernal expression data for analysis
To parse let in statement, we use the Parser of the existing
VDTable as it is. In VDTable, Converter converts
parsing data of let in statement into internal expression
data for analysis. In order to realize this, we propose data
conversion rules of let in statement.
In the data conversion of let in statement, whenmatching the token "let" from the parsing data, three
items of "from immediately after ‘let’ to before ‘in’","in", "immediately after ‘in’ to the last" areextracted.
The part of name is extracted as one item.
These are saved as String type as internal expression
data for analysis of let in statement.
3.2. Proposal of condition extraction rules andaction extraction rules of let in statement

3. Improvement of VDTable

We propose condition extraction rules and action
extraction rules of let in statement. Table 1 shows thecondition extraction rules and action extraction rules of
let in statement proposed in this paper. In the internal
expression data for analysis, let the line above "in" be thecondition 1, the line next to "in" be the condition 2, andlet it be the condition extraction pattern. Also, the partafter "name" is taken as the action extraction pattern.

To correspond to let in statement, we extend Converter
and DT-Generator. Specifically, we conduct thefollowing three items.
Proposal of data conversion rules for converting
parsing data of let in statement to internal expression
data for analysis
Proposal of condition extraction rules and action
extraction rules of let in statement© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 272

Improvement of Decision Table

Next, DT-Generator extracts each condition according
to the condition extraction rules. Then, DT-Generator
extract actions according to the action extraction rules.
Finally, DT-Generator store the values of the truth
value. In the proposed the truth table generation rules, it
is assumed that when let in statement is described as "letcondition 1 in condition 2", "if condition 1 and condition
2 are true (stores" Y "), the action is established (stores"
X ")", " when Condition 1 is true (stores" Y "), condition
2 is false (stores" N "), action is not established (stores" "). The reason is the movement of let in statement in the
VDM specification is such that the condition 1 part is
assigned to the condition 2. So, for condition 2, thecondition 1 is always true. Therefore, when generating
the truth table of let in statement, it is assumed that thecondition 1 is always true, and when the condition 2 istrue or false, the operation is determined or established.
The DT-Generator generates a decision table based on
the extracted conditions and actions, and the truth valuesof the generated conditions and actions.

Fig. 2. Specification of shape judgment of the triangle

4.

Fig. 3. Specification parsing data of shape judgment of thetriangle

Application Example

The specification used for an application example is the
specification described using VDM ++ for triangle shapedetermination, and it is determine whether triangle byusing let in statement. The specifications of shape
judgment of the triangle is shown in Fig. 2. The result of
parsing this specification with Parser is shown in Fig. 3.
Fig. 4 shows the result of converting the content of let
in statement in the specification from the syntax analysis
data in Fig. 3 to the analysis internal expression data.
Condition 1 and condition 2, and contents of let instatement including action are saved as String type. It is
understood that let in statement is correctly converted tothe internal expression data for analysis.
Fig. 5 shows the result of applying the VDM++
specification of the application example to the improved
VDTable. From the decision table of Fig. 5, it can be seenfrom Fig. 4 that the line before the token "in" is correctly
extracted as condition 1 and the line following "in" as
condition 2. Also, from Fig. 5, it can be seen that the
portion after "name" is correctly extracted as an operation.
Finally, from the truth table of the decision table of Fig.
5, when the decision table shows the condition 1 and thecondition 2 are true (stores "Y"), the action is established3.3. Proposal of truth table generation rules of let
in statement
We propose truth table generation rules of let instatement. The proposed the values of the truth table
generation method is as follows.
(1) “Generate an array that stores the values of the truth
table”(2) “Extract conditions”(3) “Extract actions”(4) “Store truth values in the array”
First, in order to store the values of the truth table, a
two-dimensional array of String type is prepared.
Describe the conditions and actions in the elements ofvertical first column, and describe the truth-values
information in the horizontal element. An example ofcreating an array is shown in Table 2.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 273

Yinuo Huang, Teturou Katayama, Yoshihiro Kita, Hisaaki Yamaba, Naonobu Okazaki

6. Conclusion
In this research, VDTable has been improved for the
purpose of expanding the application range of VDTable,
an automatic generation tool for decision tables using the
VDM++ specification. Specifically, we propose data
conversion rules of let in statement, propose a condition
extraction rules and action extraction rules of let instatement, and propose truth table generation rules of let
in statement.
We confirmed that the improved VDTable is applied
to the triangle judgment VDM ++ specification including
a let in statement to correctly generate the decision table.
From the above, it can be said that the application rangeof the VDTable improved in this paper has expanded, andits practicality has improved.
Future issues are as follows.

Fig. 4. Analysis internal representation data of the of shape
judgment of the triangle

Extension of scope
Dealing with compound conditional expressions
Improvement of readability
Correspondence to functions including multiple
syntaxFig. 5. The result of applying the specification to VDTable

Acknowledgements(stores "X"), When the condition 1 is true (stores "Y"),when the condition 2 is false (stores "N"), the action is
not established (stores "-"). Hence, it is understood thatthe truth table is correctly generated.

This work was supported by JSPS KAKENHI Grant
Number 24220001

5. Related work

1. John Fitzgerald, Peter Gorm Larsen, Paul Mukherjee, Nico
Plat, Marcel Verhoef, Validated Designs for ObjectOriented Systems, Springer (2005).
2. ISO 5806, Specification of single-hit decision tables.
3. Kenta Nishikawa, Tetsuro Katayama, Yoshihiro Kita,
Hisaaki Yamaba and Naonobu Okazaki, Proposal of a
Supporting Method to Generate a Decision Table from theFormal Specification, International Conference on
Artificial Life and Robotics, 222-225(2014).
4. VDMJ. http://sourceforge.net/projects/vdmj/ (accessed
November 30, 2016).
5. CEGTest, http://softest.jp/tools/CEGTest/ (accessed
November 30, 2016).

References

Currently, there are few cases where test design was done
from formal specifications. CEGTest5 is available as atool to support the generation of decision tables.
CEGTest automatically generates a decision table fromthe cause result graph created by the user. So the user
must make a cause effect graph created, manually. For
this reason, when creating a decision table it istroublesome and time-consuming to understand thecontents and to extract conditions and actions, and input
errors are likely to occur.
In contrast, VDTable can automatically get a decisiontable from a formal specification inputted by a user.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 274

Prototype of Test Cases Automatic Generation Tool
BWDM Based on Boundary Value Analysis with VDM++*

Hiroki Tachiyama*, Tetsuro Katayama* Yoshihiro Kita†,
Hisaaki Yamaba*, and Naonobu Okazaki*

University of Miyazaki, 1-1 Gakuen-kibanadai nishi, Miyazaki, 889-2192 Japan†Tokyo University of Technology, 1404-1 Katakura, Hachioji, 192-0914 Japan
E-mail: tachiyama@earth.cs.miyazaki-u.ac.jp, kat@cs.miyazaki-u.ac.jp,
kitayshr@stf.teu.ac.jp, yamaba@cs.miyazaki-u.ac.jp, oka@cs.miyazaki-u.ac.jp

Abstract
For software development using Formal Methods, we have developed a prototype of the boundary value test caseautomatic generation tool BWDM. The main two topics of our tool are (1) automatically generation of test cases and(2) boundary value analysis. Our tool improves the efficiency of software testing process in using VDM++ that is
one of the Formal Methods. In this research, we show the composition of our tool, application example, evaluationof the usefulness, relative research, and future issues.
Keywords: Software Testing, Boundary Value Analysis, Formal Methods, VDM++

On the other hand, either way designed using natural
languages or formal specification languages, it needs
software testing after implementation of software. In
order to test software, it is necessary to design test cases,
but it takes much time and effort to design them manually.
Therefore, by efficiently designing test cases, it ispossible to improve the efficiency of the testing process.
It is also important to design test cases focused on places
where bugs can be hidden, in order to improve theefficiency of executing of software testing.
Therefore, in this research, we develop a prototypeof test case automatic generation tool BWDM (Boundary
Value/Vienna Development Method) for the purpose of
improving the efficiency of the test process in softwaredevelopment using formal methods. We use Boundary
Value Analysis1 for test cases designing. We use the“boundary value test case” what means the test casedesigned based on boundary value analysis.

1. Introduction
A cause of coming into bugs in software is using natural
languages in process of software design. Natural
languages are ambiguous. And this causes
misunderstanding of a programmer in designing
documents. Finally, he implements software based on the
misunderstanding, and the software will have bugs.
As one method to solve this problem, Formal
Methods are suggested that used in upstream process ofsoftware development. In developing process of using
formal methods, specification is described whatcharacteristic of development target, by using not natural
languages but formal specification languages what basedon mathematical logic. For that reason, unlike
conventional development, specification can be proved
its correctness of description contents by arithmeticaltheorem proofs, mechanical inspection, etc. In otherwords, it is possible to write strict specification document
which is excluded ambiguous description.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 275

Hiroki Tachiyama, Tetsuro Katayama, Yoshihiro Kita, Hisaaki Yamaba, Naonobu Okazaki

Fig. 1. The flow of BWDM processing

Fig. 3. The flow of expected output data generator processing

when input data is input to a function that is written in the
VDM++ specification. And it outputs test cases thatconsists of input data and expected output data.
3. Application Example
Fig. 2. The flow of input data generator processing

2. Boundary Value Test
Generation Tool BWDM

Case

We apply the formal specification that mixed inequality
expression and surplus expression to BWDM. And wecheck 3 items.
(i) BWDM is operated correctly.
(ii) The boundary value is correctly extracted from the
VDM++ specification.
(iii) BWDM outputs the input data and the expected
output data correctly as the boundary value test case.

Automatic

BWDM analyzes boundary values of function definitions
that described in VDM++ specification, andautomatically generates boundary value test cases. Figure
1 shows the flow of BWDM processing. Note that VDM
is a formal method devised at the IBM Vienna ResearchInstitute in the 1970s, and VDM++ is an object-oriented
extension of VDM-SL2 which is formal specification
language of VDM.
BWDM consists of input data generator andexpected output data generator. In process of expected
output data generator, the decision table is used, what is
generated by decision table generation support tool
developed in our laboratory3. The inputs into BWDM are
VDM++ specification and decision table. Here, we
implement BWDM in Java language.

Figure 4 shows formal specification that we apply
to BWDM. This specification judges whether the firstargument is even or odd, and whether the second
argument is a positive number or a negative number.
Figure 5 shows the generated test cases by applying theformal specification of Figure 4 to BWDM.
The boundary values generated from the argument
arg1 of the specification in Fig. 4 are natMin-1, natMin,
natMax, natMax+1, 1, 2, and 3. And from the argument
arg2, intMin-1, intMin, intMax, intMax+1, -1, and 0 are
generated. Here, “natMin-1” means a value which is one
less than the minimum value of the natural type. BWDM
generates all combinations of boundary values of each of
the two variables as input data. Therefore, the number oftest cases generated from this specification is 7 * 6 = 42(cases). By Fig. 5, we can check test cases from No. 1 toNo. 42. From this, it is clear that BWDM outputs correct
test cases from the formal specification. Also, we cancheck that both of extracting boundary values from
VDM++ specification and outputting boundary value test

2.1. Process of input data generator
Figure 2 shows the flow of processing of input data
generator. The input is VDM++ specification file. And
this unit generates input data based on boundary value
analysis.
2.2. Process of expected output generator
Figure 3 shows the flow of processing of expected output
data generator. This unit generates expected output data

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 276

Prototype of Test Cases

Fig. 4. The formal specification that mixed inequality
expression and surplus expression

cases that consist both of input data and expected output
data are correct as well.
Fig. 5. The test cases generated by applying the formal
specification of Figure 4 to BWDM

4. Discussion
In this research, for the purpose of improving theefficiency of the test process in software developmentusing formal methods, we have developed a prototype ofboundary value test case automatic generation tool
BWDM. The inputs to BWDM are VDM++ specificationand decision table, and the test case automatic generationand the boundary value analysis to the function definitionin the specification are performed. Therefore, this tool
improves the efficiency both of designing of test case andconducting of testing. In the followings, we consider
BWDM.
4.1. Evaluation of usefulness
To consider BWDM usefulness, we used 3 specifications.
We measured the time to generate test cases. Table 1
shows conclusion of measuring. Within the three
specification, the if-conditional expressions ofinequalities are described. The specification 1 has 5 ifconditional expressions, specification 2 has 10
expressions and specification 3 has 15 expressions. To
measure execution time, we used System.nanoTime4
method of Java. We measured the time that is to give the
VDM++ specification and the decision table as an input
to the BWDM and to finish outputting the test cases. The
unit of time was millisecond, and measurements weremade five times for each specification, and we calculated
the average of 5 measuring as well. Here, we don’t
consider the time to prepare the VDM++ specificationand the decision table.

For the average time in each specification,
specification 1 and specification 2 were less than 0.5
seconds, and specification 3 was less than seven seconds.
The number of rules on the decision table for
specification 1 (number of columns in the decision table)
is 32, specification 2 is 1024 and specification 3 is1048576. These represent the number of states that thefunction can take, depending on the Boolean value in the
if-conditional expression. When constraint conditions
such as preconditions are described in the specification,
the number of states actually taken by the function is lessthan this number. However, it is troublesome and time
consuming to manually design test cases for functions
that can have as many states as possible. On the other
hand, BWDM can automatically generate boundary
value test cases in several seconds if the specification is
up to condition number 15.
Hence, we think that BWDM is usefulness when
testing software that implemented from VDM++
specification.
4.2. Related research
TOBIAS5 is a test cases automatic generation tool for
testingthe
VDM++
specification.
TOBIAS
automatically generates test cases according to a test
pattern input to TOBIAS. The test pattern is defined by© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 277

Hiroki Tachiyama, Tetsuro Katayama, Yoshihiro Kita, Hisaaki Yamaba, Naonobu Okazaki

Future works are as follows.
Generation of boundary values not appearing inthe specification description
Although it does not appear as a concrete
numerical value in the specification description,
there are boundary values that occur when two
or more if-conditional expressions are related,
and the current BWDM cannot generate thosevalues as input data. It is necessary to modify
the current process to generate boundary values.
Responding to various environments
Current BWDM, boundary value analysis ofargument types of function definition cannot
cope with various environments (bit number anddevelopment language) used for actual
development. It is necessary to implement afunction to allow users to set information about
test cases what they want.

Table 1. Generation time of test cases fromthree specifications
Times
1st time
2nd time
3rd time
4th time
5th time
AverageSpecification1 Specification2 Specification3
325
316
6277
283
389
5321
500
371
6236
291
334
5070
269
371
5700
334
356
5720

test designer using a regular expression to output a test
case.
Both TOBIAS and BWDM support the testing
phase of software development using VDM++. TOBIAS
supports testing the VDM++ specification. In contract,
BWMD supports testing actually implemented software.
This is what the difference of between two tools.
Therefore, when you need to test implemented software,
BWDM is superior.

Acknowledgements

5. Conclusion

This work was supported by JSPS KAKENHI Grant
Number 24220001.

In this research, we have developed a prototype of test
cases automatic generation tool BWDM based onboundary value analysis targeting the VDM++
specification with the aim of streamlining the testingprocess in software development using formal method.
We applied the VDM++ specification that mixed
inequality expression and surplus expression into
BWDM, and our tool analyzed boundary value of thespecification correctly. Finally, BWDM outputs
boundary value test cases that consist both of input dataand expected output data correctly. Furthermore, as aresult of applying three kinds of VDM++ specifications
including up to 15 if-conditional expressions to BWDM,
it generated test cases within about seven seconds.
Therefore, BWDM improves the efficiency of designing
test cases.
In conclusion, by using BWDM, it is expected to
improve the efficiency of the test process in softwaredevelopment using formal methods. Moreover, byconducting test design and test process based on the
VDM++ specification, improvement both of productivity
and quality of developed software is expected.

References
1. Blake
Neate,
Boundary
value
analysis,
http://www.cs.swan.ac.uk/~csmarkus/CS339/docs.htm,(accessed November 30, 2016).
2. International Organization for Standardization, ISO/IEC
JTC 1/SC 22/WG, Information technology--Programming
languages, their environments and system softwareinterfaces--Vienna Development Method--Specification
Language--Part 1: Base language (1996)
3. Kenta Nishikawa, Tetsuro Katayama, Yoshihiro Kita,
Hisaaki Yamaba and Naonobu Okazaki, Proposal of a
Supporting Method to Generate a Decision Table from theFormal Specification, International Conference on
Artificial Life and Robotics, 222-225 (2014)
4. Java System class,https://docs.oracle.com/javase/ja/6/api/java/lang/System.
html, (accessed November 30, 2016).
5. Olivier Maury, Yves Ledru, Pierre Bountron and Lydie duBousquet, Using TOBIAS for the automatic generation of
VDM
test
cases,
http://vasco.imag.fr/Tobias/Papers/vdm02tobias.pdf,(accessed November 30, 2016).

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 278

Prototype of Refactoring Support Tool MCC Focusing on the Naming of Variables*

Satoshi Tanoue*, Teturou Katayama* Yoshihiro Kita†,
Hisaaki Yamaba*, and Naonobu Okazaki*

University of Miyazaki, 1-1 Gakuen-kibanadai nishi, Miyazaki, 889-2192 Japan†Tokyo University of Technology, 1404-1 Katakura, Hachioji, 192-0914 Japantanoue@earth.cs.miyazaki-u.ac.jp, kat@cs.miyazaki-u.ac.jp,
kitayshr@stf.teu.ac.jp, yamaba@cs.miyazaki-u.ac.jp, oka@cs.miyazaki-u.ac.jp

Abstract
This research has implemented a prototype of refactoring support tool MCC(Make Clean Coder) which focuses on
the naming of variables. This prototype helps to describe a clean code by static analysis for the source code written
in C language. And, it can help to reduce factors that prevent programmers understanding the source code when they
modify it by pointing out improper variable names. Programmers can lower the possibility of embedded bugs, anddecrease the time required to add new functions.
Keywords: Static Analysis, Refactoring, Clean Code, Variable Namevariable names are one character or the variable name is
not included in a dictionary. For the dictionary data,
MCC uses the Dejizo Web Service3 which is a dictionary
published for experiments. By using this prototype,because it can reduce the time to understand the source
code, programmers can shorten the coding time, lower
the possibility of embedded bugs, and decrease the timerequired to add new functions.

1. Introduction
In recent years, along with the evolution of ICT, the roleof information systems in society is gradually increasing.
As a result, economic and social impacts caused bysystem failures and software malfunctions are
immeasurable. Against such a background, high-quality
systems have become more important.
There is refactoring as a method to maintain andimprove the quality of the system1. Refactoring is toimprove the source code so that it is easy to understandwhile maintaining external behavior. If there is an
unknown variable name, it becomes hard to understandwhat kind of processing2. In modifying the source code,a programmer may delete the necessary processing anduse variables and functions differently from intended
usage. Therefore, the programmer may embed bugs in thesource code.
In this research, a prototype of refactoring support
tool MCC (Make Clean Coder) has been developed forthe purpose of supporting improvement of code quality.
This prototype helps to describe a clean code by static
analysis for the source code written in C language. And,
it can help to reduce factors that prevent programmers
understanding the source code when they modify it bypointing out improper variable names. The Improper

2. Overview of MCC
Fig. 1 shows an overview of the prototype MCC. MCC
consists of the MenuBar, EditArea, and DisplayArea.
Each is explained below.
2.1. MenuBar
A user can use the function of the MCC by selecting eachmenu item on the MenuBar. When “File” is selected, thefile menu is displayed. He can use the following two
functions from the file menu.
Open File
Save File
When “Edit” is selected, the edit menu is displayed. He
can use the following two functions from the edit menu.
Clear DisplayArea
Clear EditArea© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 279

Satoshi Tanoue, Tetsuro Katayama, Yoshihiro Kita, Hisakki Yamba, and Naonobu Okazaki.

Fig. 2. The definition of the syntax analyzed by MCC written in
ENBF

please change the variable name.”. When the variablename is a snake case or a camel case, each word which is
a compound word is searched with the dictionary. In thecase of an inappropriate variable name, a warning isdisplayed in the display area as “Line number: Rolecannot be inferred from ~ in ~, please change the variablename.” .

Fig. 1. An overview of MCC

When “Run” is selected, the run menu is displayed. He
can use the following a function from the run menu.
Analyze variable names
2.2. EditArea

4. Implementation

EditArea is an area for editing the source code. MCC
opens the file and displays the code in EditArea. EditArea
has a simple editor function and can edit the source code.

Analysis Variable Names, which is one of the functionsof MCC, is implemented by combining a parser
generated using JavaCC4 and the dictionary by Web
service 3. A variable name is acquired using a parser, and
it is judged whether a variable name is appropriate by thedictionary. Each is explained as the followings.

2.3. DisplayArea
DisplayArea is an area for displaying the analysis result.
MCC selects “Run” on the menu bar and selects“Analyze variable names” for the menu item to display
the analysis result.

4.1. Get the variable name
When writing syntax rules in EBNF (Extend Backus
Naur Form) with JavaCC grammar, JavaCC
automatically generates code to perform syntax analysis
written in Java. The definition of the syntax analyzed by
MCC written in ENBF is shown in Fig. 2. We implement
the parser by describing it according to the grammar of
JavaCC and add actions. And can we get the variablename with this parser.

3. MCC Functions
The MCC has the following five functions.
Open File
Save FileAnalyze Variable Names
Clear EditArea
Clear DisplayArea

4.2. Determining whether variable names areappropriate

We explain the “Analyze Variable Names” function
which is one of the functions of MCC. The variable nameis analyzed by static analysis for the variable declaration
part in the source code. In case of an inappropriate
variable name, a warning is displayed in the display area
as “the line number: Role cannot be inferred from ~,Whether variable names are appropriate is determined byusing the dictionary3. If the variable name does not exist
in the dictionary, it is dealt with an inappropriate variablename. Here, if the variable name is 1 character, do not© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 280

Prototype of Refactoring Support

Fig. 3. The obtained XML filesearch the dictionary and make it an inappropriate
variable name.
As an example of dictionary search using Dejizo Web
Service, search of “name” is shown. As request URL in
MCC,
http://public.dejizo.jp/NetDicV09.asmx/SearchDicItem
Lite?Dic=EJdict&Word=name&Scope=HEADWORD&Match=EXACT&Merge=AND&Prof=XHTML&Pag
eSize=20 & PageIndex = 0 is used. As request parameters,
assign EJdict to Dic, Word to Word, Scope to
HEADWORD, Match to EXACT, Merge to AND, Prof
to XHTML, PageSize to 20, PageIndex to 0. “name” issearched, and the obtained XML file is shown in Fig.3.
We parse this XML file using Java XMLParser and get
the value of TotalHitCount. When the value of
TotalHitCount is 1 or more, it is registered in the
dictionary. When the value of TotalHitCount is 0, it is notexisted in the dictionary. That is, by the value of
TotalHitCount, we can know whether or not it isregistered in the dictionary.
5. Application Example
In order to verify that the MCC works properly, it wasapplied to source code written in C language. The resultsof the application are shown in Fig. 4. In line 41 of theedit area, the variable name is “r_num[50]”. Since “r” is
one character and “num” does not exist in the dictionary,
it is judged as an inappropriate variable name and awarning is displayed in the display area as “41: Rolecannot be inferred from r,num in r_num[50], pleasechange the variable name.”

Fig. 4. The results of the application

Similarly, since there are variable names that are notappropriate for 42, 45, 46, 47, 48, 49, 50, 55,56, 57,59
lines of the editing area, a warning is displayed in thedisplay area as “line number: Role cannot be inferredfrom ~, please change the variable name.”.
Therefore, it can be confirmed that MCC cancorrectly analyze.
6. Discussion
We describe the evaluation and related researches of
MCC developed in this paper.
6.1. Evaluation
MCC developed in this paper reads the source code
written in C language and performs static analysis. It canbe judged by using a dictionary whether or not eachvariable name is appropriate. This makes it easy to findout where a word is not included in the dictionary. In
addition, in the case of snake case or camel case, it ispossible to judge each part of the compound word is notin the dictionary. With these functions, it is possible tofind variable names named by one letter and words notincluded in the dictionary. The source code can be edited
and saved.
By using MCC with these functions, it is possible toreduce the factor that hinders understanding in modifying
the source code. This reduces the time required forunderstanding the source code, shortening the coding
time, decreasing the possibility of embedding bugs, andreducing the time required to add new functions.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 281Satoshi Tanoue, Tetsuro Katayama, Yoshihiro Kita, Hisakki Yamba, and Naonobu Okazaki.

6.2. Related Research

it can be considered that by using MCC developed in thispaper, it is possible to support improving the quality ofthe source code.
The future issues are shown below.

Static analysis tool for C language, a lot of researches anddevelopment has been carried out.
Splint5 is a tool for statically checking C programs forsecurity vulnerabilities and coding mistakes. Splint
performs static analysis such as unused declaration, typemismatch, use before definition, unreachable code,
ignoring return value, execution path without return,possibility of infinite loop, and so on.
AdLint6 can output a warning message about parts oflack of reliability or portability in the source code and cansimultaneously measure various quality metrics. As
quality metrics, you can measure the number of sentences
in a file, the number of statements in a function, thenumber of goto statements, and so on.
On the other hand, the MCC can use a dictionary to
decide whether it is properly named. Since it can be
determined whether the variable name is properly named,
it is possible to reduce variables that impede theunderstanding of the programmer.

Correspondence to variable names that exist in
multiple lines
If variable names extend over multiple lines, it can not
be parsed. MCC hands the source code to the parser
one line at a time. Passing variable names that exist
multiple lines will cause ParseExecption when there isa C language syntax that is not supported. It can besolved by making it possible to analyze the syntax of
all C languages.
Correspondence to variable names that are notsupported.
MCC can not parse pointer variables and variablenames of types defined in the structure. It can be solved
by adding syntax to the definition of the syntax
analyzable by MCC.
Correspondence to function names．
MCC can not judge whether function names areproperly named. It can be solved by adding syntax tothe definition of the syntax analyzable by MCC.

7. Conclusion
In this research, a prototype of refactoring support tool
MCC has been developed for the purpose of supporting
improvement of the source code quality. MCC is a toolthat performs static analysis on the source code written in
C language and supports description of clean code,
focusing on the naming of variable names. The MCC has
the following five functions.
Open File
Save FileAnalyze Variable Names
Clear EditArea
Clear DisplayArea
In static analysis, it is judged whether each variablename is properly named by using a dictionary. If thevariable name is not appropriate, MCC displays awarning for the line number and the variable name. In
addition, MCC has the source code editing function.
We have confirmed that MCC works correctly byapplying the example of source code written in C
language to MCC. MCC can point out variable namesthat are not naming properly. This reduces the time tounderstand the source code, shortens the coding time,
reduces the possibility of bug contamination, and reduces
the time required for function addition. From the above,

Correspondence to the case where the variable nameis not appropriate when existing in a dictionary.
Even if it exists in a dictionary, the variable name may
not be appropriate. For example, word_word is thecase. Such variable names are inappropriate because a
programmer do not know the meaning. It is necessary
to modify MCC so that it can judge whether variablenames are appropriate names in consideration of theorder of the variable names.
References
1. Martin Flower et al. (eds.), Refactoring Improving the
Design of Existing Code, 1nd edn. (Pearson
Education,1999).
2. Dustin Boswell and Trevor Foucher, The Art of Readble
Code (O’Reilly Media 2012).
3. Dejizo Web Service, http://dejizo.jp/dev/index.html.
4. JavaCC, JavaCC Home, https://javacc.java.net/.
5. Split, Splint Home Page, http://www.splint.org/.
6. AdLint,
Adlint::AdvancedLint,
http://adlint.sourceforge.net/.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 282

Improvement of Transitions and Flow Visualization TFVIS for Exception Handling
Takuya Sato*, Tetsuro Katayama*, Yoshihiro Kita†, Hisaaki Yamaba*, and Naonobu Okazaki**

University of Miyazaki, 1-1 Gakuen-kibanadai nishi, Miyazaki, 889-2192 Japan†Tokyo University of Technology, 1404-1 Katakuramachi, Hachioji City, Tokyo 192-0982, Japansato@earth.cs.miyazaki-u.ac.jp, kat@cs.miyazaki-u.ac.jp, kitayshr@stf.teu.ac.jp,
yamaba@cs.miyazaki-u.ac.jp, oka@cs.miyazaki-u.ac.jp

Abstract
It takes much time in debugging process. To find bugs effectively, it’s important to understand the dynamic behaviorof programs. To support understanding the dynamic behavior, we developed TFVIS(transitions and flow
visualization). It provides visualization of data transitions and data flow of Java programs. But it visualizes only
some control structures and expression. Therefore, we newly corresponded to Exception-handling. This improves
the usefulness of TFVIS as a tool to support the understanding of the dynamic behavior of programs.
Keywords: Debugging, Visualization of program, dynamic analysis, Exception-handling, Java

1. Introduction

2. TFVIS

In software development, it takes much time indebugging process1. To find bugs in a program
effectively, it’s important to understand the dynamic
behavior of the program. But it’s difficult since thedynamic behavior of the program is generally invisible2,3.
To
solve
this
problem,wedeveloped
TFVIS(transitions and flow visualization)4, which canvisualize the dynamic behavior of Java programs.
It provides visualization of data transitions in eachmethod and flow of the whole program. It has anotherfeature which can show the data transitions with arrows.
This makes it easy to follow a defect from the incident.
But it’s not useful because it visualizes only some control
structures and expressions.
We aim at the improvement of the usefulness bycorresponding an unsupported structure. This paper
focuses on Exception-handling, which is one of featuresof Java programs and especially corresponds to Try
Catch syntax.

Figure 1 shows an example of visualization by TFVIS.
TFVIS generates a data transition diagram. It shows
the behavior at the time of program execution in detail. It
is based on structural information obtained by analyzing
the program structure and executional information at thetime of execution.
The data transition line can visualize data transitions.
When the user finds a suspicious value of a variable on
the data transition diagram, by using the data transition
line, it becomes easy to specify the cause of generating
the suspicious value.
TFVIS visualizes the flow of the whole program by
the execution flow diagram based on the UML sequence
diagram. The execution flow diagram shows the usagesof a method of each class and relation of a method call.
Figure 2 shows the structure of TFVIS. It consists ofAnalyzer and Visualizer, and Analyzer consists ofStructural Analyzer, Probe File Generator, and Dynamic
Analyzer.
Structural Analyzer analyzes the structure of the
program and outputs the analysis result as structural© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 283

Takuya Sato, Tetsuro Katayama, Yoshihiro Kita, Hisaaki Yamaba, Naonobu Okazaki

Fig. 1. Example of visualization by TFVIS

information to the file. The structural information is used
for deciding the insert position of probes in Probe File
Generator and for generating diagrams in the Visualizer.
Further, Structural Analyzer obtains events occurring ineach line of the source code. The events are a specificprocess as a standard of visualization, and each event has
a value of an event type.
Based on the structural information, Probe File
Generator generates a probe file, which embedded probes
in the target source code. The probe outputs informationon the behavior at program execution. The probe outputs
information on the behavior at program execution. There
are several kinds of probes, and Probe File Generator
selects probes to be inserted for each event that occurs ineach line of the source code.
Dynamic Analyzer analyzes the behavior at the timeof execution from the probe file and outputs the analysis
result as executional information.
Visualizer visualizes the behavior at the time ofprogram execution based on the structural informationand the executional information outputted by the
Analyzer.

Fig. 2. Structure of TFVISDefinition of values of new event types in Structural
Analyzer
Definition of new probes for Try Catch syntax inProbe File Generator
Insertion of the probes for Try Catch syntax in Probe
File Generator
Visualization of events of Try Catch syntax in
Visualizer
3.1.1. Definition of values of new event types in
Structural Analyzer
In Structural Analyzer, we newly define the values of theevent types for Try Catch syntax.
The values of the defined event types are the value ofthe start of the Try block (380), the value of the end of
the Try block (382), the value of the start of the Catchblock (390), and the value of the end of the Catch block(392).
3.1.2. Definition of new probes for Try Catchsyntax in Probe File Generator
In Probe File Generator, we newly defined probes to beinserted for the event of Try Catch syntax.
The probe for the event of the Try block defines as
Try Detection Probe. This probe takes as arguments theinstance ID, method ID, method execution number, andline number at the time of execution. And, to visualize it,
outputs try event ID (380), instance ID, method ID,
method execution number, and the line number to the
Executional Information File.
In a similar way, the probe for Catch block defines as
Catch Detection Probe. In this probe, the arguments are

3. Improvement of TFVISIn this section, we describe the improvement of TFVIS
for corresponding to Try Catch syntax. We describe thechanges of each part to correspond to Try Catch syntax
and then show the data flow of TFVIS after the
improvement.
3.1. Improved points of each division
The improvement to correspond to Try Catch syntax is as
below.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 284

Improvement of Transitions andFig. 4. Flow of Visualization

3.2. Data flow of TFVIS after improvement

Fig. 3. Data Flow of TFVIS

3.2.2. Data flow in Analyzer

the same as Try Detection Probe, but the output is theCatch event ID (390) instead of the Try event ID (380).

Figure 3 shows the flow of data in which a program
including a Try Catch syntax is applied to TFVIS after
the improvement.
By improving of Structural Analyzer, we can obtain
events on Try Catch syntax. From Figure 3, we can seethat structural information file has value of the start of the
Try block (380), the value of the end of the Try block(382), the value of the start of the Catch block (390), andthe value of the end of the Catch block (392).
By the events of Try Catch syntax obtained by
Structural Analyzer, Probe File Generator inserts the
newly defined probes. From Figure 3, we can see that Try
Detection Probes are inserted just preceding eachstatements in Try block, Catch Detection Probe isinserted just following start of Catch block, and an if
statement that executes Loop Surroundings Detection
Probe and Loop End Detection Probe if it is in a loop.
By executing the probe file in which the probes areembedded by the Dynamic analyzer, we obtain
executional information.

3.1.3. Insertion of the probes for Try Catch syntax
in Probe File Generator
In the Probe File Generator, the newly defined probes areinserted for the event of the Try Catch syntax.
Try Detection Probe is inserted just preceding every
line from the start of the Try block to the end of the Try
block. At this time, the value of the line number obtainedby the probe is the line number of the immediately
following statement. Also Catch Detection Probe isinserted immediately following the Catch event.
When a Catch block is executed during Loop, there issuch a process as to break out of Loop. To correspond tothis, insert a boolean type variable (“isLoop”) definitionindicating whether it is in a Loop at the start of method.
We change Probe File Generator so that “isLoop”
becomes true if it is in the Loop, and false otherwise. If“isLoop” is true when an event of the end of Catch block
occurs, existing Loop Surroundings Detection Probe andLoop End Detection Probe are executed on theimmediately preceding line.

3.2.3. Flow of Visualization in Visualizer
Figure 4 shows the flow of visualization when
applying a program including Try Catch syntax.
Based on the structural information, the data flow
diagram and source code on data transition diagram are
drawn. Based on the executional information, the datatransition diagram is drawn.
When Visualizer reads execution information having
a Catch event ID, the execution of the event immediately

3.1.4. Visualization of events of Try Catch syntax
in Visualizer
When an Exception-handling event occurs, a box
describing “Catch” in red font is placed in the execution
place immediately preceding the data transition diagram.
Also, a red arrow that connects the occurrence part of
Exception- handling and the execution part is drawn.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 285

Takuya Sato, Tetsuro Katayama, Yoshihiro Kita, Hisaaki Yamaba, Naonobu Okazaki

improvement of TFVIS.
works.

Next, we describe relation

5.1. Evaluation
If the program to be applied contains Try Catch syntax, acompile error occurs and visualization cannot be
executed in existing TFVIS.
In order to visualize a program containing a Try Catchsyntax, firstly, we improved Structural Analyzer toobtain the events by using newly defined values of event
types for the Try Catch syntax.
Secondly, we improved Probe File Generator to insert
the newly defined probes which output executional
information for Try Catch syntax.
Finally, we improved Visualizer to draw the box
written “Catch” in red and red arrow that connects theoccurrence part of Exception-handling and the execution
part.
TFVIS after the improvement can visualize theprogram containing Try Catch syntax. From this, it canbe said that the usefulness of TFVIS has been improvedby corresponding to Try Catch syntax.

Fig. 5. Visualization of SearchInfo method

preceding is referred as the point occurred Exceptionhandling.
4.

Application Example

By the improvement of TFVIS, we confirm that
programs including Try Catch syntax can be visualized
correctly. As an application example, we show that
TFVIS correctly visualizes the method including Try
Catch syntax by using “inventory management program”
written in Java.
Figure 5 shows a data transition diagram thatvisualizes the SearchInfo method, including a Try Catchsyntax. This method requires “ID” as an argument, anddisplays instance’s “ID”, “name”, “number of stock”, and“unit price”. Since there were only 3 instances generated
as inventory, and stored in the list. At this time, assumingthat the user inputs “ID” 3, it refers to the fourth in thelist, and an exception of “IndexOutOfBoundsException”
occurs.
From Fig 5, we can see that the box “Catch” in theline referring to the list and a red arrow from the box tothe line of Exception-handling which occurred is drawn.
From this, it can be seen that the occurrence ofException-handling is correctly visualized on the datatransition diagram.

5.2. Relation works
Breakpoint debugging
Breakpoints are one of the most frequently useddebugging support approaches6. With debugging
using breakpoints, program execution can be stopped
at arbitrary points. Then, you can refer to the
execution status of the program, such as values ofeach variable at the time of stoppage.
Breakpoint debugging has a problem that it isdifficult to select the installation point of the
breakpoint. Selection of an appropriate breakpoint
location requires user’s knowledge and experience7.
TFVIS is useful in that it can obtain information only
by selecting a method having information desired by
the user and therefore does not depend on the user’s
ability.
Moreover, visualization by TFVIS can overlook
the flow of the whole program and the flow of method
process. By using the data transition line, it is also
possible to grasp the dependency relationshipbetween the variables. In comparison with breakpoint

5. Discussion
In this research, we aimed to improve the usefulness of
TFVIS by corresponding to Try Catch syntax. In thissection, we first describe the evaluation of the© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 286

Improvement of Transitions andsince the execution of the analysis section is notcompleted, execution information cannot be obtainedand visualization cannot be performed.
Implementation of inquiry function
TFVIS has no inquiry function. This problem
becomes more troublesome as the process of the
program to be visualized increases. Therefore, we
think that it is necessary to implement the inquiry
function so that the user can easily find a processsatisfying a specific condition.
Support for multithread programs
TFVIS cannot visualize multithreaded programs. In
multithread programs, process becomes complicatedeasily because data is exchanged between threads. If
we can visualize a complicated process bymultithreading, the usefulness of TFVIS will be
improved.
References

debugging, these functions are superior in that it iseasier to find out how the certain variable is defined.
JIVE
JIVE8(Java Interactive Visualization Environment) isa tool visualizing execution of Java programs.
JIVE has the function of generating UML object
diagrams and sequence diagrams from process at thetime of program execution. In addition, it supports
inquiries by queries, for example, it is possible to
make an inquiry such as “Where is the method “func”
returning NULL to the return value?”. The process
found by the inquiry is highlighted on the sequence
diagram and helps to grasp the behavior at the time of
execution.
When comparing JIVE and TFVIS, JIVE does nothave a function to indicate the dependency between
variables like data transitions. Inquiries aboutvariable updating are possible, but when examining
dependencies such as data transitions, it is necessary
to repeat inquiries. Therefore, TFVIS is more
effective for tasks such as searching for the cause
when a suspicious value is found.

1. Thomas D. LaToza, Gina Venolia, and Robert DeLine:
Maintaining mental models: a study of developer work
habits, Proceedings of the 28th international conference
on Software engineering, pp.492-501 (2006).
2. Roger S. Pressman: Software Engineering A Practitioner's
Approach, McGraw-Hill Science (2001).
3. Jonathan Sillito, Gail C. Murphy, and Kris De Volder:
Asking and Answering Questions During a Programming
Change Task, IEEE Transactions on Software
Engineering, Vol.34, No.4, pp.434-451 (2008).
4. Hiroto Nakamura, Tetsuro Katayama, Yoshihiro Kita,
Hisaaki Yamaba, Kentaro Aburada and Naonobu Okazaki:
TFVIS: a Supporting Debugging Tool for Java Programs
by Visualizing Data Transitions and Execution Flows, The
2015 International Conference on Artificial Life andRobotics, pp.376-379 (2015).
5. Gail C. Murphy, Mik. Kersten and Leah Findlater: How
Are Java Software Developers Using the Eclipse IDE?,
Software, IEEE, Vol.23, No.4, pp.76-83 (2006).
6. Gail C. Murphy, Mik. Kersten and Leah Findlater: How
Are Java Software Developers Using the Eclipse IDE?,
Software, IEEE, Vol.23, No.4, pp.76-83 (2006).
7. Cheng Zhang, Juyuan Yang, Dacong Yan, Shengqian
Yang and Yuting Chen: Automated Breakpoint
Generation for Debugging, Journal of Software, Vol.8,
No.3, pp.603-616 (2013).
8. Demian Lessa, Bharat Jayaraman and Czyz Jeffrey: JIVE:
A Pedagogic Tool for Visualizing the Execution of Java
Programs. Technical Report 2010-13, Department ofComputer Science and Engineering, University at Buffalo(2010).

6. Conclusion
In this research, we aimed to improve the usefulness of
TFVIS by corresponding to unsupported structure.
TFVIS shows behavior at the time of program execution
by data transition and data flow visualization. The
visualization of TFVIS makes it easy to grasp the
behavior at the time of execution of the program
including the defect, and it becomes possible toefficiently specify the defect.
However, there are programs that cannot be
visualized with existing TFVIS. The fact that programs
containing basic syntax of Java programs cannot be
visualized means lacking usefulness.
In this research, we improved TFVIS so that it canvisualize a program containing Try Catch syntax. It canbe said that the usefulness of TFVIS has been improvedby this improvement.
The future issues are shown below.
Correspondence to programs including occurrenceof input wait state
TFVIS does not have the function of receiving input
from the user. Therefore, when a program including
occurrence of an input wait state is applied to TFVIS,© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 287

Automatically Business Decision Making System for Software Development by using
CMMI
Hnin Thandar Tun†, Tetsuro Katayama*, Kunihito Yamamori*, and Khine Khine Oo†*

University of Miyazaki, 1-1 Gakuen-kibanadai nishi, Miyazaki, 889-2192 Japan†University of Computer Studies, Yangon, No. (4) Main Road, Shwe Pyi Thar
Township, Yangon, Myanmar
thandar123@ucsy.edu.mm, kat@cs.miyazaki-u.ac.jp, yamamori@cs.miyazaki-u.ac.jp,
k2khine@gmail.com

Abstract
The automatic business goals are effectively to provide in the software development system. In this research, thesenior level management will define goals based on previous data. We focus to generate all of the business goals
based on Capability Maturity Model Integration (CMMI) applied by Goal Question Metric (GQM) approach.
According to the GQM approach, the system will check estimated data for future and come out possible goals
automatically alignment on defined future goals by senior level management.
Keywords: CMMI, GQM approach, Project Monitoring and Control, MySQL database, Java

1. Introduction
In the current software development, business decisionmaking system is important process to support the
automatic business goals. In this research, we proposed
to make a decision model that is Capability Maturity
Model Integration (CMMI) 1.
We cannot suggest automatic goals only used to
CMMI model. Therefore, we applied the GQM approach,which is a good measurement process to provide businessgoals better and timelier decision. Thus, this approach
will support to define goals by senior management basedon CMMI model2, 3.
The system will check the defined goals of metricdata plan by senior level management. If defined goals
by senior management will match with the resources data,
the system will be operated automatically. If theresources will not match with the required goals, thesystem will realign production goals, which will bematch with the actual goals of processing.
Additionally, the metric program framework used togenerate a complete metric plan, which includes GQM
analysis, to monitor and control measurements of definedgoals. The GQM approach has three levels of
measurement of defined goals system, such as (1)conceptual level, (2) operation level, and (3) quantitative
level. In the GQM analysis, the metric plan will define
specific goals according to the project monitoring andcontrol measurements in the software development.
Therefore, the research paper focused measurement
approach of GQM analysis to define all of the businessgoals and measurable index to perform business
operation functionality4. For application concerned, themodel developed in this paper is used for dairy farms
milk production procedure. By constructing milk
production database, the proposed is able to monitor
targets of each unit to achieve goals schedule (daily,
weekly, monthly and quarterly). Then the senior level
management will be confirmed and the system willassign automatically decision goals alignment andcontrol all of the future resources of performance.
For technical construction, we collect data by using
excel data collection tool to import data in MySQL
database first. Moreover, the system will report businessgoals to senior management showed by graphicalinterface as a result for the future target. The test result
will perform the automatic business goals for futuretarget that are reporting in step by step to the senior
management.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 288

Hnin Thandar Tun, Tetsuro Katayama, Kunihito Yamamori, Khine Khine Oo

approaches that do not take into all business operationlevels.
2.2. Metric data decision of GQM approach

Fig. 1. Overview of the business goals decision-making
system

2. Framework of the CMMI model applied with
GQM approach
In this research, CMMI model applied the GQM
approach, which is an approach of metric data analysis
used to monitor business decision goals in the softwaredevelopment.
It is related the metric plan with the key process areaof Project Monitoring and Control measurements.
2.1. Applying GQM approach based on CMMI
Figure 1 shows how to perform business goals decisionmaking system of the CMMI model.
This paper is concerned with a generating of businessgoals by CMMI model. In doing so, this model appliedor adapted the Goal Question Metric (GQM) approach. It
is used to evaluate the defined goals by senior level
management that can monitor and control measurements
for the progressive measurements systematically.
This research identifies the general measures for the
specific goals and its specific practices related to theprocess area of CMMI. Monitoring and control
measurements provide business goals with progressive
measurable index to align directly with the GQM
approach. This approach is defined to align all of thebusiness goals at the levels of the organization. Moreover,it will make to control success or failure throughprogressive measurement and key performanceindication of metric definition for improvement
decisions5. Therefore, the GQM approach is an excellent
metric approach to be uses everywhere better than similar

Metric framework that is used to generate the metric planis described, followed by a complete metric plan.
The process metric plan is established by the businessdecision based on the actual progress in terms of GQM
approach according to the business goals. The metric plancontains goals and scope of implementation metrics6. The
metric plan includes data collection, data analysis, datareporting and metric decision making. This section
defines the metric plan based on monitoring and control
measurements for developing metric steps are:
1. Define the measurement goals by senior
management and obtain the objectives/goals for the
future target.
2. Utilize the GQM approach and determine thebusiness goals progressive measurements tomonitor.
3. Review measurement practices and the target ofbusiness processes in specified areas.
4. Establish reporting and monitor key performanceindicator of metric units.
5. Align with the business operation for monitoring
results per all units (daily, weekly, monthly,quarterly, and yearly).
Therefore, this paper presents the key process area of
CMMI model that is necessarily to support on business
effective process improvement, needs design
implementation, needs decision tools for achieving goals
for future target systematically. According to theillustration propose, we need to define the actual businessgoals by senior level management aligned with theresources of business data.
3. Implementation of metric analysis approach
In this section, we consider to implement the metric datathat is used to define the actual milk production diary.
According to the defined phase, the milk production
diary consists of four collection records such as
production collection records, milk storage units
collection, own farmer collection records and otherfarmer contract units. In this collection procedure, own
farm and other farmer contract collection will provide as© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 289

Automatically Business Decision Making

3.1.2. Create the storage units collection

System implementation
decision goals

In storage collection, we store the received data from own
farm and other farmer collection, amount of production
usage data, amount of remaining balance production
units.
They are related to each other to align the definedgoals of production by senior management level based on
previous data.

Production Collection
Product Code
Product Item

Automatic Business
Decision Goals

Package Type
Production

Business
Data of Milk
Collection

Required Milk

Storage Tank

Own
Farm
Collectio
n

Other
Farm
CollectionProduct
Code

Product
Item

Production(bottles)

P001

Whole
Milk

734578

P002

Raw
Milk

894208

Senior Management Goals
Product
Code

Product
Item

PackingType (ml)Production(bottles)

P001

Whole
Milk

500

734578

P002

Raw
Milk

200

8942083.1.3. Define goals by senior level management

Systemdevelopment
business goals

Based on previous data results, senior level management
defines goals for future target of production.
Moreover, the system will check estimated milk
production collection for future and come out possible
goals automatically alignment on defined future goals by
senior level management.

Fig. 2. Implementation of metric data flow

an input to the storage tank collection. Among them, thesystematic collection form involve product code, productitem, packing type, production amount of metric units,
usage amount of collection, usage amount of production
and the remaining amount of milk data to support
operation target (business goals) smoothly. This section
has to discuss two divisions such

3.2. Monitoring progress of auto suggestive goals
The senior level management specifies business goals of
the future target to compare the previous data.
As a metric data flow, all databases are used to makesuggestion goals for future target. The system willassume that senior management last input results whichmust be approximately they want to product for
appropriate products. As per previous data result, thesystem will auto align with the senior management last
input results. If the resources of milk production are notmatched with the suggestive goals by senior management,
the system will realign the production goals per eachproduct items, which will be matched with the actual
milk collection data in storage.
Based on daily actual results, weekly, monthly,quarterly, yearly data will realign the goals
systematically and report in systematically to senior level
management.

3.1. Metric data collection procedure
Based on the GQM approach, we need to store thespecific goals of metric data as a previous data collection.
The collection data refer to the milk diary production
such as milk production units, milk storage units’
collection. The storage collection is provided by the own
farm collection records and other farmer contract units as
an input to become the milk resources.
Figure 2 shows the implementation of the approach of
metric data flow. In this operation, we describe how to
implement the milk production diary.
3.1.1. Create the production collection records

3.3. System Implementation of milk production

We define the business goals of production units in themilk production system.
This system apply the production code, product item,
packing type of each product item, amount of production
per milk bottle, required milk liter per each of productitem and usage of milk liters which are able to support
operation target (defined goals by senior management)smoothly. All of the productions units are stored in themetric database of the milk production system.

This system presents evaluation of the automatic goals
for window application that is developed in Java.
Database in window application is MySQL databasethat are connect to the graphical interface by using
JFreeChart tool. In the evaluation process, it is performed
the metric parameters of the milk production system bygraphically. Milk production system shows the automatic© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 290

Hnin Thandar Tun, Tetsuro Katayama, Kunihito Yamamori, Khine Khine Oo

Bottles

Suggestive Bottles

Bottles
1600000

Bottles

700000600000500000400000300000200000100000
0

P001

P002

P003

P004

P005

P006

P007

P008

P009

Suggestive Bottles

1400000

P010

ProductCode

1200000
1000000
800000600000

Suggestive Bottles

4000002000000

Fig. 3. Yearly data progress of milk production units

Product Codedecision goals of that are implemented in window
application.
4.

Fig. 4. Suggestive goals for milk production target

Performing results of business goals

units. GQM approach can easily tested to calculate andalign the metric of milk production units.
Therefore, the system can perform the autosuggestive goals by systematically for the defined goals
by senior level management.
In the future issue, the research is used to refine theproposed method that can evaluate the quality of metrics
applying more kinds of metrics to link to the GQM
approach. The system will implement GQM plugin andmonitoring tools flexible approach to improve the quality.

In the research application, the performing results areclassified into three phases to suggest the automatic
business goals as follow as.
Phase 1: Collection of the business goals is supported
to define the business for future target by senior level
management. The amount of collection units displays
the milestone review of the production resources thatare calculated by using packing type as required milkbottles as shown in Figure 3.
Phase 2: According to the phase 1, the production ofbusiness goals are stored in the milk production
database. The production data is used for future targetof business goals defined by senior level management.
Based on the previous data results, the senior
management defines the production of goals described
as a decision-making goal for future target.
Phase 3: The system will accept auto suggestive goals
for production units matched with the storage data
resources if the storage tank units are greater thanrequirement of milk calculation. Then, the system will
be realigned by suggestive goals based on the storage
unit resources. In addition, the system will monitor
targets of business goals to achieve business goals
timely (daily, monthly, and yearly). During the system
implementation, the auto suggestive goals will comeout to specific indicators of milk products by usinggraphical interface tool. Figure 4 shows the evaluationof the auto suggestive goals for the future target.

References
1. V.Basili, G. Caldiera and D. Rombach: Goal Question
Paradigm, (Marciniak,J.J., editor), Vol.1, pp.528-532(1994).
2. R.Van Soligen and E. Berghout: the Goal Question Metric
Method: A Practical Guide for Quality Improvement ofSoftware Development, McGraw-Hill, (1999).
3. M. Chrissis, M. Konrad, S. Shrum: CMMI® Guidelines for
Process Integration and Product Development, AddionWesley (2007).
4. R. Xu, Y. Xue, P.Nie, Y.Zhang and D. Li: Research on
CMMI based Software Process Metrics, the FirstInternational MultiSymposiums on Computer andComputional Science, IMSCCS (2006).
5. S.Wanapasim,
T.
Suwannasari,andA.
Methawachananont: An Approach for Monitoring
Software Development using Timesheet and Project Plan,
International MultiConference of Engineering andComputer Scientists, Vol.1, (2013).
6. Hnin Thandar Tun, Tetsuro Katayama, Kunihito
Yamamori and Khine Khine Oo: Business Goals
Monitoring and Control Measures in CMMI, IEEE 5th
Global Conference on Consumer Electronics (GCCE), pp.
376-378 (2016).

5. Conclusion
In this system, GQM approach is modeled as a metricplan for measuring the performance of milk production© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 291

An Application of Collaborative Filtering in Student Grade Prediction

Chaloemphon Sirikayon1 and Panita Thusaraon2
College of Innovative Technology and Engineering
Dhurakij Pundit University
Bangkok, Thailand
E-mail: chaloemphon.sir@dpu.ac.th, panita.thu@dpu.ac.th
www.dpu.ac.th

Abstract
This research presents the process of student performance prediction by using the collaborative filtering (CF). The
benefit of this research includes assist instructor to identify student performance, personalized advising, and student
degree planning. The CF technique composes of similarity calculation and prediction. In our experiments, a priorcourse clustering with heuristic knowledge is adopted and different techniques of similarity calculation are compared.
The performance of each student has been predicted by using existing grades available at that time.
Keywords: grade prediction, collaborative filtering, educational data mining

correlation achieves the lowest prediction error and aprior course clustering with heuristic knowledge canenhance predictability.
The rest of this paper is organized as follows: related
work and fundamental concept is given in Section 2. The
proposed method is described in Section 3. Experiments
are conducted in Section 4. Conclusions are summarized
in Section 5.

1. Introduction
Student performance prediction in future course isimportant as it provides valuable information to facilitate
student success. In this paper, we present the process ofstudent performance prediction by using thecollaborative filtering: CF [1], which is one of the mostpopular techniques wildly used for student performance
prediction. The performances that students achieved in
the earlier courses are used to predict grade that they willobtain in future courses. The algorithm is based on theidea of finding the most similar students. We have
performed various methods to calculate students’similarity, i.e. Pearson correlation, cosine similarity, andEuclidian distance. The performance of each method is
experimentally evaluated on a dataset obtained from
Dhurakij Pundit University with enrollments of 200
undergraduate students between 2012 and 2016 from theFaculty of Information Technology. Our experimentsshows that finding students’ similarity with Pearson

2. Preliminaries
This section summarizes related work and briefly
defines the fundamental concept needed to facilitate thepresentation of the proposed algorithm.
2.1. Related work
Different models have been developed in order topredict student’s performance and many approaches rely
on collaborative filtering methods. The similarities ofstudents are calculated utilizing their study results,© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 20-22, Miyazaki, JapanP - 292

Chaloemphon Sirikayon, Panita Thusaraon

Here ru,i denotes the rating of user u for item i , andru is the average rating of all items given by user u .
Similarly, rv,i denotes the rating of user v for item i ,and rv is the average rating of all items given by user v .

represented by the grades of their previously passed
courses. A recommendation tool called the personalized
Grade Prediction Advisor (pGPA) was proposed in [3].
The system allows user to set parameters such as numberof similarity students used for prediction. Another course
recommender system for University College Dublin’s
on-line enrolment application was proposed in [2]. The
system recommends elective modules to students basedon the core modules that they have selected by using
item-based collaborative filtering.
On the other hand, [5] presented future course grade
prediction methods that utilize approaches based onlinear regression and matrix factorization. Hybrid
methods and content features are also used in [6].

2.2.1.2 Cosine similarity
The similarity sim (u , v ) between user u and v is
calculated ass(u, v)

Collaborative Filtering algorithm is based on the mainidea that people have similar preferences and interests.
One user’s behavior is compared with other user’s
behavior to ﬁnd his/her nearest neighbors, and according
to his/her neighbor’s preferences or interest to predict
his/her preferences or interest. Suppose that U = {u1, u2,...,um} is a list of m users and I = {i1, i2,..., in} is a list of n
items. Each user Ui gives rating scores for a list of items
Iui. The prediction problem is to predict the rating active
user Ua will give to an item Iua from the set of all items
that Ua has not yet rated. The CF technique composes of
3 steps as follows: 1) users similarity calculation 2) top
N nearest neighbors selection and 3) prediction.
2.2.1. Similarity and distanceLet the set of items rated by both users u and v bedenoted by I, then similarity coefficient sim (u , v )between them is calculated as

I v ( ru ,iEuclidean distance for two user u and v is calculatedbyd (u, v)i Iu I v

I v ( ru ,i

ru )

2

ru )( rv ,i

rv )

i Iu

I v ( rv ,i(ru ,irv,i ) 2(3)

Here ru,i denotes the rating of user u for item i , andrv,i denotes the rating of user v for item i . Then,obtained distance scores are converted to similarities bysim(u, v)

1
1 d (u, v)(4)

2.2.2. Prediction

Pu , i2.2.1.2 Pearson correlation

i Iu

2.2.1.3 Euclidean distanceOnce similarities are calculated, a set of top-k users
most similar to the active user u are selected and theirrating scores are used for the prediction Pu ,i of thespecific item i for user u as follow:

Various methods can be used to find similarity
between users such as Pearson correlation and cosine
similarity. On the other hand, dissimilarity calculation,i.e. Euclidean distance can be converted to similarity.

i Iu

(2)

where ru,i denotes the rating of user u for item i , andrv,i denotes the rating of user v for item i .

2.2. User-based collaborative filteringsim (u , v )i ru ,i rv ,i2
2i ru ,i i rv ,iru rv|| ru ||2 || rv ||2ru

v N s (u , v )( rv , i
v N | s (u , v ) |

rv )

(5)

3. Dataset and Method
For each student whose grade needs to be predicted, aset of similar students are identified by using their gradesfrom courses that they have already taken. The data used
for this study obtained from Dhurakij Pundit University
with enrollments of 200 undergraduate students between
2012 and 2015 from the Faculty of Information
Technology. The dataset comprised of 200 students andrv ) 2(1)© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 20-22, Miyazaki, JapanP - 293

An Application of Collaborative

their 12,000 grades. The A-F letter grades were converted
to the 4–0 scale. The performance of each student who
enrolled in semester 2, 2015 has been predicted by using
grades available at that time.

respectively. On the other hand, top 3 most similar
students form Euclidean distance are student S23, S41, andS55.
Once similarities for each student are obtained, theperformance of each student who enrolled in semester 2,2015 has been predicted as show in Table 2.

3.1. Prediction without prior courses clustering
Our study have comprised of 3 steps in user-based CF
to make a prediction for each student as follows:
Step 1: Calculate similarity between the active student
Sa and every other user by using Pearson correlation,cosine similarity, and Euclidian distance.
Step 2: Based on their similarity scores, various set of k
students, most similar to active student Sa is thenselected.
Step 3: Prediction for grade student Sa will receive fromthe course i is generated by using grades of course i thatk similar neighbors have already taken.

Table 2. Example of grade prediction for student
S1 with different similarity approach

25

CosineEuclidean

1025: 0.3033

025: 0.9466

023: 0.2450

2010: 0.2843

013: 0.9429

041: 0.2297

3013: 0.2393

010: 0.9427

055: 0.2240

4073: 0.2205

023: 0.9408

067: 0.2240

5102: 0.2121

065: 0.9400

036: 0.2222

6064: 0.2102

068: 0.9396

040: 0.2188

7022: 0.2072

073: 0.9396

052: 0.2188

8023: 0.2065

022: 0.9395

004: 0.2171

9041: 0.2040

014: 0.9392

082: 0.2171

10075: 0.2033

019: 0.9386

046: 0.2139

35

40

45

Subject: IS306 Real Grade: 2.5(C+)
2
2
2
2
2
Pearson(1.90) (1.88) (1.87) (1.87) (1.87)
2
2
2
2
2
Cosine(1.93) (1.87) (1.83) (1.86) (1.83)
2
2
2
2
2
Euclidean(1.87) (1.86) (1.85) (1.86) (1.85)

Similarity Calculation Method
Pearson30

Subject: IS203 Real Grade: 2(C)
2
2
2
2
2
Pearson(1.85) (1.85) (1.85) (1.85) (1.85)
2
2
2
2
2
Cosine(1.74) (1.74) (1.77) (1.78) (1.79)
2
2
2
2
2
Euclidean(1.86) (1.86) (1.86) (1.87) (1.87)

Table 1. Example of similarity scores for student S1
Top N
neighbors

Neighbors Size (N)Method

3.2. Performance evaluation
The performance evaluations were conducted using
accuracy measure and root mean square error (RMSE)
obtained by Eq.(6) and Eq.(7), respectively.

Accuracy

Table 1. shows an example of similarity scores forstudent S1 obtained by various approach. The result isslightly different for Pearson correlation and cosine
similarity methods. For Pearson correlation method, top
3 most similar students are student S25, S13, and S10 withsimilarity scores 0.9466, 0.9249 and 0.9427, respectively.
The result from cosine similarity method depicts that top
3 most similar students are student S25, S10 and S13,

Corrected Answer
100| Subjects | | Users |

RMSE

2
1 n ˆ
(Yi Yi )ni 1(7)

where Yˆi is a predicting value of subject i and Yi is areal value of subject i .

4.

Experiment

We compared the predicted grades with the actual
grades of students who enrolled in semester 2, 2015. The© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, JapanP - 294(6)

Chaloemphon Sirikayon, Panita Thusaraon

performance of each approach is compared as shown in
Table 3. In our experiment, Pearson correlation achieves
the best accuracy. There are hardly different between
Pearson correlation and Euclidean distance method.

Table 5. Comparison of accuracy with tolerance ±0.5
Similarity
Approach
PearsonCosineEuclidean

Table 3. Comparison of accuracy for each method
Similarity
Approach
PearsonCosineEuclidean

25
0.42
0.35
0.42

Neighbors Size (N)303540
0.42 0.42 0.42
0.35 0.37 0.39
0.42 0.41 0.42

45
0.42
0.38
0.42

Neighbors Size (N)3035400.83 0.83 0.83
0.84 0.83 0.83
0.83 0.83 0.83

45
0.83
0.85
0.83

Table 6. Comparison of RMSE with tolerance ±0.5
Similarity
Approach
PearsonCosineEuclidean

Since the A-F letter grades were converted to the 4–0
scale, which is actually discrete. Then, the result ofprediction has been compared to real performance ofstudent which has been removed from the experimentdata. However, letter grades come from marks range suchas 90-100 for A, 85-89 for B+. It means that with only 1
mark different, student who has mark at 89 will get grade
B+ while student with mark at 90 will get grade A.
Therefore, the comparison was relaxed in the boundaryof deviation error, 0, and 0.5. By(i) Error deviation 0 means the result of prediction
have to exactly match to the real performance ofstudent, and(ii) Error deviation 0.5 means the result of prediction
will be counted as corrected if it differs from the real
performance of student less than or equal to 0.5, asshown in Table 4.

25
0.54
0.56
0.54

Neighbors Size (N)3035400.55 0.54 0.54
0.55 0.56 0.55
0.54 0.54 0.54

45
0.54
0.54
0.54

References
1. S. Xiaoyuan and K. M. Taghi, A survey of collaborative
filtering techniques, Advances in Artificial Intelligencearchive (2009)
2. M. P. O’Mahony and B. Smyth, A recommender systemfor on-line course enrolment: an initial study, In
Proceedings of the 2007 ACM conference on
Recommender systems (2007), pp. 133–136.
3. M. Sheehan and Y. Park, pGPA: a personalized grade
prediction tool to aid student success. In Proceedings ofthe sixth ACM conference on Recommender systems(2012). pp. 309–310
4. H. Bydžovská, Student Performance Prediction Using
Collaborative Filtering Methods. In C. Conati, N.
Heffernan, A. Mitrovic, & M. F. Verdejo (Eds.), Artificial
Intelligence in Education (2015), pp. 550–553. Springer
International Publishing.
5. A. Polyzou and G. Karypis, Grade prediction with models
specific to students and courses, International Journal of
Data Science and Analytics (2016)
6. S. Mack, L. Jaime and R. Huzefa, Next term grade
prediction, Proceedings of the 2015 IEEE International
Conference on Big Data (2015), pp.970-975
7. H. Bydžovská, A comparative analysis of techniques forpredicting student performance, In Proceeding of the 9thInternational Conference on Education Data Mining(2000), pp. 306–311.
8. N. Thai-Nghe, L. Drumond, A. Krohn-Grimberghe and L.
Schmidt-Thieme, Recommender system for predicting
student performance, 1st Workshop on Recommender
Systems for Technology Enhanced Learning (2010), pp.
2811-2819.

Table 4. Grade converted scale with tolerance 0.5
Predicting
Scale
0
1
1.5
2
2.5
3
3.5
4

25
0.84
0.83
0.83

University grade(real student performance)F
D or D+
D or D+ or C
D+ or C or C+
C or C+ or B
C+ or B or B+
B or B+ or A
B+ or AThe corrected answers have been taking to calculate
the average of accuracy and root mean square error, as
shown in Table 5 and 6.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 20-22, Miyazaki, JapanP - 295

An improved detection method for railway fasteners
Jiwu Wang1

School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University
Beijing 100044, China1
Yan Long1

School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University
Beijing 100044, China1
Sugisaka Masanori 2

Alife Robotics Corporation Ltd, Japan and Open University, United Kingdom2
E-mail: jwwang@bjtu.edu.cn; ms@alife-robotics.co.jp

Abstract
Aiming at the disadvantages of low efficiency and poor stability of the existing methods of fastener detection, thispaper proposes a method of fastener detection based on template matching. Firstly, the image is processed by noise
reduction, this method does not need to be based on histogram can match template directly. We choose a standard
fastener image as template, read template firstly, then slide the image blocks on the input image to match thetemplate and the input image. Experimental results show that this method can effectively detect the flaw,
deformation and obstacles of railway fastener occlusion. This method has a fast matching speed and a goodrobustness, it’s detection accuracy is up to 96%
Keywords: Fastener detection, template matching, trigger device.

feature and PCA algorithm. This method need to
calculate the histogram of each image, a large amount ofcalculation, and a large number of training. The
efficiency of this method is not high. Liu Manhua, Yang
Jinfeng et al[3] take use of directional field algorithm toextract parts of the image features, and then do thetemplate matching. This method has higher computing
speed, but higher false detection rate and low accuracy.
Stella, Mazzeo et al[4]and Marino Distante et al[5] using
Wavelet analysis and, Multilayer Perceptron Neuralclassifiers to detect hexagon Al-headed bolts (a kind of
fastener reveals the presence/absence), of the fastener

1. Introduction
Railway fastener is used to tightly fixed tracks to thesleepers, as an important part of the railway system, plays
an important role in ensuring the high-speed running of
the railway train. At present, with many kinds ofliterature are used as the detection method of fastener.
Ça˘glar Aytekin, Yousef Rezaeitabar et al[1 ] using amethod which fuse various methods based on pixel-wiseand histogram similarities. The method has a large
amount of calculation and low accuracy. Xavier Gibert,
Vishal M. Patel et al[2] proposed a method of railway
fastener state detection which is based on the HOG© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 296

Jiwu Wang, Yan Long, Sugisaka Masanori

3. Fastener detection algorithm

bolts with high detection and classification rate, but thatis not applicable to our situation.
Because railway fastener has a complex background.
Characteristics of fastener has a obvious difference fromcharacteristics of track, subgrade and sleepers. distinct
characteristics.
Considering the shortage of existing methods, this paper
proposes a new method for fastener detection. Using
image and standard template matching method. Calculate
the similarity between the template and the template.
When the threshold value is beyond a given threshold,the fastener can be identified as missing, deformed or
blocked.

The method for detecting the fastener is as follows: selectan image of the complete fastener on the actual line as atemplate, and the template is sliding from the upper left
corner to the lower right corner of the image. Calculate
the similarity of each location. Match The best match
position is the fastener position. If the acquisition image
size is W*H and the template size is w*h, the output
image size is (W-w+1) * (H-h+1). The coordinates of a
pixel point on the image acquisition image is (x, y), andthe coordinate value of a pixel point on the template
image is (x ', y'). T (x ', y') represents the position of thetemplate (x ', y') of the pixel value. I (x ', y')representation (x ', y') to cover the pixel value of thecorresponding point on the image. R (x,y) represents the
final calculation results. The following algorithms are
given in this paper:

2. Fsterner detection system design
Fastener detection system is mainly composed of thebottom fastener image acquisition device and thesoftware processing system. Fastener image acquisition
device is mainly responsible for accurate, complete, clear
to capture the image of each fastener. Specific devices
such as figure 1:

SQDIFF NORMED:

R ( x, y )T ( x' , y ' ) I ( x x ' , yx ', y 'x ', y 'T ( x' , y ' ) 2y' )I ( x x' , y

2

y' )

This kind of method uses the square difference tomatch, the best match is 0 but if the match is bad, thematch value is bigger.
CCORR NORMED:

R(x, y)x', y'
x', y'

T(x', y' ) I '(x x', y y')T(x', y' )2x', y'I ' (x x', y y' )2This kind of method uses the multiplication operation
between the template and the image. Therefore, the larger
number of representative matching degree is higher. 0
represents the worst matching effect.

Fig 1. Fastener image collecting device
LED light source to provide the system with sufficientbrightness and uniform light, reducing the impact of lightand weather on the picture quality. A high speed
industrial camera with a high frequency can be
guaranteed to capture a clear image even in the case of avibration. Photoelectric sensor is used as the trigger
sensor of the fastener. When the train through eachfastener, the sensor input a signal to the control system,
the control system output a signal to the camera, shooting
a pair of images. In this way, we can ensure that each
image can only be collected to a complete image of thefastener. To avoid the image caused by the false detection
and miss detection conditions. Main control board andcircuit inside the box body.

CCOEFF NORMED:

R ( x, y )x ', y 'x ', y 'T ' ( x' , y ' ) I ' ( x x' , y

T ' ( x' , y ' )2x ', y 'y' )I ' ( x x' , yy' )2In the algorithm mentioned above

T' ( x ' , y ' ) T ( x ' , y ' ) 1 /( w h)I' ( x x '，y y' )x ", y "I ( x x" , yI ( x x' , yx ", y "T ( x" , y" )y ' ) 1 /( w h)y" )© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 297

An improved detection method

This method can match the relative value of the templateto the average value of the template and the image to themean value of the template. 1 epresents the perfect match
result, and the -1 represents a bad match, and the 0
represents no correlation.
4.

Using the above three algorithms, when the output value
is close to the ideal result, we judge for the fastener isnormal, when the output result is abnormal, we judge thefault or obstructions to fastener. Due to the small numberof samples, we use the method of artificial judgment tojudge. In the follow-up work, we will use the PCA
algorithm to classify and identify, in order to achieve themost effective. This experiment for 200 pictures,
including normal picture 177, abnormal picture 23,
including the fastener deformation 8, lost 4, the barrier
11, the statistics of the three algorithms matching results
are as follows:

Experiments

Please provide a shortened running head (not more thanfour words, each starting with a Capital) for the title ofyour paper. This will appear with page numbers on the
top right-hand side of your paper on odd pages.
Using the above three algorithms, we use 77*137 pixelsof the fastener image template, 300*400 image matching,
the matching effect as shown in figure:

Algorithm
SQDIFF NORMEDCCORR NORMED

Accuracy

75%
89%
CCOEFF NORMED 99%

False
detection

Missing
detection

9%
2%
0

16%
9%
1%

5. Summary
By using these three methods, template matching effectcan be achieved. From the SQDIFF NORMED matching
method to the CCORR NORMED matching method, andthen to the CCOEFF NORMED matching method, thematching accuracy is improved, but the corresponding
calculation time is also increased gradually. Due to thelarge size of the fasteners detecting data, we require thehigher accuracy of matching result. The NORMED
CCOEFF algorithm is more suitable for the detection offasteners. We will further optimize the efficiency of thealgorithm in the following work, select the highperformance computer configuration, in order to improvethe computational efficiency.

ab
Fig 2. (a) is template image, (b) is collecting image.

.
References
1. Ça˘glar Aytekin, Yousef Rezaeitabar, Sedat Dogru, and˙Ilkay Ulusoy, Railway Fastener Inspection by Real-Time
Machine Vision, Systems, Man, and Cybernetics, Part C:
Applications and Reviews, IEEE Trans. on, 45(7):1101–
1107, 2015. 7
2. Xavier Gibert, Vishal M. Patel, and Rama Chellappa,
Robust Fastener Detection for Autonomous VisualCd
Fig 3. (c) is calculation result image, (d) is matching
result image© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 298

Jiwu Wang, Yan Long, Sugisaka Masanori

Railway Track Inspection, IEEE Winter Conference on
Applications of Computer Vision, IEEE 2015:694-702.
3. Jinfeng Yang, Manhua Liu, Hui Zhao and Wei Tao, An
Efficient Image-based Method for Detection of Fasteneron Railway, International Conference on Test and
Measurement 2010:731-737.
4. Stella, E., P. Mazzeo, et al, Visual recognition of missing
fastening elements for railroad maintenance, Intelligent

Transportation Systems, IEEE 5th International
Conference, 2002 Proceedings:94-99.
5. Marino, F., A. Distante, et al, A Real-Time Visual
Inspection System for Railway Maintenance:Automatic
Hexagonal-Headed Bolts Detection, System, Man, andCybernetics, Part C: Applications and Reviews, IEEE
Trans. on, 37(3):418–428, 2007.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 299

A New Tool to Access Deep-Sea Floor “Sampling-AUV”
Kazuo Ishii

Dept. of Intelligence Systems, Kyushu Institute of Technology,
2-4 Hibikino, Wakamatsu, Kitakyushu, Fukuoka, Japan
E-mail: ishii@brain.kyutech.ac.jp
Takashi Sonoda

Dept. of Intelligence Systems, Kyushu Institute of Technology,
2-4 Hibikino, Wakamatsu, Kitakyushu, Fukuoka, Japan
E-mail: t-sonoda@brain.kyutech.ac.jp
Yuya Nishida

Center for Socio-Synthesis, Kyushu Institute of Technology,
2-4 Hibikino, Wakamatsu, Kitakyushu, Fukuoka, Japan
E-mail: ynishida@lsse.kyutech.ac.jp
Shinsuke Yasukawa

Dept. of Intelligence Systems, Kyushu Institute of Technology,
2-4 Hibikino, Wakamatsu, Kitakyushu, Fukuoka, Japan
E-mail: s-yasukawa@brain.kyutech.ac.jp
Tamaki Ura

Center for Socio-Synthesis, Kyushu Institute of Technology,
2-4 Hibikino, Wakamatsu, Kitakyushu, Fukuoka, Japan
E-mail: ura@lsse.kyutech.ac.jp
Keisuke Watanabe
Dept. of Navigation and Ocean Engineering, Tokai University,
3-20-1 ORido Shimizuku, Shizuoka, Shizuoka, Japan
E-mail: keisukejapan@gmail.com

Abstract
Underwater robot is one of the important research tools to explore deep-sea. Especially, Autonomous
Underwater Vehicles (AUVs) attract attentions as new tools because they are un-tethered and suitable for wide
area observation. As the next generation AUV, we have been developing a Sampling-AUV that can dive into
deep-sea and bring back marine creatures using a mounted manipulator. In the mission, the AUV transmits the
deep-sea floor images to the support ship using acoustic communication, and the operator selects the marine
creatures to sample and bring back them. In this talk, we introduce the new AUV, the underwater manipulator
and image processing techniques for sampling.
Keywords: Sampling-AUV, Next Generation AUV, Image Processing.

researchers in various fields. In spite of attractive
discoveries, we are still unable to get detailed
information about the mechanism of ecological system,
because the deep-sea is an extreme environment withspecial features of high pressure, darkness and radio
attenuation. Recently, underwater robots are employed to

1. New Tool to Survey Seafloor “SamplingAUV”
Discoveries such as mineral resources, energy
resources and marine lives in the deep-sea give deep
impacts on our society and have good reasons to attract© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 300

Kazuo Ishii, Takashi Sonoda, Yuya Nishida, Shinsuke Yasukawa, Tamaki Ura, Keisuke Watanabe

observe deep-sea floor as useful tools1,2, and they aremainly classified as Human Occupied Vehicle (HOV),
Remotely Operated Vehicle (ROV) and Autonomous
Underwater Vehicle (AUV) depending on the extent ofautonomy and mounted facilities.
Especially, AUV is expected as a new tool because
they can cruise long distance without the limitation oftethered cable, suitable for wide area observation and asa stable observation platform3-5. However, as the
disadvantages, AUV should be equipped with highautonomy and various devices such as onboard highspecification computers, expensive sensory system fornavigation (INS, DVL) and actuators for motion control,and special devices of mission-dependent sensors6.
As one of the next generation missions, AUVs are
required to sample deep-sea marine life such as crabs,
shellfish, sea cucumber or deep-sea floor sediments
which include a lot of micro-organisms. However, thesampling mission is not easy to be performed fullautonomously by the AUV, because the AUV has
difficulty to decide what should be the interesting targets
or situation, such as spawning to sample marine lives.
We have been developing a sampling system using
AUV which incorporates the interaction between the
robot and human, a man-machine system. The strategy ofdeveloping sampling system consists of several steps
which are; (i) deep-sea floor image acquisition, (ii)interesting image selection, (iii) image transmission toresearch ship, (iv) checking image by operator, (v)sending a command to AUV to go back to the interestingtarget area ordered by onboard operators, (vi) samplingtargets, and (vii) surfacing to bring back samples to theresearch ship. The mission scenario is shown in Fig.1.
We have already presented steps (i), (iii), (iv) and (v)which provides detailed discussion about image
enhancement, data compression and reconstruction
method, experimental study of image transmission using
AUV with the aid of acoustic transmitter and receiver 7
and going back to the area where the image was taken8.
However, AUV was not able to transmit all the taken
images through acoustic communication due to narrowtransmission bandwidth. Therefore, AUV has to selectthe interesting image which includes marine lives
automatically before transmitting.
In this research, we propose the next generation
AUV “Sampling-AUV”.

Fig.1 The mission Scenario of Sampling-AUV
Mission Scenario of New AUV: Sampling-AUV
A reconstructed image

It’s interesting!
We need more
information aboutthat area.

④

⑤

③

OK! I will go
to Image (t)’sarea.

This image isinteresting!

①⑥
Go back to interesting area and Sampling②

Not interesting5

2. TUNA-SAND2
The technical issues are shown in the followings.
2.1. Hardware
•
•
•
•
•

New AUV design for sampling
Sampling device: Underwater manipulator &hand
Acoustic communication device
Camera system
etc...

2.2. Software
•
•
•
•
•
•
•
•

Motion control & navigation
On-line image processing
Sea-floor image enhancement
Curious image selection
Image compression
Acoustic data transmission
Sampling device control
Visual servo for target tracking
1.
etc...

2.3. Design of TUNA-SAND2
The developed Sampling-AUV is illustrated in Fig.2,
and shown in Fig.3.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 301

A New Tool tosurvey of hydrothermal vent fields,” Proc. ofIEEE/AUV2014, (2014)
5. Y. Nishida, K. Nagahashi, T. Sato, A. Bodenmann, B.
Thornton, A. Asada, T. Ura, Development of anautonomous underwater vehicle for survey of cobalt-richmanganese crust, Proc. of IEEE/OCEANS2015, (2015)
6. Y. Nishida, T. Ura, T. Nakatani, T. Sakamaki, J. Kojima,
Y. Itoh and K. Kim, Autonomous Underwater Vehicle“Tuna-Sand” for Image Observation of the Seafloor at a
Low Altitude, Journal of Robotics and Mechatronics,
Vol.26, No.4, pp.519-521, 2014.
Underwater Manipulator

Fig.2 Overview of TUNA-SAND2.

Fig.3 The Developed Sampling-AUV“TUNA-SAND2”
Acknowledgement
This research is supported by JST CREST“Establishment of core technology for the preservation
and regeneration of marine biodiversity and ecosystems”.
References
1. Y. Junku, T. Ura , G. Bekey, Underwater robots, Springer
Science & Business Media, (2012)
2. Whitcomb, Underwater robotics: Out of the researchlaboratory and into the field, Proc. of IEEE/ICRA'00,(2000)
3. Y. Nishida, T. Ura, T. Sakamaki, J. Kojima, Y. Ito , K.
Kim, “Hovering Type AUV “Tuna-Sand” and Its Surveys
on Smith Caldera in Izu-Ogasawara Ocean Area, Proc. ofIEEE/OCEANS2013, (2013)
4. T. Maki, Y. Sato, T. Matsuda, R. T. Shiroku, T. Sakamaki,“AUV Tri-TON 2: An intelligent platform for detailed© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 302

An effective method for detecting snatch thieves in video surveillance
1Hiroaki Tsushita and 2Thi Thi Zin

1

Graduate School of Engineering, University of Miyazaki, Japan
2
Faculty of Engineering, University of Miyazaki, Japan
E-mail:hc11046@student.miyazaki-u.ac.jp, thithi@cc.miyazaki-u.ac.jp

Abstract
Nowadays, a tremendous amount of accidents and terrorisms has been occurred all over the world no exception in
Japan. Thus, detection of suspicious activities in public areas such as railway stations, shopping malls and many otherareas using video surveillance becomes important. However, very little has been achieved regarding real-time event
recognition of two person interactions such as snatch theft events. Moreover the way of snatch theft has been
increasing like using a motorcycle and so on. In this paper, we propose an effective method for detecting the snatch
theft event between two persons. Specifically, the proposed method consists of several steps: pedestrian tracking,feature computation and snatch theft detection. For feature computation, shape and motion features are used. To
confirm the validity of proposed method some experimental results in a various situations are shown by using somecollected video sequences.
Keywords: snatching, detecting, intersection, feature extraction,

1. Introduction

In addition, Tokyo Olympic Games will be held in
2020. We estimate that a great number of tourists cometo Japan for it from all over the world. Obviously, moresecurity measures should be taken with the aids ofsurveillance system for preventing and detecting various
type of crimes. In such aspect, we expect that the advanceof this research will be helpful of preventing snatching incrowded environments [2].

In modern times, a variety of crimes have reported fromthe daily news. Some of news includes the events of theft
and theft damages. In this paper, we have focused on“snatching” among a great number of thefts. Currently anumber of surveillance cameras are installed in
preparation for detecting a crime theft. Active detectionsof incidents are also required to reduce costs of security
issues. There is a limit to the number of securities bypeople, and finding the suspicious behavior in thesurveillance camera in real-time is leading to preventionof incidents and also to increase the event detection rate
[1].
The main purpose is to detect the theft, such assnatching or pickpocketing in various situations. The
human that do the criminal acts such as theft, there aresome features different from the ordinary passengers.
The automatically detecting of suspicious person byusing security camera has been processing practically inthe environment that is not crowded. But it is a little hardfor detection in case of distinguishing immediately; it isnecessary to track person for a long time.

2. The Proposed Method
In this section, we propose an effective method for
detecting snatch thieves. The overall system flowchart isshown in Fig.1.The system includes four components asshown in the figure. Among then we will emphasize on
feature extraction process in this paper [3].

Fig.1. The overall flowchart to detect snatching© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 303

Hiroaki Tsushita, Thi Thi Zin

2.1. Background subtraction
Foreground image is made by difference between
background and input image. Therefore we removed
some silhouettes except for the person by determining athreshold. It is not accurately extracted if we calculate theamount of white pixel without removing of the shadowof person silhouette. That’s why we cut the silhouette
about 10% from the bottom of bounding box (BB). The
process result is shown in Fig. 2.

(

2.2. Feature extraction
The feature extraction step is main process in our
proposed system. It is possible to classify abnormal
behaviors by extracting a person from input images andfinding the features of person. Generally speaking, there
existed two ways on what to extract features. They areappearance features and motion features. There are somecontacts like intersection between two persons when thesnatching has happened. An example is shown in Fig.3.
We compared with the intersection events before andafter, and the value of average each 15 frames before andafter events was used. Then the change amount comparedwith before and after, is used as feature value (FV).
Moreover these feature values are divided by height ofBB for normalization [4].

This motion feature value (FVM) and difference value) between before and after event can be defined by:

FVM

moving distance of head point
height of BB

(3)

DM

FV M ( after )(4)

FV M (before )Fig.2. Removal of the shadow of person silhouette

Fig.3. Flow of snatching with intersection(i) Appearance feature
We used the binary image conversed from original one,then focused on the BB of each person exists in frame.
And we created histogram in terms of the silhouette(amount of white pixels) of a person in BB. As seen in
Fig.4, shape of histogram is quite different before andafter snatching event.
This appearance feature value (FVA) and difference
value ( ) between before and after event can be defined
by:
FV A

total number of white pixel in BB
height of BB

(1)

DA

FVA (after) FVA (before)(2)

Fig.4. Difference of human silhouette histogram(ii) Motion feature
We found the point of head after extract the silhouetteof human. To compare with consecutive frames, it ismeasured the moving distance in each person by using
the coordinates of head point. We figure that there isdifference in moving distance between running person or
riding bicycle and pedestrian. The difference between
running person and pedestrian is shown in Fig.5. The
person with yellow box is original position. On the other
hand, the person with red box is the position after two
seconds.

Fig.5. Difference of moving distance© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 304

An effective method for

There are some patterns by using the way is explained
previous section. We estimate snatch theft by using somepatterns. The overview is shown in Fig.8.

2.3. Decision method of snatching
We used appearance features and motion features tojudge that snatching happens. In this time, we found eachparameter of motion feature and appearance feature byusing three training videos. The parameters were used asindicator of judgement in each features. We defined thatthe threshold of appearance feature value is ThA = 10, thethreshold of motion feature value is ThM = 0.049.
We extracted two feature values, appearance andmotion, of each person in video, and compare with thethreshold we defined. By comparing, we can confirm
some actions with person happens or not.
The way of judging action is different between eachfeatures. From next section, we will consider somecriterion to classify the events by using the outcomes ofcomparing features.

3. Experimental Result
We used two video sequences for training and four test
video sequences for test in this experiment. They weretaken as demonstration in our university. Two training
videos are data with snatching event and without
snatching event. In test video sequences, Test 1 and Test
2 have the snatching event with bicycle, and Test 3 andTest 4 have the snatching events without bicycle. If thevictim with bicycle chases suspect after snatching like
Test 1 and Test 2, he doesn’t have much difference offeature. So these feature value estimated wrong
classification. And the feature value in Test 3 does nothave big change for classifying, it is false recognition.
Table 1 shows the results from four test videos.

(i) Judging way of appearance feature
In appearance feature, the absolute value was used toclassify whether something happens or not. If the feature
value is more than threshold (ThA = 10), it can be classified
as something happens. In this case, the two persons are to
be differentiated between victim and suspect depending on
sign of feature value. It is shown by following equation.
The flow of classification is shown in Fig.6. Then we also
defined that “Something happen” is A1, and “Nothing
happen” is A2 for later process.
Then we also classify which person is a victim or asuspect. After process of classification whether
something happens or not, we focus on the feature value
without absolute value. If the feature value of appearance
takes negative value, the person can be a victim because
he lost the silhouette of bag in binary image. On the other
hand, if the feature value of appearance takes positive
value, the person can be a suspect because he gain thesilhouette of bag by snatching bag. This classification canbe defined by:VictimSuspect

if FV A ＜ 0
if FV A ＞ 0

Fig.6. The flowchart of classifying of appearance feature(3)
Fig.7. The flowchart of classifying of motion feature(ii) Judging way of motion feature
In motion feature, we found the change of velocity of
person between before and after event. If the feature
value is more than the threshold (ThM = 0.049), thevelocity of person increases compare with before
intersection. This feature was used to classify as motion
feature. The flow of classification is shown in Fig.7. Then
we also defined that “Speed up” is M1, and “No change”
is M2 for later process.

Fig.8. Overview of this method
Table 1 Result of classification on each video© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 305

Hiroaki Tsushita, Thi Thi Zin

Video
Test 1
Test 2
Test 3
Test 4

VictimSuspect

Appearance

MotionAppearance

Motion○ (A 1)○ (A 1)× (A 2)○ (A 1)× (M 2)× (M 2)○ (M 1)○ (M 1)○ (A 1)○ (A 1)○ (A 1)○ (A 1)○ (M 1)○ (M 1)○ (M 1)○ (M 1)

I would like to express my sincere thanks andappreciation to all persons who contributed towards the
success of this my paper. I would like to show mygreatest appreciation to my supervisor Professor Thi Thi
Zin whose comments and suggestions were innumerably
valuable throughout my study.

Result○○○◎From the result of Table 1, we could get almost correctclassification result in the test videos. But the result of
Test 1 and 3 figure that it is just possibility that snatchinghas happened in sequence.
To get 100 percent of possibility that snatching has
happened, we add another feature of direction which
person goes into appearance features. Because the bag
might be hidden to person shadow depends on theposition of surveillance camera. In this case, the feature
value of bag silhouette in appearance feature does notcome up well. An example of this case is shown in Fig.9.
(a) is the scene of before snatching, and the bag is seenfrom camera side. On the other hand, Fig.9 (b) is thescene of after snatching, then the bag position is opposite
side from camera. So it is hidden to person shadow.
From this if the direction of moving changes othercourse after intersection, this feature also gets a clue todetect snatching.

References

4. Conclusions
In this paper we had proposed and tested an effective
method for detecting an even of snatch thieves by using
mainly two features, ratio of the amount of white pixels
in bounding box and moving distance of head coordinates.
Then we found that it needs another feature, direction ofmoving, to detect snatching thieves events.
Moreover the way of snatch thieves is more variety inrecent times like using a bike or car. And the snatch
thieves happens not only day time but also night time.
Thus, our proposed method will be useful of preventing
snatching in various situations and ways.

1. Henri Bouma, Jan Baan, et al, “Automatic detection of
suspicious behavior of pickpockets with track-based
features in a shopping mall”, Proc. SPIE 9253, Optics andPhotonics for Counterterrorism, Crime Fighting, andDefence X; and Optical Materials and Biomaterials in
Security and Defence Systems Technology XI, 92530F,
Oct. 2014.
2. Surya Penmetsa, Fatima Minhuj, “Autonomous UAV for
Suspicious Action Detection using Pictorial Human Pose
Estimation and Classiﬁcation”, Electronic Letters on
Computer Vision and Image Analysis, vol. 13, no. 1,
pp.18-32, 2014.
3. Jasper R. van Huis, Henri Bouma, et al, “Track-based
event recognition in a realistic crowded environment”,
Proc. SPIE 9253, Optics and Photonics forCounterterrorism, Crime Fighting, and Defence X; andOptical Materials and Biomaterials in Security and
Defence Systems Technology XI, 92530E, Oct. 2014.
4. Thi Thi Zin, J. Kurohane, “Visual analysis framework for
two-person interaction”, Proc. of The 4th IEEE Global
Conf. on Consumer Electronics (GCCE 2015), Osaka,
Japan, pp. 519-520, 2015.

Fig.9. Difference of bag position

Acknowledgements© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 306

Color and Shape based Method for Detecting and Classifying Card Images

1

Cho Nilar Phyo1, Thi Thi Zin1, Hiroshi Kamada2, Takashi Toriu3

Graduate School of Engineering, University of Miyazaki, Miyazaki, Japan
2
Graduate School of Engineering, Kanazawa Institute of Technology, Ishikawa, Japan
3
Graduate School of Engineering, Osaka City University, Osaka, Japannc16004@student.miyazaki-u.ac.jp, thithi@cc.miyazaki-u.ac.jp

Abstract
This paper proposes an effective method for detecting and classifying card images by using color and shape features.
We extract the card color area using color information and remove low possibility regions based on shape feature.
Then, we classify the image by taking classroom size and camera distance. In order to confirm the proposed method,
we conduct the experiments with our own videos. Our experimental results show that the proposed method is very
promising compared to some existing methods.
Keywords: color segmentation, shape classification, interactive e-learning.

1. Introduction

2. Related Works

In today world, the education system is rapidly changing
and the video conferencing is widely used for education
due to its enormous advantages. The video conferencing
based e-learning system can make the classroom to begeographically limitless and reduce the travel cost andtime for the lecturers. One weakness of the traditional
video conferencing based e-learning system is thedifficulty of getting student’s feedback when it is using
in enlarge classroom. The computer vision based
interactive e-learning system can solve that weakness by
supporting student-lecturer interaction during the lecture
time which is the most important thing in teaching system.
In the proposed methods, we use color and shape based
color card detection and classification method for the
implementation of interactive e-learning system.
We describe some related work of shape based object
identification and color based segmentation in section 2
and the detail process of the proposed method in section
3. Then we describe some experimental results in section
4 and the conclusions of the proposed methods is
explained in section 5.

The color based object segmentation and shape based
object identification are widely used in the area ofcomputer vision research. The authors proposed1 thatpointed pattern based shape information and modified
background subtraction to identify the cows for the
implementation of automatic cow monitoring system.
The authors implement2 that image retrieval system for
trademark images using modified shape based. The
object-based retrieval in image and video databases usingmorphology and histogram based method is described inRef. 3. One proposed4 that the segmentation withmorphology and relative color for robust road sign
recognition.
3. Proposed System
In this system, we will use color cards images toimplement the interactive e-learning system in which thestudents can immediately give feedback over theunderstanding level of the lecture. So that the e-learning
system can become more active and attractive and thelecturer can find the way to explain about the lecture
based on the feedback of the students. There include four

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 307

Cho Nilar Phyo, Thi Thi Zin, Hiroshi Kamada, Takashi Toriu

main components in the proposed system as shown in Fig.
1.

Fig. 1. Overview of the detection and classification of card
images

3.1. Color Object Segmentation
The first component of the system is segmentation ofcolor object. In the proposed system, we will use HSL
color space in order to segment the color object undervarious illumination changing condition. In HSL color
space, H stands for hue that measure the purity of the basecolor, S stands for saturation that measure the degree ofwhite color embedded in specific color and L stands forlightness. HSL is the non-linear transformation of devicedependent RGB color model and widely used in the color
picker systems of many graphical applications because of
its convenience for the color perception of human being.
We can simply transform HSL from RGB color space byperforming the following steps:(i) Find maximum(Max) and minimum (Min) value in
R, G and B component.
(ii) Find L value as describe the following.
L

1
Max
2

3.2. Size Based Classification
The second part of the system is classification of cards
object using size information which is taking according
to the classroom size. We use two threshold range inorder to accuracy detect the card object in both far andnear from the camera. To define far and near area, wevertically divide the image into two parts and regards theupper part as far area and the lower part as near area fromthe camera. Then we generate the equation for finding thesize threshold depend on the relationship between 3D and
2D in photograph.

Min

3.2.1. Size Thresholding

Max Min1 2* L 1

In order to find the size threshold, we take the photographof the card by placing it on 1 to 5 meters’ distance fromthe camera. Then, we count the height pixels of color
region as shown in Table 1. Then we generate theequation to estimate the height using the following linear
equation.
y=m*x+b(iv) Find H value using the following equation.
G B
Max Min
B R
60 *Max MinR G
60 *Max Min60 *H

Thresholding is the important task in color segmentationand the performance of the system is depending on thechoosing of correct color threshold. In our system, we use
two threshold range for defining the card color (pink,
yellow, green, blue and orange), one is for day time andanother is for the night time. In order to determine thesuitable threshold range, we vertically divide the image
into two parts and check day time or night time by using
the mean intensity of the upper part. If the mean intensity
is greater than some threshold, we regard as day time
image because the mean intensity of day time image isgreater than night time. The result color mask of thesegmentation of yellow color region is as shown in Fig.2.

Fig. 2. (a) Input image, (b) Segmentation result of yellow color(iii) Find S value using L value.
S

2.1.1. Color Thresholding

mod 360

if

Max

R

120

if

Max

G

240

if

Max

B

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 308

Color and Shape based

3.4. Black Border Detection

where y is the height in pixel, x is the value 1/D, D is the
3D real world distance between the card and camera, mdetermines the slope of the straight line and b is theinterception point of y axis. We get the value of 68 and 3
for parameter m and b. distribution of the height and thestraight line equation is shown in Fig. 3. The result color
mask is as shown in Fig. 4.

The fourth component of the system is the detection of
black border in order to avoid the detection of the otherobject that have the same color and shape as the color
card objects. In Figure 6 (b), we can see that size andshape based classification result blue screen monitor as
color card object because the color, size and aspect ratioof monitor is matching with the threshold value. In orderto solve those kind of problem, we will use the detectionof black border region around the detected color objects.
We make the border region of the result of detected
object by applying morphological operation. Then weremove the border region whose value is not black color.
The result removing non-black border region is shown in
Fig. 6.

Table 1 Estimated height of color region
Distance (meter)
1/Distanceheight (pixel)
Estimated height
Error

1
1
71
71
0

2
0.5
37
37
0

80

3
0.33
26
25.67
0.33

4
0.25
20.0
20
0

5
0.2
17
16.6
0.4

y = 68 * x + 3

60
40
20
0
0

0.2

0.4

0.6

height

0.8

1

1.2

Linear (height)

Fig. 3. Linear equation for finding the height of color region

Fig. 6. (a)Input image, (b) Color mask result after size based
and shape based classification for blue color, (c)Black bordermask (d) Result after black border detection.

4. Experimental Results

Fig. 4. (a) Input image, (b) Color mask result of size basedclassification of yellow color

We test the proposed system on the self-collecting datataking under the small classroom which includes over 10
students and large classroom which contains around 200
students at both day time and night time. In order toevaluate the robustness of the proposed methods, we also
test on the images of complex environment which include
the students whose dress’s’ color is the same as card
colors. According to the experimental result, we can seethat the proposed method can give accuracy of 100% forsmall classroom images, 93.46 % for large classroom(day time) images and 88.34 % for large classroom (nighttime) images which include highlight area. Someexperimental results of the proposed method are shownin Fig. 7.

3.3. Shape Based Classification
The third component is the classification of card objects
based on shape information. We will use the aspect ratio
to classify the square color card object. The result of
shape based classification is as shown in Fig. 5.

Fig. 5. (a) Input image, (b)Color mask of shape basedclassification of yellow color.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 309

Cho Nilar Phyo, Thi Thi Zin, Hiroshi Kamada, Takashi Toriu

5. Conclusion

Acknowledgements

In this paper, we proposed color and shape based card
images detecting and classifying method that caneffectively detect and classify the various color card
objects which is very useful in the implementation of theinteractive e-learning system. According to experimental
results, we found that the proposed methods can correctly
detect and classify the card objects on both day time andnight time images with accuracy over 90% and theaccuracy decrease only at the highlight area of night time
image due to the difficulty of segmentation which is thenext challenge that will be solved in the future. By using
the dynamic threshold calculation methods for finding
the size threshold based on the lecture room size, theproposed method can give ubiquitous operation ofinteractive e-learning system which can process anytime
and anywhere in the world.

This work was supported by JSPS KAKENHI Grant
Number 15K01041.
References
1. K. Sumi, I. Kobayashi and T.T. Zin, Cow Identification by
Using Shape Information of Pointed Pattern, in Proc. 9thIntl. Conf. on Genetic and Evolutionary Computing.,(Springer-Verlag, New York, 2015), pp. 273-280.
2. T. Iwanaga, H. Hama, T. Toriu, P. Tin and T.T. Zin, A
Modified Histogram Approach to Trademark Image
Retrieval, Int. J. of Computer Sci. and Net. Security 11(4)(2011) 56-62.
3. T.T. Zin and H. Hama, A Method Using Morphology andHistogram for Object-based Retrieval in Image and Video
Databases. Int. J. of Computer Sci. and Net. Security 7(9)(2007) 123-129.
4. T.T. Zin and H. Hama, A robust road sign recognition
using segmentation with morphology and relative color, J.
of the Inst. of Image Information and Television Engineers
59(9) (2005) 1333.

Fig. 7. Some Experimental Results [row 1~2 small class room and 3~5 large class room] (a) Input images, (b) Color segmentation
result, (c) and (d) Result of size-based and shape-based classification, (e) Result of combination of color, size, shape and black borderdetection methods© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 310

Automatic Assessing Body Condition Score from Digital Images by Active Shape Model andMultiple Regression Technique1

Nay Chi Lynn1, Thi Thi Zin2, Ikuo Kobayashi3( Graduate School of Engineering, University of Miyazaki, Japan)(2Faculty of Engineering, University of Miyazaki, Japan)
1
naychelynn@gmail.com, 2thithi@cc.miyazaki-u.ac.jp, 3ikuokob@yahoo.co.jp

Abstract
Body Condition Score (BCS) of a dairy cow is a magnificent indicator for determining energy reserves of cows. The
purpose of this study is to assess BCS of dairy cattle by analyzing cows’ rear-view images. In order to do so, we firstmodel shape of cow’s tailhead area by using active shape model. Then, angle features are modelled as multiple
regression model for estimating scores. The experimental results show that proposed system is promising comparedto some existing methods.
Keywords: Body Condition Score, Active Shape Model, Multiple Regression Analysis, Angle features.

1. Introduction
Most of the existing methods for measuring Body
Condition Scores of dairy cows have been based on a
subjective assessment of tissue reserves of lactating dairy
cows.1 BCS is widely considered as an important factor
for management of dairy cattle due to its simplicity andrepeatability. Moreover, it can evaluate body fat storesand estimate cumulative energy balance through visualor tactile inspection.2 Svaluation of BCS is important foranalyzing health problems, feed intake, and optimal timeinterval between calving and first insemination. For cattle,
sheep and goats, the scoring systems most commonlyused for BCS are numerical scales with 5-point, 6-point,
8-point or 10-point scales. For dairy cows, the BCS isnormally a number in a scale that spans from 1 to 5 (0.25
increments) or from 1 to 9 (1 increment). Generally, thescore range used by dairy management advisors
describes thin animals with receiving lower scores andfat animals with receiving higher scores (1 represents
emaciated cows, 5 represents obese cows for a 5- point
system). For a certain management of farms, the BCS
should be assessed. However, this task is time consuming
and observed experts need considerable training andexperience. Roii et al. stated3 that automatic and objective
BCS will help to ensure that the cow is in the correct

condition for each stage of her annual cycle and correct
any deficiencies with appropriate dietary changes.
The aim of the proposed research is to develop a systemthat models the body shape of a cow from the back view
images and then assesses the BCS with observed angle
features in score estimation. The rest of the paper isorganized as follows: section 2 introduces some related
works on approximations for automatic body condition
scoring system, section 3 describes system overview withtheoretical methods that are mainly applied in thisresearch, section 4 explains how the experiments
performed with output results, and finally in section 5,
the conclusions and future works are described.
2. Some Related Works
Among the many attempts for estimating body condition
scores automatically, the first attempt by Coffey et al.,tested4 using line patterns painted with laser light over thetailhead area of the cows. Some attempts apply digital
images or some system used videos and an analysis of the
cow’s contour and shape that commonly involved. Roii
et al., have taken3 3D images from the above view viewof the cow acquisition of data automatically. Halachmi etal., have also taken5 thermal images and they madedecision that fatter cow’s shape are rounder. Bewley etal., use6 top view images, 23 points and 15 angles are© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 311

Nay Chi Lyn, Thi Thi Zin, Ikuo Kobayashi

extracted and show a good correlation with their expectedscore. Bercovich et al., also used7 the above view images
and they use Fourier descriptors method and regression
method for the cow signature showing that body
condition score can be automatically extracted fromthose images. Rafel et al. used8 twenty-two angle featuresfrom the rear view images with the classifiers from Weka
tool and they showed that their estimation is possiblycomparable with difference of two human experts.
Nevertheless, currently most dairy farms don’t pay somuch attention on automatic estimation of BCS in theirdairy management. This is because some systems need topreprocess like laser paint or some systems need to installsome kind of high cost camera like 3D camera, 3D Kinect
camera, thermal camera despite their usefulness. Some
commercial application like DeLaval- 2015 has appeared,
but its accuracy was not published in a scientificpublication in the statement3. So, this study will build the
BCS estimation system of using simple 2D images byexpressing observed accuracy.
3. System Overview
The proposed system for assessing BCS from digital
images contains three main components. The
preprocessing of the images, the segmentation of the
shape and the estimation of the BCS as depicted in fig 1.

Fig. 2. Cow Images with shape (the upper) and extracted
shapes from cow images.

to 5 scale with 0.25 intervals. But in this study, it isobserved that scores are ranging from 1.5 to 4.5 with 0.5
increment. Moreover, most frequent scales here are 2, 2.5,
3 or 3.5. These scores are identified and collected with
their corresponding cows’ images for subsequent
analysis. The images are then cropped to resize with theresolution of 255*170 pixels because Rafel et al.,recommended8 that most features or the rear of the cow
are distinguishable with this resolution.

Table 1. Anatomical Points Description.

3.1. Preprocessing
The images for this study were collected from the web.
We choose the back shape view of cow images because

Fig.1. Main components of the proposed system.

Ferguson et al., agreed2 that the score of a cow can beassessed by a human observer with only the rear-end
view of the cow.
Out of one hundred and thirty images of different cows,
only seventy images are selected because of somebackground noise and position of the cow (e.g clutterednoise mixing with cow’s color, very low resolution, toomuch variation of pose, etc.). Scores are assessed
according to Ferguson et al., evaluation chart that one cansee Ref. 3 for details. It is the common system usedwithin the United Kingdom (UKBCS system) using a 0

Point

Description

1-3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23-25

left hook start points
left hook midpoint
left hook angle point 1
left hook angle point 2
left hook angle point 3
left hook end point
left tailhead depress angle point1
left tailhead depress angle point 2
left tailhead depress angle point 3
tailhead peak angle point 1
tailhead peak angle point 2
tailhead peak angle point 3
right tailhead depress angle point1
right tailhead depress angle point 2
right tailhead depress angle point 3
right hook end pointright hook angle point 1
right hook angle point 2
right hook angle point 3
right hook midpointright hook start points© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 312

Automatic Assessing Body Condition

3.2. Segmentation of the back shape of the cow
Once we have the preprocessed cows’ back view images,
we need to extract the computationally manageable
representation of the anatomy of the back shape of cows.
A computer program written with Matlab 2015a was usedin annotating of cow images from the dataset. Twentyfive anatomical points are identified. Those anatomical
points according to recognizable features can influence
the important information for representation of the shape.
Those anatomical points with their description are
depicted in the figure 1. The shapes are then aligned toendure the pose (scaling, rotation and resizing). Angles
are measured according to law of cosine method using
three points. There are five angles totally computed, twoangles around the left and right hooks and two angles
around the tailhead depression area and one angle at thepeak of the tailhead as shown in figure 2.

Table 2. (a) Regression coefficients (b) Someangle features with respective BCS from 10
random images out of 70, where Y= BCS and
Y’ = predicted BCS (c) Correlation of eachangle with BCS
Fig. 2. Five Angle features from the shape of the cow.
b0
b1
b2
b3
b4
b5
-9.28631.0451-0.1265-0.3583

1.3034(a)3.3. Analysis by Regression Method
By using those angle features, we compute the regression
coefficients in estimation of BCS. The multiple
regression method is computed with the equation as
follows:

Ang1

Ang2

Ang3

Ang4

Ang5

Y

Y’

2.54911.41572.36292.36290.12122

2.5

2.83892.56052.49302.77832.91892.79802.77622.70882.8441

1.27271.40491.28701.51871.45151.56691.3964
1.25971.3426

1.8776 1.8776
2.3836 2.3836
2.3031 2.3031
2.3119 2.3119
2.5580 2.5580
2.4416 2.4416
2.4901 2.4901
2.4962 2.4962
2.2667 2.2667(b)0.13330.16950.13010.1536
0.10850.24460.08650.09480.1068

2.5
2.5
1.5
3
3.5
3
2.5
2
3

3
2
1.5
3
3.5
3
3
2.5
3

BCSi = b0+ b1Ang1+b2Ang2+b3Ang3+ b4Ang4+b5Ang5(1)Correlation with BCS
Ang1
Ang2
Ang3
Ang4
Ang5

where BCSi is the BCS of the ith cow, Ang1 and Ang5 are
the left and right hook angles, Ang2 and Ang4 are the left
and right tailhead depression angles, and Ang3 is thetailhead peak angle respectively. And b0 is the constantor intercept and b1, b2, b3, b4 and b5 are the slope(coefficients) of the three angles respectively. We
computed the angles in radian rather than degrees toreduce the variation among values.
4.

2.24500.60900.41610.10010.22290.5446(c)

Out of seventy images, twenty-four images have zerodifference, thirty-four images have 0.5 difference andeleven images have 1 difference. We assumed the 0.5
difference is feasible because even the difference
between the trained experts can have 0.5 difference inestimating the BCS. Thus, the accuracy for thisexperiment is 83% which is acceptable to estimate. We
also computed the correlation between each angle feature
with their respective BCS value as shown in Table 2(c)and we find that all these angle features have the positive
correlation with the BCS. This means that if those angle
have the higher value (wider angle) the BCS value is thehigher value. This assumption agrees with the wider

Experiments and Results

After analyzing those angle features using regression
method, the computed regression coefficient and thepredicted BCS values are shown in Table 1. In theexperiment, we take threshold to those angle values byrounding the point value and then observed that thepredicted BCS values are close and some even identicalto the estimated BCS as in shown in figure 3. The residual
values (which is the difference between predicted values
and already estimated values) are 0, 0.5 and 1.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 313Nay Chi Lyn, Thi Thi Zin, Ikuo Kobayashi

Balance Profiles Using Linear Type Scores and Body
Condition Score Analyzed Using Random Regression,”
Journal of Dairy Science, vol. 86, pp. 2205 – 2212, 2003.
5. Halachmi, I., Polak, P., Roberts, D.J., Klopcic, M.: Cow
Body Shape and Automation of Condition Scoring.
Journal of Dairy Science (2008) 4444{4451}.
6. J. M. Bewley, A. M. Peacock, O. Lewis, R. E. Boyce, D.
J. Roberts, M. P. Coffey, S. J. Kenyon, and M. M. Schutz,
Potential for Estimation of Body Condition Scores in
Dairy Cattle from Digital Images, Journal of Dairy
Science, vol. 91, pp. 3439 – 3453, 2008.
7. A. Bercovich , Y. Edan , V. Alchanatis , U. Moallem , Y.
Parmet , H. Honig , E. Maltz , A. Antler, and I. Halachmi,
J. Dairy Sci. 96 :8047–8059, 2013.
8. Rafael Tedın, J. A. Becerra1, and Richard J. Duro, Ismael
Mart´ınez Lede, Towards automatic estimation of the body
condition score of dairy cattle using hand-held images andactive shape models, Frontiers in Artificial Intelligenceand Applications 243:2150-2159 · January 2012.
9. www.infodairy.com/infodairy_upload.../0129learn%20to%20score%20bcs-e.ppsx
10. http://www.slideshare.net/jonescoleen/beginners-guideto-body-condition-scoring
11. http://www.slideshare.net/jonescoleen/learn-to-scorebody-condition-for-dairy-cows
12. http://www.techproindia.in/wws/2013/10/understandingdairy-terminology-and-body-condition-scoring-in-dairycattle.html
13. http://www.dairynz.co.nz/animal/herdmanagement/body-condition-scoring/bcs-visualexamples/
14. http://www.nadis.org.uk/bulletins/condition-score-(bcs)in-beef-herds.aspx
15. http://www.agweb.com/article/achieving-optimum-drycow-body-condition-university-news-release/

Fig. 3. Cow Images with shape (the upper) and extracted
shapes from cow images.

angles indicate a rounder cow in the statement stated5 by
Halachmi et al., in their study.
5. Conclusion
In this paper, we used the back view images of cows in
estimation of BCS. The BCS of the cows are estimatedusing the 5 point scale. Experimental results showed that
this method is effective in assessing the BCS of the cow
as the predicted scores are closed to the actual scores.
Moreover, our experiment also hold the same impression
with the assumption of the rounder shape have the higherscores. In the future, a more robust method for extractionof shape and a dataset of back view cow images usefulfor cattle management will be addressed.
Acknowledgement
This work was supported by JSPS KAKENHI Grant
Number JP15K14844.
References
1. Sharad Mishra1, Kiran Kumari1, Ashutosh Dubey, Body
Condition Scoring of Dairy Cattle: A Review, Journal of
Veterinary Sciences, 58. RRJVS| Volume 2 | Issue 1 |March, 2016.
2. D. Ferguson, D. T. Galligan, and N. Thomsen. Principal
descriptors of body condition in Holstein dairy cattle.
Journal of Dairy Science, 77, 1994.
3. Roii Spoliansky, Yael Edan, Yisrael Parmet, and Ilan
Halachmi, Development of automatic body condition
scoring using a low-cost 3-dimensional Kinect camera, J
Dairy Sci 2016 Sep 16;99(9):7714-25. Epub 2016 Dec 16.
4. M. P. Coffey, G. Simm, W. G. Hill, and S. Brotherstone,“Genetic Evaluations of Dairy Bulls for Daughter Energy© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 314

General Image Categorization Using Collaborative Mean Attraction
Hiroki Ogihara

Department of Computer Science and Systems Engineering, University of Miyazaki,
1-1 Gakuen Kibanadai Nishi, Miyazaki, 8892191, Japan
Masayuki Mukunoki

Department of Computer Science and Systems Engineering, University of Miyazaki,
1-1 Gakuen Kibanadai Nishi, Miyazaki, 8892191, Japan
E-mail: hm13015@student.miyazaki-u.ac.jp, mukunoki@cs.miyazaki-u.ac.jp
cvlab.cs.miyazaki-u.ac.jp

Abstract
In this paper, we apply Collaborative Mean Attraction (CMA) method, which has been developed for person reidentification problem, to general image categorization problem. Experimental results using Caltech101 andCaltech256 dataset reveal that CMA shows better categorization accuracy than traditional methods, particularly inthe case when the size of training data is small. Furthermore, we discuss the parameter settings for CMA throughseveral experiments.
Keywords: Generic Object Recognition, CMA method, ImageNet, Caltech dataset

images. In such cases, we need a categorization method
which works fine with a small number of training images.
In this paper, we apply Collaborative Mean Attraction(CMA) method to general image categorization
problem.1 CMA has been developed for person reidentification problem. In person re-identification
problem, we classify a person in query images into oneof known persons in gallery (training) images. Here,
since gallery images are taken while the person passes
through the view area of a surveillance camera, a few
gallery images can be available. CMA shows goodresults in person re-identification problem.
In the experiment, we apply CMA method and othercomparison methods to Caltech101 and Caltech256,
which are public general image categorization datasets,
and discuss the characteristic of CMA method.

1. Introduction
Recently, studies of general object recognition have been
progressed. In this paper, we address the general image
categorization problem, which classifies each image into
one of known categories.
In general object recognition, Deep Learning method
shows great results. Deep Learning not only classify agiven input image but also extract features of the image.
The extracted features can be used for general purpose
object recognition, and using the features we can achieve
excellent recognition results. To accomplish that, Deep
Learning demands a huge number of training images.
However, in general object categorization problem,
there are some cases when we can give only a small
number of training images. For example, when a user
wants to organize his/her personal photos based on
his/her personal criteria, the user can give only a small
number of example images, which can be used as training© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 315

Hiroki Ogihara, Masayuki Mukunoki

2. CMA method

second term and the third term are constraint terms(regularization term) for bringing the representative andapproximate point close to their respective average points.
, are weight parameters of the regularization term.

CMA is an identification method which classifies
unknown test data into one of known categories. Known
categories are given as feature sets of one or moretraining images belonging to that category. The test datais also given as the feature of the test image, but it ispossible to give a set of test images as one group andclassify the group into one of known categories at once.
CMA method consists of two stages, that is, theoptimization stage and the classification stage. In the
optimization stage, we generate a representative of test
data and approximate it with all training images. In theclassification stage, we select the category that mostcontributed to the approximation.

2.2 Classification stage

Using the coefficients α and β obtained in the
optimization stage, we find the category that mostcontributed to constructing the approximation point, andclassify the test data into that category. Each dimensionof the coefficient β corresponds to the training image. β
can be decomposed with βi as the coefficients of the
training image in i-th category. Thus, we calculate thefollowing formula for each category i and we classify thetest data into the category which has the minimum .

2.1 Optimization stage
A representative point and its approximate point are
represented by linear sums of test data and training data,
respectively. In the optimization stage, the coefficients of
the linear sum constituting the representative point andthe approximate point are obtained.
The coefficients are determined so that the distance
between the representative point and the approximate
point becomes short. At the same time, the constraint is
imposed that the representative point and theapproximate point should be near to the average of eachdata so that the properties of each data are retained.
×
A test data matrix ϵ
and a training data×
matrix∈(i∈{1,…,n}) are given, where m isthe number of dimensions of the feature vector,
is thenumber of the test images, n is the number of knowncategories,
is the number of training images in i-th
category. We denote the matrix which concatenates all×
the training data matrix as = (
… )∈
,
where
=∑
is the total number of training images.
Here, we obtain the coefficient vector , ϵbyminimizing the following expression ( , ) :( , )=‖

−

‖ +

−

+

−

(1)

= (‖ ‖∗ + ‖‖∗ ) ∙

−

‖ ‖/

( )

The smaller−is, the better the
representative point can be approximated with only i-th
category. Similarly, when the value of ‖ ‖/‖ ‖ islarger, the coefficient of i-th category plays a large rolein the entire known categories. ‖∙‖∗ is the nuclear normof the matrix (sum of singular values of the matrix). The
larger the variation of the data, the larger the nuclear
norm becomes. It is used for weighting the variance ofthe categories.
3. Image Categorization
3.1 Dataset
We used the Caltech101 and Caltech256 dataset forevaluation.2 The Caltech101 and Caltech256 dataset
consists of images with 101 and 256 categories,
respectively. The number of images per category is 31 to
800 in Caltech101, while more than 80 in Caltech256.
Overall, Caltech256 is a more challenging dataset thanCaltech101.
3.2 Feature extraction
For feature extraction, we use the model(bvlc_reference_caffenet)3 that trained using theImageNet image with Caffe,4,5 which is a Deep Learning
framework. This model was constructed using 1,000categories of ILSVRC2012, using 1.2 million training
images. The output of the coupling layer is taken as thefeature of the image. The dimension of the extracted

where 1 is the k-dimensional vector with each element
1, ‖∙‖ is thenorm of the vector.
andin
Equation (1) are the representative point and approximate
point, respectively.
The first term is a term for minimizing the distance
between the representative and approximate point. The© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 316

General Image Categorization using

feature is 4096. For implementation, OpenCV 3.1 Deep
Neural Network Module was used.

The number of training images is k=1, 2, 4, 8, 16, 30. In
order to avoid the influence of the particular selected
images for training, we calculate the accuracy for 8 kindsof training images, and let the average be theexperimental result. In all methods, the same eighttraining dataset are used. The results are shown in Fig. 1.
Experimental results show that CMA shows higher
accuracy than any other comparison methods in any k
and in both datasets. Especially, in Caltech101, when k=1,
CMA shows 12.7 points better accuracy than thecomparison methods, and it can be seen that theperformance is relatively higher in the range where thenumber of training images is small.

3.3 Comparison methods
To discuss the characteristics of the CMA method, we
compare it with three conventional methods.
The first method is Support Vector Machine (SVM).
SVM is a standard method of current pattern recognition.
SVM is basically two-class classifier. In order to apply
SVM to multi-class classification, we employ "one-toone classification" approach, which constructs SVM forall 2 class combinations and classifies by the majority
decision of the result. We use a linear kernel for the
kernel. Since SVM basically cannot handle test data as agroup, it was used only for experiments with single test
data. We used libsvm for implementation.6
The second method is Center Point Distance (CPD). In
CPD we let the distance between the average of training
data of one category and the average of test data be thedistance between that category and test data. The test data
is classified into the category that has minimum thisdistance.
The Third method is Minimum Point Distance (MPD)
between data. At first, we find the distance between two
points for all data point combinations of training and test
data. Then we let the minimum value be the distance
between the category and the test data and classify thetest data into the category with the minimum distance.
Whereas CPD considers only the center of the group,
MPD is based on individual data points, andclassification result can reflect the distribution of data.

4.2 Experiment for group test data
Then we give the test data as a group of images. In thesame way as sec. 4.1, training data is created and the rests
are used as an evaluation dataset. In this experiment,
plural images are extracted from the evaluation dataset asa group and used as a test data. The number of images ineach group is l=2, 4, 8, 16, 30. In order to avoid theinfluence of the particular selected images for testing, we
conduct 3 trials for each l and averaging the results. The
results are shown in Fig.2.
Even in the case of group test data, CMA shows higher
accuracy than the comparison methods in k=1 and 2. As
a whole, as the number l of test data increases, theaccuracy increases, and it is considered that there is anadvantage in CMA that can apply image classification to
a combined plural test data as a group. However, at k=4,8, 16 and 30, CPD shows equal to or higher accuracy thanthat of CMA.

4. Experiments

4.3 Experiment on regularization parameters

4.1 Experiment for single test data

In the previous experiments, the regularization parameterand is set to =16, =16. In order to observe theinfluences of the regularization parameters, we changed
the values ofandwith 32, 16, 8 and investigate
how the accuracy changes. Caltech256 dataset is used inthis experiment. The number of training data is k=1, 30
and the number of test images in a group is l=30. The
results are shown in Table 1.
It was found that the lower the value ofis, thehigher the accuracy rate is. This result suggests that since
the training data contains different kinds of objects, theIn this experiment, we give each test data from a single
image and investigate that how the result of identification
changes when the number of training images is changed.
We randomly extract k images from each categories inthe dataset as training images, extract features and makethem as training data. All of the remaining images areused as an evaluation dataset. The test data is extracted
one by one from the evaluation dataset and categorizedby each method. After obtaining the accuracy for theevaluation dataset for each category, we calculate theoverall accuracy by averaging them for all categories.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 317

Hiroki Ogihara, Masayuki Mukunoki

approximate point should not be close to the average ofthe training data.

Analysis of the parameters in CMA suggested that thedifference between person re-identification and thegeneral image categorization could be reduced by setting
the parameters appropriately. The method to give theoptimal parameter values is left as a future work.

Table 1 Accuracy for various parameters
k=1(%)

k=30(%)

32
32
32

32
16
8

53.4
55.1

97.7
98.7

55.7

98.9

16
16
16

32
16
8

54.7
55.4

97.9
98.6

56.6

99.2

8
8
8

32
16
8

54.8
55.9

98.1
98.5

56.3

98.9

References
1.

Y.Wu, M.Mukunoki, M.Minoh: Collaborative Mean
Attraction for Set Based Recognition, MIRU2014, OS3-3,
2014.
L.Fei-Fei, R.Fergus, P.Perona: Learning generative visualmodels from few training examples: an incremental
Bayesian approach tested on 101 object categories, CVPR
Workshop on Generative-Model Based Vision, 2004.
http://dl.caffe.berkeleyvision.org/
bvlc_reference_caffenet.caffemodel.
L.Fei-Fei: ImageNet: crowdsourcing, benchmarking andother cool things, CMU VASC Seminar, 2010.
Y.Jia, E.Shelhamer, J.Donahue, et al.: Caffe:
Convolutional Architecture for Fast Feature Embedding,arXiv preprint arXiv:1408.5093, 2014.
C.C.Chang, et al.: LIBSVM - A Library for
SVM,http://www.csie.ntu.edu.tw/~cjlin/libsvm/.

2.

3.
4.

5. Conclusion

5.

In this paper, we conducted experiments to apply CMA
method to general image categorization problem.
Although CMA was developed for person reidentification problem, it showed higher classification
accuracy in the general image categorization problem,
especially when the number of training data was small.

6.

Figure 1 Accuracy for single test data
70
60
50
40
30
20
10
0

Accuracy（%）

Accuracy（%）

100
90
80
70
60
50

CMA

SVM

CPD

MPD

40
0

10

20

Caltech101: Training images k

0

30

CMA

SVM

CPD

MPD

20

30

10

Caltech256: Training images k

Figure 2 Accuracy for group test data
90

90

Accuracy（%）

100

Accuracy（%）

100

80
70
60
50

l=2

l=4

40
30

CMA
1 2 4 8 1630

1 2 4 8 1630

l=8

l=16

CPD
1 2 4 8 1630

1 2 4 8 1630

l=30

80
70
60
50
40
30

l=2

l=4

1 2 4 8 1630

1 2 4 8 1630

20

MPD
1 2 4 8 1630

10

l=8

CMA
1 2 4 8 1630

l=16

CPD
1 2 4 8 1630

l=30

MPD
1 2 4 8 1630

Caltech256: Training images k

Caltech101: Training images k© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 318

Consideration on the Photo-Realistic Rendering of Fruits by 3DCG
Haruka Tsuboi, Makoto Sakamoto1, Sho Yamada, Kensuke Ando

Faculty of Engineering, University of Miyazaki, 1-1, Gakuen Kibanadai-nishi, Miyazaki-shi,
889-2192 Miyazaki, JapanSun Chongyang2

Graduate School of Engineering, University of Miyazaki, 1-1, Gakuen Kibanadai-nishi, Miyazaki-shi,
889-2192 Miyazaki, Japan
Makoto Nagatomo3

Interdisciplinary Graduate School of Agriculture and Engineering, University of Miyazaki,
1-1, Gakuen Kibanadai-nishi, Miyazaki-shi, 889-2192 Miyazaki, Japan
Koshiro Mitsuhashi4, Yukari Kodama

Miyazaki Multimedia Academy, 2-4-37, Takachihodori., Miyazaki-shi, 880-0812, Japan1sakamoto@cs.miyazaki-u.ac.jp, 2sun_1987@ezweb.ne.jp, 3je.suis.makoto@gmail.com, 4koshiro@multi-m.jp

Abstract
Three-dimensional computer graphics (3DCG) is the technique of creating an image having a stereoscopic effect by
converting an object in the virtual three-dimensional space into two-dimensional information. It is also possible toproduce an image that cannot be distinguished from the real thing by using the 3DCG. 3DCG is expected for theapplication to visual representations very much. However, it is difficult to express the "freshness" of fresh food by
3DCG.Therefore, expressiveness of 3DCG is considered to be improved by overcoming this weakness. In this paper,
we consider the photo-realistic rendering of fruits by 3DCG. The algorithm is implemented by using the Visual C++
2010 and C on a personal computer. Through this trial, we hope to improve the representation in photo-realistic
rendering by 3DCG.
Keywords: 3DCG, OpenGL, Blender, GIMP

the like. In this thesis we will consider how to express
the freshness of food as an example of cut apples [1-3].

1. Introduction
Today, researches and developments of three
dimensional computer graphics (hereinafter referred to
as 3DCG) and technologies related to 3DCG areextensively conducted. Among them, there is a field
pursuing ultimate reality by making full use of
rendering and bump mapping. One of them is a digital
food, which is being studied for the future practicalapplication. For digital foods, it is expected to solve
problems related to food freshness management,
disposal, development simulation of new products, and

2. Principle
In many cases, CG refers to images and images drawn
with 3DCG. One of the reasons why various
technologies using 3DCG technology such as AR and
3D printers are developed is high expressive power of
3DCG. However, it is difficult to express 'freshness' or'organic coloration' of fresh foods, fresh flowers, etc.
even with 3DCG expressive power.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 319

Haruka Tsuboi, Makoto Sakamoto, Sho Yamada, Kensuke Ando, Sun Chongyang, Makoto Nagatomo, Koshiro Mitsuhashi, Yukari Kodama

In this research, we explore what technology isnecessary to express "freshness" and "organic
coloration" which 3DCG is not good at.
3. Previous study
We used OpenGL to create CG. Modeling in OpenGL
is performed by designating a set of vertex data and arule connecting vertices. Note that since the shape of themodel changes depending on the order of the given
vertices, the order of the vertex data must be taken careof in the consolidation rule. Also, OpenGL does nothave functions to create circles or surfaces. Therefore,
triangle polygons and quadrilateral polygons must be
arranged and their shapes approximated.
Materials are set by mapping. Texture mapping wasdone to express apple skins. For the texture image, theimage of the skin was cut out from the apple photograph(Fig. 1).

Fig.2: Model applying bump mapping.

4.

Creation

This time I used Blender instead of OpenGL to create
CG. Generally, creation of CG is generally not made byprogram only like OpenGL, but because there are many
software to create sensually using GUI etc. I chose
Blender because the concept is basically the same assuch software.
I studied how to become more real on my own fromimages in previous research. Compared with actual cut
apples, dense expression is not done. In addition, CG
has not removed core and its expression is not done
either. Overall we can not feel the freshness. Solve these
problems.
4.1. ModelingIn this time we will create ordinary apple and cut
apples. First, we made ordinary apples from UV spheresand edited it to make cut apples (Fig. 3). The part withthe leather expresses a smooth curve by increasing thenumber of divisions and the number of divisions ismade smaller for the cut part so that it is expressed
linearly.

Fig. 1: Model applying texture mapping.
Bump mapping was carried out to express fineunevenness of apple fruit. The image used for bumpmapping was generated using image editing / processing
software called GIMP (Fig. 2).

4.2. Material settingMaterial setting is done by mapping as in previous
research. In the previous study, only texture mapping
and bump mapping were performed, but this time UV
mapping is performed. UV mapping is a part of texture
mapping, and by attaching a texture to a twodimensional developed view of the surface of the object,
the texture is stretched to the sterically wrapped part
© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 320

Consideration on the Photo-Realistic

without being stretched. As a result, we were able tofreely change the material and expressed it densely (Fig.
3).

Fig.5: Actual cut apple.
6. Discussion
About realistic depiction of fruits by 3DCG I have been
researching with awareness of the shape and color
reproduction. From now on, it is considered necessary toinvestigate future research and evaluation methods ofalgorithms that make it possible to reproduce thestructure of fruits finer. Also, people are affected bymany colors. Although the syrup of shaved ice has thesame taste, it feels like the taste is different only by thedifference in color. There is also a psychologicalexperiment that affects appetite and hangs glasses withblue film, which makes the dish look a little tasteless(Fig.6). As you can see, I think that rather than puttingemphasis on models to be created, understanding humanvisual information and using optical illusion will make it
more realistic. It is easy but made around so that it looks
more natural (Fig.7). For comparative purposes keep anapple that is not mapped next to it.

Fig.3: Fabrication of map by UV development.
5. Result
The cut apple I made is as shown in Figure 4. I thinkthat more realistic things could be made using means
not used in previous research. However, the mapping
looks more beautiful in the previous research. In
OpenGL, the mapping associates the vertices of theimage with the vertices of the object, so it is affixed as
expected, but it did not work in Blender so it did notwork. Also, it is impossible to say that the expression of
freshness has become a sense as it will make it just fine.

Fig.4: Finished cut apples.
Fig.6: Blue Curry Rice.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 321

Haruka Tsuboi, Makoto Sakamoto, Sho Yamada, Kensuke Ando, Sun Chongyang, Makoto Nagatomo, Koshiro Mitsuhashi, Yukari Kodama

Fig.7: Apples created including surroundings.
7. Conclusion
In the expression pursuing reality, I felt that not only
the solution of the technical problem of 3DCG but also
the ingenuity of the production process is necessary.
Since CG technology was developed, CG technology
has grown rapidly, but it is a developing technique thatstill has many problems to be solved. In the future, as
3DCG technology advances, new technology applying
3DCG can be expected to be born. The advent of suchtechnologies may bring big changes to our society in the
future.
References[1] Keisuke Ando, Consideration on pursuit of reality
in three-dimensional CG, graduation thesis ofInformation Systems Engineering, Faculty ofEngineering, Miyazaki University, 2014.
[2] Sakamoto Masato, Okaya Tetsushi, Research on
Simple 3-D CG Production by Java, Bulletin of theFaculty of Engineering, Miyazaki University, No. 36,
pp. 321-328, 2007.
[3] Sakamoto Masato, Yamada Sho, Consideration ongraphical drawing of fresh foods by three-dimensional
CG, Bulletin of the Faculty of Engineering, Miyazaki
University, No. 45, pp. 239 - 242, 2015.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 322

Consideration for the Possibility to the Tourism
by the AR Technology
Masamichi Hori1
1

1

Department of Computer Science and Systems Engineering, University of Miyazaki,
1-1 Gakuenkibanadainishi Miyazaki-shi, Miyazaki, 889-2192, Japan
Makoto Sakamoto1, Koshiro Mitsuhashi2, Yukari Kodama2,
Takeshi Tanaka3, Mihoko Fukushima3, Chikashi Deguchi3, Masahiro Yokomichi1,
Masayuki Mukunoki1, Kunihito Yamamori1, Atsushi Iiboshi4

Department of Computer Science and Systems Engineering, University of Miyazaki,
1-1 Gakuenkibanadainishi Miyazaki-shi, Miyazaki, 889-2192, Japan

2

Miyazaki Multimedia Academy, 2-4-37 takachihodori Miyazaki-shi, Miyazaki, 880-0812, Japan
3

Faculty of Regional Innovation, University of Miyazaki,
1-1 Gakuenkibanadainishi Miyazaki-shi, Miyazaki, Miyazaki, 889-2192, Japan
4

Takachiho Muratabi Co., Ltd., 6604 ohazamukouyama takachiho-cho nishiusuki-gun,Miyazaki, 882-1103, Japan

1sakamoto@cs.miyazaki-u.ac.jp, 2koshiro@multi-m.jp, 3deguchi@cc.miyazaki-u.ac.jp, 4muratabi@bz04.plala.or.jp

Abstract
The promotion of the tourism nation became the important problem of our country, and we expect increase of thenumber of the foreign tourists visiting Japan. So we must contribute plain guidance information to the foreigners who
cannot understand Japanese. On the other hand, AR (augmented reality) is becoming the boom recently. In this paper,
when we use the AR technology for tourism and sightseeing, we inspect what kind of possibilities we have throughsome experiments.
Keywords: Promotion, Augmented reality, Sightseeing, Traditional culture.

come to be seen in a wide range of fields such as
advertisement, sightseeing, education and entertainment
due to popularization of personal-owned devices such as
smartphones and tablets [1].
This laboratory has conducted numerous research on AR
technology using 3DCG in the past. We will construct asystem that combines AR technology which was one of

1. Introduction
As of 2016, virtual technology is showing great
excitement. Regarding VR technology, dedicatedequipment with a slightly high threshold is needed
although it has become a price range where individuals
can reach out. On the other hand, AR technology has© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 323

Masamichi Hori, Makoto Sakamoto, Koshiro Mitsuhashi, Yukari Kodama, Takeshi Tanaka, Mihoko Fukushima, Chikashi Deguchi,
Masahiro Yokomichi, Masayuki Mukunoki, Kunihito Yamamori, Atsushi Iiboshi

its final objectives and sightseeing which is acharacteristic of Miyazaki prefecture and carries out
tourism support in this research. This method aims at
consumers visiting sightseeing spots to experience AR
technology and to reproduce and share the experiencesthat we have defined it as “Omochikaeri” in any
environment.
2. BackgroundI will talk about Takachiho-cho which is the core in thisresearch. Takachiho-cho is located in the northernmost
tip of Miyazaki prefecture and the center of Kyushu, hasa scenic natural monument Takachiho Gorge is a city
blessed with wonderful nature that is rich in the four
seasons. Takachiho Gorge is shown in Figure 1. Also, its
origin is old, it is said that the village had existed around4000 BC [2].

Meanwhile, it is a place of descendants of descendants,
and it is regarded as a land with considerable deep
connection with Japanese mythology and the birth ofJapan. There accompanied the evening Kagura born fromthe Amanoiwato legend, and the land related to myths
exist, and in recent years has attracted more attention toa wide range of power spot[3].
Especially in the Akimoto district called OkuTakachiho,
efforts are focused on people living in the village, andefforts to have tourists make experiences in close contact
with the village popular. It is possible to touch the shrines
using regional ingredients, the environment that talks
while feeling nature in an old private house inn, and theshrines and sacred trees standing in majestic nature. The
Akimoto shrine is shown in Figure 2. Recently it has been
selected as a good example of "Treasure of Discover
Rural Mountain Fishing Village" designated by the
Ministry of Agriculture, Forestry and Fisheries, and it
seems that tourists will increase further in the future[4].
Therefore, in this research, tourism support that visitors
who visit regardless of domestic and overseas enjoy the
tourist resources as much as possible to maximize thenumber of repeat customers and new customers will be
done.

Fig.2：Akimoto shrine.
3. Methods
I will explain the technology used in this experiment.
3.1. CG modelingIn this laboratory we produce applications for terminals,
and we will cooperate with vocational schools in
Miyazaki prefecture for 3DCG model production.
Display 3DCG model with AR technology and reproduce
the beautiful landscape of the night dance performers andsightseeing spots. Until the model is completed, we willdevelop a prototype using 3DCG of Miyazaki
University's mascot character shown in Fig. 3 [5]Fig.1：Takachiho Gorge.
© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 324

Consideration for the Possibility

applications such as GPS functions in addition to thefunctions of SmartAR, creation of other models, andexploring problems for practical use. I want to improvethese points and complete it as an actual application.

Fig.3：Bird’s eye view of Miyadaimoukun.

Fig.4： Displaying the 3D CG model of
Miyadaimoukun as AR.

3.2. Unity3D
We chose Unity3D as a software that can produce
applications for mobile [6]. Unity3D is an integrated
game development environment compatible with many
platforms, and a variety of plug-ins accompanying it arealso present. In this time, we selected it from theviewpoint of wanting to develop applications that are notdependent on future developability and OS.
3.3. Selection of API for AR for Unity3D
In this time, I will concentrate on functioning only withthe application itself. We aim to create markerless typelocation-based AR applications in that it does not breakthe sightseeing landscape and costs marker preparation.
A markerless type SmartAR SDK [7], which corresponds
to the above Unity3D and satisfies the requirements, wasselected. Based on this SDK, we will add location based
functions and others.
4. Experiment and Result
I will describe the results of our experiment.
4.1. Development environment
In this research, we made prototype application using
Unity 3d and plugin. The 3DCG model used Blender.
The portable terminal to use is Xperia Z3.
4.2. Development environment
Figure 4 shows the experimental results. I was able todisplay Miya Kun's 3DCG model for the natural image
added to the dictionary. Once reading the natural image
inside the camera, I continued to display the model evenat all angles, up and down, right and left. However, when
the distance gets away, the unnaturalness of the point
where the model turns white and the model itself
becomes conspicuous becomes conspicuous.
5. Conclusion
In this research, we tried to make an application using
Unity3D. Future tasks include functions of advanced

6. Future plans
In addition to the function of SmartAR, GPSfunction,model creation, seeking problems for practicaluse, etc. are completed and completed as an application.
In addition, I would like to expand the area beyond
Takachiho focused this time. Especially in the productionof models, we are currently making intensive workon Tajikarao under construction. The model underdevelopment is shown in figure 5.

Fig.5： 3DCG model of Tajikarao.
As a concrete implementation planning function, there isa function that the performance of the 3DCG model withthe palm, which is conscious of "Omochikaeri",reproduces Kagura 's dance. The situation is shown infigure6. In addition to this, we planned functions such as
displaying the menu by holding the terminal in thepamphlet so that a beautiful image can be played bytapping, and displaying the 3DCG model reproducing abeautiful landscape when reading the brochure as well

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 325

Masamichi Hori, Makoto Sakamoto, Koshiro Mitsuhashi, Yukari Kodama, Takeshi Tanaka, Mihoko Fukushima, Chikashi Deguchi,
Masahiro Yokomichi, Masayuki Mukunoki, Kunihito Yamamori, Atsushi Iiboshi

ing. An image diagram showing a menu from a pamphletis shown in figure7.
References
1. "Idealizing AR technology" edited by I/O editorial
department. "VR" "AR" Frontline of technology.
Kohgaku-Sha Co., Ltd. 2015. pp. 20-34.
Overview of Takachiho | Introduction / History of the town
| administrative information | Takachiho cho Takachiho
cho information official website. http://www.towntakachiho.jp/administration/history/outline.html.
(accessed 2016/11/30).
Myth of Takachiho | Tourist spot | Takachiho cho tourist
association | Miyazaki ken Takachiho sightseeing,accommodation and event information Takachiho cho
information official website.
http://takachihokanko.info/sightseeing/detail.php?log=1368001475. (accessed 2016/011/30).
Takachiho Mura Council "Treasure of Discover Rural
Farm Village (Mura)" was selected. | What's New |Takachiho-cho Tourist Association Takachiho cho
information
official
website.
http://takachihokanko.info/news/detail.php?log=1479688433.
(accessed 2016/11/30).
2. Makoto Sakamoto, Keisuke Hozumi. "Study on depictionof 3D character guiding at sightseeing spots using AR
technology" Bulletin of the Faculty of Engineering,
University of Miyazaki. Faculty of Engineering, Universityof Miyazaki 2016. vol.45, pp. 231-234.．.
3. Unity Technologies Japan. “Unity - Game Engine”.
Unity3D Homepage http://japan.unity3d.com/.
(accessed 2016/11/30). Sony Digital Network
Applications Co., Ltd. "SmartAR SDK".SmartAR website.
http://www.sonydna.com/sdna/solution/SmartAR_SDK.
html. (accessed 2016/11/30).

Fig.6：Image dancing on hands.

Fig.7：Image showing menu from brochure.
Acknowledgements
Miyazaki multimedia vocational school people are very
busy, thank you hearty thank you for your cooperation in
3DCG modeling and advice etc. Also, the Miyazaki
Tourism Convention Association, Inc., abc Corporation,Inc., Phoenix Seagaia Resort Co., Ltd., people in the
Akimoto district who took care of me in the interview,other prefectures within the prefecture I would like toexpress my gratitude to all concerned parties in eachmunicipality.
© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 326

Development of Cloud Action for Seamless Robot
Using Backpropagation Neural Network
Wisanu Jitviriya

Faculty of Engineering, King Mongkut’s University of Technology North Bangkok.
1518 Pracharat 1, Wongsawang Rd., Bangsue, Bangkok, Thailand 10800
wisanu_jitviriya@hotmail.com
Jiraphan Inthiam and Eiji Hayashi

Mechanical Information Science and Technology, Kyushu Institute of Technology.
680-4, Kawazu, Iizuka, Fukuoka, 820-8502, Japan.
jiraphan@mmcs.mse.kyutech.ac.jp and haya@mse.kyutech.ac.jp

Abstract
This paper presents the cloud action model for a five DOF Seamless robotic arm using the inverse kinematics solution
based on artificial neural network (ANN). Levenberg-Marquardt method is used in training algorithm. The desired
position and orientation of the end effector is defined as the input pattern of neural network. In addition, we propose
the cloud action which is the movement patterns of the robotic arm. The cloud action platform is created in order toperform the basic behaviors of the Seamless robot such as “Catch”, “Approach”, “Interest”, “Look around”, “Alert”
and “Avoid” actions. Experimental results show the suitable structure of artificial neural network used for solving
the inverse kinematics equation, and the testing points in the robot’s workspace were verified with the robotic arm.
Keywords: Inverse kinematics solution, Artificial neural network, Levenberg-Marquardt method.

Consequently, in this paper, we present the cloud
action which is the movement patterns of Seamlessrobotic arms. The cloud action platform is created inorder to perform the behavior of robot smoothly. In
addition, we introduce the performance of the training
algorithm which is Levenberg-Marquardt learningalgorithm [4] that is used for solving the inverse
kinematics problem of the Seamless robotic arms.
This paper is organized as follows: Section 2 provides
the kinematic modeling of Seamless robotic arm. Section
3 of the paper explains the neural network fordetermining the inverse kinematics solution. Section 4
describes the process of the cloud action model. Section
5 shows the experimental results of the proposed model,
and finally, Section 6 concludes the paper.

1. Introduction
Nowadays, the service robot technology isdeveloping and advancing rapidly. Therefore, ourresearches have concentrated on exploring and studying
an artificial intelligent method to create the behavioral
and emotional models of the autonomous robot [1] - [3].
The new model for our robot is the dynamic behavior
selection model based on emotional states whichdevelops from the Consciousness-Based Architecture(CBA) model combined with Self-Organizing Map(SOM) learning and Markovian model. However, it
seems to be the efficient model of the autonomous robot,
but we face some problems in posturing the behavior andexpressing the emotion of the robot.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 327

Wisanu Jitviriya, Jiraphan Inthiam, Eiji Hayashi

z0

Loff

x0

L1

0
5T

z1 z2 Ө2

Base frame

Ө1

z3

nx

ai
0
0
0
0
0

oy

ay

py

nz

oz

az

pz

0

0

0

1

c 5 90 (c 2 90 s 4ny

s 5 90 (c 1 c 3 90
nz

αi
90
90
90
-90
0

ox

s i and cos is 1 c 2 90 s 3 90 )c 5 90 (c 4 ( s 1 s 3 90c 1 c 2 90c 3 90 ) c 1 s 2 90 s 4 )s 5 90 (c 2 90 s 4

s 2 90c 3 90c 4 )s 2 90 s 3 90c 5 90oyozs 5 90 (c 4 (c 1 s 3 90

s 1 c 2 90c 3 90 ) s 1 s 2 90 s 4 )s 1 c 2 90 s 3 90 )s 5 90 (c 4 ( s 1 s 3 90c 1 c 2 90c 3 90 ) c 1 s 2 90 s 4 )c 5 90 (s 1 c 3 90 c 1 c 2 90 s 3 90 )For this research work, Seamless robot in our robotics
laboratory is being used shown in Fig.1. Actually, it has
two arms, but in this case, we only present the left arm
for considering and determining the forward and inverse
kinematics solutions. Denavit-Hartenberg (D-H) method[5] is used to derive its kinematics. Fig.1 shows the joints
and links of the left side of robotic arm, which has thebase frame, shoulder, elbow, and wrist. The DHparameters of the robotic arm are listed in Table 1.
According, DH method, the transformation can beformed in the chain product of five homogenous matricesi 1
iT as expressed by Equation (1).

Where, sin i

s 1 c 2 90c 3 90 ) s 1 s 2 90 s 4 )s 5 90 (s 1 c 3 90 c 1 c 2 90 s 3 90 )2. Kinematics modeling of Seamless robotic arm

0 1 2 3 4
1T 2T 3T 4T 5T

s 2 90c 3 90c 4 )c 5 90 (c 4 (c 1 s 3 90

c 5 90 (c 1 c 3 90

0
5T

(2)

s 2 90 s 3 90 s 5 90

Table 1. The DH parameters for Seamless robotics arm (Left arm)di-(L1+ Loff)
0
L2
0
L3

ny

x5

Fig.1 Seamless robotics arm (Left arm)

Өi
Ө1
Ө2+90
Ө3+90
Ө4
Ө5+90

px

In Equation (2) indicates the elements of rotational oftransformation matrix ( nx , n y , nz , ox , o y , oz , ax , a y , az )and the elements of position vector ( p x , p y , p z ). Allelements can be solved as follows:

L3

Joints
1
2
3
4
5

ax

L2

Ө4

Ө5

ox

Ө3

z4

End Effector
z5

nx

ax

c 2 90c 4

s 2 90c 3 90 s 4ays 4 (c 1 s 3 90az

s 4 (s 1 s 3 90 c 1 c 2 90c 3 90 ) c 1 s 2 90c 4

px( L1 Loff ) L2c 2 90

py

L2 s 1 s 2 90s 1 c 2 90c 3 90 ) s 1 s 2 90c 4

L3 (s 4 (c 1 s 3 90pz

L3 (c 2 90c 4

s 2 90c 3 90 s 4 )s 1 c 2 90c 3 90 ) s 1 s 2 90c 4 )L2c 1 s 2 90
L3 (s 4 (s 1 s 3 90c 1 c 2 90c 3 90 ) c 1 s 2 90c 4 )3. Neural network based the inverse kinematics
solution(1)

Solving the inverse kinematics problem is one of themost important task in robot kinematics and control. The
complexity of this problem is obtained from the robot’s
geometry and the nonlinear trigonometric equations.
Therefore, there are three traditional techniques used for
determining inverse kinematics problem that can beci.

The homogeneous transformation matrix of Seamlessrobotic left arm can be shown in Equation (2).

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 328

Development of Cloud Action
Table 2. Results of each Neural Network structure
In hidden layer(Hidden node)

Epoch

MSE

Regression

30
32
34
36
38
40
42
44

205
145
193
238
276
167
230
254

51.6
51.3
51.2
51.3
51.2
50.9
52.0
52.1

0.99291
0.99293
0.99295
0.99293
0.99295
0.99296
0.99286
0.99284

Fig.2 Neural Network based inverse kinematics solution
Input layer

Hidden layer

Output layer

Σ

Σ

Ө1

Σ

Σ

Ө2

Σ

Σ

Ө3

Σ

Ө4

Σ

Ө5

px
py
pz
n, o, a(x,y,z)ΣBiasijStep 1: Determine the straight line (distance) between thetarget object and end effector position.
Step 2: Find the center point of each behavior on thestraight line. Then, the sphere of each action is created,depending on the rate action.
Step 3: Verify the response action for selecting the group
action.
Step 4: Random the point in the selecting workspace
based on the random distribution technique.

Biasoj5.
Fig.3 Multi-layered perceptron neural network structure

In this study, Seamless robotic arm was used to verify
the movement of the robot based on Cloud action model.
In Fig.2 shows the process of inverse kinematics solution.
Levenberg-Marquardt method (trainlm) from MATLAB
toolbox was used to train and update the weight and bias
values, because it is the fastest back propagation neuralnetwork algorithm (Supervised learning). The designed
neural network model is illustrated in Fig.3. The Multilayered perceptron neural network model consists of 12
node inputs (the position vector and nine elements ofrotation matrix), 5 node outputs and only one hidden
layer. For training and testing data of neural network thatwas determined by the equation of each element inEquation (2). A work space data set 33880 data. While
80% of the data set was set as the training data, 10% tovalidation and 10% data for testing data. Table 2 showsthe experiment results of the suitable structure of Neural
Network based inverse kinematics solution for Seamlessrobotic arm. After modeling the neural network to
calculate the inverse kinematics solution, the movement
paths of robotics arms were verified by the Cloud action
method as shown in Fig.4.

classified as algebraic, geometric and numericalalgorithms. These methods are time consuming, incorrect
initial estimation, the inverse kinematics cannot be
guaranteed and heavy computational calculation.
Consequently, another method can solve the inverse
kinematics problems that is Neural Networks. It is
possible to formulate the nonlinear mathematical model.
Many researchers have verified with this approach inseveral works [6] - [8].
4.

Experimental results

Cloud action of Seamless robotic arm

After solving the inverse kinematics problem base on
Neural Network, We try to generate the trajectory path
between the target position and the end effector position.
But trajectory path of Seamless robotic arm differs fromthe existing trajectory paths, because Seamless robot thatis used to perform the suitable behavior and express theintelligent emotion. Therefore, Cloud action model isintroduced in this paper. The procedure of the Cloudaction can explain by the following steps:© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 329

Wisanu Jitviriya, Jiraphan Inthiam, Eiji Hayashi

Fig.4 Results of the six actions with cloud action model

6.

3. W. Jitviriya, J. Inthiam, and E. Hayashi, “Dynamic
Behavior Selection Model based on Emotional States forConbe-I robot,” Journal of Robotics, Networking andArtificial Life (JRNAL), Vol.2, No.4, pp. 247-251, 2016.
4. D. Marquardt, “An algorithm for least-squares estimationof nonlinear parameters,” SIAM Journal on Applied
Mathematics, Vol.11, No.2, 431–441, 1963.
5. Denavit J., and R.S. Hartenberg, “A Kinematic Notation
for Lower- Pair Mechanism Based on Matrices,” ASME
Journal of Applied Mechanics, pp. 215-221, 1955.
6. Guez, A., and Ahmad, Z., “A Solution to the Inverse
Kinematic in Robotics Using Neural Network Processing,”
IJCNN89, Vol. 2, pp. 299-304, 1989.
7. Oyama, E., et.al,, “Inverse Kinematics Learning byModular Architecture Neural Networks with Performance
Prediction Networks,” Proc. IEEE Int. Conf on Robotics
and Automation, pp. 1006-1012, 2001.
8. Nguyen, L., Patel, RV., and Khorasani, K., “Neural
Network Architectures for the Forward Kinematics
Problem in Robotics,” IJCNN90, Vol. 3, pp. 393-399,
1990.

Conclusions

In this paper, we propose the new control system fordesigning and developing a 5 DOF revolute robot arm,
using a backpropagation neural network and the cloud
action model. Results have shown that the Seamlessrobot arm can move randomly in the workspace of the six
basic actions. For future work, we would like to improve
this model into the conventional action selection system(Consciousness-Based Architecture (CBA) model
combined with Self-Organizing Map (SOM) learning
and Markovian model), in order to create the intelligent
service robot perfectly.
References
1. E. Hayashi, T. Yamasaki, and K. Kuroki, “Autonomous
Behavior System Combing Motivation and Consciousness
Using Dopamine,” Proc. of 8th Int. IEEE Symposium on
Computational Intelligence in Robotics and Automation(CIRA 2009), pp. 126-131, 2009.
2. W. Jitviriya, and E. Hayashi, “Analysis of Robotic Arm’s
Behavior using Self Organizing Map combined with
Consciousness-Based Architecture Module,” in Proc.
2013 IEEE/SICE International Symposium on System
Integration (SII2013), pp. 533-538, 2013.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 330

Mathematical Modelling Of Human Fear And Disgust Emotional Reactions Based On Skin
Surface Electric Potential Changes
Gaisina Kristina

N.I. Lobachevsky Institute of Mathematics and Mechanics, Kazan Federal University, Kremlevskaya 35
Kazan, 420008, Russia
Gaisin Ruslan

Higher Institute for Information Technology and Information Systems, Laboratory of Neurobiology, Kremlevskaya 35
Kazan, 420008, Russia
E-mail: krictishaa@gmail.com, rusgaisin@gmail.com
http://kpfu.ru/eng

Abstract
The paper deals with skin surface electric potential data registration of a human while he / she is watching speciallyselected videos that are supposed to activate basic emotions of fear and disgust. We describe the selected video
materials and data logging process and present human emotional reactions model, which was implemented bynumerical methods. We demonstrate these experimental results of fear and disgust psycho-emotional states that wereobtained from 100 respondents.
Keywords: Mathematical model of emotions, skin surface electrical potential, emotion model, numerical indicatorof emotional level.

1. Introduction
The classification of human emotional reactions isnormally based on the analysis of video data,
psychological tests, electroencephalography data,
galvanic skin response and other non-invasive methods.
We propose developing a numerical model of humanemotional reactions as the way to measure the level ofexperienced emotions, not to classify them. The main
objectives of this study were to develop a hypothesis ofnumerical model to evaluate a person's emotional state
and to compare the data obtained in the experiment withthe subjective ones received from the interviews.
2. Material and Methods
During the experiment two basic emotions were analyzed,
i.e. disgust and fear. We selected them because of theeight basic emotions these two are considered to be themost prominent, easily reproducible and recorded ones.
100 respondents were interviewed. As a basis we took
the existing developments to register the skin surface
electric potential and adapted them for the investigation.
We used the SuTest Pro project source code [1] andBiopulse-TC device for the skin surface electric potential
registration. The Biopulse-TC device is characterized by
the maximum electric current of 40 µA, the output values
are from 0 to 200 standard units, which corresponds to 0
- 50 mV with 0.01 Hz sampling frequency. During theexperiment we recorded the electrical skin response inthree stages: before, during and after watching a video.
The rate of data logging was approximately from 8 to 10
records per second.
At the same time we had a psychological polling. For that
purpose we developed a psychological test to represent© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 331

Gaisina Kristina, Gaisin Ruslan

subjective emotional state in numerical form, thatconsisted of 41 questions: 10 neutral questions, 10questions related to one of the basic emotions according
to Tomkins [2] and remaining 3 questions for the rest
basic emotions. The sequence of questions was generated
randomly. The answer to the question was marked on ascale from -3 to +3, where the value of -3 meant that theemotion was not expressed and the value of +3 meant thatthe emotion was completely expressed at the highest rate.
100 respondents took part in the experiment.
To select the most appropriate video material (the mostterrible one for fear and the most "disgusting", "hideous"
one for disgust), we demonstrated each of the options to
30 respondents. We took into account personal opinionsof the respondents and external reactions to the video(mimic, facial expression).

Fig. 2. Graphs for the state of fear. Time was measured inintervals of 100 ms. The vertical line shows the beginning ofwatching video.

The graph tendency is characterized by:

3. Results and Discussion

̅ − ̅

(1)
where ̅ is arithmetic mean of initial sample (before
watching the video), ̅ is arithmetic mean of final
sample (last seconds of watching the video). The value
characterizes a common level of the experienced emotion(disgust). It is assumed that the greater the value is, thehigher the level of experienced disgust.
The indicator that characterizes fluctuations intensity of
the graph is:

To analyse the data obtained, all the results were
presented in graphical form. In case of disgust we noticed
that all the graphs had a descending tendency. Moreover,
if there was any development of action in a storyline ofthe video, we could find insignificant increase in indicesduring the most emotive parts of the video (Fig. 1).

∑

|

|

−

(2)

where n is total number of values, xi – value, xi-k – value
with the step of k size. This value characterizes
variability of the emotional state. It is assumed that thegreater the value, the more stable the emotional state is.
The standard deviation is:
Fig. 1. Graphs for the state of disgust demonstrating descending
tendency with time measured in intervals of 100 ms. The
vertical line shows the beginning of watching video.

=

∑

(

− ̅)

(3)

where n is total number of values, xi – value, ̅ –arithmetic mean. This value represents the total changeof the emotional state. It is assumed that the greater thevalue, the higher the level of experienced disgust is.

Fear expression, a peak emotional state (caused by video
material), was represented by jumps in the graphs of all
respondents (Fig. 2). In our research we supposed thatfear and disgust dominate over all human emotions [3].

Our hypothesis regarding each indicator of numericalmodel (for the state of fear) is as follows.

Our hypothesis regarding each indicator of numericalmodel (for the state of disgust) is as follows.

The biggest (maximal) fluctuation:© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 332

Mathematical Modelling Of Humanmax|| , = 1,−emotions of fear and disgust in number. The results of theresearch can be used to estimate the strength of theexternal influence on the emotional state of a person.

(5)This value represents a peak emotional state, maximal
emotion expression (the state of fear). It is assumed thatthe greater the value, the higher the level of experienced
fear is.

Acknowledgements
This work was supported by the program of competitive
growth of Kazan Federal University and the subsidy
allocated to Kazan Federal University for the state
assignment in the sphere of scientific activities.

The maximal standard deviation of fluctuations:max ( |−|) , = 1,(6)

References

It is assumed that this value represents a peak emotional
state (the maximal emotion expression (fear)) regarding
its own generalized emotional state.
The numerical model presented in our study makes it
possible to distribute respondents according to the levelof the experienced emotion. Thus, we built distribution
for each emotional state separately for all the respondents.
We made similar distribution in accordance withnumerical polling data.
After comparing psychological polling data and skinelectric response indicators for the same video we cameto the conclusion that experienced emotional intensity
distributions did not match.
The method used for subjective estimation of theemotional state cannot be used for numerical model
validation.

1. Kajsa Landgren (2008). Ear Acupuncture, Elsevier Health
Sciences, 30
2. Tomkins, Silvan S. (1962). Affect Imagery
Consciousness: The Positive Affects (Vol. 1, ch. 9), New
York: Springer.
3. Leukhin A., Talanov M., Sozutov I. (2016). Simulation ofa fear-like state on a model of dopamine system of rat brain.
Advances in Intelligent Systems and Computing , 449, 121126
4. Lövheim, H. (2012). A new three-dimensional model for
emotions and monoamine neurotransmitters. Med
Hypotheses, 78: 341–348
5. Bär, K.-J., De la Cruz, F., Schumann, A., Koehler, S.,
Sauer, H., Critchley, H., Wagner, G. Functional
connectivity and network analysis of midbrain andbrainstem nuclei (2016) NeuroImage, 134, Pages 53-63.

4. Conclusions
Mismatch between psychological polling results andfinal indicators data needs to be investigated further.
For that purpose we suggest to take a Lövheim Cube as abasis [4]. The Lövheim Cube represents 8 basic emotions
according to Tomkins, which levels depend on serotonin,
dopamine and noradrenaline neuromediator values.
The validation of the proposed numerical indicators forexperienced emotion intensity evaluation is possible only
after measuring neuromediator levels in the brain during
video watching.
We do not consider invasive ways of measurement. The
only non-invasive and the most precise way to measureserotonin, dopamine and noradrenaline levels is to study
subcortical systems of the brain with FMRT [5].
In case of successful validation, the resulting model willallow us to compare the level of the analysed basic© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 333

Neuromorphic Robot Dream: A Spike Based Reasoning System
Alexander Toschev
Laboratory of Neurobiology, ITIS,
Department of Intellectual Robotics,
Kazan Federal University, Kazan, Russia
E-mail: atoschev@kpfu.ru
Max TalanovLaboratory of Neurobiology, ITIS,
Department of Intellectual Robotics,
Kazan Federal University, Kazan, Russia
E-mail: max.talanov@gmail.com
Alexander Tchitchigin
Service Science and Engineering Laboratory, Innopolis University andLaboratory of Neurobiology, Kazan Federal University, Kazan, Russia,
E-mail: a.chichigin@innopolis.ru
Salvatore Distefano
Social and Urban Computing Group, ITIS, Kazan Federal University, Kazan, Russia and
MDSLAB, University of Messina, Messina, Italy
E-mail: s_distefano@it.kfu.ru, sdistefano@unime.it

Abstract
In this position paper we propose the approach to create a reasoning system starting from brain spikes as main
information medium to control a (quasi) real-time robotic system. Based on the robot dream architecture, the robot
input in form of spike stimulus is provided to a simulated spiking neural network, then elaborated and fed back to therobot as specific rules. The reasoning rule-based system for intelligent spike processing transforming signals into
software actions or hardware signals is thus specified.
Keywords: artificial intelligence, robotics, spikes, neurobiology, reasoning, logic.

