ICAROB2016
PROCEEDINGS OF THE 2016
INTERNATIONAL CONFERENCE
ON ARTIFICIAL LIFE AND ROBOTICS

January 29-31, 2016
Okinawa Convention Center, Okinawa, JAPANInternational Meeting Series

Editor-in-Chief
Masanori Sugisaka
Editors: Yingmin Jia, Takao Ito, Ju-Jang Lee
ISBN 978-4-9908350-1-9

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

Proceedings of The 2016 International Conference on

ARTIFICIAL LIFE AND ROBOTICS(ICAROB 2016)

January 29-31, 2016
Okinawa Convention Center, Okinawa, Japan
Editor-in-Chief: Masanori Sugisaka
Editors: Yingmin Jia, Takao Ito, Ju-Jang Lee,ICAROB 2016
ISBN 978-4-9908350-1-9©ICAROB 2016

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

Contents
1

Organization, etc.

1

2

Messages

93Time Table

13

4

Opening Ceremony

16

5

Technical paper index

17

6

Abstracts
PS abstracts
IS abstracts
OS abstracts
GS abstracts

30
31
31
52

7

Authors index

688

Conference room

75

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

©ICAROB 2016

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

SPONSERED BYAROB (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
Chinese Association for Artificial Intelligence (CAAI, P. R. China)©ICAROB 2016
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The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

ADVISORY COMMITTEE CHAIR
Moshe Kam (New Jersy Institute of Technology, Former IEEE President, USA)ADVISORY COMMITTEE
Fumio Harashima (Tokyo Metropolitan University, President, Japan)
Bruce Eisenstein (Drexel University, Former IEEE President, USA)
Hideki Kimura (RIKEN, Japan)
Masayoshi Tomizuka (University of California Berkeley, USA)
Moshe Kam (New Jersy Institute of Technology, Former IEEE President, USA)
Adam Grzech (Wroclaw University of Technology, Poland)
Kazuyuki Aihara (The University of Tokyo, Japan)
D. J.Gyln. James (Coventry University, United Kingdom)
Jeffery Johnson (The Open University, UK)
Kazuo Kyuma (Mitsubishi Electric Corporation, Japan)
Steen Rasmussen (University of Southern Denmark, Denmark)Joshua. M. Epstein (The Johns Hopkins University, USA)
Jezry Świątek (Wroclaw University of Technology, Poland)Paul Kalata (Drexel University, USA)Paul Oh (Drexel University, USA)
Robert Fischl (Drexel University, USA)
Toshio Fukuda (Meijo University, Japan)
Kai-Tai Song (National Chiao Tung University, Taiwan)
GENERAL CHAIR
Masanori Sugisaka (Alife Robotics Corporation Ltd., Japan)
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 (University of Sultan Zainal Abidin, Malaysia)
Yongguang Zhang (Academia Sinica, P. R. China)©ICAROB 2016-2-

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

PROGRAM CHAIRMAN
Makoto Sakamoto (University of Miyazaki, Japan)
SUB PROGRAM CHAIR
M. Oswald (The Vienna University of Technology, Austria)
INTERNATIONAL ORGANIZING COMMITTEE
Akira Fukuda (Kyushu University, Japan)
Eiji Hayashi (Kyushu Institute of Technology, Japan)
Hazry Desa (University of Malaysia, Perlis, Malaysia)ang
Hidehiko Yamamoto (Gifu University, Japan)
Hideyuki Suzuki (The University of Tokyo, Japan)
Hiroshi Kage ((Mitsubishi Electric Corporation, Japan)
Hiroshi Matsuno (Yamaguchi University, Japan)
Jiwu Wang (Beijing Jiaotong University, P. R. China)
Jovana Jovic (CNRS-AIST JRL, Japan, France)
Katsunori Shimohara (Doshisha University, Japan)
Kenichi Tanaka (Meji 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)
Masahito Yamamoto (Hokkaido University, Japan)
Masao Kubo (National Defense Academy of Japan, Japan)
Masanao Obayashi (Yamaguchi University, Japan)
Masafumi Yamashita (Kyushu 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)
Seiji Ishikawa (Kyushu Institute of Technology, Japan)
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)
Thomas S. Ray (University of Oklahoma, USA)
Toru Yamamoto (Hiroshima University, Japan)
Victor Berdonosov (Komsomolsk-on-Amur State University ofTechnology, Russia)
Yasunari Yoshitomi (Kyoto Prefectural University, Japan)©ICAROB 2016
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The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

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)
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©ICAROB 2016
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The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

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
M. Sakamoto (University of Miyazaki, Japan)
M. Sugisaka (ALife Robotics Co., Ltd., Japan)
Takao Ito (Hiroshima University, Japan)
HISTORY
The International Conference on Artificial Life and Robotics (ICAROB) resulted from the AROBsymposium whose first edition was held in 1996 and the eighteenth and last edition in 2013. The
AROB symposium was annually organized by Oita University, Nippon Bunri University (NBU), andALife Robotics Corporation Ltd., under the sponsorship of the Science and Technology Policy
Bureau, the Ministry of 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 of Aerospace Research and Development (AFOSR/AOARD), USA. I would like to express mysincere 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 inTokyo, Japan (AROB 6th ’01).) was organized by the International Organizing Committee of 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
expanded much by absorbing much new knowledge and technologies into it. This history andcharacter 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 thejournal. The future of the ICAROB is brilliant from a point of view of yielding new technologies tohuman society in the 21st century. This conference invites you all.

©ICAROB 2016
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The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

AIMS AND SCOPE
The objective of this conference is the development of new technologies for artificial life androbotics which have been recently born in Japan and are expected to be applied in various fields.
This conference presents original technical papers and authoritative state-of-the-art reviews on
the development of new technologies concerning robotics, networking and artificial life and,especially computer-based simulation and hardware for the twenty-first century. This conference
covers a broad 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 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©ICAROB 2016
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The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016














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 robotic
GENERAL SESSION TOPICS
GS1 Neuromorphic Systems (5)GS3 Reinforcement Learning System &Genetic Programming(6)GS5 Multi-Agent Systems &Visualization(2)GS7 RoboticsⅠ(6)GS9 RoboticsⅢ(4)GS2 Pattern Recognition &Image
Processing(6)GS4 Micro-Machine(4)GS6 Intelligent Control & System
Cybernetics(3)GS8 RoboticsⅡ(5)GS10 Poster Sessions(5)ORGANIZED SESSION TOPICS
OS1 Artificial Life and Quality
Enhancement of Life(6)OS3 Construction Engineering andManagement(6)OS5 Advanced Control Systems(6)OS7 Image Analysis and Content
Security(5)OS9 Modern approaches of MOT and
Corporate Strategy(3)OS2 Intelligence Control System and
Artificial Life(6)OS4 Construction Technology andManagement(5)OS6 Intelligent Control(6)OS8 Image, Circuit and Control(9)OS10 Reality Mining(3)OS11 Kansei Engineering andOS12 Advanced Research on Computer
Application(4)Science and Information Processing(4)OS13 Automatic Generation, Creation, andProduction of Narrative Contents(4)©ICAROB 2016
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The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

COPYRIGHTS
Accepted papers will be published in the proceeding of The 2016 International Conference on
Artificial Life and Robotics (ICAROB 2016). Some of high quality papers in the proceeding will be
requested to re-submit their papers for the consideration of publication in an international
journal ROBOTICS, NETWORKING AND ARTIFICIAL LIFE under agreement of both Editor-in- Chiefand 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 2016
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The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

MESSAGES
Masanori Sugisaka
General Chair of ICAROB

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

It is my great honor to invite you all to The 2016 International Conferenceon Artificial Life and Robotics (ICAROB 2016).
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 thesponsorship of the Science and Technology Policy Bureau, the Ministry of
Education, Science, Sports, and Culture (Monbusho), presently, the Ministryof 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 of Aerospace Research andDevelopment (AFOSR/AOARD), USA. I would like to 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) , Japanese companies for their repeated support.
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) to now. From now
on, ALife Robotics Corporation Ltd. is in charge of management. This year wehave The 2016 International Conference on Artificial Life and Robotics(ICAROB 2016). The future of The ICAROB is brilliant from a point of view ofyielding new technologies to human society in 21st century.
I hope that fruitful discussions and exchange of ideas between researchers
during Conference (ICAROB 2016) will yield new merged technologies forhappiness of human beings and, hence, will facilitate the establishment of aninternational joint research institute on Artificial Life and Robotics in future.

©ICAROB 2016
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The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

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 2016 International Conference on
Artificial Life and Robotics (ICAROB 2016), in Ginowan City, Okinawa, Japan
from Jan. 29th to 31st, 2016.
ICAROB develops from the AROB that was created in 1996 by Prof. Masanori
Sugisaka and celebrated her birthday of 21st years old in 1996. Doubtless, newmission and big challenges in the field of artificial life and 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 andspeakers, and the meeting organizing team for their excellent works.
Looking forward to meeting you at ICAROB in Ginowan City, Okinawa andwishing you enjoy your stay in Japan.

©ICAROB 2016
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The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

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 2016 International Conferenceon Artificial Life and Robotics (ICAROB 2016).This Conference is changed as
the old symposium from the first (1996) to the Eighteenth I am pleased towelcome you to The 2016 International Conference on Artificial Life andRobotics in the wonderful city of Ginowan, Okinawa, JapanThe ICAROB has long history. The former organization of the ICAROB wasdeveloped under the strong leadership of the President, Professor.
Masanori Sugisaka, the father of AROB. We gathered many researchers,
faculty members, graduate students from all over the world, and publishednumerous high-quality proceedings and journals every year.
Over the years, dramatic improvements have been made in the field ofartificial 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 life-asit-could–be. It will provide us a good place to present our new research
results, good ideas, and valuable information about artificial intelligence,complex systems theories, robotics, management of technology, etc.
In order to provide an outstanding technical level for the presentations at
the conference, we have invited more than 60 distinguished experts in thefield of artificial life in the organizing committee and program committee.
We will have 22 sessions during 3 days of conference, including 4 invited
sessions.
The conference site is Okinawa Convention Center, one of the finest
congress centers in Okinawa. It is situated near the center of the Ginowan
city in Okinawa. You can find many fantastic scenic spots and splendid
historical places in Naha city. Enjoy your stay and take your time to visit thecity of Okinawa.
I am looking forward to meeting you in Okinawa during ICAROB 2016 andto sharing a most pleasant, interesting and fruitful conference©ICAROB 2016
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The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

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 the increasingly
important role of the new interdisciplinary paradigm of science andengineering represented by the field of artificial life and robotics that
continuously attracts wide interests among scientist, researchers, andengineers 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, discuss research
directions for the future, exchange views and ideas, view the results ofapplied research, present and discuss the latest development of newtechnologies and relevant applications.
In addition, ICAROB offers the opportunity of hearing the opinions of wellknown 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 and associatedresearch area. The twenty-first century will become the century of artificial
life and intelligent machines in support of humankind and ICAROB iscontributing through wide technical topics of interest that support thisdirection.
It is a great honor for me as a Co-General Chair of the3rd ICAROB 2016 towelcome everyone to this important event. Also, I would like to extend myspecial thanks to all authors and speakers for contributing their researchworks, the participants, and the organizing team of the 3rd ICAROB.
I’m looking forward to meeting you at the 3rd ICAROB in Naha City,
Okinawa and wishing you all the best.

©ICAROB 2016
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The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

1/28(Thu.) 17:30-19:30

Welcome Party(Conference Site: Farm Café TAIYO ICHIBA)TIME TABLE (1/29)
1/29(Fri.)

RoomB2

RoomB3

8:509:10-10:25

OS4 Construction
Technology andManagement(5)Chair: C. C. Chang
Co-Chair: Y. C. Shiau

GS8 RoboticsⅡ(5)Chair: J. J. Lee

RoomB4
Registration
GS10 Poster Sessions (5)10:25-11:40

Coffee break

10:40-11:10

Opening Ceremony (RoomB2)

11:20-12:00

Chair: T. Ito
Invited Speech IS1(RoomB2)
Luigi Pagliarini

12:00-13:00

Lunch

13:00-14:00

Chair: T. Ito
Plenary Speech PS1(RoomB2)
Henrik H. Lund

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

Coffee breakOS11 Kansei Engineeringand Application(4)Chair: T. Hattori
Co-Chair: Y. Imai

15:50-16:10
16:10-18:10

OS2 Intelligence Control
System and Artificial
Life(6)Chair: K. H. Hsia
Co-Chair: K. L. Su

OS1 Artificial Life andQuality Enhancement ofLife(6)Chair: S. H. Li
Co-Chair: S. D. Wu
Coffee break
GS1 Neuromorphic
Systems(5)Chair: T. Kondo©ICAROB 2016
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OS3 Construction Engineeringand Management(6)Chair: Y. C. Shiau
Co-Chair: Y. Y. Tsai

GS4 Micro-Machine(4)Chair: J.M. Lee
GS9 RoboticsⅢ(4)Chair: A. Boonyaprapasorn

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

TIME TEBLE (1/30)
1/30(Sat.)

RoomB2

RoomB3

8:509:10-10:40

GS2 Pattern recognition &image processing(6)Chair: T. Fujita

GS7 RoboticsⅠ(6)Chair: A. Yamaguchi

10:40-11:00

Coffee break

11:00-11:40

Chair: Y. Jia

RoomB4
Registration
OS5 Advanced Control
Systems(6)Chair: Y. Jia
Co-Chair: F. Wang

Invited speech IS2(RoomB2)
Kaoru Sumi
11:40-13:00

Lunch

13:00-14:00

Chair: Y. Jia
Plenary Speech PS2(RoomB2)
Ju-Jang Lee

14:00-14:20
14:20-1S:50

OS8 Image, Circuit andControl(6)Chair: F. Dai
Co-Chair: H. Jia

15:50-16:10

Coffee breakOS12 Advanced Researchon Computer Science andInformation Processing(4)Chair: T. Ito
Co-Chair: M. Sakamoto

16:10-17:10

18:00-20:00

Coffee break
OS6 Intelligent Control(6)Chair: Y. Jia
Co-Chair: W. Zhang

Banquet: Culture Resort Festone©ICAROB 2016
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GS5 Multi-Agent Systems &Visualization(2)Chair: S. Mabu
GS6 Intelligent Control &System Cybernetics(3)Chair: M. C. Lee

OS8 Image, Circuit andControl(3)Chair: F. Dai
Co-Chair: H. Jia

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

TIME TABLE (1/31)
1/31(Sun.)

RoomB3

8:50-

RoomB4
Registration

9:10-9:55

OS10 Reality Mining(3)Chair: M. Kubo
Co-Chair: S. Iwanaga

9:55-10:10

Coffee breakOS13 Automatic Generation, Creation,
OS7 Image Analysis andand Production of Narrative Contents(4)Content Security(5)Chair: T. Ogata
Chair Y. Yoshitomi
Co-Chair: M. Tabuse
Co-Chair: Y. Kawamura

10:10-11:25

OS9 Modern Approaches of MOT and
Corporate Strategy(3)Chair: T. Ito
Co-Chair: S. Matsuno

11:25-13:00
13:00-14:30

Lunch
GS3 Reinforcement Learning Systems &Genetic Programing(6)Chair: K. Kobayashi

14:40-15:40

Farewell Party

GS1 Neuromorphic Systems(5)GS2 Pattern Recognition & Image Processing(6)GS3 Reinforcement Learning Systems & Genetic
Programing(6)GS4 Micro-Machine(4)GS5 Multi-Agent Systems & Visualization(2)GS6 Intelligent Control & System Cybernetics(3)GS7 RoboticsⅠ(6)GS8 RoboticsⅡ(5)GS9 RoboticsⅢ(4)GS10 Poster Sessions(5)(Conference Site: Farm Café TAIYO ICHIBA)

OS1 Artificial Life and Quality Enhancement ofLife(6)OS2 Intelligence Control System and Artificial
Life(6)
OS3 Construction Engineering and Management(6)OS4 Construction Technology and Management(5)OS5 Advanced Control Systems(6)OS6 Intelligent Control(6)OS7 Image Analysis and Content Security(5)OS8 Image, Circuit and Control(9)OS9 Modern Approaches of MOT and Corporate
Strategy(3)OS10 Reality Mining(3)OS11 Kansei Engineering and Application(4)OS12 Advanced Research on Computer Science andInformation Processing(4)OS13 Automatic Generation, Creation, andProduction of Narrative Contents(4)©ICAROB 2016- 15 -

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

The 2016 International Conference on
ARTIFICIAL LIFE AND ROBOTICS(ICAROB 2016)
January 28 (Thursday)
17:30-19:30

Welcome Party (Conference Site: Farm Café TAIYO ICHIBA)

January 29 (Friday)
Room B2 10:40-11:10

Opening Ceremony
Chair: Jiwu Wang (Beijing Jiaotong University, China)
Welcome Addresses
1. General Chairman of ICAROB
2. Co-General Chairman of ICAROB
3. Co-General Chairman of ICAROB
4. Co-General Chairman of ICAROB

M. Sugisaka (ALife Robotics Corporation Ltd. Japan)
Y. M. Jia (Beihang University, China)
T. Ito (Hiroshima University, Japan)
J. J. Lee (KAIST, South Korea)

January 30 (Saturday)
Banquet: Culture Resort Festone
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. Kaoru Sumi (Future University Hakodate, Japan)
Prof. Saori Iwanaga (Japan Coast Guard Academy, Japan)©ICAROB 2016
- 16 -

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

TECHNICAL PAPER INDEXJanuary 29 (Friday)
08:50-Registration
Room B2
9:10-10:25 OS4 Construction Technology and Management (5)Chair: Ching-Ching Chang (Chung Hua University, Taiwan)
Co-Chair: Yan-Chyuan Shiau (Chung Hua University, Taiwan)
OS4-1

Study on the Promotion of Steel Slags Recycling in Taiwan
Shu-Chen Chang, Ching-Jung Chang (Chung Hua University, Taiwan)

OS4-2

The Research on the Utilization of Green Building Ecological Index Group in Campus
Environment – Taking of Elementary Schools at Houli District, Taichung City as Example
Ching-Jung Chang, Yung- Feng Hsu, Chun-Hsien Chen (Chung Hua University, Taiwan)

OS4-3

The Assessment and Research on the Reconstruction of Enclosing Wall into Open Type in
Elementary School Campus – Taking of Erlin Township, Changhua County as Example
Ching-Jung Chang, Chia-Chen Li, I-Chen Wu (Chung Hua University, Taiwan)

OS4-4

The Study and Analysis on the Reuse and Transformation Strategy of Tile Kiln Space – Takingof San-He Tile Kiln as Example
Ching-Jung Chang, Hsiao-Yu Lin, Ying-Yu Su (Chung Hua University, Taiwan)

OS4-5

The Research on the Influence of Economic Benefit for High-Rise Buildings Constructed byDifferent Excavation Method – Taking of Residence Buildings at New Taipei City as Example
Ching-Jung Chang, Cheng-Min Yang, Hsiu-Hsiung Hsu (Chung Hua University, Taiwan)

10:40-11:10 Opening Ceremony
11:20-12:00
Chair: Takao Ito (Hiroshima University, Japan)
Invited Speech (IS1) Redefining robot based technologies for elderly people assistance: a survey.
1,2Luigi Pagliarini, 1Henrik Hautop Lund (1Technical University of Denmark, Denmark, 2Academyof Fine Arts of Macerat, Italy)
13:00-14:00
Plenary Speech (PS1) Disrupting the Industry with Play
Henrik Hautop Lund (Technical University of Denmark, Denmark)©ICAROB 2016- 17 -

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

14:20-15:20 OS11 Kansei Engineering and Application (4)Chair: Tetsuo Hattori (Kagawa University, Japan)
Co-Chair: Yoshiro Imai (Kagawa University, Japan)
OS11-1

Automated Multiple-Brightness Peak Image Processing Method Using Curvature andVariance Estimation
1
Yusuke Kawakami, 2Tetsuo Hattori, 2Yoshiro Imai, 2Kazuaki Ando, 2Yo Horikawa
1( DynaxT Co., Ltd. 2Kagawa University, Japan)
3
R. P. C. Janaka Rajapakse (3Tainan National University of the Arts, Taiwan)

OS11-2

Histogram Matching Based on Gaussian Distribution on the HSB Color System
1
Yusuke Kawakami, 2Tetsuo Hattori, 2Yoshiro Imai, 2Kazuaki Ando, 2Yo Horikawa
1( DynaxT Co., Ltd. 2Kagawa University, Japan)
3
R. P. C. Janaka Rajapakse (3Tainan National University of the Arts, Taiwan)

OS11-3

Quantitative Evaluation of Flash-based Educational Visualizing Simulator
Kei Takeichi, Yoshiro Imai, Kazuaki Ando, Yusuke Kawakami, Tetsuo Hattori(Kagawa University, Japan)

OS11-4

Relation between Optimal Stopping Solution and NSPR for Structural Change Point Detection
Problem1
Tetsuo Hattori, 1Yoshihide Koyama, 1Yoshiro Imai,1Yo Horikawa, 2Hiromichi Kawano
1( Kagawa University, 2NTT advanced technology Company Ltd, Japan)

16:10-17:40 OS2 Intelligence Control System and Artificial Life (6)Chair: Kuo-Hsien Hsia (Far East University, Taiwan)
Co-Chair: Kuo-Lan Su (National Yunlin University of Science & Technology, Taiwan)
OS2-1

Modified Quantum Particle Swarm Optimization for Chaos Synchronization
Ching-I Lee, Chia-Nan Ko (Nan Kai University of Technology, Taiwan)

OS2-2

A Simulation Model of Hall Sensor Misalignment in BLDC Motors
Chung-Wen Hung, Chu-Lin Hsu(National Yunlin University of Science & Technology, Taiwan)

OS2-3

Using Multi-Target Tracking and Identification TLD Algorithm for Intelligent Mobile
Robot
Jr Hung Guo, Kuo-Hsien Hsia, Kuo-Lan Su(National Yunlin University of Science & Technology, Taiwan)

OS2-4

Handheld Mobile Devices for Remote Monitoring of Factory
Bing-Gang, Jhong, Jian-Sing, Hu, Mei-Yung, Chen(National Taiwan Normal University, Taiwan)©ICAROB 2016
- 18 -

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

OS2-5

Task Assignment and Checking Process for Pattern Reformation on a Square Grid
Plane
Kuo-Hsien Hsia1, Bo-Yi Li2, Kuo-Lan Su2 (1Far-East University, 2National Yunlin
University of Science & Technology, Taiwan)

OS2-6

The Design of a Multifunctional Acousto-Optical Device With a Mobile PowerPack
Yi-Yu Lu, Wen-Bin Lin, Zheng-Ying Li (Far-East University, Taiwan)

Room B3
9:10-10:25 GS8 RoboticsⅡ(5)Chair: Ju-Jang Lee (KAIST, South Korea)
GS8-1

Visual-servo Control of 4-DOF Robot Manipulator for Sorting Moving objects
Longtan Wang, Seon-woo Kim, Hyun-Wook Ha, Jang-Myung Lee(Pusan National University, South Korea)

GS8-2

Self-identification of Mental State and Self-control through Indirect Biofeedback-Indirect Representation and Placebo EffectMadoka Takahara, Ivan Tanev, Katsunori Shimohara (Doshisha University, Japan)

GS8-3

Construction of a sense of force feedback and vision for micro-objects: Recreate the response
and a sense of force of objects
Yusei Ishii, Eiji Hayashi (Kyusyu Institute of Technology, Japan)

GS8-4

Dynamic Behavior Selection Model based on Emotional States for Conbe-I robot
Wisanu Jitviriya, Jiraphan Inthiam, Eiji Hayashi (Kyushu Institute of Technology, Japan)

GS8-5

Consideration on a Crawler Robot with Six Legs
Toyomi Fujita, Taiga Sasaki (Tohoku Institute of Technology, Japan)

14:20-15:50 OS1 Artificial Life and Quality Enhancement of Life (6)Chair: Shang-Hui Li (Far East University, Taiwan)
Co-Chair: Sheu-Der Wu (Cheng Shiu University, Taiwan)
OS1-1

The development of new products of pineapple cheesecakes
Sheu-Der Wu1, Hung-Wen Su2 (1Cheng Shiu University, 2Far East University, Taiwan)

OS1-2

The study on ballroom service quality to affect customer satisfaction
Hsiu-Min Lin , Yang Wu, Jei-Fu Ho (Far East University, Taiwan)

OS1-3

The relationship between employees’ personality traits, work values and job involvementwith the contribution to their companies – For the micro food service industry
Hsiu-Chen Chung1, Chiou-Lan Chien1, Lung-Chi Tsai2(1Tainan University of Technology,
2Southern Taiwan University of Science and Technology, Taiwan)©ICAROB 2016
- 19 -

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

OS1-4

Consumer’s acceptance to the new product- pineapple jam as example
Shang-Hui Li, Jei-Fu Ho (Far East University, Taiwan)

OS1-5

Development of an innovative design process for green products
Ming-Chieh Wang (Far East University, Taiwan)

OS1-6

Research on Nail Art 3D design with Different Materials - A Case Study of the Tang Dynasty
Style
Mei-Yin Lee, Kung-Yu Liu, Sih-Jie Guan, Jei-Fu Ho (Far East University, Taiwan, ROC)-

16:10-17:25 GS1 Neuromorphic Systems (5)Chair: Tadashi Kondo (Tokushima University, Japan)
GS1-1

Compensating Temperature-Dependent Characteristics of a Subthreshold-MOSFET Analog
Silicon Neuron
Ethan Green, Takashi Kohno (The University of Tokyo, Japan)

GS1-2

Medical image analysis of brain X-ray CT images by deep GMDH-type neural network
Tadashi Kondo, Junji Ueno, Shoichiro Takao (Tokushima University, Japan)

GS1-3

Medical image diagnosis of lung cancer by deep feedback GMDH-type neural network
Tadashi Kondo, Junji Ueno, Shoichiro Takao (Tokushima University, Japan)

GS1-4

Feature Linking by Synchronized Response in Chaotic Cellar Neural Network
for Visual Stimulus of Moving Objects
Akihiro Yamaguchi1, Satoshi Arakane1, Masao Kubo2 (1 Fukuoka Institute of Technology,2
National Defense Academy of Japan, Japan)

GS1-5

An FPGA-based cortical and thalamic silicon neuronal network.
Takuya Nanami , Takashi Kohno (The University of Tokyo, Japan)

Room B4
9:10-10:25 GS10 Poster Sessions (5)GS10-1Analysis of Postgraduates’ Entrance Examination Scores Based on Linear Regression with
Dummy Variables
Ning Xiaojun, Huang Ruocheng, Liang Xiaoyi, Ai Dongmei(University of Science and Technology Beijing, P.R. China)

GS10-2Clinical Evaluation of UR-System 2 for Recovery of Motor Function of Plegic Upper Limb after
Stroke1
Hirofumi Tanabe, 1Masahiro Mitsukane, 2Norihiro Toya, 2Ryosuke Takeichi, 2Hitomi Hattori,
2
Yoshifumi Morita, 3Yoshiaki Takagi, 3Norio Hasegawa (1Shonan University of Medical
Sciences, 2Nagoya Institute of Technology, 3Sanyo Machine Works, Ltd., Japan)©ICAROB 2016- 20 -

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

GS10-3Knee Android Model Reproducing Internal-External Rotation with Screw-Home Movementof Human Knee
Daichi Yamauchi, Sho Takei, Noritaka Sato, Yoshifumi Morita(Nagoya Institute of Technology, Japan)

GS10-4

Wood Species Recognition System based on Improved Basic Grey Level Aura Matrix asfeature extractor
1
Mohd Iz’aan Paiz Zamri, 1Anis Salwa Mohd Khairuddin, 1Norrima Mokhtar, 2Rubiyah Yusof(1University of Malaya, 2Universiti Teknologi Malaysia, Malaysia)

GS10-5

Evaluation the Performance of a New Quadrotor Model Based on the Arm’s Length Variation
Yasameen Kamil N., D. Hazry, Khairunizam Wan, Zuradzman M.Razlan(University Malaysia perlis, Malaysia)

14:20-15:50 OS3 Construction Engineering and Management (6)Chair: Yan-Chyuan Shiau (Chung Hua University, Taiwan)
Co-Chair: Yi-Yin Tsai (Chung Hua University, Taiwan)
OS3-1

Discussion on Factors Influencing the Performance of Hospital Renovation
Engineering Taking one Medical Center in Taiwan as Example
Wen-Lung Lin, Yan-Chyuan Shiau, Ting-Chi Lai, Chen-Chung Liu(Chung Hua University, Taiwan)

OS3-2

Discussion on Land Expropriation Compensation System
Hung-Chi Liu, Yan-Chyuan Shiau, Yao-Shan Huang, Chen-Chung Liu(Chung Hua University, Taiwan)

OS3-3Study on User Satisfaction of Pick Areas for Elementary Schools – Using Nanyang
Elementary School in Taichung City as Example
Chun-Feng Chang, Yan-Chyuan Shiau, Kuan-Yin Chen
(Chung Hua University, Taiwan)

OS3-4

Study on User Satisfaction in Sport Site Facilities for Senior Students of Elementary
Schools in Taichung City
Pei-Ling Lin, Yan-Chyuan Shiau, Ling-Lin Chang (Chung Hua University, Taiwan)

OS3-5

The Establishment of the Sustainability Performance Indicators for Wetland Ecological Project:
Using Construction Inspection Phase as Example
Ching-Mei Miao, Yan-Chyuan Shiau, Chen-Chung Liu, Jen-Kuo Chang(Chung Hua University, Taiwan)

OS3-6

Development of Type Control Guidelines for the Old Town District of Hsinchu City
Yi-Yin Tsai (Chung Hua University, Taiwan)©ICAROB 2016
- 21 -

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

16:10-18:10 GS4 Micro-Machine (4) + GS9 Robotics Ⅲ(4)GS4 Micro-Machine (4)Chair: Jang-Myung Lee (Pusan National University, South Korea)
GS4-1

Applying Fuzzy Sliding Mode Control on Electrowetting on Dielectric System
1
Arsit Boonyaprapasorn, 2Thavida Maneewarn, 2Eakkachai Pengwang (1Chulachomkloa Royal
Military Academy, 2King Mongkut’s University of Technology Thonburi, Thailand)

GS4-2

Development of Micro-Permanent Magnet Synchronous Reluctance Generator for TPMS on
Smart Robots
1
Chun-Chieh Chang, 1 Cheng-Tang Pan, 1Shao-Yu Wang, 1An-Yun Yang, 1Gu-Xuan Lin,2
Roger Chenglung Lee, 2 Ting-Hung Chung, 1Yu-Jen Wang
1
( National Sun Yat-sen University, 2Naroller Electronics, Taiwan)

GS4-3

Study of Micro-Flux-Switching Permanent-Magnet Generator for TPMS on Smart Robots
1
An-Yun Yang, 1 Cheng-Tang Pan, 1Shao-Yu Wang, 1Chun-Chieh Chang, 1Gu-Xuan Lin,2
Roger Chenglung Lee, 2Ting-Hung Chung, 1Yu-Jen Wang
1
( National Sun Yat-sen University, 2Naroller Electronics, Taiwan)

GS4-4

MEMS Microrobot Controlled by Mounted Neural Networks IC with Two Types Actuators
Kei Iwata, Hirozumi Oku, Yuki Okane, Yohei Asano, Masaki Tatani, Yuki Ishihara, Kazuki Sugita,
Satohiro Chiba, Satoko Ono, Mizuki Abe, Minami Takato, Ken Saito, Fumio Uchikoba(Nihon University, Japan)

GS9 RoboticsⅢ(4)Chair: Arsit Boonyaprapasorn (Chulachomklao Royal Military Academy, Thailand)
GS9-1

Social Expression of Pet Robot Based on Artificial Consciousness and Biologically Inspired Online Topological Method
Sakmongkon Chumkamon, Eiji Hayashi (Kyushu Institute of Technology, Japan)

GS9-2

Anthropomorphic robot modelling with virtual height inverted pendulum approach in
Simulink: step length and period influence on walking stability
Ramil Khusainov, Ilya Afanasyev, Evgeni Magid (Innopolis University, Russia)

GS9-3

A low cost genetic algorithm based control scheme for wheelchair control in hospitalenvironment
Karam Dad Kallu, Muhammad Jawad Khan, Wang Jie, Min Cheol Lee(Pusan National University, South Korea)

GS9-4

Simultaneous Localization and Mapping (SLAM) algorithm base on EKF and SPKF.
Zolghadr Javad, Yuanli Cai ,Yekkehfallah Majid (Xi’an Jiaotong University, P.R. China)©ICAROB 2016- 22 -

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

January 30 (Saturday)
08:50-Registration
Room B2
09:10-10:40 GS2 Pattern Recognition & Image Processing (6)Chair: Toyomi Fujita (Tohoku Institute of Technology, Japan)
GS2-1

An accurate method for the extraction of structured light stripe
Jiwu Wang1, Yaodong Li1, Zhijing Jian1, Sugisaka Masanori2(1Beijing Jiaotong University, P.R. China) (2Alife Robotics Corporation Ltd, Japan)

GS2-2

Feature Acquisition From Facial Expression Image Using Convolutional Neural Networks
Taiki Nishime, Satoshi Endo, Koji Yamada, Naruaki Toma, Yuhei Akamine(University of The Ryukyus, Japan)

GS2-3

Estimating Age on Twitter Using Self-Training Semi-Supervised SVM
Tatsuyuki Iju, Satoshi Endo, Koji Yamada, Naruaki Toma, Yuhei Akamine(University of The Ryukyus, Japan)

GS2-4Interactive musical editing system to support human errors and offer personal preferences
for an automatic piano
Kenji Tsunenari, Eiji Hayashi (Kyushu Institute of Technology, Japan)

GS2-5

Geometric parameters measurement of wheel tread based on line structured light
Jiwu Wang1, Zhijing Jian1, Yaodong Li1, Chao Yang1, Sugisaka Masanori2(1Beijing Jiaotong University, P.R. China), (2Alife Robotics Corporation Ltd, Japan)

GS2-6

Study on the ORB algorithm in the application of Monocular SLAM
Jiwu Wang1, Shunkai Zheng1, Masanori Sugisaka2(1Beijing Jiaotong University, P.R. China), (2ALife Robotics Corporation Ltd, Japan)

11:00-11:40
Chair: Yingmin Jia (Beihang Univesty, P. R. China)
Invited Speech (IS2) Affective Human Computer Interaction
Kaoru Sumi (Future University Hakodate, Japan)
13:00-14:00
Plenary Speech (PS2) A Study on Differential Evolution Using BetaCOBL, B3R, and TPBO
So-Youn Park, Ju-Jang Lee (KAIST, South Korea)
14:20-15:50 OS8 Image, Circuit and Control (6)Chair: Fengzhi Dai (Tianjin University of Science and Technology, P.R. China)
Co-Chair: Hongyan Jia (Tianjin University of Science and Technology, P.R. China)©ICAROB 2016
- 23 -

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

OS 8-1

Image Encryption Implementation Based on fractional-order Chen System
Hongyan Jia, Qinghe Wang (Tianjin University of Science and Technology, P.R. China)

OS 8-2

Mobile Camera based Motion Segmentation by Image Resizing
Chunyu Yu (Syracuse University, USA)OS 8-3

Synchronization of the fractional-order permanent magnet synchronous motor
Xue Wei, Li Xue (Tianjin University of Science and Technology, P.R. China)

OS 8-4

Analog Circuit Implementation and Full State Observation of Chua’s Circuit
Hong Niu, Dongchen Tan, Yongjun Wu(Tianjin University of Science and Technology, P.R. China)

OS 8-5

Research on visualizational rescue robot
YuanLi Yue, Fengzhi Dai, Qijia Kang, Pengfei Xie(Tianjin University of Science and Technology, P.R. China)

OS 8-6Development of training instrument for upper limb muscle rehabilitation
Qijia Kang, Fengzhi Dai, Yuanli Yue, Bo Liu, Hongtao Zhang(Tianjin University of Science and Technology, P.R. China)

Room B3
09:10-10:40 GS7 RoboticsⅠ(6)Chair: A. Yamaguchi (Fukuoka Institute of Technology, Japan)
GS7-1

Haptic system with fuzzy controller for extended control of Teleoperation mine detector
wheeled robots
Yekkehfallah Majid, Guao Yang, Yuanli Cai, Naebi Ahmad, Zolghadr Javad(Xi'an jiaotong university, P.R. China)

GS7-2

Effect of System Parameters and Controlled Torque on the Dynamics of Rigid-Flexible Robotic
Manipulator
Sachindra Mahto (North Eastern Regional Institute of Science & Technology, India)

GS7-3

Modeling of Mobile Manipulator and Adaptive Super-Twisting Backstepping Control
Seong Ik Han, Hyunuk Ha, Jangmyung Lee(Pusan National University, South Korea)

GS7-4

Self-tuned Local Feedback Gain Based Decentralized Fault Tolerant Control ofReconfigurable Manipulators
Bo Zhao1, Bo Dong2, Yan Li2, Fumitoshi Matsuno3, Yuanchun Li2(1Chinese Academy of Sciences, 2Changchun University of Technology, P.R. China),(3Kyoto University, Japan)©ICAROB 2016- 24 -

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

GS7-5

Study on Decentralized Integral Nested Sliding Mode Control for Constrained Reconfigurable
Manipulator with Harmonic Drive Transmission
Bo Dong1, Zeyu Dong1, Bo Zhao2, Yan li1, Fumitoshi Matsuno3, Yuanchun Li1(1Changchun University of Technology, 2Chinese Academy of Sciences, P.R. China),(3Kyoto University, Japan)

GS7-6

A Number of Mobile Manipulator Control for Moving Object by using Cooperative Control
Deok-Su Kim, Dong-Eon Kim, Seong-Ik Han, Jang-Myung Lee(Pusan National University, South Korea)

14:20-15:50 OS6 Intelligent Control (6)Chair: Yingmin Jia (Beihang University, P. R. China)
Co-Chair: Weicun Zhang (University of Science and Technology, P. R. China)
OS6-1

Adaptive Control of Discrete-Time Systems Using Multiple Fixed and One Adaptive
Identification Models
Zhang Yuzhen, Li Qing, Zhang Weicun(University of Science and Technology Beijing, P. R. China)

OS6-2

Adaptive Polynomial Regression and Its Applications to Gene Selection of Rat Liver
Regeneration
Juntao Li, Yimin Cao, Xiaoyu Wang, Cunshuan Xu (Henan Normal University, P.R .China)

OS6-3

Adaptive Consensus via Dynamic Output Feedback for Lipschitz Nonlinear Multi-Agent
Systems
Lin Li, Heyang Wang (University of Shanghai for Science and Technology, P.R. China)

OS6-4

Iterative Learning based Thrust Ripple Suppression for PMLSM
Caixia Gao, Fuzhong Wang, Ziyi Fu (Henan Polytechnic University, P.R. China)

OS6-5

Adaptive Sliding Mode Control for A 2-DOF Magnetic Levitation Systemwith Uncertain Parameters
Meng Duan, Yingmin Jia (Beihang University, P.R. China)

OS6-6

Undershoot Reduction in Discrete-Time ADRC of NMP Plant by Parameters Optimization
Tong Wu, Weicun Zhang (University of Science and Technology Beijing, P.R. China)

16:10-17:10 OS 12 Advanced Research on Computer Science and Information Processing (4)Chair: Takao Ito (Hiroshima University, Japan)
Co-Chair: Sakamoto Makoto (University of Miyazaki, Japan)
OS12-1Development of a Tool to Keep Consistency between a Model and a Source Code in Software
Development Using MDA.
Yuuki Kikkawa1, Tetsuro Katayama1, Yoshihiro Kita2, Hisaaki Yamaba1, Kentaro Aburada3 andNaonobu Okazaki1 (1University of Miyazaki, 2Tokyo University of Technology, 3Oita National
College of Technology, Japan)©ICAROB 2016
- 25 -

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

OS12-2

Necessary spaces for seven-way four-dimensional Turing machines to simulate fourdimensional one-marker automata
Makoto Nagatomo1, Shinnosuke Yano1, Makoto Sakamoto1, Satoshi Ikeda1, Takao Ito2,
Tsutomu Ito2, Yasuo Uchida3, Tsunehiro Yoshinaga4, and Hiroshi Furutani1(1University of Miyazaki,2Hiroshima University, 3Ube National College of Technology,
4Tokuyama College of Technology, Japan)

OS12-3

Analysis for 4×12 board of Othello
Yuki Takeshita1, Satoshi Ikeda1, Makoto Sakamoto1, Takao Ito2
(1Miyazaki University, 2Hiroshima University, Japan)

OS12-4

A space lower-bound technique for four-dimensional alternating Turing machines
Makoto Nagatomo1, Shinnosuke Yano1, Makoto Sakamoto1, Satoshi Ikeda1, Takao Ito2,
Tsutomu Ito2, Yasuo Uchida3, Tsunehiro Yoshinaga4, and Hiroshi Furutani1(1University of Miyazaki, 2Hiroshima University, 3Ube National College of Technology,
4Tokuyama College of Technology, Japan)

RoomB4
9:10-10:40 OS5 Advanced Control Systems (6)Chair: Yingmin Jia (Beihang University, P.R. China)
Co-Chair: Fuzhong Wang (Henan Polytechnic University, P.R. China)
OS5-1

Socialized Multi-Agent System Rendezvous via Networks of Networks
Yunzhong SONG, Ziyi FU, Fuzhong WANG (Henan Polytechnic University, P.R.China)

OS5-2

Global sensor selection for maneuvering target tracking in clutter
Wenling Li, Yingmin Jia (Beihang University, P.R. China.)

OS5-3

Attitude/Position Estimation of Rigid-Body using Inertial and Vision Sensors
Shihao Sun, Yingmin Jia (Beihang University, P.R. China)

OS5-4Modeling and Control of a Suspended Gravity Compensation System with the Rigid-Flexible
Coupling
Jiao Jia, Yingmin Jia, Shihao Sun (Beihang University, P.R. China)

OS5-5Trajectory Tracking Control for Omnidirectional Mobile Robots with Input Constraints
Wenhao Zheng, Yingmin Jia (Beihang University, P.R. China)

OS5-6

Parameters Tuning Approach for Prescribed Performance Function Based Active DisturbanceRejection Control
Wei Wei, Bo Liang, Weijun Su (Beijing Technology and Business University, P.R. China)©ICAROB 2016
- 26 -

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

14:20-15:35 GS5 Multi-Agent Systems & Visualization (2) +GS6 Intelligent Control & System
Cybernetics (3)GS5 Multi-Agent Systems & Visualization (2)Chair: Shingo Mabu (Yamaguchi University, Japan)
GS5-1

Moving Robots Lies and Their Minds with Degree of Confidence in a Decentralized
Autonomous FMS
Shizuka Tanaka, Hidehiko Yamamoto, Takayoshi Yamada (Gifu University, Japan)

GS5-2

Conquest Oriented Robot Knowing Its Own Availability
Sho Yamauchi, Keiji Suzuki (Future University Hakodate, Japan)

GS6 Intelligent Control & System Cybernetics (3)Chair: Min Cheol lee ((Pusan National University, South Korea)
GS6-1

A Consideration on Feature Extraction for Operation Skill Based on Control Engineering
Approach
Kazushige Koiwai1, Liao Yantao1, Toru Yamamoto1, Takao Nanjo2, Yoichiro Yamazaki2,
Yoshiaki Fujimoto2(1Hiroshima University, 2KOBELCO Construction Machinery CO., LTD., Japan)

GS6-2

Design of a Data-Driven Control System for a Hydraulic Excavator
1
Takuya Kinoshita, 1Yasuhito Oshima, 1Kazushige Koiwai, 1Toru Yamamoto 2Takao Nanjo,
2
Yoichiro Yamazaki, 2Yoshiaki Fujimoto(1Hiroshima University, 2KOBELCO Construction Machinery CO., LTD, Japan)

GS6-3

An Optimization of Spatio-Spectral Filter Bank Design for EEG Signal Classification
Masanao Obayashi, Takuya Geshi, Takashi Kuremoto, Shingo Mabu
(Yamaguchi University, Japan)

16:10-16:55 OS8 Image, Circuit and Control (3)Chair: Fengzhi Dai (Tianjin University of Science and Technology, P.R. China)
Co-Chair: Hongyan Jia (Tianjin University of Science and Technology, P.R. China)
OS 8-7

Design and Analog Circuit Implementation of a Dynamic Feedback Control System Based on
RLC Series Circuit
Hong Niu, Yongjun Wu, Dongchen Tan (Tianjin University of Science & Technology, P.R. China)

OS 8-8Maximal power point tracking technology for the solar photovoltaic battery based onmodel predictive control
Xia Zhao, Huailin Zhao (Shanghai Institute of Technology, P.R. China)

OS 8-9Analysis and unidirectionally coupled synchronization of a novel chaotic system
Li Xue, Xue Wei (Tianjin University of Science and Technology, P.R. China)©ICAROB 2016
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The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

January 31 (Sunday)
8:50- Registration
Room B3
9:10-9:55 OS10 Reality Mining (3)Chair: Masao KUBO (National Defense Academy of Japan, Japan)
Co-Chair: Saori Iwanaga (Japan Coast Guard Academy, Japan)
OS10-1

Fast collective photographic subject detection without pixels by an assumption about a shot
and its elevation angle
Sora Tanioka, Masao Kubo, Hiroshi Sato (National Defense Academy of Japan, Japan)

OS10-2

Evaluation of a safety map generated from a collection of difference of Individuals
Masao Kubo, Dang Viet-Chau, Hiroshi Sato, Akira Namatame(National Defense Academy of Japan, Japan)

OS10-3Endeavor to adopt GIS data on evacuation decision making model1
Saori Iwanaga, 2Akira Namatame(1Japan Coast Guard Academy, 2National Defense Academy of Japan, Japan)

10:10-11:10 OS13 Automatic Generation, Creation, and Production of Narrative Contents (4)Chair: Takashi Ogata (Iwate Prefectural University, Japan)
Co-Chair: Yoji Kawamura (Kinki University, Japan)
OS13-1

Automatic Generation, Creativity, and Production of Narrative Contents
Takashi Ogata (Iwate Prefectural University, Japan)

OS13-2A Design Plan of a Game System including an Automatic Narrative Generation Mechanism:
The Entire Structure and the World Settings
Jumpei Ono, Takashi Ogata (Iwate Prefectural University, Japan)

OS13-3

A Way for using the Verb Conceptual Dictionary in an Integrated Narrative Generation
System: Focusing on the use of Co-occurrence Information on the Verb Concepts
Takashi Ogata, Jumpei Ono (Iwate Prefectural University, Japan)

OS13-4

A Viewing Experiment on the Effects of Advertising Story
Yoji Kawamura (Kinki University, Japan)

13:00-14:30 GS3 Reinforcement Learning Systems & Genetic Programing (6)Chair: Kunikazu Kobayashi (Aichi Prefectural University, Japan)
GS3-1

Examination of Robotic Aerospace Engines Maintenance Supported by Augmented Reality
through Cloud Manufacturing
Mosab Alrashed, Yaser Yadekar, John Ahmet Erkoyuncu, Yifan Zhao (Cranfield University, UK)©ICAROB 2016
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The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

GS3-2

Unit Layout Design Supporting System of Cell Assembly MachineUsing Two Robots byReinforcement Learning
Yusaku Ikai, Hidehiko Yamamoto, Takayoshi Yamada (Gifu University, Japan)

GS3-3

Virtual Input Parts Decision System of Job-Shop Production Line by using GA with ON / OFF
GeneJunji Ito, Hidehiko Yamamoto, Takayoshi Yamada (Gifu University, Japan)

GS3-4

An Evolutionary Algorithm for Making Decision Graphs for Classification Problems
Shingo Mabu, Masanao Obayashi, Takashi Kuremoto (Yamaguchi University, Japan)

GS3-5

Improvement of Computational Efficiency of UPF by Automatic Adjustment of the Number ofParticles
Kenta Hidaka, Takuo Suzuki, Kunikazu Kobayashi (Aichi Prefectural University, Japan)

GS3-6

Multi Objective Evolutionary Algorithms for Association Rule Mining: Advances andChallenges
Aswini Kumar Patra (North Eastern Regional Institute of Science & Technology, India)

RoomB4
9:10-9:55 OS9 Modern Approaches of MOT and Corporate Strategy (3)Chair: Takao Ito (Hiroshima University, Japan)
Co-Chair: Seigo Matsuno (National Institute of Technology, Ube College, Japan)
OS9-1

A Study on the Structural Hole of Patent Applicant Network in R&D Management
Iori Nakaoka1, Yousin Park2, Yun-ju Chen3 (1National Institute of Technology, Ube College,
2
Prefectural University of Hiroshima, 3Shiga University, Japan)

OS9-2

The TCE-RBV framework for information systems outsourcing: Empirical testing using
survey data in Japan
1
Seigo Matsuno, 2Tsutomu Ito, 1Yasuo Uchida, 3Yoshiki Mikami, 4Takao Ito
1( National Institute of Technology, Ube College, 2Hino Motors, Ltd., 3Nagaoka University ofTechnology, 4Hiroshima University, Japan)

OS9-3

Momentum and its Implications in Corporate Management
Tsutomu Ito1, Takao Ito1, Rajiv Mehta2, Sakamoto Makoto3, Katsuhiko Takahashi1,
Katsumi Morikawa1(1Hiroshima University, 3Miyazaki University, Japan) (2New Jersey
Institute of Technology, USA)

10:10-11:25 OS7 Image Analysis and Content Security (5)Chair: Yasunari Yoshitomi (Kyoto Prefectural University, Japan)
Co-Chair: Masayoshi Tabuse (Kyoto Prefectural University, Japan)©ICAROB 2016
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The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

OS7-1

A Method for Secure Communication Using a Discrete Wavelet Transform for Sound Data
Yuji Tsuda1, Kouhei Nishimura2, Haruka Oyaizu3, Yasunari Yoshitomi2, Taro Asada2,
Masayoshi Tabuse2 (1Software Service, Inc., 2Kyoto Prefectural University, 3NHK Media
Technology, Inc., Japan)

OS7-2

An Authentication Method Using a Discrete Wavelet Transform for a Recaptured Video
Ren Fujii, Yasunari Yoshitomi, Taro Asada, Masayoshi Tabuse(Kyoto Prefectural University, Japan)

OS7-3

A System for Facial Expression Analysis of a Person While Using Video Phone
Taro Asada, Yasunari Yoshitomi, Ryota Kato, Masayoshi Tabuse, Jin Narumoto(Kyoto Prefectural University, Japan)

OS7-4

Emotion Recognition of a Speaker Using Facial Expression Intensity of Thermal Image andUtterance Time
Yuuki Oka1, Yasunari Yoshitomi2, Taro Asada2, Masayoshi Tabuse2(1NTT DATA Financial Solutions Corp. 2Kyoto Prefectural University, Japan)

OS7-5

Estimation of Learners' Subjective Difficulty in e-Learning Using Thermal Image Processing
Yuki Yoshimitsu, Masayoshi Tabuse (Kyoto Prefectural University, Japan)

PS abstracts
PS1 Disrupting the Industry with Play
Henrik Hautop Lund (Technical University of Denmark, Denmark)Decades of research into intelligent, playful technology and user-friendly man-machineinterfaces has provided important insight into the creation of robotic systems andintelligent interactive systems which are much more user-friendly, safer and cheaper
than what appeared possible merely a decade or two ago. This is significantly
disrupting the industry in several market sectors. This paper describes the componentsof the playware and embodied artificial intelligence research that has led to disruption
in the industrial robotics sector, and which points to the next disruption of the healthcare sector. This includes playful robotics, LEGO robots for kids, minimal robot
systems, user-friendly, behavior-based, biomimetic, modular robotics and intelligent
systems. The insight into these components and the use in synthesis for designing
robots and intelligent systems allows anybody, anywhere, anytime to use these systems,
providing an unforeseen flexibility into the sectors, which become disrupted with thesesystems.

PS2 A Study on Differential Evolution Using BetaCOBL, B3R, and TPBO
So-Youn Park, Ju-Jang Lee (KAIST, South Korea)
New parameters are defined to use a beta distribution without limitation and two betautilizations to control the search magnitude of thstacte algorithm are proposed: one foradding randomness to OBL (BetaCOBL) and the other for individual distributionoriented reproduction (B3R). BetaCOBL and B3R are developed into DE embedding
BetaCOBL (BetaCODE) and two-phase B3R optimization (TPBO) respectively. The
proposed algorithms are tested on various test functions and two real world applications
and compared with other algorithms with respect to the performance criteria. The
results indicate that the proposed algorithms outperform or perform comparatively tothe comparison group especially in terms of solution accuracy and reliability.

©ICAROB 2016
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The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

IS abstracts
IS1 Redefining robot based technologies for elderly people assistance: a survey.
Luigi Pagliarini 1,2, Henrik Hautop Lund 1(1 Technical University of Denmark, Denmark, 2 Academy of Fine Arts of Macerata, Italy)
We analyse the state of the art of hi-tech and robot based technologies in terms ofAssistive Technology for all patients and, in particular, elderly people assistanceand everyday activities aid. By focusing on different aspects and characteristics(such as playfulness, invasiveness, learning-speed, efficiency, short and long-term
effect, active vs. passive, etc.) we show the most important existing examples, andhighlight all the factors that might help researchers when developing technologies
for elderly care. Decades of research have not yet declared what the optimal HMI
is, in such context. Since there is an urgent need to clarify how various
technologies can be a goal or an approach for preventive, rehabilitative andassistive interaction, we try to make a first step towards a redefinition of Robotics
Assistive Technology.

IS2 Affective Human Computer Interaction
Kaoru Sumi (Future University Hakodate, Japan)
This talk introduces a study of spoken dialogue agent systems using emotional
expressions as affective human computer interaction. The paper describes anexperiment investigating the effect that the expression and words of the agent have on
people, introduces a spoken agent for customer services using expressive facial
expressions and a spoken agent for mental care using expressive facial expressions andpositive psychology as application systems for affective human computer interaction,and presents a discussion and a conclusion.

OS abstracts
OS1 Artificial Life and Quality Enhancement of Life (6)OS1-1 The development of new products of pineapple cheesecakes
Sheu-Der Wu1, Hung-Wen Su2 (1Cheng Shiu University, 2Far East University, Taiwan)
We are in view of the product - pineapple cheesecakes newly developed for this study,
the questionnaire answers proposed by consumers after sensory evaluations find themost popular flavor with 27% recipe proportion of pineapple chops added.
Furthermore, after pineapples are added into cheesecakes, a new flavor has emerged incheesecakes yet reduces the oily taste without sacrificing its original flavor and nutrient
value. In the experiment of formal sensory evaluation, genders, ages, occupations,incomes and purchase intention are served as factors to analyze consumers' preference
to pineapple cheesecakes. Research results after investigation finds the strongest
acceptability is the samples of pineapple cheesecakes added with 27% pineapple chops.
The least acceptability happens to the samples of pineapple cheesecakes added with
27% pineapple jams. After analysis, it is found the overall acceptability is high but thepurchase intention is low revealing a higher preference to cheesecakes added withpineapple chops.

©ICAROB 2016
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The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

OS1-2 The study on ballroom service quality to affect customer satisfaction
Hsiu-Min Lin , Yang Wu, Jei-Fu Ho (Far East University, Taiwan)
For hoteliers, customer expectations and the real performance of service probably
existed in perceptive variance between the emphasized details and satisfaction tobring about the gaps of service quality. As such, it naturally caused customer
unsatisfied with service quality to lose their patronage. As it is found fromresearch results, service quality exerted positive influence on demographic
variables. If it was available to improve the service quality and the expectations
to customer satisfaction, then it was naturally available to enhance the repeatedpurchase intention of customers. In 10 dimensions cited in this research, after
researching analysis, "tangibles" was satisfied by customers the most. If providers
could keep the satisfaction always, then it was naturally available to enhance thefirst image in customers' mind. Additionally, "external response" scored the leastand provides should improve their own service quality by focusing on the parts
highly emphasized by customers. Furthermore, more emphasis should be placedon the opinions of customers to win their trust and avoid external complaint lodged
by customers without losing the source of customers. The excellency or inferiority
of service quality would directly affect customer satisfaction. If providers wereplanning for sustainable business management, excellent service quality was
imperatively required. As such, it was just available to win the trust from
customers under the fiercely competitive business climate with the goal ofsustainable business management achievable.

OS1-3 The relationship between employees’ personality traits, work values and job
involvement with the contribution to their companies – For the micro food service
industryHsiu-Chen Chung1, Chiou-Lan Chien1, Lung-Chi Tsai2( Tainan University of Technology, 2Southern Taiwan University of Science and Technology, Taiwan)
1

We The purpose of the study is to discover the influence between the employee’s (forwhom who works in the micro food industry) personality, work values and job
involvement to the contribution and dedication. Research sample focus on the snackbar in night market, bakery stores and the micro food industries’ employees or owners
from Taizhong, Changhua, Yunlin, Jiayi and Tainan cities. We send 450 questionnaires,the effective questionnaires are 333, and the percentage of effective retrieve of thequestionnaire is 87.63%. After using reliability analysis, correlation analysis, one-way
analysis of variance (ANOVA), independent, work values and personality can forecast
prediction the employee’s contribution and dedication very precisely; in addition, we
find out that job involvement has a very clear effects between work values and job
contribution.

©ICAROB 2016
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The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

OS1-4 Consumer’s acceptance to the new product- pineapple jam as example
Shang-Hui Li, Jei-Fu Ho (Far East University, Taiwan)
Based on the health and environmental considerations, consumers paid theirmore attention to the pursuit of natural ingredient. In other words, a safe andnon-toxic diet is the basic consideration for the consumers. More consumers
pay more attention on natural foods; they gradually change their eating habits.
In recent years, many food safety events such as tainted milk, clenbuterol,
poison starch, plasticizers etc. have been reported. Consumers tend to buy
natural foods, less chemical additives and security food as the primary concern.
This study attempts to take the natural pineapple jam as the major structure.
And try to add dates and plum into pineapple jam instead of any artificial
additives, and enhance the taste level. Meanwhile, different flavors could
derive from the natural products according to the pineapple jam proportion o f
different formulations. The study expects to be able to create the pineapple jam
which is not the same as made by the market products. The study selected 150
consumers of Tainan area to be tested of preferences of sensory evaluation, andSPSS 12.0 statistical software was used as the data analysis of the outcomes ofthe study. The result found among123 valid questionnaires. The original flavor
pineapple jam in its appearance, aroma, stiffness, acidity, sweetness and overall
acceptability can be accepted by most consumers and there are significant
differences with other products. Consumer also showed their strongest
willingness to buy the original flavor pineapple jam and there are significant
differences with other products.

OS1-5 Development of an innovative design process for green products
Ming-Chieh Wang (Far East University, Taiwan)
We are Under the growing trend of environmental protection, many different ecodesign methods have been proposed, at the same time, there are many TRIZ tools
used to support these processes. In this study, we proposed an innovative design
process for green products. This process includes three frameworks-collaborative
design, innovative design environment, and the green product design system.
Collaborative design framework aims to assist the designer and their upstream
and downstream partners to combine their ideas by CSCW tools. The innovative
design environment includes data layer, information layer, and knowledge layer,
analyzed through the data exchange, information sharing and decision making
during the design process. The green product design system was consists of the
problem analysis, problem solving, and solution evaluation used to assist thedesign process with collaborative coordination and information recording. This
construct integrating the design ideas, knowledge exchange, problem solving andfeedback for the green product design process.

©ICAROB 2016
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The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

OS1-6 Research on Nail Art 3D Design with Different Materials-- A Case Study of the Tang Dynasty Style
Mei-Yin Lee, Kung-Yu Liu, Sih-Jie Guan and Jei-Fu Ho (Far East University, Taiwan, ROC)
Women in Tang Dynasty, China (618 A.D.-907 A.D.) had concerned with beauty. They
had painted color in their nails in addition to emphasis on clothing and makeup. Tang’s
costumes had blended the diverse characteristics of northern ethnic groups. Meanwhilea variety of engineering methods had been used such as embroidery, painting, printing,wearing gems and gemstones. Floral patterns had been appeared in large numbers inclothing, accessories and poetry. This study has tried to build the Tang Dynasty
decorative painting technique or way of painting reappeared on modern nail art and to
make the nail painting presents the different faces. After collecting a lot of relevant
documents and experimenting many combinations of different materials, such as
sculpture, super-light clay, foam paper, popsicle sticks, and Styrofoam, many handmade stereoscopic flowers, such as peony, rose, plum etc., with painting have beenmade. Hence different from the past nail art, beautiful pieces of finger nail with themood of Tang Dynasty poetry are presented. Furthermore, this study has investigatedwhether the stereoscopic nail art design with different materials could be promoted tothe daily nail decoration to enhance the quality of life.

OS2 Intelligence Control System and Artificial Life (6)OS2-1 Modified Quantum Particle Swarm Optimization for Chaos Synchronization
Ching-I Lee, Chia-Nan Ko (Nan Kai University of Technology, Taiwan)
In this study, a modified quantum-behaved particle swarm optimization (MQPSO)
based on hybrid evolution (HEMQPSO) approach is proposed to synchronize chaotic
systems, in which the proposed HEMQPSO algorithm combines the conceptions ofgenetic algorithm (GA) and adaptive annealing learning algorithm with the MQPSO
algorithm to search optimal solutions. Simulation results are illustrated to verify theperformance of chaos synchronization using the proposed HEMQPSO approach. From
the numerical simulations and comparisons with other extant evolutionary methods inchaotic systems, the validity and superiority of the HEMQPSO approach are verified.

X0

OS2-2 A Simulation Model of Hall Sensor Misalignment in BLDC Motors
Chung-Wen Hung, Chu-Lin Hsu(National Yunlin University of Science & Technology, Taiwan)
A simulation model of Hall sensor misalignment in BLDC motors is proposed in thispaper. The Hall sensor is popularly used in brushless DC motor to decide commutate
and rotational speed. However, the accuracy of Hall sensor position is limited in mostof BLDC motors, and it is lack of suitable simulation model for Hall Sensor installation
misalignment. The details of simulation model are discussed in this paper, andsimulation results are also provided to show the model workable.

©ICAROB 2016
- 34 -X  F ( X , X 0 , Q0 )X1 ,..., X M
Xˆ 1 ,..., Xˆ M

Xˆ  F ( X , X 0 , Q)

HEMQPSO_+

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

OS2-3 Using Multi-Target Tracking and Identification TLD Algorithm for Intelligent
Mobile Robot
Jr Hung Guo, Kuo-Hsien Hsia, Kuo-Lan Su(National Yunlin University of Science & Technology, Taiwan)
Image algorithms used herein are Tracking-Learning-Detection (TLD) and Speed UP
Robust Features (SURF). TLD is used for target tracking and SURF is used to identify
targets. We use zoning identification, with the use of statistical probability tostrengthen the efficiency of TLD and SURF. With such a method, the efficiency ofimage identification and target tracking can be enhanced so that the robot cansimultaneously track and identify multiple targets.

OS2-4 Handheld Mobile Devices for Remote Monitoring of Factory
Bing-Gang, Jhong, Jian-Sing, Hu, Mei-Yung, Chen(National Taiwan Normal University, Taiwan)
In this paper, a handheld mobile device system is presented for remote monitoring of
the factory to short the downtime of machines and recovery more quickly. The human–
computer interaction of the system is developed by GP-Por EX, and operates by using
commercial or free distal APP combining with sign-in-website of the company. The
way for the program linked can be linked with multi-way links, such as long-distancelink (3G/4G or Wi-Fi wireless INTERNET), medium distance (LAN) and shortdistance (ETHERNET) and can be chosen in the company portal page. With
supplemented Internet browser program for remote monitoring, the distal end operator
or the person in charge can confirm the machine condition through the distal end of theserver, port, user name and password settings. Therefore, the operational efficiency andsystem transparency can be improved with the handheld mobile devices for remotemonitoring system.

OS2-5 Task Assignment and Checking Process for Pattern Reformation on a
Square Grid Plane
Kuo-Hsien Hsia1, Bo-Yi Li, Kuo-Lan Su(1Far-East University, National Yunlin University of Science & Technology, Taiwan)
Pattern reformation problem on a grid plane is an interesting problem. Suppose there
is a group of mobile robots on a square grid plane with the number of robots equaling
to the number of columns/rows of the grid plane. The mobile robots are initially in acertain formation and they are commanded to change to another formation as soon as
possible. Any one of them has to prevent himself from colliding with others during themoving of reformation process. In this paper, we will provide a systematic process ontask assignment of route programming for the pattern reformation problem on a squaregrid plane. We will also provide a checking process to check if the suggested programis really good or not.

©ICAROB 2016
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The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

OS2-6 The Design of a Multifunctional Acousto-Optical Device With a Mobile
Power Pack
Yi-Yu Lu, Wen-Bin Lin, Zheng-Ying Li (Far-East University, Taiwan)
This study presents details of a multifunctional acousto-optic warning device thatrecently obtained a patent in Taiwan. The device comprises three subsystems, which
are RGB LEDs, warning system and mobile power pack. This device has multiple
functions, including as a traffic baton, camping light, glow sticks and for women's
security at night. The tube body of the device is adjustable, and can extend to 1.5 times
its length. The design is both functional and practical, and it is also energy efficientdue to the use of polymer optical fiber and a lithium battery. Since this device also has
a USB charge and discharge function, it can also work as a mobile power pack.

OS3 Construction Engineering and Management (6)OS3-1 Discussion on Factors Influencing the Performance of Hospital Renovation
Engineering Taking one Medical Center in Taiwan as Example
Wen-Lung Lin, Yan-Chyuan Shiau, Ting-Chi Lai, Chen-Chung Liu (Chung Hua University, Taiwan)
Renovation engineering is conducted to create the high-quality medical environment tomeet the public’s medical demands. The subjects of this study were to investigate therenovation engineering case of four hospital areas in Mackay Memorial Medical Centerin Taiwan. 50 factors influencing the engineering performance were concluded in thisstudy. The rank of each factor’s influencing severity and influencing performance in fourstages for hospital renovation engineering was summarized. The factors’ severity rank
influencing the performance in the four stages were planning and design stage,
construction management stage, bidding stage and case acceptance and closing stage.
The conclusions and suggestions of this study can be used as reference for future hospitalrenovation projects.

OS3-2 Discussion on Land Expropriation Compensation System
Hung-Chi Liu, Yan-Chyuan Shiau, Yao-Shan Huang, Chen-Chung Liu(Chung Hua University, Taiwan)©ICAROB 2016
- 36 -

es
Tax
Pay
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sfer
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Land expropriation is a necessary means for the public construction of country. Usuallysome appropriate compensation was paid to imposed people. How land expropriation
should be compensated in accordance with the spirit of the Constitution for governs toprotect people’s property rights have been explored in this study. The study results showthat the public in Taiwan is rather satisfied with the current land expropriation at the landmarket value. “Estimation of land value by real-estate estimators” and “Compensation
based on values listed on the Actual Price Registration” are much accepted by the public.
It is also suggested that the existing land expropriation compensation shall adjust to meetthe public wish to reduce incidences of struggles and improve administrative proficiency.

Be Imposed
PeopleImposition Profits
Provide Imposition Costs

General Public(Non-expropriatedPeople)

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

OS3-3 Study on User Satisfaction of Pick Areas for Elementary Schools – Using Nanyang
Elementary School in Taichung City as Example
Chun-Feng Chang, Yan-Chyuan Shiau, Kuan-Yin Chen (Chung Hua University, Taiwan)

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Most of the campus accidents happen on the way between school and home, and most of
the locations are close to their homes or schools. The elementary school children have
low autonomy and they are mainly picked by parents. This study has investigated theuser satisfaction of pick areas for elementary schools and their improvement
requirements. Questionnaire is adopted to discuss user satisfaction of the school
environment. The improvement strategies for pick areas were inspected for different
various backgrounds. The research results have showed that overall satisfaction ranges
from general to satisfaction. Some useful suggestions are proposed. These suggestions
can be served as reference for schools and educational organizations to establish an
unimpeded pick area for a safe school commuting environment.