1.

understanding) framework described in [2]. The Robot
Dream project integrates the simulated brain via spiking
neural network with the robot system.
Figure 1. depicts the “wake-dream” cycle of the Robot
Dream project architecture. The idea of the “wake-dream”
was inspired by mammalian dream and sleep cycles. We
split the computations into two phases: “wake” and“dream”. In the “wake'' phase the robotic system actively
interacts with the complex social environment in a

Introduction

This work is a part of the Robot Dream project [1]focusing on the integration of real-time robotic systemwith spiking neural network simulation of a brain. One ofthe parts of the Robot Dream project is the translation thesimulated neuron spikes into robotic system signals andthe management of the robotic system using thereasoning system, based on the TU (thinking and© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 334

Alexander Toschev, Max Talanov, Alexander Tchitchigin, Salvatore Distefano(quasi) real-time. Thus system reactions overpermanently changing environment should be performed
via light-weight rule-based system. The “dream” phase isresponsible for processing of experience accumulated
during the “wake'' phase. During the “dream” phase the“dreaming brain” plays back stored experience over thepart of the brain corresponding to the input channel(visual information over visual cortex, haptic information
over somatosensory cortex, etc.). We call this process the
direct translation.
During the reverse translation the simulated neuronal
structures are consequentially transformed into the formof the logical rules of the robotic system.
The aim of this paper is to describe the system of lightweighted logical rules operating in the real-time robotic
system which were obtaining during the reverse
translation phase.

input from sensors and actuation control logs in aformat transferable to the “dreaming brain”,
according to our bio-inspired approach basically as
spikes.
Following a biologic analogy, our solution is basedon spikes both in the representation of input
information coming from the robot and in controlling
the latter through spikes-encoded rules. This way all
sensed data is stored in form of tagged spike trains.
Later it is transmitted and “played back” over thesimulated “dreaming brain”. We propose to use
spikes as main information unit for the reasoning
system instead of textual or numerical forms. Bothapproaches are described in detail below.
3. Rule based system
Due to the (quasi) real-time requirement we propose to
use TU framework [2], based on the “Critic-SelectorWay to think” (T3) model [3]. This framework helps us
to implement the rule based system managing the robotic
system. The T3 “Critic-Selector-Way to think” triplet isinherited from the works of Marvin Minsky [4]. Thisapproach provides an option to evaluate incoming
sensory data over the stored knowledge in the knowledge
base. The input information of the TU framework [2] is
textual while in this work we propose to use spikes forthe representation of input and processing information of
the TU framework. The TU framework is based on
probabilistic rules and uses logical reasoning with the 6levels of mental activity and T3 over spikes [4].

Fig. 1. The representation of “wake-dream” cycle: during“wake” phase the robotic system accumulates experience,
during “dream” phase the accumulated experience is transferredfrom a robotic system to the “dreaming brain”. Later the“dreaming brain” updates its synaptic connections andtranslates the neuronal structures into the robotic system rules.
Robotic system during “wake” phase operates in real-timeusing light-weight rules based system.

3.1. Probabilistic Critic
Probabilistic critic is the implementation of critic fromthe T3 “Critic-Selector-Way to think” triplet. Spikes
trigger several critics, that start inbound information
processing in parallel on several levels of mental activity.
Critics are grouped in contexts based on their level ofmental activity and semantics of the processing
information (audial, visual, tactile). The activation of one
critic of the context increases the probability of triggeringcorresponding critics on the same context. This way
every critic is a temporal probabilistic predicate that
contains set of rules that are evaluated not only over the

2. Robot lifecycle
The robotic system operates in the (quasi) real-timemode during the “wake” phase. If the (quasi) realtime requirement is strict the robot should use alightweight management system. The second
requirement is that the robotic system must store the© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 335

Neuromorphic Robot Dream:

incoming information, but over current system state andcontext of recently processed information.
The sequence of the logical spiking information
processing is depicted in Fig. 2.
The incoming set of spikes S1 triggers the pattern P1 ofcritics on two levels: instinctive (basic reflexes) andlearned (simple-trained reactions). The pattern P1 and thenext set of spikes trigger two patterns P2 on learned level
and P3 on the instinctive levels. Later these two patters
trigger two more patters one on each level: P4 and P5.
This mechanism of spiking reasoning system is inspiredby the “Hierarchical temporal memory” HTM approach[5] and in a similar way has predictive mechanism: theactivation of the pattern P1 could be used as theindication of the S2 set of spikes. To use the advantagesof training we propose to extend logical rules with weightvalue similar to confidence value in NARS [6].

Fig. 2. Inbound set of spikes S1 trigger pattern of P1 critics CL1,
CI1, CI2, CI3, CI4, CI5 on two levels: instinctive reactions andlearned reactions. The pattern P1, and the spikes S2 trigger thepatterns: P2 (CL2) and P3 (CI6, CI7, CI8) they in their turntrigger P4 (CL3) and P5 (CI1, CI3, CI9). This way, when P1 istriggered it could indicate that the next incoming set of spikes
could most probably be S2.

3.2. Way to think

5.
6.

A way to think is a main activity to change the content ofmemory and perform an action [4]. A way to think runs
a workflow that could trigger the hardware controller ofthe robotic system or change the data of the information
processing context. We propose the following workflow:
1. Spike hits the system;
2. The system creates inbound context for thisspike based on its attributes: source channel(visual, audial, ...), time, number of activatedneurons, neurotransmitters, previously
processed information, see Data Structuressection for the details;
3. The system starts processing the spike;
4. Several Critics are activated by based on
resulting probability of their rules;
5. Several spikes are accumulated in the system
state and when their number reaches thethreshold the motor reaction is triggered;
6. The critic activates a way to think;
7. The way to think generates data for controllers.

4.

Neurotransmitters used to generate this spike.
Previously processed information context.

The implementation of the model of six

We implement TU framework based on "Model of Six"developed by Marvin Minsky [4] as an instance of arule-based (soft) real-time architecture.
Firstly we developed first three layers of the model:
instinctive, learned and deliberative thinking. All layers
process input data in parallel, the fastest one wins. Thismeans that whatever layer selects a next action first, that
action gets executed. This way simple instinctive
reactions get executed much faster than longdeliberative planning. Later long deliberative planning
could override the fast behavior. It's useful for
immediate handling of "emergency" situations. On theother hand, in the case no instinctive or
learned rules match the situation the system can "takesome time" to plan sequence of actions leading to a goalusing deliberative thinking.
At the bottom of our layered architecture, closest to thehardware, there is the driver layer. This layer allows to
use several drivers for different hardware and robotic
platform configurations. Next one is hardware
abstraction layer (HAL). This layer definesabstract high-level commands (like "move forward 10cm") hardware or platform agnostic. The driver is
responsible for executing these commands on a given
robotic system. The third layer implements actual
reasoning and control providing the system with the

3.3. Data structures
According to our approach, the input from robots is
encoded by spikes. A spike is an abstract object withfollowing attributes:
1. Source channel;
2. Timing: start time, duration.
3. Semantic tag of the event to be processed.
4. Number of activated neurons.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 336

Alexander Toschev, Max Talanov, Alexander Tchitchigin, Salvatore Distefano

commands to be enforced. In our case, as stated above,it implements the Marvin Minsky's "Model of Six" [4].
The specific rules managing the system are tailored on
the application running on the robotic system andsubject to modifications through the feedback from the“dreaming brain”. The initial rules for an application are
not generated by the inference engine ("Model of Six").
This architecture provides a relatively straightforward
and flexible enough framework to experiment with andadapt to different tasks and robotic systems.
5.

with spiking neurons (Advances in Neural Information
Processing Systems, 2008).
9. P. Vincent and H. Larochelle, tacked denoising
autoencoders: Learning useful representations in a deep
network with a local denoising criterion (Journal of
Machine Learning Research, 2010).

Conclusion

In the paper we propose the mechanism of the translationof neuron spikes into control signals for the robotic
system. The TU framework is used to process reasoning
life-cycle over the spikes and apply propertransformation of the brain neuronal signals (spikes) into
the pseudo-neuronal activity of the robotic system.
However, the system should be able to track existing
signals and reactions from simulated “sleeping” brain
mapping them into the robotic systems.
References
1. A. Tchitchigin and M. Talanov, Robot dream (Agent andMulti-Agent Systems: Technology and Applications: 10th
KES International Conference, KES-AMSTA 2016 Puertode la Cruz, Tenerife, Spain, June 2016 Proceedings 2016).
2. A. Toschev and M. Talanov (eds.), Thinking Lifecycle as
an implementation of machine understanding in software
maintenance automation domain (Agent and Multi-Agent
Systems: Technologies and Applications. 9th KES
International Conference, KES-AMSTA 2015 Sorrento,
Italy, June 2015, Proceedings, 2015).
3. A. Toschev, Thinking lifecycle as an implementation ofmachine understanding in software maintenanceautomation domain (Smart Innovation, Systems andTechnologies, pp. 301–310, 2015).
4. M. Minsky, The Emotion Machine: Commonsense
Thinking, Artificial Intelligence, and the Future of the
Human Mind, Simon & Schuster.2007.
5. J. Hawkins, HIERARCHICAL TEMPORAL MEMORY(Numenta, Inc., 2011), pp. 421–439.
6. H. Wang and M. Morales, Pedunculopontine andlaterodorsal tegmental nuclei contain distinct populationsof cholinergic, glutamatergic and GABAergic neurons in
the rat. (The European journal of neuroscience, 2009).
7. A. Kumar and S. Rotter, Spiking activity propagation inneuronal networks: reconciling different perspectives onneural coding (Nature reviews neuroscience, 2010).
8. L. Buesing and W. Maass, Simplified rules and theoreticalanalysis for information bottleneck optimization and PCA© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 337

Development of Behavioral Robot
Using Imitated Multiplex Neurotransmitter System
Saji Keita and Eiji Hayashi

Mechanical Information Science and Technology, Kyushu Institute of Technology.
680-4, Kawazu, Iizuka, Fukuoka, 820-8502, Japan.
saji@mmcs.mse.kyutech.ac.jp and haya@mse.kyutech.ac.jp
Wisanu Jitviriya

Faculty of Engineering, King Mongkut’s University of Technology North Bangkok.
1518 Pracharat 1, Wongsawang Rd., Bangsue, Bangkok, Thailand 10800
wisanu_jitviriya@hotmail.com

Abstract
This paper presents the design of the robot’s behavior using the imitated multiplex neurotransmitter system. The
major neurotransmitters of the nervous system are Dopamine, Noradrenaline and Serotonin, which are calledmonoamine neurotransmitter and they are related to the motivation, behavior and feeling. The proposed system isanalyzed based on Lövheim’s cube of emotion and Wundt‘s three-dimensional theory of feeling. So, Dopamine isapplied as a comfort dimension, Noradrenaline is defined as a tense dimension, Serotonin is considered as an energetic
dimension. In our system, three neurotransmitters are generated in each of the different factors, and then the
motivation is calculated. The robot’s motivation and some external information are classified into behavior andemotion maps using Self-Organizing Map learning. Finally, the robot’s behavior and emotion are decided byMarkov’s stochastic model. The experimental results confirm the effective outcomes by comparing the conventionalmodel with the proposed model.
Keywords: Neurotransmitter, Self-Organizing Map, Dimensions of Emotion(SOM) learning and Markovian model. CBA is based onanimal’s motivation system by dopamine in the brain. In
this paper, we present the multiplex neurotransmitter
system which simulates
the generation ofneurotransmitters such as serotonin, noradrenaline, anddopamine patterns. The multiplex neurotransmitter
system is created to diversify the behavior factors ofrobot and to imitate animal’s consciousness.
This paper is organized as follows: Section 2 provides
the system structure of our robot and CBA model.
Section 3 suggests a new method to improve the robot’s
behavior factors by the multiplex neurotransmitter
system. Section 4 and 5 explain the approximation in
applying the generation of neurotransmitters consistingof serotonin, noradrenaline, and dopamine. Section 6shows the experimental results of the proposed model,
and finally, Section 7 concludes the paper.

1. Introduction
Nowadays, the robot technology is applied to service
field and developing and advancing rapidly. Domestic
robot, medical robot, business robot and rescue robot areclassified as service robot. These robots are necessary to
work with people. Therefore, the robots need to haveenough ability for user affinity. We suggest somefunction, comforting, attraction, and friendship in orderto obtain the affinity. Therefore, the functions of animals(dog and cat) in real life, which is focused on improving
our robot. We propose the development of service robot’s
behavior design by imitating the animal’s consciousness
The new model of our robot is the dynamic behavior
selection model based on emotional states whichdevelops from the Consciousness-Based Architecture(CBA) model [1] combined with Self-Organizing Map© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 338

Saji Keita, Eiji Hayashi, Wisanu Jitviriya

Fig.1 the system structure

Fig.3 Lövheim’s cube of emotion

2. System Structure

Step2: Creating the maps that classified with default
emotions (natural, hope, happiness, sadness, fear,
disgust) and behaviors (look around, approach, avoid,etc.) using Self-Organizing Map method.
Step3: Selecting the candidate of emotion andbehavior of next time step by a verification of the externalsituation (including the Dopamine and the motivation)and the map.
Step4: Deciding the emotion and the behavior for
next time step by the results of the calculation. The statetransition probability of Markov model is determined by
the candidate of emotion and behavior.
Step5: The robotics arm drives the actuators
according to the selected behavior, and the two displays
which attached at the robot head present the eye images
according to the selected emotion.

In this research work, the robot in our laboratory is
being used as shown in Fig.1. This robot has two arms
which have the base frame, shoulder, elbow, and wrist (6DOF), and the robot head is made for acting the facialrecognition and expression. The web camera is attachedat the end-effector of arm that is used for recognition ofan external situation and forward tracking algorithm, inorder to recognize the target object. The CCD camera
attached at the head that is used for recognition of user’s
facial emotional expressions. Fig. 2 shows the overview
system of the robot. The procedure of the system can
explain by the following steps:
Step1: Recognizing an external situation (the objects)
by cameras and calculating the release of Dopamine anda motivation according to the circumstances.

3. Imitated Multiplex Neurotransmitter system
In this paper, we suggest the imitated multiplex
neurotransmitter system based on Lövheim’s cube ofemotion [2]. That theory is proposed theoretical model
aiming at explaining the relationship between themonoamine neurotransmitters and the emotions (Fig.3).
In addition, Wundt‘s three-dimensional theory of feeling[3] (Fig. 4), three psychological axes of feeling, pleasuredispleasure, relaxation-strain, and excitement-calm, canbe applied to these neurotransmitters. In brain,
Noradrenaline is known to be related to the attention andimpulse, and Serotonin is known to be suppressed by
fatigue substance in neuropsychology. Therefore, in thisFig.2 the overview system© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 339

Development of Behavioral Robot(a) Noradrenaline(b) Serotonin

Fig.5 the referenced waveforms
Fig.4 Wundt‘s three-dimensional theory of feelingpaper, Noradrenaline is fitted to relaxation-strain axes
and Serotonin is fitted to excitement-calm axes.
Due to generate a motivation from different factors,
the behavior factors of robot can be diversified and thesystem can imitate animal’s consciousness based on
biologically inspired artificial emotions.

(a) Noradrenaline(b) Serotonin

Fig.6 the simulated waveforms

4. Approximation of neurotransmitters
In this research, not only Dopamine (DA)neurotransmitter but also Noradrenaline (NE) and
Serotonin (HT-5) neurotransmitters are applied because
they are the element constituting the monoamine
neurotransmitters. In this robot, the generating
waveforms of these neurotransmitters are simulated
based on the experimental results when researchers test
them with the rat [3], [4] as shown in Fig.5. The
generating waveform of Noradrenaline is approximatedby a Gaussian Equation (1), and the generating waveformof Serotonin is approximated by Equations (2) and (3).

y (t )
y" 2

n

exp{
y'

y

n

2

)2

(t
2

2
n u

y

y peak e2t}(a) Conventional model(1)0(b) Proposed model(2)

Fig.7 the favorite object and the robot can catch target object(3)

relaxation. The positive Serotonin is generated when the
robot get some favorite things. That means an excitement.
On the contrary, the negative Serotonin is generated
when the robot moves and that means a calm. These
generating waveforms are calculated using Equation (1),(2) and (3). Dopamine is generated same as the usualmodel.
Finally, a motivation is calculated by a second order
response of the sum of all monoamine neurotransmitters.

5. Applying neurotransmitters
In this robot, the positive Noradrenaline is generated
while the robot cannot complete some behaviors after
deciding the behavior using Markov model. That meansa strain, but the negative Noradrenaline is generated
when the robot complete the behavior and that means a© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 340

Saji Keita, Eiji Hayashi, Wisanu Jitviriya

6.

Experimental results

In this study, the multiplex neurotransmitter system isapplied to CBA model. In Fig. 2 shows the process of the
CBA system. In each time step, the amount of eachneurotransmitter was calculated by the Equations (1), (2)and (3). Fig.7 shows the experimental results ofcomparison of the transition of emotion between
conventional model and the model was applied with themultiplex neurotransmitter system when the robot
detected a favorite object and it could complete inapproaching and catching the target object.
On the other hand, Fig.8 also shows the experimental
results of comparison of the transition of emotion when
the robot detected a target object, but it couldn’t be
successful in the task.
7.

(b) Conventional model

Conclusions(b) Proposed model
Fig.8 the favorite object and the robot cannot catch target object

In CBA, the generation process of motivation for
emotional and behavior of the robot was improved by themultiplex neurotransmitter system. For this system, in
addition to Dopamine, two neurotransmitters in brain
Noradrenaline and Serotonin was applied to thegeneration of the motivation.
As a result of that, the improvement of diversity ofemotion was confirmed and that means the behavior
factor of the robot is also diversified.
References
1. E. Hayashi, T. Yamasaki, and K. Kuroki, “Autonomous
Behavior System Combing Motivation and Consciousness
Using Dopamine,” Proc. of 8th Int. IEEE Symposium on
Computational Intelligence in Robotics and Automation(CIRA 2009), pp. 126-131, 2009.
2. Lövheim, H (February 2012). “A new three-dimensionalmodel for emotions and monoamine neurotransmitters”.
Med.Hypotheses.78:3418.doi:10.1016/j.mehy.2011.11.01
6. PMID 22153577.
3. Shahrzad Rezaie-Majd(2004), 「 Increased release ofserotonin from rat ileum due to dexfenfluramine 」 ,American Journal of Physiology – Regulatory, Vol. 287
no. 5(2004), pp.2-4.
4. Japanese Patent, Public Patent Public Relations. Santen
Pharmaceutical Co., Ltd. 6-Nitro. A pharmaceuticalcomposition comprising norepinephrine as an active
ingredient. Japanese Unexamined Patent Publication 9227368. 1997-9-2.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 341

Nonlinear Estimation Strategies Applied on an RRR Robotic Manipulator
Jacob Goodman, Jinho Kim, Andrew S. Lee

Department of Mechanical Engineering, University of Maryland, Baltimore County (UMBC),
1000 Hilltop Circle, Baltimore, Maryland, USA, 21250
S. Andrew Gadsden

School of Engineering, University of Guelph,
Guelph, Ontario, Canada, N1G 2W1
Mohammad Al-Shabi

Department of Mechanical Engineering, University of Sharjah
Sharjah, UAE
E-mail: jg4@umbc.edu, umbcjhkim@umbc.edu, alee20@umbc.edu, gadsden@uoguelph.ca, malshabi@sharjah.ac.ae

Abstract
Nonlinear estimation strategies are important for accurate and reliable control of robotic manipulators. This paper
studies the application of estimation theory to a simple robotic manipulator. Two estimation techniques are
considered; the classic extended Kalman filter (EKF), and the smooth variable structure filter (SVSF). The EKF is
included to present a basic background in estimation techniques and the SVSF demonstrates an example of the stateof-the art. We simulate the SVSF applied to a dynamically modeled three-link (RRR) robotic manipulator. The resultsof the paper demonstrate that nonlinear estimation techniques such as the SVSF can be applied effectively.
Suggestions for future estimation and robotics research are also provided.
Keywords: Estimation theory, Kalman filter, smooth variable structure filter, robotic manipulator

Many estimation strategies are well suited for linear
systems in the discrete time domain. Systems can begeneralized into four main categories (with physicalsystems only existing in the latter three): (i) linear, (ii)approximately linear systems, (iii) medium to strongly
non-linear systems, (iv) systems for whom non-linearity
is the main characteristic1. For categories i. and ii. typicalestimations strategies work well. For category iii.
estimation strategies can be extended with the use of
linearization techniques. Category iv. systems canpresent a significant challenge for any estimation orcontrol system.

1. Introduction
Accurate and predictable control of mechanical andelectrical systems is often dependent on precise
knowledge of system states. Accurate knowledge of thesestates however is rarely directly available, and insteadsome sort of estimate is required. Typically estimates will
be made based on available measurement data as well as
knowledge of the system model. Estimation theory
concerns itself with the various techniques with which
accurate and robust estimates can be made. Someestimators have become to be termed “filters” due theirability to effectively extract the true signal from system
and measurement noise.

Robotic manipulators have become quite ubiquitous overthe last several decades. Many significant advances in© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 342

Jacob Goodman, Jinho Kim, Andrew S. Lee, S. Andrew Gadsden, Mohammad Al-Shabi

manufacturing and automation are due to effective
implementation of robotic manipulators. Robotic
manipulators have achieved great utility in their ability toperform repetitive tasks in a highly consistent manner. As
such estimation techniques necessarily play a critical rolein the performance of these systems.

Gain matrix are used to apply a correction term to create
an updated a posteriori state estimate. Figure 2 illustrates
the general concept.

In this paper, we shall consider a relatively simple planer
RRR robotic manipulator. The dynamics of even such asimple system are clearly nonlinear. For context we givea basic overview of the classic estimation algorithm
known as the Kalman Filter, and its nonlinear variant theextended Kalman filter (EKF). We then present therelatively new estimation technique known as smoothvariable structure filter (SVSF). We demonstrate thelatter’s application to a simple RRR manipulator and notethe results. This paper is organized as follows: Section 1
is the introduction. Section 2 we present a briefbackground to both the EKF and SFVF with references
for further review. In Section 3 we present the system
dynamics and basic control loop. In Section 4 we present
simulation results. In section 5 we draw our conclusions
and discuss future work.

Fig 1. Predictor Estimator1

What makes the Kalman Filter so effective is the abilityof an appropriately computed Kalman Gain matrix tooptimally minimize the estimation error.
For a nonlinear system, equation’s (1) and (2) become:
= (

= ℎ(

For a linear system, state dynamics can be expressed instate representation form as follows1:

=

+

+

)+

(3)
(4)

The EKF follows the basic procedure of the KF. Whencomputing the predicted and updated state error
covariance matrices, where the nonlinear equations
cannot be used, the strategy of the EKF is to linearize fand h around the current state estimate by means of theJacobian. While this algorithm is effective, it does comeat the price of optimality as well as robustness.
Linearization can fail to account for some modelinguncertainties which can lead to instability1. More in depth
treatment of the Kalman filter can be found in theliterature.3,4,5

2.1. The Kalman Filter+)+

Where f and h are the nonlinear system and measurement
models respectively.

2. The Kalman Filter and Smooth Variable
Structure Filter

=

,

(1)
(2)

In (1), xk refers to the system states. A is the linear system
matrix, B is the input gain matrix, uk is the input vector
and wk is the system noise. In (2) zk is the measurement
vector, C is the linear measurement matrix, and vk
represents the measurement noise.

2.2. The Smooth Variable Structure Filter

The Kalman filter assumes that the system model is wellknown and linear, the initial states are known, and themeasurement and system noise is Gaussian with zeromean and known respective covariance matrices.1,2 The
KF works as a predictor-corrector; the system model isused to obtain an a priori estimate of the states,
whereupon measurements combined with the Kalman

The smooth variable structure filter is a relatively recent
development, appearing in 2007. Also formulated as apredictor corrector, it is based on variable structure
theory as well as sliding mode concepts.1,6. The basic idea
is to use a switching gain to drive estimates to within adefined boundary of the true states - termed the existencesubspace.1 The SVSF can be applied to both linear and© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 343

Nonlinear Estimation Strategies Applied

4. Simulation

nonlinear systems. Important prerequisites of the SVSF
is that the system in question be differentiable as well as
observable.7 The basic SVSF concept is illustrated in
Figure 2 below. An in-depth treatment of the concepts
can be found in the reference literature.1,6,7,8,10,11,12

For simulation we consider a simplified RRR
manipulator with unitary masses and moments of inertia.
Reference inputs are simple sinusoids. A simple PDcontroller is used to drive the system inputs. A SVSF
filter is applied to the measurement feedback loop. In thissimulation the SVSF operates with full measurements.

Fig. 2. SVSF Concept Diagram1

3. RRR Manipulator Dynamics
The basic configuration of an RRR robotic manipulator
appears in Figure 3 below. Note the important parameters
include the rod masses mi, center of gravity lengths li, andoverall lengths Li. From these the moments of inertias Ji
can be computed. θi are the joint variables. For allparameters i=1,2,3.

Fig. 7. Angle Input and Response

Fig. 3. RRR Planer Manipulator
Fig. 8. Velocity Input and Response

The generalized equation for a frictionless robotic arm
can shown to be9:̇,
̇ + ( )=( ) ̈+(5)
Where refers to the joint variable matrix with n degrees
of freedom. In the case of an RRR manipulator n=3. M is
the inertia matrix, V is the Coriolis vector, G is the gravity
vector, and u is the control input vector.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 344

Jacob Goodman, Jinho Kim, Andrew S. Lee, S. Andrew Gadsden, Mohammad Al-Shabi

5. A. Gelb, Applied Optimal Estimation, Cambridge, MA:
MIT Press, 1974.
6. S. R. Habibi, "The Smooth Variable Structure Filter,"Proceedings of the IEEE, 95(2), (2007), 1026-1059.
7. M. Al-Shabi, The General Toeplitz/Observability SVSF,(Department of Mechanical Engineering, McMaster
University, Hamilton, Ontario, 2011).
8. S. R. Habibi and R. Burton, The Variable Structure Filter,
J. of Dynamic Systems, Measurement, and Control(ASME), 125, (2003), 287-293.
9. F. L. Lewis, D. M. Dawson and C. T. Abdallah, Robot
Manipulator Control: Theory and Practice, 2nd Ed.,
(Marcel Dekker Inc., New York 2004).

Fig. 10. Angle Errors

10. S. A. Gadsden and S. R. Habibi, A New Robust Filtering
Strategy for Linear Systems, ASME Journal of Dynamic
Systems, Measurements, and Control, 135, (2013).

5. Conclusions and Future Work

11. S. A. Gadsden, Y Song, and S. R. Habibi, Novel ModelBased Estimators for the Purposes of Fault Detection andDiagnosis, IEEE/ASME Transactions on Mechatronics,
18, (2013).

Estimation theory has profound utility to a wide varietyof control problems. Robotic manipulators in theirrequirement for precise and consistent control represent
an important application of accurate and robust
estimation techniques. Filters such as the SVSF areparticularly suited for robotic manipulators as
manipulator dynamics are by necessity almost always
nonlinear. The results demonstrate that the SVSF can beeffectively applied to an idealized robotic arm. For future
work, more realistic model conditions can be considered,and the SVSF as well as other estimation strategies canbe assessed for both robustness and accuracy.

12. S. A. Gadsden, S. R. Habibi, and T. Kirubarajan, Kalman
and Smooth Variable Structure Filters for Robust
Estimation, IEEE Transactions on Aerospace andElectronic Systems, 50, (2014).

Acknowledgements
The author wishes to thank Dr. Mohammad Al-Shabi forhis generous assistance with the robotics dynamics andsimulation.
References
1. J. R. Leigh, Control Theory, 2nd Ed. (The Institution ofElectrical Engineers, London, 2004).
2. S. A. Gadsden, Smooth Variable Structure Filtering:
Theory and Applications, (Department of MechanicalEngineering, McMaster University, Ph.D Thesis,
Hamilton, Ontario, 2011).
3. R. E. Kalman, A New Approach to Linear Filtering andPrediction Problems, J. of Basic Engineering,Transactions of ASME, 82, (1960), 35-45.
4. B. D. O. Anderson and J. B. Moore, Optimal Filtering,(Prentice-Hall Englewood, NJ, 1979).
© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 345Adaptive Negotiation-rules Acquisition Methods in Decentralized AGV Transportation
Systems by Reinforcement Learning with a State Space Filter
Masato Nagayoshi, Simon Elderton

Niigata College of Nursing, 240 shinnan-cho
Joetsu, Niigata 943-0147, Japan
E-mail: nagayosi@niigata-cn.ac.jp, elderton@niigata-cn.ac.jp

Kazutoshi Sakakibara

Toyama Prefectural University, 5180 Kurokawa
Imizu, Toyama 939-0398, Japan
E-mail:sakakibara@pu-toyama.ac.jp

Hisashi Tamaki

Kobe University, 1-1 Rokkodai-cho, Nada-ku,
Kobe, Hyogo 657-8501, Japan
E-mail:tamaki@al.cs.kobe-u.ac.jp

Abstract
In this paper, we introduce an autonomous decentralized method for multiple Automated Guided Vehicles (AGVs).
In our proposed system, each AGV as an agent computes its transportation route by referring to the static pathinformation. route. Once potential collisions are detected, one of the two agents chosen by a negotiation rule modifies
its route plan. The rules are improved by reinforcement learning with a state space filter. Then, the performance isconfirmed with regard to the adaptive negotiation rules.
Keywords: reinforcement learning, AGV transportation system, negotiation rules, state space filter

In this paper, we introduce an autonomous decentralized
approach for the AGV routing problem3. At first, each
AGV as an agent creates its optimal route which satisfies
the requests assigned to it. Once potential collisions aredetected, one of the two agents chosen by a negotiationrule modifies its route. A set of negotiation-rules are used
at every collision avoidance. In this paper, we propose areinforcement learning (RL)4 approach with a state space
filter5 for improving the negotiation-rules. Furthermore,
the effectiveness of the proposed approach is discussed
through a computational experiment.

1. Introduction
Recently, the problem of planning and operation arerecognized as one of the most important issues in
production and logistic systems. In particular, thetransportation planning using Automated Guided
Vehicles (AGVs) in a lot of steel production,
semiconductor production and warehousing systems has
been widely studied from both theoretical as well aspractical viewpoints1,2. This paper focus on the AGV
routing problem2. In this problem, all requests fortransportation are given, but it is necessary to determine
the transportation route plan in which collisions among
AGVs do not occur.

2. Problem Description© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 346

Masato Nagayoshi, Simon Elderton, Kazutoshi Sakakibara,, Hisashi Tamaki

3. Autonomous Decentralized Route Planning
System

In this section, we describe a problem of routing AGVs
to the destination, under the given rail networks and thegiven assignment of transportation requests as an AGV
route planning problem. This problem is hard to findrouting plans without collisions.
The AGV route planning problem is defined as follows:
NV AGVs Vi (i = 1,⋯, NV) are available in the rail system.
The rail system is represented as NN nodes Nj (j = 1,⋯,NN) and NA arcs Ak (k = 1,⋯, NA). A node represents thearea for the loading and unloading points and branching
points of the rail. An arc represents the path for AGVs.
Vi moves to the destination node Dl which is specified by
the request Rl (l = 1,⋯, NR) assigned to Vi. Rl is given to
the system at the start time 𝑡R𝑙 , and the assignment of Rl
to Vi is given. These problem parameters can besummarized as follows:(i) AGV Vi (i = 1,⋯, NV):+ initial node Pi,(ii) Node Nj (j = 1,⋯, NN),(iii) Arc Ak (k = 1, ⋯, NA),(iv) Request Rl (l = 1, ⋯, NR):+ occurrence time 𝑡𝑙R ,+ destination node 𝐷𝑙R .
The following constraints should be taken into accountin a planning process:(i) All AGVs move synchronously.
(ii) The velocity of each AGV is constant.
(iii) Each AGV can travel on the arc and turn only oneach node. The distance between the nodes isconstant.
(iv) Each arc has a width of one AGV. Therefore, two
AGVs cannot travel between the two nodes fromthe opposite direction simultaneously.
As for the evaluation of the routing plan, various kindsof criteria may be considered. We consider here themaximum completion time Cmax:
Cmax = max 𝑡V𝑖

3.1. Algorithm based on negotiation
In this section, we introduce an autonomous
decentralized planning method3, in which each AGV as
an agent repeats planning its own route and exchanging
the route information with another until there is nocollision. The route of each AGV is calculated by
Dijkstra's method6 on the graph representation. Once
potential collisions are detected, one of the two agents
chosen by a negotiation-rule modifies its route. The
whole procedure of route planning is designed as follows:(i) Initialization Each AGV calculates its minimum
route from the start point to the destination point.
(ii) Information Exchange All AGVs within a certain
distance dF exchange their route information witheach other.
(iii) Termination If any potential collisions are detecteduntil a certain time step tL ahead, go to stage (iv),otherwise the current set of routes are outputted
and time proceeds one step.
(iv) Negotiation and Re-planning One of 2 AGVs
involved potential collisions is chosen by anegotiation-rule and re-plans its route in order toavoid collisions. Go back to stage (ii).
In stage (iv), a new route plan is acquired by re-planning
by Dijkstra's method on the network graph in such a waythat the arc adjacent to the potential collision point isgiven 1 extra value of weight.
However, it is impossible to pass through the same node
several times to avoid collisions using simply Dijkstra's
method. In this route planning, it is effective to movebackward into an adjacent node in order to avoid
collisions. For that reason, it is necessary to pass throughthe same node several times.
Therefore, we enable the AGV to pass through the samenode up to twice using Dijkstra's method by virtuallyadding one dummy node to each of the nodes, though it
may be effective to pass through the same node more than
3 times. One of two AGVs re-plans route plans in thisprocedure. Nishi et.al2 have proposed the agent-based
AGV routing algorithms, where both of two AGVs incollision modify their route plans separately. In theirapproach, both AGVs might move in the same direction
to avoid the collision points, and then new route plans are(1)

where 𝑡𝑖V represents the arrival time at the destination
node Dl assigned to Vi.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 347

Adaptive Negotiation-rules Acquisition Methods

still infeasible. In contrast, this method is free from such
oscillation phenomena.

allowing the maximum completion time Cmax to beevaluated.

3.2. Structure of negotiation rules

5. Reinforcement Learning with a State Space
Fitler

The AGV which re-plans its own route is determined bya negotiation rule. A negotiation rule consists of acondition-part and an action-part. The rule whichmatches the difference between each state of the 2 AGVs
under negotiation is selected from a set of rules. As forthe state parameters, we introduce three kinds ofvariables to represent the whole state space S:
sO: The amount of incremental change in the route
length after the re-creation.
sR: Route length from the collision point to thedestination point.
Fs : Number of nodes with 3 or more arcs.
Among the state parameters, sF represents the flexibilityof route creation. Then, S is divided into nS portions Su (u= 1,⋯, nS). The action-part expresses the index of the
AGV which re-plans its route.

We have proposed a state space filter based on theentropy which is defined by action selection probability
distributions in a state5.
The entropy of action selection probability distributions
using Boltzmann selection in a state H(s) is defined by
H(s) = -(1/log|A|) ∑𝑎∈𝑨 𝜋(a|s)log𝜋(a|s)(2)

where 𝜋(a|s) specifies probabilities of taking each action
a in each state s, A is the action space and |A| is thenumber of available actions.
The state space filter is adjusted by treating this entropyH(s) as an index of a correctness of state aggregation in
the state s. In particular, in case of mapping from theinner state space roughly digitized to the inner state space,a perceptual aliasing problem is happened. That is, theaction which an agent should select cannot be identified
clearly. Thus, the entropy may not be small in the state
space should be divided. In this paper, sufficiency of thenumber of learning opportunities is judged using athreshold value 𝜃L .
Therefore, if the entropy does not get smaller than athreshold value 𝜃H despite the number of learning
opportunities is sufficient, the state space filter isadjusted by dividing the state due to that the perceptual
aliasing problem is happened.
Similarly, if the entropy is smaller than 𝜃H in a state s
and a different state mapping from a transited input states’, and representative actions in each other’s states are
same, the state space filter is adjusted by integrating thestates due to that the states is too divided. In addition, if
a state s which never, not once, mapped during a certain
period 𝜃t exists, then the state space filter is adjusted byintegrating s and an adjacent state to s.

4. A Negotiation-rules Acquisition Method byReinforcement Learning
It is hard to design good negotiation-rules shown in Sec.
3, in advance without advanced knowledge of an object
system. In this paper, the negotiation-rules are improved
autonomously by reinforcement learning (RL)4.
Specifically we use Q-learning (QL)7 which is one of thetypical RL methods, where state space and action space
are constructed by the condition-parts of the negotiationrules and by the action-parts, namely 0 or 1, respectively.
It is necessary to design a positive reinforcement signal(reward) based on an evaluation of the route plans of all
AGVs. In this paper, the reward is given to a RL agent
only when all AGVs arrive at the destination point

6. Computational Example
The effectiveness of the proposed approach isinvestigated in this section. We have prepared an AGV
route planning problem (hereafter called ``N18''): NN=18,
NA=26, NV=8. 1 step is defined as the time step formoving to the adjacent node. The period from when all

Fig. 1. A framework of rule learning using
reinforcement learning.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 348

Masato Nagayoshi, Simon Elderton, Kazutoshi Sakakibara,, Hisashi Tamaki

7. Conclusion

Table 1. Parameters for Q-learningwith a state space filter.
Parameter
Value
0.1
𝛼Q
0.9
𝛾
0.1
𝜏
0.47
𝜃H
1,000
𝜃L
50 episode𝜃t

In this paper, we deal with the AGV route planningproblem and introduce an autonomous decentralizedroute planning method. Here, we propose areinforcement learning approach with a state space filter
for improving the negotiation-rules. Through a
computational experiment, it is observed that the
proposed approach can acquire feasible and efficient
rule-sets for the problem.
Our future projects include evaluating the effectivenessof our proposed approach under the condition that morethan one request is assigned to each AGV.

AGVs are located at the start point to when all AGVs
arrive at the destination point or 100 steps pass away islabeled as 1 episode. All occurrence time: ∀𝑙, 𝑡𝑙R = 0, thedetectable time step tL = 2, and the exchangeable distancedF = 2 which is Manhattan distance. The least number ofsteps required for all AGVs to arrive at the destination
point is 7 in N18.
To apply QL with a state space filter, a 3 dimensional
initial state space, which is constructed by the difference
between each state element of 2 AGVs which is designed
so that the state space is evenly divided into 2×2×2(hereafter called “2-2-2”'). For comparison, 3 state spaceconstructions with 1 dimensional initial state space,which are constructed by the difference between eachstate element which are designed so that the state spaces
are evenly divided into 2 (hereafter called “2-1-1”', “1-21”' and “1-1-2”'), and 1 state space construction with 1
initial state space where the size of state space is 1
(hereafter called “1-1-1”).
The reward r = 10 (reward) is given to the agent only
when all AGVs arrive at the destination point, the rewardr = -15 (punishment) is given to the agent only when theweight of the arc is more than 100., and the reward r = 0
is given to the agent.at any other points.
Computational experiments have been done withparameters as shown in Table 1. In addition, all initial Qvalues are set at 5.0 as the optimistic initial values.
The average number of steps required for all AGVs toarrive at the destination point in N18 were observed
during learning over 20 simulations with different initialstate space constructions, as described in Fig. 5.
It can be seen from Fig. 5 that all initial state spaceconstructions could acquire feasible route plans within
nearly the least number of required steps.

References
1. Y. Seo and P. J. Egbelu, Integrated Manufacturing
Planning for an AGV-based FMS, in Int. Journal of
Production Economics, 60-61, (1999) 473-478.
2. T. Nishi, K. Sotobayashi, M. Ando and M. Konishi,
Evaluation of an Agent-Based Transportation RoutePlanning Method for LED Fabrication Line using
Lagrangian Relaxation Technique, in Proc. of Int.
Symposium on Scheduling (2002), 71-74.
3. K. Sakakibara, Y. Fukui and I. Nishikawa, Genetic-Based
Machine Learning Approach for Rule Acquistion in an
AGV Transportation System, in Proc. of ISDA, 3 (2008),
115-120.
4. R. S. Sutton and A. G. Barto, Reinforcement Learning (ABradford Book, MIT Press, Cambridge, 1998).
5. M. Nagayoshi, H. Murao and H. Tamaki, A State Space
Filter for Reinforcement Learning, in Proc. of AROB
11th'06 (2006), 615-618.
6. E. W. Dijkstra, A note on two problems in connexion withgraphs, Numerische Mathematik (1959), 269-271.
7. C. J. C. H. Watkins and P. Dayan, Technical note: QLearning, Machine Learning 8 (1992), 279-292.

Fig. 2 Required steps for task N18
by QLs with state space filter© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 349

Modeling and Control of a Quadrotor Vehicle Subject to Disturbance Load
Jun Wang, Song Xin*, Yuxi Zhang

School of Electronics and Information Engineering, Beihang University, XueYuan Road No.37, HaiDian District
Beijing, 100191, P. R. China
E-mail: wangj203@buaa.edu.cn, song.xin@buaa.edu.cn, zhangyuxi@buaa.edu.cn
www.buaa.edu.cn

Abstract
In this paper, a dynamic model of quadrotor vehicle is derived for theoretic and practical evaluation. Four transfer
functions in different channels are converted from the state equations. To study the behavior of quadrotor subject tothe external disturbance load, the FLC (fuzzy logic controller) is designed to compare with the PID (proportionalintegral-derivative) controller. Subsequently, Liapounov function is applied for stability analysis. Finally, simulation
results are presented to illustrate the performance between FLC and PID. Considering model error, the evaluation
simulations are divided into two parts, which describe the ability for rejecting external disturbance, setpoint tracking
and disturbance rejection respectively. The simulation scheme demonstrates the FLC method outperforms the PID
control scheme.
Keywords: Disturbance load; Dynamic model; Fuzzy logic controller; PID; Quadrotor.

rotor aircraft, with each rotor located in endpoints.

1. Introduction
In the recent years, quadrotor vehicle has been paid
special attention due to its small size and flexible
maneuverability1. The previously developments like PID
control, Linear Quadratic control, back-stepping, slide
mode control2-3 are still used nowadays as suitable
solutions for the quadrotors autonomous flying1,4.
Considering the robust control in terms of processmodeling errors and disturbance load, Lyapunov theory
is introduced for the flight control design5-7. Backstepping is well known and widely used in the control ofnonlinear system, especially in the trajectory tracking
control of the UAV8-10.
However, the application of back-stepping depends
on the actuated modeled system dynamics. Underactuated model will dramatically decrease the control
quality. In this paper, the Liapounov function and fuzzy
logic controller is proposed to achieve better
performance for quadrotor control.

Fig. 1.

Designed Quadrotor

The quadrotor drives the posture and movement
depending on the rotational thrust and torque of four
rotors. As shown in Fig. 2, rotors 2, 4 and 1, 3 rotate in
opposite direction for eliminating the yawing torque. If
the four rotors rotate at the same speed, the aircraft willproduce vertical motion. Change the speeds of rotors 4, 2
and keep the speeds of rotors 1, 3, the aircraft willproduce pitch movement. Similarly, roll movement result
from 1, 3 rotor’s speeds change. If the yawing torque inthe different diagonals cannot be cancelled, the yaw
movement is achieved.

2. Modeling of quadrotor vehicle
As shown in Fig.1, the quadrotor is an X-shaped four-© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 350

Jun Wang, Song Xin, Yuxi Zhang
Z

F4

ZM





  r ( F4  F2 ) / I x  q     r ( F1  F3 ) / I y  ( M  M  M  M ) / I 234z   1

F3

3
4

(5)

YM



F1

XM
Y

2

X
1

Fig. 2.

Where, r is the length from rotor to the center of themass of the quadrotor. Fi denotes the thrust of ith rotor.
Ix, Iy and Iz are the rotational inertia of the quadrotor
around X, Y and Z-axis respectively. Mi is torquegenerated by ith rotor.
In terms of the equations (4), (5), the dynamic
equations can be yielded:

OM

F2

mg

The sketch map of the quadrotor

Let the vector p  x, y, z T denote the relative
position of a quadrotor with respect to an inertial
coordinate, and q   ,  ,  T represents attitude anglesof the quadrotor. Set OM as the original point. XMYMZM isthe inertia coordinates and XYZ is the body fixed
coordinates. The transformation matrix between the twocoordinates is written as:01R  0 cos0 - sin 

0  cos sin    0cos   sin coscos - cossin   sin cossin  sin sin   sin coscos

0  sin    cos 
10   sin 
0 cos    0

 sin cos 
0cossin coscos  sin sin sin - sin cos  sin sin cos

Where,- sin  
sin cos coscos T(2)Considering equations (1), (2), in inertial coordinate,
the thrust analysis is:



0
0

0

0
A  0
( 
0
1 0
) 0
0


0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
1
0
0

0
0
0
0
0
0
0
1
0

0
0
0
0
0
0
0
0
1

0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0

0
0
C
0

0

0
0
0
0

1
0
0
0

0
1
0
0

0
0
0
0

0
0
1
0

0
0
0
1

0
0
0
0

 - sin / m0
 sincos / m0
coscos / m
0


0

0

0

0
 B
0


0
0


0

0

0

0

0


0
0
0
1
Ix

0
0
0
0

0

1
Iy

0

0

0
0
0

0
0
0

0
0 
0

0


0

1
Iz 
0

0

0

0
0
, D0
0

0

The transfer function of quadrotor can be presented:
 cos cos 

ms


0
G ( s)  C ( sI  A) 1 B  D  

0



0


(3)

Let m denote the quality of the quadrotor. The
acceleration of quadrotor in the inertial coordinate isgiven by:

(7)Y  CX  Du

0 

0 
1 

 Fx 
 sin 4 F   Fy   R FM 
Fi sin  cos  
i 1
F 
cos cos   zTstate equations are given as:X  AX  Bu

Assume that the thrust generated by the four rotors isperpendicular to the aircraft. Therefore, in the bodycoordinates, the thrust is expressed as:4FM  0,0, Fi 


i 1



(6)
T  x y z   
Let X  ( x, y , z,  ,  , ,  ,  ,  ),Y  ( z,  ,  ,  ) ， the00

1/ I xs2

001/ I y

0

s2
0


0 

0 


0 

1/ I z s 2 (8)p  x y zT  Fx / m Fy / m ( Fz  mg ) / m T (4)The parameters of the designed quadrotor aremeasured as follows:

Similarly, the angular acceleration can be written as:

Table 1. Parameters of the quadrotor© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 351

Modeling and Control of

Item

Valuesr (m)m (kg)
Ix*10-3/(kg.m2)
Iy*10-3/(kg.m2)
Iz*10-2/(kg.m2)

0.245
1.05
6.2
6.2
3

NB

NM

NS

Z0

PS

PM

o

-1

PB

1 e e U

3. Control design and stability analysis
Fig. 5.

3.1. PID control method

Where, NB is negative big, NM is negative middle,
NM is negative small, Z0 is zero, PS is positive small, PM
is positive middle, PB is positive big. For instance, if e isnegative big “NB”, e is positive big “PB”, so the control
output U is supposed to be increased so as to eliminate
system error. So U should be positive big “PB”.

The PID control with simple structure has excellent
stability by selecting appropriate proportion,differentiation and integration coefficients for
disturbance rejection. Taking pitch channel as an
example, the simulation results are conducted on theblock diagram of PID control system in Fig. 3.
R

+

PID

-

G(s)

+

3.3. Convergence analysis

Y

To further illustrate quadrotor’s stability, a Lyapunov
function approval is introduced. In terms of the pitch
motion, it can be described as:e    0 x1(9)e     0  x2

DisturbanceFig. 3.

Block diagram of PID control system

3.2. Fuzzy logic modified PID control implementation

In which,  is the measured angle,  0 is the desired
angle, we usually set  0 =0.it is obtained:x1  x2    0(10)x2    0  u2 / I x  0

The fuzzy logic modified PID controller (FLC) is also
proposed in the same manner to compare with theperformance of PID controller. A block diagram of fuzzycontroller structure with a disturbance load is shown in
Fig. 4.
e
R
-

d/dt

e

F
L
C

U

++

Assume nonlinear systems:x  f ( x)  [ x1, x2 ]T  x2 , x2 T

G(s)

(11)

is asymptotically stable on the equilibrium xe  [0,0] .
The progress of looking for a Lyapunov function isorganized as follows:(1) Assume V (x) is a scalar function of x , the gradientof V (x) is:

disturbanceFig. 4.

Input fuzzy membership functions

Block diagram of FLC control system a x  a x   V 
V   11 1 12 2    1 
 a21x1  a22 x2   V2 

The error of angle (expressed by e ) and change of theangle error (expressed by e ) are regard as inputs of FLC;
U is the control output of the FLC. As a rule of a thumb,e and e can be divided into seven grades (As shown in
Fig. 5.).

(12)

Where, a11, a12, a21, a22 are unknown coefficients.
(2) Then we have:V ( x)  (V )T x  (a11x1  a12 x2 , a21x1  a22 x2 )( x1 , x2 )T (13)
 (a11x1  a12 x2 ) x1  (a21x1  a22 x2 ) x2(3) Try to choose a11  a22  1, a12  a21  0 , we getV ( x)  x1x1  x2 x2(14)

Recall (12), we have
© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 352

Jun Wang, Song Xin, Yuxi Zhangx 
V   1 
 x2 

reaches -0.85°. At about t=16.09s, the angle reaches thedesired value.

(15)

3
FLC
PID

2.5

It is obvious to meet curl equation:
V1 V2
x
x, namely, 1  2  0

x2
x1
x2 x12
1.5

angle/deg

(16)

1
0.5

which indicates the parameters chosen above is rational.
(4) Therefore, the Lyapunov function can be yield as
follows:xx1 ( x2 0)

V ( x)  (V )T dx 
0



-1
0

x2 ( x1  x1 )

 x dx   x dx
1

0
-0.5

1

0

2

Fig. 6.
2

Invoking (10), (14), we haveV ( x)  x1 x1  x2 x 2  (   0 )(     0  0 ) (   0 )(  0   0  u 2 / I x )

(17)

(18)

8

10
t/s

12

14

16

18

20

To identify the tracking abilities of PID and FLC, set
the disturbances to zero. According to Fig.3, Fig. 4, a step
signal is introduced at initial time.

0

design a fuzzy controller to adjust  ,  for satisfying
either of the following conditions:  0 and   u2 / I x  0
(19)

1.5
FLC
PID

1

(20)

angle/deg  0 and   u2 / I x  0

6

4.1.2. Setpoint tracking

To achieve the stability of the system, the conditionV ( x)  0 is necessary. Due to,  0  0 we have    0 .Therefore, to achieve V ( x)  0 , we just need
04

For FLC, the response wave reaches peak at about
t=4.37s, and reaches trough at about t= 5.41s. The
overshoot angle in negative direction is -0.8°. At about
t=13.86s, the controlled angle reaches 0°. The FLC has
faster response speed, smaller reverse overshoot than PID.
Thus, fuzzy PID control scheme has stronger ability toresist a disturbance load.

0

1 2( x1  x22 )2

2

Simulation results for fighting against a disturbance

From the mentioned above, we can make aconclusion that the convergence in the model can be
guaranteed.

0.5

0
0

4. Simulation

2

Fig. 7.

In this section, the performance of PID and FLC arecompared by simulations, which are mainly divided into
two parts. The first part is conducted in the presence ofdisturbance load; the second part presents the case withmodeling error.

4

6

8

10
t/s

12

14

16

18

20

Simulation results of setpoint tracking

In Fig.7, when a step signal is introduced, the twosystems make response at the same time. With PID, theresponse is faster than that of FLC during 1s-2s. As for
FLC, it reaches the desired value at 10.3s, which isslightly earlier than that of PID.

4.1. No modeling error

4.1.3. Setpoint tracking& disturbance rejection

4.1.1. Disturbance rejection

Fig.8 shows the simulation results of the step
response with a disturbance load. When a step signal isintroduced at the beginning, the response of two systems
just like what the Fig. 7 shows.

According to Fig. 4, the time of simulation is 20s.
During t=4-4.4s, a pulse signal disturbance load is
introduced. The simulation result is shown in Fig. 6.ThePID controller responds from peak to bottom during
t=4.4-6.59s, the overshoot angle in negative direction© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 353

Modeling and Control of
4.5

performances than its counterpart. The reason lies in thefact that the FLC can automatically adjust kp, ki, kd in time
according to the external conditions so as to maintain thestability of the control system. However, the threeparameters of traditional PID is unchanged as soon as
they are set in the initial time, that is to say, the traditional
PID does not have the adaptive characteristics.

FLC
PID

4
3.5

angle/deg

3
2.5
2
1.5
1
0.5
0
-0.5
0

2

Fig. 8.

4

6

8

10
t/s

12

14

16

18

20

5. Conclusion

Setpoint tracking & disturbance rejection

In the paper, a fuzzy logic modified PID (FLC)
control scheme is designed to control stability of thequadrotor. Based on theoretical analysis, a Liapounovfunction is constructed to prove that the stability of thecontrol system can be achieved. The difference between
the actual angle and desired angle is presented as the error.
The error and change-in-error are applied as inputs of the
FLC. To further test the performance of the designed
controller subjecting to the disturbance load, thesimulation are conducted in MATLAB/ Simulink .The
simulation results demonstrate that FLC response more
quickly than PID control method, moreover, in terms of
FLC, the angle can be controlled to its desired value
within less time compared to its counterpart.

When a disturbance is introduced within t=8-8.4s, It
can be observed that both of two controllers quickly
moves towards the maximum in negative direction.
However, the response of FLC is more sensitive to the
disturbance than that of traditional PID controller. It isapparent that the system controlled by FLC reaches thedesired angle quickly.
4.2. With modeling error
A Considering process modeling error, a third ordertransfer function in pitch channel is applied as follows:
56.95s  4391
(21)
G( s)  3s  105s 2  870s  4430
4.2.1. Setpoint tracking& disturbance rejection

References

Let the gain of disturbance be zero, a step signal isgiven to identify the ability of setpoint tracking of thethird order transfer function. Meanwhile, a disturbance isintroduced during 8s-8.4s.The simulation result ispresented in Fig.9.

1. N. Michael, D. Mellinger, Q. Lindsey, and V. Kumar,“The GRASP multiple micro-UVA test bed,” IEEE Robot.
Autom. Mag,Vol. 17, No. 3, pp. 56–65, 2010.
2. S. Bouabdallah, “Design and control of quadrotors withapplication to autonomous flying (PhD Thesis),” 2007.
3. S. Bouabdallah, A. Noth, and R. Siegwan, “PID vs LQ
Control Techniques Applied to an Indoor Micro
Quadrotor,” in Proceedings of 2004 IEEE/RSJ
International Conference on Intelligent Robots andSystems, (IROS 2004)., 2004, pp. 2451–2456.
4. B. H. Lim, J. Park, D. Lee, and H. J. Kim, “Build your own
quadrotor: open-source projects on unmanned aerial
vehicles,” IEEE Robot. Autom. Mag., Vol. 19, No. 3, pp.
33–45, 2012.
5. A. Tayebi and S. McGilvray, “Attitude stabilization of a
VTOL quadrotor aircraft,” IEEE Trans. Control Syst.
Technol., Vol. 14, No. 3, pp. 562–571, May 2006.
6. R. Zhang, Q. Quan, and K.-Y. Cai, “Attitude control of aquadrotor aircraft subject to a class of time-varying
disturbances,” IET Control Theory Appl., Vol. 5, No. 9,
pp. 1140–1146, Jun. 2011.
7. D. Cabecinhas, R. Cunha, and C. Silvestre, “A nonlinear
quadrotor trajectory tracking controller with disturbancerejection,” Control Eng. Pract., Vol. 26, pp. 1–10, 2014.

3.5
FLC
PID
3

angle/deg

2.5

2

1.5

1

0.5

0
0

5

10

15

20

25

t/s

Fig. 9.

Simulation of setpoint tracking & disturbancerejection of third order transfer function

The result shows the angle can be controlled to its
desired value within 20s. And the overshoot angle of FLC
in negative direction is smaller. Therefore, FLC provides
better performance. All the simulation results mentioned
above indicate that the FLC controller can efficiently
respond to the outside disturbance, thus show better© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 354

Jun Wang, Song Xin, Yuxi Zhang

8. F. Kendoul, I. Fantoni, and R. Lozano, “Modeling andcontrol of a small autonomous aircraft having two tilting
rotors,” in Proceedings of the 44th IEEE Conference on
Decision and Control, 2005, pp. 8144–8149.
9. F. Kendoul, I. Fantoni, and R. Lozano, “Modeling andcontrol of a small autonomous aircraft having two tilting
rotors,” IEEE Trans. Robot., Vol. 22, No. 6, pp. 1297–
1302, 2006.
10. R. Mahony and T. Hamel, “Robust trajectory tracking fora scale model autonomous helicopter,” Int. J. Robust
Nonlinear Control, Vol. 14, No. 12, pp. 1035–1059, Aug.
2004.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 355

A multithreaded algorithm of UAV visual localization based on a 3D model of environment:
implementation with CUDA technology and CNN filtering of minor importance objects
Alexander Buyval, Mikhail Gavrilenkov

Department of computer science,
Bryansk State Technical University¹,7 Oktyabrya boulevard, Bryansk, 241035,Russian Federation
Evgeni MagidIntelligent Robotic Systems Laboratory, Higher Institute for Information Technology and Information Systems (ITIS),
Kazan Federal University², 35 Kremlyovskaya street, Kazan, 420008, Russian Federation
E-mail: alexbuyval@gmail.com, gavrilenkov@umlab.ru, dr.e.magid@ieee.org¹http://iipo.tu-bryansk.ru² http://kpfu.ru/eng/itis

Abstract
Visual based navigation plays an important role in localization and path planning, especially in GPS-denied
environments. This paper presents a visual based localization algorithm for a UAV within an indoor environment.
The algorithm uses multithreaded computing CUDA technology and CNN-preprocessing filtering, which is
responsible for filtering out dynamic objects. The algorithm is simulated in ROS/Gazebo environment with two
different approaches – one uses CPU only and the other uses CPU and GPU - and their performance is compared.
Keywords: localization of UAV; particle filter; ROS; Gazebo, CUDA; CNN.

positions and thus requires some additional marking ofenvironment prior to its use; the second subclass uses
native visual features.
This paper focuses on visual localization approach,which uses object edges as key features, and proposes
several improvements to a particle filter based algorithm.
The algorithm uses multithreaded CUDA technology and
CNN-preprocessing filtering to exclude dynamic objects.
We assume that while an initial 3D model of environmentis available, the scene may undergo minor dynamicalchanges, e.g. new objects may appear in the scene as thetime passes. The localization is performed in two steps.
Initially, a neural filtering module detects new objects inthe scene and filters them out. Next, a multithreadededge-computing module processes filtered data andcompares it with the initial model. The algorithm issimulated in ROS/Gazebo environment with two

1. Introduction
Robot self-localization and simultaneous localization andmapping (SLAM)1 play important role in path planningand navigation for multiple purposes, including suchbroad fields as service robotics, industrial robotics,
search and rescue robotics, and others2. Within indoor
environment, which are typically GPS-denied, visual
localization is often a natural substitution of a GPS
approach. Due to visual sensors’ long range, highresolution, low energy consumption, and affordable pricevisual localization is often preferred over laser range
finder or sonar based localization solutions. Selection ofa passive visual sensor ranges from cheap and simple
monocular cameras to stereo or depth cameras, like
Kinect, or event-driven cameras. We may distinguish two
typical subclasses of visual localization methods: the firstsubclass uses special visual markers with known© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 356

Alexander Buyval, Mikhail Gavrilenkov, Evgeni Magiddifferent approaches – one uses CPU only and the otheruses CPU and GPU – and their performance is compared.
2. Basic localization algorithm
To solve a localization problem our system uses image
edges as visual features. Based on location model, it
compares a received from a camera real image and asimulated image of a pre-stored 3D model. The algorithm
considers a number of robot position hypotheses andrenders a set of images from the 3D model that
correspond these hypotheses. Each image corresponds toan image that would be obtained in the estimated point
under a particular hypothesis. Thus, at each algorithm
iteration, we compare one image from the camera to a setof simulated images, estimate edge matching between
them and a level of their similarity.
Edge features are formed from typical elements ofindoor environment: conjunctions of walls, ceiling andfloor, window and door frames, etc. Such edges are rather
steady features. They are steady in time and, as a rule,
illumination changes do not influence their perception.
Experimental work showed that it is enough to consider
these basic elements, which in turn significantly
simplifies a 3D model of the room.

Fig. 1. Calculation of images similarity using edges.

normals are shown as red segments. The algorithm uses
the extracted edges to perform the following calculations:(i)(Each) line weight calculated as follows:𝑔(𝑑) = 𝑒𝑥𝑝 (−

𝑑2
2𝜎 2

),

(1)

where d is the normal’s length, σ is the parameter that
determines the weight of the normal and depends on thenormal length d. Parameter σ allows to scale (to increaseor decrease) the influence of long normals.
(ii)

Total weight is calculated as follows:

2.1. Using a particle filter for robot localization𝑙=

For processing of images and locomotion sensory data,
which are followed by localization hypotheses, we use
particle filter approach that has proven to perform well insimilar tasks. This approach provides a reasonable set of
location hypotheses and allows for nonlinear models of
the system and sensory input.

∑𝑆𝑖=0 𝑔(𝑑𝑖 )

,

𝑆

(2)

where S is the total number of lines in the simulated
image.
(iii)

Total probability hypotheses is calculated
by combining the weights of each line:∑𝑚

2.2. The particle filter measurement model𝑙𝑊 =∝∙ 𝑒𝑥𝑝 (𝑘 𝑛=0 𝑛 ),𝑚

To estimate each hypothesis probability (a particle),edges from the on-board camera images are comparedwith edges of synthetic images from the 3D model,
applying the nearest edges method. This method
estimates edges’ similarity using a set of line normals thatare constructed from synthetic images’ edges3-5. Figure 1
presents the result of rendering a synthetic image of aroom at the position, which corresponds to a particularhypothesis. Edge features, which have been found in thesynthetic image and correspond to real world edges, areshown in green; blue lines depict edges of the real world
camera image, projected onto the synthetic image; line

(3)

where 𝑚 is the count of lines, and ∝, 𝑘 are empiricallyestablished parameters.
3. Particle filter implementation
The most important particle filter task is updating theparticle weights. At the first stages of the algorithm, we
detect edges within an image and find straight lines. We
implemented the system as a robot operating system(ROS6) package, and a number of computer visionfunctions from OpenCV library were utilized. The
system uses a constant number of particles, which allows© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 357

A multithreaded algorithm ofstatic memory allocation and decreases memory
management time.
3.1. Comparative analysis of the CPU and GPU
implementations
Figure 2 presents a flowchart of particle weights
calculation, which employs eq.(1-3). The main drawbackof the algorithm is its complexity of O(N*K*M), where
N is a number of particles, K is a number of straightsegments within a simulated image, M is a number ofsegment normals for a single straight line.
We parallelize the algorithm with NVIDIA’s CUDA
technology, which allows to receive a parallel computing
system that works according to SIMT-principle (SingleInstruction, Multiple-Thread), when one instruction issimultaneously executed by multiple independent threads(Fig.3). We apply CUDA technology for particle weightFig. 3. CUDA threads hierarchy.

Fig. 4. The scheme of calculating the weights of the particles
using a CPU together with GPU scheme.

better execution time, which surpassed the CPU-onlysystem in almost 17 times in average.
Table 1. Performance comparison results, ms.
Fig. 2. The scheme of calculating the weights of the particles
using a CPU only.

N
tCPU
tGPU
Ratio

calculation through usage of a separate block for eachparticle and separate GPU core for each calculation of
normal line’s weight (Fig.4).

1
123,6
7,39
16,7

2
102,836,54
15,7

3150,128,4417,7

4
131,847,45
17,6

4. Neural image filtering

3.2. Simulation results

The presented approach works well when a 3D model ofenvironment is static and almost identical to the actual
environment. However, in real world scenarios, mostscenes are rather dynamic, i.e. new objects that were notcaptured during the 3D model building process appear
and may even move through the actual environment (forexample, the black sofa and the PC blocks in Fig.5 are
new objects). To improve robot localization in such cases,
all new and moving objects should be excluded from thescene analysis. Yet, if many temporary objects are
excluded, too many edges (that belong to these objects)

We implemented two versions of the system in ROS: oneuses CPU only, while the other employs both CPU andGPU for calculations. Experiments were performed in
ROS/Gazebo environment. Table 1 presents the two
implementations’ comparison within four different
experiments: N denotes a number of the experiment, tCPU
and tGPU denote calculation time in ms for CPU only
system and CPU with GPU system implementation
respectively, ratio denotes the ratio of tCPU to tGPU. The
later system (CPU with GPU) demonstrated significantly© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 358

Alexander Buyval, Mikhail Gavrilenkov, Evgeni Magid

5. Conclusions

may be excluded as well and there is a risk of obtaining
a very small number of detected edges.
One of the most effective ways to find objects within
an image is to employ a convolutional neural network(CNN). To initialize and train, these networks use image
databases, which form a dictionary with each wordcorresponding to a graphical image7. The properly
adjusted and trained CNN allows finding objects andexcluding them from an image in real time. At the
preliminary stage, the CNN preprocesses data prior toedge detection in order to eliminate potentially dynamic
objects within the environment. Next, edge detection is
performed for an “empty” room, followed by robot
localization. Figure 5 demonstrates CNN image
preprocessing results, where the black sofa (i.e., a“dynamic” object) was properly detected. CNN returned
a two-dimensional mathematical description of an object,This paper focuses on visual localization approach,which uses environment edges as key features. Our
approach compares an obtained from a camera image
with a simulated image of indoor environment’s 3Dmodel. We suggested a particle filter based algorithm
improvement through CNN-preprocessing filtering,which eliminates dynamic objects from an image, andusage of GPU in UAVs localization system. Using
parallel threads allowed increasing of localization system
speed in almost 17 times in average. In addition, CNN
filtering decreased negative influence of dynamic objects
on localization process.
Acknowledgements
Part of the work was performed according to the Russian
Government Program of Competitive Growth of Kazan
Federal University.
References
1.

2.

3.
Fig. 5. Example of objects excluding by CNN: the black sofa
(in red rectangular) and the PC blocks (in blue rectangular).
4.

and the image area that belongs to the sofa object waseliminated from further processing.
Object detection and identification time is
proportional to system hardware capabilities. After
successful object detection, as a result of learning process,the CNN weights are redistributed. For all further
appearances of the sofa object CNN performs quick
identification in real time and thus localization process
time decreases significantly.
Integrating such CNN module into a UAV onboard
control system in addition to robot localization would
allow a specific object detection, tracking a moving
object8 and other useful functionality.

5.

6.

7.

8.

A. Buyval, I. Afanasyev and E. Magid, Comparative
analysis of ROS-based SLAM-related methods forautonomous indoor navigation, in Int. Conf. on Machine
Vision (Nice, France, 2016)
E. Magid and T. Tsubouchi. Static Balance for Rescue
Robot Navigation: Discretizing Rotational Motion within
Random Step Environment, in Lecture Notes in Artificial
Intelligence, 6472, (2010), pp. 423-435.
S. Nuske, J. Roberts and G. Wyeth, Outdoor visual
localization in industrial building environments, in IEEE
Int. Conf. on Robotics and Automation, (Pasadena, CA.,
2008), pp. 544-550.
S. Nuske, J. Roberts and G. Wyeth, Robust outdoor
visual localization using a three‐dimensional‐edge
map, in J. of Field Robotics 26(9), (2009),pp. 728-756.
A. Buyval and M. Gavrilenkov, Vision-based pose
estimation for indoor navigation of unmanned micro
aerial vehicle based on the 3D model of environment, inIEEE Int. Conf. on Mechanical Engineering, Automation
and Control Systems (Tomsk, Russia, 2015).
E. Fernandez, S. Crespo, A. Mahtani and A. Martinez,
Learning ROS for Robotics Programming Second
Edition, (Birmingham, UK, 2015).
J. Redmon and S. D. Allen, You only look once: Unified,
Real-Time Object Detection. arXiv preprint arXiv:
1506.02640v4 (2015).
G. Klein and D. Murray,Full-3D edge tracking with aparticle filter, in British Machine Vision Conference,(UK,Edinburgh,2006).

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 359

Modelling a crawler-type UGV for urban search and rescue in Gazebo environment
Maxim Sokolov, Ilya Afanasyev

Intelligent Robotic Systems Laboratory, Robotics Institute, Innopolis University,
1 Universitetskaya street, Innopolis City, 420500, Russian Federation
Roman Lavrenov, Artur Sagitov, Leysan Sabirova, Evgeni MagidIntelligent Robotic Systems Laboratory, Higher Institute for Information Technology and Information Systems (ITIS),
Kazan Federal University, 35 Kremlyovskaya street, Kazan, 420008, Russian Federation
E-mail: {m.sokolov, i.afanasyev}@innopolis.ru, {lavrenov, sagitov, sabirova, magid}@it.kfu.ru
http://university.innopolis.ru, http://kpfu.ru/eng/itis

Abstract
A long-standing goal of robotics is to substitute humans in unreachable or dangerous environments, and one of suchenvironments is urban search and rescue (USAR) domain. For USAR tasks we have selected a novel Russian crawlertype robot Engineer, and this paper presents our first steps toward modelling the robot in ROS/Gazebo environment.
We convert provided by robot developers CAD models into workable ROS-based 3D simulation and incorporate
physical parameters of the mechanisms into the model, focusing on track simulation. Robot motion and relativeinterplay of its visible mechanical parts is visualized in RViz software. The proposed model is integrated into a readyto-use ROS navigation stack and the model’s behavior is thoroughly investigated while navigating through static
obstacles populated scene in Gazebo environment. In this paper, we introduce an approximation of track-surface
interaction of Engineer robot with modeled environment, and discuss the applicability of ROS-based “Gazebo-tracks”
package for robust robot locomotion.
Keywords: USAR, ROS/Gazebo, crawler robot modelling, track simulation, system integration.

mathematical modelling3, track-surface interaction4 or asimplified approach of contact point estimation in
artificial5,6 and natural environments7 etc.
In our research, we employ Russian crawler robot
Engineer as a USAR-oriented platform8. Original
Engineer robot system does not contain any operator
oriented robotics software. Customers can use only aclient-server system to control the robot in a simple
teleoperation mode, and our main purpose is to create arobot control software based on robot operating system(ROS) framework. A next essential task is to build a 3D
simulation of the robot model in Gazebo simulator, using

1. Introduction
Crawler-type unmanned ground vehicles (UGVs) aretargeting for urban and rescue operations, especially insituations where a USAR mission deals with dangerous
environments and high risk of rescuers’ injury or life loss
(e.g. buildings with high collapse probability, ruins after
technogenic and natural disasters, etc.)1,2. In spite ofobvious progress in crawler-type mobile robotics, ourliterature review demonstrated a lack of significant
progress on track simulation and visualization. Although
there are a number of papers, which dealt with track-type
robots, considering crawler belt motion simulation2 and© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 360

Maxim Sokolov, Ilya Afanasyev, Roman Lavrenov, Artur Sagitov, Leysan Sabirova, Evgeni Magid.

3. Robot modelling

CAD models from Engineer robot designers. ROS and
Gazebo integration allows simulating Engineer robot in
3D environment, implementing navigation algorithms
and providing comfortable and fast synchronization
between the 3D model and the robot. To test workabilityof the robot’s 3D model, it is exported into a ready-to-use
navigation stack that was previously described by theauthors8, supporting point-to-point navigation in
ROS/Gazebo/RViz simulation.
The rest of this paper is structured as follows. Section
2 describes system setup. Section 3 presents Engineer
robot modelling with a focus on robot main body’s tracks
simulation. Section 4 summarizes our conclusions andhints on our future work.

3.1. Modelling in ROS
We performed Engineer robot modelling and simulationin Gazebo 2.2.3 simulator integrated with ROS Indigo.
Engineer robot 3D model consists of seven parts: a main
crawler-based body, two front flippers, a clamp (whichconnects upper part to crawler-based body), two links of
3DoF hand-type manipulator with a gripper and a head(Fig. 2). To visualize Engineer robot model in Gazebo weused CAD models from “Servosila” company, andcreated: (1) step-files, applying physical parameters ofrobot model parts for 3D model data exchange between
CAD software; (2) dae-files for ROS applications, inwhich all robot parts were transformed into separate files
with relevant parameters. Thus, initially we modeled amain crawler-based body with tracks, then robot’s upper
part with the 3DoF hand-type manipulator, grippers anda head on its end-effector8. Using CAD models, we
assembled the animated model (Fig. 2), where weconnected all robot parts with ROS joints and set angle
limits according to the robot specifications. In future, weare planning to use an adaptive PID controller to optimize
the robot manipulator control and to damp oscillations
that appear when operating the manipulator at a highspeed.

2. Robot system setup
A crawler-type mobile robot Engineer (Fig. 1) isdesigned and manufactured by Russian company"Servosila" for operating in difficult terrain conditions(e.g. pipeline and tunnel inspection, underneath asuspected vehicle, etc). The robot is equipped withradiation-hardened electronics and a sensors pack, which
includes 3D/2D laser scanner, an optical zoom camera,
and a pair of stereo vision cameras, IMU, and
GPS/GLONASS receiver. The robot is also tooled with aheadlight for operations in low illumination conditions
and a manipulator for grasping, pushing or pulling
potentially dangerous objects or opening different doors
with its gripper in teleoperated mode.

Figure 2. Simulation of Engineer robot in ROS/Gazebo (left)
and in ROS\RViz (right). Imaginary pseudo wheels are marked
in red.

3.2. Track simulation
The Engineer robot body is equipped with soft tracks, and
it is a well-know problem to simulate and animate them.
Moreover, to the best of our knowledge, track simulation
has no standard robust solution in ROS. Modelling chain

Fig. 1. “Servosila” Engineer crawler-type UGV. Courtesy of“Servosila” company.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 361Modelling a crawler-type UGVtrack belt-drive mechanism is a difficult task since it
contains multiple details, which should be properly built
into a model and animated. The difficulties in a trackmodeling arise because of a heterogeneous structure of a
track and its supporting elements, with significantly
varying properties along the track length.
One of possible solutions is based on imaginary
pseudo wheels next to robot tracks (Fig. 2, right). Suchsimplified model does not simulate physical phenomenaof track-surface contact with typical track slipping andskidding2. Further increase of pseudo wheels’ number
and decrease of their size improves the correspondence
to a real crawler physics. This solution for motion
animation of tracks and front flippers was implemented8
and provided a simple wheel control with ROS request
and response messages that synchronize wheels’ speed.
Engineer flipper wheels have different sizes, and in orderto match them with chain tracks and synchronize to eachother, limits on flipper wheel rotation were assigned androtation speed control was realized with standard ROS
modelling mechanisms. The simplified model was
visualized with ROS\RViz software. However, our next
goal is to create a realistic track simulation, taking into
account physical phenomena of track-surface contact.
Currently, there is only one ROS-based package fortracks modelling - Gazebo-tracks. It contains a script thatcreates a track driven SDF model for Gazebo
environment9. SDF model uses XML format to describe
objects and environments for ROS/Gazebo; it helps
defining robots, static and dynamic objects, lighting,terrain, physics, and could be further integrated into robot
simulation, control, and visualization. Gazebo-tracks
package supports track modelling of a system with two
equal radii rollers. Thus, it could not be directly appliedfor Engineer robot flippers that have rollers of different
radii. Gazebo-tracks takes into account a distance
between tracks, a roller radius, a distance between rollers,a track chain width and thickness, a number of trackchains. An example of a crawler robot prototype (mainbody) SDF file visualization is demonstrated in Fig. 3.
Initially, we had generated a complete Engineer robot
model in Unified Robot Description Format (URDF)8,
which is also an XML format that represents a robot
model in ROS/Gazebo. Unfortunately, it turned out that
URDF does not support involvement of track joints in a

Fig. 3. Generic robot’s main body tracks prototype modelingwith Gazebo-tracks package

loop closure configuration. Thus, only SDF format could
be directly applied for track modelling. SDF approachwith Gazebo-tracks allowed connecting modeled tracks
to the robot body (Fig. 4), although the solution
demonstrated a number of critical drawbacks:•
Typically, the modelled tracks are not stable andfall down not so long after the robot starts moving, whichis resulted by inertial forces and accumulating shift of the
tracks with regard to the rollers;•
As was previously emphasized, Gazebo-tracks
package supports only two equal radii rollers and thisrestricts its application for Engineer flippers modelling;•
Gazebo-tracks package does not have proper
ROS API, and thus rollers could not be synchronized via
ROS topics and message exchange.
Therefore, we plan to develop a ROS-based approach,which generates tracks of any configuration for different
number, location, and radii of track rollers.

Fig. 4. The result of Engineer robot modelling with Gazebotracks package for main body tracks (shown in light grey)© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 362

Maxim Sokolov, Ilya Afanasyev, Roman Lavrenov, Artur Sagitov, Leysan Sabirova, Evgeni Magid.

4. Conclusions and future plans

References

Proper modelling of a crawler-type UGVs is an importanttask within USAR domain. In this paper, we introduced
track modelling for a crawler-type UGV Engineer in
ROS/Gazebo framework using original CAD data
(provided by “Servosila” company) and Gazebo-tracks
package. This ROS-based package was integrated into
our model in trials to substitute our initial approach ofmodelling track-surface interaction with hidden pseudo
wheels.
Unfortunately,
Gazebo-tracks
packagedemonstrated a number of critical drawbacks, such as
track instability due to inertial forces, strong restrictions
on number and radii of track rollers, and a lack of proper
ROS API, which prevents its smooth integration into acrawler robot model.
Therefore, as a part of our future work, we plan tocreate a novel ROS package for modelling tracks of acrawler-type UGV within ROS/Gazebo environment.
The requirements for such package include robust robot
locomotion, flexibility with a number, location and radiiof track rollers, mechanisms for rollers synchronization,
ROS API and messaging.
Our Engineer robot model and original software files
are available for a download in the public domain of
GitHub10.

1. T. Kamegawa, N. Sato, M. Hatayama, Y. Uo, and F.
Matsuno, System Integration for Grouped Rescue Robots
System Using Robohoc Network, in Workshop on Robots
and Sensors integration in future rescue INformation
system (ROSIN, Taipei, Taiwan, October 2010)
2. S. Kohlbrecher, F. Kunz, D. Koert, C. Rose, P. Manns, K.
Daun, and O. von Stryk, Towards Highly Reliable
Autonomy for Urban Search and Rescue Robots, in Robot
Soccer World Cup (Springer International Publishing,
2014)
3. T. Inoue, T. Shiosawa, and K. Takagi, Dynamic Analysisof Motion of Crawler-Type Remotely Operated Vehicles,
in IEEE Journal of Oceanic Engineering, 38(2), (2013),
pp. 375-382
4. M. Pecka, V. Salansky, K. Zimmermann, and T. Svoboda,
Autonomous flipper control with safety constraints, inIEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS, Daejeon, Korea, October 2016)
5. E. Magid, and T. Tsubouchi. Static Balance for Rescue
Robot Navigation: Discretizing Rotational Motion within
Random Step Environment, in Lecture Notes in Artificial
Intelligence, 6472, (2010), pp. 423-435.
6. R. Sheh, M.W. Kadous, C. Sammut, and Hengst, B., 2007,
September. Extracting terrain features from range images
for autonomous random stepfield traversal, in IEEE
International Workshop on Safety, Security and Rescue
Robotics (IEEE SSRR, 2007), pp. 1-6.
7. E. Magid, T. Tsubouchi, E. Koyanagi, T. Yoshida, and S.
Tadokoro. Controlled Balance Losing in Random Step
Environment for Path Planning of a Teleoperated Crawler
Type Vehicle, in Journal of Field Robotics, 28(6), (2011),
pp.932–949.
8. M. Sokolov, R. Lavrenov, A. Gabdullin, I. Afanasyev, E.
Magid, 3D modelling and simulation of a crawler robot in
ROS/Gazebo, in Proc. Conf. on Control, Mechatronics
and Automation. MATEC Web of Conferences. (ICCMA,
Barcelona, Spain, December 2016).
9. A. Synodinos, Gazebo-tracks, ROS package repository(2013): https://github.com/progtologist/gazebo-tracks
10. M. Sokolov, Somal / Robot_Engineer, Repository with
ROS package for robot Engineer, (Innopolis University,2016), https://github.com/Somal/Robot_Engineer/

Acknowledgements
This work was partially supported by the RussianFoundation for Basic Research (RFBR) and Ministry of
Science Technology & Space State of Israel (joint project
ID 15-57-06010). Part of the work was performed
according to the Russian Government Program ofCompetitive Growth of Kazan Federal University.
Special thanks to Innopolis University and “Servosila”
company (Russia).

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 363Development of Autonomous Robot for the Labor-saving Forestry- Positioning of the Robot using IMU, GPS and Encoder Sho Yamana

Computer Science and Systems Engineering, Kyushu Institute of Technology.
680-4, Kawazu, Iizuka, Fukuoka, 820-8502 Japan.
Shoyamana0925@gmail.com
Eiji Hayashi

Mechanical Information Science and Technology, Kyushu Institute of Technology.
680-4, Kawazu, Iizuka, Fukuoka, 820-8502, Japan.
haya@mse.kyutech.ac.jp

Abstract

This paper presents about the positioning of the autonomous robot using IMU, GPS, and encoder
devices. Using acceleration and orientation of the IMU sensor that is used to calculate the position of
the robot. To locate the position, need to know the distance when the robot moved. Distance iscalculated by integrating velocity function and also the velocity is considered by integrating theacceleration function. Results showed distance measurement using only IMU. It shows the difficulty oflocating used only IMU. Therefore, GPS and encoder devices need to be combined into the system for
position measurements.
Keywords: Positioning system for wheeled robot, IMU, GPS, Encoder.

To look around automatically at the forest,
positioning system is essential to the robot. Our
positioning system is going to be composed of IMU,
GPS, and Encoder devices. This paper presents about
the positioning system using only IMU. The IMU canoutput 3-axes acceleration, 3-axes angular rate, and 3axes magnetic field. Integrating the acceleration from
IMU, and calculates velocity of the robot and integrates
the velocity and calculates the distance the robot moved.
Orientation of the robot is decided by the magnetometerof the IMU.
This paper is organized as follows: Section 2
provides the configuration of the robot. Section 3 of thepaper explains the way calculating the distance of therobot using IMU and its problem. Section 4 shows the

1. Introduction
Generally, it takes a lot of time and needs muchlabor for managing the resources of the forest. In
addition, nowadays in Japan, the population of theforestry is getting fewer and getting older. Therefore,
our researches have concentrated on developing theautonomous robot for the labor-saving forest, andparticularly this research focuses on developing thepositioning system of the robot to collect the data in theforest.
The robot is supposed to look around in the forestand collecting the data of the forest. Therefore the robot
is modeled based on ATV and some sensors andactuators are installed on it.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 364

Sho Yamana, Eiji Hayashi

3. Measuring distance with IMU device
To measure the distance using IMU, fixed the IMU
on a cart and move it 10[m] parallel to the IMU’s y-axisand start to measure before cart starts and stop measure
when car moves 10 [m] and stops. 10 times conducted.
Fig.2 shows the distance calculated by acceleration
using IMU. It shows how far it is from 10[m] and how
wide its variability, and its standard deviation is 328.04.
The distance and velocity are calculated by Equations(1) and (2), where, a is acceleration, v is velocity andx is distance.

Fig.1 The exterior of the robot

Fig.3 The acceleration and approximate line

Fig.2 The results of distance using IMU devicev  adt



(1)



(2)

x  vdt

algorithm for solving the problem. Section 5 shows
experimental results with the algorithm. Section 6
concludes the paper.

Fig.3 shows a result of acceleration (blue line) and
its approximate line (red points) in this experiment. It
shows approximate line is negative ever since about
1.5[s]. It is supposed to be zero except while startmoving and stopping. In addition, the value ofapproximate line is totally different every measurement.
Therefore, the calculating approximate curve need forour system.

2. Configuration of the Robot
Our robot developed is shown in Fig.1. KFX®90(Kawasaki Heavy Industries Motorcycle & Engine
Company) is platform of the robot to be able to run in
the rugged area (the forest). For avoiding trees, 3D
Depth sensor, CCD camera, and RFID antenna are
installed. Motors are used for controlling the movementof the robot. IMU for positioning system of the robot.
Multi blades for weeding in the forest. And three minicomputers (Intel NUCs) for controlling the all devices.

4.

Algorithm for solving the error

To solve the problem we created following system.
First, get acceleration data every 0.01 second, anddecide whether the cart moves or not. If the cart moves,
the acceleration data are accumulated in an array have
100 elements. When the array is full, start calculating
the distance and clear all elements of the array and start
accumulating from the beginning. Using moving© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 365

Development of Autonomous Robot

Fig.5 Experimental result (Distance)

Fig.4 Flowchart of the systemaverage of the acceleration, determine the duration ofacceleration of the cart. While duration of theacceleration, calculates distance using acceleration
revises by offset is determined by average ofacceleration before start moving. After the duration ofacceleration, calculates approximate curve using the all
elements of array, and calculates distance using
acceleration subtracted by approximate curve every
0.01second. The approximate curve is updated every 1
second because the number of elements of array is 100
and get acceleration from IMU every 0.01 second. Aftercalculating distance, decide whether cart moves or not.
Fig.4 shows the flowchart.
5.

Fig.6 Experimental result (Acceleration and approximate line)

approximate line is nearly to zero. Now, the effect of
this system that revising raw acceleration is verified.
However, in this algorithm assume that after duration ofacceleration, cart moves in accordance with uniform
linear motion. Therefore, when cart motion is not
uniform linear motion, this algorithm cannot be used,because revision by the approximate line clear the steep
change of acceleration. For example, when cart moves
forward and backward in a short term, the change ofacceleration is cleared by the approximate line. Becauseof this, the algorithm cannot be installed with the actual
robot.

Experimental results

To examine the effect of the system, conducted theexperiment the same as it was mentioned in the
Section3. Fig.5 shows the experimental results. Results
show that accuracy of measurement was improved, andits variability is smaller than before. In fact, its standard
deviation is 1.77, before it was 328.04.
Fig.6 shows a result of acceleration (blue line) and
its approximate line (red points) in this experiment. It
shows that the system started calculating distance after 1
second, because that need to wait accumulating data in
the array. After that, duration of acceleration,

6.

Conclusion
The system improved the accuracy of themeasurement, but the system can only be used for the
specific situation and application of the system is very
limited. Therefore, measuring the distance only using
IMU is difficult, because the shape of the approximate© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 366

Sho Yamana, Eiji Hayashi

line of the acceleration is completely different every
time even cart moves in accordance with the uniform
linear motion. Finally, the IMU can also output Euler
angle based on angular rate, so it is necessary to use
IMU for compensation of the system composed of
Encoder and GPS.
References
1. Wataru Shinyashiki，”Development of Sensing Robot～
Design and Autonomous Control～”，Kyushu Instituteof Technology, 2015
2. Naoki Tominaga, “Development of Positioining Sytem
Using Accelerometer and its evaluation”, The University
of Electro-Communications, 2003
3. Jingang Yi, et.al., “Kinematic Modeling and Analysis of
Skid-Steered Mobile Robots With Application to LowCost Inertial-Measurement-Unit-Based Motion
Estimation,” Proc. IEEE Transactions on Robotics, pp.
1087-1097, 2009.
4. G. Dissanayake, et.al., “The aiding of a low –coststrapdown inertial measurement unit using vehicle model
constraints for land vehicle application,” Proc. IEEE
Transactions on Robotics, pp. 731-747, 2002.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 367

Development of Autonomous Robot for Laborsaving of the Forestry
Detection of young plants by RGB and Depth Sensor
Nobuo Miyakawa

Department of Mechanical Information Science and Technology, Kyushu Institute of Technology
680-4, Kawazu, Iizuka-City, Fukuoka, 820-8502, Japan
Email: miyakawa@mmcs.mse.kyutech.ac.jp
Eiji Hayashi

Department of Mechanical Information Science and Technology, Kyushu Institute of Technology
680-4, Kawazu, Iizuka-City, Fukuoka, 820-8502, Japan
Email: haya@.mse.kyutech.ac.jp

Abstract
Traditional forest management and maintenance consumes excessive labor time. Particularly burdensome is theinvestigation and management of resource quantity (tree growth) in the forest and the elimination of weeds.
Therefore, this paper proposes a prototype mechanism for an autonomous self-moving robot that promises toreduce the workload of forest management investigation. For the autonomous movement task, we installed someactuators and a three-dimensional (3D) depth sensor into an all-terrain vehicle. The trajectory path of the robot isdetermined from the obtained depth-sensor data via 3D conversions and a labeling process. The handle is thencontrolled by calculating the steering angle. In an experimental evaluation mimicking a real forest environment, theprototype achieved autonomous self-movement with successful passage through the trees.
Keywords: Autonomous Self-Moving, Depth Sensor, Forest, Image processing

Forest scenarios change daily under the dynamic
conditions of tree and weed growth. Moreover, forests
are large-area, steep-grade natural environments that are
not easily patrolled by humans. For the same reasons,map information and path planning is rendered difficult
in forests. Both tasks (maintenance and path planning)
could be much more easily accomplished by anappropriately designed mobile robot.
This study proposes a robot that moves autonomously
in forest environments. When moving, the robot must
avoid the trees (resources). As mentioned above, a forest
path is difficult to plan in advance, so the robot must
detect its plausible trajectory from information of theequipped sensors. The final goal of this study is todevelop a robot that eliminates weeds and investigates
resource quality while traveling extensively through theforest. Fig. 1 shows an overview of the robot. The robot
platform is an all-terrain vehicle (ATV; Kawasaki, Inc.),

1. Introduction
Seventy percent of Japan’s land is occupied by forests,
approximately 40% (100 billion hectares) of which
comprises artificial forests. The artificial forests areplanted with Hinoki and cedars at a density of 3000 trees
per hectare. Both species are cultivated for construction
and other projects. The time between planting andshipment is 50 years. Within this term, forestry workers
must not only investigate and manage the resource
quantity (tree growth) in the forest but also eliminate
weeds. Such work places a large labor burden on forestry
workers. Moreover, the falling employment rate has
depleted the work force, causing delay of these tasks anddegradation of the forest. Our laboratory is developing anautonomous self-moving robot equipped with a weedingmechanism for forest investigation. The proposed robot
reduces the labor burden in forestry work.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 368

Nobuo Miyakawa, Eiji Hayashi

which can access the difficult forest terrain. The externalsituation is recognized by a depth sensor and a CCD
camera equipped on the ATV. The brake lever,accelerator lever and steering are controlled by a motor.
Weeding is performed by a multi-blade mechanism
attached between the front wheels of the ATV.

viewing angle are 60° and 50°, respectively. The
maximum measurable depth is 15 m. The depth values in
the grayscale depth image are converted to pixel values(Fig. 3a), and to a bird’s eye view (x–z plane image) via
three-dimensional conversions (Fig. 3b). The placement,
size and number of the trees are recognized by labelingprocessing on the x–z plane image.

Fig. 2. Flowchart of the proposed robotic system

Fig. 1. Overview of the robot
As a first step toward this goal, we are developing anautonomous self-moving robot. In previous studies,
environment recognition and autonomous movement
control have been achieved by laser range finders (LRFs)
and charge-coupled device (CCD) cameras [1-2]. In thisstudy, the external situation is recognized by a depth
sensor and a CCD camera. The depth sensor is lessprecise than the LRF, but is considerably cheaper. In
addition, it is easily introduced to the robot systembecause its information is easily interpretable fromimages. The robot always moves toward a traversable
gap between the trees.

(a)

(b)

Fig. 3. Grayscale depth image and bird’s eye view
(x–z plane image)

2. AUTONOMOUS SELF-MOVING SYSTEM
The main sensor that detects external situations is thedepth camera. This sensor provides the information(placement, size and number) of the objects ahead of therobot. A flowchart of the robotic system is shown in Fig.2.
The various processing stages are detailed below.
Recognition of external situation by depth sensor and
CCD camera
The depth sensor utilized in this study is commonly
called Infinisoleil (Nippon signal, Inc.). This sensor
measures the depth of an object from its installation
position. The horizontal and vertical components of theB. Decision of the target position
The passable gaps through the trees and weeds aredetected in the x–z plane image after the label processing.
Fig. 4 shows the measuring results of the tree gaps. After
selecting a passable gap, the robot sets the center point of
the gap as the target point. If several passable gaps aredetected, the robot selects one of these gaps (currently,
the widest one). If no passable gaps are detected, therobot measures the gaps between the trees and the line of
the sensor angle, then selects the left or right gap(whichever is larger).

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 369

Development of Autonomous robot)

3. Detection of young plant system
Fig.5. shows the environment in which the robot actually
moves. The trees and weeds are densely grown in theforest, and both plant types appear in the x–z plane image.
To enable weed avoidance by the robot, the external
environment is recognized by combining the depth
sensor data and the RGB image acquired by the CCD
camera (which is installed next to the depth sensor).

Fig. 4. x–z plane image after deciding the target position
C. Calculation of steering angle and movement distance
After detecting the steering angle and movement distanceto the target point, the robot advances toward the target.
Here we used an equivalent three-wheeled model withorigin O at the center of the front wheels. Points P and W
denote the target point and the wheel base of the robot
respectively, and the center C of the circular trajectory of
the robot is calculated by (1). The steering angle is thencalculated by (2) using the length of W and R (theEuclidean distance between C and O). The robot moves
through the arc O–P of the circle centered at C. Finally,
the chord length D is calculated by (3). From the above,the motion command issued to the robot is
u [
s] .

C

cx

s

pz
pz
W
px
2
cz
W
W
sin 1
R
D
2 R sin 1
2R

px2

3.1. Combining Depth data and RGB image
Combining depth data and RGB image using perspective
projection method. The relationship between 2D point1] ) and 3D point
position in pixel coordinates ([position in World coordinates ( [1] ) iscalculated by (4). K is intrinsic parameters of camera and,R, T are the extrinsic parameters.

x
h y
1

K R

T

X
Y
Z

(4)

1

(1)
(2)

3.2. Extraction of young plants
The trees are distinguished by the green domains in the
RGB image. The pixels of the tree domains in the depth
image are identified by the corresponding green pixels in
the RGB image. The x–z planar image captures the weed
information, as shown in Fig. 6(3)

D. Controlling each actuator
In the specified order, the system’s motors control thesteering, accelerator lever and brake levers. The control
order is governed by a rotary encoder attached to the rear
wheel. Once reach the desired distance, the processreturns to Step A.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 370

Nobuo Miyakawa, Eiji Hayashi

Extraction of the green pixel

x-z plane image

Combination image
Fig. 6. x–z plane image constructed by combining the depth and RGB image

4. Conclusion
We developed an autonomous self-moving robot. This
robot is composed of an "autonomous self-moving
system" and a "detection of young plant system”. This
robot can automatically act by avoiding obstacles in thefront. Moreover, it is also possible to judge whether theobject in front is young plants or weeds, and to avoid
only the young plants.
References
[1]

[2]

T. Ito, K. Matsumoto, and Y. Banno, “Environment recognition
method for mountain forest maintenance robot –Localization andmapping of robot using surrounding trees-,” Proceeding of the2011 JSME conference on Robotics and Mechatronics, (2011).
T. Ito, K.Matsumoto, and K. Suganuma, “Improvement ofenvironment recognition performance for mountain forest
maintenance robot,” Proceedings of the 2013 JSME conferenceon Robotics and Mechatronics,© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 371

Development of arm trajectory planning of Seamless Robot
Teedanai Pramanpol, Jiraphan Inthiam, and Eiji Hayashi

Department of Interdisciplinary Informatics,
Graduate School of Computer Science and Systems Engineering, Kyushu institute of technology,
680-4 Kawasu, Iizuka, Fukuoka, 820-5202, Japan
Tel. +819 4829 7500
E-mail: teedanai@mmcs.mse.kyutech.ac.jp, jiraphan@mmcs.mse.kyutech.ac.jp, haya@mse.kyutech.ac.jp

Abstract
This paper presents arm trajectory planning of Seamless Robot. The Seamless Robot is a Human Support Robot(HSR) which is the ongoing developed project at Hayashi Laboratory, this robot is consist of three main parts base,body and arms, this paper will focus on controlling left arm movement. The arm has been designed with four degrees
of freedom joints limit are set to values close to a human arm. In order to solve for each joint angle the Levenberg Marquardt algorithm (LMA) is used for solving the inverse kinematic problem for calculating point to create a
trajectory planning of robot`s arms. The result of trajectory planning has been proven in simulation.
Keywords: Trajectory planning, Inverse kinematics, Levenberg-Marquardt Algorithms, Robot manipulator.

1.

This paper presents the implementation of LMA for
solving inverse kinematics problem with boundary ofeach joint was set to the value close to the limit of humanarm and also shows the arm trajectory of the robot by
selecting point in workspace of the robot

Introduction

In recent years the market of household or service robot
has seen increasing growth in popularity andeconomically. The Seamless robot is also created tooperate as a service robot in an environment close tohuman. Being a service robot the robot should be able tograsp an object and be able to pass that object to humanor deliver the object to other places by using its arm, thisis a common task for service robot. Fig. 1. Shows theoverall design of the seamless robot, in this paper we will
discuss the movement control of the left arm. The left armof Seamless robot has been designed with four degrees of
freedom for a human-like agility, instead of derive for acomplete close form solution, the Levenberg-Marquardt
algorithm (LMA) is used to solve inverse kinematic
problem. The LMA is an iterative algorithm for solving
a nonlinear least square problem [3]. As [2] shows thatwhen compare the LMA with other Jacobian-based
method, the LMA shows better performance than othermethod.

2.

Arm structure of Seamless robot

The structure of the arm in Seamless robot was designed
to replicate human arm with 3 DOFs shoulder and 1 DOF
elbow. Fig. 2. (a) and (b) show the construct of robot arm.
The shoulder joint using three MAXON EC motors (two
100 watt 24V combine with 113:1 ratio gearbox and one
30 watt 24V combine with 216:1 ratio gearbox) for the
elbow joint we use one MAXON EC motor (one 15 watt
24V combine with 246:1 ratio gearbox) each motor
equipped with a 3-phase hall sensor to measure thevelocity and direction of rotation. The motor driver
ESCON from MAXON was used to control speed foreach individual motor, for position control we use
microcontroller STM32F4 Discovery to calculate therotation angle by signal from hall sensors. In Seamlessrobot we use one microcontroller to control three ofshoulder motors and another to control elbow motor.
Each microcontroller connects to computer via RS232
connection port. Fig. 3. shows the control system for
robot arm.

Fig. 1. Design of the Seamless robot© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 372

Teedanai Pramanpol, Eiji Hayashi

10 mm 14.5 mm

31.5 mm

= −

35 mm

=

(a)

(b)

=

(c)

Fig. 2. Construction of Seamless robot arm (a) actual constructof robot arm, (b) schematic model of robot arm and (c) frame
assignment of robot arm

=

(
(

(
(

(
(

=

RS232

)
)

+
+

+
+

(1)

°)

°)

−

°)

−

°)

(
(

)
)

−

=

Motor

=

Fig. 3. Diagram of control system in Seamless robot arm

3.

Kinematic model

where

,
,
shown below:

3.1. Forward kinematic

= −
=
=

The coordinate frame assignment are illustrated in Fig.
2.(c). The Denavit-Hartenberg (DH) parameters are used
to derive forward kinematic listed in Table 3.1 except
coordinate frame ( , , ) to ( , , ) which is theoffset from the center of the robot to origin frame that canbe transform by using Eq. (1) then from frame ( , , )to ( , , ) represent the frame at each joint
respectively, ( , , ) shows the coordinate frame forend effector.
From the DH parameters, the transformation
matrix from joint i to joint i+1 can be derived using Eq.
(2) to (5).

+90˚
+90˚

−

0
0

90˚

90˚
90˚
90˚
-90˚
0

(2)
°)

°)

(3)

°)

( +
( +

°)

( )
( )

(4)

(5)

(6)
T

(7)

(8)
0

0

0

1

are the global coordinate of end effector

(
−
+
[ (
−
[ (
−

where 1 = cos( 1 ),

)−
)+
)−

−
]−
]−

= sin( ), =
= sin
,
= cos
+ 90° ,°
90 , = cos( ) and = sin( )
+ 90°

(9)
(10)
(11)
+ 90° ,= sin+

3.2. Inverse kinematic

The forward kinematics equations are nonlinear. It isclearly that solving for close form solution of inverse
kinematic equation is very difficult to derive. In thispaper we use LMA to solve for each joint angle. In order
to use LMA the Jacobian matrix is necessary, we canobtain Jacobian matrix by using , , as shown in Eq.
(12). Eq. (13) is an equation for solving joint angle in each
iteration by is joint angles in current iteration,is joint angles in previous iteration, is a given positivecoefficient, is a non-zero damping coefficient ,isTable 3.1. DH Parameters
i
1
2
3
4
5

−

−

( +
( +

−

=
Microcontroller Motor Driver

( )
( )

−

0
0
0
0
0

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 373

Development of arm trajectorytarget position for end effector, andend effector from previous iteration.

where

=

=

+

⎡
⎢
=⎢
⎢
⎣

(

(

+

is position of
⎤
⎥
⎥
⎥
⎦

)) (

After the angle of each joint is determined if the input
position change there are no connection between two set
of joint angles therefore a function of time as apolynomial order 3 is used to link each set of joint angle.
The equation is shown in Eq. (14)

(12)

−

)

( )=

(13)

where

In traditional implementation of LMA [2][3], thelimitation of each joint was not consider in the algorithm,
in this paper we will also consider about this problem.
When the iteration start the algorithm will converge tothe region that give the closest result of end effectposition, however that region may contents the joint
angle that over the limitation of practical joints, so in each
iteration variable need to be check whether the valueof each joint is over limit or not, after determine the value
if it exceed the joint limit then variable will be set to avalue within the region of joint limit so when the next
iteration start it will start by using selected value thencontinue computing until we get the result for joint angle
the implemented algorithm is shown as flowchart in Fig.
4., in this algorithm λ and are set to a constant value,
is set to the initialize angle of robot arm and is a
accepted error region.

4.

+

+

+

(14)

=
= 0
= 0.0012( − )= −0.000016( − )Simulation and experiment results

The boundary of each parameter need to be set before
simulation take place, for the boundary of input
coordinates and joints angle are listed below. In
experiment we test the system by using two set of input
to create two types of movement which are two points
path, four points input for each type can be list in as(i) Boundary of input coordinate
(a)
(b)
(c)

∈ [−0.91 ,0.1599]∈ [ 0,0.665]∈ [−0.665 ,0.665](ii) Boundary of joint angle
(a)
(b)
(c)
(d)

∈ [−30° , 180° ]∈ [−90° , 10° ]∈ [−40° , 90° ]∈ [0° , 90° ](iii) Input for two points path(a) (−0.245,0, −0.665)(b) (0,0.2,0.4)(iv) Input for four points path(a) (−0.4,0.5,0)(b) (−0.1,0.5,0)(c) (−0.1,0.5, −0.4)(d) (−0.4,0.5, −0.4)

For every experiment value of and are set to 0.00755
and 0.005 respectively. The trajectory planning of both
two points path and four points path are shown in Fig. 5.
and Fig. 6. where Fig. 7. and Fig. 8. show the simulation
concept of robot movement. The joint angle of eachmovement is shown in Fig. 9. and Fig. 10.

Fig. 4. Flowchart of Levenberg-Marquardt Algorithm© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 374

Teedanai Pramanpol, Eiji Hayashi

Fig. 9. The joint angle of two points path
Fig. 5. Trajectory of two points path

Fig. 10. The joint angle of four points path

5.

Using Levenberg-Marquardt algorithm for solving
inverse kinematic problem in Seamless robot can give thejoint angle that make the end effector close to the target
point although some error occur but the calculation canbe refine by changing accepted error region, λ and formore accuracy.

Fig. 6. Trajectory of four points path

1

2

References
1. N. Bunathuek, P. Saisutjarit and P. Laksanacharoen,
Design of a Reconfigurable Spherical Robot II (HongKong, China, 2014)
2. I. Duleba and M. Opalka, a Comparison of Jacobian-Based
Methods of Inverse Kinematics for Serial Robot
Manipulator, J. Appl. Math. Comput. Sci., 23(2) (2013)
373–382.
3. Samuel R. Buss, Introduction to Inverse Kinematics with
Jacobian Transpose, Pseudoinverse and Damped Least
Squares methods. (Unpublished survey article, 2004.)
4. M. Bun, Applications of the Levenberg-Marquardt
Algorithm to the Inverse Problem .http://www.math.
washington.edu/reu/papers/2009/mark/reu/paper.pdf.,(2009).
5. M. W. Spong, S. Hutchinson and M. Vidyasagar,
Introduction Robot Modelling and Control, John Wileyand Sons, Inc., New Jersey, NJ (2006)

Fig. 7. Simulation concept of two points path

1

2

3

4

Conclusion

Fig. 8. Simulation concept of four points path

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 375

Localization Method of Autonomous Moving Robot for Forest Industry
Ayumu Tominaga

School of Computer Science and Systems Engineering
Kyushu Institute of Technology
680-4, Iizuka City, Fukuoka, Japan, 820-8502
tominaga@mmcs.mse.kyutech.ac.jp
Eeigi Hayashi

Department of Mechanical Information Science and Technology
Kyushu Institute of Technology
680-4, Iizuka City, Fukuoka, Japan, 820-8502
haya@mmcs.mse.kyutech.ac.jp
T. Sasao

Computer Science, Meiji University
214-8571, Kawasaki City, Kanagawa, Japansasao@cs.meiji.ac.jp

Abstract
In this study, we are developing an autonomous moving robot for labor-saving in forest industry. Traditional forest
management and maintenance consumes excessive labor time. Particularly, the survey and weeding in the forest placelarge labor burden on the workers. Therefore, the robot which has the weeding mechanism can achieve to reduce theworkload. In this paper, we discuss about localize of the robot in the forest. The Monte Carlo is applied for localization,
and we are considering basic system.
Keywords: Forest industry, Autonomous moving, Monte Carlo localization, Particle filter

forest on mountain and the observing the growth of eachtree are also too. As the background to it, it can be said
that planting density is high (3000 trees are planting per
a hectare) and work on steep grade is forced. As a result,the survey is not sufficiently carried out, the work isdelayed and devastation of the forest is occurring atpresent.
This study is proposing the autonomous moving robotthat automates these tasks. The robot recognizes theexternal environment and avoid the trees (resources) by
a depth sensor and a CCD camera.
In this paper, the method call Monte Carlo Localization(MCL) is used in localization system in forest. It is

1. Introduction
In this study, we are developing a field robot for laborsaving of forest industry. The robot has a weedingmechanism, and autonomously moves in the forest. The
goal is to automate both elimination of the weed plants
and observing the growth quality of each tree.
Nowadays，the decrease labor force due to the decline
the employment rate and the aging of working population
are regarded as problem.1 In the forestry, between fromplanting to final cutting takes 50 to 60 years, while thistime, the workers must do many tasks. Particularly, theweeding that carries out in summer places large laborburden on the workers. In addition, the survey of the© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 376

Ayumu Tominaga, Eeigi Hayashi, T. Sasao

thought that the localization system is necessary for thefollowing points.
Ideally, the weeding should be done all over theforest area. In order to move to an unreachable area,
it is necessary to track the movement trajectory of
the robot.
When communicate to the user (forestry workers)
about the growth of the observed trees, the location
information of the tree in the forest is required.

(InfiniSoleil FX8, Nippon Signal. Inc., Co.) installed on
the robot is a product assumed for use outdoor by the
TOF (Time Of Flight) measure method. The horizontal
and vertical components of the viewing angle are 60 and
50 degree, respectively. The maximum measureable
distance of the sensor is 15m．The Fig.2 shown flow
chart of determining of the trajectory path．
At first, 3 dimensional point cloud data get from thedepth sensor, and an image viewed in a bird’s eye viewis created. Let this image be an x-z plane image.
Next, the location, number and size of the object in frontof the robot are recognized by labeling processing on thex-z image. Thus, the gap of each object that the robot canpass through is searched, the center point of the gap is set
as the goal position of the robot should go.

2. System Structure
The robot under development used ATV (All Terrain
Vehicle) as a base vehicle in order to cope with roughterrain in the forest. The appearance of the robot is shownin Fig.1. The external environment is recognized by adepth sensor and a CCD camera equipped on the ATV.
The brake laver, accelerator laver and steering arecontrolled by DC motors. The weeding is performed bya multi-blade mechanism attached between the front
wheels.

Fig. 2. Flow chart of autonomous moving system
Fig. 1. Appearance of the robot

3. Autonomous Moving System
In this study, the functions required for the robot are to
avoid of contact with the trees and to eliminate of weeds.
It is important to have ability to keep moving more thananything else in the dynamic environment due to thegrowth of plants and so on. Therefore, we developed
autonomous moving system that searches gap of treesthat can pass through moves, mainly using depth sensor
for recognition of external situations.
3.1. Determining of a Trajectory Path Using Depth
SensorFig. 3 Motion model of the robot

This subsection describes determining of a movement
trajectory path using depth sensor. The depth sensor© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 377

Localization Method of Autonomous

At last, calculate the steering angle and the distance tothe target position based on motion model of the robot asshown in Fig.3. The actual movement distance ismeasured by a rotary encoder equipped to the rear wheel,
when reach to the calculated distance, the system will
return to the process at the top of flow chart.

4.

Localization method using the trees aslandmark

In this robot, it is thought that the localization system isnecessary for the following points.
In rough terrain within the actual forest, it is expectedthat errors will be large with localization using deadreckoning. In addition, the star reckoning by a GPS
system will be similar situation because influence ofmultipass.
We are considering the localization using the tree
observed by the robot as landmark, and based on Monte
Carlo Localization.

3.2. Mapping for Depth Information and Color
Information
In actual forest, not only the trees, but also weeds are

4.1. Expression of the Trees Map
In this study, the trees that the robot should be avoid areused a landmark for localization. The global map for
global localization is constructed by the information oftrees location. In this time, we made the trees map using
data of the trees location measured by laser measuring
instrument, and used occupancy grids map with 2
dimension expressed. The three map shown Fig. 5. The
grids map’s range is 35 m square area with 0.5 m square
grids. Each grid has occupancy value , as shown Eq.

Fig. 4. Relative transform between depth and CCD
cameragrowing. Therefore, the depth sensor will measure thedistance of weeds too. As a result, the weeds is treated as
an object to avoid target. Therefore, color information
obtained from a CCD camera and depth information are
integrated to extract three-dimensional distanceinformation of the trees based on image processing.2
The coordinate relationship between the depth sensor
and the CCD camera is shown by Fig.4. For simplicity,
they are arranged in parallel and same height.
[ , , ] is the point obtained from depth sensor,[ , ] is the coordinate in color image, apply theperspective projection transformation matrix, therotation and translation matrix [ | ] between both
sensors. Using the Eq. (1), the pixel coordinates in thecolor image with respect to the three dimensional
coordinates are obtained.

= 0
0

0

0

1

=

[ | ]

1
,[ | ] = 0
0
1

(2).

,

0
0
0

0.0 (
1.0 (

)

)

…(2)

Fig. 5. The tree map and occupancy grid map

4.2. Monte Carlo Localization
Monte Carlo Localization (MCL) is a method of
estimating the state of robot by assuming the robot's posewith particles, comparing the results observed by eachparticle with the actually observed results, calculating the
likelihood of the particle.3 It is realize by the following
three steps.

…(1)
0
0
0

=

−
0
0

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 378

Ayumu Tominaga, Eeigi Hayashi, T. Sasao

with maximum weight in a t. As the result, small diametertrees observed by the robot are contained large diametertrees observed by the particle. Therefore, it is thought that
there is a possibility of adversely affecting the likelihood
calculation in the updating step

Predict step] is the state vector of the robot, and=[prepare a set of N particles with weight (=〈 ( ) , ( ) 〉, = 1,2, … , ). Each particle is transition
according to the motion model using previous state] . In following
and the control inputu = [equation， is a noise component according to normal
disturbing．
=
,
…(3)
= +
…(4)
Update step
A likelihood of each particle is calculated by comparing
information both of the actual observation data andparticle’s observation data. First, a local map expressedby an occupied grids map by data that three-dimensional
point cloud obtained from depth sensor is created. The
coordinate transformation between the local coordinate
and the particle in the global map gives the grid in the
global map, corresponding to the grid in the local
map.
For each grid in the local map, calculate, ,the correlation value with the global map from the Eq. (5)and (6).
,

,

()

∑ ,

=

∑ ,

=

∑ ,

,

⋅(

,

∑ ,

, ,

+

,

()
, ,

()

)

Fig. 6. Result of the update step

6. Conclusions
In this paper, we discussed about localization method in
the forest. We are constructing the localization system
by MCL. In the future, we aim to improve the position
estimate accuracy of the robot.

…(5)
…(6)

, ,

References

The M is number of the grids corresponding between thelocal and global map. The correlation value takes a value()±1. The likelihood of each particle calculates by Eq.
(7). The weight of each particle is Eq. (8) withnormalized likelihood.
()

()
()

,

=

=

∑

()

()

{

,

()

,

()

,

,

()

1. http://www.rinya.maff.go.jp/j/kikaku/genjo_kadai/,
Ministry of Agriculture Forestry and Fisheries, (2016).
2. K. Nakamura, 3-D Object Tracking Based on Integrationof 3-D Range and Color Data, No.11-5 Proceedings of the2011 JSME Conference on Robotics and Mechatronics,
May 26-28, 2006.
3. S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics,
The MIT Press, 2005.

, 0} …(7)…(8)

Resampling step
According to the weight of each particle, resampling theparticles. We adopted roulette selection algorism for it.
5. Verification Experiment
We experimented the localization mentioned before in
test field. Number of the particles is 1000. Fig. 6 shown
the result of update step, and the observations of particles© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 379

Dynamic Modeling and Motion Control of a Three-Link Robotic Manipulator
Jinho Kim, Andrew S. Lee, Kevin Chang, Brian Schwarz
Department of Mechanical Engineering, University of Maryland, Baltimore County (UMBC), 1000 Hilltop Circle,
Baltimore, Maryland, USA, 21250
S. Andrew Gadsden
School of Engineering, University of Guelph,
Guelph, Ontario, Canada, N1G 2W1
Mohammad Al-Shabi
Department of Mechanical Engineering, University of Sharjah, University City Rd, Sharjah, United Arab Emirates
E-mail: umbcjhkim@umbc.edu, alee20@umbc.edu, kevicha1@umbc.edu, schwarz4@umbc.edu,
gadsden@uoguelph.ca, and malshabi@sharjah.ac.ae

Abstract
This paper presents the dynamic modeling and motion control of a three-link robotic manipulator, also known as the
RRR robot. The Kinect motion capture system by Microsoft is used in conjunction with the manipulator. A camera
is used to capture the motion of a user’s arm and tracks certain angles made by parts of the arm. We consider a pinhole
camera model to generate reference angles as per a pinhole camera model in our simulations. These desired angles
are fed into the controller and are used by the RRR robot in an effort to copy the movement of the user. A proportionalderivative (PD) controller is developed and applied to the manipulator for improved trajectory tracking. The RRR
robot manipulator is dynamically modeled and the results of the proposed control strategy demonstrate good
trajectory following.
Keywords: Three-link robotic manipulator, System modeling, Motion capture, PD controller,

are used in bomb disarming and reconnaissance. To
conduct surgery or translate human movement with arobotic manipulator on military missions remotely, it isimportant to control the robotic manipulator according tooperator’s movement simultaneously [5]-[11].
In this paper, we consider controlling a system which
consists of a camera and robotic manipulator. The
pinhole camera model extracts reference angles fromsimulations in this paper. These desired angles are fed
into the controller and are utilized by the robotic
manipulator in an effort to mimic the movement of theuser. A proportional-derivative (PD) controller is

1. Introduction
Robotic manipulators have been developed and utilized
in all sectors such as industry, medical, and military. Dueto its potential to improve precision and enhancerobustness, robotic manipulators have been applied to not
only military missions but also medical devices in recent
years. There are many research topics for medical
devices with robotic manipulators such as human-robot
collaboration, motion planning, and simultaneous control[1]-[4]. In the military field, we can easily perform
robotic manipulator applications as well. For instance,autonomous ground vehicles with robotic manipulators© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 380

Jinho Kim, Andrew S. Lee, Kevin Chang, Brian Schwarz, Mohammad AlShabi, S. Andrew Gadsden

designed and applied to the robotic manipulator for
trajectory tracking.
This paper is organized as follows: in Section 2, thepinhole camera model is introduced and the modelling of
three-link robotic manipulator is derived using the EulerLagrange equation. Based on that, a PD controller isdesigned in Section 3. Then, the simulation results arepresented in Section 4.

Figure 2. System Diagram
and=(follows;

2. Pinhole Camera Model
The pinhole camera model is used to obtain reference
angles. The camera captures the user’s arm motions, thenthe shoulder, elbow, and wrist angles are extracted
through image processing.
The pinhole camera model is shown in Fig. 1. Here, we
define as the inertial coordinate frame, ℂ as the camera
frame, and as the image frame. Then, let [ , , ] be
the axes of the inertial coordinate frame , and[] be the position vector in the camera,,
frame ℂ. Let,
be the coordinate axes of
the image frame andrepresents the principal point
where the -axis of the camera coordinate systemintersects the image plane. The focal length of the camera,, is the distance between
and . The mappingfrom a point = [ , , ] ∈ ℂ onto the image plane
can be written as Eq.(1).
=

=

(1)

The user’s arm is projected onto the image planethrough Eq.(1), then the three angles are calculated byextracting three points from the image plane using simple
geometry. Let us define two points in as = ( , )) . The angle can be calculated as
= tan

(2)

3. Dynamics and PD controller of Robotic
Manipulator
In this section, we derive the dynamics of a three-linkrobotic manipulator and design a PD controller for it. The
system diagram is depicted in Fig.2.
3.1. Dynamics of robotic manipulator
The configuration of the three-link robotic manipulator isshown in Fig.3. Here, ( = 1,2,3) denotes the angles
between each link, is the length of the link, is the
distance from the centroid to corresponding axis,isthe mass, and is the moment of inertia. Let denotes
the joint torque and its counterclockwise rotation isassumed to be positive. Then, the Euler-Lagrange
equation is applied as shown in Eq.(3)-Eq.(4) where ℒ is
the Lagrangian with kinetic energy
and potential
energy of the system [10].
ℒ=

Figure 1. Geometry and coordinate frames for thepinhole camera model

,

+

ℒ
̇ −

+

ℒ

=

−

(3)
−

−

Figure 3. Configuration of the three-link robotic
manipulator© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 381(4)

Dynamic Modeling and Motion

In this system, and of each link can be written as per
Eq.(5)-Eq.(6) where ‖ ‖ represents the magnitude of
the velocity, , is the position of the centroid that isdependent on joint coordinates.
‖ ‖ +
∑ ̇
=
(5)

3.2. PD controller
In this subsection, the PD controller for the three-link
manipulator system is presented.
In order to control the system, can be defined as acontrol input which controls with a reference input. The PD controller of the system are given by= , + , ̇
(8)
where
and
are proportional and derivative gains,respectively. The angle error, , denotes , − andthe derivative of the angle error, ̇ , denotes ̇ , − ̇ .

=(6),
By these equations, we can obtain the final dynamics ofthe system in a simple form as the following:= ( ) ̈+, ̇ + ( )(7)
where ( ) is the inertia matrix,, ̇ represents thematrix of coriolis and centrifugal elements, ( ) is thematrix of gravity elements, and, is control input. The
matrixes and its parameters can be written as follows:
( )=

, ̇ =

,
,

( +
)+
( +
=
+
+2
)+ + +
+2
+2
+2
=
(
+
)+
( +
+
+
+2
)+ +
=
(
+
+
)+
=
+
( +
+2
)+ +
=
(
+
)+
=
+
̇ −
=−
2 ̇ + ̇2 ̇ + ̇2 ̇ + ̇ + ̇+
2 ̇ +2 ̇ + ̇
̇ + ̇ ̇

=−

̇
̇ −
+
̇ + ̇ ̇ + ̇ ̇

̇ + ̇ ̇ +
̇ +2 ̇ ̇ + ̇ ̇ + ̇ + ̇ ̇̇ ̇+
2 ̇ +2 ̇ + ̇

̇ + ̇ ̇ + ̇ ̇=−
2 ̇ ̇ + ̇ ̇ + ̇ + ̇ ̇
+
̇ + ̇
̇
= [ (
(
)]
= [(
= (

+

+

).

)

+

+

)+

+
̇ ̇
(

]

+

+

̇

+

̇ +
+

Simulation Results

This section presents simulation results with initial
parameters. The simulation used the initial settings whichwere defined as follows:= [37.5, 24.2, 16.4]= [1.34, 0.87, 1.04]= [1 , 1 , 1 ]= [0.2 , 0.2 , 0.2 ], = [0.1 , 0.1 , 0.1 ]].
= [0.5 ∙, 0.5 ∙
, 0.5 ∙
We generated movements of arm and extracted theshoulder, elbow, and wrist angles as reference inputs, ,, ,, . We set these reference inputs as thefollowing:, = 3 sin(0.1 ), = 4 cos(0.1 ), = 9 sin(0.15 ),

,

( )=

where,

4.

+

+

̇ +

)+

Figure 4. History of each angle. The black dashedlines are reference inputs and colored lines are
current angles.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 382

Jinho Kim, Andrew S. Lee, Kevin Chang, Brian Schwarz, Mohammad AlShabi, S. Andrew Gadsden

Figure 5. History of three inputs

Figure 6. Errors between angles and reference inputs

where, is time from 0 to 25 seconds in this simulation.
The history of each angle is shown in Fig.4 where theblack dashed lines are reference inputs , and coloredlines are current angles. As shown in Fig.4, we canobserve that the trajectories of the system angles follow
the reference angels with satisfactory results. The initialovershoot in Fig.4 shows acceptable performance of the
PD controller for this system.
Histories of the three inputs and errors between angles
and reference inputs are shown in Fig.5 and Fig.6,
respectively. From Fig.6, we confirm that errors havevalues less than one degree which is acceptable for arobotic manipulator with linkage dimensions similar to ahuman arm.

2. H. Jiang, J. P. Wachs, and B. S. Duerstock, Integrated
vision-based robotic arm interface for operators with upper
limb mobility impairments, Proc. 2013 IEEE
International Conference on Rehabilitation Robotics(ICORR), (USA, Seattle, Jun. 2013), pp.1-6.
3. T. S. Lendvay, T. C. Brand, L. White, T. Kowalewski, S.
Jonnadula, L. D. Mercer, D. Khorsand, J. Andros, B.
Hannaford, and R. M. Satava, Virtual reality robotic
surgery warm-up improves task performance in a drylaboratory environment: a prospective randomized
controlled study, Journal of the American College ofSurgeons,216(6) (2013): 1181-1192.
4. B. Hannaford, J. Rosen, D. W. Friedman, H. King, P. Roan,
L. Cheng, D. Glozman, J. Ma, S. N. Kosari, and L. White.
Raven-II: an open platform for surgical robotics research,
IEEE Transactions on Biomedical Engineering, 60(4)(2013): 954-959.
5. X. Jian and L. Zushu, Dynamic model and motion control
analysis of three-link gymnastic robot on horizontal bar, in
Proc. The 2003 IEEE International Conference on
Robotics and Intelligent Systems and Signal Processing,(China, Changsha, Oct. 2003).
6. I. David and G. Robles, PID control dynamics of a Roboticarm manipulator with two degrees of Freedom, Control deProcesos y Robotica (2012): 1-7.
7. B. Siciliano, S. Lorenzo, V. Luigi, and O. Giuseppe,
Robotics: modelling, planning and control, (Springer
Science & Business Media, 2010).
8. M.W. Spong, Swing Up Control of the Acrobot, in Proc.
The International Conference on Robotics and Automation,(USA, San Diego, Oct. 1994), pp.2356-2361.
9. M.W. Spong, The swing up control problem for theacrobot, IEEE Control System Magazine, 15(1) (1995):
49-55.
10. M. W. Spong, S. Hutchinson, and M. Vidyasagar, Robotmodeling and control, (Wiley, New York, 2006).
11. S. A. Gadsden, Smooth Variable Structure Filtering:
Theory and Applications, Ph.D. Thesis, McMaster, 2011.

5. Conclusion
This paper presents a three-link robotic manipulator
combined with a camera for capturing the user’s motion.
For the successful control of the system, the pin holecamera model captures the user’s arm motion andextracts the shoulder, elbow, and wrist angles throughimage processing. Then, the dynamics of the system isderived and a PD controller is applied to control theangles of each link. The simulation results verified theefficacy of a PD controller showing satisfying results for
tracking movement.
References
1. S. Najarian, M. Fallahnezhad, and E. Afshari, Advances inmedical robotic systems with specific applications insurgery—A review, Journal of medical engineering &technology, 35(1) (2011) 19-33.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 383

Modeling and Simulation for a Quadrotor Vehicle with Adaptive Wing
Qunpo Liu , Fuzhong Wang, Hongqi Wang, Jikai Si

School of Electrical Engineering and Automation, Henan Polytechnic University, 2001 Century Avenue
Jiaozuo, 454003, Henan, P.R.China
Hanajima NaohikoCollege of Information and Systems, Muroran Institute of Technology, 27-1 Mizumoto-cho,Hokkaido
Muroran-shi, Hokkaido (050-8585), Japan
E-mail: lqpny@hpu.edu.cn; wangfzh@hpu.edu.cn, sijikai527@126.com, hana@mondo.mech.muroran-it.ac.jp
www.hpu.edu.cn, www.muroran-it.ac.jp

Abstract
Aiming at developing high power efficiency system for quadrotor vehicle during height keeping and long distance
flight, a quadrotor vehicle with adaptive wing is proposed. Angle of the wing respect to flying direction can be
adjusted adaptively according to flying speed, to reduce energy expenditure of motor. The dynamic model was built
up. Rotor lift were analyzed based on the mechanical structure and its flight principle. The simulation is carried out
to verify the effectiveness of high power efficiency.
Keywords: Modeling, Quadrotor vehicle, Simulation, Wing.

energy expenditure during long distance forward flight.
By now, lithium battery is used for power supply for
quadrotor vehicle. But the weight of the batteries
increases with the capacity. At the same time, the energy
expenditure is also increases for increased weight anddimension. Due to the conflict between the weight of thebatteries (capacity) and flight distance, flight distance issubject to the restriction. However, there are relatively
fewer contributions on the energy expenditure during
long distance forward flight for a quadrotor. Therefore, T
how to reduce the energy expenditure during long weightis a problem to solve for the application of quadrotor
vehicle.
A quadrotor vehicle with adaptive underbelly wing is
proposed. The underbelly wing at certain angles is
expected to obtain a horizontal direction of thrust.
Advantageously, this may result in a reduction of dip
angle for quadrotor body and also can resulting in lower
airflow resistance and lower downward force on

1. Introduction
Because of distinct features, such as vertical take-off andlanding(VTOL), sharp maneuvering, low cost, simple
mechanism, UAVs have attracted more and moreinterests in a wide range involving rescues, security,
inspection, surveillance, aerial photography, mapping,etc. Each individual rotor has a smaller diameter and lesskinetic energy than the equivalent single-rotor helicopter.
Compared to conventional helicopters, with the large
main rotor and tail rotor, the quadrotor is easier to fly,
does not have the complex swash plate mechanism and iseasier to model and control. The quadrotor has apromising application as commercial, military andacademic platforms.
Much research has focused on the Dynamic
modeling1-5, simulation, optimization, controller design2,
and control algorithm3-5 for quadrotor vehicle. However,there are still some problems in practical use, such as© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 384

Qunpo Liu, Fuzhong Wang, Hongqi Wang, Jikai Si, Hanajima Naohikoquadrotor body. And finally, it can eventually reduceenergy expenditure during long distance forward flight.
2. Dynamic modelingThis section presents a simple model of the quadrotor
dynamics according to Ref. 5-6.
As shown in Fig.1, the quadrotor possesses a simple
and symmetrical structure with four rotors and can becontrolled by the rotational speed of the four rotors.
For the sake of convenience, T1 is denoted as the headof the rotorcraft. Meanwhile rotor T3 is the tail. i.e., T1 ,T2, T3 and T4 is named front, right, rear, left rotor
respectively. Define the following body coordinate
system xb, yb, zb: the original point(Ob ) is the center ofgravity; xb is from b Ob the head of the quadrotor; yb isfrom Ob to the right side of the quadrotor; and zb follows
the law of right hand.
The notation for the quadrotor model is shown in Fig.
2. Define the following inputs to model the quadrotor:
FThrust is the thrust generated by four motors; FZ is thethe vertical upward thrust force on the quadrotor body;Fbf is the air resistance on the body; Fwa is the left force
on the wing; FWf is the air resistance on the wing; θ isthe pitch of the quadrotor body; α is the angle between
the xb and the wing; γ is the angle of between the wing
and the horizontal flying direction which marked by aline in green. mg is the weight of the quadrotor. All theanalysis is on the assumption of steady volume of air flow
situation.
The rotor speed is ωi and the thrust force is an upward
vector

FThrust4

b i2

Fig.1 The main structure of a quadrotor

Fwa

1
Cy
2

V S cos(3)

Fig.2 Force modeling of the quadrotor

where, Cy is wing section lift coefficient; S is the areaof the wing; ρ∞ is the air density; V∞ is the steady speed.
Fwa is perpendicular to the direction of air flow.
Deviation of the direction of air flow caused by wash
velocity can be ignored for low speed.
The force F’wf on the quadrotor body generated by the
air resistance on the wing Fwf is

Fwf

(1)

Fwf

Fwf cos

d

(3)

i 1

And it can be counteracted by increasing of ω3 andreducing of ω1.
The air resistance FWf on the wing isin the vehicle’s negative z-direction, where b > 0 is thelift constant that depends on the air density, the cube of
the rotor blade radius, the number of blades, and thechord length of the blade.
When the quadrotor is flying in a certain speed on
certain height, the vertical upward thrust force on thequadrotor body should be remained unchanged.

Fz

FThrust cos

Fwa mg

Fwf

1
Cy
2

V S sin()(4)

where, C’y is wing resistance coefficient.
When the quadrotor is fly on same height, the force
on the body in vertical direction is

(2)

Fz

and

FThrust cos

Fwa mg

0(5)© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 385

Modeling and Simulation for a

Therefore, the equation can be obtained using Eq. (3) and(5)

1
Cy V S cos(2

mg FThrust cos

k

) (6)

FThrust sin

Fwf

ma x

Cy

(13)

The lift-drag ratio k is simulated by wind tunnel test
data in reference [7], as shown in Fig.3. And it is used in
the conclusion of δ.

During the flying, the force on the moving direction is

Fx

Cy

(7)

where, ax is acceration of quadrotor on the moving
direction.
When the speed is stable, ax=0,

1
Cy
2

FThrust sin

V S sin() (8)In the situation without wing of quadrotor,

FThrust cos

mg(9)
Fig.3 lift-drag ratio k

In the situation with a wing of quadrotor, the following
equation can be deduced from Eq. (6) and (8)mgC y sin(

F Thrust

C y cos sin()

) C y sin cos()
(10)

Define thatC y sin(C y cos sin()

) C y sin cos()
(11)

Therefore, Eq. (10) can be simplified as

FThrustmg

In the area marked A1, the results are not valid
because it falls outside the allowable range (α+θ) ≤ 0.
In the area marked A3, the value of δ is bigger than 1,
which is means more energy needed than quadrotor
without wing.
As shown in Fig.4 and Fig.5, in the area (A2) withcolor changing from dark blue to dark violet in middle ofthe figure, the value of δ is 0 ≤ δ ≤1. It means the thrustforce is smaller than the gravity of guardrotor. Especiallyin same point, the thrust force is zero in idea situation.
And it also can be draw from the simulation results thatthe thrust force will increase with (α+θ) increasing.
Expecially when α+θ ≻ π/6 (the line as shown in Fig.5),(12)

From Eq.(12), it can be draw that the energy
1.
expenditure would be deceased if 0
3. Simulation results
To verify whether there are same cases meetrequirements, analysis is carried out based on MATLAB
platform.
In the Eq.(11), there are two parameters Cy and C’y.
Normally, Lift-drag ratio k is used instead. Lift-drag ratio
k is relate to wing's angle of attack and can be measured
from wind tunnel test:

Fig.4 D view of simulation results of δ

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 386

Qunpo Liu, Fuzhong Wang, Hongqi Wang, Jikai Si, Hanajima Naohikovalue of δ increase dramatically, which is due to decreaseof the lift-drag ratio k. In this case however, δ and thethrust force FThrust will increase very much even δ issmall in the time.
Therefore, the best to reduce energy expenditure is on
situation of 0 < α+θ <π/6. In this case, the thrust force

FThrust sin

1
Cy
2

V S sin(

FThrust will be stable invariable when (α+θ) does notchange. If θ decreases under influence of air flow, thevalue of α should increase to keep stable of FThrust . In
this case, FThrus*tcosθ will increase according to Eq.(6).
The FThrust will decrease if the flying velocity V remains
constant. Considering Eq.(8), the force on x-direction ismodel is studied firstly. The force modeling wasanalyzed. Simulation results show that the proposed
quadrotor vehicle with adaptive underbelly wing canreduce energy expenditure by arranging the attitude angleof wing within limited range.

)

Therefore, the flying velocity V should decrease until a
new stable equilibrium is reached.

References
1. Hai-Nguyen
Nguyen,ChangSu
Ha,Dongjun
Lee,
Mechanics, control and internal dynamics of quadrotor
tool operation, Automatica,61 (2015) 289-301
2. Das A, Lewis F, Subbarao K. Backstepping approach forcontrolling a quadrotor using lagrange form dynamics[J]J Intell Robot Syst, , 56: (2009) 127-151.
3. Wei Dong, et ac. A high-performance flight control
approach for quadrotors using a modified active
disturbance rejection technique, Robotics and Autonomous
Systems, 83 (2016) 177-187
4. Juhi Ajmera, V. Sankaranarayanan,point-to-point control
of a quadrotor theory and experiment, IFAC-Papers
OnLine, 49 (1), (2016), 401-406
5. Peter Corke, Robotics, Vision and Control (Springer
Verlag, Berlin Heidelberg, 2011).
6. WANG Hong-yu, et ac. Modelling and position tracking
control for quadrotor vehicle, Journal of Chinese Inertial
Technology, 20(4) (2012) 455-458
7. WANG Bingliang, et ac. Vehicle Aerodynamics(Tsinghua
University Press, Beijing, 2013)

Fig.5 Top view of simulation results of δ
4.

Conclusions

In this paper, a quadrotor vehicle with adaptive
underbelly wing is introduced. A simplified mathematic© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 387

Fuzzy Self-tuning PID Control Algorithm for Belt Conveyor Driven by Multi-motor
Caixia Gao, Fuzhong Wang, Ziyi Fu

College of Electrical Engineering and Automation, Henan Polytechnic University, 2001 Century Avenue,
Jiaozuo, 454003, P.R.China
E-mail: gcx81@126.com, wangfzh@hpu.edu.cn, fuzy@hpu.edu.cn
www.hpu.edu.cn

Abstract
Aiming at the problems of low efficiency when belt conveyor driven by multi-motor is in light or idle run as well as
electrical power unbalance, the speed setting algorithm and fuzzy PID algorithm for speed control and power balance
control are introduced. The application shows that, according to the coal flow changes, the speed can be automaticallyadjusted through this algorithm, making both speed control error and the power control error of which are lower than
2.6%.
Keywords: belt conveyor, power unbalance, fuzzy self-tuning PID

strong. Reference 3 uses the current control method toregulate the power balance of the belt conveyor, when thecurrent of each motor exceeds or falls below 5% of theaverage current. Because of the characteristics of large
inertia and severe hysteresis, the belt conveyor withmulti-motor drive is a time-varying and nonlinear control
object, it is difficult to adopt conventional fixedparameter PID control to achieve good control effect;when a single fuzzy controller is used, the response speed
is high, but its control accuracy is difficult to meet therequirements. This paper proposes a fuzzy control
algorithm based on fuzzy self-tuning PID control
algorithm for large-angle mining belt conveyor driven bymulti-motor, achieving flexible starting and stopping ofbelt conveyor, adjusting the speed according to the sizeof the load automatically, and maintaining multi-motor
power balance.

1. Introduction
In order to ensure the safe and stable operation of thetransport aircraft, the following problems need to besolved: (1) Mechanical damage of the equipment due tothe heavy load and the dynamic tension of the tape during
start-up and stopping; (2) Running state power
consumption problem; (3) Multi-motor power balance
problem. For the above problems, scholars at home andabroad have also carried out researches, but mostly forthe smaller angle (35 °and below): Reference 1 uses
direct torque control strategy to achieve motor power
balance control with fast response and good stability,
which is directly affected by the motor parameters. In
Reference2, what is proposed is a power balance control
strategy for mine belt conveyor motor based on torquecompensation. The vector dual closed-loop controller isa conventional PI controller, and its adaptability is not©The 2017International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22,Seagaia Convention Center, Miyazaki, JapanP - 388

Yunzhong Song, Ziyi Fu, Fuzhong Wang

2. The General Control Model of Highinclination Belt Conveyor
Aiming at the problem of mechanical damage to theequipment caused by the falling or rolling of the material(coal) during the heavy load start and stop of the belt
conveyor and the dynamic tension, the excessive wear
and energy wastage of the mechanical transmission
mechanism under light load or no load high speed
running condition and dynamic balance of motors, thispaper proposes the overall control model of the belt
conveyor shown in Fig.1. The control system is mainlycomposed of speed setting module, speed control module,
power balance control module and so on. It adopts double
closed-loop structure. The inner loop is composed of thepower balance controller. The strategy is using themaster-slave control mode, namely, taking the current of
the 1# motor as the given value of the current of 2# ~ 4#
motor. When the current of 2# ~ 4# motor is notconsistent with the No. 1 motor, the power balance fuzzycontroller can change the output of the motor byautomatically adjusting the opening of the servo
proportional valve of the 2# ~ 4# controllable driving
device CST, so as to realize the four motor power balance
control. The outer ring is composed of a speed control
module, and its function is to realize the belt speed
control. The basic principle is comparing the tape speedwith the given speed. When the belt speed and the given
speed is inconsistent, the controller first adjust 1# CST
servo proportional valve opening step by step to change
the output of 1# motor, then the other three motors
automatically tracking the output power of 1#motor , sofour CSTs work together to achieve the adjustment of thebelt speed. The function of the speed setting module is todetermine the speed of the tape during starting, stopping
and normal operation according to the coal flow rate in
the belt.
Speed control module and power balance control
module both use fuzzy self-tuning PID control algorithm,
and its structure is shown in Fig.2. It is composed of a
fuzzy controller and PID controller. The PID controller
mainly realizes the speed and power balance control ofthe double drum four-drive belt conveyor. The fuzzycontroller is mainly used to monitor the control effect andadjust the parameters of the PID controller. By
monitoring the tape machine speed and motor power(current) deviation e and the deviation change rate ec

continuously, the control effect can be analyzed.
Improving the control effect of double-drum four-drive
belt conveyor control system is achieved by adjusting the
proportion coefficient, integral coefficient anddifferential coefficient of PID controller using fuzzy
reasoning. Speed control fuzzy self-tuning PID input is
speed deviation e and speed deviation rate of change ec;
three power balance control fuzzy self-tuning PID input
are current deviation e and current deviation Rate ofchange ec of 2# ~ 4# motor and 1# motor. Fuzzy selftuning PID output are the respective controllable drive
CST servo proportional valve opening.
c o a l flo w

s p e e d - g iv e n+-

s p e e d fu z z yc o n tr o l
m o d u le
1 # m o to r

se r v o b r a k e

lin e a r
c lu tc h
1#C S TKW
p o w e r b a la n c e
f u z z y c o n tro lm o d u le

2 # m o to r

KW

s e rv o b r a k e

1#
r o ller

lin e a r
c lu tc h
2 #C S T

b e ltp o w e r b a la n c e
fu z z y c o n tro lm o d u le

3 # m o to r

KW

p o w e r b a la n c e
f u z z y c o n tro lm o d u le

4 # m o to r

KW

s e r v o b ra k e

lin e a r
c lu tc h
3#C S T2#r o llerspeeds e rv o b r a k e

lin e a r
c lu tc h
4 #C S T

Fig.1 Belt speed and motor power balance control principle

Fig.2 The fuzzy self-tuning PID principle diagram

PID controller uses incremental operation, position
output, and the formula is shown in (1), (2), (3).
In the formulas (1) to (3): U (k) is the output of thecontroller (also called the regulator)©The 2017International Conference on Artificial Life and Robotics (ICAROB 2017),Jan. 19-22,Seagaia Convention Center, Miyazaki, JapanP - 389

Fuzzy Self-tuning PID Controlu(k )u ( k ) u ( k 1)

K p ec ( k )K i e(k ) K d [ec(k ) ec(k 1)]
u (k )u (k 1)mechanical damage to the equipment caused by dynamic
tension because of starting and stopping of high-angle
tape conveyor, and avoid the material decline or rollingwhen start, the speed curve of belt starting and stopping
should use "S" type speed diagram, as shown in
Fig.3.0~t3 is the starting acceleration phase of the belt
conveyor; t3~t4 is constant speed running section; t4~t7 isthe deceleration stage; t4~t5 is the early deceleration
stage; t5~t6 is the constant deceleration stage; t6~t7 is the
final deceleration stage of the belt conveyor: theacceleration of the tape machine can be set at 0.1 m/s2 to
0.3 m/ s2 at the time of starting and stopping, and thestarting time is controlled in 60s~200s, and both can be
set according to site requirements.
1 2(5)
V1 (t )
k1t
2
1 2
(6)
V2 (t )
k1t amq (t t1 )
2
1 2
1
(7)
V3 (t )
k1t amq (t2 t1 )k 2 (t t2 ) 2
2
2
In the formulas (5) to (7):k1 (m/s3) is the change rateof the acceleration in the stage 0~t1; k2(m/s3) is thechange rate of the acceleration in the stage t2~t3. amq(m/s2) is the maximum acceleration in acceleration
section;V1 (t ) Vm t4 t t5
(8)

(1)

u (k )(2)ec( k ) e(k ) e(k 1)(3)First, apply the simulation tools and experimental
methods offline. According to the principle of fuzzycontrol, establish on-line adjust table of PID controller’s
proportional(fuzzy decision table), including coefficientincrement △ Kp, integral coefficient △ Ki differential
coefficient △ Kd, and store them in the PLC controller
memory for fuzzy on-line reasoning query controller. In
the control process, the fuzzy controller applies fuzzy
reasoning to find out the corresponding adjustment table(fuzzy decision table) from △ Kp, △ Ki and △ Kd according
to the deviation e and deviation change rate ec (de/dt) (4),and get the PID three parameters Kp, Ki and Kd setting
value. Finally, adjust the Kp, Ki and Kd to the PID
controller.
KP KP
E , EC ； K P K P
KP
Ki

Ki

E , EC ； K i

Ki

Ki

Kd

Kd

E , EC ； K d

Kd

Kd

(4)

In the formula (4), K'p, K'i and K'd are the initial presetting values of Kp, Ki and Kd obtained through thetraditional PID parameter setting method.

1
k3 (t t4 ) 2 t4 t t5
2
1
S (t ) Vm (t t4 )k3 (t t4 )36(9)

V (t ) Vm

3. Speed Setting of Tape Conveyor
In order to reduce the loss of mechanical transmission
mechanism and improve the efficiency of the motor, thebelt conveyor should operate at the speed in accordancewith the speed of its coal flow. At the same time, in orderto prevent damage caused by impact on the tape fromfrequent changes of tape speed, control system set six
speed diagrams. When the belt is overhauled (no load),
the speed is 0.25m/s; when the coal flow rate is less thanor equal to 120t, the speed is 0.65m/s; the coal flow rate
is more than 120t and less than or equal to 240t, the speed
is 1.35m/s; the coal flow rate is more than 240t and lessthan or equal to 360t , the speed is 2.00m/s; the coal flow
is greater than 360t less than or equal to 480t, the speedis 2.55m/s; the coal flow is greater than 480t less than orequal to 600t , the speed is 3.15m/ s.
In order to reduce the impact on the grid caused bystarting current of four drive motors, reduce the

(10)

1

V (t )(2amz S V62 ) 2 t5t

V (t ) V6

1
k z 4 (t t 6 ) 2
2

t 6 t

(11)

t6
t7

(12)

In the formulas (8) to (12): amz (m/s2) is the maximum
acceleration in the starting acceleration stage; S(t)is thetravel in the constant deceleration stage(m);kz4（m/s3）
is the change rate of acceleration in stage t6~t7.
4. Experimental Analysis
Based on the designed speed and power, the belt
conveyor control system of balance adaptive fuzzy PID
control algorithm has actually operated, with the actual
control curve speed and power of the belt conveyor
shown in Figure 3. In the figure, the belt start-up time is©The 2017International Conference on Artificial Life and Robotics (ICAROB 2017),Jan. 19-22,Seagaia Convention Center, Miyazaki, JapanP - 390

Yunzhong Song, Ziyi Fu, Fuzhong Wang

(2) According to the size of the coal flow, the speedof the belt conveyor is divided into 6 steps. The fuzzy
self-tuning PID algorithm is used to control the speed of
the belt conveyor, so the speed control error is less than
2.6%.
(3) Use the master-slave control mode and take thecurrent of the 1# motor as a given value. The fuzzy selftuning PID control algorithm makes the current of the
2#~4# motor follow the current of the 1# motor, so thecontrol error is less than 2.6%. This realizes high control
precision so that the powers of four drive motors arealmost balanced or the difference is controlled within anallowable range. By doing so, we prevent the occurrenceof motor burnout, improve the safety and reliability of thesystem, and extend the life of the belt conveyor.

180s, the belt speed is fixed at 3.15m/s. Once the belt runs
stably, the running speed will change from 3.20m/s to
3.10m/s, and the control error will be less than 2.6%. The
operating current fluctuates within the range of
20.5A~21.5A, and the control error is less than 2.6%,
which realizes the reliable, safe and energy-saving
operation of the main shaft belt transport system.

References
1. WANG Zhong-hua, CHEN Xun, YANG Guang-hui. Researchof Multi-motor Power Balance Control of Belt System in Coal
Mining, Coal Mine Machinery, Vol.34 No.8(2013), pp:61-63.
2. XU Lu-hui, LI Fu-dong, ZHANG Chao, ZHANG Xiaoyong, ZONG Cheng-guo. Research of Power Balance ofBelt Conveyor Driven by Multi-motor and Its Analysis,
Industry and Mine Automation, No.5(2011), pp: 63-65.
3. CHEN Yu. Study on Control of Belt Conveyor Based onIntelligent Algorithm Fuzzy Neutral PID Controller, Coal
Mine Machinery, Vol.35 No.42(2014), pp:39-41.

Fig.3 System speed and power curve of actual control

5. Conclusion(1) "S" speed profile is applied as speed profile in thebelt conveyor's starting and stopping, thus preventing thematerial (coal) from dropping or rolling during the startand stop of the heavy-duty conveyor, and the mechanicaldamage to equipment caused by the dynamic tension of
the tape during start and stop, so as to improve the safety
performance of equipment and operators.

©The 2017International Conference on Artificial Life and Robotics (ICAROB 2017),Jan. 19-22,Seagaia Convention Center, Miyazaki, JapanP - 391

Continuous Non-singular Fast Terminal Sliding Mode Control
for an Active Gravity Field Simulator
Jiao Jia, Yingmin Jia, Shihao Sun

The Seventh Research Division and the Center for Information and Control
School of Automation Science and Electrical Engineering
Beihang University (BUAA), 37 Xueyuan Road, Haidian District
Beijing, 100191, China
E-mail: annebuaa@hotmail.com; ymjia@buaa.edu.cn; jxcrssh@126.com
www.buaa.edu.cn

Abstract
In this study, a global non-singular fast terminal sliding mode controller (FTSMC) is designed for an active gravity
field simulator (AGFS). The stability of the control algorithm can be easily verified by using Lyapunov theory. It isshown that the proposed controller can eliminate the chattering effect without losing the robustness property.
Simulation results show that faster and high-precision tracking performance can be obtained compared with theconventional continuous sliding mode control method.
Keywords: gravity field simulator, fast terminal sliding mode, robustness, finite-time stability

1. Introduction

2. Simulation Mechanism

Terminal sliding mode (TSM) control1-4 is a finite timestability control strategy, which can stabilize system
states to their equilibrium points or track the desired
trajectory in finite time. Due to some superior properties,
such as fast convergence, strong robustness, and highprecision, TSM has attracted extensive attention incontrol theory studies and in practical applications. In
this paper, a non-singular fast terminal sliding mode
controller is applied to an AGFS.
The rest of this paper is organized as follows. The
simulation mechanism of the platform is introduced in
section 2. In section 3, a NFTSMC is developed and thestability is proved. Simulation examples are given to
demonstrate the effectiveness of the proposed control
algorithms in section 4. Finally, we end this paper withsome conclusions.

The simulation platform5 is a servo system consisting ofan x-axis direction mechanism driven by the x-axis
direction motor, a y-axis direction mechanism driven by
the y-axis direction motor, a z-axis direction mechanism
driven by the z-axis direction motor, a universal joint, aspring buffer, a dual-axis tilt sensor, a tension sensor andan unconstrained suspension section (USS). As describedin Fig. 1 the object is connected to the platform throughthe USS. The USS is linked to the spring buffer. The
spring buffer is attached to the universal joint. The
universal joint is connected to the z-axis direction
mechanism through a tension sensor to detect the force
between them. A dual-axis tilt sensor is mounted on theupper end of the spring buffer to determine an angle ofthe spring buffer with respect to the vertical axis or z-axis(the vertical).

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 392

Jiao Jia, Yingmin Jia, Shihao Sunxγ

x1 1 ,..., xn n

sgn ( x ) γ

where x

T

, x

γ

T

x1 1 ,..., xn nT

x1 1 sgn ( x1 ),..., xn n sgn ( xn )T

x1 ,..., xn

R n , and sgn( ) is the signfunction.
Hence, the TSM can be defined asswhere sThe dynamics of the AGFS5 can be written as

Define M (q)we obtain

Mm , F

F1 , Fd

M (q )q C (q , q )q G (q )(1)

M1 M 2 T F2 , andF

G0 (q) δG (q)

3

R , F

C 0 (q , q ) δC (q , q ),G (q )

3

R is the vector of applied

1

R 3 is the vector of

torque or equivalent force, Fd

driving force operating on the object, Fd

fd . Here

2

F

Fd

T (q , q , q )T (q, q, q)

b0 b1 qb2 qdiag ( 1 ,

2

,

3

) , β

1

C0 (q, q )q G0 (q) M 0 (q)qd

M0 1 (q) (b0 b1 q

b2 q

2

f d ) k1

M 0 1 (q) (b0 b1 q

b2 q

2

f d ) k2

min i k1i

1

(8)0 ,and k2

min i k2i
0 . Furthermore, the tracking errors e and ewill converge to the regions
ei
2
e
i

(3)

ei

1
e

(9)

i

i

in finite time.
Consider a Lyapunov candidate function as:
1 T
V
s s
2
By differentiating V with respect tosubstituting (6) and (7) into it, we havewhere T (q, q, q)
δM (q)q δC (q, q)q δG (q) R3 isthe lumped system uncertainty, which is bounded by thefollowing function
2

3

in finite time, where k1

M 0 (q ), C 0 (q, q ) and G0 (q ) are the nominal parts,
whereas δM (q ), δC (q, q ) and δG (q ) represent the
perturbations in the system matrices. Then, thedynamical model of the AGFS can be rewritten as
M 0 (q)q C0 (q, q)q G0 (q)

R , α

(5)

0

F1
M 0 (q)(k1 s k2 sgn( s ) ρ )(7)
where γ diag ( , , ) . Then the system trajectory will
converge to the neighborhood of s 0 ass
min 1 , 2 ,where q , q , q R are the vectors of position, velocity
and acceleration, respectively , M (q ) M 0 (q )R3 3 , C ( q , q ),) and 13

αM 0 (q) β 1γ 1 sgn(e )23

δM (q)

2

F0

(2)

Fd

T

, 2 , 3 2 . For simplicity, we
1
2
3 . The reaching law is defined as(6)s
k1 s k 2 sgn ( s ) ρwhere k1 diag ( k11 , k12 , k13 ) , k 2 diag (k21 , k22 , k23 ) ,
0
1.
1
2
3
Design the control input as
F F0 F1 ,3. Continuous FTSMC

F1 M1 M 2 T F2

s1 , s2 , s3

diag ( 1 ,chooseFig. 1. The USS and coordinate system of the AGFS

M m q2 Cq2 G

β sgn (e ) γ

αe

(4)

Let qd R3 be a twice differentiable desired
trajectory, and define the tracking error as e q qd . The
control objective is to design a feedback control F suchthat the AGFS output q tracks the desired trajectory qd
in finite time.

V

sT βγdiag ( eγ 1)(k1 s k2 sgn( s) ρ

M 0 1 (q)(T

(10)
time,
Fd )(11)which can be further changed into the following two
forms:
VsT βγdiag ( e(TBefore giving the main results, the following
vectors are defined as

γ 1)((k1 diag ( M 0 1 (q)Fd ))diag 1 ( s)) s k2 sgn( s) ρ )(12)© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 393

Continuous Non-singular Fast TerminalVsT βγdiag ( eγ 1When u (t) 0 the object’s gravity is compensatedcompletely and when u (t ) 0.2 the object’s gravity is at
normal situation. When the value of u (t) is between)(k1 s (k2 diag ( M 0 1 (q)(T Fd ))diag 1 ( sgn( s) ρ )) sgn( s) ρ )(13)
For Eq. (11), if we can keep the matrix
k1 diag ( M 0 1 (q )(T Fd )) diag 1 ( s ) positive definite,
the structure(14)V
sT k1 s sT k2 sig ( s ) ρis kept and k1

βγdiag ( eγ 1(0, 0.2) , the object’s gravity is partly compensated andwhen u (t) is negative, the object’s gravity is greater thanearth's gravity.
Firstly, define α I corresponding to the controller
designed in reference [2].
The object driving input is shown in Fig.2 which isregarded as the system disturbance.
Fig. 3 shows the system output, system output error,
the sliding surface function and system control input
when u (t )(t ) with TSM controller.

diag ( M 0 1 (q )(T

)( k1
γ 1

Fd ))diag 1 ( s)) , k 2 βγdiag ( e ) k2 . It can be proved
that
(15)
V
2 k1V 2( 1) 2 k2V ( 1) 2
and the settling time isln(( k1V (1 ) 2 2( 1) 2 k2 ) 2( 1) 2 k2
(16)
T(1 ) k2
therefore finite-time stability is guaranteed. Assume
M 0 1 (q )(T Fd ) Q [Q1 , Q2 , Q3 ]T R 3 ; then if k 1
si

0.4

Qi k 1

0.2

2

b2 q

f d ) k1

(17)

-0.4

M 0 (q ) (b0

b1 q

b2 q

f d ) k2

1

F4x

-0.6

F4y
F4z

-0.8
-1

Qi k 2
2

0
-0.2

0

2

4

6
time(s)81012Fig. 2. The object driving input
System Output with TSM for qd =[0.2 (t),0,0](18)

System Output Error with TSM for q d =[0.2 (t),0,0]

0.2

0.005
l
0

x

0.15

can be reached infinite time.
Synthesizing inequalities (17) and (18), we have thatthe region (8) can be reached infinite time. We can
explain ei 0 does not hinder the finite-timereachability of the region (17) by substituting the control(7) into (3).

y-0.005

Output Error of l
Output Error of x

-0.01

0.1

Output Error of y

q

e

s

1

40

1

can be reached infinite time. For Eq. (13), by similar
analysis for (12), we can have that the region

si

t

0.6

M 0 1 (q) (b0 b1 q

s

0.2

40

0.8

0 , the regionsitObject Driving Input

Fd

Qi

0.2sin(20t )

(t )

0.05

-0.015-0.02-0.025

0
-0.03
-0.05

0

2

4

6
time(s)81012-0.0350

Sliding Surface Function with TSM for q d =[0.2 (t),0,0]2

4

6
time(s)

8

10

1

4
s1

Fx
3

s2
s3

4. Simulationgˆ

10 m s , Rˆ

0.02 m . The parameter values are

m1

0.55kg , m2

4.4kg , m0 x

2

17.6kg , m0 y

Tx

1

s

F

ˆ 0x
ˆ 1 0.5kg , mˆ 2 4kg , m
nominal values are m
ˆ 0 y 40kg , Mˆ 10kg , kˆ 500 N m , lˆ0 1m,
16kg , m

Fy

2

0.5

The

12

System Control Input with TSM for q d =[0.2 (t),0,0]

0
-1

0
-2
-3
-0.5

0

2

4

6
time(s)

8

10

12

-4

0

2

4

6
time(s)Fig. 3. TSM control of the AGFS when u (t )44kg , M

11kg , k 550 N m , l0 1.1m, g 9.8 m s 2 , R 0.022m .
The bound parameters of system uncertainties in (4) are
assumed to be b0 0.8, b1 1, b2 8 .

8

10

12

(t )

Fig. 4 shows the system output, system output error,
the sliding surface function and system control input
when u (t )(t ) with controller (7).
Fig. 5 shows the system output with FTSMC when
the object gravity varies according to the law role of thegravity effect.

The reference signals are given by qd [u (t ), 0, 0]T
according to the simulation mechanism of AGFS andu (t) represent offload quantity of the object gravity.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 394

Jiao Jia, Yingmin Jia, Shihao Sun
System Output with FTSM for q d =[0.2 (t),0,0]

System Output Error with FTSM for q d =[0.2 (t),0,0]

0.3

0.17 s and e 1.5 10 4 is reached in the finite time
0.18s .
With the same control parameters of the FTSMC, Fig.
5 shows the system can track time-varying trajectory withe 2 10 4 when t 0.13s .
Therefore the much smaller output error and fasterconvergence speed are obtained with the FTSMC method.
t
t

0.005
l
0

x

0.25

y

-0.005

0.2

-0.01

q

e

0.15
Output Error of l

-0.015

Output Error of x

0.1
-0.02
0.05

0

-0.05

Output Error of y-0.025
-0.03

0

2

4

6
time(s)81012-0.03502Sliding Surface Function with FTSM for qd =[0.2 (t),0,0]

4

6time(s)81012System Control Input with FTSM for q d =[0.2 (t),0,0]

1

5
s1

Fx
4

s2

0.8

s3

Fy

0.6

5. Conclusions

Tx

3
2

s

F

0.4
1

0.2

In this paper, a new fast terminal sliding mode controller
is developed for the AGFS with the finite-time
convergent property. The proposed control algorithms
can guarantee the tracking errors converge to arbitrarily
small value. And the developed control approach can beused to a more general Lagrange-like second-ordernonlinear system.

0
0

-1

-0.2

-0.4

-2

0

2

4

6
time(s)

8

10

-3

12

0

2

4

6
time(s)Fig. 4. FTSMC control of the AGFS when u (t )

8

10

12

(t )

System Output with FTSM for Multi-signal
0.25
l
ld

System Output & Reference Signal

0.2
0.15
0.1
0.05

Acknowledgements

0
-0.05

This work was supported by the NSFC (61327807,
61520106010, 61521091, 61134005) and the National
Basic Research Program of China (973 Program:
2012CB821200, 2012CB821201)

-0.1
-0.15
-0.2

0

0.5

1

1.5

2

2.5
time(s)

3

3.5

4

4.5

5

Fig. 5. FTSMC control of the AGFS

For the uncertain system with bounded disturbancesand noises, the control parameters of the TSM controller
are chosen according to (8) as
k1 diag (2500,8000,8000), k 2 diag (2000, 2000, 2000),
1.6,
1 3, β diag (0.4,5, 5)
for u (t ) (t ) .
The boundary layer s 2.2 10 3 is firstly reachedin the finite time t 0.2s and e 2.3 10 3 is reachedin the finite time t 0.5s as shown in Fig. 3.
The control parameters of the FTSMC are chosen
according to (8) as
k1 diag (2000, 2000, 2000), k 2 diag (2000, 2000, 2000),
1.6,
1 3, α diag (12,10,10), β diag (0.4, 5,5)
for u (t ) (t ) .
The simulation results shown in Fig. 4 verify therobustness of the FSTM approach. The boundary layers 1.5 10 3 is firstly reached in the finite time

References
1. Y. Feng, et al., Non-singular terminal sliding mode controlof rigid manipulators, J. Automatica. 38(12) (2002) 21592167.
2. S. Yu, et al., Continuous finite-time control for robotic
manipulators with terminal sliding mode, J. Automatica.
41(11) (2005)1957-1964.
3. M. B. R. Neila and D. Tarak, Adaptive terminal sliding
mode control for rigid robotic manipulators, J.
International Journal of Automation and Computing. 8(2)(2011) 215-220.
4. D. Zhao, et al., A new terminal sliding mode control for
robotic manipulators, J. International Journal of Control.
82(10) (2009) 1804-1813.
5. J. Jia, et al., Modeling and Control of a Suspended Gravity
Compensation System with Rigid-Elastic Coupling, J.
Journal of Robotics, Networking and Artificial Life2016,
3(2) (2016)79-83.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 395

Weighted Multiple Model Adaptive Control for a Category
of Systems with Colored Noise
Yuzhen Zhang, Qing Li, Weicun Zhang

School of Automation and Electrical Engineering, University of Science and Technology Beijing,
30 Xueyuan RoadHaidian District, Beijing, 100083, China
E-mail: zhangyz_ustb@163.com, liqing@ies.ustb.edu.cn, weicunzhang@ustb.edu.cn
www.ustb.edu.cn

Abstract
The multiple model adaptive control (MMAC) of discrete-time stochastic system with colored noise is considered inthis paper. Model set contains one adaptive identification and some fixed models. Based on the output errors of themodels, a simple weighting algorithm is adopted with guaranteed convergence. The proofs of the stability andconvergence of system are presented. Besides, the influence of initial value of adaptive model on system performanceis described. Finally, computer simulation results can verify the theoretical results.
Keywords: discrete-time; colored noise; multiple model adaptive control; stability; convergence.

its model set just includes fixed models. If the real model
isn’t in the model set, the system is not good.
Considering the above problems, in this paper, afinite number of fixed and one adaptive identification
models are designed to cover the discrete-time stochastic
system with colored noise.