OS3-4 Study on User Satisfaction in Sport Site Facilities for Senior Students of Elementary
Schools in Taichung City
Pei-Ling Lin, Yan-Chyuan Shiau, Ling-Lin Chang (Chung Hua University, Taiwan)
In Taiwan, obesity rate of children and adolescents is 26.8% and physical fitness also
falls behind the neighboring Asian countries. This study has discussed senior students’actual use and satisfaction towards school sport field facilities in elementary schoolsof Taichung City. This study adopted questionnaire method and senior students inelementary schools of Taichung City were used as research subjects which were
sampled with stratified cluster sampling method. The obtained data were analyzed bydescriptive statistics, Chi-square test, T test, one-way ANOVA, and Pearson productmoment correlation. It is expected to provide reference for relevant personnel so as
to provide the ideal campus sports environment.

Student Background
Variables

Result Variables
Sports Buildings andFacilities Usage

1. School Scale
2. Gender
3. Grade

Sports Buildings andFacilities Improvement
Needs
Sports Buildings andFacilities Usage
Satisfaction

OS3-5 The Establishment of the Sustainability Performance Indicators for Wetland
Ecological Project: Using Construction Inspection Phase as Example
Ching-Mei Miao, Yan-Chyuan Shiau, Chen-Chung Liu, Jen-Kuo Chang (Chung Hua University, Taiwan)
Taiwan's government has invested considerable material, human resources and money
to build many artificial by using ecological engineering methods. The critical successfactors of sustainable performance indicator (SPI) were investigated in this research. A
wetland water purification project of a stream in Hsinchu was used as an example in thispaper. The experiment information was integrated as feedback to correct the index
library. The measurement from test results verifies the SPI from sample project case insome facet is consistent with the results of current inspection system. Adopting the SPI
integrated from this study can quickly detect the sustainability of the construction projectand dramatically reduce labor inspection and costs. The research result can be served as
reference for the sustainable development of ecological engineering achievements.

OS3-6 Development of Type Control Guidelines for the Old Town District of Hsinchu City
Yi-Yin Tsai (Chung Hua University, Taiwan)
The study employed the concept of “Building Type Control” to establish the hierarchymodel and design guideline for the urban regeneration process. GIS was used toefficiently manage database and map out the building environmental features to serve
as a visual reference tool. Existing building regulations and urban design policies set
out by the government were reviewed to set out limitation and ultimate vision for the
study district, three type control zonings were set out with type control design
guidelines respectively. Results of the study can serve as a sufficient reference tool toassist design decision making in the urban regeneration process.

©ICAROB 2016
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The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

OS4 Construction Technology and Management (5)OS4-1 Study on the Promotion of Steel Slags Recycling in Taiwan
Shu-Chen Chang, Ching-Jung Chang (Chung Hua University, Taiwan)
The fast development of global economy makes the great development of industry,causes the excess development and abuse of natural resources, and causes the resources
depletion gradually. So the topic of waste reuse has already become an energy strategy
paid more attention the worldwide research units and governments day by day. The
construction industry uses a large amount of concrete, and its basic materials are the fineand coarse aggregates and cement, which are taken from sandstone mining and limestone
calcining, and they will be exploited excessively and destroy the environment. This study
tries to use the industrial waste to replace the aggregates and cement in concrete, and use
them suitably in accordance with their different properties.

OS4-2 The Research on the Utilization of Green Building Ecological Index Group in
Campus Environment – Taking of Elementary Schools at Houli District, TaichungCity as Example
Ching-Jung Chang, Yung- Feng Hsu, Chun-Hsien Chen (Chung Hua University, Taiwan)
Due to overexploitation of the earth, the mankind causes the phenomena such as theclimate anomaly, ozone layer depletion, forest disappearance, species extinction, ocean
pollution, debris flow, global warming and sea level rise etc. Therefore every country in
the world begins to launch the comprehensive environmental protection action of earth.
This research took elementary schools at Houli District, Taichung City as study subjects,
based on “Green Building Assessment Manual” announced by official authority, andconducted “ecological index group” assessment. Through field investigation, analyzed
and compared three assessment indices of “biodiversity”, “greening amount” and “basewater preservation”, and provided recommendation of improvement, in order to raise theecological environment of campus. It is hoped there should have appropriate
contribution for the planning of similar school or environment in the future.

OS4-3 The Assessment and Research on the Reconstruction of Enclosing Wall into OpenType in Elementary School Campus – Taking of Erlin Township, Changhua County
as Example
Ching-Jung Chang, Chia-Chen Li, I-Chen Wu (Chung Hua University, Taiwan)
In recent years, Taiwan government has pursued the open campus by reconstructing theenclosing wall of campus into the friendly fence or dismantling the enclosing wall
directly, let the community residents participate in the school space together. So thegovernment subsidizes the funds and encourages the school to reconstruct or remove theenclosing wall. Because the change for the type and height of enclosing wall, the teachersand parents have the doubt for the security of students in the campus. This research
conducted the regional investigation for the school teachers, students and parents against
the friendly fence and security opinion of open campus. It hopes to provide the reference
for the reconstruction and design of enclosing wall in Elementary School campus by thegovernment authority in the future.

©ICAROB 2016
- 38 -

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

OS4-4 The Study and Analysis on the Reuse and Transformation Strategy of Tile Kiln Space– Taking of San-He Tile Kiln as Example
Ching-Jung Chang, Hsiao-Yu Lin, Ying-Yu Su (Chung Hua University, Taiwan)
Because the new technology was introduced and the abundant use of concrete and steel
products, the tile kiln industry was declined gradually in Taiwan. This paper attempts toreview a successful case of kiln space reuse that has good culture assets preservation andcombines cultural and creative industry. This research selects San-He Tile Kiln as thestudy subject. The questionnaire for investigating the visitors on different requirement
was designed through interviewing the kiln owners and experts in advance and collating
the interview information systematically. The Principal Component Analysis simplified
the questionnaire variables to become the aspects of appeal.
This research hopes toprovide the operation reference to the civilian tile kiln owners or the other related spacereuse sector.

OS4-5 The Research on the Influence of Economic Benefit for High-Rise Buildings
Constructed by Different Excavation Method – Taking of Residence Buildings at New
Taipei City as Example
Ching-Jung Chang, Cheng-Min Yang, Hsiu-Hsiung Hsu (Chung Hua University, Taiwan)
Every domestic big construction company hunted the land for development aggressively,in order to get the priority of obtaining the profit. The selection of excavation method
has great relative relationship on the construction speed of structure, influence ofadjacent building, acquisition of use license and delivery of house. This research
adopted diverse excavation methods used by the constructor to analyze the advantage
and disadvantage, construction period difference, and construction cost prediction etc.
Finally, the results are compiled and contrasted to verify the relative relationship of
excavation method on the difference of construction period, and cost etc. The purpose ofthis research is expected to provide the developer to select suitable excavation method
effectively to reach the highest benefit of delivering the finished house.

OS5 Advanced Control Systems (6)OS5-1 Socialized Multi-Agent System Rendezvous via Networks of Networks
Yunzhong Song, Ziyi Fu, Fuzhong Wang (Henan Polytechnic University, P.R. China)
Networks of networks paradigm was introduced to explore rendezvous problems ofleader-follower multi-agent system, where rendezvous actors were categorized into
target object, leader agents, informed follower agents and isolated follower agents,
respectively. Three humanized system inspired strategies were investigated. Results
demonstrated that democracy strategy could fulfill rendezvous task in expenses with along convergent time and autarchy democracy could come to targeted object quickly
with risk of failing in rendezvous, while mixed strategy cared about both of convergent
speed and utmost task, would take autarchy strategy when connections were available
for follower agents, while democracy rules had to be used if the connections were notpossible.

©ICAROB 2016
- 39 -

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

OS5-2 Global sensor selection for maneuvering target tracking in clutter
Wenling Li, Yingmin Jia (Beihang University, P.R. China.)
In this paper, we consider the problem of sensor selection for maneuvering target
tracking in clutter. By utilizing the global knowledge of all sensors locations, a costfunction that minimizes the expected filtered mean square localization error is developed
to select a given number of active sensors. As the cost function is derived based on thedecentralized structure, we investigate how to derive the decentralized fusion formulaby applying EKF and PDA technique so that the clutter information can be combined
into the cost function. By minimizing the cost function, a sensor selection scheme is
proposed based on the ’add one sensor node at a time’ strategy. Simulations are provided
to illustrate the effectiveness of the proposed approach.

OS5-3 Attitude/Position Estimation of Rigid-Body using Inertial and Vision Sensors
Shihao Sun, Yingmin Jia (Beihang University, P.R. China)
This paper is concerned with the attitude and position estimation of a rigid-body usinginertial and vision sensors. By employing the Newton-Euler method, a kinematic model
is developed for the rigid-body by treating the inertial measurements as inputs. Based on
the coordinate transformation, a nonlinear visual observation model is proposed by using
the image coordinates of feature points as observations. Then the unscented Kalmanfilter (UKF) is utilized to estimate the attitude and position recursively. Simulation
results are provided to show the effectiveness of the proposed algorithm.

OS5-4 Modeling and Control of a Suspended Gravity Compensation System with the RigidFlexible Coupling
Jiao Jia, Yingmin Jia, Shihao Sun (Beihang University, P.R. China)-3

4

System Output

x 10

Output of l
Output of bitax
Output of bitay

3

2

Output Signal

In this paper, the suspension gravity compensation system (SGCS) is modeled and thecorresponding controller is designed. The system is a servo platform consisting of threesub-systems by which a micro-gravy environment can be established. The system model
is deduced based on Lagrange equation. The system state variables are classified in two
parts based on their physical meaning and the model dimensions are reduced on theclassification. A PID controller is designed based on the feedback linearization. The
simulation results show the effectiveness of the proposed method.

1

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-2

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15
time/sOS5-5 Trajectory Tracking Control for Omnidirectional Mobile Robots with Input
Constraints
Wenhao Zheng, Yingmin Jia (Beihang University, P.R. China)
In this paper, a trajectory tracking controller based on kinematics for omnidirectional
mobile robots with input constraints is presented. The tracking error model with thecontrol law is proved to be global asymptotic stability by Lyapunov stability theory. The
input limits can be described as an octahedron in three-dimensional space, so that aspatial vector analysis method is proposed to design time-varying feedback parameters
to limit robot inputs. Simulation results show the feasibility and effectiveness of thecontrol strategy.

©ICAROB 2016
- 40 -

20

25

30

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

OS5-6 Parameters Tuning Approach for Prescribed Performance Function Based Active
Disturbance Rejection Control
Wei Wei, Bo Liang, Weijun Su(Beijing Technology and Business University, P.R. China)
Active disturbance rejection control (ADRC) is a control approach which needs lessinformation of the controlled plants/processes. However, there are many parameters in
the nonlinear functions utilized in ADRC, such parameters make the tuning of ADRC
be a challenge. Prescribed performance function based ADRC is proposed and the tuning
approach is studied in this paper. Some typical controlled plants are taken in thesimulations. Numerical results are presented to support the proposed control approachand its tuning method.

误差跟踪
0.4
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pd2
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-pd1

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OS6 Intelligent Control (6)OS6-1 Adaptive Control of Discrete-Time Systems Using Multiple Fixed and One Adaptive
Identification Models
Zhang Yuzhen, Li Qing, Zhang Weicun (University of Science and Technology Beijing, P. R. China)
The adaptive control of a linear time-invariant discrete-time system using multiple
models is considered in this paper. Based on the prediction errors of a finite number of
fixed and one adaptive identification models, a new weighting algorithm is proposed
for improving system performance. The principal contributions of the paper are theproofs of global stability and the convergence of the overall system. Computer
simulation results are included to complement the theoretical results.

C1
yr(k) +
-

.
.
.

Ci
.
.
.

p1(k)
ω(k)

pi(k)

∑

u(k)

P

y (k )

pn(k)

Cn
pθ(k)
Cθ

OS6-2 Adaptive Polynomial Regression and Its Applications to Gene Selection of Rat Liver
Regeneration
Juntao Li, Yimin Cao, Xiaoyu Wang, Cunshuan Xu (Henan Normal University, P.R. China)
This paper proposes an adaptive polynomial regression by incorporating the multi-classadaptive elastic net penalty into polynomial likelihood loss. The adaptive polynomial
regression was proved to adaptively select the relevant genes in groups in performing
multi-class classification. The proposed method was successfully applied to the gene
expression data for rat liver regeneration and the relevant genes were selected. The
pathway relationships among the selected genes were also provided to verify their
biological rationality.

OS6-3 Adaptive Consensus via Dynamic Output Feedback for Lipschitz Nonlinear MultiAgent Systems
Lin Li, Heyang Wang (University of Shanghai for Science and Technology, P.R. China)
This paper deals with the consensus problem via dynamic output feedback for multiagent systems with fixed topologies. The agents are described by Lipschitz nonlinear
systems. Under the assumption that the states of the multiple agents are unmeasured, andonly output information of each agent can be obtained from its neighbor agents. Basedon the graph and Lyapunov theory, a distributed adaptive consensus algorithm via
dynamic output feedback is proposed, in which the coupling weights between adjacentagents are time-varying and satisfy some designed adaptive laws. Provided examples areincluded to demonstrate the effectiveness of our proposed consensus algorithm.

©ICAROB 2016
- 41 -

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

OS6-4 Iterative Learning based Thrust Ripple Suppression for PMLSM
Caixia Gao, Fuzhong Wang, Ziyi Fu (Henan Polytechnic University, P.R. China)
To solve problem of thrust ripple of multi-segment primary Permanent Magnet Linear
Synchronous Motor (PMLSM), a control strategy combining fuzzy PID, which was
based on velocity regulation and iterative learning algorithm, which was based on current
compensation was proposed to deal with thrust ripple in advance. Through investigationof thrust ripple of PMLSM, mapping relationship between thrust ripple and motor criticalparameters has been established, then thrust ripple suppression model for multi-segment
primary Permanent Magnet Linear Synchronous Motor was available, and control law
can be made possible. Results show that, thrust ripple was less than 1% under different
position; strong suppression of thrust ripple caused by periodic disturbance could also
be inhibited.

fq Expectationvalue
iqk 1

Iterative
learning
controller

Memorizer 2xMemorizer 1

fk

Memorizer 3

iqkv

Thrust ripple
observerDisturbance

Table Lookup
Module+s

iqk

Velocity
controller

-

K

vs

PMLSM

OS6-5 Adaptive Sliding Mode Control for A 2-DOF Magnetic Levitation Systemwith Uncertain Parameters
Meng Duan, Yingmin Jia (Beihang University, P.R. China)
This paper investigates the stability control for a 2 degrees of freedom magnetic
levitation system with both parameters uncertainty and external disturbance. This system
includes one magnet and two coils, the magnet which levitated by two coils can only
move through x-axis and z-axis. The electromagnetic forces between the magnet andcoils are obtained by numerical calculation that brings uncertain parameter of input tothe system dynamical model. To this end, an adaptive sliding mode controller is
proposed. The system stability and robustness are proved by the Lyapunov stability
theory. Simulation results are presented to verify the effectiveness of the proposed
control strategy.

OS6-6 Undershoot Reduction in Discrete-Time ADRC of NMP Plant by Parameters
Optimization
Tong Wu, Weicun Zhang (University of Science and Technology Beijing, P.R. China)
Undershoot phenomena caused by unstable zeros of non-minimum-phase ( NMP) plant
is difficult to deal with in active disturbance rejection control (ADRC). This paper
proposes a new method to reduce undershootby optimizing the controller parameters of
discrete-time ADRC system. Simulation results are given to verify the effectiveness ofthe proposed scheme.

1
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modified discrete ADRC
regular discrete ADRC

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OS7 Image Analysis and Content Security (5)OS7-1 A Method for Secure Communication Using a Discrete Wavelet Transform for Sound Data
Yuji Tsuda1, Kouhei Nishimura2, Haruka Oyaizu3, Yasunari Yoshitomi2, Taro Asada2, Masayoshi Tabuse2(1Software Service, Inc., 2Kyoto Prefectural University, 3NHK Media Technology Inc., Japan)
We developed a method for secure communication using a discrete wavelet transform
for sound data. Two users must have one piece of music before communicating eachother. The music is beforehand transformed into a code using the scaling coefficientsobtained from a discrete wavelet transform. The sound data are transformed into anothercode using the same method as that for the music. The information on the deference
between these two codes is sent from one user to the other. The user can produce thesound similar to the original sound using an inverse discrete wavelet transform with thecode made from the music, the information on the deference between these two codes,
and values of zero for all wavelet coefficients. The voice produced by the proposed
method was audible.

©ICAROB 2016
- 42 -

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

OS7-2 An Authentication Method Using a Discrete Wavelet Transform for a Recaptured Video
Ren Fujii, Yasunari Yoshitomi, Taro Asada, Masayoshi Tabuse (Kyoto Prefectural University, Japan)
Recently, several digital watermarking techniques have been proposed for hiding data in
the frequency domain of moving image files to protect their copyrights. In this study, wedeveloped an authentication method for a recaptured video by using the previously
proposed method using a discrete wavelet transform for a static image and a method forselecting several frames in the moving image. In contrast to digital watermarking, noadditional information is inserted into the original moving image by the newly proposed
method. The experimental results show that the proposed method has a good
performance for authentication.

OS7-3 A System for Facial Expression Analysis of a Person While Using Video Phone
Taro Asada, Yasunari Yoshitomi, Ryota Kato, Masayoshi Tabuse, Jin Narumoto(Kyoto Prefectural University, Japan)
We developed a wireless local area network system, consisting of two personal
computers and one router, where a module for analyzing facial expressions of a person
while speaking with another person using a phone video is equipped in a personal
computer in front of a subject and is controlled by an operator using another personal
computer in the system. In the module, a function for determining the reference frameand locally searching the mouth-part area with the most appropriate position for eachframe to be analyzed is contained. The video saved in the personal computer in front of
the subject is analyzed using image processing software (OpenCV) and the previously
proposed feature parameter (facial expression intensity), which is measured for themouth-part area. The experimental result shows the usefulness of the proposed system.

OS7-4 Emotion Recognition of a Speaker Using Facial Expression Intensity of Thermal
Image and Utterance Time
Yuuki Oka1, Yasunari Yoshitomi2, Taro Asada2, Masayoshi Tabuse2( NTT DATA Financial Solutions Corp., 2Kyoto Prefectural University, Japan)
1

We previously proposed a method for recognizing human emotion. A video is analyzed
by thermal image processing and the feature parameter of facial expression, which is
extracted in the area of the mouth and jaw. The facial expression intensity, defined as
the norm of the difference vector between the feature vector of the neutral facial
expression and that of the observed one, is measured. The standardized mean value offacial expression intensity for a major cluster, and the standardized mean value of time
at utterance for a major cluster are used for recognizing human emotion. In this study,
the emotions of one subject were discriminable with 76.5% accuracy in speaking eachof 23 kinds of utterances with exhibiting each of the five intentional emotions of“angry,” “happy,” “neutral,” “sad,” and “surprised.”©ICAROB 2016
- 43 -

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

OS7-5 Estimation of Learners' Subjective Difficulty in e-Learning Using Thermal Image
Processing
Yuki Yoshimitsu, Masayoshi Tabuse (Kyoto Prefectural Univ., Japan)
In recent years, e-learning has been utilized as a learning system in many schools. In a
conventional class, a teacher teaches learners face-to-face, so that a teacher observes
facial expressions and posture change of learners and estimates learners’ subjective
difficulty of the lecture. On the other hand, e-learning is usually utilized in the selfstudy. A learner learns a teaching material using a computer by oneself, so that a teacher
can't observe the state of the learner. It is difficult for a teacher to estimate learners’
subjective difficulty. Therefore, we propose a learners’ subjective difficulty estimation
system. This system captures the face of a learner in e-learning with a thermal camera.
It extracts the face region, measures the temperature changes of the nose region andthe forehead region of the learner, and estimates the subjective difficulty of the learnerbased on the temperature changes.

OS8 Image, Circuit and Control (9)OS 8-1 Image Encryption Implementation Based on fractional-order Chen System
Hongyan Jia, Qinghe Wang (Tianjin University of Science and Technology, P.R. China)
In this paper, based on the fractional-order Chen system, a kind of double encryption
algorithm method is adopted to realize the image encryption. The method mainly refers
to the transformation of the pixel position and the transformation of pixel value. The
effectiveness of the double encryption method is verified by encryption and decryptionof a typical image. The experimental results show that this method not only has theideal effect of image encryption and decryption, but also possesses a better guaranteeon the image security. That is, this encryption method is practical and feasible.

OS 8-2 Mobile Camera based Motion Segmentation by Image Resizing
Chunyu Yu (Syracuse University, USA)The need of detecting moving object like human and vehicle by mobile camera isincreasing in commerce and industry. In this paper, an image-resize methodology
which can abstract motion segmentation and detect moving object from moving
background is proposed. First, edges images are computed. Then movement vector
between frame images are computed and the relative background motion iscompensated. By adjusting the parameters of resize algorithm, human liked object orvehicle liked object can be segmented separately and the segmentation can be used
for further detection. Experiments have been performed under three different
environments for human detection and vehicle detection.

OS 8-3 Synchronization of the fractional-order permanent magnet synchronous motor
Xue Wei, Li Xue (Tianjin University of Science and Technology, P.R. China)©ICAROB 2016
- 44 -

160
x1
140

y1

120

1

100

y

In this paper, the synchronization of the fractional-order permanent magnet
synchronous motor is investigated. The presented control scheme is simple andflexible, and it is suitable both for design and for implementation in practice.
According to the stability theory of fractional-order linear system, adopting thelinearization by feedback method, a nonlinear feedback controller to implement thesynchronization of the drive system and the response system is designed. The
numerical simulation results coincide with that of theoretical analysis, which canfurther demonstrate the feasibility and effectiveness of the proposed synchronization
scheme.

80
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6

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8

9

10

Variation of x1 andy1The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

OS 8-4 Analog Circuit Implementation and Full State Observation of Chua’s Circuit
Hong Niu, Dongchen Tan, Yongjun Wu(Tianjin University of Science and Technology, P.R. China)
In this paper, an inductance simulator and a nonlinear resistor, which are constructedby operational amplifiers and resistors, are applied to complete the analog circuit
implementation of Chua’s circuit as well as improve the accuracy of circuit parameters.
For the observation of all variables, the state variable z, which represents the productof the linear resistance and the inductor current in Chua’s circuit, should be observed
even if it is not an actual measurable physical quantity. It is found that z can be obtained
via scaling of the voltage of the resistor in the inductance simulator. The real chaotic
curves generated from the analog Chua’s circuit are displayed on the oscilloscope
clearly and correctly.

OS 8-5 Research on visualizational rescue robot
YuanLi Yue, Fengzhi Dai, Qijia Kang, Pengfei Xie(Tianjin University of Science and Technology, P.R. China)
Aimed at the shortcomings of the low efficiency and the limitation of the search andrescue robot, this paper presents a visualizational search and rescue robot based on
Arduino platform. The infrared detector for searching human body is used to detect
life, and the camera can transmit the information back to the mobile phone or computer
terminal in real-time. The track wheel improves the capability of shuttle and overobstacle, and can easily cope with complex terrain. The mounded manipulator cancomplete many missions, such as clean up the road block and deliver essential supplies
to small space. It can replace humans to carry out the task into the dangerous
environment and search-and-rescue person into the confined space.

OS 8-6 Development of training instrument for upper limb muscle rehabilitation
Qijia Kang, Fengzhi Dai, Yuanli Yue, Bo Liu, Hongtao Zhang(Tianjin University of Science and Technology, P.R. China)
This equipment can work in multiple degrees. By the single joint and composite
motion, it imitates human upper limb movement. Composite motion can be multiple
degrees of freedom, so that the equipment can fit with the physiological structure ofhuman body well. Upper limb strength can be recovered efficiently and the muscle
contraction can also be well preserved as expected. Obtaining vibration module, itenhances the effect of recovering. Wireless transceiver module makes the wirelessmonitoring and data wireless transmission be a feasible way. It is connected with thecomputer, and the recover information of patients could be gathered, analyzed andrecorded in computer as well. Date can also be transmitted by the internet, so as tooffer the recover information to medical institution.

©ICAROB 2016
- 45 -.

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

OS 8-7 Design and Analog Circuit Implementation of a Dynamic Feedback Control System
Based on RLC Series Circuit
Hong Niu, Yongjun Wu, Dongchen Tan (Tianjin University of Science and Technology,)
The purpose of this paper is to verify that a dynamic feedback control system can berealized by a simple small analog circuit. A normal RLC series circuit, which is anelectrical two-order circuit only consisting of one resistor, one inductor and one
capacitor, is taken as the controlled object, and the voltage of the capacitor is taken as
the output of the system. The engineering design method of regulator in DC drive
control system is applied to design the dynamic feedback compensator, so that theoutput of the system can track the given input, and the system is stabilized and has
expected performance. The simulation results of the theoretical model and thecorresponding results of the real analog circuit implementation are given in the paper
to illustrate that the circuit can accomplish the tracking function of dynamic feedback
control system.

OS 8-8 Maximal power point tracking technology for the solar photovoltaic battery basedon model predictive control
Xia Zhao, Huailin Zhao (Shanghai Institute of Technology, P.R. China)
The solar photovoltaic battery is a new type of renewable distributed energy. Its output
power is directly affected by light intensity and the temperature, and its voltage-current
characteristic has the obvious nonlinear feature. In order to improve the energy
conversion efficiency of the photovoltaic system, it’s very significant to study theeffective control algorithm’s realization on the maximal power point tracking (MPPT)
control. A new MPPT technology based on model predictive control (MPC) is
proposed. The proposed method is developed to control the output current of a boost
converter in order to extract the maximal solar power from the photovoltaic panel.

OS 8-9 Analysis and unidirectionally coupled synchronization of a novel chaotic system
Li Xue, Xue Wei (Tianjin University of Science and Technology, P.R. China)©ICAROB 2016
- 46 -

15

10

2

5

x

In this paper, a new three-dimensional autonomous chaotic system is proposed. Bymeans of theoretical analysis and numerical simulation, some basic dynamical
properties, such as equilibrium points, fractal dimension, Lyapunov exponent
spectrum and chaotic dynamical behaviors of the new chaotic system are investigated.
The obtained results show clearly that this system is a new chaotic system.
Furthermore, based on Lyapunov stability theory of the system, we observe
unidirectionally coupled synchronization of the new three-dimensional chaotic systemthrough designing the appropriate coupling coefficient. Results of numericalsimulation illustrate the effectiveness of the presented synchronization scheme.

0

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-5

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5

10

Fig.curve of statevariable( x1 , y1 )15The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

OS9 Modern approaches of MOT and Corporate Strategy (3)OS9-1 A Study on the Structural Hole of Patent Applicant Network in R&D Management
1

Iori Nakaoka, 2Yousin Park, 3Yun-ju Chen (1National Institute of Technology, Ube College, 2Prefectural
University of Hiroshima, 3Shiga University, Japan)
The arrangement of R&D staff refers to the issue of the linkage between structural
holes in a network (Burt, 2003). In order to examine the linkages between multiple
R&D projects in a company, we use the patent information as the index of R&D
capability and examine our propositions by social network analysis. In this paper we
focus on top-shared companies in Japanese cosmetics industry, and suppose that thesecompanies keep their market shares due to their smoothly changes of business fields
to cope with the threat of new entrants from other industries. The analysis in thispaper include: 1) calculate the betweenness centrality, and create heat-maps tovisualize the change of the betweenness centrality, 2) examine the structure hole.

OS9-2 The TCE-RBV framework for information systems outsourcing: Empirical testing
using survey data in Japan
Seigo Matsuno1, Tsutomu Ito2, Yasuo Uchida1, Y. Mikami3, Takao Ito4
( National Institute of Technology, Ube College, 2Hino Motors, Ltd., 3Nagaoka University of Technology,
4
Hiroshiuma University, Japan)
1This paper investigates the factors that influence the motives of make-or-buy decisions
on information systems from the viewpoints of Transaction Cost Economics (TCE)and the Resource-based View (RBV). Using our original questionnaire survey datacarried out in 2007 targeting Japanese firms, we analyze the relationships between therecognition related to the role of their information systems and the pattern of their
make-or-buy decisions. As a result, we make it clear that there are two cases in which
TCE-motive and RBV-motive are complement or contradict with each other. And inlatter case, TCE-motive tends to dominate over RBV-motive. Finally, The implications
for theory and practice are discussed.

RBV-motive

TCE-motive

Insourcing
Outsourcing

OS9-3 Momentum and its Implications in Corporate Management
Tsutomu Ito1, Takao Ito1, Rajiv Mehta2, Sakamoto Makoto3, Katsuhiko Takahashi1, Katsumi Morikawa1(1 Hiroshima University 3 Miyazaki University, Japan)(2 New Jersey Institute of Technology, USA)
In contrasting conventional theses with current strategic theories, discussions on
momentum, an important concepts, have been sparse, although in the context ofcurrent management strategies the pivotal nature of momentum recently has been
operationalized and discussed in research on marketing, and finance. The current
manuscript reviews the literature associated with corporate strategy, and proposes a
new approach of acceleration to measure momentum based on limited cycle theory.
Thus, this research manuscript makes a contribution to extant thought by: (1) Defining
momentum, (2) Discussing the nature of the relationship between momentum to its
external environment, and company scale, 3) Ascertaining the momentum period, and
4) Proposing a four-cell model composed of momentum and company scales for
judging a firm position. Additionally, the relationship between momentum and theimpact of the 2007-2008 financial crises is addressed. Based on the findings, the study
limitations are identified and directions for further research are suggested.

©ICAROB 2016
- 47 -

1500000000000

1000000000000

500000000000

01-Year
4-Year
7-YearMomentumMomentumMomentum-500000000000
Toyota Motor Corp.
Nissan Moto Co., Ltd.
Mitsubishi Motors
Honda Motor
Mazda Motor Corp.
Suzuki Motor Corp.
Daihatsu Motor Co., Ltd.

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

OS10 Reality Mining (3)OS10-1 Fast collective photographic subject detection without pixels by an assumption abouta shot and its elevation angle
Sora Tanioka, Masao Kubo, Hiroshi Sato
(National Defense Academy of Japan, Japan)
In this paper, we propose a collective intelligence to discover photographic subjects
without pixel information. A collection of information about shooting, for example,
EXIF, Geotag, and Tiff which is automatically attached into a digital photograph isonly used for this detection. So far we have to use a large number of such the not-pixel
information for this task because the original model of taking a photo is not efficient.
In this paper, we estimate the improvement of the performance to discover subject if
there is a relation between a shot and its elevation angle of the camera.

OS10-2 Evaluation of a safety map generated from a collection of difference of Individuals
Masao Kubo, Dang Viet-Chau, Hiroshi Sato, Akira Namatame(National Defense Academy of Japan, Japan)
In this paper, we propose a collective intelligence approach to generate safety maps ofroad environment from a collection of logs of vehicle behavior recorded by drivers’
smartphone. Usually a general collective intelligence uses the average of the data and it
is difficult to apply it for complex problems. This paper proposes a reasonable method
for generation of safety maps to evaluate the difference of the behavior. The accuracyof the map is evaluated by official accident record

OS10-3 Endeavor to adopt GIS data on evacuation decision making model1

Saori Iwanaga, 2Akira Namatame( Japan Coast Guard Academy, 2National Defense Academy of Japan, Japan)
1

By Multi Agent Simulation: MAS, we focused on contagion of evacuation decision
making on real map assuming that not all people evacuate at the time of disaster. Then,
we found that for contagion of evacuation decision making, local neighborhood isneeded and connection of sub network is needed. But, there we faced on difficulty ofobtaining realistic population data on map, because Census Bureau data consists of not
position data but numbers and properties. In this paper, we attempt to use geographic
information system GIS data and population data, then we simulate of population withheterogeneous agents and their decision making.

.

OS11 Kansei Engineering and Application (4)OS11-1 Automated Multiple-Brightness Peak Image Processing Method Using Curvature
and Variance Estimation
1

Yusuke Kawakami, 2Tetsuo Hattori, 2Yoshiro Imai, 2Kazuaki Ando, 2Yo Horikawa (1DynaxT Co., Ltd.,
Kagawa University, Japan) 3R. P. C. Janaka Rajapakse (3Tainan National University of the Arts, Taiwan)

2This paper describes the improvement method for the image which have multiplebrightness peak using Histogram Matching based on the Gaussian Distribution(HMGD). The previous papers, we have illustrated that the HMGD is an automatedimage processing method for improve feeling impression better, through thecomparative investigation results of feeling impression among the original image,
Histogram Equalization image, and HMGD image. However, the multiple-brightness
peak images have been hard to improve using the previous HMGD. In this paper, we
propose the processing method of HMGD to correspond image which have multiplebrightness peak, using curvature computation and variance estimation.

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The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
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OS11-2 Histogram Matching Based on Gaussian Distribution on the HSB Color System
1

Yusuke Kawakami, 2Tetsuo Hattori, 2Yoshiro Imai, 2Kazuaki Ando, 2Yo Horikawa (1DynaxT Co., Ltd.,
Kagawa University, Japan) 3R. P. C. Janaka Rajapakse (3Tainan National University of the Arts, Taiwan)

2This paper proposes Histogram Matching based on Gaussian Distribution (HMGD)
processing on the HSB color system which is close to the human visual property. In
the previous paper, we have proposed HMGD processing on the brightness axis whichis calculated from RGB color system. And we have considered that HSB color system
is more suitable for HMGD processing, because it contains brightness axis. In thispaper, we describe how to transform the color image from RGB color system into HSB
color system first. Second, we describe that the principal of HMGD on this color
system. And then, we also explain how to re-transform HSB color system to RGB color
system.

OS11-3 Quantitative Evaluation of Flash-based Educational Visualizing Simulator
Kei Takeichi, Yoshiro Imai, Kazuaki Ando, Yusuke Kawakami, Tetsuo Hattori(Kagawa University, Japan)
A Flash-based simulator of CPU scheduling has been developed and utilized for
educational visualization in the class of university lecture. We have designed andimplemented it with Flash-based scripting language in order to execute it as a standalone application as well as in various browsing environment such as Microsoft IE,
Google Chrome and/or FireFox (Mozilla). Based on questionnaire for our simulator in
the lecture, its quantitative evaluation has been carried out by means of statisticalanalysis. Our report describes overview of our Flash-based simulator and the results of
the above quantitative evaluation.

OS11-4 Relation between Optimal Stopping Solution and NSPR for Structural Change Point
Detection Problem
Tetsuo Hattori, Yoshihide Koyama, Yoshiro Imai, Yo Horikawa, Hiromichi Kawano1(Kagawa University, 1NTT advanced technology Company Ltd, Japan)
Previously, we have proposed a novel method using New Sequential Probability
Ratio (NSPR) for the structural change point detection (SCPD) of ongoing timeseries data instead of using SPRT (Sequential Probability Ratio Test). Moreover, wehave formulated the SCPD problem in time series data as an Optimal Stopping one
using the concept of DP (Dynamic Programming) and also have shown the solution
theorem in the form of Inequality. In this paper, we discuss the relation between thesolution of Optimal Stopping and NSPR for the SCPD Problem.

Optimal Solutiona   A  a   P S n S n 1

NSPR A     A  a   R
 A 
P H S R 
P H1 S nn0c A aOS12 Advanced Research on Computer Science and Information Processing (4)OS12-1 Development of a Tool to Keep Consistency between a Model and a Source Code inSoftware Development Using MDA.
Yuuki Kikkawa1, Tetsuro Katayama1, Yoshihiro Kita2, Hisaaki Yamaba1, Kentaro Aburada3,
Naonobu Okazaki1 (1University of Miyazaki, 2Tokyo University of Technology,
3
Oita National College of Technology, Japan)
This study improves efficiency of software development using MDA. This paper
develops the tool that keeps consistency between a model and a source code in softwaredevelopment using MDA. The tool has two functions: (i) Generating a source code and(ii) Keeping consistency between a model and a source code. Inputs of (ii) are an
unmodified activity diagram, the modified activity diagram, and the source code
including the detail specification. An output of (ii) is a source code which is consistent
with the modified activity diagram and includes the detail specification. In executing(ii), the tool generates EAD (Extended Activity Diagram) as intermediate data.

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Japan, January 29-31, 2016

OS12-2 Necessary spaces for seven-way four-dimensional Turing machines to simulate fourdimensional one-marker automata
Makoto Nagatomo1, Shinnosuke Yano1, Makoto Sakamoto1, Satoshi Ikeda1, Takao Ito2,
Tsutomu Ito2, Yasuo Uchida3, Tsunehiro Yoshinaga4, Hiroshi Furutani1 (1University of Miyazaki,
2
Hiroshima University, 3Ube National College of Technology, 4Tokuyama College of Technology, Japan)
We think that recently, due to the advances in many application areas such as motion
image processing, computer animation, and so on, it is very useful for analyzing
computational complexity of multi-dimensional information processing to explicate
the properties of four-dimensional automata, i.e., three-dimensional automata with thetime axis. As far as we know, there is no investigation about four-dimensional
automata. Then, in 2002, we first introduced four-dimensional finite automata in theworld. In 2003, we investigated four-dimensional alternating Turing machines. In
2015, we show the sufficient spaces for four-dimensional Turing machines to simulate
four-dimensional one-marker automata. In this paper, we continue the investigations,and deal with the necessary spaces for four-dimensional Turing machines to simulate
four-dimensional one-marker automata.

OS12-3 Analysis for 4×12 board of Othello
Yuki Takeshita1, Satoshi Ikeda1, Makoto Sakamoto1, Takao Ito2
(1Miyazaki University, 2Hiroshima University, Japan)
More than 20 years has passed since J. Feinstein (1993) found that a perfect play on
6×6 board of Othello gives a 16-20 win for the second player. A computer Othellosurpasses a much more human now. However, standard 8×8 board remain unsolved. In
our previous paper, we were able to obtain the perfect plays on 6×6 board and 4×10
board. In this paper, we challenge the unsolved problem of 4×12 board by dividing it
into about 150 small problem.

OS12-4 A space lower-bound technique for four-dimensional alternating Turing machines
Makoto Nagatomo1, Shinnosuke Yano1, Makoto Sakamoto1, Satoshi Ikeda1, Takao Ito2,
Tsutomu Ito2, Yasuo Uchida3, Tsunehiro Yoshinaga4, Hiroshi Furutani1 (1University of Miyazaki,
2
Hiroshima University, 3Ube National College of Technology, 4Tokuyama College of Technology, Japan)
Alternating Turing machines were introduced in 1981 as a generalization of
nondeterministic Turing machines and as a mechanism to model parallel computation.
On the other hand, we have no enough techniques which we can show that someconcrete four-dimensional language is not accepted by any space-bounded fourdimensional alternating Turing machines. The main purpose of this paper is to present
a technique which we can show that some four-dimensional language is not acceptedby any space-bounded four-dimensional alternating Turing machines. Concretely
speaking, we show that the set of all four-dimensional input tapes over {0,l}, which
each top half part is equal to each bottom half part, is not accepted by any L(m) spacebounded four-dimensional alternating Turing machines for any function L(m) smaller
than log m.

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OS 13 Automatic Generation, Creation, and Production of Narrative Contents (4)OS13-1 Automatic Generation, Creativity, and Production of Narrative Contents
Takashi Ogata (Iwate Prefectural University, Japan)
According to the concept of this organized session, in this presentation, we willconsider the study of automatic narrative or story generation as a research field ofartificial intelligence in the following levels: generation, creation, and production. At
first, we study the technologies of narrative or story generation systems based on
mainly artificial intelligence and cognitive science. Next, the artistic and aesthetic
problems of narrative creation are considered in the relationships with thetechnologies and systems of narrative generation. Further, issues toward
organizational or social narrative production are presented. These survey anddiscussion will be done based on some topics in this session relating to a game andadvertising systems and our integrated narrative generation system.

Generative
Parameters

Structural
Knowledge

Story
Mechanism

Discourse
Mechanism

Story
Techniques

Conceptual
Dictionaries

Expression
Mechanism
Discourse
Techniques- Natural Language- Music- Visual

Story

Discourse

OS13-2 A Design Plan of a Game System including an Automatic Narrative Generation
Mechanism: The Entire Structure and the World Settings
Jumpei Ono, Takashi Ogata (Iwate Prefectural University, Japan)
We present a plan of a narrative generation system based on a scenario generation
method in tabletop role-playing games (TRPGs). It uses an integrated narrative
generation system (INGS) that synthesizes various narrative generation mechanisms.
A TRPG is an analog game in which one or more player (PL) play each role ascharacters in a framework of narrative by the game master (GM). A PL promotes thedevelopment of scenes, and, as a result, a narrative is completed. In particular, weconcentrate on the “world setting” that has a role of rules or constricts in narrativeand consists of components defined by its rule and the stage setting of the narrative.
In the ordinary use, a GM sets a basic world setting to guide generation through theinteraction between PLs. We consider preliminarily prepared scripts corresponding
to various world settings in addition to the detailed mechanism for the use it.

.

OS13-3 A Way for using the Verb Conceptual Dictionary in an Integrated Narrative
Generation System: Focusing on the use of Co-occurrence Information on the Verb
Concepts
Takashi Ogata, Jumpei Ono (Iwate Prefectural University, Japan)
This paper presents a way for selecting verb concepts based on the analysis of cooccurrence information of verb words in the data of modern Japanese novels(“Aozora Bunko”). The proposed method will be incorporated into somemechanisms in an integrated narrative generation system (INGS) that synthesizes
various mechanisms for narrative generation that we have been developing. We show
an overview of INGS, in particular the mechanism relating to the proposed methodin this paper. In the verb conceptual dictionary, as verb concepts of a variety ofdifficulty or understandability are mixed, the objective in this paper is to adjust theusage by using co-occurrence information on verb concepts. Our hypothesis is thatthe original concepts of co-occurrence and the resulted concepts have a proportional
relation. We will investigate it by incorporating the mechanism into the INGS.

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Verb Conceptual Dictionary
Physical Move …
Gowalk …

Case Frame(agent human)(to geography)

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
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OS13-4 A Viewing Experiment on the Effects of Advertising Story
Yoji Kawamura (Kinki University, Japan)
This research lays out the conception and functions of developed Commercial Film
Production Support System (CFPSS). The research then explains the results of aviewing experiment that utilized CFPSS. In this experiment where commercial
films of beer were utilized, the following findings were obtained: in inducing
interest, the evaluation of image type of advertising story is high; in stimulating
willingness to buy, the evaluation of provider type is high; and mise-en-scène orediting attracts interest and the advertising story associated with product function
and the supporting production and distribution stimulates willingness to buy. Thesefindings gradually clarify the following creative know-how of the creator; to attract
consumer interests by focusing on the stories of consumer situations in caseconsumers do not aware or understand their products or services; to arouse their
willingness to buy by focusing on the products and stories of their acceptance andeffects in case consumers have a certain level of understanding and interests in
products or services.

GS abstracts
GS1 Neuromorphic Systems (5)GS1-1 Compensating Temperature-Dependent Characteristics of a Subthreshold-MOSFET
Analog Silicon Neuron
Ethan Green, Takashi Kohno (The University of Tokyo, Japan)
Analog silicon neurons are neuro-mimetic VLSI (very-large-scale-integrated)circuits that replicate the electrophysiological behavior of animal nerve tissue.
These circuits send and receive spike trains in continuous time and require minimal
power. This research focuses on the temperature sensitivity of a subthresholdMOSFET silicon neuron. Subthreshold operation of CMOS transistors allows for
low power consumption and desirable current-voltage characteristics, but is also
drastically sensitive to temperature changes. This critical issue must be addressed
before these circuits can be implemented into massive networks to develop futuretechnologies, which could include neuromorphic computers, artificial brains, brainmachine-interfaces, and ultra-low power computing platforms.

GS1-2 Medical image analysis of brain X-ray CT images by deep GMDH-type neuralnetwork
Tadashi Kondo, Junji Ueno, Shoichiro Takao (Tokushima University, Japan)
In this study, the deep Group Method of Data Handling (GMDH)-type neuralnetwork is applied to medical image analysis of brain X-ray CT images. The deep
GMDH-type neural network algorithm can automatically organize the deep neuralnetwork architecture which has many hidden layers, using the heuristic selforganization method which is a type of evolutional computations. This algorithm
is applied to the medical image analysis of brain X-ray CT images. The deep neuralnetwork is automatically organized from brain X-ray CT images and recognizes
and extracts the brain, bone and blood vessels regions in the brain images.

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GS1-3 Medical image diagnosis of lung cancer by deep feedback GMDH-type neuralnetwork
Tadashi Kondo, Junji Ueno, Shoichiro Takao (Tokushima University, Japan)
The deep feedback Group Method of Data Handling (GMDH)-type neural network
is applied to the medical image diagnosis of lung cancer. The deep feedback
GMDH-type neural network has feedback loops and the complexity of the neuralnetwork architecture is increased by feedback loop calculations. Each feedback
loop is constructed with the three layers which are the input, hidden and output
layers. This deep feedback GMDH-type neural network is applied to the medical
image diagnosis of lung cancer and the deep neural network which can recognize
and extract the lung cancer and blood vessel regions, is automatically organizedfrom the chest X-ray CT images.

GS1-4 Feature Linking by Synchronized Response in Chaotic Cellar Neural Network
for Visual Stimulus of Moving Objects
Akihiro Yamaguchi1, Satoshi Arakane1, Masao Kubo2(1Fukuoka Institute of Technology, 2National Defense Academy of Japan, Japan)
A feature linking mechanism by the synchronized response of neural assemblies
was studied for the chaotic cellar neural network (Chaotic-CNN). The ChaoticCNN consists of chaotic spike response neurons that show the chaotic inter-spikeinterval dynamics. In our scheme of feature linking, the features of the target
objects are linked by the synchronized spike responses that are characterized by the
temporal chaotic pattern of spike sequence. In this paper, we analyzed thesynchronized spike responses that invoked by the visual stimulus of moving bars.
As a result, neural assemblies have higher correlation for the visual stimulus ofmoving two bars with the same direction (Fig.1 (a)) than the opposite direction(Fig.1 (b)). Then we discussed a possibility of feature linking by the chaotic
synchronized response in the view point of neural coding.

(a) The same direction(b) The opposite
direction
Fig.1 Correlation ofneural assemblies

GS1-5 An FPGA-based cortical and thalamic silicon neuronal network.
Takuya Nanami, Takashi Kohno (The University of Tokyo, Japan)
A DSSN model is a neuron model which is designed to be implemented efficiently
by digital arithmetic circuit. In our previous study, we expanded this model tosupport the neuronal activities of cortical and thalamic neurons. They are regularspiking, fast spiking, intrinsically bursting and low-threshold spike. In this paper,
we report our implementation of the expanded DSSN model and kinetic-modelbased silicon synapses on a Xilinx / Spartan -6 LX45 FPGA. Here, synaptic
efficacy was stored in block RAMs. The DSP modules integrated on the FPGA chip
were utilized to facilitate efficiency in implementation of multiplication in the
DSSN model.

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The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

GS2 Pattern Recognition & Image Processing (6)GS2-1 An accurate method for the extraction of structured light stripe

Jiwu Wang1, Yaodong Li1, Zhijing Jian1, Sugisaka Masanori2( Beijing Jiaotong University, P.R. China), (2Alife Robotics Corporation Ltd, Japan)
1

In order to obtain the highest measurement accuracy in the On-line measurementof rail profile with a line structured light based on machine vision, the extractionof structured light stripe is a necessary step. An accurate extraction method is
proposed for the structural light stripe extraction in practical applications. The
noise in the captured image can be removed with region segmentation method. The
structured light stripe is separated and extracted accurately based on thecharacteristics of the structure light stripe in the binary image. The method wasverified in laboratory conditions. Experiment results show that the method caneffectively solve the problem of real-time and accurate extraction of structured
light stripe.

GS2-2 Feature Acquisition From Facial Expression Image Using Convolutional Neural
Networks
Taiki Nishime, Satoshi Endo, Koji Yamada, Naruaki Toma, Yuhei Akamine(University of The Ryukyus, Japan)
In this study, we carried out the emotion acquisition from facial expression image
using a Deep Neural Networks(DNN). Emotions we focused on are "happiness","sadness", "surprise, "anger”, "disgust", "fear" and "neutral”. By using
Convolutional Neural Networks(CNN) approaches, we have obtained an average
emotion recognition score of 58%; two emotions (happiness, surprise) recognition
score was about 70%. This result was the same tendency as preliminary experimentof human recognition. From CNN model after training, specific unit wereconfirmed to be learning the feature about happiness. The results corroborate that
CNNs work effectively for learning facial expression about happiness, surpriserepresentations.

GS2-3 Estimating Age on Twitter Using Self-Training Semi-Supervised SVM
Tatsuyuki Iju, Satoshi Endo, Koji Yamada, Naruaki Toma, Yuhei Akamine(University of The Ryukyus, Japan)
Most recent researches for estimating Twitter user’s latent attributes are concernedwith making attribute estimator from only user tweets with one's profile known,that is, ground truth data. However, it tends to be high cost to collect enough amountof ground truth data to provide estimator. In our study, we employ SVM with selftraining, with words in tweets used as features, in order to reduce the cost ofcollecting data. We carry out some parameter surveys to investigate how theseparameters affect performance of classifier. We utilize predict-probability of
LIBSVM package as a threshold to determine which of unlabeled users to belabeled during self-training. At last, we evaluate the classifier for parameters which
are found to be the best on parameter surveys.

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Japan, January 29-31, 2016

GS2-4 Interactive musical editing system to support human errors and offer personal
preferences for an automatic piano
Kenji Tsunenari, Eiji Hayashi (Kyushu Institute of Technology, Japan)
Recently, electronic musical instruments are achieving progress for developmentof electronics and seen everywhere. However, their sound quality and ambience areinferior to real musical instruments. Therefore, we developed automatic piano byusing grand piano. Pre-edit is needed to play music in the manner of a live
pianist. In the case of piano music, there are often1000 or more notes in the scoreof even a short piece of music, requiring that an editor spend a huge amount of timeto accurately simulate the emotionally expressive performance of a highly skilled
pianist. Therefore, we have developed an interactive musical editing system thatutilizes a database to edit music more efficiently.

GS2-5 Geometric parameters measurement of wheel tread based on line structured light
Jiwu Wang1, Zhijing Jian1, Yaodong Li1, Chao Yang1, Sugisaka Masanori2( Beijing Jiaotong University, China), (2Alife Robotics Corporation Ltd, Japan)
1

The positioning precision is important in the processing of geometric parameters
measurement technology of wheel tread. In this paper, a new method with nocontact measurement based on line structured light is given to solve this problem.
Here traditional mechanical locating method is used as a rough reference.
Moreover, some digital image processing techniques are developed for each of the
key dimensions of measurement. According to the feature points (particularly, thekey points that have not been worn) and lines extracted effectively on the wheel
tread, precise positioning is realized. In this paper, the experiments show that the
proposed method is feasible and excellent to design and implement non-contact
measurement of the wheel tread, wheel flange thickness and rim width. It also
ensures the accuracy of positioning and analyses the factors of measurement error.

GS2-6 Study on the ORB algorithm in the application of Monocular SLAM

Jiwu Wang1, Shunkai Zheng1, Masanori Sugisaka2( Beijing Jiaotong University, P.R. China) (2Alife Robotics Corporation Ltd, Japan)
1

In view of reducing the accumulative error, we perform loop closing (detection +correction) based on PTAM in our Monocular SLAM.As this method relies on
extracting natural environment features, we chose ORB algorithm as the feature
extraction and matching. We demonstrate that ORB features have enoughrecognition power to enable place recognition from severe viewpoint change andthey are so fast to extract and match (without needing multi-threading or GPU
acceleration) that enable real time accurate tracking and mapping. Through tooutdoor scene experiment, we validate the algorithm performance.

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The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
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GS3 Reinforcement Learning System & Genetic Programing (5)GS3-1 Examination of Robotic Aerospace Engines Maintenance Supported by Augmented
Reality through Cloud Manufacturing
Mosab Alrashed, Yaser Yadekar, John Ahmet Erkoyuncu, Yifan Zhao (Cranfield University, UK)
This Paper aims to develop a demonstration using a mobile device to apply
augmented reality to allow remote maintenance activities. This research was
focused primarily on the aerospace industry studies as there was a research gap as
there are few relevant researches in the manufacturing field and accepted industry
needs. The targets were achieved by developing and designing controls in theengineering steps to create the optimum conditions for augmented reality. The
design process pursued in four stages: first, research the current practice of using
augmented reality remotely in manufacturing maintenance; second, classify
maintenance remote assisting problems and weakness in augmented reality; third,
design and develop software and hardware, depending on the case study for a
prototype solution to remote maintenance enhanced by the augmented reality thatcan be demonstrated and tested; finally, validate the developed software anddemonstration using industrial experts and authentic reports.

GS3-2 Unit Layout Design Supporting System of Cell Assembly MachineUsing Two Robots
by Reinforcement Learning
Yusaku Ikai, Hidehiko Yamamoto, Takayoshi Yamada (Gifu University, Japan)
In this study, we explain the development of Design Supporting System
for Cell Assembly Machine System (CAMS) which systemize thedecision of the unit layout that compose the assembly machine using two
robots. CAMS uses Profit Sharing which is one of the Reinforcement
Learning methods, determining each units layout. Profit Sharing is
performed by the following flow: (1) The units layout is determined byRoulette Selection, (2) The one cycle work time of the robot is calculated
to evaluate the unit layout, (3) If the evaluation is good, the units
layout is given rewards. Efficient units layout is decided by the numberof regulation times repeating the flow 1-3. We apply CAMS to assemblyof the Differential which is automotive parts to verify its validity.

GS3-3 Virtual Input Parts Decision System of Job-Shop Production Line by using GA withON / OFF Gene
Junji Ito, Hidehiko Yamamoto, Takayoshi Yamada (Gifu University, Japan)
We propose the simulator to decide the best parts input order. We call this simulator
as Virtual Input Parts Decision by ON/OFF Genes (VIPDOG). VIPDOG has 2
configuration systems as follows: 1) The parts order decision system to indicate theparts input and processing orders of a job shop production line.2) The virtual
production system that performs the visualization of the working environments
acquired by VIPDOG. The virtual production system can check what will happen
in the future and some experienced engineers can discuss the problems to solvethem.

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The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

GS3-4 An Evolutionary Algorithm for Making Decision Graphs for Classification Problems
Shingo Mabu, Masanao Obayashi, Takashi Kuremoto (Yamaguchi University, Japan)
Classification is one of the major research in pattern recognition and a large
number of methods have been proposed such as decision trees, neural networks(NNs), support vector machines (SVMs). In order to easily understand andanalyze the reason of the classification results, decision trees are usefulcomparing to NNs and SVMs although these methods have shown
distinguished classification abilities. In this paper, to enhance the classification
ability of decision trees, a new evolutionary algorithm for creating decisiongraphs is proposed, where multi-root nodes, unique genetic operators andmajority voting mechanism based on Maximum a posteriori are introduced. In
the performance evaluation, it is clarified that the proposed method shows
better classification ability than decision trees.

GS3-5 Improvement of Computational Efficiency of UPF by Automatic Adjustmentof the Number of Particles
Kenta Hidaka, Takuo Suzuki, Kunikazu Kobayashi (Aichi Prefectural University, Japan)
In RoboCup Standard Platform League (SPL), the method using unscented particle
filter (UPF) has been proposed for self-localization.
The UPF resolves a problem of particle filter (PF) which cannot be sampled
appropriately when the likelihood is too high or low. It is also applicable to the
kidnapping problem. The filter can estimate the precise position if the number ofparticles is large enough but the computational cost may also increase in proportion
to the number of particles. In the present paper, we propose an automatic adjustment
method for the number of particles in UPF using the variance of distances between
the position of a particle and the average position of all the particles and the absolute
error between the position of a particle and the correct position of robot. Throughcomputer simulations, we confirmed the improvement of computational efficiency
of UPF.

GS3-6 Multi Objective Evolutionary Algorithms for Association Rule Mining: Advances andChallenges
Aswini Kumar Patra (North Eastern Regional Institute of Science & Technology, India)
Association rule mining, one of the most important and well researched techniquesof data mining. The challenge, posed by most of the association rule mining
methods, is the amount of time consumed for generating frequent items sets. To
overcome thisevolutionary algorithms have been used widely. Moreover, apartfrom support and confidence, there are many other metrics available to measure thequality of association rules. That is the reason why multi-objective approach plays
a crucial role. Therefore, two methodologies namely, multi-objective andevolutionary algorithms as a combination proved to be a preferred choice.
Though numerous works have been proposed for mining association rules, use ofmulti-objective evolutionary algorithms are still in its infancy stage. This paper
explores the challenges and advances that has been made in this regard in terms ofnature of algorithm, encoding mechanism, objective functions and operators.

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The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
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GS4 Micro-Machine (4)+GS9 RoboticsⅢ(4)GS4-1 Applying Fuzzy Sliding Mode Control on Electrowetting on Dielectric System
1

Arsit Boonyaprapasorn, 2Thavida Maneewarn, 2Eakkachai Pengwang (1Chulachomklao Royal Military
Academy, 2King Mongkut’s University of Technology Thonburi, Thailand)
In Lab-on-a-chip (LOC) devices, an electrowetting on dielectric (EWOD) method
can be performed to create, cut, mix, and transport droplets. Moreover, the microassembly process of micro parts can be manipulated by the EWOD system. Highaccuracy of droplet motion in both applictions is required. Based on the simplified
model, the feedback control such as sliding mode control can be applied so that the
droplet motion can achieve a high accuracy performance under disturbances.
However, the sliding mode control often has chattering problem. Thus, in thisstudy, the fuzzy sliding mode control was applied to manipulate the droplet in the
EWOD system. The study was conducted via simulation in MATLAB
environment. The result showed that the proposed control method could provide an
accurate motion control with lower chattering compared to that of classical sliding
mode control.

EWOD system.

GS4-2 Development of Micro-Permanent Magnet Synchronous Reluctance Generator for
TPMS on Smart Robots
1

Chun-Chieh Chang, 1Cheng-Tang Pan, 1Shao-Yu Wang, 1An-Yun Yang, 1Gu-Xuan Lin, 2Roger Chenglung
Lee, 2 Ting-Hung Chung, 1Yu-Jen Wang (1National Sun Yat-sen University, 2Naroller Electronics, Taiwan)
Synchronous Reluctance Generator (SynRG) is a high-functioning and robust
motor with simple structure and low cost. In this study, the rotor was embedded
magnet and developed with a miniature size of permanent magnet generators, which
can be applied to the tire pressure monitoring system (TPMS) on the smart robots.
The SynRG was studied for voltage producing to achieve sufficient power for
TPMS. The experiment focuses on the specific winding distribution, such as
distributed winding or concentrated winding, and the number of stator slots perphase. On the other hand, the structural design of the generator focuses on themagnet configuration, the rotor structure, and working air gap to optimize theparameters. In addition, the generator was considered with a different rotational
speeds and the rotor resistances. High power output, stable voltage waveform, andtorque ripple were analyzed to determine the generator. In the selection of themagnetic materials, ferrite magnet was selected which has the advantage of lowercost. Finite element method (FEM) is used to simulate the magnetic properties of
the generator. The optimized design of SynRG in this study was centralized
distribution of windings, ratio 12/4 of the slot and pole, 500-1200 rpm of therotation speed and 5 mm of lamination thickness, 50 mm of outer diameter of thestator. The results indicate that the generating capacity of 2.3 V can be achieved.

©ICAROB 2016
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The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

GS4-3 Study of Micro-Flux-Switching Permanent-Magnet Generator for TPMS on Smart
Robots1

An-Yun Yang, 1 Cheng-Tang Pan, 1Shao-Yu Wang, 1Chun-Chieh Chang, 1Gu-Xuan Lin, 2 Roger Chenglung
Lee, 2Ting-Hung Chung, 1Yu-Jen Wang (1National Sun Yat-sen University, 2Naroller Electronics, Taiwan)
Flux-Switching Permanent-Magnet Generator (FSPMG) is of high flux density andhigh efficiency due to its double salient structures. This study presents a micro
FSPMG whichcan be applied to tire pressure monitoring system (TPMS) on thesmart robots. To design a micro FSPMG, we tested the slot-pole ratio to find out an
optimal voltage output. This steady rotation generator with high generating capacity
and low torque ripple was obtained by optimizing the design of air gap, arc ratio
and salient ratio with different rotation speeds. In this paper, we used finite element
method (FEM) to simulate the magnetic properties of generator and the voltage
output. The magnetic material used in this study was ferrite magnet which has theadvantage of lower-cost. The design of FSPMG in this study was ratio 12/10 of theslot and pole, 500-1200 rpm of the rotation speed and 5 mm of lamination thickness,
50 mm of outer diameter of the stator. The results indicate that the generating
capacity achieves to 3.4 V where the air gap is the main factor to affect thisgenerator.

GS4-4 MEMS Microrobot Controlled by Mounted Neural Networks IC with Two Types
Actuators
Kei Iwata, Hirozumi Oku, Yuki Okane, Yohei Asano, Masaki Tatani, Yuki Ishihara, Kazuki Sugita,
Satohiro Chiba, Satoko Ono, Mizuki Abe, Minami Takato, Ken Saito, Fumio Uchikoba(Nihon University, Japan)
We report the hexapod microrobot controlled by the hardware neural networks.
MEMS (Micro Electro Mechanical System) was used for fabrication of the
microrobot. Hexapod walking of microrobots that mimics such motion of ants isrealized by link mechanisms. Actuators of the robots can be classified as
piezoelectric (PZT) type and shape memory alloy (SMA) type. The rotation of PZT
actuator is generated by the impact head attached to the piezoelectric element. On
the other hand, rotation of SMA actuator is generated by shrinkage of SMA fromfour directions. The microrobots are controlled by the bare chip IC of hardware
neural networks. As the result, PZT type microrobot was realized the walking
motion by bare chip IC. Moreover, SMA type microrobot could be suystemsral
networks. The walking speed was 2.4mm / min and the step width was 0.083mm.

GS5 Multi-Agents Systems & Visualization
GS5-1 Moving Robots Lies and Their Minds with Degree of Confidence in a Decentralized
Autonomous FMS
Shizuka Tanaka, Hidehiko Yamamoto, Takayoshi Yamada (Gifu University, Japan)
An autonomous decentralized FMS needs to control the entire factory and avoid theroute interference of automated guided vehicles (AGVs). To solve this problem, we
inserted an arrogant mind and a modest mind into AGVs. Also, in an autonomous
decentralized FMS, it is assumed that the information provided by agents is always
correct. However, incorrect information can sometimes be sent as a result ofmachine failures in a real factory. In this study, we define and find this incorrect
information as a "lie" of AGV. We propose the method of finding lies and sending
the lie information to the AGVs. We also propose a degree of confidence to control
the AGV's moving. The result of simulation has shown the number of production
was increased. Therefore, it is useful to control behavior by finding AGV’s lies andusing the degree of confidence.

©ICAROB 2016
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The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

GS5-2 Conquest Oriented Robot Knowing Its Own Availability
Sho Yamauchi, Keiji Suzuki (Future University Hakodate, Japan)
Robot is designed for achieving specific task. However, robot is able to do much
more things than it is expected by its own hardware. If both human and robot
become to be able to know the things robot can do in the place, robot become todeal with variable tasks that are not expected. In this paper, concept of conquest
oriented robot is proposed to know its availability systematically andautonomously. Conquest action for the robot that knows its own availability isdefined and the thing that conquest oriented robot do is to know, increase andmaintain things it can do. Algorithm of conquest oriented robot and its experimental
result in simulation field for the first step is shown.

GS6 Intelligent Control & System Cybernetics
GS6-1 A Consideration on Feature Extraction for Operation Skill Based on Control
Engineering Approach
Kazushige Koiwai1, Liao Yantao1, Toru Yamamoto1,
Takao Nanjo2, Yoichiro Yamazaki2, Yoshiaki Fujimoto2
1( Hiroshima University, 2KOBELCO Construction Machinery CO., LTD., Japan)
Technologies of automation or manpower-saving are innovated in production
fields. However, there are many industrial equipments to need human operation,i.e. construction site. The human-skill optimized by professionals exists in those
fields. However, the transfer of skills is not processing in leading countries becauseof decreasing or aging of professionals. Therefore the quantification of the humanskill is required to transfer those skills. In this study, the human-skill is considered
as a kind of controller. It consists of CMAC-PID that is learned by using system
outputs and control inputs of operators. CMACs calculate PID parameters, andhuman-skill is evaluated by trajectories of parameters. As an example, thecomparison of operator skills for an excavator will be performed.

GS6-2 Design of a Data-Driven Control System for a Hydraulic Excavator
Takuya Kinoshita1, Yasuhito Oshima1, Kazushige Koiwai1, Toru Yamamoto1,
Takao Nanjo2, Yoichiro Yamazaki2, Yoshiaki Fujimoto2
1( Hiroshima University, 2KOBELCO Construction Machinery CO., LTD, Japan)
PID control schemes have been widely used in most industrial systems. However,most systems have nonlinearity and it is difficult to achieve the desired control
performance by using fixed PID parameters. The hydraulic excavators havenonlinearity, therefore it is difficult to maintain the desired control performance. In
order to overcome such a problem, data-driven PID control scheme based on
database has been proposed. Moreover, data-driven scheme has a learning method
in off-line by using the closed-loop data. In this paper, data-driven control scheme
is applied to a hydraulic excavator in order to get desired control performance. The
effectiveness of the proposed scheme is numerically verified by using a simulation
example.

©ICAROB 2016
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The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

GS6-3 An Optimization of Spatio-Spectral Filter Bank Design for EEG Signal Classification
Masanao Obayashi, Takuya Geshi, Takashi Kuremoto, Shingo Mabu (Yamaguchi University, Japan)
Recently, studies on brain computer interface (BCI) which enables us to operate
electronic devices directly using electroencephalogram: EEG signal have been
actively done. From knowledge of brain science, in order to distinguish the EEG
signals accurately, it plays important role to make an appropriate combination ofboth optimal selection of EEG sensors and filter bank, due to theory of localizationof brain function. From this point of view, Bayesian Spatio Spectral Filter
Optimization (BSSFO) has been already proposed. However, the method still has
room for improvement in constructing optimal combination mentioned above. We
intend to improve BSSFO from the point of them, that is, a modification of apart of the optimization algorithm.

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The classification
procedure of EEG
signal in this paper

GS7 Robotics I
GS7-1 Haptic system with fuzzy controller for extended control of Teleoperation mine
detector wheeled robots
Yekkehfallah Majid, Guao Yang, Yuanli Cai, Naebi Ahmad, Zolghadr Javad(Xi'an jiaotong university, P.R. China)
This paper proposes an approach of haptic system and fuzzy logic controller with
implementation on teleoperation deminer robot, in order to reduce operator’s error
through fusion of fuzzy logic controller and haptic system, this approach provides
vibrotactile feedback and speed controller corresponding to mine detector sensor
and arm status system which are used in robot. This method can uses in all area of
teleoperation robotic but we implemented on deminer wheeled robot and forillustrating results of this work we also implemented on MATLAB fuzzy toolbox.

GS7-2 Effect of System Parameters and Controlled Torque on the Dynamics of RigidFlexible Robotic Manipulator
Sachindra Mahto (North Eastern Regional Institute of Science & Technology, India)
There are several advantages of the flexible robotic system with respect to its
counter-part convention system. Conventional robotic systems are heavy and bulky
with poor payload to weight ratio; and most of energy is used to overcome theinertia of the system in the engineering applications. As there is significant static
deflection due to the loads, it is very difficult to have high position accuracy of therobotic system. Therefore, sometimes some of the links of the conventional robotic
system are replaced by the flexible link to have the advantages of the lightweightsystem. Rigid-flexible robotics systems are the compromise in between of these
two systems. Since, one of the link is flexible, its behaviour is highly nonlinear andcomplex in nature. The dynamics of this system highly depends upon various
system parameters viz., length and mass of the links, joint motor mass, payload,
hub inertias, etc. A comparative study is also done for different input torque profiles
along with the controlled input torques. Results of the comparative study for theeffect of variation of the system parameters are very interesting technically.