1. Introduction
As the largely change of system parameters makes the
dynamical system become complicated. The classicaladaptive control can’t meet the requirements. Therefore,
MMAC developed. The most suitable controller is used
to control the system1.
The weighted multiple model adaptive control(WMMAC) was researched in 1960’s2. In that system,
Kalman filter and posterior possibility evaluator (PPE)
algorithm are important3. Later, K. S. Narendra put
forward the concept of switching multiple model
adaptive control4, 5. To some extent, WMMAC can be
regarded as a kind of slow switching4, 5. Therefore, thescheme can alleviate the vibration when switching.
However, the system stability analysis of the
WMMAC has been plaguing the researchers all the time.
In Refs. 4 and 6, the stability and convergence are proved
based on the virtual equivalent system (VES) theory. But,

2. Description of WMMAC
For analyzing system clearly, a block diagram of ageneral discrete-time WMMAC system is shown in Fig.1.
The meanings of symbols are shown: P is theuncertainty plant; Ci is a local controller corresponded
to the fixed model; C is the local controller
corresponded to the adaptive identification model; pi (k )is weighted value for its local controller; p (k ) is theweighted value for the adaptive identification controller.
Through the algorithm, the weighted values are known.
Then, the u(k ) can be obtained by weighted sum.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 396

Yuzhen Zhang, Qing Li, Weicun Zhangˆ (k ) [ y(k 1), , y(k na ), u (k d ), , u (k d nb ), ˆ (k 1), , ˆ (k nc )]Tˆ [aˆ , , aˆ , bˆ , , bˆ , cˆ , , cˆ ]T1naˆ (k )

Fig. 1. Concise block diagram of a general WMMAC system

Consider the following discrete-time dynamical plant,
the mathematical model is1d1

z d B(z 1 )u(k)(k)

B( z ) b0 b1 z

1

b2 z

bnb z

(1)

As initial values will affect the system performance,
we will discuss that specifically in simulation studies.
For each local model, its output yi (k ) is used todefine the output error ei (k ) . As shown in Fig.1, theglobal control u ( k ) is obtained by
N 1

1 k 2
ei ( j ); ei ( k )k j 1li ( k )

where

(2)

y ( k ) and u ( k ) are the output and the input. The (k ) isa zero-mean white noise. The ( k ) is colored noise.
d)

(k )

(k 1), , (k nc )]T

R( na nb 1 nc ) 1(11)li (k )

lmin (k )li (k 1)li (k )

(12)1

l (k )

i
N 1

(13)

l (k )j 1 j

The convergence character is presented in Ref. 4.
In the computer simulation, in order to prevent theweight converge to zero, we can limit the threshold value.

(4)

3. Stability and Convergence of the System
On the basis of VES4, 6, this section gives the stability
proof of the WMMAC system.

{M i , M , i 1, 2, , N } is the model set. For
every fixed model, the corresponded controller isdesigned by pole placement control. At the same time,
the one adaptive identification model is designed byindirect pole assignment self-tuning control. The
recursive extended least square is used to estimatedparameter7. That is
ˆ(k ) ˆ(k 1) K ( k )[ y (k ) ˆ T ( k ) ˆ(k 1)]
K (k )

0.

i(3)[a1, , ana , b0 , , bnb , c1, , cnc ]T R(na nb 1 nc ) 1 (5)P (k 1) ˆ ( k )ˆ T ( k ) P (k 1) ˆ ( k )(10)min{li ( k )}pi (k )

where
(k d ) [ y(k 1), , y(k na ), u(k d ), , u(k d nb ),yi ( k )lmin ( k )The plant also can be written as(ky (k )

is 0.001, avoiding li ( k )C ( z ) (k )T(8)pi ( k )ui ( k )1y (k )

ˆ (k )T ˆy (k )In this paper, the algorithm4 to calculate weight is1
p i (0) li (0)
(9)
N 1

nb

cnc z ncC ( z 1 ) 1 c1 z 1 c2 z 2

(7)

nc

i 1

ana z na2

1

u (k )

whereA( z 1 ) 1 a1 z 1 a2 z 2nby(k ) yˆ (k )1

A(z ) y(k ) z B(z )u(k) C(z ) (k)103.1. WMMAC for LTI plant
Theorem 1. If a WMMAC system satisfies:
The true model of plant, say M j , is included in the
fixed models of the model set;
Model M j generates with probability one theminimum output error in the sense that
k

2
r 1 j

(6)

1
k

T

P (k ) [I K ( k ) ˆ ( k )]P ( k 1)

e (r )k2
r 1 j

e (r )k2
r 1 i

e (r), k d 1, i

Rj ; 1k

k

2
r 1 i

e (r )

jRi

where R j is a constant, R i may be constant or
infinity, R j Ri , i j ;where© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 397

Weighted Multiple Model Adaptive

Each local controller is well defined such that Ci is3.2. WMMAC for parameter jump plantstabilizing M i , i 1, 2, , N , and the output of the

To consider the parameter jump plant, we assume that thesystem parameters are piecewise constant function oftime, and time interval of parameter jump is long enough.
According to the assumptions, we know that eachstage is a WMMAC for LTI plant. The overall system isslow switching between all the stages. Therefore,switching system theory9 can be utilized to verify thestability of the WMMAC system.

resulting closed-loop system {Ci , M i } ,say yi (k ) , istracking the reference signal yr (k ) in the sense that2klim 1kr 1

k[ yi (r ) yr (r )]

R ,R

R

.

then it is stable.
Proof. First, the weighting algorithm is convergent. So,
p j (k )1; p i ( k )0, i1, ..., N , i

0 (14)

j; p (k )4.

where p j ( k ) , p ( k ) index weights of the true modeland the adaptive identification model.
Then, the VES for WMMAC in this case is similar tothe one in Ref. 4. The influence of the adaptive model on
the system will be included in u (k ) . And,ei ( k )y (k )yi (k )Ty (k )(k

In this section, the results of computer simulations will
be presented. Consider an uncertain discrete-time plant(1 a1 z 1 a2 z 2 ) y(k ) z 1 (b0 b1 z 1 )u(k )(1 c1 z 1 ) (k )

(15)

d) i

Follow the proof process in Ref. 4, VES theory canbe used to verify the stability.
Theorem 2. If a WMMAC system satisfies:
The true model of plant isn’t included in the fixedmodels of the model set;
Model M generates with probability one theminimum output error in the sense thatr 1ke 2 (r )
k

1
k

r 1

2
r 1 i

e 2 (r )e (r ), k
R ; 1kh(18)
s 3s 2For simplicity, four fixed local models are used as theuncertainty of h , that is, h1 0.7 , h2 0.8 , h3 1.2 ,h4 1.3 . The four corresponded controllers are designedby pole placement control.
The expected closed-loop characteristic polynomial
is corresponds to the characteristic polynomial of thefollowing continuous-time second-order system
G ( s)

d 1, i 1,..., N
k

2
r 1 i

e (r )

Ri

where R is a constant, R i may be constant or
infinity, R
Ri ;The adaptive controller C is stabilizing M ,and
the output of the resulting closed-loop system{C , M } , say y (k ) , is tracking the reference
signal yr (k ) in the sense that

lim

k

1
k

2

k
r 1

[ y (r ) yr (r )]

R ,R

R

1; pi ( k )0, i1,..., N

2

2
n

where

is 0.7,

s2

2

n

is 1.

n

s

2

(19)

n

Then we can know the four fixed controller easily. At
the same time, the adaptive identification model isdesigned by indirect pole assignment self-tuning control.
The recursive extended least square is used to estimatedparameter. The initial value is 106 eye(na nb 1) . The
calculation in detail is shown in formula (6) and (7).
The true model of plant is not included in the four
fixed model, which h is 1. The initial value of adaptivemodel will affect the system performance.
The initial value
.The
( 1
2
3
4) 4
simulation results are shown in Fig.2 and Fig.3.

.

then it is stable.
Proof. First, the weighting algorithm is convergent. So,
p (k )(17)

where (k ) is obtained by sqrt (0.01) randn (1) in
MATLAB, c1 0.5 . This discrete-time model can beobtained by the simple continuous-time in (18) withsample time t s 0.5 s and the zero order hold.

where (k d ) [ y(k 1), , u (k d ), , ˆ (k 1), ] .

kSimulation Studies(16)

As the VES for this WMMAC is similar to the one in
Ref. 8. The influence of the parameter identification
process on the system will be included in ei (k ) .
Follow the proof process of Ref. 8, the VES for
WMMAC can be decomposed into three subsystems.
Then, VES theory can be used to verify the stability.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 398

Yuzhen Zhang, Qing Li, Weicun Zhang

5. Conclusions
In this paper, a finite number of fixed and one adaptive
identification models are designed for a category ofsystems with colored noise. This method can effectively
deal with issue when the real model isn’t in the model set.
And, the stability of WMMAC in the case is proven. At
the same time, an adaptive model with real-time initialvalue, the parameter value of the fixed model closest to
the real model, can improve the system performance.

Fig. 2. Controller weight signals of case 1

Acknowledgements
This work was supported by National Natural Science
Foundation of China (61520106010) and National Key
Technologies R&D Program (2013BAB02B07).
References

Fig. 3. Output, reference and control signals of case 1

The initial value is the parameter value of the fixedmodel closest to the real model. The simulation results
are shown in Fig.4 and Fig.5.

Fig. 4. Controller weight signals of case 2

Fig. 5. Output, reference and control signals of case 2

In the two cases, the controller 5 designed according
to the adaptive identification model all are chosen rapidly.
However, the latter one works better.

1. K. S. Narendra, X. Cheng. Adaptive control of discretetime systems using multiple models. IEEE Transactions
on Automatic Control, 2000, 45(9): 1669-1686.
2. Moose R L, Van Landingham H F, McCabe D H.
Modeling and estimation for tracking maneuvering targets.
IEEE Transactions on Aerospace and Electronic Systems,
1979, AES-27: 448-456.
3. J. Chen, W. Chen, J. Sun. A survey of multiple model
adaptive control. J. Sys. Sci. & Math. Scis, 2014,34(12):1421-1437.
4. W. Zhang. Stable weighted multiple model adaptive
control: discrete-time stochastic plant. International
Journal of Adaptive Control and Signal Processing, 2013,27(7): 562-581.
5. F. Li. Robust multiple model adaptive control base on
dynamic optimization model bank. Master Thesis, South
China University of Technology, 2004.
6. W. Zhang. Weighted multiple model adaptive control of
discrete-time stochastic system with uncertain parameters.
Acta Automatica Sinica, 2015, 41(3): 541-550.
7. Z. Pang, H. Cui. System identification and adaptive
control MATLAB simulation. Beihang University Press,
Beijing, China, 2009, pp:175-189.
8. W. Zhang. On the stability and convergence of self-tuning
control - virtual equivalent system approach. International
Journal of Control, 2010, 83(5): 879–896.
9. Shorten R, Wirth F, Mason O, Wulff K, King C. Stability
Criteria for Switched and Hybrid Systems. SIAM Review,
2007, 49(4): 545-592.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 399

Neutral Networks-Based Adaptive Fixed-Time Consensus Tracking Control for Uncertain
Multiple AUVs
Lin ZhaoCollege of Automation and Electrical Engineering, Qingdao University, 308 Ningxia Road, Shinan District
Qingdao, 266071, China
Yingmin Jia

The Seventh Research Division and the Center for Information and Control, School ofAutomation Science and Electrical Engineering, Beihang University (BUAA), 37 Xueyuan Road, Haidian District
Beijing, 100191, China
Jinpeng Yu

College of Automation and Electrical Engineering, Qingdao University, 308 Ningxia Road,Shinan District
Qingdao, 266071, China
E-mail: zhaolin1585@163.com,ymjia@buaa.edu.cn, yjp1109@hotmail.com
http://www.qdu.edu.cn/

Abstract
This paper is concerned with the fixed-time consensus tracking problem for multi-AUV (autonomous underwater
vehicle) systems with uncertain parameters and external disturbances. Firstly, a fixed-time terminal sliding mode is
proposed, which can avoid the singularity problem. Then, a continuous distributed consensus tracking control law
is designed based on Neutral Network approximation technique, which can guarantee the consensus tracking errors
converge to the desired regions in fixed time. A simulation example is given to show the effectiveness of proposed
methods.
Keywords: Multi-AUV systems; Terminal sliding mode; fixed-time stability; Neutral Networks.

can only guarantee the closed-loop system is
asymptotically stable. For the distributed cooperative
control, one significant requirement is the fast
convergence rate. Compared with the asymptotic
control approaches, the finite-time control approaches
can not only provide fast convergence rate but also
provide higher tracking precision and better disturbancerejection ability [4]. Therefore, many finite-time control
laws are proposed for various multi-agent systems in thepast few years [5]–[7]. However, the settling time can

1. Introduction
Distributed cooperative control of multiple AUVs has
been paid to much attention due to its potential
applications in oceanographic surveys and deep sea
inspections [1]. The distributed cooperative control for
multi-AUV systems has been investigated by using thebackstepping technique [2] and the adaptive control
approach [3]. However, the protocols proposed in them

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 400

Lin Zhao, Yingmin Jia, Jinpeng Yu

be estimated dependent on the initial conditions ofsystems in there. In practical applications, we desire thatthe settling time is estimated independent on the initialconditions of systems. In this paper, we will further
investigate the adaptive finite-time consensus tracking
problem for multiple AUVs with uncertain dynamics
using fixed-time terminal sliding mode.

M i vii

Di vi vi

gi

i

i

wi

bi pi

pd
(2)

e2i

aij pi

pj

bi pi

pd

j 1

H I 1 , e2
H I 2 , where
Then, we have e1
T
T T ,
T
T T ,
T
e1 [e11 , , e1n ] e2 [e21 , , e2 n ][ 11, , 1Tn ]T ,
1
T
T T ,
,
.
[ 21 , , 2 n ]pi pd 2i pi pd21iNow, define the FTTSM vector as s [ s1T ,[ si1 , si 2 , si 3 ]Twhere si

3

si
with

e1i

i

[ i e1i1 ,
sig

e1i

i

vi

pj

n

This paper considers the networked multiple AUV
system with n following AUVs and one virtual leader,
and the communications among them are described bya digraph G . The definitions and descriptions of graph
G are given in [7] and [9], which is omitted for brevity.
Assume that all the following AUVs have fixed
attitudes. The translational dynamics of the i-th AUV( i V ) are given as [10]:Riaij pij 1

2. Systems Description

pi

n

e1i

(1)

where pi [ xi , yi , zi ]T , i [ i , i , i ]T denote position
and attitude vectors in the inertial reference frame,
respectively, Ri i is the kinematic transformation
matrix, vi [ui , vi , i ]T is translational velocity vector in
the body-fixed reference frame, M i is the inertia matrix,
Di vi is the damping matrix, gi i is there storing
3
force vector, iis the control force vector,
3
and wiis the disturbance force vector. M i , Ri i ,Di vi , gi i are defined in [10]. In this paper, we
assume that Di vi
and gi i have uncertain
3
parameters. Note that Ri RiT I .Denote pd
as thestate vector of virtual leader and pd , pd are all assumed
to be smooth, bounded and known functions.
Assumption 1. G has a spanning tree, and the leader
node is the root node.

2i

1i

i

, snT ]T ,is given by

e2i

i

e1i

e1i 2 ,ie1i 3 ]T ,andm1

sig e1i

2i

if si

0 or sil1i e1i

l2 i sig e1i(3)

sig e1ik1

n1,0, e1i2(4),if si0, e1im1

1, 2,3, si[ si1 , si 2 , si 3 ]T , ssig( 1i sig e1i

sig e1i) , m1 , n1 , k1

,0 m1k1 1, n1k1 1,

l1i

n1 k
1

2 k1 ( 1i

( 1i

m1

2
k1

1
k1

m1
n1

2i

2
k1

n1
2i

) k1 ,

1
k1) k1 , l2 i

k1 1

0.

From (3), we can obtain the following equation

si

si

e2i

e2i

(5)

i

From the definition of e2i , we further havee2i

di bi pinaij p j bi pd(6)

M i 1 wi(7)

j 1

and from (1), we can obtainpi

3. Main resultshiRi

Mi 1 ii

Ri

i

3.1. Fixed-time terminal sliding mode (FTTSM)

Ri M i 1 g i . Assume thatd i bi Ri i M i wi
wi , wi is an unknown constant.
Then, substituting (6) into (5) yields

Denotesi

where hiRi M i 1 Di vi

Ri vi

1

si

di bi Rii

Mi 1 idi bi Rii

M i 1wi

i

(8)
n

where

i

aij p j bi pd

e2ii

di bi hi .

j 1

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 401

Neutral Networks-Based Adaptive Fixed-Time

3.2. Control law design

Proof. Denotefunction as

From the approximation property of RBF Neutral
Networks (NNs), we have

Wi

i

Zi

i

(9)

i

i

ˆi

2 i i ˆi

i
2
i

2h

siT si Ti

n

V

i

di bi

T
i i

MR

min{2 1 1 m2

s

1

1, n2

1 ,
1

2

0

2

n2 1
2

such

and

e2i

vectors

1

1
1 n2

1

1

converge into regions2max{ , (

e1i

(

s

)

1
m1

(

) ,(

s

1i

max{ se2 i1min{ 1min 2

minmin{ i },

l1i
m2 1
2

)

1
k1

oi

e1i

2
e1i

l2i

,

m2 1
2

i
i

3
0

1,

3
i1

i 1

0

2

V

n1
e1i

2i

m2 1
2

V

1

i 1
min

(H )

,

i

i

min

2
e2 i

(H )

n2 1
2

1
1 n
wi ,2
2i1

2
i

i i
i 1

i

2
i

i

2
i

i 1

i 1

V

n2 1
2

n

i

n

2

2 i i ˆi

i 1

,2

2

i 1

n

1

i

1

2
i

i

n

2 2 },

3

n

n2 1

n

i

i 1

2
i

m2 1
2

i
,2

i

Suppose that there exists a compact set, then we havei |i(2oi 1)
,
2oi

1

i 1

si

(16)

)k1 },

n2 1

2
e1i

1 T
1 ,2
si s
i
2
2

n

min{ 1i },

n2 1
min 2

3
i2

1

i 1

}, 1min

1 2
hi
2

i

Using the similar proof as in [9], we haven

( i1 em11i

s

m2 1
2

n

i

i 1

(15)

) },

,

T
i

n

m2 1

si

1 n
wi ,2
2i1

}

1
n1

min{ 2 min 2 2 ,min{ 2i }, i

1
,0
2

1 T
1 ,2
si s
wi
2
2

2

n2 1

2 min

wi si

2i

min

2

1 T
T
s s Wi
2 i i
2hi

n

and

1

i

V

respectively in fixed time, where
2
1
k1

i 1

siT

Substituting (10), (11), (14) into (13) yields

respectively in fixed time, and finally the

and

n

wi si

(14)

in fixed time, the local neighborhood state errors
e1i and e2i ,1, 2,3 converge into the regions
e1i

i 1

siT

that

1
1 n2

n

,2

n

siT di bi Ri Mi 1 i(13)1
ˆi Ti i si
2hi2
(11)

n2

i2

1
1 m2

1

si

(12)

(10)

i

sig sii1where i1 0, i 2 0, 0 m2
si converges into the region

2
i

From

m2

sig sinsiT sii 1

where i , i , hi are designed positive constants.
Theorem 1.Suppose that Assumption 1 holds for system(1), then we can choose the control law

1

1 T
1 n 1
S S
2
2i1 i

we have[ piT , piT , pTj , pTj , pTj , pdT , pdT , pdT ]T
and
ˆ
,
is
a
constant.
Denoteastheestimate0iii2Wi , then the adaptation law is designed asi

ˆ i , and choose the Lyapunov

i

V

Zi

where
of

T

i

1 n
wi ,2
2i1
n

i

i 1
n

1
wi , 2
2 i1

.

, if

, if

i

min

i
m2 1
2

n
i 1

i
,2

i

2

,2
i

such that

min

2

i
i
i
i

(17)
© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 402

Lin Zhao, Yingmin Jia, Jinpeng Yu

From (16) and (17), we can further obtain
n

V

1V

m2 1
2

1
2

n2 1
2

n2 1

V 2

(18)

2
1
It can be seen from Lemma 2 in [8] that the system(12) is practical fixed-time stability. Moreover, si will
converge into the region sis in fixed settling time.
The next proofs are similar with that of [7] and [9], thus
are omitted for brevity.
4.

Simulations

We consider a direct network with three AUVs and avirtual leader, the matrices L and B are described as:
0 0 0
1 0 0

L

1 1 0 ,B

0 0 0

1 0 1

0 0 0

Fig. 1. Response curves of pd and pi (i=1,2,3) under control
law (11).
2. R. S. Cui, S.B Ge, V. E. How and Y. S. Choo, Leader–follower formation control of underactuated autonomous
underwater vehicles, Ocean Engineering,37(17–18)(2010)
1491–1502.
3. S. P.Hou and C. C. Cheah, Can a simple control scheme
work for a formation control of multiple autonomous
underwater vehicles?, IEEE Trans. Contr. Syst. Tech.,
19(5) (2011) 1190–1201.
4. L. Zhao and Y. Jia, Finite-time attitude tracking control
for a rigid spacecraft using time-varying terminal sliding
mode techniques, Int. J. Control, 88(6) (2015) 1150-1162.
5. S. Li, H. Du and X. Lin, Finite-time consensus algorithm
for multi-agent systems with double-integrator dynamics,
Automatica, 47(8) (2011) 1706-1712.
6. D. Meng, Y.Jia and J. Du, Nonlinear finite-time bipartite
consensus protocol for multi-agent systems associatedwith signed graphs, Int. J. Control,88(10) (2015) 20742085.
7. L. Zhao and Y. Jia, Decentralized adaptive attitude
synchronization control for spacecraft formation using
nonsingular fast terminal sliding mode, Nonlinear Dyn.,
78(4) (2014) 2779-2794.
8. B. Jiang, Q. Hu and M. I. Friswell, Fixed-time attitudecontrol for rigid spacecraft with actuator saturation andfaults, IEEE Trans. Contr. Syst. Tech., 24(5) (2016)
1892-1898.
9. L. Zhao, J. P. Yu and C. Lin, Distributed adaptive
consensus tracking control for second-order multi-agent
systems using fixed-time terminal sliding mode,
submitted for publication.
10. S. Li and, X. Y. Wang, Finite-time consensus andcollision avoidance control algorithms for multiple AUVs,
Automatica, 49(11) (2013) 3359-3367.

We assume that all the AUVs have the samestructure
and
the
model
parameters
are M i diag{175.4,140.8,140.8}, Di {120 + 90 ui ,90 90 vi ,150 +90

,
,
5 i
10 i
12
[10]. The response curves under control law (11) are
shown in Fig. 1. Note that the control law (11) canensure the closed-loop system has desired robustness.

5.

i

}, i

Conclusions

This paper studied the fixed-time consensus tracking
control of Multiple AUVs. A FTTSM based adaptive
chattering-free control law was designed, which could
guarantee the closed-loop system had desired fixed-timetracking performance.
Acknowledgements
This work was supported by the National Natural
Science Foundation of China (61603204), Natural
ScienceFoundationof
Shandong
Province(ZR2016FP03), and Qingdao Application Basic
Research Project (16-5-1-22-jch).
References
1. T. I. Fossen, Marine control systems: guidance,
navigation and control of ships rigs and underwater
vehicles (Trondheim: Marine Cybernetics, 2002).

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 403

Conducted Electromagnetic Interference Prediction of the Buck
Converter via Neural Networks
Sumin Han, Fuzhong Wang

School of Electrical Engineering and Automation, Henan Polytechnic University, 2001 Century Avenue
Jiaozuo, 454000, P.R.China
hansumin@hpu.edu.cn; wangfzh@hpu.edu.cn
www.henan polytechnic university.edu

Abstract
This paper proposes an approach using neutral networks to predict conducted electromagnetic interference (EMI) on
the power supply line in the buck converter. The experimental scheme is designed to collect the input and output
target samples. It establishes a three-layer network including one hidden layer, whose activation function is of thehyperbolic tangent type .By the conducted EMI predicting model trained, interference prediction waveforms is
obtained and analyzed . The results demonstrate that the approach is decent.
Keywords: EMI prediction; DM; CM; neural networks; buck converter

Deep study of EMI prediction theory has been done
throughout the world. Some specialists construct various
analysis and prediction models such as: source model,
sensitive equipment model, the coupling model andsystem analysis model and so on. These methods finish
prediction through the physical, or equivalent circuit, orthe spice model. In general, the model study method iseffective to the certain kinds of circuits, whose
effectiveness for other circuits is difficult to verify.
Furthermore, the modeling process itself is relativecomplex 4-6.
Some scholars and engineers have proposed manymeasurement methods to obtain the EMI emission79.These methods of systems generally require relatively
large amount of calculation and specialized
equipment .Moreover, the different circuit topologicalstructure needs the specific testing scheme. Thus the
proposed methods are short of universality to enlarge
their usages. Therefore, seeking a more reasonable

1 Introduction
EMI prediction is the key point to realize electromagnetic
compatibility (EMC) of the power electronic system. It
can also provide a referential opinion for EMC evaluationand design. Because of the existence of nonlinear
switching devices in a power electronic system, which
frequently switch their working states, producing highdi/dt and du/dt signal. Consequently, the dramatic signal
can conduct to the sensitive elements by conductedcoupled channel, causing interference. Due to thenonlinearity of power electronic elements, and theuncertainty of the interference sources and coupling
channels in which parasitic elements may be physicallyinaccessible inside the module package, EMI prediction
problem has been a very complicated and challenging
task1-3. Prediction accuracy is difficult to improve formany factors of influencing interference.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 404

Sumin Han , Fuzhong Wang

forecast and analysis method without much measurement
has always been a research focus for the scientificresearch workers in EMC field
The objective of this paper is to employ neuralnetworks to predict the conducted DM EMI on the powerline of a buck converter. Section II designs the neuralnetworks of the DM EMI of the buck converter, including
the collection of input and output samples andexperiment. Section III trains the samples anddemonstrates the results and their comparison. Finally,
section IV concludes the paper.

Fig.1 Buck converter and samples collecting scheme

2. BP Neural Networks Design
The input signal is passed from the input to output layer
through the hidden layer in the typical BP network.
Namely, each layer’s neurons can only affect the state of
the next layer. If the values of the output layer is notexpected, the error is propagated backward. According to
the prediction error, BP network adjusts the weights andthresholds, so that the predicted values can approximate
gradually the expected output10-11.
The neural network design consists of the following
parts: input and output sample collection andpreprocessing, BP network training and predicting andthe analysis of the results.

Fig.2 Experiment of the input and output samples collection of
the buck converter

As shown in Fig.1, a DC-LISN(Line ImpedanceStabilization Network) is connected between the power
supply and the converter for acquiring the DM and CM
interference signals. The MOSFET of the circuit shownin Fig.2 is driven by a pulse signal working at the basic
frequency of 100kHz, which is produced by a function
generator.
All the signals are obtained by a scope recorder witha voltage probe ,or a differential voltage probe or acurrent probe ,as is shown in Fig.2 .Particularly, the DM
and CM interference signals still need processing aftergetting the potential of node a and b,as is expressed

2.1. Input and output samples’ acquisition andpreprocessing
The DM and CM interference on the power supply linein the buck converter are identified as the output samples,
which produces mainly from the nonlinear switching
devices. There are two switching devices in the common
buck converter shown in Fig.1, which are MOSFET anddiode respectively. Hence, we can preliminarily
determine the voltage at the drain and source electrode of
the MOSFET, drain current, the diode voltage are most
relevant to DM and CM signals. The experimental
electric circuit is designed and shown in Fig.2. The
values of the electrical components of the buck converterin Fig.1 are

R 100 , L

1 mH

,C

vdm

va

vb
2

,

vdm

va

vb
2

where, vdm is the DM signal ,while vcm the CM one.
Consequently we get the waveforms of the input andoutput signals illustrated in Fig.3.There are five kinds ofsignals, which are the voltage at the drain and source
electrode of the MOSFET, expressed vds, drain current
ids and the diode voltage vd respectively. Each signal is

100μF

The power supply voltage is set at 5V.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 405

Conducted Electromagnetic Interference Prediction

3. BP Neural Networks Training and
Forecastingcollected a total of 5001 samples over the five cycles, as
is illustrated in Fig.3.

Fig.3

The BP neural network adopts a three-layer structure,
namely input layer, hidden layer and output layer, withthree input variables and two output variables, which are
represented as x and y respectively. According to theexperience and continuous trial and error, we determinethat the hidden layer includes seven neurons. The
activation function of the output layer is linear identical
function, while the hidden layer activation function thehyperbolic tangent. Then, we train the selected samples
according to the following algorithm. The hidden layer
nodes are represented by hk ;output layer nodes yi;
thresholds of hidden layer nodes θi; thresholds of input
layer node θk; the connection weights between the input
layer and hidden layer nodes vki; the connection weights
between the hidden layer and output layer nodes ωjk ; thedesired output tj, ; the error between the output andexpectations Δj. Steps are as follows.
(i) Select the initial weights of v and ω, generally between(1, 1).
(ii) Calculate the information of output layer nodes for all
training input sample.
(a) Calculate the output values of the hidden layer
nodes.

Input and output samples’ waveforms

2.2. Correlation analysis
The degree of linear correlation between variables can beanalyzed by correlation coefficient. The correlation
coefficient formula is:n( xix )( yi

y)

i 1

ρxy

n

( xi

i 1

x)

2

n

(1)

( xi

x)

2

hk

i 1

Vds
0.78356-0.00795

ids
0.04220.0007886

v ki xii(b) Calculate the output values oflayer nodes)(2)

the output

q

yi

f2 (

jk

zk

k

)

k 1

Tab.1 Correlation coefficient of output and input signals
input
vdm
vcm

n
i 1

where x and y are the means of each variable.
Results are listed in Tab.1,which shows the DM
interference vdm is strongly relevant to the drain-source
voltage vds and the diode voltage vd with the correlation

Outputf1 ((c) Sum the errors between the actual output andthe expected output of all the samples.

vd-0.73634
0.04268

e

1 m q
(ti
2k1i1

yi ) 2(4)

After training the input and output samples, the BP
network predicting model is obtained. The model can beapplied to predict the DM and CM interference byinputting the tested data. The predicted waveforms andthe actual waveforms are as shown in Fig. 4.

coefficients of 0.78356 and -0.73634,respectively. Whileit has hardly relation to the drain current ids with
0.0422.However, the CM interference has a unclear
characteristic with the three input signals. Eachcorrelation coefficient is very small, almost approaching
to zero. The reason is that there may be other motivators
to produce CM interference.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 406

Sumin Han , Fuzhong Wang

Acknowledgements
This work was supported by the Control engineering
open Lab. foundation of Henan Province (KG2011-08).
This work was supported by the Science and Technology
Research Foundation of Henan Education Department(14A510022). This work was supported by National Key
Research and Development Program(2016YFC0600906).
References
1. R. Trinchero, et al.: “EMI Prediction of Switching
Converters,” IEEE Transactions on Electromagnetic
Compatibility 57.5(2015):1-4.
2. M. Ferber, et al.: “Conducted EMI of DC–DC Converters
With Parametric Uncertainties,” IEEE Transactions on
Electromagnetic Compatibility 55.4(2013):699-706.
3. S. Braun, et al.: “A Real-Time Time-Domain EMI
Measurement System for Full-Compliance Measurements
According to CISPR 16-1-1,” IEEE Transactions on
Electromagnetic Compatibility 50.2(2008):259-267.
4. G. Frantz, et al.: “EMC models of power electronics
converters for network analysis,” European Conference
on Power Electronics and Applications. 2013:1-10.
5. J. Meng, et al.: “Noise Source Lumped Circuit Modelingand Identification for Power Converters,” IEEE
Transactions on Industrial Electronics 53.6(2006):18531861.
6. Y. Liu, et al.: “Conducted EMI prediction of the PFC
converter including nonlinear behavior of boost inductor,”
IEEE Trans. Electromagn. Compat., vol. 55, no. 6, pp.
1107–1114, Dec.
7. W.Yan, et al. : “ Time domain measurement method for
conducted EMI,”
Journal of Southeast University(Natural Science Edition) vol. 39, s2 (2009):31-36.
8. Y. Li, et al.: “Time Domain Measurement System for
Conducted EMI and CM/DM Noise Signal Separation,”International Conference on Power Electronics andDrives Systems IEEE, 2005:1640-1645.
9. Q. Feng et al.: “A novel measurement system for thecommon-modeanddifferential-mode-conducted
electromagnetic
interference,”
ProgressinElectromagnetics Research Letters 48(2014):75-81.
10. T.Yang et al.: “Prediction of electromagnetic interference
based on neural network ,” Journal of Beijing Universityof Aeronautics and Astronautics, vol. 39, no.5 (2013),
pp:697-700.
11. Y.Li et al.: “Artificial neural networks-based prediction ofelectromagnetic compatibility problems,” Journal of
Chongqing University, vol. 31, no.1 (2008), pp:1313-1316.

Fig.4. Predicted and actual waveforms of the DM and CM

As can be seen from Fig.4, the predicted DM and CM
interference illustrated by vdmp and vcmp are in wellaccordance with the actual signals illustrated by vdm andvcm. Analysis data show that the relative error of thedependent variable of the scale of DM is 0.045, and CM
is 0.582.Namely, the deviation is bigger.
4. Conclusion
This paper proposes the EMI prediction scheme via BP
neural network. On the basis of the principle theconducted EMI emission of the buck converter, thestructure of the neural network is determined which
includes three-layer, three input signals and two signals.
The authors adopt BP algorithm to train the network andestablish the interference predicting model, by which
they obtain the predicted DM and CM waveforms in time
domain. Then analysis as well as comparison between themeasured and predicted waveforms also demonstrate thepredicted results is satisfactory and the validity of the
proposed approach. However, the proposed approach still
performs effective illustrated in the paper but for aslightly low precision. The predicting approach of thispaper can be extended to other topological types of powerelectronics circuits.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 407

Development of a Tomato Harvesting Robot
Shinsuke Yasukawa

Dept. of Human Intelligence Systems, Kyushu Institute of Technology
2-4 Hibikino, Wakamatsu, Kitakyushu, Fukuoka, 808-0196, Japan
E-mail: s-yasukawa@edu.brain.kyutech.ac.jp
Bingh Li

Dept. of Human Intelligence Systems, Kyushu Institute of Technology
2-4 Hibikino, Wakamatsu, Kitakyushu, Fukuoka, 808-0196, Japan
E-mail: li-binghe@edu.brain.kyutech.ac.jp
Takashi Sonoda

Dept. of Human Intelligence Systems, Kyushu Institute of Technology
2-4 Hibikino, Wakamatsu, Kitakyushu, Fukuoka, 808-0196, Japan
E-mail: t-sonoda@edu.brain.kyutech.ac.jp
Kazuo Ishii

Dept. of Intelligence Systems, Kyushu Institute of Technology,
2-4 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka, Japan
E-mail: ishii@brain.kyutech.ac.jp

Abstract
Most of commercialized robots are for industry, and the robots for agriculture, forestry and fisheries are underdeveloping and not commercialized yet. The reasons for the difficulty are caused by cost-efficiency of therobotization, safety ensuring of the works using robots, outdoor operations, and knowledge transfer problem fromfarmers to computer so on. Tomato is one of important fruit vegetables and most tomatoes are produced in thegreenhouses, or large-scale farms, where the high temperature and humidity, and long harvest age force the farmers
heavy works. With an aim to promote the automation of tomato harvesting, we have organized the tomato
harvesting robot competition and developed a tomato harvesting robot. In this paper, we propose the system oftomato harvesting robot.
Keywords: Tomato-Harvesting Robot, Agricultural Robot.

to the lack of successors is a big problem. "Smartagriculture" is proposed as a future image that will solvethese problems by introduction of robot as the tools to
realize the smart agriculture, is expected 1.
Japan is one of the countries where robots are being
introduced to production sites, but many of the robots
being put into practical use are automation robots forsecondary industries, and the ratio of robots in

1. Introduction
According to the statistical information of the Ministryof Agriculture, Forestry and Fisheries, Japan's food selfsufficiency rate (calorie base) is about 39%, which is thelowest among major developed countries. In the
agricultural field, the aging of the producers anddepopulation are progressing, and the labor shortage due© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 408

S. Yasukawa, B.Li, T. Sonoda, K. Ishii

agriculture, forestry and fishery fields is few. If we candisseminate agricultural robots, we can expect laborsaving, automation, improvement of productivity, etc.
Moreover, it becomes possible to convert informationsuch as management of agricultural crop environment,
cultivation, state and quality. From the viewpoint ofsafety, it is possible to provide accurate information to
consumers. There are major challenges to overcome, butthere is a possibility that a new robot market will becreated in the agricultural field.
Among various fruits and vegetables, tomato is one of
the major fruit and vegetables consuming a lot. Many of
the shipped tomatoes are cultivated in facilities such as
houses, but due to the high temperature and humid work
environment and long harvest period, labor load is large
and labor saving is desired. In the Netherlands exporting
tomato, various techniques are introduced to cultivate
tomatoes and realize higher productivity than Japan. In
this paper, we describe our tomato-harvesting robot.

line at a certain interval. And along the shelf rails for thebogies, the workers move and harvest tomatoes. Thissystem consists of a Kinect v.2, a USB camera, a sixaxis serial link manipulator, an end effector, a computer,and a moving mechanism that carries them and moves
on the rails.
As the strategy,1) Move to the edge of the shelf with a bogie andgenerate an environmental map of the entire shelf fromthe acquired image.
2) By analyzing the environmental map, we extract
information such as tomato condition to be harvested,
its fruit position and total harvest time.
3) Move the dolly to the front of the harvested bunches
and harvest the fruits in order.
3. Tomato Detection Method Using Infrared
Image and Specular Reflection
3.1. IR images of Tomato
From the spectral reflection characteristics, it is known
that tomatoes have high reflectance for both stems andleaves against infrared light, so in this study IR images
were taken in order to detect the fruit of tomatoes .
As a previous study, Fujiura and colleagues focused on
this characteristic and developed a two-wavelength 3D
vision sensor. In this sensor, the distance is measured byplacing near-infrared rays and red lasers coaxially andimaging the reflected light on a PSD (Position Sensitive
Device) with a lens. Three-dimensional shape ismeasured by spatially scanning this. When the laser isincident near the fruit center, the sensor captures
specularly reflected light and responds strongly. In thissystem, the pixel whose spatial response is the
maximum is detected as the fruit position. Also, thestem and fruit are distinguished by the ratio of responseto red light and near infrared light. This sensor blinks
the beam and extracts only the component of theblinking frequency at the light receiving portion,
thereby removing the sunlight component which is thestationary light.

Fig.1 The tomato-harvesting Robot
2. Tomato-Harvesting Robot
The system configuration of the tomato-harvesting
robot used in this research is shown in Fig. 1 (a), and its
external appearance is shown in (b). The robot assumes
the house environment of a large-scale tomato
production facility. In the house, there are tomato
seedlings with multiple bunches, with shelves aligned in© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 409

Development of a Tomato

Fig.2 Dichromatic vision sensor as the previous method.
3.2. Proposed Method

4.

With reference to the method in Section 3.1, we
propose a method for detecting fruits using IR images
and specular reflection. In order to acquire IR images
and RGB-D images, Kinect v.2 was adopted as a visualsensor. Since Kinect v2 acquires depth data by
irradiating with instantaneous infrared light, the IR
image can reduce the influence of sunlight.
Fig. 3 shows a flow of three-dimensional position
measurement of the fruit. Kinect gets RGB-D image and
IR image capturing the stem of tomato at 30 fps (Fig. 3(a)). Next, the IR image is processed to detect theposition of the fruit (Figs. 3 (b) to 3 (d)). From the pixel
information of the RGB-D image corresponding to thedetection position, the fruit color determination and thethree-dimensional position measurement are performed.
In this report, we propose a fruit detection anddiscrimination method in the room. The fruit on the IR
image shows a concentric reaction with a strong centerand a weak perimeter. The gradient orientation (8azimuth angle) of the IR image is calculated not todepend on the absolute value of the response intensity,and the gradient orientation image (Fig. 3 (b), eachorientation is represented by eight color) fruit pattern
template Match. We extract only the region with highcoincidence rate by the binarization process (Fig. 3 (c))
and label it to detect the position of the fruit. Finally, thecorrespondence between the red region extracted image
obtained from the RGB image and the pixel position of
the IR image is taken, and the coloring judgment iscarried out (Fig. 3 (d)).

Evaluation of proposed method using realenvironment image

At the house of Hibikinada Saien Co., Ltd., a largescale production facility, RGB-D images and IR images
were acquired using Kinect, and experiments wereconducted. 47 pairs of images were used for evaluationof the proposed method. The range that the arm reaches,that is, 260 tufts whose distance to Kinect is within 60
cm to 80 cm was targeted. The number of recognition
subjects divided by the number of successful fruit
detection and coloring judgment in all the fruits wasevaluated as the correct answer rate.
As an example, Fig.4 (a) shows the RGB image inthe house and IR image. The box surrounded by thedotted line in the figure is the object to be recognized.
Figures 4 (b) and 4 (c) show examples of bunches thatsucceeded and failed in fruit detection (red in the figure).
The correct answer rate of this method was 88.1%. An
example of failure was divided into two, fruits (14 cases)
shielding the majority of fruits of the main stem andfruits (3 cases) facing the front.

5. Conclusions
In this research, in order to realize a tomato fruit
harvesting robot, we proposed an infrared image andfruit harvesting technique using specular reflection. In
this method, fruit detection in the chamber is carried out
by detecting the center of the fruit which causes
specular reflection in the infrared image and strongly
responds. As a result of evaluation using the realenvironment image, the correct answer rate was 88.1%.
The main reason for the failure was shielding of the
fruit by the main stem, so we plan to add a detection
function for covering objects. In addition, we plan toanalyze the information such as generation ofenvironmental map of the entire shelf, selection of theharvesting target bunches and estimation of harvest timeas an issue within the action strategy.

Fig.3 The proposed tomato detection method.
© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 410

S. Yasukawa, B.Li, T. Sonoda, K. Ishii

References
1. http://www.maff.go.jp/j/tokei/index.html
2. N. Kondo, K. Tanihara, T. Shiigi, H. Shimizu, M. Kurita,
M. Tsutsumi, V. K. Chong, S. Taniwaki, Path Planningof Tomato Cluster Harvesting Robot for Realizing Low
Vibration and Speedy Transportation, Engineering inAgriculture, Environment and Food,2(3), (2009), 108115
3. Kondo, N., Yamamoto, K., Shimizu, H., Yata, K., Kurita,
M., Shiigi, T., Monta, M., Nishizu, M., A Machine
Vision System for Tomato Cluster Harvesting Robot,
Engineering in Agriculture, Environment and Food, 2(2),(2009) 60-64
4. Kondo, N., Yata, K., Iida, M., Shiiji, T., Monta, M.,
Kurita, M., Omori, H., Development of an End-Effector
for a Tomato Cluster Harvesting Robot, Engineering inAgriculture, Environment and Food, 3(1), (2010), 20-24

Fig.4 (a) Test images: RGB (left), infrared (right), (b)Successful detection, (c) Unsuccessful detection.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 411Development of SOM algorithm for Relationshipbetween Roles and Individual’s Role in Rugby
2nd Reports: University Rugby teams analysis using Physical and Psychological data

Yasunori Takemura

Department of Engineering, Nishinippon Insitute of Technology, 1-11 Aratsu Kanda
MIyako-gun, Fukuoka 800-0394, Japan

E-mail: takemura@nsihitech.ac.jp

Abstract
Victory or defeat in team sports depends on each player’s technique, physical strength, and psychologicalcondition. It follows that team performance depends on the player’s adaptation to (suitability for) a certain role(position in the team) and the relationships between different roles. We assume that team performance is related tophysical and psychological features. Many researchers have proposed that physical features determine a player’s
suitability for a position. Psychological features have also been researched as factors of position adaptation.
However, each feature has been investigated independently. The present research aims to develop a clustering
method that considers both physical and psychological features in judging an individual’s role and adaptation in thegame. This paper reports the concept of the algorithm and result of analysis using both physical data andpsychological data.
Keywords: clustering, data mining, self-organizing map

how good his/her physical features, is not athleticallysuited to team sports without having good motivation interms of setting goals and training. That is to say, for anindividual or team to be successful, a player needs tohave both good physical features (e.g., techniques,balance, height, and weight) and good psychologicalfeatures. For example, Saijo [7] presented thepsychological features of Japanese and New Zealandrugby players.
In this way, the suitability of a player in a certain
position and the relationships between different
positions in team sports are related to physical andpsychological features. As it stands now, a coach or
selector decides the player suitability and relationships
between positions him/herself. However, does it followthat good decisions are made? Previous research has notclarified athlete adaptation to positions and relationships

1. Introduction
Victory or defeat in team sports depends on eachplayer’s individual technique, physical strength, andpsychological condition. Similarly, it can be said thatthe suitability of an individual to a certain position in
the team affects the team’s performance.
In previous research, Barry and Cureton [1], Nicks
and Fleishman [2], Larson [3], McCloy and Young [4]and others clustered physical features and conductedfactor analysis in investigating sports performance. In
Japan, Tokunaga studied the diagnosis criteria forathletic adaptation (i.e., suitability) in sports [5]. These
works showed that physical features are one of thestrongest factors determining athletic adaptation.
However, Matsuda [6] showed that an athlete, no matter© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 412

Yasunori Takemura

between positions considering both physical andpsychological features.
In the present study, we develop a clustering
algorithm for positioning adaptation and relationships inteam sports. This paper reports results of analysis using
both physical data analysis and psychological data.

No. 8s, six SHs, five SOs, 10 CTBs, seven WTBs andeight FBs.
2.3. Psychological data

2. Research plan and method
2.1. Selection about sport and data
We apply a team-sport clustering algorithm to rugby.
Rugby is selected because a rugby team has a large
variety of positions and player attributes. A rugby gameis called a match. It is a competition between two teams.
Each match lasts for 80 minutes plus time added toaccount for injuries and stoppages during the match. A
match consists of two halves of 40 minutes each. The
team with the greater number of points at the end of thematch wins. Normally, a rugby team has a maximum of
15 players on the field, and seven substitutes. Each
player has a specialized position. There are eight
forwards, numbered 1 to 8, and seven backs, numbered
9 to 15. Player number 1 is a prop (PR), number 2 is thehooker, number 3 is another prop (PR), numbers 4 and 5
are locks (LOs), number 6 is the blindside flanker (FL),
number 7 is the open-side flanker, number 8 is the“number 8” (No. 8), number 9 is the scrum-half (SH),number 10 is the fly-half (or stand-off, SO), number 11
is the left wing (WTB), number 12 is the inside center(CTB), number 13 is the outside center (CTB), number
14 is the right wing (WTB) and number 15 is the
fullback (FB).
2.2. Physical features
Physical data of members of the N university rugby
club were recorded in 2014. The N University rugby
club in 2014 comprised 56 students (14 fourth-gradestudents, 14 third-grade students, 12 second-gradestudents and 16 first-grade students). There were 32
forwards (FWs) and 24 backs (BKs).
Physical features recorded were height [cm], weight[kg], body fat [%], neck length [cm], brachium length[cm] (R: right and L: left), chest circumstance [cm],waist circumstance [cm], hip circumstance [cm], lengthof thigh (R, L) [cm], length of calf (R, L) [cm],anteflexion while standing (flexibility) [negative value,cm], number of abdominal crunches completed in 30 s,
bench-press weight [kg], number of squats completed in
30 [sec], number of chin-ups completed in one effort,
and the distance run in 7 minutes [m]. The data set thus
had 19 physical dimensions. Some members played
more than one position (Therefore, number of the data isoverlapping). There were 12 PRs, 10 LOs, 12 FLs, six

Psychological data of members of the N rugby clubwere recorded in 2014. Psychological features measured
were those of the Diagnostic Inventory of PsychologicalCompetitive Ability for Athletes (DIPCA.3) [8].
DIPCA.3 measures 12 types of psychological ability
in a 48-item questionnaire. DIPCA.3 is often used
before mental training, because it reveals athletic
strengths and weaknesses. The DIPCA.3 check sheet
consists of 48 questions that measure psychologicalability and four questions that measure reliability. These
questions have already been analyzed by good–poor
analysis (G-P analysis), and the answers provide 12
criteria relating to five factors. The factors are
motivation in sport, mental stability and concentration,
confidence, operation capability and cooperativeness.
Additionally, we measure the reliability of the answers
by comparing answers to similar questions. To examine
these factors in detail, 12 criteria are described.
Motivation in sport consists of four criteria: endurance,fighting spirit, self-realization motivation andmotivation to win. Mental stability and concentrationconsist of three criteria: the ability to relax, capacity toconcentrate and self-control. Confidence consists of two
criteria: determination and confidence. Operation
capability consists of two criteria: predictive capability
and judgment. Cooperativeness has only one criterion,
which is simply described as cooperativeness. DIPCA.3
provides a total score (ability). Each criterion is scored,
and the score of each factor is the sum of scores for the
related criteria. The total score is calculated bysumming the score for each factor.
2.4. Algorithms of this Clustering system
This clustering algorithm is reported by 1st reports [9].

3. Experimental Result
The data of the feature map obtained by putting thedata group in the SOM is shown in Fig.1. Fig.1 shows
the feature map of 10x10. The position and data numberof personal data are attached to the best matching unit
(BMU) as labels. The color of the feature map indicatesthe distance from the neighboring unit, which meansthat the distance between the units is far away from blue
to red. As shown in Fig.1, forward (FW) is distributedin the upper part of the feature map, and members in the© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 413Development of SOM algorithm

position of Backs (BK) gather in the lower part.
Consider psychological feature quantities input as input

same map in Fig.1. Therefore, the individual labeling
position is the same as Fig.1. For items oncooperativeness, low-level players are gathered in theupper right, and higher-level players are arranged asthey go to the lower right. For this reason, in this team,highly level athletes on cooperativeness are often
assigned to BK and it is understood that low players are
assigned to FW. In other words, coach can analyze thepsychological state well and find out that is allocating
positions.
As shown in Fig. 3, the unit which is denoted byblack circle indicates a regular member. Analysis of thisresult shows that athletes with high psychologicalability values are selected as regular members. In otherwords, the coach knows that for the selection of regularmembers in this team, there is a tendency tocomprehensively select high level psychological players
and high cooperativeness level players as psychologicalconditions.

Fig.1 Feature map of experimental result (total score)Fig.2 Feature map of experimental result (Cooperativeness)

Fig.3 regular position in Feature maps

data. When the overall score of DIPCA.3 (total score is
not included in the input data) is examined, the overall
score will be higher as it goes to the lower left. Basedon the above results, it is considered using using
physicalcharacteristics
andpsychologicalcharacteristics that the overall superiority of positioningof this team by the supervisor.
Fig.2 shows the results of evaluation of psychologicalanalysis on cooperativeness. The feature map uses the

4. Summary
In this paper, we experimented on clustering machineusing SOM with the goal of processing data on
individual physical characteristics and psychologicalfeatures in group sports. In this research, we developed
a clustering device that takes physical features andpsychological features into 46 input data, and analyzing© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 414

Yasunori Takemura

the results. The feature map is the result of clustering,can be largely categorized into FW and BK, and canclarify psychological features and physical features.
And, we can find how coach decide the position andregular members. As a future works, for developing aclustering device, if we can use more detail input data
(add the dimensions) can perform more precise
diagnosis.
Acknowledgements
This work was supported by a Grant-in-Aid for Young
Scientists (B) 15K21570 from JSPS KAKENHI.
References
1. Barry, A.J. and T.K. cureton, “Factor Analysis of
Physique and Performance”, Res. Quart., 23-3:100 ~ 108,
1961
2. Nick, D.C. and E.A. Fleishman, “What Do Physical
Adaptation Test Meassure?,”, A Review of Factor
Analysis Studies, Educational and Psycohological
Measurement, 22-1:77-95, 1962
3. Larson, L. A., “A Factor Analysis of Motor Ability
Bariables and Tests for College Men, “, Res. Quart., 12:499-517, 1941
4. Mcloy, C.H. and R.D. Young, “Test and Meassurementin Health and Physical Education”, New York,Applenton-Century-Crofts, Inc., pp.51-65, 1954
5. M. Tokunaga, “Factor Analysis about sports adaptation”,
Japan Journal of Physical Education, Health and Sport
Sciences, Vol.22-2, pp.71-80,1977
6. I. Matsuda, R.N. Singer, “Motor learning and humanperformance”, ISBN978-4-469-26119-6, Taishukanshoten, 1986, In Japanese
7. O.Saijo, K.Suda, et., al., “On the Psychologicl Aptitude
of Rugby Foot Ball Players –From Comarision New
Zealand University Selected Team with Nippon Collegeof Physical Education Team –“, Journal of Nippon
College of Physical Education, Vol22-2, pp.135-138,
1993
8. M.Tokunaga,“Evaluation
scalesforathletes’
psychological competitive ability : development andsystematization of the scales” Japan Journal of Physical
Education, Health and Sport Sciences, Vol46-1, pp.1-17,
2001.
9. Y.Takemura, et. al., “Development algorithm for
Relationship between Role and Individual Role
Adaptation in Rugby”, CD-Proc. of WAC2014, Hawaii© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 415

Ball Dribbling Control for RoboCup Soccer Robot
Shota Chikushi

Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology
2-4 Hibikino, Wakamatsu, Kitakyushu, 808-0196, Japan
E-mail: chikushi-shota@edu.brain.kyutech.ac.jp
Tharindu Weerakoon

Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology
2-4 Hibikino, Wakamatsu, Kitakyushu, 808-0196, Japan
E-mail: weerakoontharindu@brain.kyutech.ac.jp
Takashi Sonoda

Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology
2-4 Hibikino, Wakamatsu, Kitakyushu, 808-0196, Japan
E-mail: t-sonoda@brain.kyutech.ac.jp
Kazuo Ishii

Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology
2-4 Hibikino, Wakamatsu, Kitakyushu, 808-0196, Japan
E-mail: ishii@brain.kyutech.ac.jp

Abstract
RoboCup is a platform designed to promote the research related to Artificial Intelligence (AI) and robotics. We alsoorganize a RoboCup soccer team “Hibikino-Musashi” and working on co-operated behavior control system using
multiple autonomous mobile robots. In order to realize co-operated behavior in the soccer game, the ball handlingsystem is one of the important issues for dribbling and passing the ball to teammate robots. In this paper, a control
method of the ball rotation using two active wheels, which are attached in front of the soccer robot and designed tohave friction on the upper side of the ball, is proposed. The forward and inverse kinematics between the ball-motion
and two active wheels are derived, and the ball handling mechanism is developed and evaluated based on the resultsof simulations and experiments.
Keywords: RoboCup; ball-wheel kinematics, ball handling mechanism

researchers to develop and promote Artificial
Intelligence and robotics from different aspects [1].
The soccer robots should be autonomous and make agoal in the dynamic environment where many robots
move around intricately intertwined with other friend andopponent robots. On developing the co-operated

1. Introduction
The robot competition RoboCup is proposed to be alandmark project whose target is to realize the robot
soccer team to defeat the human World Cup champion
team until 2050. The project is expected to encourage© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 416

Shota Chikushi, Tharindu Weerakoon, Takashi Sonoda, Kazuo Ishii

behaviors using soccer robots, the invention of newtechnologies is expected because not only the technicalissues of robot development but also human-robot
interactions should be considered such as safety
regulations not to damage human and robot themselves,
reading the situations, ambiguous determinations and soon. The robot technology will be utilized in service
markets and human-robot interactive tasks in the future.
We also organize a RoboCup soccer team “HibikinoMusashi” to join RoboCup Soccer Middle Sized League(MSL) and working on co-operated behavior control
system using multiple autonomous mobile robots. To
promote intelligence of robots, MSL rules are annuallyreviewed and updated. In recent years, top teams’ robots
show collaborated behaviors as a team and different ballpassing methods for various situations are developed toachieve high score. In order to realize co-operated
behavior in the soccer game, the ball handling system is
one of the important issues for dribbling and pass the ballto teammate robots. The quick motion of robots with highball retention capability has the big influence on planning
the action of the robot.
The new soccer robot of “Hibikino-Musashi” is shownin Fig. 1. The robot has omni-directional mobility and anomni-vision camera system on the top, and is controlled
by a mounted laptop computer. Comparing with ourprevious robot [2], the new one can move 1.5 times fasterand has the ball handling mechanism in front.
In this paper, a control method of the ball using two
active wheels, which are mounted in front of the soccer
robot and designed to have friction on the upper side ofball, is proposed. The forward and inverse kinematics
between the ball-motion and two active wheels is derived,
and the ball handling mechanism is developed andevaluated based on the results of simulations andexperiments.

should rotate smoothly with respect to the traveling
direction of the robot to satisfy the RoboCup regulation
and realize smooth robot motion. Moreover, the ballhandling mechanism is restricted to cover one-third of theball [5]. The RoboCup team TechUnited developed themechanism using two normal wheels and a solenoid
kicker which can keep the ball and select loop-pass or
grounder-pass [6]-[8]. CAMBADA team developed
using two omni-wheels and shows the similar function[9]. However, the forward and inverse kinematics
between ball motion and wheels are not well defined andthe ball motion is controlled by heuristic methods. We
introduce the mathematical model regarding ball motion
and active wheels.
The overview of mechanical design of the ballhandling mechanism is shown in Fig. 2, and theschematic design is in Fig. 3. In the mechanism, active
wheels are attached to the lever tips to rotate the ball. The
two levers move upward passively when the active
wheels rotate to bring the ball toward its body, and thesprings and dumpers are connected between the body andthe levers to absorb the collision impact of the ball. The
small omni-wheels in the bottom are passive rollers toprevent the ball from contacting to the body. If the robot
detects the approaching of the ball, the active wheels on
the levers start to rotate to bring the ball inside the robot,
the friction between the ball and wheels generated by the
spring force get the ball inside the robot.
To recognize the ball situation, the lever shifting
angle , which is the angle from the initial position
to the maximum angle, is measured by thepotentiometer. Then,equals to(Fig.4), the active
wheel control starts to synchronize with robot’s
movements. The ball handling mechanism is designed tohold the ball by four wheels during dribbling.

2. Development of Ball Handling Mechanism
Several different ball handling mechanisms forsoccer robot have been reported in literature [3], which
are mainly classified into two types, one is to control theball by using two active wheels mounted in the front of
the robot and another is by using arms [4]. With theselection of soccer robots going on, the ball handlingmechanism by two active wheels has become themainstream. The advantage of the ball handlingmechanism using active wheels is the capability ofkeeping the ball even in the lateral- and reverse-motion.
On the other hand, the disadvantages of the mechanism
are the complexity in design and necessity of ball motion
control algorithm suitable to the robot motion. The ballFig. 1. Soccer robot of “Hibikino-Musashi”© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 417

Ball Dribbling Control for

the wheel and the ball is required to be constant, so thatan experimental setup is developed as shown in Fig. 5
and Fig. 6.
The setup is designed to be able to change its wheels
around the ball in arbitrary position and angle. Totally,
six encoders are attached to the robot as an internal sensor
to measure two motions, ball motion and wheels. The
four encoders are to determine velocity and orientation of
the experimental setup and installed on each omni wheel.
The two encoders are attached directly to the active
wheel motors.

a. Projection viewb. Top viewFig. 2. Overview of the wheel arrangement

Fig. 3. Overview of ball handlling mechanism
Fig. 5. Experimental device of ball handling mechanism(Projection view)Fig. 4. Ball holding state

3. Kinematics and Experimental Setup
The forward and inverse kinematics between ball motion
and active wheels are represented as shown in eq.1, [10].

⃗=( ⃗ × ⃗ /)∙⃗Fig. 6. Experimental device of ball handling mechanism(Top view)(1)

Wheel angular velocity vector ⃗ can be expressed byvelocity vector at contact point and wheel radius.
Velocity vector at contact point can be expressed by ballangular velocity vector ⃗ and position vector ⃗ fromthe ball center to contact point.
To evaluate the kinematics, following experiments are
performed to obtain the state variables of ball motion andactive wheels. In the experiments, the distance between

4. Experimental Results
The kinematics between ball motion and wheel isevaluated by translational motions in surge, sway andtheir combination (see Fig.7-9). The solid and dot lines
mean the velocity in x-direction and y-direction,
respectively. Black line means the forward kinematics© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 418

Shota Chikushi, Tharindu Weerakoon, Takashi Sonoda, Kazuo Ishii

obtained from active wheels’ data, and blue are fromexperimental setup motion.
In the evaluation of surge motion (y-direction in the
robot coordinate), the ball is controlled to go backward
270 (deg.) with the velocity of 1.0 m/s. The comparisonof the velocities from setup motion and kinematics
calculated by active wheel rotation is shown in Fig. 7.
The result shows that the experimental setup motion iswell controlled to be 1.0 m/s, and velocities from the
setup and calculation of kinematics become almost samevalues.
In the sway motion (see Fig. 8), the velocity of xdirection is controlled to be 1.0 m/s. The experimental
data of setup show also good consistent with kinematics
calculation. In the experiment of Fig.9, the ball is
controlled to go in 225 degree and the velocities in x- andy-direction are equal. As the result, the ball moves inabout 240 degree, however, the ball motion data andkinematics calculation show good consistent.

Experimental device velocity(m/s)

1.5
IK_Xvelo

IK_Yvelo

EXP_Xvelo

EXP_Yvelo

1
0.5
00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
-0.5
-1
-1.5

Fig. 9.

Time (Second)Experimental results of dribble kinematics (225°)

5. Conclusion

Experimental device velocity(m/s)

1.5

In this paper, the forward and inverse kinematics between
the ball-motion and two active wheels are evaluated bysurge and sway motions using developed experimental
setup. The results show that the ball motion data andkinematics calculation show good consistent. The ballhandling mechanism has enough possibility to realize
dribbling behavior of the soccer robots.

Velocity of X direction (Forward Kinematics)

1

Velocity of Y direction (Forward Kinematics)
Velocity of X direction (Experiment)

0.5

Velocity of Y direction (Experiment)0
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16-0.5-1References-1.5

1. R. Soetens, M.J.G. van de Molengraft, B. Cunha,“RoboCup MSL - History, Accomplishments, Current
Status and Challenges Ahead” RoboCup 2014: Robot
World Cup XVIII, 624-635, (2014)
2. S. Chikushi,M. Kuwada, M. Ishikura, T. Nagao, R. Itohara,
S. Watanabe,K. Hisano, R. Shimada, K. Matsumoto, M.
Tominaga, N. Machida, K. Ishii, H. Miyamoto, T. Sonoda,
Y. Takemura “Hibikino-Musashi Team Description Paper2016” RoboCup 2016 Leipzig, (2016)
3. S. Stancliff, “Evolution of Active Dribbling Mechanisms in
RoboCup” 16-471 Project, (2005)
4. K. Fujimoto, K. Ishii, Y. Kitazumi “Development of a ballhandling mechanism for a RoboCup Soccer Robot”
Robotics and Mechatronics for Society2011, (2011)
5. M. Asada, et.al. “Middle Size Robot League Rules andRegulations for 2015” MSL Technical Committee 1997–
2015, pp. 15, (2015)
6. K.P. Gerrits, M.J.G. van de Molengraft, R. Hoogendijk, M.
Steinbuch, “Ball Handling System for Tech UnitedTime (Second)Fig. 7.

Experimental results of dribble kinematics (270 °)Experimental device velocity(m/s)

1.5
1
0.5
0
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16-0.5-1
IK_Xvelo-1.5

Fig. 8.

IK_Yvelo

EXP_Xvelo

EXP_Yvelo

Time (Second)Experimental results of dribble kinematics (0°)© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 419

Ball Dribbling Control for

7.

8.

9.

10.

Soccer Robots” Eindhoven University of Technology,(2012)
J.J.T.H. de Best, M.J.G. van de Molengraft, M.
SteinbuchA “Novel Ball Handling Mechanism for the
RoboCup Middle Size League” Mechatronics 21(2),469-478, (2011)
C. Lopez, F. Schoenmakers, K. Meessen, Y. Douven, H.
van de Loo, D. Bruijnen, W. Aangenent, B. van Ninhuijs,
M. Briegel, P. van Brakel, J. Senden, R. Soetens, W.
Kuijpers, J. Reijrink, C. Beeren, M. van 't Klooster, L. deKoning, M.J.G. van de Molengraft, “Tech United
Eindhoven Team Description 2016” RoboCup 2016
Leipzig, (2016)
B. Cunha, A. J. R. Neves, P. Dias, J. L. Azevedo, N. Lau,
R. Dias, F. Amaral, E. Pedrosa, A. Pereira, J. Silva, J.
Cunha and A. Trifan “CAMBADA’2015: Team
Description Paper” RoboCup 2015 Hefei, (2015)
S. Chikushi, T. Weerakoon, K. Ishii, T. Sonoda “Motion
Analysis and Control of the Ball Operation for Dribbling
Action in RoboCup Soccer Robot” SCIS & ISIS 2016,(2016)© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 420

Strategy Analysis of RoboCup Soccer Teams
Using Self-Organizing Map
Moeko Tominaga

Graduate School of Life Science Systems Engineering, Kyushu Institute of Technology
2-4 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka, 808-0196, Japan
E-mail: tominaga-moeko@edu.brain.kyutech.ac.jp
Yasunori Takemura

Faculty of Engineering, NishiNippon Institute of Technology
1-11, Aratsu, Kanda-town, Miyako-gun, Fukuoka, 800-0397, Japan
E-mail: takemura@nishitech.ac.jp
Kazuo Ishii

Dept. of Human Intelligent Systems, Kyushu Institute of Technology
2-4 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka, Japan
E-mail: ishii@brain.kyutech.ac.jp

Abstract
In the soccer games, the player’s behavior changes depending on the game situation such as winning or losing,score gap, remaining time. The players act more offensive when their team is losing, or more defensive when theirteam is winning with minimum score difference. Currently most of robot teams keep the same strategy during thegame so that the result of game depends on the ability of each player like speed of robot, quality of localization,
obstacle avoidance and ball handling. Next issue for the robot intelligence is collaborated team behavior andstrategy. The autonomous soccer robots are required to behave cooperatively and make decision using environmentand game situation. In this research, the team strategy is analyzed based on parameters such as the positions ofhuman player, time and actions using Self-Organizing Map (SOM).

Keywords: Strategy, Self-Organizing Map, team behavior, SOM.

robots may be possible to reduce the cost rather than amonolithic robot. Asama proposed the robotic systems
composed of multiple agent and discussed theimportance of "distributed autonomy" in terms of
autonomy and cooperativeness1. An autonomous anddecentralized robot system called ACTRESS isdeveloped and applied into multiple objects pushingproblems and showed that the system can solve by

1. Introduction
Multi-agent System (MAS) is one of the solutions to
the problems that are difficult or impossible for anindividual agent or a monolithic system. As humansolutions to the applications that require multiple
humans to works together, the use of robot teams would
be considerable solution for complex tasks. From thepoint of total cost, the development of multiple simple© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 421

M. Tominaga, Y. Takenura, K. Ishii.

parallel and independent action by each robot2. Parker
proposed a software architecture for MAS called
ALLIANCE, which facilitates fault tolerant and robust
mobile robot cooperation and demonstrated thefeasibility by hazardous waste cleanup using a physicalrobot team3.
RoboCup Soccer is a landmark project with the goal of
realizing an autonomous robots team that can win thechampion of Soccer World Cup until the 20504,5.
RoboCup Soccer has been attracting attentions as astandard problem of MAS as it is composed of multiple
interacting intelligent agents within an environment.
Human teams cannot win soccer games depending on
the ability of individual players, and the co-operation
with teammates, team behavior, is the importantsolution. Especially in RoboCup middle size league(MSL), autonomous mobile robots are affected
physically by not only teammate and opponent agents
but also their environment. It is difficult to describe all
behaviors by if-then rules, learning methods such as
reinforcement learning6, Genetic Algorithms7 have been
tried to environmental recognitions. Takahashi andAsada8 developed a multi-layered learning system using
reinforcement learning and applied into RoboCup tasks
which are to chase the ball and shoot into the goal withavoiding opponent robot. Suzuki9 applied Genetic
Algorithm into real time localization problem of
RoboCup robots.
In this research, we introduce Self-Organizing Map(SOM)10 into the classification of RoboCup teamstrategy. SOM is a topologically correct feature map
proposed by Kohonen and is well known as an attractive
tool for extracting the characteristics of data and map
the data into lower-dimensional space through its selforganizing process. We aim at developing a strategy
decision system depending on the situation andenvironment. As the first step, the classification methodof each robot using SOM is discussed.

2. Autonomous Mobile Robot Platform for
RoboCup
After a decade pass from RoboCup started in 1997, thequality of the game has risen with the development of
information and computer technology. We also organizea RoboCup team "Hibikino-Musashi", and have joined
to the MSL in the RoboCup Tournament. In recent years,
most of robots have the similar shape including the ballhandling mechanism, the kick mechanism capable ofadjusting the trajectory of the ball, environment
recognition system using an omni-directional mirror andomni-wheels that can be moved in the all direction. Our
autonomous mobile robot platform is shown in Fig.1.
Currently most of robot teams keep the same
strategy during the game so that the result of gamedepends on the ability of each player like speed of robot,
quality of localization, obstacle avoidance and ballhandling. Next issue for the robot intelligence iscollaborated team behavior and strategy. As the next
step, we have been trying to implement a strategy
selection algorithm of the team's behavior.
3. SOM for Classify Situation
The learning step of SOM is discussed in the following
steps11, and the conceptual diagram of SOM is shown in
Fig. 2. Suffix i denotes the number of input data, and jindicates the number of unit.
Step 1: Assign the weight vectors (wij) with random
values and normalize the input in the range of [0, 1].
Step 2: Compute the Euclidean distance (dj) between
a series of input vectors (xi) and all the weight vectors at
iteration by eq. (1).

Fig. 1. Soccer robot of “Hibikino-Musashi”© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 422

Strategy Analysis of RoboCup

=

(1)

−

Step 3: Find the winner unit Uc (Best Matching Unit,
BMU) which has the minimum Euclidean distance.
= argmin( )(2)

Step 4: Calculate the neighborhood radius σ( ). 0
denotes the initial neighborhood radius, and indicatesa maximum number of iteration.
σ( ) =

exp −(3)

Fig. 3: Observation environment of soccer

Step 5: The connecting weight vectors of all neurons
are updated by Eq. (4).
( + 1) =( )+ ( )∙ℎ , ( )∙̅( ) −( )̅

ℎ , ( ) = exp∙ exp(−

(4)

−

−

)

(5)

2σ( )

(6)

(7)

, )

5. Classification of Human Behavior
In the method described above, SOM’s feature map
was made from the position information of the actuallyobserved player and ball. Each parameter of SOM
algorithm is shown in Table. 2. In this paper, we focus
on 2 players and display the results. One is anexperienced futsal player and the other is inexperienced
player. Fig. 4-a shows the distance between each player
and the ball, Fig. 4-b shows the distance between eachplayer and the goal, and Fig. 4-c shows the distance
between the ball and the goal in color.
If the color of the unit is blue, it means that theevaluation factor is far. On the other hand the color ofunit becomes near as the color becomes red. As you cansee from Fig.4, we can see that they are similar distance
relationship in all factors, although there are rotated inmap. In other words, the position of the players
behavior are almost same.

rate, and ℎ , ( ) is defined as the neighborhood kernel
function, as expressed in Eq. (5) and Eq. (6). andare positions of nodes BMU and on the SOM grid.
Both the learning rate and the neighborhood radiusdecrease monotonically with time .
4.

=(

,
is the position of the player k, and,is the position of the ball. Suffix indicatesthe i-th data set. We set 2 players (k = 2) and thenumber of input data is 100 sets (maximum i = 100).

Step 6: The time increases to +1. If < T then go
to step 2; otherwise stop the training.
Where is the training step index, ( )is the learning
( )=

( )

Teaching Data Set

As the input vectors (teaching data), we had observed
and analyzed the state variables of a human soccer(futsal) game. The human soccer player performed thegame 5 to 5 at the futsal court and we observed thepositions of all the players and the ball in the field withthe coordinates of the grid. (See Fig. 3) Input vectors are
not affect behavior selection. That is, the input vector isgiven by Eq. (7).

Table. 2: Parameters setting SOM
Learning frequency
The initial learning rate
The initial neighborhood radius
Map size

t
η

σ

3000
10
0.8
10×10

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 423

M. Tominaga, Y. Takenura, K. Ishii.

6. Conclusion
In this research, the team strategy is analyzed based onparameters such as the positions of human player, timeand actions using Self-Organizing Map (SOM). We
developed the SOM map from the position of eachplayer and ball on the field. The colors of SOM map
represent the distance relationships of the player, theball, and the goal, and which kind of differences
occurred among players is observed. As a result, there
was the difference between experienced andinexperienced people. As a next problem, we aim toanalyze the positioning of players including relations
with others.
References

Experienced Player
Inexperienced Player
Fig. 4-a: A color-coded SOM of the distance between the balland each player

1. H. Asama, Distributed Autonomous Robotic System
Configurated with Multiple Agents and Its Cooperative
Behaviors, J. Robotics and Mechatronics, 4(3)(1992),
199-204
2. H. Asama, K. Ozaki, A. Matsumoto, Y. Ishida and I.
Endo, Development of Task Assignment System Using
Communication for Multiple Autonomous Robots, J.
Robotics and Mechatronics, 4(2)(1992), 122-127
3. Parker, L. E. , ALLIANCE: An archi- tecture for faulttolerant multirobot cooperation, IEEE Transactions on
Robotics and Automation 14(2)(1998) 220-240.
4. http://wiki.robocup.org/Middle_Size_League
5. R. Soetens, M.J.G. van de Molengraft, B. Cunha,“RoboCup MSL - History, Accomplishments, Current
Status and Challenges Ahead” RoboCup 2014: Robot
World Cup XVIII, 624-635, (2014)
6. Richard S. Sutton, Andrew G. Barto, Mikami. M,
Minagawa. S, Reinforcement learning, Moride
Publishing.
7. D. E. Goldberg, Genetic Algorithms in Search,
Optimization, and Machine Learning, Addison-WesleyLongman Publishing, (1989).
8. Y. Takahashi and M. Asada, Multi-Layered Learning
System for Real Robot Behavior Acquisition, AAAI 2004
Fall Symposium Series, Working Notes: Real-Life
Reinforcement Learning, (2004), 68-75
9. S. Yamada and H. Suzuki, Real-Time Self-Localization
Method for Autonomous Mobile Robot, in Proc. of 31st
Fuzzy System Symposium, (2015), 149-150 in Japanese
10. Moriyasu. K, Yoshikawa. T and Nonaka. H, Supervised
Learning for Agent Cooperative Actions Mechanism byUsing Self-Organizing Map, IPSJ SIG Technical Report,
Vol.2010-MPS-80, 2010
11. Kohonen. T, Self-Organizing Maps, Springer Japan
12. Takemura. Y, 2011, Research of the color constancy
using Neural Networks in Robocup MSL, 27th Fuzzy
System Symposium.
13. Wisanu. J, Application of Self-Organizing Map for
analyzing robotic arm’s action with ConsciousnessBased Architecture module.

Experienced Player
Inexperienced Player
Fig. 4-b: A color-coded SOM of the distance between the goaland each player

Experienced Player
Inexperienced Player
Fig. 4-b: A color-coded SOM of the distance between the balland the goal
Near
Far
Fig. 4: SOM of soccer game by human

However, the point of the view of inputs, only the
position of the ball and their own position are inputted.
In fact, it does not include input about the circumstancesand environment around the players. Therefore, it isconsidered that it was not located in feature map
considering the relationship with other people and it did
not make a difference.
In future works, we will conduct experiments with
inputs including relationships with others, situations ingames and the surrounded environments factors.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 424

Feature Extraction for Digging Operation of Excavator
Kazushige Koiwai, Toru Yamamoto

Institute of Engineering, Hiroshima University,
1-4-1, Kagamiyama, Higashihiroshima City, Hiroshima, 739-8527, Japan
E-mail: koiwaik@hiroshima-u.ac.jp, yama@hiroshima-u.ac.jp
http://www.hiroshima-u.ac.jp
Takao Nanjo, Yoichiro Yamazaki

Global Engineering Center, KOBELCO Construction Machinery CO., LTD.,
2-2-1, Itsukaichikou, Saeki-ku, Hiroshima City, Hiroshima, 731-5161, Japan

Abstract
Improvement of the work efficiency is demanded by aging and reducing of the working population in the construction
field, so that some automation technologies are applied to construction equipment, such as bulldozers and excavators.
However, not only the automation technologies but also expert skills are necessary to improve the work efficiency.
In this paper, the human skill evaluation is proposed by the human skill evaluation of the data-driven PID controller.
The proposed method is applied to the excavator digging operation. As the result, the difference between the novice
operation and the skilled operation is extracted. Moreover, the numerical difference is clarified based on the result.
Keywords: PID Controller, Human Skill Evaluation, Data-Driven

As the previous studies, skill based controllers havebeen proposed.2,3 These studies considered that thehuman skill described as a controller with the tuner of
PID gains, called the skill-based PID controller. The
operation skill have been replicated by the skill-based
PID controller experimentally. Moreover, the skill
evaluation by using the skill-based PID controller havebeen performed.4,5 These research focus on the change of
PID gains to extract the feature of the operation skill forthe excavator. However, the evaluated operation isapplied to the simple lever operation such as the swing
operation. In the actual work field, the operation for the
excavator is more complicated.
In this study, the data-driven skill-based PID controller
is adopted to the actual work operation of the excavator,
i.e. the continuous digging operation. The skill feature forthe excavator of the continuous digging operation is
extracted to compare the novice operation with the

1. Introduction
Japanese Ministry of Land, Infrastructure, Transport
and Tourism have started the policy of the integration of
construction and ICT (Information and Communication
Technology) for public works such as land survey,
engineering, construction and maintenance, that is called“i-construction”.1 Aims of this policy are theimprovement of the work efficiency and the increasing ofproductivity. The technologies of IoT (Internet ofThings), big data and AI (Artificial Intelligence) will beapplied to some construction equipments based on thepolicy. However, human operations for construction
equipments are still required continuously in the field
because skilled workers optimize their operations forhigh productivity in each complicated conditions.
Unfortunately, those skilled operators who have theunique technique are reduced in these days because of theaging and reducing of the working population.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 425

Kazushige Koiwai, Toru Yamamoto, Takao Nanjo, Yoichiro Yamazaki, Yoshiaki Fujimoto

The dataset of database, ( ), is constructed as follows:( ) = [ ( ), ( ), ( ), ( )],(5)= 1,2, ⋯ ,where the index and the total number of datasets is shownas i and N, respectively.
PID gains that is requested by the query are calculatedby the following procedure. Distances (∙) between thequery ( ) and ( )in each datasets are calculated by:professional operation based on the control engineering
approach. Moreover, the numerical difference of feature
between is verified.
Algorism of Skill Feature Extraction

The scheme of feature extraction for the human skill isshown in Fig. 1. Based on the engineering approach, theoperational objective and the operator are considered as
the system and the PID controller with the parameter
tuner that is consist of the data driven method. ∗ ( ) and∗( ) are denoted as the system output and the system
input by the operation.

( )=[
=

The following control law is considered for the skillbased controller:∆ ( )=( ) ( )−( )∆=ℎ

( − 1) .

−

( )∆

∑

( )

(1 +

(1 +

1.3. Skill feature extraction(1)

where the PID gains, ( ), ( ) and ( ) are tunedby the database. The control error signal ( ) is definedas ( ): = ( ) − ( ) and the difference operator ∆ isdefined as ∆≔ 1 −.
The operational objective is considered as the nonlinear
system is shown as follows:
∗( )

()

− min

(6)

( )

(7)

( )=

1.2. Data-driven PID controller

∗( )

max

where the index of an element in a dataset or query is
shown as l.
is a set of the element for the index.
Neighbors of data are selected according to theirdistance from query. The number of neighbor dataset,that is the user specified parameter, is donated as k. The
sum of selected PID gains( ) multiplied by aweight is desired PID gains( ) as follows:

1.1. Scheme of skill feature extraction

∗( )

( )−

( ), ( ) =Fig. 1 Scheme of Feature Extraction

( )

)

)

( )]
.

(8)
(9)

PID gains are learned by the operational data ∗ ( ) and∗( ) , which are teacher’s signals for the learning
scheme in Fig. 2. PID controller, which has the change of
PID gains can be expressed as the feature of the humanskill.

(2)

Here the ℎ(∙) is the nonlinear function using past data
and the information vector ( − 1) is defined as:∗(( − 1) =− 1), ⋯ ∗ −
,
(3)
∗(
∗(
− 1), ⋯− ), ]
whereandare orders of output and input,
respectively. The query ( ) is defined as:( ) = [ ( + 1), ( ), ∗ ( ), ∗ ( − 1), ⋯∗−, ∗ ( − 1), ⋯ ∗ ( − ) . (4)Fig. 2 The Leaning Scheme of Skill Feature Extraction© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 426

Feature Extraction for Digging

2.2. Skill evaluation procedure

The following steepest descent method (10) is
performed for the learning of PID gains in the database.

2. Evaluation Result

The most important operation for actual works is the
positioning at the stop motion because the human skill isrequired by the operation. Therefore, the target for thisevaluation is the positioning by the stop operation only.
The acceleration should be ignored in this paper. The
evaluation procedure is shown as follows.
The proposed method is applied to the specified
operation into the continuous digging operation. In thisstudy, the swing operation is selected as the specified
operation because the skill is necessary to stop at theproper position and the reference value ( ) of operation
is clearer than another operation. The digging operationis performed to record ∗ ( ) and ∗ ( ). Here, the pilotpressure and the angle of the swing are set as the input
value ∗ ( ) and the output value ∗ ( ), respectively.
Then, the skill feature is extracted by the comparison of
the professional and novice operation. Moreover, thephysical quantity that can be measured is evaluated basedon the skill feature.

2.1. Experimental equipment

2.3. Application result of proposed method

The proposed scheme is applied to the operation of an
excavator. The evaluated operation is a continuous
digging motion and the excavator, SK200-9 made inKobelco Construction Machinery, is used for theexperiment. The continuous digging motion can beclassified into four parts in Fig. 3. First part is called
digging that is the operation to scoop the earth and sandwith the bucket ①. Then, the load is lifted to the certain
height that assumed a carrier of the truck ②. Furthermore,
the load is dumped on the ground that assumed the truck
bed ③. Finally, teeth of the bucket is repositioned to thestarting point of the digging ④.

The data of the swing motion in the continuous digging
operation is extracted, normalized and resampled by the
sampling time of 200ms according to the humanquickness of a response. The proposed method is applied
to the professional and novice data. The user specificparameter of the proposed method is shown as follows.
The calculation results of PID parameter for the
professional operator and the novice operator are shownin Fig. 4 and Fig. 5. As the result, the change of PID gainat the period of deceleration which needs the operation
skill has the opposite properties to compare with them.
This is the almost same result of the skill evaluation forthe single operation. Refer to the reference 4 in the detail( )=( )− ( )

( )

(10)

where the error criterion ( ) ,the updating gradient ( )and the partial differential are expressed as follows:( )=( +

( )(− | ∗ ( ) − ( )|))

∙

=

( )=

( )=

( )
( )

( )
2

∗( )

(12)

− ( )

( )
( )

(11)

( )

(13)
.

(14)

Coefficients of a, b and c are the appropriate positive
constants.

Fig. 3 Continuous Digging Motion for Excavator

Table 1. User Specified Parameter of database
Item
SymbolValue
Number of data yny
3Number of data unu
3
Number of neighbork3
Total number of data set
N
100
Coefficient of updating gradienta100
Coefficient of updating gradientb1
Coefficient of updating gradientc100© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 427

Kazushige Koiwai, Toru Yamamoto, Takao Nanjo, Yoichiro Yamazaki, Yoshiaki Fujimoto

Stop motion

Stop motion

Fig. 5 Professional Skill Feature Extraction

Fig. 4 Novice Skill Feature Extraction
operation of the continuous digging motion. The
difference between the novice and professional isverified numerically based on the skill feature. Especially,
the skill feature that focuses on the quantity of input
signal based on the control engineering approach is
extracted numerically. In future work, statistical andstochastic approach will be applied by the increasing of
data.

of the single operation. The result shows that the
professional operator can be expressed as the proper
nonlinear controller and the novice operator can be
expressed as the ON/OFF controller.
2.4. Skill numerical evaluation
The difference between the ON/OFF controller and theproper nonlinear controller is considered based on theskill feature. Typically, ON/OFF controller has the larger
input value compared with the proper controller, that is,
the novice operator has the larger operational input. Thechange of input expresses one of the skill criterion for the
excavator operation. The Table 2 shows the change of the
input signal for the 10 cycle of the continuous digging
operation. As the result, it is verified that the novice has
the unnecessary input compared with the professional.

4. References

3. Conclusion
The human skill evaluation based on the data-driven
PID controller has been performed to the excavator
Table 2. User Specified Parameter of database
Professional Novice
Maximum of input change
0.54
0.69
Number beyond 0.5 input change
14

1. T.Kakizaki, ’i-Construction (in Japanese)” monthly
journal Kensetsu, Vol.60, No.1, (2016), pp. 6-9
2. T.Yamamoto, S.Mori and A.Sakaguchi, Data-Driven
Skill-Based PID Control of a Pilot-Scale Helicopter
Model. , Int. J. of Innovative Computing Information andControl, Vol.4, No.12, (2008), pp.3349-3358
3. Y.Liao and T.Yamamoto, Design and Experimental
Evaluation of a Human Skill-Based PID Controller, J. of
Robotics, Networking and Artificial Life, Vol.2, No.3,(2015), pp.140-143
4. K.Koiwai, T.Yamamoto, T.Nanjo, Y.Yamazaki and
Y.Fujimoto, Data-driven human skill evaluation for
excavator operation, Proc. of IEEE Conf. on AdvancedIntelligent Mechatronics, Banff (2016), pp.482-487.
5. K.Koiwai, Y.Liao, T.Yamamoto, T.Nanjo, Y.Yamazaki
and Y.Fujimoto, Feature Extraction for Excavator
Operation Skill Using CMAC, J. of Robotics andMechatronics, Vol.28, No.5, (2016), pp.715-721.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 428

Design of a Data-Oriented Kansei Feedback System
Takuya Kinoshita

Graduate School of Engineering, Hirosihma University,
1-4-1, Kagamiyama, Higashihiroshima city, Hiroshima, Japan
Toru Yamamoto

Faculty of Engineering Division of Electrical, Systems and Mathematical Engineering, Hirosihma University,
1-4-1, Kagamiyama, Higashihiroshima city, Hiroshima, Japan
E-mail: takuya--kinoshita@hiroshima-u.ac.jp, yama@hiroshima-u.ac.jp
http://www.hiroshima-u.ac.jp

Abstract
In the development of the aging society, it is important for patients with hemiplegia to introduce adaptive welfare
equipment. However, it is difficult to determine the suitable reference signal for each person. In this study, the designof a data-oriented cascade control system based on Kansei is proposed. In the proposed control system, there are twocontrollers which are a data-driven controller and a fixed controller. In particular, a data-driven controller cancalculate the suitable reference signal based on Kansei.
Keywords: PID controller, Data-driven controller, Kansei, off-line learning.

hemiplegia to move their foot by only torque τ fromtheir brain. Therefore, in the proposed scheme, the Ankle
Foot Orthosis2: AFO supports them to torque τ . In thispaper, Kansei signal is defined as walking comfortabley( ) whose maximum value is 1. Therefore, reference
comfortable signal ( ) is set as 1. Note that it isimportant to estimate reference signal of brain( )because( ) is unknown. Therefore, a data-basecontroller (primary controller) in outer loop is applied to
calculate the estimated reference signalw( ).

1. Introduction
In the development of aging society, it is important forpatients with hemiplegia to introduce the adaptive
welfare equipment. However, it is difficult to support
them by using general welfare equipment because there
are a lot of individual disabilities. Therefore, an adaptive
welfare equipment is needed in near future. However, it
is very difficult to determine the suitable reference signal
for each person. In this study, the design of a dataoriented cascade control system based on Kansei is
proposed. In the proposed control system, there are twocontrollers which are a data-driven controller1 and a fixed
controller. In particular, a data-driven controller1 is for ahuman and it can calculate the suitable reference signalof a welfare equipment based on Kansei.

3. Controlled object

The schematic figure of tow vertical joint manipulator
is shown as leg model in Fig.2. , are moment ofinertia in ankle and knee respectively.
, ,areweight of upper body, lower leg and femur. , , ,are whole length and center of gravity distance of lower
leg and femur respectively. The torqueandarecorresponding to angle ofand . The equation ofwalking motion is expressed as follows:

2. Schematic figure of proposed control system
The schematic figure of the proposed control system isshown in Fig.1. It is very difficult for patient with

+

+

+

+2

+
( )

( )

+

( )

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 429̈̈

Takuya Kinoshita, Toru YamamotoFig. 3. Block diagram of the FRIT.

( )=
( )+
( )
(4)
( ) ≔ ( ) − ( ),
(5)
where
and
respectively are proportional gain
and derivative gain.

Fig. 1. Schematic figure of the proposed control system.

4.2. Design procedure of a data-driven control

+

+

−
(

[STEP 1] Create an initial database.
The historical data is needed to use data-driven control
scheme. The database is constructed by following
information vector:( ) ≔ [ ( ), ( )]( = 1,2, , ⋯ , )(6)( ) ≔ [ ( + 1), ( ), ( ), ⋯ ,−+1 ,( − 1), ⋯ , ( − )](7)( ) ≔ [ ( ), ( ), ( )],(8)where denotes the number of data.
[STEP 2] Calculate distance and select neighbors’ data.
Distance between query ( ) and ( ) is calculated byusing the following L1–norm with some weights:

Fig. 2. Leg model: two vertical joint manipulators.
( ) ⋅ (2 ̇ ̇ + ̇ )( )⋅ ̇
) ( )+
+
( + )
=
, (1)
( + )

where ( ) denotes.
In addition, Kansei signal y( ) in Fig. 1 is expressed asfollowing equation based on Weber-Fechner law3:
1
( )=
(2)
1 + log 1 + ( )( )=( ) − ( ),(3)where ( ) is reference signal of ( ).

( ), ( ) =max( )− ( ). (9)( ) − min ( )( = 1,2, , ⋯ , )( ) denotes the th element of query ( ).
max ( ) is a maximum th element in database. In
contrast, min ( ) is a minimum th element. In
addition, the number of neighbors’ data are selected,
which data are based on smallest distance .
[STEP 3] Calculate control parameters.
Control parameters are calculated by using thefollowing linearly weighted average (LWA):

4. Design of a data-driven controller in outer
loop as primary controller
4.1. Control law of primary PID controller(t) =The primary controller in Fig. 2 is designed as a datadriven controller1 because human characteristic is
nonlinear. The primary controller is defined as follows:
( )=
( )−
( )−
( )
(4)
( ) ≔ ( ) − ( ),(5)where , andrespectively are proportional gain,
integral gain and derivative gain. denotes a difference
operator. Note that an inner loop controller of AFO isdesigned as the following fixed PD controller.

( ),

= 1,

(10)

where
is the weight corresponding to the th
information vector ( ) in the selected neighbors. It is
calculated by following equation:
1/
=
.
(11)
∑ 1/
In order to calculate effective control parameters, an
off-line learning method is described in next section.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, JapanP - 430

Design of a Data-Driven

4.3. Fictitious Reference Iterative Tuning: FRIT
Fig. 3 shows a block diagram of the FRIT4. FRIT is ascheme to calculate control parameters directly fromclosed-loop data which are input ( ), output ( ) and( ) = ̃ ( ) − ( ).
( )=
( )−
( )−
( ) (12)
Therefore, ̃ ( ) is derived as follows:( )+( )̃( ) = [( )+( )]/ . (13)+
In addition, user set a desired reference model expressedby following equation:(1)( )=̃ ( ),(14)( )( ) is reference model output and ( ) iswhere
user-specified polynomial.

Fig. 4. Walking trajectories by using fixed PID controller as
primary controller.

4.4. Off-line learning method in Data-Driven
Control scheme by using FRIT
In this section, an off-line learning method is describedby using FRIT. At first, the number of neighbors’ data( ) is calculated by equation (10)is selected andusing closed-loop data ( ) and ( ). Next, thefollowing steepest descent method is utilized to modify
the control parameters:( + 1)
( )=
( )−
(15)
( )
= [ , , ],wheredenotes the learning rate and( + 1) isdefined as following error criterion:1( )≔ ( )(16)2( ) ≔ ( ) − ( ),(17)
The each partial differential of equation (15) aredeveloped as follows:( + 1)( + 1)( + 1)̃( )⎫=( )( + 1)̃( )( )⎪( + 1) (1)
( )
⎪
=
⎪
( )
( + 1)( + 1)( + 1) ̃ ( ) ⎪=⎪( )( + 1)̃( )( ), (18)( + 1) (1) ( )
⎬
=
( )
⎪
( + 1)( + 1)( + 1)
̃( ) ⎪=⎪( )( + 1)̃( )( )⎪( + 1) (1)
( )
⎪
=
( )
⎭
Where ( ) is given by following equation:

Fig. 5. Trajectories of Kansei signal (t) and ankle angle(t) corresponding to Fig. 4.

( ) = −Δ ( ) − { ( ) + ( )} ( )+ { ( ) + 2 ( )} − ( − 1)
− ( ) ( − 2).
(19)
Therefore, equation (15) and (18) show that control
parameters can be learned off-line by using closed-loop
data.
5. Numerical Example
In this section, the effectiveness of the proposed scheme
is verified. The physical parameters5,6 in Fig. 2 are set as
follows:= 0.44[m/s ] ,= 0.72[m/s ] ,=3.26[kg],= 7.00[kg], = 0.42[m],= 0.42[m],= 0.24[m] ,= 0.24[m] , = 0.98[m/s ] , =[10 , 10 , 10 ].
Fig. 4 shows the walking trajectories corresponding to
Fig. 2 by using fixed PID controller as primary controller.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, JapanP - 431

Takuya Kinoshita, Toru Yamamoto

The dotted red line denotes the reference walking
trajectory of brain. Walking support is not well in Fig. 4
because the blue solid line does not follow red reference
signal. Therefore, the Kansei signal ( ) is not kept
around 1 in Fig. 5. Furthermore, fixed PID controller
cannot estimate the reference signal well because ( )does not follow ( ).
On the other hand, walking trajectories of Fig. 6 byusing the proposed scheme is better than Fig. 7. The
effectiveness of the proposed scheme is shown bykeeping almost ( ) = 1 in Fig. 7. The estimatedreference signal (t) is almost same as (t) using adata-driven controller and PID gains in Fig. 8 arechanged effectively.
6. Conclusion

Fig. 6. Walking trajectories by using the proposed scheme.

In this paper, the field of welfare equipment is focusedand the scheme based on the data-oriented Kansei
feedback has been proposed in order to support eachpeople adaptively. In the proposed scheme, reference
signal of a brain can be estimated by using a data-driven
controller and it can support walking well. The proposed
scheme has been verified by numerical example. In the
future, experimental result will be considered.
We are particularly grateful for the assistance given by
COI STREAM (Center of Innovation Science andTechnology
based
RadicalInnovation
andEntrepreneurship Program).
References
1. T. Yamamoto and K. Takao and T. Yamada, “Design of a
Data-Driven PID Controller”, IEEE Trans on control
systems Technology, 17(1) (2009) 29–39.
2. S. Yamamoto, "Development of Ankle Foot Orthosis for
Hemiplegic Patients Based on Gait Analysis (inJapanese)", The Journal of Japanese Physical Therapy
Association, Vol.39, No.4, pp.240-244 (2012)
3. G. T. Fechner, “Elements of Psychophysics”, New York:holt, Rinehart and Winston (1966)
4. S. Soma, O. Kaneko and T.Fujii, “A New Approach to
Parameter Tuning of Controllers by Using One-Shot
Experimental Data: A Proposal of Fictitious Reference
Iterative Tuning (in Japanese)”, Trans. of the Institute ofsystems, Control and Information Engineers, 17(2) (2004)
528–536.
5. W. T. Dempster: ``Space Requirements of the Seated
Operator'', WADC(1955)
6. I. P. Herman: “Physics of the Human Body: Biological andMedical Physics, Biomedical Engineering”, SpringerVerlag GmbH & CO. KG(2007)

Fig. 7. Trajectories of Kansei signal (t) and ankle angle(t) corresponding to Fig. 6.

Fig. 8. Trajectories of PID gains corresponding to Fig. 6.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, JapanP - 432

Parameter Estimation of a Skill Evaluation Model
Kazuo Kawada

Department of Technology and Information Education, Hiroshima University,
1-1-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8524, Japan
E-mail: kawada@hiroshima-u.ac.jp
Toru Yamamoto

Division of Electrical, System and Mathematical Engineering, Hiroshima University,
1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8527,Japan
E-mail: yama@hiroshima-u.ac.jp

Abstract
In this study, the aim is to construct a "teacher-student model" for optimal skill acquisition. To support the optimal
skill acquisition, modeling and estimation of an individual learning process are very important. The first-order systemwith time delay is introduced as an individual learning process model based the control engineering approach. Thesesystem parameters included in the skill evaluation model are estimated by using a real-coded genetic algorithm. In
order to evaluate the effectiveness of the proposed scheme, it is employed for the parts classification task.
Keywords: Skill evaluation model, Teacher-student model, Learning process, First-order system, Real-coded
genetic algorithm, Parameter estimation.

skill evaluation model is proposed by using real-coded
genetic algorithm2 (GA) from measured trial data. In
addition, the results of estimating the parameters of theskill evaluation model from the initial learning
performance data are also discussed.

1. Introduction
In order to effectively promote skill acquisition in thelearning and the work, it is necessary to grasp the learning
situation of individual learners and workers and to
appropriate support based on the learning situation. Fromthe initial learning performance, if the future learning
process can be predicted based on individual learning
characteristics, the optimum learning support according
to individuals can be performed. In response to thatproblem, the modeling was proposed in which skill
evaluation was regarded as "first order + time delay"
system in the control engineering1. The meaning of eachparameters of the time constant T, time delay L andsystem gain K in the model of skill evaluation model was
considered. However, it has not been studied how toestimate parameters of skill evaluation model from initial
learning data. In this paper, the estimate method of the

2. Parameter Estimation of Skill Evaluation
Model
2.1. Fitness value considering characteristics ofskill evaluation model
In order to estimate parameters of the skill evaluation
model, it is necessary to eliminate variations in task
execution time. Such variations can be divided into two
factors: task factors and learner's factors3,4. Variations
due to task factors are changed due to the order of tasks
to be started from among many tasks. On the other hand,the variation due to the learner's factors changes due to© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 433

Kazuo Kawada, Toru Yamamoto[2] a , b and c included in the following Eq.(2) arecalculated by the real-coded GA. The estimatedresponse time required for the n-th task is asfollows:

accidental delay in execution time or trial and error.
Therefore, the estimation of the skill evaluation model
excluding learner's factor variations is considered using"part classification task" with small variations due to thetask factors.

(2)TˆR [n] aˆ bˆe ct[ n ] .
where t[n] represents the real learning time from 1

2.2. TKL modeltime to n times, and is expressed by the following
equation.

As a requirement of the skill evaluation model, thefollowing is necessary.
1) It is exponential.
2) The parameters can be determined individually.
Generally, the controlled object given in the higher ordersystem is described as the "first-order + time delay"
system, and the control system is designed by roughsystem characteristics. In this paper, it is difficult to makethe skill evaluation model strict. However, if thereference is set at the learning start, it can be represented
by the model shown in Fig. 1.

t[ n ]n(TR [i ] aˆ )

(3)

i 1

TL is the time obtained by subtracting a such as
preparation from the Eq.(3), and denotes the
response time contributing to the learning effect.
[3] Calculate T , K and L by the following equation,using a , b and c obtained using the real-coded
GA.

1
,
c
K e c t [1] ,L TR [1] a

T

K

(4)
(5)
(6)

3. Parameter Estimation using Genetic Algorithm
The parameters of skill evaluation model a , b and c arearranged as cells included in a string, whose structure isshown in Fig. 2. These parameters included in the string
are given by real values. The real-coded GA is employed,
which is explained as follows.
(i) Initialization
The generation number G is set, and the initialindividuals are produced with random real-codes
within the initial domain which is set in advance.
Here, the number of population is set as N .
(ii) Selection
The fitness value f (l ) is calculated which is
given byFig. 1 Skill levels and learning time.

“First order + time delay” system (TKL model) is
described in the following equation.

G ( s)

K
e Ls
1 Ts

(1)

where T denotes the time constant, K denotes the systemgain, and L denotes the time delay. The method to
calculate these parameters (T, K and L) from measured
trial data.
The procedure for obtaining T, K and L from themeasured trial data is as follows.
[1] Measure each response time TR [1 n] from 1
time to n times.

f (l ) 1 /{1t end{TˆR (k ) TR (k )}2 .

t 1

(7)

where TˆR and TR respectively denote the learning
time and the estimated learning time by theparameters of skill evaluation model a , b and c .
Each individual Pl is arranged in order, based on
the fitness value. Then,percent individuals© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 434

Parameter Estimation of awith superior fitness values are selected, andsaved in the next generation.
(iii) Crossover
The (100 α ) percent remaining are generated
by the crossover. Two individuals, Pa and Pb
are chosen from among the superior α percent,and new individuals Pc and Pd are generated
by employing the following procedure:

Pc (i )

Psup (i )Pd (i )

Psup (i )Pa (i )Pb (i )
4

Pa (i ),Pb (i )
4(8)(9)

where Psup in Eq.(8) and Eq.(9) refers to theindividual with the superior fitness value, i.e., Pa
or Pb . Note that this procedure is employed forevery cell included in Pa and Pb .
(iv) Mutation
Of all individuals which are randomly selected
and given by the crossover, β percent are chosen
and replaced with randomly determined values
within the initial domain.
(v) Update
The procedure from (i) through (iv) is repeated forgenerations.
This procedure is summarized in Fig. 3.
4. Experimental Results for Part Classification
Task
The parameters T , K and L of the skill evaluation
model of the parts classification are calculated andconsidered. The fourteen kinds of parts shown in Fig. 4
were placed in the mixed state, and tasks for classifying
the same parts were prepared. The number of each part isshown in Table 1. The subject was a university student,
and he had no experience of executing parts classification
task. Subject took 3 minutes break and between each task,
and carries out the total of 11 tasks and measured the timerequired for each task. The execution results in shown in
Table 2. It is reduced to the required time 310[s] at thefirst work and 185[s] at the 11th task. The skill level isthe time obtained by subtracting the minimum required
time a from the response time in each task in Table 2,
and the learning time to each task is the skill level added.

Fig. 3 Calculation flow of the evolutionary computation.

a , b and c were searched by using the real-coded GA,

a 0.13 , b 238 and c 0.0017 were obtained. T ,K and L were obtained from Eq.(4)-Eq.(6), andT 588 , K 176 and L 178 were obtained byusing these values a , b and c . Fig.5 is shown fromthese T , K and L .
Next, Fig.6 shows the relationship between the initial
learning point and the estimated parameters. From thisfigure, T , K and L are almost estimated at the initial
learning 4 points.

Fig. 2 Structure of the GA string.
© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 435

Kazuo Kawada, Toru YamamotoFig. 4 Parts to be classified.
Table 1 Number of parts.
Fastener Components
Number
(a)
20
(b)
20
(c)
20
(d)
20
(e)
10
(f)
10
(g)
10
(h)
20
(i)
10
(j)
5
(k)
20
(l)
20
(m)
20

Fig. 6 Relationship between initial learning points and estimatedparameters.

5. Conclusion
In order to realize an optimum learning support system
by individual skill evaluation in the learning process, thevalidity of the skill evaluation model based on the control
engineering approach was discussed. Next, it applied tothe classification task of parts, calculated T , K and Lof the parameter of the skill evaluation model, examined
it and showed its effectiveness. In the future, it will beapplied to the computational learning etc. and plan todevelop the learning support system.

Table 2 Number of task and response time.
Number of Task
Response Time[s]
1
310
2
262
3
262
4
224
5
209
6
216
7
188
8
190
9
185
10
181
11
185

This work was supported by JSPS KAKENHI Grant
Numbers JP16K12760, JP16H02921.
References

Degree of Skill

1. M. Nagamatsu, T. Usuzaka, K. Kawada, T. Yamamoto and,Y. Yamane: Consideration on a Model between Teacher
and Student Based on Control Engineering Approach,
IEEJ Transactions on Electronics, Information and
Systems, Vol.134, No.10,(2014) pp.1537-1542. (inJapanese)
2. R. Loren and D. B. Benson (eds.), Introduction to String
Field Theory, 2nd edn. (Springer-Verlag, New York,
1999).
3. M. F. Mason, M.I. Norton, J.D. Van Horn, D.M. Wegner,
S.T. Grafton, and C.N. Macrae: Wandering Minds: The
Default Network and Stimulus-Independent Thought,
Science, 315,(2007) pp.393-395.
4. W. Hasenkamp, C.D. Wilson-Mendenhall, E. Duncan,
L.W. Barsalou, Mind wandering and attention during
focused meditation: A fine-grained temporal analysis offluctuating cognitive states, Neuroimage,59,(2012)
pp.750-760.

Fig. 5 Estimated skill evaluation model.
© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 436

Human Skill Quantification for Excavator Operation using Random Forest
Hiromu Imaji

Graduate School of Engineering, Hiroshima University,
1-4-1, Kagamiyama, Higashihiroshima City, Hiroshima, 739-8527, Japan
E-mail: imaji-hiromu@hiroshima-u.ac.jp
http://www.hiroshima-u.ac.jp
Kazushige Koiwai, Toru Yamamoto

Institute of Engineering, Hiroshima University,
1-4-1, Kagamiyama, Higashihiroshima City, Hiroshima, 739-8527, Japan
E-mail: koiwaik@hiroshima-u.ac.jp, yama@hiroshima-u.ac.jp
http://www.hiroshima-u.ac.jp
Koji Ueda, Yoichiro Yamazaki

Global Engineering Center, KOBELCO Construction Machinery CO., LTD.,
2-2-1, Itsukaichikou, Saeki-ku, Hiroshima City, Hiroshima, 731-5161, Japan

Abstract
In the construction field, the improvement of the work efficiency is one of important problems. However, the work
efficiency using construction equipment is depend on their operation skills. Thus, in order to increase the work
efficiency, the operation skill is required to be quantitatively evaluated. In this study, the Random Forest (RF), oneof machine learning method, is adopted as the quantitatively evaluation for the operation skill of construction
equipment. Evaluated target is the operation on an excavation to load onto a truck for a hydraulic excavator. The RF
learns to classify some states by the pilot of skilled worker’s operation. States are defined as ‘dig’, ‘lift’, ‘dump’,‘reposition’, and ‘idle’. The RF with the learning result of skilled worker is applied to other operator’s operation. It
is revealed that the ratio of ‘idle’ is related to their skill.

Keywords: human skill, machine learning, random forest, hydraulic excavator

improve the productivity in the field of constructionindustry.
In previous studies, the human skill evaluation based on
the engineering approch2 has been proposed and theevaluation of operation skills based on interviews has
been investigated.3 However, these methods focus on thequalitative evaluation only and it is difficult to utilize
them to improve the productivity.
In this paper, a quantification method of operation
skills for construction machines based on classificationsof operation state using the Random Forest (RF) is

1. Introduction
In the field of construction industry, the application ofInformation and Communication Technology (ICT) for
construction equipment has been proceeded to improve
the productivity, which is called ‘i-Construction’ inJapan.1 However, work efficiencies of some construction
equipment depend on operation skills for each operators.
Therefore, it is necessary to establish a method toevaluate the operation skills so that it is possible to© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 437

Hiromu Imaji, Kazushige Koiwai, Toru Yamamoto,Koji Ueda, Yoichiro Yamazaki

proposed. The proposed method is applied to the
excavator operation.
RF is a kind of machine learning methods proposed by
Leo Breiman in 2001.4 RF is constructed by somedecision tree. Since the decision tree is an aggregate ofbranching rules, it is widely used to perform data mining
for the certain classification problems because thelearning result is more readable than other machinelearning methods.5,6 Moreover, RF has the lower
computational cost for the classification.

min(( )=1−= { | = 1,2,3, … , },

={ ,

(3)

where is defined as the number of training data, isdefined as the dimensions of the feature vector and isdefined as the number of labels. A set of training data,
is defined as follows:

2.1.1. Training procedure

}.

∈

,

( | ) ( )
(

| )

(7)
(8)

2.1.2. Classification procedureoris called the child node. The feature vectorof validation is = ̅ = 1,2, … ,. is initializedwith 0.
[Step 2-1] Confirm existence of child nodes
If there is a child node in , step 2-2 is performed.
Otherwise step 2-3 is performed.
[Step 2-2] Go to the child node
If ̅ ̂ ≤ ̂ , step 2-1 is performed withagain,
otherwise step 1 is performed withagain.

The set of initial training data is shown as . The
branch part is expressed as a node , and the initial nodeis
. The learning procedure is shown for each step
below.
[Step 1-1] Sampling feature vector
The sampled feature vector given as follow:
=

(5)
(6)

̅)

here ( | ) is the ratio of elements in .
is a setof with label . ̂ , and ̂ are obtained with the state ofminimized ( , ) . Further, nodes
andaregenerated forand .
[Step 1-3] Save training datâ, ̂ , , , and are saved as training data of .
[Step 1-4] End judgment
When or do not satisfy the following termination
condition shown below, Step 1-1 is repeated with , →, , → .
Termination condition:(i)or
has a only single label.
(ii) The number of data in the data set is less than orequal to a certain value.
(iii) The certain depth is achieved at a branch.

The learning data for the decision tree is comprised ofa set and a set . The set consists of feature vectors,, which are one-dimensional vectors included thefeature of data. The set consists of natural number, ,which are labels for the classification. Those formula areshown by:(1)
(2)

≤ max(( , )= ( )−2.1. Classification of decision tree=
= 1,2, … ,= 1,2,3, … , ),̅∈is divided into a set with ̅ ≤ and a set with>
.Then, ̅ and which minimize the following̅( , ) are obtained.

2. Proposed method= { | = 1,2, … , },= { | = 1,2,3, … , }(

̅) <

(4)

[Step 2-3] Output of results
The label with the largest number of data is the output
at of .

where is a set of , whose components are natural
numbers selected randomly without overlap among
1,2,3, … , .
[Step 1-2] Determination of branching condition
̅ is defined as the selected index of feature vector
element. is defined as the threshold that threshold that
divide the training data into two. The selected index, ,̅
and the threshold, , have the following conditions:2.2. Random Forest
The scheme of random forest is shown in Fig. 1.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 438

Human Skill Quantification for

Fig. 1

Scheme of Random Forest
Fig. 2

General hydraulic excavator excavation work

2.2.1. Training of Random Forest
RF is composed of a plurality of decision trees.
Therefore, RF needs to generate training data of decision
trees. It is assumed that training data is given to RF as Eq.
(1) and (2). Thus, a training data of the decision tree in
RF is given as follows:= { | ∈ },= { | ∈ },(9)
Fig. 3 Pilot Pressure

four states of "dig", "lift", "dump" and "reposition". This
study classify the state of work into 5 state, which areabove states with “idle”. The “idle” state shows nooperation. The operation of the hydraulic excavator iscomposed of four operations of "bucket", "arm", "boom",and "swing". These input signals of operations wereacquired as pilot pressures. The pilot pressure is the input
pressure to the valve corresponding to the lever operation
amount. A simple configuration diagram of the hydraulicsystem is shown in Fig. 3. In this paper, eight types ofpilot pressures is adopted as the elements of the feature
vector to be used for RF, bucket dig / dump, boom up /down, arm pull / push and right turn / left turn.

where is a set of , whose components are natural
numbers selected randomly without overlap among
1,2,3, … , . The data sets expressed by (9) is generated
for the number of decision trees and used for training
each decision tree. The overlapping between data ofdecision trees is allowed.
2.2.2. Quantification
is defined as the total number of decision trees. isdefined as the number of decision trees outputting thespecific label. The ratio of in is shown as , below:
= ,

(10)

3.2. Experimental procedure

The result of ratio can express the quantification.

The experimental procedure in this paper is shown below.
[Step 1] RF learn labels of 5 states by pilot pressures of askilled worker.
[Step 2] Using the Result of Step 1, operation data of
unskilled worker, general worker, and skilled worker isclassified. At this time, the ratio of the predicted label
is evaluated.
Ta ratio of the “idle” state is related to the skill level.

3. Experiment Evaluation
3.1. Experimental device
The experiment focused on the quantification of theoperation skill by the hydraulic excavator. As shown in
Fig. 2, it is known that the general work of loading dumpof earth and sand with a hydraulic shovel is classified into

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 439

Hiromu Imaji, Kazushige Koiwai, Toru Yamamoto,Koji Ueda, Yoichiro Yamazaki

3.3. Evaluation results

Table 1parameters of experiment

Step of training data [step]
Step of verification data
Number of decision tree
Number of sampling data for decision tree
Number of sampling feature for decision tree

Table 1 shows parameters of the proposed method. Table
2 shows the result of quantification, that is the ratio of
decision trees with the "idle" state. Table 3 shows theresult of training with 50 times using the same input /output data. This result shows the the validity of RF. The
ratio of decision trees with "idle" is getting bigger fromthe skilled worker to the unskilled worker in Table 1. The
operation that have the larger difference, dump motion,is extracted in Fig. 4-6. The general worker has the single
operation with the “idle” state around 1900 step in Fig. 5.
The unskilled worker has the shortage of the operation ofdump and arm push. The pilot pressure does not reach to
80% or more. Thus, “idle” ratio represents the lack ofoperation in the work.

workerArea Ratio [%]Table 3workeraverage
Variance
Max
Min

4309
4000
31
1436
3

Table 2

Ratio of idle

Skilled
0.09

General5.32

Unskilled
13.7

General
3.45
1.48×10
6.74
1.70

Unskilled
13.3
2.06×1015.9
10.5

Verification result of RF significance
Skilled
0.15
2.92×100.26
5.00×10

4. Conclusion

This paper has proposed a quantification method of
operation skills in excavation work of a hydraulicexcavator based on RF. The paper also clarified the lackof operation by observing the proportion of decision trees
in the RF that determined the operation state to be in the"idle" state. As the future work, other state are classified
by using internal state and output. Moreover, theproposed method can be utilized to improve the work
efficiency.

Fig. 4 Part of pilot pressure by skilled worker

References
1. T. Kakizaki, i-Construction: On productivity improvementof construction site, Monthly construction (Japan), 60(1)(2016) 6-9.
2. K. Koiwai, Y. Liao, T. Yamamoto, T. Nanjo, Y. Yamazaki,
and Y. Fujimoto, Feature extraction for excavator
operation skill using CMAC,Journal of Robotics andMechatronics, 28(5) (2016) 715-721.
3. Y. Kawazoe, K. Enomoto, J. Matsumoto, S. Okabe, and S.
Handa, Acquisition of Human Operator’s Skills andDexterity using Neural Network: Automatic Generation of
Neural Controller from Chaotic Time Series Data during
Stabilizing Control of an Inverted Pendulum, Proc. of 29th
SICE Symposium on Intelligent Systems, (2002) 217-222.
4. L Breiman, Random Forests, Machine Learning, 45(1)(2001) 5-32.
5. J. Kim, J. Lee, and Y. Lee, Data-mining-based coronary
heart disease risk prediction model using fuzzy logic anddecision tree, Healthcare Informatics Research, 21(3)(2015) 167-174.
6. X. Wang, X. Liu, W. Pedryc, and L. Zhang, Fuzzy rule
based decision trees, Pattern Recognition, 48(1) (2015)50-59.

Fig. 5 Part of pilot pressure by general worker

Fig. 6 Part of pilot pressure by unskilled worker© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 440

Marker Recognition System for Localization of the Rover on the Lunar Space
Na-Hyun Lee

Dep. of Electronic Eng., Pusan National University, Jangjeon-dong, Geumjeong-gu
Busan, 609-735, Republic of Korea
Jang-Myung Lee

Dep. of Electronic Eng., Pusan National University, Jangjeon-dong, Geumjeong-gu
Busan, 609-735, Republic of Korea
E-mail: nahyun7379@pusan.ac.kr, jmlee@pusan.ac.kr
www.pusan.ac.kr

Abstract
This paper proposes a marker realization system for rover localization in the Lunar environment. CPU used in thespace environment is not as sensitive to changes of the surrounding environment differently from normal CPU, but
it has a lower performance. So it is necessary to use an algorithm reduced amount of computation. In order to reduce
the amount of computation on the rover, we propose a rover driving algorithm based on marker recognition. It makes
possible to explore the Lunar by using the low- performance CPU. For implementing this algorithm, we performed amarker detection test, and then extracted the center point coordinate of the marker by using Canny Edge Detection
and Circular Hough Transform.
Keywords: rover, Lunar space, marker, localization.

was measured through position changing of the detectedfeature points on the image. This method has anadvantage that it can localize with only the front images.
However, because this algorithm uses difference ofprevious and current images, if the rover moves too fastor the time interval for taking pictures is too long, thenumbers of identical feature points presented in both
pictures decrease, so matching accuracy is reduced. For
preventing this, it is necessary to take pictures at a shorttime interval and to process them. It leads increment ofcomputation amount, and as a result, high performance
CPU is required.
CPUs used in space have very low performance andcapacity compared with general CPUs. Nonetheless,people use CPU for space because it is insensitive toenvironment changes. General CPUs are extremely
sensitive to change of the temperature, and weak toradiation. Lunar and Mars have intensive difference of

1. Introduction
The universe is a vast, still unknown space, and an
environment that requires continuous exploration. For
stable exploration in specialized environments, manytechniques required like a rover localization and obstacle
avoidance. So exploration rovers have various sensors,
but generally use a vision sensor because it can directly
obtain images. Vision sensor usage is various dependingon which camera used, how many cameras used, whereattached in rover, which algorithm used.
The image processing algorithm for localization in thespace environment was mainly used Visual Odometry
method using Optical Flow. In this algorithm, featurepoints are extracted from previous image and matchedfeature points extracted next image. Matching is theprocess of searching for the same feature points from two
images. The movement amount and direction of rover© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 441

Na-Hyun Lee, Jang-Myung Lee

temperature more than 100℃. Also, Lunar doesn’t haveatmosphere, and Mars has very little. So Lunar and Mars
will receive the solar radiation much more directly than
Earth. In this way, the space has many environmental
changes. It is the reason that people use the CPUs forspace. But its performance is very poor and expensive.
Improving the performance and capacity of CPU forspace becomes a costly burden, so an algorithm with asmall amount of computation is required.
In this paper, we propose a new rover driving algorithm
using a low-performance CPU and marker.

earth and processed on the earth. In order to shoot 360
degrees, it is possible to confirm the presence of obstacles
by grasping the situation of the front and thesurroundings and to acquire the current position throughmarker recognition from backward image both.
For implementation this algorithm, first, marker
detecting process and extracting process of thecoordinates are required. Second, It is required that theprocess of obtaining the horizontal distance between
lander and rover by acquiring the depth value using thestereo camera. Third, it needs to integrate the coordinatesof the images and the coordinates of the rover. In the next
chapter, we will deal with the image processing
algorithm that we used for detecting and recognizing themarker.

2. Rover driving algorithm based on marker
recognition
Rover driving algorithm proposed in this paper ispossible to localization and obstacle recognition
nevertheless using low-performance CPU. Because ofusing a low-performance CPU, the vision computation is
not processed on the rover itself, but on the earth after
transmitting. Then, a rover movement command istransmitted from the earth to the lunar. In this process, acommunication delay of about 7 minutes occurs. So it is
impossible to perform rover’s moving and transmitting at
the same time. Also, distance of moving commandshould not be short. Therefore, the proposed rover
driving algorithm needs image processing algorithm thatis not affected by previous images.
In this paper, localization algorithm using marker
recognition is proposed. It can recognize the marker andget the coordinate of the marker from only current image.
By using the position which the marker is attached anddepth value obtained by the stereo camera, the horizontal
distance between lander and rover can be known. And byusing the number of pixels between two points extracted
by the marker and the actual distance between two points,
it is possible to know the angle between the rover and themarker.
But this method also has a problem. Rover drives in
direction far from lander, but rover needs to see the lander
for taking pictures of the marker. In other words, thedirection of the camera is opposite to the direction of therover’s driving. In this case, it can’t confirm whether the
obstacles are present forward. In order to solve thisproblem, we propose a method that the camera shoots
360 degrees at intervals of 15 degrees while rover is
stopped. After shooting, pictures are transmitted to the

3. Image Processing Algorithm
In this paper, edges are detected from the image and themarker is detected by recognizing a circle in the edge
image. Marker detecting algorithm used in this paper
consists of Canny Edge Detection and Circular Hough
Transform.
3.1. Edge detection
Canny algorithm used in this paper is widely used forpractical edge detection. Canny algorithm process
consists of 5 steps.
Step 1: Color conversion
Step 2: Gaussian smoothing
Step 3: Calculation of the image gradient
Step 4: Non-maximum suppression of the grad value
Step 5: Edge determination and connection
In step 1, color of the image was converted to the grayimage. In step 2, filtering processing is performed withthe converted grayscale image. This filtering is Gaussiansmoothing. It is necessary to smooth images forremoving the noise before edge detection. In step 3,
calculating the gradient is performed. To calculate theimage gradient, Canny edge detection algorithm selects a
2×2 neighboring area to get the magnitude and direction
the gradient. In step 4, in order to get sharper edges, Nonmaximum suppression is performed. Canny method use
the 3×3 neighboring area to compare a pixel with its two
adjacent pixels along the gradient direction. If a pixel is© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 442Marker Recognition System forbigger than the two adjacent have, the pixel will notchange. Otherwise, the pixel will change to 0. In step 5,
the edges are finally determined and connected.
Typeset sub-subheadings in medium face italic andcapitalize the first letter of the first word only. Section
numbers to be in Roman.

4.

Marker Detection ExperimentMarker detection experiment executed in this paper was
performed to confirm that the marker detection doing
well and that coordinates can be acquired from images innormal circumstances. We use the small marker with H
written in a circular frame. The thickness of the marker
line is 1cm, and the radius of the circle based on outerboundary is 7.5cm (radius of the circle based on innerboundary is 6.5cm). Fig. 2. indicates the shape and sizeof the marker. Please provide a shortened running head(not more than four words, each starting with a Capital)
for the title of your paper. This will appear with page
numbers on the top right-hand side of your paper on odd
pages.
The image was taken at a distance of 3m in the corridorof the building. And the marker was attached to the wall

3.2. Circular Hough Transform
Hough Transform (HT) is usually used for therecognition of contour and shapes from the images.
Circular Hough Transform (CHT) is one of the effective
approaches used nowadays for the detection of circles
arcs and finding the center of the circles. CHT normallyuse geometric and Hough region as shown in Fig. 1.
Every point in the geometric region of the original circle
edges generates a circle in a Hough space region. The
circles in the Hough region meet most at a point (x', y')that will correspond to the point (x, y) in the geometricregion of the original images. In other words, the pointthat overlap most in the Hough region is (x', y'), and thispoint is center point of circle.
The parametric equation for circle in the (x', y') space isshown in Eq. (1).

Fig. 2. The shape and size of the marker

surface. Fig. 3. indicates the captured image and theimaging area of camera is 1024×768 pixels. Edge
detection was performed from the captured image byusing Canny algorithm. Then marker was found from thedetected edge image by using Circular Hough Transformand extracted the center coordinate. Fig. 4. indicates
result image of Canny Edge Detection. Fig. 5. indicates
result image of Circular Hough Transform. Table 1.
indicates extracted center coordinate of the marker.

Fig. 1. Geometric region(x, y), Hough Region(x', y') x  x '   y  y '  r 2
2

2

(1)

If edge point in the geometric region is (a, b), radius isshown in Eq. (2).

x ' a  y ' b  r
2

2

(2)

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 443

Na-Hyun Lee, Jang-Myung Lee

Table 1. Extracted center coordinate of themarkerextracted
centercoordinate

x

y

596

354

5. Conclusion
In this paper, we propose a driving algorithm ofLunar exploring rover though marker recognition using
low-performance CPU. We carried out the marker
detecting experiment and extracted center coordinate of
the marker. As a result of the experiment, it was
confirmed that the marker was well recognized at a
distance of 3m even for small size marker. Since it ispossible to attach a large marker to the actual lander, it isexpected to be able to recognize at far distance.

Fig. 3. The captured image

Acknowledgements
This research was financially supported by the Ministryof trade, Industry and Energy(MOTIE), Korea Institutefor Advancement of Technology(KIAT) through the
Robot Business Belt Development Project(A012000009),
This research is supported by the MOTIE (Ministry ofTrade, Industry & Energy).
This research was supported by the Lunar Exploration
Program through the KARI grant funded by the MSIP
(No. SR16022)

Fig. 4. Result of Canny Edge Detection

References
1. Hyun-Seop Lim, Dong-Hyuk Lee, and Jang-Myung Lee,Moving Object Following by a Mobile Robot using aSingle Curvature Trajectory and Kalman Filters, (Journalof Institute of Control, Robotics and Systems, 2013)
2. Shi Guiming, Suo Jidong, Remote Sensing Image EdgeDetection Based on Improved Canny Operator (IEEEInternational Conference on Communication Softwareand Networks, 2016).
3. Miaomiao Zhao, Hongxia Liu and Yi Wan, An Improved
Canny Edge Detection Algorithm Based on DCT (Second
International Workshop on Computer Science andEngineering, 2009).
4. Muhammad Awais, Ibrahima Faye, Nicolas Walter,
Mohamad Naufal Saad, Automatic Removal of Auto
Fluorescence Device Part Using Circular Hough
Transform (IEEE Student Symposium in Biomedical
Engineering & Sciences, 2015).

Fig. 5. Result of Circular Hough Transform© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 444

Dynamic Model and Finite-Time SMC and Backstepping Control of a Mobile-Manipulator
SystemSeong-Ik Han

Dep. of Electronic Eng., Pusan National University, Jangjeon-dong, Geumjeong-gu
Busan, 609-735, Korea Republic
Hyun-Uk Ha

Dep. of Electronic Eng., Pusan National University, Jangjeon-dong, Geumjeong-gu
Busan, 609-735, Korea Republic
Jang-Myung Lee
Dep. of Electronic Eng., Pusan National University, Jangjeon-dong, Geumjeong-gu
Busan, 609-735, Korea Republic
E-mail: skhan@pusan.ac.kr, hyunuk.ha@gmail.com, jmlee@pusan.ac.kr
www.pusan.ac.kr

Abstract
A mobile manipulator was designed by combining three-wheeled mobile robot equipped with the DC motor andthree-links manipulator equipped with dynamixel motor. The kinematic relation and dynamic model were built via
nonholonomic constraint and Euler-Lagrange equation. For the decoupled model of this system, adaptive finitetime controllers sliding mode controller (SMC) and backstepping controller were designed respectively to obtain
fast tracking response. Simulation and experimental results show the efficacy of the proposed control scheme.
Keywords: Mobile-manipulator, Finite-time sliding mode control, Finite-time backsteppping control.

compensator is introduced to compensating unknown
parameters and uncertainty.
Simulation for the decoupled mobile platform andmanipulator was carried out to show the efficacy of the
proposed control scheme.

1. Introduction
The mobile-manipulator system has more freedom for
robot works and then it has drawn more attention
recently. However, it’s modeling is difficult due tononholonomic constraint of mobile platform andcoupling between mobile platform and manipulator.
The kinematic and dynamic coupled model for threewheeled mobile robot and three-link manipulator
system is derived using nonholomonic constraint andEuler-Lagrange
equation.
SMC[1],[2]
andbackstepping control [3] are frequently applied tocontrol robot system but these controllers are derived
based on the infinite-time stability theorem. Therefore,
the convergence time is generally slow and fast
response is not guaranteed. Finite-time control term[4],[5] is inserted in both controls to improve
convergence time of the mobile robot and manipulator.
In addition, the system parameter and uncertainty are
obtained by estimation for them via adaptive observers.
This leads to complex structure of the whole control
system. An assumed parameter feedforward

2.1Description of the Mobile-Manipulator
In this section, the dynamic equations of a threewheeled mobile manipulator system are derived using
the Euler-Lagrange equation. The derived dynamics are
modified from the relationship of forces acting on the
body and links, and constraints between the wheel andcontact surface without considering the Lagrange
multiplier method, which is used to solve thenonholonomic constraint problems of mobile robots.
The two-wheeled mobile manipulator is shown in Fig. 1,© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 445

Seong-Ik Han, Hyun-Uk Ha, Jang-Myung Lee

selecting the generalized coordinates are selected asTq [qv qm ]T [ x y[ x y ]T
1 2 3 ] , where qv
and qm [ 1 2 3 ]T . Total kinematic energy can be
expressed as:
2
1
1
1
T
( m0 m1 )( x 2 y 2 )
Io 2
I z1
1
2
2
2
1
2
m2 [ x r2 2 s 2 c 1 r2 (1 )c 2 s 1 ]2
1
2
m2 [ y r2 2 s 2 s 1 r2 (1 )c 2 c 1 ]
2
1
1
2
2
I z 2 [(
m3 [ x l2 2 s 2 c 1
1)
2]
2
2
l2 (
1 )c 2 s 1 r3 ( 23 ) s 23 c 1

r , l are theVtorques of two wheels; 1 , 2 , 3 are the torques of thejoint1, 2 and 3; r , l are the rotation angle of the left
and right wheel of the mobile platform,
respectively; v , are the forward velocity and the

2

with

respect

respectively; m p

5kg , mw

1l2 (1 )c 2 c

1

r3 (

2
1 )c 23 c 1 ]m2 gr2 sin 2 m3 g l2 sin 2

r3 sin( 2

(1)

3)

. (2)

Using the Lagrange-Euler equation, the matrix form forthe dynamic equations is written as

M ( q ) q C ( q, q ) q G (q )rotation angle of the mobile platform, respectively; 1 isthe rotation angle of the link 1 with respect
to z0 axis; 2 , 3 are the rotational angle of the link1 andlink1 )c 23 s12
I z 3 [(
( 2 3 )2 ] .
1)
2The potential energy is obtained as follows:

Fig. 1 Schematic diagram and photograph of the threelink and three-wheeled mobile manipulator
where variables are defined as follows:1m3 [ y l2 2 s 2 s 1
2
r3 ( 23 ) s 23 s 1

]2

r3 (

AT ,

B

d

(3)

where

z1 and z2 axis,
0.58kg , m1 0.5kg , m2 , m3
to

M (q)

are the masses of the mobile platform, wheel, link 1,
link 2, and link 3, respectively; I p , I z1 , I z 2 , I z 3 are the

Mv
M mv

M vm
Mm

Mx 1 Mx 2

moment of inertia of the mobile platform, link 1, link 2,
and link 3, respectively; I w is the moment of inertia of

Mvm

each wheel; d 0.145m the distance between the point
P and wheels; R 0.075m is the radius of thewheels; l1 0.1m, l2 0.2m, l3 0.1m are the lengths ofMm

the link 1, link 2, and link 3; r1 , r2 , r3 are the distance
between joints and the center of mass of links.
.
sin ,For expression simplicity, abbreviations for sccos , and 12
1
2 are introduced. By

M xx

0

Mx

0

M yy

My

M x

M y

M

, Mv

Mx 3

M 1x M 1y M 1

My 1 M y 2 My 3 , Mmv

M 2x M 2 y M 2

M 1 M 2 M 3M 3x M 3y M 3

M 11

0

0

0

M22

M 2 3 , C ( q, q )

0

M32

M33

G ( q ) [0 0 0 0 G 2 G 3 ]T

Cv
Cmv

Cvm
Cm

[Gv Gm ]T ,© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 446,,

Dynamic Model and Finite-Time

[ r
B

l

Bv
02 3

Mv
M mv

1

0
,
0

v

Av (qv )

, and

qv
qm

0
Bm

Cv
Cmv

Cvm
Cm

qv
qm

v

AvT

0

v

m

0

0

m

dv
dm

J v ( qv )(4)

matrix; C (q, q) is a matrix of velocity-dependentqvr

centripetal and Coriolis forces; G (q) is a gravitational
vector; d is a bounded unknown disturbance including
unmodelled dynamics and exogenous disturbance; B is
the input transformation matrix; and is an input torquevector.
Property 1. The inertia matrices M are symmetric,
positive definite, and bounded. The norms of C are also
bounded.
Property 2. The matrices M 2C are skew-symmetricbecause of the suitable definition of the corresponding
inertia and Coriolis matrix.
Therefore, this modeling method goes through thecomplex transformation calculation inevitably toremove the Lagrange multiplier. The resulting dynamic
equations become complicated as the DOF of theattached manipulator increases.

0,

0,

c
s

0
0 .

0

1

0 , and J v (q) can be(8)

J v (qv ) vr ,(9)

qvr as tthat qv. The trajectory tracking
problem is to track a reference mobile robot with aposture qvr [ xr yr r ]T . Therefore, we define thetracking error between the actual and desired posture
as:

qv

qvr

xr
yr

qv

x
y .

(10)

r

The posture tracking error can be expressed as:

qve

(5)

ex
ex

c
s

s
c

0
0 qv ,

e

0

0

1

(11)

where ex , ey , and e denote the tangential, normal, andBy selecting qv [ x y ]T as the generalized
coordinates of the mobile platform, the constraint canbe expressed as follows:

Av (qv )qv(7)

where qvr [ xr yr r ]T denotes the desired timevarying
position,orientation
trajectoryandT[vr r ] denotes the reference time- varying
vrlinear and angular velocity. It is necessary to find theappropriate velocity control law vc [vc c ]T , such

2.2 Kinematics of the mobile robot platform
The nonholonomic constraint for the mobile robot isthat the robot can only move in the direction normal tothe axis of the driving wheels, i.e., the mobile drives
under the condition of pure rolling without slipping.
Therefore, the three constraints can be expressed as:

x sin

0 .

A reference to the mobile platform generates a
trajectory for the actual platform to follow:

where M (q) is a symmetric and positive definite inertia

y cos

cos

of A( q) , J vT (qv ) AvT (qv )expressed as:

Gv
Gm
,

sin

The matrix J (qv ) is taken as the basis for the null spacem

03 3. (3) can be rewritten as
Bm

M vm
Mm

Bv
0

AvT
0

T
T
3] , A2

orientation tracking errors of the mobile platform andmanipulator, respectively. The error rate can beobtained as:

(6)

where
© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 447

Seong-Ik Han, Hyun-Uk Ha, Jang-Myung Lee

v

ex
ey

ey

vr cos eexvr sin ee

M v vv Cv vv
.

(12)

vc
r

c

vr cos e k x ex
k y vr e y k vr sin e

,

(13)

Mv

v

,

rv

(14)

c

[ex ex ex ]T

0 as t.

AvT v ,

M vm qm Cvm qm

qv

J v vv . Therefore, (28) can be written as

M v J v vv

(M v Jv

Cv J v )vv

v

Because of J vT (qv ) AvT (qv )the left side of (16) gives

Bv v

vvr

Fv

t
v
v1 0 sig (ev ) d

vf ,

,

(18)

(19)
sign(e )]Tv[ ev1 v sign(ev1 ), e

v1sig (ev )

AT .

vv

rv

vv

vvr

ev

v

.

and

(20)

t
v
v1 0 sig (ev ) dtv
v1 0 sig (ev ) d

.

(21)

Using (19), (20), and (21), it follows thatM v sv

M v vv M v rv
Cv vv Fv Bv v M v rv
Cv (rv sv ) Fv Bv v M v rv
Cv sv M v rv Cv rv Fv Bv v .

(22)

We define the Lyapunov function as follows:

dv . From (28), we have

Fv

vvd

sv

(15)

where Fv
J v vvvc ,1 is a constant. We then obtain the following

In (4), the mobile dynamics is separated as follows:

Bv v

Cv

The finite-time sliding mode surface sv is defined as

3.1 Design of a finite-time SMC for a mobile
platform

Fv

vm ,

0

rv

3. Design of Finite-Time Controller Design andStability Analysis

Cv qv

,

sig (ev ) v

However, in the proposed model, this kinematic
technique is not required and the controller structure issimplified.

M v qvCv J v )where

the kinematic model is then asymptotically stable withrespect to the reference trajectory:qve

J vT ( M v J v

where vi , i m, c, f are positive constants.
Consider the following signal:

0.

This is called the extended kinematic control formobile platform with link 1. If the perfect velocity
tracking is achieved as

vc

(17)

Assumption 1. There are constants that satisfy thefollowing boundedness:where k x , k y ,and k are positive constants and vrvc

Bv v ,

where M v J vT M v J v , Cv
Fv J vT Fv , and Bv J vT Bv .

r

The target or command velocity is given as:

c

Fv

Vv

1 T
sv M v sv
2

1 2
(ex
2

e 2y )

1 cos e
ky

.

(23)

Considering (24), (39), and property 2, the time
derivative of (40) becomes(16)

0 , multiplying J vT (qv ) into

Vv

svT M v sv

svT [

1 T
sv M v sv
2

M v rv Cv rve x exFv

ey ey

Bv v ]

e sin eky

k1ex2

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 448

Dynamic Model and Finite-Time

k3vr sin 2 e
vm rv

vc rv

Bv v

vf

k3 vr sin e

.

(24)

k2
The control input is specified as
v

veq

Bv

veq

vr sv ( svfvf sig ( sv )

vf

rv

vm

v)
v

1

vc

rv

vf

, (26),(27).

0 is a constant matrix.

T
vr sv sv ( sv

v)

2

1

ci svi v

1

i 1

k3 vr sin e
k2Tv 2 sv sv

2

ci si v

2Tmin ( v 2 ) sv sv

min (ci )2 min ( v 2 )Vv3
max ( v )2

i 1
( v 1)/ 2

(31)

v)

ln

kv1Vv1( sv 0 ) kv 2
.
kv 2

v

(32)

kv1Vv3 kv 2Vv 3 ,2min (ci )

x4

M m1

ym

x3 ,

Mm

Vv 3v

Fmv

Bm m ,
(33)

[ 1,

2,

T
3]

, x4

x3

[ 1,

T
2, 3]

mm ,

Cm

mc ,

Gm

mg ,

Fm

mf ,

(34)
mi , i m, c, g , f are positive constants.
The tracking error and x4 are written as

2 min ( v 2 )2( v 1)/ 2 min (ci ), kv 2.
max ( v )min (ci )Lemma 1: From the definition of finite-time stability[4],[5], the equilibrium point s 0 of is globally finitetime stable; i.e., for any given initial condition s(0) s0 ,an extended Lyapunov description of finite-time isgiven as the following inequality:
where kv1

( s) ,

Cm qm Gm

where

(29)

2V

x4 ,

, and
Fmv M mv qv Cmv qv dm .
Assumption 2. There are constants that satisfy thefollowing boundedness:( svi )( v 1)/2

min (ci )v

1V ( s )x3

where x3

1i 1

V ( s).

2

3.2. Design of a finite-time backstepping controller fora mobile platform
The state space model of the manipulator from (4) canbe expressed as follows:

2

k1ex2

2

(28)

diag (cvf , c f )Tv 2 sv sv

1
kv1 (1

tvs

Substituting (25) into (24), we haveVv)

V 1 ( s0 )
ln 1

(25)

v 2 svvr

where

1 . V (t ) converges to

Therefore, using Lemma2 and (84), the finite
convergence time of the mobile robot is given as

f ,

vr
1

1
(1
1

ts

2

k1ex2

0 , and 0

an equilibrium point in finite-time ts given by

k2

svT

0, 2

where 1

(30)

z3

x3

ymr ,

(35)

z4

x4

3.

(36)

The following Lyapunov function is defined:
Vm1

1 T
z 3 z3 .
2(37)

Using (35) and (36), we then have

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 449

Seong-Ik Han, Hyun-Uk Ha, Jang-Myung Leez3T z3Vm1

z3T ( z 4

ymr ) .

3

where km1

(38)

c3 z3

where c3

1

sig ( z3 ) 3
z3 3

3 3

ymr ,

3

diag (c 1 , c 2 , c 3 )diag (

3

3 sig ( z3 )

,

z3 1

2

,

31

3

)

tms

3 2

sgn( z3 2 ),

, and

1
km1 (1m)ln

km1Vm1 3 m ( zm 0 ) km 2.
km 2

4. Simulation Example

1 is a constant .

3

To validate the proposed control scheme, the infinitetime backstepping controller (BSC) and finite-timebackstepping controller (FBSC) with infinite-time SMC(SMC) and finite-time SMC(FSMC) were designed.
Simulation results of each control system for the mobile
manipulator system are described in Figs. 2 and 3,
where the proposed finite-time control reveals more fast
convergence performance than the infinite-time based
control sytems.

Therefore, we have
3 z3

3 1

z3T z4 .

(40)

We redefine the Lyapunov function as follows:
Vm1

1 T
z 4 M m z4 .
2

z4T M m z4

1 T
z4 M m z42

Vm3

min ( i )(39)

0 is a constant matrix,

sgn( z3 1 ), z3 2

c3 z3T z3

min ( i )0 is a constant matrix,

sgn( z3 3 ) and 0

Vm1

2m

min[( i 1) / 2], i 3, 4 . Therefore, usingm
Lemma1, the finite convergence time of themanipulator control system is given as

We specify the finite-time virtual control as follows:3

2 min (cm ), km 2max ( m )(41)

We obtain
Vm3

Vm1

c3 z3T z3
mm

z4T
3

mc

x4

mg

mf

Bm m

z3 .

(42)

Selecting the finite-time control input and adaptive laws
as follows:
m

Bm1

c3 z4

z3

mf

mm

4 sig ( z4 )

3
4

mc

,

x4

(a)

mg

(43)

Then, we obtain
4

Vm3
i 3

ci ziT zi

4
i zi

i 1

(b)

i 3

km1Vm3 km 2Vm3 ,m(44)

Fig. 2. Simulation results of the mobile platform. (a)Tracking outputs. (b) Tracking errors.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 450

Dynamic Model and Finite-Time

Acknowledgements
This work was supported by the National Research
Foundation of Korea(NRF) grant funded by the Korea
government(MSIP) (NRF-2015R1A2A2A01004457).
References
1. V. I. Utkin, J. Gulder, and J. Shi, Sliding mode control inElectromechanical systems (Taylor & Francis Ltd, 1999).
2. T. Jose, and A. Abraham, Sliding mode control ofwheeled mobile robots, IACSIT Coimbatore Conference,
Singapore, (2012) 17-22.
3. K. Kristic, I. Kanellakopoulos, and P. V. Kokotovic,
Nonlinear and adaptive control design (Wiley, New
York, 1995).
4. S. P. Bhat and D. A, Bernstein, “Finite-time stability ofcontinuous autonomous systems,” SIAM J. Control
Optim., 38(3), (2000) 751-766.
5. S. Yu, X. Yu, B. Shirinzadeh, and Z. Man, Continuous
finite-time control for robotic manipulators with terminal
sliding mode, Automatica, 41 (2005) 1975–1964.

(a)

(b)

5. Conclusion
Finite-time SMC and backstepping control schemes toguarantee the fast error convergence and small tracking
error performance for a mobile-manipulator system areproposed. A finite sliding mode surface and a virtual
finite-time error surface are defined to obtain finitetime performance. The finite-time convergence isproved by the finite-time stability analysis of Lyapunov
function. Simulation for a mobile manipulator system
confirms the theoretical proposal.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 451

Bilateral Control of Hydraulic Servo System Based on SMCSPO for 1DOF Master Slave
Manipulator
Jie Wang

Department of Mechanical Engineering, Pusan National University, 635-4Jangjeon 2 dong, Geumjeong-gu, Busan, 609735,South Korea
Karam Dad

Department of Mechanical Engineering, Pusan National University
Min Cheol Lee

School of Mechanical Engineering, Pusan National University
E-mail: wj@pusan.ac.kr, karamdadkallu@gmail.com,mclee@pusan.ac.kr
http://www.pusan.ac.kr

Abstract
In this research reaction force estimation method based on Sliding mode control with sliding perturbation observer(SMCSPO) of a hydraulic servo system for one degree of freedom (DOF) master-slave manipulators is proposed.
The reaction force at the end effector of slaver is estimated by sliding perturbation observer (SPO) without using any
torque sensor. This research verifies through simulation and experiment that slaver can follow the trajectory of themaster device using the proposed bilateral control strategy to give a reference and estimate reaction force fromobserver.
Keywords: SMCSPO, Bilateral control, Master-Slave, Hydraulic servo system, Nuclear Power Plant.

increased and the International Atomic Energy Agency(IAEA) predictable the market value of the nuclear
facilities dismantling to be about 1 trillion dollar market
by 2050 [3].In conventional bilateral control architecture,
many studies have used force sensors to detect external
force. However, the use of force sensor is the cause ofsome problems. Robot system with a force sensor isregarded as a two mass resonant system, it is difficult torealize high frequency force sensing [4-5].
Therefore, in this research we use sliding mode control
with sliding perturbation observer (SMCSPO) toestimate the reaction force of the slaver and bilateralcontrol of hydraulic servo system for 1DOF master slave

1. Introduction
There are more than 450 nuclear power plants in theworld, 210 in Europe and 24 nuclear power plant in thedomestic have been operated. Generally lifetime of a
nuclear power plant is 30-60 years, the removal of anaging nuclear power plant is being exaggerated asimportant problem. 150 nuclear power plant operating
had been stopped permanently in 19 countries. 19 nuclear
power plants completely dismantled, 100 nuclear power
plants are being dismantled and 31 nuclear power plants
are due for dismantling in the world [1-2]. Demand fordismantling of nuclear power plant is being continuously© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 452

Jie Wang, Karam Dad, Min Cheol Lee

Where , A ( ) , B( , ) , g ( ) and T are thevector joint of angles, the mass/inertia matrix, thecentrifugal/coriolis torque, the gravity torque in joint
space, and the vector of joint torques, respectively. The
dynamics equations of each link of hydraulics servo
system are as follow

manipulators. The reaction force of slaver is estimated bysliding perturbation observer (SPO) without using any
sensor.
The remaining part of the paper is organized as follow:
Section 2, describe the structure and dynamics of 1DOF
hydraulic servo system used in dismantling power plant.
In section 3, reaction force estimation method based on
sliding mode control with sliding perturbation observer(SMCSPO) is discussed. Section 4, describe the bilateralcontrol between master and slave for 1DOF hydraulicservo system. Section 5, Performance of the estimatedreaction force and bilateral control is evaluated byexperiments. Section 6, conclude the work.

(Js1 Js1) 1 (Ds1 Ds1The three links manipulator robot actuator is controlled
by sliding mode control (SMC) and reaction force isestimated by sliding perturbation observer (SPO). The
governing equation for general second order dynamics
with n-degree-of-freedom (dof) is:
n

xj

fj x

[(bji xbji x )ui ] d j t(3)
Where x≜ [X,.. . ] is the state vector and Xj ≜ [xj ̇ j] .
The terms ( ) and ∆ ( ) correspond to the nonlinear
driving terms and their uncertainties. The component bij
and bji represent the elements of the control gain
matrix and their uncertainties, while d j is the external
disturbance and u j is the control input. The terms f j
and bji are known continuous functions of the state [6].
Estimated perturbation is defined as the combination of
all uncertainties.

Fig. 1 1DOF hydraulic servo system

between forces, torques and motion. The general
dynamic equation of a manipulator in free space is
described by
g( )

fj x

i 1

The dynamics of a robot describe the relationship

B( , )T1 (2)

3. Sliding Mode Control with Sliding
Perturbation Observer (SMCSPO)

Hydraulic servo systems are heavily used in highperformance applications such as the machine-toolindustry. Examples are material handling, mobile
equipment, plastics, steel plants, and automotive testing.
In this research the hydraulic actuators of robot fordismantling a nuclear power plant are consist of two
hydraulic cylinder and one AC servo motor. Verticalmovement required a greater force as compared tohorizontal movement in dismantling power plantstructure. In this research we use only end effector of
3DOF hydraulic servo system. Size, shape and structureis shown in fig 1 of 1DOF hydraulic servo system usedin dismantling nuclear power plant.

A( )

e1

In above equations, Js1 is the inertia, Ds1 isdamper,shows the uncertainty of parameters, 1 is
viscosity of cylinder, Ms1 is mass, L 1 is length, e1 isreaction torque by contact with environment, T 1 is joint
torque of link 1,
is dynamics effect from link 1, 1 is
viscosity friction of cylinder 1.

2. Structure and Dynamics of 1DOF HydraulicServo SystemT) 0.5Ms1L1gsin1

1 1

(1)

3.1. Control Variable Transformation
Before integrating SO into SMC it is convenient todecouple the control variable using the following
transformation [7].

f j xˆ

nb ji xˆ ui

3j

uj

i 1

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 453(4)

Bilateral Control of Hydraulic

Where 3 j is an arbitrary positive number and u j is the
new control variable. The original control vector ofgeneral equation is obtained as

B 1Col[ 3 j u ju

f j xˆ ]find the reaction force at the end effector. The wholesystem for experiment includes master device, slave
device and a control system as shown in fig 3.
Fig. 3 System for experiment(5)

T
Where uand B b ji xˆ n n . Thisu 1 .... u ntransformation allows to write the system dynamics as

xj

3j

uj

5.1. Experimental results
The experiment was conducted on a three-link hydraulicservo system. In Fig. 4 shows the experimental result ofreference (Master) and end-effector (Salver) trajectories.
In Fig. 4 the reference trajectory is shown in blue line andend-effector trajectory shown in red line.

(6)j4. Bilateral Control between Master and Slavefor 1DOF Hydraulic Servo System
In this research 1DOF hydraulic servo system for masterand slave is considered. The dynamics equation of masterand slave for hydraulic servo system are described by

J m m Bm m um
J s s Bs s

us

h
e

(7)
(8)

Where Jm , J s , um and us are inertia, position andcontrol input of master and slave of hydraulic servo
system, respectively. Action force generated by operator
while maneuvering the master device is denoted by h ,while reaction force appears when slave device istouching remote environment is defined by e . The
structure of bilateral controller is shown in fig. 2.
Fig. 2 Bilateral control structure

Slaver of hydraulic servo system is follow the trajectoryof master device when operator operate the master device.
5. Experiments and experimental results
Experiments were performed on 3DOF hydraulic servo
system. Master and slave device consist of three links

Fig. 4 Experimental result of trajectories

Fig. 5 shows the experimental result of estimatedperturbation of end effector.

each, in which third link connected with base. We can© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 454

Jie Wang, Karam Dad, Min Cheol Lee

5. S. Katsura, Y. Matsumoto, and K. Ohnishi, “Analysis andExperimental Validation of Force Bandwidth for ForceControl,” IEEE Transaction Ind. Electron., vol. 53, no. 3,
pp. 922-928, Jun. 2006.
6. H. Elmali and N. Olgac, “Sliding Mode Control with
Perturbation Estimation (SMCPE)” International Journalof Control, Vol. 56, pp. 923-941,1993
7. M. J. Terra, H. Elmali and N. Olgac, Sliding mode control
with perturbation observer, J. Dyn. Syst. Measurement
Control 119 (1997) 657–665.

Fig. 5 Estimated perturbation of end-effector

6. Conclusion
This research proposed an estimation method of areaction force on end effector of one links robot
manipulator using SMCSPO without using any sensor.
The estimated perturbation showed the uncertainty ofdynamics and reaction force. However, it is useful to findthe reaction force where the reaction is big enough andnot east to use the sensor such as transportation of active
uranium in nuclear power plants or disposal of explosive
ordnances, remote cutting for nuclear power plantdismantling, debarring, welding and grinding etc. In
future research, other factors may also be included to
improve the accuracy of force estimation.
Acknowledgements
This section should come before the References. Funding
information may also be included here.
References
1. https://www.iaea.org/PRIS/WorldStatistics/OperationalR
eactorsByCountry.aspx
2. “Development of 21 Core Technologies for Dismantling
Nuclear Facilities until 2021,” Korean Prime Minister’s
Office Press, 2012.
3. K. M. Kim and J. H. Jang, “Domestic Market Prospect andCore Technology foe Decommissioning of Nuclear Power
Plant,” Korean Institute of Construction Engineering andManagement Conference Journal, pp. 37-40, 2013.
4. Y. F. Li and X. B. Chen, “On the Dynamic Behaviour of aForce/Torque Sensor for Robots,” IEEE Transaction
Instrumentation and Measurement, vol. 47, no. 1, pp. 304308, Feb. 1998.
© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 455

Advanced impedance control of haptic joystick for effective mobile robot handling
Gyung-I Choi

Dep. of Electronic Eng., Pusan National University, Jangjeon-dong, Geumjeong-gu
Busan, 609-735, Republic of Korea
Jang-Myung Lee

Dep. of Electronic Eng., Pusan National University, Jangjeon-dong, Geumjeong-gu
Busan, 609-735, Republic of Korea
E-mail: gyungi7379@pusan.ac.kr, jmlee@pusan.ac.kr
www.pusan.ac.kr

Abstract
This paper proposes optimized structure of 6 D.O.F. haptic joystick and tele-operated mobile robot system based on
haptic interfaces. Kinematic analysis of specially designed 6 D.O.F. haptic joystick derives the coordinate of end
effect. The designed haptic joystick transfers operator’s moving command to mobile platform with force controlled
coordinate information. Attached ultra-sonic sensors detect nearby obstacles including walls and retransfer the
distance between mobile platform and obstacles to the haptic joystick. Attained real-time displacement information
controls DC motors between links supporting the handle for effective user handling. Practical simulations and realexperiments verified proposed impedance control and its tele-operation system of intelligent haptic joystick.
Keywords: haptic device, tele-operation, mobile platform, impedance control.

combination of serial and parallel mechanisms. The
blended mechanism complements each other’s defects.
Also, end-effect of haptic joystick freely generates
movement of 6 D.O.F. for commend parameters.
Ultra-sonic sensors attached to mobile platform detectobstacles and retransfer distance data between the mobile
platform and obstacles to haptic joystick. DC motors
between links supporting the handle generate virtual
impedance, which transfer the distance data to user.
Advanced haptic joystick model is applicable to theindustry which requires accurate works in specificenvironment. In this paper, kinematic of speciallydesigned 6 D.O.F. haptic joystick is analyzed for thecoordinate of end effect and simple experiment ispreceded using mobile robot as a basic research before it
is applied to the industrial robot.

1. Introduction
Recently, social needs for the tele-operation have beenraised sharply in the field of industry and disasterenvironment to avoid unexpected blind accident. Teleoperation means communication between master-slave
devices from a distance. It is necessary to provide realtime feedback and to develop optimized haptic device for
precise tele-operation.
Serial mechanism haptic joystick has universally beenused in the industry. Serial mechanism has simple
structure and wide workspace, while it has relatively low
durability and accuracy. Contrastively, parallelmechanism suits with precise work and force applied toactuator disperse into parallel links. But it is difficult tocontrol in real-time. According to above characteristics,
suggested 6 D.O.F haptic joystick designed as© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 456

Gyung-I Choi, Jang-Myung Lee

2. Structure Analysis of The 6 D.O.F. Haptic
Joystick
The structure of this device is combination of 3
manipulators which has serial joint in parallel as shownin Fig.1 Each of the serial manipulator has 3 D.O.F. thatcomposed of 2 active joints, 1 passive joint and 1
spherical joint. Degree of freedom of 6 D.O.F.
serial·parallel mechanism is proved by Gruebler's
equation.
3. Kinematic Analysis
The upper platform of hybrid manipulator has been
attached between the handle of the joystick and the 3
serial manipulators. When we hold the points generated
by the connecting between the upper plate and the serial
manipulators as vertices and connect them, we can makethe shape of a triangle. The desired end effect is locatedat the center of this triangle. According to the speciallydesigned manipulator’s structural characteristics,
kinematic relationship of each parameter can be derived
from decoupled analysis of serial manipulator and upper
platform.

Fig. 2. Coordinate of parallel structure.

vector Pi ( i 1, 2,3 ) and define the coordinate of upper
platform corner based on {M} as vector Pui ( i 1, 2,3 ).
Using relationship between these 2 vectors, define therotation matrix bp R . Define the reference coordinate
system of the serial link as Li ( i 1, 2,3 ) , and based on
this define the coordinate of upper platform corner as
vector Pli ( i 1, 2,3 ). The relationship between {B} and{ Li } is defined as homogeneous transform bl H i .
Fig. 2-(b) shows the relationship between reference
coordinate of lower platform and reference coordinate ofeach serial link. bl H i , Conversion relation from reference
coordinate of lower platform {B} to serial link coordinate{ Li }, can be expressed as homogenous matrix such as
Eq. (1) and its inverse relationship can be expressed as
Eq. (2).

3.1. Relative coordinate for kinematic analysis inparallel mechanism
Fig. 2 - (a) shows the coordinate relationship for the
kinematic analysis. Set the reference coordinate system{B} (Base frame) on the center of lower platform and set
the coordinate system of end-effector {M} (Moving
frame) on the upper platform. When define the positionchange between these 2 coordinates as vector Pd , itmeans position of the {M} based on {B}. Define thecoordinate of upper platform corner based on {B} as

l
b

b
l

Hi

H i R ( b , z ) R (90, x)T (rb , z )

l
b

Hi

1

cos bi0

sin birb sin bi

sin bi0cos birb cos bi

0

1

0

0

0

0

0

1

cos bi0

sin bi

0

0

0

1

0

sin bicos bi

0

rb

0

0

0

1

(1)

i (2)

The relationship between Pi and Pli obtained using
the relative coordinates in Fig. 2 - (c) is shown in Eq. (3)and Pui is derived as Eq. (4).

Fig. 1. structure of 6 D.O.F serial·parallel mechanism haptic
device.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 457

Advanced impedance control of

Pi

0
b

Pui [ ru cos ui

desired relation between end-point (interface) force, Fint ,and position, X . Defining the desired equilibrium
position for the end-point in the absence ofenvironmental forces (the virtual position) as X 0 , a
general form for the desired force-position relation is :(3)

H i Pli0 ]T

ru sin ui(4)

3.2. Kinematic analysis of the haptic joystick
The coordinate of serial manipulator is as shown in fig.
5 Each of 3 serial manipulators is composed of two links.
The position vector Pli obtained by Denavit-Hartenberg
notation. is shown in Eq. (5).

plicos( li )(l1 cos( 2i ) l2 cos( 2i3i ))sin( li )(l1 cos( 2i ) l2 cos( 2i3il1 sin( 2i ) l2 sin( 2i

3i

))

Fint

dX J ( )d

Pd

P2

P3

x

y

Tact

z

(7)

(8)

From the principal of virtual work :

(5)

)

T

X

Compute the Jacobian, J ( ) :J t ( ) Fint

(9)

The required relation in actuator coordinates is

From given Pli , coordinate of upper platform corner Pi
is derived as (3) and position of coordinate system ofupper platform {M} Pd from coordinate of upper
platform corner pi is derived as (6).
1 3
1
Pi
P1
3i 1
3

K X0

Tact J t ( )K X 0

L( )

(10)

5. Simulation
Simulation was performed to verify the workspace ofthe constructed haptic joystick. Kinematically, both link1
and link2 were chosen equal to 19 cm. Table 1 shows theconstraint limits of the angles shown in Fig. 3.

(6)

4. Impedance control
Impedance control is the interaction between the
manipulator and the environment. Using the analogy toelectrical impedance where impedance is the ratio ofvoltage output to current input, mechanical impedance isthe ratio of force output to motion input. In other words,
the mechanical impedance is the force resisting to thedirection of the applied force. Impedance in ateleoperation system does not appear immediately in theslave device, but through the master device that controls
the slave device. The user can acquire the implied
information about environment through the masterdevice's impedance information, and instructs the masterdevice to control the slave device based on thisinformation
The lowes-order term in any impedance is the static
relation between output force and input displacement, a
stiffness. If, in common with much of the current workon robot control, we assume actuators capable ofgeneration commanded forces, Tact , sensors capable ofobserving actuator position, , and a purely kinematic
relation between actuator position and end-point position,
X L( ) , it is straightforward to design a feedback
control law to implement in actuator coordinates a

Table 1. Constraint limits of the angles
Angle

Constraint limits

1i

0 ~1802i

60 ~1603i

30 ~160

Fig. 3. Workspace of haptic joystick.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 458

Gyung-I Choi, Jang-Myung Lee

6. Experiment6.1. Experiment environment
Fig. 4 is a tele-operation system using a haptic joystick
and a mobile robot. The haptic joystick and the mobile
robot exchange the data through the bluetooth module.
The haptic joystick generates a virtual impedance basedon the distance information received through the mobile
robot attached ultrasonic sensors and transmits it to theuser. The experiment was performed in an environment
where obstacles were installed in the traveling path asshown in Fig. 5.

Fig. 6. Obstacle detection distance of right ultrasonic sensor.

6.2. Experiment result
The Fig. 6 is the graph about the relative distance
between the mobile robot and the obstacle recognized by
the ultrasonic sensor, and the graph in Fig. 5 is the resultof the impedance value generated in the joystick. The
previous result is the output of the sensor attached to theright side among the three ultrasonic sensors attached themobile robot, and the other sensors also present the same
performance. In graph 6, the ultrasonic sensor outputs avalue of 0 if it does not detect any obstacle within 20 cm.
Otherwise, the closer to the obstacles, it output the lower
value. The impedance value of the graph 7 is maintained
at a value of 5 in the normal state, and a higher impedancevalue is output as the distance from the obstacle becomescloser.

Fig. 7. Impedance value according to distance data.

7. Conclusion
Experimental results show that the impedance value
properly changes according to the output value of thesensor, but it is difficult to obtain accurate data due tocommunication problems such as time delay in datatransmission. Therefore, we will focus on this issue indeveloping a haptic joystick for improved performance.
Acknowledgements
This research was financially supported by the
Ministry of trade, Industry and Energy(MOTIE), Korea
Institute for Advancement of Technology(KIAT)
through the Robot Business Belt Development
Project(A012000009)

Fig. 4. Tele-operation system using 6D.O.F. haptic joystick.

References
1. Vu, Minh Hung, and Uhn Joo Na. "A new 6-DOF haptic
device for teleoperation of 6-DOF serial robots." IEEE
Transactions on Instrumentation and Measurement 60.11(2011): 3510-3523.
2. Burns, David T. Design of a six degree of freedom haptic
interface. Diss. Northwestern University, 1996.
3. Hogan, N., Impedance Control: An Approach to
Manipulation, American Control Conference, 1984 , vol.,no., pp.304,313, 6–8 June 1984.