©ICAROB 2016
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The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

GS7-3 Modeling of Mobile Manipulator and Adaptive Super-Twisting Backstepping Control
Seong Ik Han, Hyunuk Ha, Jangmyung Lee (Pusan National University, South Korea)
A mobile manipulator with three wheels and three DOF links is modeled by using
Euler-Lagrange method and vector orientated constraint conditions. In thismodeling process, the conventional complex nonholonomic constrainttransformation need not be considered in mobile robot system and then much
simpler dynamic model can be obtained. Next, the super-twisting sliding mode
control is combined with nonlinear backstepping control to obtain the systematic
nonlinear controller design, fast response speed, and improved robustness touncertainty due to dynamic coupling and disturbance. Simulation and experimentwere carried out to prove the effectiveness of the proposed control methodology.

GS7-4 Self-tuned Local Feedback Gain Based Decentralized Fault Tolerant Controlof Reconfigurable Manipulators
Bo Zhao1, Bo Dong2, Yan Li2, Fumitoshi Matsuno3, Yuanchun Li2 (1Chinese Academy of Sciences,
2
Changchun University of Technology, P.R. China, 3Kyoto University, Japan)

Joint 1 Position(rad)1
Desired trajectory
Actual trajectory

0.5
0
-0.5
-1

0

1

2

3

4

5
6
Time(sec)

7

8

9

10

1
Joint 2 Position(rad)This paper investigates the decentralized fault tolerant control (DFTC) scheme
based on self-tuned local feedback gain to against partial loss of actuator
effectiveness of reconfigurable manipulators. Consider the entire system as a set ofinterconnected subsystems due to its modularity property, the decentralized control
method I s proposed by employing two neural networks for the fault-free system.
For the subsystem in related failure, the self-tuned local feedback gain is added tothe proposed decentralized control method to guarantee the control performance.
Finally, a simulation example is provided to demonstrate the effectiveness of thepresent DFTC scheme.
Fig.1 shows the excellent trajectory tracking performance is derived though thereconfigurable manipulator suffers to actuator failure after t=4s. The main
contributions of this method are: i) The FTC structure is simple since it is no need
to be redesigned in the presence of partial loss of actuator effectiveness; ii) The
actuator fault can be handled in its local subsystem, it implies that the performancedegradation of the faulty subsystem cannot affect the fault-free subsystems.

Desired trajectory
Actual trajectory

0.5
0
-0.5
-1

0

1

2

3

4

5
6
Time(sec)

7

8

9

10

Fig.1 Joint tracking
curves with DFTC

GS7-5 Study on Decentralized Integral Nested Sliding Mode Control for Constrained
Reconfigurable Manipulator with Harmonic Drive Transmission
Bo Dong1, Zeyu Dong1, Bo Zhao2, Yan li1, Fumitoshi Matsuno3 and Yuanchun Li1 (1Changchun
University of Technology, 2Chinese Academy of Sciences, P.R .China, 3Kyoto University, Japan)
This paper addresses the problems of trajectory tracking control of a constrained
reconfigurable manipulator with harmonic drive transmission under a decentralized
integral nested sliding mode control (INSMC) method, and a high-performance
control is achieved without using force/torque sensor. The dynamic model of theconstrained reconfigurable manipulator is formulated with a nonlinear harmonic
drive model. Based on only local dynamic information of each module, adecentralized integral nested sliding mode control method is proposed to reduce thechattering effect of the controller and compensating the model uncertainties.
Finally, simulations are performed for a constrained 2-DOF reconfigurable
manipulator to study the effectiveness of the proposed method. Fig. 1 shown thatthe desired trajectory of robot end-effector can be tracked accurately under the
proposed INSMC method.

©ICAROB 2016
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Fig. 1 Trajectory
tracking curves ofrobot end-effector

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

GS7-6 A Number of Mobile Manipulator Control for Moving Object by using Cooperative
Control
Deok-Su Kim, Dong-Eon Kim, Seong-Ik Han, Jang-Myung Lee(Pusan National University, South Korea)
This paper proposes a method of cooperative control of a three mobile manipulator
for moving an object. These robots go to desired position independently by using
encoder data and inverse kinematics and after arriving in the position, they grasp
and lift the object. For the carrying operation, the lifting operation is implemented
by using the manipulator mounted on the top of the mobile robots cooperatively. In
this system, master-slave mode is used for finding each position of robots and for
coordinating among the robots. During the moving operation, a trajectory planninghas been kept constantly. The real cooperative carrying motions are implemented
to check the possibility of the master-slave mode control based on the mobile
manipulator’s kinematics

GS8 Robotics II
GS8-1 Visual-servo Control of 4-DOF Robot Manipulator for Sorting Moving objects
Longtan Wang, Seon-Woo Kim, Hyun-Wook Ha, Jang-Myung Lee(Pusan National University, South Korea)
This paper considers the problem of estimating position and determining class ofobjects on the moving conveyor belt. A 2D camera is utilized to capture the imageof moving objects. The control system directly integrates visual data into theservoing process. Objects are recognized by using SURF(Speeded-Up Robust
Features) key point detector and FLANN(Fast Library for Approximate Nearest
Neighbor) based matcher. Then Rotating calipers algorithm is adopted to estimate
the centroid position and orientation of the objects. The proposed system is able tocontrol the robot so that it can approach the desired position and grab the specificobject. Experimental result demonstrates that the approach is efficient and reliable

GS8-2 Self-identification of Mental State and Self-control through Indirect Biofeedback-Indirect Representation and Placebo EffectMadoka Takahara, Ivan Tanev, Katsunori Shimohara(Doshisha University, Japan)
This paper describes a possible new scheme for a user with mental
health problems to identify his/her own mental state and control it, notby visiting specialists passively, but by proactively confronting his/her
symptoms. For that purpose, the scheme should provide the user withthe ability not only to externalize and objectify his or her mental state
but also for the user him/herself to control their physiological state. So
we propose an indirect biofeedback system that represents
physiological information with colors and shapes, and enables the user
to grasp his/her inner state and to proactively change and control it
through methods of breathing. These methods facilitate the user tocontrol their autonomic nervous system by themselves. Here, we
discuss indirect representation and placebo effect.

©ICAROB 2016
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Visualization of bodyreactionStr
ess

HeartbeatFeedback
Visualization
Representation ofstress stateThe 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

GS8-3 Construction of a sense of force feedback and vision for micro-objects: Recreate theresponse and a sense of force of objects
Yusei Ishii, Eiji Hayashi (Kyusyu Institute of Technology, Japan)
Technologies that can accurately perform minute work are now beingsought for medical treatment and in the field of manufacturing
semiconductors. 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, a person skilled in the use of this technology is needed forall minute work. For the efficiency of minute work, we used a haptic
device and amplified the force feedback from a minute sample.

GS8-4 Dynamic Behavior Selection Model based on Emotional States for Conbe-I robot
Wisanu Jitviriya, Jiraphan Inthiam, Eiji Hayashi (Kyushu Institute of Technology, Japan)
Currently, the rapid development of non-industrial robots that are designed withartificial intelligence (AI) method to improve the robotics system is to have themimitate human thinking and behavior. Therefore, our works have been focused on
studying and investigating the application of brain-inspired technology fordeveloping the conscious behavior robot (Conbe-I). We created the hierarchicalstructure model, which is called “Consciousness-Based Architecture: CBA”
module, but it has limitation in managing and selecting the behavior that only
depends on the increase and decrease in the motivation’s level. Consequently, inthis paper, we would like to introduce the dynamic behavior selection model basedon emotional states, which develops by Self-organizing map learning andMarkovian model in order to define the relationship between the behavioral
selection and emotional expression model. We confirm the effectiveness of the
proposed system with the experimental results.

GS8-5 Consideration on a Crawler Robot with Six Legs
Toyomi Fujita, Taiga Sasaki (Tohoku Institute of Technology, Japan)
In this study, we consider development of a crawler-type mobile robot which isequipped with six legs at its body. This type of robot may have possibilities of both
high mobility on rough terrain and working ability with handling such as carrying
an object and removing small obstacles in its movement by using the legs as
manipulation arms. This paper presents mechanisms and characteristics of thistype of robot and some possible hybrid motions in which crawler and legs are used.
Several simulations are also performed based on statics to analyze necessary
torques for joint of the legs in the motions.

©ICAROB 2016
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The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

GS9 RoboticsⅢ
GS9-1 Social Expression of Pet Robot Based on Artificial Consciousness and Biologically
Inspired On-line Topological Method
Sakmongkon Chumkamon, Eiji Hayashi (Kyushu Institute of Technology, Japan)
Toward robot becoming friend, the social and natural behavior of the robot is themost needed function for emerging future realistic human-like robot. This paper
proposes the topological consciousness based on a pet robot using the artificial
neurotransmitter and motivation. Since, the significant is a cross-creaturecommunication to friendly companionship. This system focuses on three points.
The first, the organization of the behavior and emotion model regarding thephylogenetic. The second, the robot empathy from human expression. The third,
the social interaction by robot expression using biologically inspired topologicalon-line method for encouragement by its own emotion and the human expression.
We believe the artificial consciousness based on complexity level and the robot
social expression enhance the user affinity by the demonstration.

GS9-2 Anthropomorphic robot modelling with virtual height inverted pendulum approachin Simulink: step length and period influence on walking stability.
Ramil Khusainov, Ilya Afanasyev, Evgeni Magid (Innopolis University, Russia)
Humanoid stable walking is a complex task due the high number of degrees of
freedom, system nonlinearity and relatively small size of robot footprint. Biped
robots tend to fall down as walking speed increases or when the terrain conditions
change. This paper presents dynamically stable walking modelling of Russian
humanoid AR-601M in Simulink environment with virtual height inverted
pendulum model, an effective and simple trajectory generation method based on
inverted pendulum model (IPM). This algorithm adjusts height of the center ofmass in IPM model to reduce ZMP error and guarantees stable locomotion up tosome critical speed. We investigate influence of the step length and step period on
walking stability. Maximum torque values in leg joints are estimated in order toverify if such trajectories are attainable by robot motors. We demonstrate that the
robot model is capable to achieve significant walking speeds on flat surfaces using
this method.

GS9-3 A low cost genetic algorithm based control scheme for wheelchair control in hospitalenvironment
Karam Dad Kallu, Muhammad Jawad Khan, Wang Jie, Min Cheol lee(Pusan National University, South Korea)
In this paper, a control strategy to operate a wheelchair in a hospital is proposed toassist patients. The strategy is tested using a mobile robot, that is navigated in a
hospital using line following. The mobile robot was operated using both genetic
algorithm and A* algorithm for a thorough comparison of the control scheme. The
comparison of the results revealed that genetic algorithm is a better solution incontrolling the wheelchair in a hospital environment.

©ICAROB 2016
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The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

GS9-4 Simultaneous Localization and Mapping (SLAM) algorithm base on EKF and SPKF.
Zolghadr Javad, Yuanli Cai ,Yekkehfallah Majid (Xi’an Jiaotong University, P.R. China)
Simultaneous Localization and Mapping (SLAM) is the problem in which asensor-enabled mobile robot incrementally builds a map for an unknown
environment, while localizing itself within this map. The Kalman Filter’slinearized error propagation can result in big errors and instability in the SLAM
problem. One approach to reduce this situation is using of iteration in Extended
Kalman Filter (EKF) and Sigma Point Kalman Filter (SPKF). We will show thatthe recapitulate versions of kalman filters can improve the estimation accuracy androbustness of these filters beside of linear error propagation. Simulation results arepresented to validate this improvement of state estimate convergence throughrepetitive linearization of the nonlinear model in EKF and SPKF for SLAM
algorithms. Results of this evaluation are introduced by computer simulations andverified by offline implementation of the SLAM algorithm on mobile robot in
MRL Robotic Lab. Index Terms— Extended Kalman Filter, Sigma Point Kalman
Filter, SLAM, instability, Mobile Robot, Nonlinear Estimation.

GS10 Poster Sessions (5)GS10-1 Analysis of Postgraduates’ Entrance Examination Scores Based on Linear
Regression with Dummy Variables
Ning Xiaojun, Huang Ruocheng, Liang Xiaoyi, Ai Dongmei(University of Science and Technology Beijing, P.R. China)
Research on the main factors influencing the students’ score is a very importantpart of the student achievement evaluation system. Several factors that have asignificant influence on postgraduates’ entrance examination score including
enrollment category, university category, age, gender, fresh graduate were studied
by ANOVA in this paper. Quantitative analysis of the correlation between thediscrete variables and admission scores were performed by linear regression withdummy variables and 85% confidence prediction interval of postgraduates’
admission scores were obtained. Data support is provided by these results forgraduate school enrollment work.

 X ij     i   ij , j  1,..., ni , i  1,..., r ij ~ N (0, 2 ), i.i.d .
ri1 ni i  0GS10-2 Clinical Evaluation of UR-System 2 for Recovery of Motor Function ofPlegic Upper Limb after Stroke1

Hirofumi Tanabe, 1Masahiro Mitsukane, 2Norihiro Toya, 2Ryosuke Takeichi, 2Hitomi Hattori,
Yoshifumi Morita, 3Yoshiaki Takagi, 3Norio Hasegawa (1Shonan University of Medical Sciences,2
Nagoya Institute of Technology, 3Sanyo Machine Works, Ltd., Japan)

2

We developed a new training system UR-System 2 for restoring motor function of
the upper limb after stroke in patients with hemiplegia. And then, we conductedclinical evaluation of the therapeutic effect of training with the UR-System 2 in six
patients. The UR-System uses Proprioceptive Neuromuscular Facilitation (PNF)
to promote muscle strength. In this training, elbow flexion and extension of thesupinated forearm were repeated 250 times. As a result of the training, active
ranges of motion of elbow extension and supination of the forearm improved after
training. Moreover, the modified Ashworth scores for elbow extension andsupination of the forearm increased, indicating improvement of spastic paralysis.
These results show the immediate therapeutic effect of training with the URSystem 2 for restoring the motor function of the upper limb.

©ICAROB 2016
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The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

GS10-3 Knee Android Model Reproducing Internal-External Rotation with Screw-Home
Movement of Human Knee
Daichi Yamauchi, Sho Takei, Noritaka Sato, Yoshifumi Morita (Nagoya Institute of Technology, Japan)
It has been reported that the internal-external rotation during knee extension inhuman can be classified into three types. In our previous study, we proposed a knee
android model (Knee-AM) consisting of a tibia model, a femur model and nyloncords imitating the ligaments. We found that the Knee-AM can reproduce one type.
This paper proposed a Knee-AM that can reproduce another type. This type, towhich most of human knee motion belongs, has external rotation occurring at theend of extension, called the screw-home movement (SHM). This type were realized
by changing the lengths and the fixing points of the nylon-cords. Moreover, when
the anterior cruciate ligament (ACL) was removed from the Knee-AM to imitate
ligament injury, the SHM did not occur during extension. The results showed the
ACL played an important role for the SHM.

GS10-4 Wood Species Recognition System based on Improved Basic Grey Level Aura Matrix
as feature extractor
Mohd Iz’aan Paiz Zamri, 1Anis Salwa Mohd Khairuddin, 1Norrima Mokhtar, 2Rubiyah Yusof(1University of Malaya, 2Universiti Teknologi Malaysia, Malaysia)

1

An automated wood species recognition system is designed to perform woodinspection at custom checkpoints in order to avoid illegal logging. The system that
includes image acquisition, feature extraction and classification is able to classify
the 52 wood species. There are 100 images taken from the each wood species isthen divided into training and testing samples for classification. In order todifferentiate the wood species precisely, an effective feature extractor is necessary
to extract the most distinguished features from the wood surface. In this research,an Improved Basic Grey Level Aura Matrix (I-BGLAM) technique is proposed to
extract 136 features from the wood image. The technique has smaller feature
dimension and is rotational invariant due to the considered significant feature
extract from the wood image. Support vector machine (SVM) is used to classify
the wood species. The proposed system shows good classification accuracy
compared to previous works.

GS10-5 Evaluation the Performance of a New Quadrotor Model Based on the Arm’s Length
Variation
Yasameen Kamil N., D. Hazry, Khairunizam Wan, Zuradzman M.Razlan(University Malaysia perlis, Malaysia)
The field of UAV has exceptional level of growth; in addition it is predictable tobe one of the most prevalent fields of development and research in future. The
unmanned aerial vehicle (UAV)- quadrotor widely used as a service robotic inseveral field. This paper presents a new design of this miniature aerial vehicle inaltitude and attitude movements, based on varying the arm’s length of quadrotor
instead of varying the speed of motors to obtain a rotation around each axes. The
length of arms varying are achieved by fixing a stepper motor in each arm ofquadrotor to increase or decrease the length of these arms according to controller
command for attitude movement. The controller commands are accomplished bydesigning a PID controller with specific parameters to maintain the stability of thequadrotor in the flight path. A MATLAB software code used to evaluate thesimulation results and demonstrate the ability of the proposed design to perform a
mission.

©ICAROB 2016
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Fig.1 Quadrotor structure

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

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]

Chang

Jen-Kuo

OS3-5

21/37

Abe

Mizuki

GS4-4

22/59

Chang

Ling-Lin

OS3-4

21/37

AburadaKentaroOS12-1

25/49

Chang

Shu-Chen

OS4-1

17/38

Afanasyev

Ilya

GS9-2

22/65

Chen

Chun-Hsien

OS4-2

17/38

Ai

Dongmei

GS10-1

20/66

Chen

Kuan-Yin

OS3-3

21/37

Akamine

Yuhei

GS2-2

23/54

Chen

Mei-Yung

OS2-4

18/35

GS2-3

23/54

Chen

Yun-ju

OS9-1

29/47

Alrashed

Mosab

GS3-1

28/56

Chiba

Satohiro

GS4-4

22/59

Ando

Kazuaki

OS11-1

18/48

Chien

Chiou-Lan

OS1-3

19/32

OS11-2

18/49

Chumkamon

Sakmongkon

GS9-1

22/65

OS11-3

18/49

Chung

Hsiu-Chen

OS1-3

19/32

Chung

Ting-Hung

GS4-2

22/58

GS4-3

22/59

OS8-5

24/45

OS8-6

24/45

ArakaneSatoshi

GS1-4

20/53

Asada

Taro

OS7-1

30/42

OS7-2

30/43

OS7-3

30/43

OS7-4

30/43

GS4-4

22/59

Asano

Yohei

[D]
Dai

[B]
Boonyaprapasorn

Arsit

GS4-1

22/58

[C]
Cao

Yimin

OS6-2

25/41

Chang

Ching-Jung

OS4-1

17/38

OS4-2

17/38

OS4-3

17/38

OS4-4

17/39

OS4-5

17/39

GS4-2

22/58

GS4-3

22/59

OS3-3

21/37

Chang

Chang

Chun-Chieh

Chun-Feng

FengzhiDang

Viet-Chau

OS10-2

28/48

Desa

Hazry

GS10-5

21/67

Dong

Bo

GS7-4

24/62

GS7-5

25/62

Dong

Zeyu

GS7-5

25/62

Duan

Meng

OS6-5

25/42

Satoshi

GS2-2

23/54

GS2-3

23/54

John Ahmet

GS3-1

28/56

Ziyi

OS5-1

26/39

[E]
Endo

Erkoyuncu[F]
Fu©ICAROB 2016
- 68 -

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

Fu

Ziyi

OS6-4

25/42

Fuji

Ren

OS7-2

30/43

Fujimoto

Yoshiaki

GS6-1

27/60

GS6-2

27/60

Hsu

Hsia

OS11-4

18/49

OS2-3

18/35

OS2-5

19/35

Chu-Lin

OS2-2

18/34

Kuo-Hsien

FujitaToyomi

GS8-5

19/64

Hsu

Hsiu-Hsiung

OS4-5

17/39

Furutani

HiroshiOS12-2

26/50

Hsu

Yung- Feng

OS4-2

17/38

OS12-426/50

Hu

Jian-Sing

OS2-4

18/35

Huang

Ruocheng

GS10-1

20/66

Huang

Yao-Shan

OS3-2

21/36

Hung

Chung-Wen

OS2-2

18/34

[G]
Gao

Caixia

OS6-4

25/42

Geshi

Takuya

GS6-3

27/61

Green

Ethan

GS1-1

20/52

Guan

Sih-Jie

OS1-6

20/34

Iju

Tatsuyuki

GS2-3

23/54

Guao

Yang

GS7-1

24/61

Ikai

Yusaku

GS3-2

29/56

Guo

Jr Hung

OS2-3

18/35

Ikeda

SatoshiOS12-226/50

OS12-326/50

OS12-426/50

OS11-1

18/48

[I]

[H]
Ha

Hyunuk

GS7-3

24/62

Imai

YoshiroHa

Hyun-Wook

GS8-1

19/63

OS11-2

18/49

HanSeong Ik

GS7-3

24/62

OS11-3

18/49

GS7-6

25/63

OS11-4

18/49

Hasegawa

Norio

GS10-2

20/66

InthiamJiraphan

GS8-4

19/64

Hattori

HitomiGS10-2

20/66

Ishihara

Yuki

GS4-4

22/59

Hattori

TetsuoOS11-1

18/48

Ishii

Yusei

GS8-3

19/64

OS11-2

18/49

Ito

Junji

GS3-3

29/56

OS11-3

18/49

Ito

Takao

OS9-2

29/47

OS11-4

18/49

OS9-3

29/47

GS2-4

23/55

OS12-2

26/50

GS8-3

19/64

OS12-3

26/50

GS8-4

19/64

OS12-4

26/50

GS9-1

22/65

OS9-2

29/47

Hayashi

Eiji

Ito

Tsutomu

Hidaka

Kenta

GS3-5

29/57

OS9-3

29/47

Ho

Jei-Fu

OS1-2

19/32

OS12-2

26/50

OS1-4

20/33

OS12-4

26/50

OS1-6

20/34

Iwanaga

Saori

OS10-328/48

OS11-1

18/48

Iwata

Kei

GS4-4

22/59

OS11-2

18/49

Horikawa

Yo©ICAROB 2016
- 69 -

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

[J]

Kobayashi

Kunikazu

GS3-5

29/57

Kohno

Takashi

GS1-1

20/52

GS1-5

20/53

GS6-1

27/60

GS6-2

27/60

GS1-2

20/52

GS1-3

20/53

Jhong

Bing-Gang

OS2-4

18/35

Jia

Hongyan

OS8-1

24/44

Jia

Jiao

OS5-4

26/40

Jia

Yingmin

OS5-2

26/40

OS5-3

26/40

OS5-4

26/40

OS5-5

26/40

KoyamaYoshihide

OS11-4

18/49

OS6-5

25/42

Kubo

Masao

OS10-1

28/48

GS2-1

23/54

OS10-2

28/48

GS2-5

23/55

GS1-4

20/53

GS3-4

29/57

GS6-3

27/61

Jian

Zhijing

Jian
Jie

Wang

GS9-3

22/65

Jitviriya

Wisanu

GS8-4

19/64

Koiwai

Kondo

Kuremoto[K]

Kazushige

TadashiTakashi

[L]

Kallu

Karam Dad

GS9-3

22/65

Lai

Ting-Chi

OS3-1

21/36

Kang

Qijia

OS8-5

24/45

Lee

Ching-I

OS2-1

18/34

OS8-6

24/45

Lee

Jangmyung

GS7-3

24/62

Katayama

TetsuroOS12-1

25/49

GS7-6

25/63

Kato

Ryota

OS7-3

30/43

GS8-1

19/63

Kawakami

YusukeOS11-1

18/48

Lee

Ju-Jang

PS2

23/30

OS11-2

18/49

Lee

Mei-Yin

OS1-6

20/34

OS11-318/49

lee

Min Cheol

GS9-3

22/65

Lee

Roger

GS4-2

22/58

GS4-3

22/59

Kawamura

Yoji

OS13-4

28/52

KawanoHiromichi

OS11-4

18/49

Khairuddin,

Anis Salwa

GS10-4

21/67

Chenglung

Mohd
Khan

Muhammad

GS9-3

22/65

Jawad

Li

Bo-Yi

OS2-5

19/35

Li

Chia-Chen

OS4-3

17/38

Li

Juntao

OS6-2

25/41

Khusainov

Ramil

GS9-2

22/65

Li

Lin

OS6-3

25/41

Kikkawa

Yuuki

OS12-1

25/49

Li

Qing

OS6-1

25/41

Kim

Deok-Su

GS7-6

25/63

Li

Shang-Hui

OS1-4

20/33

Kim

Dong-Eon

GS7-6

25/63

Li

Wenling

OS5-2

26/40

Kim

Seon-Woo

GS8-1

19/63

Li

Yaodong

GS2-1

23/54

Kinoshita

Takuya

GS6-2

27/60

GS2-5

23/55

Kita

YoshihiroOS12-1

25/49

GS7-4

24/62

Ko

Chia-Nan

OS2-1

18/34

GS7-5

25/62

Li

©ICAROB 2016
- 70 -

Yan

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

Li

Yuanchun

GS7-4

24/62

MokhtarNorrima

GS10-4

21/67

GS7-5

25/62

Morikawa

Katsumi

OS9-3

29/47

MoritaYoshifumi

GS10-2

20/66

GS10-3

21/67

GS10-5

21/67

Li

Zheng-Ying

OS2-6

19/36

Liang

XiaoyiGS10-1

20/66

Liang

Bo

OS5-6

26/41

Lin

Gu-Xuan

GS4-2

22/58

GS4-3

22/59

[N]
N.

YasameenLin

Hsiao-Yu

OS4-4

17/39

Kamil

Lin

Hsiu-Min

OS1-2

19/32

Naebi

Ahmad

GS7-1

24/61

Lin

Pei-Ling

OS3-4

21/37

Nagatomo

MakotoOS12-226/50

Lin

Wen-Bin

OS2-6

19/36

OS12-426/50

Lin

Wen-Lung

OS3-1

21/36

Nakaoka

Iori

OS9-1

29/47

Liu

Bo

OS8-6

24/45

Namatame

Akira

OS10-2

28/48

LiuChen-Chung

OS3-1

21/36

OS10-3

28/48

OS3-2

21/36

NanamiTakuya

GS1-5

20/53

OS3-5

21/37

Nanjo

Takao

GS6-1

27/60

GS6-2

27/60

Liu

Hung-Chi

OS3-2

21/36

Liu

Kung-Yu

OS1-6

20/34

Narumoto

Jin

OS7-3

30/43

Lu

Yi-Yu

OS2-6

19/36

Ning

XiaojunGS10-1

20/66

Lund

Henrik

PS1

17/30

Nishime

Taiki

GS2-2

23/54

Nishimura

Kouhei

OS7-1

30/42

Niu

Hong

OS8-4

24/45

OS8-7

27/46

GS3-4

29/57

GS6-3

27/61

OS13-1

28/51

Hautop
IS1

17/31

[M]
M.Razlan

Zuradzman

GS10-5

21/67

[O]

Mabu

Shingo

GS3-4

29/57

Obayashi

GS6-3

27/61

Masanao

Magid

Evgeni

GS9-2

22/65

Mahto

Sachindra

GS7-2

24/61

OS13-228/51

Maneewarn

Thavida

GS4-1

22/58

OS13-328/51

MatsunoFumitoshi

GS7-4

24/62

Oka

Yuuki

OS7-4

30/43

GS7-5

25/62

Okane

Yuki

GS4-4

22/59

Ogata

TakashiMatsuno

Seigo

OS9-2

29/47

OkazakiNaonobu

OS12-1

25/49

Mehta

Rajiv

OS9-3

29/47

Oku

Hirozumi

GS4-4

22/59

Miao

Ching-Mei

OS3-5

21/37

Ono

JumpeiOS13-2

28/51

Mikami

Y.

OS9-2

29/47

OS13-328/51

Mitsukane

Masahiro

GS10-2

20/66

GS4-4

22/59

Ono

©ICAROB 2016
- 71 -

SatokoThe 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

OshimaYasuhito

GS6-2

27/60

OyaizuHaruka

OS7-1

30/42

[P]

OS2-5

19/35

Su

Weijun

OS5-6

26/41

Su

Ying-Yu

OS4-4

17/39

Sugisaka

Masanori

GS2-1

23/54

Pagliarini

Luigi

IS1

17/31

GS2-5

23/55

Pan

Cheng-Tang

GS4-2

22/58

GS2-6

23/55

GS4-3

22/59

SugitaKazuki

GS4-4

22/59

Park

So-Youn

PS2

23/30

Sumi

Kaoru

IS-2

23/31

Park

Yousin

OS9-1

29/47

Sun

Shihao

OS5-3

26/40

Patra

Aswini

GS3-6

29/57

OS5-4

26/40

Kumar
Pengwang

Eakkachai

GS4-1

22/58

Suzuki

Keiji

GS5-2

27/60

Suzuki

Takuo

GS3-5

29/57

Masayoshi

OS7-1

30/42

OS7-2

30/43

OS7-3

30/43

OS7-4

30/43

OS7-5

30/44

[R]
Rajapakse[T]
R. P. C.

OS11-1

18/48

TabuseJanakaOS11-2

18/49

[S]
Saito

Ken

GS4-4

22/59

Takagi

YoshiakiGS10-2

20/66

Sasaki

Taiga

GS8-5

19/64

Takahara

Madoka

GS8-2

19/63

Sakamoto

Makoto

OS9-3

29/47

Takahashi

Katsuhiko

OS9-3

29/47

OS12-2

26/50

Takao

Shoichiro

GS1-2

20/52

OS12-3

26/50

GS1-3

20/53

OS12-426/50

TakatoMinami

GS4-4

22/59

OS10-1

28/48

Takei

Sho

GS10-3

21/67

OS10-2

28/48

TakeichiKeiOS11-3

18/49

Sato

Hiroshi

Sato

Noritaka

GS10-3

21/67

Takeichi,

Ryosuke

GS10-2

20/66

Shiau

Yan-Chyuan

OS3-1

21/36

Takeshita

Yuki

OS12-3

26/50

OS3-2

21/36

Tan

Dongchen

OS8-4

24/45

OS3-3

21/37

OS8-7

27/46

OS3-4

21/37

TanabeHirofumi

GS10-2

20/66

OS3-5

21/37

TanakaShizuka

GS5-1

27/59

Shimohara

Katsunori

GS8-2

19/63

Tanev

Ivan

GS8-2

19/63

Song

Yunzhong

OS5-1

26/39

Tanioka

Sora

OS10-1

28/48

Su

Hung-Wen

OS1-1

19/31

Tatani

Masaki

GS4-4

22/59

Su

Kuo-Lan

OS2-3

18/35

Toma

Naruaki

GS2-2

23/54

©ICAROB 2016
- 72 -

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

Toma

Naruaki

GS2-3

23/54

Wu

Yang

OS1-2

19/32

Toya

Norihiro

GS10-2

20/66

Wu

Yongjun

OS8-4

24/45

Tsai

Lung-Chi

OS1-3

19/32

OS8-7

27/46

Tsai

Yi-Yin

OS3-6

21/37

Tsuda

Yuji

OS7-1

30/42

Tsunenari

Kenji

GS2-4

23/55

[X]

[U]
UchidaYasuo

Xie

Pengfei

OS8-5

24/45

Xu

Cunshuan

OS6-2

25/41

Xue

Li

OS8-3

24/44

OS8-9

27/46

OS9-2

29/47

OS12-226/50

OS12-4

26/50[Y]Uchikoba

Fumio

GS4-4

22/59

Yadekar

Yaser

GS3-1

28/56

Ueno

Junji

GS1-2

20/52

YamabaHisaaki

OS12-1

25/49

GS1-3

20/53

Yamada

Koji

GS2-2

23/54

GS2-3

23/54

GS3-2

29/56

[W]

YamadaTakayoshi

Wan

Khairunizam

GS10-5

21/67

GS3-3

29/56

Wang

Fuzhong

OS5-1

26/39

GS5-1

27/59

OS6-4

25/42

Yamaguchi

Akihiro

GS1-4

20/53

GS2-1

23/54

Yamamoto

Hidehiko

GS3-2

29/56

GS2-5

23/55

GS3-3

29/56

GS2-6

23/55

GS5-1

27/59

GS6-1

27/60

GS6-2

27/60

Wang

Jiwu

Wang

Longtan

GS8-1

19/63

Yamamoto

Wang

Heyang

OS6-3

25/41

Wang

Ming-Chieh

OS1-5

20/33

Yamauchi

DaichiGS10-3

21/67

Wang

Qinghe

OS8-1

24/44

Yamauchi

Sho

GS5-2

27/60

Wang

Shao-Yu

GS4-2

22/58

Yamazaki

Yoichiro

GS6-1

27/60

GS4-3

22/59

GS6-2

27/60

GS4-2

22/58

GS4-3

22/59

Wei

Wei

OS5-6

26/41

Wang

Xiaoyu

OS6-2

25/41

Wang

Yu-Jen

GS4-2

22/58

Yang

Chao

GS2-5

23/55

GS4-3

22/59

Yang

Cheng-Min

OS4-5

17/39

OS8-3

24/44

Yano

Shinnosuke

OS12-2

26/50

OS8-9

27/46

OS12-4

26/50

Wei

Xue

Yang

Toru

An-YunWu

I-Chen

OS4-3

17/38

Yantao

Liao

GS6-1

27/60

Wu

Sheu-Der

OS1-1

19/31

Yekkehfallah

Majid

GS7-1

24/61

Wu

Tong

OS6-6

25/42

GS9-4

22/66

©ICAROB 2016
- 73 -

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

Yoshimitsu

Yuki

OS7-5

30/44

Yoshinaga

TsunehiroOS12-226/50

OS12-4

26/50

OS7-1

30/42

OS7-2

30/43

OS7-3

30/43

OS7-4

30/43

Yoshitomi

Yasunari

Yu

Chunyu

OS8-2

24/44

Yuanli

Cai

GS7-1

24/61

GS9-4

22/66

OS8-5

24/45

OS8-6

24/45

Rubiyah

GS10-4

21/67

Mohd Iz’aan

GS10-4

21/67

GS7-4

24/62

GS7-5

25/62

Yue

YuanLi

Yusof

[Z]
Zamri

Paiz
Zhao

Bo

Zhao

Yifan

GS3-1

28/56

Zhang

Hongtao

OS8-6

24/45

Zhang

Weicun

OS6-1

25/41

OS6-6

25/42

Zhang

Yuzhen

OS6-1

25/41

Zheng

Shunkai

GS2-6

23/55

Zheng

Wenhao

OS5-5

26/40

Zhao

Xia

OS8-8

27/46

Zhao

Huailin

OS8-8

27/46

Zolghadr

Javad

GS7-1

24/61

GS9-4

22/66

©ICAROB 2016
- 74 -

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

Select the meeting facility
best suited to your needs

Conference Room B1, which may be arranged to seat 226 people in a theater-style setting, can readily serve as avenue for seminars or lectures. There are also six smaller meeting rooms with theater seating for between 49 and
112 people. These rooms are available for a broad panorama of uses, such as academic meetings, business forums
or cultural exchange events. Moreover, food and beverage are permitted in all of these areas. With a break roomand green room available for event sponsors, these facilities provide the perfect location for small parties andother such events.

- 75 ©ICAROB 2016

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

Conference hall B3 and B4 combined

Conference hall B5 and B7 combined

Interpretation service booth equipped
Partible conference hall B1

Conference hall (single use)
Conference hall B2- 76 ©ICAROB 2016

The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Okinawa Convention Center, Okinawa,
Japan, January 29-31, 2016

- 77 ©ICAROB 2016

Study on the Promotion of Steel Slags Recycling in Taiwan
Shu-Chen Chang ,Ching-Jung Chang*

Department of Construction Management, Chung Hua University
707, Sec.2, WuFu Rd., Hsinchu, Taiwan 30012, R.O.C.
E-mail: jg104@mail.jges.ntpc.edu.tw, *cjchang@chu.edu.tw

Abstract
The constant development and abuse of natural resources have led to shortage, which also causes many countries to pay
attention to waste recycling issues and promote concepts in resource sustainable recycling, in order to reduce thedepletion of natural resources. The current development trend in waste recycling has gradually shifted from single use incradle to grave to the sustainable recycling in cradle to cradle (C2C), using resource recycling to achieve the economic
concept of sustainable management.
Key words: Waste-to-Resource, Electric Arc Furnace Steelmaking Slag, Oxidative Slag, Reductive Slag

Furnace 』 steelmaking of non-integrated steelmaking
plant. Since the manufacturing process of convertor
steelmaking is smelted all the way to iron and thensmelted to steel, therefore, it is called the integrated
steelmaking plant. The slag generated during thesteelmaking process is called 『 Basic Oxygen Furnace
Slag 』 .3. As to the Electric Arc Furnace (EAF) :steelmaking of non-integrated steelmaking plant, the main
raw materials used for steelmaking are the recycling
scrapped iron and steel. The slag generated during thesteelmaking process can also be categorized into the『 Electric Arc Furnace Oxidative Slag 』 of front-end
process and『Electric Arc Furnace Reductive Slag』ofback-end process. [1, 2]
The operation of electric arc furnace smelting recycling
system in Taiwan has already well completed. The main
sources of scrapped iron and steel used for steelmaking
are: general household scrapped steel, scrap and defective
products generated during the plant manufacturing

1. Introduction
Iron & steel are the foundation of industrial development.
The construction engineering, transportation andcommercial machines & tools are all closely related to theiron/steel industries. How to dispose and utilize the waste
generated along with the manufacturing process shall becarefully faced and considered. Slag is the waste
generated during the smelting of iron & steel. According
to the different production approach, it can be categorized
into: 1. The waste generated in the Blast Furnace during
the iron smelting process is called『Blast Furnace Slag』.
Under the high temperature, the blast furnace slag is still
the liquid stage. Along with the different temperature
dropping and cooling process, it will generate 『 Aircooled Blast Furnace Slag』and『Water-quenched Blast
Furnace Slag』separately. 2. Steelmaking process. It canbe categorized into the 『 Convertor 』 steelmaking ofintegrated steelmaking plant and the 『 Electric Arc© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan1

Shu-Chen Chang, Ching-Jung Changprocess, rebar from demolition of plants and houses,
scrapped steel from obsoleted vessels, machinery andcars/motorcycles etc. According to the smelting process,the slag of EAF can be categorized into EAF oxidative
slag (hereinafter referred to as oxidative slag) and EAF
reductive slag (hereinafter referred to as reductive slag)
two major items. The EAF steelmaking plants in Taiwan
produce around 10-million metric tons (MT) of crude
steel annually and generate around 1.4-million MT ofoxidative slag and 0.4-million MT of reductive slag
simultaneously [3].
This study integrates the resource promoting strategy of
EAF steelmaking slag and brings together the relevant
technologies as the reference for promoting the slag-toresource of EAF steelmakers.

and stable which is the best way of option for promoting
recycling material to the industry promoted. To proceed
the better implementing approach for reutilization
introduction of waste-to-resource, its indexes of assessing
feasibility include the elimination of demanding amount,applied technology, process equipment, locality, productvalue, restriction of acts and specifications six items,
shown as Table 1.
Even Taiwan has already permitted the import of sandand gravel, however, the main source still depends on thenature sand and gravel obtained from the local rivers. Dueto the rising of environmental consciousness, the concept
for maintaining natural landscape, the weak concept ofquarrying industry to the mining environment andpreventing various disasters, etc., that cause theimbalance of supply and demand, and even trigger the
price escalation of aggregates which becomes the major
potential concern of construction industries. Therefore,
the development of aggregates supply source shall becoordinated with the demand, adopt the diversified supply
source development and reutilization to achieve the targetof sustainable development.
The promotional approach for waste reutilization is toproclaim the appropriate ways of using material throughthe environmental protection department; incorporate therecycling material items and using specification &proportion in the engineering guidelines through theengineering application end to induce the market channelsof construction resource recycling. Most of the waste
generated from the industrial manufacturing may suitablefor using in the civil engineering including the propertiesof hardness and anti-wear which is suitable for using as
the aggregate; with the hydration property and large
internal friction angle which is suitable for using as theground material or the ground improvement material; if
the chemical composition contains FeO, CaO, SiO2 etc.,
it may use as the cement additive; in addition, if thechemical composition contains the fertilizing elements of
CaO, SiO2, MgO, FeO etc., it is suitable for using as the
agricultural compost material. Accumulate the resource
material suitable for the aggregate material produced
yearly in Taiwan, and assess the constructions with large
demand of aggregate material, and then utilize the2. Analysis & Discussion
The Taiwan government has regulated the management ofrelated generating sources, storage, recycling, removal,
disposal, reutilization and flow with respect to the generalwaste and industrial waste, and considered in sequence toreduce the waste generation, reuse, recycling andutilization, energy recovery and proper disposal, clearly
defined the environment-friendly specification to promote
the material recycling and reutilization.
2.1. Related Status of Reutilizing Waste &Construction Industries in Taiwan
In Taiwan, the wastes were categorized into the generalwaste and industrial waste. The general waste refers to thesolid or liquid waste generated from the household or
non-industry; the industrial waste refers to the waste
generated from the industrial institutions. The total
amount of waste was around 25.35-million MT in 2012,
in which, the general waste was around 7.4039-million
MT (holding 29.21%) and the industrial waste wasaround 17.95-million MT (holding 70.79%)【4】. The
reutilizing key of waste should have the features of highand stable demand (5), and the market demand shall begreater than the generation volume of waste to facilitate
the reutilization of waste resources and reduce the stacked
space of waste and the amount of waste.
The aggregates are the most important bulk material for
construction engineering. The material demand is high

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan

2

Study on the Promotion

strategy approach that will be able to link the reutilizationof waste to the construction adequately.

Taiwan has already accumulated quite rich researchwith respect to the reutilization study of EAF slag
including the cement material, asphalt concrete
aggregate, CLSM aggregate material, basic layer ofpavement construction or bottom level aggregate material

Table 1 Waste-to-Resource Introducing Engineering Reusing
Feasibility Assessment [1]Dividing Mode

Reductive Slag【0.4-million MT, the2.2. Construction Application Market Analysis of
EAF Steelmaking Slag
The direct reutilization pathways of oxidative slag areroad subgrade layer, asphalt pavement, non-structuralconcrete respectively (such as Controlled Low Strength
Material, CLSM) [6]. The annual demand of roadsubgrade layer is around 1.5-million MT, the asphalt
pavement is around 12-million MT, however, therecycling of reclaimed asphalt pavement has already up to
40%. The annual demand of CLSM is around 2.2-million
MT, the market demand is far beyond the generating
amount of oxidative slag waste and has already reached
the reutilizing effect of waste resource. The annual
generating amount of reductive slag is around 0.4-million
MT. The reutilizing pathway of reductive slag isdistinguished by if the stabilization is to be applied,
mainly used for the cement material without requiring thestabilizing treatment and stabilizing the expansion of slag.
The reutilizing pathways and relative total demand of
EAF slag are shown as Table 2.

Same as water-quenched blast
furnace slag, directly introduce toreutilizing purpose

6

Locality
Product Value
Restriction ofActs &
Specifications

No need additional investment of hightechnology or complicated equipment toavoid increasing cost.
Nearby use to lower transportation cost.
Shall exceed disposal cost.
Shall comply with reuse purpose act, CNS
national standards, construction outlines andthe related operating specification standards.

Stabilization is not
required4
5

Process
Equipment

Table 2
Resource Market Analysis of Taiwan
EAF Steelmaking Slag [1]Mature technology and able to ensure quality.

Stabilization isrequired3

High Demand and stable market.

of year 2013 is 1.4-million MT】

2etc. Under the restriction of Waste Reuse Types of Soil
Pollution Control Standards and Industries issued by
Taiwan government, the reutilizing amount of EAF slag
was limited and could not be effectively promote.

Description of Benefits

Oxidative Slag
【0.7-million MT, the statistics

1

Assessment
Criteria
Eliminate
DemandingAmountAppliedTechnology

statistics of year 2013 is 0.4-million MT】

Item

Feasible Pathway

Annual Demand(10-Thousand MT)

Aggregate/Material(10-Thousand MT )1.Road Subgrade
Layer

150

150

2.Asphalt
Pavement

1,200

1,000

3.Non-structural
Concrete (CLSM
or cement
products)

220

165

4. CarboundumLess demand elimination

5. Other reutilizing approaches
1,920(Raw material)

1.Cement

1,200

2.Melt down
process

Reduce reductive slag 20～40％

1.CLSM

220

165

2. Other reutilizing approaches (such as Mainland China steel slag
cement, Southeast reutilization etc.)

Introduce to
Restriction
Assessment
Newly built
road, decreased
year by year
Reclaimedmaterial
already up to40%
Other waste
resources haveelimination
demand as well
Less demand
elimination
1.Able to add
raw material
10%
2.Transportation cost is highApplication
Technology has
not skillful
Other waste
resources haveelimination
demand as well
Disposal &transporting
finishedproduct are to
be assessed

In addition, the industry lacks of knowledge about thenon-toxic steelmaking slag and the hazardous steelmaking
ash collection, blended handling and improper disposal
results the trouble to the domestic steel/iron industries as
well.
Therefore, for the promotion of slag reutilization shall beable to overcome the corroded situation created by the
expansion property of slag and the metallic iron remainedon the surface, other than through the stabilization
treatment and strengthening the magnetic separation
efficiency, shall be further promoted through the planningof selecting the applicable construction purpose to avoid
further negative perception caused to the slag from thepublic.
The types of slag produced in Taiwan include thebasic oxygen furnace slag, oxidative slag and reductive

2.3. Resource Technology Assessment of EAF
Steelmaking Slag
(1) Promoting Pathway of EAF Steelmaking Slag

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan3

Shu-Chen Chang, Ching-Jung Chang

content of reductive slag, and proceeding thethermomechanical analysis (TMA) test in accordancewith the different cement proportion, the impact change
to the burning temperature of rotary kiln becomes lessafter adding the reductive slag to the cement raw mix, it
proves the feasibility of replacing partial cement raw
mix.

slag. After completing the assessment in accordance withthe material properties and applicable construction
purposes, the reutilization of EFA slag is mainly selected
the asphalt concrete pavement with coverage and CLSM
backfill material, additional assessment is implemented touse as the additive for cement raw mix.
(2) Technology Research for Oxidative Slag

4. Conclusion

The Los Angeles abrasion rate and soundness of EAF
oxidative slag are equivalent to the general natural gravel ,however, the specific gravity is higher than the natural
gravel due to higher iron content. The water absorption isgreater than the natural gravel due to more porosity ofslag itself. However, the results of running water quality
standard, radiation dose test and toxicity dissolution test
have all complied with the current specification values,
and will not cause any hazard to the environment.
Therefore, it can be considered as the general industrial
waste and reutilized for the application of road pavement.

(I). The annual capacity of EAF steelmaking slag was
almost fixed, propose the appropriate reutilization
source and transparentize the elimination flow, andcooperate with the government policy toward the
direction of diversified reutilization in order todevelop the pathway.
(II). The product of oxidative slag has the features ofhigh stability, low silt content, low abrasion rate andgood soundness, to improvement of stabilization andthe main promotional purposes shall be locked on
the asphalt pavement, road subgrade layer and nonstructural concrete.
(III). The implementation concept of waste resource
recycling management, it will be handled andprocessed by each industry competent authority
based on the announced related acts; the engineering
unit is responsible for maintaining the on-site quality.
If the prescribed use restrictions are followed, it may
indicate that resource product has no doubt about the
impact to the environmental aspect.
5. References(3) Technology Research for Reductive Slag
The main ingredients of reductive slag are
categorized into 3 parts (1) active ingredient (2) oxidized
metal and (3) other materials. The active gradient ismainly SiO2 and CaO, and its gradient is similar to thecement raw mix, however, itself contains free calciumoxide which results its instable characteristics. Therefore,
the reductive slag is used as the cement raw mix, andplace in the rotary kiln with high kilning temperature tolower its instability in order to ensure its stability andpracticality.

1. H. Motz; J. Geiseler, Products of Steel Slag an Opportunity
to Save Natural Resources, Waste Management, ,21(3),2001, pp. 285-293.
2. Geiseler, J. (1996). Use of Steel Works Slag in Europe.
Waste Management, 16(1-3), pp. 59-63.
3. Taiwan Construction Research Institute, 「 Slag
Reutilization Program Feasibility Assessment andPromotional Strategy Plan 」 . Taiwan Steel & Iron
Industries Association (2013).
4. Taiwan Steel & Iron Industries Association 50th
Anniversary Special Issue, 「 Coping Strategy ofReutilizing Taiwan Steelmaking Slag at Current
Stage」,Taiwan Steel & Iron Industries Association (2013).
5. Taiwan Construction Research Institute, 「 Road
Construction Application Effects Assessment Plan of EFA
Oxidative Slag 」 , Taiwan Steel & Iron Industries
Association (2014).

3. Assessment & Discussion
Construction industries, the promotional pathways would
be mainly locked on the road subgrade layer, asphalt
pavement, CLSM and cement additive. The strategy is toprovide the complete technical data and promote theimplementation of verification system in order. For thepart of build-up technology, the stage is to proceed fromthe asphalt concrete pavement of oxidative slag andcement raw mix of reductive slag two aspects.
As to the assessment aspect of applying the original
slag to the cement raw mix, since its chemical contents
are similar to the cement raw mix, and after replacing
partial cement raw mix in accordance with the different© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan

4

Study on the Promotion

6. Taiwan Construction Research Institute, 「 Feasibility
Assessment Plan of Applying EFA Reductive Slag to
Cement Material」, Tung Ho Steel Enterprise Corp. (2014)© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan

5

The Research on the Utilization of Green Building Ecological Index Group in Campus
Environment – Taking of Elementary Schools at Houli District, Taichung City as Example
Ching-Jung Chang*, Yung- Feng Hsu, Chun-Hsien Chen

Department of Construction Management, Chung Hua University
707, WuFu Rd., Sec. 2, Hsinchu 30012, Taiwan
E-mail: *cjchang@chu.edu.tw, jg129@mail.jges.ntpc.edu.tw, cindy4066@yahoo.com.tw

Abstract
The elementary school is the most basic educational site, and the spatial characteristics of school building haverelative large area of outdoor space. If the ecological education can be promoted from the campus, it will not beable to improve the overall environmental ecology, but also able to root the ecological education from the lowest
base. The paper research takes the field survey at the elementary school and analysis the relevant affecting factors.
It hopes to provide appropriate reference contribution to planner or government in the future.
Key words: Green Building, Ecological Index Group, Elementary School Campus, Ecological Quality,
Sustainable Campus

ecological environment, school has large area space,
if it can be converted into the quality ecologicalenvironment that will be great help to the urban-rural
ecological green network, ecological conservationand even the national ecological education [2,7].
This article takes the campus of five elementary
schools located in Houli District, aims at the
Ecological Index Group, most closely to the campus
outdoor space including biodiversity, greenery, Soil
Water Content 3 indexes to perform assessment andresearch. It is expected to enhance the environmental
quality of elementary school campus in that district
and city, and provide the basic analysis data for thereference of follow-on researchers and planning
units.
2.
Literature
The trend of thought for green building started at thetwo times of energy crisis in 1970s, due to the panic

1. Introduction
Due to the over-exploitation to Earth and dramaticincrease in energy consumption that cause theconcentration of CO2 increased year by year, andresult the environmental imbalance phenomena ofEarth warming, abnormal climate, ozone depletion,
forests gradually disappeared and debris flow etc. [1].
Taiwan government incorporated the「Green Building」
into「Urban-Rural Sustainable Development Policy」
as the implementation priority in the year of 1996,
and fully promotes the green building policy, in
March 2004.
Facing the crisis of Earth environment, the promotionand education of sustainable development are thefoundations for resolving the problem [3]. The school
ecology is the most important part in the urban-rural© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan

6

Ching-Jung Chang, Yung- Feng Hsu, Chun-Hsien Chenfrom the oil crisis, the energy-saving design was
raised in the construction industry. In recent decade,under the environmental crisis of Earth warming andclimate abnormal change, the earth environmental
design and green building concept of life cycle
assessment, CO2 reduction, biodiversity design havebeen further developed [4]. The green building is theconcept of pursuing the environment sustainable
design, it is called as ecology building Japan;
European countries call as「Ecological Building」and「Sustainable Building」, United States, Canada also
call as「Green Building」, it is the general call of
ecology, environmental protection, sustainable,
ecology building [5,6].
In the year of 1990, British Building Research
Establishment (BRE) brought the first assessment
system for green building i.e. BREEAM which
affected the follow-on assessment method e.g. LEEDof United States in the year of 1996, GBTool of
Canada in the year of 1998 etc. Taiwan establishedthe green building evaluation system of EEWH in theyear of 1999 which is the 4th green building
assessment system in the world [5]. Up to the year of
2011, there are 26 countries formerly owned thegreen building assessment system in the world shownas Fig. 1, there are 89 countries already established orunder preparation for organizing the green building
related associations.

3. Research Method
This research is assessed in accordance with theecological assessment items and standards stated in
the「Green Building Assessment Manual」issued by
Taiwan Construction & Planning Agency, take thebiodiversity, greenery and soil water content index.
3.1 Assessment Mode of Biodiversity Index
The biodiversity index is the base of biologicalhabitat and biological exchange, the base scale shallbe over 1 hectare and then be applicable to assess by
this index. According to biodiversity index BD, itsvalue shall be greater than the baseline value of
biodiversity index BDc, and the score of biodiversity
index system RS1 shall comply with 0.0≦RS1≦9.0,
its relative formula is as follows Eq.1~2:

 Xi , i  1, 2 , 3, 23  1
n

BD 

i

RS1  18.75 BD  BDc
 1.5 , 0  RS1  9 
BDc

2

BD：Biodiversity Index Calculation Value
Xi ：Sub-score, i=1，2，3 ……，23
RS1：Biodiversity Index System Score, 0.0≦RS1≦9.0
BDc：Baseline Value of Biodiversity Index
3.2 Assessment Mode of Greenery Index
The greenery index takes the plant photosynthesis
as the assessment baseline, configure the 40 years of
CO2 fixed volume as the conversion baseline ofgreening benefit, the calculation value of greeneryindex TCO2 is calculated from the total CO2 fixedvolume of all plants in the base, the system score of its
greenery index RS2 i.e. converted from its index
calculation value TCO2 and greenery index baseline
value TCO2c, the score of greenery system RS2 shallcomply with the limit of 0.0≦RS2≦9.0, its relative
calculation formula is as follows Eq.3~5:

Fig. 1. Countries with Assessment System [5]In Taiwan, it formulated green building
assessment manual in 1999, and it has been revisedfor many times until 2012 to establish EEWH
(Ecology, Energy Saving, Waste Reduction & Health)assessment indexes. The index of WWEH includes
1. Ecology: biodiversity, greenery, soil water content
three indexes2. Energy Saving: the daily energy
saving index, 3. Waste Reduction: waste reduction
and CO2 reduction 4. Health: indoor environment,water resource, improvement of sewage waste, totally
there are 9 indexes [5].

 (Gi  Ai))   (3)n

TCO 2  (i 1A  (A0 - Ap )  (1  r ) , A  0.15  A0 (4)
 TCO 2  TCO 2c   1.5 , 0  RS 2  9 (5)RS2  6.81 
TCO 2c

Gi：Unit Area of Certain Planting Species CO2 Fixed© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan

7

The Research on theVolume (kg/m2)
Ai：Planting Area Baseline
A'：Minimum Green Area (m2)α：Ecological Greening Correction coefficient
Ao：Base Area (m2) i.e.「School Land Area」of thisresearch.
Ap： Unable to greening area (m2)r：Legal Floor Area Ratio

λand baseline value λc, its system score must havethe limit of 0.0≦RS3≦9.0, its relative calculation
formula is as follows Eq.6~8:

3.3

RS3：System Score of Soil Water Content Index
λ：Calculation Value of Soil Water Content Index
λc：Baseline Value of Soil Water Content Index
Qi：Soil Water Content Volume of Various Kind of Soil
Water Content Design (m3)
Ao：Total Base Area (m3)r：Legal Floor Area Ratio
f：Base Final Infiltration Rate (m/s)
t：Maximum Rain Delay (s)

i

o

i 1

c  0.5  1.0  r Assessment Mode of Soil Water Content

The assessment of soil water content index iscategorized into the common soil water content designand special soil water content design two items, thecalculation value of soil water contentλis the relative
ratio between the nature land soil water content prior todevelopment Q0 and the land soil water content after
development Q', baseλc is the baseline of land soil
water index. The larger λ value indicates that theperformance of soil water content is better, otherwise
the worse. When its value is 1.0, it indicates that theland development activities have no damage to thefunction of soil water content of the original nature bareland. The system score of soil water content RS3,
conversion from the design value of soil water content
Table1

 Q / A  f  t  6 n

7 

   c 
  1.5 , 0  RS 3  9 
RS 3  4   c 

8 

4. Case Survey Results & Analysis
The basic data of this research scope are shown as
Table 1, the Houli elementary school has the highest
floor are ratio, up to 33.5%, the floor area ratio of otherfour schools are very similar, all less than 15%.

List of Basic Data of Elementary Schools in Houli District

School Name

SchoolLandAream2

Building Aream2

Floor Area
Ratio %

Numbers ofStudents

Tai’anHoili
Neipu
QixingYuhing

22402
19374.92
30812
11592
12912

2408.825
6490.28
3414.81562
1622.89

10.75
33.50
11.08
13.47
12.57

167
707
1484
279
262

Density ofStudent
m2 /person
134.1
27.4
20.8
41.5
49.3

Built Year of
School
1946
1946
1904
1958
1964

4.1 Status Analysis of Biodiversity Index

4.2 Status Analysis of Greenery Index

The biodiversity index assessment result ofrelative survey data are shown as Table 2 i.e., thecalculation results of biodiversity index assessment
studied by this article. From the Table 2, only the ratioof biodiversity index (BD/BDc) of Tai’an elementary
school was 1.12 since its relative smaller floor area
ratio and larger green area, it indicates that thebiodiversity part shall be further enhanced.

As summarize from the data of each elementary
school in Houli District, the calculation results ofgreenery index are shown as Table 3. From the Table
3 got the greenery and greening baseline value(TCO2/ TCO2c) of 5 elementary schools are all
greater than 1, it meet the baseline. It indicates thatthe greenery implementation status of elementary
schools in Houli District is quite well.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan8

Table 2
Primary
Category

Minor
Category

Calculation Results of Biodiversity Index Assessment
Tai’anElementary
School56
50
1.12

Design Item

Total (BD)
Baseline Vale (BDc)
BD/BDcQualification Rateable 3
School Name

School Land Area m2

Tai’anHoili

Hoili
Elementary
School
43.3
50
0.87

Neipu
Elementary
School
15.6
50
0.31
20%

QixingElementary
School
18.3
50
0.37

YuhingElementaryy
School
21.9
50
0.44

Statistical Table of Greenery
TCO2

TCO2c

TCO2/ TCO2c

Qualification Rate

System Score RS2

22402

Sports Field
m2
3,084

5,256,643

3,622,125

1.45

100%

4.57

19375

3,240

5,120,505

3,025,313

1.69

6.22

Neipu

30812

10,451

5,928,899

3,817,688

1.55

5.27

Qixing

11592

3,428

3,233,848

1,530,750

2.11

9Yuhing

12912

3,193

4,796,559

1,822,378

2.63

9elementary schools and Neipu, Qixing & Yuhing
elementary schools are all lower than 1. It did not meet
the baseline due to the insufficient green area, the main
water conservation of soil water content mainly comesfrom the green area.

4.3 Status Analysis of Soil Water Content
The soil water content are shown as Table4. From
Table 4, the index value and soil water content baseline
value (λ/λc) is greater than 1 for Tai’an & Hoili
Table 4
SchoolSchool Land Area
Name
m2
Tai’an
22402
Hoili
19375
Neipu
30812
Qixing
11592
Yuhing12912
Ecological Index Group Total
Score

Statistical Table of Soil Water Content Index

Green Aream2
13,984
8,645
4,189
2,580
3,227
27

λ

λc

λ/λc

0.64
0.5
0.48
0.23
0.34

0.5

1.28
1
0.96
0.47
0.67

10.96

7.72

6.6

Qualification
Rate
40%

9

System Score
RS3
2.64
1.5
1.33
0
0.19
9.19

is the mandatory facility for elementary school,
excessively high proportion will cause the green
area relatively reduced, and how to give balance forboth will be very important issue while planningthe campus.
4. Increase the species of trees in campus, species
diversification, it will then have variety of creatures
inhabit, breed, change the cement revetment andpool bottom to the masonry revetment with more
ecological and natural soil as the pool bottom tocreate the ecological waters space for providing
habitat.

5. Conclusion & Recommendation
The qualification rate of greenery is up to 100%,
biodiversity and soil water content are 20% and 40%
respectively, consolidate the reasons as follows:
1. Poor planning for the bio-friendly environment, thepavement common uses excess of asphalt or
concrete, causes the poor hydrophilic property ofland.
2. The green area is the important role of campus
ecological environment, only with sufficient green
area plus the complete biological environment andperfect lawn or using pavement with goodpermeability will then be able to achieve the「 qualitative 」 and 「 quantitative 」 baseline ofgreening.
3. The water biological habitat is the richest place forbiological gathering and exchange. The sports field

Reference Literatures
1.

Chen Chun-Hsien,「The Research on the Utilizationof Green Building Ecological Index Group in
Campus Environment – Taking of Elementary
Schools at Houli District, Taichung City as
Example」, Master Thesis, Department of© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan

9

2.

3.

4.
5.

6.

7.

Construction Management, Chung Hua University,
Hsinchu 2014.
Lin Xian-De,「Ecology and Energy Conservation
Planning for Sustainable School」, Chan’sArch-Publishing Co., Ltd, 2004.
Wang Xi-Zhi,「Grening」&「Soil Water Content」
in「Green Building」 Evaluation Index Applicationsat Middle/Elementary School Campus - Taking
Hsinchu City as example」, Master Thesis, Graduate
School, Department of Building & Urban Planning,
Feng Chia University, Taichung, 2002.
Lin Xian-De,「Green Building」, Chan’sArch-Publishing Co., Ltd, 2006.
Architecture and Building Research Institute,
Ministry of the Interior,「Green Building Evaluation
Manual – Basic Version」, 2015.
Yang Yi-jeng,「An Evaluation Research of Green
Building Ecological Indicator Group of Zhang HuaCounty Primary School」, Master Thesis, Graduate
School, Department of Architecture, Feng Chia
University, Taichung, 2009.
Tang Zhi-Min,「School Buildings and Campus
Planning」, Wu-Nan Book Inc., 2006.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan

10

The Assessment and Research on the Reconstruction of Enclosing Wall into Open Type in
Elementary School Campus – Taking of Erlin Township, Changhua County as Example
Ching-Jung Chang,Chia-Chen Li, I-ChenWu

Department of Construction Management, Chung Hua University
707, WuFu Rd., Sec. 2, Hsinchu 30012, Taiwan
E-mail: cjchang@chu.edu.tw, jg216@mail.jges.ntpc.edu.tw, iciness26@yahoo.com.tw

Abstract
The purpose of enclosing wall is to demarcate, segment field and blocking of school. The Taiwan government
encourages schools to perform the enclosing wall to become the open type campus with friendly affinity. Thechange of enclosing wall caused concern from the teachers and parents. This paper article takes the elementary
school at Changhua as the research objects, made field investigation, collecting data and interviewing by experts,
design the questionnaire and made analysis to explore the reaction. It hopes to provide as the reference for thedesign of elementary school.
Key words: One-Way Analysis of Variance (ANOVA), Open Type Enclosing Wall, Enclosing Form, Design
Questionnaire, Factor Analysis

1. Introduction
In the early 2000s, most of the school enclosing wall
were the solid wall made by concrete or bricks, toweringwith the barrier and protective function, and the shapefeature was monotonous, the wall surface lacked ofaesthetic feeling. Usually, the school building focuses on
the schoolhouses and sports facilities used as the activity
spots and educational environment for teachers/students.
The school enclosing wall always separates the campus
from the surrounding environment area, and just likeforming an enclosed space [1]. After 921 earthquake
occurred in the year of 1999, there were many schoolbuildings and schoolhouses collapsed and ruinous, there
were many school campus near the disaster area facing
the reconstruction. It coincided with the open education,
green school and campus open concepts promoted by
Taiwan government [2]. That promoted the open type

campus without walls idea and hoped to bring thecohesion between school environment and community
closer.
The reconstruction plan causes doubt from theparents with respect to the security level of school
children, i.e. if the open type enclosing wall isapplicable for different schools which shall be the part tobe discussed. This paper takes part of elementary school
campus located at Chung Hua, Taiwan as the research
objects, it is expected to explore the recognition ofteachers and parents with respect to the reconstructed
open type enclosing wall, and the status, problems faced
and solution of the schools. The research collected therelated literatures, questionnaire, expert interview andmade statics analysis, and provide the result to thegovernment departments as the design reference for© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan

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Ching-Jung Chang, Chia-Chen Li, I-ChenWu

planning the reconstruction of campus enclosing wall in
the future.

140cm,solid wall with partial hollow treatment, thevisual penetration is higher than the closed enclosing
wall, enhance the visual penetration but still able tokeep the privacy, shown as Fig. 2.1~2.2.
3. Open Type Enclosing Wall: Height is lower than
120cm, good visual penetration, most of them havethe beautiful styling, but due to the height islowered, barrier and blocking are lower, also able toachieve the purpose of natural monitor, comply with
Newman’s Defensible Space theory [7]. The open
type enclosing wall is categorized as follows, shownas Fig. 3.1~3.5:(1) Barrier Type Enclosing Wall: Full hollow orpartial hollow, high visual penetration, lower theheight decrease the protective, easy to create theclimbing opportunity from the schoolchildren or
outsiders.
(2) Green Hedge: Low shrubs or flower stands,
constructed with plant material, high visual
penetration, but easy create the problems ofcrossing by the outsiders or maintenance &Management.
(3) Dwarf Type Enclosing Wall: Lower the originalwall height, most of them are below 80cm, highvisual penetration, width and height are suitablefor pedestrian sitting and resting, poor protective
and blocking function.
(4) Hybrid Type Enclosing Wall: Wall combinedwith more than two different forms or material,consolidate the advantages of multiple forms or
material, good visual penetration and beautiful
styling.
(5) No Enclosing Wall Form: Demolish solid wall,
adopt full-open design, highest visual
penetration, poor protective and blocking
function.

2. Literatures
The main functions of building enclosing wall areinterval, demarcation, segmenting field, defining scope
and uses as the construction of barrier, the function ofschool enclosing wall is to segment the space between
the inside and outside of school environment. It has thefunctions of anti-strong wind, dust and noise isolation, it
also has the effect of security and blocking plus thefunction of aesthetic appearance and education [1, 3].
Currently, there are many European countries adopted
open styling for schoolhouses and open space design forclassrooms, and lower the enclosing wall or without
walls. It indicates that the school construction planningtoward to the development trend of open style [4].
In the early 2000s in Taiwan, most of the school
enclosing wall were the solid wall made by concrete or
bricks, towering with the barrier and protective function,
however, the form was monotonous, wall surface lackedof aesthetic feeling. The government prompted to theidea of campus without wall promoted by Ministry of
Education that is gradually push to expand, it expects todemolish the old enclosing wall structured by cement orbarbed wire, and replace by the greening planting or aredesign products to achieve the school opening andincrease community interaction.
The campus enclosing wall can be categorized by its
different aspects of function, height, materials, shapes,
features, performance and visual penetration,
greening/beautify degree etc. [5, 6], this article
categorizes the forms of enclosing wall as follows:
1. Close Type Enclosing Wall: Height is over 140cm,
with solid wall, no visual penetration, shown as Fig.
1.1~1.2.
(1) Traditional Solid Enclosing Wall: Most of them
are made by concrete or bricks, with solid wall.
(2) Color Painting, Collage Wall: Add color painted
patterns or mosaics collage on the solid wall.