Fig. 5. Tele-operation system using 6D.O.F. haptic joystick.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 459

Obstacle Avoidance Method for Electric Wheelchairs
Based on a Multi-Layered Non-Contact Impedance Model
Haruna Kokubo, Taro Shibanoki

Ibaraki University, Japan
Takaaki Chin

Hyogo Rehabilitation Center, Japan
Toshio Tsuji

Hiroshima University, Japan

Abstract
This paper proposes an obstacle avoidance method based on a multi-layered non-contact impedance model. The
proposed system can calculate a virtual repulsive force before the collision by multi-layered impedance fields
covered around the robot. This system therefore regulates desired path to avoid obstacles in a variety of situations.
In the experiments, the mobile robot passed through obstacles smoothly, and could stop emergently to avoid the
obstacle in front of the robot owing to virtual forces calculated by the proposed model.
Keywords: obstacle avoidance, non-contact impedance, electric wheelchairs, people with disabilities

In this regard, a method known as non-contact
impedance control supports natural collision prevention[7] – [9]. The technique can be used to generate virtual
external forces that act on the robot based on virtual
impedance between the robot and obstacles. (including
other robots), which allows the robot to steer away fromanything in its path. However, this requires the setting ofappropriate parameters for various situations.
Additionally, as such approaches are premised on thecontrol of mobile robots, it is necessary to develop anatural obstacle-avoidance method for biosignalcontrolled electric wheelchairs.
This paper proposes an obstacle avoidance system forelectric wheelchairs based on a non-contact impedancemodel. The system helps to prevent collisions using
mechanical impedance-based covered virtual walls
around the robot. It supports smooth avoidance ofobstacles and enables collision-preventing emergency
stops using virtual walls with different impedance
parameters.

1. Introduction
According to the World Health Organization (WHO),the population of people with disabilities worldwide
accounts for almost 15% and is gradually increasing [1].
Electric wheelchairs and other mobility aids are used tosupport disabled people in independent living bycompensating for lost function. In particular, an electric
wheelchair that can be controlled with biosignals such as
electroencephalograms (EEGs) and electromyograms(EMGs) has recently attracted attention [2] - [4]. The
authors’ research group also developed the Cybernetic
Human Robot Interface System (CHRIS), which can beused to operate wheelchairs, domestic appliances, video
games and other equipment using biosignals [4]. The
system has a number of distinctive features, such as
adaptability to input signal variations and various input
types in line with user requirements. However, thesesystems can malfunction due to biosignal fluctuations
associated with unintended motions. It is also necessary
to provide a motion assistance method with theseapproaches, as obstacle avoidance depends on theoperator.
A number of conventional techniques are applied todetermine appropriate paths for distinction based on
artificial potential fields and fuzzy logic using neuralnetworks and other methods [5], [6]. These systems
allow robots to avoid obstacles based on virtual force andso on, but tend to focus only on maintaining the minimum
distance necessary for avoidance. The application of suchtechnology to biosignal-controlled electric wheelchairs
requires consideration of how to achieve natural
movement rather than simple obstacle avoidance.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 460

Haruna Kokubo, Taro Shibanoki, Takaaki Chin, Toshio Tsuji

(1)

1
I

(2)

I
I

I
I

Fig. 1. Kinematics of a mobile robot

2. Control of the mobile robot based on a MultiLayered Non-Contact Impedance ModelIn this study, the mobile robot was compressed into a
mass point with two translational degrees of freedom (Fig.
1), and motion planning was performed.
The robot can determine the distance between itself andnearby obstacles using an internal sensor. Multiple
virtual walls based on mechanical impedance are used to
avoid collision with obstacles via calculation of related
virtual external forces.
Figure 2 illustrates the concept of the multi-layered noncontact impedance model. It is considered that 𝑃
obstacles are present in the direction of movement of therobot, which is surrounded by virtual circles (referred tohere as virtual walls) with radii of 𝑞𝑖 , (𝑖 = 1, 2, … , 𝐼 )based on a mechanical impedance model.
When the obstacle comes within the 𝑖th virtual wall, thenorm vector from the wall’s surface to the obstacle
𝑑𝑿𝑖𝑜 ∈ ℜ2 is represented as𝑑𝑿𝑖𝑜 = 𝑿𝑟 − 𝑞𝑖 𝒏.

(1)Here, 𝑿𝑟 is the vector of displacement from the center ofthe sphere (the mobile robot’s position) 𝑿𝑒 ∈ ℜ2 to the(𝑝)pth obstacle 𝑿𝑜 ∈ ℜ2 (𝑝 = 1, 2, … , 𝑃). The direction
vector 𝒏 reflecting the distance between the margin of
the virtual wall and the obstacle 𝑝 is defined as
𝑿𝑟 = 𝑿𝑒 −

(𝑝)

𝑿𝑜 .

(2)

𝑿

𝑟
, ‖𝑿𝑟 ‖ ≠ 0
𝒏 = {‖𝑿𝑟 ‖.
0, ‖𝑿𝑟 ‖ = 0(3)

Fig. 2. Structure of the proposed multi-layered mon-contact
impedance model

should be noted that if the obstacle is outside the 𝑖 th
virtual wall, the virtual force 𝑭𝑖𝑜 is zero.
The proposed method supports obstacle avoidance withmulti-non-contact impedance using various parameters.
The virtual extra force from all obstacles in front of therobot is calculated as𝑃

𝑭𝑖𝑜 = ∑

(𝒑) 𝑖
𝑭𝑜 .

𝑝=1

Finally, the resultant force acting on the mobile robot in
the non-contact impedance model is defined as𝐼

𝑭𝑜 = ∑ 𝑭𝑖𝑜 .

(6)

𝑖=1

Additionally, the dynamic equation for the robot
regarding non-contact impedance control is expressed as
𝑴𝑒 ∆𝑿̈ + 𝑩𝑒 ∆𝑿̇ + 𝑲𝑒 ∆𝑿 = 𝑭𝑒 + 𝑭𝑜 .
(7)
Here, 𝑴𝑒 ， 𝑩𝑒 and 𝑲𝑒 are the inertia, damping andspring matrices of the robot, respectively.
A displacement vector ∆𝑿 is given using the reference
position 𝑿𝑡 = (𝑥𝑡 , 𝑦𝑡 )T and the current position of therobot 𝑿𝑒 = (𝑥𝑒 , 𝑦𝑒 )T based on ∆𝑿 = 𝑿𝑡 − 𝑿𝑒 . The
limitations of ∆𝑿 and ∆𝑿̈ [6] and the elements 𝑲𝑒 ∆𝑿
and 𝑴𝑒 ∆𝑿̈ in Equation (7) are ignored. Thus, theequation translates to
𝑩𝑒 ∆𝑿̇ = 𝑭𝑒 + 𝑭𝑜 .

If the obstacle is inside the virtual wall (𝑞𝑖+1 < ‖𝑿𝑟 ‖ ≤𝑞𝑖 ), the virtual impedance between the robot and the
obstacle causes exertion of a virtual external force on the
robot:𝑭𝑖𝑜
𝑴 𝑑𝑿𝑖̈ + 𝑩𝑖 𝑑𝑿𝑖𝑜̇ + 𝑲𝑖 𝑑𝑿𝑖𝑜 , 𝑞𝑖+1 < ‖𝑿𝑟 ‖ ≤ 𝑞𝑖={ 𝑖 𝑜. (4)0, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
Here, 𝑴𝑖 ，𝑩𝑖 and 𝑲𝑖 are the inertia, damping and spring
matrices of the non-contact impedance, respectively. It

(5)

(8)

In Equation (8), the velocity increment ∆𝑿̇ is calculatedas1∆𝑿̇ = (𝑭𝑒 + 𝑭𝑜 ).
𝑩𝑒(9)© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 461

Obstacle Avoidance Method forFinally, 𝑿𝑣̇ is defined as the velocity of control thatallows the robot to avoid an obstacle using
𝑿𝑣̇ = 𝑿𝑒̇ − ∆𝑿̇.
3.

(10)

Simulation Experiment3.1. Experimental Conditions
To evaluate the efficacy of the proposed method, twosimulation experiments were performed with obstacles
located (1) randomly and (2) along the path of movement.
The robot had an initial position of (0.0, 0.0) [m] andmoved toward a target position (5.0, 0.0) [m] with an
initial speed of (0.6, 0.0) [m/s] in experiment (1). In
experiment (2), the robot moved in a straight line at a
speed of (0.1, 0.0) [m/s]. The parameters used in thesimulation were 𝐼 = 1, 𝑞1 = 0.6 [m], 𝑴1 = (10.0, 10.0)[kg], 𝑩1 = (5.0, 5.0) [Nm/s], 𝑲1 = (10.0, 10.0) [N/s]for experiment (1), and 𝐼 = 2 , 𝑞1 = 0.6 [m], 𝑴1 =(0.21, 0.21) [kg], 𝑩1 = (0.8, 0.8) [Nm/s], 𝑲1 =(1.0, 1.0) [N/ m], 𝑞2 = 0.4 [m], 𝑴2 = (16.8, 16.8) [kg],𝑩2 = (12.6, 12.6) [Nm/s], 𝑲2 = (1000.0, 1000.0)[N/s] for experiment (2).

Fig. 3. Trajectories of each experiment3.2. Results and discussion
Figures 3 and 4 show the results of the experiments ((a)
for (1) and (b) for (2)). Figure 3 shows the paths of therobot in each experiment, and Fig. 4 shows, from top tobottom, velocities (overall and x-/y-axis) and virtual
forces in each direction under the proposed method.
It can be seen from Fig. 3 (a) that the robot passed
straight by two obstacles and altered its path to avoid
another on the right. After the avoidance maneuver, therobot corrected its path toward the target position,
avoided obstacles on the left and finally arrived at thetarget position. Figure 4 (a) shows that the robot slowed
down due to virtual forces from the presence of two
obstacles under the proposed method at around 1.0 [s]before sensing virtual forces from behind andaccelerating to avoid an obstacle at 1.8 [s]. The robot
adjusted its velocity as appropriate in response to virtual
impedance forces indicating the distance from obstacles
on each side.
Figures 3 (b) and 4 (b) show other obstacles in the
robot’s path after collision avoidance. The robot
decelerated gradually, and finally stopped before

Fig. 4. Velocities and virtual forces of each experimentcollision under the influence of virtual forces from thevirtual wall with higher stiffness parameters from the
obstacle at around 9.4 [s].
The results indicate that the proposed method supports
collision avoidance in various situations.
4. Practical Experiment
A trajectory-tracking control experiment using an actual
mobile robot (iRobot Create 2, iRobot Corp.) was
performed with the position coordinates detailed in
Section 3. The target electric wheelchairs generally havepairs of propulsion and caster wheels, but a control
method for a robot with two independently driven wheels
was considered here for simplicity. Sufficient friction
between the drive wheels and the floor to avoid wheel
spin was assumed.
The position coordinates were regarded as target points
to be tracked by the robot. Robot position/posture data
and target position coordinates were used to determine© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 462

Haruna Kokubo, Taro Shibanoki, Takaaki Chin, Toshio Tsuji

avoidance of obstacles and emergency stops to prevent
collisions. In simulation and practical experiments using
the method, the robot avoided obstacles in response tovirtual forces based on the model.
In future research, the authors plan to implement themethod using distance image sensors and apply it inoperation of an actual biosignal-controlled wheelchair [4]to evaluate its effectiveness.
Acknowledgements
This work was supported by JSPS KAKENHI Grant
Number JP26330226.
References

Fig. 5. Experimental results for practical experiment

1. World Health Organization, Disability and health,
http://www.who.int/mediacentre/factsheets/fs352/en/,(Accessed November 2016).
2. Y. T. Lin and C. H. Kuo, Development of SSVEP-basedIntelligent Wheelchair Brain Computer Interface Assisted
by Reactive Obstacle Avoidance, in Proc. 2016 IEEE
International Conference on Industrial Technology,(Saratov, Russia, 2016), pp. 1572–1577.
3. K.Tanaka, K.Matsunaga and H. O. Wang, Electroencephalogram-based Control of an Electric Wheelchair,
IEEE Trans. on Robotics 21(4) (2005) 762–766.
4. K. Shima, R. Eguchi, K.Shiba and T.Tsuji, CHRIS:
Cybernetic Human-Robot Interface Systems, in Proc. 36th
International Symposium on Robotics, (2005) WE1C3
5. M. Massari, G. Giardini and F. B-Zazzera, Autonomous
Navigation System for Planetary Exploration Rover Basedon Artificial Potential Fields, in Proc. Dynamics andControl of Systems and Structures in Space 6th
Conference, (Riomaggiore, Italy, 2004), pp. 153–162.
6. R. M. F. Alves and C. R. Lopes, Obstacle avoidance formobile robots: a Hybrid Intelligent System Based on
Fuzzy Logic and Artificial Neural Network, in Proc. 2016
IEEE International Conference on Fuzzy Systems,(Vancouver, Canada2016), pp. 1038–1043.
7. T. Thsuji, M.Hatagi and M. Kaneko, Non-Contact
Impedance Control for Manipulators, J. the Robotics
Society of Japan 15(4) (1997) 616–623.
8. E. S. Jang, S.Jung and T. C. Hsia, Collision Avoidance ofa Mobile Robot for Moving Obstacles Based on
Impedance Force Control Algorithm, in Proc. 2005
IEEE/RSJ International Conference on Intelligent Robots
and Systems, (Edmonton, Canada, 2005), pp.383–387.
9. Z. Li, Z.Wu and Y. Fu, Dynamic Obstacle Avoidance ofMobile Robot Tele-operation Based on Non-contact
Impedance Control, in Proc. 2014 11th World Congresson Intelligent Control and Automation, (Tianyou, China,2014), pp. 1421–1426.

target velocity, which was defined for each wheel (𝑣𝐿 ,𝑣𝑅 )T using the distance 𝐿 𝑇 and direction 𝜃𝑇 between the
robot and the target position as𝑣𝐿 = 𝐾𝑣 ∙ 𝐿 𝑇 + ∆𝑣,(11)𝑣𝑅 = 𝐾𝑣 ∙ 𝐿 𝑇 − ∆𝑣,(12)∆𝑣 = 𝐾𝑡 𝜃𝑇 + 𝐾𝑡𝐷 𝜃𝑇̇ .
(13)Here, 𝐾𝑣 , 𝐾𝑡 and 𝐾𝑡𝐷 are pre-set constants.
Figure 5 (a) shows the paths observed in the simulationand practical experiments. Solid lines show the results of
10 trials in the practical experiment. Hardly any
difference between the results of the two conditions isobserved. Figure 5 (b) shows tracking in the sixth trial.
As in the simulation experiment, the robot detected the
obstacle and decelerated to pass by it with individual
wheel velocity regulation in response to virtual reaction
forces with the proposed model. The robot subsequently
decelerated to avoid collision with other obstacles along
its path in response to alternate contact with outer andinner virtual walls.
These results indicate that the proposed multi-layered
non-contact impedance model enables obstacle
avoidance in various situations. However, the scope ofthe tracking experiment was limited to the conditions
reported here; no experiment using an actual biosignalcontrolled electric wheelchair was conducted. The
authors plan to verify the efficacy of the proposed
method with implementation of the model using range
sensors.
5.

Conclusion

This paper proposes a multi-layered non-contact
impedance model and its application to obstacle
avoidance for electric wheelchairs. In this method,
multiple virtual walls based on a mechanical impedancemodel are located around the robot, enabling natural© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 463

A Voice Signal-Based Manipulation Method for the Bio-Remote
Environment Control System Based on Candidate Word Discriminations
Taro Shibanoki

Ibaraki University, JapanGo Nakamura, Takaaki Chin

Hyogo Rehabilitation Center, Japan
Toshio Tsuji

Hiroshima University, Japan

Abstract
This paper proposes a voice signal-based manipulation method for the Bio-Remote environment control system. The
proposed system learns relationships between multiple candidate words’ phonemes extracted by a large-vocabulary
speaker-independent model and control commands based on a self-learning look-up table. This allows the user tocontrol various devices even if false recognition results are extracted. Experimental results showed that the method
accurately discriminate slurred words (average discrimination rate: 94.4±2.53 [%]), and that the participant was able
to voluntarily control domestic appliances.
Keywords: environment control system (ECS), speech recognition, candidate word, learning-type look-up table1.

perspiration, long-time use of the system is difficult. To
overcome these problems, we focus voice signals as anextension input of the Bio-Remote.
A number of speech controlled ECS have beendeveloped, such as Voicecan (Voicecan co., ltd) [6] andLifetact (Asahi kasei technosystem co., ltd) [7]. Thesesystems discriminate users’ intensions from recorded
voice signals based on a speaker independent acoustic
model and control devices corresponding todiscrimination results. It is, therefore, difficult toaccurately discriminate speech of patients with dysarthria
who speak difficult, since the model used in thesesystems consider to standard adults’ speech. On the other
hand, training each user’s voice and building a speakerdependent model, it is possible to accurately discriminate
speech of patients with dysarthria [8]. However, it takes
a lot of time and needs a large amount of data to train aspeaker dependent model, it is possible to be a burden toa user.
This paper proposes a novel environment control
system for patients with dysarthria using voice signals.

Introduction

A variety of environmental control systems (ECSs) for
people with disabilities and bedridden elderlies havebeen developed to be self-sufficient and maintainindependent life in recent years.
There are number of studies to develop such systems
using biological signals [2] - [5]. As an example, the BioRemote – a new ECS developed by our research group[4][5] – has the distinctive features of (1) various input
systems such as biological signals, keyboard and mouse
input to meet user requirements, (2) flexible adaptation
to individual users depending on their capabilities, and(3) learning function to enable adaptation to variations
among individuals.
The Bio-Remote has been proven effective in support
for the everyday lives of people with spinal injuries
through its usefulness in the operation of domestic
appliances [5]. However, sensors must be attached to skinsurface with paste and medical tapes, it is user's
unpleasantness and burden because the procedure to wear
electrodes is complicated. Additionally, because of theeffectiveness of changes of skin impedance such as© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 464

Taro Shibanoki , Go Nakamura , Takaaki Chin, Toshio Tsuji

phonemes) and matching phoneme HMM to X. Then, thetop H words Wh (h = 1, 2, …, H) with maximum loglikelihood, their phonemes Mh and log-likelihoods T(Wh)are extracted.

Commandgeneration

Intention
estimation

Featureextraction

Voice SpeechControl
Infraredsignal recognition command signals
results

2.2. Operation discrimination using the learningtype look-up table

Domestic
appliances

Operator
Commanddatabase

The user’s intension is discriminated using the learningtype look-up table (LUT). The user is instructed to speaksome words used in device control, and relationships
between control commands, and extracted words Wh andphenomes Mh, which include false recognition results,
and log-likelihoods T (Wh) are learned to the LUT. Then,the control command corresponding to the extracted
word can be selected using the trained LUT.
In the learning stage, the user speaks C words, which isused in device control, some times and top V words Wcv
with maximum log-likelihood, and their phonemes Mcv
and log-likelihood T(Wcv) in H extracted words arecorresponded to each discrimination class (c = 1, 2, …,
C; v = 1, 2, …, V; V < H). In the discrimination stage,extracted phenomes of top U words with maximum loglikelihood in a new H words are used. First, extracted
phoneme (D)Mu (u = 1, 2, …, U; U < H) is compared tophoneme (L)Mci (i = 1, 2, …, Ic; Ic: number of learning datafor class c) of each discrimination class memorized in thelearning-type LUT. The coincidence between (D)Mu and(L) c
M i is then calculated as follows:(𝐷)(𝐿)( 𝑀𝑢 = 𝑀𝑖𝑐 )
1
𝑐
𝑠𝑢,𝑖
={
.
(1)
0
(𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒)
A class with a maximum value of 𝑟 𝑐 representing the𝑐average of all 𝑠𝑢,𝑖values is then taken as thediscrimination result. When the values for some classes
are same, difference between log-likelihoods T((D)Wu)
and T((L)Wci) are used to determine the result.

Learning-type LUT
Word Log likelihood Phoneme(L)(L)

W11 T ( ( L ) W11 )W21 T ( ( L ) W21 )

(L)
(L)

M11
M21

Microphone

Fig. 1. Overview of the proposed system.

The system trains individual user’s features based on thefalse recognition results using a speaker independent
model and can discriminate user’s intensions without
large amount of training data.
2.

Speech controlled Bio-Remote

Figure 1 shows the overview of the proposed system.
The proposed system consists of voice signal
measurement and feature extraction, operation
estimation, and device control stages. The details of eachstage are outlined as follows.
2.1. Voice signal measurement and feature
extraction [10]Voice signals were recorded using a microphone anddigitized using an A/D converter (sampling frequency: fs[Hz]). Mel-frequency cepstrum coefficients (MFCCs)
are then extracted as the inverse cosine transform isapplied to the log power spectra of the sampled signals.
Feature vector X used for speech recognition is definedas the low-frequency components of each frame ofextracted MFCCs [9].
Next, output probabilities P (W) of word W = {w1, w2,…, wK} (wk: word, K: number of words) is calculated
approximately using N-gram model. Additionally, output
probabilities P(X|W) of a feature vector X from W is
calculated using the acoustic model. A phoneme hidden
Markov model (phoneme HMM), which can consider acontext and a time variation, is used to calculate P(X|W).
P(X|W) is calculated dividing the words W to phonemesm = {m1, m2, …, mJ} (mj: phoneme, J: number of

2.3. Device control
The domestic appliances are controlled based on
discrimination results using the Bio-Remote. The BioRemote consists of a sensor unit to measure biologicalsignals and a main unit to control the target device using
the measured signals.
The user can directly select appliances and their control
commands with the proposed system. As an example, if© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 465

A Voice Signal-Based Manipulation

the user wishes to select the command to switch on theTV, speaks “TV” command to select the TV menu. Next,
the user speaks “power” command so that the operator
can finally choose the ultimate target – the “Power” menu
option. The control instruction corresponding to thepreviously learned TV power menu item is then sent fromthe computer to the main unit, and an infrared signal istransmitted.
3.

Julius

Proposed method

C5

C6

100
80
60
40
20

Speech recognition experiment0

3.1. Method

0

C1

C2

C3

C4

C7

Ave.

Fig. 2. Comparison of discrimination rates using the proposed
method and Julius [10]In order to verify the efficacy of the proposed method,experiments were performed to demonstrate the accuracyof discrimination. The participants are three healthy
males; and assuming slurred speech, they are intended to
speak with their tongue touching to maxillary central. A
directional microphone (audio-technica corp., AT-9942)
and an audio processor (ONKYO copr., SE-U33GXV)
are used to record voice signals. A number of
discrimination class is seven (C = 7), and participant
repeat to speak each word 50 times. 50 sets of each classdata are separated into 10 learning data sets and 40
discrimination data sets. The parameters used in theexperiment are set as fs = 16 [kHz], N = 3, H = 10, V = 10
and U = 5. The other parameters, K, J, Ic are adjusted
based on durations of input voice signals and results oflearning procedure. The Julius [10] is used to record andextract the features of each speech, and recognition
results using it are compared to the results using the
proposed method.

the difference of log-likelihood. To realize higher levelof discrimination performance, we plan to adjust
appropriate discrimination parameters such as number ofextracted words U using discrimination.
4.

ECS control experiment

Assuming operation in real life, an experiment was
performed using the Bio-Remote with the proposed
method introduced. In the experiment, the participant A
instructed to (1) turn on the light, (2) turn the TV on, (3)play DVD, (4) stop DVD, (5) turn the TV off, (6) play
the audio player and (7) turn off the light. The parameters
used in the experiment are as same as the Section 3.
An scene showing the operation of the proposed system
is given in Fig. 3, and examples of the experimental
results are shown in Fig. 4. This shows input signals,
extracted phenomes (corresponding to extracted words
with the maximum log-likelihood), discrimination resutls,
selected devices and control commands in order from thetop. From Fig. 4, the subject spoke ``shoumei (Light)”,
thereby moved to the light operation layer. Then,
speaking “onn (ON)”, the light was turned on after
approximate 1.6 [s] (see Fig. 4 (1)). The outcomes here
also showed that the subject directly move from the lightlayer to the TV layer as speaking ``terebi (TV)”, andDVD was played after the TV was turned on around 7.6[s] (see Fig. 4 (2)). It was therefore indicated that users’
intensions were correctly discriminated from slurredspeech to learn features of individual speech and devices
were controlled based on discrimination result.

3.2. Results and discussion
Figure 2 shows the discrimination rates for each classusing the proposed method and Julius. It plots the average
discrimination rates for each class while the set oflearning data and discrimination data were changed anddiscriminated at each data set for 10 times. From thisfigure, the average discrimination rate for all classes
using Julius is 3.52 ± 5.08 [%], and those using the
proposed system is 94.4±2.53 [%], respectively. These
results indicate that the proposed system can accurately
discriminate slurred speech, which is difficult todiscriminate a large vocabulary speaker independent
model, learning false recognition results in advance.
Additionally, when duplicative phenomes are extracted,
the system can discriminate user’s intensions based on© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 466

Taro Shibanoki , Go Nakamura , Takaaki Chin, Toshio Tsuji

In future work, we plan to perform operation
experiments for patients with dysarthria and validate theeffectiveness of the proposed system. In order to reduce
the level of stress involved in Bio-Remote operation, we
also plan to discuss the discrimination parameters, and toincorporate an online learning method into it.

Domestic appliances

PC

Audio processor

Acknowledgements
The authors would like to cordially acknowledge andexpress appreciation to Mr. K. Harada for his assistancein the implementation of this research. This work was
supported by JSPS KAKENHI Grant Number
JP26330226.

Main unit
Microphone

Fig. 3. Operation scene using the proposed system

References
[1]

[2]

[3]

[4]

[5]

Fig. 4. An example of experimental results

5.

[6]

Conclusion[7]This paper proposed a novel speech controlled ECS for
patients with dysarthria based on a false recognition
results for slurred speech using a large vocabulary
speaker independent model. In the experiments
performed, the accuracy of speech recognition was
evaluated comparing to results using Julius. The results
showed that the proposed method was able todiscriminate with an accuracy level of 94.4±2.53 [%] forthree healthy males. The method was therefore able tocorrectly discriminate user intensions. Further, operation
experiments using the proposed system showed that the
subject can voluntarily control domestic appliances.

[8]

[9]

Ministry of Health, Labour and Welfare, “Ministry of
Health, Labour and Welfare Fact-Finding Investigation of
Fisically Disabled,” http://www8.cao.go.jp/shougai/data/datah23/zuhyo09.html (accessed December 2016).
A. Craig, P. Moses, Y. Tran, P. McIsaac, L. Kirkup, The
Effectiveness of a Hands-Free Environmental Control
System for the Profoundly Disabled, Archives of Physical
Medicine and Rehabilitation, 83 (10) (2002) 1455-1458.
X. Gao, D. Xu, M. Cheng, S. Gao, A BCI-based
Environmental Controller for the Motion-disabled, IEEE
Transactions on Neural Systems and Rehabilitation
Engineering, 11 (2) (2003) 137-140.
T. Tsuji, K. Shima, A. Funabiki, S. Shitamori, K. Shiba, O.
Fukuda and A. Otsuka, A New Manipulation Method forEnvironment Control Systems, The Society of Life Support
Technology, 18 (4) (2006) 5-12 (in Japanese).
T. Shibanoki, G. Nakamura, K. Shima, T. Chin and T.
Tsuji, Operation Assistance for the Bio-Remote
Environmental Control System Using a Bayesian
Network-based Prediction Model, Proceedings of 37th
Annual International Conference of the IEEE Engineeringin Medicine and Biology Society (Milan, Italy, 2015), pp.
1160-1163.
Voicecan Co., Ltd, VOICECAN, http://www.voicecan.
ecweb.jp/ (accessed December 2016).
Asahi Kasei Technosystem, Co., Ltd, LIFETACT,
http://www.asahi-kasei.co.jp/ats/hukushi final.html(accessed December 2016).
M. S. Hawley, P. Enderby, P. Green, S. Cunningham, S.
Brownsell, J. Carmichael, M. Parker, A. Hatzis, P. O. Neill
and R. Palmer, A Speech-controlled Environmental
Control System for People with Severe Dysarthria,
Medical Engineering & Physics, 29 (5) (2007) 586-593.
A. Lee and T. Kawahara, Recent Development of OpenSource Speech Recognition Engine Julius, Asia-PacificSignal and Information Processing Association Annual
Summit and Conference (Hokkaido, Japan, 2009), pp. 131137.
Large vocabulary Continuous Speech Recognition Engine,
Julius, http://julius.sourceforge.jp/index.php (accessed
December 2016).

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 467

Experiments on classification of electroencephalography (EEG) signals in imagination of
direction using Stacked Autoencoder
Kenta Tomonaga, Takuya Hayakawa, Jun Kobayashi

Department of Systems Design and Informatics, Kyushu Institute of Technology,
Kawazu 680-4, Iizuka, 820-8502, Japan
E-mail: jkoba@ces.kyutech.ac.jp
lab.jkoba.net

Abstract
This paper presents classification methods for electroencephalography (EEG) signals in imagination of direction
measured by a portable EEG headset. In the authors’ previous studies, principal component analysis extracted
significant features from EEG signals to construct neural network classifiers. To improve the performance, the authors
have implemented a Stacked Autoencoder (SAE) for the classification. The SAE carries out feature extraction andclassification in a form of multi-layered neural network. Experimental results showed that the SAE outperformed theprevious classifiers.
Keywords: electroencephalography, stacked autoencoder, neural network, portable EEG headset, imagination of
direction

imagination of direction, and implemented feature
extraction with Principal Component Analysis (PCA)
and several neural networks for the classification.4,5 We
validated the classification performance and confirmedthat the best classification rate of the method using the
medical EEG device was still better than those of our
methods.
To achieve higher classification rate, we have
implemented a Stacked Autoencoder (SAE) for feature
extraction and classification of EEG signals inimagination of direction measured by the portable EEG
device. G. E. Hinton et al. said that deep autoencoder
networks can reduce the dimensionality of data muchbetter than PCA.6 Therefore, we introduced a SAE to ourstudy. Here we describe the SAE implemented for EEG

1. Introduction
Electroencephalography (EEG) is a non-invasive way formeasuring human brain activity. A lot of studies on Brain
Machine Interface (BMI) have make use of EEG becauseof its greater availability than invasive ways. In addition,
portable and low-cost EEG devices have been developed
and readily accessible nowadays. Having said that, there
are unclear points in the accuracy of those portable EEG
devices,1,2 hence the potential of applications using themshould be explored.
Seto et al. had studied on classification of EEG
signals in imagination of direction measured by a medical
EEG device.3 Following their study, we employed aportable EEG headset to record EEG signals in© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 468

Kenta Tomonaga, Takuya Hayakawa, Jun Kobayashi

signal classification and show results of comparative
experiments that validate its effectiveness.

elements. See Refs. 4 and 5 for more details about theEEG data acquisition and preprocessing.

2. EEG Data Acquisition and Preprocessing
Fig. 1 shows a wireless portable EEG headset developedby Emotiv Inc., named EPOC.7 We used the headset forEEG data acquisition in our preceding study.4,5 EPOC has
14 electrodes and two reference electrodes, recording
EEG signals at a sampling rate of 128 Hz. The electrodes
are placed on the scalp according to an extended 10-20
system for EEG measurement as shown in Fig. 2.

Fig. 1. Emotiv EPOC (wireless portable EEG headset)

Fig. 3. Experimental environment

Fig. 4. Arrows indicating directions(up, down, right, and left)

Fig. 2. Electrode placement of EPOC
Nine male university students participated inexperiments as subjects for EEG data acquisition. Theiraverage age was 21.9 years. Fig. 3 shows theexperimental environment. During the experiments, asubject imagined one figure of arrows shown in Fig. 4.
The obtained EEG signals were preprocessed to produce
input vectors to a classifier. Fig. 5 is a flowchart of the
preprocessing. The input vectors are composed of 23© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 469

Experiments on classification ofFig. 5. Flowchart of preprocessing

We constructed initial SAEs with the pretrained
autoencoders and then performed fine-tuning for 1000 or
10000 epochs. Fig. 7 shows the architecture. The SAE
has an input layer, two hidden layers, and an output layer.
Input vectors to the SAE are composed of 23 elements
produced from the EEG signals obtained through the
preprocessing shown in Fig. 5. The output layer has four
nodes in order to classify given EEG signals into the four
directions: up, down, right, and left. The number of nodesof the first and second hidden layers were tentatively set
to 20 and 10, respectively. Searching the optimum
number of hidden layers and their nodes is a future work.

3. Classification with Stacked Autoencoder
In our previous studies,4,5 we applied PCA to thepreprocessed EEG data for reducing the dimension, andtrained three-layered neural networks using the data as
feature vectors. This classification method is called“PCA-NN” in this paper.
We introduced deep neural networks in order toachieve better classification performance. Althoughtraining a deep neural network was difficult for
backpropagation due to vanishing gradient problem,
pretraining weights between nodes of a deep neuralnetwork can be a solution to the problem.
Stacked Autoencoder (SAE) is a way of constructing
a deep neural network, in which deep architectures are
initialized by stacking pretrained autoencoders. Fig. 6
illustrates a typical autoencoder that is an hourglassshaped three-layered neural network. This neuralnetwork has the same number of nodes in the input andoutput layers, and it is trained so that it can yield output
values equal to given input ones by backpropagation. As
mentioned above, autoencoders can be used for
dimensionality reduction. The anterior part between the
input and hidden layers of autoencoder works as an
encoder, compressing input signals and extracting
important information from them. The encoder parts ofpretrained autoencoders are stacked for initializing a
deep neural network. We trained the autoencoders withthe preprocessed EEG data for 1000 epochs.

Fig. 7. Stacked Autoencoder with two hidden layers
4. Results and Discussion
The classification rates of the classifiers were evaluatedwith 5-fold cross validation. Table 1 and Table 2 show
the evaluation results for the EEG signals obtained fromone of the subjects. In the PCA-NNs, the PCA kept
features with a 90% cumulative contribution ratio, andtrimmed off the others. In the result, 17-dimensionalfeature vectors were produced. Therefore, the NNs of the
PCA-NN were composed of 17-17-4 nodes in the input,
hidden, and output layers. On the other hand, thestructure of the SAEs were 23-20-10-4; the number of the
input nodes is 23 that is equal to the dimension of the
preprocessed input vector.
As shown in Table 1, the maximum classification rateby the PCA-NNs was 35.0% at FC5 electrode. It appears
that overfitting caused the poor performance in some of
the PCA-NNs trained for 10000 epochs.
Table 2 shows the classification rates of the SAEs. It
clarified that the SAEs achieved better classification

Fig. 6. Autoencoder© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 470

Kenta Tomonaga, Takuya Hayakawa, Jun Kobayashi

performance than the PCA-NNs. One of the SAEs
realized 61.7% classification rate at FC5 electrode.
Nevertheless, the work by Seto et al. using a medical
EEG device3 is still better than the results obtained in thisstudy.
It would be expected for improvement that a deeper
SAE could provide superior performance than the SAEs
with only two hidden layer used in this study. In addition,
we will use denoising autoencoders8,9 to extract more
relevant features for the classification of EEG signals.

demonstrated that the SAEs achieved the improvement,
however the achievement of the preceding study using a
medical EEG device is still better than ours using theportable EEG headset. There remains much to explore away to select the number of layers in the SAEs and to
adopt denoising autoencoders as future work.
References
1. M. Duvinage et al., A P300-based quantitative comparison
between the Emotiv Epoc headset and a medical EEG
device, in Proc. of the 9th IEEE/IASTED Int. Conf. on
Biomedical Engineering (Innsbruck, Austria, 2012).
2. M. Duvinage et al., Performance of the Emotiv Epoc
headset for P300-based applications, BioMedical
Engineering OnLine 12(56) (2013).
3. Y. Seto et al., Classification by EEG frequency
distribution in imagination of directions, in Proc. of the
18th Int. Conf. on Knowledge-Based and Intelligent
Information & Engineering Systems (2014), pp. 1300–
1306.
4. K. Tomonaga et al., Experiments on classification ofelectroencephalography (EEG) signals in imagination of
direction using a wireless portable EEG headset, in Proc.
of Int. Conf. on Control, Automation and Systems (Busan,
South Korea, 2015), pp. 1805–1810.
5. S. Wakamizu et al., Experiments on Neural Networks withDifferent Configurations for Electroencephalography(EEG) Signal Pattern Classifications in Imagination of
Direction, in Proc. of IEEE Int. Conf. on Control System,
Computing and Engineering (Penang, Malaysia, 2015), pp.
477–481.
6. G. E. Hinton and R. R. Salakhutdinov, Reducing theDimensionality of Data with Neural Networks, Science
313 (2006), pp. 504–507.
7. Emotiv EPOC, https://emotiv.com/epoc.php.
8. P. Vincent et al., Extracting and Composing Robust
Features with Denoising Autoencoders, in Proc. of the 25thInt. Conf. on Machine Learning (Helsinki, Finland, 2008),
pp. 1096–1103.
9. P. Vincent et al., Stacked Denoising Autoencoders:Learning Useful Representations in a Deep Network witha Local Denoising Criterion, J. of Machine Learning
Research 11 (2010), pp. 3371–2408.

Table 1. Classification rate percentages of PCA-NN
Electrode
AF3
F7
F3
FC5
T7
P7
O1
O2
P8
T8
FC6
F4
F8
AF4

Epoch 1000
25.8
29.2
26.7
35.0
33.3
31.7
25.0
25.0
34.2
30.0
25.8
27.5
24.2
26.7

Epoch 10000
25.8
26.7
29.2
25.8
26.7
27.5
25.8
25.0
28.3
25.8
26.7
24.2
24.2
23.3

Table 2. Classification rate percentages of SAE
Electrode
AF3
F7
F3
FC5
T7
P7
O1
O2
P8
T8
FC6
F4
F8
AF4

Epoch 1000
35.8
56.7
41.7
55.8
40.8
37.5
30.0
28.3
24.2
45.0
39.2
34.2
35.0
36.7

Epoch 10000
34.2
49.2
40.0
61.7
40.8
41.7
33.3
30.8
29.2
32.5
34.2
33.3
32.5
35.0

5. Conclusion
We implemented the SAEs for classification of EEG
signals in imagination of direction, and compared theperformance with those of the NNs trained using thefeature vectors extracted by PCA. The results© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 471

Influence of Partner Selection on Functional Differentiation:
Emergence of Diversity by Isolated Interaction and Preference Change
Saori Iwanga

Department of Maritime Safety Technology, Japan Coast Guard Academy, 5-1 Wakaba
Kure, Hiroshima 7378512, Japan
Akira Namatame

Computer Science Department., National Defense Academy, 1-10-20 Hashirimizu
Yokosuka, Kanagawa 2398686, Japan
E-mail: s-iwanaga@jcga.ac.jp

Abstract
In examining collective behavior, we focus on the interactions of individuals. We also need to describe on two
different levels: the microscopic level, where the decisions of the individual agents occur, and the macroscopic level
where collective behavior can be observed. What makes collective behavior is interesting and difficult is that theaggregate outcome is what has to be evaluated within the constraints of the environment. The performance of thecollective system depends crucially on the type of interaction as well as the heterogeneity in preference of agents.
In this study, for emergence of diversity, we found that it is needed two different speed learning processes. And it isneeded isolated interaction for emergence of diversity. But, the utility of less clustered interaction is more than thatof isolated interaction if possible. On the whole, we found that isolated interacting is effective for emergence ofdiversity. Then, once diversity is emerged enough, less clustered interaction is effective to utility of each agent andwhole population.
Keywords: collective behavior, diversity, payoff matrix, isolated interaction, preference.

example, recently a study of shape the formation by multirobots are paid attention. In the multi robots system if
there are heterogeneous robots, the robots are removed6.
There are limits to adaptation performance of multirobots system with heterogeneous robots, and it is hardly
not reproduced the emerged diversity in the populationon computer simulation.
In examining collective behavior, we focus on theinteractions of individuals. We also need to describe on
two different levels: the microscopic level, where thedecisions of the individual agents occur, and themacroscopic level where collective behavior can beobserved7. Schweitzer8 shows that interaction on

1. Introduction
The studies on multi robots and multi agent simulation
began in the late 1980s. Reynolds1 shows that a collective
behavior occurs when three rules are used at the sametime by BOIDS and shows the collective behavior is akind of the complex system. As the example of emerged
physical or functional characteristics, there are known
study of Sims2 in the block struggle environment andstudy of Terzopoulos3 about artificial fish. Kubo et.al4
show the emergence of unique individual on competition
game for food of ants. However, heterogeneity of
individual are often ignored in most of studies5. For© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 472

Saori Iwanaga, Akira Namatame

microscopic level result in the emergence of complex
behavior on the macroscopic level. What makes
collective behavior is interesting and difficult is that theaggregate outcome is what has to be evaluated within theconstraints of the environment. The performance of thecollective system depends crucially on the type ofinteraction as well as the heterogeneity in preference ofagents9.
Axelrod et.al5, 10show that agents experience morediverse contacts, the system can become less diverse. As
interaction patterns become less clustered, the experienceof interacting with more “distant others”, information
moves more rapidly throughout. The world becomes so“small” that the sociological “islands” vanish, unable tokeep their local ways and remain untouched by events
elsewhere. Early innovations spread too fast, and variety
that can provide later implements is lost, what they calls
as premature convergence. The ideal breeding ground fornovel live-forms seems to be an archipelago or a networkof mountain valleys.
Sandholm el.al10 presents a dynamic analysis of theevolution of preferences in a strategic environment.
Dynamics run at two speeds at once: while natural
selection slowly reshapes the distribution of preferences,
players quickly learn to behave as their preferences
dictate. They establish the existence and uniqueness ofthe paired trajectories of society's preferences andaggregate behavior. While aggregate behavior adjustssmoothly in equilibration games, in coordination games
aggregate behavior can jump discretely in an instant of
evolutionary time.
In this study, we clarify how diversity is emerged in
the population.

chooses the best action. We formalize these ideas in amodel with a finite population of agents in which agentsare repeatedly matched within a period to play a gameand we consider to describe game. Here are many
parameters to be considered such as payoff structure,
localization, the shadow of the future, the number of
agents and so on. Among these parameters, we examine
parameters: payoff structure and localization.
We adopt the payoff matrix given in Table 1, which
describes the outcome of the interaction between agents.
In this matrix, if both agents chose the same strategy, they
can get the positive payoff 1 − or , otherwise theyreceive nothing where 0 ≤ ≤ 1 . The game iscoordination game.
There are many situations where interacting agentscan benefit from coordinating their actions. Coordination
usually implies that increased effort by some agents leads
the remaining agents to follow suit, which gives risemultiplier effects. Examples where coordination isimportant include trade alliance, the choice of compatible
technologies or conventions such as the choice of asoftware or language. These situations can be modeled ascoordination games; in which agents are expected to
select the strategy the majority does13, 14. The traditional
game theory, however, is silent on how agents know
which equilibrium should be realized if a coordination
game has multiple equally plausible equilibria, wherethese can be Pareto ranked15,16. This silence is all themore surprising in games with common interest since one
expects that players will coordinate on the Pareto
dominant equilibrium17. The game theory has been also
unsuccessful in explaining how agents should behave inorder to improve an equilibrium situation18.
Considering that payoff parameters of both agentsare 0, it is Nash equilibrium and Pareto optimum thatboth of them choose S1. Nash equilibrium is theequilibrium state achieved to the most suitable strategy
by decisions of all agents. Pareto optimum is the state that
there is left no room to raise the benefit of the agent who
without getting down the benefit for the other agents.

2. ModelThere is a growing literature on the approach of bounded
rationality, and the hypotheses employed in theseresearches reflect the ability of each agent to receive
partial information from other agents in the course of
their interaction12. Our model can be interpreted in likemanner; however, we intend to combine the hypothesesof adaptation and local interactions in modeling
evolutionary processes. The first hypothesis reflects
limited ability to receive, decide, and act upon
information they get in the course of interactions for eachagent. The second interpretation is that the agents

Table 1. The payoff matrix of agent Ai.
behavior of other agent
S1
S2
behavior of
S1
1−0
agent Ai
S2
0

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 473

Influence of Partner Selection

2.1. Preference of agent

2.3. Decision of behavior

Payoff parameter characterizes the preference of theagent. We deal with the expression of heterogeneity ofagents. In the population, each agent has different payoffparameter and 1 − as shown in Table1. If payoffparameter 1 − is greater than of agent Ai, it meansthat the agent refers S1. And if parameter 1 − of agent
Ai is less than parameter , the agent prefers S2. For
example, if payoff parameter of agent Ai is less than0.5, the agent prefers S1. Otherwise, agent Ai prefers S2.

Agents don’t know the behavior the partners will take,but know the behavior the partners took at last. The
proportion of agents having chosen S1 at time t is definedby ( ) ( 0 ≤ ( ) ≤ 1 ) in agent’s neighbors. Eachagent can take the best response against the behavior thepartners took by calculating the expected payoff. AgentAi compares the average expected utilities by S1 with S2
at time t, which are given as flows.
( 1, ) = ( )(1 − ),( 2, ) = ( ) .
(1)Then, they act so as to maximize their utility. Agent Aichooses S1 if ( 1, ) > ( 2, ) , or chooses S2
if ( 1, ) < ( 2, ) at next time. The decision rule ofagent Ai at time t+1 is described as the following twocases depending on its idiosyncratic parameter .
If ( ) > , agent Ai chooses S1,otherwise ( ) < , agent Ai chooses S2. (2)That is best response rule. When ( ) = , we assume
that agent chooses the same behavior as last time. We
define the right-hand side of (2) as threshold of agent Ai.
The crucial point for dealing with diversity in the
population is threshold. According to the threshold,agent’s behavior is different from each other. Moreover,the proportion of agents having chosen S1 among thepartners ( ) of the agent is different from each other.
So, decision of agent is different from each other. That is,payoff matrix and the world of agent effect on the
behavior of agent and form collective behavior in the
population. We define the collective behavior ( ) thatthe proportion of agents having chosen S1 in whole
population at time t.

2.2. Social network
In many situations, agents are not assumed to be soknowledgeable as to correctly guess or anticipate theother agent's strategies, or they are less sophisticated inthat they do not know how to calculate best replies12. The
hypotheses we employ in the local interaction model
reflect limited ability to receive, decide, and act upon
information they get in the course of interactions. Eachagent to receive partial information from other agents inthe course of their interaction13. And each agent chooses
an optimal strategy based on information about what
other agents have done in its world. The consequences of
their behavior take to have an effect on agents with whomthey are not directly linked and agents gradually learn thestrategy of a population.
Lattice model is used to make clear the ecologicalcharacteristic of living beings. Nowak19 and Axelrod10
deal with the situation that each one faces the social
dilemma and indicate that importance of the structure ofspace to emerge the corporation in society. We consider
the lattice structure as shown in Fig. 1, in which eachagent interacts with only neighbors of the agent. In thispaper, we deal with von Neumann neighborhood model.
In von Neumann neighborhood model, each agent
interacts with it’s the nearest four agents.

2.4. Movable for new neighbors
If an agent wants to get a high payoff, it is effective
method to move to another site and change its partners tointeract.14, 20 in their studies, the agent changes partner
based on the properties of partners. But, it is often
difficult to observe the properties of partner. It is easy toobserve the behavior of partners. Often the character of
the agent is treated that an internal attribute, such aspreference, forms endogenously. But, we cannot observe
inside attribute from outside and can observe only anaction appeared internal attribute. And we often treat theaction of agent given internal attribute beforehand.

Agent Ai

Fig. 1. von Neumann neighborhood model© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 474

Saori Iwanaga, Akira Namatame

Table 2. Updated payoff matrix of agent Ai after chose S2 forT time steps in succession.
Behavior of other agent
S1
S2
Behavior of
S1
1− −∆
0
agent Ai
S20+∆

However, it is reasonable that action taking by an agent
forms the internal attribute. Then, in this study, we set
each agent changes partners based on the behavior.
Each agent has parameter μ. If average payoff ofagent Ai gotten by interaction with four partners isgreater than equal μ, agent Ai stay the site. Otherwise
agent Ai move to another site. The rule of change partners
is as follows, where k=1, 2.
If ( , ) ≥ , agent Ai stays the site,otherwise ( , ) < , agent Ai moves toanother site. (3)There are some agents who want to move another site
in the population. The agents make the pairs at random
and change the site each other. And all agents who want
to change can change the partners at same time. As forthe behavior of the agent on the new site, the agent
choose the behavior at first which the former agent
decided in the site because it is not familiar with the site.
After that, the agent decide the behavior by eq. (2).
If agent is movable, agents can contact diverse agentsand the interaction is less clustered. On the other hands,
the interaction when agents cannot move to another site
is clustered and corresponds to isolated interaction.

3. Simulation

3.1. Simulation settings
We arrange agents for an area of 50×50 (2500 agents)
with no gap, and four corners and ends of an area connect
it with an opposite side. We set payoff parameter of allagents as 0.5 at first. And we set μ as 0.5 based on
preliminary experiment.
At initial time step, we set the initial collective
behavior p(0), which is the proportion of agents having
chosen S1 in whole population, from 0.0 to 1.0 atintervals of 0.125, where we assume that an agent
chooses at random. And each agent makes its decision
depend on the best response rule each time step, and thencollective behavior ( ) changes. Then, we simulate 100
trials per one initial collective behavior and investigate
final collective behavior p* (at 1000 time step).
We simulated 3 cases as shown in Table.4. M-0.01 is
the case with delta ∆ of 0.01 and the agent can change its
partners. In the case, the interactions of agents are lesclustered. S-0.01 is the case with delta ∆ of 0.01 and theagent cannot change its partners that is stay the site. In
the case, all agents stay the site and always interact withsteady four neighbors, which is isolated interaction. M0.1is the case with delta ∆ of 0.1 and the agent canchange its partners. Self-reinforcement with delta ∆ of
0.1 means faster learning than that with delta ∆ of 0.01.

2.5. Self-reinforcement of preference
In this study, we clarify how diversity is emerged in the
population. Each agent can update the payoff parameterby interaction. We define this as “self-reinforcement ofpreference”. If Agent Ai has been chosen S1 for T times
in succession, agent Ai increases the payoff 1 − for S1
by delta ∆ and decreases the payoff for S2 by delta ∆.
Otherwise, agent Ai has been chosen S2 for T times insuccession, agent Ai increases the payoff for S2 bydelta ∆ and decreases the payoff 1 − for S1 by delta ∆.
Table 2. Updated payoff matrix of agent Ai after chose S1 forT time steps in succession.
Behavior of other agent
S1
S2
Behavior of
S1
1− +∆
0
agent Ai
S2
0−∆delta ∆

Table 4. Simulation patterns
Change partners
MovableStay
0.01
M-0.01
S-0.01
0.1
M-0.1
---

3.2. Criteria
We evaluate collective behavior with four criteria,stability, arrangement, efficiency and equity. The
stability is the path-dependency of collective behavior.
We evaluate final collective behavior p*, which
represents the proportion of agents to choose S1 at last,

The reinforced payoff at next time is given by Table.2
and 3. That corresponds to slow natural selection11.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 475

Influence of Partner Selection

staring from some initial collective behavior p(0). As forarrangement of collective behavior, we investigate thebehavior of all agents at last. And we compare that withthe arrangement of agents’ preferences.
Efficiency is evaluated by the average utility, which
also stands for the measure of the desirability of macrolevel. Here, high Efficiency, that is high average utility,means that agents in the population get high utilities as awhole. We define the proportion of agents g(u) who
obtained utility u per one partner at last interaction. Then,
Efficiency U is given as follows,
=∫
( ) .
(4)
If two populations have the same Efficiency, theutility distributions are different. One population may beequity, another population may be inequity. Then, Equity
stands for the measure of the desirability at the microlevel. In economics, the inequity is often calculated by
the Lorenz curve. The Lorenz curve is represented by the
cumulative proportion of the total utility, which iscumulated to the proportion at the level x starting withthe poorest agents. Using g(u), the Lorenz curve L(x) is( ) /∫
given by ( ) = ∫( ) . Equity E isobtained by the area under the Lorenz curve and is
obtained as follows,= 2∫ ( ) ,(5)where the parameter x satisfying = ∫ ( )and wis the obtained utility.

On the other hand, when initial collective behavior
p(0) is 0.5, half of agents choose S1 and the rest agentschooses S1 at last (Fig.2(1)(b)) and final collective
behavior p* becomes 0.5. As for payoff parameters,
when initial collective behavior p(0) is 0.5, payoffparameter of half of agents change to 0.0 and the rest
agents change to 1.0 at last (Fig.2(2)(b)). We found thatdiversity is emerged in the population. Moreover,comparing with Fig.2(1)(b) and Fig.2(2)(b), agents withpayoff parameter 0.0 chooses S1, that is, agents who
prefer S1 choose S1 according to the preferences. And
agents with payoff parameter 1.0 chooses S2, that is,agents who prefer S2 choose S2 according to thepreferences. And segregation is formed and agents withthe same preference can interact. We define thesegregated interaction as selective interaction.

(a) p(0) =0.125(b) p(0) =0.5

p(0) =0.875● : S1 ●: S2(1) Behaviors of agents (examples)

3.3. Simulation results
The simulation results are shown in Fig.2, 3 and 4 at last.
For case M-0.01, the arrangement of agents’ behaviors ofat last are shown in Fig.2(1) and arrangement of agents’presences at last are shown in Fig.2(2). When initialcollective behavior initial collective behavior p(0) is
0.125, all agents comes to choose S1 at last (Fig.2(1)(a))
and final collective behavior p* becomes 1.0. As for
payoff parameters, when initial collective behavior p(0)is 0.125, payoff parameter of all agents change 1.0 atlast (Fig.2(2)(a)). When initial collective behavior initialcollective behavior p(0) is 0.875, all agents comes tochoose S2 at last (Fig.2(1)(c)) and final collective
behavior p* becomes 0.0. As for payoff parameters,
when initial collective behavior p(0) is 0.875, payoffparameter of all agents change 0.0 at last (Fig.2(2)(c)).
Diversity is not emerged in the population.

(a) p(0) =0.125(b) p(0) =0.5

p(0) =0.875●: θi =0.0 ●: 0.0 < θi <1.0 ●: θi =1.0

(2) Preferences of agents (examples)Fig. 2. Assignment of Agents at last: case M-0.01

In case S-0.01 (Fig.3) and M-0.1(Fig.4), theproperties of assignment of agents at last are similar tothat in case M-0.01. But, in case S-0.01, when initialcollective behavior p(0) is 0.125 and 0.875, minority
behavior survives (Fig.3(1)(a) and Fig.3(1)(c)). Because
the agent cannot change its partners, it is possible toremain the local behavior. As for the preferences, a slightbut noticeable diversity is emerged in the population(Fig.3(2)(a) and Fig.3(2)(c)). And in case S-0.01, when

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 476

Saori Iwanaga, Akira Namatame

initial collective behavior p(0) is 0.5, it is difficult to form
selective interaction (Fig.3(1)(b)) unlike case M-0.01.
In case M-0.01 and M-0.1, when initial collective
behavior p(0) is 0.5, there are formed selective
interaction (Fig.2(1)(b) and Fig.4(1)(b))) .We found thatby change partners, there are formed selective interactionof agents with same preference. And when delta ∆ is 0.01,
segregation is clear and selective interaction are formed
than that when delta ∆ is 0.1.
The stability of collective behavior shown in Fig.5. In
this figure, x-axis represents initial collective behavior
p(0) and y-axis represents the average final collective
behavior p*.
In case M-0.01, we found that collective behavior
depends on initial collective behavior p(0). If initialcollective behavior is more than equal 0.75 collective
behavior converges to 1.0 and all agents choose S1 at lastin any trials. And otherwise initial collective behavior isless than equal 0.25 collective behavior converges to 0.0
and all agents choose S2 at last in any trials. That is the
majority behavior survives and the minority behavior
disappears. But, if initial collective behavior is between
0.375 and 0.625, collective behavior becomesintermediate, which means not extreme value like 0.0 or
1.0, depending on the trial and initial collective behavior.
If initial collective behavior p(0) is 0.375, final collective
behavior p* becomes 0.05. If initial collective behavior
p(0) is 0.5, final collective behavior p* becomes 0.54. If
initial collective behavior p(0) is 0.375, final collective
behavior p* becomes 0.97. Then both behavior S1 and S2
can coexist.
In case S-0.01, collective behavior also depends on
initial collective behavior p(0), but the property of final
collective behavior p* is more moderate than case M0.01, that is, the graph curve of case S-0.01 is gentler thanthat of case M-0.01.
In case M-0.1, collective behavior depends on initialcollective behavior initial collective behavior p(0) andfinal collective behavior p* is the most moderate and thegraph curve of case M-0.1 is the gentlest in three cases.

(a) p(0) =0.125(b) p(0) =0.5

p(0) =0.875● : S1 ●: S2(1) Behaviors of agents (examples)(a) p(0) =0.125(b) p(0) =0.5

p(0) =0.875●: θi =0.0 ●: 0.0 < θi <1.0 ●: θi =1.0

(2) Preferences of agents (examples)Fig. 3. Assignment of Agents at last: case S-0.01(a) p(0) =0.125(b) p(0) =0.5

p(0) =0.875● : S1 ●: S2(1) Behaviors of agents (examples)(a) p(0) =0.125(b) p(0) =0.5

p(0) =0.875●: θi =0.0 ●: 0.0 < θi <1.0 ●: θi =1.0

(2) Preferences of agents (examples)Fig. 4. Assignment of Agents at last: case M-0.1

In Fig.5, for reference, we also show the simulation
results of default cases which mean that all agents don’t
self-reinforce. Default M is the case that the agent canchange its partners that is movable. In default M, theagent can change its partners, and there is distinct initialvalue dependency and become 0.0 or 1.0 depend on© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 477

Influence of Partner Selection

initial collective behavior p(0). Default S is the case thatthe agent cannot change its partners that is stay the site.
In default S, the property is similar to case M-0.1 thoughagents do self-reinforce in the case. And both behavior
S1 and S2 can coexist.

The efficiency and the equity at last are shown Fig.6.
Because they are lowest when initial collective behavior
p(0) = 0.5 in all initial collective behavior, we show themas typical efficiency and equity in Fig.6. We found thatthey are increased in the order of S-0.01, M-0.1 and M0.01. The efficiency and the equity when the agent canchange its partners are higher than that when the agent
cannot change its partners. And they when delta ∆ is 0.01
are higher than that when delta ∆ is 0.1.

1

0.75

4. Discussionp*Comparing with M-0.01 and S-0.01, case S-0.01 is
effective for emergence of diversity. In case S-0.01, theagent cannot change its partners to interact and stays thesite. That is, the interaction of population is isolated. But,
the efficiency and equity of S-0.01 are less than that ofcase M-0.01. We found that less clustered interaction andutility of the population is trade-off. M-0.01 corresponds
to less clustered interaction because agents can interact
with agents far away. And S-0.01 corresponds to isolated
interaction because agents can interact with only
neighbor agents. We found what the same as Axelrod
et.al5 shows which the less clustered interaction makeworld less diverse and suggest isolated area is necessary
emergence of diversity. But, if diversity of the population
is emerged in less clustered interaction, we found that theutility is more than that of isolated interaction.
Comparing with M-0.01 and M-0.1, the efficiency
and equity of M-0.01 are more than that of M-0.1. We
found that it is effective to interact with partners of same
preferences for utility. In this study, each agent learn on
the two phases, best reply and self-reinforcement. The
speed of learning of change partner is faster than that ofself-reinforcement. As for self-reinforcement, M-0.01
corresponds to slow and M-0.1 corresponds to fastlearning. We found what the same as Sandholm10 shows
which the effectiveness of two difference speed learning
processes for emergence of diversity. Then, M-0.01
realizes more two difference speed learning process thancase S-0.01 because the learning speed of two phases ofM-0.01 has more huge differences than that of S-0.01.
Then, for emergence of diversity, it is needed twospeed learning processes. And it is needed isolated
interaction for emergence of diversity. But, the utility ofless clustered interaction is more than that of isolated
interaction. On the whole, isolated interaction is effective

0.5

0.25

0
0

0.25

0.5 p(0) 0.75

1●: M -0.01 ●: S-0.01 ■: M -0.1 ＋: default M ▲:default S

Fig. 5. Stability of Collective behavior

1

M-0.010.75

M-0.1
S-0.01
S-0.1

Udefault S
default M

0.5

0.25

0
0

0.25

0.5 E

0.75

1

Fig. 6. Efficiency and Equity at last (p(0) = 0.5)

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 478

Saori Iwanaga, Akira Namatame

10. R. Axelrod, The Evolution of Cooperation (Basic Books,
1985).
11. W.H. Sandholm, Preference Evolution Two-Speed
Dynamics and Rapid Social Change, Review of Economic
Dynamics 4 (2001)637-679.
12. A. Rubinstein, Modeling Bounded Rationality (The MIT
Press, 1998).
13. B. Huberman and N. Glance, Diversity and collective
Action, Interdisciplinary Approaches to Nonlinear
Complex Systems 62 of the series Springer Series in
Synergetics (1993) 44-64.
14. T. Schelling, Micromotives and Macrobehavior (Norton,
1978).
15. W.B. Arthur, Inductive Reasoning and Bounded
Rationality, American Economic Review 84(2) (1994)
406-411.
16. B. Fogel, K. Chellapia and P.J. Angeline, Inductive
Reasoning and Bounded Rationality Reconsidered, IEEE
Trans. or Evolutionary Computation 3 (2) (1999) 142-146.
17. J. Hansarny and R. Selten, A Game Theory of Equilibrium
Selection in Games (MIT Press, 1988).
18. D. Fudenberg and D. Levine D. The Theory of Learning inGames (The MIT Press, 1998).
19. M.A. Nowak, Evolutionary Dynamics: Exploring theEquations of Life (Belknap Press of Harvard University
Press (2006).
20. S. Parsons et.al (eds.) Selecting Partners, Game Theory
and Decision Theory in Agent-Based Systems, Multiagent
systems, artificial societies, and simulated organizations:international book series, 5(Kluwer Academic, 2002) pp.
1-29.

for emergence of diversity. Then, once diversity is
emerged enough, less clustered interaction is effective toutility of each agent and whole population.
5. Conclusion
The performance of the collective system depends
crucially on the type of interaction as well as theheterogeneity in preference of agents. In this study, foremergence of diversity, we found that it is needed two
different speed learning processes. And it is needed
isolated interaction for emergence of diversity. But, theutility of less clustered interaction is more than that ofisolated interaction if possible. On the whole, we foundthat isolated interacting is effective for emergence ofdiversity. Then, once diversity is emerged enough, lessclustered interaction is effective to utility of each agent
and whole population.
6. References
1. C. Reynolds, Flocks, Herds, and Schools: A Distributed
Behavioral Model, Computer Graphics (SIGGRAPH '87
Proceedings of the 14th annual conference on Computer
graphics and interactive techniques) 21(4) (1987) 25-34.
2. K. Sims, Evolved Virtual Creatures, Computer Graphics(SIGGRAPH '94 Proceedings of the 21st annual
conference on Computer graphics and interactive
techniques) (1994) 15-22.
3. D. Terzopoulos, X. Tu and R. Grzeszczuk, Artificial
Fishes: Autonomous Locomotion, Perception, Behavior,and Learning in a Simulated Physical World, Artificial Life
1(4) (1994) 327-351.
4. M. Kubo and Y. Kakazu, Evaluation of CoordinatedMotions of Multi - Agent Systems on Competition for
Food between Ant Colonies, IPSJ Journal 35(8) (1994)
1555-1566.
5. R. Axelrod and M.D. Cohen, Harnessing Complexity(Basic Books, 2001).
6. M. Rubenstein, A. Cornejo and R. Nagpal, Programmableself-assembly in a thousand-robot swarm, Science
345(Issue 6198) (2041)795-799.
7. M. Sipper, Evolution of Parallel Cellular Machines: The
Cellular Programming Approach (Springer-Verlag,
Heidelberg, 1997).
8. F. Schweitzer (eds.), Interactive Structure Formation with
Brownian Particles. Self-organization of complex
structures: from individual to collective dynamics (Gordon
and Breach Science Publishers, 1997) pp. 101-118.
9. W.B. Arthur et.al (eds.), The economy as an interactive
system. The Economy as a Complex Evolving System II(Addison Wesley, Reading, MA, 1997) pp. 491–533.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 479

Beacon-based tourist information system to identify visiting trends of tourists
Akihiro Yamaguchi1,*, Masashi Hashimoto1, Kiyohiro Urata1, Yu Tanigawa1, Tetsuya Nagaie1,
Toshitaka Maki1, Toshihiko Wakahara1, Akihisa Kodate2, Toru Kobayashi3 and Noboru Sonehara4
1

Fukuoka Institute of Technology, Wajiro-Higashi 3-30-1,
Higashi-Ku, Fkuoka 811-0295, JAPAN2

Tsuda College, Tsuda-Machi 2-1-1, Kodaira, Tokyo 187-8577, JAPAN3

Nagasaki University, Bunkyo-Machi 1-14, Nagasaki 852-8521, JAPAN

4

National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, JAPANE-mail: *aki@fit.ac.jp

Abstract
In this study, we propose a system that provides tourist information and obtains trends of visiting tourists using
beacons and cloud service. As part of our research, we are working on the promotion of local area tourism incooperation with a local community. A low energy Bluetooth device is used as a beacon to transmit a universallyunique identifier. In addition, beacons are placed at sightseeing spots and tourist facilities. Our proposed systemcomprises two application programs; one is a client-side application program that provides area-specific tourist
information corresponding to the detected beacon. The other is a server-side application to record time and location
information of the detected beacons. In this paper, we describe the scheme of our system, and present the results ofexperiments conducted using the prototype system in the local tourist area. In addition, we discuss an open platform
for information collection services using beacons.
Keywords: Tourism promotion, IoT, Bluetooth beacon, Stamp rally, Cloud service, Local community cooperation

numbers of tourists and the order in which they visit
sightseeing spots. Recently, beacon technology using
Bluetooth low energy devices has become very popularand various applications have been studied.3) This
technology could provide information about places
visited by tourists and collect tourist trends.
In this study, we propose a beacon-based system thatprovides tourist information and obtains visiting trendsof tourists using beacons and a cloud service. A prototypeof the proposed system was developed for AinoshimaIsland in Shingu. We then performed field tests using thedeveloped prototype system. In addition, we discuss anopen platform for information collection services using
beacons.

1. Introduction
Tourism promotion is considered to be an importantregional activity for revitalizing regional exchange andintergenerational communication.1) As part of ourresearch, we cooperate with the local community topromote local tourism in Shingu, Japan.2) Although
Shingu has many potential tourism resources, forexample, a many natural beauty and historic sites, these
tourism resources are not well known.
There are two approaches to using information
technology in tourism promotion. One is to provide
tourist information using the Internet and the other is tocollect tourist information, for example, trends in the© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 480

Akihiro Yamaguchi, Masashi Hashimoto, Kiyohiro Urata, Yu Tanigawa, Tetsuya Nagaie, Toshitaka Maki, Toshihiko Wakahara,
Akihisa Kodate, Toru Kobayashi, Noboru Sonehara

2. Beacon-based Tourist Information System
In the following sections, we present an outline of the
proposed beacon-based tourist information system andmethod for identifying sightseeing spots using beaconand Global Positioning System (GPS) information.
2.1. Outline of the Proposed System
Our proposed system consists of two parts: a client-side
application (the client) and a server-side application (theserver). A schematic drawing of the proposed system isshown in Fig. 1. Beacons are placed at the targetedsightseeing spots beforehand.
The server consists of the tourist information database(TIDB) and visiting tourists database (VTDB). Tourist
information for the targeted spots and facilities are stored
in the TIDB with their corresponding beacon ID. Beacon
detection events for each client are stored in the VTDB
as a record (user ID, beacon ID, time, and location).
The basic procedure of the proposed system is asfollows:(1) Beacon detection: The client detects the beacon
when it enters the range of the target beacon.
(2) Beacon information storage: The client sends thedetected beacon ID, client ID, time, and location to
the server, where it is stored in the VTDB.
(3) Tourist information retrieval: Visited sightseeing
spots are identified using the beacon ID, and thecorresponding area-specific tourist information isretrieved from the TIDB on the server.
(4) Identified spot notification: The client is notified
when a spot is identified.
(5) Area specific tourist information retrieval: If theclient user wants to obtain area-specific tourist
information about the identified spots, the client
downloads it from the server.
Using this procedure, data about the sightseeing spots
visited by each client are accumulated in the VTDB on
the server. We can then use these data to identify tourist
trends at sightseeing spots and the order in which tourists
visit these spots.

Fig. 1. Schematic drawing of the proposed beacon-based tourist
information system: (1) beacon detection, (2) beacon
information (user ID, beacon ID, time, and location) storage, (3)retrieval of tourist information corresponding to the beacon ID,(4) identified spot notification, (5) retrieval of area-specifictourist information, and (6) identification of visiting trends fromstored data.

spot where the beacon is installed is identified by the
beacon ID and GPS information. In iBeacon technology,
a beacon ID consists of a universally unique identifier(UUID: 128 bits), major value (16 bits), and minor value(16 bits). In this research, the UUID is assigned an
application-specific number and we use the same UUID
through our proposed system. The major value identifies
the area in which the beacon is placed. A unique minor
value is assigned for each sightseeing spot. We can thenidentify sightseeing spots by the three values UUID,
major value, and minor value. The identification isvalidated by GPS information.
To handle sightseeing spots where we cannot put
beacons, for example historical monuments in outdoor
fields, we introduce a “pseudo beacon” using GPS. The
pseudo beacon also has a UUID, major value, and minor
value like a normal beacon. When the client’s GPS
position is near the sightseeing spot where the pseudo
beacon is assigned, the client behaves as if it has detecteda normal beacon. In order to implement this pseudo
beacon, it is necessary to obtain the information about thepseudo beacons in the target area beforehand.
3. Development of the Prototype System
As a prototype of the proposed system, we developed abeacon-based tourist information system for AinoshimaIsland in Shingu. The client is implemented as an
Android application. The server is implemented using acloud service. In this prototype system, the TIDB is
implemented in the client.

2.2. Identification of Sightseeing Spots Using
Beacon and GPS Information
In this research, a low energy Bluetooth device with
iBeacon technology4) is used as a beacon. A sightseeing© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 481Beacon-based Tourist Information System

Table 1. Major sightseeing spots in Ainoshima.
Beacon ID
BeaconSightseeing Spot(minor)Type
1001
Maruyama restaurant
normal
1002
Waiting place of Ainoshima Portnormal1003
Ainoshima Tourist Information
normal1004
Wakamiya-jinjya (Shrine)
normal1005
Kizuna-Kan (Disaster support facility) normal
1006
Jingu-ji (Temple)
normal
1007
Waiting place of Shingu Portnormal
1008
Ferry boat “Shingu”
normal1009
Tsumiishi-duka (Tumulus Cluster)
pseudopseudo beacons were assigned to outdoor spots. The
major sightseeing spots are listed in Table 1. An Aplix
MyBeacon MB004 AC-DR1 was used as the beacon.
This beacon is compliant with iBeacon technology. The
advertising interval of the beacon was set to 1,285 ms to
reduce electric power consumption. The measured power,
which is the expected received signal strength indicator(RSSI) at a distance of 1 m from the beacon, was −63 dB.

The implemented functions of the prototype system
are as follows:(a) Tourist information function: The sightseeing spotsof Ainoshima are listed (Fig. 2(a)) and the user can
see tourist information about the selected spot (Fig.
2(b)).
(b) Map function: The user can see the location ofsightseeing spots where beacons are installed. Spots
identified by beacon detection are highlighted using
a colored marker (Fig. 2 (c)).
(c) Stamp rally function: The spots listed in Table 1 areassigned virtual stamps and the user obtains thestamp when the client detects the corresponding
beacon. This function is implemented for use intourism events of the local community. 3,5)(d) Notification function: When the beacon is detected,
the user is notified that the sightseeing spot has been
identified (Fig. 2 (d)). After the notification, the user
is not notified of identifications of the same spot foran hour to avoid frequent notifications.
(e) Log function: Beacon detection information is stored
on the server. New beacon detections of the same
beacon are ignored for 10 min after a previous
detection to avoid frequent communication with theserver.

In the field test, a total of nine users used the prototype
system on Ainoshima. They visited the sightseeing spots
listed in Table 1 in any order for about 2 h. As a result,
all the spots in Table 1 were identified by the detected
beacons. In total, 223 beacon detection records werestored on the server. Using these data, we can identify the
tourist path of visited sightseeing spots, as shown in
Table 2 and Fig. 3.

4.2. Results of the Field Test

5. Discussion
In this research, we proposed a beacon-based tourism
information system. The prototype system wasdeveloped for Ainoshima Island, Shingu, Japan. The
developed system worked well in the field test andtourists’ paths were identified.
The tourist paths identified by our system are based on
beacon detection log data. In comparison to identification
using GPS only, our system has two advantages. One isthat amount of data for the identification is reduced, as
our system uses log data only for sightseeing spots where
beacons are installed or pseudo beacons are assigned.
The other is that identification is possible for indoor spots
where beacons are installed.

4. Field Test of the Prototype System
In order to test the developed prototype system andevaluate the performance of the tourist data collection, afield test was performed on Ainoshima. The preparationof the field test (beacon installation) and results arepresented below.
4.1. Installation of Beacons
For 20 sightseeing spots in Ainoshima and Shingu town,
eight beacons were installed in indoor spots and 12© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 482

Akihiro Yamaguchi, Masashi Hashimoto, Kiyohiro Urata, Yu Tanigawa, Tetsuya Nagaie, Toshitaka Maki, Toshihiko Wakahara,
Akihisa Kodate, Toru Kobayashi, Noboru Sonehara

Table 2. Example of logged data for one test user.
Time
Visited sightseeing spots
11:19
Ferry boat “Shingu”
11:51
Maehato (Ruin of Port)
11:52
Ainoshima Tourist Information
11:53
Waiting place of Ainoshima Port
12:28
Ainoshima elementary school
12:43
Tsurugi-jinjya (Shrine)
12:49
Tsumiishi-duka
13:08
Ainoshima elementary school
13:20
Maruyama restaurant
13:25
Waiting place of Ainoshima Port
13:26
Ainoshima Tourist Information
13:27
Wakamiya-jinjya
13:37
Jingu-ji
13:41
Wakamiya-jinjya
13:42
Kizuna-Kan

To use our system in practice, it would be necessary toincrease the participation of local communities andextend the supported area. An open platform for
information collection services using beacons is one
potential solution for this purpose. If an open Web API
to retrieve beacon and tourist information and amanagement system for registered information areprovided, local communities could build their own
information collection service by themselves. To
construct such an open system is one of our future tasks.
Acknowledgements
The authors would like to thank Shingu Town Office,Shingu Omotenashi Kyokai, and the Consortium of“Fukuoka IT Workouts” for their cooperation. This
research was supported by open collaborative research at
the National Institute of Informatics (NII) Japan(FY2016).
References
1. Japan Tourism Agency, Case studies of creating tourism
destinations 2015, (Retrieved Nov. 20, 2016, fromhttp://www.mlit.go.jp/kankocho/shisaku/kankochi/ikiiki.
html).
2. T. Wakahara, T. Maki, K. Yoshii, N. Sato, A. Yamaguchi,
Y. Ichifuji and N. Sonehara, The local community support
system using linked open data, IEICE Technical Report,
Vol.115 No.219 (2015), pp. 61-65 (in Japanese).
3. D. Asahi, Y. Yokohata, T. Inoue, H. Maeomichi, and A.
Tsutsui, Performance Evaluation of Participatory BLE
device Monitoring in a Wide Area Stamp Rally
Experiment, IEICE Technical Report, Vol.115 No.466(2016), pp. 33-37 (in Japanese).
4. Apple inc., iBeacon for Developers, (Retrieved Nov. 20,
2016, from https://developer.apple.com/ibeacon/).
5. N. Kichiji, Network analysis of the traffic lines of the
tourists visiting Kamiikawa central district in Hokkaido,
Japan, in Econ. J. of Hokkaido Univ., Vol. 40 (2011), pp.
89–112.

Fig. 3. Identified path of visited sightseeing spots from thelogged data shown in Table 2. (Map data ©2016 ZENRIN and
Google)© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 483

Analysis of Survey on Employment Trends
Masao Kubo, Hiroshi Sato

Computer Science, National Defense Academy, Hashirimizu 1-10-20
Yokosuka, Kanagawa 239-8686, Japan
Akihiro Yamaguchi

Fukuoka Institute of Technology, 3-30-1 Wajiro-higashi,
Higashi-ku, Fukuoka, Japan
Yuji Aruka

Faculty of Commerce, Chuo University, Higashinakano
Hachioji-shi, Tokyo, Japan
E-mail: masaok@nda.ac.jp, hsato@nda.ac.jp, aki@fit.ac.jp, aruka@tamacc.chuo-u.ac.jp
www.nda.ac.jp/cs/stuff/masaok.html

Abstract
If there were no changes in the environment surrounding businesses, the numbers of people leaving and entering
employment would stay almost the same. Therefore, understanding the numbers allow us to make assumptions
about the changes inside and outside companies. However, when categorizing businesses into industry sectors andclusters of business, you will see that the numbers of people leaving and entering employment have been nearly
opposed for the last 15 years, and it is difficult to detect changes in the employment environment of Japan’sbusinesses. This study tried to improve the level of detecting changes by applying NMF (non-negative matrix
factorization) into the Survey of Employment Trends. While businesses maintain the number of people they employ
at a certain level because of severe restrictions, we assumed they respond to the surroundings by changing thecomposition of employment. Accordingly, we identified the correlation between the numbers of people leaving andentering employment in each sector characterized by employment patterns that we found by applying NMF. As aresult we successfully improved the level of detecting changes, which we would like to report in this study.
Key words: resilience, data mining, NMF, machine learning

Introduction
In principle, it is assumed that more employees leavecompanies when employers believe that the current
number of employees is too many; conversely, thenumber of people entering companies increases whenemployers believe that the number of employees is too
few. Therefore, if these numbers are the same, you canassume that the employer thinks their businessenvironment is stable. While, if the numbers are largely
different, you can assume that companies were forced tomake corporate changes due to changes in theirsurrounding environment. Applying this assumption toindustry sectors, which are clusters of businesses in thesame fields, by studying the numbers of people leaving
and entering employment, you must be able to makeassumptions about the country-wide changes in theemployment environment from the difference between
the numbers.
The survey of employment trends is statistical data basedon a questionnaire survey of businesses and people
leaving and entering employment conducted by the
Ministry of Health, Labour and Welfare (MHLW). It has© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 484

Masao Kubo, Hiroshi Sato, Akihiro Yamaguchi, Yuji Aruka

been conducted for several decades, and the results havebeen published on the internet twice a year since 2000.
This survey reported employment information of about
45 types of industries (hereinafter called “sectors”)
categorized on the basis of the Japan Standard Industrial
Classification, excluding agriculture and fisheries.
According to this open data, although a catastrophic
incident called the Lehman crisis has occurred, thenumbers of people leaving and entering employment inindustry sectors in Japan have stayed nearly the same forthe last 15 years and it is hard to say that it indicates thechanges in employment environment during the period at
a glance. One reason is based on the fact that companies
have significant restrictions and social responsibilities.
Even if companies have deteriorated businessperformance and more people leave employment,
companies are obligated to acquire skills and knowledge
provided by new employees to maintain a certain level ofbusiness performance and have the social responsibilities
to survive the competition and support permanent
employees. Therefore, it is assumed that even though thechange in the number of employees is small, there should
be large changes in the type and characters of humanresources they hire.
In this study, we visualize the changes in the environment
surrounding employment in Japanese industries,
incorporating the changes in human resources they
employee from their abilities point of view into thenumbers of people leaving and entering employment.
Therefore, here we extracted the employment patterns
using NMF and defined the characteristics of the numbersof people leaving and entering employment in each sector.
It is assumed that the required human resources should be
distributed across various sectors in industries, and incertain sectors, employers’ employing those humanresources must give impact on the number of people
leaving and entering employment in the related sectors.
Also, these types of human resources probably are likely
to be in demand in other sectors. If we can find a set of
potential patterns related to the numbers employed, we
believe that we can more clearly visualize the changes inthe surrounding environment by looking at the changes in
the composition of each pattern of the number of people
leaving and entering employment.
Non-negative Matrix Factorization (NMF) (Ref.6) is amethod to decompose the matrix with elements of either

zero or a positive value into the product of two nonnegative matrices. Using its feature that subtracting is notallowed, in principle, the method has been widely used in
the field of signal separation(Ref.2,5,3). Although there
are some application examples of this method toeconomic activities(Ref.1), there were no examples ofthis method applied to visualize the employment
environment.
1. Survey on Employment Trends
This study examined the MHLW Survey on Employment
Trends (Ref.4). The MHLW conducts the questionnaire
survey twice a year and publishes the number of
employees, those changing careers, and hired employees
by industry. This study used 15 years of annual data
between 2000 and 2014. The industry classification isbased on the Japan Standard Industrial Classification. The
following studies use the industrial sector classification
called middle classification. It is, roughly speaking, aclassification dividing industrial areas, except for
agriculture and fisheries into smaller areas. Although theclassification has been changing slightly since 2000, thisstudy classification employed a total of 55 types ofindustries reflecting the changes and including
duplication.
2. Recent employment treads in Japan
Using this data, this study provides the data of recent
employment trends in Japan. It found that there was nolarge difference between the numbers of people leaving
and entering employment in each industrial sector in thelast 15 years. The number of hired employees refers to
the number of persons who started working in the sector
as of this year, while the number of separated employees
refers to the number of persons who left the sector. Figure
1 illustrates the numbers of hired employees andseparated employees in each sector included in the survey
on employment trends in 2014. Those leaving andentering employment are shown on a bar graph in orderof sector. The vertical axis shows the number of people(unit: 1000 people).

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 485

Analysis of Survey on

3. Non-negative Matrix Factorization (NMF)
The multivariate analysis method to analyze non-negative
data into additive components is called non-negative
matrix factorization (NMF) and used in various
fields(Ref.2,5). Figure 2 shows the schematic diagram of
NMF. It decomposes matrix X, where all the factors are
non-negative, into matrix T, where all the factors are nonnegative, and V. The size K of matrix after thedecomposition is given in advance. See Ref.2 for thedetailed algorism.

Fig. 1 Results of the Survey on Employment Trends in 2014

Bars with “+” on the top of it represent the number
entering employment and bars with “×” represents thoseleaving employment. You can see that those entering andleaving employment are significantly different acrosssectors but they are nearly the same if they are in thesame sector and same year.
Table 2 Similarities among the numbers of entering and leaving
employment between 2000 and 2014
2000
2001
2002
2003
2004
0.998
0.993
0.992
0.997
0.996
2008
2009
2010
2011
2012
0.996
0.936
0.996
0.997
0.996

2005
0.997
2013
0.997

2006
0.997
2014
0.994

2007
0.999

Fig. 3 matrix X in this analysis

3.1. Structure of matrix X

Table 2 shows the results of these changes in aquantitative manner. When the number of people entering
employment in the year y in the sector i is set as ay,j, andthe number of people leaving employment is sy,i, thecosine of the vector of the people entering employment ay= (ay,0, ..., ay,i, ..., ay,54) and that of the people leaving
employment sy = (sy,0, ..., sy,i, ..., sy,54) isThis section explains the structure of the decomposed
object, matrix X. Figure 3 shows the structure of matrix
X. This matrix is structured by listing the numbers ofpeople entering and leaving employment in sector i inrow i by year. The data in blue boxes shows factors
related to the number of people entering and red shows
those related to the number of people leaving
employment.

(1).
The value given from this formula in each year is shownin Table 2. The values except 0.936 in 2009, immediately
after the Lehman crisis, were over 0.99, which shows thatthe numbers of people entering and leaving employment
are nearly the same across all the sectors at all times
except in 2009.
Fig. 4 Decomposition errors

Fig. 2 NMF© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 486

Masao Kubo, Hiroshi Sato, Akihiro Yamaguchi, Yuji Aruka

pattern of the number of people leaving employment ineach year is shown in the size of each symbol. For
example the row 10 shows the number of people entering
employment in 2005 and row 11 shows that of the people
leaving in 2005. You can see that in both rows pattern 4
accounts for a large portion.
Looking at the structure of pattern 4 in Figure 5,
employment in accommodation, wholesale and retailindustries have large patterns. Also, in some industries
the breakdowns of patterns of people entering and leaving
employment vary. For example, the 2nd row and 3rd row
represents the numbers of people entering and leaving
employment in 2001 respectively. The importance ofpeople entering employment is high in pattern 8, whilethat of those leaving is high in pattern 16. In pattern 8more employment is seen in service industry, while inpattern 16 more employment is seen in constructionindustry. As a result of these, when using matrix V, you
can see that the breakdowns of the numbers of people
entering and leaving employment change.

Fig. 5 Patterns obtained

Fig. 6 Pattern’s support in each year

3.2. Analysis results
Figure 4 shows the relationship between the number ofparameters K and the accuracy of NMF decomposition.
The vertical axis shows the sum of squares of thedifference between the products of T, original matrices
listing the numbers of people entering and leaving
employment in sectors for 15 years, and V, matrices
gained by NMF decomposition. The horizontal axis
shows K, the number of patterns, which is equivalent to
the horizontal axis of matrix T. It was found that when K
is about 20, the error becomes nearly zero. Figures 5 and6 show the results of decomposition when K=20. Figure 5
shows the patterns where matrix T is transposed. The
vertical axis shows pattern numbers and horizontal axis
shows sectors. The size of each symbol indicates theimportance of the sector included in the pattern. In Figure
5, the combination of sectors where employment
increases around the same time and the numbers of theemployees are estimated. For example, in manufacturing
and retailing industries you can see that growth in
employment is expected in any patterns. Also, in the 17th
pattern, when the numbers in wholesales and services
industries significantly increase, that in manufacturingindustry also increases though the number is small.
Figure 6 shows matrix V; the vertical axis shows patterns
and horizontal axis shows the data of entering andleaving employment. In even rows the breakdown of eachFig. 7 Differences between the pattern’s support of the people enteringand leaving employment in each year

Fig. 7 Differences between the pattern’s support of thepeople entering and leaving employment in each year
Now, in order to quantify the difference of breakdowns,
the cosines were obtained in accordance with eq.1 and by
setting the breakdowns of the numbers of people enteringand leaving employment in Figure 6 as the vector, whichwere then illustrated in Figure 7. The horizontal axis
shows years and vertical axis shows cosines. Cosines
related to the number of people entering and leaving
employment each year described in the Table 2 where K= {20, 25} with different NMFs are shown in thick lines.
We found that although simple calculation hardly
provides any changes even in the time of Lehman crisis,© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 487

Analysis of Survey on

the suggested method using NMF visualizes detailed
changes.
4. Conclusions
This study suggests a method of detecting the changes inthe employment environment based on the Survey of
Employment Trends. We suggest vectorizing the numbersof people entering and leaving employment based on theindustrial sector middle classification of the Survey of
Employment Trends by employment patterns found in
NMF and using the cosines as the detection index. As aresult, we were able to clearly visualize the impact ofLehman crisis, which we assume to have increased thelevel of detection of the changes in employment
environment.
References
1. K. Kajitori, An Attempt to Detect Industry Clusters byUsing Non-negative Matrix Factorizations, Journal of
National Fisheries University 64 (4),pp227-239(2016)
2. H. Kameoka, Non-negative Matrix Factorization. Journalof the Society of Instrument and Control Engineers 51,9,
pp. 835–844(2012)
3. http://hdl.handle.net/10086/15136.
4. Y. Kubo, M. Kubo, H. Sato, and A. Namatame: Estimationof Locations of Densely Distributed Subjects Using NMF
with Nonpixel Information. Journal of Advanced
Computational Intelligence and Intelligent Informatics Vol.
18 No. 4, 2014
5. Ministry of Health, Labour and Welfare (2016) Survey on
Employment Trends
6. http://www.mhlw.go.jp/toukei/list/9-23-1.html.
7. H. Sawai: Nonnegative Matrix Factorization and Its
Applications to Data/Signal Analysis. Journal of theInstitute of Electronics, Information, and Communication
Engineers 95(9), pp. 829–833 (2012)
8. P. Smaragdis, J.C. Brown: Non-Negative Matrix
Factorization for Polyphonic Music Transcription, 2003
IEEE Workshop on Applications of Signal Processing to
Audio and Acoustics, pp.177-180 (2003)© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 488

Direction switch behavior to enclose a target
Masao Kubo, Nhuhai Phung, Hiroshi Sato

Dep. of Computer Science, National Defense Academy of Japan, Hashirimizu 1-10-20
Yokosuka, Kanagawa 239-8686, Japan
E-mail: masaok@nda.ac.jp, em54052@nda.ac.jp, hsato@nda.ac.jp
www.nda.ac.jp/cs/stuff/masaok.html

Abstract
This paper discusses a target enclosing behavior when a part of the orbit is blocked. Usually, a orbit to enclose atarget supposes to be clear. However this often takes place in realistic environment. For this issue we propose
microscopic methods to emerge macroscopic search capability to enclose a target. The group of robots change theirdirection according to the blocked part.
Keywords: Self organized swarm system, target enclosing, swarm robotics, phase transition.

formation by trial and error. In general such a system
has to balance exploration and exploitation of collective
level adequately. The phase transition property ofboids[12] is adopted as this controller.

1. Introduction
Recently, self-organized swarm system[3] which has
more sophisticated and intelligent plasticity is required
for real world application because it is too complex anddynamic. In this paper, target enclosing behavior ofrobot collective is discussed from this point of view.

Fig. 2. Target enclosing under a difficult condition

This paper is composed as follows. Firstly, the proposed
scenario that macroscopic states of a collective can beswitched between exploration and exploitation is
explained. Next a method that the scenario is realized
for enclosing task for a robotic swarm. Here 2
microscopic rules, namely, an action selection scheme
based on majority decision and a self generating noise
rule are proposed. Finally a series of computer
simulation shows that the proposed robotic collective

Fig. 1. Target enclosing under an ideal condition

When a part of the orbit to be enclosed with a robot
collective is blocked there is no way of producing
alternative formations to enclose. For this issue we
proposes micro level modifications to emerge intelligent
macroscopic dynamics as they find a new better© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 489

Masao Kubo, Nhuhai Phung, Hiroshi Satocan behave flexible under conditions which a simple
collective is suspended.

Fig. 7. Model of the enclosing a target algorithm: α, β.

2. The proposed scenario
macroscopic statesofswitchofFig.1 shows a typical example of the problem of
enclosing a target that a target is surrounded by moving
robots. Target enclosing, target capturing andcircumnavigation discuss similar topics [13] [5] [4] [9][2] [11] [6] [7] [14]. This seems to be a fundamental
formation of a collective for disaster site because thisbehavior builds up a dense but ordered robots around a
given spot.
Usually, research in these domain suppose that the orbit
is clear. However, this is not true in real environment.
For example, a cliff around a target in mountain range
makes this difficult because of space limitation. Also aninvisible unusual strong air current makes robots fly
difficult. We aim to build a self organized swarm system
to enclose targets which can perform more adaptable.
In this paper, one of the following 2 formations
according to their environment arises. The firstformation is the ideal one shown in Fig.1. The second
formation is shown in Fig.2. The group of the robots
stays the part of the orbit to find a path. Once theblocked region is solved, the group can form the firstformation immediately.
In order to realize such various group behaviors, if aproblem occurs, it is necessary for the group to develop
itself that searches for a better formation. Also, if it
finds a good formation, the group also needs toconverge it that every agent seems to agree with. It isnecessary to balance both searching for formation at thecollective level and utilizing the search results.
For this issue the phase transition trait of boids based onnoise [12] is adopted. It is known that when the noise
applied to particles in a particle group simulating boids
exceeds a certain strength, the behavior of boids
changes from a consistent state to a disordered state.

Fig. 3. Phase transition of boids.

Fig. 4. Example of a synchronized formation.

Fig. 5. Example of a disordered formation.

Fig. 6. concept of proposed collective level adaptation by self generating
noise.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 490

Direction switch behavior to

Fig.3 shows an example of the transition. This indicatesthe degree of consistency of boid directions when noise
is applied to boids whose Separation, Cohesion, andAlignment ratios are 1.3, 1.0, and 1.0 [10]. In the casewhere a small amount of noise is added (Noise < 0.3),
since the deviation of the corresponding boids iscorrected by the remaining of the group, the overallbehavior does not change. The state of the group at thistime is shown in Fig.4. Almost all are moving in thesame direction. On the other hand, when the amount ofnoise exceeds the threshold (Noise > 0.3), it varies
sharply. Fig.5 shows the state when Noise = 1. It turns
out that individuals are facing different directions.
Here, utilizing this dynamics, realization of collective
search activity to recover from a failure and switchingof modes of its use are realized. This conceptual
diagram is shown in Fig.6. When a group is in an
appropriate formation (A), it is assumed that sometrouble has occurred. An agent that detects thismalfunction selects an action which is different to others.
As a result, the whole swarm changes to the state of B.
If this detected defect is temporary, this noisy action iscorrected and returns to state A. On the other hand,when many similar defects are detected, the macro state
shifts to the C state. In the C state, each agent randomlychanges their actions, so multiple formations will betested as future formation candidates. As the number of
agents supporting a good formation increases, theswarm shifts to macro state D and eventually reaches A.
In the following, we show how to implement the target
enclosing problem as a method to embody the conceptof this collective search state and decision making
process.
We have already made a similar presentation[7].
However, the verification was not done with a general
simulator and there was no way to verify. Therefore, inthis paper we adopted a general agent model [10] thatthe source code is published and a model at the time ofcollision, and we verify this concept again.

3. Implementation to the target enclosing
algorithm
3.1. The proposed algorithm
The proposed algorithm is shown as follows[7]. This
uses only bearing information about target and its
neighbor[9][14]. Additionally this model can change
their direction to enclose a target.
Let n be the number of agents. The agents arenumbered as P1,…,Pn. Agent Pi determines its action
according to its nearest agent Pj and the nearest target t
as follows.
vi
i

f i

(1)

v
dir(Pi )( i
r

k cos

(2)

i)

where dir(Pi ) {1, 1} represents its direction to enclose
the target.
dir(Pi )1
direction(Pi ) CW
1 direction(Pi ) CCW(3)f, k are positive constants． direction(Pi ) {CW ,CCW }means the direction of agent Pi. direction(Pi ) is
determined basically by majority decision as follows.
A i,t{Pi | PiP j | rcom }CW

1| A i,t 1 |direction(Pi,t )

CCW

(4)
dir(P j )P j A i,t 1

dir 0

(5)

otherwise

dir is a random number chosen with a uniform
probability from the interval [ A i,t , A i,t ] .
A i,t

dir

A i,t

(6)

where is a positive constant. As the number of agentsin the neighborhood increases, the noise applied
becomes stronger, and the agent randomly selects theenclosing direction. Similarly, if there are many agentsin the vicinity that select its direction randomly, they
become disorderly locally and search for new collective
behavior is done. This state is performed until the highdensity state is eliminated.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 491

Masao Kubo, Nhuhai Phung, Hiroshi SatoIn this section, we show the performance of the proposedmodel. The parameters are defined as follows: r 300 ,n 20 , T 1 , rcom 100 ,0.1 , f 1 , f 1 .

Fig.8 shows the result of enclosing the targets without
any blocked area. Each half of agents enclose the target
from different direction. The two sub groups collide andconsequently the direction of all agents becomes sameby the equation 8.
Fig.9(1) - (8) shows the result of the proposed algorithm
in case of orbit restricted. The line shown for crossing acircle represents a wall that is difficult to pass. Whenhitting this wall the robot bounces according to the ruledescribed in subsection 3.2. Here we explain a typicaltransition. When the agent reaches the obstacle thedirection is reflexively changed. As a result, agents
gather near the wall as shown in Fig.9(4), (5). As aresult, trying to avoid defect by try and error by theequation 6 near here. As the density gets higher, thenoise of the equation 5 becomes larger, and the numberof agents moving in a direction away from the wall
increases. As a result, as shown in Fig.9 (6), it escapesfrom the vicinity of the obstacle. As they get out, thedensity decreases, so the direction to enclose the targetof the agent is fixed.
From the above, it turns out that the proposed method
can generate group behavior suitable for the difference
in environment around the target.

Fig. 8. A snapshot of the formation of the proposed swarm under an ideal
condition.

Fig. 9. Snapshots of the formation of the proposed swarm under condition
which 2 parts of the orbit are blocked.

Fig. 10. Interval time span of switch of their direction at obstacles.

3.2. Collision to wall
Instead of the past work [7], in case of collision with thewall, according to [10] we changed direction
reflectively and the speed was taken as the maximum
speed.
Fig. 11. Transition of the proportion of CW.

4.

Experiment© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 492

Direction switch behavior to

4.1. The trend of changes of their direction
Next, we investigated the relationship between the timeinterval at which the enclosure direction changes and .
In the experiment in the previous subsection, we foundthat the group moves between two obstacles. It isthought that swarms can be reconstructed promptly if
the time required for conversion is short. The timerequired T c sec is shown in Fig.10 using the cumulative
probability density distribution P T c t . Although it
was found that 60% could change direction quickly, itturned out that it was difficult to change direction evenafter 12,000 seconds had elapsed. Fig.11 shows the ratioof CW in the direction of direction. 1 or 0 indicates that
all correspond to CW or CCW. However, it became 1 attime = 200, and since it was 1 many times again before
reaching 0, it was confirmed that it took time for trial
and error. Improving the time required for this change isa future task.
5.

3.

4.

5.

6.

7.

8.

Conclusion

In this paper, we investigated the case where the orbit toenclose a target is physically restricted in the target
enclosing problem as a part of the basic technology
development for the self - organizing swarm system.
Here we proposed micro level algorithms, namely
behavior selection based on majority voting andspontaneous noise generation function in order torealize macroscopic state switching between
exploitation and exploration. This is based on the factthat the phase transition phenomenon occurs due to themagnitude of noise in the boids model. As a result of
verifying the proposed method by computer
experiments, it was found that formation can beswitched according to the environment if there issufficient time.

9.

10.
11.

12.

13.

14.

References
1. E. Castello, T. Yamamoto, Y. Nakamura, H.Ishiguro :Task Allocation for a Robotic Swarm Based on an
Adaptive Response Threshold Model, 2013 13th
International Conference on Control, Automation and
Systems(ICCAS2013),DOI:10.1109/ICCAS
2013.6703905(2013)
2. Y. Cao, UAV Circumnavigating an Unknown Target
using Range Measurement and Estimated Range Rate,2014 American Control Conference (ACC) June 4-6,
2014. Portland, Oregon, USA,pp4581-4586.
E. Castello, T. Yamamoto, F. Dalla Libera, W. Liu, A. F.T.
Winfield, Y. Nakamura, H. Ishiguro: Adaptive Foraging
for simulated and Real robotic Swarms: The dynamical
response threshold approaches, swarm intelligence, vol10, issue 1,pp1-31(2016)
Tae-Hyoung Kim, T. Sugie: Cooperative control fortarget capturing task based on a cyclic pursuit strategy,
Automatica 43,1426-1431(2007)
Y. Kobayashi, K. Otsubo, S. Hosoe, Autonomous
Decentralized Control of Capturing Behavior by Multiple
Mobile Robots, Transactions of the Society of Instrument
and Control Engineers, 43(8), 663-671(2007)
M. Kubo, H. Sato, T. Yoshimura, A. Yamaguchi, T.
Tanaka, Multiple targets enclosure by robotic swarm,
Robotics and Autonomous Systems 62, pp12941304,2014.
M. Kubo, H. Sato, Nhuhai Phung, A. Namatame,
Direction switch behavior to enclose a pack of targets
based on phase transition, Proceedings of SWARM 2015,
pp295-298(2015).
J. A Marshall, M E.Brouvke and Bruce A Francis:
Formations of Vehicles in Cyclic Pursuit; IEEE
Transactions on Automatic Control, vol49, no11 (2004)
Y. Takayama, S. Yamamoto, T. Takimoto, Distributed
Formation Control of a Nonholonomic Multi-agent
System for Target-enclosing Operations, Proceedings of
9th SICE System Integration Division Annual Conference(2008)
Daniel Shiffman, Nature of Code,(2012)
Sarah Tang, Dylan Shinzaki, Christopher G. Lowe,
Christopher M. Clark, Multi-Robot Control for
Circumnavigation of Particle Distribution, Distributed
Autonomous Robotic Systems Springer Tracts in
Advanced Robotics Volume 104, 2014, pp 149-162
Tamás Vicsek, András Czirók, Eshel Ben-Jacob, Inon
Cohen, and Ofer Shochet, Novel Type of Phase
Transition in a System of Self-Driven Particles, Phys. Rev.
Lett. 75, 1226.1995
Yamaguchi H., A Cooperative Hunting Behavior by
Nonholonomic Mobile Robot Troops, Transactions of theJapan Society of Mechanical Engineers. C, 69(688),
3285-3292(2003)
Ronghao Zhenga, Yunhui Liub, Dong Suna,Enclosing atarget by nonholonomic mobile robots with bearing-onlymeasurements, automatica, Automatica 53 (2015) 400–
407

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 493Force and Motion Analysis of larval zebrafish (Danio rerio)using a body dynamics model
Naohisa Mukaidani

Department of System Cybernetics, Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama,
Higashi-Hiroshima, Hiroshima, 739-8527, Japan
Zu SohDepartment of System Cybernetics, Institute of Engineering, Hiroshima University, 1-4-1 Kagamiyama, HigashiHiroshima, Hiroshima, 739-8527, Japan
Shinichi Higashijima

National Institutes of Natural Sciences, Okazaki Institute for Integrative Bioscience, National Institute for Physiological
Sciences, Okazaki, Aichi 444-8787, Japan
Toshio Tsuji

Department of System Cybernetics, Institute of Engineering, Hiroshima University, 1-4-1 Kagamiyama, HigashiHiroshima, Hiroshima, 739-8527, Japan
E-mail: mukaidani@bsys.hiroshima-u.ac.jp, sozu@bsys.hiroshima-u.ac.jp, shigashi@nips.ac.jp, tsuji@bsys.hiroshimau.ac.jp
www.bsys.hiroshima-u.ac.jp

Abstract
This study proposes a method of body dynamics analysis for larval zebrafish incorporating a viscoelastic body modeland a fluidic environment model to support the study of development mechanisms in motion generation. The results
showed that the estimated fluid drag coefficients enabled the body dynamics model to approximate the paths of actual
larvae with an error level of 0.76±0.74 [%] to the total body length.
Keywords: zebrafish, larval zebrafish, dynamics model, drag coefficient

1. Introduction
Zebrafish (Danio rerio), which measure about 3 – 40[mm] in length, have been widely used in the field of
biology to study genetic diseases and the brain structureof vertebrates. Their transparency at the embryonic andlarval stages of development facilitates in vivo
observation of their internal organs and neural circuits,
making them highly useful as a model organism for the
study of phenomena observed during the stages of growth.
For this reason, the behaviors of larval fish have been
extensively investigated. For example, changes inswimming distance and velocity during ontogeny weresystematically analyzed in the 1980s (Webb, 1984; Webb
and Weihs, 1986; Fuiman and Webb, 1988). Subsequentdevelopment of computer vision techniques enableddetailed quantification of larval motion, and further
analysis helped to elucidate the ontogeny of fin function(Danos and Lauder, 2008), the relationships between
drag force and fin/body morphological developmentfrom the larval stage to the adult stage (McHenry and
Lauder, 2006) and the effects of hydrodynamics on
locomotor development (Danos, 2012). Locomotor
development was also analyzed in the context of larval© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 494

Naohisa Mukaidani, Zu Soh, Shinichi Higashijima, Toshio Tsuji

and Lauder (McHenry and Lauder, 2006). Against sucha background, this paper proposes a body dynamics
model incorporating the considerations of body form andviscoelasticity to support the study of developmental
mechanisms in the movement of larval zebrafish. An
algorithm for estimation of the external drag forces acting
on such fish is also presented. The proposed model
enables simulation of target phenomena in uniform
virtual environments and with individual conditions, andalso allows analysis for the interaction of larval zebrafish
morphology and swimming movement.

body waves (Müller and Leeuwen, 2004), and thefunction of pectoral fins at the larval stage wasinvestigated by comparing the movements of wild larvalfish with those of others lacking pectoral fins as a resultof genetic manipulation (Green et al., 2011).
Based on the extensive body of behavioral analysis
data obtained from such studies, the mechanisms
supporting coordination between neural circuits and the
body for appropriate motor function have recently
attracted attention. For example, Budick and O’Malley
classified the behavior of larvae into three repertories(Budick, 2000), and Throsen et al. analyzed fin-axis
coordination (Throsen et al., 2004) to support discussionof neural control. In addition, Higashijima et al. succeed
in indirect measurement of neural activities in both the
motoneurons and spinal interneurons during escapebehaviors using a calcium indicator called cameleon(Higashijima, 2003).
The experimental studies described above succeededin elucidating a number of important mechanisms. The
clear-cut experimental results obtained have contributed
to enormous efforts regarding the control of complex
experimental conditions and static analysis to eliminate
the influences of individual differences in bodymorphology, learning ability and stages of development.
However, the effects of individual differences andexperimental conditions cannot be completely eliminatedin experimental approaches. In addition, it is technicallydifficult to measure the forces produced by tiny larvae
despite the importance of such information in clarifying
the neural control of muscles and swimming behavior. In
light of such limitations, computer simulation can be seen
as a potentially effective approach. For example,
Katsumata et al. constructed a body dynamics model andcarried out fluid simulation for free-swimming larvalzebrafish to evaluate the properties of fluid flow aroundthem (Katsumata et al., 2009). This zebrafish larva modelallowed precise description of fluid behavior.
Considering the needs of biological study, however,both fluid dynamics and local body forces generated bylarvae are important in discussing the relationships
between ontogeny and nerve control development. In thiscontext, the development of a larva model that can beused to calculate forces generated from the body andthose acting on the environment based on motion
recorded from actual fish can be seen as effective foranalyzing behavioral aspects of fish during ontogeny,
such as the relationships between drag force andmorphological development as analyzed by McHenry

2. Body dynamics model
As larval zebrafish measure only about 3 [mm] in length,
the forces exerted between them and their environments
are difficult to measure. In this study, a body dynamics
model was constructed based on the sizes and shapes ofeach part of these fish. A novel method of estimating dragcoefficients between larval zebrafish and theirenvironment as well as the driving torque of each joint
based on dynamic analysis techniques was also
introduced. Fig. 1 shows a zebrafish larva at protrudingmouth stage (72 [h]) and a 3D morphology of the body
dynamics model configured from this image. The
proposed model is a rigid-link type composed of fiveparts: a head part consisting of one link, left and rightpectoral fin parts with 10 links each, a trunk part with 10
links, and a tail fin part with 10 links. The links areinterconnected by joints allowing rotation about the zaxis. The rotational motion of each link and thetranslational and rotational motion of a representative
point =,of the model can be calculated fromthe following equation based on the Newton-Euler
method:I( ) ̈ + h( ̇ , ) + g( )= − ( − )−c ̇ +∑ , ,I ( ) ̈ + h ( ̇, ) + g ( ) = ∑

(1)
, ,

(2)

where I(q) and I ( ) represent the inertia matrix,h( ̇ , ) and h ( ̇ , ) are the centrifugal force and the
Coriolis force, g( ) and g ( ) represent the force ofgravity, = ( ) ∈is the rotational angle vector of
the joints,
is the equilibrium angle of the stiffness
component at a joint, =∈is a vector ofrotational driving force, k is the stiffness coefficient (Fig.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 495

Force and Motion Analysis

Fig. 2. Viscoelasticity of the body dynamics model. The whitecircles indicate inter-link joints, and qri denotes the angle
between the links. A pair of damper and spring units is attachedto each link in parallel to represent the viscoelastic
characteristics of muscles on the left and right sides. As only
two-dimensional motion was considered in the model for
simplification, the pairs of muscles on the dorsal and ventral
parts were combined.

the centroid of a link as shown in the following equation(Mochizuki and Ichikawa, 2010):
Fig. 1. Shape of a larval zebrafish and the body dynamics
model. (a) shows a zebrafish larva at protruding-mouth stage(72 [h]) photographed by Kimmel et al. (Kimmel et al. 1996).
Thin transparent fins surround the dorsal and ventral parts ofthe trunk. (b) shows the configuration of the body dynamics
model approximating the image of the larva. The model’s
parameters were determined using this approximated shape.
Position x1 is the centroid of the head link, and x2 is the joint
connecting the head link and the trunk. The time traces of thesetwo points were used to determine the drag coefficients of a
fluid environment.

=

(3)

where
is the drag coefficient, is the fluid density
andis the surface area.
As drag force significantly affects the model’s motion,needs to be estimated. However, if different dragcoefficients were assigned to each of part r, direction dand link j, the number of coefficients to be estimatedwould be as many as 82. Accordingly, it was assumed forsimplification that the drag coefficients for the
rostrocaudal direction of the pectoral fins and the tail fin(,,) were 0 because the fins are negligibly
thin compared to their total surface area. It was also
assumed that the drag coefficients for the same part r
were identical. As a result, the friction force acting oneach link can be determined via estimation of 6 dragcoefficients.

2), c is the viscous damping coefficient (Fig. 2),
isthe Jacobian matrix, andis the friction force exerted
between the center of gravity of a link and the external
environment. In addition, the subscript ∈ (H, B, C, R, L)
denotes parts of the model, with H representing the head,
B the trunk, C the tail fin, R the right pectoral fin and L
the left pectoral fin. The subscript ∈ (A, N) denotes
direction, with A representing the rostrocaudal directionof the fish and N the normal direction in the rostrocaudal
direction. The subscript j denotes links sequentiallynumbered as = 1, 2, ⋯ , 10 from that connecting withthe head part. Although the parameters on the left of the
motion equation can be determined based on themeasured shapes and weights of actual larval zebrafish,drag force is difficult to determine due to the size andcomplex morphology of the fish. In this study, the dragforce
was approximated as a viscous resistancevalue proportional to the square of the velocity
at

3. Drag coefficient estimation algorithm
As Eqs. (1) and (2) describe the relationship between the
motion of larval fish and related drag coefficients, thesecoefficients can be estimated via dynamic analysis bysolving Eqs. (1) and (2). However, direct solution of dragcoefficients from these equations for each time sample
can yield large errors caused by video analysis noise.
Accordingly, the coefficients were determined in thisstudy by minimizing the following evaluation function
for robustness:© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 496

Naohisa Mukaidani, Zu Soh, Shinichi Higashijima, Toshio Tsuji

Fig. 3. Four-step flow of the algorithm used to estimate thedrag coefficient of the environmental fluid. As drag coefficientsare related to the paths of the body dynamics model, theproposed algorithm minimizes the difference between the pathsof the actual larva and the body dynamics model. To avoid local
minima, the algorithm sequentially performs global search,
local search and generalized reduced gradient search where thedrag coefficient sets with the smallest errors were chosen ateach step.

=∑

∑

( )−

( )

Fig. 4. Video image analysis procedure with recording
conducted at 1000 [fps]. (a) As the thinness of pectoral finsmakes it difficult to perform image analysis, the trunk and finsare manually separated and lines are drawn on the blurred
images of fins for interpolation. (b) Using the processed images,
joint bending angles and positions are calculated using Wriggle
Tracker software (Library Inc. Tokyo). (c) The outcomes of
motion calculation are input to the body dynamics model, andthe resulting paths are calculated based on motion equations(Eqs. (1) and (2)) .

(4)

where P is the number of comparison points,
represents positions on an actual fish, andrepresents
the corresponding positions on the body dynamics model
as shown in Fig. 1. This evaluation function represents
the error of paths between an actual fish and the model as
simulated using a set of drag coefficients. The proposed
algorithm minimizes this evaluation function in M steps
as shown in Fig. 3.
In Step 1, the movement of larval zebrafish is recorded
using a high-speed camera and video image analysis isconducted (see Fig. 4). First, the tail and pectoral fins are
separated as shown in Fig. 4 (a). Image processing steps
including binarization, noise reduction and thinning arethen performed for each frame of all the separated images
to extract the centroid line of the trunk and the pectoral
fins. This line is divided by points corresponding to thoseof the model's joints, and the time-dependent joint angles
between the divided points are calculated. As themeasurement results obtained from actual fish include
noise, the joint angles
are filtered using an n-degree
low-pass Butterworth filter. Finally, the joint angles are
input to the model as shown in Fig. 4 (c).
In Step 2, a global search of the drag coefficients is
performed. First, a combination of drag coefficients isselected from a set (
,
,
,
,
,
)

defined using the equation below and substituted into Eq.
(3).
,

,
,
∈{ ∆
∆
,

,

,

, ∆
, ∆
, ∆
,
∆
| , , , , ,= 0,1, ⋯ ,}(5)where ∆
is the step size for the global search yieldingthe= 1,2, ⋯ , (+ 1)variation of dragcoefficient sets, and the superscript 1 indicates the step
size used in the first optimization step. Data on the
motion of actual larvae, including the joint angle andjoint angular velocity of each link, are then substituted
into Eqs. (1) and (2), and the paths are calculated byrepeatedly determining the forward and inverse dynamics
for each sample time. This algorithm is described below.
(i) The initial position and initial posture of arepresentative point on a larval zebrafish(0) arecalculated from image analysis results. Other initialvalues such as translational velocity ̇ (0), joint angle
̇ (0), angular state velocity
̇ (0) and angularacceleration ̈ (0) are also calculated and substituted
into Eqs. (1) and (2).

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 497

Force and Motion Analysis(ii) The translational velocity( ) of each link isderived from the translational velocity ̇ ( ) of arepresentative point and the angular velocity ̇ ( ) of
each joint. The translational velocity of each link issubstituted into Eq. (3) along with the selectedcombination of drag coefficients to determine the friction
force
( ).
(iii) The driving torque ( ) and the acceleration ̈ ( )are then derived from Eqs. (1) and (2) based on thesubstitution of( ), ( ), ̇ ( ) and ̈ ( ).
(iv) The position ( ) of the representative point on
the model is derived via second-order integration for theacceleration ̈ ( ). By repeating steps (ii) through (iv) for
each time step, the path is calculated for the selected dragcoefficients. This path is then used to evaluate thecoefficients by comparing it to the path of the fish using
the evaluation function (Eq. (4)). After evaluation of all
combinations of drag coefficients given by Eq. (5), a setof drag coefficients (
,
,
,
,
,
) with smaller[%] values for path error E are
selected for a further search procedure in which=
1,2, ⋯ , (+ 1) /100.
In Step 3, a local search is performed on the neighborsof the drag coefficient extracted in the previous step. First,
a set of combinations of drag coefficients
(
,
,
,
,
,
) is selected. The
search region for these coefficients is defined by thefollowing equation:
,

,
∈{
+
+
, ,

,
+

,
,
=−

,

,
,

+
+
,
+
|
, ⋯ ,0, ⋯ ,

,

}

,

,

,

(6)

where Δis the step size of the drag coefficient(Δ>Δ) . The selected combination ofcoefficients is substituted into Eq. (3). Here,
represents the number of drag coefficients in the step 2.
Dynamic analysis is performed as described in Step 2,
and the simulated path is compared with that of an actual
larva using the evaluation function (Eq. (4)). Afterevaluation of all combinations of the drag coefficientsshown in Eq. (5), another set of drag coefficients
(
,
,
,
,
,
) with smaller [%]values of E is extracted for the end of step 3.

In Step 4, a combination of drag coefficients (
,
,
,
,
,
) withthesmallest
evaluation value E is searched using the generalized
reduced gradient method (Lasdon, 1974). The initialvalue for this method is selected from the dragcoefficients extracted in step 3, i.e., (
,
,
,
,
,
).
With appropriate drag coefficients obtained using themethod described above, it is possible to determine thelocal force generated by the fish as the torque in Eq. (1)driving each link.
4. Experiments
To verify the efficacy of the proposed method, dragcoefficient estimation was carried out using video imagesof swimming larvae, and the effect of pectoral fins on
their swimming behavior was then explored.
4.1. Experimental condition
The experimental conditions configured for the dragcoefficient estimation algorithm (Fig. 3) are shown below.
4.1.1. Video image analysis conditions
・Frame rate: = 1000 [fps]・Measurement duration: t = 0.27 [s]・Low-pass filter:= 70 [Hz], n = 2
・The pectoral fin images used in the experiment weremanually interpolated for every frame due to an
insufficient level of clarity for analysis. In addition, thepectoral fins and the trunk were separated via manual
processing by painting unrelated parts of each frame inwhite as a background color (see Fig. 4 (b)).
・ Software used for video image analysis: Wriggle
Tracker (Library Inc., Tokyo)
4.1.2. Configuration for drag coefficient estimation
・Step size: ΔC= 1.3×10 ,Δ= 6.3×10 , Δ= 1.3×10 ,Δ= 1.3×10 , Δ= 1.3×10 ,Δ= 1.3×10 , ΔC
= 1.3×10 ,Δ= 0.63, Δ= 1.3×10 ,Δ= 1.3×10 , Δ= 1.3×10 ,Δ= 1.3×10
・Number of searches:= 100,=4© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 498

Naohisa Mukaidani, Zu Soh, Shinichi Higashijima, Toshio Tsuji

Fig. 7. Video images of the body dynamics model after
optimization. When optimal drag coefficients were chosen, themodel accurately traced the paths observed from an actual larva.

Fig. 5. Video images of wild larval zebrafish motion. The
pictures show temporal changes in posture and position forevery 0.03 seconds from when the larva started swimming until
it stopped.
Fig. 8. Video images of the body dynamics model after
optimization. When optimal drag coefficients were chosen, themodel accurately traced the paths observed from an actual larva

4.1.4. Configuration reading evaluation function
・Number of comparison points in evaluation function(Eq. 4): = 2
・Position of fish for evaluation (Eq. 4):
・Centroid of head ( = 1),
・Point of joint connecting head and trunk ( = 2) (seeFig. 1)

Fig. 6. Video images of the body dynamics model before
optimization. The pictures demonstrate a typical example withrandomly chosen drag coefficients in which drag force
precluded body thrust.

4.2. Drag coefficient estimation
Drag coefficients were estimated based on the algorithm
presented in Section 2.1. Fig. 5 shows video images of
the wild larval zebrafish. Fig. 6 shows the simulated
movement of the body dynamics model before
optimization, and Fig. 7 shows that after. Fig. 8 shows
the average path error between a wild fish and the body
dynamics model as given by Eq. (4), and indicates a
gradual decrease in each step. After drag coefficient
optimization using the generalized reduced gradientmethod, the path error approached 0 at about 0.76±0.74[%] compared to the body length. Fig. 9 shows the pathof larval movement, with (a) and (b) indicating a positionof 70 [%] normalized by body length with the tip of thehead as 0 [%] and the end of the tail as 100 [%]. The solidline shows the path of the larval zebrafish, and the dashed4.1.3. Dynamic analysis conditions
・Equilibrium angle (Eq. 1): = 0
・Density of water (Eq. 3): = 1.00 [g/cm ](at a water temperature of 20 [℃])・Larvalmodel
length:= 2.78 [mm]・Mass density of larval model: 3.33 [g/cm ](Calculated based on reference of Avella et al. (2012))
・Form of link sets: as shown in Figs. 1 and 4
・Joint stiffness: 15.5 [kNm ⋅ rad/m ] (Long, 1998)
・Joint viscosity: 0.84 [kNms ⋅ rad/m ] (Long, 1998)© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 499

Force and Motion Analysis

Fig. 10. Resultant force of the trunk and fins. The broken line
shows the resultant force of the pectoral fin part, and the solidline shows that of the trunk and tail part. Overall, the force
generated from the pectoral fins is negligible compared to thatgenerated from the trunk, which is consistent with biologicalexperiment results [Green et al. 2011]. However, in period (a),the larvae initiated swimming and the model predicted that the
pectoral fins would generate force to push the fish backward.

Fig. 9. Comparison of paths obtained before and after
optimization. (a) shows a typical path of the body dynamics
model when drag coefficients were chosen randomly, andcorresponds to the images shown in Fig. 6. (b) demonstrates thepath of the body dynamics model given optimal dragcoefficients, and corresponds to the images shown in Fig. 7. It
is particularly notable that the model successfully traced the
backward swimming observed when the actual fish began
swimming.

The resultant force generated by the trunk and tail fin part(=+) and that of the pectoral fin part() were calculated. In Fig. 10, period=+(a) shows the time immediately after the larva started
swimming (0 – 0.045 [s]) during which it moved
backward, and period (b) shows the duration of forward
swimming (0.045 – 0.25 [s]). For period (b), the analysis
results showed that the average resultant force of thetrunk and tail fin part was about=78 [ N] and thatof the pectoral fin part was about=4068 [ N]. As theresultant force of the pectoral fin part accounts for only 2[%] of the total force generated by the body dynamics
model, its influence on swimming can be considerednegligible. This result is consistent with those of theexperimental studies. Green et al. assumed that pectoral
fins affected the movement of larval zebrafish andconducted motion analysis on actual fish (Green et al.,2011). The results showed a minimal influence frompectoral fins on swimming in the larval stage; rather, thefins are used to supply oxygen to the gills at this stage ofdevelopment. This outcome verified the efficacy of the
proposed model and drag coefficient estimationline shows the path of the model before and after dragcoefficient optimization. Comparison of the optimized
model and the larval zebrafish shows that the former
captured the characteristics of fish motion in which boththe model and the larval zebrafish started by swimming
backward then forward. These results indicate that the
body dynamics model with drag coefficients estimatedusing the proposed algorithm successfully reproduces the
movement of wild larval zebrafish.
5.

Results and Discussion

Although the pectoral fins of adult zebrafish are known
to contribute to thrust (Webb, 1973), their function indevelopmental-stage larval zebrafish has not been fully
elucidated. In this study, the influence of pectoral fins on
swimming was examined by comparing the resultant
force generated from the pectoral fin part with those ofother parts. These resultant forces were calculated bysimply adding all force vectors acting on thecorresponding links via the following equation:=∑(7)© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 500

Naohisa Mukaidani, Zu Soh, Shinichi Higashijima, Toshio Tsuji

algorithm, indicating their suitability for the prediction of
unknown dynamic characteristics. In this context, focus
was subsequently placed on period (a), for which theaverage resultant force of the trunk and tail fin part wasabout=39 [ N] and that of the pectoral fin part was
about=121 [ N]. These results indicate that thetrunk and tail fin part have a smaller influence in period(a), allowing pectoral fin movement to contribute more
to swimming. Indeed, the fish was observed to maneuver
its pectoral fins toward its head, thereby creating force
for backward swimming. Such pectoral fin movement
may thus be applied to generate force in order todetermine the direction of swimming. In this way, theproposed model can provide guidelines for biologicalexperiments.
6. Conclusions and future work
This paper proposed a body dynamics model for larvalzebrafish and reported on dynamic analysis of the species.
The measured paths of wild larval fish were comparedwith those obtained from swimming simulation using the
proposed model. The results indicated that the model canbe used to approximate paths with a high accuracy levelof 0.76±0.74 [%] in relation to body length. It was also
found to be capable of reproducing paths withcharacteristics similar to those of wild larval fish as wellas supporting estimation of how pectoral fins affectswimming.
In future work, the author plans to elucidate theswimming mechanism of larval fish with focus on the
motion generation mechanisms hidden behind the neuralcircuit. To this end, muscle and neural circuit models will
be constructed, and the mechanisms behind thedevelopment of swimming behavior in larval zebrafish
will be analyzed
7. Reference
1. M. A. Avella, A. Place, S.-J. Du, E. Williams, S. Silvi, Y.
Zohar and O. Carnevali, (2012). Lactobacillus rhamnosus
accelerates zebrafish backbone calcification and gonadal
differentiation through effects on the GnRH and IGF
systems, PLoS One 7, (2012), e45572
2. S. Budick and D. O’Malley, Locomotor repertoire of thelarval zebrafish: swimming, turning, and prey capture, J.
Exp. Biol. (2000), 203, 2565-2579.
3. N. Danos and G. V. Lauder, The ontogeny of fin function
during routine turns in zebrafish Danio rerio, J. Exp. Biol.
210, (2008), 3374-3386.

4. L. Fuiman and P. W. Webb, Ontogeny of routine
swimming activity and performance in zebra danios(Teleostei: Cyprinidae), Anim. Behav. (1988), 36, 250-261.
5. M. H. Green, R. K. Ho and M. E. Hale, Movement andfunction of the pectoral fins of the larval zebrafish (Daniorerio) during slow swimming, Exp. Bio. 214, (2011), 31113123.
6. S. Higashijima, Y. Hotta and H. Okamoto, Visualizationof cranial motor neurons in live transgenic zebrafish
expressing green fluorescent protein under the control of
the islet-1 promoter/enhancer, J. Neurosci, 20, (2000),
206–18.
7. Y. Katsumata, U. K. Muller and H. Liu, (2009).
Computation of Self-Propelled Swimming in Larva Fishes,
Bio. Sci. and Eng. 4, 1, 54-66.
8. C. B. Kimmel, W. W. Ballard, S. R. Kimmel, B. Ullmann
and T. F. Schilling, Stages of Embryonic Development of
the Zebrafish, Dev. Dynam. 203, (1995), 253-310.
9. L. S. Lasdon, R. L. Fox and M. W. Ranter, Nonlinear
optimization using the generalized reduced gradientmethod, Revue Francaise d’Automatique. Informatique etRecherche Operationelle. 3, (1974), 73-104.
10. J. H. Long Jr. Muscles, Elastic Energy, and the Dynamics
of Body Stiffness in Swimming Eels, Amer. Zool. 38,(1998), 771-792.
11. M. McHenry and G. Lauder, Ontogeny of form andfunction: locomotor morphology and drag in zebrafish(Danio rerio), J. Morphol. 267, (2006), 1099-1109.
12. O. Mochizuki and S. Ichikawa, Hydrodynamics forlearning from creatures (in Japanese), Yokendo, (2010).
13. U. K. Müller and J. L. van Leeuwen, Swimming of larvalzebrafish: ontogeny of body waves and implications forlocomotory development, J. Exp. Biol. 203, (2004), 853868.
14. D. H. Thorsen, J. J. Cassidy and M. E. Hale, Swimming oflarval zebrafish: fin-axis coordination and implication forfunction and neural control, J. Exp. Biol. 207, (2004),
4175-4183.
15. P. W. Webb, Effects of partial caudal-fin amputation on
the kinematics and metabolic rate of underyearlingsockeye salmon (Oncorhynchus nerka) at steadyswimming speeds, J. exp. Biol. 59, (1973), 565-581.
16. P. W. Webb, Form and function in fish swimming. Scient.
Am., 251, (1984), 72-82.
17. P. W. Webb and D. Weihs, Functional locomotor
morphology of early life history stages of fishes, Trans.
Am. Fish. Soc. 115, (1986), 115-127.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 501

Behavior Analysis on Boolean and ODE models for Extension of Genetic Toggle Switch from
Bi-stable to Tri-stable
Masashi Kubota1 Manabu Sugii2 Hiroshi Matsuno3

1,3

Graduate School of Sciences and Technology for Innovation, Yamaguchi University,1677-1 Yoshida, Yamaguchi-shi,
Yamaguchi 753-8512, Japan
2
Faculty of Global and Science Studies, Yamaguchi University,1677-1 Yoshida, Yamaguchi-shi, Yamaguchi 753-8541,
Japan
E-mail:1w006vb@yamaguchi-u.ac.jp 2manabu@yamaguchi-u.ac.jp 3matsuno@sci.yamaguchi-u.ac.jp

Abstract
The artificial genetic circuit (AGC) is a gene network in which its expression timing, period and function are designedby computational and biological techniques. The AGCs for the functions of electronic circuits like a toggle switch andan oscillation circuit were realized in E.coli by Gardner and Elowitz in 2000[1],[2], respectively. We visualized andverified the structure and behavior of Boolean network (BN) and ordinary differential equation (ODE) models for
2-variable genetic toggle switch in the phase plane by using GINsim and Scilab. ODE is the most widely used
formalism for biological processes, while BN model allows us to easily understand mathematical expression of
biological processes. Then, we extended the models to 3-variable genetic toggle switch from the 2-variable ones
demonstrating a mathematical formalization by showing correspondence between state transitions of BN and trajectory
paths of ODEs in the phase space.
Keywords: synthetic biology, artificial genetic circuit,

1.Introduction
Currently, artificial genetic circuits can be constructusing genetic engineering, which is called synthetic
biology. We investigated the behavior and mathematicalformalization about genetic toggle switch as artificial
genetic circuits. The genetic toggle switch works as anelectronic toggle switch so that the state of the gene
expression between two genes can be switched by theregulation of related factors. For the bi-stable genetic
toggle switch, Gardner et al. showed the constructing
method of the bi-stable genetic toggle switch circuit in2000[1].They specified and confirmed the conditions forthe bi-stable behavior, the logical model of mutual
suppression and the ordinary differential equations(ODEs).
In this paper, we obtained the state transition
graph(STG) from the Boolean Network (BN)model
based on the logical model for the bi-stable genetic toggle
switch, and associated the STG with the ODE model toinvestigate the correspondence between the statetransitions in STG and trajectories of the ODEs. After
that, we extended the bi-stable genetic toggle switch totri-stable one, and analyzed the dynamic behavior of the
ODE model comparing with the state transitions in the
STG. In the case of a bi-stable genetic toggle switch,
nullcline could be shown in a 2-dimensional diagram. On

the other hand, nullcline of the ODEs for the tri-stable
genetic toggle switch is drawn as a 3-dimensional graph.
A graph of each nullcline can be associated with its STG.
The phase plain can be divided into 4 areas centered on
the unstable point based on the correspondence between
the state transitions in STG and trajectories of the ODEs
for the bi-stable genetic toggle switch. The tri-stable
genetic toggle switch has 8 state transitions in STG andthe phase space can be divided into 8 spaces centered on
unstable point. By extending to tri-stable genetic toggle
switch, the stable point increased from 2 to 3, nullcline isrepresented as planes from lines. As a result, the genetic
toggle switch has been extended from bi-stable totri-stable, and we demonstrated a mathematicalformalization by the correspondence between statetransitions of BN and trajectory paths of ODEs in thephase space.
2.Dynamic behavior, Boolean network and Gene
structure of bi-stable genetic toggle switch
The bi-stable genetic toggle switch gets steady state
when either repressor 1 gene or repressor 2 gene isexpressed in Figure1. Thus, it has 2 stable steady states.
Figure 2 shows the logical model and Figure 3 shows thestate transition diagram for bi-stable genetic toggle© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 502

Masashi Kubota Manabu Sugii Hiroshi Matsuno

switch. The gene structure of the bi-stable genetic toggle
switch produced by Gardner[1] is shown in Figure 1, andODEs is shown in Figure 4.
When the value of ODEs do not change with time, the setof x and y( = 0, = 0) is called nullcline (Figure 4,5).The intersection of the trajectories of the ODEs iscalled the stationary point, and the bi-stable genetic
toggle switch has 2 stable steady state points (XA,XB) and1unstable point (XU).The line drawn diagonally from theorigin is called separatrix. The convergent stable steadystate point is determined depending on the initial value of
the ODEs on the phase plane which is divided into the 2regions (SA and SB)by the separatrix (Figure 5-T , T ).
The state transition graph of bi-stable genetic toggle
switch shown in Figure 2 has 4 states. In Figure 5, let θ
be the value of x and y of the unstable point, and let it bethe (xi = (θ, θ)) threshold. Then the phase space can bedivided into 4regions.Each region with each state in the
STG are defined in the following conditional formula(Figure 6). R denotes the set of real numbers.
P
P
P
P

Figure 2 Logical model==

Figure 3 state transition graph

+

+

−

−

Figure 4 ODEs

= {(x, y) ∈ R | x ≤ θ and y ≤ θ },= {(x, y) ∈ R | x ≤ θ and y > },= {(x, y) ∈ R | x >≤ },= {(x, y) ∈ R | x >> },The stable steady state points (XA,XB) are in the regions
P01 and P10, respectively in Figure 6. Because thetrajectories never go across the separatrix, the state P01
and P10 do not transit between the two regions. There isno transition path between [0,1] and [1,0] in the STG.
Thus, the four states in the STG of the bi-stable genetic
toggle switch can be associated with 4 regions obtainedby dividing the phase plane.

Figure 5 Nullcline

Figure 1 Gene structure© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 503

Behavior Analysis on Boolean

P
P
P
P
P

Figure 6 Associate the state in the STG with the phaseplane for bi-stable genetic toggle switch

3. Extension of tri-stable genetic toggle switch
We extend the STG as a BN model and thecorrespondence between state transitions of BN andtrajectory paths of ODEs in the phase plane representing thebi-stable genetic toggle switch to the tri-stable ones. The
tri-stable genetic toggle switch gets stable steady states
when one of the repressor genes (A, B and C) is
expressing. Thus, it has 3 stable steady states. Figure 7
shows a logical model, and Figure 8 shows a STG oftri-stable genetic toggle switch. Figure 9 shows the ODEs
for tri-stable genetic toggle switch obtained by extending
the ODEs for the bi-stable ones.
The nullcline of the ODEs for tri-stable genetic toggle
switch has 4 intersections, and these intersections are
consisted of 3 stable steady state points (XA,XB,XC) and1unstable point (XU). In the case of tri-stable genetic
toggle switch, it is also possible to control the gene
expression by giving various initial value for eachparameter (x, y and z)(Figure 10-T1,T2,T3).
In Chapter 2, we distributed the states in the STG andnullcline of the bi-stable genetic toggle switch. There are
8 states in the STG. Let θ be the value of x, y, z of theunstable point, and let it be the (XU = (θ, θ, θ)) threshold.
Then the graph of nullcline can be divided into 8 regions.
Each region with each state in the STG are defined asshown in the following conditional formula (Figure 11).
PPP

= {(x, y, z) ∈ R | x ≤ θ &= {(x, y, z) ∈ R | x ≤ θ &= {(x, y, z) ∈ R | x ≤ θ &

≤
≤
>

&
&
&

≤
>
≤

= {(x, y, z) ∈ R | x ≤ θ &= {(x, y, z) ∈ R | x > &= {(x, y, z) ∈ R | x > &= {(x, y, z) ∈ R | x > &= {(x, y, z) ∈ R | x > &

>
≤
≤
>
>

&
&
&
&
&

>
≤
>
≤
>

},
},
},
},
},

The stable steady state points are in the regions [001],[010] and [100] respectively in Figure 11.Trajectories
T1, T2 and T3 transit to one of the stable steadystate points through the spaces which correspondto the state transition path of STG. In other words,
there is no trajectories in the phase space not according to
the state transition paths in STG. For example,T1 is the
trajectory of the ODEs for the tri-stable genetic toggle
switch when (x, y, z) = (1.8, 1.1, 1.5) is given as an initialstate.
Trajectory start from the region [1,1,1] which is
associated with the STG. After that, it goes to the region[1,0,1]→[1,0,0] so as to correspond to the state transition
path in the STG and reaches to the stable steady state
point XA (Figure 12).Therefore, the state transition in the
STG based on the logical model corresponds to the
behavior of the ODE model in the phase space for thetri-stable genetic toggle switch after the extension frombi-stable to tri-stable.

Figure 7 Logical model

−
−

−

},
},
},

Figure 8 state transition graph

A

=

B

=

C

=

Figure 9 ODEs

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 504

Masashi Kubota Manabu Sugii Hiroshi Matsuno===
Figure 14 Region SB

Figure 15 Region SC

Figure 16 separatrix of the ODEs
for tri-stable genetic toggle switch

Movement of Figure 11 from P000 to any one of P001,
P010 and P100 is determined by the starting point. Alsofrom P111 to P001, P010, P100 are similar. We considered how
to transit from P111 to which regions (P001,P010,P100) andstabilize. We divided the region P111 in detail as in thefollowing formula.(Similarly for P000, P001, P010, P100, P011,
P101, P110)

Figure 10 Nullcline

Figure 11 Associate the state in
the STG with the phase space
for tri-stable
genetic toggle switch

Figure 13 Region SA

Figure 12 T1 change
regions
P111→P101→P100

P
P
P
P

=P
∪P
∪P
where
= {(x, y, z) ∈ P | x > & >= {(x, y, z) ∈ P | y > & >= {(x, y, z) ∈ P | z > & >

P

=P

P

= {(x, y, z) ∈ P | x >> },= {(x, y, z) ∈ P | x >

&
&
&

> },
> },
> },

>

& x>

We divided the areas further on P111A as in the following
formula. (Similarly for P111B, P111C)

4. Separatrix of tri-stable genetic toggle switch
The separatrix of the ODE model for the tri-stable genetic
toggle switch in the phase space is shown in Figure
16.The trajectories transit to one of the stable steady state
points without crossing the separatrix (Figure13,14,15).
In other words, there are three regions divided by theseparatrix which decide the destination point of the
trajectory as a stable steady state point. The separatrix of
the ODEs for the bi-stable genetic toggle switch isrepresented as a line. After the extension from bi-stable totri-stable, the separatrix is also extended from a line toplanes.

P
P

},
},

∪P

= {(x, y, z) ∈ P

∪P

| x>

where
&

&

&

>

>

>

&

& y<z & x

& y=z & x>

We can understand how the trajectory is drawn from thestarting point value.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 505

Behavior Analysis on Boolean

generalize the extension to multi-stable genetic toggle
switch. The ODEs for multi-stable genetic toggle switch
are shown below.
du
a
=
dt
1+∑

(a)

u

−u

(i ≠ j)

6. References[1] Gardner T.S., Cantor C.R., and Collins J.J.
Construction of the genetic toggle switch in Escherichia
coli. : Nature, 403,339-342, (2000)[2] Elowitz, M.B. & Leibler, S. A synthetic oscillatory
network of transcriptional regulators. : Nature 403,
335-338, (2000)(b)
Figure 18 trajectories started from P111Ay (P111Bx,P111Cx)and P111Az(P111Bz,P111Cy) approach to XA (XB,XC)via different regions, P110 and
P101, respectively

Figure 17 P000 and P111 are divided into three regions

5. Conclusion
In this paper, we confirmed the state transition of thelogical model of the bi-stable genetic toggle switch
conducted by Gardner, and found the correspondence
between the BN model and the ODE model. By setting
the value of the unstable point of the ODEs as θ, thephase plane can be divided into 4regions.We showed thecorrespondence between the STG and the behavior of the
trajectories Then, We extended a genetic toggle switchfrom bi-stable to tri-stable. The nullcline of the ODEs forbi-stable one isrepresentedas2 dimensional curve in thephase plane but the nullcline of ODEs for tri-stable one isrepresented as 3 dimensional diagram. The stable steadystate point indicated by the intersection of nullcline
increased from 2 to 3. Likewise, we distributed the statetransitions based on the logical model to the phase space
based on the ODE model. We confirmed thecorrespondence between state transitions of BN andtrajectory paths of ODEs in the phase space. Finally wesought the separatrix of the ODEs for the tri-stable
genetic toggle switch. The separatrix is also extended
from a line to planes after the extension from bi-stable totri-stable.
By extending from bi-stable to tri-stable, we can© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 506

Boolean modeling of mammalian cell cycle and cancer pathways
Hideaki Tanaka, Adrien Fauré and Hiroshi Matsuno

Graduate School of Sciences and Technology for Innovation, Yamaguchi University, 1677-1 Yoshida, Yamaguchi-shi,
Yamaguchi 753-8512, Japane-mail: w015vb@yamaguchi-u.ac.jp, afaure@yamaguchi-u.ac.jp, matsuno@sci.yamaguchi-u.ac.jp

Abstract
The cell division cycle is controlled by a complex molecular network: a recent model of the cell cycle andcancer pathways includes close to a hundred genes [Fumiã and Martins, 2013]. To cope with suchcomplexity, different approaches have been used by modelers. Recently, Deritei et al [2016] haveemphasized modularity as a key organizational principle. Their model however, includes only 21
components. This raises the question how the approach would fare on larger models such as the one
published by Fumiã and Martins.To explore that question we first convert these two models to acommon modeling framework [Naldi et al., 2009]. Preliminary results show that there is only limited
overlap between the two models, with shared variables being controlled by different regulators. Thissuggests that at this stage module definition may still depend on the modeler and thus may not yet
reflect actual biological organization.
Keywords: Boolean modeling, Cell cycle, Cancer pathways

1. Introduction
When the same system is modeled by different authors,
to the extent that the system is well understood and themodeling approaches are consistent, we should expect
converging results. The mammalian cell cycle, due to its
fundamental and practical importance in various areas of
biology and medicine, is a heavily studied biologicalsystem. Yet a single look at two recent logical models [[1]Deriteiet al., [2] Fumiã et al.] reveals importantdifferences in terms of complexity. Our aim is to evaluate
the origin, extent and significance of these differences.
In this paper, we first convert the two models to acommon modeling framework [[3] GINsim] and checkthat the transposed models are equivalent to the originalmodels. This preliminary study reveals discrepancies
within one of the models, as described in the original
paper in terms of logical equations, or in terms ofregulatory graph and simulation results [1]. Then wecompare the converted models focusing on the common
elements. We hoped that the common elements havecommon logical expression; however, this is not what we
found. This suggests that module definition may still
depend on the modeler and thus may not yet reflect actual
biological organization.

2. Cell cycle
The mammalian cell cycle functions as an excellent casestudy for coordination between closely connected bistable
circuits. It consists in a series of events that take place
within the cell, leading to division and duplication of
DNA to produce two daughter cells [4]. The cell cycle
consists of the following two phases, named inter-phaseand M phase (Mitotic phase). Inter-phase consists of thefollowing three phase, named G1 phase (Gap 1 phase), S
phase (Synthesis phase), and G2 phase (Gap 2 phase). In
G1 phase, the cell gets big for replication and the enzyme
required in the next S phase is synthesized. This phase iscalled preparation phase by researchers.
At the end of G1 phase there is a cell cycle checkpoint.
This is a series of safety mechanisms to confirm that the
DNA is defect free and that the function of the cell isnormal. The G1 checkpoint is known as the restriction
point, and requires the presence of growth factors. Cells
that proceed through this checkpoint are committed toentering the S phase, during which chromosomal DNA isreplicated. When DNA synthesis is completed and all
chromosomes have been replicated, S phase is terminated.
During the S phase, the amount of intracellular DNA
doubles.
The period from the completion of DNA replication to© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 507

Hideaki Tanaka, Adrien Fauré, Hiroshi Matsuno

the entry into M phase is called the G2 period. In the G2phase, active protein synthesis is again performed, andmicrotubules necessary for mitosis are mainly produced.
In the M phase, mitosis and cytokinesis are performed. It
is a relatively short period of the cell cycle.
In addition to these four phases, there is a G0 phasewhich leaves the cell cycle and stops dividing. The cell
cycle starts with this phase. Fig.1. shows the cell cycle.

the paper that we use are used synchronous simulation.
So in this paper, we use also synchronous simulation toconfirm their results.
4. The mammalian cell cycle modelFirst, we convert the three models proposed by Deritei
et al., namely the restriction switch, the phase switch andthe full cell cycle model, [3] into GINsim's fromalism.
4.1. The restriction switch
This restriction switch works between G1 phase and S
phase in the cell cycle. The restriction point is the pointof the G1 phase of the animal cell cycle where the cells
are involved in the cell cycle and then extracellular
proliferation stimulants are no longer required. This
switch model consists of 6 nodes.

Fig.2.1. Schematic of cell cycle
3. Method
GINsim (Gene Interaction Network simulation) is acomputer tool based on a logical formalism forsimulation and analysis of regulatory networks [3]. In
GINsim, regulatory networks are modeled as logicalregulatory graphs (LRG), and state transition graphs
represent dynamical behaviors.
Nodes in a logical regulatory graph represent thecomponents system such as protein and mRNA, etc.
Arrows connect the nodes, and represent the relationshipand effect between the nodes.
To complete the logical regulatory graph, we have to be
expressed the relationship between each node using the
logical expression. Operator AND, OR and NOT in
logical expression are represented by &, | and !
respectively.
Dynamic movement is represented by a State Transition
Graph (STG). In this type of graph, node represents thestate to give a value of the logical variable for eachcomponent. Variables are arranged in the order deﬁned at
the modeling stage. The values of 0 represent inactive
and 1 represent active state of each components.
We have two types of simulation: asynchronous andsynchronous. In asynchronous graph, each value isupdated individually. Conversely, in synchronous graph,
all values will be updated at the same time. The authors

Fig.4.1. The Restriction Switch
When we run simulation, this switch has two stablestates, consistent with the original results. This restriction
point is a switch-like transition controlled by a bistablemolecular control circuit. In the restriction switch, multistability is essential.
Table 1. Stable state of the restriction switch
Name

CyclinD1
10

CyclinE
1
0

E2F1
1
0

Myc
1
0

P27Kip1
0
1

RB
0
1

4.2. The phase switch
The phase switch is the network responsible for toggling
the cell from G2 into mitosis, then past the spindle
assembly checkpoint into cytokinesis. This switch
consists of 11 nodes.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 508

Boolean modeling of mammalianFig.4.2. The Phase Switch
When we run simulation, this switch has three stablestates. This result is also the same result as them. In thephase switch, multi stability is also essential.
Fig.4.3. The full cell cycle model
5. Boolean network model for cancer pathways
The cancer network proposed, although highly
simplified by Fumiã et al [2]. But this model has 96
nodes. So we think this model is complicated anddifficult. These equations are update rules of the model.

Table 2. Stable state of the phase switch
Name
Cdc20
Cdc25A
Cdc25C
Cdh1
Cdk1
CyclinA
CyclinB
Mad2
pAPC
UbcH10
Wee1

0
0
0
1
0
0
0
0
0
0
1

0
1
1
0
0
1
1
0
0
1
1

0
0
1
0
1
0
1
1
1
1
0

σ

σ

4.3. The full cell cycle model
The cell cycle model is a combination of the restriction
switch and the phase switch. These cell cycle switches
mutually toggle to generate cyclic dynamics. These two
switches within the cell are tightly bound to form acontrol network of the mammalian cell cycle. This model
is formed by four nodes plus two switches. These nodes
represent regulatory sub-networks rather than molecularspecies. The restriction switch has two stable states, andthe phase switch has four stable states. These restriction
switch and phase switch drive the mammalian cell cycle.
So these switches are important for cell cycle. In order todrive the cell cycle of mammals, because themulti-stablility is essential, we can confirm that it meetsthe conditions.

σ(t + 1) = input;(t + 1) = input;(t + 1) = input;(1)⋮(t + 1) = +σ(t) − σ(t) − σ (t)σ(t) − 1 ;+σ
It includes proteins related to oncogenes, tumor
suppressor genes and stability genes, which are the threemajor classes of mutation targets involved intumorigenesis. These nodes represent a significant subsetof the proteins involved in the cancer and the networkedge represents a number of parallel pathways in which
the transformed cells maintain an aberrant gene
expression pattern and survive and develop further
malignancies. The network has input nodes for applying
to cells, different environmental stimuli and stresses suchas hypoxia, carcinogens, nutrient depletion, proliferationand growth suppression signaling. Here, threshold
function is given by these equation.
sgn(x) = 0 if x ≤ 0(2)
sgn(x) = 0 if x > 0This kind of expression can also be represented as
logical rules in the formalism used by GINsim. Here,when σ=1 the protein is functionally active. On thecontrary, when σ=0 the protein is inactive. We calculate
the logical expression for each node and input it. As a

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 509

Hideaki Tanaka, Adrien Fauré, Hiroshi Matsuno

result, Fig 5.1. was obtained. The topological structure ofthis cancer network was characterized by Fumiã et al interms of its shortest path length, clustered coefficient andconnectivity.

Fig.5.1. The cancer pathways
6. Comparison of the models
A recent model of the cell cycle and cancer pathways
includes close to a hundred genes [2]. To cope with suchcomplexity, different approaches have been used bymodelers. So we did a comparison of the model of the
Cell Cycle model and Boolean Network Model for
Cancer Pathways. These models represent the cell cycle.
So we examined the same or different parts of thesemodels. First, we have focused on the elements. Looking
at these graph, There is a big difference in the number of
elements. The cell cycle model has 21 elements and thecancer pathways has 96 elements. In cell cycle model
made by Deritei et al [1] have emphasized modularity as
a key organizational principle. So there is a big
difference.
Next we focused on the common parts of these models.
As a result, there were 12 common elements between thecell cycle model and the cancer pathways.
Table 3. Common elements
Name

GF
E2F
Cdh1

p21
Myc
CyclinA

CyclinD
Rb
CyclinB

CyclinE
Cdc20
UbcH10expression is different. This suggests that at this stage
module definition may still depend on the modeler andthus may not yet reflect actual biological organization.
7. Conclusion
The primary purpose of this study was to create aboolean model based on a recent paper on cell cycle andcancer pathway.
In chapter 3, we describe to convert the models to acommon modeling framework . We used GINsim as acommon framework. We converted it into a common
framework using logical regulatory graph. After that, we
use state transition graphs for checking operation etc.
Further, GINsim has circuit analyze and expression of
stable state that is useful for analysis of biologicalsystems.
In chapter 4, we describe the cell cycle model. Deritei etal [1] have emphasized modularity as a key
organizational principle. This cell cycle model is acombination of the restriction switch and the phaseswitch. Here, we explained the timing of working and thenumber of nodes in each parts. In the two switches, we
confirmed that there are multi stable state. This is arequired condition.
In chapter 5, we describe the boolean network model forcancer pathways. First, we show the equations. There are
96 equations. These equations are update rules of themodel. We used these equations and thresholds function
to decide the logical expressions.
Comparing the cell cycle model and cancer pathways, itappears to differ. However, these models have the same
parts. Some elements were common. Further, we foundthat many of these elements has received a lot of
influence from the common elements. From this result,many cell models can be presumed to have common
parts.
Finally, future tasks of this study are comparison of
behaviors of these models and compare them with othercell cycle models. In this time, we run synchronous
simulation only. We have to run asynchronous simulation.
References

Further, all of the elements were also common in the
restriction switch. We found that many of these elements
has received a lot of influence from the common node.
For example, all elements affected by UbcH10 are incommon. But this result is not expected. We hoped that
all nodes of cell cycle model would be in common withcancer pathways.
From this result, we have to examine the logicalexpression. But the logical expression was not the same.
Because it contains elements that are not in common. All
affected elements are the same UbcH10 also the logical[1] D. Deritei, W. Aird, M. Ercsey-Ravasz and E. Regan,
Principles of dynamical modularity in biological regulatory
networks, Sci Rep. 6:21957. doi:10.1038/srep21957, 2016[2] H. Fumiã and M. Martins, Boolean Network Model for
Cancer Pathways: Predicting Carcinogenesis and Targeted
Therapy Outcomes, PLoS ONE 8(7):e69008.
doi:10.1371/journal.pone.0069008, 2013
[3] C. Chaouiya, E. Remy, B. Mosseand and D. Thieﬀry,
Qualitative analysis of regulatory graphs: A computational tool
based on a discrete formal framework, Lecture Notes in Control
and Information Sciences 294: 119-126 2004
[4] B. Alberts, A. Johnson, J. Lewis, M. Raff, K. Roberts and P.
Walter, MOLECULAR BIOLOGY OF THE CELL FOURTHEDITION, 2004

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 510

An Estimation Method for Environmental Friction Based on Body Dynamic Model of
Caenorhabditis elegans
Zu SohDepartment of System Cybernetics, Hiroshima University, 1-4-1 Kagamiyama
Higashi-Hiroshima, Hiroshima 739-8527, Japan
Michiyo Suzuki

Department of Radiation-Applied Biology, National Institutes for Quantum and Radiological Science and Technology,
1233 Watanuki, Takasaki, Gumma 370-1292, Japan
Toshio Tsuji

Department of System Cybernetics, Hiroshima University, 1-4-1 Kagamiyama
Higashi-Hiroshima, Hiroshima 739-8527, Japan
E-mail: sozu@bsys.hiroshima-u.ac.jp

Abstract
Caenorhabditis elegans is a small worm which is approximately 1.3 mm in length. The present study proposes anestimation method for frictional force using locomotion information obtained from video analysis of actual worms.
The results indicate that the body model driven by the estimated frictional force can trace the locomotion of theworm within 4% of the body length. The proposed method may be able to be applied to analyze the relationshipbetween friction and gait control.
Keywords: Caenorhabditis elegans, frictional force, dynamics model, video analysis, locomotion

the worm's information processing mechanism can beinvestigated by recording its behavior and examining theinternal states of its neural network. Recently, dopamine
was identified as a key chemical2 intermediating thedistinctive gait changes observed when the worm swims
in water and crawls on agar.3,4 It was also revealed that
dopamine-related changes are induced by mechanicalstimuli2. However, experimental conditions and current
technology limit measurement of the amount of force thatcan be applied to the worm without

1. Introduction
Caenorhabditis elegans is a small soil-dwelling
worm with a slender translucent body composed ofaround 1,000 cells. It is approximately 1.3 mm long andweighs 5.0 g (Figure 1(a)). Its neural network, which iscomposed of only 302 neurons, allows the processing ofenvironmental information such as temperature andchemical gradients so that the worm can act in response
to its surroundings. Due to the small scale of its neuralnetwork, C. elegans has become a favored modelorganism for the analysis of information processing
mechanisms hidden inside neural networks, especiallysince White et al. revealed a connective structure
between its neurons and muscle cells.1
In this context, the mechanism of gait control is aninteresting analysis target among various life phenomena
because commands generated by neural networks can beeasily observed through body locomotion. Accordingly,© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 511Zu Soh, Michiyo Suzuki, Toshio Tsuji

of C. elegans and the estimation method forenvironmental friction. Chapter 3 covers verification of
the proposed algorithm and reports on the results ofenvironmental friction estimation. Chapter 4 discusses
the relationship between translational force and friction
force, and Chapter 5 details the conclusion and outlines
future work.

yxqi
Point of action( xg , yg , qg )(b) Body Model(a) C. elegans
Joint axis

y

z

y
x
t

Tangental
direction

2. Materials and Methodsx

0.2 [mm]

When C. elegans moves on a solid surface such as anagar plate or swims in liquid, it is exposed to different
environmental drag forces. As these forces are the basisof propulsion, different drag characteristics or strengths
produce different propulsive movements. Drag force cantherefore be derived by observing the motion of worms
to solve dynamic problems. The following section
describes the materials and methods used to record worm
motions, and highlights the algorithms used to estimate
drag force.

0.1[mm]Normaln direction(c) Model of each link(d) Cross section of x-y planeFig 1. Body dynamics model of C.elegans
influencing its behavior. As a result, the input for its
neural network system remains unclear.
Computer simulation provides a potential solution tothis problem because the worm's thrust is generated byreaction force based on the friction between its body andits environment in the absence of other external forces,
and can be calculated using motion equations. The body
dynamics model of C. elegans first proposed by Niebur
and Erdos5 was able to simulate the worm's undulatory
locomotion so that a model worm could travel as fast asa real one. As friction cannot be measured from an actual
worm, the friction coefficients included in the model
were estimated from the friction measured between glass
fiber and agar6. Recently, more precise friction
coefficients in a gelatin solution and water were
calculated by Berri et al. and Sznitman et al. based on theprinciples of fluid dynamics.7~9 However, previous
methods have limitations in terms of the range ofenvironments in which they can be applied. An algorithm
to determine friction independent of environmental
conditions is therefore needed to support estimation of
the mechanical stimuli affecting C. elegans.
In this paper, the authors propose a body dynamics
model incorporating the considerations of dynamic andviscous friction. An estimation method for friction
coefficients based on the model is then proposed. As thismethod requires only locomotion information obtained
from video analysis of worms, it enables estimation offriction regardless of the environment and the worm's
behavior. Chapter 2 describes the body dynamics model

2.1. Strains and culture
The C. elegans wild-type Bristol N210 and the
Escherichia coli OP50 strain were obtained from the
Caenorhabditis Genetics Center. Using standard
methods10 worms were grown at 20°C on 6-cm plates
containing 10 ml of nematode growth medium (NGM)
agar spread with E. coli (food). Well-fed worm at the
young adult stage were used in all assays.
2.2. Sample preparation
6-cm plates containing 4 ml of NGM agar for assay
were prepared on the same day of experiments. The
plate for the crawling assay (plate A) was uncovered
and placed on a clean bench for approximately 1h to dry
up excess fluid from the surface. The plate for theswimming assay (plate B) was not dried up and wasadded 300 l of S basal buffer11. A worm was picked up
from a culture plate and washed twice in a few drops of
S basal buffer. Immediately after wash, a worm wastransferred to an assay plate A or B.
2.3. Rigid link modelTo estimate the drag forces acting between C.
elegans and its environment, the worm's body was first
approximated using the N rigid link model shown in© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 512

An Estimation Method forActual worm

driving each link, and the second term represents
friction, whereis the Jacobean matrix of the j-th
joint with a representative point. = , , ,represents the drag force acting on the gravity point ofeach joint, and can be described in arbitrary form
depending on the surrounding environment. In thisstudy, drag force was modeled using the properties of
Coulomb friction and viscous drag in a Newtonian fluid
as follows:(iii) Skeletonizing,(i) Binarization(iv) Division of body line

(xg, yg, qg)(v) - (vii) Calculation concerningwith body form

Fig. 2. Image processing

( )=

,

,

−

,

,

(3)

2.4. Friction estimation algorithm

Figure 1 (c), which also illustrates its coordinate
configuration. The motion of the model is restricted tothe two-dimensional x-y plane. The links l2 - lN-1 wereconnected with adjacent links via rotational joints toallow rotation around the z-axis, and the head link l1 andtail link lN had one free end and a joint respectively
connected to links l2 and lN-1. Using this model, therelationship between drag and motion can be derived
from Newton-Euler-type motion equations as shown
below.
̈0( ) ̈ + ℎ( , ̇ ) + ( ) =−, (1)( ) ̈ + ℎ ( , ̇) +,where ∈ ( , ) denotes the tangential and normal
directions related to the link, , are the velocities at thegravity point of link j,
is the coefficient of Coulomb
friction, andis the viscous drag coefficient.
Equation (2) indicates that the drag coefficients andtorque driving each joint can be derived from local
bending angles and the related velocities andaccelerations ( , ̇ , ̈ ) and translational acceleration ̈ ,which can be determined from video analysis. Based on
this equation, an algorithm for friction coefficientestimation is proposed below.

qi(ii) Denoising=−

The motion equations (1) and (2) can be solved fordrag coefficients given the motion information ( , ̇ , ̈ ,̈ ). Among these values, the local bending angle andthe position
can be determined by recording the
motion of a worm and analyzing the resulting video, andthe related time derivations can be used to determine
velocity and acceleration. The information obtained canthen be used to solve the motion equations for the dragcoefficients in relation to each video. However, there
are two problems in the implementation of thiscalculation.
Firstly, it is an ill-posed problem because only N+2
coupled equations can be derived from Equations (1)and (2), while there are four unknown drag coefficientsplus N-1 unknown torque values ∈ ℝ. To solve
this problem, the proposed algorithm involves
simultaneous analysis of two different motions of aworm placed in the same environment in which
identical drag coefficients are assumed. In this manner,
the number of unique coupled equations can be doubled
without increasing the number of unknown dragcoefficients.

(2)

) ∈ℝ
where = ( , , …
is a vector of
local bending angles, = ( , ) and denotes the tip
position and its posture angle for the head link related to
the global coordinate as shown in Figure 1. The terms( ), ( ) on the left-hand side are inertial forces,ℎ( , ̇ ), ℎ ( , ̇ ) are Coriolis forces and centrifugal
forces, and ( ), ( ) are gravity forces. The firstterm ∈ ℝ
on the right-hand side is the torque© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 513

Zu Soh, Michiyo Suzuki, Toshio Tsuji

Secondly, the drag coefficients calculated from only
one sampling can be significantly affected by video
analysis measurement error. To reduce this impact, theproposed algorithm involves the solution of a motion
equation for ̈ and for the time duration T withdifferent drag coefficients. The coefficients thatgenerate the paths closest to those of the actual worm
are then explored by minimizing the following
evaluation function:
=

2(

1

+ 1)

|| ( ) −( )||,

procedures (i)-(iv) and Move-tr/2D (Library Co. Ltd.,Tokyo) for procedures (v)-(vii).
Block 2: Selection of drag coefficient
In this block, a set of coefficients is selected in threephases as described below.
[Phase 1]
In Phase 1, drag coefficients are selected from theset ( , , , ) ∈ {( Δ , Δ ,
,
)|
, , ,
= 0, 1, ⋯ ,} based on iterative
alteration of , , , .
After the search for all combinations is complete,,,,
d1 % of the drag coefficients,,, ,with smaller evaluation values (Equation (4) areselected, and the iteration proceeds to Phase 2, where= 1, 2, … , (+ 1) /100.
[Phase 2]
In Phase 2, better drag coefficients are sought fromthe set selected in Phase 1. The search is carried out,
with the set of ( , +
,
+
,
,
,
+
,
+
) where ~ ={−1,0,1} . After the search for all combinations iscomplete,% of the drag coefficients
,
,
,
,
,
,
,
with smaller evaluation values(Equation (4)) are selected, and the iteration proceeds to(
Phase 3, where = 1, 2, … , 3+ 1) /10000.
[Phase 3]
In this phase, the generalized reduced gradientmethod13 is applied for the selection of drag coefficients
,
,
,
,
using
,
,
,
as initial values so thatthe friction coefficients with the smallest evaluation
values are calculated.

(4)

Where
represents the position of the i-th evaluation
point on the worm, represents the corresponding
position on the rigid-link body model, and subscript j
denotes an individual worm.
The proposed algorithm is applied to estimate thedrag coefficients for a worm swimming in liquid andcrawling on an agar surface. The proposed algorithm
consists of four blocks as outlined below.
Block 1: Video analysis

To analyze the body form of C. elegans in crawlingand swimming, the worm on an assay plate (A for
crawling and B for swimming) was video-recorded
using a digital camera EXILIM EX-F1 (CASIOCOMPUTER CO., LTD., Tokyo) mounted on astereomicroscope for approximately 10 seconds at 300
frames per second. The resolution of each frame was
512 x 384 pixels. Distinct worms picked up from aculture plate were used for crawling assays andswimming assays. The body form in crawling andswimming of C. elegans was analyzed off-line based on
the previous method12 as shown in Figure 2. Briefly,
each frame of the video was processed using thefollowing procedures: (i) binarization, (ii) denoising,(iii) skeletonizing, and (iv) division of body line into the
13 parts. After image processing, time-series data
concerning with the body form were obtained using thefollowing procedures: (v) length-scale calibration, (vi)acquisition of the x- and y-coordinates of each point on
the body, and (vii) calculation of values concerning withthe body form such as the relative angle between
adjacent dividing points. Video analyses describedabove were carried out using the following software:
Wriggle Tracker (Library Co. Ltd., Tokyo) for

Block 3: Calculation of Motion Equations
In this block, calculation for the path of two rigidlink body models in the time span T s is performed withthe friction coefficients selected in Block 2. Calculation
to solve Equations (1) and (2) for q0, r is carried out
using ADAMS (MSC Software Corporation, Tokyo)
multi-body dynamics simulator.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 514

An Estimation Method for

tmax
l
m
Nmax

Table 1 Parameters used in simulation
5sΔ
1.0×103
Δ
1.0×103.84×10 g
1.0 %
10
0.2 %
True value
Estimated value

1.0

[％]

h

m
0

0.01 10

mn

hn

mt

(a) Result of F1

ht

0

0

hn

mn

mt

ht

av

(b) Percentage errors

Fig. 3. Estimated results of friction coefficients

Fig. 5. Locomotion of C. elegans
3.1. Verification
The locomotion of the body model was simulatedusing Equations (1) and (2) with artificially preset
friction coefficients and time-dependent joint angles. The
paths determined from this simulation were then usedwith the proposed algorithm for friction coefficientestimation. Finally, the errors observed between thepreset and estimated friction coefficients were calculated
to verify the performance of the algorithm. Here, timedependent joint angles with the following four patterns
werechosen:− = 0.4sin(5 − 0.6 ),0.6 sin(5 − 0.6 ) , 0.4 sin(5 − 0.6 ) + 0.1,
0.6 sin(5 − 0.6 ) + 0.1 which are respectively denoted
as Locomotion 1~4. In addition, 10 sets of friction
coefficients (F1~F10) were chosen as uniform random
numbers in the range of [0,1] for dynamic friction
coefficients and [0, 0.01] for viscous friction coefficients.
The locomotion pair of 1 and 2 was simulated with thefriction coefficients of F1~F5 and the other pair
(Locomotion 3 and 4) was simulated with the friction
coefficients of F5~F10. The parameters of the body
dynamics model and the proposed estimation algorithm
are shown in Table 1.