3.1

3.2

3.3

3.4

3.5

Fig.3. Open Type

3. Research Method
1.1

1.2

Fig. 1.Close Type

2.

2.1

This research adopts the literature exploration,
environmental status survey, expert interview andquestionnaire, through the statistical analysis on the

2.2

Fig. 2. Semi-open Type

Semi-open Type Enclosing Wall: Height is over

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan

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The Assessment and Researchquestionnaire results, use ANOVA to inspect thedifferences of various different opinions from theteachers and parents, simplify the questionnaire opinion
variables to the demand dimensions through theprinciple component analysis, and able to briefly andconcisely express the user demands [8].
The questionnaire objects are the teachers andparents of elementary schools where the campus
enclosing wall has been reconstructed to the open type,through the approach of questionnaire survey tounderstand the recognition and satisfaction ofquestionnaire objects with respect to the reconstructionof open type enclosing wall in the campus.
Questionnaire adopts five-level of Likert scale, survey
scene first, collect data and after interviewing by experts,
design the pretest questionnaire items, adopt the closed
questionnaire which will allow the participants
answering fully in accordance with the options provided
by the researcher, item design are all 18 items for bothteachers and parents questionnaire. First start withpretest questionnaire, randomly issue 120 copies in the
research area, After performing the statistical analysis,
test its reliability and validity analysis, and thendetermine based on the test results, and re-establish theformal questionnaire items.
This research takes the questionnaire results
performing the descriptive statistics, ANOVA and factor
analysis to analyze and compare the objects withdifferent background, and their views to reconstruct thecampus enclosing wall to the open type enclosing wall.
ANOVA inspects the opinion differences between the
different identities, ages, principle component analysis
simplifies the questionnaire variables to demanddimensions. When perform the principle component
analysis, the test of KMO and Bartlett shall be
performed, it is applicable for performing the principle
component analysis when KMO value is over 0.8; thelarger value acquired by Bartlett sphericity test, the moresuitable for analysis.
After determining the data suitable for the principle
component analysis, select suitable factor dimensions inaccordance with the eigenvalue, select eigenvalue large
than 1 or the factor selecting point when the slope ofscree plot starts to downward, the factor with small
eigenvalue does not have the explained variable
difference which can be discarded, and the selectedcumulative explained variance percent shall reach 70%.
The principle component analysis is to show thecorrelation between the measurement variable andextracted component through the eigenvalue, if thedifference of component loading is not too large, therotation may be used to change the factor loading ofeach component, after the rotation, the variance of newcomponent become the largest, this research uses the
Varimax rotation.
4. Research Results & Analysis
The total formal questionnaire for the teacher portionare 155 copies, valid questionnaire are 137 copies, theparents portion are 210 copies, valid questionnaire are
163 copies. Take the questionnaire results performing
the descriptive statistics, one-way analysis of varianceand principle component analysis. The descriptive
statistics and ANOVA analyze and compare the objects
with different background, and the view difference withrespect to the campus enclosing wall reconstructed tothe open type enclosing wall.
The principle component analysis simplifies thequestionnairevariables,
after
analyzingthequestionnaire, select the one with eigenvalue large than
1, its referred scree plot is shown as Fig. 4, simply to 4
dimensions, namely as「Reconstruction Effectiveness」,「Visual Perception」,「Safety Sense」,「Environmental
Facility」。

Fig. 4. Scree Plot

Analysis Results:
1.As found out from the survey results, teachers andparents have higher recognition to the perception ofopen type enclosing wall, and the recognition ofparents is generally higher than teachers.
2. As to the part of invasion by outsiders, easy climbing byschoolchildren, both parents and teachers also recognize,
it indicates that even the open type enclosing wall has
enhanced the visual penetration and slightly enhanced© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan

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Ching-Jung Chang, Chia-Chen Li, I-ChenWu

the security, however, due to the wall height is lowered,form is changed, there is still doubt on the teachers andparents mind with respect to the safety.
3. As to the selection of favorite enclosing wall form,
collage enclosing wall receives the highest number ofvotes (shown as Fig. 1.2), the next one is the hollow
enclosing wall (shown as Fig. 2), hybrid enclosing wall(shown as Fig. 3.4), it indicates that the teachers andparents recognize the various functional performance ofopen type enclosing wall, however, as viewing from thesecurity of schoolchildren, the enclosing wall withtraditional height, blocking, visual beautify andvariability of color painting, collage wall and hollow
wall is more favorable by the interviewee. In addition,although the height of hybrid enclosing wall is lowered,however, due to coordinate with planting, it has the plus
effect to the overall campus greening which is alsofavorable by the teachers.

the school to construct the concept and education.
3. Although the teachers and parents recognize thevarious functional performance of open typeenclosing wall, but viewing from the security ofschoolchildren, the enclosing wall with traditionalheight, blocking, visual beautify and variability ofcolor painting, collage wall and hollow wall is morefavorable by the interviewee. In addition, although
the height of hybrid enclosing wall is lowered,however, due to coordinate with planting, it has theplus effect to the overall campus greening which isalso favorable by the teachers.
This research takes the suburban schools as the researchscope, due to the environmental factor, part of schools
have reservation about the open type enclosing wall, theresearch results may not applicable for the metropolitan
areas. Therefore, the follow-on researchers may focus on
the metropolitan areas proceeding research andexploration to propose recommendation for thereconstruction of enclosing wall with different city
attributes.

5. Conclusion
This research inspects the perception ofreconstructing the campus to the open type enclosing
wall as viewing from the teachers and parents angle
through the questionnaire and expert interview, inspect
by the statistical analysis and simplify the opinion trend
dimensions of questionnaire items by factor analysis.
This is able to distinguish the opinion difference ofdifferent identity in details, and also understand thecommon demands, the research results may provide as
the reference for the follow-on planning and researchers,
the research conclusions are described as follows:
1. After reconstructing to the open type enclosing wall,the overall response of teachers and students is good,
however, due to the wall height is lowered, theblocking is more weak, easy to cause insecurity,
therefore, the location of enclosing wall, circumstanceof surrounding roads, community customs,
environment conditions shall be considered when thereconstruction is selected, if the planting is collocated,then the geology, climate, monsoon, human impact
factors shall be considered.
2. After reconstructing to the open type enclosing wall,
even the school security event does not increase, butthere are circumstances of taking shortcut to step
cross the open type enclosing wall by the outsiders,
the school shall reinforce the dissuasion andpropaganda; the schoolchildren will also depend on

Reference
1.

2.

3.

4.
5.

6.
7.
8.

Wu I-ChenWu,「The Assessment and Research on the
Reconstruction of Enclosing Wall into Open Type in
Elementary School Campus - Taking of Erlin Township,
Changhua County as Example」, Master Thesis,
Department of Construction Management, Chung Hua
University, Hsinchu 2014.
Li Mei-Huei,「A Research of Campus Rebuilding
Planning of Primary School in Nan-Tou after Ji-Ji
Earthquake」, Master Thesis, Graduate School,
Department of Spatial Design, National Yunlin Universityof Science & Technology, Yunlin, 2003.
Chen Mi-Tao,「Exploration of Certain Practical Problems
on School Buildings」，Journal of Education Research,
pp2-7, 1997.
Tang Zhi-Min,「School Buildings and Campus Planning」,
Wu-Nan Book Inc., 1992.
Fan Yin-Jo,「A Research on the Interaction between
Campus Wall Reform in Elementary Schools and Security
Identification Among Teachers and Students：Tainan City」
Master Thesis, Graduate School, Department ofArchitecture, National Cheng Kung University, Tainan,
2010.
Yu Zheng-Lu,「City Environment Creation: Landscape &Environmental Facilities Design」, Garden City, 2004。
Newman Oscar, Defensible Space, New York：Macmillan
Publishing Co.,Inc,1973.
Chen Shun-Yu, Multivariate Analysis, Huatai Bookstore,
Taipei City, 2005.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan

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The Study and Analysis on the Reuse and Transformation Strategy of Tile Kiln Space –Taking of San-He Tile Kiln as Example
Ching-Jung Chang,Hsiao-Yu Lin, Ying-Yu Su

Department of Construction Management, Chung Hua University
707, WuFu Rd., Sec. 2, Hsinchu 30012, Taiwan
E-mail: cjchang@chu.edu.tw, jg333@mail.jges.ntpc.edu.tw, btofi@ms41.hinet.net

Abstract
Due to the reduction of brick demand, Taiwan traditional brick kiln is declined gradually. The policy forpreservation of local cultural and spatial utilization, it is successfully transformed the traditional kiln into thebusiness model. This research selects San-He tile kiln as the case study, through the interview with the kilnowner, experts, and tourists, and made the questionnaire to perform the statistical analysis to find out thecommon demand and expectation of business model. It could provide the analysis to the kiln owner as thereference for operational strategy.
Key words: Brick Kiln, Spatial Reuse, One-Way Analysis of Variance (ANOVA), Factor Analysis

culture & creation, tourism and preservation viewpointof cultural assets, make detailed case study to thespatial reuse of kiln industry.
The reuse of abandoned industrial culture assets is
the important strategy to strengthen industrial culture
preservation, if the cultural assets preservation andspatial reuse are able to combine with the local cultural
memory, it will definitely generate multiplying effect.
This research selects San-He tile kiln, in Kaohsiung
City, since the kiln owner recognizes the importance ofindustrial and cultural assets preservation, preserves theold kiln plant and utilizes the landscape with local
cultural memory, successfully reuses the vacant space
and transforms into the business model of cultural &creative industry, successfully continues the old kiln
plant and activates the business industry.

1. Foreword
During the year of 1895 and 1945, Taiwan traditional
brick kiln, due to the public construction andproceeding the large-scale urban planning, the demandof red brick was significantly increased, that caused thebrick kiln industry entering into the industrializationand possessed different types and forms of old kiln
plants. However, after the year of 1970, due to theintroduction of new technology and change of building
mode, extensive use of concrete and rebar plus steel
reinforced, that caused the brick kiln industry declined
gradually. Most of the brick kiln industry requires thevast space, when the industrial recession would cause
idled or abandoned. This paper tries to explore thedeclined traditional brick kiln plant and consolidate© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan

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Ching-Jung Chang,Hsiao-Yu Lin, Ying-Yu Su

This research goes through the advanced interview
with the kiln owner, experts, local tourists, afterorganizing the interview information systematically,
then design the questionnaire, makes the investigation
with focusing on the different demanding for thebusiness model of brick kiln industry from the tourists.
From the view point of experts, artists and tourists toverify the analysis results, provide the different level ofthinking to the civil owners with respect to thepreservation and reuse strategy.

The basic theory and technology of monument
preservation and historical space preservation/reuse areclose, if the value of preserved object is not high interms of science, memorial or other academic value,and has less relationship with historical event or person
but only presents the architectural style, settlement
context, or localism, make flexible use of the historicalspace preservation is the effective method not only able
to preserve the monument but also able to protect therights of residents [4].
For Taiwan experience, the vacant space is not only
a new space utilize mode or concept, but also thehandling memories of city sentimental [5]. In the formof city space with the rapid development of real estate,lack of an emotion link with the emotional memories
to land, consequently, form a kind of the 「New/Old
Street Bloc Mode」to segment the significant urban
space [6]. After many years of experience in promoting
the reuse of vacant space by the government andscholars, most of the vacant space in these years havethe aesthetics and tourism effect; create very uniquespatial reuse culture.

2. Literatures
After the industrial revolution in 18th century, theindustry was rapidly developed, however, followed theindustrial transformation in 20th century which causedpart of the traditional industries declined, started in theyear of 1960, European and America enthusiastic
research the topics of industrial archaeology, industrial
assets preservation and reuse, industrial culture tourism,
becomes the city planning and development trend [1].
Taiwan government started to promote the industrial
culture assets regeneration project in the year of 2006,
supports the industries to evaluate the cultural assets bythemselves, the concept and actions of industrial
culture assets reuse are gradually valued [2]. It createdmany sightseeing landscapes such as railways/station,
docks & warehouses, rice granary, old plant areas of
Taiwan Sugar Corporation, traditional brick & tilekilns., these vacant space were reused and combinedwith the creative industries successfully develop as the
new tourism landscapes.
The vacant space may be the abandoned public
quarters, administrative offices, military facilities,
religious facilities, educational facilities. It may also be
the「Dissimilation Space」during the connecting processof new metropolitan space, especially the transportation
facilities. The historical buildings shall have thecontinuation and regeneration value, it shall contain thehistorical facts, culture and emotion, and the major
consideration of historical facts is the features oforiginal style. The cultural value is the special group
perception or behavior action, and the emotional value
is the support exists in the memory and spirit. The
expected regeneration value of historical buildings
includes use, economics and education, the major
consideration of abandoned space reutilize is the newfunction of building, and the economic value is thebasic source of continuous survival, and the educational
value is to inherit the historical culture [3].

3. Research Method & Application
This article takes the methods of literature review,questionnaire survey and expert interview, organize thequestionnaire data, use the independent sample T
test/inspect the independent samples or group of two
unrelated groups to determine if the average at a certain
variable reaches the significant difference. Use
one-way analysis of variance (ANOVA) to understandif there is significant difference between thequestionnaire data with various different conditions, if
the significant difference exists, then proceed the post
hoc comparisons with Scheffe method [7], simplify thequestionnaire opinion variable to the constructs throughthe principle component analysis, explore the strategy
of reuse brick/tile kiln space and the satisfactory
condition of kiln industry business model from thetourists.
This paper research surveys scene first, collect data
and after interviewing by experts, design the pretest
questionnaire items, adopt the closed questionnaire
which will allow the participants answering fully inaccordance with the options provided by the researcher.
The questionnaire adopts five-level of Likert scale,after performing the statistical analysis to the pretest
questionnaire, proceed the items, validity and reliability
analysis inspection, and then determine based on thetest results, and re-establish the formal questionnaire© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan

16

The Study and Analysis

items, and issue the questionnaire at San-He tile kiln,
let the tourists personally fill the questionnaire.
This research uses T test to analyze the different
gender (male, female) with regard to if there issignificant difference between the satisfactory degree
variables of the various reuse and transformation
strategy. The one-way analysis of variance (ANOVA)
analyzes if there is significant difference between thevariables of different tourist background and thevariables of satisfactory degree and the various reuse
and transformation strategy. The main purpose ofprinciple component analysis is to simplify thequestionnaire variables, simplify the designed variables
to factor constructs. Before performing the principle
component analysis, the statistics test of KMO and
Bartlett shall be performed, it is applicable for
performing the principle component analysis when
KMO value is over 0.8; the larger value acquired by
Bartlett sphericity test, the more suitable for analysis.
After determining the data suitable for the principle
component analysis, select suitable factor constructs inaccordance with the eigenvalue, select eigenvalue large
than 1 or the factor selecting point when the slope ofscree plot starts to downward, the factor with small
eigenvalue does not have the explained variable
difference which can be discarded, and the selectedcumulative explained variance percent shall reach 70%.
The principle component analysis is to show thecorrelation between the measurement variable andextracted component through the eigenvalue, if thedifference of component loading is not too large, therotation may be used to change the factor loading ofeach component, after the rotation, the variance of newcomponent become the largest, this research uses the
Varimax rotation.
4. Research Results & Analysis
This case locates at San-He tile kiln, Kaohsiung
City, after the tile kiln industry of Kaohsiung area
declined, only San-He tile kiln continues the production.
In 2004, it was registered by the government as thehistorical building and able to continue the traditional
tile kiln culture, and forms the monument preservation
district together with the surrounding theKaohsiung-Pingtung old iron bridge and water intake
station.
This research issues 350 copies of questionnaire,
retrieves 331 copies, screen the answering, if there is
missing item, the questionnaire is invalid, therefore,
invalid questionnaire has 11 copies, valid questionnaire
has 320 copies, the retrieving rate of valid sample is91.43%. After the formal questionnaire items throughthe validity analysis, KMO =0.929, it indicates that it isapplicable for proceeding the principle component
analysis [6.7]. After processing through the principle
component analysis and the maximum variance rotation,
its component eigenvalues are shown as Table 1, takethe components with eigenvalue large than 1 andsimplify to 4 dimensions, 4 main components are
named respectively as 1.Cultural creation marketing
2.Environmental space planning. 3. Service quality. 4.
Willing for re-visit.

Table 1. After Varimax Rotation
Factor

Eigenvalue

Explained Variance %Accumulated Variance %Factor 1

4.874

24.36824.368

Factor 2

4.026

20.13144.499

Factor 3

3.144

15.71960.219

Factor 4

1.999

9.994

70.213

Use the principle component analysis to integrate
the data and perform the statistical result analysis,
obtain the common needs tendency and expectation ofkiln industry business model from the tourists andcategorize into 4 dimensions: 1. Cultural creation
marketing dimension. 2. Environmental space planning
3.Service quality 4.Visit willing. After analyzing thequestionnaire, interview with the experts, cultural &creative dealers, art workers for cross-comparison, thebusiness ideas of kiln owner, tourist view are close to
the cultural & creative dealers, this is able to verify that
this reuse of brick/tile kiln space and transformation
strategy is a successful case.
5. Conclusion
This article explores the historical meaning ofbrick/tile kiln industrial culture assets in Taiwan,© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan

17

Ching-Jung
Chang,Hsiao-Yu
Lin,Lin,
Ying-Yu
Su Su
Ching-Jung
Chang,Hsiao-Yu
Ying-Yu

according to the results of literature review, personal
experience of participating the visiting activity in the
brick/tile kiln, questionnaire survey and expert
interview, organize and analyze/investigate thesatisfactory degree of brick-tile kiln reuse strategy fromthe tourists. Data integration, statistical analysis, result
analysis, reduce questionnaire variables, simplify toobtain the common needs tendency and expectation ofkiln industry business model from the tourists,categorize into 4 major dimensions, add with expert
interview, interview with the operator of brick-tile kiln,
cultural & creative dealers, art workers for comparison,verify that this reuse of brick/tile kiln space andtransformation is a successful business strategy.
The summarized research conclusions are describedas follows:i. Diversified business model of San-He tile kiln:
Operate the traditional making of brick/tile in parallelwith the cultural & creative merchandises, and linkwith Kaohsiung-Pingtung old iron bridge landscape,old kiln culture combines with leisure tourism,
provide the feasible strategy for the old industry andspatial reuse.
ii. San-He tile kiln takes the traditional craft combinedwith the young craftsmen, participant the craft design
exhibition regularly, establish diversified marketing
channels, cultural & creative products adopting
together with LOGO recognition, establish the newthinking with both of product practicality and artistry.
iii. San-He tile kiln is able to coordinate with the overall
community creation, cultivates the local newcraftsmen, explore the pass, present and future of kiln
industry from the history, geography, culture,
economy, arts etc., in order to extend education to
help the next generation paying more attention to thepreservation of traditional technology & culture.
iv. The overall environment, Exhibits and exhibition
space of brick/tile kiln allow the visitors enjoying
cultural and spiritual relaxation, cultural products andart space environment, attract the re-visit willing of
tourists.
As shown from the research survey, San-He tile kiln
shall combine with the government entity power in
the aspect of environmental space planning, improve
the tourism road signs and marks, and the riversewage water in front of its entrance, and then beable to completely enhance the comfortable level ofoverall environment.

Reference Literatures
1.Su Ying-Yu,「An Exploration and Analysis of the Space
Reutilization and Transformation Strategies of Brick andTile Kilns -San-He Tile Kiln as an Example」, Master Thesis,
Department of Construction Management, Chung Hua
University, Hsinchu 2013.
2.Huang Hai-Ming, "Promote the Reuse of Vacant Space
Operating Reference Manual」, Ministry of Culture of the
Republic of China, Taipei, 2003.
3. Chen,Ching-Tju,「A Study of Application the idea ofEnvironmental Symbiosis to the Regeneration ofHistorical Buildings - A case study of the Red-House ofWest Gate Market in Taipei」, Master Thesis, Graduate
Institute of Architecture & Urban Design, Chinese Culture
University, 2000.
4.Xue Qin, "Change of Taiwan Historical Space fromPreservation to Re-use」, Graduate School, Cultural Assets,
Chung Yuan Christian University, 2001.
5.Xiao Li-Hong, Huang Rui-Mao Edited, 「 Creative
Programming in Reuse of Space ： An International
Perspective」，Ministry of Culture of the Republic of China,
Taipei, 2002.
6.Chen Shun-Yu Writing, 「 Multivariate analysis Third
Edition」, Huatai Bookstore.
7.Wu Ming-Long Writing, 「 SPSS Statistics Application
Learning Practice」, Yi Xi Books.
8.Sun Yan-Ling, Ho Yuan, Li Yang-Xu, Writing,「Learning
SPSS Statistical Analysis from Examples」, DrMaster Press
Co., Ltd.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan

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The Research on the Influence of Economic Benefit for High-Rise Buildings Constructed byDifferent Excavation Method – Taking of Residence Buildings at New Taipei City as Example
Ching-Jung Chang1, Cheng-Min Yang2, Hsiu-Hsiung Hsu3
Department of Construction Management, Chung Hua University
707, WuFu Rd., Sec. 2, Hsinchu 30012, Taiwan
E-mail:cjchang@chu.edu.tw1, m10316009@chu.edu.tw2, andy@mail.cylee.com3

Abstract
The application of engineering technology could influence the cost and progress, and the excavation method forthe infrastructure might cause significant difference of construction planning and cost. This research studies theconstruction time and cost efficiency for various infrastructure excavation methods. Based on the designed case,carry on the evaluation of construction time, construction price, and performance in accordance with different
excavation method. It could provide the developer to select the best method at the initial design and planningstage, in order to obtain the highest benefit.
Keywords: Top-down Construction, up-up Construction, the high-rise buildings, the diaphragm wall

property and different management method for various
construction projects, so the risk management ofconstruction project are higher than other industries.
From the data of government, this paper first reviews
development trend of the domestic real estate in recent
ten years as fig.1, in order to understand the prosperous
fluctuation situation of construction industry. The
prosperity pulse of construction industry is closely to thedomestic and international economic boom. This paper
uses the excavation methods commonly used forhigh-rise buildings, as bottom-up, top-down and up-up
construction, to evaluate the construction time and cost
efficiency. An actual project was studied to assess andcompare in order to get the most favorable construction
time and cost efficiency to the developer.

1. Introduction
In the metropolis, there is limited land with theovercrowding in Taiwan, so the building land wasdifficult to get in the downtown. Because ofovercrowding, convenient traffic and life function, it
caused the expensive land costs [1]. The developer
always expected the project could achieve the highbenefit and satisfy the parking requirement, which was
the trend for high-rise buildings and deeper
infrastructure excavation in the metropolis. This paper
tried to study the performance of deep excavation
method on construction time and cost efficiency.
The construction industry is changed from theconstructor into the project administrator gradually, toensure the execution life cycle of project can becontrolled in a predictable range [2]. If the same scale
and type of project is implemented by different
construction method, there will be a significant
difference for the cost efficiency. There is unique

2. Literature
In Taiwan, the r construction industry was integrated theplanning, automation, financial administration and risk
management instead of the previous labor intensive. The© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan

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Ching-Jung Chang1, Cheng-Min Yang2, Hsiu-Hsiung Hsu3

selection for the method of construction project has
close relationship with the cost. The price of material,
the different technique, equipment, personnel andschedule might cause direct or indirect influence.
The land developers always follow the market trend
and expect to construct the building by faster andeconomic way. If the infrastructure of high-risebuildings took the tradition excavation and supported it
might be increase duration due to the topographical,
weather factors or space limited and poor management[3]. For the mention reasons, the innovative research,expert and scholar develop Top-Down Method andUp-Up construction method to overcome the dilemma[4].
The quantities of building permits and building
usage licenses showed in Fig.1, issued by Taiwan
Government from 2005 to 2015, reveal the informationthat the constructor could make the investment
opportunity by following the wave band peak of housemarket [5].

2.1.1 Bottom-Up Method
The Bottom-Up construction method normally followed
the procedure 1. making the diaphragm wall 2. complete
piling works 3. king post work 4. excavation 5.
horizontal supports. After finishing the mat foundation,
the basement structure was constructed from the bottomof the basement to the top floor.. The construction
procedures are shown in Fig. 2. 〔7〕Fig. 2 The construction sequence of Bottom-Up Method

Most of the building project adopted the Bottom-Up
construction method, because the construction procedure
is simple, suitable for general medium-scale buildings
without special construction time requirement etc., allregard this method as the prior choice.

Fig. 1 Construction permit and usage permit issued by
Taiwan Government

2.1.2 Top-Down Method
First to construct the diaphragm wall and the bore-pilefoundation with steel stanchion place, afterwards
completed the ground floor to be transition floor. Thismethod could construct both the Superstructure /infrastructure at the same time after completing thetransition floor. During the excavation, the building
structure would become the horizontal supports, and thecompleted bore-pile foundation with steel stanchion
place work as the vertical support. Because of theSuperstructure / infrastructure could constructsimultaneously, which would arrive the target to shorten
theconstruction
duration.
The
infrastructure
construction for the building constructed from top tobottom, so the construction method was known as the
Top-Down. Its construction procedures are shown in Fig.
3. [7]

2.1 Excavation Method
Because of the fast development of economy andconstruction in the 1970s, the high-rise buildings werebuilt gradually at urban area in Taiwan. Most of the
excavation of infrastructure used the Bottom-Up
construction method. Because of the space limited forworking requirement and duration reduce, the contractor
used the new technology by the Top-Down construction
method. This method could construct both theSuperstructure and infrastructure simultaneously after
completing the ground floor, which would get the resultof shortening construction time. The Up-Up method waseven developed subsequently, the various methods wereused with different project and building condition.

Fig.3 The construction sequence of Top-Down Method© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan

20

The Research on the Influence

The Top-Down method constructed both theSuperstructure and infrastructure at the same time,
which could effectively shorten construction duration
and avoid the neighboring building security during theconstruction period [6]. This construction method
required with complicated technology and might meet
more unexpected risk, so this method was suitable forlarge-scale building with space limited and tightschedule requirements.

3.1 Difference Analysis for Excavation Method toConstruction Time, Cost
Because the construction complicated, difficult degreeand construction condition, the various method werewith different duration and construction cost. This caseon the initial planning stage, the planner made analysis
by the Bottom-Up, Top-Down, and Up-Up construction
method to evaluate the benefit on duration and cost. The
owner finally adopted the Top-Down Method throughthe research before the construction.
To consider the geological condition withliquefaction reaction, the building project designed the
deep foundation with boring-pile which prevent theeffect of liquefaction of soil. All of the threeconstruction method designed in same condition withboring-pile.
The various construction method was exhaustive
planned and made analysis, the infrastructure could becompleted in 209 days by Top-Down Method. The
Bottom-Up method took 380days and the Up-Up
method took 254 days relatively. If the Top-Down
method acted as a benchmark, the Bottom-Up method
took more 171 days and the Up-Up method needs more
45 days. The cost estimations were NT$ 50,142,815,
30,939,065 and 40,706,905 for the Top-Down, theBottom-Up and the Up-Up method relatively. If the
Top-Down method acted as a benchmark, theBottom-Up method took 1.52 times and the Up-Up
method needs 1.23 times.

2.1.3 Up-Up Method
For the target to shorten the project duration, thecontractor always expected to construct both theSuperstructure and infrastructure at the same time. They
modified the top-down method and developed the up-up
construction method, whose procedure as 1. thediaphragm wall 2. piling work 3. king post work 4.
excavation and support 5. Repeat the step 5 and finishedthe mat foundation 6. construct the Superstructure
structure from 1st floor up 7. construct the infrastructure
structure from mat foundation up to ground floor. The
infrastructure structure excavation and each layer of
excavation support and finish, and after finishing inlower structural raft base, can carry on the first floor
edition and construct at once. The UP-Up construction
method constructed both the Superstructure andinfrastructure simultaneously.
3. Case Study
The case was the high-rise building in New Taipei City.
Its plane drawing of building is shown in Fig. 4.

4. Conclusion
The infrastructure was constructed by the Top-Down
Method to get 171 and 45 days less compared with the
Bottom-Up and the Up-Up Method respectively. The
construction cost of Top-Down Method was 1.52 timesof Bottom-Up Method and 1.23 times of Up-Up Method.
Although the construction cost of Top-Down Method
was most expensive, it created more value and benefit
than the other two methods due to the construction
duration saved. The overall benefit can be summed up as
follows:

Fig. 4 The Plan of the case study building

1. As for higher construction cost of Top-Down Method,
because upon excavating, the steel columns have to be
used as the support, the PC has to be used at every
floor, the consumption of forms is higher, and thenon-shrink cement has to be used at the joint of piles.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan

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Ching-Jung Chang1, Cheng-Min Yang2, Hsiu-Hsiung Hsu3

The high construction cost can be compensated by theshort construction time.
2. As for shorter construction time of Top-Down Method,
because after the end of story change, the upper partstructure and underpart structure can be constructed at
the same time. When the construction time and urban
space are limited, the largest benefit can be developed.
3. Only the benefit of construction time and construction
cost of the project is studied in this case. The next
research goal will focus on the interest effect ofconstruction time and the expedient analysis.
5. References
1. Shu, H. H., “Study on the Influence of Excavation
Methods to the Construction Cost and Time of High-Rise
Buildings - Taking of Residence Buildings at Xinzhuang
District of New Taipei City as Example”, Master Thesis,
Department of Construction Management, Chung Hwa
University, June 2013.
2. Shen, S. C., “Investigation of Hidden Costs for Building
Construction – A Case Study of Top-down Construction
Building Projects”, Master Thesis, Department of Civil
Engineering, National Central University, June 2008.
3. Huang, W. L., “Construction Build to Use Wall-type
Foundation and Other Manner Results to Analyze
Research”, Master Thesis, Department of Civil
Engineering, National Central University, November
2008.
4. Paek, J. H., and Ock, J. H. (1996), ‘‘Innovative Building
Construction Technique: Modified Up/Down Method’’, J.
Constr. Engrg. and Mgmt., ASCE, 122(2), 141–146.
5. Lee, H. S., and Lee, J. Y. (1999), “Nonshored Formwork
System for Top-Down Construction”, J. Constr. Engrg.
and Mgmt., ASCE, 125(6), 392–399.
6. Wu, C. H., “Investigations of the Variations of Unit Prices
and Cost-down Methods”, Master Thesis, Department ofCivil Engineering, National Chiao Tung University, 2005.
7. Chang, C., J. Hsu, H., H., and Lee, C., H., “Evaluation of
the Construction Cost and Time for Different Excavation
Methods on High-Rise Buildings’’, the NineteenthInternational Symposium on Artificial Life and Robotics
2014, Beppu, Japan, Jan. 22-24, 2014

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan

22

Redefining robot based technologies for elderly people assistance: a survey
Luigi Pagliarini1,2Henrik Hautop Lund1

1

Center for Playware, Technical University of Denmark, Building 326, 2800 Kgs. Lyngby, Denmark2
Academy of Fine Arts of Macerata, Via Berardi, 6, 405111 Macerata, Italy
E-mail: luigipagliarini@gmail.com
www.playware.elektro.dtu.dk

Abstract
We analyse the state of the art of hi-tech and robot based technologies in terms of Assistive Technology for all patients
and, in particular, elderly people assistance and everyday activities aid. We focus on different aspects andcharacteristics of these tools, such as playfulness, invasiveness, learning-speed, efficiency, short and long-term effect,
active vs. passive, etc. We do so by showing the most important existing examples, and by taking into account all thepossible factors that might help researchers when thinking of developing appropriate technologies for elderly care,
as well as, for their relative assistance personnel. Indeed, while in rehabilitation robotics, a major role is played by
the human–machine interface (HMI) used to gather the patient’s intent from biological signals, and convert them into
control signals for the robotic artefacts, surprisingly, decades of research have not yet declared what the optimal HMI
is in this context [1]. Further, there is an urgent need to clarify how various technologies can be a goal or an approachfor preventive, rehabilitative and assistive interaction. Therefore, we try to make a first step towards a redefinition of
Robotics Assistive Technology.
Keywords: Assistive Technology, Robotics, Prevention, Rehabilitation, Healing, Production.

Therefore, the definition of assistive technologies relieson the classification of either social or medical models of
disability. This is an important point that tends todistinguish ludic productions from assistive technologies
themselves. Although, this is surely a good way to lookat Assistive Technology, along this paper we will try toenrich such a vision by including in the set of Assistive
Technology Products tools that might work at aprevention level, a level that from our point of view isstill one of the most desirable ones.

1. Introduction
Quite recently, a new field has emerged from both private
and public research that has been denominated Assistive
Technology. In few cases such research reaches the final
state, and releases specific Assistive Technology
Products. As matter of fact, Assistive Technology seems
to be a generic definition [2] that aims at covering
technologies, services, apparatus, products as well as
systems mostly used by disadvantaged people, likedisabled and elderly ones. The aim is to increase thosepeople’s quality of life by helping them reaching the best
possible autonomy either in social or private life. In otherwords, it means giving them the chance to recuperate
their impaired or weakened skills, both motor andcognitive ones so that they can execute tasks, whichmight have become too difficult or too dangerous to them.

Normally, the definition of assistive technology products,
as described by Hersh [3], can be classified in at least five“flexible” categories, such as:Mainstream Products (i.e. general population
products that are available everywhere);© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

23

Luigi Pagliarini, Henrik Hautop Lund










Universal Design Products (i.e. products thatare conceived to be used by the largest possiblegroup of users) ;Assistive Products (i.e. designed to remove
barriers for elderly and disabled people);
Rehabilitation Products (i.e. products designed
to restore functions in elderly and disable
people);Medical Products (i.e. products designed tosupport a range of health care practices andpromote healing).







It is to be noticed that the difference between assistiveand rehabilitative products stand in the fact that theassistive products modify the people interaction with theenvironment by removing the “obstacles” in some way,while the rehabilitative ones are meant to directly reenable persons, in order to overcome their “limitations”.

In this perspective, we will here report some examples ofAssistive Technology Products based on Robotics andwill compare them to both review the state of the art insuch fields’ intersection, and to try to underline how itfits with the general standards of Assistive Technology.

Once such a product is released, there are a number ofdifferent ways of classifying evaluation and evaluation
methods (e.g. evaluation can be purely evaluative or
diagnostic). By generalising a categorisation inheritedfrom human computer interaction [4], assistive products
gives at least four classes:







Product advantages and expected market
reaction;
Expected financial potential, including market
share, sales and market growth and expected
profitability;
The implications for the firm's financial,physical and human resources;
Fit to current business, organisation, technology,
marketing and managerial skills;
The potential or availability of new products
and alternative opportunities, as well as the
product range policy.

2. Assistive Technology - Examples
The first examples of Assistive Technology Product we
present are the HONDA Walking Assist Device and its
Cyberdyne precursor HAL (Fig. 1). They both represent
the classical Assistive Technology devices designed toremove barriers for elderly and disabled people, and that
help users to support bodyweight to reduce the load on
the user's legs while walking, going up and down stairs,
and in a semi-crouching position.

Analytical methods: specialists manipulate
system models to predict performance.
User reports: users work with representations or
their memories of particular products. (i.e.
interviews and questionnaires, etc.).
Specialist reports: human factors specialists orother professionals assess real products,
prototypes or simulations.
Observational methods: real users assess real
products, prototypes or simulations.

Considering the pragmatic of productions, it has been
proven that there are factors that are most important inproduct success and product screening decisions, as well
as factors that influence decisions on whether or not todiscontinue a product. They are somehow crucial inunderstanding the effective value of a product and they
include [5, 6, 7, 8]:

Fig. 1. Cyberdyne HAL, and HONDA’s Walking Assist Device

Both HAL and Walking Assist are examples of an
efficient production that is professionally evaluatedthroughout specialist reports. They both fit their firm’scurrent business, organisation, technology, marketingand managerial skills and financial potential, including a
big potential market share. In other words, in theirassistive niche, they represent the perfect product and© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan24

Redefining robot based technologies

product design, although they almost exclusively act in
the no-rehabilitation zone.
On the same line are the Assistive Robotic Manipulator,
as InMotion’s Arm, Wrist, Hand and Exoskeletal Arm
Robot produced by the Interactive Motion Technologies.
These technologies should help patients with Stroke,
Cerebral Palsy, Traumatic Brain Injury, Spinal Cord
Injuries, Multiple Sclerosis, and Parkinsons to add a levelof independence to their lives. These products areevaluated either by the patients and the specialists arewell tested and seem to be promising for Assistive
Technology Clinical practice, at least.

Fig. 3. RoboCoach.

Vice versa, on the Rehabilitative Robotics Products side
there seems to be a quite fruitful production that results
in an enlarging number of companies specialized on
different branches of artefacts. AlterG Inc., that makes
anti-gravity treadmills and bionic legs for athletic
training and physical therapy. Ekso Bionics, that makes
robotic exoskeletons providing gait training andneurorehabilitation by extending the individual's strengthand also helping the therapist to work with their patients.
Hocoma, that provides spine evaluation devices thatenables computer-assisted analysis and display of thespine's shape and mobility, or robotic gait orthosis inwhich the patient’s legs are guided to a pre-programmed
physiological gait pattern on the treadmil. Bioextreme,
specialized in robotics rehabilitation innovative Error
Enhancement treatment for patients. Intead Technologies
and Kinestesica, specialized in neuromotoric disorders.
A very interesting approach is the one from the Kinetic
Muscles that is merging digital gaming, robotic, andtelemedicine technologies with the most up-to-datediscoveries in neurorehabilitation science to provide
solutions that are practical and clinically effective forengaging post-stroke rehabilitation. MediTouch, that
produces wearable motion capture devices and dedicatedrehabilitation software that allow patients with upper
and/or lower extremity movement dysfunction to practiceintensive virtual functional task training. SynTouch thatmakes the only sensor technology in the world thatendows robots with the ability to replicate the humansense of touch, or TouchBionics provider of worldleading prosthetic technologies and supporting services.
Besides them, the technologies realized by Kinova,
Rehab-Robotics, Reha-Stim, YouRehab and the above
mentioned Interactive Motion Technologies are very
interesting.

Further, there are a long number of productions oriented
towards becoming caregiver robots. Amongst them, the
RIBA (or Robear) developed by researchers at RIKEN
and Tokai Rubber Industries (TRI) is the most advanced
example. It is in between an Assistive and a Medical
Product since besides removing barriers, it is meant totake care of patients, substituting some nursery functions.
Besides those, there are other fields of research, like forFig. 2. The RIBA Robot lifting a patient.

example Socially Assistive Robotics - see "The Robotics
Primer" [9] for further information - which are currently
trying to develop robot-assisted therapies for children
with autism spectrum disorders, stroke and traumaticbrain injury, and individuals with Alzheimer's Disease
and other forms of dementia. But, despite of the fact thatthe field seems to be promising and important, and withsome good indications from the Paro robot by Shibata,
there is not an effective Assistive Products outcome, yet.
On the contrary, on the side of what we will here call
Active Assistive Robotics the production can beconsidered much more concrete. One example could be
RoboCoach, developed by Yaacob Ibrahim at the Ngee
Ann Polytechnic, that coaches the elderly in performing
15 types of arm exercise each week, recognizing a humanvoice that tells it to start the workout.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

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Luigi Pagliarini, Henrik Hautop Lund

All of them work with patients suffering from partial
spinal cord injuries, strokes, multiple sclerosis, traumaticbrain injuries, cerebral palsy, Parkinson’s disease:generally speaking with nervous system injuries andpatients with minimal forearm strength and problems instanding and walking. Some of these have a mainly
passive approach while other push a proactive exercising.
Amongst these last ones a few are also targeting
playfulness as a philosophy. They also differ as level of
Human-Machine Interface (HMI) invasiveness, although
in different cases appropriate and engaging HMI design
to be the only effective way.

targeted for the assistive technology market (e.g.
Cyberdyne).
3. Moto tiles
In this context, we present our Moto tiles, robot tiles thatlight up when you touch them and that are mostly used to
improve the balance of the elderly, and strengthen theirmuscles and endurance with a high grade of efficiency,
as it has been demonstrated by scientific tests [11, 12].
Despite of their specific Medical Technology target andrehabilitative application, Moto tiles use can be easilytranslated in quite different contexts since they fulfil
almost all of the typical criteria of the Universal Design
Products. Their use is easy to learn. They are multimodaland can easily adapt to any kind situation, either indoor
or outdoor, since they are very portable, reconfigurable
and well fit to any context and user’s age. Therefore, theymight be used either as gaming or educational platform.
More than that, they show their best when combining
playfulness with fitness, rehab or educational exercises,
all together. Besides, if compared with the mainstream
assistive technology or medical technology products,
they show their peculiarity in being a mostly “active” tool.
Indeed, the software platform they use promotes users’
motor and/or cognitive activation at the best and tends toadapt to the rate of users’ responses in a fairly adaptiveway.

On the Robotics Medical Products side, there is a large
production. Medical apparatus, especially in surgery, areday-by-day moving towards a robotics-based practice.
As, for example, Magnetic Microbots, a group of tiny
robots used in various operations, such as removing
plaque from a patient's arteries or helping with ocularconditions and disease screenings. In short, robotic
advancements are used to better the day-to-day lives ofpatients, helping them eat (e.g Bestic Arm), or helping apatient regain her ability to walk (e.g Toyota's Healthcare
Assistants). Obviously, robotics is a very attractive
industry since within the hospitals, a robot’s work canfulfil the full-time work of one or more employees(e.g Aethon TUG) and outside of them caregivers use
robots to enhance telemedicine and care for those
restricted to their homes (e.g. Vasteras Giraff) or,
surprisingly enough, in the open (e.g. Anybots). Some ofthem are playful and non-invasive ones, as those used toenhance the therapy of developmentally disabledchildren between 5 and 12 years old (e.g. CosmoBot).
On the Mainstream and Universal Design Products side
there is a large number of contributions which mainly
focus their attention on fitting products in the ecologicalsystem of humans. Therefore the products try to beportable, fit in the apartment or outside homes trying tofollow the scheme of the “walk up and use” design
philosophy, to be scalable, to allow user to initiateinteraction, to provide different choices for any giventask, to provide multiple and multimodal output andfeedback, to provide mutable functionality and to target
the largest number of people and contexts. See [10] forfurther information.

Fig. 4. Moto tiles – an example of an active tool for prevention
and rehabilitation (www.mototiles.com).

Furthermore, they represent a rather unique example ofAssistive Technology Robotics applied to “prevention”,
a “medical zone” which is rarely considered within thefield and that, on the contrary, might represent one of themost important fields of robotics applications, in the
future.

Good examples can be considered the iRobot products,
the Toyota’s i-REAL, the Universal Robots products,
and many other products for the toys and gaming industry(e.g. WowWee) or, sometimes, those that are specificallyIn other words, the Moto tiles might represent, bythemselves, a new dimension of how to apply robotics
within an Assistive Technology context, by promoting© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

26

Redefining robot based technologies

4. A. Whitefield, Wilson F, Dowell J. 1991. A framework forthe idea of cure as “prevention” and therefore by
stressing the “activation” at its best. The Moto tiles also
show how, by focusing on such a direction, Assistive
Technology can reach important results in either short orlong term effects.

5.
6.

4. Discussion and Conclusion
In this paper we discussed the most important generalprinciples of Assistive Technologies and, in particular,
analysed the state of the art Robotics Assistive
Technologies. At a first sight, it seems that much is to bedone and that despite of decades of research, both the
optimal HMI is in this context is still to be understoodand the borders of such a field is to be defined. We also
reported the most successful examples of market andscience industries trying to underline differencesamongst what is to be considered a good or a bad example
of production. By doing that we also highlighted thedifferent application fields and how production itself ismoving within them, by providing possible solutions
under different circumstances.

7.

8.
9.
10.
11.

12.

In this context, we presented the Moto tiles robotic
system as an innovative example on how to approach
Assistive Technology, by opening new fields ofapplication and new approaches to users’ attitude. Indeed,in our opinion, it is important to continuously change thephilosophy by which we approach robotics (i.e. either as
research and as an applicative field of production), trying
to find new technological and behavioural scenarios that
better fit to users’ need of easily understanding and useof technology in assistive, rehabilitative, and preventive
robotics. We believe that, ideally, as already happened intelecommunication technologies, user and patientoriented robotic technologies must get simpler, adaptable,
and enlarge the range of action as much as possible.

human factors evaluation. Behaviour and Information
Technology 10(1):65-79.
G. J. Avlonitis 1993. Project Dropstrat: what factors domanagers consider in deciding whether to drop a project.
European Journal of Marketing 27(4):35-57.
Brentani de U. 1986. Do firms need a custom-designed
new product screening model. Journal of ProductInnovation Management 3:108-119.
J. R. Schmidt, Sarangee, KR, Montoya MM. 2009.
Exploring new product development project review
practices. Journal of Product Innovation Management
26:520-535.
X. M. Song, Parry ME. 1996. What separates Japanese
new product winners from losers. Journal of ProductInnovation Management 13:422-439.
https://mitpress.mit.edu/books/robotics-primer
http://repository.cmu.edu/cgi/viewcontent.cgi?article=10
44&context=hcii
H. H. Lund, Modular Robotics for Playful Physiotherapy,
in Proceedings of IEEE International Conference on
Rehabilitation Robotics (IEEE Press, 571-575, 2009)
H. H. Lund, and J. D. Jessen, Effects of short-term trainingof community-dwelling elderly with modular interactive
tiles. GAMES FOR HEALTH: Research, Development,
and Clinical Applications, 3(5), 277-283, 2014.

References
1. http://journal.frontiersin.org/article/10.3389/fnbot.2014.0
0024/full?utm_source=newsletter&utm_medium=email&utm_campaign=Robotics_and_AI-w49-2015
2. M.A. Hersh, and M.A. Johnson. Assistive technology forvisually impaired and blind people. (Springer Verlag. 9781-84628-866-1. 2008)
3. M.A. Hersh. 2010. The Design and Evaluation of Assistive
Technology Products and Devices Part 2: Evaluation ofAssistive Products. In: JH Stone, M Blouin, editors.
International Encyclopedia of Rehabilitation.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan27

Disrupting the Industry with Play
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
Decades of research into intelligent, playful technology and user-friendly man-machine interfaces has provided
important insight into the creation of robotic systems and intelligent interactive systems which are much more userfriendly, safer and cheaper than what appeared possible merely a decade or two ago. This is significantly disrupting
the industry in several market sectors. This paper describes the components of the playware and embodied artificial
intelligence research that has led to disruption in the industrial robotics sector, and which points to the next disruptionof the health care sector. This includes playful robotics, LEGO robots for kids, minimal robot systems, user-friendly,
behavior-based, biomimetic, modular robotics and intelligent systems. The insight into these components and the use
in synthesis for designing robots and intelligent systems allows anybody, anywhere, anytime to use these systems,
providing an unforeseen flexibility into the sectors, which become disrupted with these systems.
Keywords: Playware, user-friendly, modular robots, playful robotics and intelligent systems.

Since these industrial robots had their clear place inautomating the industry, it became standard to develop
and utilize such robots with this approach, and mostresearchers and roboticists would adhere to this standard.

1. Introduction
Over the last half century, most robots have beencharacterized by being large, squared, metallic and hard
objects, which are controlled by fairly complicatedcontrol software to the novice user. This led to large
robots, for instance employed in the heavy industries incar factories, shipyards, etc. installed by highly skilled
engineers. These robots were automatic in the sense thatthey were self-regulating, but did not make the laws that
their regulatory activities seek to satisfy. These laws were
given to the robot, or built into the robot. They were done
so by engineers with years of training, and experience
with control theory and practice. In many cases, the large,
squared, metallic and hard robots were kept in a cage toperform their actions in order to screen away humanbeings for security reason, since being hit by one of thelarge robots could be deadly to a human being.

Nevertheless, decades of research into intelligent, playful
technology and user-friendly man-machine interfaces has
provided important insight into the creation of robotic
systems and intelligent interactive systems which aremuch more user-friendly, safer and cheaper than what
appeared possible merely a decade or two ago, and this is
now disrupting the industry.
2. Play and playful robotics
The basis of such disruption of the industry is the quest
to create truly user-friendly robotic systems. Since the1990’s, with a bottom-up approach, we have investigated
how to create minimal robot systems that are playful tointeract with, in order to develop technological systems© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan28

Henrik Hautop Lund

that anybody can interact with. Play is important, since
play is a free and voluntary activity that we do for noother purpose than play itself [1]. We play for theenjoyment. So installing play in the interaction can leadto people interacting with the system out of their own free
will and enjoyment. Further, when in play, people
performs an ontological shift according to Gadamer [2],and almost become another representation of themselves
when they forget about time and place. In such a state ofbeing, i.e. in a play dynamics, people can often perform
more than they would normally do [3].

The experiments resulted in an understanding of how
minimal robot systems can result in task-fullfilling
behavior guided by the user in different ways. One
example is the development of user-guided behaviorbased systems, in which the engineer develops theprimitive behavior modules, and the end-user is allowed
to combine the primitive behaviors to coordinate these
into the overall behavior of the robotic system. Since theengineer performs the difficult task of handling thedetails (e.g. reading, interpretation and classification of
sensory signals; and complicated motor commands) inthe design and debugging of the primitive behaviors, theend-user can work on the higher abstraction level ofcombining these primitive behaviors with no need ofknowledge of the (more complicated) low-level details.
This approach of creating user-friendly robot control for
any user with user-guided behavior-based systems wasdeveloped and verified on LEGO Mindstorms robots for
RoboCup Junior [4], for the LEGO Mindstorm Europe
launch tour with a robotic orchestra, robot fashion show,
and robot art show, for the RoboCup Humanoids
Freestyle World Champion 2002 [5]. Further, it became
the basis for the development of user-friendly modular
robotic systems such as the I-Blocks [6], in which eachof the primitive behaviors is a module, and the user isallowed to physical build with the modules to create theoverall robotic system. Further, this became theinspiration to create other user-friendly modular robotic
systems such as the music cubes, MusicTiles
MagicCubes, modular interactive tiles [7], and Moto tiles(www.mototiles.com).

The first playful robot systems were in the form of
Khepera miniature robots and LEGO robot prototypes,
before we started the collaboration with LEGO on thedevelopment of LEGO Mindstorms robots. The
development of these robotic systems in the 1990’s
facilitatedthedevelopment
andpractical
experimentation of modern artificial intelligence withartificial life experiments, evolutionary robotics, userguided evolutionary robotics, and user-guided behaviorbased robotics.

3. Disrupting the Industrial Robot Sector
Based on these studies of playful robotics as user-friendly
interfaces, our students during a decade went on to form
the Universal Robots company to create an industrial
robot arm, which should be light-weight, safe, and easily
programmable by anybody.

Fig. 2. A robot orchestra that we developed for the LEGO
Mindstorms promotion tour in Europe in late 1990’s.

Despite of the core research effort in these embodied
artificial intelligence themes, all experiments had theunderlying focus of creating robotic and interactive
systems that anybody – even young children – could
interact with and understand within minutes. This focus
on the user and understandable interaction for anybody
within minutes distinguished the research anddevelopment from the traditional robot, automation, andcontrol research.

Indeed, the founder of Universal Robots, EsbenØstergaard reports that “in the mid 90’s, we built robots
with pure ferocity. It culminated in 1998 when we won
the football World Cup for robots, which was seen by 50
million people. Football robots could be programmed bychildren without any technical knowledge, and if children
can learn to program robots, then all people can learn it.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan29

Disrupting the Industry withThis approach was one of the fundamental values of ourcompany Universal Robots. We found out that Danish
food companies needed to be able to adapt quickly. It wasabsolutely impossible to do that with the existing robots.
So we built a robotic arm that could be reprogrammed
easily and without specialist knowledge, and the idea
turned out to be pretty good.” [8]Further, crowdfunding sites such as Kickstarter are
seeing several projects funded to build low-cost table-top
robotic arms for personal use, e.g. for the makermovement and light manufacturing.
In our case, the lessons learned were that long-term focus
on play and user-friendliness can lead to disruption of anindustrial sector such as the industrial robot sector, where
the growth now is characterized by the development andsales of such low-cost, user-friendly robots. The
development of such robots was based upon the extensive
knowledge gained in playful robotics, LEGO robots forkids, minimal robot systems, user-friendly, behaviorbased, biomimetic, modular robotics and intelligent
systems.
4.

Disrupting the Health Sector

One of the next sectors to experience a similar disruption
may be the health sector. In many areas of the healthsector, patient compliance to the treatment is important.
This is the case in prevention and rehabilitation, forinstance, in which the subject is asked to perform acertain series of actions at regular intervals. But
unfortunately compliance to the protocol is often low,
due to little intrinsic motivation to perform those actions
that may be viewed as tedious and repetitive.

Fig. 2. One of the Universal Robots industrial robot arms.

Indeed, Universal Robots created such robotic arms,
which suddenly made it feasible for SMEs to invest in
robots and automation, since the SMEs could afford therobot arm and could easily adapt the robot arm to newprocesses. This disrupted the industrial robot industry,
the Universal Robots company was sold for 285 million
USD to Teradyne, and most other robot companies had
to follow up to create similar light-weight, safe and userfriendly robotic arms. A good example is Rodney Brooks’
Rethink Robotics company and their Baxter robot. Like
the Universal Robots company did, also Rethink
Robotics tries to develop user-friendly robots thatanybody can control by allowing the factory worker tograb the robot arm and show the robot what to do bymoving it around while the robot learns what to perform
and with which objects.

Also here, play and playware may disrupt the sector byproviding new technological opportunities that radicallychanges the way such practices in prevention andrehabilitation is performed. 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 [9].
Therefore, we developed modular playware in the formof the modular interactive tiles called Moto tiles(www.mototiles.com). The Moto tiles activates the user
to perform playful interaction with play and games on thetiles that light up in different colors and registers whenusers step on them. The Moto tiles are connected to a

Fig. 3. Robots from Rethink Robotics and Universal Robots.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan30

Henrik Hautop Lund

tablet with the ANT+ radio protocol. On the tablet, theuser can select between numerous games that challengeboth the physical and cognitive abilities of the user.
Further, the tablet shows the score in each games, shows
statistics for the user, and make automatic documentationof effect.

on the possibility for the end-user to easily adapt todifferent tasks and practices, e.g. for different kinds ofpatients.
5. Discussion and Conclusion
The developments of disruptive technologies are basedon the Playware ABC concept. The Playware ABC
concept focusses on the development of technology to be
used by anybody, anywhere, anytime based on building
bodies and brains, and thereby facilitating that users canconstruct, combine and create. Indeed, the possibility foranybody to create new interactions with the systems seem
to provide new opportunities, as is showcased in theindustrial robot sector and health sector. At the same time,
the concept leads to solutions that are cheaper, safer andmore flexible than previous solutions in the sectors.

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 [10]. Also, all other measured functional
abilities (strength, mobility, agility, and endurance of theelderly) improved with statistical significant effect.
Qualitative studies suggest that the high health effectfrom playing with the Moto tiles arises from the fact that
the Moto tiles act as a play force, which pushes theparticipants into a play-dynamics, in which they forget
about time and place, and thereby perform more than theywould normally do. In play, they may forget about theirfears of falling and forget about their perceived physicaland cognitive limitations.

Acknowledgements
The author would like to thank colleagues at the Center
for Playware, Technical University of Denmark.
References
1. J. Huizinga, Homo Ludens. (1938) (Homo Ludens. Beacon
Press 1971).
2. H.-G. Gadamer, Truth and Method. (Crossroad, New York,1989)
3. C. Jessen and H. H. Lund. On play forces, play dynamics,
and playware. Unpublished manuscript.
4. H. H. Lund and L. Pagliarini, LEGO Mindstorms Robot
Soccer. In Proceedings of RoboCup’98, LNAI 1604,(Springer-Verlag, Heidelberg, 1999).
5. H. H. Lund and L. Pagliarini, Modular behavior-based
control for team humanoids. Advanced Robotics, 18(7),659–676, 2004
6. H. H. Lund, Intelligent Artefacts. In Sugisaka and Tanaka(Eds.), Proc. of 8th International Symposium on Artificial
Life and Robotics (Oita: ISAROB, 2003).
7. H. H. Lund, Modular Robotics for Playful Physiotherapy,
in Proceedings of IEEE International Conference on
Rehabilitation Robotics (IEEE Press, 571-575, 2009)
8. Børsen newspaper (In Danish), 22-09-2015.
9. H. H. Lund, Play for the Elderly - Effect Studies of Playful
Technology, In Human Aspects of IT for the Aged
Population. Design for Everyday Life. (LNCS Vol. 9194,
pp 500-511, Springer-Verlag, 2015)
10. H. H. Lund, and J. D. Jessen, Effects of short-term trainingof community-dwelling elderly with modular interactive
tiles. GAMES FOR HEALTH: Research, Development,
and Clinical Applications, 3(5), 277-283, 2014.

Fig. 4. The Moto tiles and the tablet interface.

With such quantifiable health effect results of play, we
believe that playware technology may disrupt certain
areas of the health sector. Already, the Moto tiles are usedin the health sector for the benefit of cardiac patients,
stroke patients, elderly citizens at risk of falling,dementia patients, children with cerebral palsy, and inspecial schools in three continents. As is the case with thedisruptive technology for the industrial robot sector, also
this disruptive technology for the health sector is based© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan31

Automated Multiple-Brightness Peak Image Processing Method
Using Curvature and Variance Estimation
Yusuke Kawakami

DynaxT Co., Ltd., 2217-6 Hayashi
Takamatsu City, Kagawa 761-0301, Japan
Tetsuo Hattori, Yoshiro Imai, Yo Horikawa

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, horikawa}@eng.kagawa-u.ac.jp, janakaraja@gmail.com

Abstract
This paper describes the improvement method for the image which have multiple-brightness peak using Histogram
Matching based on Gaussian Distribution (HMGD). The previous papers, we have illustrated that the HMGD is an
automated image processing method for improve feeling impression better, through the comparative investigation
results of feeling impression among the original image, Histogram Equalization image, and HMGD image. However,
the multiple-brightness peak images have been hard to improve using the previous HMGD. In this paper, we propose
the processing method of HMGD to correspond image which have multiple-brightness peak, using curvaturecomputation and variance estimation.
Keywords: Image processing, Curvature, Variance estimation, Histogram matching, HMGD

estimation of histogram. Last, we illustrate the results ofthe HMGD processing for multiple-brightness peak
image through the experimentation.

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
HMGD processing could improve feeling (or Kansei)
impression better than original image6.
In this paper, we present the improvement HMGD
processing method for multiple-brightness peak image
using curvature computation and variance estimation ofimage histogram. We explain the principle of HMGD,
and we also explain peak detection and variance2. Principle
2.1. Histogram Matching based on GaussianDistribution
In the section, we explain 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© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan32

Yusuke Kawakami, Tetsuo Hattori, Yoshiro Imai, Yo Horikawa, R. P. C. Janaka Rajapakseof image brightness level between variables x and y 7-9.
And let y=(x) be defined by Eq. (1).

transformation the original histogram into Gaussian
histogram9.

xy   x   L  f x dx.

(1)yh y  h(y)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).
f x   h y  ' x   h y Lf x .

(x)
L

(2)

L

(3)
z0

 z   2 
.
exp 2 2 
2 2
y    z   L  Gaussz dz
0

1



0

x

(4)

2.2. Peak Detection of Histogram(5)

The HMGD processing method for multiple-brightness
peak image need to calculate transforms function for eachbrightness peak of histogram. And the solution to detect
it is curvature computation of the histogram.
Let y be a function with respect to x, the definition
curvature R is given by Eq. (9)6-9.

0 z   2 
exp   2 2 dz.
2 2 0
L

z



(6)

(9)

Let g(x) and K be Gaussian distribution function and acoefficient which is defined by following equation,
respectively.

xz z   2 ddLdz. (7)L  f x dx exp dx 0dz 2 2 0
2 2   x  a 2 
,exp 2 2 
2 2
L u  a 2 K
du  1.
exp 
2 2 
2 2 0g x  

If we perform Eq. (7),f x   Gaussz .

32
d 2 y   dy   2
R  2 1     .
dx   dx  

And we can derive Eq. (7) from differential of Eq. (6).

L ' x   L ' z ,f(x)Fig. 1. Conceptual image of HMGD6, 9.

Here, Fig.1 shows the relationship between y=(x) andy=(z). So we can be obtained following Eq. (6).
L  f x dx xGauss(z)

2

2

x0+dx

z z0+dz1Lz    dz.

 exp  2
2
2

x0

z

z



(x0+dx)

x

1
.
L

1

z

y=(x)

(L)y

We understand the histogram of original image f(x)becomes uniform distribution h(y) by Eq. (3). This meansthat brightness level of original image f(x) is expanded toh(y).
Then, let Gauss(z) and (z) be the function that is definedby Eq. (4) and (5), respectively.
Gaussz  yy=(z)

0

(8)

K

(10)

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 theapproximation of curvature R as Eq. (14).

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 histogram© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan33

Automated Multiple-Brightness Peak Imagex

f x    g u du 0 u  a 2 
exp   2 2 du. (11)2 2 0 u  a  dyK.
 g x 
exp 2dx
2 2 
2


(12)

d 2 y dg x  a  x g x 
dx2
dx
2

(13)

2

a  x  g x 2R g a 

xK

1  g x 

3
2 2

H



.

Then, we understand that we can obtain reference
histogram variance 2 from Eq. (17). For example, letv  2 be the distance from average a,

(14)

2



2
e1  g a  2 Ra  v   Ra  v  

a  x  g x .


(18)
3

2

2 2



H

4
v2
H ,  2  . (19)
v
2

y
g(a)

From Eq. (14), we understand that the curvature R varies
the sign according to the value of x9. That is, if x<a →R>0 (downward convex shape), and if x<a → R<0(upward convex shape).
2.3. Variance Estimation
In this section, we propose how to optimize the shape ofthe reference histogram, which is used in the HMGD
processing method for multiple-brightness peak image.
Fig. 2 shows the conceptual image of the original image
histogram which is variance 2 and average a. From Eq.
(10), let g(a) be a Gauss density function with variance2 at average a.
g a  

K
2 2

0







1

2





 a  a 2  g a  2 1 ga  2   . (16)3
2 2

R a  2 

2From Eq. (15), we can derive Eq. (17) and (18).













3


2 2
2
R a  2    2  g a  2 1  g a  2  (17)  2g a 
2
   2    2  H .
3



2

 


2
e1  g a  2 





a

2

x
L

In the previous chapter, we have described about the
principle for HMGD processing for multiple-brightness
peak. In this section, we propose the concrete process ofit.
(i) Detect the brightness-peak values xm (m=1,…,n) oforiginal image histogram.
(ii) Perform variance estimation and generate thereference histogram for brightness-peak value xm.
(iii) Perform HMGD processing and make an image im
which has histogram hm.
(iv) Proceed to (v) if original image has no brightnesspeak value. Otherwise, set the brightness-peak value
to xm+1 and return to (ii).
(v) Calculate the average of the HMGD processed
image Ia using Eq. (20).

  K2 exp  a  22  a   g xe . (15)a

3. HMGD Processing for Multiple-Brightness
Peak

Using Eq. (14), we can describe g a  2 and its
curvature as Eq. (15) and (16), respectively.
g a  2 

2Fig. 2. Conceptual image of the original image histogram8.

(14)

.

a

Ia 



1 n
 im
n m1

(20)

(vi) Output the processed image Ia.
4.

Experimentation© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan34

Yusuke Kawakami, Tetsuo Hattori, Yoshiro Imai, Yo Horikawa, R. P. C. Janaka Rajapakse

Fig. 3(a) shows the example of results for HMGD and
HMGD for multiple-brightness peak (HMGD-MBP).
And Fig. 3(b) and Fig. 3(c) show the corresponding
histogram and cumulative histogram, respectively.
In this case, we understand that HMGD-MBP processing
image is enhancing contrast naturally than HMGD
processing image. And in the HMGD-MBP image, theedges in the mountain (located left below of image)
become clear and detailed.

From the experimentation results, HMGD-MBP
processing image is enhancing contrast naturally and theedges of image becomes clear and detailed. That is, weconsider that the HMGD-MBP processing method isuseful than previous HMGD.

References
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 of Japan
Society of Kansei Engineering 8(3) (2009) 667-674.
6. Y. Kawakami, T. Hattori, D. Kutsuna, H. Matsushita,
Y. Imai, H. Kawano, R.P.C. Janaka Rajapakse, AutomatedColor 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, 85-93.
7. Y. Kawakami, T. Hattori, Y. Imai, H. Matsushita,
H. Kawano, R. P. C. Janaka Rajapakse, Kansei Impression
and Automated Color Image Arrangement Method andKansei Impression, Journal of Robotics, Networking andArtificial 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, Automated Color
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, AutomatedColor Image Arrangement Method for Multiple-Peak
Image, in Proceedings of The Second International
Conference on Computer Science, Computer Engineering,& Social Media (Lodz, Poland, 2015), ISBN 978-1-47991789-1, pp. 27-30.

1200

1000

1000

800

800

800

600

600

Frequency

1200

1000

Frequency

Frequency(a) Image: Original (left), HMGD (center), HMGD-MBP (right)
1200

600

400

400

400

200

200

200

0

0
19 37 55 73 91 109 127 145 163 181 199 217 235 253 1

0
19 37 55 73 91 109 127 145 163 181 199 217 235 253 1

70000

70000

70000

60000

60000

60000

50000

50000

50000

40000

40000

40000

30000

30000

30000

20000

20000

20000

10000

10000

10000

0

0

0
19 37 55 73 91 109 127 145 163 181 199 217 235 253 1

1

1

19 37 55 73 91 109 127 145 163 181 199 217 235 253(b) Histogram:Original (left), HMGD
(center), HMGD-MBP(right)Brightness
Brightness
Brightness

19 37 55 73 91 109 127 145 163 181 199 217 235 253 1

19 37 55 73 91 109 127 145 163 181 199 217 235 253(c) Cumulative histogram: Original (left), HMGD (center), HMGD-MBP (right)

Fig. 3. Example of results (HMGD, HMGD-MBP, and thecorresponding histograms).

5. Conclusion
In this paper we have aimed improvement automatedprocessing for multiple-brightness peak image, we haveproposed “Histogram Matching based on GaussianDistribution for multiple-brightness peak (HMGDMBP)”.
As for the concrete processing method, we have usedcurvature computation and variance estimation for
detecting brightness peaks and optimizing the shape ofthe reference histogram. Then we have taken an averageof HMGD processed images.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

35

Histogram Matching Based on Gaussian Distribution on the HSB Color System
Yusuke Kawakami

DynaxT Co., Ltd., 2271-6 Hayashi
Takamatsu City, Kagawa 761-0113, Japan
Tetsuo Hattori, Yoshiro Imai, Yo Horikawa

Kagawa University, 2217-20 Hayashi
Takamatsu City, Kagawa 761-0113, 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, horikawa}@eng.kagawa-u.ac.jp, janakaraja@gmail.com

Abstract
This paper proposes Histogram Matching based on Gaussian Distribution (HMGD) processing on the HSB color
system which is close to the human visual property. In the previous paper, we have proposed HMGD processing on
the brightness axis which is calculated from RGB color system. And we have considered that HSB color system ismore suitable for HMGD processing, because it contains brightness axis. In this paper, we describe how to transformthe color image from RGB color system into HSB color system first. Second, we describe that the principal of HMGD
on this color system. And then, we also explain how to re-transform HSB color system to RGB color system.
Keywords: Histogram matching, RGB, HSB, Image processing, HMGD

detect multiple-brightness peak of image. Also, we haveapplied variance estimation to optimize for reference
histogram which is used HMGD. Through these
improvements, HMGD processing works well not only
single-brightness peak image but also multiplebrightness peak image.
However, we have considered that HSB color system4, 5
is more suitable for HMGD processing, because HSB
color system is more close to visual feature of human.
In this paper, we describe about HSB color system first.
Next, we illustrate principle of transform the color image
from RGB color system into HSB color system. Then, we
propose the principle of HMGD on this color system.
Last, we also propose the method of HMGD on the HSB
color system.