Fig. 4. Estimated paths
Block 4: Evaluation of Drag Coefficients
In Block 4, evaluation is performed for dragcoefficients by substituting the paths calculated in Block3 into an evaluation function (Equation (4)), and theiteration returns to Block 2.
3. Experiments
As a way of verifying the performance of the
proposed body dynamics model and the estimation
algorithm for friction coefficients, testing was performed
to determine whether the algorithm enabled estimation of
artificially preset friction coefficients. Such coefficientswere then estimated for worms crawling on agar andswimming in a drop of water. Major headings should betypeset in boldface with the first letter of important words
capitalized.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 515

Zu Soh, Michiyo Suzuki, Toshio Tsuji

Fig. 6. Resulted value of evaluation function of
crawling simulation

Fig. 7. Resulted value of evaluation function ofswimming simulationworm

y [mm]

Start

End
0

0

End

(a) Paths of crawling

End
Start
End

Start

Start
x [mm]

2.0
y [mm]

model

1.5

2.0

0

0

x [mm]

2.0(b) Paths of swimming

Fig. 8. Paths of joint 7
Figure 3 shows the friction coefficient estimation
results and the related percentage errors against the preset
values. Figure 3(b) shows that the average percentage
errors were less than 2% with a standard deviation of 2%.
Figure 4 shows an example result for the paths of Joint 7
as well as average path errors for all simulations
performed. It can be seen that the average error was
1.9×10 ± 1.1×10mm, which indicates that the
proposed algorithm is capable of generating locomotion
values with errors smaller than 2.9×10 % in relation
to the worm’s length.

Fig. 9. Estimated friction coefficients (Agar)
Figures 6 and 7 show the evaluation function values,
which are path errors determined from crawling andswimming simulation for actual worms. The averagecrawling simulation error was about 0.02 mm (2 % of theworm's length), and the average swimming simulation
error was 0.05 mm (4 % of the worm’s length). Figure 8
shows the paths of Joint 7 in crawling and swimming
simulation. These outcomes indicate that each of theestimation results represented worm paths well. Figures
9 and 10 show friction coefficients estimated from eight
video images for crawling and eight for swimming, with
the numbers under each picture distinguishing individual
worms. It can be seen that the estimated friction
coefficients differed greatly even for the sameenvironments. In particular, in Figure 9(A1) and (A3)representing situations in which friction coefficients were
estimated from the same worm crawling on the same agar
plate, the viscous friction coefficient for the normal

3.2. Estimation of friction coefficients of actual
worms
Friction coefficients for actual worms were
estimated using video of their movement on agar and ina drop of water (Figures 5). Each of the time-dependent
joint angles determined from image processing wassmoothed using a two-order Butterworth low-pass filter(high cutoff frequency: 5 Hz) for denoising. The analysis
times for crawling and swimming were respectively set
as tmax=4 s, 1 s, and the other parameters were the sameas those used in the verification simulation.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 516

An Estimation Method forFig. 11. Comparison of trajectories (Agar)

Fig. 10. Estimated friction coefficients (Water)

Fig. 12. Comparison of friction force
even when different friction coefficients (A1, A2 and A3)
were used. Figure 12(a) highlights the friction force ateach link accumulated at the point of the head tip. The
right-hand side of Equation (2) shows this calculation.
From the figure, it can be seen that the total friction
generated from friction coefficient A1 showed a strong
correlation and similar values to that generated fromfriction coefficients A2 and A3. As the total friction
reactive force is the basis of the propulsion force theworm uses to crawl and swim, this result indicates thatthe proposed algorithm with path errors as an evaluation
function can be used to estimate propulsion force.
In previous studies regarding the dynamic modelingof C. elegans, it was reported that a worm's path can be
determined from the ratio between the normal andtangential friction coefficient.5,7 Indeed, the friction
coefficient ratio is a dominant factor in determining
worm paths. However, according to the simulation
performed here, identical locomotion cannot bedirection shown in (A1) was much larger than that shownin (A3). The next section discusses this friction
coefficient variability.
4.

Discussion

This section discusses the cause of variances foundin the estimated friction coefficients A1, A2 and A3 (Fig.
10) or the same worm on the same agar plate.
To compare the effects of different friction
coefficients adopted for the same joint motion, the timedependent joint angles qA1(t) extracted from thelocomotion of A1 were substituted into Equations (1) and(2), and paths with the friction coefficients of A2 and A3
were calculated. Figure 11 shows paths resulting fromsimulation under the time-dependent joint angles qA1(t)and the friction coefficients A2 and A3. The errors
observed between the actual and simulated worm's paths
are also shown in Figure 12(b), which indicates that the
average path error was about 4% of the worm's length© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 517

Zu Soh, Michiyo Suzuki, Toshio Tsuji

estimation of friction coefficients close to actual values
with an acceptable level of accuracy.
Finally, the effect of friction in different
environments was quantified. Figure 14 shows theaverage RMS value of the total friction forces ascalculated from the crawling and swimming simulation
results. The result of a t-test to determine the RMS
between crawling and swimming showed a significant
difference between the two (p<0.001). Fig. 15 indicatesthat worms experience stronger friction forces whenswimming than when crawling. Using results obtained
from the proposed estimation algorithm allows
estimation of the mechanisms behind gait control8 at thefriction level.

Fig. 13. Paths under the same friction coefficientratio

5. Conclusion and Future Work
This paper proposed a method for estimating friction
coefficients based on a body dynamics model and video
images of C. elegans. The study verified that the
proposed algorithm can be used to estimate friction
coefficients under artificially preset configurations, andthe results of experiments using actual worms confirmedthat it can also be used to generate locomotions withsmall errors and track worm paths closely. The algorithm
can be used to estimate true friction coefficient values
under ideal noise-free conditions. Although estimation
results were affected by noise contamination from image
processing, the propulsion forces affecting locomotion
paths could be estimated.
In future works, the authors plan to improve therobustness of the proposed method to support thecalculation of time-dependent friction changes acting on
worms, and will analyze the mechanisms behind gait
control against environmental factors in the context ofbody dynamics.

Fig. 14. Average RMS
generated if the same ratio but different friction
coefficients are adapted. Figure 13 shows locomotion
paths observed when the dynamic friction coefficientswere kept at a constant ratio （2 t = n） and the viscous
friction coefficient was set to 0 （ t= n =0）. It can beseen that there were path differences depending on thevalue of n, even though the dynamic friction coefficientswere kept at a constant ratio. This suggests that if image
processing on actual worms can be carried out with a highdegree of accuracy, friction coefficients can be
specifically determined because verification simulation
under artificially preset configurations (3.1) enabled the

Acknowledgements
This work was supported by JSPS KAKENHI Grant
Numbers 15H03950 and 20115010.

References
1. J. G. White, E. Southgate, J. N. Thomson and S. Brenner,
The Structure of The Nervous System of Nematode
Caenorhabditis elegans, Philosophical Transactions of
the Royal Society of London, 314 (1986) 1–340.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 518

An Estimation Method for

2. A. Vidal-Gadea, S. Topper, L. Young, A. Crisp, L. Kressin,
E. Elbel, T.,Maples, M. Brauner, K. Erbguth, A. Axelrod,
A. Gottschalk, D. Siegel and J. T. Pierce-Shimomura,
Caenorhabditis elegans selects distinct crawling andswimming gaits via dopamine and serotonin, Proc. Natl.
Acad. Sci. U.S.A. 108(42) (2011) 17504-17509.
3. J. Gray and H. W. Lissmann, The Locomotion of
Nematodes, J. Exp. Biol. 41 (1964) 135-154.
4. J. Korta, D. A. Clark, C. V. Gabel, L. Mahadevan and A.
D. T. Samuel, Mechanosensation and mechanical load
modulate the locomotory gait of swimming C. elegans, J.
Exp. Biol. 210 (2007) 2383-2389.
5. E. Niebur and P. Erdos, Theory of the locomotion ofnematodes, Biophys. J. 60 (1991) 1132–1146.
6. H. R. Wallace, Wave formation by infective larvae of theplant parasitic nematode Meloidogyne javanica,
Nematology. 15 (1969) 65-75.
7. S. Berri, J. H. Boyle, M. Tassieri, I. a Hope and N. Cohen,
Forward locomotion of the nematode C. elegans isachieved through modulation of a single gait, HFSP J. 3(3)(2009) 186–193.
8. J. T. Pierce-Shimomura, B. L. Chen, J. J. Mun, R. Ho, R.
Sarkis and S. L. McIntire, Genetic analysis of crawling andswimming locomotory patterns in C. elegans, Proc. Natl.
Acad. Sci. U.S.A. 105(52) (2008) 20982–20987.
9. J. Sznitman, X. Shen, P. K. Purohit and P. E. Arratia, The
Eﬀects of Fluid Viscosity on the Kinematics and Material
Properties of C. elegans Swimming at Low Reynolds
Number, Exp. Mech. 50(9) (2010) 1303–1311.
10. S. Brenner, The Genetics of Caenorhabditis elegans,
Genetics 77(1) (1974) 71-94.
11. E. R. Sawin, R. Ranganathan, and H. R. Horvitz, C.
elegans Locomotory Rate is Modulated by theEnvironment through a Dopaminergic Pathway and byExperience through a Serotonergic Pathway, Neuron,26(3) (2000) 619–631.
12. Y. Hattori, M. Suzuki, Z. Soh, Y. Kobayashi and T. Tsuji,
Theoretical and evolutionary parameter tuning of neuraloscillators with a double-chain structure for generating
rhythmic signals, Neural Comput., 24 (2012) 635-675.
13. G. A. Gabriele and K. M. Ragsdell, The Generalized
Reduced Gradient Method: A Reliable Tool for Optimal
Design, J. Eng. Ind., 99(2) (1976) 394–400.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 519

Mouse Cursor-like Control System in Consideration of the DC-EOG Signals
using EOG-sEMG Human Interface
Mingmin Yan

Department of Environmental Robotics, Faculty of Engineering, University of Miyazaki
Miyazaki, Japan
Yu Cheng

Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Miyazaki
Miyazaki, Japan
Keiko Sakurai

Interdisciplinary Graduate School of Agriculture and Engineering, University of Miyazaki
Miyazaki, Japan
Hiroki Tamura

Department of Environmental Robotics, Faculty of Engineering, University of Miyazaki
Miyazaki, Japan
Koichi Tanno

Department of Electrical and Systems Engineering, Faculty of Engineering, University of Miyazaki
Miyazaki, Japan
E-mail: yanmm@cc.miyazki-u.ac.jp, hc11047@student.miyazaki-u.ac.jp, z3t1401@student.miyazaki-u.ac.jp,
htamura@cc.miyazaki-u.ac.jp, tanno@cc.miyazaki-u.ac.jp
www.miyazaki-u.ac.jp

Abstract

Patients who suffered with the limb disorders cannot take care of themselves by communication barrier.
However, it is possible to improve the communication abilities of the patients by using biological signal
such as ocular potential and muscle potential which is caused by moving eyes or facial muscle. Therefore,
we have developed the human interface using the electrooculogram (EOG) and the facial surface
electromyogram (sEMG) signals which can control PC. However, this system could not control the mouse
cursor in accordance with the direction and magnitude of the movement of eyes to control PC smoothly
and intuitively. Thus, we proposed a new mouse cursor-like control system in consideration of the DC
elements of EOG signals using the EOG-sEMG human interface. Our proposed method has both drift andblink countermeasures which had better performance in mouse cursor control, and all subjects could
control the mouse cursor for both moving and clicking flexibility.
Keywords: Ocular potential, Facial muscle potential, Biological signal.

improve the communication abilities of patients who
suffer from diseases by using a biological signal such as

1. Introduction
There are a large number of persons with disabilities
around the world currently. However, it is possible to© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 520

Mingmin Yan, Yu Cheng, Keiko Sakurai, Hiroki Tamura, Koichi Tanno

ocular potential and muscle potential caused by
movement of the eyes or facial muscle1.
The aim of this paper is to present a system using
biological signals that can efficiently be used as a human
computer interface. The authors conducted joint research with multiple companies to develop an interface
device called “FARG” that can detect biological signals
such as ocular potential and muscle potential on the face
as shown in Fig. 1. A conventional method can control a
PC as a switch with 7 patterns: 4 patterns of eyes moving
in 4 directions (up, down, left, and right), and 3 patterns
of facial muscle operation (left blink, right blink, andbite) 2. However, the purpose of this paper is to control
the mouse cursor in accordance with the direction andmagnitude of movement of the eyes to control the PC
more smoothly and intuitively by using the proposed
methods.

vertical direction and horizontal direction (up, down, left,
and right) as the thresholds that show the EOG signals
changes. When the patients gazed on the up, down, left,or right side of the screen. The formula used forcomputing the pixel of the mouse cursor is shown in Eq.
(1), (2) and the update interval is set as 0.1 [sec]. W is thewidth of the screen; the benchmark is changed into the
pixel of the mouse cursor at 0.1 [sec] before (Xpre, Ypre).
Further, we imported the blink counter-measure.

x

DC1 DC 2 W＋X pre
8 TLR
2

(1)

if x<0, x=0, and if x>W, x=Wy( DC1 DC 2) H
＋Y pre
4 TLR
2

(2)

if y<0, y=0, and if y>H, y=H
3. Experimental Method
In this study, we tried four experiments in order toevaluate the performance of the mouse cursor control
system which we built by the methods we proposed. Allfour experiments are designed step by step in thefollowing sequence: A) horizontal movements only, B)
horizontal movements and clicking, C) both horizontal
and vertical movements, D) practical application.

Fig. 1. Appearance of FARG

2. Proposed Method

3.1. Experimental Method 1

As we introduced in section 1, our method could
recognize 7 patterns of biological signals. However, thepatients do not have such great m to operate the PC under
their mind except by using support software3.
In this paper, we propose a modified method to move themouse cursor to the vertical direction and horizontal
direction freely by using the EOG signals and the clickby the EMG signals. We set the mouse cursor to click by
a right blink and to double-click by biting. The mouse
cursor was set to move 50 pixels to the right when theeyes moved to the right direction. In the case of theoperation in the sequence of “move to right”, “move toright”, “bite”, the operation performs double-click after
the mouse cursor has moved 100 pixels to the right.
In the modified method proposed, we defined thethresholds used in the basic method for recognizing the

In experiment 1, we set the desktop as shown in Fig. 2.
We asked the subjects, who are 5 healthy males (subject
A, B, C, D, E) in their 20s and 1 female (subject F) in her

Fig. 2. Desktop for experiments 1 and 2.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 521

Mouse Cursor-like Control System

20s, to focus on the center of the screen. Next, they
moved their sight to the first icon 1, on the left side fromthe center and stopped on the icon for 1 sec, then moved
to the next icon and stopped for 1 sec one by one fromicons 1 to 8. After that, they continued moving to the iconon the right side from the center, from icons 9 to 16 on
the right edge of the screen. We measured the time fromfirst moving the mouse cursor until moving to the final
icon 16. Each subject performed the same experiment 3
times.

Fig. 3. Desktop wallpaper for experiments 3.

3.2. Experimental Method 2

or right 1/10 area to the sides of the screen, the mouse
cursor control function paused until a left blink, rightblink or bite were recognized by the EMG signals.

In experiment 2, we used the same desktop as inexperiment 1. We asked the subjects, who are 2 healthy
males (subjects G, H) in their 20s and 1 female (F), tofocus on the center of the screen. Then they moved theirsight to the next icon on the left side from the center andclicked the icons one by one from 1 to 8. After that, they
continued moving to the icon on the right side from thecenter and clicking the icons from 9 to 16 on the rightedge of the screen. We measured the time from starting
the moving mouse cursor until clicking the final icon, 16.
Each subject performed the same experiment 3 times.

4. Results
4.1. Results of Experiment 1
In experiment 1, subjects move their sight to the 16 icons
one by one. The time includes 1 [sec] stopped on eachicon. Everyone finished the experiments at one time, so
it means the correct rate is 100%. The average time of 6
subjects for one task is 87.5 [sec].

3.3. Experimental Method 3

4.2. Results of Experiment 2

In experiment 3, we set the desktop wallpaper as shownin Fig.3 and cleared all icons. Next, we asked subjects G
and H to focus on the ‘start’ point, then to move theirsight to follow the arrows and do their best to finish one
circle for 3 tasks at 5 min rest intervals. The EOG signals
changed and the times used for one task were recorded as
the evaluation standards. The size of the screen was 22
inches with full HD (1920*1080)

Table 1 shows the time taken by 3 subjects in experiment
2. The average time of 3 subjects for one task is 111 [sec].
As in Experiment 1, the influence of a blink caused theresults of subject I to be the worst because the muscle
potential caused unstable ocular potential. In addition, we
consider one of the reasons is that the threshold isdifferent due to individual differences.
Table 1. Time of 3 Subjects in Experiment 2.

3.4. Experimental Method 4

Subject

In experiment 4, we tried to control Google Earth4 asverification experiments using the mouse cursor control
system that we built. We opened Google Earth and put
the map of Japan on the center of the screen. Then we
asked the subject to rotate the globe and put the map ofBrazil on the center of the screen by the click and drop
action of the mouse. Finally, subjects double left clicked
the map of Brazil to zoom in the camera. The same
subjects were asked to try the operations for 3 times in 5
min intervals. When the mouse cursor moved into the left

F

G

H

Task
1
2
3
Ave
1
2
3
Ave
1
2
3
Ave

Time[sec]
143
168
204
172
62
60
52
58
123
87
100
103

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 522

Mingmin Yan, Yu Cheng, Keiko Sakurai, Hiroki Tamura, Koichi Tanno

4.3. Results of Experiment 3
Table 3. Results of experiment 4.

The experiments results of subjects G are shown in
Figure 4. The left-top chart shows the ideal value, and thenext three ones show the actual data of three tasks. From
the results, we can see the influence of a blink wasdecreased, and with subject G, although there were
mistakes that recognized a blink as up in task 3, all of thetasks could finish matching the arrows roughly. Table 2
shows the pixels and time of 2 subjects with the modified
method we proposed.

Subject
G

H

Task

Time [sec]

1
2
3
Ave
1
2
3
Ave

23.2
23.1
38.1
28.1
49.4
52.0
29.3
43.6

Suspended
count
0
0
0
0
1
0
0
0.3

5. Conclusion
In this paper, we introduced a PC control method using a
biological signal measuring device that could be used for
ALS patients. With the proposed method and theexperiments results, it is possible to control a mouse
cursor by moving the eyes and clicking by moving thefacial muscle when using the mouse cursor control
system we built. The patients also can control theapplication as they wish. We validated that the influenceof drift and blink cannot be ignored simply, and those
influences are also the difficult points in our research.
The modified method having both drift and blinkcountermeasures had better performance of mouse cursor
control, and all subjects could control the mouse cursor
for horizontal moving and clicking flexibility.

Fig. 4. Experimental results of subject G.

Table 2. Pixels and T of 2 subjects in experiment 3.
Subject
G

H

Task

Pixels

1
2
3
Ave
1
2
3
Ave

28874
24800
77192
43622
84391
47347
60780
64172

Time
[sec]
44.4
41.0
67.1
50.8
60.7
56.2
57.8
58.2

References
1. R. Barae, L. Boquete, and M.Mazo: “System for assisted
mobilityusing
eyemovements
basedonelectrooculography”, IEEE Transaction on Neural
Systems and Rehabilitation Engineering, vol. 10, no. 4,
2002, pp. 209-218.K. Elissa.
2. H.Tamura, etc.: “Development of the electric wheelchair
hands-free semi-automatic control system using thesurface-electromyogram of facial muscles”, Artificial Lifeand Robotics, Volume 17, Issue 2, pp 300-305, Dec. 2012.
3. M. Yan ， H. Tamura ， K. Tanno: “ A study on gazeestimation system of the horizontal angle using
electrooculogram signals” IEICE Trans on Informationand Systems ： E97-D (9), pp 2330 - 2337, 2014.
4. Google Earth, https://www.google.co.jp/intl/ja/earth/,
accessed on January 20th, 2016.

4.4. Results of Experiment 4
In experiment 4, we asked the subjects to control Google
Earth by using the mouse cursor control system andfollowing the steps in the verification experiments. The
time that a subject took and the suspend count results areshown in Table 3. With subject G, the experiments could
be finished smoothly without any pauses, and withsubject H, the experiments were suspended once in thecase that the mouse cursor moved from the left 1/10 area
to the sides of screen. But in task 3, subject H could finish
the experiments in the same time as did subject G.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 523

A Study on Eyes Tracking Method using Analysis of Electrooculogram Signals

1

Keiko Sakurai 1, Mingmin Yan, 2, Hiroki Tamura2 and Koichi Tanno2

Interdisciplinary Graduate School of Agriculture and Engineering, University of Miyazaki, Japan
2
Faculity of Engineering, University of Miyazaki, Japan1

z3t1401@student.miyazaki-u.ac.jp
http://www.miyazaki-u.ac.jp/

Abstract
Gaze estimation has been an active research and important technique for seriously physically handicapped person tocommunicate. In this paper, we proposed eye tracking method using electrooculogram signals which is small-burden.
However, there is a problem that the resolution is not so high. Our experiments were conducted to investigate theEOG component strongly correlated with changes in eye movements. The experiment result shows the possibility ofeyes tracking method using the analysis of electrooculogram signals.
Keywords: Gaze estimation , Electrooculogram Signal.

Table.1 shows the characteristic of the proposed method
and conventional method. In previous studies[4][5] thepositions of the electrodes were set as plus-channels samewith direction of the eye movements. The horizontal
channel was in charge of horizontal EOG signals and thevertical channel was in charge of vertical EOG signals.
The mainstream of eye movement classification is 4
patterns or 8 patterns using an alternating current (AC)signals conventionally.
We proposed the cross-channel method in order toimprove the accuracy of the EOG signals. Our proposed
technique classified in 4 patterns using AC and DC
signals and this method is able to put the electrodes inplace far from the eyeballs (Table.1). The proposed
technique can say that wearability is good better thanconventional method.

1. Introduction
Gaze estimation has been an active research in the past
years and gaze estimation is an important technique forseriously physically handicapped person who cannot
move their body to communicate. In this paper, we
proposed eye tracking method using electrooculogram(EOG) signals which is widely applied in medical field
because of the merit that proposed method have lowburden of the patients. In eyes movements, a potential
across the cornea and retina exists, and it is the source ofEOG. The eyeball can be modeled by a dipole. There areseveral EOG-based Human-Computer Interface [1][2][3] studies in literature. Investigating possibility ofusage of the EOG for human-computer interface, a
relationship between gaze angle and the EOG isdetermined. However, in-depth studies evoked that slow
changing baseline drift is the difficult point to estimate incontinuous EOG signals and this drift only appears in DC
signals in the circuit. We have already developed EOG
system has the center parameter update technique which
reduces baseline drift by segmentation of the signal [2].
Our proposed system [2][3] is possible to improve thecommunication abilities of those patients who can movetheir neck or/and eye, though there is the problem not tobe so high in resolution.

Table.1. Characteristic of the proposed method
technique analysis

We had already developed eyes input application fordesktop PC with high accuracy gaze estimation[3].

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 524

Keiko Sakurai, Mingmin Yan, Hiroki Tamura, -Koichi Tanno

3.1. Experiment environment

Furthermore, we carried out the large space experiments(the range is -60degrees to 60degrees) and estimate thegaze by the multiple regression analysis using the DC
integral value [6]. Although the regression analysis
results are good results, the narrower range was better. In
this paper, we analyzed DC, AC, DC difference value,
DC integral value for regression analysis and we also
checked if the accuracy was improved than previous
studies [3]. Moreover, we considered the limiting angleof gaze estimation in a wide space ranging from -90
degrees to 90 degrees.

The experiments were designed in order to confirm theeffectiveness of the proposal system. Experimentcondition is Fig.2 and three healthy subjects participatedin this study. We ask the subjects to focus at these targets
by moving the eye only as the sequence as 0, -30, 0, -60,
0, -90, 0, 30, 0, 60, 0, 90 degree. Time zoom for watchingeach target was set as 1 second and repeated 10 times.

2. Measurement System using EOG Signals
In this section, cross-channel EOG measurement
system design is shown [3]. Fig.1 shows the formal
scheme for the acquisition and analysis of the EOG signal
for the control organization and flow of information
through the system. Our proposal system is based on thefive features, 1) Amplifier, 2) Low pass filter for channel
1 and 2, 3) High pass filter for channel 3 and 4, 4) A/Dconvertor, and 5) PC. Our system consists of fiveelectrodes, an A/D-convertor, a personal computer, and amonitor. In order to effectively filter functions, channel 3(and channel 4) is used two amplifiers. The horizontal
signals and the vertical signals could be recorded by bothchannels at the same time which is much easier to analyze
data using double signals.

Fig.2 Experiment Environment

3.2. Extraction method of feature value
We carried out the experiments to make a study thecalculation method of the EOG element with strong
correlation for the change of the eyeball movements. The
feature values are (1) AC, (2) DC, (3) DC-Difference
value, (4) DC-Integral value.
 Feature value of AC
The feature value of AC was assumed to be themaximum value when the AC is more than thethreshold in the right direction and the minimum
value when the less than the threshold in the left
direction (Fig.3 (a)).
 Feature value of DC, DC Difference Value
The feature value of DC is the maximum value andthe minimum value (Fig.3(b)), however it isnecessary to take the difference with a baseline DC
value and the DC value because drift occurs as forthe DC signals. The changing baseline drift makes
difficult to estimate the EOG signals. The baseline isshown by the dashed line in Fig.3(b). DC difference
value(𝐷𝑑𝑖𝑓 ) is express by Eq.(1) and the maximum𝑅𝐿value 𝐷𝑚𝑎𝑥
, 𝐷𝑚𝑎𝑥is as below (Eq.(2), Eq.(3)).
When 𝐷𝑑𝑖𝑓 exceeds a certain threshold value,𝑅𝐿
𝐷𝑚𝑎𝑥
and 𝐷𝑚𝑎𝑥
is taken as the maximum value.

Fig.1 EOG measurement system

3. Method

𝐷𝑑𝑖𝑓 (𝑖) = 𝐷(𝑖) − 𝐷𝑏𝑎𝑠𝑒

In this study, we carried out the experiments by our
proposal EOG system to make a study the calculation
method of the EOG element with strong correlation forthe change of the eyeball movements.

(1)

𝑖𝑓 ∶ 𝐷𝑑𝑖𝑓 (𝑖) > 𝑇ℎ𝑅
𝐷𝑚𝑎𝑥
= 𝑚𝑎𝑥𝐷𝑑𝑖𝑓 (𝑖) ∶ 𝐷𝑑𝑖𝑓 (𝑖)𝑖𝑠 𝐸𝑂𝐺 𝑎𝑐𝑡𝑖𝑣𝑒 𝑜𝑛 𝑅𝑖𝑔ℎ𝑡 𝐷𝑖𝑟𝑒𝑐𝑡𝑖𝑜𝑛 (2)𝐿𝐷𝑚𝑎𝑥= 𝑚𝑖𝑛𝐷𝑑𝑖𝑓 (𝑖): 𝐷𝑑𝑖𝑓 (𝑖)𝑖𝑠 𝐸𝑂𝐺 𝑎𝑐𝑡𝑖𝑣𝑒 𝑜𝑛 𝐿𝑒𝑓𝑡 𝐷𝑖𝑟𝑒𝑐𝑡𝑖𝑜𝑛(3)
𝑖: 𝑡ℎ𝑒 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑟𝑎𝑤 𝐸𝑂𝐺 𝑑𝑎𝑡𝑎

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 525

A Study on Eyes

3.3. Data Analysis

Feature value of DC Integral Value.
DC integral value is the linear weighted sum of the
DC difference value subtracting baseline and we
obtain the value of the eyes position. By taking the
maximum value of the DC integral (XRmax, XLmax) is
stable when feature can be expressed. The dashedline is the value of DC, and solid line is the sum of
the value of DC (Fig.3 (c)). Eq. (4),(5),(6) show DC
integral value. The maximum value XRmax, XLmax is as
below.
𝑋(𝑖) = ∑𝑁
𝑖=1 𝐷𝑑𝑖𝑓 (𝑖): 𝑁 = 20 0

In our previous studies [3][6], we have carried out thegaze estimation in the range from -60 degrees to 60
degrees. Therefore, in this paper we have estimated thegaze from -90 degrees to 90 degrees.
In order to confirm the accuracy of EOG system, we
try regression analysis. We tried two types of regression
analysis. First is the multiple regression analysis as linear
regression analysis, second is the logistic regression
analysis as non-linear regression analysis.
DC was known to have a linear relation to the eyeballangle, so we performed a multiple regression analysis
with the next explanation variable ： AC, DC, DC
integral, DC difference. However, most previous works
were carried out the experiments in the small space suchas desktop PC [3]. We assumed that DC elements might
not be linear shape characteristics in the large space suchas this experiment and performed a nonlinearity logisticregression analysis with an explanation variable same asa multiple regression analysis. We computed thepredicted gaze degree by two type of regression analysis
for each subject. We compared the explanation variables
which is the most suitable variable using EOG in large
space.

(4)𝑅
𝑋𝑚𝑎𝑥= 𝑚𝑎𝑥 𝑋(𝑖) ∶ 𝑋(𝑖)𝑠 𝐸𝑂𝐺 𝑎𝑐𝑡𝑖𝑣𝑒 𝑜𝑓 𝑅𝑖𝑔ℎ𝑡 𝑑𝑖𝑟𝑒𝑐𝑡𝑖𝑜𝑛
(5)
𝐿
𝑋𝑚𝑎𝑥
= 𝑚𝑖𝑛 𝑋(𝑖) : 𝑋(𝑖)𝑖𝑠 𝐸𝑂𝐺 𝑎𝑐𝑡𝑖𝑣𝑒 𝑜𝑓 𝐿𝑒𝑓𝑡 𝑑𝑖𝑟𝑒𝑐𝑡𝑖𝑜𝑛(6)(𝑖 = 0,1,2 … 𝑁)
𝑖: 𝑡ℎ𝑒 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑟𝑎𝑤 𝐸𝑂𝐺 𝑑𝑎𝑡𝑎

4.

(a) Feature value of AC

Experimental Results

In this section, we describe the gaze estimation
results of -30degrees, -60degrees, -90degrees 30degrees,
60degrees and 90degrees. In this experiment, theproposed analysis method is the major analysis items of
the angle of the target which subjects look to clarify thecorrelation between. The result of calculating therespective correlation coefficients R2 are shown in Table
2. All the result over R2 >0.83, that shows the correlationof all analyses. In addition, DC integral value is higher inthe results.

(b) Feature value of DC

Table.2. The Correlation Coefficient of the multiple regression
analysisAC
DC Difference
DC Integral
DC Difference

0.83560.79060.9257

SubjectB

0.84400.90800.9222

SubjectC

0.84520.84510.9064

From the result of the multiple regression analysis,
we conducted the logistic-regression analysis about DC
integral value. In addition, we calculated the average
error between the predicted value and the true value
obtained by multiple regression analysis, logistic
regression analysis (Fig.4).

(c) Feature value of DC integral Value
Fig. 3.

SubjectA

Feature value of proposed method© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 526

Keiko Sakurai, Mingmin Yan, Hiroki Tamura, -Koichi Tanno

5. Conclusion

Fig. 4.

In this paper, we conducted experiments to examine
EOG elements that have a strong correlation with eye
movement changes and from the experiments, we foundthat the following three points in this study.
1) From Table 2, DC Integral value is the most effective
EOG signals to gaze estimate.
2) In the gaze movement in large space, the right andleft 60 degrees is the best success rate.
3) DC integration is possible to classify 5 patterns by
combining linear and nonlinear regression analysis
results.
When gaze estimation is performed in a large space,
we understood that the gaze estimation of 60 degrees on
the left and right is the most stable. Therefore, by
arranging the object at 60 degrees, our system is possible
to perform input of 5 patterns with line of sight. By
setting the lower limit of 60 degrees on each side, it iseasier to use with success rate of Subject A: 85%, Subject
B: 100%, Subject C: 79% without calibration. Therefore,
the gaze estimation system of this study is a possibility to
use as the application of the communication tool ofpatients with brain disease.
References

The average error

Fig.4 shows that 60 degrees and -60 degrees were thesmall average error among each angle at left and right
direction. And the gaze estimation by nonlinear analysis
is better for angles bigger than 60degrees or less than 60degrees. The results of all data from -90 degrees to 90
degrees, the multiple regression analysis were 19.5 andlogistic regression analysis was 16.2. Fig 4 shows thatwhether the relation of DC element and eyeball angle islinear or not depends on eyeball angle. Therefore, weestablished the boundary line (DC Integral Value:±15V)
and separated a linear/nonlinearity and combined theresults of the logistic-regression analysis and the multiple
regression analysis (Fig.4 and Fig.5).
The eyeball angle of the 60degrees and -60 degrees
are most easy to judge by EOG and the success rate is
88% without considering the individual difference of 3
subjects. At 90 degrees and -90 degrees, the judgment isdifficult because individual is wide and the value of EOG
tends to be saturated. About 30 degrees, -30 degrees, twoof three people were 60% of success rates. One of thecauses having low success rate is the influence of theindividual differences. Based on 60 degrees, our analysis
method can be classified as pattern 1) center (Fig.5),
pattern 2) center to 60 degrees range, pattern 3) 60 degree
over, pattern 4) center to -60 degree range and pattern 5)-60 degree less. Our analysis method can be said that 5
patterns are the appropriate number of judgment patterns.

Fig. 5.

1. S. Venkataramanan, P. Prabhat, S. R. Choudhury, H. B.
Nemade, and J. S. Sahambi, Biomedical instrumentation
based on Electrooculogram (EOG) signal processing andapplication to a hospital alarm system, in Proceedings ofthe 2nd IEEE International Conference on Intelligent
Sensing and Information Processing (Chennai, India,
2005), pp. 535–540.
2. H. Tamura, M. Yan, M. Miyashita, T. Manabe, K. Tanno,
and Y. Fuse, Development of Mouse Cursor Control
System using DC and AC Elements of Electrooculogram
Signals and its Applications, International Journal ofIntelligent Computing in Medical Sciences & Image
Processing, Vol 5 No1, 2013, pp.3-15.
3. M. Yan, H. Tamura, and K. Tanno, A Study on Gaze
Estimation
Systemusing
Cross-Channels
Electrooculogram Signals, in Proceedings of The IMECS
2014(Hong Kong,2014), pp112-116.
4. A. B. Usakli, S. Gurkan, F. Aloise, G. Vecchiato, and F.
Babiloni, On the Use of Electrooculogram for Efficient
Human Computer Interfaces, Computational Intelligence
and Neuroscience 2010, Article ID 135629.
5. Zhao Lv, Xiao-Pei Wu, Mi Li, Development of a human
computer Interface system using EOG, Health ,Vol.1 No.1,
June 2009, PP. 39-46.
6. Keiko Sakurai , Mingmin Yan, Kazuhiko Inami, et al, A
study on human interface system using the direction ofeyes and face, Artificial Life and Robotics, December 2015,
Volume 20, Issue 4, pp 291-298.

The relation between the predicted value and the DC
Integral value© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 527

Development of Diagnosis Evaluation System of Facial Nerve Paralysis Using sEMG
Shogo Okazaki†, Misaki Syoichizono, Hiroki Tamura‡

Faculty of Engineering, University of Miyazaki,
1-1,Gakuen-kibanadai-nishi, Miyazaki-shi, Japan
Takahiro Nakashima*, Eiji Kato*, Tetsuya Tono**Department of Otolaryngology, Head & Neck surgery, Faculty of Medicine, University of Miyazaki,
1-1,Gakuen-kibanadai-nishi, Miyazaki-shi, Japan
E-mail: hc10010@student.miyazaki-u.ac.jp†, htamura@cc.miyazaki-u.ac.jp‡

Abstract
In Japan, diagnostic of facial nerve paralysis is mainly used the electroneuronography (ENoG) method. Previous ourresearch showed the certain correlation between ENoG and surface-electromyogram (s-EMG) data. Therefore, wedeveloped the evaluation system of facial nerve paralysis using s-EMG. We compared with our developed softwareand manual by experimental person. Form simulation result, our developed software showed the correlation
coefficient (R2) between ENoG is 0.68.
Keywords: Facial Nerve Paralysis, Surface Electromyogram, Electroneuronography, Multiple Regression Analysis

1. Introduction

2.

Although cause of facial nerve paralysis is unclear, thecause is believed that is one of the causes of viral
infection. The main treatments are medication andsurgical operation when the symptom is serious.
Electroneuronography (abbr. ENoG) test is one of facial
nerve diagnosis methods currently in using. However, theENoG test can’t carry out within one week after onset.
For quantitatively diagnose, we focused on the surfaceelectromyogram (abbr. s-EMG) 1. Previous our research,
we found that there is a correlation between the ENoG
value and features values of the s-EMG. We developed
software to extract the features of s-EMG. In this paper,
we compared with the results using software and manual
results by correlation coefficient R2 of ENoG value andfeature values of s-EMG. In addition, we confirmed how
much the manual method can be reproduced in thesoftware method.

Previous ResearchPrevious our research, we investigated the relationshipbetween the ENoG value and the feature values obtained
from the s-EMG. Fig.1 shows the attachment position of
the electrodes. In the experiment, the following two
patterns of facial muscle activity performed five times,
and the generated s-EMG measured.
· Motion the mouth horizontally (Motion A)· Motion closing the eyes strongly (Motion B)
We measured 19 times of facial nerve paralysis: 6
subjects measured twice. Fig.2 shows an example of thes-EMG measured, and sampling frequency is 1000Hz.
For these s-EMG, we performed each of frequency
analysis by Fast-Fourier-Transform (abbr. FFT) andintegrated calculation of s-EMG, and the difference
between ch1 and ch2 obtained as the feature values. The© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 528

Shogo Okazaki, Misaki Syoichizono, Hiroki Tamura, Takahiro Nakashima, Eiji Kato, Tetsuya Tono

reason for taking the difference is that in the Ref.2 asignificant difference in the frequency of the s-EMG
confirmed on the affected side and the healthy side. As aresult, we obtained four kind of feature values. Next, weinvestigated the relationship between ENoG value andfeature values and parameters that strongly influence
using Adaptive – Network – Based – Fuzzy – Inference System (abbr. ANFIS, ANFIS is one of fuzzy inference).
Based on the results obtained from ANFIS, we prepared
the three rules (shown in Table.1). Here, freA isdifference between ch1 and ch2 frequency of the motion
A, integA is difference between ch1 and ch2 integrated
value of s-EMG of the motion A, and integB is difference
between ch1 and ch2 integrated value of s-EMG of the
motion B. We prepared three groups that divided thefeature values by three rules. For each of the three groups,
multiple regression analysis performed with the feature
values as the explanatory variables and the ENoG values
as the objective variable. The predicted of ENoG value
was calculated from the result of multiple regression
analysis, and feature values confirmed the correlation
with ENoG values. As a result, R2 was 0.79, and a strong
correlation was found. This value set as the result of themanual method, and compare results between softwareand manual method.

Fig.1. Attachment position of electrodes.

Table 1. Three rules.
frequency
Motion A[Hz]
Rule 1

freA≧14

Rule 2

freA<15

Rule 3freA≦30

integrated
Motion A[mV]
integA<15orintegA≧42
integA<15orinteg≧42

Fig.2. Example of measured s-EMG (Motion B)

integrated
Motion B[mV]

3. Algorithm
We developed software that implements the methods
for extracting feature values used in previous ourresearch. Fig.3 shows the flow of processing. Thissection explains the processing of the developed software.

integB≦34

3.1. Search of Activity Start Position
In this subsection explain the method of searching forthe position on the active part of N times. Y(n) (in Fig.3)
is the integrated value of absolute value of X(t) for every
100 data. When for 2 seconds from start of s-EMG
measurement, motion had set to do nothing. There B (inFig.3) is threshold, it set a twice of average of integration
values for 1 second from measurement start. If Y(n) are
greater than threshold B and more than 7 times for 10 data,

integB≧30© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 529

Development of Diagnosis Evaluation

activity start position P(N) (in Fig.3) is set the position at
initially beyond threshold B.

In this procedure performed on the sEMG data of ch1
and ch2 to extract the frequency. Then, difference infrequency of ch1 and ch2 obtained, and took out as afeature value. Since there are two kinds of motion, thefeature value obtained by frequency analysis is two.

Fig.4. Result of frequency analysis and moving average ofspectrum.

3.3. Integrated s-EMG
Integrated values calculated using s-EMG data from
P(N) and the number of FFT data. Then, as the frequency
case, the difference between the integrated values of ch1
and ch2 obtained, and two feature values obtained.
Finally, a total of four feature values of frequency andintegrated value extracted and used as parameters forcalculating the predicted value of ENoG value.

Fig.3. Processing flow of developed software.

3.4. Execution Screen of Software

3.2 Frequency Analysis

Execution screen of developed out software are show
Fig.5, Fig.6, and Fig.7. Fig.5 is reading files of s-EMG
measurement. Fig.6 shows the results of position ofactivity part. These positions can also change bymanually. Fig.7 shows the result of ENoG predicted value
and condition of facial nerve paralysis diagnosed.
Method of diagnosis will described later.

In this subsection, frequency analysis by FFT performedon the activity parts of the s-EMG. First, prior tofrequency analysis, commercial power supply noise
removed by a notch filter, and the frequency band of
62.5Hz cut. After that, FFT performed with 1024 s-EMG
data from the starting point P(N) of the activity parts.
Next, moving average performed on the spectrum
obtained by FFT. The moving average performed every
10 spectrum. Then, the peak value of the spectrum after
moving average searched, and the frequency at that time
extracted. Fig.4 shows an example of the result. The solidline shows the spectrum obtained by FFT and dash line
shows the result of moving average on the spectrum.

4. Analysis
In the previous section, we presented the method of
calculating the feature values. We divided the obtainedfeature velues according to the rules in Table.1, andmultiple regression analysis performed for each divided
data groups. After that, we obtain the predicted value of© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 530

Shogo Okazaki, Misaki Syoichizono, Hiroki Tamura, Takahiro Nakashima, Eiji Kato, Tetsuya Tono

6. Conclusion
Predicted values using software, the result of R2 = 0.68
was obtained. Although the correlation coefficientdecreased as compared with R2 = 0.78 when manual
analysis performed in the previous research, we
confirmed a strong correlation still observed with the
ENoG value. However, there were two results that the
ENoG predicted value of serious patients weresignificantly different from the actual ENoG value. From
now on, it is necessary to predict the ENoG value for sEMG data of newly measured facial nerve palsy patients,
and investigate whether the correlation between thefeature values of s-EMG used this time and the ENoG
value is reproducible. Also, as necessary, we intend to
update the coefficients of the regression equation andadjust the rules in Table.1.

Fig.5. Execution screen for reading files.

Fig.6. Execution screen to show the results
search position of activity parts.

Fig.7. Execution screen to show the results of ENoG
predicted value and condition of facial nerve paralysis
diagnosed .

Fig.8. Correlation between the predicted value calculated fromthe output feature quantities and the ENoG values.

EnoG, and we compare the predicted value of EnoG withactual EnoG. Then, medical condition diagnose using
predicted value of ENoG. Diagnostic criteria for each
symptom are mild if ENoG is 40 or more, moderate if
ENoG is greater than 10 and less than 40, and severe if
ENoG is 10 or less.

References
1.

5. Result2.

Fig.8 shows vertical axis is for the predicted value of
ENoG value, and horizontal axis is for the ENoG
value.The Area 1, Area 2, and Area 3 are areas of each ofstates severe, moderate and mild. From the figure, thecorrelation coefficient R2 was 0.68. The success rate
when diagnosing the condition using the predicted ENoG
was 78.95%.

Kohei Nagatomo, Analysis of Facial Nerve
Paralysis Using s-EMG and Movement of the Facial
Muscles During Exercise, FACIAL NERVERESEARCH JAPAN, 33, 2013, 95-96p (Japanese)
Katsuo Nishimoto, Deliberation of measure of
peripheral facial palsy -Especially using s_EMG-,
Japanese
Physical
Therapy
Association,
19(Supplement), 1992-3, 355p

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 531High CMRR and Wideband Current Feedback
Instrumentation Amplifier Using Current Conveyors
Shota Mago, Hiroki Tamura, Koichi Tanno

University of Miyazaki
1-1, Gakuen Kibanadai Nishi, Miyazaki,889-2192, Japantanno@cc.miyazaki-u.ac.jp

Abstract
This paper presents a high CMRR and wideband current feedback Instrumentation Amplifier (IA). The proposed IA
architecture consists of Fully Balanced Differential Difference Amplifier (FBDDA) and Differential Difference
Amplifier (DDA) based on 2nd generation current conveyor (CCII) with a buffer. From the simulation results
evaluated by HSPICE, the proposed IA exhibits average CMRR was 109.3 dB higher than the conventional one.
Furthermore, the proposed IA has higher closed-loop gain over a larger bandwidth than corresponding voltage
feedback.
Keywords: Instrumentation Amplifier, CMRR, Current Conveyor, Differential Difference Amplifier, Current
Feedback

1. Introduction
Instrumentation Amplifier (IA) with high CMRR, highinput impedance, and configurable differential gain
characteristics is used in many application areas, such as
medical instrumentation, the read-out circuit ofbiosensors, signal processing, and data acquisition [1].
Fig.1 shows the conventional IA, which consists of 3
operational amplifiers and 7 resistors. Each resistors
require the condition of R1 = R2, R3 = R4, R5 = R6. In thiscase, the output voltage is described by Eq. (1).

Vout 

R5 
2R 
1  1 V1  V2 
R3 
RG 

Fig.1. Conventional IA

To overcome this problem, we have proposed an IA
architecture which consists of Fully Balanced
Differential Difference Amplifier (FBDDA) andDifferential Difference Amplifier (DDA)[4]. Its
CMRR is extremely higher than that of the conventional
IA under the condition of resistor mismatch [3].
On the other hand, in a signal processing system, oneof the considerable characteristics is the bandwidth.

(1)

However, CMRR, especially common-mode gain (Ac) of
this IA is highly dependent on strict resistor matching [2].
For example, if only 0.1% mismatch between R5 / R3 andR6 / R4, CMRR deteriorates from ideal (infinity) to 66 dB,
on condition that differential gain is 0 dB [5]. Thus,
resistor mismatches are serious problem for IA.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 532

Shota Mago, Hiroki Tamura, Koichi Tanno

Especially, in the biological signal processing, thechopper stabilization technique is often employed foravoiding 1/f noise in MOS devices [4]. In this kind of
signal processing, wide bandwidth circuits are required
strongly. However, the conventional IA exhibits a narrow
bandwidth that is highly dependent on the closed-loop
gain, due to the fixed gain bandwidth product.
In this paper, high CMRR and wideband IA, which
consists of FBDDA and DDA using current feedback
technique, is presented. The proposed IA has advantages
that common-mode gain is insensitive to the resistor
mismatches and bandwidth is wider than theconventional one and is independent of gain. The
proposed IA is evaluated through HSPICE with the set ofthe parameters of 0.6m CMOS process. In this paper,
we report the detailed simulation results.

Fig.2. Block Diagram of Voltage Feedback

2. Voltage Feedback and Current Feedback
Voltage feedback is majority feedback method for theconventional IA. Fig.2 shows the block diagram ofvoltage feedback. A(s) is the open-loop gain as a functionof frequency, β is a feedback factor which is amount offeedback from output. Fig.3. represents frequency
response of Fig.2. Fig.3 indicates that if we get higher
closed-loop gain reducing β, closed-loop bandwidth will
be narrow caused by constant Gain Bandwidth Product(GBP). Therefore, IA with voltage feedback is notsuitable for implementation of IA with the chopper
stabilization technique and high gain.
Fig. 4 shows the block diagram of current feedback.
Z(S) is the open-loop trans-impedance gain as a functionof frequency, gin is a trans-conductance which converts
input voltage to current, gf is a trans-conductance which
feedback output signal as current. Fig.5 denotes typicalfrequency response of Fig.4. As long as gf is fixed, thebandwidth is constant, even if the closed-loop gain isvaried by changing gin [6]. Therefore, current feedback
has wider bandwidth than voltage feedback, and its
bandwidth is independent of the closed-loop gain.
Therefore, the current feedback is suitable for high gain
IA with the chopper stabilization technique.

Fig.3. Frequency Response of Voltage Feedback

Fig.4. Block Diagram of Current Feedback

Fig.5. Frequency Response of Current Feedback
stage and 2nd stage shown in Fig.6. 1st and 2nd stages aredesigned based on FBDDA and DDA respectively, andFBDDA and DDA are realized by using 2nd generation
Current Conveyors (CCII) and buffers for
implementation of current feedback. As for CCII,

3. Proposed Instrumentation AmplifierFig. 6 shows the proposed architecture for the high
CMRR and wideband IA. The proposed IA consists of 1st© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 533

High CMRR and Wideband

terminal Yi (i=1, 2, 3) exhibits an infinite input
impedance in ideal, the voltage at Xi (i=1, 2, 3) follows
that applied to Yi, X exhibits a zero input impedance inideal. The current supplied to X is conveyed to the highimpedance output terminal Zi [7]. Theoretically, eachresistors of the proposed IA no require resistor matches
for high CMRR. Furthermore, the proposed IA has
higher closed-loop gain over a larger bandwidth thancorresponding conventional IA.
The output voltage of 1st stage (VA and VB) is given as
follows in the case of R1 = R2.

VA 

2R 
1
1  1 V1  V2 2RG 

Fig.6. Proposed IA

(2)

2R 
1
VB   1  1 V1  V2 
2
RG 

(3)

From Eq. (2) and (3), common-mode signal can berejected at VA and VB before 2nd stage. In addition, output
voltage of 2nd stage is given byR 
Vout  1  4 V A  VB 
R3 


(4)

Then, from Eq. (2), (3) and (4), we can derive Vout asfollows.


R 
2R 
Vout  1  4 1  1 V1  V2 
R3 
RG 


Fig.7. Histogram of CMRR

(5)
4.1. CMRR

In mismatch condition (R1 ≠ R2), Vout,m can be given byR 
R  R2 V1  V2 
Vout ,m  1  4 1  1
R3 
RG 


(6)

Eq. (6) represents common mode signal is rejected even
if the mismatches in all resistors are occurred. Thus, theproposed IA has much higher CMRR than theconventional one.
4.

Simulation Results

In this chapter, simulation results of CMRR andfrequency response are shown. The conventional IA andproposed IA were evaluated using HSPICE with 1P 3M
0.6μm CMOS process.

For comparison of conventional and proposed IA, wehave simulated 300 times with ±30% all resistor
mismatches by Monte Carlo analysis. The simulation
result is shown in Fig.7. To evaluate the effect of AC
which is more sensitive to resitor mismatch thandifferential gain (AD), AD of both IAs are set 0 dB for easy
estimation.
From Fig. 7, average CMRR of conventional andproposed IA are 24.9 dB and 134.2 dB respectively. The
average CMRR of the proposed IA is 109.3 dB higher
than conventional one.
4.2. Frequency Response
Fig.8 and Fig.9 show the frequency response of theconventional IA and proposed IA, respectively under thecondition that the differential gain of both IAs are varied© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 534

Shota Mago, Hiroki Tamura, Koichi Tanno

from 12 dB to 20 dB by changing RG. As closed-loop gainof the conventional IA is getting higher, the cut-off
frequency is getting lower. On the other hand, the cut-off
frequency of the proposed IA is constant, regardless ofchange of the closed-loop gain. Therefore, proposed IA
is superior to conventional IA in terms of bandwidth.
Lastly, the detailed simulation results are listed in Table
1. From these results, the performance of the proposed
IA is much better than that of conventional one.
5.

Fig.8. Frequency Response of Convention IA

Conclusion

In this paper, high CMRR and wideband current
feedback instrumentation amplifier using CCII have beenpresented. The CMRR and frequency response of
conventional and proposed IA were evaluated by
HSPICE. As a result, we confirmed the proposed IA has
109.3 dB higher CMRR than the conventional one under
the resistor mismatches. Moreover, it was verified
proposed IA has higher closed-loop gain over a larger
bandwidth than corresponding conventional IA.
The evaluation through the actual fabrication of the
LSI is the future work.

Fig.9. Frequency Response of Proposed IA

Acknowledgements

Table 1. Summary of the Simulation Results.

This work is supported by VLSI Design and Education
Center (VDEC), the University of Tokyo in collaboration
with Synopsys, Inc. and Cadence Design, Inc.

Item

Conventional IA

Proposed IA

AC analysis
Cut-off Frequency(Differential Gain = 20 dB)
Unity Gain Frequency
Power Consumption

References
1. Y. H. Ghallab, W. Badawy, B. J. Maundy, “A Novel
Current-Mode Instrumentation Amplifier Based on
Operational Floating Current Conveyor,” IEEE Trans. On
Instrumentation and Measurements, Vol. 54, No. 5, pp.
1941-1948, 2005.
2. J. Szynowski, “CMRR analysis in instrumentation
amplifiers,” Electronics Letters, vol. 19, no. 14, pp. 547549, 1983.
3. Z. Abidin, K. Tanno, S. Mago, H. Tamura, “LowCommon-Mode
Gain
Instrumentation
AmplifierArchitecture Insensitive to Resistor Mismatches,” Int.
Journal of Electrical and Computer Engineering, pp. 1-7,
May 2013.
4. Z. Abidin, K. Tanno, S. Mago, H. Tamura, “Novel
Instrumentation Amplifier Architectures Insensitive to
Resistor Mismatches and Offset Voltage for Biological

Average CMRR
(Differential Gain = 0dB)

750 kHz

7.8 MHz

5 MHz
16.5 MHz
2.6 mW
1.6 mW
Monte Carlo analysis
24.9 dB

134.2 dB

Signal Processing,” proc. of IEEE Int. Symp. on MultipleValued Logic, pp. 194-199, May 2016.
5. B. Baker, “Understanding CMR and instrumentation
amplifiers,” EDN, pg 14, Nov 26 2014.
6. G. D. Cataldo, A. D. Grasso, S. Pennisi, “Two CMOS
Current Feedback Operational Amplifiers,” IEEE Trans.
on Circuits and Systems -2: Express Briefs, vol. 54, no. 11,
pp. 944-948, Nov. 2007.
7. A. S. Sedra, G. W. Roberts, F. Gohh, “The Current
Conveyor: History, Progress and New Results,” IEE
Proceedings, vol. 137, Pt. G, no. 2, Apr, 1990.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 535

Voltage Rectifier Circuit with Voltage Doubler Using New Active Diode
Masayuki Uchihara, Hiroki Tamura, Koichi Tanno†

University of Miyazaki, 1-1 Gakuenkibanadai-Nishi,
Miyazaki City, Miyazaki, Japan
E-mail: tanno@cc.miyazaki-u.ac.jp†

Abstract
In this paper, voltage rectifier circuit with voltage doubler using new Active Diode (AD) is proposed for battery
less biological signal measurement system using smartphone. Firstly, we propose the new AD which consists of
AD core circuit and Bulk Regulation Transistor (BRT). The advantage of proposed AD (ADB) is insensitive to thethreshold voltage of MOSFETs and very useful for low power and low voltage operation. Next, the voltage rectifier
circuit with voltage doubler using the ADB is presented. Thanks to the good performance of the ADB, the designed
rectifier could operate as theory by only the sin waves from the earphone terminal of smartphone. The ADB were
fabricated actually using 1-P 3-M 0.6- m CMOS process. The detailed simulation and measurement results arereported in this paper.
Keywords: Active diode, Bulk regulation transistor, Voltage rectifier, Voltage doubler

1. Introduction
Recently, healthcare systems using mobile devices
receive attention to maintain personal health condition.
In the case of realizing this kind of systems, additional
sensor to measure biological signals for mobile devices
are necessary. However, it is difficult to use justconnecting sensor to mobile devices because theelectrical characteristics of sensors are different
depending on sensors. In order to overcome the problem,
we have proposed the sensor conditioner IC (SCIS) for
mobile devices such as smartphone. Fig. 1 shows thearchitecture of the proposed SCIC. In SCIC, we focus
on the earphone-mic terminal, which is used for
input-output interface and power supply to SCIC. SCIC
consists of amplifier which is to amplify the signal fromthe additional sensor, filter which eliminates noises and
AC/DC converter for supplying DC power to theamplifier and filter. In many cases, this kind of circuits,

analog-to-digital (A/D) converter is often necessary;
however, A/D converter built in mobile devices foranalog input from earphone-mic terminal is used in the
proposed SCIC.
The battery less SCIC is very important for mobile
devices because of light weight and small package.
Therefore, in the proposed SCIC, we employed the
AC/DC converter as mentioned previously. The AC/DC
converter converts from AC signal, which is generated
by application program on the mobile devices, to DC
voltage. The DC voltage is used for supplying the DC
sources for operating the amplifier and filter in SCIC. In
the past, some AC/DC converters were proposed. In
these circuits, some diodes were used for voltage
rectifiers. However, a threshold voltage of the diodecannot be ignored because amplitude of the AC signal isvery small. In order to overcome this problem, voltage
rectifier using Active Diode (AD), which has ideal
diode characteristic, were proposed [1] [2]. The circuit© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 536

Masayuki Uchihara, Hiroki Tamura, Koichi Tanno

is used a Bulk Regulation Transistor (BRT) for
eliminating the dead zone. However, because BRT has atiming at which the back-gate potential becomesindefinite, there is the problem that the AD does notcorrectly operate at low input voltage.
In this paper, we propose a new AD. The proposed AD
is very simple circuit which adds an inverter toconventional AD. Furthermore, we propose new voltage
rectifier circuit with voltage doubler using the proposed
AD. The proposed AD is evaluated using HSPICE andactual fabrication based on 0.6 m standard CMOS
process. We report the detail simulation andexperimental results in this paper.

M1 to drain terminal of M1 without flowing theback-gate current.
(ii) In the case of VA ≥ VB
On condition that VA≥ VB, A and B are the sourceand drain terminals M1, respectively. In this case, M1
turns OFF because the output of comparator is high.

Moreover, M2 and M3 turn ON and OFF, respectively
because VA ≥ VB. As the results, the back-gate

terminal of M1 is connected to the source terminal. In
this case, the current from Vin doesn’t flow from thesource terminal of M1 to drain terminal of M1 neither isthe back-gate current.
In the above explanation, threshold voltage of
MOSFET is ignored. If threshold voltage is consideredin the circuit operation, M2 and M3 cannot turn ON or
OFF immediately because Vin, which is a sin wave,

Fig. 1 Architect of sensor conditioner IC(SC-IC)

gradually increases or decreases. Therefore, M2 and M3
turn ON or OFF halfway, as the results, the

2. Conventional AD with BRT

conventional circuit has a dead band until Vin reaches

A schematic of the conventional AD with BRT isshown in Fig. 2 [3] [4]. The gate, drain and source
terminals of the MOSFET in AD (M1) are connected tothe output terminal of the comparator, the positive phase
input terminal, and the negative phase input terminal,
respectively. Output of comparator in AD makes
MOSFET ON or OFF state. At the same time, BRT
makes back-gate terminal of M1 connect source terminal,
therefore BRT prevent the back-gate current fromflowing. When VA and VB are the potential of theterminals A and B, respectively, the operation of thecircuit of Fig. 2 is as follows.

the threshold voltage.

Fig. 2 Conventional ADB(i) In the case of VA < VB
On condition that VA < VB, A and B are the drain andsource terminals of M1, respectively. In this case, M1
turns ON due to the output of the comparator are low.
And also, M2 and M3 turn OFF and ON, respectively
because VA < VB. As the results, the back-gate terminalof M1 is connected to the source terminal. In this way,
the current from Vin flows from the source terminal of© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 537

Voltage Rectifier Circuit with

3.

Proposed AD with BRT

The schematic of the proposed AD with BRT is shownin Fig. 3. The difference between the proposed ADB
and conventional circuit is the control signal for BRT.
In the conventional circuit, Vin, which is a sine wave
and gradually increase or decrease signal, is directly
used as the controlling the BRT, therefore, the BRT has
dead band. In the proposed ADB, the output signal of
the comparator, which is digital signal such as Hi(VDD) or Low (GND), is used as the control signal for
BRT. Therefore, the dead band can be omitted, so that
we can overcome the problem.

Fig. 4 Simulation and experiment results ofthe conventional ADB

Fig. 5 Simulation and experimental results potential of
the proposed ADB

Fig. 3 Proposed ADB

5. Application to AC-DC Converter Using ADB
In this chapter, we explain AC-DC converter using
the ADB. Fig. 6 shows the voltage doubler half-wave
rectifier circuit, which is widely used. However, thiscircuits includes diodes, therefore, the problem
described in the previous (problem of the thresholdvoltage and dead band of diode) is remained. Fig. 7
shows the proposed AC-DC converter, which is
replaced by the proposed ADB instead of diodes andexpanded to bipolar (plus and minus) supply voltage. In
order to confirm the usefulness of the proposed AC-DC
converter, the circuits were evaluated through HSPICE
with 0.6 m CMOS device parameters.
Fig. 8 shows the output voltage at each output load.
From Fig. 8, the desired voltage (2.6V) can be obtained
from about 10 k . Fig. 9 shows the power efficiency ateach output load. As the output resistance increase, theimpedance of the comparator apparently decrease andthe total amount of current consumed by the comparator
becomes larger than the amount of current consumed by
the load. Therefore, the power efficiency decreases from50 k and the value is approximately 40%. Fig. 10shows the results of the mask layout of the proposed

4. Simulation and Experiment Result
The conventional and proposed ADB were fabricatedusing 0.6 m standard CMOS process. Fig. 4 shows
both the simulation and experimental results of theconventional ADB. From Fig. 4 it can be confirmedthat the back-gate voltage of M1 is an indefinite value.
Fig. 5 shows simulation and experimental results ofthe proposed ADB. When the change of the back-gate
terminal of M1 was confirmed in the range of Vin from
-2 [V] to 2 [V], a desired voltage characteristic was
obtained. Therefore we could confirm that the
proposed ADB operates as the AD without dead band.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 538

Masayuki Uchihara, Hiroki Tamura, Koichi Tanno

AC-DC converter. The estimated chip area is 310
m × 650 m.

Fig. 6 General Voltage doubler half-wave rectifierFig. 10 Doubled voltage half-wave rectifier circuit layout
using ADB

6. Conclusion
In this paper, we have proposed a novel active diode(ADB) firstly. As a result of pointing out the problem
about the indefinite value of the back-gate potential in
the conventional active diode were improved. The
improvement was confirmed by HSPICE simulation andexperiment using the actual chip fabricated with 0.6um
CMOS process. Next, AC-DC converter using the
proposed ADB for SCIC using mobile devices has been
proposed and designed. The circuits were evaluatedthrough HSPICE. From the simulation results, we could
obtain approximately 70% efficiency up to the load of
40 kΩ. For future work, the actual chip fabrication of
the sensor conditioner ICs and its evaluation.

Fig. 7 The proposed AC-DC Converter

References
1. Y. Rao and David P. Arnold, An AC/DC Voltage
Doubler with Configurable Power Supply Schemes for
Vibrational Energy Harvesting (IEEE Applied PowerElectronics Conference and Exposition, 2013), pp.
2844-2851
2. E. Dallago et al, Active Autonomous AC-DC Converter
for Piezoelectric Energy Scavenging Systems, (IEEE
Custom Integrated
Circuit Conference, 2008)
pp.555-558.
3. C. Peters and J. Handwerker, A Sub-500mV Highly
Efficient Active Rectifier for Energy Harvesting
Applications (IEEE Trans. Circuits Syst. I, Reg. papers,
vol.58, no.7, 2011), pp.1542-1549
4. D.Maurath, C. Peters, M. Ortmanns, and Y. Manoli,
Highly Efficient Integrated Rectifier and Voltage
Boosting Circuits for Energy Harvesting Applications,(Advances in Radio Science, vol.6, 2008), pp.219-225

Fig. 8 Output voltage at each output load

Fig. 9 Power efficiency at each output load

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 539

Towards Unified Human-Robotic Societies
Peter Sapaty

Institute of Mathematical Machines and Systems,
National Academy of Sciences, Kiev, Ukraine
Masanori Sugisaka

ALife Robotics Corporation Ltd., Oita, Japan
Takao Ito

Department of Engineering, Hiroshima University, Japan
Email: peter.sapaty@gmail.com, icarob@alife-robotics.co.jp, itotakao@hiroshima-u.ac.jp
http://alife-robotics.co.jp/

Abstract
Large numbers of robotic facilities have been accumulated worldwide, but existing robots still remain specialized
devices rather than intelligent collaborators for humans. To integrate massive robotics into human societies, more
universal approaches are needed. Higher-level, semantic, model supported by special language SGL is described
expressing operations and decisions in distributed spaces in a compact form, with traditional system management
shifted to automatic language interpretation. Communicating SGL interpreters associated with humans and robots
form goal-driven teams under unified control, with corresponding examples presented.
Keywords: world dynamics, human-robotic systems, spatial grasp technology, networked language interpretation,
self-evolving patterns

with humans and between themselves under unified
control.
The paper briefs a high level formalism andsupporting technology that can express operations andtop decisions in physical, virtual and executive
environments regardless of who (humans) or what(robots) should perform them, and in which quantities.
This can allow us to make implementation in dynamic
environments where manned and/or unmanned
resources may not be known a priori but rather definedat runtime, depending on circumstances.

1. Introduction
The world has changed dramatically for the last decade,with numerous conflicts and crises includinginternational terrorism, ethnic, religious and military
conflicts, endless floods of refugees, economy collapses
too. To withstand this dynamics, new ideologies andsystem management technologies are desperately
needed. Of particular effectiveness may be thoseallowing for seamless embedment of massive robotics
into human societies, with robots taking care ofdangerous and critical situations, acting cooperatively

2.

Spatial Grasp Technology (SGT)© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 540

Peter Sapaty, Masanori Sugisaka, Takao Ito

Key ideas of the developed technology and its possible
implementation will be revealed in brief.

operating over spatially scattered data or other, lower
level, distributed systems of arbitrary natures.

2.1. Self-evolving patterns

2.3. Networked SGL interpreter

Within SGT, a high-level scenario for any task in a
distributed world is represented as active self-evolving
pattern rather than traditional program, sequential orparallel, inheriting holistic and gestalt [1-2] ideas rather
than of communicating agents [3]. This also reflects
integral style of human thinking and brain activity [4, 5]
when perceiving images as a whole, treating parts andtheir sense within this whole rather than vice versa.
The self-evolving pattern, expressing top semantics
and key decisions of the problem to be solved andstarting from any point, spatially propagates, grows,
replicates, modifies, covers, interlinks and matches theworld. It creates distributed operational infrastructures
throughout the space covered, with final results retainedin the environment or returned as high level knowledge
to the starting point, as shown in Fig. 1.

The interpreter (its architecture stemming from [7])consists of a number of specialized modules handlingand sharing specific data structures. A backbone of the
distributed interpreter is its spatial track systemproviding global awareness and automatic C2 overmultiple distributed processes, also creating andmanaging distributed information and control resources.
The distributed SGL interpreter may have any numberof nodes, up to millions even billions, distributed
worldwide. It can operate with dynamic and changeable
topology with varying number of interacting nodes.

Fig. 2. SGL recursive syntax.

3. Elementary Example

Fig. 1. Spatial pattern growth & coverage & matching.

Giving a command to soldier John to use robot
Shooter to fire by coordinates (x, y) withconfirmation of the robot’s success or failure, see Fig. 3.

The spreading & matching patterns can createknowledge infrastructures arbitrarily distributed
between system components (humans, robots, sensors).
Covered by same or other patterns, these can effectively
support distributed databases, command and control,
situation awareness, and autonomous decisions, also
simulate any other models.

Fig. 3. Ordering soldier to use robot to shoot by coordinates

2.2. Spatial Grasp Language, SGL

hop(John);report_if((hop(Shooter); fire(x, y)),success, failure)

SGL [6], the core of the approach, allows us to directly
move through, observe, and make any actions anddecisions in fully distributed environments (whether
physical, virtual, executive, or combined). It hasuniversal recursive structure, shown in Fig. 2, capableof representing any parallel and distributed algorithms

4. Integral Human-Robotic Teams
In the previous example we showed selective tasking ofa human and a robot, whereas will consider here simple© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 541

Towards Unified Human-Robotic Societies

scenarios for mixed teams with humans and robots
having equal status, as symbolically shown in Fig. 4.

Integration of the above four cases within a single
united scenario allowing the whole group randomly
move while keeping threshold distance between units,
regularly redefining its changing center and hierarchicalinfrastructure, and collection and analysis of targets istrivial – can be done starting from any human or robotic
unit (a related case in [8]). Any other collective
scenarios can be generated too, often on the fly.
5. Fully Semantic Scenario in SGL

Fig. 4. Mixed team with same status of units.

At this highest level, it is possible to describe in SGL
only what should be done in a distributed space andwhich top operations and decisions to make, likefollows: Evaluate damage after disaster in points withphysical coordinates X1_Y1, X2_Y2, and X3_Y3, andreport the maximum one. The SGL expression will be:

The highlighted scenario parts may be executed bybiological brain & sensors (in a dialog mode, say, via a
local screen) if appear in SGL interpreter copies
associated with humans, otherwise handled by robots,with overall group control fully automatic too.

report_max_assess(X1_Y1, X2_Y2, X3_Y3)

Randomized group movement, starting in any
node, with Range distance allowed between units whenmoving; units reporting individually if “aliens” seen.

This semantic description is fully formal, and can be
automatically implemented in physical space byavailable manned, unmanned or mixed units. The
solution by robotic units R1 and R2 and manned M1,
scattered somewhere in the region (all having
communicating SGL interpreters installed) is shown in
Fig. 5.

hop(all);nodal(Limits = (dx(0,8), dy(-2,5)),Range = 200, Shift);
repeat(if(seen(unknown), report(‘alien’));
Shift = random(Limits);
if(empty(Shift, Range), WHERE += Shift);sleep(delay))

Starting from any node, finding topologically
central unit of the moving group and hopping into it.
frontal(Aver) = average(hop(all); WHERE);
min_destination(hop(all); distance(Aver, WHERE));

Creating hierarchical infrastructure from thecenter found using oriented links infra and depth ascertain maximum allowed linking distance:

Fig. 5. Automatic solution of semantically defined problem

6. Coastal Waters Cooperative Patrol

repeat_linkup(+infra, firstcome, depth))

This is another scenario example, where manned andunmanned units can work together cooperatively and be
substituted by each other at any time, with new ones
involved too, as in Fig. 6, following coastline inchanging directions and regularly reporting if discover(sensors dependent) “aliens” in key points.

Using the created infrastructure, collect at its top
and analyze all objects (symbolically: targets)
discovered throughout the whole territory covered by
the group, issuing OK or alarm if danger.
frontal(Seen) = repeat(
free_detect(targets), hop(+infra));if(analyze(Seen), out(OK), out(alarm))© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 542Peter Sapaty, Masanori Sugisaka, Takao Itotherefore needing integration with other linguistic
facilities and organizational levels. On the contrary,
SGL, being fully formal and universal system language,
allows for effective and compact semantic expression of
any battlefield scenarios and orders, also directly
supporting robotized up to fully robotic systems [8].
SGT, allowing for holistic and intelligent solutionsof complex problems in distributed environments, mayalso be considered as inheriting some ideas (albeit two
thousand years old) of Sun Tzu [10] like “win by brain,
not by brute force”, which has been confirmed by many
presentations at different world events [11-14].
The latest and most advanced version of thetechnology can be put within a short time on any
platform by a small group of system programmers,
within existing university environments too. This iscurrently being discussed with different institutions,
Hiroshima University including.
The authors would be glad to communicate with anyorganizations and individuals who may get interested inthis area of research and cooperation.

Fig. 6. Simultaneous coastal patrol.

At the beginning we will create a discrete coastal map
as a semantic network consisting of coordinates of key
points linked with each other by oriented links (allnamed r). Vehicles will follow this chain along or
opposite orientation of the links, changing direction at
the end or when see a “colleague” ahead, with thescenario oriented on starting simultaneously in points
x1_y1, x5_y5, x9_y9.
stay_create(x1_y1; (+r, x2_y2); ...;(+r, x9_y9));hop(x1_y1, x5_y5, x9_y9);
frontal(R) = random(+r, -r)
WHERE = CONTENT;
repeat(repeat(check_report(vision_depth);
WHERE = hop(R); none(distance));invert(R))

References
1. M. Wertheimer, Gestalt theory, Erlangen, Berlin, 1924.
2. P. Sapaty, “Gestalt-Based Ideology and Technology for
Spatial Control of Distributed Dynamic Systems”,
International Gestalt Theory Congress, 16th Scientific
Convention of the GTA, University of Osnabrück,
Germany, March 26 - 29, 2009.
3. M. Minsky, The Society of Mind, Simon & Schuster,
New York, 1988.
4. K. Wilber, “Waves, Streams, States, and Self: An
Outline of an Integral Psychology”, The Humanistic
Psychologist, Vol. 31(2-3), 2003.
5. L. Gabriel, “Brain Wave Basics – What You Need toKnow about States of Consciousness”, Thought
Medicine. Exploring the Power of Mind from Science toSpirituality, 2011.
6. P.S. Sapaty, “Spatial Grasp Language (SGL) for
Distributed Management and Control”, Journal of
Robotics, Networking and Artificial Life, Vol.4, Issue 2,
2016.
7. P.S. Sapaty, A Distributed Processing System. European
Patent No. 0389655. European Patent Office (1993).
8. P.S. Sapaty, “Military Robotics: Latest Trends andSpatial Grasp Solutions”, International Journal ofAdvanced Research in Artificial Intelligence, Vol. 4,
No.4, 2015.
9. Coalition Battle Management Language (C-BML), RTO
Technical Report TR-MSG-048, February 2012.
http://www.dtic.mil/dtic/tr/fulltext/u2/a559456.pdf

This semantic level scenario can, for example, be
executed by unmanned UPV1 and UPV2 vehicles andmanned MPV1, as in Fig. 6. In case of a manned vehicle
engaged, the boldfaced operations can be performed
manually, whereas in robotic cases -- automatically.
7. Conclusions
The current paper is pioneering on formalization ofsemantic level operations and top intelligence as regards
large distributed systems, which can be implemented byany available resources regardless of being human or
robotic, thus paving a real way to integration of multiple
robotics into human societies.
Some remotely related works in this direction are
conducted in military on formalization of Command andControl (C2) to simplify multilingual international
cooperation and also improve chances of formal
engagement of robotic facilities in advanced operations.
But the developed specialized Battle Management
Languages (BML) [9] for unambiguous expression of
C2 are not programming languages themselves,© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 543

Towards Unified Human-Robotic Societies

10. Sun Tzu (Translated by Thomas Cleary), The Art of War,
Shambhala, Boston & London 2005.
11. P. Sapaty, M. Sugisaka, “Optimized Space Search byDistributed Robotic Teams”, Proc. World Symposium
Unmanned Systems 2003, Jul. 15-17, 2003, Baltimore
Convention Center, USA.
12. P. Sapaty, M. Sugisaka, “Countering AsymmetricSituations with Distributed Artificial Life and Robotics
Approach”, Proc. Fifteenth International Symposium on
Artificial Life and Robotics (AROB 15th’10), B-Con
Plaza, Beppu, Oita, Japan, Feb. 5-7, 2010.
13. Р. Sapaty, M. Sugisaka, “Advanced Networking andRobotics for Societal Engagement and Support ofElders”, Proc. 16th International Symposium on
Artificial Life and Robotics (AROB 16th ’11), B-Con
Plaza, Beppu, Oita, Japan, January 27-29, 2011.
14. P.S. Sapaty, “Advanced Maritime Operations Under
Network-Centric Organizations”, Proceedings of theInternational Conference Warship 2016: Advanced
Technologies In Naval Design, Construction &Operation, 15-16 June 2016, Guildhall, Bath, UK.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 544Development of English Text for the engineers
to preserve the environment of North-East Asia
Yuji Minami

National Institute of Technology, Ube College
2-14-1 Tokiwadai, Ube, 755-8555, Japan
Fukuchi Kenji, Shinya Tagawa

National Institute of Technology, Ube College
2-14-1 Tokiwadai, Ube, 755-8555, Japan
E-mail : minami@ube-k.ac.jp, fukuchi@ube-k.ac.jp, tagawa@ube-k.ac.jp
www.ube-k.ac.jp

Abstract
Our research began in 2007 as a part of the activities of ‘Good Practice’ for Education of Engineers in cooperation
with the UMICNEA, that is, “Union of Machinery Industrial Cities in North-East Asia”. Through our study, we
found it necessary to acquire the knowledge of ‘Fish Breeding Forest’ when developing and preserving the area ofNorth-East Asia, where the river Amur plays an important role for sustaining the environment of this region. We
made the research of the origin of the river Amur, named ‘the river Onon’, located in the area of ‘Strictly Protected
Area’ in Mongolia by the agreement of Mongolia, Russia, and China in 2005. We plan to make it clear that futureengineers should find a good way of cooperation through English as a communication tool for the purpose ofplaying an active part together.
Keywords: North-East Asia, Strictly Protected Area, Amur Green Belt, Fish Breeding Forest‘ICAP : International Conference of Aqua-polices’ held
every 4 years since 1990.
After taking those 3 projects into consideration, we
continued to send students to the foreign internship, toexchange students mutually, and planned to make atextbook in English for all those engineers who wish toplay active part and to take an important role fordeveloping and preserving the environment of the areaof North-East Asia .

1. Introduction
The purpose of this thesis is to make it clear that the
younger generation of engineers who are expected towork especially in the area of North-East Asia should
learn the basic concept of safe and sound but dynamic
circulation system of environment for the preservationof Amur River Basin.
Before reaching the above-mentioned recognition of
the importance of preserving the origin of the River
Amur, chronologically speaking, we learned some basic
ideas through three major projects in the North-East
Asia, such as ‘the modern GP at NIT, UC from 2007 to
2009’, ‘Amur-Okhotsk project from 2005 to 2009’, and

2. Projects Taken into Consideration
2.1 Modern GP at NIT, UC from 2007 to 2009
We’ve learned to continue the mutual exchange© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 545

Yuji Minami, Kenji Fukuchi, Shinya Tagawa

28th of November, 2016. Aiming to continue ourforeign internship by using UBE airport, opening of thisnew line has long been expected among researchers.
Referring to the Incheon international airport, it candeal with 40,000 passengers a day according to our
research in 2012 at the meeting of ‘Project of theInternationalization of UBE airport’ committee. So the
new line mentioned above can surely support thesustainability of the activities of NIT,UC foreigninternship.

program of foreign Internship after 3 years activities
of “Education of engineers in cooperation with
UMICNEA; the ‘Union of Machinery Industrial Cities
In North-East Asia’” adopted and supported forcontemporary education needs by the Ministry of
Education, Culture, Sports, Science & Technology inJapan(2007-2009).
Students of NIT, UC (National Institute of Technology, Ube College, Japan) began their foreigninternship activities in 3 universities, such as DIT(DongEui Institute of Technology, South Korea), HIT,
WH (Harbin Institute of Technology at Weihai, China),
and KnASTU (Komsomolsk-na-Amure State Technical
University, Russia). We had one week acceptanceprogram and its PDCA worked well as follows.

2.2 Amur-Okhotsk project from 2005 to 2009

Table 1. PDCA Example of Acceptance Program
PeriodUniversity
1Arrival
2Reception
3Day1

4Day 2

5Day 3
6Day4

7Day5

22-29/April/2013
KnASTU, Russia*(KHV-NRT-FUK)
HAKATA→NIT,UC
President Interview
Campus Tour
Welcome Party
Class Activities
Visit Ube City Hall
Tokiwa Park Tour
Student Academic
Conference
Factory TourUBE Industries
Culture Tour
1. Rurikoji Temple
2. Hofu Tenmangu
Shrine20-29/April/2014
KnASTU, Russia*(KHV-NRT-FUK)
HAKATA→NIT,UC
President Interview
Campus Tour
Welcome Party
Class Activities
Visit Ube City Hall
Tokiwa Park Tour

Student Academic
Conference
Factory Tour
1. UBE Industries
2. UBEKAMA
3. YANAGIYA
Machinery

NIT,UC→HAKATA

Culture Tour
1. Rurikoji Temple
2. Akiyoshido Cave
8Departure*(FUK-NRT-KHV)
NIT,UC→HAKATAFree*(FUK-NRT-KHV)(* refers to the names of international airport, such as
KHV;Khabarovsk, NRT;Narita, FUK;Fukuoka )In comparison with the present situation of airport
convenience, Air Seoul, a South Korean low-cost carrier,launched the new line between Ube and Incheon fromIn the process of continuous activities of foreigninternship for 3 years, we happened to find that the big
project concerning the river Amur among 3 countries,
Russia, China, and Japan had almost done by the end of
GP activities mentioned above.
This big project made efforts to prove the essential
object, soluble iron, circulating in the Amur River
covering the Amur River basin in the North-East Asia
including Russia, China, and Mongolia.
This systematic circulation of soluble iron enables thesea of Okhotsk abundant enough to sustain the industryof fisheries in Japan. In brief, as ‘Fish-Breeding Forest’
directly suggests, soluble iron can breed fish and thisobject exists abundantly in the area of North-East Asia,
that is, the Amur River Basin. Researchers of this field
especially call this area ‘Giant Fish-Breeding Forest’.
Moreover, we found out that there existed the agreement of protected area (PA) among 3 countries such as
Russia, China, and Mongolia in 2005, which was called“Amur Green Belt” by WWF (World Wide Fund for
Nature). “Amur Green Belt” is defined as the networkof protected areas (PAs) in the Amur River Basin.
Consequently, we recognized that along with theforeign internship activities as young engineers in thearea of North-East Asia, environmental preservationmust be taken into consideration.
2.3 ICAP: International Conference of Aqua-polices
According to ‘Busan Declaration’ in 2008 at the endof the 5th International Conference of aquapolices(ICAP), all efforts are supposed to be made on the baseof ‘environment-first principle’.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 546

Development of English Text

3. Strictly Protected Area in Mongolia

In the end of 2.1, Incheon air port is referred to havethe capacity of dealing with 40,000 people a day.
While this city can be called the biggest hub city of
Korea at present, Korea needs the second biggest hub
city to become the gateway to Asian Highway covered
countries including the area of North-East Asia.
Along with the environment-first principle, Busanmunicipality has shown its future vision in public. First
Master plan of U-City was established in 2005. Among
many projects for developing the U-City, there exists
U-Environment project.
Thanks to this U-Environment project, Nakdonggang River and its riverside trees are monitored,
managed and protected well enough to sustain its ecological cycle.

As is mentioned in the previous chapter 2, especiallyin North-East Asia, there exists ‘PA’, the Protected Area
with the agreement of 3 countries, Russia, China, andMongolia. This area holds the Amur River Basin, andthis basin is called ‘Giant Fish-Breeding Forest’ by‘Amur Okhotsk Project’ researchers.
Through our activities mentioned above, we recognize
the origin of the River Amur must be taken into
consideration for the sustainable completion of
ecological cycle in the North-East Asia along with theactive young engineers playing an important role in thisarea.
For the sake of learning geographical view of thisarea in English, the following table is prepared.

Table 2. The Amur River Basic Geographical Data with local city names
City(Local)Никола́евск-на-Аму́реКомсомо́льск-на-Амуре
Хабаровск
Благовещенск
黒竜江省 黒河市

City(Global)
nikol.ru
kmscity.ru
khabarovskadm.ru
admblag.ru
heihe.gov.cn

lat
53° 8'32.60"N
50°34'1.33"N
48°30'9.83"N
50°16'21.99"N
50°14'42.46"Nlong/lon/lng
140°43'53.30"E
137° 0'1.88"E
135° 3'58.54"E
127°32'25.45"E
127°31'41.86"E

黒竜江省大興安嶺地区漠河県北極村
ШилкаЧиндант
УлачаОнонУлаанбаатар

Bĕijí
ShilkaChindant
UlachaOnon
ulaanbaatar.mn

53°28'49.94"N
51°50'55.43"N
50°34'31.71"N
50°24'12.99"N
48°37'7.01"N
47°55'11.44"N

122°21'32.69"E
116° 1'58.17"E
115°24'27.24"E
113°16'2.22"E
110°36'18.30"E
106°55'11.81"E

Table 3. The Amur River Basic Geographical Data with altitude report columns
City(Global)
nikol.ru
kmscity.ru
khabarovskadm.ru
admblag.ru
heihe.gov.cn
Bĕijí
ShilkaChindant
UlachaOnon
ulaanbaatar.mn

lat
53° 8'32.60"N
50°34'1.33"N
48°30'9.83"N
50°16'21.99"N
50°14'42.46"N
53°28'49.94"N
51°50'55.43"N
50°34'31.71"N
50°24'12.99"N
48°37'7.01"N
47°55'11.44"Nlong/lon/lng
140°43'53.30"E
137° 0'1.88"E
135° 3'58.54"E
127°32'25.45"E
127°31'41.86"E
122°21'32.69"E
116° 1'58.17"E
115°24'27.24"E
113°16'2.22"E
110°36'18.30"E
106°55'11.81"E

altitude
Report1
Report2
Report3
Report4
Report5
Report6
Report7
Report8
Report9
Report10
Report11© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 547

Yuji Minami, Kenji Fukuchi, Shinya Tagawa

4. S.Tagawa, K. Fujita, “The Recent Efforts of Global
Human Resouces Development”, The 9th International Symposium on Advances in Technology
Education (ISATE2015), p.125, Nagaoka University of Technology, Nagaoka, Japan, 2015.

Geographically speaking, two rivers, the Rivers ofSilka and Arugun are joined to flow down east into theAmur river making the border of Russia and China.
This Silka River comes from the Onon River which
originates at the eastern slope of the Khentii mountains
located at the Northeast area of Ulaanbaatar, capital cityof Mongolia. This area is administered by the Mongolian government and called ‘the Khan Khentii Strictly
Protected Area’.
In table 2 and 3, Silka and Onon can be found. Morecity names can be added by young engineers themselves
who wish to protect the Amur River Basin, in otherwords, ‘Giant Fish-Breeding Forest’. What should beobserved in table 2 is to write each city name in English
for the students of different nationalities.
In learning table 3, altitude of each city should bereported by students because the river depends on theupper or lower locations when flowing down to the sea.
This is the first step of learning the Amur River Basinby young active engineers in the North East Asia.
Fortunately, Air Busan, subsidiary of Asiana Airlines,
launched the new Busan – Ulaanbaatar line this June.
Moreover, Korean Emart newly opened its store in
Ulaanbaatar this July. As we point out in 2.3, Busan is
stepping up toward the second biggest hub and gateway
city of Korea by launching the new air line and opening
the new store. So, we are sure that young engineers in
the North East Asia, especially those of Russia, China,
Mongolia, Korea, and Japan can cooperate to makeefforts to sustain the systematic circulation of ‘solubleiron’.

5. K. Fukuchi, Y. Minami, ”Development of environmental education concerned water in northeast
Asia, Mongolia and far-east Russia along theAmur river”, PCV06, IGNITE-2016, Penang,
Malaysia(2016).

Acknowledgements
This work was supported by JSPS KAKENHI Grant
Number 25370686.
References
1. M. Yoshida, K. Fukuchi, S. Tagawa, Y. Park, T. Ito,and Y. Minami, “Education of engineer incooperation with the Union of Machinery Industrial
Cities in North-East Asia”, Kosen Kyoiku, No34,
pp. 865-870, 2011.
2. Y. Minami, M.Yoshida, K. Fukuchi, K. Fujita, S.
Tagawa, “On the Acceptance of Foreign Internship”，
All Kosen Education Forum, C-1 AP25_3_2,
Kanazawa University, 2014.
3. “Busan Declaration” signed by 41 delegates, the 5thInternational Conference of Aquapolices, Busan
Metropolitan City, Korea, May 21-22, 2008.
© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 548

Measuring Fragility and its Implications in Network System
Tsutomu Ito*, Katsuhiko Takahashi, Katsumi Morikawa, Takao Ito

Graduate School of Engineering, Hiroshima University,1-4-1 Kagamiyama,
Higashi-Hiroshima, 739-8527, Japan
E-mail: fw.eldorado.500cuin@gmail.com
Rajiv Mehta

School of Management, New Jersey Institute of Technology, University Heights,
Newark, New Jersey, 07102-1982, U.S.
Seigo Matsuno

Department of Business and Administration, Ube National College of Technology, Tokiwadai 2-14-1
Ube, Yamaguchi, 755-0096, Japan
Makoto Sakamoto

Faculty of Engineering, University of Miyazaki, 1-1 Gakuen Kibanadai-Nishi,
Miyazaki, 889-2192, Japan

Abstract
A survey of the relevant literature reveals that a plethora of network analytic facets, such as degree, eigenvalue,density, block, cluster, have been developed and employed to further our understanding of network structures. To
extend our understanding of network systems, additional dimensions need to be identified that shed light on the
dynamic processes among individual member nodes within network structures. Specific to the context of networked
systems, this paper proposes a new concept of fragility. Specifically, it develops and empirically tests a mathematicalmodel of fragility from the standpoint of how ties among network members significantly influence corporate
performance, thus uniquely contributing to extant knowledge. Using data drawn from two well-known networkorganizations, Mazda and Toyota, this research attempts to shed light on the relationship between degree and fragility,
to confirm the validity of the new concept as well as enabling a contrast of Mazda and Toyota’s network structures.
[RM1] Based on the findings, the managerial implications are discussed, the study limitations are identified anddirections for further research are suggested.
Keywords: degree, fragility, Keiretsu, relationship, Yokokai.

eigenvalue, density, block, and cluster, reveal the static
nature of relationships among individual nodes and othermembers within the whole network. However, dynamic
processes are considered to be even more salient in theorganizational sciences and allied fields. Accordingly, as
newer models and paradigms that illustrate dynamic
processes are more desirable, this paper proposes the new

1. Introduction
As in sociology and psychology, many mathematicalmodels have been developed to increase ourunderstanding of the organizational sciences. As an
additional organizational form, within the context of
networked structures, dimensions, such as degree,© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 549

Tsutomu Ito, Katsuhiko Takahashi, Katsumi Morikawa, Takao Ito, Rajiv Mehta, Seigo Matsuno, Makoto Sakamoto

concept of fragility that is grounded in the review of therelevant literature as well as rooted in a systematic
repeated trial and error assessment. Drawing data fromtwo well-known network organizations, Yokokai in
Mazda and Kyohokai in Toyota, from 2004 to 2007, thepurpose of this research endeavor is to calculate therelationship between degree and fragility to confirm thevalidity of new model. Further, the relationship between
fragility and corporate performance, is also assessed that
forms the basis of the managerial implications discussed
in the study. Specifically, this paper makes a uniquecontribution to extant thought by: 1) Defining the conceptof fragility, 2) Discussing the nature of the relationshipbetween fragility and corporate performance, and 3)
Empirically testing the dimensional differences between
fragility and corporate performance, thus enabling
Mazda’s networked organization to be contrasted with
Toyota.
This paper is structured as follows. Section 2 reviews theliterature focusing on network analysis. In section 3, thepaper explicates the calculation of degree and fragility.
Section 4 discusses our findings based on which the study
limitations are identified and directions for further
research are proffered in the final section.

network members can be manifest in the form of equity
ties, personal ties, transactional ties and work-flow ties,
this study examines transactions within a network toreveal the nature of the relationships between businessties and corporate performance. More specifically, owed
to the sparsity of knowledge, this investigation
contributes to the literature by advancing a newprocedure for measuring the interrelationships between
members of a keiretsu by suggesting the new concept offragility to be a determinant of corporate performance.
3. Research Method
Two concepts are applied in this paper: degree andfragility.
3.1. Degree and Fragility
Degree, as one of the basic indices of centrality, isconsidered as the basic index in network analysis. It canbe calculated as follows [1].
(1)nCD ( pk )a( pi , pk );i 1

2. Background and Literature Review
Indexes, such as degree, eigenvalue, density, block, andcluster have been used widely in empirical research tomeasure different dimensions of organizational networks.
Comprising a most basic facet of centrality, degree was
firstly proposed by Nieminen [1] in his research onorganizational constellations. Simply put, it is defined as
the number of links incident upon a node in a graph. In
real society, most of the relationships between members
are considered to be mutual. Furthermore, nodes within a
network are known to interact with each other.
Consequently, direction with weight have beencommonly employed to analyze different phenomena
within social networks. In this context, Freeman
proposed a new index of entire degree, which identifies
the centrality of the whole network [2].
More recently, Ito and Sakamoto proposed a new
approach to identify the importance of each individual
node based on Freeman’s model [3], but noted that much
more research is necessary to fully understand keiretsu
constellations. Although the relationships between

where
i≠j;
a (pi, pk) = 1 if and only if pi and pk are connected by alinea (pi, pk) = 0 otherwise
In an asymmetric network, two indexes of out-degree andin-degree should be calculated
Basically fragility is a physical term, which characterizes
how rapidly the dynamics of a material slow down as it
is cooled toward the material transition. Accordingly,
fragility is defined as the ratio of the entire degree of andthe entire degree after moving a specific node. It will beillustrated as follows.
n 1[C D ( p * ) C D ( pi )]i 1

FCD ( p)CD( n 1) 2 3( n 1) 2n

[C D ( p * ) C D ( pi )]i 1

n 2 3n) 2

(2)

where
CD: Entire degree of a given networkCD( p ): Entire degree after removing node p
CD ( p*) max C D ( pi )© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 550

Measuring Fragility and its

Table 2. Results of out-degree and in-degree-sales regression model.
ModelsYokokai
Sales
Out-degree
Partial regression coefficientStandard coefficientt value
Probability
Correlation coefficientPartial correlation coefficient

2004

2005

2006

2007

2004

Kyohokai
2005
2006

2007

5153.172
0.06450.86110.3919-0.4908
0.09837124.583
0.0904
1.49570.1384-0.3858
0.1601-8079.95-0.0964-1.04250.3003-0.4298-0.1151-15695.4-0.1447-1.59710.1146-0.3185-0.8137-7537.79
0.1133-2.7502
0.007-0.4726-0.2615-4448.25-0.0667-1.691
0.0941-0.4713-0.1692-3779.68-0.0471-1.2655
0.2087-0.4388-0.1262-3619.42-0.0389-0.9838
0.3277-0.448-0.1015

In-degree
Partial regression coefficientStandard coefficientt value
Probability
Correlation coefficientPartial correlation coefficient

14162.85
0.896811.9655
00.85690.80827124.583
0.922115.2559
00.87540.85599322.689
0.6465
6.991700.6962
0.613520726.5
0.61416.775900.655
0.62146465.408
0.872
21.159200.91870.90167605.123
0.906
22.971500.93580.9191

9597.788
0.921124.734100.94110.927711958.24
0.922
23.341500.93930.9242

Coefficient of determination
Multiple correlation coefficient
F value
Degree of freedom
AIC
DW ratio
Data number

0.73677
0.85835
106.3622, 762441.18
1.933179

0.772310.87881144.1595
2, 852706.92
1.653188

0.491550.7011139.15432, 812678.46
2.566484

0.448360.669629.66672, 73
2466.462.468276

0.85474
0.92452
303.038
2, 1033182.65
1.56091060.879210.93766
353.0187
2, 972989.13
1.4931100

0.88753
0.94209390.6121
2, 99
3078.191.4393102

0.88346
0.93992352.4917
2, 932929.59
1.381796

Table 3. Results of fragility-sales regression model.
ModelsYokokai

Kyohokai
2006

Sales
Fragility
Partial regression coefficientStandard coefficientt value
Probability
Correlation coefficientPartial correlation coefficient

2004

2005

2006

2007

2004

2005

2007

21109945
0.2154
1.93540.05660.21540.215422124395
0.19571.8511
0.0676
0.1957
0.195711933200.01770.16020.87310.01770.017712793233
0.1262
1.09440.27730.12620.1262-19942684.1-0.7684
12.24380-0.7684-0.7684-20889146.6-0.7653-11.7689
0-0.7653-0.7653-26007968.4-0.7752-12.27050-0.7752-0.7752-29733934.04-0.7798-12.0763
0-0.7798-0.7798

Coefficient of determination
Multiple correlation coefficient
F value
Degree of freedom
AIC
DW ratio
Data number

0.045390.21538
3.74558
1, 77
2540.88
1.230279

0.03832
0.19575
3.42656
1, 86
2831.71
1.1155880.000310.01769
0.02566
1, 82
2733.250.967184

0.015930.126210.019779
1, 742508.45
1.164176

0.590410.76838
149.9108
1, 1043290.540.91581060.585640.76527138.50651
1, 98
3110.400.9054100

0.60090.77518150.56441
1, 1003205.68
0.8992102

0.608070.77979145.83724
1, 943044.020.9052
96

network indexes. Data on Toyota’s Kyohokai and
Mazda’s Yokokai from 2004 to 2007is reported in Table
1.
Table 1. Firms in Yokokai and Kyohokai.

3.2. Data Collection
Widely considered as successful examples of prominent
Japanese networked organizations, data were drawn fromToyota’s Kyohokai and Mazda’s Yokokai from 2004 to2007 to establish the status quo of keiretsu as well as
longitudinally ascertain changes in their keiretsu
structure. Both of these keiretsu organizations include
singletons, which refers to a partner firm in the keiretsu
that has no relationship with other member firms.
However, singletons were removed from the data-set
because they have no impact on the calculation ofFirm in Yokokai

Firm in Kyohokai

Total

Singleton

Total

Singleton

2004

188

97

215

93

2005

191

89

216

97

2006

190

92

213

93

2007

189

104

213

101

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 551

P - 552

Tsutomu Ito, Katsuhiko Takahashi, Katsumi Morikawa, Takao Ito, Rajiv Mehta, Seigo Matsuno, Makoto Sakamoto

A diagrammatic representation of the inter-firmtransactional relationships in 2007 is illustrated in Fig. 1.

means that greater fragility is associated with higher
sales—as predicted. However, as the probabilities ofeach year are larger than 0.05, additional longitudinal
data should be collected.
5. Conclusions
Grounded in the review of the relevant literature, thispaper proposed a new approach called fragility to shedlight on interfirm behaviors within network organizations
known as keiretsu. Data were drawn from Yokokai in
Mazda and Kyohokai in Toyota to ascertain therelationship between degree and fragility. The results
look similar between both the keiretsu when we
investigate the relationships between degree and sales.
However, different behaviors are manifest when fragility
is viewed as a determinant of sales.

Fig. 1. Transaction network of Yokokai in 2007.
4. Results and Discussion
Using regression analysis, we first tested the relationshipbetween out-degree and in-degree as determinants ofsales. The results of out-degree and in-degree-sales
regression model is shown in Table 2.

6. Directions for Future Research

4.1. Sales and out-degree and in-degree
All the probabilities of in-degree are significant while
out-degree are not significant except for Kyohokai in
2004. Thus, evidently in-degree has a statisticallysignificant impact on sales. In contrasting the results of
the Yokokai, the correlation coefficients as well as thecoefficients of determination are higher. Thus, it can beinferred that the regression model of Kyohokai has
stronger power to explain the relationship between indegree and corporate performance—as measured by sales.
Table 2 indicates that the difference between Yokokai
and Kyohokai looks similar, but different in degree.
4.2. Sales and Fragility

Based on the results obtained, four fiscal years of data is
not sufficient to support contentions of internal validity.
In addition to degree, other centrality indexes, such as
closeness and betweenness, should be tested asdeterminants of corporate performance. Furthermore, theoriginal definition of fragility in Physics is a derivativeof a mathematical function. Thus, in the future themodels tested in this study should be investigated using
data drawn from other settings, such as, information
technology, the ship-building industry for comparative
research as well as replicating the current findings.
Acknowledgements
This work was supported by JSPS KAKENHI Grant
Number 16K01243.
References

The results of out-degree and in-degree-sales regression
model is shown in Table 3.
Compared with Yokokai, all the probabilities of fragility
from 2004 to 2007 are significant, and coefficients ofdetermination are higher. Thus, the model assumptionsof Kyohokai hold.
All correlation coefficients are negative. Thus, theassumption of higher fragility having an inverse
association with sales is confirmed. Because the meaningof fragility denotes the value of entire degree of a given
network after removing a specific node, it is evident thatfirms in Kyohokai have a higher possibility to improvesales. Conversely, all correlation coefficients are positive

1. Nieminen J. (1974). On centrality in a graph, Scandinavian
Journal of Psychology 15, 322-336.
2. Linton C. Freeman (1978/79). Centrality in Social
Networks Conceptual Clarification, Social Networks 1,
215-239.
3. T. Ito and M. Sakamoto (2005), Importance Analysis of
Each Firm in the Keiretsu of Toyota, Proceedings of the2005 Information Resources Management Association,
International
Conference,Managing
ModernOrganizations with Information Technology, 930-933,
May 15-18, 2005, San Diego, USA.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 553

A Formation of Standard Setter to Transplant Global Standards into Domestic Institution
Kensuke Ogata

Department of Business Administration, University of Nagasaki
123 Kawashimo, Sasebo, Nagasaki, 858-8580, Japan
E-mail: ogata@sun.ac.jp

Abstract
Japanese accounting standard setter, the ASBJ, developed lots of domestic accounting regulations to converge withthe global standards in the period of 2005-2008, which substantially differs from the period of 2001-2004. A general organizations theory states that organizations make a change in their strategy and structure in order to change
their performance. Based on this theory, we analyzed the change in organizational structure of the ASBJ using social network analysis. According to our result, the ASBJ formed a network in which accounting professions playeda central role. The reason could be that the ASBJ made use of knowledge and wisdoms on the global standards Japanese big accounting firms has through their global networks.
Keywords: ASBJ, global convergence, standard setting process, social network analysis

1. Introduction
This paper aims to clarify how an accounting standard
setter changes its organizational structure when it
changes its standard setting behavior. The theoreticalbackground is Strategy-Structure-Performance paradigm to be extensively studied in organizations theory.
The ASBJ developed a lot of standards which mainly
aimed at harmonizing with global standards producedby the International Accounting Standards Board(IASB) so as to reform the conventional ones, by meansof adopting non-conventional techniques and/or narrowing down the ones to be previously accepted as options
for four years after 2005. Under what type of organization did the ASBJ take such a standard setting behavior?
To do so, this paper shows the organizational structureof ASBJ at that time using Social Network Analysis(SNA).
This paper is organized as follows. In the second section,
we review the previous literature regarding to standard
setting processes, and refer to the reason why we focus
on the organizational structure of the standard setter andthe effectiveness of network theory as an organizational
structure analysis. In the third section, we confirm feature of standard setting activities of ASBJ in the periodof 2005-2008. The fourth section conducts the organizational structure of ASBJ using SNA. Finally, we mention some reasons why the ASBJ formed that network atthat time.
2. Studies on Accounting Standard Setting
2.1. Previous Literatures
It is said that accounting standard setting process is apolitical forum for various stakeholders who can be
economically or socially influenced by creations, revisions or removals of specific standards, which can force
to alter meanings and amounts of accounting figures(Zeff [1]). Since the early 1970s when researches on theprocess began in the USA, empirical researches to investigate activities or motivations of lobbyists in a formal process of inviting public comment letters havepredominated. Though demonstrating the influence of
certain political factors on standard setting process, such

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 554

Kensuke Ogata

researches cannot have a full picture of the process,cannot shed light on behavioral principle of accounting
standard setter that has the greatest responsibility formaking rules, and cannot explain the dynamics of power,interests and resources in the accounting standard setting process.
In order to illustrate these points, a small number ofstudies on intra-board political activity and politicalactivity regarding regulatory structures were carried out(Walker and Robinson [2]). Among them, some researches emphasized that the behavior of the setter
could be affected by the strategies its chairpersons, its
parent organizations, or its regulatory bodies adopted(e.g., Hope and Gray [3]).
In addition, according to traditional organizations theorists, organization could in general change its organizational structure in order to execute a specific strategy.
This is called to the strategy-structure-performance paradigm (e.g., Miles and Snow [4]). If we apply this paradigm to the behavior of accounting standard setter, it
could (re)organize its own structure to carry a given
strategy into effect (Mattli and Büthe [5]).
2.2. Clarifying Organizational Structure Using SNAThis paper employs SNA for the purpose of confirming
the organizational feature of the standard setter. SNA
seeks to model relationships among actors in the network to depict the network structure. The most basic
feature of SNA is to quantify the relationships amongactors in the network. Another feature of SNA is to describe the network structure using a graph in which the
network relationship is drawn with nodes and edges(e.g., Wasserman and Faust [6]). In recent times, someresearches which incorporated this analytical method
made an appearance for the purpose of defining the organizational feature of standard setters (Perry and Nöelke [7]; Botzem [8]; Richardson [9], and Ogata [10]).
Among a wide variety of analytical methods to capture
the network structure in SNA, this paper adopts a continuous coreness analysis. This analytical method isused to identify a set of actors who have a high densityof ties among themselves (the core) by having many
events in common and another set of actors who have a
low density of ties among themselves (the periphery) by
having few events (Borgatti and Everett [11]). Here,“common” event refers the organizations, for example,

companies, financial institutes, investment institutes,
accounting firms, universities, governmental agencies,
and other various organizations, which members of thestandard setter and/or its related organizations belonged
to and/or engaged with as constituencies before they
became a member. It rests on an underlying idea that
individual preferences and minds on accounting standards and techniques depend on their backgrounds or
careers. Analysis based on this kind of data-set canhighlight dominant actors in the standard setter in termsof human resources. As a result, we can understand on
what value the standard setter has placed or would placemuch.
3. Standard Settings of the ASBJ
3.1. Overview of the ASBJ
In 2001, the ASBJ was created as the first Japanese private accounting standard setter, under mutual cooperation between the ten stock market-related stakeholders,
including some industry groups of bank, insurance andinvestment, business leaders groups and accounting
profession community and the regulatory agency, theMinistry of Finance and the subsequent Financial Service Agency of Japan.
The ASBJ is an intra-organization of the Financial Accounting Standards Foundation (FASF). The FASF at
the inception was comprised of four internal bodies:
Board of Directors, Board of Councilors, ASBJ, andTheme Advisory Council. In 2004, the FASF built a
newly organization, Advisors; and furthermore in 2007,
the FASF compounded Theme Advisory Council andAdvisors into Standards Advisory Council.
3.2. Standard Settings of the ASBJ from 2005 to 2008
As shown in Table 1, the ASBJ developed standards atslow pace until 2004. In 2005, it developed seven standards which had not been adopted ever, for example,
accounting for business divestitures and share-based
payment. Since 2006, it kept issuing standards to thesame effect. These standards adopted non-conventional
techniques and/or narrowed down the alternatives to be
accepted for the global convergence. This triggered amajor reform in Japanese financial reporting system.
In the next section, this paper attempt to identify theorganizational characteristics of the ASBJ at that time.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 555

A Formation of Standard)

4.2. Features of Organizational Structure of the ASBJ

Table 1: Transition on Volume of Issued Standards

A result of the coreness score in this period is Table 2.
This table shows the top four scorers left other organizations far behind. Included were Azusa (coreness score:
0.545, rank: #1), Tohmatsu (0.444, #2), ShinNihon(0.410, #3), and FASF (0.373, #4). The former threeorganizations are all big accounting firms in Japan thatform alliances with the global big accounting firms1. In
the second group, preparers of financial statements appeared. There were non-financial sectors such as Tokyo
Electric Power (0.139, #6), Central JR (0.116, #10), andNEC (0.116, #10) as well as financial sectors such as
Nomura Securities (0.127, #7), Mitsui Sumitomo Insurance (0.121, #8), and Meiji Yasuda Life (0.120, #9), ona similar level.

4. The Organizational Structure of the ASBJ
4.1. Using data
In conducting organizational structure analysis, thispaper uses data based on organizations which provided
members to intra-organizations of the FASF. As mentioned before, this assumes that individual preferences
and opinions on the accounting standards or techniquesshould be determined by his/her background or career.
Therefore, shedding light on the relations among theorganizations in which the members have engaged before taking these seats in the network can give a fullaccount of what actors dominate and what kinds of values are predominant in the standard setter.
This paper prepares a matrix data-set, composed of theorganizations which provided human resources to theintra-organizations of the FASF from 2005 to 2008, on
the basis of annual reports of the FASF, in rows, and theintra-organizations of the FASF every period in columns. In this case, the larger numbers of members
or/and the longer period organizations supply, the higher scores these organizations obtain. This paper as thesecond step transform this original data-set into the organization-to-organization data-set by means of the affiliation process, contained in social network analysis
software, UCINET VI (Borgatti, et al. [12]). We perform a coreness analysis and draw a graph using NetDraw (Borgatti [13]), based on this affiliated data-set.
An original matrix data-set in the period of 2005-2008
comprises 77 organizations. To relativize the standard
setting process during this period, we make up anotherdata-set in a period of 2001-2004, which include 69
organizations.

Table 2: Coreness Result of the ASBJ (Top 15 Actors)

According to Graph 1, which shows a network graph ofthen-ASBJ, the above actors occupied central positionsin this network. Especially, accounting profession actors
which are plotted on the right center of this graph constituted the core position of this network. In this paper,
Graph 1: Network Graph of the ASBJ

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 556

Kensuke Ogata

the line gets bigger on the five-scale level on the basisof the strength of relationships.
To emphasize the change of the ASBJ’s organization
structure, this paper makes a comparison between thisperiod and the previous four years. Table 3 which presents the proportion of the total coreness score acquiredby each attribute group to the whole score in the network of the both periods and the difference can show
the transition of central actors by attributes2.

of global standards. Third is that since the accounting
profession organization was the strongest proponent of
harmonizing Japanese standards with global standards
in Japan, the ASBJ probably made use of its momentum.
Reference[1] Zeff SA (2002), “Political” Lobbying on Proposed Standards: A Challenge to the IASB. Accounting Horizons 16:
43-54[2] Walker RG and Robinson P (1993), A Critical Assessmentof the Literature on Political Activity and Accounting
Regulation. Research in Accounting Regulation 7: 3-40
[3] Hope T and Gray R (1982), Power and Policy Making:
The Development of an R&D Standard. Journal of Business Finance and Accounting 9: 531-557[4] Miles RE and Snow CC (1978), Organizational Strategy,
Structure, and Process. McGraw-Hill[5] Mattli W and Büthe T (2005), Accountability in Accounting? The Politics of Private Rule-Making in the Public Interest. Governance 18: 399-429[6] Wasserman S and Faust K (1994), Social Network Analysis: Methods and Applications. Cambridge Univ Press[7] Perry J and Nöelke A (2005), International AccountingStandards Setting: A Network Approach. Business and Politics 7(3.5): 1-32
[8] Botzem S (2012), The Politics of Accounting Regulation,
Organizing Transnational Standard Setting in Financial
Reporting. Edward Elgar Publishing[9] Richardson AJ (2009), Regulatory Networks for Accounting and Auditing Standards: A Social Network Analysis of Canadian and International Standard-Setting. Accounting, Organizations and Society 34: 571-588[10] Ogata K (2010), A Study of Accounting Standard-Setting
Using Graph Theory. Artificial Life and Robotics 15: 279283[11] Borgatti SP and Everett MG (1999), Models of
Core/Periphery Structures. Social Networks 21: 375-395[12] Borgatti SP, Everett MG, Freeman LC (2002), UCINET
for Windows: Software for Social Network Analysis. Harvard, MA: Analytic Technologies[13] Borgatti SP (2002), NetDraw: Network Visualization.
Harvard, MA: Analytic Technologies

Table 3: Difference of Coreness between Two Periods

The results are as follows. The first is that accounting
profession group sharply increased the prominence inthe network (20.2% to 43.5%). The second is that othergroups except for accounting profession and regulator
reduced the importance, among them the decline of academic community by 15.9% was the most remarkable.
Besides, the third is that financial statements preparers,
including financial and non-financial, who might havethe strongest influence in the previous four years, also
reduced the importance (21.8% to 16.2% in the former
case; 25.5% to 18.1% in the latter case). Thus, the ASBJ
formed an accounting-profession-centric network.
5. Concluding Remarks
There are three assumed reasons that the ASBJ formed
the accounting-profession-centric network in the periodof 2005-2008 when conforming Japanese accounting
standards to the global standards was needed. First isthat, as evidenced by cases of IASB and the US standard setter, accounting standard setters is likely to rest on
accounting professions when they should develop thestandards being substantially different from the existing
ones. Second is that the ASBJ could make use of global
networks which some big Japanese accounting firms
hold, with an aim to attain the knowledge and wisdoms© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 557

A Comparison Study on the Vertical Integration and Horizontal Specialization of Chinese ICT
Companies
Yunju Chen

Faculty of Economics, Shiga University,
1-1-1, Banba, Hikone City, Shiga Pref.,522-8522, Japan
yun-chen@biwako.shiga-u.ac.jp
Yousin Park

Dept. of Business Administration, Prefectural University of Hiroshima,
1-1-71 Ujina-Higashi, Minami-ku, Hiroshima City, Hiroshima Pref., 734-8558, Japanecventure@pu-hiroshima.ac.jp
Iori Nakaoka

Dept. of Business Administration, National Institute of Technology, Ube College,
2-14-1 Tokiwadai, Ube City, Yamaguchi Pref., 755-8555, Japan
nakaoka@ube-k.ac.jp

Abstract
The debate between vertical integration vs. horizontal specialization appears to be reinvigorated. The decision ofvertical integration or horizontal specialization affects a firm’s profit and competitive advantage, especially in the
ICT industry which is difficult to create added value due to the commoditization of digital products. The ongoing
commoditization of smartphone brings the rising of Chinese ICT companies but these companies adopt different
strategies/operation systems to create their own competitive advantages. In this paper, we focus on top-shared
Chinese ICT companies in global smartphone industry, Huawei and Xiaomi, to examine how they design theiroperation systems in R&D and gain competitive advantages, also to compare their systems with each other. The
patent information of these two companies is used to visualize their technical orientations and operation systems in
R&D by text mining.
Keywords: patent analysis, social network analysis, Chinese ICT companies, the boundary of the firm, vertical
integration, h orizontal specialization

Although the open source of Android caused therising of Chinese ICT companies, they do not simply
adopt the cost leadership strategy. For example, Xiaomi
is an internet startup with short company history, and its
rapid growth tells a story that it may grow up by
outsourcing. On the other hand, Huawei has been thegiant smartphone firm with long history, and it is also
one of the innovative Chinese firms that have top classfilings of patents.

1. Introduction
No doubt smartphone has replaced feature phone tobecome the core device in electronics industry now.
Since iPhone was launched at 2007, the global
smartphone market has been changed rapidly. Recently,
Chinese ICT companies are on the rise in the global
smartphone market and they almost occupy the global
top ten besides Samsung and Apple.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 558

Yunju Chen, Yousin Park, Iori Nakaoka

However, how Chinese ICT companies adopt
different strategies, even the operation systems? It
seems that Chinese ICT companies, like Xiaomi and
Huawei, develop their operation systems by either makeor buy. Actually, the decision of make or buy, in otherwords, vertical integration or horizontal specialization
affects a firm’s profit and competitive advantage. The
debate between vertical integration vs. horizontal
specialization appears to be reinvigorated especially in
the ICT industry which is difficult to create added value
due to the commoditization of digital products.
In this paper, we focus on top-shared Chinese ICT
companies in global smartphone industry, Xiaomi and
Huawei, to examine how they design their operation
systems in R&D and gain competitive advantages, also
to compare their systems with each other. The patent
information of these two companies is used to visualize
their technical orientations, R&D networks andoperation systems in R&D by text mining.

private sectors whose activities and interactions initiate,
import, modify and diffuse new technologies [2].
Patent documents are an ample source of technical andcommercial knowledge. The patent is one of theindicators of capacity for technological development.
There are some researches aimed at visualizing andanalyzing patents, or proposing efficient text-mining
approaches for creating patent maps. However, there
are few researches focusing on R&D management
strategies based on text-mining analysis of patents.
Therefore, this paper describes the features of Chinese
ICT companies’ R&D management based on the patent
analysis.
2.2 The overview of Chinese ICT companies
The global market share of smartphone out of analyst
firm IDC shows a major shakeup in the Chinese
smartphone market from 2013 Q1 to 2015 Q4.
According to Figure 1, 3 Chinese ICT companies ofHuawei, Xiaomi and Lenovo occupied the top 3rd-5th
share in the global market. Huawei has made abreakthrough in global market share, following
Samsung and Apple, but Xiaomi is stalling from the endof 2014.
Xiaomi (Xiaomi Technology Co., Ltd) founded in2010 by a well-known angel investor Lei Jun, andachieved impressive growth soon after its existence.
Xiaomi is a mobile internet company and focuses its
businesses on smartphones (Xiaomi Phones), including
the OS of MIUI, MiTalk, the e-commerce platform of
XIaomi.com, MiBox etc. On the other hand, Huawei(Huawei Technologies Co. Ltd.) was founded in 1987,
and started its business by producing private branchexchange switches. Now the company has become oneof the top telecommunications equipment suppliers andhas active R&D activities since its start. Its businesses
comprise mobile and fixed broadband networks,
smartphones, tablet computers etc.
Figure 2 shows that while Huawei stably maintains
high ROE, Xiaomi explosively expanded ROE in 2013
and 2014, but has declined since then.

2. Research Background
2.1 Open innovation and patent analysis
Companies comprise various activities. In order tocarry out their activities, companies inevitably
cooperate with other companies to use external
resources. At the same time, companies face the issueof what to outsource from other companies and how tomanage their own systems while cooperating with othercompanies. The decision making of what and how to
outsource from others refers to the issue of boundary of
the firm. The issue of make or buy in the vertical flowof production is the typical issue in the boundary of thefirm.
In fact, the boundary of the firm is an important issue
in not only production, but also in R&D. Theoretically,much attention has been drawing to using external
resource to drive innovation, which is defined as open
innovation. Open innovation is the use of purposive
inflows and outflows of knowledge to accelerateinternal innovation, and expand the markets for external
use of innovation, respectively. This paradigm assumes
that companies can and should use external ideas aswell as internal ideas, and internal and external paths tomarket, as they look to advance their technology [1].
Horizontal specialization oriented companies tend tooutsource and cooperate with other companies, hence
benefit from open innovation in R&D.
And the boundary of the firm in R&D also concerns
the national innovation system. The national innovation
system is the network of institutions in the public and© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 559

A Comparison Study on
Discharging
Coded Data Generation or Conversion
Liquid Crystal Cells, Elements and Systems
Electricity: Electrical Systems and Devices
Multiplex Communications

35%

H03, H04
G02
H02, F21
H04,G01, G06,
G08
H03, H04
H04
G02, H04
H04
G06, G10

30%
25%
20%
15%
10%
5%
0%

Pulse or Digital Communications
Telephonic Communications
Optical Communications
Telecommunications
Data Processing: Speech Signal Processing,
Linguistics, Language Translation, and Audio
Compression/Decompression
Data Processing: Artificial Intelligence
Data Processing: Database and File Management or
Data Structures
Data Processing: Presentation Processing ofDocument, Operator Interface Processing, andScreen Saver Display Processing
Interprogram Communication or Interprocess
Communication (IPC) (Electrical Computers andDigital Processing Systems)

2013Q1 2013Q2 2013Q3 2013Q4 2014Q1 2014Q2 2014Q3 2014Q4 2015Q1 2015Q2 2015Q3 2015Q4

SamsungApple

LenovoXiaomi

Huawei

G06
G06

Figure 1 Global market share by vendors

G06
200
G06

150
100

4. Analysis

50
0

4.1 An approach based on the number of patentpublications

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015-50

As our first approach, the numbers of patentpublications associated with ICT in each of thecompanies are shown in Figure 3. Every company
obtains related patents to a certain extent and thenumber of patents of all of them kept on increasing
during the years except for 2016.

-100
HUAWEI TECHNOLOGIES CO., LTD.

XIAOMI TECHNOLOGY CO., LTD.

Figure 2 ROE of Huawei and Xiaomi
3. Methodology and dataInthefollowing
sections,the
R&D
strategies/operation systems of Chinese ITC companies
are analyzed. In order to examine target companies’
R&D strategies and the change of core researchers intheir R&D projects, we visualize their patent
information in 3 steps: the number calculation of patentpublications, text mining, and social network analysis.
We use the IPC (International Patent Classification)code for analyzing the smartphone industry. IPC,
established by the Strasbourg Agreement 1971, provides
for a hierarchical system of language independent
symbols for the classification of patents and utility
models according to the different areas of technology towhich they pertain.
Table 1 is most frequently used IPC in smartphoneindustry. Based on these IPC related to smartphone,patents related to smartphone published by eachcompany are extracted and collected from the patent
information.
Table1
IPC
H01, H02

250

2500
2000

200

193

150

1500

114

1000

100
66

500

36
1

0

2010

6

0
2011

2012

50
0

2013

2014

2015

2016

HUAWEI INVESTMENT & HOLDING CO., LTD
XIAOMI COMMUNICATIONS CO., LTD.

Figure 3 The number of patents of Huawei and Xiaomi
4.2 An approach by correspondence analysis
We use the correspondence analysis based on text
mining to disclose the technical trends and features by
IPC codes associated with smartphone. The reference
data in the analysis is the numbers of each their patentdocument in each year. These figures are based on datesof patent publication, and patents are applied to
products in the companies. Figure 4 shows that Xiaomi
probably develops different technologies in each year,

IPC classes of smartphone
Description

Electricity: Battery or Capacitor Charging or© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 560

Yunju Chen, Yousin Park, Iori Nakaoka

and Huawei tends to constantly develop technologies
that highly related.

Figure 5 R&D Networks of Xiaomi and Huawei
4.3.2. The evaluation of core rigidity in R&D
We define that patent applicants are key persons
attaining high scores calculated by centrality of social
network analysis. Centrality is a well-known index inthis field. Freeman proposed three distinct conceptionsof centrality: degree, betweenness and closeness [3].
And, it indicates that conditional probability appears
where whether or not upper rank j% of person at i year
appears upper rank j% of person at i+1 year. When suchprobability scores are high, they have core rigidity [4].
Core Rigidity = Conditional Probability (upper rank j% of person at i + 1 year)
upper rank j% of person at i year

We visualize the human resource reallocation of
personnel engaged in R&D project by heat-map. Where
the color is dark red in heat-map, it indicates an unexecuted reallocation of a core engineer; otherwise, thecolor is light red, it means a radical change of core
member in that year. As one of the example, the corerigidity of human resource and the change of R&D area
the personnel involved in Xiaomi and Huawei areshown in Figure 6. There is more change in Xiaomi’score rigidity degree than change in Huawei. This isbecause Xiaomi must obtain to new patent for entering
new market such as U.S. and Huawei has been
exploiting research and develop for long time.

Figure 4 Results of correspondence analysis
4.3 An approach by the social network analysis
4.3.1. R&D network Figure 5 is the R&D networksof Xiaomi and Huawei. The results show that Xiaomi
and Huawei have different R&D patterns. In Xiaomi’scase, several islands that connected only to related
patents are observed. It seems there is little relationshipbetween technologies.
On the other hand, thetechnology network of Huawei concentrated on several
core patents and technologies are expanded based onthese core patents.

Figure 6 The core rigidity of Xiaomi and Huawei
5. Conclusions
This paper focused on Xiaomi and Huawei, the two
major Chinese ICT companies to examine how they
design their strategy/operation systems in R&D. Patent
data were used to investigate Xiaomi’s and Huawei's
technology strategies and technical networks. Analysis
includes: (1) the number of patent publications, (2)technological orientations, (3) R&D networks, (4)degree of core rigidity of R&D.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 561

A Comparison Study on

From the analysis of R&D network of Xiaomi, we
supposed that it probably acquires external related
patents rather than develops its own technologies inside
the firm. Actually, Xiaomi did buy patents from othercompanies, for instance, Microsoft. In addition, in the
degree of core rigidity of R&D, Xiaomi has rough R&D
organization structures and hence has low level ofintegration. In fact, Xiaomi has few manufacturing
facilities of its own, instead it outsource all production
to contract manufactures such as Taiwan’s Foxconn.
And they also have not much invested R&D. Xiaomi’s
problem for new market entry is a lack of patent
holdings and faces intellectual property-related lawsuits.
And Xiaomi have tried to apply new patent recently.
But Xiaomi’s patent portfolio is still thin.
On the other hand, Huawei has high weight on internal
R&D, and explores technologies related to coretechnologies. The heat map of core rigidity of Huawei
shows that its R&D structure is more highly integrated
than Xiaomi. Huawei is increasing its emphasis on
R&D to become more innovative as a foundation forsurviving in a highly competitive and rapidly
consolidating industry. Being able to continuously andsuccessfully innovate through vertical integration,
Huawei is trying to do what can create a competitive
advantage.
References
1. H. Chesbrough (2006) Open Innovation: Researching a
New Paradigm, Oxford University Press
2. L. C. Freeman (1987) Technology and Economic
Performance: Lessons from Japan, Pinter, London
3. D. A. Levinthal, and J. G. March, The myopia of learning,
Strategic Management Journal, vol.14 (1993), pp.95–112
4. Leonard-Barton, D. (1992). Core Capabilities and Core
rigidities: A Paradox in Managing New ProductDevelopment. Strategic Management Journal, vol. 13:
111-125.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 562

The Optimized Function Selection Using Wolf Algorithm for Classification
Duangjai Jitkongchuen and Worapat Paireekreng

College of Innovative Technology and Engineering
Dhurakij Pundit University
Bangkok, Thailand{duangjai.jit,worapat.png}@dpu.ac.th
www.dpu.ac.th

Abstract
Several classification techniques have been widely explored during the past decade. One of the novel approaches inrecent years is Nature Based Algorithm. This approach is appropriate to imbalanced dataset. The focus of Nature
Based Algorithm mostly is related to selection the optimized functions for self-learning. This is used to solve the NPhard problems. However, Some Nature Based Algorithms are suitable for general situation; some may be suitable forcustomized situation. This research proposes the Featured-Wolf (F-Wolf) algorithm to optimize the function selection
problem in classification. The proposed algorithm applies the movement of a wolf and characteristics of wolves’
leaders which can be more than one leader in a pack. Therefore, the pack can have more than one dominant leader
which can help to select the most optimized functions to selection the most relevant features in the dataset. The
experiment shows the comparison among other popular Nature Based Algorithms such as Ant Colony Optimizationand other classification techniques. The results show that F-Wolf performs better results in terms of accuracy rate.
Keywords: Nature Based Algorithms, Wolf algorithm, Classification

the NP-hard problems. It also works well withimbalanced dataset. The examples of NBA are such as
Grey Wolf Optimizer (GWO)2 and Ant Colony
Optimization 3.
Furthermore, there is an attempt to improve the speedof computational time for classification performance.
One of the stage in the pre-processing phase is feature
selection. The feature selection has been used for theobjective of selecting the most relevant attributes of thedata set as input variables. These variables impact on theperformance of classification if the relevant variables arechosen to build the classification model. The preprocessing stage eliminates the irrelevant attributes inorder to increase the computational time of the modelbuilding stage. The example of feature selection can beseen from several data mining, machine learning, andpattern recognition4. Moreover, the input variables are
the challenge of this area by selecting the appropriate

1. Introduction
Data science is now crucial for almost all areas. Data
from various sources needs to be analyzed and uses
specific analytic tools. One of the most important domain
to present the analyzed data is classification. The
common techniques used in data mining to solve theclassification problem. Some research combined theclassification techniques using ensemble technique toimprove the classification performance1.
In addition, the new classification techniques havebeen investigated to enhance the performance because
the characteristics of each dataset have their uniqueness.
The adjusted classification model and classifiers needs tobe improved. One of the novel approaches in recent years
is Nature Based Algorithm (NBA). The approach isrelated to selection the optimized functions for selflearning. This is the most appropriate technique to solve© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 563Duangjai Jitkongchuen, Worapat Paireekreng

subset of attribute with maintaining of classification
performance 5,6.
However, Some Nature Based Algorithms are
suitable for general situation; some may be suitable forcustomized situation. Therefore, some enhancing
technique is needed for improving the performance ofclassification using NBA approach. Feature selection
technique can be included in the NBA to perform betterin terms of accuracy rate.
This paper proposed the Featured-Wolf (F-Wolf).
The main idea of F-Wolf is to imitate the grey wolf
behavior including feature selection in the pack. The
algorithm uses information exchange within population
which leads to generate new candidate individuals forfeature selection and perform the better results forclassification.

True Positive (TP) and True Negative (TN) are thepositive instances and negative instances correctlyclassified respectively whereas False Positive (FP) andFalse Negative (FN) are the negative instances andpositive instances misclassified respectively.
To measure the performance of the classification, thetraditional accuracy rate (1) has been used.

=

2.2. Soft Computing and Nature Based Algorithm
The soft computing has been introduced to solve theuncertain problems with intensive computation. The
principle of soft computing can implement probabilistic
reasoning approach to solve the problems. The examplesof techniques used in soft computing are such as
evolutionary algorithm. One of the commonly usedtechnique is Ant Colony Optimization (ACO) which canbe seen in the recent research3.
However, to handle different type of data such as
categorical and continuous data in the real world,
Customized techniques is needed to obtain the better
performance in terms of accuracy. Some problems
domain such as NP-hard problems also needs the specifictechniques and methodology to solve the uncertain
variables and alternatives. Therefore, it is also thechallenge to implement the adapted Nature Based
Algorithm for classification performance.

2. Related Works
2.1. Data Mining and Classification Techniques
There are many classification techniques that have beenused to deal with classification problems and predictions.
Examples of these common used classification
techniques are Decision Trees (DT), Naïve Bayes (NB),
Artificial Neural Networks (ANN) and Support Vector
Machines (SVM)7. There are different in terms ofadvantages and disadvantages based on each technique.
The factors to decide which technique is suitable fordataset are such as simplicity of the algorithm, upsampling scale, robustness and outlier handing.
Classification techniques have also been incorporated
into the several applications. For example, the mobile
services incorporated Artificial Neural Networks (ANN)with feed-forward back-propagation neural network inorder to select the different types of particular services8,9.
The performance of classification model can beassessed by confusion matrix. It is shown in Table 1. The
confusion matrix presents the results of amount of correctand incorrect instance in each class.

2.3. Grey Wolf Optimizer
The grey wolf optimizer algorithm (GWO) was proposedby Mirjalili et al10. It is the recent Nature Based
Algorithm which simulate the grey wolf behavior to live
in a pack. The social dominant hierarchy of the wolf packcan be defined as leaders, subordinate wolves andmembers which consist of scouts, sentinels, elders,hunters and caretakers. Each member has a different rolein a pack. For example, Sentinels protect and guarantee
the safety of the pack. Therefore each level and member
is defined as variables called alpha, beta and delta
respectively. In addition, the lowest level is omega. The
omega wolves have to comply with all the otherdominant wolves.
The grey wolves show naturally ability to encircle
and identify the position of a prey and other wolves help
the hunting for a pack. This behavior can be explained inmathematical model which assumes the leader to bealpha (α). The beta (β) and delta (δ) is similar to theTable 1. Confusion Matrix Terminology.

Positive class
Negative class

Positive
Prediction
True Positive(TP)
False Positive
(FP)

(1)

Negative
Prediction
False Negative(FN)
True Negative (TN)© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 564

The Optimized Function Selection

second and the third optimal solutions, respectively.
Whereas, the rest of the candidate solutions are assumed
to be omega (ω). The hunting is guided by alpha, beta anddelta. Besides, the omega wolves would consider the bestsolutions from three different positions and update theinformation to the pack.
Nevertheless, the traditional grey wolf algorithm
basically simulate with only one pack which leads toencircle on local search. Therefore, splitting the pack of
the grey wolf is challenge. The proposed method is
included algorithm to reduce the optimum ratio anddistribute value to in order to obtain the larger search
space. This is also including feature selection.

3.2. The Proposed Featured Wolf (F-Wolf) andProcedure
The overall and details of F-Wolf procedure are shown
below in “Fig. 1.”.

2.4. Feature Selection for Classification Problem
Due to the consuming computational time of building theclassification model for training dataset, somemethodologies are needed to address to problem. The
common methodology used is feature selection. The
purpose is to select the subset of input variables which isuseful and impact on performance of the classifier. This
includes elimination of some irrelevant variables withfew contributions towards the prediction results. The useof feature selection can be seen from data mining,machine learning and pattern recognition4, 11. The
objective of feature selection is to select a subset of usefulfeatures from the input variables that impact on accuracyof the classifier and eliminate irrelevant features withlittle contribution towards the prediction results.
Moreover, the challenge is to choose the minimum subset
of features with little or no loss of classification
accuracy5,6.
Feature selection can be divided into two categories:
model-free method and model-based methods. Modelfree methods are based on statistical tests, properties offunction and available data such as linear regression,
whereas, model-based methods such as neural network
develop model to find the significant features andminimize the model output error12,13. Therefore, Nature
Based Algorithm (NBA) can be also incorporated intoclassification model in order to select the most suitablevariables for classification problem with less impact on
performance.

Fig. 1. Procedure of F-Wolf

4. Experimental Results
After the datasets have been prepared, the next step is tobuild the classification model for prediction. There were
4 different types of algorithms used to compare in theexperiment, specifically Featured Wolf (F-Wolf), Ant
Colony Optimization (ACO), C4.5 and PART. In thisresearch, the metric to determine the performance of theclassification is based on accuracy rate. The preliminary
results are shown in Table 2.
Table 2. Comparison Results.
Technique
DatasetCredit-G
Haberman
Heart-C
Heart-HHeart-statlog
Horse
Iris
Pima diabets
ShuttleSonar
Average

3. Research Methodology

Accuracy Rate (%)
F-Wolf
87.96
76.30
78.00
70.00
86.67
82.50
99.09
87.00
96.30
97.59
86.14

ACO
79.29
79.63
69.38
73.47
77.24
88.00
98.22
78.89
75.60
87.07
80.68

C4.5
71.70
70.59
49.17
64.29
62.22
82.00
94.67
73.31
53.33
76.92
69.82

PART
71.90
70.59
55.12
62.59
64.07
82.33
95.33
73.44
53.33
73.56
70.23

It appeared that the proposed F-Wolf
classification model can perform better in terms ofaccuracy rate compared to other classification
techniques. In addition, it performed better than C4.5
and PART. To compare with ACO, the Nature Based
Algorithm, the results show that the proposed

3.1. Experimental Design
The data source used for the experiment was obtained
from University of California Irvine (UCI) machinelearning repository. There are 10 datasets used in thisresearch.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 565Duangjai Jitkongchuen, Worapat Paireekreng

3. W. Paireekreng, D. Jitkongchuen, W. Sukpongthai and R.
Suwannakoot, Improving Soft Computing Performancewith Ant Colony Optimization for MulticlassClassification: The Application for Learning StyleClassification, in Proc. 7th International Conference onIntelligent Systems, Modelling and Simulation( ISMS2016), (Bangkok, Thailand, 2016) pp.101-105.
4. P. Mitra, C. A. Murthy and S. K. Pal, Unsupervised feature
selection using feature similarity, IEEE Transactions on
Pattern Analysis and Machine Intelligence, 24(3) (2002)
301–312.
5. H. Almuallim and T. G. Dietterich, Learning boolean
concepts in the presence of many irrelevant features,
Artificial Intelligence, 69 (1-2) (1994) 279–305.
6. D. Koller and M. Sahami, Toward optimal feature
selection, in Proc. 13th International Conference on
Machine Learning, (1996).
7. X. Wu, et al., Top 10 Algorithms in Data Mining,
Knowledge and Information Systems, 14 (2008) 1-37.
8. Q. H. Mahmoud, et al., Design and implementation of asmart system for personalization and accurate selection ofmobile services, Requirement Engineering, 12 (2007) 221230.
9. A. Cufoglu, et al., A Comparative Study of Selected
Classifiers with Classification Accuracy in User Profiling,in Proc. World Congress on Computer Science andInformation Engineering, (Los Angeles, California, USA,2009).
10. S. Mirjalili, S. M. Mirjalili and A. Lewis, Grey wolf
optimizer, Advances in Engineering Software, 69 (2014),
46-61.
11. Miller, Subset Selection in Regression, 2nd edn.
(Chapman & Hall/CRC, 2002).
12. S. M. Vieira, J. M. C. Sousa and T. A. Runkler, Twocooperative ant colonies for feature selection using fuzzymodels, Expert Systems with Applications, 37(4) (2010)2714-2723.
13. A. E. Isabelle Guyon, An Introduction to Variable andFeature Selection, Journal of Machine Learning Research,
3 (2003) 1154-1182.

technique presented the better results with 7 out of 10
datasets. Furthermore, the average accuracy rate for
F-Wolf is higher significantly compared to othertechniques.
5. Discussion and Conclusions
Classification is an important problem domain for data
related area. Several techniques have been proposed todeal with classification. One of the novel approaches inrecent years is Nature Based Algorithm (NBA) whichappropriate to imbalanced dataset including NP-hardproblems. Grey Wolf Optimizer (GWO) is one of the
NBA to address the classification problems. However, it
may be suitable for one pack situation, therefore,
Featured Wolf Algorithm (F-Wolf) is proposed using
information exchange within population. This whichleads to generate new candidate individuals for feature
selection in order to enhance the classification
performance.
The results from the experiments based on UCI
datasets have shown that the proposed F-Wolf provided
the better classification results. The average of accuracy
rate for all datasets in the experiment was also higher thanother classification techniques. The F-Wolf usedcrossover to keep the best value in each round but thetraditional did not cover.
In the future works, it is important to consider theimbalanced data sets with other Nature Based Algorithm.
This includes the investigating the improved
performance on prediction results of the classification
model. The hybrid Nature Based Algorithm is therefore
needed to address the problem. Moreover, the other soft
computing techniques such as fuzzy-based techniques ormachine learning techniques can be incorporated in thefeature selection stage. This is to build the improved
classification model.
References
1. W. Paireekreng and T. Prexawanprasut, An Integrated
Model for Learning Style Classification in University
Students Using Data Mining Techniques, in Proc. 12th
International
ConferenceonElectricalEngineering/Electronics, Computer, Telecommunications
and Information Technology (ECTI-CON), (Hua Hin,
Thailand, 2015).
2. D. Jitkongchuen, P. Phaidang and P. Pongtawevirat, Grey
Wolf Optimization Algorithm with Invasion-based
Migration Operation, in Proc. 15th IEEE/ACISInternational Conference on Computer and Information
Science (ICIS 2016), (Okayama, Japan,2016).

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 566

Tell Agent Where to Go: Human Coaching for Accelerating Reinforcement Learning
Nakarin Suppakun

Institute of Field roBotics, King Mongkut's University of Technology Thonburi
Bangkok, 10140, Thailand
Suriya Natsupakpong

Institute of Field roBotics, King Mongkut's University of Technology Thonburi
Bangkok, 10140, Thailand
Thavida Maneewarn

Institute of Field roBotics, King Mongkut's University of Technology Thonburi
Bangkok, 10140, Thailand
E-mail: nakarin_sup@hotmail.com, suriya@fibo.kmutt.ac.th, praew@fibo.kmutt.ac.th
www.kmutt.ac.th

Abstract
In this work, we proposed a method to accelerate learning by allowing a human to coach a robot behavior byinserting an intermediate target at the early phase of the reinforcement learning. By using an intermediate target, thedifferent pair of policy and reward function was temporarily used to select an action that most likely to drive therobot toward the intermediate location, while the global reward function is still used for updating the state-action
value. Q learning algorithm was used to test with the proposed method on three learning tasks: ball following,
obstacle avoidance, and mountain car. The proposed technique resulted in better learning performance than the
traditional RL.
Keywords: learning from demonstration, reinforcement learning, human assisted learning, semi-supervised
learning, robot coaching

this work, a human coach guides an agent by placing avirtual intermediate target so that the agent would movetoward. Another pair of policy and reward function
would recognize this virtual target as a new goal state. A
greedy policy is used in conjunction with an aggressive
reward function until an agent reaches the virtual target.
Meanwhile, the agent updates state-action value for every
state-action encounter with the global environmental
reward. After reaching the virtual target, the agent would
be switched back to exploit and explore the states andactions through an e-greedy policy until next human

1. Introduction
In Reinforcement Learning (RL), long time training is
usually required in order to train an agent to perform agiven task. A large number of learning episodes isnecessary to effectively propagate environmental
rewards to each state action pair. However, instead of
unnecessarily exploring states and actions freely, if there
is a way that a human teacher could guide an agent
toward the task completion at an early learning state, theagent could reach the goal state, and get theenvironmental reward within less learning iteration. In© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 567

Nakarin Suppakun, Suriya Natsupakpong, Thavida Maneewarn

3. Methodology

input is provided. In this work, a human only assists anagent in the early phase of learning process.
2. Related Work
Various techniques which using human inputs to assist an
agent in learning a given task can be found in many
literatures. In the work of A.L. Thomaz, and C.
Breazeal1,2, a human teacher gives a scalar reward after arobot performs a task in a computer simulation. Humanteacher was supposed to give the feedback after eachaction has taken; however, many people misunderstoodand give the signal as if guiding what the robot should donext. In work of M. Hirokawa and K. Suzuki3, a humancoach would give a subjective evaluation as a binary
feedback signal by modifying the reward function. Thereis also check for inconsistency of feedback and the signal
is discarded if some inconsistency was found. There arealso literatures about reward shaping technique4,5, theconcept is that the feedback given by the human was used
to modify the reward function by adding the interpreted
value to the initial predefined reward (environmental
reward). A list of certain feedback words was fixed andeach word was predefined their corresponding value
based on intensity level of meaning. In the work of S.
Griffith et al.6, the policy shaping technique from anadvice feedback was presented. However, they used asimulation feedback instead of an actual human feedback
in order to control the consistency and likelihood of the
feedback provided. W.B. Knox and P. Stone7-9 presented
the system which allowed human teacher to give a binary
feedback on the action taken. Human feedback model
was trained by supervising technique and used for action
selection as a policy. A variety of combinations onhuman input and traditional RL were tested8; The best
combination was to use human input for an action
selection only, which convince us to the same idea as well.
In the work of W. D. Smart and L. P. Kaelbling10, they
allowed a human to take over the policy for action
selection on the early phase of learning, and then let thelearning to proceed as usual later on. A human controlled
the action directly via a joystick, while an agent updates
its state-action value properly. In our work, a humancoach assists the learning process by placing anintermediate virtual target to guide the agent toward thedesired location. In this proposed method, there is noneed for a human coach to manually select an action foreach state-action that the agent encounters.

In the proposed method, an agent performs both
exploitation and exploration by ϵ-greedy policy. (greedy
with probability 1-ϵ or random). Whenever a humancoach assist an agent by giving an intermediate virtual
target, a separated set of aggressive reward function is
generated. With this reward function, a separated
aggressive policy, which the action with the most reward
received would be selected, is used. An agent would
move toward the intermediate virtual target until theintermediate target is reached. In the meantime, eachstate-action encounter along the trajectory path would be
updated the state-action value with a corresponding
environmental reward (regardless of aggressive reward
which was used solely for action selection). Afterreaching the guided target location, the agent would beswitched back to ϵ -greedy policy again until the next
guide target is given by a human coach. The process isrepeated until the global goal state is reached. In our
experiment, the human guidance was only given in theearly phase (the first 30 training episodes) and the
traditional reinforcement learning was resumed later on.
4. Experimental Setup
All experiments were conducted in a computer
simulation program. The first two tasks are in a robot
soccer environment as shown in Fig. 1. The first task (ballfollowing) is for the robot to move toward the ball. In thesecond task (obstacle avoidance), an obstacle (i.e. an
opponent robot) was placed between the ball and therobot. From this program, a human coach could assign avirtual intermediate target for the robot by left clicking at
the desired coordinate on the screen.

Fig. 1. Simulation program for ball following and obstacle
avoidance. Soccer field represents 6 x 4m.

The third task is mountain car problem from Ref. 11. The
purpose of this task is to drive a car back and forth in avalley between 2 mountains until the car can reach the© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 568

Tell Agent Where to

distance and the sine and cosine angle to the obstacle. The
reward r(s,a) condition was modified by including that if
the robot collides with an obstacle (<= 0.3 m), then the
penalty of -0.3 is given, and the episode is failed. List ofactions, an aggressive reward function and policy are thesame as in section 4.1.

top of the mountain on the right. From this program, ahuman coach give a guided intermediate target for the carby left clicking at the screen. The horizontal position
information would be used as a guidance input.
Q reinforcement learning using a radial basis functions(RBFs) network as function approximator was used inour experiment (see Ref. 11). Information for eachexperimental task was explained as follow.

4.3. Mountain car

4.1. Ball following
State descriptors consist of the ball linear (Euclidean)distance, the sine and cosine of an angle from the robot
heading to the ball, the sine and cosine of an angle tocenter of the opponent goal, and the linear distance fromthe opponent goal field line.
List of actions are combination of linear and angularmovement. Linear actions consist of go forward (0.1 m),
stop, and go backward (-0.1 m). Angular actions consistof turn left (15 degrees), no turn (0 degree), and turn right(-15 degrees). Thus, we have the total of nine actions(3x3). Note that, the completely stop action (stop & noturn) is excluded.
A global reward function r(s,a) was defined as follow. If
the robot reaches the ball ( <= 0.15 m) and the ball is in
front of the robot (in between angle -60 and 60 degrees),
a positive reward +1.0 is given and the episode issuccessfully finished. If the ball is not in front of the robot,
no score is given and the episode is failed. If the robot
moves further away from the ball than the given
threshold distance (6 m), a negative penalty score of -0.1
is given and the episode is failed. If the robot goes outside
the field on either side, a penalty of -0.1 is given and theepisode is failed. Otherwise, no score is given.
An aggressive reward function ^( , ) extended theprevious conditions by using the intermediate target
given by a human coach instead of a global reward. If thecurrent distance to the intermediate target is shorter thanprevious state’s, the positive score of +0.1 is added to thereward. If the current angular distance between the agent
heading and the intermediate target is smaller than theprevious state’s, the positive score of +0.1 is also added.
The Greedy approach was used for the aggressive policythat is used in conjunction with the aggressive reward^( , )

State descriptors consist of the car position (in horizontalaxis) and the velocity. List of actions consist of full
throttle reverse, zero throttle, and full throttle forward.
The reward function r(s,a) was implemented differently
from the original work11. A positive reward (+1.0) is only
given when the car reaches the target location (top of theright mountain), and no reward for other cases. An
aggressive reward function ^( , ) was implemented
separately as follow. If the current state is closer to theintermediate target than the previous state, a positive
reward +0.1 is given. Again, greedy approach was used
for an aggressive policy.
5. Results
Figs. 2, 3, and 4 illustrate the learning performance forthe three tasks: ball following, obstacle avoidance, andmountain car, respectively. Each method and each task
was repeated for 3 trials. In all three tasks, the guidance(coached) approach has higher accumulated rewards than
the traditional RL.

Fig. 2. A comparison learning performance for ball following.

4.2. Obstacle avoidance

All state descriptors explained in section 4.1 are also usedwith an addition of the obstacle linear (Euclidean)© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 569

Nakarin Suppakun, Suriya Natsupakpong, Thavida Maneewarn

with reinforcement learning has accelerated the learning
performance compared to the traditional RL.
References
1. A.L. Thomaz and C. Breazeal, Reinforcement Learningwith Human Teacher: Evidence of Feedback and Guidancewith Implications for Learning Performance, in Proc. 21stNat. Conf. Artificial Intell. 1 (AAAI 2006) (Massachusetts,
Boston, 2006), pp. 1000-1005.
2. A.L. Thomaz and C. Breazeal, Teachable Robots:
Understanding Human Teaching Behavior to Build More
Effective Robot Learners”, Artificial Intell. J. 172(6-7)(2008), pp. 716-737.
3. M. Hirkoawa and K. Suzuki, Coaching Robots: Online
Behavior Learning from Human Subjective Feedback, inInnovations in Intelligent Machines-3, Studies in
Computational Intell. 442 (2013), pp. 37-51.
4. A. C. Tenorio-Gonzalez, E. F. Morales and L. VillaseñorPineda, Dynamic Reward Shaping: Training a Robot by
Voice, in Advances in Artificial Intell. (IBERAMIA 2010)(2010), pp. 483-492.
5. L.A. Leon, A.C. Tenorio and E.F. Morales, Human
Interaction for Effective Reinforcement Learning, in
European Conf. Mach. Learning and Principles andPractice of Knowledge Discovery in Databases(ECMLPKDD 2013) (Prague, 2013).
6. S. Griffith, K Subramanian, J. Scholz, C.L. Isbell and A.L.
Thomaz, Policy Shaping: Integrating Human Feedback
with Reinforcement Learning, in Advances in Neural
Inform. Process. Syst. (2013), pp. 2625–2633.
7. W.B. Knox and P. Stone, Interactively Shaping Agents via
Human Reinforcement: The TAMER framework, in Proc.
5th Int. Conf. Knowledge Capture (2009), pp. 9-16.
8. W.B. Knox and P. Stone, Combining Manual Feedback
with Subsequent MDP Reward Signals for Reinforcement
Learning, in Proc. 9th Int. Conf. Autonomous Agents andMultiagent Syst. 1 (AAMAS 2010) (2010), pp. 5-12.
9. W.B. Knox and P. Stone, Reinforcement Learning from
Simultaneous Human and MDP Reward, in Proc. 11th Int.
Conf. on Autonomous Agents and Multiagent Syst. 1(2012), pp. 475-482.
10. W. D. Smart and L. P. Kaelbling, Effective Reinforcement
Learning for Mobile Robots, in Proc. IEEE Int. Conf. on
Robotics and Automation (ICRA’02) 4 (2002), pp. 34043410.
11. R.S. Sutton and A.G. Barto, Reinforcement Learning: An
Introduction (MIT Press, Cambridge, 1998).

Fig. 3. A comparison learning performance for obstacle
avoidance.

Fig. 4. A comparison learning performance for mountain car.

6. Conclusion
The results showed that with the guided information froma human coach, an agent learn a task more effectively.
An agent could use the guided information at the earlylearning episodes instead of exploring randomly at the
beginning phase of the learning. As the agent reached thevirtual intermediate target given by the human coach, theagent has performed what the human coach expected andalso learned the list of useful actions along the way. The
chance for exploration is still available as the policy isswitched back to ϵ-greedy after the agent reached theintermediate target and possibly could find the evenbetter solution. The experimental results from threedifferent tasks also show that the proposed method that
including an intermediate target from a human coach© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 570

Fall Risk Reduction for the Elderly
Using Mobile Robots Based on the Deep Reinforcement Learning
Takaaki Namba

Graduate School of Engineering, Nagoya University,
Furo-cho, Chikusa-ku, Nagoya City, 464-8603, Japan
Yoji Yamada

Graduate School of Engineering, Nagoya University,
Furo-cho, Chikusa-ku, Nagoya City, 464-8603, Japan
E-mail: namba.takaaki@b.mbox.nagoya-u.ac.jp, yamada-yoji@mech.nagoya-u.ac.jp
www.nagoya-u.ac.jp

Abstract
Slip-induced fall is one of the main factors causing serious fracture among the elderly. This paper proposes a deeplearning based fall risk reduction measures by mobile assistant robots for the elderly. We use a deep convolutional
neural network to analyze fall risks. We apply a deep reinforcement learning to control robots and reduce slip-induced
fall risks of the elderly. The results suggest that the applicability of our method to other cases of the fall and othercases of accidents.
Keywords: Safety, Risk Reduction, Mobile Robot, Deep Learning, Reinforcement Learning.

1. Introduction

2. Risk analysis

An increase tendency of the elderly’s accidents isshowing no signs of stopping in some countries including
Japan.1-2 In order to reduce the increasing fall accidents
among the elderly, we take an approach to use mobile
assistant robot. We consider safety in the environments
where humans and assist robots coexist in the fields of
medical welfare and assist living.
This paper proposes a deep learning based fall risk
reduction measures by mobile assistant robots for theelderly. First, we collect preparatory data regarding past
incidents and accidents data as input data to analyze fallrisks and to learn examples of risk reduction measures.
Second, we use a deep convolutional neural network3 toanalyze fall risks of the elderly. Third, we apply a deep
reinforcement learning4-5 to control mobile robots andreduce slip-induced fall risks of elderly. Moreover, weevaluate the effect of risk reduction.

The risk analysis is carried out through methods such as
FTA (Fault Tree Analysis),6 HAZOP (Hazard And
Operability Study),7-8 and FMECA (Failure Modes
Effects and Criticality Analysis).9 Conventional risk
analyses conducted manually or partially use aprobability calculation tool when employed inapplications such as system safety requirement analysis
or design. This type of analysis has the following
limitations:
Failures to identify hazards may occur depending on
the proficiency of the safety management officers,that is, their experience and capability,
Risk analysis procedures are complex and require a
specific number of man-hours depending on thescale of the assessment.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 571

Takaaki Namba, Yoji Yamada

Most analyses and assessments are carried out offline
based on prior information and require time to be
conducted. The results are not immediately available.
Therefore, the measures of risk reduction are often
delayed, and it becomes difficult to respond promptly andflexibly in continually changing situations.
In this paper, we use online real-time risk analysis basedon the deep convolutional neural network. The input isenvironmental sensing data aimed at detecting HEs(Hazard Elements), IMs (Initiating Mechanisms, triggersof the accident), and T/T (Target and Threat) in the.
Ericson’s Hazard Theory.10 According to the Ericson’s
Hazard Theory, the accident (mishap) occurs when HE,
IMs and T/T are appeared at the same time in specifictiming. The sensing data is mainly time-series images ofthe environment and distance information to the objects
and parts of human body.
The main output is the result of risk analysis and thelevel of risk as quantitative information. The risk ispresented with following formula:

ℎ=

=

+

∗ ℎ.
+

This paper proposes online real-time risk reduction
based on the deep reinforcement learning. Figure 1
illustrates that inputs are following environmental data:
the elderly’s outer factors and inner factors. Outer factors
are some sensing data, for example, images from stereo
camera and laser range finder. Inner factors are elderly’s
physical condition, for example, breathing. The other
input is the risk level that is the result of risk analysis asa negative reward.
This deep neural network is trained with the Q-learningalgorithm, 4-5 with Adam15 for optimization of parameters.
Moreover, this network uses batch normalization forimproving the learning rate and robustness of initial valueof weight.16
The output is the action as the risk reduction measures.
4. Experiment
We performed an experiment on a simulation. Figure 2
shows a visualization of an environment and an agent. A
scenario is that an elderly person who is assisted bymobile robot goes to the toilet at a certain interval in the

(1)
(2)

. Eq. (1) shows that the risk is multiplication of theseverity of the harm (S) and the probability of the harm(Ph). Eq. (2) displays the probability of the harm (Ph) is
the combination of following: frequency of exposure tohazards (F), the probability of occurrence of hazardous
event (Ps), the possibility of avoiding or limiting theharm (A). Furthermore, according to the result of risk
analysis, we generate a painting of the future by blending
a representative hazardous image per each risk category
with a current image.

Fig. 1. Reinforcement learning for fall risk reduction of theelderly.

hospital. Sometimes the floor is partially wet. The mobile
robot (agent) assists preventing from slip-induced fall forthe elderly.
Figure 3 presents a process how the reward changes
during training on the simulation. The increase of the
reward indicates the reduction of the risk. We evaluate
the effects of our approach through these change ofgraphs. We obtained the result of simulation that theagent is able to act reducing slip-induced fall risks
automatically.
Figure 4 depicts an example of the deep neural networkmodel generating risk reduction measures. We apply the
combination of 3-streams (the current flow, the chaging

3. Risk reduction
In general, about the safety of machinery, risk reduction
carries out through methods such as inherently safe
design measures, safe guarding implementation ofcomplementary protective devices and information foruse.11-13 In addition to the above, risk reduction learns
from the previous incidents and accidents, 14 which
improves the environment, for example, removes hazard
elements, supports avoiding hazardous situation andstops the trigger of incidents and accidents.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 572

Fall Risk Reduction forsFig. 3. Change of reward during training on the simulation.

of our method to other cases of the fall and other cases ofaccidents.
References
1. Takaaki Namba, and Yoji Yamada, A study on an
assistance by mobile robots in preventing the elderly fromfalling, Proceedings of annual meeting of the the Japan
Society of Mechanical Engineers 2015, Paper No.
J1630205, 2016 (in Japanese).
2. Takaaki Namba, and Yoji Yamada, A study on an
assistance by mobile robots in preventing the elderly fromfalling (Deep Convolutional Neural Network for Fall risk
Analysis), Proceedings of annual meeting of the the Japan
Society of Mechanical Engineers 2016, Paper No.
J1640304, 2016 (in Japanese).
3. Alex Krizhevsky, Ilya Sutskever, and Geoff Hinton.
Imagenet classification with deep convolutional neuralnetworks, In Advances in Neural Information Processing
Systems 25, (2012), 1106–1114.
4. Christopher J. C. H. Watkins and Peter Dayan, Q-learning,
In Machine Learning, vol. 8, (1992), pp. 279-292.
5. Mnih, Volodymyr et al. Playing Atari with Deep
Reinforcement Learning". In:CoRR abs/1312.5602,(2013).
6. C. Ericson, Fault tree analysis – a history, In:17th
International System Safety Conference. Unionville, VA,(1999), pp.1-9.

Fig. 2. A simulation environment of experiments. ER is anelderly person assisted by a robot as an agent. E1 is anotherelderly person. From a hospital room, ER goes to the toilet ata certain interval by walking on the corridor between Wall_1
and Wall_2. E1 also goes to the same toilet. Sometimes it is
wet on the floor and ER detects wet area. The fan-shaped areashows the sensing rage of ER and E1.

flow and the changing rate flow) deep convolutional
neural network and full connection network to determine
the risk reduction measures. The data of input layercomposes state and reward vectors.

5. Conclusion
In this paper we proposed a fall risk reduction measures
for the elderly using mobile robots based on the deep
reinforcement learning, and have presented its usability.
The results of experiments suggest that the applicability

Fig. 4. A deep neural network model generating a risk reduction measures.
© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 573

Takaaki Namba, Yoji Yamada

7. Faisal I Khan, S.A Abbasi, Techniques and methodologies
for risk analysis in chemical process industries, Journal of
Loss Prevention in the Process Industries, Vol.11, Issue 4,(July 1998), pp.261–277
8. Jordi Dunjóa, Vasilis Fthenakis, Juan A. Vílchez, Josep
Arnaldos, Hazard and operability (HAZOP) analysis, A
literature review, Journal of Hazardous Materials, Vol.
173, Issues 1–3, (15 January 2010), pp.19–32
9. MIL-STD-1629A, Military Standard, procedures for
performing a Failure Mode, Effects and Criticality
Analysis, (Department of Defense, Washington, DC,1980).
10. Crifton A. Ericson II, Hazard Analysis Techniques For
System Safety, Second Edition, (John Wiley & Sons, Inc.,
2016).
11. ISO12100, Safety of machinery – General principles fordesign – Risk assessment and risk reduction, 2010.
12. IEC61508-ed2, Functional safety of electrical /electronic /programmable electronic safety-related systems, 2010-04.
13. ISO13482, Robots and robotic devices -- Safety
requirements for personal care robots, 2014.
14. David L. Cooke, Learning from Incidents, System
Dynamics Society, (2013)
15. Diederik Kingma and Jimmy Ba., Adam: A Method for
Stochastic Optimization, 3rd International Conference forLearning Representations (ICLR), San Diego, (2015)16. Sergey Ioffe and Cristian Szegedy, Batch Normalization:
Accelerating Deep Network Training by Reducing
Internal Covariate Shift, Journal of Machine Learning
Research, (2015)© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 574

Histogram Matching Based on Gaussian Distribution Using Regression Analysis
Variance Estimation
Yusuke Kawakami

DynaxT Co., Ltd., 2217-6 Hayashi
Takamatsu City, Kagawa 761-0301, Japan
Tetsuo Hattori, Yoshiro Imai, Kazuaki Ando, Yo Horikawa

Graduate School of Kagawa University, 2217-20 Hayashi
Takamatsu City, Kagawa 761-0396, Japan
R. P. C. Janaka Rajapakse

Tainan National University of the Arts, 66 Daci
Guantian District, Tainan 72045, Taiwan
E-mail: riverjp2002@gmail.com, {hattori imai, ando, horikawa}@eng.kagawa-u.ac.jp, janakaraja@gmail.com

Abstract
This paper describes an improvement method for variance estimation which is used in Histogram Matching basedon Gaussian Distribution (HMGD). In the previous papers, based on curvature computation, we have described that
how to estimate the variance of reference histogram, which is used in HMGD processing. However, we haveconsidered that the histogram of original image is not always ideal shape. And the variance estimation method
based on curvature computation might not have high reliability. In this paper, we propose improvement varianceestimation method using regression analysis. As for the method, first, we detect the histogram peak of original
image by using curvature computation; next, we perform regression analysis using approximation formula ofcurvature. Then, we illustrate processing results through some experimentation.
Keywords: Image processing, Curvature, Variance estimation, Histogram matching, HMGD

comparative investigation, we have illustrated that
HMGD processing could improve feeling (or Kansei)
impression better than original image6. And we haveaimed to improve HMGD processing, we have proposedthat how to estimate the variance of reference histogram,which is used in HMGD processing based on curvaturecomputation, and also illustrated these results.
However, we have considered that the histogram oforiginal image is not always ideal shape (i.e., Gaussian
distribution, etc). And the variance estimation method

1. Introduction
These days, automated image processing forenhancement of color images has been more familiar tous, for example, Digital Signage, Smart Phone, etc 1-3.
In the previous paper, we have presented that theHistogram Matching based on Gaussian Distribution(HMGD) processing is one of the automated image
arrangement method using Elastic Transformation 4-5
based on the brightness axis. And through the© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 575

Yusuke Kawakami, Tetsuo Hattori, Yoshiro Imai, Kazuaki Ando, Yo Horikawa, R. P. C. Janaka Rajapaksezbased on curvature computation might not have highreliability. In this paper, we propose how to perform
regression analysis using approximation formula ofcurvature.

yz

L Gauss z dz

(5)

0
z

1
2

2

2. Principle

2

z

exp

dz .

2

2

0

Here, Fig.1 shows the relationship between y= (x) andy= (z). So we can be obtained following Eq. (6).

2.1. Histogram Matching based on GaussianDistribution (HMGD)

x

x

y

x

x

d
L f x dx
dx 0

L ' x

1
.
L

h y

y

1
2

2

exp

L ' z,

2
2

.

f x

y

h(y)

y= (z)

22

dz. (7)

Gauss z .

(8)y= (x)(L)(x0+dx)

(x)

(3)

z
2

z
2

y

L
L

We understand the histogram of original image f(x)becomes uniform distribution h(y) by Eq. (3). Thismeans that brightness level of original image f(x) is
expanded to h(y).
Then, let Gauss(z) and (z) be the function that isdefined by Eq. (4) and (5), respectively.
Gauss zzdLexp
dz 2 2 0(2)

h y Lf x .

(6)

dz.

2

That is, we understand that f(x) becomes Gaussian
distribution Gauss(z) when we take the transformfunction as (1) and (5). Thus, HMGD processing is thefunction which defined by cumulative histogramtransformation the original histogram into Gaussian
histogram9.

At first, we have to expand brightness level of original
image histogram and convert into uniform distribution
histogram, because we aim to match Gaussian
distribution. From Eq. (1) and Fig. 1, we can derive Eq.
(2) and (3).
h y ' x02

If we perform Eq. (7),

0

f x

z
2

exp

And we can derive Eq. (7) from differential of Eq. (6).

(1)

L f x dx.

220

In the section, we describe the principle of HMGD
processing.
Fig. 1 shows the conceptual image of HMGD. Let f(x)and h(y) be two probabilistic density functions (PDF) on
real variables x and y, respectively. The PDF iscorresponding to histogram of image brightness level
which is discretely defined.
In addition, let y= (x) be a continuous and monotonic
increase function corresponding to cumulative
histogram of image brightness level between variables x
and y 7-9. And let y= (x) be defined by Eq. (1).

z

L

L f x dxxz0

z z0+dzx0z
1
LGauss(z)

x0+dx

x

f(x)

(4)
Fig. 1. Conceptual image of HMGD processing6, 9.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 576

Histogram Matching Based on

2.2. Peak Detection of Histogram

2.3. Variance Estimation

The HMGD processing need to calculate transforms
function for brightness peak of histogram. And thesolution to detect it is curvature computation of thehistogram.
Let y be a function with respect to x, the definition
curvature R(x) is given by Eq. (9)6-9.

In this section, we propose how to estimate the varianceof original-image histogram.
Fig. 2 shows the conceptual image of the original image
histogram which is variance 2 and average a. From Eq.
(14), we can describe R(x) following Eq. (15).

d2y
1
dx 2

Rx

2

dy
dx

.

K
L

K
2

u a
2 2

exp

2

x a
2 2

exp

2

2

0

Rx

(10)

Ri

2

du 1.

f x

x

i

K

g u du

2

2

0

u a
2 2

exp
0

2

a x g x . (15)

CH x

(16)

Now, we can calculate a constant C by using leastsquare regression analysis10 following Eq. (17).

CH i
2

n

C

i

~ N 0,

That is, we evaluate

Next, let y=f(x) be a function representing the
cumulative histogram which is represented Eq. (11).
That is, dy/dx and d2y/dx2 be described as Eq. (12) and(13), respectively. From Eq. (12) and (13), we obtain
the approximation of curvature R(x) as Eq. (14).
x2And, let C=1/ 2 and H(x) = (a-x)g(x) respectively, we
can derive Eq. (16).

2

,

1

g x

2

(9)

Let g(x) and K be Gaussian distribution function and acoefficient which is defined by following equation,
respectively.
g x

a x

Rx

3
2

,

2

i 1,

(17)

n

as follows;

H i Ri

i 1
n

,
Hi

2

2

1
C

(18)

i 1

du. (11)

y
dy
dx

g x

d2y
dx 2

K
2

2

a x
2

g x

1 g x

u a
2 2

exp

dg x
dx
a x

Rx

2

3
2 2

2

.

g x

a x
2

g x.

(12)

g(a)

(13)

(14)

0

a

L

x

Fig. 2. Conceptual image of the original image histogram.

From Eq. (14), we understand that the curvature R(x)varies the sign according to the value of x.9 That is, if
x<a → R>0 (downward convex shape), and if x<a →R<0 (upward convex shape).

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 577

Yusuke Kawakami, Tetsuo Hattori, Yoshiro Imai, Kazuaki Ando, Yo Horikawa, R. P. C. Janaka Rajapakse

3. Experimentation

References

Fig. 3 shows the example of results and thecorresponding histogram for original image and HMGD
image which applied variance estimation using
regression analysis.
In this case, we understand that HMGD image isreducing contrast than original image. And in the
HMGD image, the color tones become unnatural.

1. R. C. Gonzalez and R. E. Woods, Digital Image
Processing (Addison-Wesley Publishing Company,
1993).
2. B. Jahne, Digital Image Processing --Concepts,
Algorithms, and Scientific Applications-- 4th edition(Springer, 1995).
3. E. S. Umbaugh, Computer Vision and Image Processing:
A Practical Approach Using CVIP tools (Prentice Hall
PTR, 1998).
4. W. Burger and J. M. Burge, Principles of Digital Image
Processing: Fundamental Techniques (Springer 2009).
5. T. Izumi, T. Hattori, S. Sugimono, and T. Takashima,
Color Image Arrangement Using Elastic Transform on
Principal Component Axis (in Japanese), Journal ofJapan Society of Kansei Engineering 8(3) (2009) 667674.
6. Y. Kawakami, T. Hattori, D. Kutsuna, H. Matsushita,
Y. Imai, H. Kawano, R.P.C. Janaka Rajapakse,
Automated Color Image Arrangement Method Based on
Histogram Matching - Investigation of Kansei impression
between HE and HMGD -, International Journal of
Affective Engineering 14(2) (2015) ISSN 2187-5413, 8593.
7. Y. Kawakami, T. Hattori, Y. Imai, H. Matsushita,
H. Kawano, R. P. C. Janaka Rajapakse, Kansei
Impression and Automated Color Image Arrangement
Method, Journal of Robotics, Networking and Artificial
Life 1(1) (2014) ISSN 2352-6386, 60-67.
8. Y. Kawakami, T. Hattori, Y. Imai, H. Matsushita, H.
Kawano, and R. P. C. Janaka Rajapakse, AutomatedColor Image Arrangement Method Using Curvature
Computation in Histogram Matching, in Proceedings ofInternational Conference on Artificial Life and Robotics(ICAROB 2015) (Oita, Japan, 2015) ISBN 978-49902880-9-9, pp. 272-277.
9. Y. Kawakami, T. Hattori, Y. Imai, Y. Horikawa,
H. Matsushita, R. P. C. Janaka Rajapakse, AutomatedProcessing of Multiple-Brightness Peak Histogram Image
Using Curvature and Variance Estimation, Journal of
Robotics, Networking and Artificial Life 3(1) (2016)
ISSN 2352-6386, 55-60.
10. D. G. Kleinbaum, L. L. Kupper, A. Nizam, E. S.
Rosenberg, Applied Regression Analysis and Other
Multivariable Methods (Brooks/Cole Pub Co, 2013).

(a) Image: Original (left) 5, Variance estimated HMGD (right)
900

3000

800
2500

700
600

2000

500
1500

400
300

1000

200
500

100
0

0

1

21 41 61 81 101 121 141 161 181 201 221 241

1

22

43

64 85 106 127 148 169 190 211 232 253(b) Histogram: Original (left) 5, Variance estimated HMGD (right)

Fig. 3. Example of results and the corresponding histogram.

4. Conclusion
In this paper we proposed we have described that how
to estimate the variance of reference histogram, which isused in HMGD processing by using regression analysis.
As for the method, first we have detected the brightness
peak on the original-image histogram. Then we have
performed regression analysis based on the cumulative
histogram and its curvature value.
Through the result of experimentation, the varianceestimation based on the regression analysis image isreducing contrast than original one, and its color tones
become unnatural. That is, we consider that we have tofurther improvement of this method.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 578

Histogram Matching Based on Gaussian Distribution Using Variance Estimation- Comparing between Curvature Computation and Regression Analysis Yusuke Kawakami

DynaxT Co., Ltd., 2217-6 Hayashi
Takamatsu City, Kagawa 761-0301, Japan
Tetsuo Hattori, Yoshiro Imai, Kazuaki Ando, Yo Horikawa

Graduate School of Kagawa University, 2217-20 Hayashi
Takamatsu City, Kagawa 761-0396, Japan
R. P. C. Janaka Rajapakse

Tainan National University of the Arts, 66 Daci
Guantian District, Tainan 72045, Taiwan
E-mail: riverjp2002@gmail.com, {hattori imai, ando, horikawa}@eng.kagawa-u.ac.jp, janakaraja@gmail.com

Abstract
This paper describes variance estimation method comparing between regression analysis and curvature computation
which is used in Histogram Matching based on Gaussian Distribution (HMGD). In the previous paper, we havedescribed and illustrated that the variance estimation method have been considered of value for HMGD processing
results. Though we have considered that histogram of original image is not always ideal. So, in this paper we
propose improvement variance estimation method using regression analysis. First of all, we describe the principleof variance estimation methods using curvature computation, and regression analysis. Then, through some HMGD
processing experiment, we compare between curvature computation results and regression analysis.
Keywords: Image processing, Curvature, Variance estimation, Histogram matching, HMGD

HMGD processing could improve feeling (or Kansei)
impression better than original image6. And we haveaimed to improve HMGD processing, we have proposedthat how to estimate the variance of reference histogram,which is used in HMGD processing based on curvaturecomputation, and also illustrated these results.
However, we have considered that the histogram oforiginal image is not always ideal shape (i.e., Gaussian
distribution, etc). That is, the variance estimation
method based on curvature computation might not havehigh reliability.

1. Introduction
These days, automated image processing forenhancement of color images has been more familiar tous, for example, Digital Signage, Smart Phone, etc 1-3.
In the previous paper, we have presented that theHistogram Matching based on Gaussian Distribution(HMGD) processing is one of the automated image
arrangement method using Elastic Transformation 4-5
based on the brightness axis. And through thecomparative investigation, we have illustrated that

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 579

Yusuke Kawakami, Tetsuo Hattori, Yoshiro Imai, Kazuaki Ando, Yo Horikawa, R. P. C. Janaka Rajapakse

d2y
dx 2

In this paper, we describe principle of estimate
brightness peak of the original image, and also describe
two principle of variance method; (1) curvaturecomputation based, (2) regression analysis based. Then,
we illustrate some HMGD processing experiment andcompare between curvature computation results andregression analysis results.

a x

1 g x

Rx

3
2

In this section, we describe how to optimize the shapeof the reference histogram, which is used in the HMGD
processing9. First, we explain the conventional method
which is based on the curvature computation, andsecond, explain the proposed method which is based on
the regression analysis.
2.1.1. Variance Estimation based on the Curvature
Computation
Fig. 1 shows the conceptual image of the original image
histogram which is variance 2 and average a. And Fig.
2 shows its cumulative histogram. From Eq. (2), we
have the following Eq. (7) and Eq. (8).

(1).

Let g(x) and K be Gaussian distribution function and acoefficient which is defined by following equation,
respectively.
K

g x

L

K
2

2

2
2

x a
2 2

exp
u a
2 2

exp
0

f x

K

g u du

2

0

dy
dx

g x

2

K
2

2

exp
0

exp

K
.
2

2

2

(7)

2

,

(2)

ga

K
exp
2

2

2

x

K

ga

du 1.

u a
2 2

u a
2 2

a

2
2

a

(8)

2

gae1

Next, let y=f(x) be a function representing the
cumulative histogram which is represented Eq. (3).
That is, dy/dx and d2y/dx2 be described as Eq. (4) and (5),respectively. From Eq. (4) and (5), we obtain theapproximation of curvature R(x) as Eq. (6).
x

(6)

g x.

2

2.2. Variance Estimation

In the section, we describe the principle of brightness
peak detection of original image.
The Histogram Matching based on the GaussianDistribution4-9 processing need to calculate transforms
function for brightness peak of histogram. And thesolution to detect it is curvature computation of thehistogram.
Let y be a function with respect to x, the definition
curvature R(x) is given by Eq. (1)6-9.
2

a x

3
2 2

(5)

g x

2

From Eq. (6), we understand that the curvature R(x)varies the sign according to the value of x 9. That is, if
x<a → R>0 (downward convex shape), and if x<a →R<0 (upward convex shape).

2.1. Brightness Peak Detection of Original Image

dy
dx

a x

g x

2

Rx

2. Principle

d2y
1
dx 2

dg x
dx

2

.

2

du. (3)

Then, the value of curvature R at a
Eq. (9).

Ra

2

a

a

2

is represented

2

ga

2

1 ga

2

2

3

2

(9)

gae1

2
2

2

1

gae1

2

3

2

Let S=g(a)e-1, we have Eq. (10).

(4)© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 580

Histogram Matching Based on

Ra
H

2

2

S

S
1 S2

3

2

2
3

1 S

2

gae

1

gae1

1

2

H.

2

3

(10)

y

.

g(a)

2

That is, we understand that we can obtain reference
histogram variance 2. For example, let v
2 be thedistance from average a,
Ra v

2 2

Ra v

4
H,
v

H

2

v 2 (11).
2

Fig.01. Conceptual image of athe original image histogramx8, 9.
L
a
2
a
2

y

2.1.2. Variance Estimation based on the Regression
Analysis

R a

In the previous subsection, we show the Fig. 1 as theconceptual image of the original image histogram which
is variance 2 and average a. From Eq. (6), we candescribe R(x) following Eq. (12).

a x

Rx

1

g x

2

2

R(a)
Ra

a x g x . (12)

0

CH x

CH i

i

~ N 0,

Thus, we can evaluate
n

C

i

2

,

2

i 1,

n

H i Ri
,
Hi

2

2

1
C

2

a

a

2

x

Fig. 3 shows the example of results and thecorresponding histogram for original image and for
HMGD image which applied variance estimation,
respectively.
In this case, we understand that HMGD which appliedcurvature computation based variance estimation image
is softer and more natural contrast and than original
image. However, HMGD which applied regression
analysis based variance estimation image is reducing
contrast than original image, and the color tones become
unnatural.

(14)

as following Eq. (15).

i 1
n

a

3. Experimentation(13)

Now, we can calculate a constant C by using leastsquare regression analysis10 following Eq. (14).

Ri

2

L
Fig. 2. Conceptual image of the original image cumulative
histogram 8, 9.

And, let C=1/ 2 and H(x) = (a-x)g(x) respectively, we
can derive Eq. (13).

Rx

2

(15)

i 1

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 581Yusuke Kawakami, Tetsuo Hattori, Yoshiro Imai, Kazuaki Ando, Yo Horikawa, R. P. C. Janaka Rajapakse

reducing contrast than the others, and its color tones
become unnatural. That is, we consider that we have toimprove algorithm of variance estimation based on the
regression analysis.

900
800
700
600
500
400

References

300
200

1. R. C. Gonzalez and R. E. Woods, Digital Image
Processing (Addison-Wesley Publishing Company,
1993).
2. B. Jahne, Digital Image Processing --Concepts,
Algorithms, and Scientific Applications-- 4th edition(Springer, 1995).
3. E. S. Umbaugh, Computer Vision and Image Processing:
A Practical Approach Using CVIP tools (Prentice Hall
PTR, 1998).
4. W. Burger and J. M. Burge, Principles of Digital Image
Processing: Fundamental Techniques (Springer 2009).
5. T. Izumi, T. Hattori, S. Sugimono, and T. Takashima,
Color Image Arrangement Using Elastic Transform on
Principal Component Axis (in Japanese), Journal ofJapan Society of Kansei Engineering 8(3) (2009) 667674.
6. Y. Kawakami, T. Hattori, D. Kutsuna, H. Matsushita,
Y. Imai, H. Kawano, R.P.C. Janaka Rajapakse,
Automated Color Image Arrangement Method Based on
Histogram Matching - Investigation of Kansei impression
between HE and HMGD -, International Journal of
Affective Engineering 14(2) (2015) ISSN 2187-5413, 8593.
7. Y. Kawakami, T. Hattori, Y. Imai, H. Matsushita,
H. Kawano, R. P. C. Janaka Rajapakse, Kansei
Impression and Automated Color Image Arrangement
Method, Journal of Robotics, Networking and Artificial
Life 1(1) (2014) ISSN 2352-6386, 60-67.
8. Y. Kawakami, T. Hattori, Y. Imai, H. Matsushita, H.
Kawano, and R. P. C. Janaka Rajapakse, AutomatedColor Image Arrangement Method Using Curvature
Computation in Histogram Matching, in Proceedings ofInternational Conference on Artificial Life and Robotics(ICAROB 2015) (Oita, Japan, 2015) ISBN 978-49902880-9-9, pp. 272-277.
9. Y. Kawakami, T. Hattori, Y. Imai, Y. Horikawa,
H. Matsushita, R. P. C. Janaka Rajapakse, AutomatedProcessing of Multiple-Brightness Peak Histogram Image
Using Curvature and Variance Estimation, Journal of
Robotics, Networking and Artificial Life 3(1) (2016)
ISSN 2352-6386, 55-60.
10. D. G. Kleinbaum, L. L. Kupper, A. Nizam, E. S.
Rosenberg, Applied Regression Analysis and Other
Multivariable Methods (Brooks/Cole Pub Co, 2013).

100
0
1

21 41 61 81 101 121 141 161 181 201 221 241(a) Original5.
3000

2500

2000

1500

1000

500

0
1

22

43

64 85 106 127 148 169 190 211 232 253(b) HMGD processing which applied curvature computation based varianceestimation5.
3000

2500

2000

1500

1000

500

0
1

22

43

64 85 106 127 148 169 190 211 232 253(c) HMGD processing which applied regression analysis based varianceestimation.

Fig. 3. Example of results (left) and the correspondence
histogram (right).

4. Conclusion
In this paper we described the principle of brightness
peak detection, estimate the variance of reference
histogram, which is used in HMGD processing by using
curvature computation, and we propose varianceestimation based on the regression analysis.
Through the comparing experimentation, HMGD
processing image which applied variance estimation
based on the curvature computation image is becomes
soften and natural contrast from original. In contrast,
HMGD processing image which applied the varianceestimation based on the regression analysis image is© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 582

An Extended Optimal Stopping Method for Structural Change Point Detection Problem
Yoshihide Koyama

Kagawa University, 2217-20 Hayashi Cho
Takamatsu City, Kagawa 761-0396, Japan
Tetsuo Hattori, Yoshiro Imai, Yo Horikawa, Yusuke Kawakami

Kagawa University, 2217-20 Hayashi Cho
Takamatsu City, Kagawa 761-0396, Japan
Hiromichi Kawano

NTT advanced technology Company Ltd, Mitaka, Tokyo, Japan
E-mail: {hattori, imai, horikawa}@eng.kagawa-u.ac.jp, hiromichi.kawano@ntt-at.co.jp

Abstract
Previously, we have proposed and formulated the SCPD (Structural Change Point Detection) problem in time series
data as an Optimal Stopping one using the concept of DP (Dynamic Programming). And also we have shown thesolution theorem in the form of inequality. In this paper, based on the relation between the solution of Optimal
Stopping and NSPRT (New Sequential Probability Ratio Test), we present the extended Optimal Stopping Method
in order to obtain more practical one, considering a loss cost and an action cost involved by failure prediction.
Keywords: Time series data, Structural change point detection, Optimal stopping problem, NSPRT (New Sequential
Probability Ratio Test)

Above all, it is very important to detect the change
point as quickly and correctly as possible in the second
stage, in order to renew the accurate prediction model assoon as possible after the change detection.
For the problem of the structural change point
detection (SCPD) problem, or change point detection(CPD), some methods have been proposed 1-4. The
standard well known method is Chow Test used ineconometrics2. Chow Test carries out a statistical test by
setting the hypothesis that the change has occurred attime t for all of data obtained so far.

1. Introduction
There are three stages to be considered for ongoingtime series analysis as follows;(i) the stage of prediction model construction,(ii) the stage of structural change detection (and/or
disparity detection between the model and observingdata),(iii) the stage of renewal of prediction model.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 583

Yoshihide Koyama, Tetsuo Hattori, Yoshiro Imai, Yo Horikawa, Yusuke Kawakami, Hiromichi Kawano.

As for the SCPD problem, we have previously
formulated the change detection method as a solving
method for Optimal Stopping Problem with an action
cost, using the concept of DP (Dynamic Programming)
5-6. Moreover, we have proposed a model-introduced
SPRT (Sequential Probability Ratio Test) as a New
Sequential Probability Ratio (NSPR) test method7-8.
In this paper, we extend the Optimal Stopping
Problem as a more general problem, and we show theextended solution. Moreover, we present the relation
between NSPR and the extended optimal solution
theorem for SCPD.

E
1-R
R

1-R

Fig.2. Internal model of the State E.

Ec
1-Rc

Rc
Rc

in

2.1. Structural Change Model 5-6

λ

out
1-Rc

We assume that the structural change is Poisson
occurrence of average λ, and that, once the change has
occurred during the observing period, the structure does
not go back to the previous one. The reason why we set
such a model is that we focus on the detection of thefirst structural change in the sequential processing (orsequential test). The concept of the structural change
model is shown in Fig. 1.
Moreover, we introduce a more detailed model. Let
R be the probability of the failing when the structure isunchanged. Let Rc be the probability of the failing when
the structure change occurred. We consider that Rc is
greater than R, i.e., Rc>R. The detailed model for theState Ec and E are illustrated as similar probabilistic
finite state automatons in Fig.2 and Fig.3, respectively.

E

out

in

2. Definitions and Equations

1-λR

Fig.3. Internal model of the State Ec.

2.2. Optimal Stopping Formulation and its Slution
Theorem 5-6
Let the cost(n) be a n as a linear function for n,where a is the loss caused by the one-time prediction
failure. And for simplicity, let T and A denote the Total
loss cost and an involved action cost, respectively. Then,the evaluation function is denoted as the following
equation (1).
T A an(1)
We recursively define a function ET(n, N ) to obtain
the optimum number of times n that minimizes the
expectation value of the evaluation function of Equation(3), using the concept of DP (Dynamic Programming).
Let N be the optimum number. Let the function EC(n, N )be the expectation value of the evaluation function at
the time when the failing has occurred in continuing ntimes, where n is less than or equal to N, i.e., 0 n N .
Thus the function is recursively defined as follows.

1.0
Ec

Ec : State that the structural change occurred.
E : State that the structure is unchanged.
λ: Probability of the structural change occurrence.
(Poisson Process.)
Fig.1. Structural change model.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 584

An Extended Optimal Stopping(if n = N ) ET (n, N )

A a・N

(2)

P( Sn 1 | S n ・) a・nn(1 P(Sn 1 | S )) ET (n 1, N )(if n < N ) ET (n, N )(3)where S means the state of failing in continuing n times,
Sn 1 the state of hitting at the (n+1) th observed data,
and P( S n 1 | S n ) means the conditional probability
that the state Sn 1 occurs after the state Sn.
The first term in the right-hand side (RHS) of theequation (3) indicates the expectation value of theevaluation function at the time when hitting happens atthe (n+1)th data after the continuing n times failing. The
second term in the RHS of the equation (3) indicates the
expectation value of the evaluation function for the time
when failing happens at the (n+1)th data after
continuing n times failing.
Then, from the definition of the function ET (n, N ) ,the goal is to find the N that minimizes ET (0, N ) ,because the N is the same as n that minimizes the
expectation value of the evaluation function of (1).
[Optimal Solution Theorem (OST)]The N that minimizes ET(0,N) is given as the largest
number n that satisfies the following Inequality (4).
na ( A a )・P( Sn | S n 1 )(4)

where the number N+1 can also be the optimum one thatminimizes ET(0,N), i.e., ET(0,N) = ET(0,N+1), only ifa ( A a )・P( S N 1 | S N )[Extended Optimal Solution Theorem (EOST)]The N that minimizes ET(0,N) is given as the largest
number n that satisfies the following Inequality (5).

a* ( A a* )・P( Sn | S n 1 )(5)where the number N+1 can also be the optimum one thatminimizes ET(0,N), i.e., ET(0,N) = ET(0,N+1), only ifa*( A a * )・P( S N 1 | S N )2.4. New Sequential Probability Ratio (NSPR)
Based on Structural Change Model 7-9
Let a1a2 , , ai , an ai {IN, OUT} be a string(or symbol sequence) obtained from the observed data.
~Let θi and θi be the conditional probability that
outputs the observed data (or above symbol sequence,
Cn a1a 2 a n in the state S0 and S1, respectively. That~is, it means that θi {R, 1 R} and θi {Rc , 1 Rc } ,respectively.
And let P (a1 a n , H 0 ) and P ( a1 a n , H1 ) be thejoint probability of the symbol sequence Cn happens
with the event H0 (the structural change is not occurred)and H1 (the change is occurred), respectively.
Then, the following New Sequential Probability
Ratio (NSPR)
n is represented as the following
Eq.(6).

NSPR

P(H1 | a1
P(H 0 | a1nn2.3. Extended Optimal Stopping Formulation

P(H1 | Cn )P(H 0 | Cn )k 1
n ~) k 1･ θj )( θi )

((1

k 1

j 0

P (C n , H 1 )P(Cn , H 0 )i kn) n Πθi(1

We extend the meaning of the loss a as the averageof loss cost a* caused by the “so far n-time prediction
failure”. The “so far n-time prediction failure” does notmean the continuous n-time prediction failure9. And A
means the loss cost involved by some action, e.g.,renewal and/or disposal of some equipments and/or
some systems after the structural change point detection.
Then, Sn does not mean “the state of failing incontinuing n times”, but means “the state of so far ntimes prediction failure”. Thus we obtain an extended
OST as follows.

an )
an )

i 1

(6)

From Eq.(6), we have the following recursive
equation (7).
~1(Λn 1 γ)( nn )
1 γ

Λn
where

0

1,

~
0

1,Λ0

0

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 585(7)

Yoshihide Koyama, Tetsuo Hattori, Yoshiro Imai, Yo Horikawa, Yusuke Kawakami, Hiromichi Kawano.

If the NSPR is greater than 1.0, we can regard that thestructural change has been occurred before the present
time.

relation between New Sequential Probability Ratio(NSPR) method and the Extended Optimal Solution
Theorem. From this relation, we can use the NSPR as anextended optimal stopping method for structural change
point detection in ongoing time series data.

3. Relation between NSPR and EOSTWe show the relations using the probability in the
Extended Optical Solution Theorem, considering Rc >>R.
P Sn 1 | S n

1 R 1 P Ecn | S n
1 RReferences
1. R.Jana and S.Dey, Change detection in Teletraffic
Models, IEEE Trans. Signal Processing, vol.48, No.3,
pp.846-853, 2000.
2. Chow,G.C., Tests of Equality Between Sets of
Coefficients in Two Linear Regressions, Econometrica,
Vol.28, No.3, pp.591-605, 1960.
3. S.MacDougall,
A.K.
Nandi
and
R.Chapman,Multisolution and hybrid Bayesian algorithms forautomatic detection of change points, Proc. of IEEE
Visual Image Signal Processing, vol.145, No.4, pp.280286, 1998.
4. E.S.Page, Continuous inspection schemes, Biometrika,
Vol.41, pp.100-115, 1954.
5. Tetsuo Hattori, Katsunori Takeda, Izumi Tetsuya,
Hiromichi Kawano, "Early Structural Change Detection
as an Optimal Stopping Problem (I) --- Formulation
Using Dynamic Programming with Action Cost ----",
Proc. of the 15th International Symposium on Artificial
Life and Robotics (AROB15th'10), ISBN 978-4-99028804-4, pp.763-766, 2010.
6. Hiromichi Kawano, Tetsuo Hattori, Katsunori Takeda,
Izumi Tetsuya, "Early Structural Change Detection as an
Optimal Stopping Problem (II) --- Solution Theorem and
its Proof Using Reduction to Absurdity ----", Proc. of the
15th International Symposium on Artificial Life andRobotics (AROB15th'10), ISBN 978-4-9902880-4-4,
pp.767-772, 2010.
7. Yoshihide Koyama, Tetsuo Hattori, Hiromichi Kawano,“Model Introduced SPRT for Structural Change
Detection of Time Series (I)”, Journal of Robotics,
Networks and Artificial Life (ISSN: 2352-6386), Vol.1,
No.1, pp.55-60 (Atlantis Press, Jun. 2014)
8. Yoshihide Koyama, Tetsuo Hattori, Katsunori Takeda,
Hiromichi Kawano, “Model Introduced SPRT for
Structural Change Detection of Time Series (II)”, Journalof Robotics, Networks and Artificial Life (ISSN: 23526386), Vol.1, No.3, pp.237-243 (Atlantis Press, Dec.
2014).
9. Tetsuo Hattori, Yoshihide Koyama, Yusuke Kawakami,
Yoshiro Imai, Yo Horikawa, Hiromichi Kawano:"Relation between Optimal Stopping Solution and NSPR
for Structural Change Point Detection Problem”, Proc. ofThe 2016 International Conference on Artificial Life andRobotics (ICAROB 2016), ISBN 978-4-9908350-1-9,
pp.44-47, Jan. 29-31, Okinawa, Japan.

1 Rc P Ecn | S n

P Ecn | S n Rc

R

(8)Therefore, we have

P Ecn | S
P E |SP Sn 1 S n

1 R

n

Rc

R

,
(9)

P Sn 1 | S n
1 Rc
Rc R

n

Since
P Ecn | S n

P H1 | S n , P E | S n

P H0 | S n

we haven

P H1 | S n
P H0 | S n

P Sn 1 | S n

1 R

P Sn 1 | S n

1 Rc

(10)

If P S n 1 S n is a monotonous decreasing function
with respect to n, NSPR becomes an increasing one.
From the EOST, the optimal N is the maximum n thatsatisfies (5), so the N is the the maximum n that satisfies
the following Inequality (11).
P H1 S nn

P H0 Sn

A
A
Rc

R

a*
A
A

If R 0.05, Rc 0.9, A 10 , a *have the threshold21.5 .

a*(11)

1 , then we

4. Conclusion
We have proposed the extended Optimal Stopping
Formulation for the SCPD problem and its extended
solution theorem. Moreover, we have presented the© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 586

A Study of Sentimental Value Analysis for Tweeting Message
Shunsuke Doi, Shinya Hara, Yoshiro Imai, Tetsuo Hattori

Department of Electronics and Information, Faculty of Engineering, Kagawa University
2217-20 Hayashi-cho Takamatsu City, Kagawa pref 761-0396, Japan
Yusuke Kawakami

DynaxT Co., Ltd.
2271-6 Hayashi-cho Takamatsu City, Kagawa pref. 761-0113, Japan
E-mail: {s13t243, 13t258y}@stmail.eng.kagawa-u.ac.jp, {imai, hattori}@eng.kagawa-u.ac.jp
riverjp2002@gmail.com

Abstract
In the recent years, social media analysis has brought several kinds of effective results to us in the domains ofindustries, education, social science, economics, and so on. This study focuses on Twitter used by students ofuniversities, analyzes tweeting data (messages) by students from Twitter, and calculates sentimental values fromthe according data. Our approach includes deciding to select and cheese suitable Twitter accounts who tweet
interesting messages and acquire a possible mount of their relevant message, specifying and defining value forword of messages for sentimental dictionary, categorizing and listing such a calculated value for each word or
expression as item of the dictionary, using these values as "Sentimental Values" for calculation of tweetingmessages, calculating Sentimental Values for tweeting message from students of selected universities, checking
statistical relation between the above "Sentimental Values" and published data. It also investigates existence ofsome relations between calculated sentimental values and practical thought of students. At the same time, it tries todiscuss whether the above procedure and analysis can visualize conventionally hidden relationship between
contents of tweeting messages and characteristic behavior of students in categorized universities.
Keywords: Twitter, Sentiment analysis, Data mining, Visualization, Hidden relation.

This study is to perform some kind of Big Data
Analysis2 as an example of visualization of sentimental
words extracted from messages of Twitter and toanalyze hidden or unknown relation between
sentimental value and the relevant behavior/phenomena.
We have defined translation scheme from words inTwitter's message into sentimental values, applied thescheme into data mining with calculation of sentimental
values for tweeting messages acquired from Twitter,and demonstrated relation between sentimental values
and categorized human behavior.
This paper includes the following five sections, namely
the next section describes our related works, the third

1. Introduction
In recent years, young people use social media such as
Twitter. And information processing for social media
can bring several kinds of effective results to us in thedomains of industries, education, social science,
economics, and so on. We can expect that our current
environment will bring us useful facilities to analyze
messages from Twitter based on data mining1 and their
attractive results to visualize normally hidden relations
among several kinds of events and phenomena.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 587

Shunsuke Doi, Shinya Hara, Yoshiro Imai, Tetsuo Hattori, Yusuke Kawakami

section illustrates our approach of sentiment analysis for
Twitter, the fourth section discusses our results and theirvisualization of relation between sentimental values andbehavior/phenomena, and finally the last section
summarizes our conclusion.

work are: a) the pre-processing of tweets to normalize
the language and generalize the vocabulary employed toexpress sentiment; b) the use minimal linguistic
processing, which makes the approach easily portable toother languages; c) the inclusion of higher order ngrams to spot modifications in the polarity of thesentiment expressed; d) the use of simple heuristics toselect features to be employed; e) the application ofsupervised learning using a simple Support Vector
Machines linear classifier on a set of realistic data.”
Referring the above works and our previous research,
we have tried to apply sentiment analysis and visualize
normally hidden relation between sentimental values
and behavior/phenomena.

2. Related Work
This section introduces some related works about
twitter-based data-mining, knowledge discovery andsentiment analysis.
Ley Zhang of HP Laboratory reported in their article3“With the booming of microblogs on the Web, people
have begun to express their opinions on a wide variety
of topics on Twitter and other similar services.
Sentiment analysis on entities (e.g., products,
organizations, people, etc.) in tweets (posts on Twitter)thus becomes a rapid and effective way of gauging
public opinion for business marketing or social studies.
However, Twitter's unique characteristics give rise to
new problems for current sentiment analysis methods,
which originally focused on large opinionated corpora
such as product reviews.”
Mikalai Tsytsarau and Themis Palpanas described intheir journal article4 “In the past years we havewitnessed Sentiment Analysis and Opinion Mining
becoming increasingly popular topics in Information
Retrieval and Web data analysis. With the rapid growthof the user-generated content represented in blogs, wikis
and Web forums, such an analysis became a useful tool
for mining the Web, since it allowed us to capture
sentiments and opinions at a large scale. Opinion
retrieval has established itself as an important part ofsearch engines. Ratings, opinion trends andrepresentative opinions enrich the search experience ofusers when combined with traditional document
retrieval, by revealing more insights about a subject.
Opinion aggregation over product reviews can be very
useful for product marketing and positioning, exposing
the customers' attitude towards a product and its
features along different dimensions, such as time,
geographical location, and experience.”
Alexandra Balahur of European Commission Joint
Research Centre pointed out in her international
conference paper5 “This paper presents a method for
sentiment analysis specifically designed to work with
Twitter data (tweets). The main contributions of this

3. Sentimental Analysis as Data Mining
Schematic diagram of our sentimental analysis flow canbe illustrated and explained in Fig.1. We have focusedon specific data based on speakers' attributes as target
users. After connecting Twitter as Web-based Social
Media, our system has acquired an amount of data to beanalyzed with "twpro API" and "Twitter API". With
these data, the main part of our system, which isconstructed with multiple numbers (#1-#n) of modules,
performs a series of manipulations for information
processing from data into generated results which
include some temporary files.

Fig.1. Schematic Diagram for System Configuration of Data
Mining of Tweeting Message and Sentimental Analysis.

From top to bottom in Fig.2, our system sequentiallydefines target users of Twitter, obtains tweetingmessages from defined users, prepares Sentimental
dictionary based on those messages, computes
sentimental values for each users, and then generates
sets of such values focused groups (universities in ourcase). This part is the main body of our system, showing"System Configuration and Processing Flow" and can© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 588

A Study of Sentimental

accomplish some kind of sentiment analysis andgenerate our interesting results. We have obtained
related relation between the above files based onmessages from Twitter.
With the sentimental value for each university, we willcompare the published data and try to visualize
normally hidden relation between tweeting messages
and behavior and/or phenomena in the next section.

Starbucks Coffee shops, urban in other words, used totweet relatively negative messages.

Fig.3. Relation between Sentimental values and numbers ofStarbucks Coffee shops.

The next year, 2015, we have faced to the different
phenomena. When we dare to ignore narrow range ofsentimental values from 0.4 to 0.5 in Fig.4, it may also
describe that there seems to be positively-correlated
between positive sentimental values and numbers ofStarbucks Coffee shops. In 2015, students of university
located in the city which has relatively more numbers ofStarbucks Coffee shops, namely urban, have changed totweet relatively positive messages.
Fig.2. System Flow of Data Mining of Tweeting Message andSentimental Analysis with Dictionary of Sentiment Value.

4.

Results and Discussion

This section compares results in 2014 and 2015 andthen discusses comparison of sentimental values in 2014
and 2015.
4.1. Comparison of analyzed results in 2014 and2015
For the cases of 2014 and 2015, we have compared ournormalized sentimental values (from 0.0 to 1.0) with thenumbers of Starbucks Coffee shop and show the resultsin Fig.3 and Fig.4 respectively. Our normalized
sentimental values have characteristics where the range
from 0.0 to 0.5 means negative feelings and the range
from 0.5 to 1.0 means positive ones.
Fig.3 may describe that there is negatively-correlated
between negative sentimental values and numbers ofStarbucks Coffee shops. In 2014, students of university
located in the city which has relatively more numbers of

Fig.4. Relation between Sentimental values and numbers ofStarbucks Coffee shops.

4.2. Discussion of Sentimental Values in 2014
and 2015
By comparison with results in 2014 and 2015, we
discuss our approach of sentimental analysis. Table 1
shows our results of sentimental analysis for tweetingmessages from students of previously defined
universities, whose identifier, IDs are specified from
001 to 026 in Table 1.In 2014, there is only one, ID=018,© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 589

Shunsuke Doi, Shinya Hara, Yoshiro Imai, Tetsuo Hattori, Yusuke Kawakami

whose score of sentimental values is more than 0.5,
namely others are less than 0.5, so almost in 2014 have
tweeted negative messages. In 2015, sentimental values
have varied from 0.4 to 0.90 approximately. Fig.5.
shows bar chart about normalized sentimental values in2014 and 2015. Generally speaking, such values in 2014
are relatively lower than ones in 2015.
We can assume that two patterns of relations between
2014 and 2015. One pattern is a group of students who
have tweeted negatively in 2014 and 2015. Anotherincludes students who have tweeted negatively in 2014
but done positively in 2015. Fig.6 demonstrates thattargets can be divided into two groups described before.

analyzed results and several information from the above
documents, such as population of district, number ofgovernmental universities' students, number ofStarbucks coffee shops, and so on.

Table 1. Sentimental Values in 2014 and 2015.
ID
001
002
003
004
005
006
007
008
009
010
011
012
013

2014
0.398
0.362
0.328
0.447
0.109
0.321
0.325
0.418
0.229
0.364
0.428
0.218
0.313

2015
0.682
0.420
0.402
0.533
0.567
0.875
0.638
0.451
0.628
0.439
0.532
0.467
0.497

ID
014
015
016
017
018
019
020
021
022
023
024
025
026

2014
0.462
0.439
0.352
0.374
0.672
0.431
0.495
0.338
0.311
0.306
0.268
0.284
0.357

2015
0.509
0.503
0.591
0.739
0.595
0.464
0.504
0.509
0.518
0.434
0.753
0.599
0.574

Fig.6. Relation between Sentimental values in 2014 and 2015.

References
1. A. K. Choudhary, J. A. Harding and M. K. Tiwari,
Data mining in manufacturing: a review based on
the kind of knowledge, J. Intelligent
Manufacturing, 20(5) (Oct. 2009 ) pp. 501–521.
2. S. C. Lewis, R. Zamith and A. Hermida, Content
Analysis in an Era of Big Data: A Hybrid
Approach to Computational and Manual Methods.
J. Broadcasting & Electronic Media, 57(1) (2013)
pp. 34–52.
3. L. Zhang, R. Ghosh, M. Dekhil, M. Hsu and B. Liu,
Combining Lexicon-Based and Learning-Based
Methods for Twitter Sentiment Analysis, Technicalreport, HP Laboratories, (2011) 7 pages.
http://www.hpl.hp.com/techreports/2011/HPL2011-89.pdf
4. M. Tsytsarau and T. Palpanas, Survey on mining
subjective data on the web, J. Data Mining andKnowledge Discovery 24(3) (May 2012) pp.478514.
5. Alexandra Balahur, Sentiment and Social Media
Analysis, in Proc.
4th Workshop on
Computational Approaches to Subjectivity (June2013) pp. 120-128.

Fig.5. Bar Chart of Sentimental values in 2014 and 2015.

5. Conclusion
We have tried to demonstrate and visualize normallyhidden or unknown relations between messages fromTwitter and information of published documents bymeans of sentimental analysis. For example, we havebeen analyzing relationship between sentimental© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 590

Molecular Artificial Intelligence by using DNA reactions

Yasuhiro Suzuki

Graduate School of Information Science, Nagoya, University, Furocho, Chikusa
Nagoya City, 464-8601, Japan
Rie Taniguchi

Graduate School of Informatin Science, Nagoya University, Furocho Chikusa,
Nagoya City, 464-8601, Japan
E-mail: ysuzuki@nagoya-u.jp, mesomokyuu@gmail.com
http://sci-of-harness.info

Abstract
We have developed molecular Artificial Intelligence system by using DNA molecules, where "intelligence" meansthat the reaction system can "select" DNA molecules to sustain their reactions. We have bio-chemically
implemented the reaction system by using the DNA strand-displacement reaction and have obtained several
mutated DNA sequences that can sustain the reactions. We confirmed that when we give non-mutated input andmutated input sequences (the system can also use them as input), the system is able to select higher concentration
one, regardless of it having mutation or not. And we confirmed that reaction behaviors in the time series ofconcentration of non-mutated input and mutated input show oscillations; it would show that the system selects
higher concentration one in between non-mutated and mutated one according to its concentration. Since the system
exhibits adaptive autonomous behaviors, this DNA reaction networks system realize molecular Artificial
Intelligence.
Keywords: Molecular Robotics, Molecular Artificial Intelligence, DNA computing, Seesaw gate

On the definition of intelligence has been discussed not
only in AI but also in philosophy and there haveproposed many standpoints and definitions from each ofthem. We will take the standpoint proposed by Kazuhisa
Todayama [1] and Ruth Millican [2]; in order let
systems to be autonomous likewise a living systems,(there are several requirements that they proposed but)
one main requirement is having problems to take care ofthemselves; such problems are affordable from its
environment including us; for example, an electric robot
needs electric power supply, in the most of cases, usersof the robot (environment) take care of and recharge it

1. Introduction
The aim of this contribution is composing molecular
artificial intelligence, molecular AI for molecular robots
that are made by organic materials such as DNA orproteins.
It has been developed bio-computers; various biocomputers with natural media such as DNA, slime
molds have been realized; and many kinds of parts tosynthesize a bio-computer, by using mainly DNA, suchas logic gates or circuits have been produced. Based onthese progresses, molecular robots have been created.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 591

Yasuhiro Suzuki, Rie Taniguchi

or pre-programmed in the robot to go to recharge place
themselves, such as a clean robot [3].
Their claim is that in order let the robot to beautonomous, we should give only minimal settings toadapt its environment; it almost impossible to give sucha perfect minimal setting so Millican claims thatevolution mechanisms are irreducible to refine theminimal settings by evolutional searching with
mutations and natural selections [2].
On this standpoint, we reconsider what is intelligence ofliving systems?; living systems in wild life must selectappropriate food sources from the environment; even if
parents do not teach appropriate and not toxic foods,
even if they have instructors or not, must selectappropriate one and avoid harmful ones, otherwise willgo extinct. Hence select-ability, ability to makeappropriate selections must be one factor of theminimal settings.
We prepare DNA reaction networks that keep on
maintaining the start sequence, where the start sequenceinteracts with an input (food) sequence and theytransformed into intermediate sequences and from theinteractions in the intermediate sequences, start
sequence is re-produced. Hence the reaction system canmaintain start sequence.
Firstly, we modified a well-known DNA reaction
systems, the seesaw gate and constructed the selfmaintaining reaction system, experimentally andconfirmed it works (it is notice that it is not simulation
by PC).
Next, we mutated an input sequence randomly andexamined if the reaction behaviors were changed. We
confirmed that, in 6 cases in 38 trials, even if it has
mutation in the input sequences, the system still works
correctly and reproduces the start sequences. We
examined efficiency of such reactions by comparing
reaction speed and quantity changes of input sequences
and it has shown that they are not significant different inefficiency between no-mutation and mutated input cases.
Finally, we gave not mutated (normal) input
sequences and mutated sequences to the system andobserved if the system is able to select appropriate one,
where appropriate means to select the input sequencesin the higher concentration regardless of it has mutation
or not. We confirmed that the system can select higher
concentration irrespective of having mutation or not.
And we also confirmed behaviors in the time series ofconcentration of normal input and mutated input
sequences showed oscillations; we conjecture that the
because of system select higher concentration one,selected input sequences are switched among nonmutated and mutated input sequences.
These results illustrate that the system made of DNA
reaction networks are able to select input sequences inhigher concentration so it will able to adapt theenvironmental change. Therefore, from Biological andphilosophical points of views, this system realizes
molecular Artificial Intelligence.
2. Methods
Since simple hybridization reactions cannot be cascaded,other reaction mechanism such as strand displacement
reactions [4] have been used. Strand displacement is theprocess which two strands partially or fully hybridize toeach other, displacing one or more pre-hybridized
strands in the process. Strand displacement can beinitiated at complementary single-stranded domains(referred to as toeholds) and progresses through arandom walk-like branch migration process. Strand
displacement reactions can be cascaded to eliminate thisneed for external triggers at every step;
this enables the engineering of complex autonomous
systems.
Strand displacement releases at least one single
stranded nucleic acid product, such as the output. In a
DNA strand displacement cascade, this output serves as
the input to the downstream reaction. Unlike electric or
biological circuits that are powered by a standardizedenergy source (electrical voltages or ATP
concentrations), strand displacement-based circuits
cannot be easily recharged because the reactant species
for each strand displacement reaction are different.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 592

Molecular Artificial Intelligence by

Fig. 2. Fig. 2. Seesaw gate reaction of DNA sequences: (a)Fig.1. Strand displacement reaction; Strand Displacement
Reaction; Dynamic DNA nanotechnology often makes use of
toehold-mediated strand displacement reactions. In thisexample, the short arrow binds to the single stranded toehold
region on the strand, and then in a branch migration processacross region 2, the strand is displaced and freed from thecomplex. Reactions like these are used to dynamicallyreconfigure or assemble nucleic acid nanostructures. In
addition, the strands can be used as signals in a molecular
logic gate.

We modified the Seesaw gate reaction [4] by composedby the strand displacement reaction (Fig.2.); in order toconfirm these reactions (1) and (2) circulate, we add thereaction; fuel + gate:marker gate:fuel + marker (3);input + gate:fuel ↔ gete:input + fuel (1),gate:input + output ↔ gete:output + input (2),fuel + gate:marker gate:fuel + marker (3).
The reaction (3) examines the fuel sequence is produced
and confirm the reaction (2) regenerate the input
sequence. We confirm that the input sequence has been
regenerated.

Abstract gate diagram. Red numbers indicate initialconcentrations. (b) The DNA gate motif and reaction
mechanism. S1, S2, S3 and S4 are the recognition domains; T
is the toehold domain; T0 is the Watson–Crick complement ofT, etc. Arrowheads mark the 30 ends of strands. Signal strands
are named by their domains from 30 to 50, All reactions arereversible and unbiased; solid lines indicate the dominant
flows for the initial concentrations shown in (a), while thereverse reactions are dotted. (c)3. ResultFrom biochemical experiments, we observed that when
we added non-mutated input and mutated input, systemselect higher concentration one (Fig.3). And we also
confirmed behaviors in the time series of concentrationof normal input and mutated input sequences showedoscillations (Fig.3). And when the concentration
decreases, the concentration of another input increases,
hence we conjecture that the because of system selecthigher concentration one, selected input sequences are
switched among non-mutated and mutated input
sequences. These results illustrate that the system madeof DNA reaction networks are able to select input
sequences in higher concentration so it will able toadapt the environmental change. Therefore, from
Biological and philosophical points of views, thiesystem realizes molecular Artificial Intelligence (Fig.3).

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 593

Yasuhiro Suzuki, Rie Taniguchi

Fig. 3. Time series of concentration of normal input(below) and mutated input (upper); where mutated input
was selected and concentration decreases.
Acknowledgements
We thank for thought provoking discussion with prof.
Kazuhisa Todayama (Nagoya University). This work
supported by Grant in Aid for Scientific Research on
Innovative Areas, Molecular Robotics, No: 24104003,(B) No. 16H3093.
References
1. K. Todayama, Tetsugaku Nyumon (in Japanese,
introduction to philosophy, Chikuma shobo publishing,2014)
2. R. G. Millican, Varieties of Meaning, (MIT press, 2002).
3. D.Y. Zhang, G. Seelig, Dynamic DNA nanotechnology
using strand-displacement reactions Nature Chemistry 3,
103–113 (2011) doi:10.1038/nchem.957
4. L.Qian and E. Winfree, A simple DNA gate motif forsynthesizing large-scale circuits, J R Soc Interface. 2011
Sep 7; 8(62): 1281–1297.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 594

Neural Networks by using Self-Reinforcement Reactions

Yasuhiro Suzuki

Graduate School of Information Science, Naogya University, Furocho Chikusa
Naogya City, 464 860, JAPAN

Abstract
We consider a chemical reaction network model in which selections of reaction are stochastic and depend on pasthistory. In this chemical reaction network, we found the emergence of Auto-Catalytic Sets (ACS) and complex
dynamics in which ACS are repeatedly created and destroyed; we have called this reaction system as the SelfReinforcement Reactions, SRR. We developed a neural-networks system by using SRR and confirm the neuralnetwork of SRR can solve a linear classification problem.
Keywords: Artificial Chemistries, Chemical Reaction Networks, Perceptron, Linear Classification Problem, Self
Reinforcement Reaction, Neural Networksof element. We denote the duplication (multiplicity) ofan element as M (a), for a ∈ A and in case c /∈ A, M(c) = 0; for example M (a) and M (b) of {a, a, b, b} are
2, and M (c) = 0; in the mathematical description, amultiset is described as; < sup, M () >, in which sup is a
simple set of elements, in this paper we describe amultiset by denoting the same alphabet in its number ofmultiplicity such as {a, a, b, b} or a vector w =(M (a1)M (a2) . . . M (an)).
The union of two multisets M1, M2 is the same as
the union of simple set and in vector description, theunion of multisets is addition of vectors w1 and w2.
And inclusion of sets is also the same as the simple set,when M1(a) ≤ M2(a) for all a ∈ A, the multiset M1 isincluded in M2 and we write M1 ⊆ M2.
A reaction rule is a pair of multiset, we denote A# asa set of all combinations of multisets over A and in the
combinations, an empty multiset is included. A reaction
rule l → r, l, r ∈ A# is described as a pair of multiset
likewise chemical equations or a pair of its vector
expression; and in some case, we can describe a reaction
rule as a vector r, r = −l + r, it is simple and good for
examining the dynamics of an ARMS, but this

1. Introduction
We consider an abstract model of chemical
reactions, a type of “abstract chemistry”. The model
assumes that substances can interact with each otheraccording to reaction rules which change the amounts ofthe substance, and that reaction tendencies change
depending on the reaction history. Reaction rules are
assumed to be of the form x y, meaning that theamount of substance x decreases and the amount of
substance b increases. Reactions occur at rates which
depend on the amount of the first substance x and thestrength of the reaction x
y. The strength of eachreaction is increased in proportion to how often thereaction has occurred recently.
1.2 An Artificial Chemistry, Abstract Rewriting
System On Multisets
We have been proposed an artificial chemistry, based on
the Abstract Rewriting System, ARS; the ARS is atheoretical model of computation; we expand ARS on
rewriting system on the multiset. A multiset is defined
as a simple set and a map, which returns the duplication© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 595

Yasuhiro Suzuki

description cannot illustrate when there are the same
species of element in the left-hand side and right-handside such as a, b → a, c; in this case l = (1, 1, 0) and r =(1, 0, 1) and r = −(1, 1, 0) + (1, 0, 1) = (0, −1, 1).
A reaction is described as the rewriting of a multiset,
if the left-hand side of a reaction rule is included in amul- tiset, these elements in the multiset are excluded
and the right-hand side of the rule is merged to themultiset; the case when the multiset is a, a, b, b and thereaction rule is a, b → c, d, the left-hand side of the rule
is included in the multiset, {a, b} ⊆ {a, a, b, b} so the{a, b} is excluded from the multiset and it istransformed to {a, b} and the left-hand side of the rule{c, d} is merged to the set and we obtain {a, b, c, d} bythis reaction.
We have developed a simple computational model,
the Abstract Rewriting System, ARMS based on
stoichiometric chemistry and reaction rate model. When
we consider fundamental mechanism of living systems.
ARMS is a hybrid-model and it is a discrete andcontinuous model, so if the number of elements(molecules) is small, ARMS is discrete model but when
the number of element is large it is equal to Differential
equation. AC including ARMS, in the most of models,
have been described as an algorithm and implemented
in digital media and have not been able to communicate
with physical environments.

substance y increases from [y] to [y]+1. The strength of
the reaction x y is w = (q*R+1) where q is the numberof times the reaction has occurred in the last M reactionsof x and R is the strength of the reinforcement. If
reactions have not been selected recently, or there is no
reinforcement, then reactions have minimal strength of
w = 1 (Fig.1).

Fig. 1. The schematic example of SRN; in this example,
reaction rules are {ab, a d, c b, c d}; thickness ofarrows illustrate speed of reactions (thicker is faster)

The stochastic model we use is based on a wellknown model in probabilistic theory, Polya’s Urn(Feller 1957) Selecting a node is equivalent to picking
up a ball from an urn, where a node name is painted oneach ball. In the urn, there are N-1 types of balls,
excepting itself. Before picking up a ball from the urn,a number R balls is added to the urn for each of the M
most recently selected nodes, in addition to one extra("permanent memory") ball for each node in the
network. Initially, the urn will contain just one ball foreach node, and the probability of choosing any node
will be the same. The model reaction system can bethought of as a network. Each substance corresponds to

1.3 Self Reinforcement Reaction on ARMS
Specifically, we considered a form of this model
which is implemented according to the following
procedure. The state of the system is represented by theamounts [x] of the chemical substances, and strengths
wy:x of the reaction rules. (For generality, we use“amount” rather than “concentration” or “number ofmolecules”.) Reactions are executed one at a time. The
first substance and the reaction rule are each selectedstochastically. The probability of selecting substance x
is proportional to the relative amount of the substance[x], and the probability of selecting the reaction x
y is
proportional to the relative strength wy:x of the reaction.
When the reaction x
y occurs, the amount of
substance x changes from [x] to [x]-1 and the amount ofFig. 2. The schematic example of SRN; in this example,
reaction rules are {ab, a d, c b, c d}; thickness ofarrows illustrate speed of reactions (thicker is faster)a node of the network, and each directed connection
between one node to another corresponds to a catalyzed
reaction rule. However, since each connection has a© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 596

Neural Network by Using

probability which depends on recent activity, it isdifferent from other models such as models based on
Random Graph (Erdos and Reny 1960). We can think
each node has a “memory storage” where it memorizes
the names of the M most recent outgoing links and uses
this memory to determine the probability for selecting
an outgoing link. Another way of description is asfollows. There are two types of links. Each node has (N1) + M outgoing links. (N-1) permanent links and M
dynamic links. There is one permanent link to every oneof the other (N-1) nodes. Other dynamic links which are
created and destroyed dynamically. Once a link is
activated to a node, then an additional replica link isadded, and the oldest dynamic links are destroyed. Oneof the (N-1) +M links is chosen randomly with a bias

where w is a vector of real-valued weights, w*x isthe dot product ∑, where m is the number of
inputs to the perceptron and b is the bias. The bias shifts
the decision boundary away from the origin and does
not depend on any input value.
2. Neural Networks implemented by ARMS
From Neural Network viewpoints, strength of weight
can be regarded as Hebb rule and change ofconcentrations as anti-Hebb rule; because theconcentration of left hand side of the fired rule isdecreased and the firing probability of this rule isdecreased. We confirmed that Self Reinforcing Neural
Network, SRNN (Fig.2 and 3).

2.1. Linear Classification Problem

Fig. 3. Schematic image of SRNN; start product is randomly
selected in proportion to WTX, in this example aX is selected
and a is decreased by LC and the reaction destination is also
randomly selected in proportion to WTX, in this example by isselected and is increased by LC; and this interaction ismemorized.

weight of R for dynamic links compared to permanent
links (Fig.1).

3. Result
We compare the ability of system of SRNN with thesingle layer neural network; in order to characterizedifficulty of problems, we change the distance between
the center of clusters (xcenter, ycenter) from -10 to 10,
where the negative distance means that the two clusters
are overlapped.

1.3 Perceptron
A perceptron is an algorithm for learning a binaryclassifier: a function that maps its input x (a real-valued
vector) to an output value
( )=

1
0

∗ +
ℎ

A linear classifier achieves is making a classification
decision based on the value of a linear combination of
the characteristics; in this contribution, the classify
sample data X=(xn, yn), n=1,2,3,…,N by ax + by + c=0,
the system decide the weight vector w = (a, b, c) andconjectures the sample data is upper or lower than theclassification equation. We set an initial weight vector
by random value w = (0.1, -0.3, -0.2) and learning
coefficient, LC is 0.5.
In the SRNN, reactions exchange value of elementin the weight vector by learning coefficient; select anelement in w randomly in proportion to∙ ; forexample ax is selected, a is decreased by LC; in the
beginning reaction destination is also randomly selectedin proportion to∙ and the reaction is memorized
as in SR and the selected reaction destination isincreased by LC (Fig.3).

>0© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 597

Yasuhiro Suzuki

When the center of clusters are closed, the single layer
perceptron cannot classify, while the SRNN can classify
the clusters (Fig. 4 and 5).
We compare SRNN with the single layer perceptron
by changing the center distance of clusters andexamined error rate, where error rate is defined as theratio of number of correctly classified elements to thetotal number of data.
Acknowledgements

1.5
1
0.5
0
-9 -7 -5 -3 -1
SRNN

2

4

6

8

Perceptron

Grant in Aid for Scientific Research on Innovative
Areas, Molecular Robotics, No: 24104003, (B) No.
16H3093.

Fig. 4. Compare the SRNN with the single layer Perceptron,
the horizontal axis illustrates the distance of the center ofclusters, vertical axis illustrates the error rate of clustering.

References

1. Dittrich P., T. Kron, W. Banzhaf (2003) On thescalability of social order: Modeling the problemof double and multi contingency following
Luhmann , Journal of Artificial Societies andSocial Simulation, 6(1),http://jasss.soc.surrey.ac.uk/JASSS.html
2. Erdos P. and A. Reny (1960) On the evolution of randomgraphs, Publ. Math. Inst. Hung. Acad. Sci. 5, 17-61.
3. Eigen M. and P. Schuster (1979) The Hypercycle,
Springer Verlag, Berlin.
4. Feller W.(1957) An Introduction to Probability Theory
and Its Applications, John Wiley & Sons, Inc
5. Ikegami T. (2002) An origin of combinatorial complexity
in replicator networks, Proceedings of the Seventh Intl.
Sym. on Artificial Life and Robotics 1, 300-301.
6. Jain S. and S. Krishna (2001) A model for the emergenceof cooperation, interdependence and structure in evolving
networks, Proc. Nat. Acad. Sci. 98 (2) 543-547.
7. Suzuki Y., Y. Fujiwara, J. Takabayashi and H. Tanaka(2001) Artificial life applications of a class of P systems:
Abstract rewriting systems on multisets in multiset
processing. Lec. Notes in Comp. Sc., 2235, 299-346,
Springer Verlag London,
8. Suzuki Y., Q. Zhang. and H. Tanaka (2001) Emergent of
networks based on relationship among components, Proc.
Of World Congress on Medical Informatics, ISO Press,984-987.
9. Suzuki Y. and H. Tanaka (1997) Symbolic chemicalsystem based on abstract rewriting system and its
behavior pattern, Journal of Artificial Life and Robotics,
1, 211-219, Springer Verlag, Tokyo

Fig. 4. Fig. 5. The results of sample run of the single layer
Perceptron (top) and the SRNN (below) in the same distanceof clusters; it shows that SRNN can classify even if the centerof clusters are closed.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 598

Artificial Chemistry by Sound Waves

Yasuhiro Suzuki

Graduate School of Information Science, Nagoya University, Furocho, Chikusa
Nagoya City, 464-8601, Japan

Abstract
We consider a chemical reaction network model in which selections of reaction are stochastic and depend on pasthistory. In this chemical reaction network, we found the emergence of Auto-Catalytic Sets (ACS) and complex
dynamics in which ACS are repeatedly created and destroyed; we have called this reaction system as the SelfReinforcement Reactions, SRR. We developed a neural-networks system by using SRR and confirm the neuralnetwork of SRR can solve a linear classification problem.
Keywords: Artificial Chemistries, Chemical Reaction Networks, Computer Human Interaction, Fourier Analysis,
Sound waves

reactions. It is well-known that, in a living system suchas a cell, the number of molecules is small andfluctuation of reactions also large. Hence, “noise
canceling” in information processing would be
important.
AC including ARMS, in the most of models, havebeen described as an algorithm and implemented indigital media and have not been able to communicate
with physical environments.

1. Introduction
Living systems are composed of chemical reactions;
almost all interactions in living systems are chemical
reactions. However, bio-chemical reactions in living
systems are complicated and it is difficult to consider
and treat. Hence, abstract model of chemical reactions
have been proposed and such Artificial Chemistry, AC
has been used as a method of describing interactions formodeling; Multi Agent Systems, Petri Nets, etc. and the
AC is one of the methods which based on the
Stoichiometric
Chemistry.
The
Stoichiometric
Chemistry is a method for describing chemical reactions
that treat chemical reactions as changes of quantity ofchemical molecular. We have developed a simple
computational model, the Abstract Rewriting System,
ARMS based on stoichiometric chemistry and reaction
rate model. When we consider fundamental mechanismof living systems. ARMS is a hybrid-model and it is adiscrete and continuous model, so if the number of
elements (molecules) is small, ARMS is discrete model
but when the number of element is large it is equal to
Differential equation.
Since living systems are composed of bio-chemical
reactions, information processing are also chemical

2. An Artificial Chemistry, Abstract Rewriting
System On Multisets
We have been proposed an artificial chemistry, based on
the Abstract Rewriting System, ARS; the ARS is atheoretical model of computation; we expand ARS on
rewriting system on the multiset. A multiset is defined
as a simple set and a map, which returns the duplicationof element. We denote the duplication (multiplicity) ofan element as M (a), for a ∈ A and in case c /∈ A, M(c) = 0; for example M (a) and M (b) of {a, a, b, b} are
2, and M (c) = 0; in the mathematical description, amultiset is described as; < sup, M () >, in which sup is a
simple set of elements, in this paper we describe amultiset by denoting the same alphabet in its number of© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 599

Yasuhiro Suzuki

synthesizing W1 and W2: and inclusion of Wi is W1(a) ≤W2(a) for all a ∈ A, the W1 is included in W2 and wewrite W1 ⊆ W2. A reaction rule is a pair of Sin waves,
we denote A# as a set of all combinations of elements
over A and in the combinations, an empty multiset isincluded. A reaction rule Wl → Wr, l, r ∈ A# isdescribed as a pair of Sin waves likewise chemical
equations.
A reaction is described as the transforming of a
Sin wave, if the left-hand side of a reaction rule isincluded in W, waves represent left-hand side of therules are filtered and the waves of right-hand side of therule is synthesized; the case when the multiset is a, a, b,b and the reaction rule is a, b → c, d, the left-hand sideof the rule is included in the multiset, {a, b} ⊆ {a, a, b,b} so the {a, b} is excluded from the multiset and it istransformed to {a, b} and the left-hand side of the rule{c, d} is merged to the set and we obtain {a, b, c, d} bythis reaction.

multiplicity such as {a, a, b, b} or a vector w =(M (a1)M (a2) . . . M (an)).
The union of two multisets M1, M2 is the same as
the union of simple set and in vector description, theunion of multisets is addition of vectors w1 and w2.
And inclusion of sets is also the same as the simple set,when M1(a) ≤ M2(a) for all a ∈ A, the multiset M1 isincluded in M2 and we write M1 ⊆ M2.
A reaction rule is a pair of multiset, we denote A# asa set of all combinations of multisets over A and in the
combinations, an empty multiset is included. A reaction
rule l → r, l, r ∈ A# is described as a pair of multiset
likewise chemical equations or a pair of its vector
expression; and in some case, we can describe a reaction
rule as a vector r, r = −l + r, it is simple and good for
examining the dynamics of an ARMS, but thisdescription can not illustrate when there are the same
species of element in the left-hand side and right-handside such as a, b → a, c; in this case l = (1, 1, 0) and r =(1, 0, 1) and r = −(1, 1, 0) + (1, 0, 1) = (0, −1, 1).
A reaction is described as the rewriting of a multiset,
if the left-hand side of a reaction rule is included in amul- tiset, these elements in the multiset are excluded
and the right-hand side of the rule is merged to themultiset; the case when the multiset is a, a, b, b and thereaction rule is a, b → c, d, the left-hand side of the rule
is included in the multiset, {a, b} ⊆ {a, a, b, b} so the{a, b} is excluded from the multiset and it istransformed to {a, b} and the left-hand side of the rule{c, d} is merged to the set and we obtain {a, b, c, d} bythis reaction.

4. Neural Networks implemented by ARMS
In the system, which we propose in this paper, Neural
Network, NN is implemented by using ARMS; NN in
this paper is Self-Reinforcement Neural Network,
SRNN, which we have been proposed; in SRNN , weassumes that substances can interact with each otheraccording to reaction rules which change the amounts ofthe substance, and that reaction tendencies change
depending on the reaction history. Reaction rules are
assumed to be of the form x
y, meaning that theamount of substance x decreases and the amount of
substance b increases. Reactions occur at rates which
depend on the amount of the first substance x and thestrength of the reaction x
y . The strength of eachreaction is increased in proportion to how often thereaction has occurred recently.
Specifically, we considered a form of thismodel which is implemented according to the following
procedure. The state of the system is represented by theamounts [x] of the chemical substances, and strengths wof the reaction rules. (For generality, we use “amount”
rather than “concentration” or “number of molecules”.)
Reactions are executed one at a time. The first substanceand the reaction rule are each selected stochastically.
The probability of selecting substance x is proportional
to the relative amount of the substance [x], and the

3. ARMS by Sound Waves
We model the ARMS by using sound waves; where
each type of element is characterized as a frequency ofsin wave in fixed amplitude, A and n duplication(multiplicity) of an element is differed as nA,; forexample, when we define a as 0.1Sin(2) and b is
0.1Sin(3), the multiset M1={a, a, b, b} is denoted as
W= 2×0.1 (2) + 2× (3); we denote a multiset in
Sin wave form as W and amplitude (duplications) of a in
W is denoted as W(a); when M(a)= empty set, 0×(2) .The union of two multisets M1, M2 is the© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 600

Artificial Chemistry by Soundand input to SRNN is {A0, A1, A2,…,Am}. The SRNN
changes amplitude of inputted wave, in this paper we
propose unsurprised learning.

Fig. 1. Neural Networks implemented by ARMS (SelfReinforcement Neural Networks) and the ARMS
communicate with environment via waves; it analyzes input
waves by using Fourier Analysis and filtered input waves canbe detected by the it (detectable frequencies are given, in thispaper but is not mandatory) and transform and synthesize
output wave.

probability of selecting the reaction x
y is
proportional to the relative strength wx,y of the reaction.
When the reaction x
y occurs, the amount of
substance x changes from [x] to [x]-1 and the amount of
substance y increases from [y] to [y]+1. The strength of
the reaction x y is wx,y = (q*R+1) where q is thenumber of times the reaction has occurred in the last M
reactions of x and R is the strength of the reinforcement.
If reactions have not been selected recently, or there is
no reinforcement, then reactions have minimal strengthof w = 1.
From Neural Network viewpoints, strength ofweight can be regarded as Hebb rule and change ofconcentrations as anti-Hebb rule; because theconcentration of left hand side of the fired rule isdecreased and the firing probability of this rule isdecreased. We confirmed that SRNN can work as a NN.

Fig. 2. Sample run of the system with frequency spectrums
and time developments is up to bottom; in the sample run, weused the equipped microphone in the PC (MacBook) andrecord environment sounds for 30 sec; the synthesized wave
was outputted through equipped sound speaker.

5. Communicating with ARMS via waves

6.

In proposed model, input wave is analyzed by Fourier
analysis with Fast Fourier Transform, FFT; for thesystem, detectable frequencies are given, it
corresponding to the hearable frequencies (in the case ofhumankind, it is around 30Hz to 20,000Hz); so even if
amplitude is large, if its frequency is out of hearable
frequencies, it cannot detect it; practically selecting
hearable frequencies is realized by a sensor, which
detects frequencies and their amplitudes (Fig. 1). So, incase detectable frequencies are a0,…,am inputted wave fis transformed as=∑( ), (i = 0,…,m),Communicate with SRNN via Sound Waves

As the input device, we used a microphone, which isequipped with the Personal Computer, PC (MacBook);the system record environment sound for 30 seconds
then analyzes sound wave by using FFT and filtereddetectable frequencies and their amplitude. In thesample runs reported in this paper, we did not define
specific reaction rules but the system changes amplitudeof frequencies; the system selects a frequency randomly,
where the probability of selecting frequency is in
proportion to its amplitude; then the amplitude is© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 601

Yasuhiro Suzuki

3. Suzuki Y., Q. Zhang. and H. Tanaka (2001) Emergent of
networks based on relationship among components, Proc.
of World Congress on Medical Informatics, ISO Press,984-987.
4. Suzuki Y. and H. Tanaka (1997) Symbolic chemicalsystem based on abstract rewriting system and its
behavior pattern, Journal of Artificial Life and Robotics,
1, 211-219, Springer Verlag, Tokyo

decreased by Adf; then selects a frequency to be addedby Adf; in the beginning, it is selected randomly
according to its amplitude and by the interaction theconditional probability of P(x|y) is changed; which
denotes the probability of selecting y when thefrequency x is selected and it corresponds to weight oflink wij in SRNN.
In the sample run, environmental sound wascreated by voice, making noise by hitting desk for 30
sec. then the system analyzes sounds and create soundoutput; sound spectrums of system illustrate the systemchanges output sounds through environmental sounds
changing (Fig. 2).
7. Future Remarks
We propose a method to communicate withcomputational models implemented inside a PC fromoutside via sounds. This framework would be applicablein Artificial Life, Robotics and Human Computer
Interactions and Evolutional Language; by using several
PCs or robots having this framework can interact witheach other by using this sound communications; byintegrating computational vision, these agents would be
able to create link between sounds and visual
information and novel language would emerge among
agents / robots; it is important that animals or ourhuman also can participate the process of thisemergence of a common language among agents, robots,animals and humankind.
Acknowledgements
Grant in Aid for Scientific Research on Innovative
Areas, Molecular Robotics, No: 24104003, (B) No.
16H3093, 15K00268, 24520106
References

1. Dittrich P., T. Kron, W. Banzhaf (2003) On thescalability of social order: Modeling the problemof double and multi contingency following
Luhmann, Journal of Artificial Societies andSocial Simulation, 6(1),http://jasss.soc.surrey.ac.uk/JASSS.html
2. Suzuki Y., Y. Fujiwara, J. Takabayashi and H. Tanaka(2001) Artificial life applications of a class of P systems:
Abstract rewriting systems on multisets in multiset
processing. Lec. Notes in Comp. Sc., 2235, 299-346,
Springer Verlag London,© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 602

Variants of Spiking Neural P Systems with Energy Control
Rudolf Freund
Marion Oswald

Institute of Computer Languages, TU Wien, Favoritenstraße 9-11
Wien, 1040, Austria
E-mail: rudi@emcc.at, marion@logic.at

Abstract
We consider several variants of extended spiking neural P systems (ESNP systems), theoretical frameworks forbrain modeling. Special emphasis is laid on ESNPS systems with different colors, and, instead of choosing the rulesin the neurons based on the current contents being in a regular set, we also consider the way of choosing the rule tobe applied in each neuron consuming the minimal energy. This choice can be accomplished by assigning energy
values to each rule or by assigning (different) energy values directly to the colored spikes.

Keywords: colored spikes, computational completeness, energy control, spiking neural P systems

1. Introduction

2. Definitions

Spiking neural P systems were introduced in Ref. 1.
Energy-controlled P systems have already been
considered in Refs. 2 and 3, where energy was assigned
to the rules and the multisets of rules consuming the
maximal amount of energy and the maximal amount ofenergy within a specific range were investigated.
Computational completeness results for everal variantsof P systems – P systems with cooperative rules,
catalytic and purely catalytic P systems, P systems withtarget selection – with different derivation modes andusing energy control, i.e., taking only those multisets or
sets of rules consuming the minimal amount of energy,
have been proved in Ref. 4. Especially for the set
derivation modes, introduced in Ref. 5, evendeterministic simulations of the computations of registermachines could be obtained.

We assume the reader to be familiar with the underlying
notions and concepts from formal language theory, e.g.,
see Ref. 6.
2.1. Prerequisites
We assume the reader to be familiar with the underlying
notset of non-negative integers is denoted by N, the setof integers by Z. Given an alphabet V , a finite nonempty set of abstract symbols, the free monoid
generated by V under the operation of concatenation isdenoted by V*. The elements of V* are called strings,
the empty string is denoted by λ, and V*\{λ} is denotedby V+. For an arbitrary alphabet V={a1,...,an}, thenumber of occurrences of a symbol ai in a string x isdenoted by |x|ai. With respect to a specific order on theelements a1,...,an of the alphabet V, the n-tuple© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 603

Rudolf Freund, Marion Oswald(|x|a1,...,|x|an} is called the Parikh vector of x. A finite
multiset over an alphabet V={a1,...,an} is a mapping f :V → N. The set of all multisets over V is denoted by Vo.
The families of recursively enumerable string languages
is denoted by RE. For any family of languages X, Ps(X)denotes the set of Parikh sets of the languages in X; if
we do not distinguish between different symbols andonly consider sets of numbers, we write N (X).
2.2. Register machines
An d-register machine is a tuple M = (d,B,l0,lh,P), whered is the number of registers, B is a set of labels, l0∈B is
the initial label, lh∈B is the final label, and P is the set of
instructions bijectively labeled by elements of B. The
instructions of M can be of the following forms:p:(ADD(r),q,s), with p∈B\{lh}, q,s∈B, 1≤j≤n.
Increases the value of register r by one, followed bya non-deterministic jump to instruction q or s. This
instruction is usually called increment.
p:(SUB(r),q,s), with p∈B\{lh}, q,s∈B, 1≤j≤n. If
the value of register r is zero then jump to
instruction s; otherwise, the value of register r isdecreased by one, followed by a jump to instruction
q. The two cases of this instruction are usuallycalled zero-test and decrement, respectively.
lh: HALT (halt instruction) Stop the machine. The
final label lh is only assigned to this instruction.
The register machines are known to be computationallycomplete, equal in power to (non-deterministic) Turingmachines: they generate or accept exactly the sets ofvectors of non-negative integers which can be generated
by Turing machines.

initial value (of spikes);
R is a finite set of spiking rules of the form i:E/ak→P such that i∈{1,...,m} (specifying that this rule
is assigned to neuron i), E is the checking set, aregular set over a (the current number of spikes in
the neuron has to be from E if this rule shall be
executed), k ∈ N is the “number of spikes” (theenergy) consumed by this rule, and P is a (possibly
empty) set of productions of the form (l,wl) where l∈{1,...,m} (thus specifying the target neuron), wl∈{a}* is the weight of the energy sent along theaxon from neuron i to neuron l.
Depending on the purpose of the ESNP system, we may
also specify input and output neurons.
Definition 2. A configuration of the ESNPS system Π isdescribed by the actual number of spikes in each neuron.
A transition from one configuration to another one
works as follows: for each neuron i, i∈{1,...,m}, we
non-deterministically choose an applicable rule i:E/ak→P, i.e., the number of spikes in neuron i is in theregular set E; the application of this rule reduces thenumber of spikes in neuron i by k and adds wl spikes toeach neuron l specified in P. The computation of Π halts
if no spiking rule can be applied any more.
3.1. ESPN systems with colored spikes
We may extend this model of ESNP systems by
allowing more than one variant of spikes, i.e., we may
use colored spikes a, b, etc.; the set of these colored
spikes is denoted by U. In that case, the spiking rules
then are of the form i:E/u →P where E is a regularmultiset over the finite set of colored spikes U, u is a
finite multiset over U, and P is a (possibly empty) set of
productions of the form (l,wl) where l ∈{1,...,m} (thusspecifying the target neuron) and wl is a finite multisetof colored spikes over U sent along the axon fromneuron i to neuron l.
A configuration of the ESNPS system with colored
spikes is described by the actual multiset of colored
spikes in each neuron.
The application of a spiking rule i:E/u →P reduces themultiset of colored spikes in neuron i by u and adds themultiset wl to each neuron l specified in P.

3. Extended Spiking Neural P Systems
The reader is supposed to be familiar with basic
elements of membrane computing, e.g., see Ref. 7;
comprehensive information can be found on the P
systems web page, see Ref. 8. For the definition of an
extended spiking neural P system we refer to Ref. 9.
An extended spiking neural P system (of degree m ≥ 1)
(an ESNP system for short) is a construct Π = (N,I,R)
where
N is the set of cells (or neurons); the neurons may
be uniquely identified by a number between 1 andm or by an alphabet of m symbols;
I describes the initial configuration by assigning an© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 604

Variants of Spiking Neural

4. ESNP Systems with Colored Spikes andEnergy Control

The ESNP system with only one neuron Π = ({1},I,R)
now can be constructed as follows:
An increment instruction p:(ADD(r),q,s) then can be
simulated by the two spiking rules
1: {a0p}Uo/ a0p →(1,ar a0q) and
1: {a0p}Uo/ a0p →(1,ar a0s).
A zero-test and decrement instruction p:(SUB(r),q,s)then can be simulated by the two spiking rules
1: {a0p}(U\{ar})o/ a0p →(1,ar a0s) and
1: {a0p ar}Uo/ a0p ar →(1,a0q).
Assuming that the halt instruction has label m, wefinally get the simulation of the halt instruction as
1: {a0m}Uo/ a0m → λ.
After the application of this rule, no spike a0 is present
any more, hence, no spiking rule can be applied, i.e., the
ESNP system Π halts.

We consider two variants: the first variant assigns fixed
integer values of energy to each colored spike in thesystem, i.e., instead of a colored spike a∈U we consider
the pair [a,f(a)] with f(a) ∈Z. We extend f in the natural
way to multisets over U. The energy balance of aspiking rule i:E/u →P then is z = f (v) − f (u) where v isthe sum of all multisets wl in P. Such variants of P
systems will be called symbol energy-controlled ESNP
systems.
In the second variant, the energy is directly assigned to
the rules only, and we write spiking rule i:E/u →P <z>
where z is the assigned integer energy value. Suchvariants of P systems will be called rule energycontrolled ESNP systems.
In the case of energy-controlled ESNP systems we now
will only cosider spiking rules i:E/u →P with E being
the set of all multisets over U, i.e., we can omit E andsimply write spiking rule i:u →P and i:u →P <z>,respectively. In order to control the application of rules,
we will impose the condition that the resulting energybalance of the applied spiking rules must be minimal.

5.2. ESNP systems with energy control
We may omit the regular checking sets by replacing
them by the condition that the resulting energy balanceof the applied spiking rules must be maximal.
Theorem 2. The computations of any register machine
can be simulated by an ESNP system with colored
spikes and energy control (symbol or rule energy
control) in only one node.

5. Computational Completeness Results
We now investigate the computational power of some ofthe variants defined above.

Proof. Again we consider an arbitrary d-registermachine M = (d,B,l0,lh,P). For each register i, 1≤i≤d, of
M, we use a different colored spike ai with 1 being theenergy assigned to it. An additional colored spike a0
with energy 2 is used to encode the label l in
B={1,...,m} in B, i.e., l is encoded by a0l. Moreover, we
use a special energy spike e with energy -1 to balance
the spiking rules.
A symbol energy-controlled ESNP system Π = ({1},I,R)
with only one neuron simulating the computations of M
now can be constructed as elaborated in the following.

5.1. ESNP systems with colored spikes
With colored spikes, the simulation of register machines
becomes trivial. In contrast to the proof given in Ref. 9,
where the contents of the registers had to be encoded byusing exponential prime number encoding, we now candirectly use the numbers contained in the registers.
Theorem 1. The computations of any register machine
can be simulated by an ESNP system with colored
spikes in only one node.

The corresponding rule energy-controlled ESNP system
is obtained by omitting the energy assigned to the spikes
and just taking the energy listed together with the rules
between the brackets < and >; we observe that theseenergy values exactly coincide with the energy balanceof the rule in the case of assigning energy values to each
spike. Finally we remark that we can omit the target

Proof. Consider an arbitrary d-register machine M =(d,B,l0,lh,P). For each register i, 1≤i≤d, of M, we use a
different colored spike ai. An additional colored spike a0
is used to encode the label l in B={1,...,m} in B, i.e., l is
encoded by a0l. We here denote U:={ai :1≤i≤d}.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 605

Rudolf Freund, Marion Oswald

7. Conclusion

neuron 1 as we have only one neuron in the whole
ESNP ystem:
An increment instruction p:(ADD(r),q,s) then can be
simulated by the two spiking rules
1: [a0,2]p → [ar,1] [a0,2]q [e,-1]2q+1 < -2p > and1: [a0,2]p → [ar,1] [a0,2]s [e,-1]2s+1 < -2p > .
A zero-test and decrement instruction p:(SUB(r),q,s)
now can be simulated by the two spiking rules
1: [a0,2]p → [a0,2]s [e,-1]2s < -2p > and1: [a0,2]p [ar,1] → [a0,2]q [e,-1]2s < -2p-1 > .
As the energy balance of the decrement rule has asmaller value, in case it is applicable it has priority overthe zero-test rule, which fact, in contrast to the previous
construction, makes this one even deterministic.
In all cases it holds that we have to consume all copiesof a0 as otherwise there would exist a rule having
smaller energy balance.
Assuming that the halt instruction has label m, the halt
instruction can be simulated by the spiking rule
1: [a0,2]m → λ < -2m >which has the minimal energy balance of all spiking
rules in the ESNP system Π.
Yet the ESNP system Π is not going to halt, too, after
having applied this rule, as it still has to eliminate the"garbage" of spikes e, which task can be accomplishedby the spiking rule
1: [e,-1] → λ < 1 > .
As this spiking rule has even positive energy balance, itcannot be applied as long as spikes a0 are present, i.e.,
as long as the ESNP system Π still simulates thecomputations of the register machine M.
This observation completes the proof.

We have considered several variants of extended
spiking neural P systems (ESNP systems) with colored
spikes. Computational completeness has been shown for
the variants using colored spikes, i.e., more than one
kind of spikes. Only the simplest ESNP systems with
one neuron are needed if we use colored spikes andeither regular multisets as checking sets or energy
control, i.e., integer energy values assigned to the spikes
or the spiking rules themselves. In case of symbol orrule energy-controlled ESNP systems with colored
spikes the simulation of deterministic register machines
can even be carried out in a deterministic way.
References
1. M. Ionescu, Gh. Păun, T. Yokomori, Spiking neural P
systems, Fundamenta Informaticae 71(2–3) (2006) 279–
308.
2. Gh. Păun, Y. Suzuki, H. Tanaka, P systems with energy
accounting, Int. J. of Computer Mathematics 78(3)(2001) 343–364.
3. R. Freund, Energy-controlled P systems, in WMC 2002,
Lecture Notes in Computer Science, vol. 2597, eds. Gh.
Păun, G. Rozenberg, A. Salomaa, C. Zandron (Springer,
2003), pp. 247–260.
4. A. Alhazov, R. Freund, and S. Ivanov, Variants ofEnergy-Controlled P Systems, NIT 2016 (Valencia, 2016).
5. A. Alhazov, R. Freund, S. Verlan, Computational
completeness of P systems using maximal variants of theset derivation mode, in Proc. 14th Brainstorming Week
on Membrane Computing (Sevilla, February 1–5, 2016).
6. G. Rozenberg, A.Salomaa (eds.), Handbook of Formal
Languages, vol. 1-3 (Springer, 1997) .
7. Gh. Păun, G. Rozenberg, A. Salomaa (eds.), The Oxford
Handbook of Membrane Computing (Oxford University
Press, 2010).
8. The P Systems Website: http://ppage.psystems.eu
9. A. Alhazov, R. Freund, M. Oswald, M. Slavkovik,
Extended spiking neural P systems, in Workshop on
Membrane Computing, Lecture Notes in Computer
Science 4361 (Springer, 2006) pp. 123–134.

6. Applications
Besides these theoretical computational completenessresults as elaborated above ESNP systems with colored
spikes allow for using the different colors of the spikes
to encode the signal sizes of different sensor inputs aswell as of various output effector signals. In contrast to
standard spiking neural P systems with only one spike,
all these signals can be directly stored in parallel even in
one neuron and do not need an exponential prime
number encoding, which allows for feasible
implementations of control systems.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 606

Motion Improvement of Four-Wheeled Omnidirectional Mobile Robots for Indoor Terrain
Amornphun Phunopas1
Department of Production Engineering, King Mongkut’s University of Technology North Bangkok, 1518 Pracharat 1
Road Bangsue, Bangkok, 10800, Thailand1

Shinichi Inoue2
Department of Interdisciplinary Informatics, Kyushu Institute of Technology, Laboratory, 680-4 Kawazu, Iizuka
Fukuoka, 820-0067, Japan2
E-mail: amornphun.p@eng.kmutnb.ac.th, inoue.shinichi.1800@gmail.com
www.kmutnb.ac.th

Abstract
The four-wheeled omnidirectional platform is great to use for an indoor mobile robot. It can increasingly move andchange heading directions. However, the robot is easy to slip when it is moving. One or more wheels are sometimes
not touching the ground. This paper approaches to solving these problems by computational simulation in locomotion.
The mathematical models simulate the robot movement. The robot struggles to go to the target with randomly
simulated slips and untouched ground circumstances. The robot can estimate the positions using the Kalman filter
and readjust itself to the planned path. Consequently, this paper demonstrates the motion improvement and compares
the results of decreasing errors.

Keywords: Omnidirectional wheel, Indoor mobile robot, Kalman filter, Motion improvement

The four-wheeled omnidirectional mobile robot has
four points to make contact with the floor. Its geometry
is a rectangular shape with four connecting points, which
are supposed to align all wheels in the same plane. In fact,
the floor is not completely flat, and after running therobot many times, each wheel will become eroded
unequally. The robot becomes unbalanced or shaky eventhough all wheels are designed and aligned in the sameplane. It is possible that some wheels will not touch thefloor when it moves. It is possible that the robot will getstuck and not reach its goal. These problems can besolved by mechanically designing it for rough terrain
conditions.3 For example, the omnidirectional mobile
robot employs a suspension system4 for supporting all
wheels to touch the floor in order to cancel out themechanical noise.
The computational algorithm can reduce error fromnoises and make the robot move on its planned path.
Dead reckoning is the motion estimation of the robot base

1. Introduction
It is known that an omnidirectional mobile robot is aholonomic motion. It can travel in multi-directions andchange direction rapidly. This platform is famous for use
in the automated guided vehicles (AGVs), which use is
growing in service robots.1 The robots can support homecare. The robots can flexibly add on many functions forworks or for human assistance.
The omnidirectional mobile robot has many wheelsthat are placed in different directions. It has acomplicated mathematical model to control the
movement of robots.2 There is a constraint when moving
on a flat, indoor terrain. However, it is difficult to movethe robot to the desired position autonomously because
the robot's motion is slippery. Therefore, the robot may
miss its planned path or, in the worst-case scenario, getlost due to unrecognized circumstances.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 607

Amornphun Phunopas, Chinichi Inoueon speed estimation, direction, and traveling time.5 The
Kalman filter is great to use in noisy systems such as
localization and navigation.6
This research is about to use the Kalman filter to solve
the slip without the wheel touching the ground. The robot
will use sensors and computation to resolve the problems.
The research is done in simulation by creating thepossible conditions of motion randomly using
MATLAB. The first section is about the kinematics
model; next section is the trajectory to define the state of
motion. After that, the simulation section describes thesteps of simulation and the conditions of motion, and thenthe motion improvement section uses the Extended
Kalman Filter and shows the simulation results. Finally,
conclusion section discusses and concludes the resultsfrom the simulation.

omnidirectional movement in Table 1. Each wheel turns
in different directions. CW is clockwise, and CCW is
counterclockwise.
Table 1. Eight directions of the robot’s motion
relate to the four wheels’ rotation direction.
Direction
Forward
Left
Right
Back
+Diagonal Right-Diagonal Right+Diagonal Left
-Diagonal Left

W1
CW
CW
CCW
CCW
0
0
CW
CCW

W2
CCW
CW
CCW
CW
CCW
CW
0
0

W3
CCW
CCW
CW
CW
0
0
CCW
CW

W4
CW
CCW
CW
CCW
CW
CCW
0
0

2.1. Forward Kinematics
2. Kinematics

The input of forward kinematics regards the wheels’speed, and the output regards the robot’s velocities as in
Eq. (1) to simulate the robot's motion. Converting thefour wheels’ speed into the robot’s linear velocity
requires the Jacobian matrix as seen in Eq. (2). The
parameters R and r are set to 10 cm and 5 cm respectively
for the simulation.

The omnidirectional mobile robot uses holonomic
motion and has four wheels to move in multiple
directions. It can be controlled through the planned pathin three degrees of freedom. There are two kinematics
models: the first is forward kinematics, and the second isinverse kinematics. The robot platform uses the forward
kinematics to determine the robot’s velocities ( , , ̇ )and the inverse kinematics to determine the wheel's
angular velocities ( , , , ). The robot motion isin the XY coordinate as in Fig. 1. The robot's heading
angle is determined by . The robot can move throughthe planned paths of motion from the kinematics models.
As such, there are eight basic directions for

=

̇

=

(1)

+
+
⎡−
⎤
⎢
⎥
−
+
+
⎢
⎥
= ⎢
(2)
⎥
+
+
⎢−
⎥
⎢
⎥
+
+
⎣−
⎦
Where W1 is the angular velocity of wheel 1, W2 is the angularvelocity of wheel 2, W3 is the angular velocity of wheel 3, andW4 is the angular velocity of wheel 4. R is the radius of the robot
from the center to the wheel, r is the radius of the wheel, Vx isthe robot’s velocity on the x-axis, Vy is the robot’s velocity on
the y-axis, and ̇ is the heading angular velocity of the robot.

2.2. Inverse Kinematics

To inverse Eq. (1) will obtain Eq. (3), which provides
the robot’s velocities.

=
Fig. 1. The robot’s configuration and eight directions of
motion.

⎡−
⎢
⎢−
1
= ⎢
⎢−
⎢
⎢
⎣−

+

4
3
+
4
5
+
4
7
+
4

+

4
3
+
4
5
+
4
7
+
4

⎤
⎥
⎥
⎥
⎥
⎥
⎥
⎦
̇

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 608(3)

Motion Improvement of Four-Wheeled

The planned path of the robot's velocities is assigned as
input parameters to obtain the speed of four wheels.

However, the wheels still continue as the assigned
wheels’ speed.
There are two effects from changing the conditions of
motion randomly. First, the robot position shifts from the

2.3. Trajectory model
The robot can move smoothly. The trajectory is for path
planning, which determines speed and acceleration ofeach wheel. The trajectory advantage is to increase andslow down velocity properly. The robot can rotate tochange directions while it is moving. Accordingly, thetrajectory of path planning will be applied to the inverse
kinematics and then control the speed of four wheels. In
reality, the robot has to have odometry sensors for
feedback control to control the wheels’ speed and therobot's direction. For simulation, the velocity of four
wheels can be applied to drive the animated robot. The
trajectory uses a polynomial function of time.
Polynomials are simple to compute and can provide therequired smoothness and boundary conditions using
Peter Corke’s MATLAB library.7 A quintic (fifth-order)
polynomial is used,
( )=
+
+
+
+ +
(4)
̇( ) = 5
+4
+3
+2 +
(5)
̈ ( ) = 20
+ 12
+6 +2
(6)

Fig. 2. The simulation procedures.

where S is function of robot motion ( , , ), ̇ is derivative of
S as robot velocity ( , , ̇ ) , ̈ is derivative of ̇ as robot
acceleration, , ̈ , t is interval time in step sampling, and(A,B,C,D,E,F) are coefficients.

3. Simulation
The simulation is implemented by a kinematics modeland a trajectory model. The planned path is created forthe robot motion, a straight line and a curved line. The
trajectory is ready to use to control the robot's position,
velocity, and acceleration using Eq. (4-6). Then, the robot
converts its body velocity to four wheels’ speed using Eq.
(3). To display the robot's motion, in animation, see Eq.
(1). The Kalman filter is used to improve the robot's
motion and simulate every condition of motion as in Fig.
2.
3.1. Conditions of Motion
The robot may not move as the planned path because offriction and external force. However, the robot normallyhas the feedback control to compensate for the motor’s
speed to maintain the speed input and direction of therobot. Another problem is the robot possibly getting
stuck and being unable to move out from the trap field.

Fig. 3. There are eight possible cases involving the four
omnidirectional wheels. This shows the rotation direction ofeach omnidirectional wheel, clockwise and counterclockwise.
In case 1, not all wheels touch the ground. This is not used in
the simulation.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 609

Amornphun Phunopas, Chinichi Inoue

planned path, and second, the robot cannot reach thegoal's position. There are eight conditions of motion asshown in Fig. 3. The wheels can change the states of
motion, as in Table 2, the four wheels drive in Case 2, thethree wheels drive in Cases 3 through 6, and the twowheels drive in Cases 7 and 8.

cannot move along to the goal position when it is inmotion case condition 3-8. The robot needs to adjust its
position to the goal even when the robot goes into a trap
field. Motion improvement aims to help the robot
approach the goal position using the Kalman filter.

Table 2. The omnidirectional wheels touch and do
not touch the floor in the conditions of motion.
Eight cases have been determined.

4.1. Kalman Filter
The Kalman filter was invented by Rudolf Kalman. It isa well-known algorithm that is used to estimate theoutput from a system model and sensor measurements.
The Kalman filter was invented for the linear system, butmany problems are non-linear. Thus, it needs to linearize
the system model. Estimating the measurements of a nonlinear system requires a tool known as the extended
Kalman filter (EKF). This paper uses the EKF for the
four-wheeled omnidirectional mobile robot. The
algorithm is iterative to predict, and an update follows
Eq. (7-11). However, much research is needed in order toimplement the EKF within the omnidirectional mobile
robot. Guidelines following the algorithm's steps aredescribed in Ref. 6-8. A difference in this work ascompared to other works is the use of random states of
motion for the state process.
Predict:
= (
, )+
(7)
=
+
+
(8)
Update:
=
(
+ )
(9)
=
+ ( − ̂)
(10)
=( −
)
(11)

case
W1
W2
W3
W4
1
0
0
0
0
2
1
1
1
1
3
1
1
1
0
4
1
0
1
1
5
1
1
0
1
6
0
1
1
1
7
1
0
1
0
8
0
1
0
1
Note: 1 is touching and 0 is not touching the ground.

In the example, W1 and W3 are stuck, and the robot
cannot keep moving in the desired direction in case 8.
The robot needs to get out by struggling to the left andright, back and forth, as shown in Fig. 4.
In motion case 2, all wheels are touching the floor
while the robot is moving. This is the ideal condition formotion. However, not all wheels can touch the ground all
the time in cases 3 through 8.

Where is the estimated state. F is the state transition matrix(translation between states). is the control variables. is themodelled random white noise. is the state variance matrix(error of estimation). is the control input error covariance.
is the process variance matrix (error due to process). is themeasurement variables. is the measurement matrix (mapping
measurements onto state).
is the Kalman gain. is themeasurement variance matrix (error from measurements).
Subscripts are as follows: t current time period and t-1 previous
time period. Superscript (-) is intermediate steps.

Fig. 4. The omnidirectional mobile robot attempts to move
under the conditions of motion case 8. The trap field, or the
freewheel, is random in the simulation. This means that theomnidirectional wheel is not touching the floor.

The EKF framework has various sensors for the step
update that is referred to as a "sensor fusion.8" The values
from simulation compared to the original sensors are theheading angle from a compass, the yaw rate from agyroscope, the wheel’s speed from an encoder, thefreewheel check from the wheel's rotation and the robot's

4. Motion Improvement
The planned path trajectory is assigned to the robot's
moving path profile. This omnidirectional mobile robot© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 610

Motion Improvement of Four-Wheeled

movement using an encoder and an accelerometer, andtarget bearing from a camera.

planned path and had not reached the goal position when
the time ended. The robot’s actual position is shown in
Fig. 5.
Accordingly, each wheel of the robot had the wheel’s
speed, as shown in Fig. 6. The wheel’s speed dropped tozero in the trap field in case of free running, or the wheel
did not touch the floor.
The total simulation time was 0.5 seconds, with every
0.01 second per step. After applying the EKF to the robot,

5. Simulation Results
The planned path was a linear path from the start position(x = 20, y = 20) to the goal position (x = 150, y = 150). It
started moving to the +diagonal right, as in Table 1, andthen attempted to rotate itself because the heading angle
was changed. The robot’s heading angle started from 0rad, as in the configuration in Fig. 1. Finally, the robot’s
heading angle stopped at π ∕4 rad. Moving under theconditions of motion randomly, the robot shifted from theFig. 7. The robot’s actual positions x and y shifted from theplanned path. The estimated position x and y from the EKF had
a better motion.
Fig. 5. The robot randomly moved under the eight conditionsof motion, following the planned path from the start position to
the goal position.

the estimated positions x and y were smooth and could
reach very close to the goal position as shown in Fig.7.
The robot maintained the position on the planned
path. However, the robot’s heading angle from

Fig. 6. The four wheels’ speed trajectory is related to theplanned path. W1 and W2 rotated counterclockwise. W3 and W4
rotated clockwise.

Fig. 8. The robot’s heading angle simulated the actual position.
The estimated and measured heading angle were the motion
improvement from the EKF.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 611

Amornphun Phunopas, Chinichi Inoue

measurement and estimation changed a lot as shown in
Fig. 8.
The robot in x and y position could be compared inFig. 9. The measured position was noisy and affected by
the error of the actual position.

came from the robot motion model, which changed thenumber of the driving wheel. Generally, the EKF reduces
the effects of slip noise and sensor noise in the deadreckoning problem. However, the simulation resultshows that the EKF produces better robot motion along
the planned path. The robot struggles to traverse its
planned path with its assigned trajectory.
The behavior of the robot during the real test may
considerably differ from the simulation. Noises fromsignals are reduced, but the dynamic motion of the robot
may not work like the control signals. For real tests infuture work, the omnidirectional mobile robot has to have
speed control sensors to monitor its wheel speed, and it
will be important to check the freewheel and the floor
touchless condition when some of the wheels rotate, but
the robot does not move, or it moves in the wrong
direction.
Acknowledgements

Fig. 9. Comparing the motions of the robot from path planning,measurement, estimation, and actual position.

This work was funded by Science and Technology
Research Institute, King Mongkut’s University ofTechnology North Bangkok.

For the motion improvement, each wheel’s speed was
generated from the estimated positions. The estimatedwheel’s speed was very noisy compared to the actual
wheel’s speed because the robot struggled to the newpositions as in Fig. 10.

References
1. Z. Jie-Tong, The Development of the Omnidirectional
Mobile Home Care Robot, (Mobile Robots – Current
Trends, InTech, 2011) pp.345-362
2. R. Raul and G.F. Alexander, Holonomic Control of a robot
with an omnidirectional drive, KI Künstliche Intelligenz,
BöttcherIT Verlag, 2006.
3. U. Martin and I. Karl, Analysis Design and Control of an
Omnidirectional Mobile Robot on Rough Terrain (Journalof Mechanical Design, ASME, 2009) vol. 131.
4. S. Jae-Bok and B. Kyung-Seok, Design and Control of an
Omnidirectional Mobile Robot with Steerable
Omnidirectional Wheels, Mobile Robotics, Moving
Intelligence, Jonas Buchli (Ed.), InTech.
5. R.C. Dixon, G. Bright and R. Harley, Robust localisationof automated guided vehicles for computer-integrated
manufacturing environments. S. Afr. J. Ind. Eng. 2013,vol.24, n.1, pp.81-90.
6. S. Caius, C. Cristina and M. Florin, Mobile Robot Position
Estimation Using the Kalman Filter, Scientific Bulletin of
the Petru Maior University of Tirgu Mures, vol. 6, 2009.
7. C. Peter, Robotics Vision and Control (Springer Tracts in
Advanced Robotics, 2011), pp. 107–128.
8. S. Antonio, N. Urbano and A. Rui, Fusion of Odometry
with Magnetic Sensors Using Kalman Filter andAugmented System Models for Mobile Robot Navigation,
IEEE ISIE 2005, pp. 1551-1556

Fig. 10. Comparing the estimated and the actual speed of thefour wheels.

6. Conclusion
The robot randomly changed its states of motion in fourwheel, three-wheel, and two-wheel drive. The main error© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 612An Improved Algorithm for Obstacle Avoidance by Follow the Gap Method Combined
Potential Field
Chakhrit La-orworrakhun and Suriya Natsupakpong

Institute of Field Robotics, King Mongkut's University Technology Thonburi,
26 Pracha Uthit Rd., Bang Mod, Thung Khru, Bangkok 10140, Thailand
E-mail: chakhrit.yong@mail.kmutt.ac.th, suriya@fibo.kmutt.ac.th
http://www.fibo.kmutt.ac.th/

Abstract
A novel obstacle avoidance algorithm for boat survey, “Follow the Gap Plus”, is proposed by combining the “Follow
the Gap” method and adapting the “Potential Field” method with an attractive field of obstacles. Then the simulation
environment for testing algorithm are generated in various situations with real-world-like environment. The results
show that the proposed algorithm can go to the desired destination with better performance than “Follow the Gap”
method in the environment with obstacles about 4.47% of total distance, about 7.63% of total time used, and about
54.33% of average change of direction.
Keywords: Survey boat, Obstacle Avoidance, Follow the Gap Method, Potential Field Method, Simulation.

Most survey robots are using on the ground and in theair, whereas no robots for survey is on the water. Survey
robots on water is interesting because an environment isdifferent from the ground and have many problems, suchas dynamic of the water. Therefore, the simulation ofsurvey robot on the water is selected and developed.
The paper is organized as follows. In section 2, therelated algorithms are summarized. In section 3, theproposed algorithm is presented in details. In section 4,
the navigation system for testing environment are setup.
In section 5, the simulation results are shown withdiscussion. Finally, section 6 shows the conclusions.

1. Introduction
Currently robot technology have more demand formaking life better. Knowledge and techniques applied torobots have more complicated as well. For example,
robot movement to the designated destinations needinteract with environment in different ways, such as
obstacle avoidance, navigation, localization, trajectory
planning etc.
In unknown environment, robot must have intelligent
navigation system for planning a safety path to the goal.
An algorithm for obstacle avoidance should makedecision automatically with simple computation.
Algorithm must find a safety path, shorter path andsmooth change1. And when the robot found the obstacles,
the algorithm must make quick decision with accuracy.
For the objectives mentioned above, a novel obstacle
avoidance algorithm for boat survey is proposed byfocusing on the development of efficient algorithms usedin shorter path and smooth change.

2. Related Works
2.1. Artificial Potential Field Method2-4
Artificial potential field method is based on the principleof potential field. The robot and obstacle are determinedwith positive value and the goal is determined withnegative value. Therefore, obstacles will be pushed arobot out by created repulsive force. The goal is to attract© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 613

Chakhrit La-orworrakhun, Suriya Natsupakpong
C. La-orworrakhun, S. Natsupakpong

a robot by created attractive force. So, final force on robot
is the summation of all repulsive and attractive force.
This algorithm is popular implemented in mobile
robot. Many researchers3 used [SN1] this algorithm
because efficiency and simplicity of mathematical model,
however this algorithm has a problem in local minimum
and produces a short path.
2.2. Follow the Gap Method5
Follow the gap method avoids obstacles by finding the
gap between obstacles and calculates the gap angle.
Afterward, many researchers used this algorithm toimplement in autonomous robot, such as autonomous
car6-8 or improve this algorithm by combined with others
algorithm1 for solving local minimum problem andresearcher has noticed that this algorithm familiar withavoid obstacles of birds9. However, follow the gap
method has issues about weight between gap angle andgoal to find optimal ratio.

Where
and
are the distance of obstacles 1 and 2
from robot respectively. ∅ and ∅ are angles of
obstacles respectively. ∅is the final calculated gap
center angle.
Step 3: combining with attractive field and calculating
distance to goal and creating attractive field to obstacleby using inverse repulsive field. Then, finding an angle
between robot and obstacle using Eq. (2).
θ = tan ()(2)(

Determine the position of the robot changes ∆ , ∆)as follow2.

< ,
∆ =−

(cos )∆ =−≤ ≤ + ,
∆ = ( + − ) cos(sin )∆ = ( + − ) sin≥ + ,∆ = ∆ = 0[SN2]3. Follow the Gap Plus Method
Follow the gap method has problem when user wants therobot to avoid obstacles safely. Users need to set theweight of center gap angle with high value. The result of
this makes the distance to the target increases. This
proposed algorithm is intended to improve follow the gap
method by using the concept of artificial potential field
method applied in created repulsive force pushed out
from obstacles and turned into attractive force instead.
This algorithm is named with “follow the gap plus
method”.

If position of robot is outside of the circle of inﬂuence,
the attractive potential ﬁeld is zero. Within the circle ofinﬂuence but outside the radius of the obstacle, theattractive force decreases from maximum value to zerodepending on distance to obstacle. The constant β > 0 is
given by allowing the agent to scale in the strength of thisﬁeld. Then the summation of attractive force from every
obstacles is calculated in the result of angle to that
position. Then, the summation of gap center angle iscalculated using Eq. (3).
∅

∅

∅
=
(3)
Step 4: calculating the final heading angle. This canbe achieved by combining the gap center angle with thegoal angle in Eq. (4).
∅

∅

∅

Where
is minimum distance to obstacle. ∅calculated gap combined attractive force angle. ∅goal angle. and are weight coefficients.

Fig. 1 Concept of follow the gap plus method

(4)
is
is

4. Navigation System

Follow the gap plus method has four main step.
Step 1: calculating the gap array and finding the
maximum gap.
Step 2: calculating the gap center angle using Eq. (1)(∅ ∅ )∅= arccos(
)
(1)
(∅

=

Autonomous survey boat requires a system fornavigating to the goal. This paper proposed a simple
navigation system used in survey boat experiment. The
survey boat’s navigation system after initial system has
three main process.
First, obstacle detection process for detecting
obstacle in front of the survey boat is calculated. In this∅ )© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 614

Improved Algorithm for Obstacle Avoidancewater is simulated. The performance about the shorter
path shown in the distance, and about smooth change
shown in number of times to change heading direction
and change heading angle over 15 degree. [SN3]Two[SN4]interesting scenarios are presented consisting of simple
obstacles and similar real-environment[SN5].

process, laser scanner is used to detect obstacles. Start bychecking tilt of boat, if the value is less than the thresholdthen the environment in front of boat begins to scan andsave. Second, avoidance strategy process and a decision
making of boat’s heading is calculated by an obstacle
avoidance algorithm, such as follow the gap method orfollow the gap plus method. Third, control steering
process for changing boat’s heading according to theangle is calculated.
These three processes run respectively and repeat
until the goal is reached. If it has other goal, system will
update position of new goal and begin the first process.
The Flowchart of this system is shown in Fig.2.

5.1. Scenario 1: Avoiding simple obstacle
In scenario 1, considers the situation with few and easy
shape obstacle. The shortest path is almost a linear path.
This situation is suitable for basic testing an algorithm forusability. The results of trajectory path are shown in Fig.3.

(a)

(b)

Fig. 3 Scenario 1 robot trajectory (a) Follow the gap method(b) Follow the gap plus method
Table. 1 The comparison performance of the algorithm inscenario 1.

Time used (sec)Distance (m)Follow the gap
method37.70
254.42

Follow the gap
plus method
36.53
251.38

Heading direction change

39

37

Heading change > 1500

Table. 1 shows that the follow the gap plus method
has better performance about 1.19 percentage of total
distance, about 3.08 percentage of total time used, lessheading direction change 2 times, and both algorithms
have no heading change over 15 degrees. Follow the gap
plus method has better performance in this scenario
because an obstacle closes to the shortest path andattractive force to obstacle makes the path to close
obstacle, therefore the path nearby the shortest path.
While follow the gap method has only gap angle incalculation, therefore the path is far from the shortest path.

Fig.2. Navigation system flowchart

5. Simulations and Results

5.2. Scenario 2: Environment similar real

The effectiveness of the proposed algorithms has been
demonstrated by using the Unity3D software. In theexperiments, the environment of canal with dynamic

In scenario 2, a real-life environment is simulated instraight canal, width about 60 meters with obstructions in© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 615

Chakhrit La-orworrakhun, Suriya Natsupakpong
C. La-orworrakhun, S. Natsupakpong

the canal. No dynamic obstacle is consider in theexperiment. The result of trajectory path is shown in
Fig.4.

the performance with safety path, testing with dynamic
obstacle and testing with real robot and real environment.
Acknowledgements
Thanks to Hydro and Agro Informatics Institute andInstitute of Field Robotics, King Mongkut's University
Technology Thonburi for supporting the researchfunding.
References
1. M. Zohaib, S. M. Pasha, N. Javaid, J. Iqbal, & A. Salaam,
An improved algorithm for collision avoidance inenvironments having U and H shaped obstacles, Studies inInformatics and Control 23(1) (2014), pp. 97–106.
2. M. Goodrich, Potential Fields tutorial, 2002.
3. L. Tang, S. Dian, G. Gu, K. Zhou, S. Wang, & X. Feng, A
novel potential field method for obstacle avoidance andpath planning of mobile robot, 3rd International
Conference on Computer Science and Information
Technology (2010), pp. 633–637.
4. E. N Sabudin, R Omar. and C. K. A. N. H Che Ku Melor,
Potential Field Methods and Their Inherent Approaches
for Path Planing, ARPN Journal of Engineering andApplied Sciences (2016), pp. 10801-10805
5. V. Sezer, & M. Gokasan, A novel obstacle avoidancealgorithm: “Follow the gap method, Robotics andAutonomous Systems, 60(9) (2012), pp. 1123–1134.
6. Design V., Combined fuzzy approach for online speed
planning and control with real vehicle implementation
68(4) (2015), pp. 24–27.
7. F. You, R. Zhang, G. Lie, H. Wang, H. Wen, & J. Xu,
Trajectory planning and tracking control for autonomouslane change maneuver based on the cooperative vehicle
infrastructure system, Expert Systems with Applications,42(14) (2015), pp. 5932–5946.
8. R. Kala, & K. Warwick, Motion Planning of Autonomous
Vehicles on a Dual Carriageway without Speed Lanes,
Electronics, 4(1) (2015), pp. 59–81.
9. H. T. Lin, I. G. Ros, & A. A. Biewener, Through the eyesof a bird: modelling visually guided obstacle flight,
Journal of the Royal Society, Interface / the Royal Society(2014), pp. 2-39.

(a)(b)
Fig. 4 scenario 2 robot trajectory (a) Follow the gap method(b) Follow the gap plus method
Table. 2 The comparison performance of the algorithm inscenario 2.
Follow the gap Follow the gap
methodplus method133.06122.91Time used (sec)
780.15745.24
Distance (m)Heading direction change

177

168

Heading change > 15

42

22

Table. 2 shows that follow the gap plus method has
better performance about 4.47 percentage of total
distance, about 7.63 percentage of total time used, lessheading direction change 9 times and less heading changeover 15 degrees 20 times. The simulation result showsthat the follow the gap plus method uses the different pathfrom the follow the gap method. From waypoint 2 to
waypoint 3, the follow the gap method uses path far fromobstacle because it uses only center gap degree fordecision. Whereas the follow the gap plus method used
center gap degree and attractive force to obstacle, in the
result the selected path is near obstacle and straight to
waypoint 3.
6. Conclusions
The proposed algorithm, follow the gap plus method, ispresented for autonomous navigation robot with theshorter and smoother path results and uses less times thanfollow the gap method. The simulation results show thisalgorithm can implement to real robot for survey on canal.
However, this algorithm emphasized about shorter path,therefore robot approaching near an obstacle. So
performance about short and safety path are being turned
upside down. So, the future work will focus on improving© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 616

Study on the target positioning for an Omni-directional 3 DOF mobile manipulator based onmachine vision
Jiwu Wang1

School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University
Beijing 100044, China1
Yao Du1, Wensheng Xu1

School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University
Beijing 100044, China1
Masanori Sugisaka2

Alife Robotics Corporation Ltd, Japan and Open University, United Kingdom 2
E-mail: 15121244@bjtu.edu.cn1; ms@alife-robotics.co.jp2
www.bjtu.edu.cn

Abstract
The omni-directional mobile robot with multi DOF, because the operation posture and operation accuracy of themanipulator can be better controlled in a narrow or crowded workplace compared with the general manipulator, isgetting more interested in practical applications. The present problem is to improve its flexibility for operating
multiple different targets. Target recognition with image processing is an effective solution. Based on the image
processing, the position and posture of the target can be determined. Then the signal will be sent to the arm control
system. In this paper, the illumination conditions, distortion, etc. are studied in the target recognition. The target
position with image processing, is verified with real coordinates. The experiments show target recognition withimage processing can effectively improve the flexibility of our robot.
Keywords: mobile robot; machine vision; 3 DOF manipulator; Omni-direction.

1.

increase its function.
Firstly, through the analysis of the 3 DOF manipulator,
established the manipulator kinematics model by the
D-1H method, and on the basis of this model, derived
solving method to the manipulator kinematics equationof the manipulator and the inverse kinematics problem,the analytical solution of problem of manipulator

Introduction

Today, the degree of automation of factories andwarehouses becomes increasingly higher and higher. 1
However it is difficult to expand manipulator’sfunctions because its fixed base. This article describes
an Omni-directional 3 DOF mobile manipulator, which
can increase flexibility of the manipulator and greatly© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 617

Jiwu Wang, Yao Du, Wensheng Xu, Masanori Sugisaka

inverse kinematics.
Then the analytical solutions of the forward kinematics
equations and inverse solutions are verified andanalyzed.
The experimental results prove the effectiveness of themethod and the method of the target location.
2.

a DC brush motor alone, with both left-handed andright-handed rotations.

System Overview

In order to realize the automatic control of the robot,
the robot control system plays an indispensable role. In
this paper, the robot uses four Mecanum wheels to
realize the omnidirectional movement of the robot
platform. Robot control system is divided into fourparts, they are IPC, the main, chassis drives andmechanical arm drive plate, as shown in Figure 1.

Fig.3.Analysis of chassis movement

Figure 4 shows the Mecanum wheel four-wheel robot
chassis model coordinate system. The coordinate
system OiXiYi(i=1,2,3,4) is established with the centerof each wheel position as the origin, and the reference
coordinate system OXY is established with the center ofthe robot as the origin.

Fig.1. System Overview

IPC runs the Windows operating system, through theserial port and the main control board to achievecommunication between the main control board and thetwo driver boards through the CAN bus
communication.
2.1

Fig.4. Coordinate modeling of omnidirectional mobile robot

As shown in Figure4, the robot body length is 2L, thebody width is 2l, the speed of the robot chassis is = (𝑉𝑥 ,𝑉𝑦 ), the angular velocity of the robot around the Z-axis
is 𝜔𝑧, the linear velocity of the wheel is 𝑖𝑤, there𝑖𝑤 =𝜔𝑖𝑅. (Where 𝜔𝑖 is the angular velocity of each wheel
and R is the radius of the wheel). The roller and theground contact when the line speed 𝑖𝑟. Suppose that the
speed of each wheel is 𝑖= (𝑉𝑖𝑥, 𝑉𝑖𝑦). Under
OiXiYi(i=1,2,3,4), can be decomposed along 𝑋𝑖 and 𝑌𝑖,
as𝑖𝑤 and 𝑖𝑟, and 𝑉𝑖𝑥 can be expressed as −𝑉𝑖𝑟 sin𝛼𝑖, 𝑉𝑖𝑦is the sum of 𝑖𝑤 and 𝑖𝑟 cos𝛼𝑖. Which satisfy the relation:

Design of Omnidirectional Mobile Chassis
Based on Mecanum Wheel

In this paper, four Mecanum wheels are installed torealize the omni-directional movement of the platform,as shown in Figure 2. 2

𝑉1𝑥 = - 𝑉1𝑟 sin𝛼1，𝑉1y = - 𝑉1𝑤+V1𝑟 cos𝛼1𝑉2𝑥 = - 𝑉2𝑟 sin𝛼2，𝑉2y = - 𝑉2𝑤+V2𝑟 cos𝛼2𝑉3𝑥 = - 𝑉3𝑟 sin𝛼3，𝑉3y = - 𝑉3𝑤+V3𝑟 cos𝛼3𝑉4𝑥 = - 𝑉4𝑟 sin𝛼4，𝑉4y = - 𝑉4𝑤+V4𝑟 cos𝛼4

Fig.2. Mecanum wheel(1)

In the formula 𝛼𝑖-i round roller axis and the hub axis
angle (𝑖 = 1, 2, 3, 4).
Overall, the speed of each wheel 𝑖 is the speed of therobot and the relative speed of the wheel center O speed

From Figure 3 you can see the four Mecanum wheels
combined installation situation. Each wheel is driven by© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 618

Study on the target

vector, that is, =𝑖 +z∗l, consider vector directionality,
along the coordinates and 𝑋𝑖𝑌𝑖 Coordinate
decomposition formula can be obtained:

𝑉1𝑥 = 𝑉x –L𝜔z，𝑉1y = 𝑉y +l𝜔z
𝑉2𝑥 = 𝑉x –L𝜔z，𝑉2y = 𝑉y –l𝜔z
𝑉3𝑥 = – 𝑉x +L𝜔z，𝑉3y = 𝑉y –l𝜔z
𝑉4𝑥 = 𝑉x +L𝜔z ，𝑉4y = 𝑉y +l𝜔zd (𝑦, 𝑧) is(6)
Where d is in the range of [0,]. When d is evenly
changed in this interval, the movement of the hand (𝑦, 𝑧)obtained by the above-described equation is substituted
into the joint position expression so as to be able torealize a straight line movement of the hand

(2)

Taking into account the pipe speed for the
non-controlled volume, to eliminate, that is derived
omni-directional robot inverse kinematics equation is:

W1 = – 𝑉x cot𝛼 + Vy + (l + L cot𝛼) 𝜔z
W2 = 𝑉x cot𝛼 + Vy – (l + L cot𝛼) 𝜔z
W3 = – 𝑉x cot𝛼 + Vy – (l + L cot𝛼) 𝜔z
W4= – 𝑉x cot𝛼 + Vy + (l + L cot𝛼) 𝜔z

2.2.2

It can be seen that the relationship between the spatial
position of the robot hand and the joint variable is(7)
Derivative of the above formula can be obtained(8)
Written in matrix form
(9)
Is known from the definition of the Jacobian matrix:(10)
The Jacobian matrix of the manipulator can be obtained(11)
Jacques array is mainly used to describe the joint
displacement and space pose differential relationship,
the relationship between the spatial velocity of the robot
hand and the angular velocity of the joint is given
v=J(12)
Conversely, the speed of movement of each joint can be
determined from the desired hand speed =J-1 v.
According to calculations:
J-1= (13)(3)

From the above equation, the control of the whole robot
can be realized.
2.2

Design of Multi - DOF Gripping
Manipulator

The design of the robot arm a total of three degrees of
freedom, the direction of the X-axis rail, twoconnecting rods were Y, Z axis.
2.2.1

Robot manipulator arm speed control

Motion Control of Manipulator 's Hand

Linear Interpolation
Calculation shows that the position of the robot joint
position and the relationship between the hand space:

(4)

2.3

Design of Image Processing.

Template matching is to find the target image in theimage. 3 The principle is to measure the similarity
between two images by some similarity criteria.

For the robot hand space position, the line isinterpolated as it moves from (𝑦1, 𝑧1) to (𝑦2, 𝑧2) in astraight line.
Because of simple interpolation from the x-axis or
y-axis, there is a great deal of non-uniformity.
Assuming the slope of the line segment is k, when𝑦1≠𝑦2, 𝑘 = (𝑧2- 𝑧1)/( 𝑦2- 𝑦1), the straight line inclination
angle is:α = arctank(5)
Assuming that d is the distance from any point (𝑦, 𝑧) to(𝑦1, 𝑧1) on this line segment, the point corresponding to

2.3.1

Template Matching Based on Gray Value

When the template slides in the image, do similarity
matching. The simplest measure of similarity is tocalculate the sum of the absolute values of thedifferences between the template and the image or thesum of squares of all the differences (SAD and SSD).

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 619

Jiwu Wang, Yao Du, Wensheng Xu, Masanori Sugisaka(14)(𝑟+𝑢,𝑐+𝑣) denotes the gray value of the row r + u
column c + v of the image to be measured, and 𝑡(𝑟,𝑐)denotes the gray value of the template image T row r
column c. 4 The difference between the values after
the change and their mean values will be smaller
regardless of whether all the gray values become larger
or smaller and then become zero mean and unit
variance by dividing the variance so that the image isless sensitive to changes in the brightness level , andhas strong robustness.
(15)
𝑚𝑐 is the average gray value of the template, 𝑠𝑡2 is thevariance of all the gray values of the image to bemeasured, (𝑟,) is the average gray value of the image tobe measured.

Fig.7. Steps to find items

As shown in figure 7. First, the original image
distortion correction and smoothing noise; then extract
the image of the saturation S component from theimage HSV space, we can clearly see that saturation
component of the object we want to crawl is relatively
high; The third step is to get the image region with highsaturation through the dynamic threshold technique.
The final step is to display the obtained area and get theorientation information of the area and the orientation
information of the bottle.
3.

Fig.6.A schematic diagram of the image pyramidThis paper introduce an Omni-directional 3 DOF
mobile manipulator based on machine vision. However,in real-world experiments, the robot was controlled tograsp the bottle. Our experiments have shown that thedesired relative position and orientation between thecamera and the object have been kept within errors of
few centimeters. The next step is to further improve theimage processing method to make the image processing
faster, so that the robot more accurate understanding of
the surrounding environment. Adding some sensors,
such as lidar, gives the robot a path planning andobstacle avoidance function. Increase the chassis
suspension system to adapt to a richer terrain.

In the pyramid-based search strategy: from the top to
the bottom of the search, in the high-level image search
to track the template instance to the bottom of theimage pyramid, then in a small area to match, in thenext search region to locate the results of the matchingof a small area the similarity degree is calculated,segmented by the threshold and the local extrema is
extracted.
2.3.2

Conclusion

Realization of crawl

In order to achieve the robot automatic items crawl, we
must get the actual location of items in the world
coordinate system.

References
1. Wang J, Zhang X and Zhang W, Study on the
Improvement of Flexibility for an Industrial Robot basedon Machine Vision, J. Journal of Robotics, Networking& Artificial Life, 2014, 1(1):45-48.
2. Du B, Zhao J and Song C, Dexterity Analysis for
Omni-directional Wheeled Mobile Manipulator Based on© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 620

Study on the target

Double Quaternion, J. Chinese Journal of MechanicalEngineering,2013,03:585-593.
3. Wang J, Zhang X and Dou H, Study on the Target
Recognition and Location Technology of Industrial
Sorting Robot based on Machine Vision, J. Journal of
Robotics, Networking & Artificial Life, 2015,
2(2):108-110.
4. Wang J, Dou H and Zheng S, Study on the Target
Recognition based on Machine Vision for Industrial
Sorting Robot, J. Journal of Robotics, Networking &Artificial Life, 2015, 2(2):100-102.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 621

Informational Narratology and Automated Content Generation
Takashi Ogata

Department of Software Informatics, Iwate Prefectural University
Takizawa, Iwate, 020-0693, Japan
Yoji Kawamura

Kindai University
Higashi-osaka, Osaka, 577-8502, Japan
Akihito Kanai

Hosei University
Machida, Tokyo, 194-0298, Japan
E-mail: t-ogata@iwate-pu.ac.jp, kawamura@bus.kindai.ac.jp, kanai@hosei.ac.jp

Abstract
Several studies have proposed a concept called “informational narratology” or “information narratology.” In thispaper, we integrate narratological studies, including narratology and various literary theories, into information
technologies, such as artificial intelligence and cognitive science, in order to mainly design and develop systems that
automatically generate, create, or produce digital narrative contents related to a variety of existing or future narrative
genres. Under this framework, we design and develop narrative generation systems, advertise image processing
systems, and develop narrative film operation systems. In this paper, we present an overview of the concept of
informational narratology, and introduce the above three studies as concrete approaches to its development.
Keywords: Informational narratology, automated content generation, narrative generation, multiple narrative
structures, integrated narrative generation system.

techniques. Ogata & Kanai1 and Ogata2 reviewed
narrative studies in diverse academic fields including
narratology, literary theories, AI, and cognitive science.
We have performed narrative-related studies fromvarious viewpoints. However, a common outstanding
feature is the objective to integrate narratological themes
and materials related to literary theories into information
science and technologies such as AI and cognitive
science. For narrative-related studies from the above
perspective, we have proposed a new research frameworkand concept called “informational narratology” or“information narratology.” Informational narratology

1. Introduction“Narrative” is one of the most important words in thefields of literature and humanities. Furthermore, it is also
an academic term that is related to wider areas such as
clinical psychology, artificial intelligence (AI), andcognitive science. The concept of the narrative in AI andcognitive science is used particularly as a kind of
schemata through which a variety of information in anenvironment is synthetically recognized and represented.
Studies of narrative text analysis have also been
attempted using various narrative-relating methods and© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 622

Takashi Ogata, Yoji Kawamura and Akihito Kanai

focuses on the areas of narrative generation and creation,and is different from the previous or traditional
narratology, which focuses on the narrative aspects ofreading and interpretation. Informational narratology
also focuses on automated narrative content generation
using computers and AI to utilize the formalized
characteristics of narratology.
In this paper, we present an overview of three
approaches to the informational narratology, which weredeveloped by the respective authors of this paper, to show
the potential and effectiveness of automated narrative
generation. We mainly designed and developed systems
that automatically generate, create, and produce digital
narrative contents for several existing or future genres
through narrative communication and simulation. In
particular, we designed and developed narrative
generation systems (Ogata), advertising image
processing systems (Kawamura), and narrative film
processing systems (Kanai). In this paper, we propose an
overview of the above three systems as mutually related
approaches to embody the informational narratology.

narrative level, and the narrative productionconsumption process at the social narrative level. As themodels and systemization of these processes, INGS and
GIS correspond to the former and latter, respectively. In
other words, both the narrative generation-reception
model and narrative production-consumption model,
which are integrated by the next multiple-narrative
structure model, are implemented by the INGS
corresponding to the narrative generation-reception
model and the GIS corresponding to the narrative
production-consumption model.
From the perspectives of the mutual relationship or
inclusive relationship, as well as a larger narrative model,
narrative production, i.e., the consumption process,includes the narrative generation-reception process.
Therefore, from a broad perspective, a narrative as a
generation-reception process is constructed by a
production-consumption process. We use the term
“multiplenarrative structure model” to show such
multiple narrative phenomena. In addition, at othervarious levels, the multiple-narrative structures model isa very significant characteristic in narrative phenomena.
According to the above theoretical frameworks,
Ogata aims to develop a practical method of narrative
generation through the collaboration of narrativegeneration systems and humans. At the same time, at alarger social level, he envisions a narrative generationsociety, where diverse narratives are created and received
through the interaction between humans and machines,
as mutually equivalent existences, although they havedifferent characteristics and merits, respectively.

2. Theories and Systems of Narrative
Generation by Ogata
Although narrative generation systems called Integrated
Narrative Generation System (INGS)2 and Geino
Information System (GIS)3, which are central parts in thecurrent work by Ogata, have been designed anddeveloped, they are based on the following theories for
informational narratology.
2.1. An Overview

2.2. Phenomenological Multiplicity of Narrativeand Expanded Literary Theory

Ogata recognized that there are narratologicalphenomena through which diverse narratives always
emerge and exist. Ogata4 tentatively showed ahierarchical list that comprehends diverse narrative
genres to indicate the large space of the narratologicalphenomena. Another reason for this attempt is to create
new narrative works that absorb and re-construct avariety of elements and characteristics of previous or
traditional narratives in the relationships with future
digital narratives and narrative generation systems (INGS
and GIS) developed by Ogata.
Ogata considered two types of narrative processes:
the narrative generation-reception process at the personal

Ogata’s plan is based primarily on considering a narrative
phenomenon that appears with very diverse forms as asingle framework. We refer to a personal level’s narrativemodel as a narrative generation-reception model and asocial level’s narrative model as a narrative productionconsumption model. These models form an integrated
and entire model including multiple narrative structures,
where the former model is included in the latter model,
and where the repetition of the former constructs thelatter.
Further, we refer to previous or traditional
narratology and literary theories in order to apply them to© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 623

Informational Narratology

the expanded literary theory as a theoretical foundation.
Although recent narratology and literary theories havefocused on destroying the constructive ideas that rely on
the terms of de-construction and others, they have gained
an important and novel academic value by consideringnarratives as objects that can be analyzed technologicallydepending on structuralism theories. The expanded
literary theory5 returns to this original idea of narratology
in an effort to seek to newly create model-oriented
narratology and literary theory.

dependent on the above two levels of narrative generationand the interaction model. Although the analysis andmodeling of a phenomenon are objective and static tasks,
we insert a subjective and dynamic method into theobjective and static model.
The INGS is a mechanism that systematizes theabove narrative generation-reception model. The purposeof this mechanism is to simulate narrative generation in asingle level. Ogata1 provided a detailed description.
There are corresponding relationships between the
INGS and humans. For example, the conceptual
dictionaries in the INGS are equivalent to the conceptual
memory of a personal human. The narrative content
knowledge bases in the INGS are similar to humanepisodic memory. Further, narrative generation
techniques such as the story-generation techniques in the
INGS correspond to the knowledge for making andoperating event sequences in the human brain.

2.3. Model of Multiple Narrative Structures
The narrative generation-reception model refers to thenarrative generation level by single subjects, namely thelevel of narrative generation realized by each human as
an individual. We refer to this level as a “human systemof narratives.” On the other hand, the narrative
production-consumption model is a social and collective
narrative generation level in which many narrative
generation-reception systems are included, and this levelis considered a “social system of narrative.” In therelationship between narrative generation-reception andproduction-consumption levels, both levels interact witheach other to create an entire narrative phenomenon.
The multiple narrative structures model1 is an ideathat focuses on a narrative phenomenon from theviewpoint of the multiplicity of the narrative. In anotherfeature, a single narrative work is structured using suchvarious types of narrative elements as a story or stories,
characters, a world or worlds, and so on. These elements
are not constructed as a hierarchical structure, and it isdifficult to determine the most important element. The
narrative elements in a narrative work are composed
through appealing each existence and many types ofmutual relations among narrative elements. Ogata has
surveyed and analyzed kabuki as a narrative genre toshow multiple narrative structures from various
perspectives.3

2.5. Geino Information System: GIS
On the other hand, the GIS is a systematization of themodel of the narrative production-consumption process.
The GIS is a narrative generation model at the social level,
which actualizes both narrative generation-reception andnarrative production-consumption processes throughcollaboration with the INGS.3 Fig. 1 shows an overview
of the GIS architecture, which includes the INGS.
Although this system is not actually executed in thecurrent timing different from the INGS, in the near future,
we plan to implement it as a system including several
INGSs.
The GIS is a social level system considering thefollowing points: First, a society is a space where diverse
narratives are continuously generated and received inparallel. However, on the other hand, with respect to a
specific narrative production system, it is a subject that
continuously repeats narrative production cycles. A
society refers to a situation where diverse narrative
production-consumption subjects, including individuals,
are produced in parallel (namely spatially) andsequentially (namely temporally). According to ourtheoretical framework, a society refers to a temporal andspatial field where there are many INGS as well as the
GIS as a collection of INGSs. Therefore, although asingle GIS does not indicate an entire society, the GIS ismodeled as a geino production mechanism that is a kind2.4. Integrated Narrative Generation System:INGS
The narratological study by Ogata is not sociology. It
does not remain within the range of the model
development and the analysis of societies. The study
aims to design and construct actual systems that are© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 624

Takashi Ogata, Yoji Kawamura and Akihito Kanai

variations of a narrative generation case involving thecollaboration of both humans and machines. There aremany possibilities for collaboration methods. For
example, we can consider various possibilities such as
methods through which a human completes a narrative,
where once a narrative generation system is made, it adds
a new part to a narrative that a human made. As these arevery practical problems, we will not be able to classify inadvance all of the possibilities normatively.
On the other hand, in the case of the narrative
generation at a social level, there is a situation where bothhuman-narrative generation and social-narrative
generation coexist. If we consider each narrative
generation mechanism, such as the GIS, it is related tothe problem of the specific gravity of narrative generation.
The most extreme type is a narrative productionconsumption mechanism that does not involve humans atall. Then, the most extreme type in the reverse sense is aprevious type’s narrative generation mechanism, which
is formed by only humans or which at least does notinclude any machines as narrative generation systems. In
addition, with respect to concrete usage forms of the GIS,
we seek to undertake various narrative generation andrepresentation forms where humans and machines
coexist.
As stated above, based on the model of multiple
narrative structures, we can envision the phenomenon
where both narrative generation mechanisms as humans
and narrative generation mechanisms as machines
coexist at various levels, and this applies also in theconcrete implementation level.

Geino History (Sequence) <Contents><Receiver Mechanism>
Receptionmechanis

Geino
event

Narrativeas image

Life event
sequence

Life
event

Reactionmechanism

Geino event
sequenceTrailinformation
Life
work
Interpretation
Strategy(Meta level)

Geino
work

<Narrator>
Integrated narrative
generation system

Generation
mechanismContents
knowledgebaseGeino work
resource
Life work
resource
Geinojinresource<Sender> (Author as a whole)Fig. 1. GIS Architectureof social-narrative production and a consumption system
as a small-scaled social mechanism or system.
2.6. Vision of Human-Machine Symbiosis System
of Narrative
From the above theories and systems, we seek toapproach a situation that we refer to as a “human-society
symbiosis system.” Alternately, we consider that such asituation will need to be realized in the future society.
This is a narrative situation where human authors andmachine authors live together with no mutual
discrimination in order to continue to collaboratively
create narrative works in various genres. This is notnecessarily just a wish, but an expectation that thehistorical progress will take such a route.
In such a social situation, we aim to determine thepositioning of narrative-generation systems. There areseveral phenomenological forms shown below:
With respect to a narrative generation system as asingle unit, first, we consider a form where a narrative
generation system autonomously exists as itself. Next,
there is a form where a human makes narratives using anarrative generation system or a narrative generation
system makes narratives using humans. These are

3. An Attempt of the Commercial Film
Production Support System (CFPSS) Based on
the Image Rhetoric of Commercial Film
There are case studies into effective commercial films
with a focus on marketing and advertisement.6 Some ofthese studies include elements of advertising expressionthat have been classified and extracted to enhance
advertising effects. Alternatively, other studies
classifying advertising expressions according to thenature of the information (comparative, unique selling
proposition, preemptive, hyperbole, generic-informatic),
and the nature of transformation (user image, brandimage, use occasion, generic-transformatic) examine theideas of advertising expressions.7 However, these studies© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 625

Informational Narratology

provide insight into advertising expressions from aparticular vantage point, and are not conducive todeveloping specific rules for creating images at the
microcosm level. Related to print advertisements (text,
photo, poster etc.), studies examine an interpretation andanalysis of the effect of advertising rhetoric onviewers.8,9,10 However, these studies do not include
analyses related to commercial films. Moreover, as theseresearch approaches aim to interpret and analyze existing
advertisements, these studies also exclude information
systems to generate specific advertisements according tooperating rhetoric. Under such circumstances,
Kawamura provided one of the few studies aiming tobuild an information system that analyzes the advertising
image techniques (advertising story, editing). 11,12,13
Fig. 2 shows the plan of CFPSS.11 The concept of
CFPSS is to generate the various commercial films
adapted to the user’s keywords and sentences (lifescenario) input. CFPSS includes a database of 3643
image shots converted to a commercial film, thatsearches and classifies image shots based on keywords
and sentences. The system includes a function to generate
the storyboard based on the selection of advertising story,
and playback in the order arranged in the storyboard. The
menu of advertising story is as follows;12

Fig. 2. CFPSS Plan.

Using an advertising story technique, a system wasdeveloped to generate commercial films through aninteraction between the users and the system, which wastested with an observational experiment. Since thesystem easily generates commercial films that comply
with a variety of requirements, it can be utilized forvarious experiments in the future. These types ofexperiments will clarify and enhance the creative knowhow (image techniques and rhetoric) to produce
individual advertisements.
4. Cutting Techniques and Non-Story Type
Rhetoric

Provider story type: The primary structure expresses
the provider’s story (production and distribution,
product function, effect on company). This is the“story of the product” and “story of the company.”
Consumer story type: This primary structure
expresses consumers’ stories (consumption situation,
product acceptance, consumption effect). This is the“story of consumption.”
Overall type: This structure generally expresses boththe provider’s and consumer’s stories. This isrepresented as provider story type + consumer story
type.
Image type: This structure expresses images related
to the consumer situation, though does not express
product acceptance and consumption effects in thefilm. Rather this structure represents a productfunction. This category also includes structures
expressing an image that does not belong to theconsumer, provider, or product function.

For computational and informational narrative film
generation, many kinds of cognitive effects can be
actualized using a strategy that employs story and nonstory rhetorical aspects17. Many cognitive effects fromnarrative films are created through the interaction
between cognitive process, story, discourse, and rhetoric.
For example, irrational rhetoric cutting techniques fornarrative space and time in the film can change theviewer's cognitive transition from story-driven to
rhetoric-driven processing. Based on cognitive transition
due to narrative processing from story-based to non-story
based, cognitive effects such as intensive nostalgia
effects can emerge.
The process of the viewer coming to understand thepast story may lead to some cognitive effects. Conversely,
many films have a primary purpose other than to tell astory. This happens when the purpose of the director isthe film rhetoric itself, or the non-story rhetorical aspects
rather than the story. When the above issues are
considered, a system for computational and© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 626

Takashi Ogata, Yoji Kawamura and Akihito Kanai

informational narrative film generation must include
story and non-story rhetorical aspects.
Based on the cutting techniques used to makegenerate irrational relationships with time or charactersin continuous shots, a viewer or a reader can feel lessconstrained with regard to story comprehension and reset
the viewpoint to subtle elements, such as individual shots.
In addition, there are two different cognitive process
types for the rhetoric of film. Cognitive effects fromcertain rhetoric, such as story type rhetoric, are primarily
generated by the story or consistent narrative (cognitive
process type-1). Conversely, the cognitive effects fromthe other rhetoric, such as non-story type rhetoric, arecaused by the rhetoric of films, in particular the cutting
techniques (cognitive process type-2). Many films
combine both type of rhetoric. Therefore, the viewers
transfer cognitive processes from type-1 to type-2 while
viewing the same film.
Cognitive process type-1, the process in which theviewers understand the story, has some cognitive effects.
In this process, the “experience” of the film's goaldirected characters and consistent narrative may lead tosome affective response. However, the cognitive effects
such as nostalgia effects may arise from other sources, aswell as cognitive process type-2.
When the constraint is relaxed, an irrational cut of
non-story type rhetoric may cause a “resetting ofviewpoint” and “affect, which does not arise throughcomprehending a story, but through the audiovisual
situations.” A resetting of viewpoint indicates that theviewer's viewpoint shifts from the need to be told a story
to a grasp of the non-story narrative. This reflects theobject associations within the moving image and the“mood” or the details of the moving image. “Affect,
which does not arise through comprehending a story, but
through the audiovisual situation” is caused by theaudiovisual situation that emerges from the film's moodand object associations.
In non-story type rhetoric, the editing, recording,and photography are not always subordinate to theoverall story. As stated above, the focus is on non-story
narrative, the film's mood and object associations or thereality related to the unfamiliar situations rather than on
the events in the story. Such associations and situations
cause cognitive effects that do not arise in the process ofcomprehending a story. Kanai14,15 indicated that when aviewer is able to relax his or her constraint with regard to
the need to comprehend a story, this strengthens thosecognitive effects including nostalgia effects, which do
not arise in the process of comprehending the story. On
the contrary, with story type rhetoric, the cognitiveeffects are those generated by the overall story.
The unfamiliar situations derived from non-story
rhetorical elements create new realities. Unfamiliar
situations can emerge through the sudden appearance ofa section of non-story type rhetoric. In this case, a viewer
sometimes cannot understand the consistent story.
Therefore, attention to the situation itself is enhanced.
The unfamiliar situations generate new cognitive effects
related to unknown memories and affects that do not arise
through story comprehension.
Non-story type rhetoric is created through cutting
techniques used to make irrational relationships between
1) the rhetorical elements of two shots; 2) events andimages; or 3) sounds and images16. The irrational
relationships through the cutting techniques are used inmany artistic films and music videos in order to generatesituations unfamiliar to the viewers and to create new
realities.
5. Conclusions
In this paper, we described the basic ideas of
informational narratology as a fusion of narratology,
including literary theories and informatics such as AI andcognitive science. In particular, an important direction of
informational narratology is the point that it focuses oncontent generation or production through narrative
communication and simulation. Previously, we
performed research that focused on each narrative
content generation, as discussed above in this paper.
Ogata stated the idea and concept of the model ofmultiple narrative structures, and presented an overview
of two mutually related systems, the Integrated Narrative
Generation System (INGS) and Geino Information
System (GIS), to show the implementation of the model.
Kawamura discussed the Commercial Film Production
Support System (CFPSS) based on the Image Rhetoric ofCommercial Film. Kawamura’s study mainly focuses on
the perspective of the reception in narrative
communication. Kanai, who focuses on computational
and informational narrative film generation, mainly
analyzed the cognitive effects by receivers for movie© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 627

Informational Narratology

7.H. A. Laskey, E. Day and M. R. Crask, Typology of main
message strategies for television commercial, J.
Advertising 18(1) (1989) 36-41.
8.E. F. McQuarrie and D. G. Mick, Figures of rhetoric inadvertising language, J. Consumer Research 22(4) (1996)
424-438.
9.B. A. Huhmann and P. A. Albinsson, Does rhetoric impact
advertising effectiveness with liking controlled?,
European J. Marketing 46(11/12) (2012) 1476-1500.
10.I. G. Theodorakis, C. Koritos and V. Stathakopoulos,
Rhetorical maneuvers in a controversial tide: assessing theboundaries of advertising rhetoric, J. Advertising 44(1)(2015) 14-24.
11.Y. Kawamura, An analysis of the rhetoric of commercial
film -toward the building of a commercial film production
support system based on image rhetoric, in Proc. 2003
IEEE Int. Conf. Systems, Man and Cybernetics,(Washington, DC., 2003), 993-1000.
12.Y. Kawamura, An analysis on the story and editing
techniques of commercial film -toward the building of acommercial film production support information system, J.
Advertising Science 50 (2009) 16-32. (In Japanese).
13. Y. Kawamura, An attempt of the commercial film
production support system based on the image rhetoric ofcommercial film (Chapter 4), in T. Ogata and T. Akimoto,Computational and Cognitive Approaches to Narralotogy,(IGI Global) (2016) 117-138.
14. A. Kanai, The story as a constraint on film. In Proceedingsof the Third International Conference on Cognitive
Science (Beijing, China, 2001a), pp. 503-506.
15. A. Kanai, Cognitive process model toward the rhetoric of
the film, Cognitive Studies, 8 (2) (2001b) 139-150.
16. A. Kanai and T. Ogata, Non-story Processing on the film
rhetoric composition system. In Proceedings of 18th
Congress of the International Association of Empirical
Aesthetics (Lisbon, Portugal, 2004), pp. 433-406.
17. A. Kanai, Non-Story, Nostalgia, and film cognition:
nostalgia-based
narrative
rhetoric
composition.
Computational and Cognitive Approaches to Narratology,
eds. T.Ogata and T. Akimoto (IGI Global, 2016) pp. 376390.

films, and used a strategy that employs story and nonstory rhetorical perspectives.
However, these studies were not integrated in theprevious research. If these researches are synthesized, inparticular by developing narrative content-generation
systems, it will enable the expansion of the constructive
feature, namely an academic approach through executing
system development, of the informational narratology.
Therefore, one or our future plans is to integrate theabove narrative content generation systems proposed by
the three authors into a larger system. This will also help
to bridge informational narratology studies to the social
experimental stage for the distribution of generated or
produced narratives.
Acknowledgements
This work is supported by JSPS KAKENHI Grant
Number 26330258 and Support Center for Advanced
Telecommunications Technologies Research Grant(2015-2017).
References
1. T. Ogata and A. Kanai, A, An introduction to Informatics
of Narratology: Towards the Thoughts and Technologies
of Narrative Generation (Gakubunsha, Tokyo, Japan,2010).
2. T. Ogata, Computational and cognitive approaches tonarratology from the perspective of narrative generation,in Computational and Cognitive Approaches toNarratology, eds. T. Ogata and T. Akimoto (IGI Global,
Hershey, Pennsylvania, USA, 2016), pp. 1-73.
3. T. Ogata, Kabuki as multiple narrative structures, inComputational and Cognitive Approaches to Narratology,
eds. T. Ogata and T. Akimoto (IGI Global, Hershey,
Pennsylvania, 2016), pp. 400-431.
4. T. Ogata, A comprehensive consideration of a narrative
genre system, in Literature, in Proc. of 2nd Annual
Conference of Literature, Cognition, and Computer
Research Group in Japan Cognitive Science Society(Tokyo, Japan, 1999), pp.85-91.
5.T. Ogata, Expanded literary theory for automatic narrative
generation, in Proc. of Joint 7th Int. Conf. on Soft
Computing and Intelligent Systems and 15th Int. Sympo. on
Advanced Intelligent Systems (Kitakyushu, Japan, 2014),
pp. 1558-1563.
6.D. W. Stewart and D. H. Furse, Effective television
advertising: A study of 1000 commercials (Lexington
Books, Lexington, MA, 1986).

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 628

Analyzing Multiple Narrative Structures of Kabuki based on the Frameworks of Narrative
Generation Systems
Takashi Ogata

Department of Software Informatics, Iwate Prefectural University
Takizawa, Iwate, 020-0693, Japan
E-mail: t-ogata@iwate-pu.ac.jp

Abstract
Although kabuki is a genre of, so to speak, traditional performing arts, it also has many of the characteristics of acontemporary genre, including such various elements as narrative, drama, dance, and music. This analysis and study
will contribute to the design and development of studies on digital-narrative generation. The author has previously
surveyed and analyzed kabuki’s narrative structures from the viewpoint of a multiple narrative structure model,
particularly the generation or production processes involved in its creation and its reception and consumption
processes. Kabuki’s multiple-narrative-structure model means that the entire structure of kabuki is constructed
through multiple usages of related information. For example, in kabuki the element of the “person” is divided into
three: a “character” within a narrative work, an actor with a history of performances and who uses a stage name, anda real human with a true name. This multiplicity provides the person with multiple and deep characteristics. In thispaper, based on these previous studies, the author discusses a method that bridges this kabuki analysis with systemdesign and involves the use of two narrative generation systems: an Integrated Narrative Generation System (INGS)
and a Geino Information System (GIS).
Keywords: Kabuki, Multiple narrative structures, Integrated narrative generation system, Geino information system.

Kawatake,3 Shoyo Tsubouchi (1859–1935), who was a
Japanese researcher and author, compared the nonhierarchical and collective characteristics of kabuki to achimera. However, the author of this paper would like to
use this analogy to suggest the “multiplicity” of kabuki’s
characteristics, applying the multiple narrative structure
model4 that forms the theoretical basis of the narrative
generation study presented here. In particular, in kabuki,
a variety of forms (“kata”), which means collections ofpaternal knowledge relating to various fields, including
the dances performed by actors in each kabuki scene,frequently use story patterns, etc., to enable the use of
free combinations of elements to construct multiple
structures in a work.5,6,7

1. Introduction
Examining its history and characteristics1, kabuki, which
originated during the beginning of the Edo era, can be
regarded as a synthesized genre of drama thatincorporates a variety of geino genres, such as dance,
Noh, kyogen, and ningyo-joururi (puppet drama), into arich form and also includes various narrative elements. In
the West, the basis of drama and other narrative forms
originates from Aristotle’s opinion2: that the story or plot(“mythos”) manages the other narrative elements such as
characters and thoughts. In contrast, in kabuki, the storyor plot was originally an element that was added to aid
comprehension and to help control a collection of
performances by an actor or actors. In kabuki, allnarrative elements have equal competence. According to© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 629

Takashi Ogata

The author has been studying kabuki in order tointroduce the acquired knowledge into two types ofmutually related narrative-generation systems, called
Integrated Narrative Generation System (INGS)8 andGeino Information System (GIS).1 In particular, theauthor has been studying kabuki from the viewpoint of
the multiple-narrative-structure model. We generally
consider a narrative work, its generation, and its
production process from the viewpoint of its construction
through multiple structures. For example, a narrative’s
flow comprises the multiple structures of a story in asemantic flow and a plot in a constructive flow. A kabuki
narrative is a particularly rich object for the multiplenarrative-structure model. Consequently, we haveanalyzed the multiplicity of kabuki regarding fifteen
kinds of elements.1 For instance, the element of a “person”
in kabuki can be divided into the following three aspects:
the real “person,” who lives in the actual world; the“person” as an actor who has a history of performingroles; and the “person” of the kabuki character thatappears on stage. Each person has their own history or
story. In the course of this study, knowledge on theanalyzed multiple-narrative structures in kabuki will be
introduced into the INGS and GIS.
In the author’s previous research on kabuki, his
studies referred to related books and papers 9,10,11;
however, for concrete application and modeling in the
INGS and GIS, he must perform a more formal andelaborate analysis of kabuki. This paper is the first step
towards attempting a more formal analysis of kabuki,
aiming at the introduction of the performance art into the
INGS and GIS.
Before beginning the main discussion on the above
theme, the author would like to explain why multiplicity
is important, effective, and significant in kabuki: (1) First,
the multiplicity in kabuki enables it to interest a varietyof receivers through social-distribution processes. (2) Its
narrative values are useful for effecting a diverse anddifferential interpretation of the world through the powerof imaginative- and fictional-narrative production.
Multiplicity contributes to this diversity anddifferentiation of world understanding. (3) Multiplicity
can enhance the depth of works.
However, the above characteristics may also be
common in other narrative genres. Why then is kabuki
worthy of being included in a study on multiple-narrative

structures? A reason for this is that kabuki, which has along history and exists in the current society, contains
multiplicity in very diverse aspects and is part of a genrethat combines performing art and drama. Kabuki is anembodiment of a very rich multiplicity.
Although, in a previous paper,1 the author
comprehensively researched and analyzed themultiplicity of kabuki, in this paper, the author is
focusing on the concept of the “person” and is attempting
to consider it through concrete modeling by analyzing it
using the INGS and GIS.
In addition, the INGS corresponds to the narrativegeneration-reception process at the personal narrative
level. On the other hand, the GIS corresponds to thenarrative-production-consumption process at the socialdistribution level. Both of these processes construct anentire narrative process or a mechanism as a whole. The
kabuki research and analysis discussed in this paper will
be extended to an entire narrative mechanism that
includes these two processes.
Therefore, we explain the relationships between
kabuki and the narrative in order to avoid causing
misunderstanding and confusion. In kabuki, a narrativemeans the work’s story or plot, which is a wider sense ofnarrative; it can also encompass the narration performed
by a character on stage, which is a narrower sense ofnarrative. In any case, both form partial elements of an
entire kabuki.
However, the concept of narrative discussed here
has wide and comprehensive meanings. It includes alevel of representation or expression in addition to levelsof story or plot. The media for representation or
expression include language, music, image, and further
body. From the author’s viewpoint, the whole of a kabukistage corresponds to a narrative. In this sense, thenarrative generation of kabuki naturally has the meaningof generating the whole of a kabuki performance.
2. “Person” in kabuki: A Basic Consideration
In kabuki, there are various kinds of “persons,” such as
an “actor” and a “character.” These are temporally andspatially formed and possess multiple existences andrelationships.
First, a person, who is an actor, plays a character ona kabuki stage. Concurrently, the actor themselves has avery important and essential value. In kabuki, “actor
plays” sometimes carry a more important meaning than

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 630

Analyzing Multiple Narrative Structures

universal dramas that focus on characters. Originally,
especially during the Edo era, kabuki scenarios were notwritten using characters’ names, but using actors’ names.
In other words, many kabuki works were written bykabuki authors to emphasize and strengthen theirpersonalities and characteristics.
Further, although the person as an actor is a humanwith a real body, a real person in kabuki is divided into
two parts: the real actor who is represented by his “stage
name,” and the real human who is symbolized by his“real name.” These correspond to the spatial forms of aperson. Concurrently, a unique point is the fact that a
stage name, in many cases, has a temporal tradition andhistory or a kind of story that is inherited over generations.
This means that actors who retain the same stage nameover multiple performances exist in a current temporal
point in continuity with other “real” people. An actor is
located in a temporal and historical continuity. Therefore,
the existence of various persons and stories can be
multiplied in various ways.
On the other hand, a “person” as a character is not
limited to the range of a single kabuki play. Certain
characters, for example, can occasionally appear in otherkabuki plays, other works in other genres, non-fictional
histories, etc. Therefore, a character is an existence that,through its other appearances in the temporal and spatial
situations of a kabuki play, represents a particularscenario. Concurrently, a character also exists as anoverlapped representation of its appearances in otherkabuki plays and other genres.
Bando Tamasaburo V is an example of an actor who
has multiple constructions, as can be seen from thefollowing description:
He is a man, and his real name is Morita Shinichi.
He has a history of acting and he inherited the name“Bando Tamasaburo” from his adoptive father andfurther past persons with the same name.
He is an actor who has performed many different
characters in classical kabuki.
He is something of an innovator who has continued
to introduce new dramas in other kabuki-related
genres, such as “Tenshu Monogatari” (Castle Tower
Story) (1917) by Izumi Kyoka (1873–1939).
He is a director of diverse works in the kabuki genre.
He is a performer and an artist who transcends theboundaries of various genres such as dance, classicalChinese opera, and reading aloud.
He is a movie director, who, among other works,
directed “Gekashitsu” (Operating Room) (1992),

which is based on “Tenshu Monogatari” (Castle
Tower Story), an 1895 novel by Izumi Kyoka.
In recent years, he has fulfilled the role of an
educator or trainer and has trained many kabuki
actors by performing with them.
Discussing “onnagata” is necessary in order tounderstand the “persons” of kabuki. Although kabuki
originated from yujo (“prostitutes”) kabuki, the transition
from wakashu (“boys”) kabuki to yaro (“adult men”)
kabuki transformed it into a drama genre performed byadult men only3,9 Real women do not appear on the
kabuki stage; however, actors, as onnagata, further
exhibit the multiplicity of kabuki by incorporating real
men’s sexuality. As onnagata is considered to be one of
the most important elements that contribute to theattraction of kabuki, it has been the focus of essential
discussions.
Additionally, concerning “person,” the motif of the“jitsu ha” (actually)12 transformation also plays animportant role. Many characters in kabuki are
multiplexed and they present complex images through amanipulation of the “actually.” In many cases, thetransformation of the “actually” is created bytransforming the character of an unknown commoner
into a famous and historical character, namely a hero.
Two contrasting sub-genres in kabuki, sewa-mono andjidai-mono, are related to this feature. The sewa-mono is
a comparatively real kabuki genre where the characters
are common people. In contrast, the jidai-mono is acomparatively formal and stylistic kabuki genre based on
historical worlds and characters. Concurrently, these arealso used as concepts to show the performance styles ofkabuki actors. The style of sewa sometimes infuses withthe style of jidai, creating a unique impression on a
kabuki stage. In jidai-mono, the transformation of acharacter through the “actually” occasionally creates a
trigger for moving from a of sewa-style scene to an
original jidai-style scene. Further, these concepts, sewamono and jidai-mono or sewa and jidai, occasionallyappear in the micro performances of a character. For
instance, in a sewa-mono, a character or an actor
occasionally acts and speaks in the jidai-mono style.
Sewa-mono and jidai-mono or sewa and jidai areconcepts that show two types of kabuki performancestyles, namely realism (sewa) and formalism (jidai). The
multiple rhythm through which the formal style isinserted into the realistic style, and the contrary rhythm
through which the real style is inserted into the formal
style, produces a unique spectacle on the kabuki stage.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 631

Takashi Ogata

3. GIS/INGS models

not necessarily mean that the GIS simply generates
narratives according to the desires and expectations of thereceivers. The INGS is placed in the central position of
narrative generation by the sender mechanism.
The sequence and repetition by the sendermechanism forms a mechanism for geino histories. In
order to continuously generate geino histories, the GIS
utilizes special knowledge bases called “work resource,”“life resource,” and “geino-jin resource,” which are notpresent in the INGS. Although these are similar to thenarrative content knowledge bases in the INGS, uniquecontent knowledge is contained in the GIS.
By the geino-jin resource we mean a place in whichto store geino-jin information, which includes character
information on geino performers and actors as well as
their personal information. The life resource corresponds
to the part in the geino-jin resource where only the life
histories or life courses are extracted. The work resourcemeans that the knowledge relating to geino works isanalyzed and processed using various methods.
Comparing this to the context knowledge present in the
narrative-content-knowledge bases in the INGS, thework resource in the GIS stores more specific narrative
information. Analyzing and studying kabuki aims atstoring the specific narrative content knowledge in the
GIS.
In addition to human receivers, the receivermechanism also includes reception subjects as machinerymechanisms. These receiver mechanisms output, throughvarious methods, information on reactions based on thereception experiences to a work, a sequence of works, thelife history of a geino-jin, etc. A geino-history issequentially formed through circulative and repetitive
interaction between both mechanisms of the sender andthe receiver. The content of a geino history is classified
into geino events and their sequence, and life events andtheir sequence. The works also include scandals andgeino news that focuses on geino-jins’ lives themselves.

In the author’s research framework, kabuki narrative
generation is implemented using the GIS and INGS.
Basically, the GIS is a comprehensive systemarchitecture that contains the INGS as one of its parts.
The first two sub-sections in this section describe the GIS
and INGS, and their mutual relationship is explained in
the last sub-section.
3.1. GIS: Geino Information System
Fig. 1 shows an overview of the architecture of the GIS.
This architecture corresponds to a narrative productionconsumption mechanism that includes the use of many
INGSs as a narrative generation-reception mechanism.
The GIS is composed of, at its highest level, a sendermechanism, a receiver mechanism, and a geino history.
The first sender mechanism receives external
information, which is mainly traces of information
outputted by the reception mechanism, through the GIS’mechanism of “interpretation strategy” and selects aninterpretation according to an interpretation framework.
Lastly, the sender mechanism generates information
based on the interpretation. However, this process does
Geino History (Sequence) <Contents><Receiver Mechanism>
Receptionmechanis

Geino
event

Narrativeas image

Life event
sequence

Life
event

Reactionmechanism

Geino event
sequenceTrailinformation
Life
work
Interpretation
Strategy(Meta level)

Geino
work

<Narrator>
Integrated narrative
generation system

Generation
mechanismContents
knowledgebaseGeino work
resource
Life work
resource
Geinojinresource

3.2. INGS: Integrated Narrative Generation
SystemFig. 2 shows an overview of the INGS architecture. At
the macro level, the INGS is divided into the threefollowing generation processes: story, discourse, andrepresentation. Parts corresponding to the mechanisms inthis figure concern the “story-generation mechanism,”<Sender> (Author as a whole)Fig. 1. GIS Architecture© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 632

Analyzing Multiple Narrative Structures“narrative-discourse mechanism,” several “narrativeexpression mechanisms,” and several “control
mechanisms.” On the other hand, parts corresponding to
the content or knowledge in this figure are part of a“state-event transformation knowledge base,” several“conceptual dictionaries,” “several language notation
dictionaries,” and “narrative-content knowledge bases.”
Ogata8 presents a detailed list of all of the modules in thecurrent INGS.

from the GIS and then generates narrative conceptual
structures for a story as well as a narrative discourse
before expressing these structures for narrative
representation. This part of narrative expression or
representation is conducted by the staging mechanism in
the GIS. Furthermore, in the business model, we canconsider a variety of possibilities for the actual
distribution: expressing or representing on the PC and on
the stage, developing and distributing second works
through books and other media, etc. However, thesematters exceed this paper’s range.