1. Introduction
Recently, the automated image arrangement has become
more familiar to us1-3. For example, if we take a photousing Digital Camera or Smartphone, these devices willprocess which aimed to improve feeling impression fromraw taken one. And the method of image processing is in
the research stage.
In the previous papers, we have proposed histogram
Matching based on Gaussian Distribution (HMGD), as
the automated image processing which is one of theelastic transform. This HMGD has been the processing
on the brightness axis which is calculated from RGB
color system.
As for the method of improvement the feeling (or Kansei)
impression, we have apply curvature computation for© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan36

Yusuke Kawakami, Tetsuo Hattori, Yoshiro Imai, Yo Horikawa, R. P. C. Janaka Rajapakse

2. Principle

interconversion between RGB color system and HSB
color system in this section.

2.1. HSB color system
2.2.1. Color system conversion from RGB to HSB

HSB (or HSV) color system is the color system due toHue, Saturation and Brightness, as shown in Fig. 1. This
color space has been proposed from A. R. Smith, theauthor of Ref. 5 published in 1978.
Hue means the primary color and it is the angulardimension5 starting at the red primary at 0º. And green
primary and blue primary is located at 120º and 240º,respectively, and then primary color become red at 360º.
Next, saturation is corresponding to the clearness of color.
This means that the high saturation color is close to
primary, and the low saturation color is close toachromatic.
Then, brightness is corresponding to brightness of color.
This means that the high brightness color is bright color,
and the low brightness color is close to black.
That is, HSB color system is the most common
cylindrical-coordinate representations of points in a RGB
color system.

Let bmax and V be the maximum value of RGB color
system in one pixel and the brightness value on HSB
color system in one pixel, respectively. And, let H and S
be the hue value and saturation value. Then, V is definedby Eq. (1).
V  bmax .

(1)

If bmax= 0, let H and S is defined by Eq. (2).
H  0, S  0.

(2)

And let be bmin the minimum value of RGB color system
in one pixel, S is defined by Eq. (3).

b bS  255 max min .
bmax

(3)

From Eq. (1) ~ (3), we understand that we can derive
saturation (S) and brightness (V) of HSB color system.
And, let R, G, and B be defined red, green, and blue valueof RGB color system, we can derive the H by Eq. (4).


 B G .
 H  60  
 bmax  bmin  RB   120.
 H  60  
 bmax  bmin  H  60   G  R   240.
 bmax  bmin bmax  R bmax  G  (4)bmax  B 

In Eq. (4), H may be taken negative value H’. If H is H’,
we have to correct by following Eq. (5) because H isdefined 0º ~ 359º.
Fig. 1. Conceptual image of HSB color space

H  H '360.

(5)2.2. Color system conversion
In the previous section, we have described the principleof HSB color system. So, we describe the principle of© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan37

Histogram Matching Based on

2.2.2. Color system conversion from HSB to RGByyy=(z)

R  0, G  0, B  0.

S  0h(y)Let R, G, and B be defined red, green, and blue value of
RGB color system, respectively. And let H, S, and V bedefined hue, saturation, and brightness value of HSB
color system, respectively. If we take into account ofdefinition of HSV color space, S=0 must be black color
by Eq. (6).

y=(x)

(L)(x0+dx)
y

(x)
L

(6)

L
x

And here, let HF, I, J, K, and L be defined following
equations.
H F  H 60

I  H 60  H F

z0

1
L

x

f(x)

Gauss(z)(8)

Fig. 2. Conceptual of Histogram matching based on GaussianDistribution (HMGD) 8-10.

From Eq. (7) ~ (9), conversion equation of R, G, and B isderived by Eq. (10) and (11).

By Eq. (12), we define that (x) means the cumulative
histogram of f(x).
First, we transform from original histogram f(x) into
uniform distribution histogram h(y) because we need to
expand brightness value for histogram matching. We canderive Eq. (13) and (14) from Eq. (12) and Fig. 2.

(10)f x   h y  ' x   h y Lf x .

H F  3  R  J , G  K , B  V .
H F  4  R  L, G  J , B  V . (11)H  5  R  V , G  J , B  K .
 Fh y  

1
.
L

(13)
(14)

Then, let Gauss(z) and (z) be the function that is definedby Eq. (15) and (16), respectively. And we obtain Eq.
(17) from the relationship between y=(x) and y=(z) as
shown in Fig. 2.

2.3. Histogram Matching based on GaussianDistribution
In this section, we describe the principle of Histogram
Matching based on Gaussian Distribution (HMGD).
HMGD is one of a kind of automated image arrangement
method based on the Elastic Transformation6, 7.
Fig. 2 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. PDF is corresponding
to the brightness value histogram of HSB color system.
And, we define the relationship between x and y by Eq.
(12).

Gaussz    z   2 
.
exp 2 2 
2 2

1

(15)

z

y   z   L  Gaussz dz.

(16)

0

x

L  f x dx 0 z   2 dz. (17)
exp
2


2 
2

2 0


L

z

So, we can derive Eq. (18) and (19) from differential of
Eq. (17).

xy   x   L  f x dx.

x0+dx

z z0+dz

(7)

 S 
 S 
 S

J  V 1  , K  V 1 F , L  V 1  1  F . (9)
255
255
255







H F  0  R  V , G  L, B  J .

H F  1  R  K , G  V , B  J .
H  2  R  J , G  V , B  L.
 F

x0

z

(12)

0

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan38

Yusuke Kawakami, Tetsuo Hattori, Yoshiro Imai, Yo Horikawa, R. P. C. Janaka RajapaksexL  f x dx 0 z   2 
exp   2 2 dz.
2 2 0L

L ' x   L ' z ,zf x   Gaussz .

4. S. Matsuhashi, K. Fujimoto, O. Nakamura, T. Minami, A
Proposal of the Modified HSV Colour System Suitable for
Human Face Extraction (in Japanese), Journal of
Television 49(6) (1995) 787-797.
5. A. R. Smith, Color Gamut Transform Pairs, in Computer
Graphics 12(3), (1978), 12-19.
6. W. Burger and J. M. Burge, Principles of Digital Image
Processing: Fundamental Techniques (Springer, 2009).
7. T. Izumi, T. Hattori, S. Sugimono, and T. Takashima,
Color Image Arrangement Using Elastic Transform on
Principal Component Axis (in Japanese), Journal of Japan
Society of Kansei Engineering 8(3) (2009) 667-674.
8. Y. Kawakami, T. Hattori, D. Kutsuna, H. Matsushita,
Y. Imai, H. Kawano, and 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, ISSN 2187-5413, 14(2) (2015) 8593.
9. Y. Kawakami, T. Hattori, Y. Imai, H. Matsushita, H.
Kawano, and R. P. C. Janaka Rajapakse, Automated Color
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.
10. Y. Kawakami, T. Hattori, Y. Imai, Y. Horikawa,
H. Matsushita, R. P. C. Janaka Rajapakse, AutomatedColor Image Arrangement Method for Multiple-Peak
Image, in Proceedings of The Second International
Conference on Computer Science, Computer Engineering,& Social Media (Lodz, Poland, 2015), ISBN 978-1-47991789-1, pp. 27-30.

(18)
(19)

That is, we understand that we can match the original
histogram f(x) to Gaussian distribution histogram
Gauss(z).
3. Method of HMGD on the HSB Color System
In the previous chapter, we described the principle of
HSB color system, its conversion, and HMGD. In thischapter, we propose the method of HMGD on the HSB
color system.
(i) Load original image.
(ii) Perform color system conversion from RGB to HSB.
(iii) Perform HMGD on the brightness axis of HSB color
system.
(iv) Perform color system conversion from HSB to RGB.
(v) Output processed image.

4.

Conclusion

In this paper, we have proposed about Histogram
Matching Based on Gaussian Distribution (HMGD) on
the HSB color system, and we have described principles
of color conversion and HMGD.
As the proposed method, we have performed color
system conversion and HMGD on the brightness axis of
HSB color system image. And then, we have performed
re-conversion of color system as the post processing.
For the further study, we have to validate investigation
feeling (or Kansei) impression evaluation through thequestionnaire survey, for the review proposed method for
effectiveness. Also we have to review the proposed
method for effectiveness.
References
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).

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan39

Quantitative Evaluation of Flash-based Educational Visualizing Simulator
Kei Takeichi, Yoshiro Imai, Kazuaki Ando, Tetsuo Hattori

Kagawa University, 2217-20 Hayashi
Takamatsu City, Kagawa 761-0113, Japan
E-mail: {hattori, imai, ando}@eng.kagawa-u.ac.jp
Yusuke Kawakami

DynaxT Co., Ltd., 2271-6 Hayashi
Takamatsu City, Kagawa 761-0113, Japan
E-mail: riverjp2002@gmail.com

Abstract
A Flash-based simulator of CPU scheduling has been developed and utilized for educational visualization in the classof university lecture. We have designed and implemented it with Flash-based scripting language in order to execute
it as a stand-alone application as well as in various browsing environment such as Microsoft IE, Google Chrome
and/or FireFox (Mozilla). Based on questionnaire for our simulator in the lecture, its quantitative evaluation has been
carried out by means of statistical analysis. Our report describes overview of our Flash-based simulator and the resultsof the above quantitative evaluation.
Keywords: Educational visualization, Questionnaire-based Evaluation, Statistical analysis

is better, or which is optimal of them. SPTF (Shortest
Processing Time First) is to be chosen as one of thepriority-based algorithms in this study. In other view
point, RR (Round Robin) is evaluated as a policy ofalgorithm to realize equality of opportunity around thetargets. It is a little complicated but very useful strategy
to choose item with equal opportunities.
In order to teach students these above algorithms
efficiently, we had better utilize some suitableeducational tool to visualize their behavior and results for
specific conditions1,2. A visual simulator is one of theuseful solutions to provide educational tool(s) for
students who wants to understand such theme of
information processing education in an efficient and1. Introduction
As a matter of course, software system covers many
important areas from fundamentals to applications. Thistime, we focus on Operating System, and particularly
CPU scheduling algorithm. It must be cover several kindsof themes that students should understand during theirschool days. A simple algorithm, namely FCFS (First
Come and First Served) is very natural so that it is one
the most fundamental strategies to decide its priority forusers, clients, processes/tasks and so on. Priority basedalgorithm is another candidate to determine the order of
execution. It is very significant idea to choose a suitable
item around potentially selected targets. It means which© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan40

Kei Takeichi, Yoshiro Imai, Kazuaki Ando, Tetsuo Hattori, Yusuke Kawakami

3. Quantitative Evaluation

effective manner3. In this study, we have developed some
useful educational tools to demonstrate practical CPU
schedulingalgorithm(s)andprovidevisualunderstanding for students in an effective way.

This section presents quantitative evaluation of ourvisualizing simulator. As one of the quantitativeevaluation for our CPU scheduling simulator, at first, we
compare the execution time of simulation of native Flashplayer with ones on the below three major browsers. The
result is summarized in the following Table 1.

2. Overview of Visualizing Simulator
In a lecture of Operating System of our university, mostimportant algorithms of CPU scheduling are FCFS,SPTF,
and RR, we think. They are very trivial but sometimes
very useful in the real operating systems. So we have
employed these above algorithms as fundamental
procedures to decide CPU scheduling in our visualizing
simulator. The above three procedure based on FCFS,
SPTF and RR are as follows;(1) FCFS is very much simple, but clearly well-defined
strategy to decide next candidate to be performed, justlike First-In First-Out(FIFO queue). This algorithm issometimes the target to be compared with otheralgorithm(s). And moreover, a result applied by thisalgorithm will be not so worse than other ones derived
from complicated algorithm(s).
(2) SPTF is one of the most famous priority-based
assigning/allocating algorithm. Whenever every event
happens, namely conditions have changed, it must be
investigated which candidate has the best priority at thattime. So we had better call this algorithm Shortest
Remaining Processing Time First (SRPTF), because wemust consider not the total processing time but remaining
processing one in order to decide which candidate has thebest priority at that time or later.
(3) RR is a typical non-priority based algorithm in orderto provide 'equality of opportunity' which can realize
taking turns at it. This algorithm can retrieve candidates
which are waiting for service and select/assign one ofthem who wait for the longest time or longer than others.
It is a little complicated for beginners to understanddetails of RR-based procedure and/or develop a kind of
corresponding programs. And we may sometimes meet
its results with not suitable performance for specificapplications. But equality of opportunity is very
important for several users to receive their necessary
services.

Table 1. Comparison of Execution
between Different Environments.
Host Application
Flash Player (Ver.10) Stand alone
MS-Internet Explorer 11.0.9600
Mozilla FireFox 35.0.1
Google Chrome 40.0.2214.11 m

Time(s)Execution Time
32.25 (sec.)
28.95 (sec.)
35.19 (sec.)
93.61 (sec.)

As another quantitative evaluation for our simulator,
secondly, we have carried out questionnaire in theclassroom lecture of Operating System in our university
after using our simulator. The questionnaire includes
following six questions;
Q#1 Is it easy to utilize this simulator? (yes: 2,neutral:1,
no:0)
Q#2 Is it effective to learn CPU scheduling algorithmwith this visualizing simulator? (yes: 2, neutral:1, no:0)
Q#3 Do you understand CPU scheduling algorithm moresuitably with this simulator? (yes: 2, neutral:1, no:0)
Q#4 Are you interesting in CPU scheduling algorithm bymeans of this simulator? (yes: 2,neutral:1, no:0)
Q#5 Are you interesting in other themes of Operating
System after usage of this simulator? (yes: 2, neutral:1,
no:0)
Q#6 Do you need to utilize another type of simulator inorder to learn Operating System? (yes: 2, neutral:1, no:0)
Our questionnaire described before can obtain just 20
answers from students of the class because of carryingout on a voluntary basis, although we used to have theclass for Operating System with 40 students or more. The
result of such a questionnaire is summarized in Table 2.
Q#1 has 17 numbers of answer "yes" per 20 students (i.e.
85%), Q#2 has 18 numbers of answer "yes" per 20
students (i.e. 90%) and Q#3 has 12 numbers of answer"yes" per 20 students (i.e. 60%). From the questionnaire,
many students do feel easy to utilize our CPU scheduling© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan41

Quantitative Evaluation of Flash-based

simulator and consider to be effective for learning CPU
scheduling algorithm by means of using our simulator.
And majority, namely six out of ten, of replying students
understand CPU scheduling algorithm more suitably
with this simulator.

Based on Table 3, a two-way contingency table for Q#1
and Q#2 can be introduced, which is shown in Table 4,
while another two-way contingency table for Q#1 andQ#3 can be also done, which is shown in Table 5.
Table 4. Two-way Contingency Table for Q#1
and Q#2.

Table 2. Result of Questionnaire about our
Simulator.
Student
S01
S02
S03
S04
S05
S06
S07
S08
S09
S10
S11
S12
S13
S14
S15
S16
S17
S18
S19
S20

Q#1

2
2
1
2
2
2
2
2
2
2
2
2
1
2
2
2
2
2
2
1

Q#2

2
2
1
2
2
2
2
2
2
2
2
2
2
1
2
2
2
2
2
2

Q#3

2
1
0
2
1
2
1
2
2
2
1
2
1
2
2
1
1
2
2
2

Q#4

1
1
2
1
1
1
1
1
1
1
1
1
1
1
2
1
2
2
1
2

Q#5

Q#2

1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
2
1
2

2
2
2
2
1
2
2
2
1
2
2
1
2
2
2
2
2
2
2
2

yes

Q#1

neutral

16
2

1
1

SUM

16(18*17/20)
2(18*3/20)
18

1(2*17/20)
1(2*3/20)
2

17
3
20

C

R

2: goodness-of-fit statistic for Table 4 can be calculated
in the following expression (Eq-1);
22

 2   {( y ij 
i 1 j 1

sumR * sumC 2 sumR * sumC
) /
}
Total
Total

 {16  (18 * 17 / 20)}2 /(18 * 17 / 20)   {1  (2 * 3 / 20)}2 /(2 * 3 / 20)  2.135

yesneutral

no

11
1

6
1

0
1

(Eq-1)As described in expression (Eq-1), degree of freedom for
Table 4 is  = (2-1) x (2-1) = 1. So we can have 2
=0.05( =1) = 3.8415 from the 2 distribution table.
"Statistical independence" between results from Q#1 andQ#2 can be confirmed so that users of our simulator not
only consider it to be easy to utilize but also recognize
effectiveness to learn CPU scheduling algorithm with it
respectively and independently.
Table 5. Two-way Contingency Table for Q#1
and Q#3.
Q#3

Q#1

yes
neutral

C

SUM

yes

neutralno11(12*17/20)
1(12*3/20)
126(7*17/20)
1(7*3/20)
70(1*17/20)
1(1*3/20)
1

17
3
20

Just like the same way, 2: goodness-of-fit statistic for
Table 4 can be calculated in the following expression(Eq-2);

Q#3
noneutralSUM

Table 3. Relation between Results from Q#1 andQ#2 (left-hand) & from Q#1 and Q#3 (right-hand).
Q#2

yes

Q#1

SUM

neutralneutralQ#6

At the same time, however, Q#4 has just 5 numbers ofanswer "yes" per 20 students (i.e. 25%) and Q#5 has only
3 numbers of answer "yes" per 20 students (i.e. 15%). In
order to perform test of independence among Q#1, Q#2
and Q#3, we will demonstrate to calculate "2 test of
goodness-of-fit" for relation between results from Q#1
and Q#2 as well as one for Q#1 and Q#3, respectively.
Relation between results from Q#1 and Q#2 is expressedin the left-hand of Table 3, while relation for Q#1 andQ#3 is done in the right-hand. The former has 2 x 2 tableitems and the latter has 2 x 3 ones.

yes

yes

no

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

42

R

Kei Takeichi, Yoshiro Imai, Kazuaki Ando, Tetsuo Hattori, Yusuke Kawakami
22 2   {( y ij 
i 1 j 1

CPU scheduling algorithm more suitably with oursimulator respectively and independently.

sumR * sumC 2 sumR * sumC
) /
}
Total
Total

References {11  (12 * 17 / 20)}2 /(12 * 17 / 20)   {1  (1 * 3 / 20)}2 /(1 * 3 / 20)  0.1365

1. Sarjoughian,H., Yu Chen, Burger,K. : A component-based
visual simulator for MIPS32 processors. Proceedings of
38th Annual Conference on Frontiers in Education(FIE2008)., pp. F3B-9 – F3B-14 (October 2008)..
2. Kabir,M.T., Bari,M.T., Haque,A.L. : ViSiMIPS: Visual
simulator of MIPS32 pipelined processor. Proceedings of
6th International Conference on Computer Science &Education (ICCSE2011), pp. 788 – 793 (August 2011).
3. Imai,Y., Tomita,S., Niimi, H., Kitamura,T. : Web-Based
Computer Visual Simulator. Technology Enhanced
Learning (IFIP International Federation for Information
Processing), Volume 171, pp 111–120(Summer 2005).
4. Lee, K.-C., Lee, J. : Programming physics softwares inFlash. Computer Physics Communications, Volume 177,
Issues 1-2, pp 195–198 (July 2007).
5. Stodulka, P., Privitzer, P., Kofranek, J., Tribula, M., Vacek,
O. : Development of WEB accessible medical educational
simulators. Proceedings of 6th EUROSIM Congress on
Modelling and Simulation, 6 pages (September 2007).
6. Imai,Y., Takeichi, K. : Development and Evaluation of
Adobe Flash based CPU Scheduling Simulator Executableon Major Multiple Web Browsers. Proceedings of 2015
IEEE International Conference on Intelligent Networking
and Collaborative Systems (INCoS-2015, Tamkang
University, Taipei, TAIWAN(RoC)), pp.149–155(September 2015).

(Eq-2)As described in expression (Eq-2), degree of freedom for
Table 5 is  = (2-1) x (3-1) = 2. So we can have 2
=0.05( =2) = 5.9915 from the 2 distribution table.
"Statistical independence" between results from Q#1 andQ#3 can be confirmed so that users of our simulator not
only consider it to be easy to utilize but also understandCPU scheduling algorithm more suitably with oursimulator respectively and independently.
In other words, both of results from Q#1 and Q#2 are not
only very good scores, namely 85% of the former's
answers are "yes" and 90% of the latter's answers are"yes", but also the two scores are statistically independent
each other, namely there is no reason that one scores canbecome good because another scores are good. And
major part (i.e. 60%) of users, whose answers from Q#3
are "yes", understand CPU scheduling algorithm moresuitably by means of our simulator independently fromits operability.
4. Conclusion
The paper describes an Adobe-Flash based educational
visualizing simulator for students to learn CPU
scheduling algorithm graphically and practically. Our
Flash-based simulator can execute on the major Web
browsers such as Microsoft Internet Explorer, Mozilla
FireFox and Google Chrome and provide efficient
explanation for students of lecture "Operating System".
As quantitative evaluation for our simulator, we havecarried out questionnaire for the students using thesimulator and apply statistical analysis for the results of
the questionnaire. We can obtain and confirm a goodresults from the above performance through analysis.
Namely,(1) It can be confirmed that users of our simulator not
only consider it to be easy to utilize but also recognize
effectiveness to learn CPU scheduling algorithm with ourvisualizing simulator respectively and independently.
(2) It can be confirmed that users of our simulator not
only consider it to be easy to utilize but also understand© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan43

Relation between Optimal Stopping Solution and NSPR for Structural Change Point Detection
Problem
Tetsuo Hattori

Kagawa University, 2217-20 Hayashi Cho
Takamatsu City, Kagawa 761-0396, Japan
Yoshihide Koyama, Yusuke Kawakami, Yoshiro Imai, Yo Horikawa

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 a novel method using New Sequential Probability Ratio (NSPR) for the structuralchange point detection (SCPD) of ongoing time series data instead of using SPRT (Sequential Probability Ratio
Test). Moreover, we have formulated the SCPD problem in time series data as an Optimal Stopping one using theconcept of DP (Dynamic Programming) and also have shown the solution theorem in the form of Inequality. In thispaper, we discuss the relation between the solution of Optimal Stopping and NSPR for the SCPD Problem.
Keywords: Image processing, Curvature, Variance estimation, Histogram matching, HMGD

1. Introduction

the hypothesis that the change has occurred at time t for
all of data obtained so far.
Meanwhile, we have previously formulated thestructural change detection method in time series as an
Optimal Stopping Problem with an action cost, using theconcept of DP (Dynamic Programming) 5-6. Moreover
for the change point detection problem, we haveproposed a model introduced SPRT (Sequential
Probability Ratio Test ) as a New Sequential Probability
Ratio (NSPR) test method7-8.
In this paper, we present that there is a relation
between the NSPR and the optimal solution theorem for
CPD that dealt as an Optimal Stopping Problem anddescribe it concretely.

For ongoing time series analysis, three stages are
considered: prediction model construction, structuralchange detection (and/or disparity detection between themodel and observing data), and renewal of prediction
model. Above all, it is important to detect the change
point as quickly and also correctly as possible
Especially in the second stage, in order to renew theaccurate prediction model as soon as possible after thedetection.
As the structural change detection, or change point
detection (CPD), some methods have been proposed 1-4.
The standard well known method is Chow Test that isused in econometrics2. It does a statistical test by setting© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

44

Tetsuo Hattori, Yoshihide Koyama, Yusuke Kawakami, Yoshiro Imai, Yo Horikawa, Hiromichi Kawano

2. Definitions and Equations
2.1. Structural Change Model 5-6

2.2. Optimal Stopping Formulation and its
Slution Theorem 5-6

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.

Let the cost(n) be a  n as a linear function for n,where a is the loss caused by the failing in one time.
And for simplicity, let T and A denote the Total_cost andcost (A), respectively. Then, the evaluation function isdenoted as the following equation (1).
T  A an(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.

(if n = N ) ET (n, N )  A  a・N

λ

1-λ

1.0

) a・n
(if n < N ) ET (n, N )  P( Sn 1 | S ・ (1  P( Sn 1 | S n )) ET (n  1, N )Ec

E

Fig.1. Structural change model.
E
R
R
out

in
1-R

Fig.2. Internal model of the State E.
Ec
1-Rc

(3)

where Sn 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).

Ec : State that the structural change occurred.
E : State that the structure is unchanged.
λ: Probability of the structural change occurrence.
(Poisson Process.)

1-R

(2)

n

Rc
Rc

in

[Optimal Solution Theorem (OST)]The N that minimizes ET(0,N) is given as the largest
number n that satisfies the following Inequality (4).

out
1-Rc

a  ( A  a )・P( S n | S n 1 )(4)

Fig.3. Internal model of the State Ec.
© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan45

Relation between Optimal Stoppingwhere the number N+1 can also be the optimum one thatminimizes ET(0,N), i.e., ET(0,N) = ET(0,N+1), only if

If the NSPR is greater than 1.0, we can regard thatthe structural change has been occurred before thepresent time.

a  ( A  a)・P( S N 1 | S N )3. Relation between NSPR and OST

2.3. New Sequential Probability Ratio (NSPR)
Based on Structural Change Model 7-8

We show the relations using the probability in the
Optical Solution Theorem, considering Rc >> R.

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  a1a2 an 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 an , H 0 ) and P(a1 an , H1 ) be thejoint probability of the symbol sequence C n happens
with the event H0 (the structural change is not occurred)and H1 (the change is occurred), respectively.
Then, the following equations hold.










 1  R   PE | S R  R 

P S n 1 | S n  1  R  1  P Ecn | S n  1  Rc P Ecn | S n



n

cn

c

(8)
Therefore, we have

1  R   P Sn1 S n PEcn | S  
Rc  R n

Similarly,(9) PS |RS R1  R 



n

n 1

P E | Sn 

c

(10)

c

P(a1 an , H 0 )  P(Cn , H 0 ) (1   ) θ1 θn  (1   )nSince
P Ecn | S n  P H1 | S n and P E | S n  P H 0 | S n



n

Πθ

n

(5)

i

i 1

 

we haveP ( a1 an , H1 )  P (Cn , H1 )n

NSPR  n nθ  ((1   )θ)( θ)
~

i

~

1

i

i 1

 ((1   ) θ1θ2 )(
2

n



k 1



((1   ) k 1･



k 1

θj )(

j 0

n

 






 (11)



Since P Sn 1 S n is a monotonous decreasing function~
θi )  with respect to n, NSPR becomes an increasing one.
From the OST, the optimal N is the maximum n thatsatisfies (4), so the N is the the maximum n that satisfies
the following Inequality (12).

i 3




 



1  R   P Sn 1 | S n
P H1 | S n

P H0 | S n
P Sn 1 | S n  1  Rc 



i 2

n






θ)
~

i

ik



(6)

A 


R


A a 
NSPR    A P H S R 
P H1 S nn

New Sequential Probability Ratio (NSPR)  n that
we propose is represented using the aforementioned
equations as following Eq.(7).

NSPR  n 
n

n

j



k 1

(1   ) n

~

i

j 0

ik

n

Πθ
i 1

 A a (12)

Meanwhile, since NSPR is a probability ratio, NSPR
makes unnecessary the restriction condition of n times
continuous failures (or “OUT”) in the Optimal Stopping
Formulation.

 ((1   ) ･θ )( θ)k 1cIf R  0.05, Rc  0.9, A  10 , a  1 ,   21 .5 .

P(H1 | a1 an ) P(H1 | Cn ) P(Cn , H1 )P(H 0 | a1 an ) P(H 0 | Cn ) P(Cn , H 0 )
k 1

n

0

i

(7)

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

46

Tetsuo Hattori, Yoshihide Koyama, Yusuke Kawakami, Yoshiro Imai, Yo Horikawa, Hiromichi Kawano

4. Conclusion

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)

We have described the relation between New
Sequential Probability Ratio (NSPR) method and the
Optimal Solution Theorem for CPD that dealt as an
Optimal Stopping Problem. From this relation, we canuse NSPR as well in the case where we have to consider
some constraints with respect to loss cost.

References
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.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan47

Modified Quantum Particle Swarm Optimization
for Chaos Synchronization
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
In this study, a modified quantum-behaved particle swarm optimization (MQPSO) based on hybrid evolution(HEMQPSO) approach is proposed to synchronize chaotic systems, in which the proposed HEMQPSO algorithm
combines the conceptions of genetic algorithm (GA) and adaptive annealing learning algorithm with the MQPSO
algorithm to search optimal solutions. Simulation results are illustrated to verify the performance of chaos
synchronization using the proposed HEMQPSO approach. From the numerical simulations and comparisons withother extant evolutionary methods in chaotic systems, the validity and superiority of the HEMQPSO approach areverified.
Keywords: Quantum-behaved particle swarm optimization, Chaotic system, Genetic algorithm,
Chaos synchronization, Hybrid evolution.

1. Introduction

researchers have endeavored to improve thesynchronization of chaotic time series.4-8 In this article,
we propose a scheme to synchronize Chen system andGenesio system with known parameters.6
Although the original PSO algorithm possesses theability of high convergent speed, easily falling in somelocal optima is its fatal defect. Many researchers9,10 havepresented revised PSO algorithms and obtained goodresults. Another improvement on traditional PSO
algorithm is quantum-behaved particle swarm
optimization (QPSO).11-14 However, in QPSO, particles
fall into local optimal state in multimode optimization

A chaotic system is a nonlinear deterministic systemthat has some special features of sensitive dependence on
initial conditions and unstable bounded trajectories in thephase space. Synchronization in chaotic dynamic systems
attains much interest among scientists from various fields.
Applications in chaotic systems synchronization are verysignificant in nonlinear fields. Recently, some researchers
have paid much attention to identification andsynchronization of chaotic systems.1-3 Due to theircharacteristics sensitivity to initial conditions, chaotic
systems are not easy to synchronize. Recently, some

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan48

Chia-Nan Ko, Ching-I Lee

to the center of potential field, pf c  [ pf1c , pf 2c ,, pf nc ] ,and is defined as:( c  r  p l  c2  r2  p g )pf i c  1 1 i, i  1,2,, n .
(3)( c1  r1  c2  r2 )problems and cannot find any better state, the QPSO
algorithm will take on the premature phenomenon.
To overcome the premature phenomenon in QPSO, a
modified quantum-behaved particle swarm optimization(MQPSO) based on hybrid evolution (HEMQPSO)
algorithm is proposed to synchronize chaotic systems will
be proposed to perform the synchronization of chaotic
systems in this study. In HEMQPSO, the significant
improvement is that the evolutionary algorithm combines
the concept of mutation algorithm in GA and adaptive
annealing learning similar to SA with QPSO to achieve
global search and defeat premature phenomenon in
searching optimal solutions. From the illustrated results
for three chaotic dynamical systems, the synchronization
performance of the proposed HEMQPSO approach isdemonstrated.

2. Modified
Quantum
Optimization

Particle

Inspired by the behavior that particles move in abounded state and preserve the global search ability, Sunet al.15 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 as12:
L 1
(4)
pi  pf i c   ln  ,
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.15 employ a mainstream thought
point to evaluate the parameter. The mainstream thought
point and can be expressed as the following forms:n p
n p n p
mbest    i ,1 ,  i , 2 ,,  i ,n , i  1,2,, n,(5)
i 1 n  i 1 n i 1 n

Swarm

In the PSO algorithm, each particle keeps trajectory of
its coordinates in the problem space. The coordinate ofeach particle is related to its own best position (local best
position) and the global best position achieved so far. The
trajectories of particles are updated according to thefollowing equations:vi ( k  1)  w  vi ( k )  c1  r1 ( pil  pi ) (1)c2  r2 ( p g  pi ), i  1,2,, n,pi (k  1)  pi (k )  vi (k  1), i  1,2,, n,

L  2   mbest  pi ,(6)where  is a creative coefficient which is used to adjust
the convergence speed of an individual particle and theperformance of this algorithm.
Firstly, in order to achieve global searching, should be set to a large number at the beginning. Then theparameter  is adjusted decreasingly. The decreasingrate of  can be linear, but nonlinear revision according
to the convergence of optimization process is morereasonable. The creative coefficient  with adaptive
annealing learning mechanism according to the change
rate of optimal estimation has the form:
(7)   max    fit ,(2)where n denotes the number of particles in a population;pi (k ) and vi (k ) are position and velocity of the ith
particle at generation k in n-dimensional search space;
pil and p g are the best position of the ith particle andthe global best position; w is the inertia weight; c1 andc2 are cognitive and social constriction coefficients,
respectively; r1 and r2 are random numbers between 0
and 1.
From the view of classical dynamics, to avoid
explosion and guarantee convergence, particles must bebounded and fly in an attractive potential field. Clerc andKennedy13 have proved that if these coefficients areproperly defined, the particle’s position pi will converge

fit  p g  pil ,(8)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. Another improvement© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan49

Modified Quantum Particle Swarmof the HEMQPSO is elitist reproduction. The mutation
mechanism is usually used for keeping diversity andavoiding premature.

initial values of the drive and response systems arexˆ1 (0)  1, xˆ 2 (0)  0, xˆ3 (0)  1, x1 (0)  5, x2 (0)  10,
respectively.
In
HEMQPSO,thex3 (0)  5,parameters,  max and  in Eq. (7), are set as 0.5 and 0.5.
Comparisons of HEQPSO16 and HEMQPSO are shown in
Figs. 1 and 2, respectively, in which the superiority of the
proposed HEMQPSO is verified.

3. Problem Formulation
This section presents two chaos systems of Chensystem and Genesio system to synchronize their behavior.
The dynamic equation of Genesio system is given by xˆ 1  xˆ 2 ,(9) xˆ 2  xˆ 3 , xˆ  r xˆ  r xˆ  r xˆ  xˆ 2 , 3 1 1 2 2 3 3 1

5. Conclusions
This paper presents the proposed HEMQPSO to
synchronize chaotic systems. The evolutionary algorithm
can overcome the stagnation in searching global solutions
for synchronizing two chaotic systems. From thesimulation results, we can conclude that the proposed
HEMQPSO method has good performance for chaos
synchronization. The future work is to apply HEMQPSO
for investigating more complex chaotic systems.

where xˆ1 , xˆ 2 , xˆ3 , are state variables, r1  6, r2  2.92,

r3  1.2, for the chaotic system (9). Chen system is

q2  28,

0.5

q3  3, the system (10) is

0
-0.5

0

20

40

60

80

100

120

140

160

180

200

120

140

160

180

200

120

140

160

180

200

k

0.5
e2

when q1  35,
chaotic.

(10)
e1

described by x1  q1 ( x2  x1 ), x 2  ( q2  q1 ) x1  q2 x2  x1 x3 ,
 x  x x  q x ,
1 2
3 3
 3

4. Simulation Results

0
-0.5

0

20

40

60

80

100
k

Consider that Genesio system (9) is the drive systemand the controlled Chen system (11) is the response
system. The synchronization behavior between Chensystem and Genesio system using active control isobserved.
 x1  q1 ( x2  x1 )  u1 ,(11)
 x 2  ( q2  q1 ) x1  q2 x2  x1 x3  u2 , x  x x  q x  u ,
1 2
3 3
3
 3
Three control functions u1 , u2 , u 3 , are introduced insystem (11), in order to determine the control functions torealize synchronization between systems (9) and (11), we
subtract (9) from (11) and get the deviation of errors
system can be expressed ase1   q1e1 ,(12)e2   q2 e2 ,e   q e ,3 3
 3
where e1  x1  xˆ1 , e2  x2  xˆ2 , e3  x3  xˆ3 , The error
system (12) is asymptotically stable by linear control
theory.6
The sampling time is equal to 0.005 and the number ofstates is set as 200 for three simulated examples. The

e3

0.5
0
-0.5

0

20

40

60

80

100
k

Fig. 1. Synchronization errors between Chen and Genesio
systems via active control with HEQPSO.
e1

0.5
0
-0.5

0

20

40

60

80

100

120

140

160

180

200

120

140

160

180

200

120

140

160

180

200

k

e2

0.5
0
-0.5

0

20

40

60

80

100
k

e3

0.5
0
-0.5

0

20

40

60

80

100
k

Fig. 2. Synchronization errors between Chen and Genesio
systems via active control with HEMQPSO.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan50

Chia-Nan Ko, Ching-I Lee

Acknowledgements
This work was supported in part by the Ministry of
Science and Technology, R.O.C., under grants MOST
104-2221-E-252-004.

9.

References10.

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adaptive synchronization of uncertain chaotic systems,
Physics Letters A 334 (2005) 295-305.
2. J. P. Yeh, Identifying chaotic systems using a fuzzy model
coupled with a linear plant, Chaos, Solitons and Fractals
32 (2007) 1178-1187.
3. H. Modares, A. Alfi, and M. M. Fateh, Parameter
identification of chaotic dynamic systems through an
improved particle swarm optimization, Expert Systems
with Applications 37 (2010) 3714-3720.
4. C. H. Lee, F. Y. Chang, and C. M. Lin, An efficientinterval type-2 fuzzy CMAC for chaos time-series
prediction and synchronization, IEEE Transactions on
Cybernetics 44(3) (2014) 329-341.
5. M. P. Aghababa and A. Heydari, Chaos synchronization
between two different chaotic systems with uncertainties,
external disturbance, unknown parameters and input
nonlinearities, Applied Mathematical Modeling 36 (2012)
1639-1652.
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synchronization between Chen system and Genesio system,
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© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan51

A Simulation Model of Hall Sensor Misalignment in BLDC Motors
Chung-Wen Hung

Department of Electrical Engineering, National Yunlin University of Science & Technology,
123, Sec. 3, University Road, Douliou, Yunlin, 64002, Taiwan
Chu-Lin Hsu

Department of Electrical Engineering, National Yunlin University of Science & Technology,
123, Sec. 3, University Road, Douliou, Yunlin, 64002, Taiwan
E-mail: wenhung@yuntech.edu.tw, B10100061@yuntech.edu.tw
www.yuntech.edu.tw

Abstract
A simulation model of Hall sensor misalignment in BLDC motors is proposed in this paper. The Hall sensor ispopularly used in brushless DC motor to decide commutate and rotational speed. However, the accuracy of Hallsensor position is limited in most of BLDC motors, and it is lack of suitable simulation model for Hall Sensor
installation misalignment. The details of simulation model are discussed in this paper, and simulation results arealso provided to show the model isworkable.
Keywords: simulation model, BLDC motor, Hall sensor, misalignment.

reference for BLDC motor control integrated circuit.
However, those two studies didn’t discuss any
simulation. [3] focused on commutation behavior
situation of BLDC motor, and proposed a simulation
model in MATLAB. [4] presented an efficientsimulation model for variable sampling system, which is
caused by low resolution effect of Hall sensors.
However, it is a lack of simulation model for the Hallsensor position misalignment.

1. Introduction
BLDC motors are popularly used nowadays due to its
high power density, low maintenance requirement, andeasy control. Most BLDC motors have Hall sensors
which are used to decide commutation timing andestimate rotational speed. However, it is impossible toinstall Hall sensors in accurate position without error.
This misalignment effect will cause torque ripple due towrong commutation timing and incorrect speed
feedback.
This phenomenon is highlighted in some studies andthese papers tried to propose solution. [1] proposed a
Hall-effect-sensor-based position observer to mitigate
torque ripple in permanent-magnet synchronousmachines, and the hardware and simulation validation
showed the proposed algorithm is workable. [2]proposed an auxiliary circuit for BLDC motors, to reject
the Hall sensor signal noise and capture time interval.
The circuit is built in field programmable gate array,
FPGA, and the experimental results show the circuit
works as expecting. This work could be a good

2. The Simulation Method
To simulate the Hall sensors in BLDC motors, therotation information, distance, is necessary. It is used todecide the hall sensor signals. The most of BLDC motor
simulation models only provide the rotational speed
output, so an integrator will be used to perform therotation distance. As shown in Fig. 1, to simplify
explanation, the pole pair number is set to 1; Hx
indicates Hall sensor whether x is a, b or c. When the
BLDC motor rotational angle arrives 0 or π radians,
means the point O reaches origin or Ha, the output ofHall sensor A will change due to the change of magnetic© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan52

Chung-Wen Hung, Chu-Lin Hsu

field direction. The detail is presented in Fig. 2. If
without reverse, the output of the integrator is monotony
increasing. However, only the angular information
within 0 to 2π is used, so the remainder divided by 2π isuseful for the decision of Hall sensor signal. Similarly,
when point rotes 5π/3 or 2π/3 radians, the output of theHall sensor B will change. Again, Hall sensor C willchange at π/3 and 4π/3 radians.

and described as (1)-(3). Here, % indicates remainder
operator and θpresents the integrated rotational angle.

Fig. 3. Simulation model of Hall-sensor

Fig. 1. Diagram of Hall-sensor and relative rotor positions

Fig. 4. Simulation model of Hall-sensor B and C

Fig. 2. The relationship between Rotation angle and Hallsensor output

3. The Proposed Simulation Model
As discussed in the previous section, an integrator isused to integrate rotational speed to rotational distance,
which is equivalent to rotational angle; a remainder
block is built to calculate rotational angle within 0 to 2π.
This block comprises a divider, a rounder, and an adder.
A relational operator is applied to decide Hall sensor
signal. The simulation model is shown in Fig. 3. Note,
to simplify the calculation, the output of the remainder
block is ratio of 2π, and the second input of therelational operator is changed to 1/2 from π. The otherrelational operator could be skipped, because remainderfunction will change the output to zero when divisible.
However, the output of Hall Sensor B is positive, whenrotational angle is larger than 5π/3 or less than 2π/3.
And again, Hall Sensor C output high when lager thanπ/3 and less than 4π/6. The details are shown in Fig. 4,

HallA(θ) = 0,
= 1,

if (θ % 2π) < πif (θ % 2π) ≧ π(1)

HallB(θ) = 1,
= 0,

if (θ % 2π) ≦ 2π/3, or (θ % 2π) > 5π/3
o. w.

(2)

HallC(θ) = 1,
= 0,

if (θ % 2π) ≧ π/3, and (θ % 2π) < 4π/3
o. w.

(3)

Fig. 5. Equivalent simulation model

4. The Proposed Hall Sensor Misalignment
Effect Simulation
Before discussion of Hall Sensor Misalignment Effect, amodified simulation model is proposed which isequivalent to the model discussed in previous section.
The model shown in Fig. 4, every hall sensor channel
includes two relational operators and a logic operator.
However, an equivalent operation could be performed
with adding an angle shifter to the integrator output. For
the Hall sensor B, the output is high till the point O runs
to 2π/3, and then keep in low before it runs to 5π/3. If
4π/3 is added into to the integrator output, the© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan53

A Simulation Model ofsummation will be fed into the remainder block andrelational operator block, which are the same as Hall
channel A. The outputs sill are equivalent to theprevious model. Similarly, a 4π/3 shifter performs thesame effect for channel C. The calculation requirement
may not less than previous one. However, it is simpler
and easier to understand. Moreover, it implies anadvance what will be introduced in next section.
In most BLDC motor, the accuracy of hall sensor
installation is limited, as presented in Fig. 6. The hallsensor may not be installed in correct position, it willcause wrong interval of HALL sensor signal. A novel
simulation model is proposed to simulate thismisalignment effect, and the position error is added tothe two relational operators in Fig. 4. As shown in Fig. 7,
the Hall sensor B channel is modified, and it can work
well to reflect the misalignment effect.

shifter which is used to add position error is needed, as
shown in Fig. 8. It is more intuitive and can avoid
missing the other relational operator in Fig. 7. Note that,an extra shifter could be added into channel A if there isany position error for the Hall sensor A.

Fig. 8. Equivalent misalignment simulation model(a) Matlab Simulink demonstration(b) proposed models
Fig. 9. Hall sensor signal comparison without misalignment

Fig. 6. Diagram of Hall-sensor with misalignment

Fig. 10. Simulation result of Hall sensor B with misalignment

5.

Simulation Results

To verify the proposed simulation model work well, theproposed model is add to the BLEDM DEMO model in
Matlab Simulink. The input of the proposed model isthe BLDC motor speed, and the output is Hall sensor
output. Fig. 9 presents the Hall sensor signal
comparison
betweenthe
MatlabSimulink
demonstrational and proposed models without
misalignment. It shows that the two outputs of Hall

Fig.7. Misalignment simulation model of Hall-sensor B

However, misalignment effect could be simulated easier,if the previous equivalent simulation model is selected.
Because rotor and Hall sensor are symmetric, only one© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan54

Chung-Wen Hung, Chu-Lin Hsu

sensors are the same. Next, to show the misalignment
effect, the proposed misalignment model is used toreplaced, and the channel B result is shown in Fig. 10.
Obviously, the proposed simulation model could
simulate the misalignment effect well.
6. Conclusion
A Simulation Model of Hall Sensor Misalignment in
BLDC Motors is proposed in this paper. For most
BLDC motors, it is very difficult and expensive to
install Hall sensors in correct position accurately. And it
will cause torque ripple and wrong speed feedback. The
proposed model could simulate exactly thismisalignment effect, and it is can provide a good
simulation reference to engineers for designs of BLDC
motor controllers.
Acknowledgements
This work is partially supported by the Ministry of
Science and Technology, ROC, under contract No. NSC
103-2622-E-224-056 and 104-2221-E-224-012.
References
1. P. B. Beccue, S. D. Pekarek, B. J. Deken, and A. C.
Koenig, Compensation for Asymmetries and Misalignment in a Hall-Effect Position Observer Used in
PMSM Torque-Ripple Control, IEEE Transactions On
Industry Applications, Vol. 43, NO. 2, MARCH/APRIL
2007, pp. 560-570.
2. C.-W. Hung and J.-H. Chen, A Hall Sensor Auxiliary
Circuit with Noise Rejection and Time Interval Capture
Functions for BLDC Motors, Sensor Letters, Vol. 10,
2012, pp. 1178–1184.
3. B. Tibor, V. Fedák, and F. Ďurovský, Modeling and
Simulation of the BLDC Motor in MATLAB GUI, in
Proc. 2011 IEEE International Symposium on Industrial
Electronics (ISIE), 27-30 June 2011 pp.1403-1407
4. C.-W. Hung, C.-T. Lin and C.-W. Liu, An Efficient
Simulation Technique for the Variable Sampling Effectof BLDG Motor Applications, in Proc.33rd Annual
Conference of the IEEE Industrial Electronics Society,Nov. 5-8, 2007 (Taipei, Taiwan, 2007), .pp. 1175 - 1179

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan55

Using Multi-Target Tracking and Identification TLD Algorithm for Intelligent Mobile Robot
Jr-Hung Guo

Department of Electrical Engineering, National Chin-Yi University Of Technology, No.57, Sec. 2, Zhongshan Rd.,
Taiping Dist., Taichung 41170, Taiwan
Kuo-Hsien Hsia*

Department of Electrical Engineering, Far East University, No.49, Chung Hua Rd., Hsin-Shih. 74448 Tainan, 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
Image algorithms used herein are Tracking-Learning-Detection (TLD) and Speed UP Robust Features (SURF). TLD
is used for target tracking and SURF is used for identifying targets. We use zoning identification, with the use ofstatistical probability to strengthen the efficiency of TLD and SURF. With such a method, the efficiency of image
identification and target tracking can be enhanced so that the robot can simultaneously track and identify multiple
targets.
Keywords: multi-target tracking, TLD, SURF, image identification, mobile robot.

The image tracking algorithm that we use is TLD [4]which was developed by Zedenek Kalal, University ofSurrey, Czech. The TLD algorithm used for tracking
single-target for a long time. In this paper we extend the
TLD algorithm to multi-target tracking based on theoriginal single-target tracking. But this algorithm cannot
identify the tracked target. The TLD framework
improves the tracking performance by combining a
detector and an optical-flow tracker. Since this algorithm
is based on optical flow, the accuracy of TLD cannot behigh. So we use Speed UP Robust Features (SURF) toassist the targets identification and as an auxiliary for
TLD algorithm to track the targets.

1. Introduction
Image recognition and tracking technology are important
issues on robotics research. Fabian et al. [1] used
Microsoft Kinect and Simulink for instant object tracking.
Marković et al. [2] used omnidirectional camera on amobile robot to track and follow objects. Huang et al. [3]used the SURF algorithm to make the robot have theability to follow the object. As can be seen from previous
studies, image recognition and tracking technology
usually require dedicated imaging equipment or complex
algorithms. But this will cause an increase in the cost ofmobile robot, or reduce the efficiency of the mobile robot.
Therefore, how to enhance the efficiency of the mobile
robot on image identification and tracking is the main
research purpose of this paper.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan56

Jr Hung Guo, Kuo-Hsien Hsia, Kuo-Lan Su

SURF is based on the scale-invariant feature transform
Scale-invariant feature transform (SIFT) to identify theobject. Although SURF operation is relatively light to the
SIFT, it will still affect the effectiveness of the mobile
robot. Therefore, in this paper, the image is divided intosome separate regions. Each region is detected in turn.
Supplemented with the statistics and probability, it can
assist TLD on tracking. The target tracking andidentification process with SURF and TLD is shown in
Fig. 1. The relevant algorithms will be explained in thenext section.
2. Algorithms
2.1. TLD
TLD technology is divided into three parts. They are thetracker, the learning process and the detector. Tracker
and detector operate in parallel in TLD. Both the tracking
and detecting results will feed into the learning process.
The new model after learning is feedback to the tracker
and detector resulting in a real-time updating. The overall
process ensures that even if the target appearancechanges, the target can still be tracked. The architectureof TLD tracking is shown in Fig. 2.
TLD's tracker estimates the motion of the target using theLucas-Kanada [6] [7] optical flow, which is a frame toframe tracking method. However, the precision of the
optical flow tracking is not high, thus it often results in a
tracking failure. The detector records the history of target
locations and surface information, called learning, andwill re-detect while tracking failure.
P-N learning [9] is a very important part of tracking in
TLD. Here P is Positive Constraint, also known as Pexpert or growing event, and N is Negative Constraint,
also known as N-expert or pruning event. In practice, Pexperts as well as N-experts will have a certain bias, but
the study of Zdenek Kalal found that, although there is anerror, under certain conditions, the error is acceptable,and performance testing module will therefore be
improved. The P-N learning consists of four parts:(i) A classifier for learning;(ii) A training sample set;(iii) Supervised learning;(iv) P-N experts: to generate positive and negative
samples in the learning process;The relationship between these parts is shown in Fig. 3.

Fig. 1. Target tracking and identification process with SURF
and TLD.

Fig. 2. TLD tracking architecture [4].

The “random forest” classifier, which can instantly
update and forecast, is used in the detector [4, 6]. The
information for the feature detector is generated byrandom forests 2bitBP (2 bit binary pattern). The gradientorientation information of a specific area will be© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan57

Using Multi-Target Tracking and

The sign of the determinant value can be used to classify
the point and to determine the point is an extreme point
or not. In the SURF algorithm, the function value f(x, y)
is replaced by the image pixel I(x, y), and a second-orderstandard Gaussian function is chosen as a filter. Thenelements of H matrix can be obtained by convolution andsecond-order partial derivatives as following:

Fig. 3. TLD learning architecture [8].

converted into encoded output. 2bitBP is similar to harrlike feature which includes the feature type and thecorresponding characteristic values. Therefore, whenusing nFeat type to express the object, each Fern is a
Quad tree because of the 2bitBP characteristics.
TLD tracking module is based on the Median Flow
tracker method. This method is performed on a frame.
Select some pixels as the feature points in the previous
frame and then find the corresponding positions in thecurrent frame by the feature values. Then sort thedisplacements of these features pixels and find the
median of these displacements. Finally let the pixels lessthan 50% of the median as the next feature pixels. Since
this approach assumes that the tracked object is in theframe, it will fail once the object is out of the frame or
obscured. The SURF will be used for confirmation at thattime. Once it is confirmed, the new tracking target will
be selected by the TLD.

(1)

2 f 2 f
x

2

y

2

(

2 f 2
)
xy

(2)

(4)

1
2

e  ( x  y ) / 2 2
2

2

2

(5)

and  is the scale. The determinant of the H matrix ofeach pixel of the image can be obtained. Hence each point
can be classified. For the ease of application, Herbert Bay
proposed to replace the L by an approximation [9]. The
discriminant of H matrix can be expressed as:
G (t )  2 g (t )
x 2

(6)

Finally, what we want is a transformed image of theoriginal image, because we have to look for the featurepoints on the transformed image and then reflect it onto
the original image. And this is constituted by theapproximate determinant of the Hessian matrix of eachpixel with the approximate determinant defined by:det( H approx )  Dxx Dyy  (0.9Dxy ) 2(7)

Since SURF process the pixels at the image for Hessian
matrix processing simultaneously on both X and Y
directions, its efficiency is better than SIFT. But it stillrequires a large amount of computation. So we take thesolution as follows:(i) Identify region by region to reduce the amount ofpixel processing. Because the entire image is divided
into nine regions for TLD tracking, SURF will
therefore use the same area for identification andtracking.
(ii) Determine the identification sequence according tothe orientation. Since the orientation of the robot canbe known, we design the scanning sequence
following the orientation of the robot, as shown in

The determinant of H is:det( H ) L( x, y,  )  G( x, y,  )  I ( x, y)

G( x, y,  ) 

SURF is a technology for image recognition and tracking
based on SIFT. The main difference is that SIFT uses
DoG (Difference-of-Gaussians) image, and SURF uses
the determinant of a Hessian matrix to approximate theimage. The Hessian matrix of a pixel in an image can be
defined as [5]:
2 f 
xy 
2 f 

y 2 

(3)

where

2.2. SURF 2 f
x 2
H ( f ( x, y ))   2
 f xy Lxx ( x, y,  ) Lxy ( x, y,  ) H ( x, y ,  )   Lxy ( x, y,  ) L yy ( x, y,  )© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan58

Jr Hung Guo, Kuo-Hsien Hsia, Kuo-Lan Su

Fig. 4. The central region is of higher priority, thenthe upper and lower regions. With this strategy, thetarget tracking for the mobile robot can be of higher
efficiency.
3. Experimental Results(a) turn left

A Microsoft's LifeCam Cinema was used in our
experiments. Because of its large aperture and highresolution, images with better quality can be obtained.
For the image tracking and identification system, theentire image was cut into nine regions, and TLD wasapplied to have the tracked object, and then SURF wasused for identification. The CPU of the computer for theexperiment is INTEL I5-3470 with 8G memory, and thesystem was developed by EMGUCV 2.4 and Microsoft
Visual Studio VB2010. The experimental results areshown in Fig. 5. The red box marks the tracking target of
TLD, and the blue box marks the SURF tracking andidentification results with the identification result of
SURF marked at the upper right corner.

(b) forward(c) turn rightFig. 4. Robot orientation relationship with SURF scanningorder.

Fig. 5. Tracking and identification result by TLD and SURF.
2. I. Marković, F. Chaumette and I. Petrovć, Moving object
detection, tracking and following using an omnidirectional
camera on a mobile robot, 2014 IEEE Int. Conf. Robotics
and Automation (ICRA) (Hong Kong, China, 2014), pp.
5630-5635.
3. P.T. Huang, C.Y. Li, C.C. Hsu, and C.M. Hong, Object
following based on SURF for mobile robots, 2012 IEEE
1st Global Conf. Consumer Electronics (GCCE) (Tokyo,
Japan, 2012), pp. 382-386.
4. Z. Kalal, K. Mikolajczyk, and J. Matas, Tracking-learningdetection, IEEE Trans. on Pattern Analysis and MachineIntelligence, 34(7) (2012) 1409-1422.
5. C. Evans, Notes on the OpenSURF library, (University ofBristol, Tech. Rep. CSTR-09-001, January, 2009).
6. B.D. Lucas, Generalized image matching by the method ofdifferences, (doctoral dissertation, tech. report, Robotics
Institute, Carnegie Mellon University, 1984).
7. B.D. Lucas, and T. Kanade, An iterative image registration
technique with an application to stereo vision, inProceedings of the 7th Int. Joint Conf. Artificial
Intelligence (IJCAI) (1981) 674-679.
8. Z. Kalal, J. Matas, and K. Mikolajczyk, P-N learning:
Bootstrapping binary classifiers by structural constraints,
2010 IEEE Conf. Computer Vision and Pattern
Recognition (CVPR) (San Francisco, CA, 2010) 49-56.
9. H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, SURF:
Speeded Up Robust Features, Computer Vision and Image
Understanding (CVIU), 110(3) (2008) 346-359.

4. Conclusion
The TLD and SURF algorithms are successfully
combined in this paper so that the robot's vision system
can track and identify the targets. The entire image isdivided into nine regions, in order to reduce the loadingon image processing, and to enhance the efficiency of
tracking and identification. You used a mid-level
webcam in this paper resulting a lower image quality andhigher failure rate in tracking and identification. Highlevel camera and CUDA (Compute Unified Device
Architecture) can increase the quality of image andefficiency of image processing. Thus this algorithm canbe used in a more complex environment. We believe thatthe algorithm can be widely used in robot systems in thenear future.
Acknowledgements
This work was partially supported by National Science
Council of Taiwan. (NSC 104-2221-E-224 -015 -).
References
1. J. Fabian, T. Young, J.C. Peyton Jones, and G.M. Clayton,
Integrating the Microsoft Kinect with Simulink: Real-time
object tracking example, IEEE/ASME Transactions on
Mechatronics, 19(1) (2014) 249-257.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan59

Handheld Mobile Devices for Remote Monitoring of Factory
Bing-Gang, Jhong

Department of Mechatronic Engineering, National Taiwan Normal University,
162 Section 1 Heping E. Rd., Taipei City 106, Taiwan
Jian-Sing, Hu; Mei-Yung, Chen

Department of Mechatronic Engineering, National Taiwan Normal University,
162 Section 1 Heping E. Rd., Taipei City 106, Taiwan
E-mail: Jhong.bing.gang@gmail.com, cmy@ntnu.edu.com.tw
www3.ntnu.edu.tw

Abstract

In this paper, a handheld mobile device system is presented for remote monitoring of the factory to shortthe downtime of machines and recovery more quickly. The human–computer interaction of the system isdeveloped by GP-Por EX, and operates by using commercial or free distal APP combining with sign-inwebsite of the company. The way for the program connected can be connected with multi-way links, suchas long-distance link (3G/4G or Wi-Fi wireless INTERNET), medium distance (LAN) and short distance(ETHERNET) and can be chosen in the company portal page. With supplemented Internet browser
program for remote monitoring, the distal end operator or the person in charge can confirm the machinecondition through the distal end of the server, port, user name and password settings. Therefore, theoperational efficiency and system transparency can be improved with the handheld mobile devices forremote monitoring system.
Keywords: Human Machine Interface, Remote monitoring, GP-Por EX, GP-Viewer EX.

panel design. As technology advances, HMI now is not
the button-only panel, but with logic control,
Multi-machine interconnected communication.
Therefore, due to its versatility, wide adaptation, highreliability and other advantage, HMI has widely used in
the automated machine equipment control to propose
high quality automated machine service.
The inner workings of monitoring and abnormal
maintenance both need engineering staff to maintenancein site in the past, which is time-consuming and effortless.
If we can use the network function, and with the internet
and wireless network, the dream of remote monitoring
may become true [1-2]. For our case as example, due tothe technology limit for the early construction of the

1. Introduction
Human Machine Interface, often used with touch
panel, is a control equipment connecting with human andmachines. Because of within CPU, memories andconnect interface units, it can connect to other hardware,such as PLC, PID controller and servo controller, so that
theirs information can be shown on the screens for theoperators to understand the state of machines. Developedby different companies and different purposes, HMI may
have different functions.
In early stage, the aim of developing HMI is only forsimplifying the control interface, reducing the risk ofdesign, and increasing the modification of electrical© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan60

Bing-Gang Jhong, Jian-Sing Hu, Mei-Yung Chenfactory, only few machines can be connected to internet.
However, thanking for the continuous evolution of
network technology, now we can use the free APP withhandheld devices to connect to the program of company
entrance, build the HMI by GP-Por EX software forcoordination, so that all the automation equipment havethe abilities of remote monitoring.
The different HMIs are used in different location.
3G/4G and Wi-Fi is for where is far away from factory.
Therefore, if the data of company entry, such as server IP,
port, user name and password are known and alreadypreset, then we can achieve remote monitoring to get the
information about the states of automation equipment in
the factory.
This paper is organized as follows. The methodology
is proposed in Section 2. Section 3 presents experimentresults. Finally, some conclusions are summarized in
Section 4.

Fig. 2. The main window of monitoring program

2.2. Automation equipment malfunction reset
To describe reset process in detail, we propose thediagram of malfunction reset in fig. 3.

2. The Main Text
2.1. HMI Monitoring program
Our HMI program is developed by GP-Por EX
software, and import the network components form thecommunication software named GP-Viewer EX. With
connecting to the server of factory, GP-Viewer EX will
read the HMI after running, and check this link isConnect or not. If the link is correct, all the state ofautomation equipment will be shown on the screen byswitching the screen. However, if the link is not correct,
this program will try to do malfunction reset to recoverits production. The screen of monitor program is shownin fig. 2.
malfunction reset
Fig. 3. The diagram of abnormal reset

2.3. The choosing of monitoring HMI
Porface company has developed many HMI. Withdifferent developing targets, theirs types are also
different, and the connecting ways need to be used are
different, too. Therefore, it is necessary to choose thetypes of HMI at the program beginning, so that thisprogram can make correct choice for different type. For
our monitoring program, it can work for the series ofGT3000 and series of GT4000.

Fig. 1. The diagram of communication equipment© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan61

Handheld Mobile Devices forentry and the network of factory. Finally, the interface
uses the communication software to connect thefunctions proposed from GP-Viewer EX, so that all thestates of automation equipment appear in the software
window at handheld mobile devices shown in fig. 8, or at
PC shown in fig. 9. If the link is successful, it will returna running normally message and able to observe all thestates of automation equipment. Otherwise, it will stop
the foregoing actions.
Fig. 4. The illustration of system setting

2.4. The handheld mobile devices and thecommunication interface
The handheld mobile devices APP requests theconnecting data, including server IP, port, user name andpassword, shown in fig. 5. The select settings ofcommunication interface of HMI against the
ETHERTER internet is shown in fig. 6. With the correct
setting IP, network number and station number shown infig. 7, and all correct data in fig. 5 and fig. 6. The
connecting between GP-Viewer EX program and HMI
could be successfully.

Fig. 6. The communication preferences page of HMI

Fig. 7. The communication page of GP-Viewer EX

Fig. 5. The communication preferences page of handheld
mobile devices

2.5. The connect between monitor APP and HMI
In the handheld mobile devices monitor APP, theinterface connecting to HMI needs through the company

Fig. 8. The monitor program at handheld mobile devices© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan62

Bing-Gang Jhong, Jian-Sing Hu, Mei-Yung Chen

3. Experiment Results
In our research, we use the tablet Nexus II with 2X
RDP APP as our handheld mobile devices for remotemonitoring. In the local part, we use the server PC with
GP-Viewer EX software to get the information of HMI
through intranet. This server PC also connects to theentry of company. The handheld mobile devices are more
popularize now, and the bandwidth of mobile network islarger and larger, so the location can be for grasping theoperating of factory is not limited at established place.
We test the time cost of start-up with different connection
ways, including Wi-Fi, 3G and 4G. The result is given in
Table 1, and can learn that the time cost will be shorter
with larger download speed.

Fig. 9. The monitor program at PC

2.6. Exception Handling by HMIWhen the online staff or equipment engineer findthere are some errors which can’t be solved, and the
major engineer is also not in the factory at the same time,then he/she will start the remote monitoring programfrom his/her handheld mobile devices, and get the alarmby the HMI screen. With this information, the major
engineer can try to analysis which parts are going wrong
and do abnormal reset, so that the production equipment
can recover to the normal state as soon as possible.

Table 1. The cost time with different connection mode.

Mode
3G
4G
Wi-Fi

Update1.12 Mb/s
17.86Mb/s
2.78Mb/s

Download
9.15Mb/s
21.23Mb/s
25.18Mb/s

Cost time
120s
96s
90s

4. Conclusion
In this paper, we develop a handheld mobile device
program using mobile internet through the entrance ofcompany to the HMI of automated production equipment
for remote monitoring. In the beginning, because of thefirewall of server PC, we need to find some method, such
as VNC, to connect the server PC. However, it will causesignificant delays and only with the monitor screen andunable to fulfil the requirements of remote monitoring.
Therefore, we change our research to replace the HMI ofequipment with independent development of connection
systems to accomplish mission.
References
1. Kusunoki, K., A CORBA-based remote monitoring
system for factory automation, International Symposiumon
Object-Oriented
Real-timeDistributed
Computing(Kyoto, Japan, 1998), pp. 396–402.
2. Xiaolan Xie et al., A Factory Remote Monitoring System
Based on Pervasive Environment, International
Conferenceon
Pervasive
ComputingandApplications(Birmingham, UK, 2007), pp. 647–651.
3. Sharp Electric, Sharp Programmable Controller User
Manual, Sharp Electric (1997).
4. Sharp Electric, Sharp Human Machine Interface User
Manual, Sharp Electric (1997).

Fig. 10. The monitor program when get alarms© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

63

Task Assignment and Route Programming for Pattern Reformation on Grid Plane
Kuo-Hsien Hsia

Department of Electrical Engineering, Far East University
No.49, Zhonghua Rd., Xinshi Dist., Tainan City 74448, Taiwan (R.O.C.)
Bo-Yi Li

Graduate School of Engineering Science & Technology (Doctoral Program)
National Yunlin University of Science & Technology
No. 123, University Road, Section 3, Douliou, Yunlin 64002, Taiwan (R.O.C.)
Kuo-Lan Su

Department of Electrical Engineering, National Yunlin University of Science & Technology
No. 123, University Road, Section 3, Douliou, Yunlin 64002, Taiwan (R.O.C.)
E-mail: khhsia@cc.feu.edu.tw, g9910813@yuntech.edu.tw, sukl@yuntech.edu.tw
www.feu.edu.tw

Abstract
Pattern reformation problem on a grid plane is an interesting problem. Suppose there is a group of mobile robots ona square grid plane with the number of robots equaling to the number of columns/rows of the grid plane. The mobile
robots are commanded to change to another formation as soon as possible. Any one of them has to prevent himself
from colliding with other mobile robots during the moving of reformation process. In this paper, we will provide asystematic process on task assignment of route programming for the pattern reformation problem on a square gridplane. We will also provide a checking process to check if the suggested program is really good or not.
Keywords: Task assignment, pattern reformation, grid plane.

1. Introduction

2. Problem Formulation

There are mainly two category of programming problem:
one is maximization problem and the other one isminimization problem. Minimum cost and minimum
time are two of the most commonly used criterion of theminimization problem. Usually, the objective function ofa minimum time problem is the sum of the spent time.
However, for a problem that people start their works incooperation at the same time and they want to complete
the works as soon as possible, the objective function
should be the maximum of the spent time. Pattern
reformation problem is one of these problems.

Suppose there are some mobile robots placed on a squaregrid plane and forming certain pattern. The number of themobile robots are the same as the rows of the square gridplane. There may or may not be obstacles on the gridplane. The mobile robots are commanded to reform
another pattern in the shortest time and they can only
move along the rows or the columns of the grid plane.
This is definitely a minimum-time programming problem.
Figure 1 is an example for six mobile robots on a 6x6 gridplane where the black squares form the target pattern, i.e.
the 6 mobile robots are commanded to form the newpattern designated by the circles.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan64

Kuo-Hsien Hsia, Bo-Yi Li, Kuo-Lan Su

D1 D2 D3 D4 D5 D6
3 4 5 6 7 8
R1
3 2 3 4 5 6R2 
 = R3 5 4 5 4 5 6

R4 5 4 5 4 5 4R5 7 6 7 6 7 6

R6 7 6 7 6 7 6
After the initial formation and target formation aredetermined, one can do the task assignment process. We
have developed two algorithms for task assignment
process [3-4]. The algorithm in [3] is simple but cannot
guarantee the obtained result being with shortest time.
When there is 0’s in the distance matrix, the assignment
result will often be wrong. Hence we developed a newalgorithm, which is more complex but with better result,in [4]. However, there is still no mathematical proof forthe optimality. Hence, in this paper, we develop a newtask assignment algorithm, with which we believe thatthe shortest time can be guaranteed, as in the following.
Step 1: Use the algorithm in [3] or [4] to determine thefirst assignment for the original distance matrix,
1. Determine the necessary moving time t1 forthe reformation.
Step 2: Replace the elements greater than or equal to t1of the distance matrix by a sufficiently large
number Z, e.g. 999. This forms the 2nd distancematrix.
Step 3: Use the Hungarian algorithm [5] to check theminimum working time T2 for the 2nd distancematrix 2. If T2 >= Z, then the obtained task
assignment is the optimal one, else add 1 to theindices ofand t, and go to Step 1.
Because the Hungarian algorithm is a well-known
method for solving minimum-cost task assignment
problem, and the sufficiently large number Z is muchlarger than the required time for the task, the check can
guarantee that there is no better alternative for the task
assignment. Hence, each mobile robot will have its
destination to form the overall new pattern.
There is one thing that should be noticed. The optimal
task assignment is not unique. But one cannot find anyother assignment way that will have a shorter time tocomplete the task.
After the target position determined, there may be morethan one possible route to the target position. Refer to Fig.