3.3. Relationship between the GIS and INGS
Simply speaking, specific knowledge in kabuki is stored
in the GIS and the other general knowledge required fornarrative generation is stored in the INGS. Althoughnarrative generation by the INGS concerns narrative
generation for each unit of work, narrative generation by
the GIS means narrative generation at a more complete

4. Toward Designing the “Person” using the GIS
and INGS
In this section we show a design plan of the “person” inkabuki using the GIS and INGS. The main aspects of aperson in kabuki are as follows:

OutputInput(i) A person as an actor, performer, or a player on the
kabuki stage(ii) A person as a real human(iii) A person as a character in a kabuki work

Entire control
Story
Contents
KB
State-event
Transformati
on KB
Conceptual
Dictionaries

Story
Mechanism

Discourse
Mechanism

Expression
Mechanism

Language
Notation
Dictionaries

Music KB

Image KB

Function call

Data flow

Fig. 2. INGS Architecturelevel, where a sequence of narrative works is generated
by the INGS. The author’s basic idea here is that the latter
type of narrative generation is also performed by a kindof narrative technique that transcends a mere accidental
sequence.
Ogata1 has surveyed and analyzed the kabuki
themes shown below: (1) real incident, (2) work, (3)genre, (4) material or topic, (5) person, (6) story and plot,(7) actor and place, (8) time (era or age), (9) style (formor pattern), (10) theatre (stage and seating), (11) audience,(12) text, (13) production of scenario (daicho), (14)direction, and (15) stage performance. In this researchplan, these surveys and analyses will be formalized foruse in the GIS, including the INGS.
In a generation cycle, the INGS receives the above
various knowledge and additional related knowledge

The person relating to the above (i) has a stage name.
He is defined in the GIS as a part of the geino-jin resource.
This geino-jin resource includes several geino-jins who
have the same name. These data describe a set ofcommon personalities for actors who have this name inaddition to different types of personality each actor
possesses.
(ii) concerns a person who has a real name and isliving as a real human in the real world. In the geino-jin
resource, this type of person is continually represented
with the first type of person.
(iii) relates to a person who should be defined inrelation with a specific kabuki work in order to form anelement of the work resource in the GIS. As this person
can occasionally be used in many kabuki works, it isnecessary to store the person as a specific character, notjust a character who forms a part of a specific work.
To ensure a concrete execution method during the
GIS’ narrative-generation cycle, when the INGS
generates a narrative it receives various information,
including the above three types of person-related
information, to create the basic narrative structure.
Concurrently, general information except for the basic© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 633

Takashi Ogata

information on persons is supplied from the INGS side.
Although the GIS provides more specific informationand knowledge, the INGS prepares more general
information.
Further, through the GIS’ narrative productionconsumption cycle, various sides of the person continue
producing or distributing narratives in a wider sense. In
particular, the next narrative production in the cycle canbe executed by triggering all three of the above types of
person. For instance, the kabuki narrative productionconsumption system can create a series of stages for theactor. It can also produce a series of stages where akabuki character continuously appears.
A primary effect for narrative generation involving
the above method is to employ a concrete method with aperson setting to strengthen the impression of thegenerated narratives and upgrade the quality.
Furthermore, this contributes to enlarging the diversity inthe narrative distribution in the GIS, namely the deviationand evolution of narratives. In particular, narrative
distribution and expansion proceeds from a variety ofnarrative parts or sides, including the whole of a narrativeand narratives. The above method produces diverse
possibilities and provides relatively thick profile
information on each character that provides concretenessand depth in the development.

References
1. T. Ogata, Kabuki as multiple narrative structures, inComputational and Cognitive Approaches toNarratology, eds. T. Ogata and T. Akimoto (IGI
Global, Hershey, Pennsylvania, 2016), pp. 400-431.
2. Aristotle, The Poetics of Aristotle (trans. S. H.
Butcher, Macmillan, London, UK, 1895). Retrieved
fromhttps://archive.org/details/poeticstranslate00arisuoft
3. T. Kawatake, Kabuki: Baroque Fusion of the Arts,trans. Connel Hoff, F. and J. (The International
House of Japan, Tokyo, Japan, 2003).
4. T. Ogata and A. Kanai, An introduction to
Informatics of Narratology: Towards the Thoughts
and Technologies of Narrative Generation(Gakubunsha, Tokyo, Japan, 2010).
5. S. Sugiyama, Joururi Shiroto Koshaku I, II [Joruri
Amateur Lecture I, II] eds. M. Uchiyama and H.
Sakurai (Iwanamishoten, Tokyo, Japan, 2004).
6. T. Miki, Kangeki Guhyo [Reviews on Theater Plays]
ed. T. Watanabe (Iwanamishoten, Tokyo, Japan,
2004).
7. T. Watanabe, Kabuki–Kajo naru Kigo no Mori
[Kabuki: The Forest of Excessive Signs] (Shinyosha,
Tokyo, Japan, 1989).
8. T. Ogata, Computational and cognitive approaches
to narratology from the perspective of narrative
generation, in Computational and Cognitive
Approaches to Narratology, eds. T. Ogata and T.
Akimoto (IGI Global, Hershey, Pennsylvania, USA,2016), pp. 1-73.
9. T. Kawatake, Kabuki: Its Beauty and Tradition,trans. C. Holmes (Japan Arts Council, Tokyo, Japan,
1992).
10. T. Imao, Kabuki wo Miru Hito no Tameni [ForPeople Viewing Kabuki] (Tamagawa University
Press, Tokyo, Japan, 1979).
11. T. Watanabe, Kabuki no Mikata [Kabuki’s
Viewpoints] (Kadokawa Gakugei Shuppan, Tokyo,
Japan, 2009).
12. T. Imao, Henshin no Shiso [The Theory of
Metamorphosis] (Hosei University Press, Tokyo,
Japan, 1970).

5. Conclusions
In this paper, the author focused on the narrative
multiplicity in kabuki as a means of considering future
directions of expansion for narrative-generation research.
This examination was based on the system developmentof two mutually related systems: INGS and GIS. In
particular, this paper showed the various types of person
involved in kabuki towards discussing kabuki’s
conceptual design using the INGS and GIS. In the future,
the author plans to elaborate on designing the person andother elements of kabuki in order to approach an
experimental implementation using/involving the INGS
and GIS.
Acknowledgements
This work is supported by the JSPS KAKENHI Grant
Number 26330258 and Support Center for Advanced
Telecommunications Technologies Research Grant(2015-2017).

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 634

Rhetoric of Commercial Film and the Response of Viewers
Yoji Kawamura

Faculty of Business Administration, Kindai University, 3-4-1 Kowakae
Higashi-osaka, Osaka 577-8502, Japan
E-mail: kawamura@bus.kindai.ac.jp
www.kindai.ac.jp

Abstract
This study first outlines a view of the rhetoric of commercial films as a combination of commercial film techniques.
Next, it discusses the relationship between the rhetoric of commercial films and perception/positive factors based ona variety of surveys. It then shows the structure of the relationship between the rhetoric of commercial films andperception/positive factors based on the discussion. Finally, it summarizes the rhetorical patterns in advertisement
based on the structure thus identified. This study showed that there is a correlation between the overall rhetorical type
and perception/positive factors by introducing the framework of “overall rhetorical types” as a combination ofcommercial film techniques. Interestingly, the overall rhetorical type, which is an approach to classify commercial
films based on commercial film techniques, is found to correlate with the viewer’s perception and positive factors.
On the basis of the structure identified in the study, expressive characteristics of commercial films can be summarized
as 4 expressive characteristics.
Keywords: Commercial Film, Rhetoric, Overall Rhetorical Type, Perception, Positive Factor

representation elements. In particular, in the case ofcommercial films with time constraint (between 15 and
30 seconds), it is speculated that viewers use theirintuition to capture physiological elements, which cannot
be verbalized and form their perception, by focusing on
the differences from their own reality.
Commercial films are visual representations that bringcertain disturbance to the physical reality in which
consumers consume products in the real world.
Commercial film techniques and rhetoric are classified inreference to the disturbance (certain manipulation:
addition or deletion). In this study, to grasp commercial
film techniques and rhetoric systematically, they aredefined as “rules that add some manipulation (addition ordeletion) to the basic advertisement content, mise-enscène, editing and sound effects.” Here, “the basic
advertisement content, mise-en-scène, editing and soundeffects” refer to the general consumer’s behavior of
consuming products in the real world. Advertising story

1. Introduction
This study first outlines a view of the rhetoric ofcommercial films as a combination of commercial film
techniques. Next, it discusses the relationship between
the rhetoric of commercial films and perception/positive
factors based on a variety of surveys. It then shows the
structure of the relationship between the rhetoric ofcommercial films and perception/positive factors basedon the discussion. Finally, it summarizes the rhetoricalpatterns in advertisement based on the structure thus
identified.
2. Views on Commercial Film Techniques andRhetoric
Commercial film techniques contained in commercial
films are complex with a variety of representation
elements, but viewers do not form their perception of thefilms by comprehending all these techniques or© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 635

Yoji Kawamura

technique expresses “the product function (the productitself or its performance)” and “product reception (thescene in which the consumer receives the product).”
Mise-en-scène technique refers to “the real space;” “oneconsumer;” “the protagonist is not a celebrity with astrong character;” “the protagonist or the product is notin fast movement or in slow motion;” and “the fixedcamera shot.” Editing technique refers to “a slow tempo(a large number of average number of seconds per shot);”“continuation (movements are usually connected);” and“temporal series (there is no manipulation tochronology).” Sound effect technique refers to “silent(slow tempo music or no music).”
Moreover, advertising story techniques are classified into
two categories: “representation of reception (arepresentation of the scene in which the consumer
receives the product)” and “deletion of reception (thescene in which the consumer receives the project is not
represented).” Mise-en-scène techniques are classified
into two categories: “dynamic (the protagonist or productmoves fast, slow or circles; or the camera shot is not
fixed)” and “stable (the protagonist or product does not
move fast or is not in slow motion; the camera shot isfixed).” Editing techniques are classified into two
categories: “dynamic (fast tempo or discontinues
movements)” and “stable (slow tempo or no tempo).”Sound effect techniques are classified into two

categories: “dynamic (fast tempo)” and “stable (slowtempo or no tempo).” These categories are referred to as
technique types. The combination of the technique types
constitutes the overall rhetorical types. The number of
combination is 16 (2 x 2 x 2 x 2).
Kawamura1 analyzed the overall rhetorical types of 100
commercial films. 1A and 1B, which are “dynamic” interms of mise-en-scène, sound effect, and editing
techniques, are most frequent and account for 46% of thetotal films (Fig. 1).
3. Relationship between the Rhetoric ofCommercial Films and Perception
Viewers’ response is defined as perception and positive
factors, and the relationship between the rhetoric ofcommercial films and perception/positive factors isoutlined based on a number of surveys.
3.1. Rhetoric of Commercial Film and Perception
Kawamura2 analyzed the correlation between the rhetoricof commercial films and perception with 16 films of beerand 22 films of personal computers. The relationship(correlation) between the overall rhetorical type (from
1A to 8B) and perception shows that there is a certain
structure between the two as seen in 1A being related to“looks delicious,” “fresh,” and “looks cool;” 1B to“rhythmical,” “powerful,” and “looks cool;” 2B to

Fig. 1. System and percentage of overall rhetorical types. 1© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 636

Rhetoric of Commercial Film

3.2. Rhetoric of Commercial Films and Positive
Factors“creative” and “vivid;” 3A to “feels clean” and“fashionable;” 7A to “rich nature,” “feels cool,” and“idyllic;” and 8B to “somehow sad,” “simple,” and “nonorganic.” It is shown that out of 106 image words
extracted from the data, 80 (76%) correlate with theoverall rhetorical type.

Kawamura1 analyzed the correlation between the rhetoricof commercial films and perception/positive factors with100 commercial films that were ranked among top ten for
the new film category in each month (2004–2005)
published by CM Research Institute. The analysis

Table 1. Relationship between the rhetoric of commercial films and perception/positive factors.

Type

Rhetorical TypeMiseSou
en-scène
nd

Edit
ing

Perception2

1A/B

D

D

D

looks delicious, fresh,looks cool, dazzling, cold,
pleasant, lively, congenial,
impatient, desperate, sporty,
haste, fighting, speedy,
light, conspicuous, tense,
surprised, rhythmical,
thrilling, powerful, fast,
futuristic, speed,
contemporary

2A/B

D

D

S

creative, vivid

3A/B

D

S

D

speed, stylish, feels clean,fashionable, elegance,
simple, refresh

5A/B

S

D

D

Perception1

Positive Factors1

Effect Type

speedy, realistic,eerie, surprise“because it issexy”“the firm’s
attitudes appear tobe sincere”

thrillingfierce, millet, light,
gorgeous, colorful
serious, healing,funny, happy,
peaceful, sad, pitiful,puzzled, miserable
impact, cool, gentle,
magnificent

light, vivid”it is humorous”“a good story”“cute”

tempo,
enjoyable“attracted by the
product”

magnificent

4A/B

D

S

S

simple, heartwarming,
daily, couple, refresh, relax,
tranquil, my pace, fierce,
slow, design, transparent,
white, chic, mood, change

6A/B

S

D

S

tired, a comedy

tranquil, comical,
enjoyable, hope“not fashionable
but likeable”

heartwarming,enjoyableDrich nature, feels cool,
idyllic, running feeling, idle
feeling, wonder,
expectation, doubt, anxiety,indoor, lifestyle feeling

elegance, natural, richknowledge, wonder“reassuring”“it is pleasantly
persuasive”

nature,reassuring

relaxing, calm,
gorgeous,
heartwarming, fresh,dim, hard, powerful“it is pleasantly
persuasive”“it feels at the
cutting-edge oftime”“impressive catchphrases”“good visual
images/videos”

rest, aesthetic

7A/B

8A/B

S

S

S

S

S

quiet, domestic, pure,
warm, somehow sad, drift,
simple, non-organic, calm,
home, stinking, relaxing,
magnificent, Japan, chic,dark

fashionabledaily

D: Dynamic S: Stable© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 637

Yoji Kawamura

showed that there is a structure between the overall
rhetorical type (from 1A to 8B) and positive factors as
seen in 1A being related to “because it is sexy;” 1B to“the firm’s attitudes appear to be sincere;” 3A to “it ishumorous” and “a good story;” 3B to “cute;” 5A to“attracted by the product;” 6B to “not fashionable but
likeable;” 7A to “reassuring;” 7B to “it is pleasantly
persuasive;” 8A to “it is pleasantly persuasive” and “itfeels at the cutting-edge of time;” and 8B to “impressive
catch phrases” and “good visual images/videos.” It isshown that out of 77 image words extracted from the data,
59 (77%) are correlated with the overall rhetorical types,
and out of 15 positive factors, 13 (87%) are correlatedwith the overall rhetorical type.
4. Structure of the Rhetoric of Commercial
Films and Perception/Positive Factors

and leaves a strong impression of the actors and thecompany.
Overall rhetorical types 2, 3, or 5 (one of mise-enscène, sound effects, and editing is set as “stable”)
attracts the viewer’s attention and interest to theactor’s movement, the story, or the product.
Overall rhetorical types 4, 6, or 7 (two of mise-enscène, sound effects, and editing are set as “stable”)
encourages the viewer to relate to/sympathize withthe advertisement content or representation.
Overall rhetorical type 8 (“stable” mise-en-scène,sound effects, and editing) encourages the viewer toevaluatetheadvertisement
concept
andrepresentation aesthetically.
Acknowledgements
This work was supported by JSPS KAKENHI Grant
Number 22500102.

Table 1 shows the relationship between the rhetoric ofcommercial films and perception/positive factors as
examined in 3.1 and 3.2. Kawamura2 analyzed
commercial films of beer (16 films) and personal
computers (22 films); Kawamura1 analyzed commercial
films according to product types other than beer andpersonal computers. (Of the 100 films analyzed, three arefilms of beer, one of personal computers, and 96 of otherproducts.) The tale shows that there is a close relationship(structure) between the overall rhetorical type andperception regardless of product category.

References
1. Y. Kawamura, A study on the technique-rhetorics andeffects of commercial film, Cognitive Studies 14(3) (2007)
409-423. (In Japanese).
2. Y. Kawamura, An analysis on rhetoric of the commercial
film of beer and personal computer – a symbolic approachon rhetoric of commercial film, J. Advertising Science 47(2006) 33-48. (In Japanese).

5. Rhetorical Patterns in Commercial Films
As viewers use diverse knowledge and knowledge
processing when perceiving commercial films, it isdifficult to structure or verbalize the viewer’s perceptions.
Consequently, it is not easy to achieve perception that isloyal to the image that the advertiser wants to convey.
However, the study showed that there is a correlation
betweentheoverallrhetoricaltype
andperception/positive factors by introducing the frameworkof “overall rhetorical types” as a combination ofcommercial film techniques. Interestingly, the overall
rhetorical type, which is an approach to classify
commercial films based on commercial film techniques,is found to correlate with the viewer’s perception andpositive factors.
On the basis of the structure identified in the study,
expressive characteristics of commercial films can besummarized as follows:
Overall rhetorical type 1 (“dynamic” mise-en-scène,sound effects and editing) overwhelms the viewer© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 638

Comparison Between Variational Autoencoder and Encoder-Decoder Models for Short
Conversation
Shin Asakawa

Center for Information Sciences, Tokyo Women's Christina University, Zempukuji, 2, 6, 1
Nerima, Tokyo 1678585, Japan
Takashi Ogata

Faculty of Software and Information Science, Iwate Prefectural University
Takizawa, Iwate, 020-0693, Japan
E-mail: asakawa@ieee.org, t-ogata@iwate-pu.ac.jp

Abstract
We provide a point of view concerning generative models such that they could deal with short conversation. These
include the standard recurrent neural network language, sequence to sequence, vector embedding, and variational
autoencoder models. These models seem to be possible candidates to describe such conversations, there are several
differences among them.
Keywords: neural network language model, recurrent neural networks, vector embedding, variational autoencoder,sequential data processing, variational inference, narrative generation.

1. Introduction

2. Recurrent Neural Network Models

Conversation includes understanding and generation.
Therefore, modeling of conversation play an importantrole in natural language processing (NLP) and machine
intelligence. Although previous approaches exist, they
are often restricted to specific domains (e.g., booking an
airline ticket) and require hand-crafted rules. In this paper,
we will provide a brief survey about recent progressconcerning sentence generation. First the recurrent neuralnetwork language models (RNNLM) will be introduced(section 2). Then we will make brief explanations about
recent models: vector embedding models (section 3),variational autoencoder (VAE) models (section 4), andcombining models between RNNLM and VAE. In
section 6, we will discuss possible contributions of themodels cited here to narrative studies.

RNNLMs (RNNLM7) represent the state of the art inunsupervised generative modeling for NLP modeling. In
supervised settings, RNNLMs decode conditioned ontask specific features in machine translation1,11. Standard
RNNLMs can predict each word of a sentence
conditioned on the previous word and an evolving hidden
state. RNNLMs can generate sentences word-by-wordbased on an evolving distributed state representation.
RNNLMs can make their internal representation
probabilistic models without any significant independentassumptions. However, by breaking the model structure
down into a series of next-step predictions, the RNNLM
does not expose an interpretable representation of globalfeatures like topic or of high-level syntactic properties.
Vinyals and Le12 showed RNNLM (Recurrent Neural
Neural Network Language Model) could deal withconversation (Fig. 1).

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 639

Shin Asakawa, Takashi Ogata

4. Variational autoencoder models
The variational autoencoder (VAE3) is a generativemodel that is based on a regularized version of thestandard autoencoder. This model imposes a priordistribution on the hidden codes z which enforces aregular geometry over codes and makes it possible todraw proper samples from the model using ancestral
sampling. The VAE models the autoencoder architecture
by replacing the deterministic function φ(x) with alearned posterior recognition model, q(z|x). This model
parametrizes an approximate posterior distribution overz (they employed a diagonal Gaussian) with a neuralnetwork conditioned on x. The VAE learns sentences notas single points, but as soft ellipsoidal regions in latent
space, forcing the sentences to latent the space rather thanmemorizing the training data as isolated sentences. If the
VAE were trained with a standard autoencoder'sreconstruction objective, it would learn to encode its
inputs deterministically by making the variances in q(z|x)
vanishingly small9. Instead, the VAE uses an objective
which encourages the model to keep its posterior
distributions close to a prior p(z), generally a standard
Gaussian (= 0; σ2 = 1). Additionally, this objective
is a valid lower bound on the true log likelihood of thedata, making the VAE a generative model. This objective
takes the following form:

Fig. 1. A schematic diagram of sequence to sequencemodel, modi ed form Ref. 12

3. Vector embedding models
In order to incorporate a continuous latent sentence
representation, we need a method to map between
sentences and distributed representations that can betrained in an unsupervised setting. While no strong
generative model is available for this problem. In thissection we tried to deal with vector embed ding models:
1. skip-thought4,
2. paragraph vector5,
Sequence autoencoders have been success ingenerating complete documents6. An autoencoder
consists of an encoder function φenc and a probabilistic
decoder model p(x|z =φ(x)), and maximizes thelikelihood of an example x given z. In the case of asequence autoencoder, both encoder and decoder are
RNNs and examples are token sequences.
The intermediate sentences are generally
ungrammatical and do not transition smoothly from one
to the other. This suggests that these models do not
generally learn a smooth, interpretable feature system forsentence encoding. In addition, since these models do notincorporate a prior over z, they cannot be used to assign
probabilities to sentences or to sample novel sentences. It
was proposed that unsupervised learning models mighttake the same model structure as a sequence autoencoder,but generate text conditioned on a neighboring sentencefrom the target text, instead of on the target sentence
itself4. Paragraph vector models5 are non-recurrent
sentence representation models. In a paragraph vector
model, the encoding of a sentence is obtained by
performing gradient-based inference on a prospective
encoding vector with the goal of using it to predict thewords in the sentence.

L (θ; x) = −KL (q (z |x ) ∥p (z))+Eθqθ (z|x)[log p (x |z )]θ≤ log p(x).
(1)
This forces the model to be able to decode plausible
sentences from every point in the latent space that has areasonable probability under the prior.
It was used diagonal Gaussians for the prior andposterior distributions p(z) and q(z|x), using the Gaussian
reparameterization trick3. The model was trained withstochastic gradient descent, and at each gradient step they
estimated the reconstruction cost using a single sample
from q(z|x), but compute the KL divergence term of thecost function in closed form.
5. Combining model between RNNLM and GAE
A variational autoencoder model was proposed Ref. 2. It
was found that on a standard language modeling
evaluation where a global variable was not explicitly
needed, their model yielded similar performance to© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 640

Comparison Between Variational Autoencoder

existing RNNLMs. The model was also evaluated usinga larger corpus on the task of imputing missing words.
For this task, it was introduced a novel evaluation
strategy using an adversarial classifier, sidestepping theissue of intractable likelihood computations by drawing
inspiration from work on non-parametric two-sample
tests and adversarial training. In this setting, the model's
global latent variable allowed to do well where simpler
models fail. In this model several qualitative techniques
was introduced for analyzing the ability of their model tolearn high level features of sentences. They could
produce diverse, coherent sentences through purely
deterministic decoding and that they could interpolatesmoothly between sentences.

recent advances in deep learning. Using this construction,
the model could learn the data by the gradient
backpropagation that allowed for optimization of theparameters of both the generative and recognition models
jointly.
Fig. 4 shows the corresponding computational graphof Fig. 3. Black arrows indicate the forward pass ofsampling from the recognition and generative models.
Solid lines indicate propagation of deterministic
activations, dotted lines indicate propagation of samples.
Red arrows indicate the backward pass for gradientcomputation. Solid lines indicate paths where
deterministic backpropagation is used, dashed arrows
indicate stochastic backpropagation.

Fig.2. A VAE models from Ref. 2

The model of Ref. 2 allows it to explicitly model
holistic properties of sentences such as:
1.

style,

2.

topic

3.

high-level syntactic features

Fig. 3. A plate notation of a variational RNN model

Samples from the prior over these sentence
representations remarkably produce diverse and wellformed sentences through simple deterministic decoding.
By examining paths through this latent space, we are able
to generate coherent novel sentences that interpolate
between known sentences. In order to solve the difficult
learning problem, the method implemented in this model
to demonstrate its effectiveness in imputing missing
words, explore many interesting properties of the model'slatent sentence space, and present negative results on theuse of the model in language modeling.
A class of deep, directed generative models with
Gaussian latent variables at each layer10. In order to allow
for efficient and tractable inference, they introduced an
approximate representation of the posterior over thelatent variables using a recognition model that acts as astochastic encoder of the data. For the generative model,
they derive the objective function for optimization using
variational principles; for the recognition model, theyspecify its structure and regularization by exploiting

Fig. 4. A computational graph corresponing to Fig. 3

6. Discussion
So far, we tried to compare several methods proposed
recently. Studies cited here might suggest a trends towardto generate sentences or topics with computational
accountabilities with both RNNLMs and VAEs.
Additionally, the authors also aim at the application
to narrative generation8. Conversation in narratives, suchas folktales and novels, is used for a scene’s description
where several characters appear in many cases. There are© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 641

Shin Asakawa, Takashi Ogata

novels that conversation is the central part in narrative
progression, such as many novels by F. Dostoyevsky, E.
Hemingway, and Y. Kawabata. Main effects of
conversation of narratives are, for example, “realistic
effect” (Hemingway and Kawabata) and “dramatic effect”(Dostoyevsky). Although the current version of an“Integrated Narrative Generation System (INGS)”8 thathave been developing has not conversational part and themain narratological element is “event”, conversation, in
addition to description and explanation, is a necessary
and important element for completing a narrative’s
structure and representation. Furthermore, although the
INGS is currently an approach to narrative generation
based on semiotic processing and knowledge, studying
paternal processing approaches by neural networks statedin this paper is an important direction for aiming at the
fluency of text generation or representation and theacquisition of diverse narrative knowledge including
episodes and scripts.
7. Conclusion

7.

8.

9.

10.

11.

We introduced computational explanations for sentence
generations and showed relations between such studies
based on neural networks and narrative studies. Despite
many studies should be required, it seems to worth trying
to go forward along these ways that we employed here.
The RNNLMs and VAEs might converge in some ways
in the near future, then the results that these method mightbring us further steps.

12.

Proceedings of the 53rd Annual Meeting of the
Association for Computational Linguistics and the 7thInternational Joint Conference on Natural Language
Processing (Volume 1: Long Papers), (China: Association
for Computational Linguistics, Beijing, 2015), pp. 11061115.
T. Mikolov, S. Kombrink, L. Burget, J. H. Cernocky, and
S. Khudanpur, Extensions of recurrent neural network
language model, in IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP),(Czech Republic, Prague, 2011).
T. Ogata, Computational and cognitive approaches tonarratology from the perspective of narrative generation,in Computational and Cognitive Approaches toNarratology, eds. T. Ogata and T. Akimoto (IGI Global,
Hershey, Pennsylvania, USA, 2016), pp. 1-73.
T. Raiko, and M. Berglund, Techniques for learning
binary stochastic feedforward neural networks, (2015),
arXiv:1406.2989.
D. J. Rezende, S. Mohamed, and D. Wierstra, Stochastic
backpropagation and approximate inference in deep
generative models, in Proceedings of the 31st
International Conference on Machine Learning (Vol. 32),
(Beijing, China, 2014) pp. 1278-1286.
I. Sutskever, O. Vinyals, and Q. V. Le, Sequence tosequence learning with neural networks, In Z.
Ghahramani, M. Welling, C. Cortes, in Advances in
Neural Information Processing Systems (NIPS) (Vol. 27),
eds. N. Lawrence, and K. Weinberger (Canada, Montreal,BC, 2014) pp. 3104-3112.
O. Vinyals, and Q. V. Le, A neural conversational model,(2015), arXiv:1506.05869.

References
1. D. Bahdanau, K. Cho and Y. Bengio, Neural machinetranslation by jointly learning to align and translate, inProceedings in the International Conference on Learning
Representations (ICLR), eds. Y. Bengio and Y. Le Cun(USA, CA, San Diego, 2015).
2. S. R. Bowman, L. Vilnis, O. Vinyals, A. M. Dai, R.
Jozefowicz, and S. Bengio, Generating sentences from acontinuous space, (2016), arXiv:1511.06349.
3. D. P. Kingma, and M. Welling, Auto-encoding variational
Bayes, (2014), arXiv:1312.6114v10.
4. R. Kiros, Y. Zhu, R. Salakhutdinov, R. S. Zemel, A.
Torralba, R. Urtasun, and S. Fidler, Skip-thought vectors,(2015), arXiv:1506.06726.
5. Q. Le, and T. Mikolov, Distributed representations ofsentences and documents, (2014), arXiv:1405.4053v2.
6. J. Li, T. Luong, and D. Jurafsky, A hierarchical neuralautoencoder for paragraphs and documents, in© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 642Changing and Transforming a Story in a Framework of an Automatic Narrative Generation
Game
Jumpei Ono

Graduate School of Software Informatics, Iwate Prefectural University
Takizawa, Iwate, 020-0693, Japan
Takashi Ogata

Department of Software Informatics, Iwate Prefectural University
Takizawa, Iwate, 020-0693, Japan
E-mail: g236m001@s.iwate-pu.ac.jp, t-ogata@iwate-pu.ac.jp,

Abstract
We propose the concept of a game system that includes an automatic story-generation function based on a table-toprole playing game (TRPG). In this idea, stories are generated or transformed based on communication between a“game manager (GM),” who controls the story generation, and “Players (PLs),” who change and transform thecontent of a story proposed by a GM. This paper focuses on the mechanism for changing and transforming a story
proposed by the GM. In particular, the proposed mechanism has various techniques ranging from macroleveltechniques, which are related to the entire structure of a story, to microlevel techniques, which are related to thepartial structures of a story. For instance, when the GM is a real human, these techniques in the PLs, who arecomputer agents, can produce changed and transformed stories that were not originally conceptualized by the GM.
Keywords: Automatic narrative generation game (ANGG), Integrated narrative generation system (INGS), Tabletop role playing game, Story generation, Discourse.

1. Introduction
In this study, we propose a mechanism for a computer
game system using an automatic story generation
function based on a tabletop-top role playing game
(TRPG). Here, stories are generated and transformed via
a communication loop between a “game manager (GM),”
who generates the first framework of a story andcontrols or manages the subsequent story generation
process, and “Players (PLs),” who modify andtransform the story that was first generated by the GM.
This paper focuses on the problem associated with themechanism for changing and transforming a story
proposed by the GM. Although this mechanism iscurrently implemented using a simple method, in thispaper, we discuss various techniques that range frommacrolevel techniques, which are related to the overall
structure of a story, to microlevel techniques that are
related to the partial structures of a story. We aim todesign and implement the future expansion of thesystem.
We refer to the game system, including theautomatic narrative generation mechanism, as an“automatic narrative generation game (ANGG).” In thissystem architecture, each GM and PL can be either acomputer agent or a human. When the GM is a realhuman, a variety of narrative techniques employed by
PLs who are computer agents enable the production ofchanged and transformed stories that the GM did not
imagine originally. When both the GM and PLs arecomputer agents, they can use the narrative generation
mechanisms in the integrated narrative generation© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 643

Jumpei Ono, Takashi Ogata

system (INGS) described in the following section to
generate a story framework and transform the story
framework into the expanded stories. Thus, the ANGG
is regarded as a kind of narrative generation systemthrough the corporative relationships between the GM
and the PLs.
As Ogata1 presented a detailed description, Fig. 1
shows an overview of the INGS architecture. From amacro perspective, the INGS is divided into three
generation processes or mechanisms, namely a story,
discourse, and representation. Ogata1 presents a detailed
list of all of the modules in the current INGS. Generally,
the story generation mechanism generates a story
structure that contains a sequence of events. Then, thediscourse mechanism transforms the story structure
generated by the story generation mechanism into a
discourse structure that contains edited by the narrative
logic, namely the structure of a narrative to be narrated.
Further, the representation mechanism transforms thenarrative structure into a surface narration using natural
language, music, and a picture image.

of the GM to expand it through the communication
cycle between the GM and the PLs.
Ono and Ogata2-5 presented the architecture of an
ANGG system. Ono and Ogata2 located a mechanismthat set a story world in the ANGG, and Ono and Ogata6
proposed a mechanism for expanding a scene in theframework story generated by the GM in order to makea longer story. In this paper, we focus on the aspect of
the story generation and discourse generation techniqueswith the aim of extending narrative generation
techniques in future ANGGs. These correspond to thetechniques that are used to arrange the story-generation
mechanism and discourse generation mechanism in the
INGS, and the many parts can be used in both the
ANGG and INGS. However, important differences arethat the PLs in the ANGG generate and transform aprepared story framework through the collective andcorporative process, and narrative generation is
generally conducted considering the interaction and theGM and the PLs.
2. ANGG Overview and Story and Narrative
Generation

OutputInput
Entire control
Story
Contents
KB
State-event
Transformati
on KB
Conceptual
Dictionaries

Story
Mechanism

Discourse
Mechanism

Expression
Mechanism

Language
Notation
Dictionaries

Music KB

Image KB

Function call

Data flow

Fig. 1. The INGS Architecture.

The GM and PLs in the ANGG respectively
correspond to the story generation mechanism and thediscourse mechanism in the INGS. From the perspectiveof the GM, a story that the GM generated is changed
and transformed by the PLs into a more interesting story.
In this case, the story is called a discourse in the INGS.
The GM also controls and manages the transformationand change process to make an expanded discourse or toindicate the need to make an alternative discourse. On
the other hand, the PLs collectively change andtransform the story under the control and management

Fig. 2 shows an overview of the story generation
process in the ANGG. From the perspective of story or
narrative generation, the ANGG comprises two types ofgeneration mechanisms by the GM and PLs. In
particular, in the first step in the communication cycle,
the GM generates the framework of a story that is to be
incrementally extended into an extended story. Then,the PLs receive the framework story to generate apartial discourse to be inserted or added to the story.
Finally, in the PL mechanism, several PLs collectively
generate a new partial narrative. Currently, this requires
a method that inserts an event between the two events inthe framework story until the decided connection
condition is satisfied for the next event in theframework story. The framework story refers to a story
in the INGS that is generated using the story generation
mechanism. On the other hand, the transformed orchanged narrative corresponds to a discourse in the
INGS that is transformed according to the discourse
mechanism. However, in practice, the PLs can also use
techniques in the story generation mechanism. In
addition, the ANGG uses the “world setting,” whichmeans both a set of elements for stories and a set of© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 644Changing and Transforming a

constraint information that is used to define the range ofpossibilities.
GM

until the GM decides to adopt it. After the second
process, this cycle, which is referred to as narrativetransformation by the PLs and narrative evaluation by
the GM, continues until a complete narrative is
eventually generated. As stated above, in the ANGG,
the GM has roles that include generating the first story
framework and controlling the post-game progression.
On the other hand, the main role of the PLs is totransform and change the framework story generated by
the GM in an attempt to propose partial stories.
In the above system’s architecture, the ANGG canuse story and discourse generation techniques in the
INGS that we have presented. The main types that are
relevant to the PLs are described in section 3. The
ANGG can also refer to previous research to elaborately
design the interactive mechanisms between the GM and
the PLs as well as between the PLs. In section 4, we
introduce the related previous research.

PLs①

Generation

Story
sequence

③-a

Story
sequence’

Control③-b

Story
sequence

PL

②

Story
sequence’

PL

PL

INGS
OutputInput
Entire control
Story
Contents KB
State-event
Transformat
ion KB
Conceptual
Dictionaries

Story
Discourse Expression
Mechanism Mechanism Mechanism
Language
Notation
Dictionaries

Music KB
Function call

Image KB
Data flow

Fig. 2 A story generation process in ANGG.

A variety of collective or corporative narrative
generation or transformation methods can be considered
in order to extend the ANGG. Based on the current
method, all PLs propose candidates for expansions of an
original story or discourse, and only one is selected to
be given to the GM. Discourses that are made by all PLs
can also be combined into the final proposed discourse.
In addition, various types of discourses may be
proposed to the GM, and include methods that embody
or extend a part of the framework story generated by theGM, and that transform the flow of the entire story.
Further, each PL plays a character in a story world, andthe narrative generation by the PLs is executed based on
the characters’ perspectives. A function in thecommunication process among PLs determines which
PL is the main character in the story world. Thus, goalplanning story generation is one of the basic narrativegeneration methods that are used. We discuss thesenarrative generation problems faced by PLs in anotherpaper.
After the PLs first propose a discourse thattransforms and changes the framework story generated
by the GM, the GM evaluates whether to directly adopt
the proposition. If the proposed discourse (or the
fragmental extension) is adopted, the GM creates a newstory that reflects the discourse proposed by the PLs. In
contrast, if the proposed discourse or the fragmental
extension is not adopted, the GM instructs the PLs torepeat the narrative generation for the framework story

3. Methods for Story Generation andTransformation
In this section, we introduce various mechanisms thattransform a story into various discourse structures.
3.1. Story and Discourse in a Narrative
First, the “narrative discourse mechanism” in the INGS
architecture shown in Fig. 1 provides the mostfundamental narrative mechanism that is used forchanging and transforming a story generated by thestory generation mechanism. Fig. 3 shows an example
where a story structure is transformed into a discourse
structure; a relationship specified to discourse, “presentbackward” is added to the story structure. The structures
for both the story and the discourse have similar tree
forms. Each leaf node in a story tree corresponds to an
event described with conceptual representation, which isfurther described by a case structure that consists of one
verb concept and eight types of cases, such as “agent”
and “object.” Each internal node in the tree is equivalent
to a “relationship,” such as “cause-effect” and “serial,”
combined with the child nodes. The ANGG can use thistype of method as a narrative discourse technique tochange and transform the framework of a story that is
generated by the GM.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 645

Jumpei Ono, Takashi Ogata$Sequence $Sequence
E1 E2 E3 E4

E5$Present-backward
Discourse technique($Present-backward)

E5

E4$Cause-effect$Sequence $Present-backward
E1 E2 E3

E5

E4

Fig. 3. Transformation from story structure to discourse
structure.

Table 1. Classification of TV-CF rhetorical techniques.
ObjectIrregular

We began this study by modifying the theory’s
context proposed by Genette7, who is a Frenchnarratologist, and we used a computational viewpoint tocreate a technological method for the design orformalization of the narrative discourse generation
mechanism. This allows each of the classified
techniques applied by Genette to be used as a rule for
associating an input story structure with a transformed
output discourse structure. However, the aim ofstudying the narrative discourse mechanism is not only
to apply the narratology proposed by Genette. It is also
expanded to other narrative discourse techniques. We
designed a more comprehensive narrative discourse
system by integrating various narrative discourse types,
including the following techniques, into a systematic list.

Table 1 shows 12 defined types of rhetoric. A“rhetoric” refers to a technique that is used to introduce
a product in an impressive way by employing anadvertising scenario, while a “single event” refers to aunit that contains several elements such as a person,
object, location, and time, which are associated with asingle action in the flow of the story. In the table,
rhetorical techniques from R1 to R3 generate scenes ofstandard or ordinary use methods of products. On theother hand, the rhetorical techniques from R4 to R12
generate defamiliarization scenes by applying theextraordinary rhetoric. The irregular techniques partially
use the “changing constraint” method, which changes
usable concepts in the conceptual dictionaries in the
INGS (Fig. 1) to adjust the semantic range of concepts
to enable them to be set into an event.

Regular$Cause-effect3.2. Defamiliarization Techniques
Zhang, Ono and Ogata8 classified the methods of therelationships between a product and the story byperforming an analysis of over 5000 televisioncommercial films to define its result as “rhetorical
techniques of introducing a product into a single event.”
The previous study classified the rhetorical techniques
into either regular rhetoric or standard rhetoric during
the of production, distribution, and utilization processesof a product. On the other hand, they defined irregularrhetoric based on “defamiliarization” (“ostranenie” inRussian) or a deviation from the regular rhetoric.
Defamiliarization rhetoric indicates techniques thattransform the whole of a part or an element included ina story into an extraordinary object that is different frompeople’s ordinary feeling to strengthen the impressionof the element itself.

Type of Rhetoric
R1 original method of manufacturing: the original
way that the product is manufactured.
R2 original method of purchasing: the original way
that the product is purchased.
R3 core rhetoric: the way that the product is originallyused.
R4 defamiliarization: the defamiliarized way that the
Action product is used.
R5 none use of rhetoric: no deviation, just "hold."R6 character as a narrator: rather than being involvedin the story, the character sends a message to viewers
directly.
R7 state defamiliarization of the character: changing
Person all or part of the character, or the character daily uses
the product in an impossible way.
R8 action defamiliarization of the character: thecharacter does not perform the original action, but
performs defamiliarity.
R9 state defamiliarization of the product: changing allor part of the product.
R10 state defamiliarization of related things of the
product: changing all or part of the related things of theThings product.
R11 product subjectification: instead of being usedby a character, here, it emphasizes that the productexists independently.
R12 background defamiliarization: all or part of the
background is changed into
Location something entirely decorative, which is impossible to do
daily.

3.3. Film Rhetoric by Kanai
Story cutting techniques proposed by Kanai9 aim to cut
a story’s consistency in order to strengthen only aspecific element in the story. This is done by lacking atemporal sequence and a character’s consistency incontinuous scenes in a film. For example, in a battle
scene in a war film, the use of cutting techniques by© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 646

Changing and Transforming a

inserting a silent scene and a scene where the temporalcontinuity is ambiguous contribute to strengthening theimpression of scenes that are independent of a story’s
semantic progression.
Kanai and Kodama9 proposed a high-levelclassification of cutting techniques that (1) emphasizes
editing itself by making a differentiation or
discontinuity in the elements among continuous shots,
which includes destroying the continuity of events, (2)emphasizes the image itself by irrationalizing or
destroying the relationships between an event and theimage, which includes repetitive filming of the same
object, and (3) emphasizes the sound itself byirrationalizing the relationships between sound and theimage, which includes lacking the relationships between
speech and the visual image.
Furthermore, considering the above large
classification, Kanai9 presented the following sixteen
types of cutting technique. In the description, “dialectic”
refers to a unified whole. On the other hand, in “cutting,”
it appears as itself, and the temporal order cannot berecognized by the user. In “metric,” the continuity timeof all shots is the same. On the contrary, the continuity
time in each shot varies according to the action’s time.
“Tonal” and “overtonal” do not initially determine thecontinuity time.
(1) Dialectic metric, (2) Dialectic rhythmic, (3)Dialectic tonal, and (4) Dialectic overtonal: (1), (2),and (3), respectively have different lengths of shots,and (4) mixes a shot with different sounds into asequence of shots.
(5) Temporal sequence cutting metric, (6)Temporal sequence cutting rhythmic, (7) Temporalsequence cutting tonal, and (8) Temporal sequence
cutting overtonal: These prevent a receiver from
representing the temporal sequence of events in a
cognitive manner.
(9) Characters cutting metric, (10) Characters
cutting rhythmic, (11) Characters cutting tonal, and(12) Characters cutting overtonal: These cut thecontinuity of a character, and there are no shots
where the same character continuously appears.
(13) Temporal sequence/characters cutting metric,(14) Temporal sequence/characters rhythmic, (15)Temporal sequence/characters tonal, and (16)Temporal sequence/characters overtonal: These
prevent a receiver from representing the temporalsequence of events in a cognitive manner. In
addition, there are no shots where the samecharacter continuously appears.
4. Generation by Narrative Communication
Akimoto and Ogata10,11 presented a computational
narrative model in which literary works arecontinuously changing owing to the interaction between
an author or narrator and a reader or narrator (these arecomputer agents within the system) for a narrative
discourse control mechanism in the INGS. In the systemmodeling, Jauss’ reception theory12 is simply interpreted
as a mechanism to limit the discourse process throughthe interaction between a narrator mechanism withgenerative parameters and a narratee mechanism withexpectation parameters. In this study, we mixed thisJauss-based mechanism with the Genette-based
mechanism mentioned in section 3.1 to form a modeland system that control the deviation of a narrative
discourse mechanism based on the interaction between anarrator mechanism and a narratee mechanism within anarrative.
This system is a model of Jauss’ reception theory,
where literary works are repeatedly produced according
to a reader’s expectations and the corresponding
responses from an author; the interaction cancontinuously or historically change a literary work. In
particular, texts of narrative discourses are repeatedly
produced through a process according to parameters fora reader (or narratee) as the expectation, and using thesame parameters for an author (or narrator) as thegenerative goals. These parameters are the same as
those in the Genette-based mechanism.
The cycle continues according to the interaction
between a narrator and narratee. The narrator
mechanism has a set of rules for controlling theapplication of discourse techniques based on thegenerative parameters to transform a discourse structure.
We performed the direct transformation of a discourse
structure using techniques defined by Genette’s theory.
The narratee mechanism evaluates the results bycomparing the narratee’s expectation parameters with
the narrator’s generated parameters. In the next cycle,
the narrator applies corrections according to thenarratee’s evaluation in order to achieve reader
satisfaction. However, the process eventually reaches a© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 647

Jumpei Ono, Takashi Ogata

point where the narratee becomes tired or when his/her
satisfaction begins to fall. When this happens,“deviation” occurs and the narrator abandons some ofthe old generative parameters and moves to a new cycleof discourse grounded on the newly found strategy. The
narratee’s expectations change according to thereconstruction.
Fig. 4 is a detailed process flow of the narrative
discourse control based on the interaction between the
narrator and narratee mechanisms. As seen in (1), thesystem selects the corresponding techniques according
to the parameter values. In (2), the Genette-based
techniques fulfill the functions in the context of the
Jauss-based control. In (3), a higher satisfaction can beobtained when a more strongly desired parameter issatisfied. The narratee indicates one parameter with thelowest satisfaction. In (4), the narratee rewrites an
expectation parameter using two different procedures.
The first rewrites the sufficiency number and degree ofdesire. The former is increased each time the narratee
receives the sufficed discourse. Finally, (5) includes“deviation” from the expectation.
Narrator mechanism
Generative
goal(1) Selection of
discourse
techniques to be
applied

Story

Table 2. Classification of narrative techniques.
Macro level techniques
Story-generation techniques
Story grammar techniques
Story-discourse transformation techniques [Section 3.1]
Medial level techniques
Goal-planning techniques
Micro level techniques
Event transformation techniques
Rhetorical techniques [section 3.2]
Cutting techniques [section 3.3]

In the future, we plan to extend the design of an
integrated mechanism of the ANGG to incorporate
various narrative techniques, including those proposedin this paper, interactive processing between the GM
and the PLs, and collective processing among the PLs
into a system architecture that enables changeable anddiverse narrative generation.

Narratee mechanism

Acknowledgements

Expectation(2) Techniqueapplication(5) Adjustment ofgenerative parameters
according to narratee
indications

considering the interaction between the GM and the PLs
and the collective method among PLs. Table 2 shows acomprehensive list of narrative techniques for the GM
and the PLs including the techniques described in thispaper.

DiscourseThis work is supported by the JSPS KAKENHI Grant
Number 26330258 and the Support Center for
Advanced Telecommunications Technologies Research
Grant (2015-2017).

(3) Reception
evaluation

Evaluation
results

User input: story and initial value of generative goal and expectation(4) Expectation
rewriteReferences
Data flow Procedure

1. T. Ogata, Computational and Cognitive Approaches
to Narratology from the Perspective of Narrative
Generation, in Ogata, T. & Akimoto, T. (eds),
Computational and Cognitive Approaches toNarratology (IGI Global, Pennsylvania, 2016) 1-74.
2. J. Ono and T. Ogata, Architecture of a narrative
generation system based on a TRPG model: the useof an integrated narrative generation system forknowledge acquisition (preliminary version),
Bulletin of Netw., Comput., Syst., and Softw. 5(1)(2016) 40-48.
3. J. Ono and T. Ogata, A design plan of a gamesystem including an automatic narrative generation
mechanism: the entire structure and the world
settings, J. Robotics, Networking and Artificial Life2(4) (2016) 243-246.

Fig. 4. Interaction between narrator and narratee.

5. Conclusions
In this paper, after we presented an overview of the
ANGG system that we developed, we considered story
generation and discourse techniques to utilize the GM
and PLs used in the ANGG in the future. These
techniques are provided by the story-generation
mechanism and the discourse mechanism in the INGS.
Therefore, although there is common narrativeknowledge to be used in both the INGS and the ANGG,
an important difference between the two systems is thatnarrative generation by the ANGG is performed© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 648

Changing and Transforming a

4. J. Ono and T. Ogata, Towards a narrative
generation system based on a TRPG model: the useof an integrated narrative generation system for an
application system, in Proc. 21nd Int. Symp. on
Artificial Life and Robotics (2016).
5. J. Ono and T. Ogata, Architecture of a narrative
generation system based on a TRPG model: the useof an integrated narrative generation system, in
Proc. 4th IIAE Int. Conf. on Industrial Application
Engineering 2016 (2016) 138-145.
6. J. Ono and T. Ogata, Implementation of a Scene
Expansion Mechanism using an Event sequence:
As a Mechanism in an Automatic Narrative
Generation Game, Proc. 3rd Int. Conf. on
Knowledge Engineering (2016). (In printing).
7. G. Genette, Narrative discourse: An essay inmethod, trans. J. E. Lewin (Cornell University Press,
New York, 1972).
8. Y. Zhang, J. Ono and T. Ogata, Single Event andScenario Generation based on Advertising
Rhetorical Techniques using the Conceptual
Dictionary in Narrative Generation System, The 4thIEEE Int. Conf. on Digital Game and Intelligent
Toy Enhanced Learning (2012) 162-164.
9. A. Kanai and K. Kodama, Film edit design: Storyand irrational cutting, Cognitive Studies 17(3)(2010) 444-458.
10. T. Akimoto and T. Ogata, A narratologicalapproach for narrative discourse: Implementationand evaluation of the system based on Genette andJauss, in Proc. of the 34th annual conf. of the
Cognitive Science society (2012) 1272-1277.
11. T. Akimoto and T. Ogata, An information design ofnarratology: The use of three literary theories in anarrative generation system, Int. J. Visual Design7(3) (2014) 31-61.
12. H. R. Jauss, Literaturgeschichte als provokation,(Suhrkamp Verlag, Frankfurt am Main, 1970).

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 649

A Rule-Based Classification System Enhanced by Multi-Objective Genetic Algorithm
Kenzoh Azakami
Graduate School of Science and Engineering, Yamaguchi University, Tokiwadai 2-6-11 Address
Ube, Yamaguchi 755-8611, JAPAN
Shingo Mabu
Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Tokiwadai 2-6-11 Address
Ube, Yamaguchi 755-8611, JAPANMasanao Obayashi
Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Tokiwadai 2-6-11 Address
Ube, Yamaguchi 755-8611, JAPANTakashi Kuremoto
Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Tokiwadai 2-6-11 Address
Ube, Yamaguchi 755-8611, JAPANE-mail: {v001vk, mabu, m.obayas, wu}@yamaguchi-u.ac.jp

Abstract
Recent years, data mining techniques have been developed for extracting rules from big data. However, there aresome problems to be considered, for example, it is difficult to judge which rules are important and which are notimportant; and even in simple classification problems with the small number of classes, a various sub-patterns to beconsidered potentially exist in each class. To solve the above problems, a rule clustering algorithm using multiobjective genetic algorithm is proposed.

classification system. In this paper, a rule clustering
algorithm using the multi-objective genetic algorithm(MOGA) is proposed to enhance the conventionalclassification system. To confirm the effectiveness ofthe proposed method, the accuracy of the conventional
method and proposed method is compared. In addition,
the accuracy of the proposed method and otherclassification methods: Tree-based classifier (J4.8),
Multi-Layer Perceptron (MLP), Support Vector
Machine (SVM). The data used for the comparisons are
Credit approval, Pima Indian, and German, which were
downloaded from UCI machine learning repository [3].

1. Introduction
Data mining is a technique of extracting effective rules
from big data. However, data mining has someproblems to be considered. That is, a large number ofrules are extracted, so it is difficult to judge which are
the important rules and which are not important; andeven in simple classification problems, e.g., two-classproblems, a variety of patterns potentially exist in eachclass, which makes the problems more difficult. In theconventional method [1], a genetic algorithm (GA)based clustering was applied to the class association
rules extracted by genetic network programming (GNP)[2] to solve the above problems and enhance the2. Association rule extraction by GNP© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 650

Kenzoh Azakami, Mabu Shingo, Masanao Obayashi, Takashi Kuremoto

In the conventional method, a clustering using GA wasapplied to the rules extracted by GNP. In this section,
the method of extracting class association rules is
explained.
2.1 Class association rule
As X is the antecedent, and Y is the consequent, anassociation rule is denoted by X⇒Y. For example, arule “-people who buy milk and egg also buy bread” ,is denoted by {milk, egg}⇒{bread}. A classassociation rule is denoted by, (A1=1)∧(A2=1)⇒
class1 where the consequent is changed to a classlabel.

Fig. 1．Flow of rule extraction by GNP

2.2 Rule extraction by GNPClass association rules are extracted by GNP from a
database with attributes of binary values and classlabels (Fig. 1). GNP has a structure as shown in Fig.
2. GNP consists of initial nodes and attribute nodes
for rule extraction. According to the transition of
attribute nodes, many candidate rules are generated.
The node transition is repeated by the predefined
number of times. The candidate rules that satisfy theevaluation criteria are stored in a rule pool. The
evaluation criteria are support, confidence, and .

Fig. 2．Structure of GNP in rule extraction

2.3 Evolution of GNP

3.

After storing rules, crossover and mutation are
performed. In GNP, the crossover is performed by
selecting two individuals, and the selected individuals
exchange their node connections with each other. The
mutation selects one individual, and connections ofnodes are randomly changed. Fitness is calculated by
Eq. (1). Eq. (1) means individuals that extract
important rules are highly evaluated.

In this section, the conventional GA clustering for theclass association rules extracted by GNP is explained.

fitness = ∑(( ) + 10( ( ) − 1) ) +Conventional rule clustering by GA

3.1 Individual initialization
First, several integer arrays are prepared, where thenumber of arrays is the same as the number of classes,
and the length of the arrays are the same as the numberof rules in the corresponding classes. All the arrays are
initialized by random values ranging from 1 to n,
where n means the number of subclasses. The random
values in the arrays show the cluster numbers to which
each rule is assigned. The flow of GA Clustering isshow in Fig. 3.

(1)( ): χ value of class association rule rn(r) : Length of rule r: Reward when a new rule is found
R : Set of extracted rule numbers
The crossover and mutation are repeated by thepredefined number of times.

3.2 Evolution of individuals
The fitness of GA is defined as the classification© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 651

A Rule-Based Classification Systemaccuracy for training data, and the individuals forcrossover and mutation are selected by tournament
selection. Then the crossover exchanges the genes ofthe selected individuals by crossover, and the mutation
changes the genes randomly.

Inter-cluster variance, intra-cluster variation andaccuracy are used as evaluation functions ( =accuracy, =, = 1/). The evaluation ishigher as the values of the evaluation functions arelarger.
4.1. Multi-objective algorithm
Considering the trade-off between the evaluation
functions, MOGA is applied to the optimization.
4.2.1 . Pareto solution
Pareto solutions of the individuals in MOGA are
represented as shown in Fig. 4, where each axis
corresponds to each evaluation function. The
individuals that are not inferior to any otherindividuals are called Pareto solutions (non-dominatedsolutions). An example of Pareto solutions is shown in
Fig. 5.

Fig. 3．Extracted rule clustering by GA

4.

4.2.2 . Evolution in the proposed method

Proposed rule clustering by Multi-Objective
GA (MOGA)MOGA obtains the plural number of Pareto solutions(elite individuals), not one elite individual unline
standard GA. The evolution of MOGA is performed bycrossover and mutation. For selecting crossover andmutation individuals, individuals are ranked, where theindividual rank is defined as the number of superior
individuals to itself, e.g., the rank of Pareto solutions
is 0.

In this section, a class association rule clustering using
MOGA is explained. In the conventional method, thefitness is based only on the classification accuracy forthe training data. Therefore, the direction of theevolution is biased toward the correct classification for
the training data, which loses the generalization
performance for new data. The proposed method adds
two new criteria, applies MOGA to optimize thesethree criteria simultaneously, and aims to enhance theclustering accuracy. The two new criteria are intercluster variance and intra-cluster variance. Intercluster varianceand intra-cluster variationarecalculated as follows:
= ∑

= ∑
=

(
∑

∑

−

(

)

−

(2)
)

(3)
(4)

: Value of kth attribute of rth rule in cluster ia : the total number of attributes in a databaseni : the number of rules in cluster i

Fig. 4. Pareto solutions on axis

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 652

Kenzoh Azakami, Mabu Shingo, Masanao Obayashi, Takashi Kuremoto

Table. 2. Comparison of accuracy between the proposed
method and other classifiers
GermanOther classifiers
Proposedmethod
J4.8
MLP
SVM
Accuracy[%]
71.5
72.5
76.0
75.3
Pima Indian
Other classifiers
Proposedmethod
J4.8
MLP
SVM
Accuracy[%]
75.8
77.1
77.1
80.4
Credit approval
Other classifiers
Proposedmethod
J4.8
MLP
SVM
Accuracy[%]
86.4
82.4
85.9
88.0

Fig. 3. Pareto solutions example

5.

Simulation results

6.

The datasets named German, Pima Indian and Credit
approval are used for evaluating the classification
accuracy. The results of the accuracy obtained by the
proposed method and conventional method are shown in
Table 1. The accuracy of the proposed method shown in
Table 1 is the best one obtained by Pareto solutions.
From Table 1, the proposed method showed higher
accuracy than the conventional method. The comparison
with J4.8, MLP, and SVM are shown in Table 2. From
Table2, the proposed method showed equal to or higher
accuracy than J4.8, MLP, and SVM.

In order to enhance the performance of theclassification system using class association rules,
MOGA is applied to the rule clustering, and its
classification ability was evaluated using threedatasets. The results showed that the proposed methodwith MOGA generated solutions with high accuracy.
The future work will be developing a method selecting
the best solutions from the generated Pareto solutions.
In addition, the Pareto solutions should be analyzed indetail to find the importance of each evaluation
function.

Table. 1.Comparison of accuracy between the conventional
method and proposed method

References1.

German k=20
Conventional
method72.5

Proposedmethod75.3

Conventional
methodAccuracy [%]77.1
Credit approval k=20
Conventional
methodAccuracy [%]86.6

Proposedmethod80.4

Accuracy [%]Pima Indian k=20

Conclusion2.

Proposedmethod88.0

3.

K. Azakami, S. Mabu, M. Obayashi, A. Kuremoto,
Performance Enhancement of Classification Systems by
Clustering Class Association Rules with Pruning and Its
Evaluation, The Conference Record of 66th Chugokubranch Joint Convention of Institutes of Electrical andInformation Engineers, (Yamaguchi, 2015)
K. Shimada, K. Hirasawa, and J. Hu, Genetic network
programming with acquisition mechanisms of
association rules, Journal of Advanced Computational
Intelligence and Intelligent Informatic, Vol. 10(1)(2006) 102-111.
UCI Machine Learning Repository,
URL:http://archive.ics.uci.edu/ml/datasets.html/(accessed 2016-5-20)

k :Number of clusters(Specified value by the designer)© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 653

A Method of Feature Extraction for EEG Signals Recognition Using ROC Curve
Takashi Kuremoto1, Yuki Baba2, Masanao Obayashi1, Shingo Mabu1, Kunikazu KobayashiI3
1

Graduate School of Science and Technology for Innovation, Yamaguchi University, Tokiwadai 2-16-1,
Ube, Yamaguchi 755-8611, Japan
2
Graduate School of Science and Engineering, Yamaguchi University, Tokiwadai 2-16-1,
Ube, Yamaguchi 755-8611, Japan3

School of Information Science and Technology, Aichi Prefectural University, 1522-3 Ibaragabasama,
Nagakute, Aichi 480-1198, Japan
E-mail: {wu, v033vk, m.obayas, mabu}@yamaguchi-u.ac.jp, kobayashi@ist.aichi-pu.ac.jp
Abstract

The feature extraction of Electroencephalograph (EEG) signals plays an important role in mental task recognition ofbrain-computer interaction (BCI). In this study, a novel method of EEG signal feature extraction is proposed using
techniques of fast Fourier transform (FFT) and receiver operating characteristic (ROC) curve. In the proposed method,
the raw EEG data was transformed into power spectrum of FFT at first, and then to find frequencies decided by area
under curve (AUC) of ROC between the value of spectrums of different classes of metal tasks. Experiment results
using benchmark data of EEG signals showed the effectiveness of the proposed feature extraction method when support
vector machine (SVM) was used as a classifier.
Keywords: EEG, FFT, ROC, AUC, SVM

with a powerful classifier: kernel-support vector
machine (k-SVM). Benchmark data of EEGclassification [7] and BCI competition II data [8] wereused in the experiments.

1. Introduction
The electronic potential signals of electrodes,
which are arrayed on the surface of head, are measured
by electroencephalograph and it is called as“Electroencephalogram”, i.e., “EEG”. When different
metal tasks such as motor imagery, calculation, number
counting, article considering, and so on, different
patterns of EEG signals can be observed. So the analysis
and recognition of EEG signal is one of the way torealized brain computer interface (BCI), or brain
machine interface (BMI), i.e., people can control themachines or robots by their imaginations [1] [2].
Feature extraction of the EEG signal plays a very
important role of mental task recognition to enhance theaccuracy and there have been many studies of thistheme [3] [4]. In [5], Obayashi et al. proposed to use thenonlinear normalized feature spaces of fast Fouriertransformation (FFT) of EEG signals. And the method
was improved by selecting the most characterizedphases of raw EEG signals in our previous works [6].
In this paper, we propose a novel method using
FFT and the value of area under curve (AUC) ofreceiver operating characteristic (ROC) curves to extract
the feature of EEG signal and verified its effectiveness2. Method
To extract the feature vector space of EEG signal for
mental task recognition, we propose to use FFT and
ROC to find the limited frequencies which AUC values
are high when two classes EEG data are compared in
the training process, and the power spectrums of thesefrequencies are used as input vector of classifiers.
2.1. Fast Fourier Transform (FFT)FFT is a kind of discrete Fourier transform (DFT),
which is used for frequency analysis of signals in thefield of signal processing, invented in 1965 by J. Cooley
and J. Tukey at first, and improved continuously. The
function of FFT is to show the power spectrum ofdifferent frequencies of the time series signals as sameas the original Fourier transform but easily to berealized by computers. In this study, we used the FFT© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 654

Takashi Kuremoto, Yuki Baba, Masanao Obayashi, Shingo Mabu, Kunikazu Kobayashi

function provided by free software package R [9] toprocess the raw EEG signals.

propose to use the value to AUC of ROC and FFT todistinguish the two classes EEG signals.

2.2. Receiver operating characteristic (ROC) curve
Assume that two classes data class A and class B
have their probability density functions as shown in Fig.
1. The true positivity of class A will be the shadowed
area α in the left and the false positives area 1 – β inmore shadowed area. As α and 1 - β are plotted whilesliding the threshold along the x axis, a graph as shownin Fig. 2 is obtained. The curve in this graph is called
the ROC curve and it shows the divisibility of the two
probability density functions. If two distributions
completely overlap, the for any position of thethreshold we have that α =1 – β.

3. Feature Extraction
3.1. Method of using FFTSuppose that an EEG signal x m,n (m=1, 2, …, M,
n=1, 2, …, N) is given. m indicates the number ofchannels, and n indicates the number of samples inchannel m. The procedure of the conventional method
using FFT to extract the feature of EEG [6] is as follows.
Step 1 Divides the EEG signal of all the channels m into
multiple windows l (l=1, 2, …, L) along the time
domain.
Step 2 Perform FFT on all of the divided windows, andlet the result be Fm,l .
Step 3 Compare the power spectrum of the window l ofthe channel m to its adjacent window l + 1, andcalculate the difference Dm (l ) of the frequency
spectrum.
Dm, (l )l L 1| Fm,l 1

l 1

Fm,l |(1)

Step 4 From Step 3, the maximum Dm (l ) in the channel
m is determined, and power spectrum in a regionof 4 to 45 Hz in the frequency band of thewindow l is used as a feature space for classifiers
such as SVM, multi-layer perceptron (MLP), andso on.

Fig. 1 Overlapping of the probabilities of two classes data.

3.2. Method of using FFT and ROC Curve
Let the input signals be xkc ,m,n (c=1 or 2, k=1, 2, …,
K), where k indicates the kth EEG signal of a set of EEG
data, and c indicates the class of mental task. m and n
are the same as in section 3.1. The procedure of thenovel method is as follows.

Fig. 2 AUC of a ROC curve

Step 1 Perform FFT to all the EEG signals xkc ,m,n and

In Fig. 2, the area below the ROC curve is called“area under curve” (AUC). This value takes from 0.0 to
1.0, and it is an indicator of the divisibility of the two
distributions. If the value of AUC becomes to 0.5, two
distributions completely overlapped. Conversely, whenthe value of AUC reaches 1.0 (or 0.0), it means that thetwo distributions are completely separated.
ROC was used to classify cDNA microarray
successfully [10] and applied to EEG classification withwavelet transformation (WT) recently [11]. Here, we

let the result be Fkc ,m, p where p indicates theorder number of frequencies.
Step 2 Obtain Pk1 , m, p and Pk 2 ,m, p , which are two
probability density functions of Fkc ,m, p at p
frequency where class c = 1 and 2 of K signals ofchannel m.
Step 3 Calculate the ROC curve and its AUC Am, p of
Pk1 , m, p and Pk 2 , m, p .

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 655

A Method of Feature

Step 4 Repeat Step 2 and Step 3 on all channels, a set
Am, p of frequency p in channel m is obtained.

bottom of monitor and move a cursor up or down, andIb with the same tasks but with not only visuallypresentation but also auditorily. Details of EEG data
used in our experiments are listed in Table 2.

Step 5 Find P points of frequencies which Am, p is high.
Step 6 Power spectrum Em, p (p=1, 2, …, P) of theunknown EEG signal are used to as input feature
vector of a classifier.

Table 1 Mental tasks in a benchmark database [7].
Contents
Relaxing as much as possible
Calculating multiplication mentally.
Considering the contents of the letter
Imagining rotation of a 3-D object
Imagining writing a number in order

Mental Task
Baseline
Multiplication
Letter-composing
Rotation
Counting

Table 2 EEG data used in the experiments.
(a) The raw EEG data
Database
Benchmark[7]
Ia [8]
Ib [8]

Mental Data
Tasks Sets

Chan Samples
nels perChannelSampling
Freq.

5

10

7

2500

250

2
2

135/133
100

6
7

896
1152

256
256

(b) FFT results(c) AUC of FFT of 2 kinds of mental tasks
Fig. 3 The procedure to obtain AUC

The procedure of how AUC of FFT of 2 kinds of
EEG data patterns is shown in Fig. 3 Frequencies withenough high values of AUC in Fig. 3 (c) are chosen as
input features to classifiers.
4.

Fig. 4 Results of different feature extraction methods for thebenchmark EEG data [7] (5 classes)

Kernel SVM (k-SVM) was used as the classifier, its
package and ROC package in R [9] was utilized in our
experiments.
As shown in Fig. 4, the average classification rate of
the conventional method [6] (FFT, 140 Data) was
76.0%, meanwhile 100.0% of the proposed method(FFT+ROC, 140 Data) for the 5 tasks EEG data. 140
Data indicates that the input dimension to SVM was 140
which came from 20 points of FFT (P in Step 5 of the
proposed procedure) signals in 7 channels.
For BCI competition II data Ia and Ib [8] (2 classes),
the proposed method also showed its priority to theconventional method as shown in Fig. 5 and Fig. 6
respectively. Additionally, higher dimensionality

Recognition Experiment

Mental task recognition experiments using EEG data
and the methods introduced in Section 3 were
performed. Two kinds of EEG data are used: (i) abenchmark data provided by Colorado State University[7] and (ii) BCI Competition II generated by Birbaumer[8]. In the case of (i), there are 5 kinds of mental tasks(see Table 1) were requested to subjects, and their EEG
data were obtained 10 trials with 7 channels. Meanwhilein (ii), there are 2 data sets named “Ia” and “Ib” which
are EEG data obtained by 2 kinds of tasks: Ia was asked
to recognize visual objects displayed on the top or© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 656

Takashi Kuremoto, Yuki Baba, Masanao Obayashi, Shingo Mabu, Kunikazu Kobayashi

showed higher recognition rate according to theexperiments results. For Ia, 120 Data means 20 points of
one channel data, and 6 channel data were used, as well
as 216 Data chosen by 36 points per channel. For Ib,
140 Data were obtained by 20x7, and 315 Data from 45
points per channel. The highest recognition rates of our
method for Ia and Ib are 91.23% and 77.65%, higher
than the best classification rates 90.10% and 56.67% of
T. Nguyen et al. [11] respectively.

and ROC of wavelet transform (WT) as the input of kSVM, and experiment results showed that the method
described in Section 3, i.e., features extracted by ROC
of FFT, was superior to others.
The future work of this study is to confirm theperformance of the proposed method by other classifiers
such as MLP, K-nearest neighbors (KNN),
convolutional neural networks (CNN), and so on.
Acknowledgements
This work was supported by JSPS KAKENHI Grant No.
26330254 and No.25330287.
References
1.

N. Birbaumer, L. G. Cohen, Brain-computer interfaces:
communication and restoration of movement in paralysis,
Journal of Physiology, Vol. 579, pp. 621-636, 2006
2. F. Lotte, M. Congedo, A.Lecuyer, F. Lamarche, B.
Arnaldi, A review of classification algorithms for EEGbased brain-computer interfaces, Journal of NeuralEngineering, Vol. 4, pp. 24-48, 2007
3. N. Jrad, M. Conedo, Identification of spatial andtemporal features of EEG, Neurocomputing, Vol. 90,
pp.66-71, 2012
4. A.S. Al-Fahoum, A. A. Al-Fraihat, Methods of EEG
signal features extraction using linear analysis infrequency and time frequency domains, ISRN
Neuroscience, Vol. 2014, 7 pages, 2014
5. M. Obayashi, K. Watanabe, T. Kuremoto, K. Kobayashi,
Development of a brain computer interface using
inexpensive commercial EEG sensor with one-channel,
Proceedings of the 17th International Symposium on
Artificial Life and Robotics (ISAROB 2012), pp. 714-717,
2012
6. T. Kuremoto, Y. Baba, M. Obayashi, S. Mabu, K.
Kobayashi, To extraction the feature of EEG signals formental task recognition, Proceedings of 54th Annual
Conference of the SICE, pp. 353-358, 2015
7. Colorado State University, Brain-Computer Interfaces
Laboratory:
http://www.cs.colostate.edu/eeg/
8. BCI competition II:
http://www.bbci.de/competition/ii/#datasets
9. The R Project for Statistical Computing: https://www.rproject.org/
10. H. Mamitsuka, Selecting features in microarrayclassification using ROC curves, Pattern Recognition,
Vol. 39, Issue 12, pp. 2393-2404, 2006
11. T. Nguyen, A. Khosravi, D. Creighton, S. Nahavandi,
EEG signal classification for BCI applications bywavelets and interval type-2 fuzzy logic systems, Expert
Systems with Applications, Vol. 42, pp. 4370–4380, 2015

Fig. 5 Results of different feature extraction methods for the
BCI competition II data Ia [8] (2 classes)

Fig. 6 Results of different feature extraction methods for the
BCI competition II data Ib [8] (2 classes)

5. Conclusion
In this paper, we proposed a feature extraction
method for EEG pattern recognition. The proposed
method uses the AUC value of ROC curves of FFT
results for two classes EEG signals to decide the feature
vector space for classifiers. Classification experiments
were performed using the Gaussian kernel SVM (kSVM) to confirm the proposed method. As the result of
the experiments, the proposed method showed its higher
recognition ability than the conventional methods
including the conventional FFT feature extraction
method and others. We also used ROC of raw EEG data

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 657

Forecasting Real Time Series Data using Deep Belief Net and Reinforcement Learning
Takaomi Hirata1, Takashi Kuremoto2, Masanao Obayashi2, Shingo Mabu2, Kunikazu Kobayashi3
1

Graduate School of Science and Engineering, Yamaguchi University, Tokiwadai 2-16-1,
Ube, Yamaguchi 755-8611, Japan
2
Graduate School of Science and Technology for Innovation, Yamaguchi University, Tokiwadai 2-16-1,
Ube, Yamaguchi 755-8611, Japan3

School of Information Science and Technology, Aichi Prefectural University, 1522-3 Ibaragabasama,
Nagakute, Aichi 480-1198, Japan
E-mail: {v003we, wu, m.obayas, mabu}@yamaguchi-u.ac.jp, kobayashi@ist.aichi-pu.ac.jp
Abstract

Hinton’s deep auto-encoder (DAE) with multiple restricted Boltzmann machines (RBMs) is trained by theunsupervised learning of RBMs and fine-tuned by the supervised learning with error-backpropagation (BP).
Kuremoto et al. proposed a deep belief network (DBN) with RBMs as a time series predictor, and used the same
training methods as DAE. Recently, Hirata et al. proposed to fine-tune the DBN with a reinforcement learning (RL)
algorithm named “Stochastic Gradient Ascent (SGA)” proposed by Kimura & Kobayashi and showed the priority
to the conventional training method by a benchmark time series data CATS. In this paper, DBN with SGA isinvested its effectiveness for real time series data. Experiments using atmospheric CO2 concentration, sunspot
number, and Darwin sea level pressures were reported.
Keywords: deep learning, restricted Boltzmann machine, stochastic gradient ascent, reinforcement learning, errorbackpropagation

deep neural networks [5]-[7]. In [5], a deep Q-network(DQN) is proposed and applied to game (named
ATARI) control and reached human level. In [6], acomputer software named AlphaGo, using a deep neuralnetwork and RL, won the world champion of the gameGo. In [7], we adopted a policy gradient RL algorithm[8] [9] into a deep belief net (DBN) proposed by
Kuremoto et al. [10]-[13] instead of its fine-tuning
method BP. And using a benchmark data CATS which
is used by time series forecasting competition with
ANNs [14] [15], the DBN with RL showed the highest
prediction precision comparing to all conventional
methods in the competition and the conventional DBN
with BP learning [12] [13].
In this paper, we concentrate to investigate theeffectiveness of the DBN with RL for real time series
forecasting. Three kinds of real time series data which
are weekly average of CO2 concentration in atmosphereat Hawaii, monthly average of sea level pressures at
Darwin, the number of sunspot monthly provided by

1. Introduction
Deep learning (DL) is the novel kernel technique of
artificial intelligence (AI) developed rapidly innowadays. As the training method of artificial neuralnetworks (ANNs), in 2006, DL firstly is introduced byHinton’s deep auto-encoder (DAE) [1], which has
multiple stacked restricted Boltzmann machines(RBMs). The learning process of DAE is divided into
two phases: firstly, pretraining, which is a kind of
unsupervised learning using the gradient of network
energy of RBMs, and secondly fine-tuning using thesupervised learning: error-backpropagation (BP) [2].
To deal with the adaptive behavior acquisition
problem in unknown environment, reinforcement
learning (RL), which is a kind of machine learning
method adjusting is output by the rewards/punishmentfrom the environment when a learner (system) changed
its state by the policy of output, has been studied for
decades [3] [4]. Recently, RL is also introduced into

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 658

Takaomi Hirata, Takashi Kuremoto, Masanao Obayashi, Shingo Mabu, Kunikazu Kobayashi

Aalto University [16] were used in the forecasting
experiments, and the prediction precision was comparedto the conventional BP learning method. And as theresults, DBN with RL showed the higher performance
than the conventional DBN with BP in the process offine-tuning of the network.

which are parameters of Gaussian distribution functionused in the case of SGA learning method [4] [7].
2.2. BP learning for DBN
Let E is the mean squared error (MSE) between the
output of DBN and the teacher signal, the weight of

Fig.1. A structure of DBN composed by RBMs and a MLP

connections w between layers of RBMs and MLP, andthe bias of RBMs b are modified as following.

2. DBN with BP
In [10] [11], Kuremoto et al. firstly applied Hinton &Slakhutdinov’s deep belief net (DBN) with restricted
Boltzmann machines (RBMs) to the field of time series
forecasting. In [12] and [13], Kuremoto, Hirata, et al.
constructed a DBN with RBMs and a multi-layer
perceptron (MLP) to improve the previous time series
predictor with RBMs. In [7], Hirata et al. adopted areinforcement learning named “stochastic gradientascent” (SGA) [8] [9] into DBN instead of the BP
learning used in the fine-tuning of the network. In thissection, the structure of DBN and learning methods are
introduced.

(1)

(2)
where a is the learning rate.

2.3. SGA learning for DBN
The SGA algorithm and the learning rule for theweight of DBN’s layers and parameters of the stochastic
policy (Gaussian distribution function) were introducedin [7].

2.1. DBN with RBMs and MLP
As a neural predictor model, a DBN composed bymultiple RBMs and a MLP is shown in Fig. 1 [7] [12][13]. The visible layer of RBM 1 (1st Layer) are input
with raw data of time series data (omitted in the figure).
The hidden layer of RBM L+1 are used as the input
layer of the MLP. The output of MLP is with one unit in
the case of DBN using BP learning, and it has two units

3. Prediction Experiments and Results
We predicted three types of natural phenomenon timeseries data given by Aalto University [16].
CO2: Atmospheric CO2 from continuous air
samples Weekly averages atmospheric CO2© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 659Forecasting Real Time Series

Table.1. Prediction MSE of real time series data [16]concentration derived from continuous air samples,
Hawaii, 2225 values
Sea level pressures: Monthly values of the Darwin
Sea Level Pressure series, 1882-1998, 1300 values
Sunspot Number: Monthly averages of sunspot
numbers from 1749 through the present, 3078 values

CO2
Sea level pressure
Sun spot number

DBN with BP
0.26710.9902733.51

DBN with SGA
0.20470.9003364.05In Fig. 2 to Fig. 4, the one-ahead prediction resultsof DBN with BP and DBN with SGA were shown. In
Table 1, the comparison of forecasting precision (MSE)of these different learning methods for DBN was given.
The DBN with SGA showed its priority to the DBN
with BP in all cases of real time series data. In Table 2,
the number of samples and structures of different DBNs

Fig.4. Prediction result of Sun spot number data

Fig.2. Prediction result of CO2 data

Fig.5. Changes of evaluation function in random search[17] (CO2, DBN with SGA)

were listed. To decide the number of RBMs, and thenumber of units on different layers of RBMs and MLP,
random search (RS) [17] was used in the experiments.
As an optimization method, RS used random values ofparameter vector spaces to find the lower forecastingerror (MSE). The change of evaluation function in theFig.3. Prediction result of Sea level pressure data

Table.2. Sizes of time series data and structures of prediction networks
SeriesTotal size

Testing size

DBN with BP

DBN with SGA

CO2
Sea level pressure

2225
1400

225
400

15-17-17-1
16-18-18-1

20-18-7-2
16-20-8-7-2

Sun spot number

3078

578

20-20-17-18-1

19-19-20-10-2© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 660

Takaomi Hirata, Takashi Kuremoto, Masanao Obayashi, Shingo Mabu, Kunikazu Kobayashi

Fig.6. Changes in learning MSE (CO2, DBN with SGA)
case of DBN with SGA for CO2 forecasting is shown in Fig. 5
as a sample. In Fig. 6, the change of MSE in SGA learning
process is shown. As a stochastic forecasting method of RL,
the vibration of MSE needs to be reduced by tuning learning
rates and rewards and we leave it as a future work.

4. Conclusion
In this paper, a reinforcement learning (RL) method“stochastic gradient ascent (SGA)” for fine-tuning of adeep belief net (DBN) with multiple restricted
Boltzmann machines (RBMs) and a multi-layer
perceptron (MLP) was compared to the conventional
method error backpropagation (BP) in the case of realtime series forecasting. Different to the supervised
learning method which uses learning error exhaustively,
RL a reward function which allows a range of errors
between the output of the model and the teach signal
and it may raise the forecasting precision for the realtime series data.
Acknowledgements
This work was supported by JSPS KAKENHI Grant No.
26330254 and No.25330287.
References
1. G.E. Hinton, R.R. Salakhutdinov, Reducing thedimensionality of data with neural networks, Science,
Vol.313, 2006, pp. 504-507.
2. D. E. Rumelhart, G. E. Hinton, & R.J. Williams,
Learning representation by back-propagating errors,
Nature, Vol. 232, No. 9, 1986, pp. 533-536.
3. R.S. Sutton, A.G. Barto: Reinforcement Learning: An
Introduction, 1998, The MIT Press
4. T. Kuremoto, M. Obayashi, M. Kobayashi, Neural
Forecasting Systems. In Reinforcement Learning, Theory
and Applications (ed. C. Weber, M. Elshaw and N. M.
Mayer), Chapter 1, pp. 1-20, 2008, INTECH.

5. V. Mnih, et al., Human-level control through deep
reinforcement learning, Nature, Vol. 518, 2015, pp. 529533
6. D. Silver, et al., Mastering the game of Go with deep
Neural networks and tree search, Nature, 529, 2016, pp.
484-489
7. T. Hirata, T. Kuremoto, M. Obayashi, S. Mabu, K.
Kobayashi, Deep Belief Network Using Reinforcement
Learning and Its Applications to Time Series Forecasting,
Neural Information Processing: 23rd International
Conference (ICONIP 2016), Vol.3, Oct. 2016, pp.30-37
8. H. Kimura, S. Kobayashi, Reinforcement learning forcontinuous action using stochastic gradient ascent,
Proceedings of 5th Intelligent Autonomous Systems, 1998,
pp. 288-295.
9. R.J. Williams: Simple statistical gradient-following forconnectionist reinforcement learning, Machine Learning,
Vol. 8, 1992, pp. 229-256
10. T. Kuremoto, S. Kimura, K. Kobayashi, M. Obayashi,Time series forecasting using restricted Boltzmann
machines, Proc. 8th International Conference onIntelligent Computing (ICIC 2012), July 2012, pp.17–22.
11. T. Kuremoto, S. Kimura, K. Kobayashi, M. Obayashi,Time series forecasting using a deep belief network withrestricted Boltzmann machines, Neurocomputing,
Vol.137, No.5, Aug. 2014, pp.47–56.
12. T. Kuremoto, T. Hirata, M. Obayashi, S. Mabu, K.
Kobayashi, Forecast Chaotic Time Series Data by DBNs,
Proceedings of the 7th International Congress on Imageand Signal Processing (CISP 2014), Oct. 2014, pp. 13041309.
13. T. Hirata, T. Kuremoto, M. Obayashi, S. Mabu, Time
Series Prediction Using DBN and ARIMA, International
Conference on Computer Application Technologies(CCATS 2015), Matsue, Japan, Sep. 2015，pp.24-29.
14. A. Lendasse, E. Oja, O. Simula, M. Verleysen, Time
Series Prediction Competition: The CATS Benchmark,
Proceedings of International Joint Conference on Neural
Networks (IJCNN'04), 2004, pp1615-1620.
15. A. Lendasse, E. Oja, O. Simula, M. Verleysen, Time
Series Prediction Competition: The CATS Benchmark,
Neurocomputing, Vol. 70, 2007, pp.2325-2329.
16. AML research group, The Aalto University School of
Science: http://research.ics.aalto.fi/eiml/datasets.shtml
17. J. Bergstra, Y. Bengio, Random Search for HyperParameter Optimization, Journal of Machine Learning
Research, Vol. 13, 2012, pp.281-305.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 661

Improvement of Robot's Self-localization by Using Observer View Positional Information
Yo Aizawa, Takuo Suzuki, Kunikazu Kobayashi

Aichi Prefectural University
1522-3 Ibaragasama, Nagakute, Aichi 480-1198, Japan.
Tel::+81-561-64-1111; Fax: +81-561-64-1108
kobayashi@ist.aichi-pu.ac.jp

Abstract
This study aimed to improve the precision of the robot’s self-localization in the standard platform league of RoboCup,i.e. a robotic soccer competition. For improving the precision of the self-localization, we suggest a new techniquethat uses a camera out of the field for assistance. Robots in the field use the unscented particle filter that estimates
their position from landmark. When a robot which is equipped with the filter cannot recognize landmarks exactly,
particles spread and the precision of the self-localization decreases. Therefore, the overlooking camera out of the
field observes each robot's position. When particles spread, the camera out of the field estimates the foot of a robot,
and then the robot sprinkles particles on the neighborhood again. In this way, even if a robot cannot recognize
landmarks exactly, assists of camera out of the field revise the position of particles and improve the precision.
Keywords: Self-localization, Perspective image, Image processing, Homography, RoboCup

1. Introduction
The RoboCup (Robot Soccer World Cup) project sets agoal that a fully autonomous robot team shall win against
the most recent winning team of FIFA World Cup insoccer by 2050.
The RoboCup Soccer Standard Platform League (SPL) isa league that all teams compete with the same standard
humanoid robot called NAO developed by Softbank
Robotics1. The robot operates fully autonomously, that iswith no external control, neither by humans nor bycomputers. In RoboCup Soccer SPL, the robot must
process all the calculations on vision processing anddecision making using low-end CPU (Intel Atom
1.6GHz). In addition, the robot must devote a lot ofcomputation time to percept a white goal and a mostly
white ball in vision processing. Each team has five player
robots and one coaching robot that can send instructions
at a perspective view from outside the field. An exampleof the positional relationship between the field and thecoaching robot is shown in Fig.1.

Fig. 1. Coaching Robots can observe the almost whole field１.

In RoboCup Soccer SPL, a self-localization mechanismthat estimates its own position and orientation is required.
We use an unscented particle filter (UPF)2 which iscurrently a mainstream method3 for self- localization.
However, a robot cannot accurately grasp any landmarks,
then particles do not converge, so the estimation error ofself-localization becomes large.
In addition to the conventional method, by using thecoaching robot as the observer, an area where a player islikely to exist is specified. We propose a method topromote convergence of particles by correcting the© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 662

Yo Aizawa, Takuo Suzuki, Kunikazu Kobayashi

coordinates of scattering particles based on theinformation from the coaching robot. From this method,
estimation error of the self-location is assumed to be
suppressed when the player cannot accurately recognize
landmarks.

3.2. Estimation of a player’s position
We estimate a straight line with a high possibility that a
robot exists. Only the uniform region is extracted fromthe image after homography transform. Then, the region
is denoising by opening processing7 (see Fig.4). After
that, we extract a region of the own team's jersey, it iscertain that the player robot will be on the line calculated
by simple linear regression analysis (see Fig.5).

2. Unscented Particle Filter (UPF)
The UPF is a combination of an unscented Kalman filter(UKF)4 and a particle filter (PF)5. The filter can solve theproblem in particle filter which resampling will fail if the
new measurements appear in the tail of prior or if thelikelihood is too peaked in comparison to the prior2. The
difference between UPF and PF is whether UPF is used
for updating prediction step of PF.
3. Proposed method
When UPF cannot accurately grasp landmarks, particles
may not converge. When such a situation occurs, thecoaching robot behaves as an observer, assists to estimate
the self-localization of the player from the outside, andencourages the convergence of the particles.

Fig. 4. Extraction of uniform

3.3. Estimation of a foot position

3.1. True perspective image

Finally, the foot position of the player robot is estimatedas the bottom point of non-green regions on the line
(Fig.5). We transform the color space of the perspective
image into L*a*b* to detect the color of the field. L*
stands for lightness and a* and b* are hue and saturation,
respectively. The color approaches red as the value of a*becomes high and green as it becomes low, and yellow
as the value of b* becomes high and blue as it becomeslow. We binarize the image of a* by Otsu's thresholding
method. As applying the homography transform to theimage, the true perspective image is shown in Fig.6. The
estimated foot position is illustrated in Fig.7 as a red dot.

At first, the coaching robot gets a perspective image asshown in Fig.2. Then we transform it to a true perspective
image using homography transform6 (see Fig.3). Since
the homography transform requires more than four
coordinates on an image, the coaching robot will selectmore than four points out of 17 candidates, i.e. four
corners of the field, eight corners of the penalty area, two
penalty crosses, two intersections of the center line andthe side lines, and a point of the center mark. In Fig.2, we
use eight points by indicating red circles.

Fig. 2. A perspective image froma coaching robot.

Fig. 5. An estimated line by
the simple linear regression
analysis.

Fig. 3. A true perspective
image by homographytransform.

Fig. 6. A binary image ofthe player robot.

Fig. 7. An estimated footposition (red dot).

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 663

Improvement of Robot’s Self-localization

proposed method by measuring the actual foot position.
in addition, we compare the estimation error of the selflocalization with the conventional method.
In this experiment, we verifies whether an error changes
depending on the distance between the coaching andplayer robots. We then prepared two kinds of routes asshown in Fig.9. The difference of pattern A and B iswhether the player robot approaches the coaching robot
or not. Experiments were conducted three times each andthe errors are averaged.

3.4. Determination of resampling position
Based on the foot position, the locations where particles
are scattered are determined. Taking into account theerror of the estimated foot position, the positions ofparticles are determined according to the normal
distribution as given by eq. (1).

( )=

1

√2

−

( − )
2

,

(1)

where μ is the mean and σ2 is the variance. In this paper,
the value of μ is defined by the foot position x, and thevalue of σ is set to 1 / α. The particles are gathered into
the foot estimated by increasing the value of α. Based on
the above, the positions of particles are indicated byyellow circles in Fig.8.

Fig. 8. Resampling Position of Particles

Fig. 9. Two kinds of routes in Experiment 1

4. Experiment4.2. Result of Experiment 1

We verify the accuracy whether the player can assist the
self-localization using the image acquired by thecoaching robot. Firstly, it is evaluated how the estimatedfoot position is closer to the true one by comparing the
proposed and the conventional methods. Secondly, after
correcting the position of the particle by the proposed
method, it is verified whether it is close to the true
position as compared with the conventional method.
The experiments were conducted using one player under
an LED uniform lighting environment using natural light.
We use the OpenCV 3.1 library as a tool for image
processing. The value of α in the normal distribution in
Section 3.4 is empirically set to 6 in order to prevent
particles from spreading. The number of particles is 12.

The experimental results in Experiment 1 are shown in
Table 1. From Table 1, it is shown that the accuracy ofthe proposed method is better than that of theconventional method irrespective of pattern A and B. In
addition, the improved rate of the accuracy of theestimated foot position is 78%. Therefore, using theestimated foot position, resampling particles is expected
to improve the accuracy.
Table 1. Average error of the estimated foot position(Experiment 1)
Average error [mm] (Improved rate [%])

4.1. Experiment 1 (Verification of the accuracy ofthe estimated foot position)
When distributing particles using the proposed method,
the accuracy of the estimated foot positionin Section 3.3
is critical the resampling coordinates of the particles ishighly baased on the foot position. Therefore, we verify
the accuracy of the estimated foot position using the

Method \ Pattern

Pattern A

Conventional method500

Proposed method

180

Pattern B
(64)

624
71

(89)

4.3. Experiment 2 (Verification of the accuracy ofself-localization after resampling)
After resampling particles using the proposed method,when the player robot moves again, we verify the© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 664

Yo Aizawa, Takuo Suzuki, Kunikazu Kobayashi

accuracy of the self-localization. We also compare theestimation error of the self-localization with theconventional method. As seen in Section 4.1, the player
robot moves by two kinds of routes as illustrated in
Fig.10. Experiments were carried out three times eachand the errors against the true position are averaged tocompare the accuracy. When resampling is performed
using the proposed method, the direction of the particles
is determined according to the normal distribution basedon the estimated direction. The normal distribution is

we could correct the position of the particles using theobserver (coaching robot) and assist the subject (player
robot) who performs self-localization estimation.
As future work, we will assist the estimation of the selflocalization for multi-player robots.
Acknowledgements
This work was partly supported by Aichi Prefectural
University, Japan.
References
1. RoboCup Technical Committee, RoboCup Standard
Platform League (NAO) Rule Book (2016).
2. R. van der Merwe, A. Doucet, N. de Freitas, and E. Wan,
The Unscented Particle Filter, Proc. of NIPS (2000),
pp.584-590.
3. T. Röfer, T. Laue, J. Richter-Klug, M. Schünemann, J.
Stiensmeier, A. Stolpmann, A. Stöwing, F. Thielke, BHuman Team Report and Code Release 2015 (2015).
4. S. J. Julier and J. K. Uhlmann, A New Exten- sion of the
Kalman Filter to Nonlinear Systems, Proc. of AeroSense:
Proc. of The 11th Int. Symp. On Aerospace/Defence
Sensing, Simulation and Controls (1997), pp.182-193.
5. S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics,
The MIT Press (2005).
6. R. Hertley and A. Zisserman, Multiple View Geometry incomputer vision, 2nd Edition, Cambridge University Press(2003).
7. R. Szeliski, Computer Vision: Algorithms andApplications, Springer (2011).

Fig. 10. Two kinds of routes in Experiment 2

given by eq. (1). In self-localization, the direction iscorrected by recognizing landmarks. Therefore, the valueof μ is set to the previous estimated direction and thevalue of σ is empirically set to π/8.
4.4. Result of Experiment 2
The results in Experiment 2 are shown in Table 2. From
Table 2, it is shown that the proposed method is moreaccurate than the conventional method irrespective ofpattern A and B. In addition, the improved rate of theaverage accuracy is 64%.
Table 2. Average error of self-localization(Experiment 2)
Average error [mm] (Improved rate [%])
Coach \ Pattern

Pattern A

Pattern B

w/o coach

1,151

724

w coach

364

(68)

302

(58)

5. Conclusion
In this paper, we have proposed the method for
improving the accuracy of the self-localization of UPF
used in RoboCup Soccer SPL. In the proposed method,© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 665

Consideration on the Recognizability of Three-Dimensional Patterns
Chongyang Sun, Makoto Sakamoto, Makoto Nagatomo, Yu-an Zhang, Shinnosuke Yano, Satoshi Ikeda

Faculty of Engineering, University of Miyazaki, 1-1, Gakuen Kibanadai-nishi, Miyazaki-shi, 889-2192
Miyazaki, Japan
Takao Ito, Tsutomu Ito

Institute of Engineering, Hiroshima University, 4-1, Kagamiyama 1-chomeHigashi-Hiroshima, Hiroshima 739-8527
Hiroshima, Japan
Yasuo Uchida

Department of Business Administration, Ube National College of Technology, Tokiwadai Ube, Yamaguchi 755-8555
Yamaguchi, Japan
Tsunehiro Yoshinaga

Department of Computer Science & Electronic Engineering, National Institute of Technology, Tokuyama College
GakuendaiShunan, Yamaguchi 745-8585
Yamaguchi, Japan

Abstract
Due to the advances in computer vision, robotics, and so forth, it has become increasingly apparent that the study ofthree-dimensional pattern processing should be very important. Thus, the study of three-dimensional automata as thecomputational model of three-dimensional information processing has been signiﬁcant. During the past about thirty
years, automata on a three-dimensional tape have been obtained. On the other hand, it is well-known that whether or
not the pattern on a two- or three-dimensional rectangular tape is connected can be decided by a deterministic onemarker ﬁnite automata. As far as we know, however, it is unknown whether a similar result holds for recognition of
the connectedness of patterns on three-dimensional arbitrarily shaped tape. In this paper, we deal with therecognizability of three-dimensional patterns, and consider the recognizability of three-dimensional connected tapesby alternating Turing machines and arbitrarily shaped tapes by k marker finite automata.
Keywords: connectedness, finite automaton, marker, pattern, recognizability, three-dimension, Turing machine

1.

by
any
three-dimensional
deterministicornondeterministic finite automaton. By the way, it is wellknown that two-dimensional digital pictures have 4- and8-connectedness, and three-dimensional digital pictures
have 6- and 26-connectedness. It is also known thatvarious topological properties can be defined by making
use of these connectedness. For example, Nakamura andAizawa proposed a new topological property of threedimensional digital pictures ― the interlocking
component which is a chainlike connectivity. They
showed that three-dimensional deterministic one-marker
automaton cannot detect interlocking components in athree-dimensional digital picture [3]. Moreover, in [3],Sakamoto, et al. proposed various new three-dimensional
automata, and studied several their properties. In general,
however, to recognize three-dimensional connectedness

Introduction

Recently, there have been many interesting
investigations on digital geometry [2]. These works form
the theoretical foundation of digital image processing.
Among them, the problem of connectedness is one of themost interesting topics. For instance, Yamamoto, Morita
and Sugata showed that a three-dimensional
nondeterministic one-marker automaton can recognize
connected tapes. In the case of L(m) space-bounded fiveway three-dimensional deterministic Turing machine,
they proved that space m2logm is necessary andsufficient amount for recognizing connected tapes of size
m×m×m [3]. Nakamura and Rosenfeld showed thatthree-dimensional connected tapes are not recognizable© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 666

Chongyang Sun, Makoto Sakamoto, Makoto Nagatomo, Yu-an Zhang, Shinnosuke Yano, Satoshi Ikeda, Takao Ito, Tsutomu Ito, Yasuo Uchida,
Tsunehiro Yoshinaga

seems to be much more difficult than the twodimensional case, because of intrinsic characteristics ofthree-dimensional pictures.
In this paper, we consider about recognizability of
three-dimensional patterns. First, we deal with therecognizability of three-dimensional connected cubic
tapes by three-dimensional alternating automata. Next,
we consider whether or not the pattern on a threedimensional arbitrarily shaped tape is connected can bedecided by a deterministic multi-marker ﬁnite automaton.
2.

Σ) and one semi-inﬁnite storage tape, initially ﬁlled withthe blank symbols. M begins in state q0. A position is
assigned to each cell of the input tape and the storage tape,as shown in Fig.1. A step of M consists of reading one
symbol from each tape, writing a symbol on the storagetape, moving the input and storage tape heads in speciﬁed
directions, and entering a new state, according to the next
move relation δ [3].

Preliminaries

Deﬁnition 2.1. Let Σ be a ﬁnite set of symbols. A threedimensional tape over Σ is a three-dimensional
rectangular array of elements of Σ. The set of all threedimensional tapes over Σ is denoted by Σ(3). Given a tapex ∈ Σ(3), for each integer j (1 ≤ j ≤ 3), we let lj(x) be thelength of x along the jth axis. The set of all x ∈ Σ(3) with
l1(x)=n1, l2(x)=n2, and l3(x)=n3 is denoted by
Σ(n1,n2,n3). When 1 ≤ ij ≤ lj(x) for each j (1 ≤ j ≤ 3), letx(i1,i2,i3) denote the symbol in x with coordinates(i1,i2,i3) . Furthermore, we deﬁne x[(i1,i2,i3), (i0 1,i0
2,i0 3)], when 1 ≤ ij ≤ i0 j ≤ lj(x) for each integer j (1 ≤ j≤ 3), as the three-dimensional input tape y satisfying thefollowing (1) and (2): (1) for each j (1≤j ≤3), lj(y) = i0 j
−ij + 1; (2) for each r1, r2, r3 (1≤r1 ≤l1(y), 1≤r2 ≤l2(y),1≤r3 ≤l3(y)), y (r1,r2,r3)=x (r1+i1−1, r2+i2−1, r3+i3−1).
(We call x[(i1,i2,i3), (i0 1,i0 2,i0 3)] the [(i1,i2,i3), (i0 1,
i0 2,i0 3)]-segment of x.) For each x∈Σ(n1,n2,n3) andfor each 1≤i1 ≤n1, 1≤i2 ≤n2, 1≤i3 ≤n3, x[(i1, 1, 1), (i1,n2, n3)], x[(1, i2, 1), (n1, i2, n3)], x[(1, 1, i3), (n1, n2,i3)], x[(i1, 1, i3), (i1, n2, i3)], and x[(1, i2, i3), (n1, i2,i3)] are called the i1th (2-3) plane of x, the i2th (1-3)
plane of x, the i3th (1-2) plane of x, the i1th row on thei3th (1-2) plane of x, and the i2th column on the i3th (12) plane of x, are denoted by x (2-3)i1, x (1-3)i2, x(1-2)
i3, x[i1,∗,i3], and x[∗,i2,i3], respectively[3].

Fig.1:Three-dimensional alternating Turing machine.
Deﬁnition 2.3. A three-dimensional k marker finite
automaton M(k) is deﬁned by the six-tuple M =(Q,q0,F,Σ,{+,−},δ), where (1) Q is a ﬁnite set of states;(2) q0∈Q is the initial state; (3) F⊆Q is the set of
accepting states; (4) Σ is a ﬁnite input alphabet (#/ ∈Σis the boundary symbol); (5) {+,−}is the pair of signs ofpresence and absence of the marker; and (6) δ:(Q×{+,−})×((Σ ∪ {#})×{+,−}) → 2(Q×{+,−})×((Σ ∪{#})×{+,−})×{east,west,south,north,up,down,no move}) is the next-movefunction, satisfying the following: For
any q,q0∈Q, any a,a0∈Σ, any u,u0,v, v0∈{+,−}, andany d ∈ {east,west,south,north,up,down,no move}, if((q0,u0),(a0,v0),d) ∈ δ ((q,u),(a,v)) then a=a0, and(u,v,u0,v0)∈{(+,−,+,−),(+,−,−,+),(−,+,−,+), (−,+,+,−),(−,−,−,−)}. We call a pair (q,u) in Q×{+,−} an extended
state, representing the situation that M holds or does nothold the marker in the ﬁnite control according to the sign
u = + or u =−, respectively. A pair (a,v) in Σ×{+,−}
represents an input tape cell on which the marker exists
or does not exsit according to the sign v = + or v =−,respectively. Therefore, the restrictions on δ above imply
the following conditions. (A) When holding the marker,
M can put it down or keep on holding. (B) When notholding the marker, and (i) if the marker exists on thecurrent cell, M can pick it up or leave it there, or (ii) if
the marker does not exist on the current cell, M cannot
create a new marker any more (see Fig.2) [3].

Deﬁnition 2.2. A three-dimensional alternating Turingmachine (denoted by 3-ATM) is a 10-tuple M = (Q, q0,
U, E, S, F, Σ, Γ, δ), where (1) Q = U ∪E∪S is a ﬁnite
set of states, (2) q0 ∈Q is the initial state, (3) U is the setof universal states, (4) E is the set of existential states, (5)F ⊆Q is the set of accepting states, (6) Σ is a ﬁnite input
alphabet (# ∉Σ is the boundary symbol), (7) Γ is a ﬁnite
storage tape alphabet containing the special blank symbol
B, (8) δ ⊆(Q×(Σ∪{#})×Γ)×(Q×(Γ−{B})×{east, west,
south,north,up,down,no move}×{left,right,no move}) isthe next move relation. As shown in Fig.2, M has a readonly cubic input tape with boundary symbols #’s (# ∉© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 667

Consideration on the Recognizability

L[TR2DTM(m)], and thus in L[TR2-SUTM(m)]. (TR2SUTM(m) means m space-bounded three-way twodimensional synchronized alternating Turing machinewith only universal states). By applying the same idea ofsuch a two-dimensional case, we can easily get the factthat ¯ Tc is in L[FV 3-SUTM(m2)]. (The proof ofnecessity) Suppose that there is an FV 3-SUTM(L(m)) M
accepting ¯ Tc, where L(m) = o(m2). We assume without
loss of generality that M enters an accepting state only on
the bottom boundary. Let T0 c ={x ∈{0,1}(4m+1,4m+1,4m+1) |m≥1 & ∀i1(1≤i1 ≤m + 1)∀i2(1≤i2 ≤2m + 1) [ x [(2i2 −1, 1, 2i1 −1), (2i1 −1, 4m−2i1+ 3, 2i1 −1)], x[(2i2 −1, 1, 4m−2i1 + 3), (2i2 −1, 4m−2i1+ 3, 4m−2i1 + 3)]}, x[(2i1 −1, 4m−2i1 + 3, 2i1 −1), (2i2
−1, 4m−2i1 + 3, 4m−2i1 + 3)]∈{1}(3) ] &∀i2(1≤i2≤2m) [ x [(2i2, 1, 2m + 1), (2i2, 2m + 1, 2m + 1) ]∈{1}(3)] &∀i1(1≤i1 ≤2m)∀i2(1≤i2 ≤2m + 1) [ x(2i2 −1,
1, 2i1) = x(2i2 −1,1,4m−2i1 + 2) ] & (the other part of x
consists of 0’s)}, where we deﬁne ¯0 = 1 and ¯1 = 0.
4).Clearly T0 c ⊆ Tc. Let s and t be the numbers of states(of the ﬁnite control) and storage tape symbols of M,
respectively. For each m(m≥1), let V(m) ={x ∈ T0 c
|l1(x) = l2(x) = l3(x) = 4m+1}. For each x in V(m), let
S(x) and C(x) be sets of conﬁgurations of M deﬁned asfollows. S(x) = {((i1, i2, 2m + 1), (q, α, k)) | there exists
a computation path IM(x) `∗ M (x, ((i1, i2, 2m), (q0, α0,k0)))`M (x, ((i1, i2, 2m + 1), (q, α, k))) of M on x (that is,(x, ((i1, i2, 2m+1), (q, α, k))) is an ID of M just after the
point where the input head left the (2m+1)th plane of x)},C(x) ={{ρ1,ρ2}|ρ1 and ρ2 are conﬁgurations in S(x) suchthat (i)in case of ρ1 = ρ2, there exists a sequential
computation of M which starts with ID(x,ρ1) and either
terminates in a rejecting ID, or enters an inﬁnite loop, and(ii)in case of ρ1 ≠ ρ2, there exist two sequential
computations of M which start with ID’s(x, ρ1) and (x,ρ2), respectively, and terminate in sync ID’s withdifferent sync elements}. (Note that, for each x in V(m),C(x) is not empty, since x is not in ¯ Tc, and so not
accepted by M.) Then the following proposition must
hold.
Proposition 3.1. For any two different tapes x, y∈V(m),C(x)∩C(y)= φ.
[ Proof: For otherwise, suppose that x6= y(x, y ∈V(m)), C(x)∩C(y)6= φ, and {ρ1, ρ2} ∈C(x)∩C(y). Let z (with l1(x)=l2(x)=l3(x)=4m + 1) be the tapesuch that (i)z [(1, 1, 1), (4m + 1, 4m + 1, 2m + 1)] =x[(1,1,1), (4m + 1, 4m + 1, 2m + 1)], and (ii)z [(1, 1, 2m+ 2), (4m + 1, 4m + 1, 4m + 1)] = y[(1, 1, 2m + 2), (4m+ 1, 4m + 1,4m + 1)]. Since {ρ1,ρ2}∈ C(x), there exist
computation paths IM(z) ┠∗ M (z,ρ1) and IM(z) ┠∗ M

Fig.2:Three-dimensional k marker finite automaton.
3. Recognizability of Connected Tapes byThree-Dimensional Alternating Turing machines
Deﬁnition 3.1. Let x be in {0,1}(3). A maximal subset, Pof N3 satisfying the following conditions is called a 1component of x. (1)For any (i1, i2, i3)∈P, we have 1≤i1
≤l1(x), 1≤i2 ≤l2(x),1≤i3 ≤l3(x), and x(i1,i2,i3)=1. (2)Forany (i1, i2, i3), (i0 1, i0 2, i0 3) ∈ P, there exists a
sequence (i1,0, i2,0, i3,0), (i1,1, i2,1, i3,1),···, (i1,n, i2,n,i3,n) of elements in P such that (i1,0, i2,0, i3,0) = (i1, i2,i3), (i1,n, i2,n, i3,n) = (i0 1, i0 2, i0 3), and|i1,j−i1,j−1|+|i2,j −i2,j−1|+|i3,j −i3,j−1|≤1 (1≤j ≤ n). A tape x∈{0,1}3 is called connected if there exists exactly one
1-compnent of x. We denote the set of all the cubic
connected tapes by Tc. It is shown in [3] that a 3-ATM
can accept Tc. From this fact and from the fact L[FV 3AFA] ⊇L[3-AFA] by using a technique similar to that in[15], the following theorem holds. (3-AFA means 3ATM without the storage tape and the storage-tape head,
FV3-AFA means 3-AFA which cannot move up.)
Theorem 3.1. Tc ∈L[FV 3-AFA]. It is shown in [3] thatlogm space is necessary and sufﬁcient for FV 3-ATM’s
to accept Tc. We below show the necessary and sufﬁcientspace for FV 3-SUTM’s to accept ¯ Tc (=the complementof Tc).
Theorem 3.2. m2 space is necessary and sufﬁcient for FV
3-ATM’s to accept ¯ Tc.
Proof: (The proof of sufﬁciency) It is shown in [3] that
Tc is accepted by a deterministic one-way
parallel/sequential array acceptor (DOWPS), and it isshown that L[DOWPS] = L[TR2-DTM(m)] (TR2DTM(m) means m space-bounded three-way twodimensional deterministic Turing machine). From thesefacts and the fact that L[TR2-DTM(m)] is closed undercomplementation, it follows that ¯ Tc is in© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 668

Chongyang Sun, Makoto Sakamoto, Makoto Nagatomo, Yu-an Zhang, Shinnosuke Yano, Satoshi Ikeda, Takao Ito, Tsutomu Ito, Yasuo Uchida,
Tsunehiro Yoshinaga

p-tape T. M(k) is started on a 0,1-voxel in its start statewith carrying its markers . The markers can be placed onor collected back to the ﬁnite control from only the voxel
the input head is currently scanning. In each step, M(k)can change its internal state, place a marker ‘carried’ by
the ﬁnite control (or collect back a marker (if it exists) to
the ﬁnite control) on (or from) the voxel the input head iscurrently scanning, and move the input head to a 6adjacent cell, according to the current state, the symbol
and the presence of marker on the voxel currently
scanned by the input head. M(k) is called deterministic if
its next-move function is deterministic, otherwise it iscalled nondeterministic. We assume that M(k) can visit
any #-voxel which is 6-adjacent to some 0,1-voxel in T,
but can never fall oﬀ the tape T beyond these #-cells.
By using the same technique as in the proof of Theorem
3.1 in [1], we get the result.
Theorem 4.1. whether or not the pattern on a p-tape isconnected can be decided by a deterministic threemarker finite automaton.
It is shown in [1] that there is no deterministic one
marker ﬁnite automaton which is able to search all mazes(i.e., p-tapes). Moreover, it is shown in [3] that the set of
all three-dimensional connected tapes is not recognizable
by any three-dimensional nondeterministic multi-inkdot
ﬁnite automaton (an inkdot machine is a conventional
machine capable of dropping an inkdot on a given input
tape for a landmark, but unable to further pick it up[3]).
This result means that whether or not the pattern on a ptape is connected cannot be decided by any deterministic
one marker finite automaton.

(z,ρ2). Since{ρ1,ρ2}∈C(y), in case of ρ1=ρ2, there
exists a sequential computation of M which starts with
the ID (z,ρ1) and either terminates in a rejecting ID, orenters an inﬁnite loop, and in case of ρ1 6= ρ2, there
exist two sequential computations of M which start with
ID’s (z, ρ1) and (z, ρ2), respectively, and terminate insync ID’s with different sync elements. This means thatz is not accepted by M. This contradicts the fact, that z
is in ¯Tc = T(M).
□]Proof of Theorem 3.2(continued): Let p(m) denote thenumber of pairs of possible conﬁgurations of M just after
the point where the input head left the (2m+1)th planesof tapes in V(m). Then p(m) =KC 2+ K where K = s(4m +3)2 L(4m + 1)tL(4m+1). On the other hand, |V(m)| =2m(2m+1). Since L(m) = o(m), we have|V(m)|≥ p(m) forlarge m. Therefore, it follows that for large m there mustbe two different tapes x, y in V(m) such that C(x)∩C(y)≠φ. This contradicts Proposition 6.1 and completes theproof of necessity.
□
4. Recognizability of Three-Dimensional
Arbitrarily Shaped Tapes by k Marker Finite
Automata
Let Σ(3) be a set of points in the three-dimensional
Euclidean space with integer coordinates. Each point inΣ(3) is called a vertex, Each unit-length segment
connecting two vertices is called an edge. Each region ofunit area enclosed by twelve edges is called a voxel. Eachvoxel can have an input symbol ‘0’ or ‘1’, or a boundary
symbol ‘#’. A voxel is called 0-voxel (1-voxel, or #voxel) if it has symbol 0 (1, or #). Two-voxels are 6adjacent (or 27-adjacent) if they share a common edge (ora common vertex) [3]. A 6-adjacent (or 27-adjacent) path
is a sequence of voxels c(1),c(2),...,c(i) such that for each1 ≤ j ≤ i−1, c(j) and c(j + 1) are 6-adjacent (or 27adjacent). A three-dimensional arbitrarily shaped
pathwise-connected tape (p−tape) T is a set of 0, 1-voxels
surrounded by #-voxels, where any two 0,1-voxels in T
are connected by a 6-adjacent path with only 0,1-voxels
in T. (Note that T can contain some ‘holes’ in its inside.)
the pattern P on a p-tape T is the set of all 1voxels thatappear there. For the pattern P on a p-tape, a 1-component
C is any maximal set of 1-voxels such that any 1-voxels
in C are connected by a 6-adjacent path with only 1voxels in C. A pattern P is connected if and only if any
two 1-voxels are connected by a 6-adjacent path with
only 1-voxels in P. That is, P is connected if and only ifthere exists only one 1-component. A k-marker ﬁnite
automaton M(k) consists of a ﬁnite control with a readonly input head and k (labelled) markers operating on a5.

Conclusion

In this paper, we considered about recognizability of
three-dimensional patterns by some three-dimensional
automata. It is an open problem whether the set of all thethree-dimensional connected tapes is not accepted by any
three-dimensional nondeterministic Turing machine withspaces of size smaller than logm, and by any threedimensional alternating one marker finite automatonwith only universal states.
References[1] M.Blum and C.Hewitt, Automata on a twodimensional tape, in IEEE Symposium on Switching andAutomata Theory:155-160 (1967).
[2] A.Rosenfeld, Three-dimensional digital topology,
Information and Control 50:119-127 (1979).
[3] M.Sakamoto, Three-Dimensional Alternating Turing
Machines, Ph.D. Thesis, Yamaguchi University (1999).

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 669

Four-Dimensional Homogeneous Systolic Pyramid Automata
Makoto Nagatomo, Makoto Sakamoto, Chongyang Sun, Shinnosuke Yano, Satoshi Ikeda

Faculty of Engineering, University of Miyazaki, 1-1, Gakuen Kibanadai-nishi, Miyazaki-shi, 889-2192
Miyazaki, Japan
Takao Ito, Tsutomu Ito

Institute of Engineering, Hiroshima University, 4-1, Kagamiyama 1-chomeHigashi-Hiroshima, Hiroshima 739-8527
Hiroshima, Japan
Yasuo Uchida

Department of Business Administration, Ube National College of Technology, Tokiwadai Ube, Yamaguchi 755-8555
Yamaguchi, Japan
Tsunehiro Yoshinaga

Department of Computer Science & Electronic Engineering, National Institute of Technology, Tokuyama College
GakuendaiShunan, Yamaguchi 745-8585
Yamaguchi, Japan

Abstract
Cellular automaton is famous as a kind of the parallel automaton. Cellular automata were investigated not only in the
viewpoint of formal language theory, but also in the viewpoint of pattern recognition. Cellular automata can beclassified into some types. A systolic pyramid automata is also one parallel model of various cellular automata. A
homogeneous systolic pyramid automaton with four-dimensional layers (4-HSPA) is a pyramid stack of fourdimensional arrays of cells in which the bottom four-dimensional layer (level 0) has size an (a≥1), the next lowest
4(a-1), and so forth, the (a-1)st four-dimensional layer (level (a-1)) consisting of a single cell, called the root. Eachcell means an identical finite-state machine. The input is accepted if and only if the root cell ever enters an accepting
state. A 4-HSPA is said to be a real-time 4-HSPA if for every four-dimensional tape of size 4a (a≥1) it accepts thefour-dimensional tape in time a-1. Moreover, a 1- way four-dimensional cellular automaton (1-4CA) can beconsidered as a natural extension of the 1-way two- dimensional cellular automaton to four-dimension. The initialconfiguration is accepted if the last special cell reaches a final state. A 1-4CA is said to be a real- time 1-4CA if whenstarted with four-dimensional array of cells in nonquiescent state, the special cell reaches a final state. In this paper,
we propose a homogeneous systolic automaton with four-dimensional layers (4-HSPA), and investigate someproperties of real-time 4-HSPA. Specifically, we first investigate a relationship between the accepting powers of realtime 4-HSPA’s and real-time 1-4CA’s. We next show the recognizability of four-dimensional connected tapes byreal-time 4-HSPA’s
Keywords: cellular automaton, diameter, finite automaton, four-dimension, parallelism, pattern recognition

animation, VR(virtual reality), AR (augmented reality),and so on, the study of four-dimensional patternprocessing has been of crucial importance. And thequestion of whether processing four-dimensional digital

1. Introduction
In recent years, due to the advances in many application
areas such as dynamic image processing, computer© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 670

Makoto Nagatomo, Makoto Sakamoto, Chongyang Sun, Shinnosuke Yano, Satoshi Ikeda,Takao Ito, Tsutomu Ito, Yasuo Uchida, Tsunehiro
Yoshinaga

patterns is much more difficult than three-dimensional
ones is of great interest from the theoretical and practicalstandpoints. Thus, the study of four-dimensional
automata as a computational model of four-dimensional
pattern processing has been meaningful[4-23]. On theother hand, cellular automata were investigated not onlyin the viewpoint of formal language theory, but also inthe viewpoint of pattern recognition. Cellular automata
can be classified into some types [2]. A systolic pyramid
automaton is also one parallel model of various cellular
automata. In this paper, we propose a homogeneous
systolic pyramid automaton with four-dimensional layers(4-HSPA) as a new four-dimensional parallel
computational model, and investigate some properties ofreal-time 4-HSPA.
Let Σ be a finite set of symbols. A four-dimensional tapeover Σ is a four-dimensional rectangular array ofelements of Σ. The set of all four-dimensional tapes over
∑ is denoted by Σ(4). Given a tape x ∈ Σ(4), for each
integer j (1≤ j ≤4), we let lj(x) be the length of x along thejth axis. The set of all x ∈ Σ(4) with l1(x) = n1, l2(x) = n2,l3(x) = n3, and l4(x) = n4 is denoted by Σ ( , , , ) . When1≤ ij≤ lj(x) for each j(1≤ j ≤4), let x(i1, i2, i3, i4) denote thesymbol in x with coordinates (i1, i2, i3, i4). Furthermore,
we defineFig.1 : Four-dimensional input tape.
A four-dimensional homogeneous systolic pyramid
automaton (4-HSPA) is a pyramidal stack of fourdimensional arrays of cells in which the bottom fourdimensional layer (level 0) has size 4a (a ≥ 1), the next
lowest 4(a − 1), and so forth, the (a−1)st fourdimensional layer (level (a − 1)) consisting of a single
cell, called the root . Each cell means an identical finitestate machine, M = (Q, Σ, , #, F), where Q is a finite setof states, Σ ⊆ Q is a finite set of input states, # ∈ −Σ isthe quiescent state, F ⊆ Q is the set of accepting states,
and : 17 → Q is the state transition function, mapping
the current states of M and its 16 son cells in a 2 × 2 × 2×2 block on the four-dimensional layer below into M’s
next state. The input is accepted if and only if the root
cell ever enters an accepting state. A 4-HSPA is said tobe a real-time 4-HSPA if for every four-dimensional tapeof size 4a (a ≥ 1) it accepts the four-dimensional tape intime a − 1. By £R[4-HSPA] we denote the class of the setsof all the four-dimensional tapes accepted by a real-time
4-HSPA[1].
A 1-way four-dimensional cellular automaton (1-4CA)
can be considered as a natural extension of the 1-way
two-dimensional cellular automaton to four dimensions[3]. The initial configuration of the cellular automaton istaken to be an l1(x) × l2(x) ×l3(x) × l4(x) array of cells
in the nonquiescent state. The initial configuration is
accepted if the last special cell reaches a final state.
A 1-4CA is said to be a real-time 1-4CA if when started
with an l1(x) × l2(x) ×l3(x) × l4(x) array of cells in thenonquiescent state, the special cell reaches a final state intime l1(x) + l2(x) + l3(x) × l4(x) − 1. By £R[1-4CA] wedenote the class of the sets of all the four-dimensional
tapes accepted by a real-time 1-4CA [3].

x [(i1, i2, i3, i4), (i’1, i’2, i’3, i’4)],
when 1 ≤ ij ≤ i’j ≤ lj(x) for each integer j(1≤ j ≤4), as thefour-dimensional input tape y satisfying the following
conditions :(i)for each j(1≤ j ≤4), lj(y)= ij - ij+1;(ii)for each r1, r2, r3, r4 (1≤ r1 ≤ l1(y), 1 ≤ r2 ≤ l2(y), 1 ≤r3 ≤ l3(y), 1 ≤ r4 ≤ l4(y)), y(r1, r2, r3, r4) = x(r1 + i1 - 1, r2 +i2 - 1, r3 + i3 - 1, r4 + i4 - 1). (We call x[(i1, i2, i3, i4), (i’1, i’2,
i’3, i’4)] the [(i1, i2, i3, i4), (i’1, i’2, i’3, i’4)]-segment of x.)
For each x ∈ Σ ( , , , ) and for each 1 ≤ i1 ≤ n1, 1 ≤i2 ≤ n2, 1 ≤ i3 ≤ n3, 1 ≤ i4 ≤ n4, x[(i1, 1, 1, 1), (i1, n2, n3, n4),x[(1, i2, 1, 1), (n1, n2, n3, n4)], and x[(1, 1, i3, 1), (n1, n2, n3,n4)] are called the i1th (2-3) plane, the i2th (1-3) plane,
and the i3th (1-2) plane of each time of x, and are denotedby x[i1, ∗, ∗, ∗], x[∗, i2, ∗, ∗], and x[∗, ∗, i3, ∗], respectively.
x[∗, ∗, ∗, i4] also has analogous meaning.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 671

Four-Dimensional Homogeneous Systolic Pyramidx(DNWF), x(USWF), x(USEF), x(UNEF) are the statesof C(UNWP), C(USWP), C(USEP),C(UNEP), C(DNWP), C(DSWP),
C(DSEP), C(DNEP), C(UNWF), C(USWF), C(USEF),C(UNEF), C(DNWF),
C(DSWF), C(DSEF), C(DNEF), at time t-2, respectively. Let ( )= (x(UNWP), x(USWP), x(USEP), x(UNEP), x(DNWP),
x(USWP), x(USEP), x(UNEP)),( )=(x(UNWF),
x(USWF), x(USEF), x(UNEF), x(DNWF), x(USWF),
x(USEF), x(UNEF)), and ( ) = (x(UNWP), x(USWP),
x(USEP), x(UNEP), x(DNWP), x(USWP), x(USEP),
x(UNEP), x(UNWF), x(USWF), x(USEF), x(UNEF),
x(DNWF), x(USWF), x(USEF), x(UNEF)). Then, thefollowing two propositions must hold.
Proposition 2.1. (i) For any two tapes x, y ∈ W(n) whose
1st cubes are same, ( ) = ( ). (ii) For any two tapes x,y ∈ W(n) whose nth cubes are same, ( ) = ( ) .

[Proof : From the mechanism of each cell, it is easily
seen that the states of C(UNWP), C(USWP), C(USEP),C(UNEP),
C(DNWP), C(DSWP), C(DSEP), C(DNEP) are not influenced by the
information of 1st cube. From this fact, we have (i). The
proof of (ii) is the same as that of (i). □]Fig.2 : Four-dimensional homogeneous systolicpyramid automaton.

2. Main Results

Propositon 2.2. For any two tapes x, y ∈ W(t) whose 1st
cube are different, ( ) ≠ ( ).

We mainly investigate a relationship between theaccepting powers of real-time 4-HSPA’s and real-time 14CA’s. The following theorem implies that real-time 4HSPA’s are less powerful than real-time 1-4CA’s.

[Proof : Suppose to the contrary that ( ) = ( ). We
consider two tapes x’, y’ ∈ W(t) satisfying the following :(i) 1st cube of x and nth cube of x are equal to 1st cube ofx’, respectively(ii) 1st cube of y’ is equal to 1st cube of y, and nth cubeof y′ is equal to 1st cube of x.

Theorem 2.1. £R[4-HSPA] ⊊ £R[1-4CA].

Proof : Let V = { x ∈{0,1}(4) | l1(x) = l2(x) = l3(x)=l4(x)&[∀ 1 ,∀ 2 ,∀ 3,∀ 4 (1 ≤ i1 ≤ l1(x), 1 ≤ i2 ≤ l2(x), 1 ≤ i3
≤ l3(x) , 1 ≤ i4 ≤ l4(x))[x(i1, i2, i3, 1) = x(i1, i2, i3, l4(x))]]}.
It is easily shown that V ∈ £R[1-4CA]. Below, we showthat V ∉ £R[4-HSPA]. Suppose that there exists a realtime 4-HSPA accepting V. For each t ≥ 4, let

As is easily seen, x′ ∈ V and so x′ is accepted by M. On
the other hand, from Proposition 2.1(ii), ( ′) = ( ′).
From Proposition 2.1(i), ( ) = ( ′), ( ) = ( ′). It
follows that y′ must be also accepted by M. This
contradicts the fact that y′ is not in V . □]W(n) = { x ∈{0,1}(4)| l1(x) = l2(x) = l3(x) = l4(x) & x[(1, 1,
1, 2), (t, t, t,t-1)] ∈ {0}(4)}.

Proof of Theorem 2.1 (continued) : Let p(t) be thenumber of tapes in W(t) whose 1st cubes are different,
and let Q(t) = { ( ) | x ∈ W(t)}, where k is the numberof states of each cell of M. Then, p(t) =2 , and Q(t) ≤ k4.
It follows that p(n) > Q(t) for large t. Therefore, it follows
that for large t, there must be two tapes x,y in W(t) suchthat their 1st cubes are different and ( ) = ( ). This
contradicts Proposition 2.2, so we can conclude that V ∉£R[4-HSPA]. In the case of four-dimension, we can showthat V ∉ £R[4-HSPA] by using the same technique. This
completes
the
proof
of
Theorem
2.1.
□

Sixteen sons of the root cell A(t−1,1,1,1,1) of M
A(t−2,1,1,2,1), A(t−2,1,2,2,1), A(t−2,2,1,2,1), A(t−2,2,2,2,1), A(t−2,1,1,3,1),
A(t−2,1,2,3,1), A(t−2,2,1,3,1), A(t−2,2,2,3,1), A(t−2,1,1,2,2), A(t−2,1,2,2,2),A(t−2,2,1,2,2), A(t−2,2,2,2,2), A(t−2,1,1,3,2), A(t−2,1,2,3,2), A(t−2,2,1,3,2),A(t−2,2,2,3,2) are denoted by C(UNWP), C(USWP), C(USEP),C(UNEP),
C(DNWP), C(DSWP), C(DSEP), C(DNEP), C(UNWF), C(USWF),
C(USEF),C(UNEF), C(DNWF), C(DSWF), C(DSEF), C(DNEF),
respectively. For each x in W(n), x(UNWP), x(USWP),
x(USEP), x(UNEP), x(DNWP), x(USWP), x(USEP),
x(UNEP), x(UNWF), x(USWF), x(USEF), x(UNEF),© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 672

Makoto Nagatomo, Makoto Sakamoto, Chongyang Sun, Shinnosuke Yano, Satoshi Ikeda,Takao Ito, Tsutomu Ito, Yasuo Uchida, Tsunehiro
Yoshinaga

References
We next show the recognizability of four-dimensional
connected tapes by real-time 4-HSPA’s by using the same
technique of Ref.[3]. Let x in {0,1}(4). A maximal subset
P of N4 satisfying the following conditions is called a 1component of x.

1. K.Culik II, J.Gruska and Salomaa, Sytolic trellis automata,Part I, International Journal of Computer Mathmatics,(1991), 55:pp.99-121.
2. C. Iwamoto, K.Inoue, and I.Takanami, Some Properties of
Homogeneous Pyramid Automata, The IEICE
Transactions on Information and Systems (Japanese
Edition), (1990), J73-D-I(9) : 778-780.
3. C. Iwamoto, K. Inoue, and I. Takanami, Some Properties
of Homogeneous Pyramid Automata, The IEICE
Transactions on Information and Systems (Japanese
Edition), (1990), J73-D-I(9) : 778- 780.
4. K. Krithivasan and M. Mahajan, Systolic pyramid
automata, cellular automata and array languages,
International Journal of Pattern Recognition and
Artificial Intelligence, (1989), 3(3 4):405-433.
5. M.Sakamoto, Three-Dimensional Alternating Turing
Machines, Ph.D. Thesis, Yamaguchi University, (1999).
6. M.Sakamoto, H.Okabe, and K.Inoue, Some properties offour-dimansional finite automata, in 2002 Chugoku −Section Joint Convention Record of Insistes of Electricaland Information Engineerings, Japan, (2002), p.351.
7. M.Sakamoto, S.Nagami, K.Kono, A relationship between
the accepting powers of three- dimentional layers, Trans.
of SCI(Japan), Vol.17, No.10, (2004), pp.451-458.
8. M.Sakamoto, H.Okabe, S.Nagami, S.Taniguchi, T.Maki,
Y.Nakama, M.Saito, M.Kono, and K.Inoue, A note on
four-dimensional finite au- tomata, WSEAS Transactions
on Computers, Issue 5,Vol.3, (2004), pp.1651-1656.
9. M.Sakamoto, T.Ito, N.Tomozoe, and H.Furutani, Asurveyof three-dimensional automata, The papers of Technical
Meeting on Information Systems, IEE, Japan, IS-07-12,(2007), pp.11-17.
10. M.Sakamoto, T.Ito, H.Furutani, and M.Kono, Some
accepting powers of three-dimensional parallel Turning
machines, in 1st European Workshop on Artificial Life andRobotics, Vienna, Austria, (2007), pp.73-79.
11. M.Sakamoto, N.Tomozoe, H.Furutani, M.Kono, T.Ito,
Y.Uchida, and H.Okabe, A survey of automata on threedimensional input tapes, WSEAS Transactions on
Computers, Issue 10,Vol.7, (2008), pp.1638-1647.
12. M.Sakamoto, T.Ito, H.Furutani, and M.Kono, Some
accepting powers of three-dimensional parallel Turning
machines, International Journal of Artificial Life andRobotics, Springer, Vol.13,No.1, (2008), pp.27-30.
13. M.Sakamoto, M.Fukuda, S.Okatani, T.Ito, H.Furutani,and M.Kono, Path-bouded Three- dimensional finite
automata, International Journal of Artificial life andRobotics, Springer,Vol.13,No.1, (2008), pp.54-57.
14. M.Sakamoto, S.Okatani, M.Fukuda, T.Ito, and H.Furutani,and M.Kono, A relationship between Turning machines
and finite automata on four-dimensional input tapes,(i)For any (i1, i2, i3, i4 ∈ P, we have 1≤i1≤l1(x), 1≤i2≤l2(x),1≤i3≤l3(x), 1≤i4≤l4(x), and x(i1, i2, i3, i4) = 1.
(ii) For any (i1, i2, i3, i4), (i1’, i2’, i3’, i4’) ∈ P, there exists
a sequence (i1,0,i2,0,i3,0,i4,0),(i1,1,i2,1, i3,1,i4,1), . . . ,(i1,4,i2,4,i3,4,i4,4) of elements in P such that
(i1,0,i2,0,i3,0,i4,0) = (i1,i2,i3,i4), (i1,4,i2,4, i3,4, i4, 4) = (i′1,i′2, i′3, i′4), and |i1, j -i1, j−1| + |i2, j -i2, j−1| + |i3, j -i3, j−1|
+ |i4, j –i4, j−1|≤ 1(1 ≤ j ≤ 4). A tape x ∈ {0, 1}(4) is called
connected if there exists exactly one 1- component of x.
Let Tc be the set of all the four-dimensional connected
tapes. Then, we have
Theorem 2.2. Tc ∉ £R[4-HSPA].
3. Conclusion

The technique of AR (augmented reality) or VR(virtual
reality) progresses like the Pokemon GO and the Virtual
Cinema in the world. The virtual technique will spread
steadily among our societies in future. Thus, we thinkthat the study of four-dimensional automata is very
meaningful as a computational model of fourdimensional pattern processing. In this paper, we
proposed a homogeneous systolic pyramid automatonwith four-dimensional layers, and investigated a
relationship between the accepting powers ofhomogeneous systolic pyramid automaton with fourdimensional layers(4-HSPA) and one-way fourdimensional cellular automata (1-4CA) in real time, andshowed that real-time 4-HSPA’s are less powerful thanreal time 1-4CA’s.
It will be interesting to investigate about an alternating
version or synchronized alternating versionofhomogeneous systolic pyramid automaton with fourdimensional layers. Moreover, we think that there aremany interesting open problems for digital geometry.
Among them, the problem of connectedness, especiallytopological component is one of the most interesting
topics[17].
Finally, we would like to hope that some unsolved
problems concerning this paper will be explicated in thenear future.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 673

Four-Dimensional Homogeneous Systolic Pyramid

15.

16.

17.

18.

19.

20.

21.

22.
23.

International Journal of Artificial life and Robotics,
Springer, Vol.13,No.1, (2008), pp.58-60.
M.Sakamoto,
S.Okatani,
K.Kajisa,
M.Fukuda,
T.Matsuawa, A.Taniue, T.Ito, H.Furutani, and M.Kono,
Hierarchies based on the number of cooperating systemsof three-dimensional finite automata, International
Journal of Artificial Life and Robotics, Springer,
Vo.4,No.3, (2009), pp.425-428.
M.Sakamoto, T.Matsukawa, R.Katamune, H.Furutani,
M.Kono, S.Ikeda, T.Ito, Y.Uchida, and T.Yoshinaga,
Four-dimensional synchronized alternating Turning
machines, Proceedings of the 10th American Conference
on Applied Mathmatics, Harvard University, Cambridge,
USA, (2010), pp.195-200 (CD-ROM).
M.Sakamoto, R.Katamune, T.Matsukawa, H.Furutani,
M.Kono, S.Ikeda, T.Ito, Y.Uchida, and T.Yoshinaga,
Some result about hierarchy and recognizability of fourdimensional sychronized alternating Turning machines,
Proceedings of the 10th American Conference on Applied
Mathmatics, Harvard University, Cambridge, USA,(2010), pp.201-205(CD-ROM).
M.Sakamoto, T.Matsukawa, R.Katamune, H.Furutani,
M.Kono, S.Ikeda, T.Ito, Y.Uchida, and T.Yoshinaga,
Synchronized Alternating Turning Machines on FourDimensional In- put Tapes, WSES TRANSACTIONS on
COMPUTERS,Issue 4,Vol.9, (2010), pp.319-328.
M.Sakamoto, R.Katamune, T.Matsukawa, H.Furutani,
M.Kono, S.Ikeda, T.Ito, Y.Uchida, and T.Yoshinaga,
Hardware Hierarchies and Recognizabilities of FourDimensional Synchronized Alternating Turning Machines,
WSEAS TRANSACTIONS on COMPUTERS, Issue 4,(2010), pp.329-338.
M.Sakamoto, T.Ito, X.Qingquan, Y.Uchida, T.Yochinaga,
M.Yokomichi, S.Ikeda, and H.Furutani, A Note on Threedimensional Probabilistic Finite Automata, The
Seventeenth International Symposium on Artificial Lifeand Robotics 2012, Oita, Japan, (2012), pp.492- 495 (CDROM).
M.Sakamoto, Y.Uchida, T.Hamada, T.Ito, T.Yoshinaga,
M.Yokomichi, S.Ikeda, and H.Furutani, A Relationshipbetween the Accept- ing Powers of Nondeterministic
Finite Automata and Probabilistic Finite Automata on
Three-Dimensional Input Tapes, in the 2012 IEICE
General Conference, Okayama University, (2012), p.4(CD-ROM).
T. Toffoli and N. Margolus, Cellullar automaton machines− new environment for modeling, MIT Press, (1987).
J. Wiedermann, Parallel Turing machines, Technical
Report RUU-CS-84-11, Department of Computer Science,
University of Utrecht, the Netherlands, (1984).

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 674

Reduction of the search space to find perfect play of 6×6 board Othello
Yuki Takeshita

Graduate School of Engineering, Miyazaki University,
Japan, hf11031@student.miyazaki-u.ac.jp
Makoto Sakamoto

Department of Computer Science and System Engineering, Miyazaki University,
Japan, sakamoto@cs.miyazaki-u.ac.jp
Takao Ito

Graduate School of Engineering, Hiroshima University,
Japan, itotakao@hiroshima-u.ac.jp
Tsutomu Ito

Graduate School of Engineering, Hiroshima University,
Japan, 0va71-2538f211n@ezweb.ne.jp
Satoshi Ikeda

Department of Computer Science and System Engineering, Miyazaki University,
Japan, bisu@cs.miyazaki-u.ac.jp

Abstract
In 1993, mathematician Feinstein found out perfect play on 6×6 board Othello gives 16-20 loss for the first player byusing computer. He reported on the Web that it took two weeks to search forty billion positions in order to obtain the
result. In our previous papers, we confirmed the perfect play he found is correct. And we also found another perfectplay different from the one he found to search 884 billion positions. In order to search efficiently, we attempted toreduce the search space to find some perfect play. In this paper, we introduce some techniques to solve 6×6 board
Othello by searching about nine billion positions.
Keywords: combinatorial theory, combinatorial optimization, perfect play, miniature Othello

players choose constantly the best move, these areclassified into win, loss or draw game for the first player3(the sequence obtained in this way is called perfect play).
6×6 board Othello is solved by mathematician Joel
Feinstein in 1993. He reported on the Web that perfectplay of 6×6 board Othello gives 16-20 loss for the firstplayer4. He also reported, he needed two weeks to obtainhis perfect play by searching 40 billion positions. On theother hand, standard 8×8 board Othello is still open inspite of the fact that over twenty years have passed since
he solved 6×6 board Othello.

1. Introduction
Othello is a board game derived from Reversi by Goro
Hasegawa (JPN) in 1973. Othello rules1 are completely
unified, whereas Reversi has many local rules. According
to the Japan Othello Association established by
Hasegawa, World Championship has been held every
year since 1977, the tournament is held in Japan, theUnited States and European countries.
Othello is categorized into two-player zero-sum finite
deterministic games of perfect information2. Games inthis class are possible to look ahead in theory, thus if both

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 675

Yuki Takeshita, Makoto Sakamoto, Takao Ito, Tsutomu Ito, Satoshi Ikeda

3.1. MiniMax with Alpha-Beta Pruning

In our previous papers5, 6, we examined his results
and showed that the perfect play he found is correct. In
addition, we also found new perfect play. However, we
needed five and a half days to find it by searching 884
billion positions.
In the future, in order to solve standard 8×8 board
Othello, it is necessary to greatly reduce the search space.
Therefore, in this paper, we introduce some techniques tosolve 6×6 board Othello by searching about nine billion
positions. Moreover, we explain an idea that solves bydividing the problem of large search space into multiple
sub-problems using 4×12 board Othello.

As the technique in computer Othello, regardless of the
middle or the end, MiniMax with Alpha-Beta pruning iscommonly used. MiniMax algorithm achieves the best
moves by both players, Alpha-Beta method cuts
unnecessary search in MiniMax. These are commonlyused together since MiniMax with Alpha-Beta pruning
gives the same results as the simple MiniMax search.
3.2. Move Ordering
Appropriate order in search causes pruning by AlphaBeta method. In our program, all positions progressed
one move (from current position) are extracted, and thenthey are sorted. To realize this, we calculate theevaluation values by machine learning in advance.
Therefore, the higher the accuracy of machine learning,
the higher the reduction effect of search space.

2. Othello*
Othello always begins with the setup as shown in Fig. 1.
One player uses the black side of the pieces (circularchips), the other the white side. Black always moves first.
Each player puts a piece of own color to an empty
square in own turn. A player’s move consists ofoutflanking the pieces of his opponent, he flips
outflanked pieces to his color. To outflank means to place
the piece on the board so that his opponent’s rows of thepiece are bordered at each end by the piece of his color.
If a player cannot make any move, then he has to pass. If
he is able to make a valid move, then passing is notallowed. The game ends when neither player can makeany valid move. The winner is the player who has more
pieces than his opponent.

Machine Learning
We adopt reinforcement learning using Monte Carlo
method. First, it starts at root node of the tree, several
moves of random selection are running. Second, they
select optimal child nodes until a leaf node is reached.
Third, computer uses the final disc difference as a reward,
it updates evaluation value of the position while returning
moves. In our program, one cycle including the above
three steps is repeated a million times.
Evaluation function
The evaluation value is the sum of evaluation values ofseveral patterns. For example, in 6×6 board Othello, it
consists of the patterns shown in Fig. 2.

Fig. 1. Othello’s setup.

Fig. 2. Evaluate pattern of 6×6 board Othello.

3. Techniques
Perfect analysis of Othello is useful to refer thinking
routines of game program. This is because end-gameroutine of game program is the perfect analysis exactly,
and evaluation function in the middle-game routine isavailable for the ordering of the search in perfect analysis.

*

3.3. Adding Hash Table
To cut the search of duplication positions, we added ahash table. However, since the same positions hardly
occur in rectangular boards, we only implemented on 6×6
board Othello. By this revision, we were able to reduce
the search space of 6×6 board Othello by 40 percent.

Othello is a registered trademark.
© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 676

Reduction of the search

4. Experiments
Table 1 shows number of final positions for 6×6 board
Othello. From Table 1, comparing the first row with thethird row, we can see that our current program has
reduced Feinstein’s search space by about 78%. Besides,
comparing the second row with the third row, we can seethat our current program has reduced the search space by
about 99%. This is due to the implementation effect of
the Move Ordering described Section 3.2.

Fig. 5. Depth 4.

Table 1. Number of final position for 6×6 board Othello.
Experimenter

Execution time

Final position

Feinstein(‘93)
Takeshita(‘15)
Takeshita(‘16)

2 weeks
5.5days2.5hours

40 billion
885 billion
9 billion

Fig. 6. Depth 2.

Table 2 shows a part of the position fixed from move
1 to move 6 in 4×12 board Othello. These are currently
executing sub-problems, however, blacked out cells are
already finished. For every element in Table 2, the first
Greek alphabets show the places as mentioned above.
The following character strings are a sequence to move 6
corresponding to the coordinates in Fig. 7. For further
details on the sub-problems, refer to reference 8.

4.1. 4×12 board Othello
We found an initial position of 4×12 board Othello as
shown in Fig. 3. This position gives win for the firstplayer. The sequence is perfect play which is consistent
with 4×8 board and 4×10 board. This is why we choose
the position. If the sequence of Fig. 3 is the best moves
for both players, 4×12 board Othello gives win for the
first player. Therefore, it is necessary to check somepositions which branched from white turn.

Table 2. Executing sub-problems to prove our perfect play.
Depth 6(α)(β)

Depth 4(γ)f4g1(δ)e2g1(δ)e2h1(ε)h3f4(ε)h3h4

Depth 2(ζ)f4e1d1g1(ζ)f4e1d1h2(ζ)f4g1h1h2(η)h1e1d1e3(η)h1e1d1f4(η)h1e1d1h2(η)h1e3g4h3

Fig. 3. A position fixed from move 1 to move 6.

Fig. 4, Fig. 5 and Fig. 6 are three positions that must
be checked in order to prove that gives win for the firstplayer on 4×12 board. The Greek alphabets in thesefigures mean all of places that the second player can put
in his turn. ‘F’ in each figure means ‘Finish’, this is aplace which already finished the search when we checkin descending order (Depth 6, Depth 4 and Depth 2).

Fig. 7. Coordinates on 4×12 board.

As we can see (Table 2), eleven problems havealready finished, the rest is three problems. If their results
are all the black win, it will show that the first player winsin 4×12 board Othello.
5. Consideration
Fig. 8 shows the number of final position for miniature
Othello solved by us. The horizontal axis shows eachboard size and the vertical one represents number of final
position by the exponent. The elements in Fig. 8 meanthat circles are square boards and the triangles arerectangular boards.

Fig. 4. Depth 6.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 677

Yuki Takeshita, Makoto Sakamoto, Takao Ito, Tsutomu Ito, Satoshi Ikeda

References.
1. Japan Othello Association: Nihon osero renmei kyougi ruru, [online]www.othello.gr.jp/r_info/rule.html (Retrieved
9 November. 2016)
2. John von Neumann and Oskar Morgenstern: Theory ofGames and Economic Behavior, Princeton University
Press (1944).
3. Jonathan Schaeffer, Neil Burch, Yngvi Björnsson, Akihiro
Kishimoto, Martin Müller, Robert Lake, Paul Lu and Steve
Sutphen: “Checkers Is Solved,” Science, Vol. 317, pp.
1518-1522 (2007).
4. British Othello Federation: Forty Billion Nodes Under The
Tree - The Newsletter of the British Othello Federation,[online]www.britishothello.org.uk/fbnall.pdf (Retrieved 9
November. 2016), pp. 6–8 (1993).
5. Yuki Takeshita, Satoshi Ikeda, Makoto Sakamoto andTakao Ito: “Perfect Analysis in miniature Othello,”
Proceedings of the 2015 International Conference on
Artificial life and Robotics, pp.39 (2015).
6. Yuki Takeshita, Makoto Sakamoto, Takao Ito and Satoshi
Ikeda: “Perfect Play in Miniature Othello,” Proceedings of
the Ninth International Conference on Genetic andEvolutionary Computing, pp.281-290 (2015).
7. Yuki Takeshita, Makoto Sakamoto, Takao Ito and Satoshi
Ikeda: “Analysis for 4×12 board of Othello,” Proceedingsof the 2016 International Conference on Artificial life andRobotics, pp.349-352 (2016).

Fig. 8. Number of final position for miniature Othello.

Firstly, in rectangle board, it is reasonable to expect
number of final position in 4×12 board is about 103 times
larger than 4×10 board. Now, given that the execution
time in 4×10 board is four hours, the execution time in
4×12 board is considered to be about four thousand hours(when we analyze perfectly from move 1).
Secondly, in square board, number of final position
increased about 108 times in the transition from 4×4
board to 6×6 board. From this fact, we assume that
number of final position in 8×8 board is about 1018 atleast. Now, given that the execution time in 6×6 board is
2 hours, we know that perfect analysis in 8×8 board is
impossible as it stands.
6. Conclusion
In this paper, we attempted to reduce the search space of
6×6 board Othello by implementing Move Ordering(Section 3.2) and Hash Table (Section 3.3). As a result,
we succeeded in reducing Feinstein’s search space by
about 78%. In addition, the technique described in
Section 4.1 is available for all boards. Therefore, it could
be one key factor for solving even larger boards.

7. Future work
To challenge 6×8 board, 4×14 board and 8×8 board, it isnecessary to reduce the search space by exponential order.
For that purpose, it is most effective to improve accuracyof Move Ordering. Specifically, we strengthen
reinforcement learning and improve search algorithm.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 678

A Comparative Study On The Delisting Ratings Of Firms From The Un Global Compact In
The International Management Environment

Kanako Negishi

Business Administration, National Institute of Technology, Ube College,
Tokiwadai, Ube City, Yamaguchi Pref., 755-8555, Japan
E-mail: negishi @ube-k.ac.jp
www.ube-k.ac.jp/en/

Abstract
This study clarifies unique characteristics of Japanese manufacturing firms in the international management
environment using the United Nations Global Compact delisting ratio. Firms have adopted voluntary standards
provided by non-governmental organizations and international institutions. Although there are various standards thatare not legal binding, there is a recent trend toward integration to achieve comparability for firms’ management,
which then binds firms’ managements as a de facto standard. This study examines the firms’ delisting ratio bycomparing countries.
Keywords: United Nations Global Compact delisting ratio, multi-stakeholder initiative, Japanese manufacturing,international management environment

common way to report a firm’s sustainability
worldwide.2
One of the feature of these guidelines and principles isthat as the organizations are based on multi-stakeholder
initiatives, firms not only adopt the guidelines and/or
principles in their management but also participate in theplatforms to discuss them with other stakeholders tomake decisions and develop a process for the guidelines,
principles, and related actions.3
Sustainability is a very important agenda for a firm’s
international management. However, not all firms
archive their sustainable management with suchinternational actors; some firms are expelled from GC at
fixed intervals. The agenda surrounding firm’s
environment are complicated recently. It is difficult forfirms to solve it solely. However, firms are often
expected to approach it by their stakeholders.
Using the GC delisting list, this study clarifies
Japanese manufacturing firms’ characteristics in the

1. Introduction
The international environment regarding firms’
sustainability has changed, and there is also a transitionin the firms’ international relationship with stakeholders
and solutions to sustainability. After the 1990s, many
guidelines and principles for firms were developed bypublic institutions and non-governmental organizations
such as the United Nations Global Compact (GC),Global Reporting Initiative (GRI), and Integrated
Reporting (IR) to improve international firms’ activities.
For example, over 9,000 firms participated in GC.
Although there are various guidelines and principles
that are not legally binding such as GC, there is a recent
trend toward integration to achieve comparability forfirms’ international management of sustainability. Thisbinds firms’ management as a de facto standard.1 GRI
does not have legal power to force firms to use the GRI
guideline; however, nowadays, this guideline is the most

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 679

Kanako Negishi

international environment. It contributes
international strategy on sustainability.

to3. Method and Dataset

itsThis analysis used the data from the GC database ofparticipants. Firms are expelled if they fail to submit
their reports to GC at least one year. The first date ofdelisting in GC is March 27, 2002.Therefore, the data offirms are used from March 27, 2002 to November 11,
2016. The number of delisting firms during this term isshown in Table 1.
According to GC, a company is defined as a firm
having over 250 employees, while an SME has under
250 employees except for under 10 employees.
The target countries are Japan, Spain, China, and the
U.S. Because Spain ranks at the top of the GC
participants by country. I selected the countries based on
the ranking in each region.
This study focuses on the manufacturing sector.
According to GC, this sector includes automobile andparts, construction and materials, electronic andelectrical equipment, healthcare equipment and service,
household goods and home construction, industrial
engineering, personal goods, and technology hardwareand equipment.

2. Literature Review and Research Object
The recent international environment is a complex webof “soft” low that has constructed new social norms forseveral important dimensions of business conduct. The
part regarding dimensions has been institutionalized incorporate and industry codes, multi-stakeholder
initiatives, and private standard-setting bodies.4
How do firms develop good relationships with suchbodies to archive their sustainable management goals
efficiently? Many Japanese firms participate in GC anduse the GRI guideline.5 It is one of the result that firms
responded to the pressure from their stakeholders.
GC is one representative initiative, and its
participant firms are from both advanced and developing
countries. Business associations seem to be mostinvolved in the discussion at the UN level, as reflected inthe ‘post-2015 business engagement architecture’,leading to an issue area priorities framework publishedby GC.6
As noted above, some firms are delisted from GC
although the participation norm is only to submit thereport to GC. Particularly, the delisting ratio of Japanese
Small and Medium Sized Enterprise (SME) is thehighest in the GC participants’ top-10 ranking by
country including all sectors7, however, ref. 8 shows thatdomestic governance institutions are crucial in assisting
firms fulfill the GC requirement based on the WorldBank Governance Indicators which shows high level ofthe governance in Japan.
The emerging literature lacks information on thepractice of Japanese firms. Because, most of guidelines
and principles have been developed in advanced Westerncountries as the concept of sustainability and Corporate
Social Responsibility (CSR) also developed mainly inthe countries historically. Some of guidelines andprinciples oriented in Japan adopted by Japanese firms.
None of them have not spread globally like GRI whose
head office is in Netherlands.
This study focuses on the manufacturing sector, with
one of the largest values of foreign direct investment
from Japan, to clarify Japanese firms’ characteristics in
the international environment through a comparative
study of Japanese, Chinese, Spanish, and US firms bysize, sector, and country.

Table 1. The number of delisting firms by country and size
Country and size

NumberSpanish company
Spanish SME
Chinese company
Chinese SME
Japanese company
Japanese SME
US company
US SME

54
320
53
44
0
64
36
113

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 680

A Comparative Study On

4. Result and Findings
Table 2 shows the delisting ratios of the four countries
by size and sectors.
Table 2. Delisting ratio of the Spanish, Chinese, Japanese, and US firms by size and sectors
Spain

China

Japan

USA

SectorAutomobiles and parts

Rating ofcompany
delisting
9.09%

Rating ofSME
delisting58.33%

Rating ofcompany
delisting
38.46%

Rating ofSME
delisting
75.00%

Rating ofcompany
delisting
0.00%

Rating ofSME
delisting
91.67%

Rating ofcompany
delisting
22.22%

Rating ofSME
delisting
50.00%

Construction and materials
Electronic and electrical equipment
General industrials
Healthcare equipment and service
Household goods and home construction
Industrial engineering
Personal goods
Technology hardware and equipment

34.78%
40.00%
46.15%
12.50%
100.00%46.15%
56.25%
25.00%

60.29%
41.86%
55.70%
30.56%
35.71%
41.10%
60.00%
50.00%

66.67%
60.71%
75.00%
25.00%
50.00%
75.00%
57.14%
80.00%

46.67%
70.83%
64.71%
33.33%
50.00%
72.73%
14.29%
88.89%

0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%

88.57%
80.88%
77.78%
0.00%
81.82%
100%
0.00%
100%

27.78%
60.00%
60.87%
50.00%
0.00%
50.00%
14.29%
28.57%

77.78%
61.54%
70.49%
72.73%
57.14%
75.00%
46.15%
60.00%

4.1. Sizeratios are higher than those of their SMEs.
Only Japanese SMEs have ratios over 80% in all
sectors, and only Japanese companies’ have ratios at 0%.
There are no gaps among sectors.

Actions undertaken by large firms are more visible, sofirm size is an important factor for analysis.3 It isapplicable to Japanese and US firms, but firms in China
and Spain are not influenced by size, as shown in Table
1.
Focusing on the manufacturing industry, the featuresof Japanese firms are shown as the same. In brief, SMEs’delisting ratios are higher than any company in Japan. US
firms show the same trend. However, the gap in the ratio
between Japanese firms is larger than that in US firms.
On the other hand, Spanish and Chinese firms’delisting ratios are different. They depend on the sector,
and gaps between their companies and SMEs are smaller
than those of Japanese firms.

5. Considerations and Further Research
From the comparative study in the manufacturingindustry, it is clarified that Japanese companies’ delistingratios are the lowest, whereas that of SMEs are thehighest, and the trend is similar to the U.S.
Ref.6 noted what multinationals do face, however,
especially if they are large, visible and active in countries
with different norms and standards than their homecountry, is the growing pressure to account for social,environmental and ethical problems occurring in various
locations of operation. Although not all company in the
GC do business globally, companies tend to be pressured
stronger than SMEs as the reasons. The result of ratios
in Japan and US by size support it.
It is also clarified Japanese companies respond to the
expectation by their stakeholders globally. Additionally,
there is not large change about the Japanese companies

4.2. Sector
The ratios of SMEs delisting are higher than forcompanies in the automobiles and parts, electronic andelectrical equipment, and technology hardware andequipment sectors in four countries.
In the construction materials, personal goods, andgeneral industrials sectors, only Chinese companies’© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 681Kanako Negishi

and SMEs delisting ratios between manufacturing sector
and all sectors7.
It is believed that delisting ratios are influenced by
the number of firms whether they are supplier or not in
the global supply chain. Some firms request their
suppliers to behave virtuously. 1 The results indicate a lotof firms in Spain and China participate in GC and use the
GRI guideline although Japanese SMEs have better
financial conditions. However, western firms provide
little to no financial assistance to suppliers to meet theirstandards.4 The result of their delisting ratios partly
explains it quantitatively.
How can the Japanese SMEs’ ratios be explained? It
is thought that their main clients are Japanese companies
that generally do not request such norms for their
suppliers instead of each company’s code. In addition,
their home country’s consumers do not strongly request
to be more responsible than in Europe and the U.S.
However, more research is needed to explain the
result explicitly to add more data; reason of delisting, theother sectors and countries. It is also needed to explore
the relationship between the GC and internal elements
such as the governance of management.

References
1. The Global Compact and Global Reporting Initiative,
Making the Connection: Using the GRI G4 Guidelines toCommunicate Progress on the UN Global Compact
Principles,
https://www.globalreporting.org/resourcelibrary/UNGCG4-linkage-publication.pdf (25,Nov. 2016 last access)2. Global
Reporting
Initiative
homepage,https://www.globalreporting.org/information/aboutgri/alliances-and-synergies/Pages/UNGC-andGRI.aspx%20 (25, Nov. 2016 last access)3. K. Tanimoto, Kigyou to syakai no gabanansukouzou nohenka, in Proc. 90th Annual Meeting of the Japan Academyof Business Administration (2016).
4. D. Vogel, The Market for Virtue: The Potential and Limitsof Corporate Social Responsibility (The Brookings
Institution, Washington, DC, 2005).
5. KPMG, The KPMG Survey of Corporate Responsibility
Reporting 2013.
https://www.kpmg.com/Global/en/IssuesAndInsights/ArticlesPublications/corporateresponsibility/Documents/corporate-responsibilityreporting-survey-2013-exec-summary.pdf (25 Nov. 2016
last access)6. A. Kolk, The social responsibility of international
business: from ethics and the environment to CSR andsustainable development, J.World Bus., 51(1),(2016) 2334.
7. K. Negishi, Multinational Enterprises’ Global Supply
Chain: Study of the Global Reporting Initiative and United
Nations Global Compact in Industrial Renaissance: New
Business Ideas for the Japanese Company (Institute ofBusiness Research, Chuo University, Tokyo, 2017).
8. J. S. Knudsen, Company delisting from the UN Global
Compact: Limited business demand or domestic
governance failure? J. Bus. Ethics, 103 (2011) 331–349.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 682

Application of ViSC to the Natural Grazing in Qinghai Tibet Plateau
Chengshui Niu

Department of Computer Technology and Applications,
Qinghai University, 810016, Xining China
Bingfen Li

School of Mechanical Engineering,
Qinghai University, 810016, Xining China
Yu-an Zhang

Department of Computer Technology and Applications,
Qinghai University, 810016, Xining China
Makoto Sakamoto

Faculty of Engineering, University of Miyazaki,
Miyazaki City 889-2192 Japan
Abstract“SanjiangyuanRegion” is a complex ecosystem with typical characteristics of alpine grass land, which the livestock
production relies mainly on the yak grazing. In recent year, grassland degradation, the reduction of herdsmen income
and on-going deterioration of the ecological environment caused by human disturbance are gradually intense, whicha scientific and rational grazing mode of combination of ecological animal husbandry and sustainable utilization ofgrassland should be established urgently. Applying the ViSC combined with the optimization theory, will study
establish the optimization of alpine grassland yak grazing system, which provides a theoretical basis for scientificgrazing.
Keywords: Sanjiangyuan Region, ViSC, Optimization theory, Grazing systemt

high-speed, large capacity, parallel, easy to interact[2].
This article builds an system that displays the scientificanalysis of the data by the graphics, which can be aperfect solution to problem of the computer data difficult
to understand. So that herdsmen can be clear and intuitive
understanding of pastures need to optimize, and make the
Qinghai-Tibetan Plateau herdsmen can to carry out
scientific wisdom grazing(Wisdom Graze WG).

1. Introduction
The Qinghai-Tibet Plateau is a natural laboratory in thefields of earth sciences, life sciences, resources andenvironmental sciences[1]. Scientific research in thisarea is relatively scarce at present, so the significance of
its research becomes more valuable.
Due to overgrazing, the ecological balance of the
Qinghai-Tibet Plateau is seriously imbalanced anddesertification is aggravated. The main reason is that theunreasonable herdsmen seasonal grazing methods andpopulation selection is not standardized. In this paper, thegrazing intensity of the four seasons is rationallyarranged by scientific calculation, and the whole
structure of the population tends to be normalized byscientific distribution, which can reduce environmental
damage to realize the sustainable grazing.
The unfamiliar with the computer science of QinghaiTibetan Plateau area herdsmen leads to them can’t
directly use the scientific computing for analyzing thedata. The scientific visualization of visual information
processing possesses prominent merits in terms of

2. WG Model Is EstablishedIn the wisdom grazing, how to standardize the flock
structure and plan the four seasons grazing, so that to get
the maximum profit is the herdsmen most concerned the
problems. Limited stocking of yaks within limited
meadows is a major constraint for maximum profitability.
So Good allocation of grassland and yak is the basis of
the herdsmen to get the maximum profit.
Define1 Set there are Task head of yak, Work acres ofgrass. Task=F(s)i M(s)i F(a)i M(a)i, F means the bull，M means the cow，s means the summer，a means thespring and autumn, i=1,2,…8，means ages.
According to the conventional rangeland custom, theflock of yak from summer into autumn will grows one
year old, The initial copulation age of the cow is 2 to 3
years old, the bull is 4 to 5 years old, the conception time

Corresponding author: Yu-an Zhang, e-mail:2011990029@qhu.edu.cn© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 683Cheng-shui Niu, Bing-fen Li, Yu-an Zhang, Makoto Sakamoto

According to the different food intake, this article divided
the yak into four types, calves (newborn yaks), young yak,adult yak and old yak. The amount of eating grass ofcalves is almost zero in the summe, the amount of eating
grass of young yak has a increasing trend from autumn.
The amount of eating grass of adult yak almost unchange
is 19kg per day, the old yak’s is 15kg per day[5].
Define 2 Assuming the number of young cattle, adultcattle and old yak are expressed with TaskT1, TaskT2 and
TaskT3; the amount of eating grass of them are expressedwith TaskTS1, TaskTS2 and TaskTS3 respectively;
theyak required grass within a year is SumWork.

is about 6 to 9 months per year, the reproduction rate of
68% survival rate is 62%, Adult cattle affected byweather and disease mortality rate of 5%, aging cattle
mortality rate of 30%[3]。
Assume the survival rate is SR(i), as follows:
62%,=0SR(i)= 95%, 0 ≤ < 8
70%,
≥8

(1)

The variation equation of yak population as follows:
( )
( )
( )

( )

( )

= 0.5

0.68 ∗

= 0.5

=

=

=
( )
=

∗

∗

∗
∗

∗

0.68 ∗

( ),

( ),

( )

∗

=∑

(2)

( )

= 0,1,2 … 6; = 1,2,3, … 7
∗

∗

( )

( )

During the summer, the grass yield will reach the peakvalue(167kg=Ha)[4]. Assume the maximum value is
MaxGras and the yield of grass in winter, summer, spring
and autumn expresses by WinterGrass, SummerGrass,
SpringGrass and AutumnGrass respectively.
A large amount of data from the Zaduo County wascollected to demonstrate that the grass production in thespring pasture was growing exponentially. Different
rainfall and grassland quality will correspond to different
base-a. Spring production of grass can be expressedas:
=

3
=1

( )

( +
TaskTS1= ( +( +0.5 + 1) , [0,1],
0.5 + 7 ), [1,2],
0.5 + 14) , [2,3],
where i is the age of yak and x∈ [0,11].