Fig. 1. Example of 6 mobile-robots on a 6x6 grid plane withthe black squares forming the target formation.

There are two sub-problems that have to be considered
for solving this problem. The first one is the taskassignment problem that each mobile robot’s destination
has to be determined for the minimum time requirement.
The second one is the route programming problem that
how each mobile robot should move to the target positionin the shortest time without colliding with any other one
mobile robot once the task assignment is completed.
While dealing with the second sub-problem, it is possible
to return to the first sub-problem because of the route
conflict of any two mobile robots and one of them should
wait for a step or detour to a further routing.
3. Algorithm for Task Assignment with
Verification
Let each mobile robot has its ID and each target is also
assigned an ID for convenience. The grid points on thegrid plane are numbered sequentially with column
majored from up to down and from left to right. For
example, the grid point on the cross of the ith row and jth
column on a 6x6 grid plane is numbered as (j-1)x6+i.
With these information, we can form a distance matrix
with the row number represents the robot ID and thecolumn number represents the destination ID. The
element at row i and column j represents the steps needed
for the mobile robot i to the destination j. This matrix canbe determined by the diffusion method [1-2].
For the case of Fig. 1, suppose the robots are numberedfrom top to bottom as 1 to 6. By diffusion method¸ thedistance matrix is:© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan65

Task Assignment and Route2, both Routes 1 and 2 for Robot 1 are of distance 3. If
both robots choose the dashed route, then they will
collide after one step. If anyone of them waits a step toprevent the collision, then the task will be delayed for 1
unit of time. If any one and only one of them chooses thesolid route, then there will be no collision and the task
will be completed on time.
4.

Experimental Results

Consider the example shown in Fig. 1. This means thatthe distance matrix is as equation (1).
3 4 5 6 7 8 
3 2 3 4 5 6
5 4 5 4 5 6 
1 = 
(1)

5 4 5 4 5 4
7 6 7 6 7 6 
7 6 7 6 7 6
Use the algorithm in [3] to solve this task assignment
problem and we will have:
Robot 1  Destination 1, Robot 2  Destination 3
Robot 3  Destination 5, Robot 4  Destination 2
Robot 5  Destination 4, Robot 6  Destination 6
The required shortest time t1 for the pattern reformationis 6. Replace the elements greater than or equal to 6 ofthe 1 by 99, and we have the second distance matrix as:
4
5 99 99 99
3
3
2
3
4
5 99

5
4
5
4
5 99
2 = 
(2)

4
5
4
5
4
5
99 99 99 99 99 9999 99 99 99 99 99
Applying the Hungarian Algorithm on 2, we have theminimum cost for this problem is 211 which is much
more large than 99. Thus the obtained task assignment
result is the best one. And the routes for the pattern
reformation are shown in Fig. 3.
For this problem, there is another assignment way thatwill result in the same time 6 for the pattern reformation.
This way is:
Robot 1  Destination 1, Robot 2  Destination 2
Robot 3  Destination 5, Robot 4  Destination 3
Robot 5  Destination 6, Robot 6  Destination 4

Fig. 2. Route select on route programming.

Consider another example shown in Fig. 4. There are 7
mobile robots on a 7x7 grid plane. The target positions
are prescribed. The distance equation is:1 2 1 6 4 3 2 
6 5 4 5 5 8 7  2 5 4 3 1 4 3
1 =  3 4 3 4 2 5 4 (3)
4 3 2 5 3 6 5
2 5 4 5 1 2 1 4 3 2 9 7 6 5

Use the algorithm in [3] to solve this task assignment
problem and we will have:
Robot 1  Destination 7, Robot 2  Destination 4
Robot 3  Destination 5, Robot 4  Destination 1
Robot 5  Destination 2, Robot 6  Destination 6
Robot 7  Destination 3
The required shortest time t1 for the pattern reformationis 5. Replace the elements greater than or equal to 5 ofthe 1 by 99, and we have the second distance matrix as:
2 1 99 432
1
99 99 4 99 99 99 99 2 99 4 3
1
4
3


4 3 4
2 99 4 
2 =  3(4)
4
3 2 99 3 99 9921
 2 99 4 99 1
4
3 2 99 99 99 99Applying the Hungarian Algorithm on (4), we have theminimum cost for this problem is 20 which is less than
99. Thus we have to use the algorithm in [3] to solve (4)and we will have:© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan66

Kuo-Hsien Hsia, Bo-Yi Li, Kuo-Lan Su

Fig. 3. Programmed routes for Example of Fig.1.
Fig. 5. Programmed routes for Example of Fig.4.

the task has been obtained. The programmed route inshown in Fig. 5.
5. Conclusions
In this paper, we developed a new algorithm, which
combines our early developed algorithm with the
Hungarian algorithm, to guarantee the task assignment
result to be the best one. Since the Hungarian algorithm
has been well applied on minimum cost programming
problem, we believe that the proposed algorithm in thispaper can be well applied on any size of grid plane.

Fig. 4. Example of 7 mobile-robots on a 7x7 grid plane with thesquares forming the target formation.

References

Robot 1  Destination 7, Robot 2  Destination 3
Robot 3  Destination 4, Robot 4  Destination 1
Robot 5  Destination 5, Robot 6  Destination 6
Robot 7  Destination 2
The required shortest time t1 for the pattern reformationis 4. Replace the elements greater than or equal to 4 ofthe 1 by 99, and we have the third distance matrix as:
2
1 99 99 32
1
99 99 99 99 99 99 99 2 99 99 3
1 99 3 3 =  3 99 3 99 2 99 4  (5)99 3
2 99 3 99 9921
 2 99 99 99 1
99 3
2 99 99 99 99

1. E. W. Dijkstra, A note on two problems in connexion withgraphs, Numerische Mathematik, 1 (1959) 269-271.
2. T. H. Cormen, C. E. Leiserson, R. L. Rivest and C. Stein,
Section 24.3: Dijkstra's algorithm in Introduction to
Algorithms, Third ed., (MIT Press and McGraw–Hill,2009) pp. 658–663.
3. K.H. Hsia, B.Y. Li and K.L. Su, Task assignment on
pattern reformation for multiple mobile robots, The
nineteenth Int. Symp. on Artificial Life and Robotics 2014
(AROB 19th 2014) (Beppu, Japan, 2014), pp.438-441.
4. K.H. Hsia, B.Y. Li and K.L. Su (2015). Task assignment
on grid plane for multiple mobile robots, Tenth Int. Conf.
on Innovative Computing, Information and Control(ICICIC2015) (Dalian, China, 2015), p.193.
5. H.W. Kuhn, Variants of the Hungarian method for
assignment problems, Naval Research Logistics Quarterly,
3 (1956) 253–258.

Applying the Hungarian Algorithm on (5), we have theminimum cost for this problem is 114 which is greaterthan 99. Thus we can feel free that the shortest time for© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

67

The Design of a Multifunctional Acousto-Optic Device
with a Mobile Power Pack
Yi-Yu Lu and Zheng-Ying Li

Department of Electrical Engineering, Far-East University
No. 49, Zhonghua Rd., Xinshi Dist., Tainan City, 74448, Taiwan
E-mail: yiyulu@cc.feu.edu.tw, marspopopo431@gmail.com
Wen-Bin Lin

Department of Optoelectronic Engineering, Far-East University
No. 49, Zhonghua Rd., Xinshi Dist., Tainan City, 74448, Taiwan

Abstract
The new designed acousto-optic device has adjustable, it is 1.5 times to extend its length. Fitting plastic optic fiber
into transparent tube, the path of light can be increased and the light area will be enlarged. The device can present
different color mode of light with RGB and high light LEDs as the light source. Adding the USB charge and discharge
function into the device, make it has the function of mobile power pack.
Keywords: Multifunction, acousto-optic warning device, mobile power pack.

and extend the product life-cycle. Moreover, sincesmartphones can also work as flashlights, the flashlightindustry is facing a crisis of falling sales. Further
investigate the destruction of mobile phone innovation,
just like the original online music under carrier service
arise, causing the collapse of the traditional record
industry is facing the same crisis [1]. Today, the mobile
phone also undermine innovation in many industries
began to face a crisis of survival. Flashlight if not actively
seek to change, they may face the fate of being eliminated.
This study thus presents the design of a multifunctional
acousto-optic warning device with a mobile power packusing plastic optical fiber [2]. Since the late 1990s, highperformance plastic optical fiber based on perfluorinatedpolymers began to appear on the marketplace [3-4], but
it has not yet been used in flashlights. Plastic optical fiber
has the advantage of bending easily and not breaking, andits falling price means that it is now used for medical andrecreational purposes. The advantages of the area of the

1. Introduction
Traffic batons plays an important role in policemen daily
works. Their accoutrements also need a flashlight andwhistle when on duty, and thus this study aimed tointegrate these into one device. This work thus presents
the design of a single apparatus with the functions of a
glow stick, warning system and camping light in the formof an acousto-optic warning device. The multifunctional
acousto-optic device with a mobile power pack will be
introduced for the demand of amusement and convenientconsideration.
A typical flashlight is constructed from a light bulb andfocusing mirrors, and over the last twenty years lightemitting diodes (LEDs) have been applied on more
lighting devices, as they can reduce electricity
consumption and have a long lifecycle. Flashlightmanufacturers are also facing the product innovation andorientation problem and how to improve the applications© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan68

Yi-Yu Lu, Zheng-Ying Li, Wen-Bin Lin

light guide path increasing are implemented for thisacousto-optic device design.
Conventional flashlights usually project light in only one
direction only, and while this enables of directivity andconcentration, the relation between the light source anddirection of sight is unclear, and thus people may beunsure of the location of the light source [5]. This is an
issue that could be improved.
C1

2. Materials and methods

LED1

R1
R.125W

The device includes five major parts: (1) adjustable focus,(2) plastic optical fiber, (3) LED driver, (4) battery, and(5) micro USB power pack. The structure ofmultifunction acousto-optic warning device is depicted inFig. 1.

D5

FIGURE 2. MCU circuit
A 555 Oscillator is a type of relaxation oscillator for
generating stabilized square wave output waveforms ofeither a fixed frequency of up to 500 kHz or of varying
duty cycles from 50 to 100%. The OP amplifier 741 isused to increase the volume. The warning function circuit
is shown in Fig. 3.

R7
R.125W

R1
R.125W
CT2

R3
R.125W

R4
R.125W
3

Figure 1. The multifunctional acousto-optic warning
device.

6

R2
R.125W
2.1. Hardware

R5
R.125W

7
4

2

1
5

The basic circuit of the microcontroller unit (MCU) is
shown in Fig. 2. The related program is written in Keil C.

R6

CT1 R.125W

FIGURE 3. The warning function circuit© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

69

The Design of a2.2. USB power pack

3. Results and Discussion

The USB boost and buck module are shown in Figs. 4
and 5.

The device comprises three subsystems which are RGB
LED, warning system and mobile power pack. Be able togive consideration to people’s recreation and demand ofamusement at the same time, an acousto-optic warning
device with multifunction was designed. The transparent
tube used in the device contains a plastic optical fiber,
which is not currently seen in any similar product. The
tube can increase the light guide and improve theluminous area. The device uses both RGB LEDs andhigh-brightness white LEDs as dual light sources, thus
enabling different patterns of light and color, so thedevice is both functional and usable. The flashlight canbe extended to 1.5 times its length, increasing the lightguide and the illuminated area. In addition, the use of a
USB charge and discharge function from lithium batterymeans that this device can also work as a mobile powerpack.
The body of the flashlight is flexible, and it is convenient
for use at night and for directing traffic. It combines aflashlight and a baton, and can be used when camping
and to make warning sounds. On the other hands, it
introduced the design of polymer optical fiber andlithium battery for energy-conservation and increasing
efficiency consideration. Further, since it includes RGB
LEDs, it can be used as a glow stick in concerts. The
LEDs can make the light cover the whole flashlight via
the optical fiber. The flashlight has an additional micro
USB charging socket. The multi-functional design is thegoal of various products now. This design has alreadybeen patented as a multi audible warning device at theIntellectual Property Office of Taiwan on September 21,
2014. Images of different colors of flash lighting areshown in Fig. 7.

FIGURE 4. USB boost module

FIGURE 5. USB buck module

2.3. Software
The flowchart and interrupt subroutine for the
acoustooptical warning device are depicted in Fig. 6.

Figure 6. Flowchart for acousto-optic warning device© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan70

Yi-Yu Lu, Zheng-Ying Li, Wen-Bin Lin

References
1. Industrial technology, vol. 283, no. 5, pp. 4-7, 2015.
2. C. M. Okonkwo, E. Tangdiongga, H. Yang, D. Visani, S.
Loquai, R. Kruglov, B. Charbonnier, M. Ouzzif, I. Greiss,
O. Ziemann, R. Gaudino and A. M. J. Koonen, “Recent
results from the eu pof-plus project: multi-gigabittransmission over 1 mm core diameter plastic opticalfibers,” vol. 29., no.2., pp. 186–193, 2011.
3. S. Randel and C. Bunge, “Spectrally efficient polymer
opticalfiber
transmission,”
Coherent
OpticalCommunications, Subsystems and Systems, Proc. of SPIE,
vol. 7960, 2011.
4. F. Beguin and E. Frackowiak ed., “Supercapacitors:
Materials, Systems and Applications,” New Jersey: Wiley,
pp. 515, 2013.
5. J. Zubia and J. Arrue, “Plastic optical fibers: anintroduction to their technological processes andapplications,” Optical Fiber Technology, vol. 7, pp. 101140 2001.

FIGURE 7. Photos of different colors of flash lighting

4. Conclusions
The design have functionality and practicability
concurrently. The concept of a multifunctional acoustooptic warning device is introduced in this work. A
multifunction acousto-optic warning device has obtained
our country's new patent for this study. There are
multifunction in this device which are as the traffic baton,
camping light, glowing sticks in the concert and using for
women's security at night, etc. The new designed
acousto-optic device has adjustable, it is 1.5 times toextend its length. The advantage of this device is thatwhen the body is exposed outside the transparent tube,
the LED light source can diffuse along the light guide,
and thus the lamp body can be projected in one direction,and so achieve the effect of centralized lighting. The
structure also has the effect of concentrated and omnidirectional diffusion of light.

Acknowledgements
This work was in part supported by the Ministry of
Science and Technology, Taiwan, Republic of China,
under the research grants 104-2815-C-269-018-E.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan71

Visual-servo Control of 4-DOF Robot Manipulator
for Sorting Moving objects
Longtan Wang

Dep. of Electronic Eng., Pusan National University, Jangjeon-dong, Geumjeong-gu
Busan, 609-735, Republic of Korea
Seon-Woo Kim

Dep. of Electronic Eng., Pusan National University, Jangjeon-dong, Geumjeong-gu
Busan, 609-735, Republic of Korea
Hyun-Wook Ha

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: longtan7379@pusan.ac.kr, seonwoo7379@pusan.ac.kr, hyunuk.ha@gmail.com, jmlee@pusan.ac.kr
www.pusan.ac.kr

Abstract
This paper considers the problem of estimating position and determining class of objects on the moving conveyor
belt. A 2D camera is utilized to capture the image of moving objects. The control system directly integrates visual
data into the servoing process. Objects are recognized by firstly removing the background using adaptive
thresholding algorithm, then find objects outer contours and determine the size and shape by topological structure
analysis tracking boards, then Rotating calipers algorithm is adopted to estimate the centroid position andorientation of the objects. Finally control the robot manipulator pick different moving objects to different position.
The proposed system is able to control the robot so that it can approach the desired position and grab the specificobject.
Keywords: Robot; Adaptive thresholding; Topological Structure Analysis; Rotating calipers

However different kinds of objects holds different
features, classify them often needs different algorithms.
For a sorting system, if the sorting objects changes, thesegmentation algorithm always need be altered. The
past research mostly devoted to segment and recognize
objects by surface features. For example, when sorting
the transparent or reflective objects, the surface featureschanges as the illumination changes, typical vision

1. Introduction
Sorting objects on the moving conveyor belt by robot
is an essential part in a production line [1]. Despite
many methods to sort the objects, vision based robot
may be one of the most favored one in terms of the costand efficiency.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan72

Visual-servo Control of 4-DOF

algorithms first extract the features of the surface andthen compare it with the database to determine the classof it. So recognizing them by using feature related
algorithms becomes a difficult job.
In this paper a sorting system with objects holds
different size is addressed. As mentioned above, thesurface appearance of the object is easily affected by theillumination, which makes it difficult to extract features
and thus segment them becomes impossible. Instead ofsegment them by surface feature, we propose a way tosegment and recognize objects by its size.

Vision Part

Control Part

Background
Subtraction

Find Contours

CameraGripper Controlling

Position andPose Estimator

Driver Controlling

Circumcircle
Command Analyzing
Matching

2. Develop Environment

Robot controller

As shown in Fig. 1. the robot system consists of a 4DOF manipulator and a finger like gripper, a tablet PC,
a conveyor belt, a Basler industry camera with ring light,
which is installed on top of conveyor belt and in front ofrobot operation area.

Fig. 2. System architecture

3. System Operation
The system Operation is make up of several main steps,
each step is an essential part.
3.1. Background Subtraction
Background subtraction is a very important procedurefor of many vision based applications. Backgroundmodel is a relatively static model and its modeling is atheart of any background subtraction algorithm. Due toits flat and monochrome surface, consider the conveyor
belt as a time invariant background, and it is sensitive tothe illumination. Apply the background subtraction
process in order to eliminate noise and correct uneven
illumination. Also can determine whether the objects
have come in view of camera. If so, the vision system
will save the image for further processing. If not, thevision system will continue monitoring the conveyor
belt.
Adaptive thresholding [2] is a form of thresholdingthat takes into account spatial variations in illumination.
Due to the video stream, we have to maintain real-time
performance, which means the less iteration times thebetter. A simple and fast adaptive thresholding
technique is adopted. The first step is to get the integral
image.

Fig. 1. Experiment environment

As shown in Fig. 2. once the object appears in the viewof the camera. The vision system will begin to find all
the outer contours and thus get each shape. This way wewill get the size of each object in the view. After that,the system will continue to compute its current positionand orientation. According to information got fromvision system, the PC will figure out a suitable strategy
of motion planning and control the robot sorting objects.

I(x,y) = f (x,y)+I(x−1,y)+I(x,y−1)−I(x−1,y−1)(1)

Eq. (1) shows the process of integral image. To
compute the integral image, we store at each location,© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan73

Longtan Wang, Seon-Woo Kim, Hyun-Wook Ha, Jang-Myung Lee

3.3. Position and Orientation Estimation

I(x,y), the sum of all f (x,y) terms to the left and above
the pixel (x,y). This is accomplished in linear time using
the following equation for each pixel (taking into
account the border cases.
Fig. 3. shows adaptive thresholding effects, which
decrease the influence of uneven illumination.

(a)

In order to get the centroid position of an object, what
we need we have to know firstly is its shape. Assume
that the centroid is the center of its minimum bounding
rectangle(NBR), which means this rotated rectangle isthe smallest one that enclosing the object in the image.
While the pose is represented by the angle this rectangle
has rotated. What we need is to find its minimum
bounding rectangle.
We adopt Rotating calipers algorithm [4] which is awell-known algorithm for finding the minimum
rectangle [5]. As shown in Fig. 5.

(b)Fig. 3. (a) Original image, (b) After adaptive thresholding

3.2. Find Contours
After removing background, we need to find thecontours by using topological structural analysis [3].
This step is the used for finding all the objects appearedin view of the camera. Each outer contour represents anobject.
Find the contours by border following using
topological structural analysis. Since the outer borders
and the hole borders have a one-to-one correspondence
to the connected components of l-pixels and to the holes,
respectively, the topological structural analysis byFig. 5. Minimum bounding rectangle

3.4. Motion Planning and Control
The control strategy is assigned based on information
got from vision part. The manipulator and gripper will
work together to move the object to specified area [6][7].
4. Experiment Result
We design an experiment which will test the result of
the recognizing effects and real-time performance.
In this experiment, different kinds of objects are placedin order continually. The robot will recognize theirkinds and place different kinds of objects to different
places.
In this experiment the camera continues capturing
gray-level image at 500*690 resolution. The camera
capture images in real-time and each frame of the image
will be checked to find and recognizing the objects.
Then the robot manipulator will grab the object andplace each to a specified area. Fig. 6. shows theexperiment process. Different kind of object will berecognized.

(b)

(a)

(b)

Fig. 4. (a) Origin binary image; (b) Contour of object

border following yields a representation of a binary
image, from which one can extract some sort of features
without reconstructing the image. As shown in Fig. 4.
The shape can be recognized© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan74

Visual-servo Control of 4-DOF

3.

4.

(a)

(b)

5.

6.

7.

(c)

(d)

Suzuki, Satoshi. "Topological structural analysis ofdigitized binary images by border following." Computer
Vision, Graphics, and Image Processing 30.1 (1985) 3246.
Toussaint, Godfried T. "Solving geometric problems
with the rotating calipers." Proc. IEEE Melecon, Vol. 83,(1983).
D. Chaudhuri, A. Samal. "A simple method for fitting ofbounding rectangle to closed regions." Patternrecognition 40.7 (2007) 1981-1989.
Hyun Woo Kim, Hu Chen, Jang Myung Lee, "Pathplanning of 5-DOF manipulator." Advanced Intelligent
Mechatronics (AIM), (2014) 877-881.
Dirk Buchholz, et al. "Combining visual and inertial
features for efficient grasping and bin-picking." Robotics
and Automation (ICRA), 2014 IEEE International
Conference on. IEEE, (2014).

Fig. 6. Experiment results. (a) Object 0; (b) Object 1; (c)Object 3; (d) Object 4

5. Conclusion
This paper proposes a design of robust recognizing andgrasping objects in real time. High robustness has been
demonstrated in recognizing featureless objects. The
adaptive thresholding algorithm and topologicalstructure analysis are used in recognizing objects, whilerotating calipers algorithm is applied to estimate thecentroid position and orientation of the objects. A path
planning scheme is thus yielded based on theinformation gotten from vision system. Experimental
results illustrate the good efficiency performance.
Acknowledgements
This research was supported by the MOTIE (Ministryof Trade, Industry & Energy), Korea, under the Industry
Convergence Liaison Robotics Creative Graduates
Education Program supervised by the KIAT
(N0001126).
This work was supported by the National Research
Foundation of Korea(NRF) Grant funded by the Korean
Government(MSIP) (NRF-2013R1A1A2021174).
References
1.

2.

Ik Sang Shin, et al. "Conveyor visual tracking using
robot vision." Proceedings of 2006 Florida Conference
on Recent Advances in Robotics, (2006) 1-5.
Derek Bradley, Gerhard Roth. "Adaptive thresholding
using the integral image." Journal of graphics, gpu, andgame tools 12.2 (2007) 13-21.
© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan75

Self-identification of Mental State and Self-control through Indirect Biofeedback--- Indirect Representation and Placebo Effect --Madoka Takahara, Jilin Huang, Ivan Tanev, Katsunori Shimohara

Graduate School of Science and Engineering, Doshisha University,
1-3 Tatara-miyako-dani, Kyotanabe, Kyoto, 610-0321 Japan
E-mail:{takahara2012, jhuang2014, itanev, kshimoha}@sil.doshisha.ac.jp

Abstract
This paper describes a possible new scheme for a user with mental health problems to identify his/her own mental stateand control it. For that purpose, we propose an indirect biofeedback system which represents physiological information
with color and shape, and enables the user to grasp his/her inner state and to proactively change and control it throughmethods of breathing. Those methods facilitate the user to self-control the autonomic nervous system. Here, we discuss
indirect representation and placebo effect.
Keywords: Indirect biofeedback, Autonomic nervous system, Breathing, Placebo effect, Indirect representation.

1. Introduction

We would ultimately like to determine whether it ispossible for individuals to learn to easily andautomatically identify their own internal state and control
it through breathing.

The number of people with stress and mental problems
has been gradually increasing in Japan. However, theirsituation is often not improved by consultation withspecialists in psychiatry or psychosomatic medicine.1 We
hypothesize that consulting such specialists is not very
effective because it is merely a passive experience.2 We
believe that if the patient is instead asked to approach thesymptoms of their disease in a voluntary and proactive
way, the treatment is more likely to succeed. In order toelicit proactive behavior from the client, he or she must
become aware of their current mental condition. The
client can then act appropriately to maintain his/her selfcontrol. A device or mechanism is needed to externalize
the internal state of the client, while establishing a senseof unity between the external device and him/herself.
Here, we propose an indirect biofeedback system thathelps the client to be self-aware of his/her current mental
condition by monitoring a device with visual features thatvary according to the client’s heartbeat. That is, thedevice not only externalizes the mental state of the self
but also keeps a sense of unity with the self. We also
investigate how effectively clients could control theirmental condition through breathing techniques such as
abdominal and costal breathing.

2. Biofeedback for Identifying the Self and Selfcontrol
2.1. Concept
It has already been reported that individuals can, to someextent, control their autonomic nervous system (ANS)
using biofeedback. Specifically the report claims that
individuals can use biofeedback to produce usefulimprovements in their physiological functions, such as
decreases in anxiety symptoms and physical disorders.3
Biofeedback systems are generally used at medical
institutions and seen as medical treatments to be executed
under a medical doctor’s direct supervision. Acquired
physiological data are usually represented numerically
and/or as waveforms. Such systems are not designed to
be used by ordinary clients on a daily basis.
In the research reported here, we aim to provide a systemby which ordinary users can identify their physiologicalstate and exercise self-control on a daily basis. For that

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan76

Madoka Takahara, Jilin Huang, Ivan Tanev, Katsunori Shimohara.

purpose, we propose an indirect feedback enables users
to understand their internal state intuitively with a userfriendly representation of physiological data.
In addition, in this research, we employ breathing
techniques as a means of exercising self-control overinternal state, since this is the only known means bywhich individuals can control their ANS.

Stress
Heart
beat

Wi-Fi

Visualization

Biofeedback

Representation of stress stateFig. 1. System configuration

2.2. Acquisition of information on balance ofautonomic nervous system from heartbeat
fluctuation

Physiological information is acquired through a heartbeat
sensor pasted on a user’s chest, and heartbeat fluctuations
are used as the basis for generating a biofeedback signal.
To generate this signal, heartbeat fluctuation is analyzed.
A measure of the balance of the SNS and PNS within the
ANS is calculated and displayed to the user as changes in
the color and shape of a circle. Fig. 2 shows an exampleof the indirect biofeedback representation which we
propose here.

In this research, we estimate the balance of the autonomic
nervous system (ANS) by analyzing the frequency ofheartbeat fluctuation. Heartbeat fluctuation can bemeasured simply and non-invasively, and a frequency
analysis of heartbeat fluctuation gives us an index ofstress manifesting itself by way of the influence of the
ANS on the heart. It is well known that heartbeat can bechanged by regulatory control by the nervous andendocrine systems, as well as by physical position andmovement. ANS works autonomously and regulates
itself automatically. Unlike motor nerves, we cannot
control it intentionally. The ANS consists of thesympathetic nervous system (SNS) and theparasympathetic nervous system (PNS). The SNS mainly
activates and tenses the body; for example, when we havesweaty palms and a racing heart. The activation of the
SNS constricts blood vessels and increases heart rate. As
a result, blood pressure goes up, and blood flow toperipheral areas is increased. On the other hand, the PNS
activates the internal organs, and puts the body into a
resting state.
This research is focused on the proposition that we cankeep our body and mind in good condition byintentionally controlling the ANS and by that means
recover its proper balance. The relationship between the
ANS and heartbeat fluctuation is as follows: The low frequency (LF) component of heartbeat
fluctuation is observed when SNS and PNS are
activated The high frequency (HF) component of heartbeat
fluctuation is observed when the PNS is more active
than the SNS, and this state is reflected in breath
fluctuation.

Before experiment
Green: 67.38%
Red: 25.61%
Blue: 7.01%
Switching times: 2

Fig. 2. Example of Indirect Biofeedback Representation

We designed the indirect biofeedback display as follows: Feedback was provided via a circular display that canassume one of three colors. Red was used to indicate
active situations with SNS superiority. Green showsmood-stabilizing situations. Blue shows depressive
situations with PNS superiority
 The shape of the display can vary from a circle to aflattened ellipse, depending on the rate of switchingfrom a SNS to a PNS dominated condition. This isinferred from heart rate fluctuation.
 If the switching rate is high, the user’s mental
condition is not stable. At such times the display
becomes more strongly elliptical.
3.

Experiments and Results

3.1. Self-identification of mental state and selfcontrol: indirect biofeedback
We conducted 4 different types of experiments withhuman participants by the proposed system. In eachexperiment, there were 2 different loads of stress and 2
different ways of breathing with 16 study participants.
The results indicate that study participants could properly
control their inner states after the loads of stress, since
they are healthy. 4

2.3. Indirect biofeedback
Fig. 1 shows the configuration of the proposed systemin this research.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan77

Self-identification of Mental State

According to the results of the subjective evaluation(via the questionnaire), some participants said they wereable to relax by using the proposed biofeedback system.
On the other hand, the results of objective evaluation
indicate that there were no significant differences in the
inner state of the participants before and after theexperiments.
In this study, objective experimental results did notindicate an improvement in the study participants’physiological condition due to the use of biofeedback.
However, the participants reported that they felt morerelaxed. We think the discrepancy between the subjective
and objective results indicates that the proposed systemcauses a placebo effect. Here we define a placebo effectas reporting feeling relaxed regardless of the actual
objective experimental results. And some of theparticipants wanted to see the balance of physiologicalinformation displayed as a graph rather than a circle.

(%)

*

20

Real
biofeedback

15
10

Fake
biofeedback

50*: P=0.027

Fig. 3. T-test Results of Placebo Effect(i.e. Increase of the good balance of ANS)As shown in Fig.3, the real biofeedback was significantly
better at producing a balanced situation in ANS than was
the fake biofeedback. Some participants doubted the
behavior of the fake biofeedback readout because itchanged even when they were not feeling anything. The
participants felt that the real biofeedback reflected theirmental states more closely.
3.3. Comparison
biofeedback

3.2. Effects of fake biofeedback: Placebo effect3.2.1. Experiments

withexisting

methodsof

3.3.1. Experiments

We conducted an additional experiment in which theparticipants evaluated both real and fake biofeedback. In
the fake biofeedback, the user evaluated the data
provided by another healthy participant.
We designed this experiment as follows. The
participant should use the following 2 kinds of systems;*1: Real biofeedback system*2: Fake biofeedback system
In the real feedback system, the participant gets his/herown biological information. On the hand, in the fakefeedback system, the participant gets another healthy
participant’s biological information as the feedback. The
participant is asked to use abdominal breathing as thestress control techniques.

We additionally conducted an experiment comparing the
proposed indirect biofeedback display with aconventional direct biofeedback display with waveform
representation.
We designed this experiment as follows. The
participant should use the following 2 kinds of systems;*1: Indirect feedback system*2: Direct feedback system
In the indirect feedback system, the participants get theirown biological information as a form of circular
representation proposed in this study. On the other hand,in the direct feedback system, the participants get theirown
biologicalinformation
withwaveform
representation. The participant is asked to use abdominal
breathing as a stress control technique.

3.2.2. Subjective evaluation
In a subjective evaluation, 17 out of 23 participants
judged the real biofeedback to reflect their mental state
better than the fake biofeedback.

3.3.2. Subjective evaluation
Twenty out of 23 study participants reported, in a
subjective evaluation, that the indirect biofeedback
display was better than the direct one.

3.2.3. Objective evaluation
As an objective evaluation, after the participants selfcontrolled using both real and fake biofeedback, we
compared the effectiveness of the real and fakebiofeedback in increasing of the green area that
represents the balanced situation between SNS and PNS.
The result is illustrated in Fig. 3.

3.2.3. Objective evaluation
As an objective evaluation, we compared the ability ofparticipants using the circle image and a conventional
waveform display to achieve a good balance of their ANS.
The result is illustrated in Fig. 4, which shows that theindirect feedback system was much more effective than

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan78

Madoka Takahara, Jilin Huang, Ivan Tanev, Katsunori Shimohara.

user’s physiological state for the purpose of allowing theuser to self-control their inner state.
The indirect biofeedback, which we have introduced
here allows the user to keep a sense of unity between adevice that externalizes the internal state of the self, andthe user him/herself. For that purpose, we designed thesystem so that the circle represents the user him/herself.
We evaluated the measured data as well as thequestionnaire results carried out after the experiments.
The results indicate that study participants could properly
control their inner states after the loads of stress, since
they are healthy.
Moreover, we conducted the additional two
experiments on the Placebo effect and on representationof biological information. In the experiment on thePlacebo effect, when a participant has the fakeinformation, we couldn’t confirm any Placebo effect,
however, when the participant has the real information,
we could confirm the Placebo effect.
In the comparative experiment on representation of
biological information, we could confirm that the indirect
biofeedback is much better than the direct one.

the direct one. The result (p=0.04) was significant by the
T-test with p<0.05.
(%)

20
10

*

Indireck
biofeedback
system0

Directbiofeedback
system-10*：P=0.04

Fig. 4. T-test Results of the Experiment about Representation(i.e. Increase of the good balance of ANS)

Some possible reasons for this result are:
Direct Biofeedback
1) It might be difficult for the subjects to grasp theirinner state during the given time.
2) The waveform image sometimes seemed to cause
negative reactions response in the subjects throughstraight expressions.
3) The direct feedback causes the participants to havestress with certain amount of tense or irritated andthereby reduce their level of ANS balance.
Indirect Biofeedback
1) The subjects can easily grasp their inner statethrough expressions easy to understand.
2) The circular image and easily understandable
expressions do not cause negative reactions which
may occur in Direct Biofeedback to theparticipants.
Thus, we conclude that users can control themselvesmore easily using the proposed indirect biofeedback thanthe direct biofeedback common to existing biofeedback
systems.

References
1. About medical plan (mental disease), (2015.Available:)
http://www.mhlw.go.jp/seisakunitsuite/bunya/kenkou_iry
ou/iryou/iryou_keikaku/dl/shiryou_a-3.pdf
2. Keiji Hoshikawa, Religions and Others --- Study on
Language and Reality---,( SHUNJUSHA Tokyo, 2011)
3. Taishi Aoyama and Mieko Osuga, Breath inducing system
for relaxation,(Japanese Society of Biofeedback
Research,33(2006), pp.61-62.
4. Madoka Takahara el at, Self-identification of mentality
and self-control through indirect biofeedback, Int. Conf.
on Electronics and Software Science, (2015), pp.377-380.

4. Discussion
In the experiment on the Placebo effect, most of theparticipants doubted the fake feedback image. Even if theparticipants see the green area increase on the display, it
is usually hard for them to believe that the data reflect
their own mental state.
From the comparative experiment on representation of
feedback information, the participants preferred thecircular representation proposed here to the previous
meansofrepresentation,
namely,waveform
representation.
5. Conclusion
In this research, we developed an indirect biofeedback
system. This system externalizes and objectifies the© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan79

Construction of a micro sense of force feedback and vision for micro-objects: development of a
haptic device
Yusei Ishii

Graduate School of Computer Scienceand System Engineering
Kyushu Institute of Technology
680-4, Iizuka City, Fukuoka, Japan, 820-8502
ishii@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 was to develop a combined sense system that uses both force feedback and visualfeedback to determine the shape of the microscopic features of a sample. We constructed a haptic device that gives
a sense of that force to the operator when touching the sample. We recreated touch of salmon roe as one of minutesamples this time.
Keywords: force feedback, haptic interface, simulation.

manipulator. For this fundamental research, a system
was created to assess the reaction force when a minutesample was touched. A cantilever was used to touch thesample, and the reaction force was obtained from thedegree to which the sample bent. In addition, we used ahaptic device and amplified the force feedback from aminute sample of a virtual object.

1. Introduction
Technologies that can accurately perform minutework are now being sought for medical treatment and in
the field of manufacturing semiconductors. Such minutework is improved by using micromanipulators, but theiroperation is difficult because the operator has no senseof force; he or she relies only on sight through a
microscope. As a result, a person skilled in the use ofthis technology is needed for all minute work. The
efficiency of minute work would be improved if theoperator were 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 was to develop asystem using not only the sense of sight through a
microscope but also a sense of force from the

2. System Structure
The system structure is shown in Fig. 1. This system
consists of a microscope with an automatic x-y stage, apiezo stage, a feedback stage controller to control the xy stage, a piezo stage controller, a haptic device fortransmitting force feedback, a cantilever, and a PC via
which the user can control and operate thesecomponents. The sample was fixed on the x-y stage by
an injector and a holding pipette. When the cantilever,© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan80

Yusei Ishii, Eiji Hayashi

which was fixed to the piezo stage, touched the sample,
the operator could maintain the cantilever’s position byobtaining the value of the reaction force through theinterface. The resolution of the piezo stage is 1 nm.
Fig. 2 provides a diagram of the haptic device. It
consists primarily of a rotor, a laser, and a positionsensitive device (PSD). We installed a coil on the rotor
withapolaritymagnet,which
generated
electromagnetic induction by an electric current and amagnetic force. The angle of the rotor can be measured
Fig.3. Structure of the haptic device.
by the laser and the PSD. The rotor was able to follow
any input waveform.

The actuator, whose actions are governed by the PD
control, is operated through a digital differential
calculus device. A transfer function for the quadratic
function system shown in Fig. 4 is provided for theactuator servo system. The role of each parameter of thecontrol system is to adjust the total offset to a master in
Gi/Gif, to regulate the item viscosity/resonance point in
Gp/Gv, and to regulate the total gain in Gm.
Table 1 provides a list of the control parameters of theservomechanism system.

Fig.1. Photograph of the system structure.

Fig.4.Block diagram of the servomechanism system.
3. Measuring the reaction force
The reaction force was used to calculate the force
applied by the minute object. In this experiment, wetouched the minute object with the cantilever, and thereaction force was obtained based on the degree of bendof the cantilever. The environment of the experiment isshown in Figs. 5. Based on this experiment, we wereable to determine the reaction force applied by theminute object.

Fig.2. Diagram of the haptic device.
The actuator is controlled by a servomechanism on
the actuator. The system driving the actuator therefore
consisted of four actuators: a microcomputer, an
inputting AD/DA port, an outputting microcomputer,and a PC outputting order value. The system controls
the actuator during each part of the process. Fig. 3
shows the structure of the haptic device.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan81

Construction of a micro

applied at a mass point, the simulation 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 aspring on a bitmap (Fig. 9). In addition, a sample can beseen from various viewpoints, and the deformation of
the sample, which is impossible to observe by
microscope, can be checked. The shape of this object
can be either a cube or a sphere, and any point may be
selected as a fixed point or an operating point.

Fig.5. Environment of the experiment.

Fig.6. The cantilever moving
Left of the figure 6 shows the cantilever touching the
tip of the holding pipette. Right of the figure 6 showsthe experiment that measures the reaction force of thedowny hair. The image-processing speed of thecantilever was improved by the tracking process. The
bend of the cantilever is assumed to be linear-elastic sothat Hooke’s law may be applied. The restoring force, F,of the bend of the cantilever is given by(1)
F  kxwhere x is the compression distance from theequilibrium position, and k is the spring constant.

Fig.7.Simulator.

4. Deforming the sample in simulation

Fig.8.Arrangement of masspointsFig.9. 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. Figure 7 shows the graphical userinterface (GUI) of the simulator. A graphic tool was
created using OpenGL to draw the object and to choose
the shape of the sample, for instance, a cube or sphere.
The dynamic model of the sample consisted of a springmass array of mass points in both the vertical andhorizontal directions. An example of the arrangement ofmass points is shown in Fig. 8. When a force was

5. Characteristic measurements and areappearance experiment for an object
Dynamic characteristics of the object were measured
and then recreated using the haptic device. The object
used this time was salmon roe. We measured responseof salmon roe and showed the displacement of its
surface a figure. Fig. 10 shows the response of thesalmon roe.
Salmon roe is 6 millimeters in diameter and we cannot
precisely feel the sense of touch. Hence values in the© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan82

Yusei Ishii, Eiji Hayashi

6. Conclusion

figure are multiplied by 68 so that human can feel thesense of salmon roe’s touch. Fig. 11 shows that
response of the salmon roe and haptic device. Table1
shows the frequency, constant of spring, and damping
coefficient in each ectopic focus.
In this experiment, there was a tendency for a touchfeeling close to that of real and big salmon roe to becreated when characteristic was recreated.

0.0

0.2

Time [s]
0.4
0.6

0.8

In the present study the characteristics of salmon roe
were measured and recreated, the evaluations also werecarried out with regard to response and sense of force
using a haptic device. The characteristics of various
objects will be measured in the future, and thecharacteristics should recreate.
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.

1.0

0.1
Displacement [mm]0.0

7. Acknowledgement-0.1This research was partially supported by the Ministryof Education, Science, Sports and Culture, Grant-in-Aid
for Scientific Research, 2012.

-0.2
-0.3
Salmon roe

-0.4
-0.5

8. References

-0.6

1.

Fig.10. Time response of Salmon roe

Table1. Haptic device parameter of dynamic characteristic
Displacement
[mm]
0～6
6～35
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

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
Institute of Technology. Proceedings of the 15th
International Symposium on Artificial Life and Robotics,
Beppu, Oita, Japan, 2012

1.0

Salmon roe (seventy
times)(68 times)
HapticFig.11. Time response of Salmon roe and Haptic device© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan83

Dynamic Behavior Selection Model based on Emotional States for Conbe-I robot
Wisanu Jitviriya and Jiraphan InthiamComputer Science and Systems Engineering, Kyushu Institute of Technology.
680-4, Kawazu, Iizuka, Fukuoka, 820-8502, Japan.
wisanu@mmcs.mse.kyutech.ac.jp, jiraphan@mmcs.mse.kyutech.ac.jp
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
Currently, the rapid development of non-industrial robots that are designed with artificial intelligence (AI) methods
to improve the robotics system is to have them imitate human thinking and behavior. Therefore, our works havefocused on studying and investigating the application of brain-inspired technology for developing the conscious
behavior robot (Conbe-I). We created the hierarchical structure model, which is called “Consciousness-BasedArchitecture: CBA” module, but it has limitation in managing and selecting the behavior that only depends on theincrease and decrease of the motivation levels. Consequently, in this paper, we would like to introduce the dynamic
behavior selection model based on emotional states, which develops by Self-organizing map learning and Markov
model in order to define the relationship between the behavioral selection and emotional expression model. We
confirm the effectiveness of the proposed system with the experimental results.
Keywords: Behavior selection model, Self-organizing map (SOM) learning, Markovian model.

But the conventional (CBA) model has limitation in
managing and selecting the behavior that only depends
on the increase and decrease of the motivation levels. For
a truly effective of the robotic system, the robots should
not only perform the behavior, but also express theappropriate emotional state. Thus, the general objectiveof this research, we would like to introduce the proposedsystem, which can autonomously select the suitablebehavior based on transition of emotional states.
Our paper is organized as follows: Chapter 2 explains
the autonomous behavior selection system which has
three processes. In Chapter 3, our results are shown how
our robot can choose behavior depending on its
emotional states. In the final section we conclude thepaper and describe our future works.

1. Introduction
Nowadays, the focus of research of service robots isa development of the robots that are able to express
emotions, imitate human thinking and change their
behavior based on variety surrounding environment.
However, development of these robots that are still ahuge challenge when they have interaction with humans.
Therefore, Hayashi laboratory’s works [1]-[2] havefocused on studying and investigating the application ofbrain-inspired technology by developing the robots witha hierarchical structure module based on Tran Duc
Thoa’s research [3]. This model is called“Consciousness-Bead Architecture (CBA)” model which
relates between consciousness field and behavior group.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan84

Wisanu Jitviriya, Jiraphan Inthiam, Eiji Hayashi

variable for calculating the robot’s motivation. The
motivation waveform can estimate by the 2nd ordersystem of differential equation as expressed by Eq. (3).

2. Autonomous Behavior Selection System
In this paper, we would like to propose the behavior
selection model which autonomously selects andperforms the suitable action based on variation ofemotional expression. The proposed system has threemajor processes as described below.

Motivation ''(t )  2n Motivation '(t )n2 Motivation(t )  n2Total _ dopamine(t )2.1. Computation dopamine and robot’s
motivation(3)

2.2. Classification emotions and behaviors basedon Self-organizing map (SOM) learning
After recognition process, the robot’s motivation andthe visual information are used to define as the input
parameters of the Self-Organizing Map (SOM) learningin order to generate the behavior and emotion maps. The
robot is set the nine behaviors and six emotions. To
classify and select the most appropriate behavior andemotion correspond to the surrounding environment. The
results of the SOM learning the will be verified.

2.1.1. Determination of occurring dopamine model
The Conbe-I robot uses only the acquired images
from the CCD camera and web camera for operating theactions and emotional expression in recognition process.
The robot is not usable the other sensors (tactile sensor or
laser range finder sensor). Thus, the robot can evaluate
the rough position of the target object by only using theimage processing. The system may simplify the acquired
images by dividing into four color groups: green, blue,
flesh-color and the other colors. With the simplification,
the sequential recognition process has the reduced image
size, the changed color model of image from RGB
method to HSV method, the labeling process based on
the color of visual information and the calculation of theposition of the target object. Then, the visual information(the color, shape, size, labeling and distance of the target
object) is used to calculate and form the naturally
occurring dopamine waveform [4]. The computation of
the typical pattern of dopamine model is explained. The
dopamine waveform can be evaluated and divided intorise and fall portions. The rising part and falling part are
created by the 2nd order and 1st system equations as shownin Eq. (1) and Eq. (2) respectively, where the input
variable x (t ) is an accelerator of dopamine, the output
variable y (t ) is a naturally dopamine,  is damping
factor,  n is the natural frequency and Tc is time
constant.

y ''(t )  2n y '(t )  n2 y(t )  n2 x(t )(1)

Tc y '(t )  y(t )  x(t )(2)

2.2.1. SOM algorithm
The Self-Organizing Map is a neural networkarchitecture which is proposed by T. Kohonen [5]. It is
well known as an effective pattern classifier that is anunsupervised learning method. The SOM method canprovide topologically preserved mapping from highdimensional input data to a low - dimensional array basedon competitive learning. The procedure of the basic SOM
learning can explain by the following steps:
Step 1: Each node must to be initialized with weight value
based on setting the small standardized random value.
Step 2: Calculate and find the minimum Euclidean
distance that is called “winner node or best matching unit
(BMU)”.
Step 3: After finding the BMU, the weight vectors of the
BMU, and its topological neighbors are moved closer tothe input vector. The new weight vectors are updated by
Eq. (4), where t is the training step index is,  (t ) is thelearning rate, and h j ,c (t ) defines as the neighborhood
kernel function around the winner nodes.

w j (t  1)  w j (t )   (t )h j ,c (t )  x  w j (t ) 



(4)

2.1.2. Calculation of the robot’s motivation
Step 4: The training step increases to t+1. Steps 2 to 4 arerepeated with decreasing the width of the neighborhood
function.

Since the computation of the naturally occurring
dopamine as described above, the sum of positive andnegative dopamine values that is used as the input© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan85

Dynamic Behavior Selection Model

2.2.2. Formation of behavior and emotion maps
In order to verify the SOM learning, the motivation
value, the slope of robot’s motivation, the pixels of colors(green, blue and flesh colors) are defined as the input data
for testing. For example, if the robot recognizes the green
object, which is near the robot’s hand. The testing data
are the motivation flag = 1 (positive level), the motivation
value = 0.45, the slope of robot’s motivation = 0.003, themotivation waveform is a positive slope = 1, the green
pixels = 0.6, the blue pixels = 0.02 and the flesh-color
pixels = 0.2. During the SOM learning process, we have
not only considering the changing position of the winner
nodes in behavior and emotion maps, but also observe theevolution of the form of both maps as shown in Fig.1 andFig.2. The blue cells showed on behavior and emotion
maps that were the winner nodes of each behavior andemotion pattern. The green cells represented as the
response action according the testing input data.
Therefore, in this situation, the robot should perform theaction as “Approach”, and express the emotion as“Happiness” at the same time. Because, the green object
is a favorite object, thus it tries to possess the target object.

Look around (B)Avoid (B)Be alert (B)Interest (G)Approach (G)Be alert (F)Look around (G)Catch (G)Interest (F)Fig.1 Behavior map
1

5

Happiness

Hope

Cell Number (y)1015Sadness
Neutral20

2.3. Behavior selection based on Emotional States
25

The last process is the behavioral-emotional selection
process, which is modeled based on a Markov model [6].
The Markov model is memoryless property that means
the next state will be changed based on a transition ruleand depends only on the current state.
In this study, the Emotional Markovian model thatconsists of the six nodes representing the basic emotion
states. The arrows are the probabilities of getting out of
the states. The next emotional state can determine by Eq.
(5), where, A is the emotional state transition matrix.

Emotionk 1  A  Emotionk

Fear

Disgust
30
1

5

10

15

20

25

30

Cell Number (x)Fig.2 Emotion map

Behaviork 1  C  Emotionk 1(6)

All element values in the behavioral state transition
matrix (cbehavior/emotion) can be modified later based on the
behavioral and affective factors.

(5)

3.

Experimental results

The intelligent behavioral-emotional selection system
was tested and evaluated, that confirmed its effectivenessin the surrounding environment.
The robot was verified its behavior and emotional
expression when it recognized the favorite object (green
object) and disliked object (blue object) in a realistic
environment. Throughout the task period (T1-T8), therobot was able to act the appropriate behaviors andemotional expressions as illustrated in Fig.3. After the

All elements of the emotional state transition matrix
can be updated by the affective factors, which calculates
by averaging the sum of the weights of individual groupof behavior and emotional patterns in SOM learning
process. In addition, the behavior selection module isbased on its emotional expression, thus the behavior state
will be selected by the emotional state that can estimate
with Eq. (6), here, C denotes as the behavioral statetransition matrix.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan86

Wisanu Jitviriya, Jiraphan Inthiam, Eiji Hayashi“Hope” emotion, when it did not see the disliked object
or the robot’s motivation increased to the positive level.
4. Conclusions
In this paper, the overview of the proposed systemthat consists of the recognition process, cognitive process
and behavioral-emotional selection process. The system
is executed by the Conbe-I robot in the realistic
environment. All experimental results confirm theeffectiveness of the proposed system such as the system
can classify and generate the behavior and emotion maps
based on the SOM learning method in the cognitive
process, and the robot can suppress and change all
emotional states based on the Markov model. In addition
the robot can perform its behavior based on updating theemotional transition state. For the future work, we would
like to combine the other neurotransmitters(noradrenaline and serotonin models) into the dopamine
model in order to generate the dynamic emotional
expression model.
References
1. N. Goto and E. Hayashi, “Design of Robotic Behavior that
imitates animal consciousness,” Journal of Artificial Life
Robotics, vol. 12, pp. 97-101, 2008.
2. 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.
3. T. D. Thao, D. J. Herman, and D.V. Morano,
Phenomenology and dialectical materialism (Boston
Studies in the Philosophy of Science), D. Reidel Pub.,
1986
4. H. Kimura, “A trial to analyze the effect of an atypicalantipsychotic medicine, risperidone on the release of
dopamine in the central nervous system,” J. of Aichi
Medical University Association, vol. 33, no. 1, pp. 21-27,
2005.
5. T. Kohonen, “Self-organized formation of topologicallycorrect feature map,” J. Biological Cybernetics, vol. 43, pp.
56-69, 1982.
6. K. Koiji, and B. Martin, “Towards an emotion core basedon a Hidden Markov Model,” Proc. of 13th IEEE
International Workshop on Robot and Human Interactive
Communication, Sep. 20-22, Kurashiki, Okayama, Japan,
pp. 119-124, 2004.

Fig.3 Behavioral and emotional selections at period T1-T8

robot was started. The robot selected “Look around” and“Interest (Flesh)” actions when it recognized the fleshcolor. At T1, the robot was able to capture the favorite
object, thus the robot’s motivation increased and therobot performed the behavior between “Look around”
and “Interest”, the robot also express emotion as“Neutral”. From T2-T3, the robot performed the actions
as “Interest” and “Approach” meanwhile it expressed“Hope” and “Happiness” emotions. Then, the blue object
was presented in order to decrease the robot’s motivation.
At T5, the motivation model achieved in the negative
level, during periods T5-T6, the robot felt unhappiness
when it confronted the disliked object, the robot
expressed “Sadness” and “Fear” emotions meanwhile itperformed the action as “Look around” and “Alertness”.
After that (T6-T7), the blue object was moved closer tothe robot, it felt dislike this situation, thus it performed“Avoidance” behavior and express “Disgust” emotion.
Moreover, during period T6-T8, the robot expressed© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

87

Consideration on a Crawler Robot with Six Legs
Toyomi Fujita and Tiga Sasaki

Department of Electronics and Intelligent Systems, Tohoku Institute of Technology,
Sendai, 982–8577, Japan
E-mail: t-fujita@tohtech.ac.jp
www.eis.tohtech.ac.jp/study/labs/fujita/FUJITALAB

Abstract
In this study, we consider development of a crawler-type mobile robot which is equipped with six legs at its body.
This type of robot may have possibilities of both high mobility on rough terrain and working ability with handlingsuch as carrying an object and removing small obstacles in its movement by using the legs as manipulation arms.
This paper presents mechanisms and characteristics of this type of robot and some possible hybrid motions in whichcrawlers and legs are used. A running motion over a large gap by this type of robot is also considered as a typicalhybrid motion with crawlers and legs. Necessary joint-torques of the legs in the motion are also analyzed in acomputer simulation.
Keywords: Crawler-type mobile robot, legged robot, hybrid motion, handling task with locomotion, running motion
over a gap

crawler hybrid system, even though it has an advanced
locomotion ability4, the mechanism in which a leg itself
drives as a crawler5, 6 may not be suitable for our aim
with respect to capability of handling task. A crawlertype robot with four legs presented by Hirose et al.7
have not been considered on handling tasks very muchbecause movement mechanisms and how to obtain
information from the surrounding environment havebeen mainly focused on. On the other hand, some legcrawler hybrid robots are considered on some handlingwork. For instance, HELIOS IX8 is equipped with amanipulator for rescue operations. However, the type oftask and the size of target object are restricted becauseof its single arm.
We thus consider another type of hybrid mobile
robot with crawlers and six legs which are used as
manipulators. Basically, in order to achieve stable static
walking, six or more legs are necessary.9 Even though
the increase of the number of legs brings the decrease ofmobility due to heavy weight and energy consumption,

1. Introduction
With the recent spate of disasters in the world, the needfor robots which can perform rescue activities in adisaster area has been growing. Such rescue-purpose
robots should not only move to obtain information but
also complete handling tasks by themselves in theirworking area. Therefore, the robot that has a hybridmechanism with crawlers and robotic arms is useful
because it can perform tasks for manipulation. In fact,some literatures describe that hybrid systems havecapabilities of not only locomotion but also
performance of work such as handling task.1
However, conventional types of robot which has the
hybrid mechanism have not been satisfied with both of
movement and working ability very much. For example,
in leg-wheel hybrid systems, many types such as RollerWalker2 and ATHLETE3 have a wheel at the end ofeach leg, so these are not suitable due to limitation of
the handling task. In case of the mobile robots with leg-

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan88

Toyomi Fujita, Taiga Sasaki

we consider the robot which has six legs is appropriate
to the leg-crawler hybrid mobile robot with respect toboth locomotion and working abilities.
This paper describes the structure and mechanism ofdevised crawler-type mobile robot which has six legs.
Some possible hybrid motions by crawlers and legs are
considered as an advantage of this type of robot. A
computer simulation is also performed based on statics
to analyze necessary joint-torques of the legs in therunning motion over a gap.
2. Crawler-type Mobile Robot with Six Legs
Fig. 1. Overview of crawler-type mobile robot with six legs.

2.1. Overview
driving pulleys for the left and right crawlers should belocated diagonally to balance the body such that theright crawler has a driving pulley at the rear side and theleft crawler has it at the front side. Symmetric locationof the motors and encoders will enable the robot to run
straight stably.

We have devised a crawler-type mobile robot which
has six legs. Fig. 1 shows an overview of the robot.
The robot consists of two crawlers and six legs. Threelegs are mounted on the left or right side of robot body.
Each leg has 4-DOF mechanism and can be used as amanipulator.
This mechanism enables the robot to have highmobility such as running over a large gap by using front
and rear legs to support the robot body appropriately,
climbing a steep slope by assisting the crawler
movement using legs, and so on. In addition to the highmobility, this type of robot is able to perform some
useful tasks at a disaster area by acting the legs as
manipulators. For example, the robot will be able toremove a small obstacle such as stone on the pathway of
the robot in its movement using one leg, carry a large
object to destination using multiple legs, get into a large
rubble with supporting or removing it using multiple
legs and retrieve a target object using the other legs, andso on. Fig. 2 shows a typical example of motion
indicating an advantage of the robot; running over alarge gap, which normal type of robot is not able toperform, by four legs with carrying a box by two legs.

2.3. Leg Mechanisms
Fig. 3 shows the mechanism of devised leg. The leg has
four joints: two yaw joints (J1, J4) and two roll joints (J2,
J3). This mechanism enables the robot to perform awalking motion using each leg as 3-DOF mechanism by
fixing the angle of J4. On the other hand, the joint J4
will be used when the robot performs a manipulation
task such as carrying an object. Servo motors will beable to be used as joint actuators. These can be selectedbased on a statics analysis of necessary torques inhybrid locomotion such as running over a gap, which
will be presented later.
3. Consideration of Hybrid Running Over Gap
We consider a running over a long gap as a hybrid
motion by crawlers and legs with carrying a box by the
robot as shown in Fig. 2. If the robot doesn’t have nor
use legs, the robot will fall in the gap when the center ofgravity of main body goes out of the edge of the gap. In
this case, however, the presented crawler-type robot isable to use front and rear legs so that the robot prevents
falling. Specifically, the robot can ground the tips offront legs on the opposite edge before the center ofgravity of the robot come to just above the edge of thegap, continue movement by the crawlers with the

2.2. Crawler Mechanisms
Two crawlers are attached at both sides of the robot
body. Each crawler can be made by a belt with
attachments and pulleys. One pulley which is at themost front or rear side can acts as a driving pulley by
connecting a DC motor. The other pulleys then act as
idling pulley. A rotary encoder is also attached to apulley for the control of crawler movement. The two© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan89

Consideration on a Crawler

Fig. 2. Running over a large gap with carrying a box bycrawler-type mobile robot with six legs.

Fig. 3. Mechanism of a leg.

Fig. 4. Models in the motion of running over a gap (a): front
leg auxiliary supporting (b): front and rear legs supporting (c):rear leg auxiliary supporting.

support of the front tips, touch the tips of rear legs at alittle behind the edge before the robot body completely
comes above the gap, then get across the gap by
supporting the body by four legs. When the front part ofbody touches the ground at the opposite side of the edge,
the robot can release the tips of front legs from theground, then continue running by the crawlers with thesupport of the rear tips. When the center of gravity ofthe body comes above the opposite edge of the gap, therobot can release the rear tips and return to normal
locomotion using crawlers only. In this motion, therobot doesn’t have to use the fourth joint J4 for each leg
so that it acts as 3 DOF.
Let us suppose that the height of ground between the
gap is same and the robot moves slowly. The models inthis motion in the side view are shown in Fig. 4. We
can divide the motion into three states: (a) front leg
auxiliary supporting, (b) front and rear legs supporting,and (c) rear leg auxiliary supporting.
In the state of front leg auxiliary supporting, thedistance between the edge of the gap and the center ofgravity of the robot, L, meets 0 ≤ 𝐿 ≤ 𝐵ℎ where 𝐵ℎ is
the length between the center of gravity and the rear endof the body of the robot. In this state, the balance of
moment around the edge point of the gap gives
𝐿𝑚𝑔 = 𝐿1 𝐹𝑧𝑓of the gap and the contact point of the front tip, and Fzf
is the vertical component of the force to be generated at
the front tip, so reaction force −Fzf are acted to thecontacting point of the tip.
In the state of front and rear legs supporting,because of supporting by four legs, the balances ofmoment around the both contact points of front and reartips should be considered. The balance of moment
around the rear tip gives
𝐹𝑧𝑓 (𝐿𝑟 + 𝐿𝑓 ) = 𝐿𝑏 𝑚𝑔,(2)

and the balance of moment around the front tip gives
𝐹𝑧𝑟 (𝐿𝑟 + 𝐿𝑓 ) = 𝐿𝑓 𝑚𝑔(3)

where Lf and Lr are the horizontal distances between thecenter of gravity of the robot and the front and rearcontact points respectively, and Fzr is the verticalcomponent of the force at the rear tip.
In the state of rear leg auxiliary supporting, it meets(𝑊 − 𝐵ℎ ) ≤ 𝐿 ≤ 𝑊 where 𝑊 is the horizontal distanceof the gap. In this state, the balance of moment aroundthe opposite edge point of the gap gives(𝑊 − 𝐿)𝑚𝑔 = 𝐿2 𝐹𝑧𝑟

(1)

(4)

where Fzr is the vertical component of the force at therear tip and L2 is the horizontal distance between thefront edge of the gap and the contact point of the rear tip.

where m is the mass of the robot, 𝑔 is the acceleration ofgravity, L1 is the horizontal distance between the edge

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan90

Toyomi Fujita, Taiga Sasaki

to perform variety of tasks as well as high mobility. For
future work, we plan to develop this type of robot andperform hybrid locomotion and working motions withlegs and crawlers.
Acknowledgements
This work was supported by the Ring Ring Project of
the JKA Foundation.
References
Fig. 5. Joint torques for the front and rear legs in the motionof running over a large gap.

1. Bruzzone, L., and Quaglia, G., 2012. “Review article:
locomotion systems for ground mobile robots in unstructured environments”. Mechanical Sciences, 3(2), pp.
49–62.
2. Hirose, S., and Takeuchi, H., 1996. “Study on rollerwalk (basic characteristics and its control)”. In
Proceedings, 1996 IEEE International Conference on
Robotics and Automation, 1996, Vol. 4, pp. 3265–3270.
3. Wilcox, B. H., Litwin, T., Biesiadecki, J., Matthews, J.,
Heverly, M., Morrison, J., Townsend, J., Ahmad, N.,
Sirota, A., and Cooper, B., 2007. “Athlete: A cargohandling and manipulation robot for the moon”. Journalof Field Robotics, 24(5), pp. 421–434.
4. Gao, X., Li, K., and Gao, J., 2009. “A Mobile Robot
Platform with Double Angle-Changeable Tracks”.
Advanced Robotics, 23, pp. 1085–1102.
5. Michaud, F., Letourneau, D., Arsenault, M., Bergeron, Y.,
Cadrin, R., Gagnon, F., Legault, M.-A., Millette, M.,
Pare´, J.-F., Tremblay, M.-C., et al., 2005. “Multimodal
locomotion robotic platform using leg-track-wheel
articulations”. Autonomous Robots, 18(2), pp. 137–156.
6. Yokota, S., Kawabata, K., Blazevic, P., and Kobayashi,
H., 2006. “Control law for rough terrain robot with legtype crawler”. In Mechatronics and Automation,
Proceedings of the 2006 IEEE International Conference
on, IEEE, pp. 417–422.
7. Hirose, S., Hodoshima, R., Fukumura, Y., and Amano, H.,
2010.
“Development of Track-changeable Quadruped
Walking Robot TITAN X –Design of Leg Driving
Mechanism and Basic Experiment-”. In IEEE/RSJ
International Conference on Intelligent Robots andSystems (IROS), pp. 3340–3345.
8. Guarnieri, M., Takao, I., Debenest, P. C., Takita, K.,
Fukushima, E. F., and Hirose, S., 2008. “HELIOS IX
Tracked Vehicle for Urban Search and Rescue
Operations:Mechanical Design and First Tests”. In 2008
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and Systems, pp. 1612–1617.
9. Siegwart, R., Nourbakhsh, I. R., and Scaramuzza, D.,
2011. Introduction to autonomous mobile robots. MITpress.

From Eqs. (1)-(4), we can obtain necessary forces at
the tips of the front and rear legs, then computenecessary torques for the joints J1, J2, and J3 of each leg,τ = (τ1, τ2, τ3)T based on statics by𝛕 = 𝑱T 𝑭(5)

where J is Jacobian corresponding to the tip position ateach time, and F = (Fx, Fy, Fz, τx, τy, τz)T is a vector
which consists of x, y, z components of the force, Fx, Fy,
Fz, and moments around x, y, z axis, τx, τy, τz generated
at the tip position.
Fig. 5 shows the computational result of the joint
torques for the front and rear legs to L. In this case, we
supposed that the distance between the bases of front
and rear legs is 150 mm, the lengths of five links of eachleg shown in Fig. 3 are l1 = 45, l2 = 42, l3 = 150, l4 = 68,
and l5 = 95 mm. W = 312 mm was also assumed as the
distance of the gap. In the result, 0 ≤ L ≤ 90 shows thestate of front leg auxiliary supporting, 90 ≤ L ≤ 226
shows the state of front and rear legs supporting, and226 ≤ L ≤ 312 shows the state of rear leg auxiliary
supporting. For each state, Fzf or Fzr was applied to Fz,
and 0 was given the other components of F in Eq. (5).
The torque of the first joint is not shown because it wasalways zero.
From this result, we can select appropriate joint
motors which are enough for performing this motion;
their maximum torques should be equal to 4 Nm at least.
4. Conclusions
In this study, we have considered development of acrawler-type mobile robot which has six legs. Because
each leg can be used as a manipulator, the robot is able

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

91

The development of new products of pineapple cheesecakes
Sheu-Der Wu

Department of Food and Beverage Management , Cheng Shiu University, No. 840, Chengcing Road, Niaosong Dist.,
Kaohsiung City 83347, Taiwan (ROC)
E-mail: wsd8245@yahoo.com.tw
Hung-Wen Su

Department of Food and Beverage Management, Far East University
No.49, Zhonghua Rd., Xinshi Dist., Tainan City 74448, Taiwan (R.O.C.)

Abstract
In view of the product - pineapple cheesecakes newly developed for this study, the questionnaire answers proposedby consumers after sensory evaluations find the most popular flavor with 27% recipe proportion of pineapple chops
added. Furthermore, after pineapples are added into cheesecakes, a new flavor has emerged in cheesecakes yet
reduces the oily taste without sacrificing its original flavor and nutrient value. In the experiment of formal sensory
evaluation, genders, ages, occupations, incomes and purchase intention are served as factors to analyze consumers'
preference to pineapple cheesecakes. Research results after investigation finds the strongest acceptability is the
samples of pineapple cheesecakes added with 27% pineapple chops. The least acceptability happens to the samplesof pineapple cheesecakes added with 27% pineapple jams. After analysis, it is found the overall acceptability ishigh but the purchase intention is low revealing a higher preference to cheesecakes added with pineapple chops.
Keywords: Pineapple, Cheesecakes, Sensory evaluations..

1.

The science of Chinese Medicine proposes pineapples
are featured with sweet yet sour flavors. It can stopthirst and facilitate digestion after eating with its major
healing effect of deficient stomach secretion, frequent
thirst, indigestion, appetite loss and diarrhea [2].
Because pineapples are featured with aforesaid effect,affordable prices and easy availability, this study
focuses on the raw material of pineapples to develop
new products and also takes people's purchase intention
into consideration. The pineapples are further combinedwith the cheesecakes with a higher preference to explore
consumers' preference for this kind of product.