=

( )

+

)19 ∗ 360

（8）

(9)

(10)

Define 3 Assume that the supplementary feed amount is
Fodder. Through the relation between the amount ofgrass and grass yield, we can get the fodder, as shown:
Fodder= (SumGrass-SumWork)/15（11）

wher 1 pounds of fodder is equal to 15 pounds of grass
Define 4 Assume the maximum feeding amount of eachyak is TaskI(i), i∈ [1,3], which represent young yaks, adultyaks and old yaks, respectively. The formula of TaskI(i)as follows
()=

⁄

()

Define 5 Assume that the total number of yaks is
TaskSum. The value of TaskSum is:(4)

Where 1/√/3 < < 1 ， x=1,2,3 which
respectively represents the three month of autumn.
The relationship between summer and winter can beobtained by the actual situation, the formula is asfollows:
MaxGrass=WinterGrass+ SummerGrass(

TaskTS3=15*360*(F + M )

(3)

−∑

)(30 ∗ ∑)(30 ∗ ∑)(30 ∗ ∑2= ∑where1 < </3， x=1 ,2 ,3 respectively
represents the three month of spring.
In the autumn, pasture harvest and grass dying lead tonegative production growth. So the expression of theequation as shown:
4(7)

where i = 1,2, 3, which represents young yak, adult yak,old yak respectively.
Let the alternate date that summer into autumn within
one year is a new year, the total amount of grass neededby rangelands according to different yaks age can be
expressed as:= 0,1,2 … 6; = 1,2,3, … 7

( )+
( )+

()

（13）

=∑

1=∑

(

+

3=(

+

)

2=∑

（5）

Suppose the total pasture grass production within ayear is SumGrass. Formula (6) can be obtained by theformula(3),(4),(5), as shown:

( )+(

(

+

)

+

(12)

)

)

Define 6 Suppose that the sum of all the yaks produced
profit in a year is Profit：Cows lactation is period of 5months, Total milk production was 350 kg, The milk fat
rate of 5 ~ 7.5[6] , 2016 annual the milk fat price is 600 ¥ /kg, Therefore, Assume that pasture yak milk production

SumGrass=MaxGrass+AutumnGrass+SpringGrass（6）© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 684

Application of ViSC to[0,8]
Step6: The values plug into the formula(8), formula (9), formula (10).
Get TaskTS1, TaskTS2,TaskTS3. put these three values into formula (7)get SumWork.SumWork。
Step7: if ∑< (∑)*10 && ∑ TaskTS(i) <TopWork&&
while [a….i] conditions are met, Execute Step8.
Step8：Get Pack, Fur profits by the formula (15) and formula(16).
Step9：Get Mi,F0,M0 by formula (2); Get Milk by formula (14); Get
the Calf by formula (17).
Step10：Get Meat by the formula (18).
Step11: Get SpringGrass, AutumnGrass, SummerGrass,WinterGrassby formula (3), formula (4), formula (5). Then,Put them into formula(6) to get SumGrass.
Step12：Get Fodder by the formula (11).
Step13：Get profit by the formula (19).
Step14：Placing the values and itself of Fodder, Milk,Pack, Fur, Calf,
Meat in Key and V alue of V alueMap. Transform the Xi to a string andput it and its value into theKey and V alue of KeyMap.
Step15： ∑>(/945) || ∑(/(19 ∗30)) || >TopWork*15. Judge whether or not the condition issatisfied, if yes, go on, else, goto Step6.
Step16：The V alue of V alueMap is sorted by total profit.Meanwhile
the KeyMap is sorted too.
Step17: Return KeyMap.

within a year to get the total income is Milk.The value ofMilk is:0.68 ∗( )= 500 ∗ ∑F

= 2 ∗ 72 ∗ ∑(= 0.5 ∗∗ (350 ∗ 0.05) ∗ 600(14)

Yak has a great profit in the field of traffic. Eachtransportation per yak average can produce 500¥ profit.
Assume that the profits that can be generated are Pack.
The value of Pack is:(15)

The yak tail cow-hair cut once every two years and one
can get 0.25kg, other coarse cow-hairs cut once a year,
each yak can get 0.5-3kg[6]. Yak hair’s the latest price is
72 ¥ / kg in2016. Assuming that the yak fluff in the whole
pasture can make profits is Fur. The value of Fur is:
+

)

(16)

The price of a calve is 1700￥ [7]. Assume a gain fromcalves in a year as Calf, The value of Calf is:
Calf= (

( )

+

( )

)*1700(17)

By the growth characteristics of yak, “Summer strong,
autumn fat, winter thin”, we can know the fastest
growing in the autumn. According to the literature, we
can know that the maximum weight gain of young cattle
was 29.41kg. Winter and spring and summer will be acorresponding reduction. The weight gain was 16.1kg,
18.5kg, 25.2kg[7]. In the latest market research, we can getthat the price of yak is 23 ¥ / Kg, Assume that a young yak
can generate a profit in one year is Meat. The value ofMeat is:=(29.41 + 16.1 + 18.5 + 25.2) ∗ 46

4. WG Of Visc System
The process of ViSC system is divided into:acquisition layer-integration layer-expression layer[8].
By all levels of rational division of labor and context
consistent, the system implements highly available.
The acquisition layer is the key parts of the wholesystem data. The data collection includes the herdsmen
input data and the known data. The collected data istransmitted to the integration layer. Integration layer
mainly collecting, selecting and combining the data ,and
then returning the required data to the WG system.
Eventually, the data are sent to the presentation layer.
The presentation layer displays all the databy images toachieve the readability.
The entire process of visualization as the algorithm 2
shown:
Algorithm 2 Based on SG model generation ViSC
algorithm
Input: Model related parameters, Pasture grassland area and(18)

Through all of the above profits and expenses, we canbe obtained the final profit in one year as follows:
Profit=Calf+Fur+Pack+Milk-Fodder（19）

3. WG Algorithm Analysis

The 0-1 knapsack problem is used in the process ofresolving the WG model. The main algorithm as follows:
Algorithm 1: An Algorithm Based on SG Model

Maximum grass yield in summer;
Output: Yield of grass, Need grasses, required fodder the
relationship between the graphics chart; Dynamic display the data of
yak flock chart; Dynamic growth of individual profit in yak chart; Caninteractive automatic flock selection chart; The population relationshipof yak in pasture chart; Pasture in a variety of profits chart;
Step1: Initialization. //Initialize the existing data.
Step2: The relationship between Yield of grass, Demand of grasses andrequired fodder. Show the relationship of grass data by the D3 pie chart;
Step3: Dynamic display the data of yak flock. Dynamic display the dataof KeyMap and MapValue;
Step4: Dynamic growth of individual profit of yak. Calves profit form
the increasing of the weight; Cows profit form the milk production andpropagate; Bull profit from consignment; Adult yak profit from

Step1 ： Define variables: Work, MaxGrass, dFodder, TaskTS1,
TaskTS2, TaskTS3, Fodder, Milk, Pack, Fur, Calf, Meat，KeyMap,
ValueMap; //ValueMap collection used to store the same age andgender of the yak different profit, KeyMap collection used to store
ValueMap basis on different yak number.
Step2：Initialize the variable Work, MaxGrass, dFodder
Step3: HightWork=MaxGrass*Work // Maximum pasture grazing.
Step4: TopWork= HightWork+ dFodder*30*15// Maximum numberof farms raised.
Step5: The total number of yaks for each age group is set to be X (i) i∈© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 685

Cheng-shui Niu, Bing-fen Li, Yu-an Zhang, Makoto Sakamoto
trimming fluff. All of them will expressed by the yak dynamic map;
Step5: Automatic interactive flock selection. Get young yak, adult yak,old yak by the Key of KeyMap. Choose them as X-axis. Find the V alueby Key, all profit is mapped to the Y -axis according to Key;
Step6: The population relationship of yak in pasture: By the numbering
all the yaks in the pasture and using weight of it, displaying therelationship by mapping out by point and point. When two adult yaks
to crossover, the overlap of each other is greater than 35%. If therelationship between them is a mother-child, then the overlap will beless than 35%, likewise, if the relationship between calves and adultcattle is direct descendant, the relationship between points and points
will not be less than twice the diameter of children, if there is a direct
relationship, the point between the distance between the point will be
no more than three times the diameter of the point;
Step7: Pasture in a variety of profits. Display the V alue of the V
alueMap by histogram.

Figure3. Dynamic display yak population dataknow how to produce profits of each yak in the traditionalfarming process. Fig.4 shows the yak profits growth
process of all ages within a year. Using the latest data, theprocess can be displayed by the form of chart. As shownin Fig.4:

Fig.1. Input data

Figure4. Dynamic growth of individual profit in yak

According to the process in algorithm 2, using
scientific computing generated chart carries out wisdom
grazing. First, herdsman enter the basic data of grasslandarea and grassland maximum production value forsystem. The best pasture profits, the number of three
types of yak and the ranch yak age of the number ofdifferent age groups in whole year will can be produced.
As shown in Fig.1:

Herdsman have basically know of the situation in the
pasture by Fig.2, Fig.4. According to Fig.3, the herdsmen
select the yak population and profits, and then producing
the profits and flock structure according to the selectedorder, as shown in Fig.5.
When the herdsmen selecting the flock structure data
for their own pastures, choosing its data in Fig.3 and rightclicking “yak population diagram”, it will generate theyak population diagram as shown in Fig.7. Herdsmen canintuitively analyze whether or not the whole yak
population is infected by diseases .

Fig. 2. Yield of grass, Need grasses, required fodder the
relationship between the graphics

By entering the basic data, 50 acres of grassland and yield
2500kg, we can gain the grass yield, demanded grass, themaximum of supplementary feeding and the relationshipbetween them of four seasons of the year. As shown in
Fig.2: Meantime, All flock structure, profit in the wholeranch canbe dynamic displayed. As shown in Fig.3 Herdsmen
don’t

Figure5. Can interactive automatic flock selection© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 686

Application of ViSC toprevention.
The herdsmen in Zadou County gives me a lot ofexperimental opportunities and data and Sanjiang source
data center of Qinghai University provides a systemservice for my system and provides a convenientcondition for the smooth promotion of my system. We
appreciate your support.
Acknowledgment
This work is supported by Natural Science Foundation ofChina(No.61363019, No.61640206) , Chunhui Research
Program from Ministry of Education (Z2015050) and theState Foundation for Studying Abroad to visit the japan .

Figure6. The population relationship of yak

References[1] Zhu Liping. “Development and Prospect of the QinghaiTibet Plateau Research[C]”. Academic Exchange Seminar,
2010.
[2] Zhanglv Tan.“he theoretical development of information
visualization and the construction of the frame system[J]”.
Information theory and Practice, 2013, 36(1):16-19.
[3] Liang YN, et al. “Resource value and conservation strategy
of wild yak [J]”. Acta Zoologica Sinica, 2004, 25 (6): 6-7.
[4] Liu Yuan.“National Grassland Monitoring Report, 2014
[J]”. Chinese Journal of Animal Husbandry, 2015, (8): 18-31.
[5] YE Xue-Bing. “Estimation of Feed Intake and Several
Digestive Physiological Indices of Grazing Yaks[J]”. Chinese
Herbivorous Animal Science, 2003, 23 (5): 5-7.
[6] Wenxue Hong, Jinjia Wang. “Genetic algorithm-based
clustering techniqueVisualization and Visualization Analysis
Study[J]”. Yanshan University, 2010, 34(2):95-99.
[7] Deng YF, et al. “Studies on the growth performance of yak[C]”. China Cattle Industry Development Conference.2012.
[8] WANG Qing-hua, LIU Shen-qing. “Study on the Optimum
Mathematical Model of Yak Herd Structure and Its Slaughter
Scheme [J]”. ChineseYaks, 1990 (2): 27-36.
[9] Georgin Lau and LeiPan. “A 5-step guide to datavisualization[J]”.
Elsever,
28
January
2015,“https://www.elsevier.com/connect/a-5-stepguide-to-datavisualization”.

Fig. 7. Pasture in a variety of profits

With the above steps, the herdsmen will be get thedata that suits for their pasture situation. Generating theFig.8 by right clicking the “Pasture in a variety of profits”
in Fig.3. After the above steps, the herdsmen can cleanly
select the data that can obtain the maximum profits which
suit for the reality of pasture. The above steps can clearlyreflect the source of profits and costs and can intuitively
help the herdsmen to understand how to grazing in eachquarter, which makes herdsmen of the whole the nomadsof the Qinghai-Tibet plateau graze scientifically andintelligently.
5. Summary
Visualization technology can convert the model datadifficult to understand to the graphical form which easy
to analyze and view. Showing the data by the five stepsof data visualization not only improves the reliability ofvisual graphics[9], but also demonstrates the dependency
between the various data. In the traditional plateau
farming, herdsmen can’t reasonable use the pastures ofgrass production, which results in excessive grazing.
Visualization can instruct pastoralists in yak breeding,from the experiments in the ZaDuo area, we can conclude
that not only the profit of farming is increasing, but also
the overall fertility of the pasture is improving. At thesame time, the breeding of the whole rangeland can be
more standardized, and have a very well influence ingenetic diseases, infectious diseases and other diseases© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 687

Clinical Evaluation of UR-System-PARKO for Recovery ofMotor Function of Severe Plegic Hand after Stroke
Hirofumi Tanabe

Department of Health Sciences, Shonan University of Medical Sciences
16-48 Kamishinano, Totsuka-ku, Yokohama, Kanagawa 244-0806, Japan
E-mail: hirofumi.tanabe@sums.ac.jp
Yoshifumi Morita

Nagoya Institute of Technology
Gokiso-cho, Showa-ku, Nagoya, Aichi 466-8555, Japan

Abstract
The first author has developed TANABE therapy for severe hemiplegic stroke patients. In the therapy, he performed
repeated facilitation training using his hands on patients to help them recover their motor function and achieved agood treatment outcome. In this paper we developed a training system (UR-System-PARKO) on the basis of
TANABE therapy. The clinical test of the therapeutic effect of the UR-System-PARKO was performed in severe
plegic hand. As a result, the active ranges of motion of finger extension were improved, and Electromyogram ignition
increased after the training. Moreover, the Modified Ashworth Scale scores of finger extension were increased. These
results show the effectiveness of the training by the UR-System-PARKO for recovery of motor function for finger
extension of the severe plegic hand.
Keywords: List four to six keywords which characterize the article.

Recently, it has been shown that the motor functionof stroke patients may be restored even six months ormore after the occurrence of the stroke.1,2 However, theseverity of hemiplegia after the stroke was moderate inall patients who recovered motor function in these reports.
More than half of the stroke survivors have a severe
plegic hand with difficulty in extending the fingers.3
Therefore, the need for developing a method to treat
severe plegic hands is significant.
The first author in an occupational therapist who
developed the TANABE therapy for severe hemiplegicstroke patients. With this therapy, he performed repeatedfacilitation training using his hands on patients to help
them recover their motor function, and a good treatment
outcome was achieved.4 In addition to a book on thistechnique, he has trained many therapists5 (Fig. 1). As a

1. Introduction
With Japan's gradual advancement towards a so-calledsuperannuated society, the number of people physicallychallenged by disease and by aging is increasing.
The corresponding increase in the workload of therapists
has become a social issue.
Moreover, according to proposals for health carereform in the rehabilitation field, a system with empiric
treatment for evidence-based medicine (EBM) has to be
implemented. This change will be accelerated byintroducing technology, such as robotics, mechatronics,
information processing, and motion control, into therehabilitation exercises administered by therapists inorder to reduce their workload and to improve thetherapeutic effects.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 688

Hirofumi Tanabe, Yoshifumi Morita

result, many therapists have successfully restored finger
movement in patients with severe plegic hands. Thisfacilitation method increases the output of the extensor
digitorum muscle force through extension exercises ofthe elbow joint while applying resistance to the tips of the
fully extended hemiplegic fingers. In this type of training,
the therapist has to spend a lot of time with each patient.
Therefore, we developed a rehabilitation support system(UR-System-PARKO) designed to reduce the burden on
the therapist for resistance training．
The simplified training system previously developedby us for resistance training of hemiplegic upper limbs
was applied to a new support system for the hands. This
simplified training system provides unidirectional
resistance training. We have also developed a brace forsecuring the plegic hand to the apparatus. The objectiveof this study was to verify the effect of training on
increasing the output of the extensor digitorum muscle
force using the UR-System-PARKO on a severe plegic
hand.

Fig. 1. The author has developed the TANABE therapy forsevere hemiplegic stroke patients.

program. The resistance force is measured by the strain
gauge and is used only for monitoring.
2.2. Functions of the previous training system
In the earlier study7, it was found from an interview thatthe therapist selectively uses four different resistancepatterns during resistance training. These patterns werealso detected from the movement analysis performed
during the manual training by the therapist using his
hands. The four different resistance patterns, namely astep mode, a slope mode, a wall mode, and a constantmode, are installed in the controller. Control programs
based on these results are installed in the controller. Thissystem is equipped with the six functions. The Resistancedisplay function enables therapists to perform various
types of resistance training by changing the arm lengthand the resistance level. The maximum resistance was 49
N when the length of the training arm was set to 0.75 m.
The touch panel parameter setting function enables
therapists to easily set the parameters of the resistancepatterns by pushing the buttons on the touch panel
display. The parameters consist of the magnitudes andthe positions of the resistance patterns. The magnitudes
are selected from among nine levels. The positions are
determined by moving the training arm and stopping it at
the desired position. This function provides goodvisibility and ease of use for therapists.

2. Training System for Recovery of Motor
Function
2.1. The previous training system
We developed rehabilitation support systems forresistance training of hemiplegic upper limbs, namely
UR-System 16,7 and UR-System 28. The UR-System 2 isreferred to as the previous training system in this paper.
The previous training system is a force display systemwith one degree-of-freedom that has a mechanical system
and a controller. The mechanical system consists of abrace, a training arm, and a powder brake (SINFONIATECHNOLOGY CO., Ltd., PRB-2.5H). The patientmoves the training arm by himself/herself. The brace is
attached to the tip of the training arm, which is used tosecure the patient's forearm to the apparatus. The brace
has two degrees-of-freedom, by which the yawing andpitching motions of the forearm are possible. The powder
brake generates a brake force that serves as the resistanceforce during training. Because this system is not
equipped with motors, it is extremely safe and low cost.
This system is equipped with an encoder and a strain
gauge as a force sensor. The angle of the training arm ismeasured by the encoder and is used in the control© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 689

Clinical Evaluation of UR-System-PARKO

2.3. Modification of the training system tofacilitate finger extension

In order to improve motor function, especially tofacilitate finger extension, an additional device wasinstalled in the training apparatus. A thermoplastic
splint for fixation was prepared to fix the plegic handof a hemiplegic patient in complete extension. Rails
were laid on the arm-fixing plate to allow sliding thesplint on the plate. Rods were set individually to theindex, middle, and ring fingers of the plegic hand, andthe hand was set on the arm-fixing plate so that thepatient could push the arm with her/her fingertips (Fig.
2).

(a) Lateral view(b) View from directly above

3. Clinical Evaluation of Therapeutic EffectWe conducted clinical tests on patients with severe
chronic plegic hands to verify the therapeutic effects ofthe training with the UR-System-PARKO.
3.1. Assessment
The therapeutic effects of the training with the URSystem-PARKO were assessed by using the surface
electromyogram (EMG), the modified ashworth scale(MAS), and the active range of motion (A-ROM) test.
An EMG electrode pad was applied to the extensor
digitorum muscle, and firing of the extensor digitorum
muscle during exercise was measured. The MAS is oneof the quantitative evaluation methods for spastic
paralysis, which is rated in 6 levels. Level 6 indicates noincrease in muscle tone.
Level 0 indicates that the affected parts are rigid ineither flexion or extension. The A-ROM test is performed
to evaluate voluntary motor performance and has been
widely used in clinical settings. The A-ROM is measured
when a patient moves the joint without any assistance tothe muscles surrounding the joint.

(c) Training
Fig. 2. UR-System-PARKO.

PARKO. The study content was explained and writtenconsent was obtained from the patient.
3.3. Methods
3.3.1. Setting
The fingers of the patient were set on the splint in the
fully extended position followed by fixation to the arm of
the UR-System-PARKO. First, the therapist tried thetechnique facilitating the extensor digitorum muscle ofthe plegic hand to confirm the exercise initiation andcompletion positions and the grade and timing ofresistance loading.
Then, the subject’s forearm was set on the splint andfixed to the arm-fixing plate of the UR-System-PARKO.
Then, the rods were set to the fingertips of the plegic hand

3.2. Patient
The subject was a hemiplegic patient in his 40s who had
suffered a stroke 8 years ago. The MAS score presenting
the grade of spasticity was 3. (He had a more marked
increase in muscle tone through most of the ROM, but
the affected part (s) easily moved). The patient did notexperience any pain in the training with UR-System-© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 690

Hirofumi Tanabe, Yoshifumi Morita

to push the lever. The exercise initiation and completion
positions, resistance value, and initiation and completion
positions of resistance loading confirmed by the therapist
beforehand were entered into the UR-System-PARKO
using the operating the panel. The resistance value wasset at 15 N.
3.3.2. Comparison of the output of the extensor
digitorum muscle force between the positions of applying
resistance load
For manual facilitation treatment of the extensor
digitorum muscle, the output of the extensor digitorum
muscle force can be increased by loading resistance on
the fingertips. The position of pushing the UR-SystemPARKO lever was set at 2 sites: 1) the fingertips of theplegic hand and 2) the forearm. The subject pushed the
UR-System-PARKO lever against the resistance 10times with each site, and the muscle force output
measured using the EMG was compared between the 2sites.

(a) The paralyzed fingers push a lever.

3.3.3. Comparison of the active range of motion andflexor spasticity of the plegic hand between before andafter training with the UR-System-PARKO
Training was set so as to load resistance on the fingertips.
The subject moved the hand from the initiation to thecompletion position and repeated the motion 30 times. AROM and MAS were evaluated before initiation and after
completion of the exercise training.

(b) The paralyzed forearm pushes a lever.
Fig. 3. Electromyograms of the extensor digitorum muscle
while pushing the lever of the UR-System-PARKO (Firing
increased when the subject pushed the lever with the fingertipsof the plegic hand compared with that caused by pushing withthe forearm.)

3.3.4. Results and considerations
On comparison of the output of the extensor digitorum
muscle force between the different resistance loading
positions, the muscle force output by pushing the leverwith the fingertips of the plegic hand was greater thanthat by pushing with the forearm on visual observation(Fig. 3). The training with the UR-System-PARKO
increased the A-ROM of the finger extension whencompared with that before training (Fig. 4). The MAS of
the finger extensors was 2 before training, and it
increased to 4 after training.

improve the motor function of patients with a severe
plegic hand after a stroke. The spasticity of the finger
extension is reduced after the training. The A-ROMs ofthe finger extension are expanded after the training.
References
1. S. L. Wolf, C. J. Winstein, J. P. Miller, P. A. Thompson,
E. Taub, G. Uswatte, D. Morris, S. Blanton, D. NicholsLarsen, P. C. Clark, The EXCITE Trial: Retention of
Improved Upper Extremity Function Among Stroke
Survivors Receiving CI Movement Therapy, Lancet
Neurol. 7(1) (2008) 33–40.

4. Conclusion
A therapeutic effect of the training with the UR-SystemPARKO was shown. The UR-System-PARKO can© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 691

Clinical Evaluation of UR-System-PARKO

2. J. Whitall, S. M. Waller, K. H. Silver, R. F. Macko,
Repetitive bilateral arm training with rhythmic auditory
cueing improves motor function in chronic hemiparetic
stroke, Stroke 31(10) (2007) 2390-2395.
3. H. Nakayama, H. S. Jørgensen, H. O. Raaschou, T. S.
Olsen, Recovery of upper extremity function in stroke
patients: the Copenhagen Stroke Study, Arch. Phys. Med.
Rehabil. 75(4) (1994) 394-398.
4. H. Tanabe, T. Nagao, R. Tanemura, Application ofConstraint-induced Movement Therapy for People with
Severe Chronic Plegic Hand, Asian J. Occupational
Therapy, 9(1) (2011) 7-14.
5. H. Tanabe, The approach that recovers for a central nerve
disorder -TANABE Therapy- (Human Press, Tokyo, 2016).
6. Y. Morita, N. Sato, H. Ukai, H. Tanabe, T. Nagao, R.
Tanemura, Y. Takagi and Y. Aoki, Clinical Evaluation ofTraining System for Recovery of Motor Function after
troke in Patients with Hemiplegia, in Proc. 2nd Int. Conf.
on NeuroRehabilitation (ICNR2014 in Aalborg, Denmark)(2014), pp.83-92.
7. H. Tanabe, M. Mitsukane, N. Toya, R. Takeichi, H.
Hattori, Y. Morita, Y. Takagi, N. Hasegawa, Clinical
Evaluation of UR-System 2 for Recovery of Motor
Function of Plegic Upper Limb after Stroke, J. Robotics,
Networking and Artificial Life, 3(1), (2016) 33-36.
8. Y. Morita, N. Toya, R. Takeichi, H. Hattori, H. Tanabe, Y.
Takagi, N. Hasegawa, Development of 'UR-System 2': atraining system for recovery of motor function of plegic
upper limb after stroke, Advanced Robotics, 30(21) (2016)
9. Y. Iida, Y. Hiramatsu, K. Yamazaki, Y. Morita, H. Ukai,
H. Tanabe, R. Tanemura, K. Yokoyama, Development ofresistance training and evaluation support system forupper limbs - Study on resistance training using robot in
CI therapy, in Proc. 11th SICE System Integration
Division Annual Conf., (2010), pp.1808–1811. (inJapanese).

(a) Before training(b) After training
Fig. 4. Finger extension before and after training.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 692

A Piston Finger Device for Restoring the Motor Function of Chronic Plegic Fingers:
Analysis of the Piston Finger TechniqueMengsu Wang*

Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology,
Gokiso-cho, Syouwa-ku, Nagoya, Aichi 466-8555, Japan
Hirofumi Tanabe†

Department of Health Sciences, Shonan University of Medical Sciences,
16-48 Kamishinano, Totsuka-ku, Yokohama, Kanagawa 244-0806, Japan
Kenji Ooka*

Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology,
Gokiso-cho, Syouwa-ku, Nagoya, Aichi 466-8555, Japan
Yoshifumi Morita*

Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology,
Gokiso-cho, Syouwa-ku, Nagoya, Aichi 466-8555, Japan
E-mail: m.wang.188@nitech.jp, hirofumi.tanabe@sums.ac.jp, k.oooka.446@nitech.jp, morita@nitech.ac.jp
http://www.nitech.ac.jp/eng/index.html

Abstract
A piston finger technique (PFT) was developed for restoring the motor function of chronic plegic fingers, withgood treatment outcomes, including reduced spasticity and improved muscle shortening. Before developing a piston
finger device (PFD) simulating the PFT, we analyzed the PFT by using a motion capture system. The motion of achronic plegic index finger was investigated after administering two types of PFT-based treatment. The ranges of
motion of the proximal interphalangeal and metacarpophalangeal joints of the index finger, and the vibration widthand frequency during treatment were measured. These results will be useful for developing a PFD.
Keywords: Piston finger technique, Plegic finger, Spasticity, Motion analysis, Piston finger device

and many patients are forced to give up self-sustaining
living.
In hand rehabilitation therapy, the therapist performs
treatment by moving the patient’s fingers passively.
However, because of the lack of therapists, many
patients do not receive adequate treatment. In view ofthis situation, we aimed to develop a rehabilitation
device to support therapists.

1. Introduction
The number of patients with cerebrovascular diseasein Japan in 2014 has reached 1.179 million.1 The main
sequelae of cerebrovascular disease is hemiplegia, andapproximately 80% of patients with chronic hemiplegia
have chronic finger paralysis. Among the symptoms ofhemiplegia, the decreased motor function of the fingers
is the most difficult to recover. Owing to hand paralysis,
nursing care is needed in many situations in daily life

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 693

Mengsu Wang, Hirofumi Tanabe, Kenji Ooka, Yoshifumi Morita(a) Before PFT treatment.

(a) Treatment with piston finger technique(b) After PFT treatment.
Fig. 2. Electromyograms of the flexor digitorum
superficialis muscle of a plegic index finger.

(b) Pattern 1(c) Pattern 2
Fig. 1. Piston finger technique [6].

good treatment outcomes, including reduced spasticity
and improved muscle shortening.5
In this study, we performed myoelectricmeasurement to demonstrate the effectiveness of PFT
for a paralyzed index finger. We also analyzed thefinger motion with the PFT by using a motion capturesystem to clarify the finger joint angles and thefrequency during the repeated motion by PFT.

Currently, in Japan, a treatment method has beenused for therapeutic stimulation and motion
reconstruction of paralyzed limb according to uppermotor neuron disorders such as stroke and spinal cord
injury by therapeutic electrical stimulation andfunctional electrical stimulation.2 In the development ofequipment supporting finger rehabilitation, a perception
support system has been developed that allows
biofeedback transfer from the paralyzed side motion to
the non-paralyzed side so that awareness can be directedto the paralyzed side movement sensation through thenon-paralyzed side.3
In other countries, a robotic interface for training
hand and finger functions has been developed. It is acable-actuated rehabilitation system where each finger
is attached to an instrumented cable loop that allows
force control and a predominantly linear displacement.4
However, only a few studies have been conductedon the validation and motion analysis of manual
treatment methods. Moreover, only few research studies
have been conducted to develop rehabilitation devices
simulating the treatment method.
On the other hand, the second author developed apiston finger technique (PFT) for restoring the motor
function of chronic plegic fingers and has achieved

2. PFT and Its Treatment Effect2.1. What is PFT?
In general, when stretching treatment is applied tofingers with spasticity, the spastic muscle is temporarily
improved. However, the spasticity often occurs again
when the patient bends the affected finger. This is oneof the troubles of many therapists engaged in thetreatment of hemiplegia.
PFT is a technique for improving adhesion of soft
tissues and spasticity. In the PFT, the therapist quickly
repeats flexion and extension of the patient’s finger
joints. The PFT can decrease flexor spasticity in a shorttime and improve muscle contraction. The effect lasts
longer than that by stretching treatment. Treatment with
the PFT has two patterns as shown in Fig. 1.5© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 694

A Piston Finger Device(a) Pattern 1.
(b) Pattern 2.
Fig. 3. Marker attachment positions and vectors
defined for computation of joint angles.

Fig. 4. MP and PIP joint angles.
spastic paralysis was reduced. Electromyograms of theflexor digitorum superficialis muscle of the patient’s
plegic index finger before and after treatment are shownin Fig. 2. Figure 2 shows that the abnormal muscle toneof the paralyzed finger during MAS testing by thetherapist. The abnormal muscle tone was markedly
reduced after PFT treatment. This means that spasticity
was reduced by PFT and that the therapeutic effect was
confirmed.

In Pattern 1, the therapist, with one hand, holds andfixes the patient’s plegic thumb with full abduction, andwith the other hand, holds and moves the distal
phalange of the patient’s index finger. From the start
positions, the metacarpophalangeal (MP) and proximal
interphalangeal (PIP) joints are bended and extended,
respectively. Then, the metacarpophalangeal (MP) joint
is bended and extended quickly and repeatedly, while
the proximal interphalangeal (PIP) joint is extended andbended quickly and repeatedly. PFT treatment is applied
to the index, middle, and ring fingers individually or
together. After PFT treatment with Pattern 1, the flexor
spasticity of the four fingers diminishes.
In Pattern 2, the start positions of the MP, PIP, anddistal interphalangeal (DIP) joints are extended fully.
Then, the MP joint is bended and extended quickly andrepeatedly, while keeping the DIP and PIP joints in theextended position. After PFT treatment with Pattern 2
the MP joints of four fingers can bend to the vicinity ofthe end feel within the range without feeling pain.

3. Analysis of the PFT
We analyzed the patient’s finger motion during PFT
treatment by using the VICON motion capture system(Vicon Motion Systems Ltd.).
3.1. Motion measurement

2.2. Treatment effect of PFT
To verify the therapeutic effect of the PFT treatment,
the therapist applied the PFT treatment to the paralyzed
index finger of a male adult in his forties. The paralyticside was the left. Nine years had passed since the onsetof stroke.
After treatment, the Modified Ashworth Scale(MAS) scores of the extension of the patient’s plegicindex finger increased from 1 to 4. This means thatIn Pattern 1, as the DIP joint was kept fully
extended, we measured the PIP and MP joint angles of
the index finger as shown in Fig. 3(a). Six markers
consisting of two markers between the DIP and PIP
joints, two markers between the PIP and MP joints, andtwo markers between the MP joint and the wrist wereattached on the center line of the middle phalanges,
proximal phalanges, and metacarpals of the index finger,
respectively. Vectors v11, v12, v13, and v14 were defined
as shown in Fig. 3(a).
In Pattern 2, as the DIP and PIP joints were kept
fully extended, we measured only the MP joint angle ofthe index finger as shown in the Fig. 3(b). Four markers
consisting of one marker between the DIP and PIP
joints, one marker between the PIP and MP joints, and© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 695

Mengsu Wang, Hirofumi Tanabe, Kenji Ooka, Yoshifumi Morita

two markers between the MP joint and the wrist wereattached on the center line of the middle phalanges,
proximal phalanges, and metacarpals of the index finger,
respectively. Vectors v22 and v23 were defined as shownin Fig. 3(b).
3.2. Computation of joint angles
We computed the joint angles of the index finger
during treatment. For this purpose, we measured thethree-dimensional coordinates of the markers with
VICON and computed the joint angles from the vectors
shown in Fig. 3 (a) and (b). The PIP and MP joint
angles were defined as shown in Fig. 4.
In Pattern 1, the PIP and MP joint angles were
denoted by PIP1 and P1, respectively. The angles werecomputed by applying vectors v11, v12, v13, and v14 to thefollowing equations:
1 v11 v12 ,1 v13 v14 .
(1)
PIP1 cos
MP1 cosv11 v12v13 v14

Fig. 5. Time history of PIP joint angle in Pattern 1.

In Pattern 2, the MP joint angle was denoted by MP2.
The angle was computed by applying vectors v22 and v23
to the following equation:
1 v 21 v 22 .
(2)MP2 cosv 21 v 22

Fig. 6. Time history of MP joint angle in Pattern 1.

4. Analysis of the Results of the PFT

Fig. 7. Time history of MP joint angle in Pattern 2.

Table 1. Summaries of the joint angles in Patterns 1
and 2. (Start and End: start and end positions of therepeated motions, Diff.: difference between Start andEnd, x (y − z), x: mean value, y: maximum value, z:
minimum value)
Pattern 1 (degrees)
PIP
joint
angle

Start

MP
joint
angle

Pattern 2 (degrees)

155.4 (135–178)

End

86.7 (73–96)

Diff.

68.7 (39–105)Start

172.6 (168–177)

141.4 (136–148)End

140.4 (135–145)

109.5 (102–119)

Diff.

32.2 (23–42)

31.9 (17–46)

We analyzed the patient’s finger motion during
treatment by the therapist for 10 seconds. The time
histories of the PIP and MP joint angles during
treatment of Patterns 1 and 2 are shown in Figs. 5 to 7.
The summaries of the mean, maximum, and minimum
values of the joint angles at the start and end positionsof Patterns 1 and 2 are shown in Table 1.
In Pattern 1, we analyzed the joint angles in Figs. 5
and 6 from 2 to 10 sec. We found that the PIP joint
angle was moved by the therapist between 86.7° and
155.4°, and the MP joint angle was moved between
140.4° and 172.6°. Moreover, the frequency of therepeated motion was found to be 4.25 Hz.
In Pattern 2, we analyzed the joint angle in Fig. 7
from 0 to 10 sec. We found that the MP joint angle was
moved between 109.5° and 141.4°. Moreover, thefrequency was found to be 3.6 Hz.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 696

A Piston Finger Device

5. Conclusion
The therapeutic effect of PFT was confirmed by thecomparison of MAS scores and electromyograms before
and after treatment. The motion analysis revealed thejoint angles and frequencies of finger motion during
PFT treatment. Future studies are needed to develop the
specifications of a PFT treatment apparatus.
References
1.

2.

3.

4.

5.

6.

Ministry of Health, Labor and Welfare, The overview ofthepatient
surveyin2014.
http://www.mhlw.go.jp/toukei/saikin/hw/kanja/14/index.
html (in Japanese)
T. Matsunaga, The NESS H200 Hand Rehabilitation
System for upper extremity paralysis [in Japanese], in
Bull. Jpn. Soc. Prosthet. Orthot. Educ. Res. Dev., 29(2)(2013) 80–82.
H. Iwata, K. Enjyoji, R. Komagata, S. Sugano, Somaticsensation-biofeedback system for paralyzed-finger
rehabilitation: development of a self-rehabilitation
assisting device simultaneously cross-displaying somaticsensation of fingers [in Japanese], in Proc. the 2008
JSME Conf. on Robotics and Mechatronics, 2008, 1P1D14(1)-(2).
R. Kato, S. Nakagawa and H. Yokoi, Development of
EMG-controlled Powered Exoskeleton for Hand
Rehabilitation, in Precision Engineering Society
Academic Lecture Papers Collection, (2011), pp. 499–
500.
L. Dovat, O. Lambercy, R. Gassert, T. Maeder, T.
Milner, T. C. Leong and E. Burdet, HandCARE a cableactuated rehabilitation system to train hand function
after stroke, IEEE Trans. Neural. Syst. Rehabil. Eng.
16(6) (2008) 582–591.
H. Tanabe, Effective approach to reconstruct central
nervous system diseases: Tanabe therapy [in Japanese].
(Human Press, Tokyo, 2016).

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 697

Verifying the Sleep-Inducing Effect of a Mother’s Rocking Motion in Adults
Hiroaki Shibagaki

Department of Computer Science and Engineering, Graduate School of Engineering, Nagoya Institute of Technology
Gokiso-cho, Syouwa-ku, Nagoya, Aichi 466-8555, Japan
Keishi Ashida

Department of Computer Science and Engineering, Graduate School of Engineering, Nagoya Institute of Technology
Gokiso-cho, Syouwa-ku, Nagoya, Aichi 466-8555, Japan
Yoshifumi Morita

Department of Electrical and Mechanical Engineering, Graduate School of Engineering, Nagoya Institute of Technology
Gokiso-cho, Syouwa-ku, Nagoya, Aichi 466-8555, Japan
Ryojun Ikeura

Division of Mechanical Engineering, Graduate School of Engineering, Mie University
1577 Kurimamachiyacho, Tsu, Mie 514-8507, Japan
Kiyoko Yokoyama

Graduate School of Design and Architecture, Nagoya City University
2-1-10 Kitachikusa, Chikusa-ku, Nagoya, Aichi 464-0083, Japan
E-mail: h.shibagaki.502@nitech.jp

Abstract
Previously, we concluded that a mother's rocking motion is the most effective motion for inducing sleep in adults.
We call it the candidate rocking motion. In this study, we confirm that the candidate rocking motion is more
effective in inducing sleep than using no rocking motion. Moreover, we find that different to aromatherapy, theeffectiveness of the candidate rocking motion varies only slightly between individuals. We conclude that thecandidate rocking motion is effective for inducing sleep in adults.
Keywords: rocking motion, mother's rocking motion, sleep-inducing effect, electroencephalogram analysis

the causes of insomnia. For the caregivers in nursing
homes, it is a large burden to ensure the wellbeing of thepatients who suffer from these problems.2 Many people
in modern society would benefit from stress reduction
and sleep induction. It is known that when a motherembraces and rocks her baby, the baby feels
comfortable and falls asleep quickly. We assume that arocking motion simulating a mother’s embrace would

1. Introduction
Recently, the number of people suffering from highlevels of stress has increased. This phenomenon iscalled a stressful society.1 Every year, an increasing
number of people suffer from stress-related illnesses.
Moreover, sleep disorders are becoming morewidespread in modern society because stress is one of© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 698

Hiroaki Shibagaki, Keishi Ashida, Yoshifumi Morita, Ryojun Ikeura, Kiyoko Yokoyama

have the same effect on adults. The aim of our project isto develop a relaxation machine that reduces stress andinduces sleep using a rocking motion similar to that of amother. We expect that our research results will be
useful for other relaxation systems and robots, such as
the Robot for Interactive Body Assistance (RIBA),
which assists caregivers in lifting patients in and out of
their beds and wheelchairs.3
In our previous study, we analyzed a mother’sembrace and rocking motion, and we designed andconstructed an excitation apparatus that simulates amother’s rocking motion. We found that a mother uses
two types of rocking motions and constructed a model .4
Moreover, we examined ten types of rocking motions
and found that the linear motion component of amother's rocking motion was the most effective rocking
motion for inducing sleep in adults. We refer to thismotion as the candidate rocking motion.5 However, we
did not compare the sleep-inducing effect of thecandidate rocking motion with other sleep-inducing
methods, such as aromatherapy or music. 6, 7, 8
In this paper, we report on our comparison of thesleep-inducing effect on adults of the candidate rocking
motion without stimulus and aromatherapy. We
evaluated the sleep-inducing effect through anelectroencephalogram analysis.

Fig. 1. Photograph of excitation apparatus.

rocking motion were determined as Ax = 0.098[m], fx =2.734[Hz], A = 0[deg], f 0[Hz],π/2 [rad] , andB = 25[rad].5
3. Evaluation Method
3.1. Electroencephalogram analysis and sleep
stage
We computed the sleep stage through anelectroencephalogram (EEG) analysis. We used the
SYNA ACT MT11 system (NEC Medical Systems Ltd.)
to measure the brain waves and the MATLAB
computing environment (The MathWorks, Inc.) to
conduct a frequency analysis. We placed the EEG
electrodes according to the International 10-20 system.
To compute the sleep stage, we used the quantitativeevaluation method10 that was developed based on thedefinition by the Japanese Society of Sleep Research.
We differentiate four different sleep stages. Sleep stage
W indicates the state when being awake with mental
activity, sleep stage 1 indicates the awakening andresting state, sleep stage 2 indicates a light sleeping state,and sleep stage 3 indicates a deep sleeping state.

2. Excitation Apparatus Simulating a Mother'sRocking Motion
In our previous work, we analyzed a mother's embraceand rocking motion to identify the motion’s features.4, 5
Based on the analysis results, we created a model of amother's rocking motion, which is represented byxref(t)=Ax sin(2 fxt),ref(t)=A sin(2 f t+ )+B ,,(1)

3.2. Evaluation index of sleep-inducing effectWe evaluated the sleep-inducing effect with focus on"sleep latency" and "sleep depth." We define "sleep
latency" as the time until the sleep enters stage 2 anddenote it as tmin. We define "sleep depth" as the total
time spent in sleep stage 2 and sleep stage 3, and wedenote it as Ssum.

where xref(t) is the position and ref(t) is the angle of thecenter of gravity of the baby. B was determined fromthe angle of a comfortable sitting position reported inRef. 9. We identified the rocking vibrations using theexcitation apparatus shown in Fig. 1. The excitation
apparatus has two degrees of freedom for linear motion
and rotational motion. The parameters of the candidate© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 699

Verifying Sleep-Inducing Effect

4. Experiment4.1. Experimental method
Fig. 2 shows the experimental protocol. Eachexperiment took 42 minutes. For each test subject, we
conducted three experiments in the morning under
different conditions and on different dates. The start
times are the same for each test subject. To conduct a
comparative study, we prepared three different
conditions, namely without rocking motion, with thecandidate rocking motion, and with aromatherapy. We
refer to them as Case 1, Case 2, and Case 3, respectively.
For the aromatherapy, we picked genuine lavender(SANOFLORE) because of its verified relaxing
effect.11
4.2. Experimental results and consideration
The test subjects consisted of ten healthy males. The
mean value and standard deviation of their ages were
21.8 ± 0.6. We conducted 30 experiments with a meanroom temperature and standard deviation of 23.1 ±2.3 °C, a mean humidity and standard deviation of 26.8
± 5.6%, and a mean illuminance and standard deviationof 22.3 ± 14.9 lx.
4.2.1. Sleep stage
We
computed
the
sleep
stage
from
an
electroencephalogram (EEG) power spectrum for eachinterval. Fig. 3 shows the sleep stages of Subject D as
an example. Each time interval was 4 minutes. We hadnine analysis sections (AS) of sleep stages. AS No. 1
refers to closed eyes and resting state. AS No. 2 refers
to calculation. AS No. 3 to AS No. 9 refer to either CaseW

Fig. 3. Sleep stages of Subject D in three cases.

1, Case 2, or Case 3. We compared Case 2 with Case 1
and Case 3 in AS No. 3 to AS No. 9.
On the other hand, it was reported that there areindividual differences in aromatherapy.12 We had threetest subjects for whom tmin was larger or Ssum was
smaller in at least three sections of Case 3 as compared
to Case 1. We can conclude that the sleep induction of
the three test subjects became worse by applying
aromatherapy. Therefore, we prepared two test groups,
Group A and Group B. Group A consisted of all ten
subjects, while Group B did not contain the three testW
subjects mentioned above and consisted only of theseven remaining subjects. For Group B, we can compare
the sleep-inducing effect of the candidate rocking
motion with that of aromatherapy under severe
conditions.
Figs. 4 and 5 show the mean values and standard
deviations of the sleep stages of Group A and Group B,
respectively. Most of the mean values of the sleep© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 700

Fig. 4. Mean values and standard deviations of sleep
stages in Group A.

Hiroaki Shibagaki, Keishi Ashida, Yoshifumi Morita, Ryojun Ikeura, Kiyoko Yokoyama

Fig. 6. Mean values and standard deviations of the two
performance indices in Group A.

Fig. 7. Mean values and standard deviations of the two
performance indices in Group B.

stages in Case 2 were larger than the mean values inCase 1. In Fig. 5, most of the mean values of the sleep
stages in Case 3 were larger than the mean values inCase 1. In Fig. 4, however, most of the mean values ofthe sleep stages in Case 3 were equal to or smaller thanthe mean values in Case 1 from AS No. 6 to AS No. 9.

performed a one-way variance analysis and applied the
Bonferroni method on Ssum of both groups. We also
performed the Friedman test and the Wilcoxon signed
rank sum test adjusted by the Bonferroni method on tminof both groups.
We observed a significant trend (p=0.055) between
the values of the Ssum index in Case 1 and Case 2 ofGroup A and significant differences (p=0.040) between
the values in Case 1 and Case 2 of Group B.

4.2.2. Evaluation of sleep-inducing effect4.2.3. Consideration

We evaluated the sleep-inducing effect using theevaluation indices tmin and Ssum. The mean values andstandard deviations of tmin and Ssum of Group A andGroup B are shown in Figs. 6 and 7, respectively.
Figs. 6 and 7 show that the mean values of tmin and
Ssum in Case 2 were equal to or greater than those inCase 3. We performed a statistical analysis using the
IBM SPSS Statistics 22 software package. First, weverified the normal distribution by a Shapiro-Wilk test
and the homoscedasticity by a Levene test. The
distribution of tmin did not exhibit a normal distribution
and showed homoscedasticity in both groups. The
distribution of Ssum exhibited a normal distribution andshowed homoscedasticity in both groups. Thus, we

Fig. 6 shows that in Group A, Case 2 has the smallest
mean value of tmin. However, there were no significant
differences between any two pairs of all the cases. Fig. 7
shows the same results for Group B. However, Fig. 6
and Fig. 7 show that Case 2 has the largest mean valueof Ssum for both, Group A and Group B. There were no
significant differences except between Case 1 and Case2.
Fig. 7 shows that the mean values of Ssum in Case 2
and Case 3 are almost the same and that there is asignificant difference between Case 1 and Case 2.
However, there is no significant difference between
Case 1 and Case 3. Therefore, we assume that, different
to the candidate rocking motion, in the case of

Fig. 5. Mean values and standard deviations of sleep
stages in Group B.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 701

Verifying Sleep-Inducing Effectaromatherapy there are individual differences in thesleep-inducing effect.

9.

5. Conclusion
In this paper, we demonstrated that the candidate
rocking motion promotes a deep sleep in adults. We also
showed that individual differences in the sleep-inducing
effect were smaller in the case of the candidate rocking
motion than in the case of aromatherapy.
As future work, we plan to compare the sleepinducing effect of the rocking motion with sleeping pills.

10.

11.

References
1. Ito, H. and Ishino, Y., Adverse Effects of Sleep
Deprivation, Iho Fuji, No.124 (2003) (in Japanese), p.1.
2. Mishima, K., Sleep Problems in Dementia, Proceedingsof the Annual Meeting of the Japanese Research Groupon Senile Dementia, Vol.17 (2010) (in Japanese), pp.109113.
3. RIKEN-TRI Collaboration Center for Human-Interactive
Robot Research (RTC), World’s first robot that can lift
up a human in its arms, RIBA, (online), available from<http://rtc.nagoya.riken.jp/RIBA/index-e.html>,(accessed on 28 November, 2016)
4. Morita, Y., Yamaguchi, K., Ashida, K., Ikeura, R.,
Yokoyama, K., Verification of Sleep-Inducing Effect by
Excitation Apparatus Simulating Mother's Embrace andRocking Motion, Proceedings of the 9th International
Workshop on Robot Motion and Control (RoMoCo '13),
pp.80-85 (2013)
5. Ashida, K., Morita, Y., Ikeura, R., Yokoyama, K., Ding,
M., Mori, Y., Effective rocking motion for inducing sleepin adults - Verification of effect of mother’s embrace androcking motion -, Journal of Robotics, Networks andArtificial Life, Vol. 1, No. 4, pp.285-290 (2015)
6. Yoshida, K., Mizuta, T., Takeshima, Y., Nishida, K.,
Tsukida K. and Takeda, C., The Effects of Foot Bathing
on the Central Nervous System with or without Lavender
Oil-An Electroencephalographic Study-, Fukui Medical
University research magazine Volume 2, No. 1, No. 2
merger issue (2001) (in Japanese)
7. Momose, K., Fujisawa, Y. and Uchiyama, A., Effects ofMusic on Physiological Measurements and Mood States,
and Their Correlation with Impression of the Music,
Journal of International Society of Life Information
Science 22(2), 545-551, 2004(in Japanese)
8. Kanazawa, Y., Moritani, K., Momose, I., Furuhashi, S.,
and Ohtsuka, Y., Examination of Stress relieving Effects
of Drinking Chamomile Tea and Peppermint Tea in the

12.

Morning, BULLETIN OF TENSHI COLLEGE,
vol.10(2010) (in Japanese), pp.23-34
Sasaki, Y., Kawamoto, T. and Yamazaki, N.,
Development of an Experiment Bed for Searching
Comfort Semi-Sitting Position, Japanese Journal ofErgonomics, Vol.42, No.6 (2006), pp.373-380.
Ashida, K., Yamaguchi, K., Morita, Y., Sato T., Ikeura, R.
and Yokoyama, K., Verification of Sleep Inducing Effectby Mother’s Embracing and Rocking Motion Consideration by Sleep Stage Judgment Based on EEG
Analysis -, Proceeding of the 2013 JSME Conference on
Robotics and Mechatronics, 2A2-G05(1)-(4), 2013(inJapanese)
Yurugi Y. and Suzuki T., A review of Lavender
Fragrance and Neural Function，The Bulletin of Kansai
University of Health Sciences 6(2012) (in Japanese),
109-115
Miyajima, M., Moriya, K., and Agishi, Y., The effects ofbathing with, lavender oil on relaxation as assessed fromchanges in skin temperature, JAPANESE JOURNAL OF
BIOMETEROLOGY Vol. 34 (1997) (in Japanese) No. 4
P 139-146© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 702

Design of Automated Real-Time BCI Application
Using EEG Signals
Chong Yeh Sai, Norrima Mokhtar, Hamzah Arof

Department of Electrical Engineering, University of Malaya
Kuala Lumpur, Malaysia
Masahiro Iwahashi

Department of Electrical Engineering, Nagaoka University of Technology
Nagaoka, Niigata, Japan
E-mail: chongyeh.sai@siswa.um.edu.my, norrimamokhtar@um.edu.my, ahamzah@um.edu.my,iwahashi@vos.nagaokaut.ac.jp
www.um.edu.my, www.nagaokaut.ac.jp

Abstract
This study proposed a design of real time BCI application using EEG recording, pre-processing, feature extraction
and classification of EEG signals. Recorded EEG signals are highly contaminated by noises and artifacts that originate
from outside of cerebral origin. In this study, pre-processing of EEG signals using wavelet multiresolution analysis
and independent component analysis is applied to automatically remove the noises and artifacts. Consequently,
features of interest are extracted as descriptive properties of the EEG signals. Finally, classification algorithms using
artificial neural network is used to distinguish the state of EEG signals for real time BCI application.
Keywords: Electroencephalogram, EEG Pre-processing, Feature Extraction, Supervised Machine Learning,
Artificial Neural Network.

namely, biological and environmental artifacts.1,2Biological artifacts are signals arising from non-cerebral
origin of the human body, such as cardiac, ocular or
muscles activity. On the other hand, environmental
artifacts are signals that originate from outside of human
body, such as interference from external devices.
Artifacts recorded in EEG signals distorted power
spectrum and influenced the decision-making process of
BCI application. Therefore, a pre-processing step toremove the noises and artifacts of EEG signals isnecessary.

1. Introduction
Electroencephalogram (EEG) is the recording ofelectrical activities of human brain using electrodes
attached to the scalp. Conventionally, EEG is used forclinical diagnosis of epilepsy and sleep disorder. In
recent decades, EEG have been studied and finding
increasing use in Brain Computer Interface (BCI)
application. The EEG signals are described in frequency
bands of delta (0.5 to 4 Hz), theta (4 to 8 Hz), alpha (8 to
12 Hz) and beta (12 to 32 Hz), each of which is attributed
to different aspects of brain activity.

Conventional methods using linear filters incurred
substantial loss of cerebral activity because of theinherent spectral overlap between neurological activity

In practical settings, EEG signals are often contaminatedby noises and artifacts during the recording. There are
two types of artifacts that contaminated the EEG signals,© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 703

Chong Yeh Sai, Norrima Mokhtar, Hamzah Arof, Masahiro Iwahashi

and signal artifacts.3,4 Wavelet based multiresolution
analysis using Discrete Wavelet Transform (DWT) is
more effective in removing the target artifacts, while
retaining the cerebral activities of interest in EEG
signals.5,6 Meanwhile, Independent Component Analysis(ICA) algorithm using blind source separation is able toisolate the target artifacts into a separated Independent
Component (IC).2,7 Combinatorial use of Wavelet
Multiresolution Analysis (WMA) and ICA are able toisolate and remove noises and artifacts without incurring
substantial loss of cerebral activities of interest in EEG
signals.1,8Fig. 1. Block diagram of Discrete Wavelet Transform (DWT)of a discrete signal, x[n]. The annotation ↓2 denotes sampling
reduction by a factor of 2, i.e. two-fold down-sampling of thesignal.

x As

In this study, we designed an automated pre-processing
step using WMA and ICA to remove noises and artifacts
in EEG signals. Then, feature extraction andclassification of EEG signals is applied for real time BCI
application.

(1)

In equation (1), A is the unknown mixing matrix
estimated by using the ICA algorithms.7,10 The un-mixing
matrix, W is then computed as the inverse of estimatedmixing matrix. The source components, s are revealed byusing the equations

2. Methodology
2.1. Wavelet Multiresolution Analysis
WMA incorporates the steps of DWT and inverse DWT.8
The DWT is an implementation of wavelet transformusing discrete set of wavelet scales and transitions.6,9DWT consists of sequential application of low- and highpass filters to decompose a discrete signal into multiple
wavelet components, as shown in Figure 1. Here, x[n]represents a channel of EEG signal passed through a low
pass filter, g[n] and a high pass filter, h[n] simultaneously.
This process is repeated until each channel of the EEG
signal is decomposed into n levels of wavelet details, i.e.
D1(t), D2(y), ..., Dn(t) and a mother wavelet of An.
On the other hand, inverse DWT is applied in a similar
but reversed sequence by combining wavelet details andmother wavelet into a discrete EEG signal.
2.2. Independent Component Analysis
ICA model describes multivariate signals as a mixture ofits source components, by assuming that the multivariate
signals, x are separable into their statistically independentand non-Gaussian source components, s. The
relationship between the signals and its sourcecomponents is described by the equation

Wx

(1)

On the other hand, inverse ICA is accomplished bymultiplying the inverse of estimated mixing matrix, W-1
with the source components, s.
2.3. Artificial Neural Network
Artificial Neural Network (ANN) is a computationalmodel based on interconnected adaptive neurons that
resemble the biological neural network.11 Eachprocessing neurons operating in parallel receives input
from the input layer or its preceding tier and further theprocessed information to its successive tier or output
layer. ANN can be described by the transfer functions of
their neurons, the learning rules and the connection
formula.12 ANN are adaptive and able to learn byobserving and weighting on the importance of input
datasets. In supervised machine learning, ANN modeltrained with sufficient number of training data can beused to make prediction or classification on test data to
determine the dataset in which the test data might belong.
3. Design of System
This study proposed a design of real time BCI application
using the following steps: EEG recording, pre-processing,feature extraction and classification. The design of thesystem is illustrated in Figure 2.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 704

Design of Automated Real-Time

Fig. 2. Block diagram of the design of system for BCI
application using EEG signals.
Fig. 3. The recorded 16-channel EEG signals before preprocessing is applied. Eye blinks artifacts is seen on 1s and 3-4s
segments of the signals. Also, high frequency noises is seen on
channel 6, 10, 14, 15 and 16 (T7, T8, O1, Oz and O2).

3.1. EEG Recording
EEG acquisition equipment g.USBamp (g.tec, Austria) isused to acquire the EEG signals. The electrodes areplaced as specified by the 10-20 system. A total of 16
electrodes corresponding to channels FP1, FP2, F3, Fz,
F4, T7, C3, Cz, C4, T8, P3, Pz, P4, O1, Oz, and O2 areused in this study. The ground electrode is set at FPz andreference point at the left earlobe (A1). The scalp
impedance is kept below 5 kΩ. The recording is
conducted with sampling rate of 256 Hz. A notch filter of
50/60 Hz and band pass filter of 0.5 to 100 Hz are applied
to remove the electrical noise and frequency bands
outside of EEG signals respectively. A sample of fiveseconds segment of the recorded EEG signals is shownin Figure 3.

Fig. 4. Similar segment of EEG signals after pre-processing
step is applied. Eye blink artifacts and high frequency noises
are removed while underlying cerebral activities withinfrequency range of interest (0.5 to 32 Hz) is retained.

frequency bands of EEG signals defined as delta (0.5 to4 Hz), theta (4 to 8 Hz), alpha (8 to 16 Hz) and beta (16to 32 Hz) bands. The coefficients of the wavelet details
are taken as features that characterize the properties of
the EEG signals in each corresponding frequency band.

3.2. EEG Pre-processing
Automated pre-processing step is applied to the recorded
EEG signals using the following steps: (1) Wavelet
Multiresolution Analysis is applied to decompose eachchannel by DWT to 8 levels with mother wavelet of db8.5
Then, only the wavelet components of D3 to D8
corresponding to the frequency range of 0.5 to 32 Hz is
retained to remove unwanted noises and artifacts outside
the frequency range of interest. (2) Blind source
separation using ICA is applied to isolate the artifactual
components, in this case, the eye blink artifacts to be
removed by wavelet denoise algorithm.1,13 (3) Separated
ICs and wavelet components are recombined using
inverse ICA and inverse DWT respectively to reconstruct
the clean EEG signals as shown in Figure 4.

3.4. Classification
Classification of EEG signals is conducted using ANN.
The ANN model is trained using both target and nontarget datasets and applied to classify the test data in realtime BCI application. In this study, we applied the system
to classify target state of “alert with eyes-open” and nontarget state of “relax with eyes-closed”.14 By using 1190
epochs of data and 10 layers of hidden neurons, an overall
accuracy of 97.6 % calculated using 10 fold crossvalidation is achieved as shown in Table 1.

3.3. Feature Extraction
Table 1. Classification result for “alert with eyesopen” and “relax with eyes-closed” EEG signals.

The pre-processed EEG signals are separated into 1
second epochs and features of interest are extracted byusing DWT. Each channel of the EEG signal isdecomposed to wavelet details each represent theSensitivity
98.0 %

Specificity
97.2 %

Accuracy
97.6 %© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 705

Chong Yeh Sai, Norrima Mokhtar, Hamzah Arof, Masahiro Iwahashi

4.

References

Discussion

1. R. Mahajan and B. I. Morshed, Unsupervised Eye Blink
Artifact Denoising of EEG Data with Modified Multiscale
Sample Entropy, Kurtosis, and Wavelet-ICA, IEEE
Journal of Biomedical and Health Informatics 19 (2015)
158-165.
2. T. P. Jung, S. Makeig, C. Humphries, T. W. Lee, M. J.
McKeown,V.
Iragui,etal.,Removing
electroencephalographic artifacts by blind source
separation, Psychophysiology 37 (2000) 163-78.
3. N. A. de Beer, M. van de Velde, and P. J. Cluitmans,
Clinical evaluation of a method for automatic detection
and removal of artifacts in auditory evoked potential
monitoring, J Clin Monit 11 (1995) 381-91.
4. D. P. Subha, P. Joseph, R. Acharya U, and C. Lim, EEG
Signal Analysis: A Survey, Journal of Medical Systems 34(2010) 195-212.
5. M. Mamun, M. Al-Kadi, and M. Marufuzzaman,
EffectivenessofWaveletDenoisingon
Electroencephalogram Signals, Journal of Applied
Research and Technology 11 (2013) 156-160.
6. S. Khatun, R. Mahajan, and B. I. Morshed, Comparative
Study of Wavelet-Based Unsupervised Ocular Artifact
Removal Techniques for Single-Channel EEG Data, IEEE
Journal of Translational Engineering in Health andMedicine 4 (2016) 1-8.
7. C. Chunqi, D. Zhi, Y. Sze Fong, and F. H. Y. Chan, Amatrix-pencil approach to blind separation of colored
nonstationary signals, IEEE Transactions on Signal
Processing 48 (2000) 900-907.
8. N. Mammone, F. L. Foresta, and F. C. Morabito,
Automatic Artifact Rejection From Multichannel Scalp
EEG by Wavelet ICA, IEEE Sensors Journal 12 (2012)533-542.
9. G. W. Wornell, Emerging applications of multirate signal
processing and wavelets in digital communications,
Proceedings of the IEEE 84 (1996) 586-603.
10. A. Hyvarinen, Fast and robust fixed-point algorithms forindependent component analysis, IEEE Transactions on
Neural Networks 10 (1999) 626-634.
11. I. A. Basheer and M. Hajmeer, Artificial neural networks:
fundamentals, computing, design, and application,
Journal of Microbiological Methods 43 (2000) 3-31.
12. S. Agatonovic-Kustrin and R. Beresford, Basic conceptsof artificial neural network (ANN) modeling and its
application in pharmaceutical research, Journal of
Pharmaceutical and Biomedical Analysis 22 (2000) 717727.
13. J. H. Zhang, K. Janschek, J. F. Bohme, and Y. J. Zeng,
Multi-resolution dyadic wavelet denoising approach forextraction of visual evoked potentials in the brain, IEE
Proceedings - Vision, Image and Signal Processing 151(2004) 180-186.
14. R. J. Barry, A. R. Clarke, S. J. Johnstone, C. A. Magee,
and J. A. Rushby, EEG differences between eyes-closed
andeyes-open
resting
conditions,
Clinical
Neurophysiology 118 (2007) 2765-2773.

The proposed system design function to automaticallyfilter and classify target data of EEG signals in real time
BCI application. Recorded raw EEG signals are preprocessed using a combination of WMA and ICA toremove noises and artifacts while retaining cerebral
activities in frequency bands of interest. The preprocessing steps are important as EEG signals are highly
contaminated by noises and artifacts in a practical setting.
Wavelet based multiresolution analysis are applied as
wavelet decomposition is more effective in preserving
the structure of the EEG signals in both time andfrequency domains.5,13 Mother wavelet of db8 is selected
due to its balanced performance and computational
simplicity for real time application.5
After the pre-processing steps, feature extraction using
wavelet decomposition is applied to extract the featuresof interest. The features of interest are the coefficients ofwavelet details each corresponding to frequency bands
activities of delta, theta, alpha and beta band. The wavelet
coefficients are computed due to its better performancein characterizing the structure of EEG signals. Lastly,
supervised machine learning using trained ANN model isapplied for classification of EEG signals. We are eager toapply the system in BCI applications in real time setting.
5. Conclusion
This study proposed a design of automated real time
BCI application using EEG signals. EEG signals arenotably noisy and contaminated by artifacts. The
proposed pre-processing steps using WMA and ICA
effectively removed the noises and artifacts with minimal
distortion to the cerebral activities in frequency bands ofinterest. Then, feature extraction is applied using DWT
to extract the coefficient of wavelet details corresponding
to each frequency band of interest. Lastly, classification
algorithm using ANN are applied to classify the EEG
signals in real time BCI application.
Acknowledgements
The authors wish to acknowledge HIR grant(UM.C/HIR/MOHE/ENG/16
Account
Code:
D000016-16001) and PPP grant (PG260-2015B) for thefinancial support provided over the course of the study.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 706

A Hybrid Simulated Kalman Filter - Gravitational Search Algorithm (SKF-GSA)
Badaruddin Muhammad, Zuwairie Ibrahim, Mohd Falfazli Mat Jusof

Faculty of Electrical and Electronics Engineering, Universiti Malaysia Pahang,
26600 Pekan, Pahang, Malaysia
Nor Azlina Ab Aziz, Nor Hidayati Abd Aziz

Faculty of Engineering and Technology, Multimedia University,
75450 Melaka, Malaysia
Norrima Mokhtar

Faculty of Engineering, University of Malaya,50603 Kuala Lumpur, Malaysia
E-mail: badaruddinmuhammad@yahoo.com, zuwairie@ump.edu.my, mfalfazli@ump.edu.my, azlina.aziz@mmu.edu.my,
hidayati.aziz@mmu.edu.my, norrimamokhtar@um.edu.my

Abstract
In this paper, simulated Kalman filter (SKF) and gravitational search algorithm (GSA) are hybridized in such a waythat GSA is employed as prediction operator in SKF. The performance is compared using CEC2014 benchmark
dataset. The proposed hybrid SKF-GSA shown to perform better than individual SKF and GSA algorithm.
Keywords: hybrid, simulated Kalman filter, gravitational search algorithm, CEC2014 benchmark problem.

initialization of n agents, in which the states of each agent
are given randomly. The maximum number of iterations,tmax, is defined. The initial value of error covarianceestimate, (0) , the process noise value, , and themeasurement noise value, , which are required in
Kalman filtering, are also defined during initialization
stage. Then, every agent is subjected to fitness evaluation
to produce initial solutions {X1(0), X2(0), X3(0), …, Xn2(0), Xn-1(0), Xn(0)}. The fitness values are compared andthe agent having the best fitness value at every iteration,t, is registered as Xbest(t). For function minimization
problem,

1. Introduction
The simulated Kalman filter (SKF) and gravitational
search algorithm (GSA) are examples of populationbased optimization algorithms. GSA has been introduced
in 2009 by Rashedi et al. [1]. On the other hand, as a new
estimation-based metaheuristic [2], the SKF has been
introduced by Ibrahim et al. [3] in 2015. Even thoughboth algorithms are population-based, however, they are
inspired differently. In particular, GSA is inspired by
Newtonian law of gravity and law of motion while SKF
is inspired by the estimation capability of Kalman filter.
In this paper, hybridization between GSA and SKF is
proposed. Specifically, GSA is employed during theprediction stage of SKF.

( ) = min∈ ,….,( ( ))(1)

The-best-so-far solution in SKF is named as Xtrue. The( )<Xtrue is updated only if the Xbest(t) is better ((( )>for minimization problem, orfor
maximization problem) than the Xtrue.

2. Simulated Kalman Filter Algorithm
The SKF algorithm is illustrated in Figure 1. Consider nnumber of agents, SKF algorithm begins with© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 707

Badaruddin Muhammad, Zuwairie Ibrahim, Kamil Zakwan Mohd Azmi, Nor Azlina Ab Aziz, Nor Hidayati Abd Aziz, Mohd Saberi Mohamad

The sin(×2 ) term provides the stochastic aspectof SKF algorithm andis a uniformly distributed
random number in the range of [0,1].

Generate Initial Solution
Evaluate Fitness of Each Agent

The final step is the estimation. During this step, Kalman
gain, ( ), is computed as follows:

Update Xbest
Update Xtrue

( )=

(6)

Then, the estimation of next state, Xi(t), is computed
based on Eqn. (7).

Predict
Measure( + 1) =( | )+ ( )×( ( )−( | )) (7)and the error covariant is updated based on Eqn. (8).

Estimate

No

( | )

( | )

( + 1) = 1 − ( ) × ( | )(8)Finally, the next iteration is executed until the maximum
number of iterations, tmax, is reached.

Stopping Condition?
Yes

3. Gravitational Search Algorithm

Return best solution (Xtrue)

In GSA, agents are considered as an object and theirperformance are expressed by their masses. The positionof particle is corresponding to the solution of the problem.
best( ) and worst( ) denote the best and the worst
fitness value of the population t. The best and the worst
for the case of function minimization problem are defined
as follows:( ) = min( )∈{ ,…, }(12)( ) = max
( )Fig. 1. The SKF algorithm.

The subsequent calculations are largely similar to thepredict-measure-estimate steps in Kalman filter. In theprediction step, the following time-update equations arecomputed as follows:( | )=( )(3)( | )= ( )+∈{ ,…, }while gravitational constant is defined as a decreasing
function of time, which is set toat the beginning anddecreases exponentially towards zero with lapse of time.

(4)

where Xi(t) and Xi(t|t) are the current state andtransition/predicted state, respectively, and P(t) and P(t|t)
are previous error covariant estimate and transition error
covariant estimate, respectively. Note that the error
covariant estimate is influenced by the process noise, Q.

To give a stochastic characteristic to GSA, the total force
acted on agent i in dth dimension is a randomly weighted
sum of dth components of the forces exerted from otheragents.

The next step is measurement, which is modelled suchthat its output may take any value from the predicted state
estimate, ( | ), to the true value,. Measurement,
Zi(t), of each individual agent is simulated based on thefollowing equation:( )= ( | )+sin(×2 ) × | ( | ) −|According to law of motion, the current velocity of any
mass is equal to the sum of the fraction of its previous
velocity and the variation in the velocity. Acceleration ofany mass is equal to the force acted on the system divided
by mass of inertia. Finally, the next iteration is executed
until the maximum number of iterations, tmax, is reached.
In summary, the algorithm of standard GSA is shown in
Figure 2.

(5)© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 708

A Hybrid SKF-GSA

Fig. 2. The GSA algorithm.

4. Hybrid SKF-GSA Algorithm

Fig. 3. Hybrid SKF-GSA algorithm.

Note that even though the SKF follows predict-measureestimate steps as in Kalman filter, the states are not
updated during the predict step. Hence, in the proposed
hybrid SKF-GSA algorithm, GSA is employed as theprediction operator in SKF. An additional variable isintroduced in hybrid SKF-GSA, which is the jumping
rate, Jr, that is a predefined constant in the range of [0,1].
Prediction based on GSA is performed if jumping rate
condition is satisfied. The hybrid SKF-GSA algorithm isshown in Figure 3.

if rand < Jr
apply GSA in prediction
else
proceed to measurement and estimationend
where rand is a random number in the range of [0,1]. If
rand < Jr, agents’ velocity is updated according to GSA.
For the position update, Xpredict, is required and it iscalculated as follows:( ) = ( ) + ( + 1)
(12)

In detail, the hybrid SKF-GSA algorithm begins withinitialization of n agents, in which the states of each agent
are given randomly. The maximum number of iterations,tmax, the initial value of error covariance estimate, (0),the process noise value, , the measurement noise value,, and jumping rate value, Jr, are also defined duringinitialization stage. Then, every agent is subjected tofitness evaluation to produce initial solutions. After that,
Xbest(t) and Xtrue are updated according to SKF algorithm
and pbest is updated according to GSA algorithm.

The algorithm continues with measurement andestimation similar to SKF. The next iteration is executed
until the maximum number of iterations, tmax, is reached.
5. Experiment, Result, and Discussion

The
CEC2014benchmarkfunctions(http://www.ntu.edu.sg/home/EPNSugan/index_files/C
EC2014/CEC2014.htm) have been employed for
performance evaluation. Table 1 shows the setting
parameters used in experiments.

In hybrid SKF-GSA, the purpose of jumping rate, Jr, is tocontrol the occurrence of the prediction. Based on ourobservation, the performance of SKF cannot be enhanced
when GSA is executed at every iteration as the prediction
operator of SKF. The following jumping condition isconsidered:

The experimental result for CEC2014 benchmark
functions are tabulated in Table 2. Result in bold
represents the best performance.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 709

Badaruddin Muhammad, Zuwairie Ibrahim, Kamil Zakwan Mohd Azmi, Nor Azlina Ab Aziz, Nor Hidayati Abd Aziz, Mohd Saberi Mohamad

Table 3. Wilcoxon Signed-Rank Test Result

Table 1. Setting Parameters
Experimental Parameters
Number of agent
Number of dimension
Number of run
Number of iteration
Search space

Comparison
Hybrid SKF-GSA vs GSA
Hybrid SKF-GSA vs SKF

100
50
50
10,000Initial gravitational constant, Go
SKF-GSA Parameters
Jumping rate, Jr

This paper report an attempt to hybrid SKF algorithmwith a well-established GSA algorithm. In this study,
GSA is chosen as the prediction mechanism in SKF
algorithm. In addition, jumping rate is also incorporated
in the proposed SKF-GSA algorithm. During theprediction, GSA is executed not only when the jumping
rate condition is satisfied but also if the predicted solution
is better. The findings proved that the proposed hybrid
SKF-GSA is superior to individual SKF and GSA
algorithms

1000
0.5
0.5
20
100
0.1

Table 2. The Average Fitness Values Obtained by SKF, GSA,
and SKF-GSA
Function
F1
F2
F3
F4
F5
F6
F7
F8
F9
F10
F11
F12
F13
F14
F15
F16
F17
F18
F19
F20
F21
F22
F23
F24
F25
F26
F27
F28
F29
F30

SKF
4702013.17
24498691.7
18147.7005
532.77148
520.010016
633.441686
700.246225
807.981323
1059.13877
1335.18324
6249.36725
1200.23641
1300.55973
1400.30009
1551.6584
1619.12553
908272.092
6941389.77
1950.223
34799.058
1186640.91
3429.1058
2645.6890
2667.2498
2730.4018
2766.3853
3883.3415
7223.3697
5997.8302
19753.2888

SKF-GSA
4295218.33
8265.524
16725.114
538.979519.9999
627.7067
700.0133
816.2199
1056.2279
1543.5057
6178.1897
1200.0562
1300.5183
1400.2931
1549.9115
1619.2702
880075.612
3285.3104
1950.84943
25328.3173
1092401.31
3339.9376
2644.6327
2660.3817
2731.4575
2786.2986
3763.4039
7757.2433
4109.6202
18853.2102

R+
246
104

6. Conclusion[-100.100]

SKF Parameters
Error covariance estimate, P
Process noise value, Q
Measurement noise value, R
GSA Parameters

R219
361

GSA
1400195.11
7166.7916
64249.0520
653.4553
519.9997
636.3361
700.00014
1076.43847
1217.9866
7456.3162
8637.6403
1200.0001
1300.3749
1400.3012
1504.4108
1622.5712
161088.839
3731.2078
1923.7518
26574.2424
187636.63
3857.9572
2500.0000
2600.0283
2700.0000
2800.0315
4577.5301
6261.3240
3100.1482
8695.4410

Acknowledgements
This research is supported by a Fundamental Research
Grant Scheme (FRGS) awarded to Universiti Malaysia
Pahang (RDU160105).
References
1. E. Rashedi, H. Nezamabadi-pour, S. Saryazdi, GSA: agravitational search algorithm, Information Sciences. 179(13) (2009) 2232-2248.
2. N. H. Abdul Aziz, Z. Ibrahim, S. Razali, N. A. Ab. Aziz,
Estimation-based metaheuristics: a new branch ofcomputational intelligence, in Proc. 3rd National Conf.
Postgraduate Research (Pekan, Malaysia, 2016), pp. 469476.
3. Z. Ibrahim, N. H. Abdul Aziz, N. A. Ab. Aziz, S. Razali,
M. I. Shapiai, S. W. Nawawi, M. S. Mohamad, A Kalmanfilter approach for solving unimodal optimization
problems, ICIC Express Letters. 9(12) (2015) 3415-3422.

Based on the averaged performances, Wilcoxon signed
rank test is performed and the result is tabulated in Table
3. Based on the level of significant, σ = 0.05, it is foundthat statistically, the proposed SKF-GSA is significantly
superior to SKF and GSA in solving continuous
numerical optimization problems.
© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 710

Simulated Kalman Filter with Randomized Q and R Parameters
Nor Hidayati Abdul Aziz, Nor Azlina Ab. Aziz

Faculty of Engineering and Technology, Multimedia University
Bukit Beruang, Melaka, Malaysia
Zuwairie Ibrahim, Saifudin Razali, Mohd Falfazli Mat Jusof

Faculty of Electrical and Electronics Engineering, Universiti Malaysia Pahang
Pekan, Pahang, Malaysia
Khairul Hamimah Abas, Mohd Saberi Mohamad

Universiti Teknologi Malaysia
Skudai, Johor, Malaysia
Norrima Mokhtar

Faculty of Engineering, University of Malaya
50603 Kuala Lumpur, Malaysia

E-mail: hidayati.aziz@mmu.edu.my, azlina.aziz@mmu.edu.my, zuwairie@ump.edu.my, saifudin@ump.edu.my,mfalfazli@ump.edu.my, khairulhamimah@utm.my, saberi@utm.my, norrimamokhtar@um.edu.my

Abstract
Inspired by Kalman filtering, simulated Kalman filter (SKF) has been introduced as a new population-based
optimization algorithm. The SKF is not a parameter-less algorithm. Three parameter values should be assigned to P,
Q, and R, which denotes error covariance, process noise, and measurement noise, respectively. While analysis of P
has been studied, this paper emphasizes on Q and R parameters. Instead of using constant values for Q and R, random
values are used in this study. Experimental result shows that the use of randomized Q and R values did not degrade
the performance of SKF and hence, one step closer to the realization of a parameter-less SKF.
Keywords: Optimization, simulated Kalman filter, random, error covariance, process noise, measurement noise.

1. Introduction
The simulated Kalman filter (SKF) [1] is a relatively new
optimization algorithm compared to some well-known
optimizer such as genetic algorithm (GA) [2], particle
swarm optimization (PSO) [3], and gravitational searchalgorithm (GSA) [4]. Since the first introduction of SKF,
it has been modified and applied extensively. These
include fundamental improvements to SKF. For example,

to solve combinatorial optimization problems, anglemodulated SKF [5], binary SKF [6], and distanceevaluated SKF [7] have been presented to enable the SKF
to operate in binary search space. The SKF also has been
hybridized with PSO and GSA [8-9]. From theapplication point of view, airport scheduling has beensolved using SKF [10]. Also, recently, the SKF has been
employed to select the most significant features for peakdetection of EEG signal [11].

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 711

All Author’s Full Names

2. The Original and Modified SKF Algorithms
The simulated Kalman filter (SKF) algorithm isillustrated in Figure 1. Consider n number of agents, SKF
algorithm begins with initialization of n agents, in which
the states of each agent are given randomly. The
maximum number of iterations, tmax, is defined. The
initial value of error covariance estimate, (0) , theprocess noise value, , and the measurement noise value,, are also defined during initialization stage. Then,every agent is subjected to fitness evaluation to produce
initial solutions, X(0) = {(0),(0),…,(0),(0)} where d refers to the dimensional of the problem.
The fitness values are compared and the agent having thebest fitness value at every iteration, t, is registered as
Xbest(t). For function minimization problem,( ) = min∈ ,….,( ( ))(1)( ) = max

( )

(2)

whereas, for function maximization problem,∈ ,….,

The-best-so-far solution in SKF is named as Xtrue. The( )<Xtrue is updated only if the Xbest(t) is better ((( )>for minimization problem, orfor
maximization problem) than the Xtrue.
Fig. 1. The SKF algorithm.

The prediction step is subjected to the following timeupdate equations:( | )=( )( | )= ( )+

( )=

(3)
(4)

( + 1) =( | )−( | ) + ( ) ×(( )−( | )) (7)( + 1) = 1 − ( ) × ( | )(8)and the error covariant is updated based on Eq. (8).

The next step is measurement. Measurement, Zi(t), ofeach individual agent is simulated based on the following
equation:( )= ( | )+×2 ) ×(6)

Then, the estimation of next state, Xi(t), is computed
based on Eq. (7).

where
( ) and( | ) are the current state andtransition/predicted state, respectively, and P(t) and P(t|t)
are previous error covariant estimate and transition error
covariant estimate, respectively.

sin(

( | )

( | )

Finally, the next iteration is executed until the maximum
number of iterations, tmax, is reached.
The SKF algorithm requires the determination of P, Q,
and R parameters. Earlier, P = 1000 and Q = R = 0.5 has
been suggested [1]. However, based on an analysis on P,
it was found that the parameter P alone did not affect theperformance of SKF when Q = R = 0.5. Hence, Q and R
parameters greatly affect the performance of the SKF.
Since the determination of Q and R parameters is critical,
this study suggests the determination of those parameters

(5)

where
is a uniformly distributed random number inthe range of [0,1].
The final step is the estimation. During this step, Kalman
gain, ( ), is computed as follows:© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 712

Paper Title (4 Words)

Table 1. Performance comparison between the original SKF with SKF with random Q and R values.
Function No.

Original SKF

Random QR SKF

Sign

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30

4.70E+06
2.45E+07
18148
532.77520.01
633.44700.25807.981059.11335.26249.41200.21300.6
1400.31551.71619.1
9.08E+05
6.94E+06
1950.234799
1.19E+06
3429.12645.72667.22730.42766.43883.37223.45997.819753

4.70E+06
3.53E+07
19599
522.09520.01631.86700.31807.731064
1366.46166.41200.21300.6
1400.31547.81619.3
8.80E+05
3.06E+06
1940.833350
1.10E+06
3473.62645.52664.62730.82778.43876.16931.79398.7
19184

=
<
<
>
=
>
<
>
<
<
>
=
=
=
>
<
>
>
>
>
>
<
>
>
<
<
>
>
<
>

= similar> Random QR SKF is better

Subsequently, Eq. (8) is replaced with Eq. (11).

could be avoided. Thus, in this work, random numbers,
randn ∈ [0,1], normally distributed at the mean of 0.5, isgenerated whenever Q and R values are required, at every
iteration, every agent, and every dimension.

3.

( )+

(9)

( | + 1)]/[( | + 1) +] (10)

since the P parameter is dependent on Q. On the other
hand, Eq. (6) is replaced with Eq. (10) since the Kalman
gain, Ki(t), is dependent on R.
( )=[( + 1) = (1 −( )) ×( | + 1)

(11)

Experiment, Result, and Discussion

The experiment was conducted based on CEC2014
benchmark dataset for function minimization problems(http://www.ntu.edu.sg/home/EPNSugan/index_files/C
EC2014/CEC2014.htm), which includes 30 set ofproblems. Every run consists of 10,000 function
evaluations. After a single run, the same experiment was
implemented again until 50 results were recorded andaverage values were calculated. The average value forevery function is depicted in Table 1. Note that fororiginal SKF algorithm, P = 1000 and Q = R = 0.5. The

In the proposed SKF with randomized Q and R values,
Eq. (2) is replaced with Eq. (9) as follows:( | + 1) =< Original SKF is better© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 713

All Author’s Full Names

References

sign indicates performance comparison between twoalgorithms for every problem. Based on the result,however, general conclusion cannot be made. Figure 2
shows an example of convergence curve, which mostly
the same.

1. Z. Ibrahim, N. H. Abdul Aziz, N. A. Ab. Aziz, S. Razali,
M. I. Shapiai, S. W. Nawawi, M. S. Mohamad, A Kalmanfilter approach for solving unimodal optimization
problems, ICIC Express Letters. 9(12) (2015) 3415-3422.
2. D. Goldberg, Genetic Algorithms in Search, Optimization,and Machine Learning (Reading, Addison-Wesley
Professional, MA, 1989).
3. J. Kennedy, R. Eberhart, Particle swarm optimization, in
Proc. IEEE Int. Conf. Neural Networks (1995), pp. 1942–
1948.
4. E. Rashedi, H. Nezamabadi-pour, S. Saryazdi, GSA: agravitational search algorithm, Information Sciences. 179(13) (2009) 2232-2248.
5. Z. Md. Yusof, Z. Ibrahim, I. Ibrahim, K. Z. Mohd Azmi,
N. A. Ab. Aziz, N. H. Abd Aziz, M. S. Mohamad, Angle
modulated simulated Kalman filter for combinatorial
optimization problems, ARPN Journal of Engineering andApplied Science. 11(7) (2016) 4854-4859.
6. Z. Md. Yusof, I Ibrahim, S. N. Satiman, Z. Ibrahim, N. H.
Abd Aziz, N. A. Ab. Aziz, BSKF: binary simulated
Kalman filter, in Proc. 3rd Int. Conf. Artificial Intelligence,
Modeling and Simulation (Sabah, Malaysia, 2015), pp.
77–81.
7. Z. Md. Yusof, Z. Ibrahim, I. Ibrahim, K. Z. Mohd Azmi,
N. A. Ab. Aziz, N. H. Abd Aziz, M. S. Mohamad, Distanceevaluated simulated Kalman filter for combinatorial
optimization problems, ARPN Journal of Engineering andApplied Science. 11(7) (2016) 4904-4910.
8. B. Muhammad, Z. Ibrahim, K. H. Ghazali, K. Z. Mohd
Azmi, N. A. Ab. Aziz, N. H. Abd Aziz, M. S. Mohamad,
A new hybrid simulated Kalman filter and particle swarm
optimization for continuous optimization problems, ARPN
Journal of Engineering and Applied Science. 10(22)(2015) 17171-17176.
9. B. Muhammad, Z. Ibrahim, K. Z. Mohd Azmi, K. H. Abas,
N. A. Ab. Aziz, N. H. Abd Aziz, M. S. Mohamad, Fourdifferent methods to hybrid simulated Kalman filter (SKF)with gravitational search algorithm (GSA), in Proc. 3rd
National Conf. Postgraduate Research (Pekan, Malaysia,
2016), pp. 854–864.
10. K. Z. Mohd Azmi, Z. Md. Yusof, S. N. Satiman, B.
Muhammad, S. Razali, Z. Ibrahim, N. A. Ab. Aziz, N. H.
Abd Aziz, Solving airport gate allocation problem using
angle modulated simulated Kalman filter, in Proc. 3rd
National Conf. Postgraduate Research (Pekan, Malaysia,
2016), pp. 875–885.
11. A. Adam, Z. Ibrahim, N. Mokhtar, M. I. Shapiai, M.
Mubin, I. Saad, Feature selection using angle modulated
simulated Kalman filter for peak classification of EEG
signals, SpringerPlus. 5(1580) (2016).

Table 2 shows further analysis based on Wilcoxon
signed-rank test. The R+ and R- values were calculated
and those values were compared to a threshold value,
which is 137. Since both R+ and R- values are greater than137, there is no significant difference between results
produced by both algorithms.
Table 2. Wilcoxon Signed-Rank Test
Original SKF vsRandom QR SKF

R+

R-

171.0

264

Function 5
521.4
Original SKF
Random QR SKF

521.2

521

520.8

520.6

520.4

520.2

520

0

100

200

300

400

500

600

700

800

900

1000

Fig. 1. Convergence curve for function 5.

4.

Conclusion

Parameter tuning of an optimization algorithm could bea tedious task, especially, if the algorithm is employed tosolve a specific problem. This study proves that Q and R
tuning of SKF can be avoided, which is important
towards the parameter-less SKF.
Acknowledgements
This research is supported by a Fundamental Research
Grant Scheme (FRGS) awarded to Universiti Malaysia
Pahang (RDU160105).

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 714

Real Detection of 3D Human Hand Orientation Based Morphology
Abadal-Salam T. Hussain

Center of Excellence for Unmanned Aerial Systems (CoEUAS)
Universiti Malaysia Perlis, 01000 Kangar, Perlis, Malaysia
Hazry D.a, Waleed A. Oraibib, M.S Jawadb, Zuradzman M. Razlana, A. Wesam Al-Muftib, S. Faiz Ahmeda, Taha A. Tahab,
Khairunizam WANa & Shahriman A.Baa

Center of Excellence for Unmanned Aerial Systems (CoEUAS)b
School of Electrical System Engineering
Universiti Malaysia Perlis, 01000 Kangar, Perlis, Malaysia
E-mail: abadal@unimap.edu.my, hazry@unimap.edu.my
www.unimap.edu.my

Abstract
This paper describes a new methodology to track a human hand movement in 3D space and estimate its orientationand position in real time. The objective of this algorithm development is to ultimately using it in robotic spherical
wrist system application. This method involves image processing and morphology techniques in conjunction withvarious mathematical formulae to calculate the hand position and orientation. The advantage of this technique is that,
there is no need for continuous camera calibration which is required in other conventional methods in similar
applications. The result of proposed method shows correctly identifying a large number of hand movements and its
orientations. The proposed method could therefore be used with different types of tele operated robotic manipulators
or in other human-computer interaction applications. This method is more robust and less computationally expensive,
unlike other approaches that use costly leaning functions. The high performance is achieved during experiments
testing because of its accurate hand movement identification and the low computational load. Keywords: List four tosix keywords which characterize the article.
Keywords: 3D human hand, 3D detection, 3D orientation, SCARA robot

without any prior data base information. The plain
background is used for hand detection and the results
obtained from it shows high consistency with hands ofdifferent sizes and colours and the simulated movement
and rotation matched that of the subject’s hand.
After obtaining and matching the hand’s orientationand position in 3D space, these results used as the input
to a simulated robotic manipulator and the position andorientation of its end-effector accurately matched theuser’s hand. Furthermore, additional information fromthe hand, such as the opening or closing of the endeffector gripper, is obtained and directly sent to thesimulated manipulator robot, which emulated the handaction [6].

1. Introduction
Vision-based tracking of hand motions is one of thehottest area of research, many researchers are working on
this area e.g. [1], [2], and [3]. However, the problem ofcorrectly identification of a hand and completely
simulating its movement is not yet solve. Several
previously proposed algorithms use features extraction
from the hand and then classify it using save data or
information, and others (e.g. [4] and [5]). Although someof these techniques have reported some levels of success,but suffers from many restrictions such as present
gestures and positions which are fed into the systembeforehand. Other solution required invasive. In thispaper a new method is presented to identify the human
hand in real time and analyze the resultant information to
obtain the hand’s position and orientation in real time

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 715

Real Detection of 3D

subsequent change in this property is noted and comparedto the original registered width.
Original registration of the human hand was
calculated with a 0° rotation about the y-axis, then thewidth at this initialization stage is the maximum value.
Therefore, each decrease in the hand width reflects a
relative rotation about the y-axis. If it is assumed that afull rotation of 90° results in a hand width of 1/2 themaximum hand width, a relationship between the angle
and the width can be formed, as shown in (1).

2. Deduction of 3D Hand Orientation
First the hand position is determined and then, thesame set of features can be used to estimates theorientation of the hand in 3D space. The three quantities
that would need to be determined are the angles that thehand has rotated with respect to the three principal axes,
namely, the x, y and z-axes.
The rotation about the z-axis (depth) can be easily
determine, which occurs on the plane parallel to thescreen. This angle is measured by using two previously
obtained features the reference point (located in the
middle of the wrist) and the tip of the hand (farthest pointon the hand from the reference point). After obtaining thecoordinates for these points on the plane parallel to thescreen (the xy plane), a line is drawn between them.
Therefore, the angle on which hand is rotated about thez-axis can be computed using atan2 function in
MATLAB. In practice, the rotation of a hand lies in therange of 0° - 180°.
Where, the value of b is equal to the difference
between the x coordinate of the reference point and the xcoordinate of the human hand tip. Similarly, the value of
a is equal to the difference between the y coordinate of
the reference point and the y coordinate of the hand tip.
Using these calculations, the first angle of the orientationof the human hand is obtained.
Besides that, if the hand rotated towards the screen,means the upper half of the hand shape gets larger, refer
to Fig. 1. (c) otherwise when the hand rotated backwards,
fare from the screen, the hand image become smallerfrom the upper half refer to Fig. 1. (b) that is recognisable
by eye.

ycos 1 2

w
w o

1
2

(1)

This variation, which differs between humans andeven between different hands (right or left), can beconsidered by changing the equation that relates thewidth to the rotation about the y-axis. Fig. 2. shows anexample that demonstrates the algorithm used todetermine the rotation about the y-axis.

Fig.2. Differing hand widths correspond to rotations
about the y-axis (a) no rotation (0°), (b) partial rotation,and (c) full rotation (90°).
3. Robotic Wrist Manipulation
Finally, the accurate hand position and orientation in 3D
space obtained from proposed methodology are fed into
a robotic manipulator to control the position andorientation of the robotic end-effector such that it mimics
the hand movements in real time. The robotic
manipulator used in this study is a modified SCARA
robot. The proposed design is a SCARA manipulator
with a spherical wrist as its end-effector, which gives itcomplete freedom of movement in 3D space with regards
to position and orientation.
Each of the links of the robotic manipulator is
assigned to a coordinate frame. The Denavit andHartenberg (DH) algorithm proposes a matrix method ofsystematically assigning coordinate systems to each linkof an articulated chain. The axis of revolute joint iisaligned with axis zi−1. The xi−1 axis is directed along
the normal of axes zi−1 to zifor intersecting axes parallelFig.1. Rotation of the human hand about the z-axis (a)angle of rotation approximately 90°, (b) angle of rotation
less that 90°, and (c) angle of rotation greater than 90°.
The calculation of the next two angles required tocomplete the detection of the orientation of the hand ismore difficult. The first of these, the angle about the yaxis (the vertical axis) utilizes the hand width property,
which was previously obtained. As noted, the hand widthis recorded at the beginning of the algorithm and any© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 716

Abadal-Salam T. Hussain, Hazry D., Waleed A. Oraibi, M.S Jawad, Zuradzman M. Razlan, A. Wesam Al-Mufti, S. Faiz Ahmed, Taha
A. Taha, Khairunizam WAN & Shahriman A.B.

to zi−1 × zi. The link and joint parameters may besummarized as follows:(i)
Link length aiis the offset distance between axes
zi−1 and zialong the xi -axis;(ii)
Link twist αiis the angle from the zi−1-axis to
the zi-axis about the xi-axis;(iii)
Link offset diis the distance from the origin offrame i-1 to the xi-axis along the zi−1-axis;(iv)
Joint angle θiis the angle between the xi−1 andxi-axes about the zi−1-axis.

L3 sin 2
L2 L3 cos 2

L2 L3 cos 2 sin 1
L3 sin 2
cos 1

px
py

(4)

Where, ϴ1 = atan2 (sin ϴ1, cos ϴ1). The desiredorientation (rotation) matrix uses the notation shown in
(5).

r11

r12

r13

r21

r22

r23

r31

r32

r33

These parameters and conventions are applied with
our robotic manipulator. The resultant DH parameters areshown in Table 1.

R desTable 1. DH parameters for the proposed simulated
robotic manipulator link

The corresponding rotation matrices for the three rotation
angles can then be multiplied, as shown in (6), to obtain
Rdes

Link (i)

i 1

ai 1

di

i

qi

(5)

(Variable)
1

0°

0

L1

2

0°

L2

0

3

180°

L3

d3

4

-90°

0

0

5

90°

0

0

6

-90°

0

0

1
2

0

(6)

d

The orientation matrix can then be used to obtain thedesired rotation angles, as shown in (7).

4
5
6Using these parameters, the inverse kinematics
model describing each link angle and link length withrespect to the desired position and orientation of themanipulator wrist can be calculated using (2) and (3)

5

atan2

r212 r222 , r23

4

atan2

r33
r
, 13
sin 5 sin 5

(8)

r22
r
, 21
sin 5 sin 5

(7)

6

d3

2

L1

cos 1

z d es

p x2

(2)

p y2

Consequently, for any desired orientation andposition of the robotic manipulator, we can obtain thelink angles and lengths required. The matching of thestate of the human hand in 3D space to a real robot canbe easily accomplished through simple mathematical
calculations. An example of this mapping is shown in Fig.
5. and Fig. 6. that the simulated manipulator follows thehand movements.

L23 L22

2L2 L3atan2(3)Where, zdes is the desired orientation and position of therobot arm, which should be identical to the human hand
position and orientation. After obtaining, ϴ2 the system
for sin ϴ1 and cos ϴ1 shown in (4) can be solved.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 717

Real Detection of 3D

4. Application and Result

References

In this manuscript, a new algorithm has been proposedthat correctly and accurately extracts the 3D position andorientation of a human hand and then apply thisalgorithm to a simulated robotic manipulator directly totest its accuracy. In all our experiments, the manipulator
was successfully tele operated using a single webcam, thedetails representation of robot parts in corresponding tohuman hand parts as shown in Fig. 6.
The extraction of the human hand rotation about thex and z-axes was obtained. In addition, the range ofangles for the hand orientation is considered from a
realistic point of view but may be deemed insufficient for
certain applications in which the exact calibrated angle ofthe 3D hand orientation is required.

1.

2.

3.

4.

5.

A. T. Hussain, S. Faiz Ahmed & D. Hazry, Tracking andReplication of Hand Movements by Teleguided Intelligent
Manipulator Robot. (Robotica, Cambridge University
Press, Vol. 33, Issue 01, Jan. 2015) pp 141-156. DOI:
http://dx.doi.org/10.1017/S0263574714000083(ISSN:
0263-5747 EISSN: 1469-8668).
A. T. Hussain, S. Faiz Ahmed & D. Hazry, Tracking andReplication of Hand Movements by Teleguided Intelligent
Manipulator Robot. (Robotica, Cambridge University
Press, 2014) (ISSN: 0263-5747 EISSN: 1469-8668).
M. Kamran Joyo, D. Hazry, S. Faiz Ahmed, M. Hassan
Tanveer, Faizan. A. Warsi, A. T. Hussain, Altitude andHorizontal Motion Control of Quadrotor UAV in thepresence of Air Turbulence. (Systems, Process & Control(ICSPC2), 2013 IEEE International Conference,
Published at IEEE Xplore, 2013) PP 16-20. ISBN: 978-14799-2208-6, (2013).
Hussain, A. T., Said, Z., Ahamed, N., Sundaraj, K., Hazry,D., Real-time robot-human interaction by tracking hand
movement & orientation based on morphology. (IEEE
Xplore, 2012) PP. 283-288. ISBN 978-1-4577-0243-3.
Syed Faiz Ahmed, Ch. Fahad Azim, Hazry Desa, AbadalSalam T. Hussain, Model Predictive Controller-based,
Single Phase Pulse Width Modulation (PWM) Inverter for
UPS Systems, (Acta Polytechnica Hungarica, Journal of
applied science Volume 11, Issue Number 6, 2014) pp. 2338. (ISSN 1785-8860).

Fig. 5. Simulation of the configuration of a human handwith a robotic manipulator (a) original registeredconfiguration, (b) rotation about two angles, and (c)rotation and translation.

Fig. 6. Show the representation of hand feature points on
a SCARA manipulator robot.
© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 718

Multilevel Non-Inverting Inverter Based Smart Green Charger System
Abadal-Salam T. Hussain a,baCenter of Excellence for Unmanned Aerial Systems (CoEUAS)
Universiti Malaysia Perlis, 01000 Kangar, Perlis, Malaysia

Waleed A. Oraibib, Hazry D.a, Zuradzman M. Razlana, S. Faiz Ahmeda, Taha A. Tahab , Khairunizam WANa &Shahriman AB ab

Center of Excellence for Renewable Energy (CERE)
Universiti Malaysia Perlis, 01000 Kangar, Perlis, Malaysia
E-mail: abadal@unimap.edu.my, hazry@unimap.edu.my
www.unimap.edu.my

Abstract
This paper discusses about the development of low cost efficient battery charging system using PIC Microcontroller.
The demand of efficient battery charging system are going increase day by day, because of its usage in various
applications such as it use in Hybrid electric cars, PV solar electric generation system, other energy storage systems
and many more. In this paper an efficient and cost effective battery charging system is presented that use buck-boost
converter topology. First, the software (PIC Programming) simulation is performed using Micro C software and thenhardware is developed and tested to check the performance of the developed battery charger system. The efficiencyof circuit is 85.66% and it can be use in any battery charging application
Keywords: Battery, Solar Cells, Microcontroller and Battery Charger.

in the circuit to control on the whole events and to be
developed and efficient. As well as the PIC 16F877A isused to control on the input voltage by fixing it to selectedlevel.

1. Introduction
The Fossil Fuel is the conventional source of energy
which is going to be exhausted day by day. According to
the energy information administration (EIA) departmentof energy United States, “the world will face shortage ofliquid fossil fuels in the next few years”. In the light offorecasting of EIA, in upcoming future fossil fuel may
not able to cover the requirement of world. Therefore thealternate energy systems are needed to fill the upcoming
shortage(AhmedK.
Abbas,
2014.
The buck-boost is used to do both of the operations same
like step up and down the output voltage depending on
the duty cycle (Abadal-Salam T. Hussain, 2015). The
economical battery charger controller for solar panel will
be beneficial to optimize the output power as per loadrequirement. In this paper a PIC Microcontroller based
battery charging system is presented that use
PIC16F877A microcontroller as the main processor used

2. Related Works
The renewable energy in the resent years is the solution
for the problems to produce the clean electrical energy,
such as the photovoltaic (PV) technology. The PV
technology is based on the conversion of the sunlight into
electricity (Abadal-Salam T. Hussain, 2015). Now, theresearchers are worked on PV and solar penal systems
that trade on this energy (Abadal-Salam T. Hussain,2015). Lee, Young-Joo Khaligh, one who introduced anddo the experimental about the buck-boost non-inverting
converter or can called a positive buck-boost converter in2009 (Syed Faiz Ahmed, 2014). He has done a highly
efficient control technique by using pulse widthmodulation (PWM) controlling dc/dc positive buck–© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 719

Abadal-Salam T Hussain,Waleed A. Oraibi, Hazry D., Zuradzman M. Razlan, S. Faiz Ahmed, Taha A. Taha, Khairunizam WAN & Shahriman AB

boost converter. In his proposed control scheme, cancontrol the output voltage for an input voltage, whichchanges based on the charge status of the power supply.
There are many techniques to charge the battery areproposed according to the (Abadal-Salam T. Hussain,2015).

3. Application and Result
M The microcontroller is main control unit of the circuit
which is programmed using Micro C software. First ofall, the input voltage coming from solar cell is divided byusing voltage divider to decrease the voltage and provide
a protection to the PIC. The output voltage from thevoltage divider is connected to ADC that including inside
the PIC microcontroller. The PWM that producing fromthe PIC is depended on the input voltage that comingfrom the voltage dividers . Shown in Fig. 1. simulation
circuit at proteus software.

Table 1. Output voltage results for different resistive load

VIN
(V)

RESISTIVE
LOAD(Ω)V out(V)Current
(A)

6
6
6
8
8
8
14
14
14
16
16
16

8
12
18
8
12
18
8
12
18
8
12
18

11.83
11.9
11.92
11.6
11.67
11.72
11.85
11.9
11.935
11.86
11.88911.921

1.478
0.989
0.66
1.437
0.967
0.65
1.48
0.9911
0.66271.4811
0.99
0.666

As have seen below at Fig. 3. and Fig. 4.: shown the resultof hardware implementation at buck and boost noninverting converter.

Fig. 1. Buck- Boost Simulation Circuit

The PWM waveform is generated by using PIC
microcontroller, depend on the variation of the duty cycle,
and input voltage as shown in Fig. 2. The output voltage
and current from buck-boost non-inverting converter
controlled by using pic microcontroller (PWM) andsystem loaded with different resistive load are presented
in the Table 1 shows the I/O voltages results.

Fig. 3. Shown the result at buck converter.

Fig.2. Wave form of gate driver on 50% of duty cycle.
© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 720

Multilevel Non-Inverter

Resolution = 5V/1024= 4.887 mV
Therefore, 4.887mV is the step for each change in input
voltage. The programming of ADC’s converter is toconvert the analog input voltage to digital number.
V=(v ×4.89)
×120
20

(1)

Voltage across 0.8 ohm resistor = V,The load current =Current across 0.8 ohm resistor = V/0.8
Thus,

Fig. 4. Shown the result at boost converter.

=

The current and voltage have followed from theconverter to the battery across relay (RL). The yellow andred LED is used to give alarm when the battery is charged
fully or when an overload is occurred. The Analog todigital converter ADC had been modeled in the
PIC16F877A to convert the analog battery voltage todigital. The battery sense voltage is reduced by using
some resistors and sent to the ADC input as show in theFig. 5. simulation circuit.

(2)
0.8

The reduced voltage from divider is used as a sense
voltage from the load to the PIC16F877A to be displayedon the LCD as shown in the FIGURE (4). The one of themain objective was to design an efficient battery charger
device based PIC microcontroller that would able tocharge the battery with variable dc source as well as toprotect the battery from the overcharging by cutting off
the supply voltage when the battery is charged fully. The
charger circuit was tested by using Proteus simulation
software.
The data is collected from the simulation and lap todescribe the drawn charging current per the battery
voltage until cutoff as show in Fig. 7. and Fig. 8.
1.6
1.4

Current

1.2
1
0.8
0.6
0.4
0.2
0
6
6.5
7
7.5
8
8.5
9
9.5
10
10.5
11
11.5
12
12
12

Fig.5. Battery charger circuit simulation

Voltage

The modeling of ADCs inside the PICs, convert
automatically the analog input voltage on the 10 bit
digital number. To get higher resolution from ADCs, asmaller step size is provided. The resolution step size is:Fig.7. Battery Charging Current© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 721

Abadal-Salam T Hussain,Waleed A. Oraibi, Hazry D., Zuradzman M. Razlan, S. Faiz Ahmed, Taha A. Taha, Khairunizam WAN & Shahriman AB

3. Abadal-Salam T. Hussain, F. Malek, Nur Amira Azhar, M.
S. Jawad, Syed F. Ahmed, Mohd Asri Jusoh, Mohd Irwan
Yusoff, Muhammad Irwanto Misrun, Gomesh Nair
Shasidharan, Shouket A. Ahmed & Taha A. Taha,“Efficient Power Generation for Smart Homes BasedTitanium Dioxide (TiO2)“, Trans Tech Publications,
Journal of Applied Mechanics and Materials, Vol. 793(2015)
pp430-434,
ISSN:
1662-7482,
doi:10.4028/www.scientific.net/AMM.793.430;
4. Syed Faiz Ahmed, Ch. Fahad Azim, Hazry Desa, AbadalSalam T. Hussain, "Model Predictive Controller-based,
Single Phase Pulse Width Modulation (PWM) Inverter for
UPS Systems", Acta Polytechnica Hungarica, Journal of
applied science (ISSN 1785-8860), Volume 11, Issue
Number 6, pp. 23-38, ( 2014);
5. Abadal-Salam T. Hussain, Syed F. Ahmed, F. Malek, M.
S. Jawad, Nursabrina Noorpi, Gomesh Nair Shasidharan,
Mohd Irwan Yusoff, Muhammad Irwanto Misrun, Taha A.
Taha & Shouket A. Ahmed, “Wind Turbine Farm as an
Alternate Electric Power Generating System in Perlis Malaysia”, Trans Tech Publications, Journal of Applied
Mechanics and Materials, Vol. 793 (2015) pp 333-337,
ISSN:1662-7482,
doi:10.4028/www.scientific.net/AMM.793.33.

Battery voltage (V)

14
12
10
8
6
4
2
1.4
1.37
1.34
1.27
1.05
0.81
0.63
0.45
0.28
0.18
0.11
0.06
0.04

0

Charging current (A)Fig. 8. Charging Current and Battery Voltage

4.

Conclusion

In this paper buck-boost inverter based an efficientbattery charger system for solar cell application ispresented which is based on PIC microcontroller, it isprogrammed, simulated and design to control on the
charging process and to protect the battery against theovervoltage that might occur during the charging process.
It automatically cutoff the voltage when the battery is
charged fully. When a battery voltage reaches theregulation set point, the charging algorithm slowly
reduces the charging current to avoid heating. The circuit
is tested and it performed well.
References
1. Ahmed K. Abbas, Abadal-Salam T. Hussain, F.
Malek,Waleed A. Oraibi,Qais H. Jeflawi, Israa A.
Dahham .” High-Low Voltage Risk Prevention Of UAV
Electrical Generation System Based Buck-Boost Inverter”
International Journal of Advanced Technology inEngineering and Science (ISSN 2348- 7550), Volume 02,
Issue 06, June 2014;
2. Abadal-Salam T. Hussain, Qais H. Jeflawi, F. Malik, Israa
A. Dahham, Jailani O. Mahmoud, Mohd Asri Jusoh, Mohd
Irwan Yusoff, Muhammad Irwanto Misrun & GomeshNair Shasidharan, “Integrated DC-AC Inverter for Hybrid
Power System“, Trans Tech Publications, Journal ofApplied Mechanics and Materials, Vol. 793 (2015) pp
252-256,
ISSN:
1662-7482,
doi:10.4028/www.scientific.net/AMM.793.252;© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 722

Design A New Model of Unmanned Aerial Vehicle Quadrotor Using The Variation in The
Length of The Arm
Yasameen Kamil

Center of Excellence for Unmanned Aerial Systems (CoEUAS)
Universiti Malaysia Perlis, 01000 Kangar, Perlis, Malaysia
D. Hazry, Khairunizam Wan, Zuradzman M. Razlan, Shahriman AB

Center of Excellence for Unmanned Aerial Systems (CoEUAS)
Universiti Malaysia Perlis, 01000 Kangar, Perlis, Malaysia
E-mail: moonom2002@gmail.com, hazry@unimap.edu.my
www.unimap.edu.my

Abstract

The direction of technological advancement toward autonomous aerial vehicle is increased. As aconsequence of this evolution, the quad rotor used to accomplish a complex task in several fields. This
paper presents the dynamic model of this miniature aerial vehicle in altitude and new dynamic model foryaw attitude movement, based on varying the arm length of quadrotor instead of varying the speed of
motors to obtain a rotation around z-axis. This achieved by fixed a stepper motor in the arm of quadrotor
to increase or decrease the length of the arm according to controller command for yawing movement.
The controller command achieved by design PID controller with specific parameters to maintain thestability of the quadrotor in the flight path. The equipped energy to the motors during flight andmaneuvering is reduced by selecting the motors’ speed. The MATLAB software code used to evaluate
and presents the comparison between the proposed and conventional quadrotor dynamic model.
Simulation results show the robustness performance of the proposed model due to the movement aroundthe z-axis with high system stability
Keywords: Altitude; Arm’s Length; PID; Quadrotor; UAV.

used PD controller and backstepping based on PID tocontrol the attitude. A.L Salih 2010, used PID controller
for developing the stability of pitch, roll, yaw and altitudeof the vehicle. Unfortunately, the system contains sometransient overshoot because of disturbance and someother reasons like some certain of mechanical parameterand simplification of the controller. M. K. Joyo etc 2013,
the position and altitude of quadrotor are controlled byusing PID controller in the condition of wind gust andconclude that the controller worked effectively under thiscondition. H. K. Kim, 2013 proposed PID based on
feedback linearization combined with block control
technique for controlling the position of QR.
In this paper, a new model for QR yawing attitude is
proposed based on varying the length of arms to generate

1. Introduction
The autonomous miniature aerial vehicle has attracted
the interest of researchers and represents a challenge inthe world of Unmanned Aerial Vehicle (UAV) from few
years ago. One of the UAV is the QuadRotor (QR), which
has an endless list of applications in civil, military andcommercial. The QR is under actuated nonlinear system.
Many methods have been proposed to control thestability of quadrotor. S. Bouabdallah & R. Siegwart,
2007 are focused on the design and the control ofquadrotor by using integral backstepping to control
position, attitude and altitude. According to the results,
the vision sensors and population group must be
improved to be dependable. A. Azzam & X. Wang 2010,© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 723

Variable Arm Length (VAL) Quadrotor̈=̈=variable torque without altering the motors’ speed. This
torque can rotate the QR to the desired position andkeeping QR balance. Varying the arm’s length have been
done by using stepper motor in each arm to increase or
decrease the length of arm depending on controller
command.

̈=

2. Architecture and Dynamic Model of
Quadrotor

= .(
= . (
= . (
= .(

−

∅

∅

∅

∅
∅

Where, c means cos ( ), and s means sin ( ).

+ ∅
−
∅

0

−

] −

(4)

⎭

+
−
−
+

)
)

+
−

+

)

−

)

(5)

3. Proposed Design Model
The quadrotor faces many problems the main problem isthe consumption energy during flight andmaneuverability for long time due to the motors’ speed,
where the two motors’ speed must be increased and twoother motors must be decreased to produce the yaw
movement.
The QR arms are designed to comprise two parts,
first part is fixed with the QR frame and the second partis the sliding arm. This design is proposed to varying thearm’s length(1)

The thrust force and control torque which acting on
the QR body is generated by the propellers rotation. The
thrust vector converts from B frame to E frame by
applying Newton-Euler method for the rigid body (L. R.
G. Carrillo, 2013) as̈ = . + [0
̇ = × +⎬⎪⎪The UAV-QR has a great advantage in variety
fields of work. The main challenges in the UAV-QR
design are represented by its size and simplicity,robustness control and reducing the consumption energy.

R represents the rotational matrix from body to earth
frame.
∅++⎫⎪⎪

2.2 Proposed Yawing Movement Dynamic Model1

The motion of any rigid body in space can be represented
by rotational and translational motions (L. R. G. Carrillo,2013). QR is nonlinear system with (6_DOF) and only
four inputs which are motor speed and 3-translational and3-rotational motion.

−

̇

−

̇

These input vectors are represented a

2.1 Dynamic Model of Quadrotor∅̇ ∅̇ +−

̇The thrust induced by variation the speed of the motors
is the input vector to the system and defined as

The front motor (m1) and the rear motor (m3) are rotate
counter clockwise (C.C.W). The right motor (m2) and theleft (m4) are rotate clockwise (C.W). Therefore, thedirections of the motors’ rotation are maintained abalance torque at the QR frame and try to lift the QR in
vertical motion. The lift motion is obtained by increasing
the speed of all motors (m1, m2, m3, m4) gradually .

=(cos ∅ sin cos + sin ∅ sin ) −(cos ∅ sin sin − sin ∅ cos ) −̈ = (cos∅) −−̇̈ =( − ) ̇ ̇+ −̇∅̈ =− ∅̇ ̇ + − ∅̇

4. Control Strategy

(2)
(3)

The QR requires a stable and robust controller during the
maneuverability and flight path (A. L. Salih, 2010). In
this paper, a PID controller is applied in altitude and yaw
attitude movement.
The parameters of PID are adjusted to a specificvalue to reach steady- state system. The equation of PID
defined mathematically (M. K. Joyo) as

Where, x ̈ is the linear acceleration vector, F is thethrust force generated by motors, fa is the frictional force,τ is the control torque generated by motors, m is the massof the body, I am the moment of inertia matrix, ω is theangular velocity. Therefore, from (3) and (2), can be
write the QR dynamic equation as,

( )=

( )+

∫ ( )

+

( )

(6)

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 724

Variable Arm Length (VAL) Quadrotor

k4=k5=k6

4.1 Altitude Implementation Control Algorithm

0.012 Ns/m

The QR must be at desired value from the ground tomaintain this distance the altitude controller is used (M.
H. Tanveer). Therefore, the QR lift force must be greaterthan its weight and the earth’s gravity to keep the QR athovering and take-off (M. H. Tanveer, 2013).
( )=

( )+

∫

+

(7)

4.2 Yaw Movement Implementation Control Algorithm
QR attitude controlled the angles of orientation
pitch, roll, and yaw. In this paper, we present only the
yaw angle and only choose yaw angle differential
equation. Shown in Fig.1.
̈=

+

̇

Fig. 1. Altitude and hovering performanceFig. 1. Shows the yaw movement according the
proposed design that based on PID controller andchanging the arm’s length and shows the ability of the
proposed design to orient the QR to the desired angle (0.5rad) from reference input angle within 3sec.

(8)

By solving the differential equation can obtain as theoutput value, so the control design for the PID controller
error signal will be:
=

−

(9)

Where ref the desired value andis the measured
output signal, then, the PID controller become
( )=
+ ∫
+
(10)
5. The Results and Discussion(a)
The physical parameter of simulation as mention isshown in Table I.
Table I. System Parameters
mass

2kg

Arm length∆ll2 = l4
l1 = l3
gravity
Ix
Iy
Iz
b
d
k1= k2=k3

0.25 meter± 0.05 meter
0.3 meter
0.2 meter
9.8m/s2
1.25Ns2/rad
1.25Ns2/rad
2.5Ns2 /rad3.31*10-5
7.5*10-6
0.010 Ns/m(b)
Fig. 2. The comparison of yaw movement between (a)The Proposed Control Design (b) Conventional Control
Design© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 725

Variable Arm Length (VAL) Quadrotor

6. Conclusion
The result shows a good performance in altitude motion
by automatically tracking the desired path. A new
approach was proposed in yaw attitude based on varying
the arms’ length to ensure the rotation around the z-axis.
The arm length is varying by fixed a stepper motor insideeach arm to extend or reduce the length of the arm. In
addition, the proposed model in this approach is reduced
the energy consumption and extend the motor’s lifetime
through fixing the motors speed (hovering speed).

References
1.

S. Bouabdallah and R. Siegwart, "Full control of aquadrotor," in Intelligent robots and systems, 2007.
IROS 2007. IEEE/RSJ international conference on,
2007, pp. 153-158;

2.

A. L. Salih, M. Moghavvemi, H. A. Mohamed, andK. S. Gaeid, "Modelling and PID controller design
for a quadrotor unmanned air vehicle," in Automation
Quality and Testing Robotics (AQTR), 2010 IEEE
International Conference on, 2010, pp. 1-5;

3.

M. K. Joyo, D. Hazry, S. F. Ahmed, M. H. Tanveer,
F. A. Warsi, and A. Hussain, "Altitude andHorizontal Motion Control of Quadrotor UAV in the
Presence of Air Turbulence.” IEEE Conference on
Systems, Process and Control, ICSPC 2013, pp.1620;

4.

H. K. Kim, T. T. Nguyen, S. J. Oh, and S. B. Kim,
"Position Control of a Small Scale Quadrotor Using
Block Feedback Linearization Control," in AETA
2013: Recent Advances in Electrical Engineering andRelated Sciences, ed: Springer, 2014, pp. 525-534;

5.

A. Nagaty, S. Saeedi, C. Thibault, M. Seto, and H. Li,"Control and navigation framework for quadrotor
helicopters," Journal of Intelligent & Robotic
Systems, vol. 70, pp. 1-12, 2013;6.

S.-h. Lee, S. H. Kang, and Y. Kim, "Trajectory
tracking control of quadrotor UAV," in Control,
Automation and Systems (ICCAS), 2011 11th
International Conference on, 2011, pp. 281-285;

7.

M. H. Tanveer, S. F. Ahmed, D. Hazry, M. K. Joyo,and F. A. Warsi, "Disturbance and Noise Rejection
Controller Design for Smooth Takeoff/Landing and
Altitude Stabilization Of Quad-rotor," Journal ofApplied Sciences Research, vol. 9, pp. 3316-3327,
2013© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 726

Development of Automatic Take Off and Smooth Landing Control System for Quadrotor
UAV
Syed. F. Ahmed

Universiti Kuala Lumpur, British Malaysian Institute, Malaysia
Center of Excellence for Unmanned Aerial Systems (CoEUAS)
Universiti Malaysia Perlis, Malaysia
D. Hazry

Center of Excellence for Unmanned Aerial Systems (CoEUAS), Universiti Malaysia Perlis, Malaysia
Kushsairy Kadir

University Kuala Lumpur, British Malaysian Institute, Malaysia
Abadal Salam T. Hussain, Zuradzman M. Razlan, Shahriman AB

Center of Excellence for Unmanned Aerial Systems (CoEUAS), Universiti Malaysia Perlis, Malaysia
E-mail: hazry@unimap.edu.my
www.unimap.edu.my

Abstract
This paper covers the development of automatic takeoff and smooth landing control system for UAV Quadrotor. This paper includes
development, simulation, mathematical modeling and experimental results of flight test. Developed UAV model has a system to stabilize
the quadrotor. Altitude stabilization in quadrotor make the quadrotor can perform take off and smooth landing perfectly and can avoid
crash during landing which was proved during experiment. Yaw, pitch, and roll in quadrotor body is detected by gyro sensor when
flying is balanced by gyro sensor. Gyro sensor act as input that detect stabilization problem and input data will be sent to microcontroller
to make new output for quadrotor.
Keywords: UAV, Quadrotor

cannot enter for example danger zone. Uses of quadrotor
at this case can save the people life from danger. We candeploy quadrotor for monitoring hazardous zone such as
power plant and radioactive leakage area. Installment of
automatic takeoff and smooth landing system can makenew user easily to handle quadrotor and can avoid air
crash (Tanveer, 2013). The knowledge in kinematics anddynamics is very important and useful for understanding
the physics and characteristic of quadrotor. Quadrotor
attitude and height from bodies to ground can bemeasured by using two type of sensor. Inertial
measurement unit (IMU) is a main part that be used while
to predict distance ultrasonic sensor and infrared sensor
are used. Micro control unit is a part that handled dataprocessing and control algorithms and generate signal to

1. Introduction
The Quadrotor is one of the Unmanned Aerial Vehicle(UAV) that use four propeller for flying. They are four
unit of brushless DC motor that mount at each of end
quadrotor structure (M. Kamran Joyo, 2013). Flying
object needs to take off and landing safely. Therefore thedevelopment of automatic takeoff and smooth landing
system is very crucial for safety system in Quadrotor. The
advantages of this system are totally eliminate the use of
the pilot to airborne the UAV and it is more secure andreliable for autonomous flight. Quadrotor is very usefulin many applications for example in monitoring,surveillance, search & rescue, agriculture and military.
Quadrotor also suitable to use at the place that human© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 727

Syed. F. Ahmed, D. Hazry, Kushsairy Kadir, Abadal Salam T. Hussain, Zuradzman M. Razlan, Shahriman ABfour brushless DC motor that was mounted at thestructure.
2. Objective, Scope and Problem Statement
The flying quadrotor is not easy to take off and landing
for new user because quadrotor tends to easily crashwhen the user controlled it recklessly and strong windwhen flying outdoor. The development of automatic
takeoff and landing will solve this problem when thesystem detect emergency or receive the automatic
landing command and this will save the quadrotor fromcrash. When to deploy the quadrotor without automaticcommand, the user will be difficult to predict the lift forceof quadrotor and this will consume more power of battery.
The development of automatic takeoff will help new user
to solve this problem. Another problem in this project is
to make quadrotor maintain its stability in air. The usesof gyroscope in quadrotor will help the quadrotor
microcontroller accept four output roll, pitch, yaw andthrottle. This input will be process by microcontroller andgenerate new output for quadrotor to change the speed ofquadrotor to make it stable.
3. Literature Review
In 1920 - 1930 quadrotor is already has been design andbuild at that time but the quadrotor is handle by a man.
The quadrotor build at that time is bad performance, lackof stability and very large shape and heavy. People
cannot make it unmanned at that time because there a no
microcontroller, sensor and electronic control system.
Nowadays many of the microcontrollers developed bymanufacturer for example: Arduino, PIC18 microchip
and basic stamp. All of this microcontroller can be used
for making the electronic control system of quadrotor to
make it unmanned.

Fig. 2. Flow Chart of the works
In quadrotor front and back propeller must rotate anticlockwise and right and left propeller must rotate
clockwise as shown in Fig. 1. to eliminate torqueproduced by the motor (Tanveer, 2014). By have acorrect orientation of propeller we can hover thequadrotor.
X, Y and Z represent the fixed body frame ofquadrotor. The blue arrow is a weight of quadrotor W =mg. The red arrow in Fig. 1. show the angular speed ofquadrotor. Ψ is for yaw angle, ϕ is for pitch angle and ϴ
is for roll angle (Warsi, 2014)(Joyo, 2014).

4. Methodology

5. Quadrotor Control System
Closed loop control system is important during takeoff
and landing of a quadrotor. Control system is importantin the systems that need accuracy. Accurate value of
PWM will make quadrotor fly at actual output that
already set by the user. This closed loop control system

Fig. 1. Direction of propeller and free body diagram© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 728

Development of Automatic Takeoff and Smooth Landing Control System
is also can be used to perform smooth landing. When
automatic landing button is pressed, the quadrotor will
decrease the motor speed due to height level that read by
ultrasonic sensor.

Rpm
12000
10000
8000

6. Results & Analysis

6000

Table 1. Total mass of quadrotor

4000

Part
Propeller
Receiver
Brushless DC motor

Quantity414Mass(g)4012140Electronic speed
controllers
Quadrotor structure

4

80

1

500

Screw, nuts
WiringArduino board

20
1.5 meter
1
TOTAL

80
38
30
920

2000
0

Fig. 4. Automatic smooth landing graph

6.4 Roll, Pitch and Yaw Error
Analysis of roll error
Performance of roll stabilization error of roll angle whenquadrotor is in static condition is less than one degree.
The data taken in this experiment is in 10 second. The redline is in figure below show the good stability without
error that we need. The blue wave is the gyro detection
in change of roll during static fly and the black wave iserror of roll during static fly (M. Kamran Joyo, 2013)

6.1 Analysis of Quadrotor Thrust Force
The average mass of quadrotor is 920 grams. Therefore,
we need thrust force equal to quadrotor mass to lift up
quadrotor. By using static thrust equation, we canestimate analytical data to make the quadrotor mass equal
to thrust force. Firstly we need to find at what rpm themass of quadrotor is equal to the thrust. Thrust must be
multiply by four because quadrotor has four propellers.

6

Rpm

HeightHeight4
2
0Fig. 5. Error of Roll

Analysis of pitch error
Fig. 3. Automatic takeoff graph

Figure below shows the performance of pitch
stabilization pitch angle also less than 1 degree. The datataken in this experiment is in 10 second. The red line isin figure below show the good stability without error that
we need. The blue wave is the gyro detection in changeof pitch during static fly and the black wave is error ofpitch during static fly.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 729

Syed. F. Ahmed, D. Hazry, Kushsairy Kadir, Abadal Salam T. Hussain, Zuradzman M. Razlan, Shahriman AB
Error of yaw in figure above during static is less than 2
degree while when turning to another direction the yaw
error become high that 2 degree and less than 4 degree.
7. Conclusion
Development of automatic takeoff and landing that movealong Z-axis is look like a simple task but its require
many concept such as thrust study, control system, sensor
interface, stability study, PWM, and Newton second lawof motion. In this paper shows the first step ofdevelopment quadrotor starting from the body structure.
References
1.

Fig. 7. Error of pitch2.

Error of pitch and roll must be low to make a goodstabilization and the longitudinal acceleration condition.
The movement of pitch and roll almost same but thedifferent is in a different axis. Will occur if one of these
two angles was different than zero degree during static.

3.

Analysis of yaw error

4.

5.

M. Kamran Joyo, D. Hazry, S. Faiz Ahmed, M.
Hassan, “Position Controller Design for Quad-rotor
under Perturbed Condition,” Journal Article,
Wulfenia, volume 20, issue 7, pg. 178-179, 2013;
Tanveer, M. Hassan, S. Faiz Ahmed, D. Hazry,
Faizan A. Warsi, and M. Kamran Joyo. "Stabilized
Controller Design For Attitude And Altitude
Controlling Of Quad-Rotor Under Disturbance And
Noisy Conditions." American Journal of Applied
Sciences 10, no. 8 (2013): 819;
Tanveer,M.H. Hazry, D. ; Ahmed, S.F. ; Joyo,
M.K. ; Warsi,
F.A. ; Kamaruddin,
H. ; Razlan,
Z.M. ; Wan, K. ;Shahriman, A.B. “NMPC-PID based
control structure design for avoiding uncertainties in
attitude and altitude tracking control of quad-rotor(UAV)” IEEE 10th International Colloquium on ;Warsi, F.A. Hazry, D. ; Ahmed, S.F. ; Joyo,
M.K. ; Tanveer, M.H. ; Kamarudin, H. ; Razlan,
Z.M., “Yaw, Pitch and Roll controller design for
fixed-wing UAV under uncertainty and perturbed
condition”, IEEE 10th International Colloquium on
Signal Processing & its Applications (CSPA), 2014
(pp. 151-156);
Joyo, M.K. Hazry, D. ; Faiz Ahmed, S. ; Tanveer,
M.H. ; Warsi, F.A. ; Hussain,
A.T.,“Altitude and horizontal motion control
ofquadrotor UAV in the presence of air turbulence”,
IEEE 10th International
Colloquium on Signal
Processing & its Applications (CSPA), 2014 (pp.
16-20);

Figure below show the performance of yaw stabilization.
Longitudinal acceleration does not effected by error ofyaw during static condition. The change of yaw angle ofquadrotor can be -90 ̊, 90 ,̊
-180 ̊, 180 ̊ and high thanthat.
Fig. 8. Error of yaw© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 730

Auto Pilot Ship Heading Angle Control Using Adaptive Control Algorithm
Abadal-Salam T. Hussaina,baCenter of Excellence for Unmanned Aerial Systems (CoEUAS)
Universiti Malaysia Perlis, 01000 Kangar, Perlis, Malaysia.

Hazry D.a, S. Faiz Ahmeda, Wail A. A. Alwardb, Zuradzman M. Razlana & Taha A. TahabaCentre of Excellence for Unmanned Aerial Systems (CoEUAS)
b
Center of Excellence for Renewable Energy (CERE)
Universiti Malaysia Perlis, 01000 Kangar, Perlis, Malaysia.
E-mail: abadal@unimap.edu.my, hazry@unimap.edu.my
www.unimap.edu.my

Abstract
In this paper discussed about development of an auto pilot system for ships using adaptive filter. Adaptive filter in
the application of auto pilots for ships is presented for controlling the ship such that it follows its predeterminedtrajectory. Due to random environmental effects such as wind speed or direction and sea current, the path of the ship
may alter. The objective of this research is to investigate that whether proposed system will adapts to the randomchanges and maintain the desired ship trajectory. The proposed auto pilot system is developed using Least Mean
Square algorithm (LMS) adaptive filter. The performances of the system are analyzed based on accuracy andcomputational times. MATLAB Simulink model tool is used for execute the simulations of the auto pilot system forships.
Keywords: Autonomous Controls, Ship Heading Angle, Adaptive Controls

early autopilot for ships were entirely mechanical innature and had a very simple process, wherein the rudder
was proportional to the heading error.
Major disadvantage s of traditional controllers suchas PID and PD are their inability to adjust with alterations
automatically in the environmental and operating
conditions. These settings had to be adjusted manually by
the user which in many cases may not be completely
optimal. Moreover, these obligatory setting which had tobe done repeatedly were exhausting and time consuming.

1. Introduction
The history of ships, boats and sailing is spread overcenturies. Ship steering control has been an essential
topic for researchers for more than 95 years. Early
steering control systems were based on an instrument
called gyroscope, which was used to determine thedirection of travel. In 1911, Elmer Sperry invented an
automatic mechanism for ship steering control based on
the gyroscope (Sperry, 1992).
In 1922, Minorsky published his work on automatic
ship steering, which was an essential breakthrough in thefield (Minsky, 1954). Later in the same year, Sperry
presented the first automatic ship control system. These© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 731

Abadal-Salam T. Hussain, S. Faiz Ahmed, Hazry D., Wail A. A. Alward, Zuradzman M. Razlan & Taha A. Taha

1.1 Steering Control Overview

and developed for designing ship-autopilots (Barbos,
2008), (M. Kamran Joyo, 2014), ( Hussain, 2011).

Fig. 1 shows a general block diagram of a ship steering
control system made up of reference model, sensor
system and feedback control system. The reference
model receives the data on the position and speed of theship from the DGPS (Differential Global Position
System) and also receives external data provided by anoperator (Deck Officer) and other data concerning theenvironmental conditions (height and slope of the waves
and speed and direction of the wind and currents). The
control system also receives information that supplied by
the measurement system and determines the forces andmoments which are to be supplied to the ship in
accordance with the established control objective in mostcases together with the reference model system. In thisthesis, we will be focusing only on the course keeping
characteristics of the auto pilot controller.

2.2. Adaptive Filter
Fig. 2 represents the general block diagram of any
adaptive filter. In this application i.e. the ship steering
auto-pilot is designed using an adaptive filter andcompletely replacing the traditional controllers.
Fig. 2. Adaptive filter

2.3 Least Mean Square (LMS)The LMS algorithm was established by Widrow and Hoffin 1959. Compared to other algorithms, LMS algorithm
is relatively simple; it does not require correlation
function calculation nor does it require matrix inversions.
This algorithm is basically the type of adaptive filter
known as stochastic gradient-based algorithms and it
consists of two major processes: Filtering process andadaptive process (A.T. Hussain, 2015). LMS algorithm
worked on steepest decent method and also took help
from the theory of Wiener solution (optimal filter tap
weights). This algorithm is basically using the formulas
which updates the filter coefficients by using the tap
weight vectors w and also update the gradient of the costfunction accordingly to the filter tap weight coefficient
vectorFig. 1. Steering control

2. Modeling2.1. Adaptive Controllers in Auto Pilot Ship
SystemAutopilot is one of the most important section used inships. Autopilots are not just used to lead the ship on adesired trajectory, but also raise the safety level of thejourney and control the ship economically. A good
autopilot can help to avoid undesired situations on
maneuvering and remarkably reduce the numbers of ship
operators. In the last few decades, taking the advantage
of drastic development of electronics and control theory,
several new and effective methods have been proposed(n) .
w( n 1)w( n )( n)w(n +1) = w(n) + m E {e(n)x (n)}*(1)

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 732

Auto Pilot Ship Heading

2.5 Ships Use for Simulation Purposes

In practice, the value of the expectation E { } is normallyunknown, therefore we need to introduces theapproximation or estimated as the sample mean.

1 L 1
e(n
L l 0

E e(n) x * (n)l ) x * (nFor simulation purpose ROV Zeefakkel ship (ferry) istaken in account which is 45m ferry. The ship model ofthe ferry can be formed using table 1. The desired
heading response is represented by the third order modelof Eq 8 with am= 0.9341, bm= 0.2040 and cm= 0.0182.
The maximum rudder limit selected is and the maximum
rudder rate is.

l)(2)With this estimate we obtain the updating weight vector
as,

w(n

1)

L 1

w(n)

L l 0

e(n

l ) x * (n(3)

rAnd finally, the weight vector update equation become
the simple form.

w(n +1) = w(n) + m e(n)x * (n)

cm

d

l)

s

3

am s

2

bm s

cm

(8)

Where am, bm and cmare constants.
Van Amerongen demonstrates that the motion of this ship
can be described adequately by the Norrbin’s nonlinear
model with the following parameter values:
When the speed of the ship changes, the values of K and
T also vary as found out by Van Amerongen andillustrated in the table below:(4)

2.4. Ship Dynamics & ModelIn this research, Norrbin Model of the ship is considered.
This model is extension of the Nomoto's first-ordermodel (Syed Faiz Ahmed, 2014) in an empirical way. To
describe large rudder angles as well as course instability,
the following model is proposed:

3. Results
ROV Zeefakkel performance of LMS Adaptive
Controller with Reference heading angle: 20° & 50°,
Constant speed: 5m/s, Step Size = Optimal (1x10-5for
LMS ) and Filter Length : Larger than the optimal value
and Smaller than the optimal value.

(5)

1 for
Where a1 = +1 for course-stable and 1
course-unstable ships. Of course, this model can also be
extended with a constant and a quadratic term. This0yield, in the steady state, when

HN ( )

3
3

(6

2
2

1

0

)

With Eq. 5 and Eq. 6 can be rewritten as:

T ( ) HN ( ) K(7)© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 733

Abadal-Salam T. Hussain, S. Faiz Ahmed, Hazry D., Wail A. A. Alward, Zuradzman M. Razlan & Taha A. TahaU(m/s)
1
2

T
155.0000077.5000

K
0.1
0.2

3

051.6667

0.3

4
5
6
7
8

038.7500
031.0000025.8333
022.1429
019.3750

0.4
0.5
0.6
0.7
0.8

9
10

017.2222
015.5000

0.9
1.0

the performance of LMS algorithm is better to minimize
Mean Square Error (MSE) for different heading degree
angles to maintain the desired trajectory using theperformance function of the algorithm that minimized theaverage power in the error signal. As the degree ofheading angle was switched from 20° to 50°, we canobserve that the filter length and step size µ has to bechanged to get an optimum performance from thecontroller.
References
1. Sperry, E. A. (1922). Automatic steering. Transactions,Society of Naval, Architects and Marine Engineers, 61–
63;
2. Minsky, M. L., 1954. Theory of Neural-Analog
Reinforcement Systems and Its Application to the Brain
Model Problem, PhD Thesis, Princeton, University,
Princeton, NJ;
3. Barbos, M. ; Cristescu, C. , Technical Overview on
Designing Wireless Remote Control Steering Mechanisms
for Small Ships and Scaled Model Ships, IEEE
International Conference on Automation, Quality andTesting, Robotics, 2008 Vol. 3, pp. 287 – 291;
4. M. Kamran Joyo, D. Hazry, S. Faiz Ahmed, M. Hassan
Tanveer, Faizan. A. Warsi, A. T. Hussain, “Altitude andHorizontal Motion Control of Quadrotor UAV in thepresence of Air Turbulence”, Systems, Process & Control(ICSPC2), 2013 IEEE International Conference,
Published at IEEE Xplore, PP (16-20), ISBN: 978-1-47992208-6, (2013) ;5. Hussain, A.T., et al.; “Real-Time Robot-Human
Interaction by Tracking Hand Movement & Orientation
Based on Morphology”, International Conference on
Signal and Image Processing Applications (ICSIPA 2011),
Published at IEEE Xplore, PP (283-288), ISBN: 978-14577-0243-3, (Feb. 2012) ;6. A.T. Hussain, S. Faiz Ahmed & D. Hazry; "Tracking andReplication of Hand Movements by Teleguided Intelligent
Manipulator Robot", Robotica, Cambridge University
Press, Vol. 33, Issue 01, Jan. 2015, pp 141-156. DOI:
http://dx.doi.org/10.1017/S0263574714000083(ISSN:
0263-5747 EISSN: 1469-8668) ;7. Syed Faiz Ahmed, Ch. Fahad Azim, Hazry Desa, AbadalSalam T. Hussain, "Model Predictive Controller-based,
Single Phase Pulse Width Modulation (PWM) Inverter for
UPS Systems", Acta Polytechnica Hungarica, Journal of
applied science (ISSN 1785-8860), Volume 11, Issue
Number 6, pp. 23-38, (2014).

Fig.4. LMS 50° at 5m/s, µ =1x10-6, step size = 40

4. Conclusions
The LMS filter gives very good results in terms ofachieving the heading angle that is desired for both 20°
and 50°. It is ascertained from the simulation results that

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 734

Classification of Hippocampal Region using Extreme Learning Machine
Muhammad Hafiz Md Zaini, Mohd Ibrahim Shapiai

Electronic System Engineering, MJIIT, Center of Artificial Intelligence and Robotics (CAIRO), UTM,
54100 Kuala Lumpur, Malaysia
Ahmad Rithauddin Mohamed

Institut Pediatrik, Hospital Kuala Lumpur,
Jalan Pahang Kuala Lumpur, 50586 Kuala Lumpur, Malaysia
Norrima Mokhtar

Faculty of Engineering, University of Malaya,50603 Kuala Lumpur, Malaysia
Zuwairie Ibrahim

Universiti Malaysia Pahang,
26600 Pekan, Pahang, Malaysia
E-mail: muhdhafizzaini@gmail.com, md_ibrahim83@utm.my, drahmadrm@gmail.com, norrimamokhtar@um.edu.my,
zuwairie@ump.edu.my

Abstract
Important brain parts like hippocampal usually being manually segmented by doctors. But with the introduction of
hybrid between machine learning along with neuroimaging technique, it has proved to shows some promising results
regarding on segmenting subcortical structures. However, it is known that Extreme Learning Machine (ELM) is tobe superior machine learning technique. This study will investigate on the usage of ELM to segment hippocampal byusing various hidden nodes configuration. This study also will address on the usage of full image and region ofinterest (ROI) using ELM. Bag of features is used as a feature extractor where it will segment the hippocampal of the
MRI in order to get its visual words. ELM will used it to learn its feature. Results shows that with suitable hidden
nodes, it could achieve up to 100% performance on both cases for full image and ROI in hippocampal segmentation.

Keywords: Extreme learning machine, Hippocampal segmentation, Magnetic resonance imaging, Neuroimaging

such as Magnetic Resonance Imaging (MRI), Positron
Emission Tomography (PET), radiography and many
more to detect stroke, cancer, Alzheimer and epilepsy.
However the problem arises when doctors need to makefurther diagnosis as it only provide raw images for them

1. Introduction
With the introduction of biomedical imaging modality, it
has brings great help especially for doctors and medical
expert community. Medical imaging can be defined as amodality that deliver information on the subject of thevolume underneath the skin [1]. There are otherbiomedical imaging modalities that has been introduced© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 735

Muhammad Hafiz Md Zaini, Mohd Ibrahim Shapiai, Ahmad Rithauddin Mohamed, Norrima Mokhtar, Zuwairie Ibrahim

to diagnose [2]. This can be seen in the cases where
doctors usually detect hippocampal manually which canconsume a lot of their time and also lead to error at somepoint [2, 3].
Computer Aided Diagnosis (CAD) is introduced to solve
this problem, whereas it can be describe by the means of
the usage of machine learning and image processing toprocess image and interpret it in order to assist doctors
and experts [4]. The process to segment subcorticalstructures is difficult and important task at the same time
too especially in brain analysis, this is because, it is where
the hippocampal is located [5]. Many research has shownthat a patient’s hippocampal who is effected by Epilepsy
or Alzheimer tends to become smaller in size compare toa healthy person [6]. The likes of Atlas-based
Segmentation, Statistical Model and Machine Learningis the result of a research that has been done in this past
years in order to segment hippocampal [6-8]. Machinelearning, technique such as Structure Vector Machine(SVM) [5], radial basis [9], Artificial Neural Network(ANN) [10] has been widely used as the learning method
in order to learn the structure of the hippocampal.
Although it is proven that the aforementioned machinelearning could achieve good result, however ELM is
known to be superior in machine learning technique pointof view as it offer a unified framework where thegeneralized decision from training data can be used tosolve binary classification and predicting a scalar number
for regression problem [11]. Therefore, this study will
investigate on the usage of ELM to segment hippocampal
by using various hidden nodes configuration with normal
control from ADNI dataset as its data. Furthermore, thisstudy also will address on the usage of full image andregion of interest (ROI) using ELM. Bag of features [12]will be used as a feature extractor where it will segment
the hippocampal of the MRI in order to gets its visual
words. Thus, it could be feed into ELM to learn its feature.
2. Related Works
Lately, a lot of research has been done particularly in thearea of segmenting subcortical structure. Technique suchas Atlas selection, Statistical model and Machinelearning which can be seen in Figure 1 are some of theresult achieved by extensive research that have been done[5-7].

Fig. 1. Hippocampal segmentation technique2.1. Atlas Selection, Statistical Model andMachine Learning
One of the technique that is popular among researcher isAtlas-based segmentation, where it will register atlas
image to the subject image. And the result of theregistered atlas will be used to map the coordinates of thestructure of interest from the atlas image to the subject
image [7]. Since image registration is basically the
essence of an extensive variety of medical applications
together with visualisation, and image guided surgery
and voxel-based morphometry [6, 7]. Hence allowing
Atlas-based segmentation to gain benefits frommethodological advances driven by a wide range ofapplication areas. However, it has been noted that its
performance depends on the image registration accuracy
and anatomical differences between the target image [5].
Another technique is statistical models. The idea ofstatistical model is that a priori shape information will be
used as its learning set so that it could learn the variationfrom it and limit the search space to only acceptableinstances defined by the trained model [6]. This
technique is widely used among researchers as expert
knowledge can be captured in the forms of training
examples [13].
Machine learning is one of the method that vastly becomefavourite among researcher [8, 14, 15], it is used not only
to segment subcortical structure but also to detect tumour[6]. Besides, it also can handle large amount of data
especially in segmenting or classifying brain anatomical
as it needs a lot of raw data in order to get better result[8]. However, machine learning has several limitations
that could hinder its accuracy, it needs a lot of data in© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 736

Classification of Hippocampal Region

order to be more accurate [6], and thus a lot ofexperiments need to be done in order to get its optimal
parameter.

This permit an exploratory approach looking for groupeffects across the entire brain, or a hypothesis-driven
approach whereby common ROIs may be utilised acrossall subjects, keeping away from the necessity for ROI
tracing for each subjects [18].

2.2. SVM and neural network
The idea of SVM is that it separates the data in the future
space by looking for support vectors is one of the reasonof the growing interest in applying SVM in neuroimaging
as it has good generalization ability [14]. Besides, it also
has the capability to classify non-linearly separable data
too [6]. Nevertheless, SVM also has several
disadvantages that might deter the performance duringclassification as it is difficult to determine the optimal
parameters and to understand its structure algorithm too.
Apart from that, SVM also takes a lot of time for training
compare to other machine learning approach [6]. Besides
that, SVM also is not a sparse model since support vector
tends to grows along the size of training samples [9].

3.2. Bag of Feature
Bag of Feature (BoF) is an approach where it represent
the whole image or an ROI as a histogram occurrence
quantized visual features that also known as “visualsignature” of the image [12]. It is also one of the mostpopular method for content-based visual information
retrieval (CBVIR) especially in medical field. One of theadvantages of BoF is that it represent a direct
identification of the features instead of their quantization,
thus this approach is able to classify the intended
classification whether the image represent hippocampal
or not- hippocampal [12].

Whereas ANN is a data processing technique that can be
categorised by the form of architecture between
connections of the neurons, its method to determine theweights on the connections which is the training or
learning algorithm and its activation function [16]. Its
performance depends entirely on the structure of the
training set as if it is not well design it might hinder theclassification result and might be resulting to overfitting
too [6]. But on the hindsight, researcher still opt for ANN
as it is capable for classification and regression and it istolerant towards noise because of the ANN structure
which it keeps on improving until it finds the optimal
parameter. Plus, ANN can handle and classified morethan one output too [6].

3.3. ELM
ELM is a supervised learning technique which is basedon single-hidden layer feedforward neural networks(SLFN) [19]. The main idea of ELM is that the hidden
node parameters don’t need to adjust as they can be
assigned with random values. Unlike conventional neuralnetwork, ELM also does not need much parameterselection which make it appropriate to be applied inneuroimaging, and ELM also fast in training with good
generalisation performance too [20].
ELM can be formulated by:Lf ( x)

iG ai , bi , x

(1)

i 1

3. Employed Techniques

From the formula above, ai are the input nodes and bi

This section will briefly explain the techniques that has
been employed in this study.

is the bias of the i th , which they are defined as thelearning parameters of the i th hidden nodes. Whereas x
is defined as the input vector with d dimensions and i

3.1. Spatial normalization
Spatial normalization is a step where it will transform abrain subject by establish a one-to-one correspondence
between the brains by matching it to standard brain form
called “template” [12]. This technique is developed
mainly to simplify inter-subject comparisons by placing
all subjects into a standardized stereotactic template
space [17]. Spatial normalization is an important process
in this research as it involves large inter-individual
variability of human brain, thus with spatial
normalization it helps to decrease the number of interindividual variability and also to ease subject comparison.

is the output weight from the i th hidden node. As for
G ( a i , b, , x ) is the output of i th hidden node with respect
to input x and G activation function. The activation
function can be nonlinear continuous functions such as
sigmoid, Gaussian and many more.
During training phase, hidden nodes and output nodes
parameter must be determined. According to ELM theory,the hidden node parameters ai and bi are assigned withvalues randomly regardless of its nature and will

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 737

Muhammad Hafiz Md Zaini, Mohd Ibrahim Shapiai, Ahmad Rithauddin Mohamed, Norrima Mokhtar, Zuwairie Ibrahim

where,
is the estimated output weight vector inequation (4) and ℎ( ) is a vector that maps the ddimensional input space to L-dimensional hidden layer
feature space.

remained fixed after that. But in this scenario, i is the
only parameter that needs to be determined based on
training data. In ELM, given a training data ( , ), =1, . . . , where ∈and ∈ {−1, +1}, thus in orderto minimize the training error in the cost function that isformed in least square sense and given can be seen inequation (2).
E

N

L

j 1

i 1

min

4. Proposed Methodology
Since the objective of this study is to investigate on theusage of ELM to segment hippocampal by using various
hidden nodes configuration. And also, to investigate on
the usage of full image and region of interest (ROI), theoverall process is being highlighted on flowchart in
Figure 2.

2
i G a i .x j

bi

tj

(2)

Equation 2 can be further simplified as shown in formula
(3).
H
T (3)
h( x1 ),

Hh( x N )G (a1 , b1 , x1 )G (a L , b L , x1 )

G (a1 , b1 , x N )G (a L , b L , x N ) N L

HFig. 2. Experiment methodology

t1

1

and T
tN

L L 1

The process first started with acquiring dataset fromADNI database, it can be downloaded directly fromadni.loni.usc.edu website. A total of 68 MRI from
Normal Control (NC) data from the ADNI1: Annual 2Year 1.5T dataset has been downloaded fromhttp://ida.loni.usc.edu details can be seen in table 1. Next,
is the pre-process step, all of them has undergoes throughspatial normalisation process that will register the MRI
image into template’s image. Then, the normalised image
will be fed into BoF in order to get its visual words where
it will be later on used as the training and testing feature
for the ELM.

N 1

Where is the hidden layer output matrix of SLFN.
While thecolumn of is thehidden node output
with respect to input , , … , . ℎ( ) is defined asℎ( ) = [ ( , , ), . . . , ( , , )] and called thehidden layer feature mapping. The
row
is thehidden layer feature mapping with
input . In thiscase, solving the linear system in equation forisequivalent to training the network. If the number oftraining samples equal to the number of hidden
nodes, = then is a square matrix and can befound by calculating the inverse ofwhere a zero
training error is obtained. Thus, if L < N then it is not asquare matrix and a solution can be found using Moore–
Penrose generalized inverse of matrix
as given in
Equation (4)Table 1. Dataset descriptions

H †T (4)In binary classification problems, the decision function
for ELM with one output node can be written in a vector
form from Equation (1) and is given in Equation (5).
signi G ( a i , bi , x )Description

Age

61 – 91 years’ old

GenderMale (11), Female (9)4.1. Spatial Normalisation
The first step is to correct the origin of the raw MRI data,
the main purpose is to reposition the crosshair position sothat it would provide better normalisation result of thesubject’s structural MRI to the template’s structural MRI
which in this case, it is the MNI152 space.

Lf ( x)

Specification

sign .h( x) (5)i 1© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 738

Classification of Hippocampal Region

Next, is to realign and reslice. Fundamentally, thefunction of realign and reslice is to registers all images ofa subject to generate parameter files so that it can be usedlater on to correct for head motion. By realigning theimages of the subject, it will match the image bytransforming it to manipulate the scan. The realignfunction only allows translations by moving the image in
X, Y and Z direction and rotation. After the realignment
process the next step will be reslice. Reslice is a function
where it will refine which images are needed as
sometimes the MRI might not have the same thickness asothers thus corrects its motion.

5. Experiment, Result, and Discussion
This part will discuss on the result of the experiment thathave been conducted for hippocampal classification in
two experiments 1) full MRI image and 2) ROI of MRI
image. 68 MRI from NC has been pre-processed using
spatial normalisation technique. From the pre-processed
MRI, 2-4 hippocampal from sagittal, axial and coronalhas been extracted resulting to 140 extracted images ofhippocampal and non-hippocampal. All of them has been
fed into bag of feature in order to extract its feature
vector. Both of the experiment will used 60% from theextracted images as the training sample and another 40%
as testing set. Table 2 below shows the clarification on
how the experiment has been setup. Whereas theparameter for both of the experiment can be seen on table
3

The last step of spatial normalisation is to normalise thesubject’s MRI to the template’s MRI, so that it could put
the subject’s MRI into standardised MNI space. The
function of this step is mainly to determine thetransformation that minimises the differences between
two scans by minimising the sum of squares of intensity
differences. The result of the normalised MRI can be seenin Figure 3

Table 2. Distribution dataset for training and testing
Model

Testing
Set

Training Set

Full
image

Training 60%: 84 feature vector

ROI

Test 40%: 56 feature vector

Table 3. Experiment's parameter setup

Fig. 3. Spatial normalisation result
Parameter
4.2. Feature extraction using BoF
The next step is to segment hippocampal and nonhippocampal so that the segmented image can be feed
into BoF to get its Visual Words. There are several steps
involved in order to extract the Visual Words which are
to extract its features, learn “Visual Vocabulary” andquantize features using Visual Vocabulary and lastly torepresent each image by frequencies of Visual Words
which can be seen in Figure 4.

Experiment
1

Experiment2

HiddenNodes

10, 30, 50, 100, 300, 500, 800,
1000, 1200, 1500

Activation
Function

Sigmoid function

Experiment
Runs

30

As mentioned previously, two set of experiment havebeen conducted where the first experiment is to test on
different configuration of hidden neurons for full imageof MRI. The investigated hidden nodes are 10, 30, 50,
100, 300, 500, 800, 1000, 1200 and 1500 with 30 set ofexperiment where all of the image will be sorted by using
pseudorandom method. The results for the firstexperiments are tabulated in Table 4.
Fig. 4. Hippocampal and non-hippocampal visual word
© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 739

Muhammad Hafiz Md Zaini, Mohd Ibrahim Shapiai, Ahmad Rithauddin Mohamed, Norrima Mokhtar, Zuwairie Ibrahim

parameters setup as given in Table 3. The results for thesecond experiments are tabulated in Table 5. Experimentwill be setup with the same configuration as experiment
1 which can be seen in table 1 and table 2. The result canbe seen on table below.

Table 4. Experiment 1 result
HiddenTraining
AVG

MIN

MAX

STD

10

0.684325

0.619048

0.755952

0.028971

30

0.905357

0.863095

0.940476

0.02212650

0.969048

0.952381

0.982143

0.009431

100

0.999405

0.994048

10.001816

300

1

1

1

0

500

1

1

1

0

Table 5. Experiment 2 result
HiddenTraining ROI
AVG

MIN

MAX

STD

10

0.803968

0.744048

0.845238

0.026247

0.940476

0.898810.964286

0.014327

800

1

1

1

0

30

1000

1

1

1

0

50

0.984325

0.952381

1

0.011107

1200

1

1

1

0

100

1

1

1

0

1500

1

1

1

0

300

1

1

1

0

500

1

1

1

0

HiddenTesting
AVG

MIN

MAX

STD

800

1

1

1

0

10

0.63869

0.544643

0.75

0.044719

1000

1

1

1

0

30

0.823810.741071

0.883929

0.03632

1200

1

1

1

0

1

1

1

0

50

0.892262

0.839286

0.946429

0.025463

1500

100

0.882440.803571

0.928571

0.029106

Hidden300

0.971131

0.919643

0.991071

0.01718500

0.982143

0.955357

10.013055

800

0.98869

0.96428610.010469

1000

0.993452

0.964286

1

Test ROI
AVG

MIN

MAX

STD

10

0.763988

0.696429

0.839286

0.03491130

0.891071

0.848214

0.93750.024072

0.00843250

0.905952

0.821429

0.955357

0.035462

0.88006

0.794643

0.93750.036976

1200

0.988393

0.955357

1

0.011523

100

1500

0.996131

0.991071

1

0.0045300

0.982738

0.964286

10.009649

500

0.993155

0.9732141

0.006062

From the table result above, it shows that ELM could
clearly distinguish between hippocampal and nonhippocampal image where both training and testing result
could achieve as high as 100%. This could be seen in bar
chart below that shows the average of training and testing
accuracy. When the more the number of hidden neuron isapplied, the higher the accuracy will be. In thisexperiment 1000 hidden neurons are the optimum hidden
neuron parameter for this experiment set, because theaverage of the testing accuracy will start to decrease alittle bit when the number of hidden neuron applied isincreased to 1200 but when 1500 hidden neuron isapplied it started to increased back.

The results of the second experiment show that it could
offer a better result compare to experiment 1. This isbecause, the average testing accuracy in experiment 2 ishigher than experiment as tabulated in Table 5. The
comparison performance between experiment 1 andexperiment 2 are shown in figure 5

As for the second experiment, all of the segmented imageof both hippocampal and Non-hippocampal will beclassified using ROI of MRI image using similar

The optimum hidden neurons for experiment 2 is 500
because after that, the testing accuracy remains the same
when the number of hidden neurons applied is increased.

800

0.998214

0.991071

1

0.003632

1000

0.999405

0.991071

1

0.0022651200

0.999702

0.991071

10.001631500

0.999702

0.991071

10.00163© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 740

Classification of Hippocampal Region

However, some things worth to be mention, where ELM
could not give a good classification result when theparameter between hippocampal and non-hippocampal isalmost the same. This could be seen in the result ofExperiment 2 on hidden neurons 500 to 1500. Which inthis case the testing result shows 1 of the testing isclassified wrong. One of the reason that could be
deducted is its visual word’s is almost the same with thenon-hippocampal’s visual words, figure 6 shows thecomparison between the classified wrong image’s visual
words and non-hippocampal’s visual words. Aside from
that, this study proves that ELM could be applied in
Neuroimaging problems especially on segmenting brain
subcortical problem.

influenced by the number of hidden neurons applied.
This clearly shows the ability of ELM’s where it has good
generalisation performance, as its only need to adjust thehidden neuron’s parameter in order to increase its
accuracy. For future works, ELM could be further
improved into structured-ELM so that it could be
experimented in more complicated problem especiallyregarding hippocampal segmentation problems.
Acknowledgements
The author would like to thank to Universiti Teknlogi
Malaysia for financially supporting this research workthrough
University
Matching
Grant,
vote
R.K130000.7343.4B188.

1.5
1References

0.5

1.

0

2.
full_AVGROI_AVG

Fig. 5. Comparison test average between full image and ROI

3.

4.

5.

Fig. 6. Visual word occurrences between wrongly segment
hippocampal and non-hippocampal

6. Conclusion
In general, ELM has been tested to two different type ofhippocampal segmentation which are full image and ROI.
Besides that, several hidden nodes configuration for
ELM has been investigated too. From the result ofexperiment 1 and 2, this study could deduct that ELM
could easily classify between hippocampal and nonhippocampal image this is because the result for bothexperiment 1 and 2 could achieve as high as 100%!
Besides that, from the experiment conducted also, ahypothesis could be made, where the accuracy result is

6.

7.

8.
9.

I. Bankman, Handbook of medical image processing
and analysis: academic press, 2008.
M. Mizotin, J. Benois-Pineau, M. Allard, and G.
Catheline, "Feature-based brain MRI retrieval for
Alzheimer disease diagnosis," in 2012 19th IEEE
International Conference on Image Processing, 2012,
pp. 1241-1244.
P. Suppa, H. Hampel, L. Spies, J. B. Fiebach, B.
Dubois, and R. Buchert, "Fully automated atlas-based
hippocampus volumetry for clinical routine:
Validation in subjects with mild cognitive
impairment from the ADNI cohort," Journal of
Alzheimer's Disease, vol. 46, pp. 199-209, 2015.
C. L. Leggett and K. K. Wang, "Computer-aided
diagnosis in GI endoscopy: looking into the future,"
Gastrointestinal Endoscopy, vol. 84, pp. 842-844,
2016.
Y. Hao, T. Wang, X. Zhang, Y. Duan, C. Yu, T. Jiang,
et al., "Local label learning (LLL) for subcorticalstructure segmentation: application to hippocampus
segmentation," Human brain mapping, vol. 35, pp.
2674-2697, 2014.
J. Dolz, L. Massoptier, and M. Vermandel,"Segmentation algorithms of subcortical brain
structures on MRI for radiotherapy and radiosurgery:a survey," IRBM, vol. 36, pp. 200-212, 2015.
O. T. Carmichael, H. A. Aizenstein, S. W. Davis, J.
T. Becker, P. M. Thompson, C. C. Meltzer, et al.,"Atlas-based
hippocampus
segmentationin
Alzheimer's disease and mild cognitive impairment,"
Neuroimage, vol. 27, pp. 979-990, 2005.
N. Oktar and Y. Oktar, "Machine Learning and
Neuroimaging," Journal of Neurological Sciences(Turkish), vol. 32, pp. 001-004, 2015.
Y. Wang, Y. Fan, P. Bhatt, and C. Davatzikos, "Highdimensional pattern regression using machine© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 741

Muhammad Hafiz Md Zaini, Mohd Ibrahim Shapiai, Ahmad Rithauddin Mohamed, Norrima Mokhtar, Zuwairie Ibrahim

10.
11.

12.

13.
14.

15.

16.
17.
18.

19.
20.

learning: from medical images to continuous clinicalvariables," Neuroimage, vol. 50, pp. 1519-1535,
2010.
M. Lai, "Deep learning for medical image
segmentation," arXiv preprint arXiv:1505.02000,
2015.
Z. Bai, G.-B. Huang, D. Wang, H. Wang, and M. B.
Westover, "Sparse extreme learning machine forclassification," IEEE transactions on cybernetics, vol.
44, pp. 1858-1870, 2014.
O. B. Ahmed, J. Benois-Pineau, C. B. Amar, M.
Allard, and G. Catheline, "Early Alzheimer disease
detection with bag-of-visual-words and hybrid fusion
on structural MRI," in Content-Based Multimedia
Indexing (CBMI), 2013 11th International Workshop
on, 2013, pp. 79-83.
T. F. Cootes and C. J. Taylor, "Statistical models of
appearance for computer vision," ed: Technicalreport, University of Manchester, 2004.
J. Morra, Z. Tu, A. Toga, and P. Thompson,"Machine learning for brain image segmentation,"
Biomedical Image Analysis and Machine Learning
Technologies: Applications and Techniques:
Applications and Techniques, p. 102, 2009.
J. H. Morra, Z. Tu, L. G. Apostolova, A. E. Green, A.
W. Toga, and P. M. Thompson, "Comparison of
AdaBoost and support vector machines for detecting
Alzheimer’s disease through automated hippocampal
segmentation," IEEE transactions on medical
imaging, vol. 29, p. 30, 2010.
L. Fausett, Fundamentals of neural networks:
architectures, algorithms, and applications: PrenticeHall, Inc., 1994.
P. T. Fox, "Spatial normalization origins: Objectives,
applications, and alternatives," Human brain
mapping, vol. 3, pp. 161-164, 1995.
S. Krishnan, M. J. Slavin, T.-T. T. Tran, P. M.
Doraiswamy, and J. R. Petrella, "Accuracy of spatial
normalization of the hippocampus: implications for
fMRI research in memory disorders," Neuroimage,
vol. 31, pp. 560-571, 2006.
A. Annema, K. Hoen, and H. Wallinga, "Precision
requirements for single-layer feedforward neuralnetworks," 1994.
N.-Y. Liang, G.-B. Huang, P. Saratchandran, and N.
Sundararajan, "A fast and accurate online sequential
learning algorithm for feedforward networks," IEEE
Transactions on Neural networks, vol. 17, pp. 14111423, 2006.

© The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), Jan. 19-22, Seagaia Convention Center, Miyazaki, JapanP - 742