Introduction

Nowadays, because people prefer to chase after
innovative and novel products. Currently, there are few
products of pineapple cheesecakes available on themarkets. Because Kuanmiao, a town in southern Taiwan,
is rich in pineapples with its agricultural yields offamous Diamond Pineapples (Tainon Number 17)
throughout the year. Compared with the pineapples
originated from tropical regions, our pineapples raisedin the subtropical hilly regions are merited with theappropriate proportion of sweetness against acidity due
to the remarkable temperature differences between days
and nights in summer. Our pineapples combined withhighly distinctive sweetness and acidity can intertexture
a stunning flavor in mouths [1].

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan92

Sheu-Der Wu, Hung-Wen Su

2.

The Review of Scientific
Literatures

Pineapples (Ananas comosus) are a kind of evergreen
perennial trees with its English name of the pineapple
and the Chinese aliases like Buoluo and Huangli.
The Nutrients of Pineapples：
In view of the nutrients of pineapples, the pulps ofpineapples are abundant in nutrients. Every 100g of
pineapple pulp contain 52 calories, 87g of water, 0.9g ofprotein, 0.2g of fat, 11.6g of carbohydrate, 1.4g of fibers
and 0.3g of ash [3].
Pineapple pulps containable bromelain with the effectlike those of papain or pepsin available to decompose
protein and facilitate digestion. Fruits contain a large
volume of sugars (such as glucose, fructose, sucrose)and organic acids (Most ingredients are citric and others
like malic acid and tartaric acid, etc.) available toeliminate fatigue. There are also some inorganic
components such as iron, magnesium, potassium,
sodium, calcium, phosphorus. Furthermore, pineapple
fruits can also exert the effect of relieving heat, diureses,
detoxification and stopping thirst [3] [4].
3.

Fig. 1. Pineapple cheesecakes are combined with different content
proportions for experimental tests.

Fig. 2. Illustrates the finished products of pineapple cheesecakes
made from different content proportions prepared for formal tests.

respondents to answer. In the 7-point scoring evaluation,
7 points mean the strongest flavor or preference and 1
point means the weakest flavor or preference. In viewof the tests of sensory evaluation, "1" means "dislike
very much"; "4" means "accept reluctantly"; "7" means"like very much". Meanwhile, to understand if the basic
information of respondents will affect their own
preference and acceptability to pineapple cheesecakes.

Research Method & Goal

(1) The Design of Questionnaires
Questionnaire design can be divided into two majorparts. In previously prepared experimental products, theControl Group - Product B is added with pineapple
chops and pineapple jams and divided into 3 different
kinds of food stuffs for scoring evaluation analysis.
Among them, 2 kinds of cheesecake recipes containing
pineapple chops and pineapple jams with higher
popularity are further analyzed available for formal
sensory evaluation.

4.

Production & Analysis(1) Evaluation Method
The Methods for Experimental Preparation:
By referring to the cheesecake recipes adopted fromcommercial cooking books, a group of food stuffs
combined with Recipe A and B is viewed as the control
group and further improved as the group C and D withtwo best recipes selected. Cream cheese, whippedcream, gelatin powder, sugar, yogurt and lemon juice
are served as raw materials with 3 different mixture
proportions of Recipe C and D ready for formal sensory
scoring evaluation few days later. The experiment is
randomly conducted on 15 people passing through newurban regions for blind sensory evaluation.
Blind Test: Every sampling doze weighs 25g. During
tests, respondents are not aware of food contents andpineapple weight. Figure 1 illustrates the finished(2) Formal Sensory Evaluation:
This study continues referring to the recipe results
adopted from commercial cooking books and sets
Recipe A as a control group. Thereafter, two better
flavors of the Group C with pineapple chops and theGroup D with pineapple jams after screened fromexperimental preparation are further added with therecipes of the Control Group and the Group B withpineapple juice for 7-point scoring evaluation. Also,
every asking item is clearly described convenient for© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

93

The development of new

products of pineapple cheesecakes combined withdifferent content proportions for experimental tests.
Formal Sensory Evaluation: The sampling recipes forformal sensory evaluation are exactly the same as thosepreviously prepared for experiments, but new random
codes are marked. Figure 2 illustrates the finishedproducts of pineapple cheesecakes made from different
content proportions prepared for formal tests.

show higher preference to cheesecakes without any
additive fruit granule.
Table 1 reveals the ANOVA variance analysis of pineapple
cheesecakes with different recipe proportions felt by those of different
occupations
Purchase
Intention

Count

Average

S.D.

987 Overall
Acceptability

Yes

84

4.70

1.487

No

20

4.80

1.704

711 Overall
Acceptability

Yes

84

4.87

1.412

No

20

5.20

1.105

261 Overall
Acceptability

Yes

84

4.71

1.393

No

20

5.30

1.302

Yes

84

5.02

1.388

No

20

5.55

1.191

Yes

84

4.64

1.580

No

20

4.90

1.373

Yes

84

4.56

1.524

No

20

5.15

1.226

Question Item

(2) The Analysis of Demographic Variables of Formal
Sensory Evaluation
Experimental respondents are those consumers on newurban regions with the frequency analysis focusing on
their genders, ages, occupations, incomes and purchaseintention .
In gender survey, there are 62 females occupying 59.6%
and 42 males occupying 40.4%. In age survey, the agesof respondents include 32 respondents aging below 20(inclusive) occupying 30.8%, 30 respondents aging
21~25 (inclusive) occupying 28.8%. About income
survey, there are 48 respondents with incomes below
20,000 NTDs (inclusive) occupying 46.2%, 31
respondents earning 20,001~30,000 NTDs occupying
29.8%. In occupation, there are 20 respondents with
their occupations separately in academic, agriculturaland industrial fields occupying 19.2%, 21 respondents
in the commercial field occupying 20.2%, 43 students
occupying 41.3% and 20 unemployed or retired
respondents occupying 19.2%.
About purchaseintention : In this survey, there are 84 respondents withpurchase intention occupying 80.8%.
The Variance Analysis of purchase intention : This
study is operated with the Independent Sample T-test to
explore and analyze if there is any remarkabledifference happening to purchase intention against
different recipe proportions added to pineapple
cheesecakes. Results are shown as Table 1. It is foundthere is no remarkable difference happening to thepurchase intention from those of different occupations.
Among them, no matter if there is any purchaseintention, respondents show highly similar degrees in
acceptability.
However, respondents show stronger purchase intention
to sample 711 and 897 wherein sample 897 receives
stronger preference. It means consumers feel moreenjoyable in chewing acceptability. Some consumers

897 Overall
Acceptability
421 Overall
Acceptability

685 Overall
Acceptability

5.

F-testSignificance

1.213

.273

2.305

.132

.002

.965

.671

.415

.753

.387

1.435

.234

Conclusion & Prospect

In view of the product - pineapple cheesecakes newly
developed for this study, the questionnaire answers
proposed by consumers after sensory evaluations findthe most popular flavor with 27% recipe proportion of
pineapple chops added. Furthermore, after pineapples
are added into cheesecakes, a new flavor has emerged incheesecakes yet reduces the oily taste without
sacrificing its original flavor and nutrient value.
In the experiment of formal sensory evaluation, genders,ages, occupations, incomes and purchase intention areserved as factors to analyze consumers' preference topineapple cheesecakes.
Research results afterinvestigation finds the strongest acceptability is the
samples of pineapple cheesecakes added with 27%
pineapple chops. The least acceptability happens to thesamples of pineapple cheesecakes added with 27%
pineapple jams. After analysis, it is found the overall
acceptability is high but the purchase intention is low
revealing a higher preference to cheesecakes added withpineapple chops.
We assume not everyone like the product of pineapple
cheesecakes. However, in view of overall flavors, theproduct is acceptable but no purchase intention shows.
Also, respondents show no remarkable feeling to the© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan94

Sheu-Der Wu, Hung-Wen Su

acidity of pineapple cheesecakes proposed in this study.
Furthermore, the sweetness after sugaring making theacidity of pineapple vaguer also affects respondents'
preference to samples with research results influenced.
Because of the limitation of research fund, it is hoped in
the future, the vague acidity of pineapple cheesecakes
after the sugaring of pineapple chops and jams can be
improved. Additionally, purchase intention can befurther investigated to continuously find out the productof pineapple cheesecakes with the best preference fromconsumers to improve product diversities.
Reference
1. Taiwan Fruit Exports (2012/4/29)
http://trade.coa.gov.tw/index_b.jsp.
2. Gao, Kai-Ping (2009) Herbal Healing Science, China:
The Chemical Industry Publisher.
3. The Department of Health (2011/05/06)
http://www.doh.gov.tw/cht2006/index_populace.asp.
4. Kang, Pei-Ci (2007) The Study on the R&D of
Pineapple High-fiber Bread and its Quality. The
Master's Degree Disseminate of the Graduate Instituteof Applied Science, Tainan Science and Technology
University, Tainan City, Unpublished Article.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

95

The Study on Ballroom Service Quality to Affect Customer Satisfaction

Hsiu-Min Lin *Department of Food and Beverage Management, Far East University
No.49, Zhonghua Rd., Xinshi Dist., Tainan City 74448, Taiwan (R.O.C.)
E-mail: linhsiumin@cc.feu.edu.tw
Yang Wu

Department of Food and Beverage Management, Far East University
No.49, Zhonghua Rd., Xinshi Dist., Tainan City 74448, Taiwan (R.O.C.)
Jei-Fu Ho

Department of Food and Beverage Management, Far East University
No.49, Zhonghua Rd., Xinshi Dist., Tainan City 74448, Taiwan (R.O.C.)

Abstract
For hoteliers, customer expectations and the real performance of service probably existed in perceptive variance
between the emphasized details and satisfaction to bring about the gaps of service quality. As such, it naturally
caused customer unsatisfied with service quality to lose their patronage. As it is found from research results, service
quality exerted positive influence on demographic variables. If it was available to improve the service quality andthe expectations to customer satisfaction, then it was naturally available to enhance the repeated purchase intentionof customers. In 10 dimensions cited in this research, after researching analysis, "tangibles" was satisfied bycustomers the most. If providers could keep the satisfaction always, then it was naturally available to enhance thefirst image in customers' mind. Additionally, "external response" scored the least and provides should improve theirown service quality by focusing on the parts highly emphasized by customers. Furthermore, more emphasis shouldbe placed on the opinions of customers to win their trust and avoid external complaint lodged by customers without
losing the source of customers. The excellency or inferiority of service quality would directly affect customer
satisfaction. If providers were planning for sustainable business management, excellent service quality was
imperatively required. As such, it was just available to win the trust from customers under the fiercely competitive
business climate with the goal of sustainable business management achievable..
Keywords: Ballroom, Customer Satisfaction, Service Quality

in the future and even enhance the intention to hold aballroom festival event [2].

1. Introduction
Ballroom incomes occupied from 40% to 80% of the total
revenue of a restaurant department roughly with its
undoubted importance. It was quite a critical income
source.
Ballroom visitors were all the potential
customers of restaurants and excellent ballroom festival
service would be certainly the most powerful marketing
tool to gain such potential interest [1]. Joining awonderful ballroom service would be an impressive
occasion in mind to add the frequencies of repeated visit

2. Literature Review
There were various combinations and types of festival
service available with the markets mainly focusing on theevents held by associations, commercial organizations
and social groups. An ordinary association was normallycomposed of members less than one hundred. The annual
meetings and other campaigns held by such associations
were mostly combined with meals and meetings together©The 2016International Conference on Artificial Life and Robotics (ICAROB 2016),Jan. 29-31, Okinawa Convention Center, Okinawa, Japan96

Hsiu-Min Lin, Yang Wu, Jei-Fu Ho.

wherein it also included rest time and the service ofproviding coffee and snacks, cocktail parties and formal
festivals.
As for service quality, service quality meant the qualityof service and products provided by service industries incustomers’ mind, a comparison between the quality
expected by customers and the quality practicallyexperienced by customers [3]. Service was not only akind of intangible products but also a kind of virtual
stuffs without fixed standards and models. It did not only
mean products themselves but also emphasize theintangible service quality available for customers. As [4]supposed, the excellency of service quality depended on
the comparison between the expectations to service andproduct quality and the quality practically experienced bycustomers. If the service and product quality practicallyexperienced by customers was beyond expectations, thencustomers showed higher satisfaction with a positive
perception of better service quality and vice versa.

Figure 1, The Map of Research Structure

3.2. 3.2. The Operational Definitions of Research
Variables
The researching dimensions adopted the SERVQUAL
Scales modified by Parasuraman (1985) by focusing onservice quality as a basis. Also, the correction was madein accordance with the natures of occupations to serve as
the questionnaires to test service quality. The scoringmeasurements were conducted in accordance with the
Likert 5-Point Scale available for respondent evaluation
wherein "1" meant very much disagree; "2" meantdisagree; "3" meant fair; "4" meant agree and "5" meant
very much agree.
Higher scores meant higher
satisfaction.

The firstly proposed the concept of customer satisfaction
and health contended on a certain occasion, timing or
setting, in view of the perceive status after customers
sacrificed the cost and obtained the reward throughpurchasing products, whenever perceived reward washigher than sacrificed cost, the satisfaction was arisen[5].
As Parsuraman, Zeithaml, and Berry contended, there were
5 kinds of service gaps existing in service processes andinterfering service experience[6]. The said 5 kinds ofservice gaps were respectively service positioning,
service explanation, service delivery, service
communication and service delivery. In this research,
further based on the research data proposed by [6], the"natures of service quality" were summarized as researchgoals. etc. In this study, the preferences evaluation wasused to research the consumers’ taste.

3.3. 3.3. Questionnaire Design
Based on contents and structure, this research could be
divided into two major parts with every part respectively
described as below questionnaire contents: The statisticaldemographic variables were divided into 6 dimensions inaccordance with the demands of this research including
genders, ages, occupations, education levels andconsumption frequencies.

3. Research Methods
3.1. 3.1. Research Structure
In this research, the research structure was summarizedin accordance with research motivations, goals andscientific literatures by using statistical demographic
variables to analyze and understand the variance ofcustomer satisfaction among various levels of customers.

4. The Analysis of Research Results
This research was meant to explore the customer
satisfaction against the service quality provided by hotels.
In this article chapter, the valid questionnaire replies© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

97

The Study on Ballroom

were retrieved for data processing and analyzing with the
SPSS 18 Suite Application as a kit. Research results
were respectively explained on the basis of customer
basic information including the analysis on the evaluatedresults of satisfaction, customer genus against service
quality and the influence on customer satisfaction. This
chapter was divided into 3 sections with Section 1 todescribe the analysis of formal questionnaire surveys,
Section 2 to describe the analysis on basic information
further subdivided into two parts, namely service quality
and customer satisfaction for detailed explication andSection 3 to describe the Independent Sample T-test. In
respect of independent sample T-test, It could be known
from tested results the significant two-tailed P-value(0.049) of different genders against the dimension of
external response was below 0.05. It indicated thecustomers of different genders caused significant
variance in the dimension of external response meaning
the customers of different genders showed their special
preference or detest to the average of the dimension of
external response. For the customers of different genders,
all the values against other dimensions were above 0.05
and it indicated the customers of different genders
showed no special preference or detest.

dimension of "pay more" wherein the item - "I would still
patronize this hotel repeatedly even if the prices wereraised!" scored the least. It indicated the customers with
self-employed occupations contended the service
provided by hotels could not fulfill their special demands.
The customers with monthly incomes within
25,001~35,000 NTDs showed a lower average rated at
3.31 in the dimension of transit. It indicated thecustomers with such monthly incomes respectively
showed different opinions to the dimension of transit
wherein the item - "The first priority was based on thetransportation convenience!" scored the least.
It
indicated the customers with monthly incomes within
25,001~35,000 contended the service provided by hotels
could not fulfill their special demands.
The customers with festival frequencies within 1~2 times
annually showed a lower average rated at 3.60 in theempathy dimension. It indicated the customers with suchmonthly incomes respectively showed different opinions
to the empathy dimension wherein the item - "I would ask
for extraordinary service from the service givers!" scoredthe least. It indicated the customers with festival
frequencies within 1~2 times annually contended theservice provided by hotels could not fulfill their special
demands.

5. Conclusion and Suggestion
5.2. Suggestion
5.1. Research Conclusion

A company should enhance its own education andtraining projects for their employees and allow
employees to aggressively ask about customers' opinions
after meals. It is available to understand and solve
problems immediately.
A company should introduce novel and excellent
facilities to replace the old ones and allow customers withthe entirely novel atmosphere.
It is available to serve customers with customary service
to fulfill their special demands and consolidate customer
royalty with admirable words of mouth won (such as
special menu, music bands and small giveaways etc.).
A company can hold irregular favorable deduction events,
charity campaigns and advertisement to promote theuniqueness quite different from other competitors,
availably allowing customers with totally different
experience.
A company can provide employees with some training
courses to fulfill their professionalism and knowledge. It

Female customers showed a lower average rated at 3.64
in the dimension of external response. It indicated female
customers respectively showed different opinions to thedimension of external response wherein the item - "If I
was unsatisfied with the service of a hotel, I would lodge
a complaint to external organizations." scored the least.
It indicated female customers the service provided byhotels could not fulfill their demands.
The customers aging above 50 (inclusive) showed alower average rated at 3.50 in the dimension of reliability.
It indicated the customers at theses ages respectively
showed different opinions to the dimension of reliability
wherein the item - "If service givers could answer
questions appropriately?" scored the least.
The customers with self-employed occupations showed alower average rated at 2.99 in the dimension of "paymore", It indicated the customers with self-employedoccupations respectively showed different opinions to the© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan98

Hsiu-Min Lin, Yang Wu, Jei-Fu Ho.

is available to enhance the responsiveness of employees
when facing various problems and improve employees'
self-confidence.
References
1.

2.

3.

4.

5.

6.

Ministry Y. X. Zhang, The Dissatisfaction at the 1st
Restaurant Management Symposiu., (The Foundation ofChinese Dietary Culture. Taipei, Taiwan 1996).
S. W. Xu, The Management of Ballroom Festival Service:
Theories & Practical Affairs.(The Yang-Chih Book Co
Ltd. Taipei, Taiwan 2005)
F. J. Su, The Management & Practical Affairs of
Hospitality Service. (The Yang-Chih Book Co Ltd. Taipei,
Taiwan 2008).
P. G. Klaus, Quality Epiphenomenon: The Conceptual
Understanding of Wuality in Face-to-Face Service
Encounters. In J. Czepial, M. Solomon, and C. Surprenant,eds. The Service Encounter. (Lexington, KY: Lexington
Books, 17-33. 1985).
R. N. Cardozo, An Experimental Study of Customer Effort,
Expectation, and Satisfaction. Journal of Marketing
Research, 2(3)(1965), pp. 244-249.
A. Parsuraman, V. A. Zeithaml, and L. L. Berry. A
Conceptual Model of Service Quality and Its Implications
for Future Research. Journal of Marketing . 49 (1985),
pp.44-50.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

99

The Relationship between Employees’ Personality Traits, Work Values and Job Involvementwith the Contribution to their Companies – For the Micro Food Service Industry
Hsiu-Chen Chung

Department of Food and Beverage Services, Tainan University of Technology,
No.529, Zhongzheng Rd., Yongkang District, Tainan City 71002, Taiwan (ROC)
Chiou-Lan Chien*

Department of Food and Beverage Services, Tainan University of Technology,
No.529, Zhongzheng Rd., Yongkang District, Tainan City 71002, Taiwan (ROC)
E-mail: t10156@mail.tut.edu.tw
Lung-Chi Tsai

Department of Leisure, Recreation, and Tourism Management, Southern Taiwan University of Science and Technology,
No.1, Nan-Tai Street, Yongkang District, Tainan City 71005, Taiwan (ROC)

Abstract
The purpose of the study is to discover the influence between the employee’s (for whom who works in the micro
food industry) personality, work values and job involvement to the contribution and dedication. Research sample
focus on the snack bar in night market, bakery stores and the micro food industries’ employees or owners from
Taizhong, Changhua, Yunlin, Jiayi and Tainan cities. We send 450 questionnaires, the effective questionnaires are
333, and the percentage of effective retrieve of the questionnaire is 87.63%. After using reliability analysis,correlation analysis, one-way analysis of variance (ANOVA), independent, work values and personality canforecast prediction the employee’s contribution and dedication very precisely; in addition, we find out that job
involvement has a very clear effects between work values and job contribution.

Keywords: personality, work values, job involvement, contribution.

would affect individual performance and efficiency on
work〔5〕〔6〕〔7〕. A staff's high job involvement,satisfaction, spontaneous professional proactive
behavior with passion, and low intention of turnoverrefer to a high employee engagement, and therefore
leveling up the efficiency of an enterprise〔8〕〔9〕〔10〕.
This study aimed to discuss how the personality traits,
personal work values, and job involvement of staff
affect the employee engagement, and hope to offer
advices to the Micro Food Service Industry runners on
staff election and keep-on.

1. Introduction
Micro-Enterprise is an easier way of starting a personalowning business due to the relative low market entrance.
In Taiwan, it only costs five hundred thousand New
Taiwanese Dollars to start an eating house〔1〕, andtherefore most of the people make livings on MicroEnterprise, which is important and irreplaceable tosocial development〔2〕. Staff is the most importantresource of an enterprise〔3〕, only by choosing andkeeping key staff long can an enterprise increase its
competitive advantage〔4〕.
Researchers pointed out that the personal values,
personality traits, and the job involvement of staff

2. Study Purpose© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan100

Hsiu-Chen Chung, Chiou-Lan Chien, Lung-Chi Tsai

vitality facet α= .841, concentrating facet α= .803, andprofessional effectiveness facet α= .901.

The study was focused on investigating: 1) The effect of
personal backgrounds on personality traits, work values,
job involvement and employee engagement of
individual staff. 2) The relationships among personality
traits, work values, job involvement of staff andemployee engagement. 3) The effect of personality traits,
work values, job involvement of staff on its employee
engagement. 4) The mediation of job involvement onwork values and employee engagement.

4.

Results and discussions

4.1. Descriptive statistics
1) For personality traits facet, the significant others forpromotion, and the luck of having a good job are highly
valued by employees involved in micro catering andbeverage businesses.
2) For work value facet, participants regarded goodinteraction relationships, the improvement and learning
experiments the job could provide, and the increasing
opportunities of promotion due to learning hard asimportant factors.
3) For job involvement facet, high-involvement
participants regarded assuming responsibility as animportant factor.
4) For employee engagement facet, most of theparticipants regarded professional ability as the mostimportant factor.

3. Research Methods
The study applied convenience sampling from
employers and staff in eating houses, small restaurants,
combinative bakery, and food stands in the nightmarkets in Taichung, Changhua, Yunlin, Chiayi, andTainan. 450 designed questionnaires were released and380 of them were returned, with the valid response rateof 87.63%. All data were analyzed with statisticalsoftware SPSS 12.0 for frequency distribution, mean,
standard deviation, non-paired t-test, one-way ANOVA,
Pearson's correlation coefficient, and regression forfurther discussions.
The questionnaire with high reliability was designed
including the following scales. Personality traits scale
applied for the study was adopted from Work Locus ofControl Scale〔11〕, including internal and external
facets. And the items on the scale were condensed from
16 into 5 for the study. Originally there were 15 items inpersonal traits variable scale, and this study only
concerned the 7 items of external facet, with total scale
α= .892. Items of work values scale including
instrumental value and objective value evaluation parts
were designed according to Wu〔12〕〔13〕〔14〕.
These 17 items have a total scale of α= .905, with
α= .915 and α= .877 on instrumental vale and objective
value facet, respectively. Job involvement scale
including 13 high-involvement items were designed
according to Kanungo 〔15〕, and Lodahl & Kejner〔 16 〕 , with total scale of α= .902. 15 items of
employee engagement scale including vitality,
concentrating, and professional effectiveness parts were
designed according to Lee, Lain, & Chen (1999) 〔17〕,and Schaufeli, Salanova, Gonzalez-Roma, & Bakker〔18〕 (2002). In this part, the total scale α= .916,

4.2. Correlation coefficient analysis
There were no significant correlation of personality
traits and employee engagement (r=-.027). Work value
and job involvement are significantly positive correlated
to employee engagement, with r= .707 and .734,
respectively. The results suggested that if participants of
micro catering and beverage enterprises have morepositive attitudes, involving more in their jobs, and havehigher instrumental and objective values, their vitality,
concentrating and professional effectiveness are higher,
and the same as their employee engagement.
4.3. ANOVA
1) There were significant differences on external
personality traits among different age groups.
2) There were significant differences on position andjob grade among external personality traits, job
involvement (high involvement), and employee
engagement (professional effectiveness).
3) There were significant differences on external
personality traits, objective value, and employee
engagement(concentrating
andprofessional© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

101

The Relationship between Employees’

effectiveness) among participant groups with different
years of working experience and different.
4) Experienced participants have higher job
involvement and vitality than non-experienced ones.
5) There were significant differences on job
involvement (high involvement) among participantgroups with different years of working experience
related to catering and beverage businesses.
6) The objective value of male was higher than female.
7) There were significant differences on job grade
among external personality traits, objective value, highjob involvement, concentrating, and professional
effectiveness.

3) The affects of objective value and instrumental value
toward vitality facet, concentrating facet, andprofessional effectiveness facet are all partiallymediated by job involvement.

5. Conclusions and suggestions
5.1. Conclusions
1) Staff with external personality traits hope to get abetter job with significant others and luck, and this isextremely different from the career expectations of staff
with high working passion, be willing of facing
challenges, and sticking on one's place on catering andbeverage business.
2) Once the staff confirm that they can have highindividual achievements for the future in a company oran enterprise, they are more likely to involve themselvesin their work.
3) Staff with high involvement is staff whom ispassionate to their jobs with vitality, and also thinks
highly on the SOP and their working efficiency.
4) Although external personality traits has negative
effects on employee engagement and professional
effectiveness, the study found that to the supervisors,
the most important thing are that staff can be familiar
with their job content and working progress, and finish
their individual missions conscientiously and effectively.
5) Understanding the items staff are willing to devoted
to, e.g. accomplishment, interests, and chances oflearning thins, would help increasing the work value tothe staff, and therefore increase staff's passion, vitality,
responsibility, and working efficiency.
6) Among variables related with individuals, age,position, job grade, and years of working experience
have significant effects on external personality traits,
but have no significant effects on work value. Position,
job grade, related working experience, and years ofworking experience related to catering and beveragebusiness have significant effects on job involvement.
Position, job grade, and related years of working
experience have significant effects on employee
engagement and professional effectiveness. Job grade
and related years of working experience have significant
effects on concentrating, while related working
experience has significant effects on vitality.

4.4. Regression analysis
1) The more the participants think highly of job
involvement and job value, the more employee
engagement they contribute, and jobs involvement
affects employee engagement more than job value.
2) The more the participants think highly of high job
involvement, objective value, and instrumental value,
the more employee engagement, concentrating, andprofessional effectiveness they contribute. Among those,high job involvement affects employee engagement andconcentrating facet the most, and objective value affects
professional effectiveness the most.
3) The more the participants think highly of high job
involvement and objective value, the more vitality they
performed, and high job involvement affects vitality themost. The more the participants think highly of high job
involvement, objective value, and instrumental value,
the larger the concentrating sub-facet is, and high job
involvement affects the most on concentrating sub-facet.
4) The more the participants think highly of high job
involvement, objective value, and instrumental value,
the larger the professional effectiveness sub-facet is, andobjective value affects the most on it.
4.5. Mediation
1) The affects of work value toward employee
engagement is partially mediated by job involvement.
2) The affects of objective value and instrumental value
toward employee engagement are both partiallymediated by job involvement.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan102

Hsiu-Chen Chung, Chiou-Lan Chien, Lung-Chi Tsai

5.2. Suggestions
1) Elect and apply staff with internal personality traits
as the staff.
2) Build a fair managing system on working efficiency
evaluation and promotion to help building a positivesocial interaction of the entire enterprise.
3) Build a reasonable and transparent celery managing
system to lower the intention of staff turnover.
4) Build an encouragement system that fits the cultureof the enterprise to make staff to be more willing todevote to the enterprise.

8.
9.

10.

11.

Table 1. The verification of research hypotheses.

Hypothesis
1) Individual background has significant
effects on personality traits, work value, job
involvement, and employee engagement.
2) Personality traits, work value, and job
involvement are significantly positive
correlated to employee engagement.
3) Personality traits, work value, and job
involvement have significant positiveeffects on employee engagement.
4) Work value and employee engagement
are mediated by job involvement.

12.

ResultPartially
accepted

13.

Partially
accepted

14.

Partially
accepted
15.

Accepted

16.

References
1. MINISTRY of LABOR, A Case Study of a MicroEnterprise Program (MINISTRY of LABOR, Taipei,
2014).
2. Z. K. Zheng, May the Micro-Entrepreneurs Springing up
all over the Place (Common Wealth Magazine, Taipei,
2014).
3. Z. X. Chen, Small and Medium Enterprise Managementof Sharing (Reinvent Corporates Values and Strategies,
Taipei, 2007).
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Development Agency, Taipei, 2005).
5. S. P. Robbins, Essentials of Organizational Behavior(New Jersey: Prentice Hall, 1992).
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Driving customer satisfaction and financial successthrough employee engagement, People & Strategy. 32(2)(2009) 22-27.
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161-188。
M. Buckingham, and C. Coffman, First, Break all therules. (New York: Simon & Schuster, 1999).
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behavior: A new look at the interface between nonwork
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518-528.
W. B. Schaufeli, and A. B. Bakker, Job demands, job
resources, and their relationship with burnout andengagement: A multi-sample study, Journal of
Organizational Behavior. 25 (2004) 293-315.
P. E. Spector, Development of the work locus of control
scale, Journal of Occupational Psychology. 61 (1988)
335-340.
T. H. Wu, The Study of the Construction of the SalesWork Value Scale (National Youth Commission, Taipei,
1996).
I. C. Huang, and P. W. Huang, The relation between
practical training values and job involvement: A study ofcollege students majoring in Tourism, Leisure,
Recreation, and Hospitality, Journal of Tourism Studies.
10(1) (2003) 63-79.
S. H. Lin, and K. C. Hung, The study on the view ofvalue toward work of the students who major in businessadministration and related departments of vocational
colleges, Journal of International Esthetic Science. 5(2)(2008) 177-214.
R. N. Kanungo, Measurement of job and work
involvement, Journal of Applied Psychology, 67(3),(1982) 341-349.
Lodahl, T. M., and M. Kejner, The definition andmeasurement of Jjob involvement, Journal of Applied
Psychology, 49(1), (1965) 24-33.
Y. D. Lee, J. W. Lain, & C. Y. Chen, A study on themeasurement of productivity for White-Collor employees:
A case of electronic industry in Taiwan. The Chinese
Military Academy Journal. (1999) 345-361.
W. B. Schaufeli, M. Salanova, V. Gonzalez-Roma, & A.
Bakker, The measurement of engagement and burnout: A
two sample confirmatory factor analytic approach.
Journal of Happiness Studies, 3(1) (2002) 71-92.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan103

Consumer’s Acceptance to The New Product- Pineapple Jam as Example

Shang-Hui Li*

Department of Food and Beverage Management, Far East University
No.49, Zhonghua Rd., Xinshi Dist., Tainan City 74448, Taiwan (R.O.C.)
E-mail:a0956358700@gmail.com
Jei-Fu Ho

Department of Food and Beverage Management, Far East University
No.49, Zhonghua Rd., Xinshi Dist., Tainan City 74448, Taiwan (R.O.C.)

Abstract
Based on the health and environmental considerations, consumers paid their more attention to the pursuit of naturalingredient. In other words, a safe and non-toxic diet is thebasic consideration for the consumers. More consumers
pay more attention on natural foods; they gradually change their eating habits. In recent years, many food safety
events such as tainted milk, clenbuterol, poison starch, plasticizers etc. have been reported. Consumers tend to buy
natural foods, less chemical additives and security food as the primary concern. This study attempts to take the natural
pineapple jam as the major structure. And try to add dates and plum into pineapple jam instead of any artificial
additives, and enhance the taste level. Meanwhile, differentflavors could derive from the natural products according
to the pineapple jam proportion of different formulations. The study expects to be able to create the pineapple jam
which is not the same as made by the market products. The study selected 150 consumers of Tainan area to be testedof preferences of sensory evaluation, and SPSS 12.0 statistical software was used as the data analysis of the outcomesof the study. The result found among123 valid questionnaires. The original flavor pineapplejam in its appearance,
aroma, stiffness, acidity, sweetness and overall acceptability can be accepted by most consumers and there are
significant differences with other products. Consumer also showed their strongest willingness to buy the original
flavorpineapple jam and there are significant differences with other products.
Keywords: Pineapple, Pineapple Jam, Sensory Evaluation.

In this study, the innovative pineapple jam was as theresearch theme to discuss the acceptance and purchaseintension of consumers on different types of pineapple
jams.

1. Introduction
Pineapple is low in calories, rich in vitamin C, B 1 and E,
also with minerals, dietary fiber, water etc., it is widelyused in health food and fruit juice [1]. However, themedia often reported that pineapple could be sold out
cheaply, destroyed directly or to be used as a plantfertilizer during in season period. Because fresh fruit isvery difficult to save, it is necessary through using fruit
processing to continue its value. Pineapple jam, acommercial commodity, is the other way to keep its “life“.

2. Literature Review
Pineapple is a kind of tropical fruit originated to the
Amazon River. Presumably, it was introduced to Taiwan
in 1664, or Emperor Kangxi, Qing Dynasty, fromsouthern China. Current pineapple cultivars in Taiwan
can be divided into local, foreign and hybrid species [1].

©The 2016International Conference on Artificial Life and Robotics (ICAROB 2016),Jan. 29-31, Okinawa Convention Center, Okinawa, Japan104

Shang-Hui Li, Jei-Fu Ho

Codex Alimentarius Commission, CODEX, regulated
only solidified or sticky products which have more than
65% sugar could be defined as "fruit jam". However, theliquid sugar and cooked sauce was taken as a jam in Japan
marketing, even the sugar content was reduced to about
40 percent [2]. 50% - 65% sugar content of the jam could
be acceptable for most of Taiwan consumers [3].

Tai-farmer #2 Pineapple

Different Formulas of Pineapple
Jam

Preferences Sensory Evaluation“Purchase intention” is an attempt to buy productpossibilities [4]. The information resources will create
the different opinions and preferences to consumers,
thereby affecting their purchase intention [5]. In 1988,
proposed to measure the purchase intention by perceived
value such as possible purchase, want to purchase,
consider to purchasing etc[6]. The study results of Wu
and Peng in 2010 showed that the attitude to the brandwill affect the purchase intention of consumers [7].

Data Analysis

Results and Discussion
Fig. 1. Pineapple Jam Test Flowchart

official test samples. Then, preferences sensory
evaluation test was conducted. A "five-point" score wasused. “5”represents “very much like or very dark”;“1”represents “did not like very much or too
light”.Repeatevaluation was acceptable. The differenceof evaluation results was analyzed by SPSS 12.0.

Institute of Food Technologists, U.S. defined sensory
evaluation named in line with the basic human capacity
for food or something of the sense of taste, smell, etc.
Also, to obtain a correct conclusion of systematic
knowledge through the scientific booting, measured,analyzed and judged the test done [8].

4. Data Analysis
In this study, 150 convenience samples were collectedfrom Tainan City residents to fill up the sensory
evaluation survey, 150 questionnaires were distributed,
and a total recovery of 123 valid questionnaires, 81%,
while 27 were for invalid questionnaires,17%.

There are many kinds of sensory evaluation methods to
be used. To select the appropriate method in accordancewith the purpose of the test, then the results make sense[9]. In 2003, pointed out that consumer preferencesreferred to test scores of palatability [10]. However, thehigh or low score was not directly related to the actual
strength of a certain taste. Hobby of tasting referred toappearance, flavor and overall acceptability, etc. In thisstudy, the preferences evaluation was used to research theconsumers’ taste.

Regarding the gender, male respondents accounted for
49.59% and 50.40% for female. Respondents of 21 to 30
years old were at most（36.58%）. 48.78% received thehigher education majority. 72.35% respondents
considered that they always put their attention on theissue of processed food safety.

3. Research Methods
Kuanmiao-planted “Tai-farmer #2” pineapples weretaken as the research object, because it contains lesswater, making jam time can be shortened and easy toobtain.
The study process was shown as in Fig. 1.
In this study, two preliminary tests were conducted inorder to find a better formula for an official test. Original
pineapple jam, date-added pineapple jam, plum-added
pineapple jam and on the market pineapple jam were thePreferences analysis results of pineapple jam were shownin Table 1. The analysis result on the difference of© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

105

Consumer’s Acceptance to The

Table 1. Preferences analysis results of pineapple jam
Item

Appearance

Aroma

Stiffness

Acidity

Sweetness

Overall
Acceptance

Purchase
Intention

Sample
254
396
642
875
254
396
642
875
254
396
642
875
254
396
642
875
254
396
642
875
254
396
642
875
254
396
642
875

Standard
F test Distinctivenessdeviationa2.71 ±1.233
4.05c±1.047
35.3170.000***
2.74a±1.240
3.07b±1.146
2.89a±1.144
3.63b±1.141
16.4300.000***
2.91a±1.138
2.68a±1.081
3.14b±1.019
3.70c±0.949
10.2210.000***
3.14b±1.019
3.05a±1.144
2.98b±1.127
3.53c±0.969
15.4580.000***
2.99b±1.149
2.61a±1.013
3.10a±1.190
3.76b±1.066
13.9680.000***
3.08a±1.178
2.89a±1.065
3.15b±1.248
3.84c±1.074
20.7100.000***
3.11b±1.248
2.63a±1.266
3.03b±1.152
3.69c±1.174
19.8830.000***
3.05b±1.156
2.54a±1.160

Number Average
123
123
123
123
123
123
123
123
123
123
123
123
123
123
123
123
123
123
123
123
123
123
123
123
123
123
123
123

Note 1 :*<p0.1，**<p0.01，***<p0.001
Note 2 :396 = pineapple + apple + sugar + lemon; 254 = pineapple + apple + sugar + lemon + dates;
642 = pineapple + apple + sugar + lemon juice + plum; 875 = on the market jam

appearance preferences degree showed that the original
flavor pineapple jam can be accepted by most
respondents (396, average 4.05). The plum-added
pineapple jam was followed by (642, average 2.74) andthere was a significant difference (P<0.000 ***) between
them.

Concerning the overall acceptance, the differences
analysis showed that the original flavor pineapple jam
can be accepted by most respondents (396, average 3.84),
the on the market pineapple jam (875, average 2.63) was
the minimum, and there were significant difference (P<0.000 ***).

As for aroma analysis, the analysis result on the
difference of aroma showed that the original flavor
pineapple jam can be accepted by most respondents (396,average 3.63) and there were a significant difference (P<0.000 ***) with the other three products (P <0.000 ***).
The original flavor pineapple jam can also be acceptedby most respondents (396) in stiffness, acidity andsweetness.

Finally, concerning the purchase intention, the analysis
result on the difference of purchase intention showed thaton the market pineapple jam(875, average 2.54) anddates-added pineapple jam(254, average 3.03) can’t be
accepted by most respondents.
The plum-added
pineapple jam was followed by (642, average 3.05).
However, the original flavor pineapple jam can be
accepted by most respondents (396, average 3.69) and© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan106

Shang-Hui Li, Jei-Fu Hothere were a significant difference (P <0.000 ***) amid
them.

5.

Y. Liebermann and A. Flint-Goor, Message strategy by
product-classtype: A matching model. International
Journal of Marketing Research,13 (1996) pp. 237-249.
6. V. A. Zeithaml, Consumer Perceptions of price, quality,and value:Ameans-end model and synthesize of evidence.
Journal of Marketing, 52 (1998) pp. 2-22
7. S. I. Wu and P. K. Da, The Effect of Consumers to Airlines’
Brand Awareness, Perceived Quality and Perceived Valueon Brand Attitude and Purchase Intentions. ChineseManagement Review. 13(2) (2010) pp. 1-17.
8. G. Jellinek, Sensory evaluation of food. VCH Publishers.
(Florida. U. S. A. 1985).
9. Q. S. Lin and R. Y. Ye, Application and Practice of
Sensory Evaluation. (Rui Yu Publishing Co., Pingtung.
Taiwan, 2013).
10. S. Ou, Andi M, Food Sensory Evaluation and Practice.
(Wagner Publishing. Taipei, Taiwan 2003).

5. Conclusion and Suggestion
The finding results showed that the consumers had thehighest preference on the appearance of original flavor
pineapple jam. It might be that consumers preferred theoriginal color of jam. Both the colors of plum-added anddate-added jams were too dark, but, the contents of “onthe market” pineapple jam was too little. The study
suggested that the manufacturers should be noted thecolor and contents of pineapple jam.
The consumers had the highest overall acceptance on theoriginal flavor pineapple jam. It might be that consumers
considered that no any inference or other ingredients on
pineapple jam would bring less health burden. The study
suggested that the manufacturers should be noted with noadded artificial additives or starch while making
pineapple jam, although the shelf life is shorter, but will
give less effect to health.
The study found only Tainan area consumers showed thestrongest purchase intention to buy the original flavor of
pineapple jam. The study suggested that manufacturers
could be pay their attention on its differences with theother jams for their future market strategy such as theidea of safety and no artificial additives etc., or some freesamples for tryout at the super market, so that consumers
can experience the delicious pineapple jam directly.
References
1.

2.
3.
4.

Ministry of Health and Welfare, Taiwan (2015).Taiwan
Area Food Nutrition Database. Download fromhttps://consumer.fda.gov.tw/FoodAnaly
sis/ingredients.htm?nodeID=640. August 14, 2015.
A. Shimosako. The Magic of Jam. (Candied Fruits andWiping , SauceUnited Culture, Taipei 2012).
J. L. Yi, Best quality Jam. (Apple House Publishing Co.,
Taipei, Taiwan 2011).
W. B. Dodds, K. B. Monroe and D. Grewal, The effects
of price, brand,andstore information on buyer’s
productevaluations. Journal of MarketingResearch, 28(3)(1991) pp. 307-319.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan107

Development of An Innovative Design Process for Green Products
Ming-Chieh, Wang

Department of Food and Beverage Management, Far East University, No.49, Chung Hua Rd.,
Hsin-Shih. Tainan County 744, Tainan, Taiwan
E-mail:wang10071009@yahoo.com.tw

Abstract
Under the growing trend of environmental protection, many different eco-design methods have been proposed, atthe same time, there are many TRIZ tools used to support these processes. In this study, we proposed an innovative
design process for green products. This process includes three frameworks-collaborative design, innovative design
environment, and the green product design system. Collaborative design framework aims to assist the designer andtheir upstream and downstream partners to combine their ideas by CSCW tools. The innovative design environment
includes data layer, information layer, and knowledge layer, analyzed through the data exchange, information
sharing and decision making during the design process. The green product design system was consists of the
problem analysis, problem solving, and solution evaluation used to assist the design process with collaborative
coordination and information recording. This construct integrating the design ideas, knowledge exchange, problem
solving and feedback for the green product design process.
Keywords: col laborative design, innovative design environment, green product design system

1. Introduction
Under the growing trend of environmental
protection, many different eco-design methods havebeen proposed. In various fields, researchers all do theirefforts to create new products to fit the new global
standard. Many advanced countries even listed Green
Design Rules as the first priority in government policy.
In the past, products have been designed without
consideration of the impact on environment. Traditional
factors considered in the product design process arefunction, quality, cost, and safety. Now, it is imperative
to consider the environmental impact of products
throughout their life cycle. At the end of the traditional
instruction or regulations only focused on
manufacturing process. However, the adverse effects on
the environment may occur in the life cycle stages, suchas the recycling, distribution, and any kind of material
collected. Therefore, companies need to analyze andevaluate the influence from product's entire life cycle, inorder to focus on the core issues, and solve the problem
effectively.
In recent years, many eco-design methods havebeen proposed [1] and many innovative tools andmethods were used to eco-design process[2]. [3]. But
how to analyze the design problem and how to evaluate
the design result are seldom explored in previous
researches. For that reason, how to construct anintegrated method to solve green design problems
would be the key point. In this study, some design tools
such as the TRIZ tools [4], computer supported
cooperative work tools collaborative design concept andknowledge management environment creation wereused to construct innovative green product design
system.
2. Literature Review
2.1 TRIZ
TRIZ was a theory developed by Genrich Altshallerin Russia, who had analyzed over 400,000 patents to© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan108

Ming-Chieh Wang

construct the contradiction matrix and 40 inventive
principles [5]. TRIZ was based on the patents and used
for system analyzed and problems solved throughinventive principles. TRIZ was a complex system
including Inventive Principles, Contradiction Matrix,
Ideal Final Result (IFR), S-Fields, Function Analysis,
Separation Principles, Subversion Analysis, Trimming,
etc. Among these tools, the contradiction matrix alongwith 40 inventive principles is the most famous tool.
TRIZ also can be seen as a symbol of a set of tools, amethod, a way of thinking and a philosophy [6].
Altshaller analyzed thousands of patents and identified
the same fundamental problems and found solutions.
TRIZ was a tool with knowledge and experiences of theworld's best inventive concept for design engineers tohandle these conflict conditions during the innovative
design problem solving process [7]. The most importantparts of TRIZ was the contradictions, through analyzed
large amount of patents' problems and solutions, theideas were created to solve the problems. In the field of
science, TRIZ was an innovative tool providing asystematic process to confirm and solve problem, manyscholars have proposed some new contradiction
matrices in their researches. A hierarchical view of
TRIZ was shown in Fig. 1.

environmental design, environmentally conscious
design, environmentally responsible design, sustainable
design, green design , etc. The purpose of green design
was to decrease the environmental impact during the
product life cycle: raw materials, preliminary design,detailed design, manufacturing, assembly, packaging
and transportation, use, and disposal [8]. Liu & Chen
(2001) combined engineering parameters in TRIZ
method with the 7 Eco-Efficiency Elements put forth by
the World Business Council for Sustainable
Development (WBCSD), allowing designers discovered
the engineering parameters needed [9]. Michelini and
Designer

Marketing
PersonKnowledge
layercollaborativedecision

Data
layer

Experience
share

Information layer

Data transformation

Manufacturing
Customer Supplier
Engineer
Knowledge
layer
Data
layer
Information layer

Product information exchange
C S C W tool

Product design information
Product idea design knowledge
Knowledge management environment

Razzoli proposed a model including three types ofinnovations, product-innovation, function-innovationand method-innovation [10]. Horváth presented that theeco-design research was used to investigate the conceptsof corrective products, to reduce the environmental
humiliation, and ameliorative products, and to cope withthe environmental effects [11]. Dewulf and Duflou
discussed how to integrate different levels into thebusiness operations, and proposed a concept of the 3layered framework for eco-design [12]. Trappey, Chen,
Hsiao, and Lin proposed an integrated green productdesign methods and system. Though there are many
researches try to develop a systematic method in orderto design products complying with natural andeconomic needs [13].

Excellence
Ideality
Resource
Functionality
Contradiction
Space / Time / Interface

Philosophy

A complete problem
definition/solving process
Inventive principles

IFR

Contradiction Matrix
S-Fields
PI Tools
Trimming

Function
AnalysisResources

MethodKnowledge/
Effects

TrendsTool

Subversion
Analysis
Separation Principles

Fig. 1. Hierarchical view of TRIZ

2.2 Green design
Product design concerning environmental impacts
has many expressions that include ecological design,© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan109

Development of An Innovative

3. Framework and methodology
In this paper, a framework and its related productdesign process for eco-innovative product design were
proposed and shown in Fig. 1.and Fig. 2.
The design environment framework for green
innovative product was proposed and shown in Fig.1. In
this framework includes three layers: data layer,
information layer, and knowledge layer, which deal
with the collaborative (products) detail design, idea
design and activities of knowledge management.
Products data exchange provided by data layer, it also
used for data standard exchange. Information layer
provided information sharing with products design.
Knowledge layer can provide experience andknowledge to make right decision. Therefore, if green
design and innovative design could be integrated inknowledge layer, it could be efficiency to develop
products for highly value.
In Fig.2, a green product design system was
proposed and shown. At left side, collaborative design
knowledge includes general product design knowledge,
TRIZ innovative principles, green design guidelines andregulations. At right side, design team includes designer,customer, Manufacturer and vendor. The CSCW tool
can support the collaborative tools and method formembers located on different places. Design Team
could share or exchange information through the CSCW
tools immediately. At the top, green design process andknowledge management includes: product design anddevelop process, knowledge exchange and collaborative
design information system development.

4.

Conclusions
In recent years, in order to fulfill end user demands
and environmental awareness, TRIZ and eco-designconcept get more attention of the academy and industry.
The most important contribution of this paper is to
propose an innovative design process for green products.
This process includes three frameworks-collaborative
design, innovative design environment, and the green
product design system. Collaborative design frameworkaims to assist the designer and their upstream anddownstream partners to merge ideas by CSCW tools.
This eco-innovative product design system was consistof data flow from designer, consumer, manufacturer…
to transport, collect and feedback during the life-circle,
then it also could be used for data exchange,
information sharing and collaborative decision-making
through CSCW. The collaborative design in this model
consists of three layers: data layer, information layer,
and knowledge layer, which corresponding to different
level from lower-higher in the design process. The
lower layer could support higher layer, higher layer
provided lower design with directives and regulations to
avoid form the wrong decision-making or the unsuitable
design, then delayed the products entered to the market
and production costs increasing.
This innovative product design system was based on
knowledge management construct illustrated a newcollaborative model to create green products. Sincemany research aimed to green design and innovative
design, but very less study focused on collaborative
design within knowledge management. In the future wewill try to build the prototype of knowledge
management for the green product design.

Fig. 1. Design environment framework

5. References

Fig. 2. The proposed framework of eco-innovative productdesign system
Database &Information Resource

Green Product Design System

STEP-based
Data

Problem Analysis

1.

CSCW

Desinger

2.

TRIZ-knowhow

Solution Evaluation

Eco-based Data

Customer

3.

Manufacturer

A. J. Gottberg Morris, S. Pollard, C. Mark-Herbert, and
M. Cook, Producer Responsibility, Waste Minimisationand the WEEE Directive: Case Studies in Eco-Designfrom The European Lighting Sector, Science of the Total
Environment, 359 (2006) pp. 38-56.
D. Pujari, Eco-Innovation and New ProductDevelopment: Understanding the Influences on Market
Performance, Journal of Technovation , 26 (2006) pp.
76-85.
M. T Smith, Eco-Innovation and Market Transformation,
J. Sustainable Product Design, 1(1999) pp. 147-161.

Problem Solving© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan
PatentInformation

Investor

110

Ming-Chieh Wang

4.

5.

6.
7.
8.

9.

10.

11.

12.
13.

J. L. Chen and C. C. Liu, Green Innovation Design ofProducts by TRIZ Inventive Principles and Green
Evolution Rules, Int. Conf. CIRP Design Seminar, (HongKong, China, 2002) , pp.16-18,.
G. Ashuller, Suddenly the Inventor Appeared –TRIZ thetheory of Inventive Problem Solving(Worcester,Technical Innovation Center, MA, 1996).
D. Mann, Hands-On Systematic Innovation. (Creax Press,
IEPER, 2002) .
S. d. Savransky, Engineering of creativity. (Boca Raton,
CRC Press, FL, 2000)
E. Jones and D. Harrison, Investigating the use of TRIZ
in Eco- innovation, The TRIZ Journal, (http://www.trizjournal.com/, 2000).
C-C Liu, J.L Chen, Development of product green
innovation design method. Proceedings of Eco-design, in
Proc. 2nd Int. Conf. Symposium on Environmentally
Conscious Design and Inverse Manufacturing.(Tokyo,
Japan, 2001), pp.168-173.
Michelini R.C. and R.P. Razzoli, Product-Service Ecodesign: Knowledge-based Infrastructures, Journal of
Cleaner Production, 12 ( 2004) pp. 415-428.
I. Horváth, A Treatise on Order in Engineering Design
Research, Journal of Research in Engineering Design, 15(2004) pp. 155-181.
W. Dewulf and J.R. Duflou, Product Engineering.
(Springer science, Business Media, USA,2005 ).
A. J. C. Trappey, M. Y. Chen, D. W. Hsiao, and G. Y. P.
Lin, The Green Product Eco-design Approach and
System Complying with Energy Using Products (EuP)
Directive, Global Perspective for Competitive Enterprise,
Economy and Ecology(Springer, London, 2008).

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan111

Research on Nail Art 3D Design with Different Materials-- A Case Study of the Tang Dynasty Style
Mei-Yin Lee*

Department of Cosmetic Application and Management
Department of Innovative Design and Entrepreneurship Management
Far East University
No.49, Zhonghua Rd., Xinshi Dist., Tainan City 74448, Taiwan (R.O.C.)
E-mail:mey.lee@msa.hinet.net
Kung-Yu Liu, Sih-Jie Guan

Department of Cosmetic Application and Management, Far East University
No. 49, Zhonghua Road, Xinshi District, Tainan City, 74448, Taiwan (R.O.C)
Jei-Fu Ho

Department of Food and Beverage Management, Far East University
No. 49, Zhonghua Road, Xinshi District, Tainan City, 74448, Taiwan (R.O.C)

Abstract
Women in Tang Dynasty, China (618 A.D.-907 A.D.) had concerned with beauty. They had painted color in theirnails in addition to emphasis on clothing and makeup. Tang’s costumes had blended the diverse characteristics ofnorthern ethnic groups. Meanwhile a variety of engineering methods had been used such as embroidery, painting,printing, wearing gems and gemstones. Floral patterns had been appeared in large numbers in clothing, accessories
and poetry. This study has tried to build the Tang Dynasty decorative painting technique or way of painting
reappeared on modern nail art and to make the nail painting presents the different faces. After collecting a lot of
relevant documents and experimenting many combinations of different materials, such as sculpture, super-light clay,
foam paper, popsicle sticks, and Styrofoam, many hand-made stereoscopic flowers, such as peony, rose, plum etc.,with painting have been made. Hence different from the past nail art, beautiful pieces of finger nail with the mood of
Tang Dynasty poetry are presented. Furthermore, this study has investigated whether the stereoscopic nail art design
with different materials could be promoted to the daily nail decoration to enhance the quality of life.
Keywords: Nail art, sculpture, different materials, Tang Dynasty poetry.

In this study, 3D sculpture techniques were used withdifferent materials and the Tang Dynasty poetry mood on
nail painting techniques. Finally, painted nail sheets
different from past works are presented.
This paper is organized as follows. Section 2 states a brief
literature review. In Section 3, the creative elements andanalyses, and the creative inspiration are described.

1. Introduction
Nail painting has gradually swept in Taiwan in recent
years. The continuous painted modeling has been
innovated. Especially, the city women are looking
forward to having a beautiful painting color on their nail.
Meanwhile, they hope to make changes in their nails withthe different materials.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan112

Mei-Yin Lee, Kung-Yu Liu, Sih-Jie Guan, Jei-Fu Ho

Section 4 shows some nail creative works. Finally, someconclusions and recommendations are made in Section 5.

Poetry, one of the Chinese great culture, is the mostwidespread literature in Tang Dynasty (618 A.D.-907
A.D.) particularly. There were many elaborate floral
works in Tang poetries. There were many elaborate floral
works in Tang poetries such as Yuxi Liu (poet, 772 A.D.842 A.D.), he referred to the “lotus and peony” in his
poem of “Peony Viewing”; The other one, Pian Gao(General, 821 A.D.- 887 A.D.) in his famous poem “A
Summer’s Day in a Hill Garden”, he described as “Greentrees, greener shades, and the summer’s day is long. Finebuildings studied their own reflections in the pond. The
crystal curtains stir when a light breeze descends. A bedof roses permeates the garden with their scents”.
In this study, a three-dimensional relief patterns called“Flourishing” and “Fragrant Yard” were created underthe base of the above-mentioned two poems combining
3D technology with different materials and nail painted
sculpture. There were many elaborate floral works in
Tang poetries such as Yuxi Liu, he referred to the “lotus
and peony” in his poem of “Peony Viewing”; The otherone, Pian Gao in his famous poem “A Summer’s Day in
a Hill Garden”, he described as “Green trees, greener
shades, and the summer’s day is long. Fine buildings
studied their own reflections in the pond. The crystal
curtains stir when a light breeze descends. A bed of roses
permeates the garden with their scents”.

2. Literature Review
The nail art was used in ancient times. [1] Women in
ancient Egypt used red nail polish and got the color fromhenna, which was a plant used to color hair and nails. [2]Some documents showed that the ancient Egyptian had
dyed their nails golden. Also, they had refined the bronze
oil from insect secretions as the nail dying. Nails
paintings had begun to legalize in 1830s. In 1916, thedecorative nail polish was begun to use. About 3,000
B.C., some Chinese documents showed that bees wax,
proteins and gelatin had been used as nail polish [3].
Pigment in nail polish had been successfully introduced
and have been started to commercialization in 1950s to
1960s.Women around the world regardless of class had
begun a crush on nail polish. Subsequently, the chemicalengineer had invented the silk nails and crystal nails, suchinvention of artificial acrylic nails had helped the nails
become more slender and beautiful.
Nail cosmetic, art nails, crystal nail and painting nailhave been developed rapidly by the end of 20 th century.
Nowadays, with a pair of gorgeous nail hands areindispensable for many fashion women [4]. The nailmanicurist and the related industries have been risen by
the popularity of the nail art. The professional nailmanicurist must undergo a rigorous training with hand,foot care, nail and other aspects of professional
knowledge providing multivariate and higher quality nailservices.
In 21st century, nail painting arts have made hugeprogress due to the different new technologies. They are
painting nails, bright crystal nails, French-style crystal
nails, 3D crystal nails, phototherapy nails, watery nails,
nail inkjet, 3D sculpture nails, and so on. These new nailarts keep the consumers’ eyes greatly feasted on [5].
There are many kinds of commonly used nail painting
techniques, e.g., painting, sponge rubbings, stack heap
color dying, crystal nails and sculpture painting andothers [4-7].
In this study, 3D stereoscopic sculpture painted was used
for the creation of nail arts. For 3D stereoscopic sculpture,different colors of nail sculpting powder are painted on anail piece resulting in a three-dimensional effect like oilpainting.

3. Creative Design
3.1. Creative elements and analyses
In general, sculpture powder is used for creating patterns
for a 3D sculpture nail. In this study, we use multimaterials, including super light clay, sculpture powder,and foam paper, and auxiliary tools including glass
drilling, oil-paper umbrella, paints, nail glue, artificial
grass, thin wire, scissors, luminescent nail polish andcrystal monomer, for the series of creation works.
3.2. Creative inspiration
The creative inspiration comes from some kinds offlowers with good imagery in China. Peony, known as
the "king of flowers" in China, has a lofty status Aromatic.
In Chinese people's minds, other flowers are neverattainable like the on the prominent of prestige and thestatus of honor. Lotus, being lightness finds, always gives
the feeling of pure and noble. Multiflora rose, often© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan113

Research on Nail Art

Pen Drawing
Peony

3D with different materials
painting
Peony combined by different
materials

Lotus and peony

Sculpture lotus,
leaves and lotus

Tang Woman and Rendezvous

Sculpture Tang Woman

Flourishing

Fragrant Yard

Fig. 2. The Completed "Flourishing" and "Fragrant Yard"

4. Nail Creative Works
Rose

Roses designed bydifferent materials

The design draft of "FragrantYard"

The design patterns bydifferent materials

All kinds of creative ideas and characters of flowers,
garden exhibited. The creative design and actual creation
were shown as Figure 1. The design idea of the first work
“Flourishing” is a scenery of poem “lotus and peony” by
Yuxi Liu. A woman of the Tang Dynasty standing next
to a gazebo with grace and elegance along the lotus andpeony, fully presents the brilliant flourishing Tang
Dynasty. The design idea of the second work “Fragrant
Yard” is also a mood emulation of Tang’s poem. In a
quiet courtyard in the mountains, the only things are trees
and the reflections in the pond. When the breeze blows,
the curtain sways gently and the aroma of multifloraroses is also spread to everywhere of the yard. The
Completed “Flourishing” and “Fragrant Yard” are shownas Figure 2.

5. Conclusion and Recommendation

Fig. 1. The creative design and actual creation

In this study, 3D sculpture techniques were used with thedifferent materials. Meanwhile, flowers and backgroundmood of Tang poetry were reflected on nail painting
techniques. Finally, to express the innovative nailpainting works.

densely forested and full of branches and quite brilliant
when flowering, is loved by poet and general people in
China.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan114

Mei-Yin Lee, Kung-Yu Liu, Sih-Jie Guan, Jei-Fu HoThis study expects to promote the 3D different materials
and innovative nails design to the daily nail decoration.
To provide more choices for anyone who love nailcreation or nail design, to enhance the taste and qualityof life.

References
1. Wikipedia, Nail art, https://en.wikipedia.org/wiki/
Nail_art, retrieved Nov. 30, 2015.
2. Alyssa1, The BEST Guidelines to Makeup and Nailpolish!, http://www.teenink.com/hot_topics/all/article/
334907/The-BEST-Guidelines-to-Makeup-and-Nailpolish/, retrieved Nov. 30, 2015.
3. H.D. Peng (Translator), History of Beauty, U. Eco ed.
(Linking Publishing, Taipei, Taiwan, 2006) (in Chinese)
4. H.S. Zhan and X.J. Chen, Nail Art (New Wun Ching
Developmental Publishing, Taipei, Taiwan, 2005) (inChinese)5. E.F. Hsiao, Study on the vocational high school students’preference for nail design techniques, J. Aesthetics andVisual Arts. 3 (2011) 1-14. (in Chinese)
6. C.C. Cheng, C. C. 2011. Application of Artificial Nail to
the Creation of Accessories. Unpublished thesis of
Institute of Applied Design, Shu-Te University (Shu-Te
University, Kaohsiung, Taiwan 2011). (in Chinese)
7. H. Tsai 2011. Study on Painting Techniques and Designof Nail Art. Unpublished thesis of Institute of Applied
Design, Shu-Te University (Shu-Te University,
Kaohsiung, Taiwan 2011). (in Chinese)© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

115

Compensating Temperature-Dependent Characteristics of a Subthreshold-MOSFET Analog
Silicon Neuron
Ethan Green

Department of Electrical Engineering and Information Systems, 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
Analog silicon neurons are neuro-mimetic VLSI (very-large-scale-integrated) circuits that replicate theelectrophysiological behavior of animal nerve tissue. This research focuses on the temperature sensitivity of asubthreshold-MOSFET analog silicon neuron. Subthreshold operation of CMOS transistors allows for low power
consumption, but is also drastically sensitive to temperature changes. This critical issue must be addressed before
these circuits can be implemented into massive networks to develop future neuromorphic technologies.
Keywords: neuromorphic engineering, analog VLSI, silicon neurons

temperatures are found and parameter sets forintermediary temperatures are obtained by interpolation.

1. Introduction
The field of neuromorphic engineering seeks to design
circuits that mimic the electrophysiological behavior
and network structure of neurons to create biologicallyinspired or neuromimetic technology that can perform
calculations in ways fundamentally different fromtraditional digital computers. Motivations for researchin this field include developing brain-like computersand bio-silico hybrid systems for medical devices.
Analog silicon neurons are electronic circuits that
exploit the characteristics of transistors to mimic how
nerve cells control membrane potential. These circuits
operate in continuous time, can replicate a variety of
spike shapes and spiking behavior, and require low
power1,2,3,4. They are expected to be a promising tool toconstruct neuromorphic systems. However, manytechnical challenges must be addressed before thesecircuits can be implemented in large-scale networks. A
possible method to solve a key issue, temperaturesensitivity, is to actively tune the circuit parameters
depending on temperature. We report our approach inwhich appropriate parameter sets for several

2. Circuit Description
The circuit used in this research (Fig. 1) is an ultra-low
power VLSI silicon neuron based on qualitative
neuronal modeling1, which seeks to use judicious
approximation and mathematical understanding5 toconvert the model of membrane potential to equations
with fewer variables that are more suitable for circuit
implementation. In the model for this circuit, variable v
represents membrane potential and variable n represents
abstracted ionic activity. The value of each variable iscalculated by subtracting the voltage over eachcapacitor from Vdd (1.0V). These two variables control
the output currents of transconductance circuitcomponents fx(v), gx(v) (x=v,n), and r(n) that charge anddischarge capacitors attached to the v-block and n-blockwithin the circuit. This circuit was shown to support the
dynamical behaviors of Class I and II neurons in the
Hodgkin’s classification6. The system equations of thismodel are as follows:© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan116

Ethan Green, Takashi Kohno

fv(v)

Istim
r(n)

gv(v)

Cv

Iav

v-block

fn(v)
r(n)

gn(v)

Cn

Ian

n-blockFig. 2. Circuit components. (a) fv(v) and fn(v), (b) gv(v), gn(v),and r(n).

Fig. 1. Circuit block diagram of the silicon neuron circuit.

dv= fv (v) - gv (v) + I av - r(n) + I stim
dt
dv
Cn = fn (v) - gn (v) + I an - r(n)
dt
Cv

(1)
(2)

Istim is an external stimulus current, and Iav and Ian are
constant currents. The current-voltage characteristics ofthe fx(v), gx(v) (x=v,n), and r(n) components are
expressed by the following sigmoidal relationships:
fx (v)  Mx (1  exp( g x (v)  I 0 exp(
r (n)  I 0 exp(


UT


UT


UT

(v   x ))) x ) (1  exp(  r ) (1  exp( 


UT


UT

(3)
(v   x )))(4)(v   r )))(5)

Fig. 3. Nullclines in Class I mode.

(Fig. 3). The points are characterized mathematicallythrough analysis with nonlinear dynamics5.
The temperature sensitivity of this circuit originatesin the intrinsic temperature sensitivity of MOSFETs inthe subthreshold regime. Figure 4 illustrates periodic
spiking behavior in the Class I mode in response to a 4
pA sustained stimulus over a narrow range oftemperatures from 25 to 26.5°C. The stimulus begins at
0.2 seconds. As can be seen from the figure, the spiking
frequency varies by temperature, and ceases at 26.5°C.

I0 is the PMOS transistor off-current, UT is the thermal
voltage (~26 mV at room temperature), and κ is thecapacitive coupling ratio. Parameters Mx, θx, δx (x=v,n),and θr can be tuned by bias voltages applied to thecircuit. All transistors are operated in the subthreshold
regime. Figure 2 (a) shows a differential pair attached toa current mirror used for fv(v) and fn(v). Figure 2 (b)shows the circuit used for gv(v), gn(v), and r(n). This
circuit is a cascoded transistor with source degeneration
and a detached bulk voltage.
This silicon neuron circuit includes a nullclinedrawing function which assists in the diagramming of aphase plane to evaluate the circuit’s dynamical structure.
For example, a sigmoidally shaped n-nullcline andcubic-shaped v-nullcline that cross at 3 distinct points
yields Class I behavior in the Hodgkin’s classification

3.

Parameter Compensation by Interpolation

In this work, we suppose a temperature sensitivity
management algorithm in which the parameter voltages
applied to the circuit are adjusted depending on thetemperature. Such an algorithm will be used by simple
on-chip components to control circuit parameters, thus
complementing the low-power characteristics of thecircuit. We examined an approach to finding appropriate
parameter sets for each temperature which maintain theimportant characteristics of a neuron: spike frequency in© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

117

Compensating Temperature-Dependent Characteristics

benchmark behavior at each temperature. The bottom 3
rows of the table show the circuit behavior at eachtemperature. The script was then used to generateparameter sets for all intermediary temperatures in
0.5°C steps from 17 to 37°C. Parameters were rounded
to the nearest 0.5 mV. These 42 parameter sets werethen simulated with Spectre in their corresponding
temperatures. The "simple" column in Table 2
summarizes the results. "Average % change" refers to
the average percent difference from the benchmark
behavior. Firing frequencies were relatively close to thebenchmark behavior, with the exception of the 5 pA
stimulus at 29°C which failed to fire. The thresholdcurrent showed a downward trend with higher values
above 200 pA at lower temperatures and lower values
around 140 pA at higher temperatures.

Fig. 4. Periodic spiking at different temperatures.

response to sustained stimuli and the threshold pulse
stimulus for spike generation.
First, circuit behavior was analyzed at 27°C andthese results were considered the benchmark for
operation at other temperatures. The data recorded
included frequency response to 5 pA and 10 pA
sustained stimuli, and the threshold current Ith of a pulse
stimulus (500µs pulse width) required to generate an
action potential. The v and n-nullclines were alsoplotted. Next, the circuit was simulated at 17, 22, 32,
and 37°C. Selected circuit parameters were tuned togenerate behavior that most closely matched the 27°C
benchmark. The nullcline-drawing function was used
extensively, with the conjecture that similarly shaped
nullclines lead to similar circuit behavior. Finally, thetuned parameter sets were input into a script whichinterpolates functions from these data points and returns
circuit parameter sets for any intermediary temperature.
This procedure was performed with the Class I setting
simulated by the Spectre circuit simulator.

Table 1. Parameter sets with corresponding circuit
behavior using the simple approach
Temperature (°C)
17
22
27
32
37
gv_Vm (mV)

388

417

432

443 449.5

fn_Vb (mV)

257

248

237

231

Iav_Vin (mV)

434

450

461 468.5 473.5

Ian_Vin (mV)rn_Vm (mV)

420 449.5 464.5
480.5 462.5

4.1. Simple approach
We selected 5 significant circuit parameters because
exploring high-dimensional parameter space isinefficient. The tail current of the fn(v) component, thebias voltages of the gv(v) and r(n) components, and theconstant current sources Iav and Ian were tuned with allother circuit parameters held constant. The gn(v)component was turned off. Table 1 shows the bestparameter sets which most closely replicated the475 481.5

445

430

185 178.5

415

Ith (pA)

201

164

147

5 pA response (Hz)16.3 15.8 15.1 15.7

18.5

10 pA response (Hz)39 38.8 36.2 38.7

39.4

Table 2. Analysis of interpolation results
simple4. Simulation Results

219

full

5 pA stim average freq. (Hz)5 pA stim average % change

15.8
5.2

11.9
-9.3

5 pA stim frequency range (Hz)10 pA stim average freq. (Hz)

3.7–27.1
38.7

2.4–26.1
35.5

10 pA stim average % change
10 pA stim freq. range (Hz)

6.8
34.6–43.2-0.6
26.8–40.4

Average threshold current Ith (pA)
Average Ith % change

172
–3.6

185.4
-0.1

Ith range (pA)

115.5–216

131–303.5

Table 3. Parameter sets with corresponding circuit
behavior using the full-parameter approach© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan118

Ethan Green, Takashi Kohno

Temperature (°C)
fv_Vb (mV)

17

22

27

256.5 248.5 238

32

5. Discussion37

230 219

fv_Vdlt (mV)567

571 580

588 596

gv_Vm (mV)464

451 432

420 398

fn_Vb (mV)

255

246 237 228.5 219

fn_Vdlt (mV)517

520 520521 524

Iav_Vin (mV)437

452 461469 472

gn_Vm (mV)

220 178.5 156 132.5 113

Ian_Vin (mV)

395 445.5 461

473 479

rn_Vm (mV)

480.5 463.5 445

427 413Ith (pA)

250

191 186

169 145

5 pA response (Hz)

13.3

17.1 13.2

13.2 13.2

10 pA response (Hz)32.7

36.5 35.7

36.0 38.6

The above results can be improved by setting theinterpolation script to return parameters rounded to thenearest 0.1 mV, but this accuracy would be difficult toreplicate with real-world circuits because of thermal
noise and other restrictions including the limited
precision of bias voltage generator circuits.
The relative success of the simple approach suggeststhat limiting the available parameters leads to asmoother progression of values over a range oftemperatures, allowing more success in intermediary
steps. The full-parameter approach in contrast offered
too much fine tuning capability. At a given temperature,some parameters were tuned more aggressively thanothers, depending on which yielded the most desirable
behavior. This is a potential cause of the problems in theintermediary steps. Using a meta-heuristic algorithm toobtain the pillar parameter sets may solve this problem.

4.2. Full-parameter approach
A second approach augments the strategy used above by
incorporating all the influential circuit parameters
including the tail current and offset voltage of fv(v), theoffset voltage of fn(v), and the bias voltage of gn(v). First,
each of the circuit components were simulated
individually and circuit parameters were sought thatmaintained I-V characteristics over a range oftemperatures. These parameters were then used as astarting point for tuning the entire circuit at different
temperatures. Focus was placed on matching thenullclines as closely as possible. Through analysis of theindividual components, it was discovered that the gv(v),and gn(v) components experience a temperature induced
offset, which can be compensated for by tuning theoffset voltages of the fv(v) and fn(v) components.
The parameters for the pillar temperatures are listed
in Table 3. Those for the intermediary temperatures in
the range 17–37°C in 0.5°C steps were determined in
the same way as in the previous approach. The "full"column in Table 2 shows the results of the Spectre
simulations. Ith showed a downward trend and the firing
frequencies were relatively close to the benchmark, but
the 5 pA sustained stimulus failed to induce periodic
spiking at 17.5–21°, 29.5°, 34.5°, 35.5°, and 36.5°C.
While the temperature compensation with this approach
was less effective, nullclines that more accurately
matched the benchmark were obtained, suggesting that
future modifications may yield better results.

Acknowledgements
This study was supported by JST PRESTO and VLSI
Design and Education Centre (VDEC) at the Universityof Tokyo with collaboration from Cadence Corporation.
References
1. 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.
2. S. Liu, et al., Analog VLSI: Circuits and Principles, MIT
Press, 2002.
3. E. Chicca, F. Stefanini, C. Bartolozzi, and G. Indiveri,“Neuromorphic electronic circuits for building
autonomous cognitive systems,” Proceedings of the IEEE,
102, 9, pp. 1367-1388, 2014.
4. S. Brink, S. Nease, and P. Hasler, “Computing with
networks of spiking neurons on a biophysically motivatedfloating-gate based neuromorphic integrated circuit,”
Neural Networks, 45, pp. 39-49, 2013.
5. J. Rinzel and B. Ermentrout, “Analysis of Neural
Excitability and Oscillations,” Methods in Neuronal
Modeling, Massachusetts Institute of Technology, pp.
251-291, 1998.
6. A. Hodgkin, “The local electric changes associated withrepetitive action in a non-medullated axon,” The Journalof Physiology, 107, 2, pp. 165-181, March, 1948.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

119

Medical Image Analysis of Brain X-ray CT Images By Deep GMDH-Type Neural Network

Tadashi Kondo†
Graduate School of Health Sciences, Tokushima University
3-18-15 Kuramoto-cho Tokushima 770-8509 Japan
Email: †kondomedsci@gmail.com
Junji Ueno
Graduate School of Health Sciences, Tokushima University
3-18-15 Kuramoto-cho Tokushima 770-8509 Japan
Shoichiro Takao
Graduate School of Health Sciences, Tokushima University
3-18-15 Kuramoto-cho Tokushima 770-8509 Japan

Abstract
The deep Group Method of Data Handling (GMDH)-type neural network is applied to the medical image analysisof brain X-ray CT image. In this algorithm, the deep neural network architectures which have many hidden layers
and fit the complexity of the nonlinear systems, are automatically organized using the heuristic self-organization
method so as to minimize the prediction error criterion defined as Akaike’s Information Criterion (AIC) or
Prediction Sum of Squares (PSS). The learning algorithm is the principal component-regression analysis and theaccurate and stable predicted values are obtained. The recognition results show that the deep GMDH-type neuralnetwork algorithm is useful for the medical image analysis of brain X-ray CT images.
Keywords: Deep neural networks, GMDH, Medical image recognition, Evolutionary computation

nonlinear systems can be identified using this deep
GMDH-type neural network. In our previous work2, wehave applied the deep multilayered GMDH-type neural
The deep GMDH-type neural network algorithms
1,2
network to the medical image recognition of brain andwere proposed in our early works
and canblood vessels. The brain and blood vessel regions areautomatically organize the neural network architectures3
recognized and extracted accurately. In this study, theusing heuristic self-organization method which is a typedeep logistic GMDH-type neural network algorithm isof the evolutionary computation. In this study, deep
1
applied to the medical image analysis of brain X-ray CT
GMDH-type neural network algorithm is applied to theimages and the skull and the blood vessels in the brain
medical image analysis of brain X-ray CT images. The
are recognized and extracted. The recognition results arelearning calculations of the weights is the principal
compared with those obtained using the conventional
component-regression analysis and the accurate andneural networks trained using the back propagation
stable predicted values are obtained. In this GMDH-type
algorithm and it is shown that this deep neural network
neural network, two type neurons such as the sigmoidis accurate and useful for the medical image analysis offunction neuron and the polynomial neuron are used tothe brain X-ray CT image.
organize the deep neural network and very complex© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa, Japan
1. Introduction120

Tadashi Kondo, Junji Ueno, Shoichiro Takao

2. Deep GMDH-type neural network1

the first and second layer. The weights wi (i=0,1,2,,5)are estimated by using the principal componentThe GMDH-type neural network architecture isregression analysis. This procedure is as follows:shown in Fig.1. Here, nonlinear function gi is describedFirst, the values of zk are calculated using theby the following Kolmogorov-Gabor polynomial:
following equation:gi (x1,x2,,xp)= a0+aixi+aijxixj+ (1)zk=loge (’/(1-’))
(5)
i
i j
where ’ is the normalized output variable. Then theThis nonlinear function is automatically organizedweights wi (i=0,1,2,,5) are estimated by using theby using the polynomial neurons. The architecture ofprincipal component-regression analysis.
the GMDH-type neural network is automatically[Principal component-regression analysis]organized using the heuristic self-organization and is
In the GMDH-type neural network, the multiproduced as follows:colinearity is generated in the function  of the neurons
In the GMDH-type neural network, the original data
because heuristic self-organization method is used. In
are not separated into training and test sets because
this study, the function  is calculated using the
AIC4 or PSS5 can be used as the test errors.
principal component-regression analysis.
In the case of Eq.(3), orthogonal vector v is calculated .
v=Cu
(6)
Here,
v=(v1,v2,…,v5)
u=(ui uj, uiuj, ui2, uj2)
v is orthonormal vectors and C is orthonormal matrix. C
is calculated using the following eigenvalue equation.
R・C＝C・
(7)
Here, R is a correlation matrix. Then, variable zk is
gi (x1,x2,,xp)
calculated using orthogonal regression analysis.
zk = wT v
 : (Nonlinear function)=w1v1+w2v2+…+w5v5(8)zk =  wi gi (x1,x2,,xp)
Using the principal component-regression analysis,f : (Nonlinear function)
variable zk in the function  is calculated without multi =1/(1+exp ( - zk))Fig.1 Architecture of the deep GMDH-type neural network
colinearity. In (8), useful orthogonal variables i(i=1,2,…,5) are selected using AIC or PSS.
2.1 The first layer
From these generated neurons, L neurons which
uj=xj (j=1,2,,p)(2)
where xj (j=1,2,,p) are the input variables of theminimize AIC4 or PSS5 values are selected. The output
system, and p is the number of input variables.
values (yk) of L selected neurons are set to the input
2.2 The second layer
values of the neurons in the third layer.
All combinations of two variables (ui,uj) are generated.
2.3 The third and succeeding layers
For each combination, the neuron architecture is
In the third and succeeding layers, the samedescribed by the following equations:computation of the second layer is iterated until AIC or
: (Nonlinear function)
PSS values of L neurons are not decreased. When thezk=w1ui+w2uj+w3uiuj+w4ui2+w5uj2 –w01(3)
iterative computation is terminated, the followingf : (Linear function)
calculation of the output layer is carried out.
yk= zk(4)
2.4 The output layer
where 1 =1 and wi ( i=0,1,2,,5) are weights between
In the output layer, the output values of the neural© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa, Japan

121

Medical image analysis of

network are calculated from zk as follows:yk= 1/(1+exp ( - zk))
(9)
So, in the output layer, the neuron architecture becomes
as follows:: (Nonlinear function)zk =  wi gi (x1,x2,,xp)(10)
f : (Nonlinear function) =1/(1+exp ( - zk))(11)
The complete neural network architecture is produced
by selected neurons in each layer.
In this algorithm, the principal componentregression analysis is used for the learning calculationof the neural network and the accurate and stableprediction values are obtained. The structuralparameters such as the number of layers, the number ofneurons in hidden layers and useful input variables areautomatically selected to minimize prediction error
criterion defined as AIC or PSS. The GMDH-type
neural network is organized with two types of neurons
which are the sigmoid function neuron and thepolynomial neuron and this neural network can identify
very complex nonlinear system.

skull image was carried out. Fig.5 shows the output
image after the post-processing. The output image after
the post-processing was overlapped to the original
image (Fig.2) as shown in Fig.6. The recognized skull
region are very accurate. The skull region was extracted
from the original image as shown in Fig.7. These image
processing were carried out for all slices by theorganized deep GMDH-type neural network and the 3dimensional skull image was generated as shown in
Fig.8. A conventional neural network trained using theback propagation algorithm was applied to the same
recognition problem. The output images, when thenumbers of neurons in the hidden layer (m) are 5, 7 and
9, are shown in Fig.9. These images contain moreregions which are not part of the skull.

3. Application to the medical image analysis ofbrain X-ray CT images.

Fig. 2 Original image of the brain

In this study, the regions of the skull and the blood
15
vessels in the brain were recognized and extracted
automatically. Multi-detector row CT (MDCT) imagesof the brain are used in this study.
10
3.1 Extraction of the skull regions
PSS
An brain MDCT image shown in Fig.2 was used fororganizing the deep GMDH-type neural network. The
5statistics of the image densities and x and y coordinates



    
in the neighboring regions, the NN pixel regions, were0
used as the image features. The neural networks were
4
5
6
7
2
3
organized when the values of N were from 3 to 10.
Layer
When N was equal to 3, the neural network architecture
Fig. 3 Variation of PSS values in each layer (1)had the smallest recognition error. Fig.3 shows thevariation of PSS values in each layer. The PSS values
were decreased gradually through the layers and small
PSS value was obtained in the seventh layer. The deep
GMDH-type neural network output the skull image(Fig.4) and the post-processing analysis of the output© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa, Japan122

Tadashi Kondo, Junji Ueno, Shoichiro Takao

Fig. 4 Output image(1)3.2Extraction of the blood vessel regions
Another deep GMDH-type neural network wasorganized and applied to the recognition of the bloodvessel regions in the brain using the MDCT image
shown in Fig.10 which is extracted from the original
image in Fig.2. Fig.11 shows the variation of PSS
values in each layer. Very small PSS values wereobtained in the seventh layer. The deep GMDH-type
neural network output the blood vessel image as shownin Fig.12. The output image after the post-processing(Fig.13) was overlapped as shown in Fig.14. The
recognized blood vessel regions were very accurate. The
blood vessel regions were extracted from the original
image as shown in Fig.15. A conventional neuralnetwork trained using the back propagation algorithm
was applied and the output images, when the numbersof neurons in the hidden layer (m) are 5, 7 and 9, areshown in Fig.17. These images contained many noisecompared with the output image of the deep GMDHtype neural network which is shown in Fig.12.

Fig. 5 Output image after
the post processing

Fig.6 Overlapped image(1)Fig.7 Extracted image (1)Fig.8 The 3-dimensional skull image
Fig.10 Original image of blood vessels
54(a)m=5PSS(b) m=7

3



2
1












  

2

3

4

5

0

6

7

Layer

(c)m=9Fig.9 Output images of the conventional neural network (1)Fig. 11 Variation of PSS values in each layer (2)© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa, Japan123

Medical image analysis ofFig.12 Output image (2)(c) m=9

Fig.13 Output image after theFig.17 Output images of the conventional neural network (2)post-processing (2)4. Conclusions

Fig.14 Overlapped (2)In this paper, the deep GMDH-type neural network
algorithm using principal component-regression
analysis was applied to the medical image analysis of
the brain X-ray CT image, and these results werecompared with those of the conventional sigmoid
function neural network.
Acknowledgment
This work was supported by (JSPS) KAKENHI
26420421.
References

Fig.15 Extracted image (2)1.

T. Kondo, J. Ueno and S. Takao, Logistic GMDH-type
neural network using principal component-regression
analysis and its application to medical image diagnosis oflung cancer, Artificial Life and Robotics 20(2)(2015) 137144.

2. T. Kondo, J. Ueno and S. Takao, Deep multi-layered
GMDH-type neural network using principal componentregression analysis and its application to medical image
recognition of brain and blood vessels, Proceedings of theFig.16 The 3-dimensional blood vessel image

twentieth international symposium on artificial life androbotics (2015) pp.92-95.
3.

S. J. Farlow ed., Self-organizing methods in modeling,
GMDH-type algorithm, New York: Marcel Dekker Inc.,
1984.

4.

H. Akaike, A new look at the statistical model
identification, IEEE Trans. Automatic Control, AC-19,(6) (1974) 716-723.

5.
(a)m=5

H. Tamura, T. Kondo, Heuristics free group method ofdata handling algorithm of generating optimum partial(b) m=7

polynomials with application to air pollution prediction,
Int. J. System Sci. 11 (9) (1980) 1095-1111.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa, Japan124

Medical Image Diagnosis of Lung Cancer by Deep Feedback GMDH-Type Neural Network

Tadashi Kondo†
Graduate School of Health Sciences, Tokushima University
3-18-15 Kuramoto-cho Tokushima 770-8509 Japan
Email: †kondomedsci@gmail.com
Junji Ueno
Graduate School of Health Sciences, Tokushima University
3-18-15 Kuramoto-cho Tokushima 770-8509 Japan
Shoichiro Takao
Graduate School of Health Sciences, Tokushima University
3-18-15 Kuramoto-cho Tokushima 770-8509 Japan

Abstract
The deep feedback Group Method of Data Handling (GMDH)-type neural network is applied to the medical image
diagnosis of lung cancer. The deep feedback GMDH-type neural network can identified very complex nonlinear
systems using heuristic self-organization method which is a type of evolutionary computation. The deep neuralnetwork architectures are organized so as to minimize the prediction error criterion defined as Akaike’s Information
Criterion (AIC) or Prediction Sum of Squares (PSS). In this algorithm, the principal component-regression analysis
is used for the learning calculation of the neural network. It is shown that the deep feedback GMDH-type neuralnetwork algorithm is useful for the medical image diagnosis of lung cancer because deep neural networkarchitectures are automatically organized using only input and output data.
Keywords: Deep neural networks, GMDH, Medical image recognition, Evolutionary computation

type neural network and these lung regions wereextracted and then the lung cancer regions wereextracted using the image post processing of theextracted lung regions. In another previous our work3,
we applied the hybrid feedback GMDH-type neuralnetwork to medical image diagnosis of liver cancer. The
liver cancer regions were recognized and extractedusing the same image processing using the hybrid
feedback GMDH-type neural network.
In this paper, we apply the deep feedback GMDHtype neural network and the lung cancer regions arerecognized directly using the deep feedback GMDHtype neural network and the lung cancer regions are
extracted accurately. The recognition results shows that

1. Introduction
The deep GMDH-type neural network algorithms
were proposed in our early works1~3 and can
automatically organize the neural network architecturesusing heuristic self-organization method4 which is a typeof the evolutionary computation. In this study, deep
feedback GMDH-type neural network algorithm1 isapplied to the medical image diagnosis of lung cancer.
The learning calculations of the weights is the principal
component-regression analysis. In previous our work2,
we applied the logistic GMDH-type neural network to
the medical image diagnosis of lung cancer. First, thelung regions were recognized by the logistic GMDH-© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa, Japan125

Tadashi Kondo, Junji Ueno, Shoichiro Takao

zk= w1u1+w2u2+w3u3+ ··· +wrur - w01 ( r<p ) (4)f : (Nonlinear function)
1
yk 
(5)
1  e(  z )(2) Radial basis function neural network
1) The first type neuron
: (Nonlinear function)zk=w1ui+w2uj+w3uiuj+w4ui2+w5uj2- w01
(6)
f : (Nonlinear function)2(7)
y k  e (  zk )2) The second type neuron
: (Linear function)zk= w1u1+w2u2+w3u3+ ··· +wrur - w01 ( r<p ) (8)f : (Nonlinear function)2(9)y k  e (  zk )(3) Polynomial neural network
1) The first type neuron
: (Nonlinear function)zk=w1ui+w2uj+w3uiuj+w4ui2+w5uj2 - w01
(10)
f : (Linear function)yk= zk(11)2) The second type neuron
: (Linear function)zk= w1u1+w2u2+w3u3+ ··· +wrur - w01 ( r<p) (12)f : (Linear function)yk= zk(13)
Here, 1 =1 and wi (i=0,1,2,,5) and wi (i=0,1,2,,r)
are weights between the input and hidden layer. Weights
wi (i=0,1,2,) in each neural network architecture are
estimated by the principal component-regression
analysis.
[Estimation procedure of weight w i ]First, values of zk** are calculated for each neuralnetwork architecture as follows.
a) Sigmoid function neural network
'
(14)
**
zk  loge ()'
1b) RBF neural network**(15)
zk   loge  'c) Polynomial neural network
zk**=
(16)
where  is an output variable and ’ is the normalized
output variable whose values are between 0 and 1.
[Principal component-regression analysis]

the deep feedback GMDH-type neural network canrecognize the lung cancer regions directly and extract
accurately.

k

2. Deep feedback GMDH-type neural network1
The architecture of the deep feedback GMDH-type
neural network used in this paper has a feedback loop as
shown in Fig.1.
2.1 First loop calculation
First, all data are set to the training data. Then thearchitecture of the input layer is organized.
2.1.1 Input layer
uj=xj (j=1,2,…,p)(1)
where xj (j=1,2,…,p) are the input variables of thesystem, and p is the number of input variables.
 f
X1

f

X2

 f

X3
.
.
.
XP

.
.
.

 f







.
.

f
 f
f

Fig.1 Architecture of the deep feedback GMDH-type neuralnetwork

2.1.2 Hidden layer
All combinations of the r input variables are
generated. For each combination, three types of neuron
architectures which are the sigmoid function neuron, theredial basis function (RBF) neuron and the polynomial
neuron, are generated and L neurons which minimize
AIC5 and PSS6 value are selected for each type ofneuron architectures.
(1) Sigmoid function neural network
1) The first type neuron
: (Nonlinear function)zk=w1ui+w2uj+w3uiuj+w4ui2+w5uj2 - w01
(2)
f : (Nonlinear function)
1
yk 
(3)
1  e(  z )2) The second type neuron
: (Linear function)k© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa, Japan

126

Medical image diagnosis ofMulti-colinearity is generated in the function  ofthe neurons. In this study, the function  is calculatedusing the principal component-regression analysis.
In the case of Eq.(2), orthogonal vector v is calculated .
v=Cu
(17)
2
2
Here, v=(v1,v2,…,v5) , u=(ui uj, uiuj, ui , uj )v is orthonormal vectors and C is orthonormal matrix. C
is calculated using the following eigenvalue equation.
R・C＝C・
(18)
Here, R is a correlation matrix. Then, variable zk iscalculated using orthogonal regression analysis.
zk = wT v=w1v1+w2v2+…+w5v5(19)
Using the principal component-regression analysis,
variable zk in the function  is calculated without multicolinearity. In (19), useful orthogonal variables
i(i=1,2,…,5) are selected using AIC5 or PSS6 criterion.
L neurons having the smallest AIC or PSS values
are selected for three types of neuron architectures. The
output variables yk of L selected neurons for three typesof neuron architectures are set to the input variables of
the neurons in the output layer.
2.1.3 Output layer
For three types of neural network, the outputs yk of
the neurons in the hidden layer are combined by thefollowing linear function.
(20)

is organized by the L selected neurons in each feedbackloop.
3. Application to the medical image diagnosis oflung cancer
In this study, the lung cancer regions wereautomatically recognized using the deep feedback
GMDH-type neural network and these regions wereextracted. Multi-detector row CT (MDCT) images ofthe lung were used in this study.
3.1 Results of the medical image recognition by the
deep feedback GMDH-type neural network
The MDCT image shown in Fig.3 which was
extracted from the MDCT image in Fig.2, was used fororganizing the neural network. x and y coordinates andthe statistics of the image densities in the neighboring
regions of the NN pixels at the positions of the learning
points are used as the input variables of the neuralnetwork. Only five input variables which are the mean,
the standard deviation, the variance and x and ycoordinates were automatically selected as useful input
variables. The output value of the neural network is zeroor one. When NN pixel region is contained in the lungcancer and blood vessel regions, the neural network set
the pixel value at the center of the NN pixel region to
one and this pixel is shown as the white point. The
neural networks were organized when the values of N
were from 3 to 10. It was determined that when N was
equal to 3, the neural network architecture had thesmallest recognition error. Five useful neurons wereselected in each hidden layer. Fig.4 shows the PSS
values of the three types of neurons in the second layer.
The RBF neural network architecture was selected by
the deep GMDH-type neural network algorithm. Fig.5
shows the variation of PSS values in each layer. PSS
values decreased gradually and very small PSS values
were obtained at the seventh layer. The lung cancer andblood vessel regions were recognized by using theorganized neural network and was extracted
automatically. Fig.6 shows the output image of the deep
feedback GMDH-type neural network. This output
image was processed by the post-processing analysis. In
the post-processing, the small isolated regions were

Here, L is the number of combinations of the input
variables and yk is the intermediate variables. Eq. (20) is
calculated for three types of neural networkarchitectures. The neural network architecture which
has smallest AIC or PSS value is selected. Then, theestimated output values * which is selected in the
output layer is used as the feedback value.
2.2 Second and subsequent loop calculationsFirst, the estimated output value * is combined with
the input variables x and all combinations between theestimated output value * and the input variables x are
generated. The same calculation as the first feedback
loop is iterated. When AIC or PSS value of the linear
function in (20) is not decreased, the loop calculation is
terminated and the complete neural network architecture© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa, Japan127

Tadashi Kondo, Junji Ueno, Shoichiro Takao

eliminated and the outlines of the lung cancer regions
were expanded outside by N/2 pixels. Fig.7 shows theoutput image after this processing. In order to check thematching between the original image and the output
image of the neural network, the output image wasoverlapped on the original image in Fig.3. The
overlapped image is shown in Fig.8. From Fig.8, we
can see that the output image was very accurate. Fig.9
shows the extracted lung cancer image. These image
processing were carried out for all slices by theorganized deep feedback GMDH-type neural networkand the 3-dimensional lung cancer image was generated
as shown in Fig.10.

0.7
0.6
0.5

PSS

0.4
0.3
0.2
0.1

0
0

1

2

3

4

5

6

7

Number of feedback loops
Fig. 5 Variation of PSS value in each layer

Fig. 6 Output image

Fig.7 Output image
after the post processing

Fig.2 Original image

Fig. 8 Overlapped image

Fig. 9 Extracted image

Fig.3 Original image extracted from Fig.2


1.0

PSS


0.5
５
0


Fig.10 The 3-dimensional image of lung cancer

Sigmoid

RBF Polynomial

Fig. 4 PSS values of three types of neurons© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa, Japan

128

8

Medical image diagnosis of

3.2 Extraction by the conventional neural network
using sigmoid function.
A conventional neural network trained using theback propagation algorithm was applied to the same
recognition problem. The learning calculations of theweights were iterated changing structural parameterssuch as the number of neurons in the hidden layer andthe initial values of the weights. The output images,
when the numbers of neurons in the hidden layer (m) are
5, 7 and 9, are shown in Fig.11. These images containmore regions which are not part of the lung cancer andblood vessel regions, and the outlines of the lung cancer
are not extracted with required clarity compared withthe output images obtained using the deep feedback
GMDH-type neural network algorithm, which are
shown in Fig.6.

recognition of lung cancer regions and the results werecompared with those of the conventional sigmoid
function neural network trained using the backpropagation algorithm. It was shown that the deep
feedback GMDH-type neural network algorithm was auseful method for the medical image diagnosis of thelung cancer because the deep feedback neural networkarchitecture is automatically organized so as tominimize the prediction error criterion defined as AIC
or PSS.
Acknowledgment
This work was supported by (JSPS) KAKENHI
26420421.
References
1.

T. Kondo, J. Ueno and S. Takao, Deep feedback GMDHtype

neuralnetworkusing

principal

component-

regression analysis and its application to medical image
recognition of abdominal multi-organs, Journal of
Robotics, Networking and Artificial Life 2(2)(2015) 9499.
2.

T. Kondo, J. Ueno and S. Takao, Logistic GMDH-type
neural network using principal component-regression(a) m=5

analysis and its application to medical image diagnosis of(b) m=7lung cancer, Artificial Life and Robotics 20(2)(2015) 137144.
3.

T. Kondo, J. Ueno and S. Takao, Medical image
diagnosis of liver cancer by hybrid feedback GMDH-type
neural network using principal component-regression
analysis, Artificial Life and Robotics 20(2)(2015) 145151.

4.

S. J. Farlow ed., Self-organizing methods in modeling,
GMDH-type algorithm, New York: Marcel Dekker Inc.,
1984.

(c) m=95.

identification, IEEE Trans. Automatic Control AC-19 (6)Fig. 11 Output images of the conventional sigmoid(1974) 716-723.

function neural network
6.

4.

H. Akaike, A new look at the statistical model

H. Tamura, T. Kondo, Heuristics free group method ofdata handling algorithm of generating optimum partial

Conclusions

polynomials with application to air pollution prediction,

In this paper, the deep feedback GMDH-type neuralInt. J. System Sci. 11 (9) (1980) 1095-1111.
network algorithm was applied to the medical image© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa, Japan

129

Feature Linking by Synchronized Response in Chaotic Cellar Neural Network
for Visual Stimulus of Moving Objects
Akihiro Yamaguchi *, Satoshi Arakane

Department of Information and Systems Engineering, Fukuoka Institute of Technology,
3-30-1 Wajiro-higashi, Higashi-ku, Fukuoka, 811-0116, JAPANMasao Kubo

Department of Computer Science, National Defense Academy of Japan,
1-10-20 Hashirimizu, Yokosuka, Kanagawa, 239-8686, JAPANE-mail: *aki@fit.ac.jp

Abstract
A feature linking mechanism by the synchronized response of neural assemblies was studied for the chaotic cellar
neural network (Chaotic-CNN). The Chaotic-CNN consists of chaotic spike response neurons that show the chaotic
inter-spike-interval dynamics. In our scheme of feature linking, the features of the target objects are linked by thesynchronized spike responses that are characterized by the temporal chaotic pattern of spike sequence. In this paper,
we analyzed the synchronized spike responses that invoked by the visual stimulus of moving bars. As a result, neuralassemblies have higher correlation for the visual stimulus of moving two bars in the same direction than the opposite
direction. Then we discussed a possibility of feature linking by the chaotic synchronized response in the view pointof neural coding.
Keywords: chaotic synchronization, neural coding, spike response model, feature linking

information. Therefore, there is a possibility to increase
the performance of information representation by using achaotic synchronization of spike responses3,4.
In the view point of neural coding, authors havestudied the formation of the chaotic cell assemblies in the
chaotic cellular neural network (Chaotic-CNN) that is a
two dimensional coupled network of chaotic neurons5-7.
The chaotic neuron is modeled by the chaotic spike
response model (Chaotic-SRM) that is an extended SRM
to show chaotic inter-spike interval by adding thebackground sinusoidal oscillation5-7.
In our previous study, chaotic cell assemblies wereformed in the Chaotic-CNN for the visual stimulus when
the stationary image was inputted6,7. In this research, we
analyze the formation of chaotic cell assemblies for the

1. Introduction
For the brain system, feature linking is one of theprincipal functions to realize the recognition of visualobjects. From the physiological experiments, thecorrelated firing among neurons is regarded as a
candidate of the mechanism of such feature linking 1,2. In
the neural coding scheme using the synchronization ofneurons, the feature linking information is represented by
the synchronized firing among the corresponding
neurons such that the features detected by neurons arelinked if their firing pattern are synchronized.
On the other hand, the chaotic system is well known
for its complex behavior. Comparing with the periodic
spike pattern, the chaotic spike pattern is expected tohave an advantage for the variety of represented© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

130

Akihiro Yamaguchi, Satoshi Arakane, Masao Kubo

and 𝑦 ∈ {0,1, ⋯ , 𝑀 − 1}. The membrane potential 𝑢𝑥,𝑦of the neuron 𝑛𝑥,𝑦 is defined as

visual stimulus of moving objects and the feature linking
property of them.

∗𝑢𝑥,𝑦 (𝑡) = 𝑢𝑟𝑒𝑠𝑡 + 𝜂(𝑡 − 𝑡𝑥,𝑦) + 𝛽𝑥,𝑦

2. Chaotic Cellular Neural Network+𝜉 ×

The Chaotic-CNN is defined as a two dimensional
coupled system of the Chaotic-SRM5-7. In the following,
the definition of the neuron model and the network is
described, respectively.

𝑛𝑥′,𝑦′ ∈𝐵(𝑥,𝑦;𝑟)

𝐵(𝑥, 𝑦; 𝑟) = {𝑛𝑥 ′ ,𝑦′ | (𝑥 ′ , 𝑦 ′ ) ≠ (𝑥, 𝑦),max(|𝑥 − 𝑥 ′ |, |𝑦 − 𝑦 ′ |) ≤ 𝑟 ,0 ≤ 𝑥 ′ ≤ 𝑁 − 1, 0 ≤ 𝑦 ′ ≤ 𝑀 − 1 }.

(1)𝑜𝑥 ′,𝑦′ (𝑡) =∑(𝑘)𝜀(𝑡 − 𝑡𝑥 ′,𝑦′ − Δ𝜀)

(𝑘)

∗ <𝑡
𝑡𝑥,𝑦
+Δε<𝑡
𝑥′ ,𝑦′(6)(𝑘)

where 𝑡𝑥 ′ ,𝑦′ denotes the 𝑘-th firing time of the neuron𝑛𝑥 ′ ,𝑦′ and Δ𝜀 is the time delay of synaptic connection.
The kernel function 𝜀 describes the response of thesynaptic connection. The definition of the kernel function
𝜀 is

(2)

where 𝜏𝜂0 is the time constant of the spike response, and𝛩 is a step function such that 𝛩(𝑠) is 1 for 𝑠 ≥ 0 and 0
for the other values.
In this model, when the membrane potential exceeds
the threshold value 𝜃 , the neuron is firing and themembrane potential is reset by the update of the last
firing time 𝑡 ∗ . The term −𝜂𝑖𝑛𝑖𝑡 is an initial value of thekernel function 𝜂 after firing.
We extended the original SRM to show the chaotic
response by adding a background sinusoidal oscillationin the same way as the bifurcating neuron9 and thechaotic pulse coupled neural network4. In our model, thebackground oscillation is added to the term 𝜂𝑖𝑛𝑖𝑡 suchthat𝜂𝑖𝑛𝑖𝑡 = 𝜂0 − 𝐴𝜂0 𝑠𝑖𝑛(2𝜋𝜔𝜂0 𝑡 ∗ + 𝜙),(5)

The function 𝑜𝑥 ′ ,𝑦′ is the output from the connected
neuron 𝑛𝑥 ′ ,𝑦′ and it is also defined aswhere 𝑢𝑟𝑒𝑠𝑡 , 𝑡 ∗ , and 𝛽 denote the resting potential, thelast firing time, and the external input, respectively. The
kernel function 𝜂 is defined as𝑡 − 𝑡∗
𝜂(𝑡 − 𝑡 ∗ ) = −𝜂𝑖𝑛𝑖𝑡 𝑒𝑥𝑝 () Θ(𝑡 − 𝑡 ∗ ),
𝜏𝜂0

(4)

∗
where 𝑢𝑟𝑒𝑠𝑡 , 𝑡𝑥,𝑦
, 𝛽𝑥,𝑦 , 𝜉 and 𝐵(𝑥, 𝑦) denote the resting
potential, the last firing time, the external input, thecoupling weight, and the set of connected neurons,
respectively. A set of connected neurons is defined as

2.1 Neuron Model
As a neuron model, we use the Chaotic-SRM that is anextended spike response model (SRM). The spike
response model (SRM) was introduced by Gerstner andKistler8. The definition of Chaotic-SRM is as follows.
The membrane potential 𝑢(𝑡) of the neuron at the time 𝑡
is defined as𝑢(𝑡) = 𝑢𝑟𝑒𝑠𝑡 + 𝜂(𝑡 − 𝑡 ∗ ) + 𝛽,

𝑜𝑥 ′ ,𝑦 ′ (𝑡)∑𝜀(𝑠) =𝑠𝜏𝜀𝑠

𝑒𝑥𝑝 (− ) Θ(𝑠),𝜏𝜀(7)where 𝜏𝜀 is the time constant of the synaptic connection.
2.3 Gradient Field of Phase Shift
In this research, to emphasize the synchronized response
in one direction, a gradient field of the phase shift is
introduced in Eq.(3). The gradient field of the phase shift
is defined as,
𝜙𝑥,𝑦 (𝛼) = 𝐷(cos(𝛼) 𝑥 + sin(𝛼) 𝑦) 𝑚𝑜𝑑 2𝜋,(8)where 𝐷 is the constant of gradient and 𝛼 is the directionof gradient. Therefore 𝜙𝑥,𝑦 is the same value in theorthogonal direction of 𝛼 in the Chaotic-CNN.

(3)

where 𝐴𝜂0 , 𝜔𝜂0 , and 𝜙 denote the amplitude, thefrequency, and the phase shift of the backgroundoscillation, and 𝜂0 is constant. The Chaotic-SRM shows
various chaotic behaviors depending on the parameters𝐴𝜂0 and 𝛽.

3. Numerical Analysis of Coupled two Neurons
As a preliminary numerical experiment, we examined thesynchronizing property of the coupled two neurons that
corresponds to the Chaotic-CNN 2 × 1. In this case, theneuron 𝑛0,0 and 𝑛1,0 are coupled to each other. In the
numerical simulation, the parameter values of the
Chaotic-SRM are set as follows: 𝑢𝑟𝑒𝑠𝑡 = −70 mv, 𝜃 =2.2 Definition of Network
In the Chaotic-CNN, each neuron is located in the 𝑁 × 𝑀
lattice and connected to neighbors. Let 𝑛𝑥,𝑦 be a neuron
located at the position (𝑥, 𝑦) where 𝑥 ∈ {0,1, ⋯ , 𝑁 − 1}

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan131

Feature Linking by Synchronized
0.75

case of 𝛥𝑡 = 0, the cross-correlation 𝐶𝐶(𝑆𝑥,𝑦 , 𝑆𝑥′,𝑦′ ; 0)
corresponds to the ratio of synchronized spikes.
We simulated these coupled two neuros andcalculated the cross-correlation 𝐶𝐶(𝑆0,0 , 𝑆1,0 ; 0) for
generated spike sequences. Results are shown in Fig. 1
and Fig. 2. Fig. 1 shows the dependency of the crosscorrelation to the coupling weight 𝜉. In the case that thephase shift is equivalent for two neurons(𝜙0,0 = 𝜙1,0 ),the complete synchronization such that 𝐶𝐶(𝑆0,0 ,𝑆1,0 ; 0) ≃ 1 is achieved as increasing the couplingweight 𝜉. Fig. 2 shows the dependency of the crosscorrelation to the difference of phase shifts (𝜙1,0 − 𝜙0,0 ).
The cross-correlation exhibits a selective preference forthe case that the difference of phase shifts is 0. In thisresearch, we apply this selective preference of coupledsystem to the feature linking.

−35 mv , 𝜂0 = 55 , 𝜏𝜂 = 10 , 𝜔𝜂0 =, 𝛢𝜂0 = 10.9 ,2𝜋
𝜏𝜀 = 1.5 and 𝛥𝜀 = 0.1. The external input is chosen as
𝛽0,0 = 𝛽1,0 = 52.5 such that the single neuron exhibits
chaotic spike response.
As an index of the synchronization, the crosscorrelation between two spike sequences is analyzed. Let
𝑆𝑥,𝑦 be a set of the firing times of the neuron 𝑛𝑥,𝑦 . The
cross-correlation between 𝑆𝑥,𝑦 and 𝑆𝑥 ′,𝑦′ is defined as
(𝑘)

(𝑙)

𝐶𝐶(𝑆𝑥,𝑦 , 𝑆𝑥′,𝑦′ ; Δ𝑡) = #({𝑡𝑥,𝑦 ∈ 𝑆𝑥,𝑦 |∃𝑡𝑥′,𝑦′ ∈ 𝑆𝑥′,𝑦′ ,
(𝑘)

(𝑙)

(9)

|𝑡𝑥,𝑦 − 𝑡𝑥′,𝑦′ − Δ𝑡| ≤ 𝛥𝑠}) / #(𝑆𝑥,𝑦 )where #(𝑋) denotes the number of elements of 𝑋, and𝛥𝑠 is the time resolution of the coincident firing. In thisresearch, 𝛥𝑠 is set to 0.5 ms. The auto-correlation is also
defined as 𝐴𝐶(𝑆𝑥,𝑦 ; 𝛥𝑡) = 𝐶𝐶(𝑆𝑥,𝑦 , 𝑆𝑥,𝑦 ; 𝛥𝑡) . In the

4. Numerical Experiment
4.1 Experimental Model
In order to examine the feature linking property by thesynchronized response, we construct the simulation
model of the Chaotic-CNN 40 × 40 and the motion
picture of moving two bars is inputted as visual stimulus
like the physiological experiments of cat visual cortex1.
In this research, we also make two motion patters. One is
the moving two bars in the same direction (Fig. 3(a)) andthe other is in the opposite direction (Fig. 3(b)).
The image of bar is represented by a rectangle of
5 × 12 pixels. The velocity of bar is 0.07 pixels /msec.
The external input 𝛽𝑥,𝑦 is 52.5 for the neurons inputted
the image of the rectangle and 0 for the others. The
parameter of the connection 𝑟 is 2. The gradient field of
the phase shift is set as 𝐷 = 0.353 and 𝛼 = 0.

Fig. 1. The cross-correlation vs. the coupling weight 𝜉 for the
coupled two neurons.

4.2 Cross-correlation analysis
Numerical simulations are performed for the spike
responses during 1,000 ms. Then, the cross-correlation is
calculated for the neuron 𝑛20,10 and the others. The
neuron 𝑛20,10 locates at the center of the way of the bar 1
in Fig. 3.
The cross-correlation for the moving two bar in thesame direction (Fig. 3(a)) is shown in Fig. 4(a). Fig. 4(a)shows the formation of neural assemblies in which
neurons are synchronized. Furthermore, the neuralassembly corresponds to the bar 1 has high crosscorrelation with the assembly of the bar 2. On the other
hand, the cross-correlation for the moving two bar in the
opposite direction (Fig. 3(b)) is shown in Fig. 4(b).
Although Fig. 4(b) also shows the formation of neuralFig. 2. The cross-correlation vs. the difference of the phaseshift (𝜙1,0 − 𝜙0,0 ) for the coupled two neurons.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

132

Akihiro Yamaguchi, Satoshi Arakane, Masao Kubo

assembly, the cross-correlation between the assembly of
the bar 1 and the bar 3 is low.

By the cross-correlation analysis, the invoked neuralassemblies have high correlation for the moving objects
in the same direction and low correlation for the opposite
direction. These results indicate a possibility of thefeature linking by the synchronized spike responses in the
Chaotic-CNN in terms of the cross-correlation. In ourmodel, this linking property might be achieved by thepreference for the difference of phase shifts mentioned inSec. 3. Analysis of the dynamics of invoked spikesequences along the moving assembly and theapplication to the real image are our future works.

5. Discussions
For the visual stimulus of moving objects, the formationof neural assemblies were observed in the Chaotic-CNN.

Acknowledgements
This work was supported by JSPS KAKENHI Grant
Numbers 24650120.
(a) The same direction(b) The opposite direction

References

Fig. 3. Visual stimuli for the numerical experiment. The
motion of moving two bars in the same direction (a) and the
opposite direction (b).

1. Gray, C.M., Koenig, P., Engel, A. K., and Singer, W.,
Oscillatory responses in cat visual cortex exhibit intercolumnar synchronization which reflects global stimulus
properties, Nature, 338, pp.334-337, 1989.
2. Eckhorn, R., Reitboeck, H.J., Arndt, M., Dicke, P., Featurelinking via stimulus-evoked oscillations: experimental
results from cat visual cortex and functional implications
from a network model, International Joint Conference on
Neural Networks, pp 723-730, 1989.
3. Yamaguchi, A., Okumura, N., Chaki, H., and Wada, M.,
Chaotic synchronized cluster in the network of spike
response neurons, IEICE Tech. Rep., Vol.99, No.685,
NC99-119, pp. 15-20, Mar. 2000 (in Japanese).
4. Yamaguti, Y., Ishimura, K., and Wada M., Chaotic
synchronized assembly in Pulse Coupled Neural Networks,
IEICE Tech. Rep., Vol.101, No.615, NC2001-98, pp.127134, Jan. 2002 (in Japanese).
5. Yamaguchi, A., On a chaotic synchronization of one-way
coupled two spike response neurons, Fukuoka Institute ofTechnology Reports of Computer Science Laboratory,
Vol.24, pp.1-6, 2013 (in Japanese).
6. Yamaguchi, A., On an information coding using localized
synchronization in the two dimensional coupled system ofchaotic spike response neurons, Fukuoka Institute ofTechnology Reports of Computer Science Laboratory,
Vol.25, pp.1-6, 2014 (in Japanese).
7. Fujiwara, M., Yamaguchi, A., and Kubo M., Synchronized
Response to Grayscale Image Inputs in Chaotic Cellular
Neural Network, Journal of Robotics, Networking andArtificial Life, Vol.2, No.1, pp.26-29, 2016.
8. Gerstner, W., and Kistler, W., Spiking Neuron Models:Single Neurons Populations Plasticity, Cambridge
University Press, 2002.
9. Lee G., and Farhat, N.H., The Bifurcating Neuron
Network 1, Neural Networks, Vol. 14, pp.115-131, 2001.

(a) The same direction(b) The opposite direction
Fig. 4. The cross-correlation between the neuron 𝑛20,10 and theothers when the visual stimuli as shown in Fig. 3 were inputted.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan133

An FPGA-based cortical and thalamic silicon neuronal network
Takuya Nanami

School of Engineering, The University of Tokyo
Tokyo, Japan
Takashi Kohno

Institute of Industrial Science, The University of Tokyo
Tokyo, Japan

Abstract
A DSSN model is a neuron model which is designed to be implemented efficiently by digital arithmetic
circuit. In our previous study, we expanded this model to support the neuronal activities of several corticaland thalamic neurons; Regular spiking, fast spiking, intrinsically bursting and low-threshold spike. In thispaper, we report our implementation of this expanded DSSN model and a kinetic-model-based silicon
synapse on an FPGA device. Here, synaptic efficacy was stored in block RAMs, and external connection
was realized based on a bus that conform to the address event representation. We simulated our circuit by the
Xilinx Vivado design suit.
Keywords: silicon neuronal network, neuron model, FPGA, cortex, thalamus.

1.

Introduction
computational efficiency, diversified neuronal models areused in silicon neuronal networks. For example, theionic-conductance models can reproduce a neuronal
activity accurately but consumes excessive computational
resources in large-scale implementations. On the other
hand, neuronal models that approximate a spiking processby resetting of the state variables (integrate-and-fire(I&F) -based models), such as the LIF, exponential I&F
model[], adaptive exponential I&F model[8] and IZH
models, can be implemented at low computational cost.
However, it has reduced reproducibility of complex
neuronal activities. For instance, these models assume
fixed maximum membrane potentials during the spikeprocess, whereas these potentials are non-uniform in the
nervous system.
In our previous research [9], we expanded the DSSN
model to cover cortical and thalamic neuron classes,
including regular spiking (RS), fast spiking (FS),low-threshold spike (LTS) and intrinsically bursting (IB).
We confirmed that the model behaves in the same way as
the ionic-conductance model [10] in response to various
magnitude of step input in each neuron classes.
In this paper, we designed digital arithmetic circuit for
the DSSN model on an FPGA. We simulated our circuiton Xilinx Vivado design suit. The remainder of this paper
is organized as follows. Section 2 introduces our neuronmodel and details of its implementation. The simulation

Silicon neuronal networks can simulate neuronal activity
with low power consumption and in high speed. They arethought to be a promising way to realize an extremely
large-scale network comparable to the human brain in
future. The Field-Programmable Gate Array (FPGA)
devices are commonly used for silicon neuronal networks
because they can implement user-designed circuits withlow cost. Many silicon neuronal networks on an FPGA
can run at a higher speed than the biological real-time[1][2]. For example, a fully-connected 1024-neuron
network that is 100 times the real-time speed has been
implemented on a single Virtex-5 FPGA [3]. Thomas et al.
adopted the Izhikevich (IZH) model that can simulate
various types of neuronal activities with theircorresponding parameter sets. Li et al. [4] constructed anauto-associative memory with 256 fully connected digital
spiking silicon neuron (DSSN) models on an FPGA. The
DSSN model [5] is an qualitative neuronal model
designed for efficient implementation by efficiently in adigital arithmetic circuit. This model is a non-I&F-basedmodel and can realize several neuronal activities
including Class I and II in the Hodgkin’s classification [6].
This model can reproduce the gradient response in Class
II neurons [7], because it does not abbreviate thecalculation of the spiking process. Due to a trade-off
between the reproducibility of neuronal activity and© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan134

Takuya Nanami, Takashi Kohno

result is shown in Section 3. Section 4 summarizes thework and suggests ideas for future.

Av

B
x 1.00101001
×1
×2

v_v0n

×2

Cv_v

×2

x 0.0011111 (= 0.01 – 0.0000001)

v

u
MUX

MUX

MUX

v_v

MUX

vnext

v_vv
v_n
v_q
v_u
v_c

×2 2

v_v0p

× −2

v_v0n
v_v0p
v_v1n
v_v1p
v_v2n
v_v2p

the fast and slow variables, respectively, that abstractly

Fig. 2 The architecture of 16 fully-connected network. The
network is composed of 16 DSSN, 16 silicon synapse, and an
accumlator unit

Fig.1 Block diagram of the v-circuit. Symbols ×, + and MUXmean a multiplier, adder, multiplexer, respectively.
(A)
Multiplication realized by shift operation and adder. (B) Add
operation of the differential equation of v. (C) Correct 𝑣𝑖𝑗 arechosen by multiplexer depending on v and u.

describe the activity of the ion channels. The slow
variable q controls the slow dynamics of the neuronal
activity and has a key role to realize spike-frequency
adaptation and burst firing. Variable 𝑢 is the slowest thatmodifies the structure of the fast subsystem comprising v
and n. If sustained stimulus to the fast subsystem is taken
as a bifurcation parameter, a saddle-node bifurcation isobserved in the RS, FS, and LTS modes, and ahomoclinic - loop bifurcation in the IB mode. The
parameter 𝐼0 is a bias constant and 𝐼𝑠𝑡𝑖𝑚 represents the
input stimulus. The other parameters determine thedynamical properties of the model. All of the variables
and constants in this qualitative model are purely
abstracted and have no dimension.
The model is solved by the Euler’s method ( ∆t =0.0001). We developed differential equations for efficient
implementation. The equations of the circuit forcalculation of v are

2. Methods
2. 1 DSSN model
The DSSN model is a qualitative neuronal model.
It is designed so that diverse neuronal activities with fixed
point Euler method. Given appropriate parameter sets,
this model can reproduce various neuron classes
including regular spiking (RS), fast spiking (FS),intrinsically busting (IB), and low-threshold spike (LTS).
The model is given by
𝑑𝑣 ∅(𝑢)(𝑓(𝑣) − 𝑛 − 𝑞 + 𝐼0 + 𝐼𝑠𝑡𝑖𝑚 ),
=
𝑑𝑡
𝜏
𝑑𝑛 1
= (𝑔(𝑣) − 𝑛),𝑑𝑡 𝜏𝑣next = 𝑣_𝑣𝑣 + 𝑣_𝑣 + 𝑣_𝑛 + 𝑣_𝑞 + 𝑣_𝐼 + 𝑣_𝑐,
𝑣_𝑣𝑣𝑖𝑗 = 𝑣2 ∙ (∆𝑡 ∙ 𝑎𝑓𝑗 ∙ ∅𝑖 /𝜏),𝑑𝑞 𝜀= (ℎ(𝑣) − 𝑞),
𝑑𝑡 𝜏
𝑑𝑢
𝑑𝑡

𝜏

𝑎𝑓𝑛 (𝑣 − 𝑏𝑓𝑛 ) + 𝑐𝑓𝑛
2

𝑎𝑓𝑝 (𝑣 − 𝑏𝑓𝑝 ) + 𝑐𝑓𝑝
2ℎ(𝑣) ≡ {𝑣_𝑞𝑖𝑗 = 𝑞 ∙ (−∆𝑡 ∙ ∅𝑖 /𝜏),(𝑢 < 𝑟𝑢0 )(𝑟𝑢0 < 𝑢 < 𝑟𝑢1 )(𝑟𝑢1 < 𝑢),2𝑔(𝑣) ≡ {𝑣_𝑛𝑖𝑗 = 𝑛 ∙ (−∆𝑡 ∙ ∅𝑖 /𝜏),𝜀

= 1 (𝑣 − 𝑣0 − 𝛼𝑢),∅0∅(u) = {∅1∅2
𝑓(𝑣) ≡ {𝑣_𝑣𝑖𝑗 = 𝑣 ∙ (∆𝑡 ∙ 𝑏𝑓𝑗 ∙ 𝑎𝑓𝑗 ∙ ∅𝑖 /𝜏 + 1),

𝑎𝑔𝑛 (𝑣 − 𝑏𝑔𝑛 ) + cgn
2

𝑣_𝑥0𝑛
𝑣_𝑥0𝑝
𝑣_𝑥1𝑛
𝑣_x =
𝑣_𝑥1𝑝
𝑣_𝑥2𝑛
{𝑣_𝑥2𝑝(𝑣 < 𝑟𝑔 )(𝑣 ≥ 𝑟𝑔 ),𝑎ℎ𝑛 (𝑣 − 𝑏ℎ𝑛 )2 + chn(𝑣 < 𝑟ℎ )(𝑣 ≥ 𝑟ℎ ),𝑎ℎ𝑝 (𝑣 − 𝑏ℎ𝑝 ) + 𝑐ℎ𝑝

𝑣_𝑐𝑖𝑗 = ∆𝑡 ∙ (𝑏𝑔𝑗 ∙ 𝑏𝑔𝑗 ∙ 𝑎𝑔𝑗 + 𝑐𝑔𝑗 )/𝜏,(𝑣 < 0)(𝑣 ≥ 0),𝑎𝑔𝑝 (𝑣 − 𝑏𝑔𝑝 ) + 𝑐𝑔𝑝
2

𝑣_𝐼𝑖𝑗 = 𝐼stim ∙ (∆𝑡 ∙ ∅𝑖 /𝜏),

when
when
when
when
when
when

𝑢 < 𝑟𝑢0 and 𝑣 > 0,
𝑟𝑢0 < 𝑢 < 𝑟𝑢1 and 𝑣 > 0,
𝑟𝑢1 < 𝑢 and 𝑣 > 0,𝑢 < 𝑟𝑢0 and 𝑣 > 1,
𝑟𝑢0 < 𝑢 < 𝑟𝑢1 and 𝑣 > 1,
𝑟𝑢1 < 𝑢 and 𝑣 > 1,
for 𝑥 = 𝑣𝑣, 𝑣, 𝑛, 𝑞, 𝐼, and 𝑐.

where, i denotes 0, 1, or 2 and j denotes n or p. In these,

where v denotes the membrane potential, and n and q are© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan135

An FPGA-based cortical and16𝑗𝑖
𝐼stim = 𝐶0 ∑ 𝑤𝑖𝑗 𝐼𝑠 + 𝐼𝑒𝑖
𝑗=1

equations, we can calculate a product of parameters inadvance and store it as a constant value. The

Fig. 3 The architecture of external connection. This module has

16 silicon synapse units and 16 accumlator units and an
AER decoder.
multiplication of a variable and a parameter is realized byshifters and adders (Fig.1(A)). To reduce the number ofadders, coefficient was approximated by the sum of 2 𝑥 ;𝑥 denotes arbitrary integer number. Multiplier circuit isused once in an Euler’s method’s step for calculation of𝑣 2 . 𝑣𝑖𝑗 are screened by the multiplexer depending on thevalue of v and u (Fig. 1(C)). We constructed n-circuit andq-circuit in the same way. The DSSN unit consumes 1
multiplier and 4 multiplexers. The numbers of adders are
35, 33, 60, and 88 for RS, FS, LTS, and IB classes.
We adopted the silicon synapse circuit in [4] based on
the kinetic synapse model [11].The equations are givenby𝑑𝐼𝑠𝛼(1 − 𝐼𝑠 )
={
−𝛽𝐼𝑠
𝑑𝑡

Fig .4 Waveforms generated by the DSSN unit of RS (a), FS (b),LTS (c, d), IB (e). Excitable step inputs rise at t = 0.1 (a-c,e).
inhibitory step inputs are provided from t = 0.2 to t = 1.0 (d).

where i and j are the indices of the post- and presynaptic neurons respectively, and 𝐼𝑒𝑖 denotes an external
input to the i th neuron, respectively. The parameter 𝑐0 is
a coefficient to scale 𝐼stim into an appropriate range.
The units for the external connection is composed of 16
silicon synapse units and 16 accumlator units and an AER
decoder (Fig.3). AER decoder receives 14-bit input signal
by a clock. First 12-bit of input signal specifies an addressof 4096 synapses by AER decoder. Next 1-bit represents
rising or trailing of the pulse input, and the value of the
bit is stored in the register corresponding to thespecified address. Therfore, we can assigne input pulse
having an arbitrary length. Last 1-bit is an enable signal.
Synaptic weights are loaded from the 16 18kb blockrandom acces memory. A synapse unit updates 256
post-synaptic current depending on the decoded input
signal at each ∆t step, and the accumulator unit
calculates an weighted sum of the post-synaptic inputs.

([𝑇] = 1)([𝑇] = 0)

where, 𝐼𝑠 represent the post-synaptic current and thevalue of [T] is 1 when the membrane potential of thepresynaptic neuron is over the threshold (0). This siliconsyanpse unit costs 1 multiplexer and 2 adders, and it
requires 2 clocks by a step.
2.2 Circuit architecute
Our circuit is composed of 16 fully-connected neurons
and external input from an address event representation(AER)–basedstimulus
receivermodule.
The
fully-connected neuronal network consumes 16 DSSN
and 16 silicon synapse units and an accumlator unit
comprising 1 multiplier and 1 adder and an 18kb blockrandom access memory that stores synaptic weights (Fig.
2). The synapse units calculate postsynaptic input
depending on the 1-bit signal that denotes whether themembrane potential v of the pre-synaptic DSSN is over
the threshold or not. The accumlator unit calculates
weighted sum of the post-synaptic input. The 𝐼stim iscalculated as follows.

2 6

𝐼𝑒𝑖 = 𝑐1 ∑ 𝑤𝑖𝑘 𝐼𝑠𝑘
𝑘=1

where i and k are the indices of the neurons andsynapses, respectively. The parameter 𝑐1 is a coefficient
to scale 𝐼𝑒𝑖 into an appropriate range.
In our system, a ∆t step corresponds to 1024 clocks,
and 1024 input signals are acceptable by 1 step. The
DSSN unit require 10000 steps to simulate a second, andit runs 10 times faster than real-time under theassumptuion that a clock correspond to 10 ns. We
represent all variables and synaptic weights using 18-bit
signed fixed point with 14-bit fractions.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan136

Takuya Nanami, Takashi Kohno

bursting which were already realized by software
simulation. Larger-scale network will also be pursued.
5. Acknowledgement
This work was supported by JSPS Grant-in-Aid for scientific
Exploratory Research Number 25240045.

6. Reference
1. R. K. Weinstein and R. H. Lee. Architectures forhigh-performance fpga implementations of neural models.
J. Neural Eng, 3(1):21–34, 2006.
2. M. J. Pearson, A. G. Pipe, B. Mitchinson, K. Gurney, C.
Melhuish, I. Gilhespy, and M. Nibouche. Implementing
spiking neural networks for real-time signal-processing andcontrol applications: A model-validated fpga approach.
IEEE Transactions on Neural Networks, 18(5):1472–1487,
2007.
3. D. B. Thomas and W. Luk. Fpga accelerated simulation of
biologically plausible spiking neural networks. In K. L.
Pocek and D. A. Buell, editors, FCCM, pages 45–52. IEEE
Computer Society, 2009
4. J. Li, Y. Katori, and T. Kohno. An fpga-based siliconneuronal network with selectable excitability siliconneurons. Frontiers in neuroscience, 6(183), 2012.
5. T. Kohno and K. Aihara. Digital spiking silicon neuron:
Concept and behaviors in gj-coupled network. Proceedingsof International Symposium on Artificial Life and Robotics
2007, 2007.
6. A. L. Hodgkin. The local electric changes associated withrepetitive action in a non-medullated axon. The Journal ofphysiology, 107(2):165–181, Mar. 1948
7. X.-J. Wang and J. Rinzel. Oscillatory and bursting
properties of neurons. In M. A. Arbib, editor, The
Handbook of Brain Theory and Neural Networks, pages
835–840. MIT Press, MA, 2nd edition, 2003.
8. R. Brette and W. Gerstner. Adaptive Exponential
Integrate-and-Fire Model as an Effective Description of
Neuronal Activity. J. Neurophysiol., 94:3637–3642, 2005.
9. T. Nanami and T. Kohno. Simple cortical and thalamic
neuron models for digital arithmetic circuit implementation.
Inpreparation.
10. M. Pospischil, M. Toledo-Rodriguez, C. Monier, Z.
Piwkowska, T. Bal, Y. Fregnac, H. Markram, and A.
Destexhe. Minimal hodgkin-huxley type models fordifferent classes of cortical and thalamic neurons.
Biological Cybernetics, 99(4-5):427–441, 2008.
11. Alain Destexhe, Zachary F. Mainen, and Terrence J.
Sejnowski. Kinetic Models of Synaptic Transmission. in:
Methods in Neuronal Modeling, Edited by Koch, C. andSegev, I. (2nd Edition) MIT Press, Cambridge, 1998

Fig.5 Raster plots of the spikes in the network. Neurons are RS(Neuron ID = 0-3), FS (4-7), LTS (8-11), IB (12-15).

3. ResultHere, we show the waveforms generated by the DSSN
unit in response to external stimulus inputs. Figure 4(a)shows the waveform of the RS class that is a most typicalneuron class in the cortex. A characteristic behavior ofthis class is spike-frequency adaptation; that is, the spikefrequency decreases over time in response to a constantstimulation input. On the other hand, FS neurons fire withalmost constant frequency (Fig.4(b)). LTS neurons exhibit
periodic firing in reaction to excitable input stimulus(Fig.4(c)) and generate a burst firing just after thetermination of a sufficient hyperpolarizing stimulus(Fig.4(d)). IB neurons generate the bursting at the onsetof a stimulus, then continue to spiking (Fig. 4(e)).
Figure 5 shows raster plots of the spikes in the network.
We applied 12-bit signal correspond to each neuron byrotation and evoked spike, and additional spikes were
evoked by the inner connection. Synaptic weights wereassigned randomly.
4. Conclusion
In this paper, we constructed digital circuit of the DSSN
model that supports four cortical and thalamic neuron
classes. The implemented circuit could generate activities
qualitatively comparable to the ionic-conductance modelfor each neuron class. It consumed only 1 multiplier for
calculation of an Eular’s Method’s step, which is an
expensive module in an FPGA. We confirmed the
behavior of a network of 16 fully-connected DSSNs thatcan receive stimuli via 4096 synapses. Each synapse iscontrolled by 12-bit input signal through an AER decoder.
Our previous study implemented the DSSN model that
supports the Class I and Class II in the Hodgkin’sclassification. We expect the circuit constructed in thiswork will be a basis for digital silicon neuronal networks
that can support a wide variety of neuronal activities moreelaborately than the I&F-based circuits.
In future works, we will implement the DSSN modelthat supports the square-wave bursting and elliptic© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

137

Analyses of Postgraduates’ Entrance Examination Scores Based on Linear Regression with
Dummy Variables
Ning Xiaojun,

Graduate School, University of Science and Technology Beijing, Beijing, China
Huang Ruocheng, Liang Xiaoyi, Ai Dongmei*

School of Mathematics and Physics, University of Science and Technology Beijing, Beijing , China*
corresponding author :Ai Dongmei , E-mail: aidongmei@ustb.edu.cn

Abstract
Detecting the main factors influencing the students’ score is an important part of the student evaluation system.
Several factors that have significant influence on postgraduates’ entrance examination scores including enrollment
category, university category, age, gender, fresh graduate were studied by ANOVA in this paper. Quantitative
analysis of the correlation between the discrete variables and postgraduates’ entrance examination scores were
performed by linear regression with dummy variables and 85% confidence prediction interval of postgraduates’
entrance examination scores were obtained. Data support were provided by these results for graduate school
enrollment work.
Keywords: Postgraduates’ Score; ANOVA; Dummy Variables; Linear Regression

1. Introduction

2. Method

A digital campus information management system isbeing promoted in universities recently. Especially,
complete postgraduate information management systems
are being used by graduate school in universities [1].
Thus, a large number of postgraduates’ data have formed
valuable information resources. However, these data are
only used for simple query and statistics at present, andits inherent information was not properly mined andutilized [2].
In this paper, enrollment information of
postgraduates within three years was extracted andanalyzed by ANOVA and stepwise regression analysis
with dummy variable, in order to explore the factors that
affect the postgraduates’ entrance examination scoresand measure the quality of enrollment postgraduates. The
attribute variables (e.g. gender, university category) wereanalyzed using ANOVA in order to judge if they have asignificant impact on the postgraduates’ entrance
examination scores. Those significant attribute variables
were then applied in stepwise regression and obtained
regression equations. Prediction interval with 85%
confidence of postgraduates’ entrance examination
scores were obtained and the forecasted scores for eachcollege were compared.

2.1 Analysis of Variance
The attribute variable which has a significant impact on
the postgraduates’ entrance examination scores wereselected using one-way ANOVA. Let a factor (attribute
variable) A with r levels: A1 , A2 , Ar , collecting data
X ij by n times observation under A level, wherej  1, 2,..., ni , i  1, 2,..., r . Let  be the grand mean of
all the samples,  i is the significance level,  ij is therandom error, and then comes the following ANOVA
models [3]: X       , j  1,..., n , i  1,..., r
i
ij
i
 ij
2
 ij ~ N (0,  ), i.i.d .
ri1 nii  0whose null hypothesis is H 0 : 1   2  ...   r . Then
its test statistic can be constructed base on the Sum of
Squares of Deviation: F ( r  1, n  r ) . If the null
hypothesis is rejected, it shows that there are significant
differences in different levels. The variance homogeneity
assumption of variance analysis can be tested by
Bartlette’s test or Levene’s test [4]© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan138

Ning Xiaojun, Huang Ruocheng, Liang Xiaoyi, Ai Dongmei

2.2 Stepwise Regression with Dummy Variables

for postgraduates’entrance examination scores by aovfunction. The result showed that admission category,graduate university and register age groups are theimportant factors to influence the postgraduates ’entrance examination scores, while gender is not asignificant factor. Take admission category as an
example. Its ANOVA result is shown in Table 1, where
the p-value is 0.0005, which indicates that there are
significant difference among distinct admission category
values. The significant differences among values ofadmission category can be shown in Fig. 1.
Table 1ANOVA for admission category

The quantitative relation between each attribute variableand postgraduates’ entrance examination scores can beanalyzed using stepwise regression with dummy
variables.
The basic idea of linear regression is as follows: let

Y  X    , where X is independent variables, Y isdependent variables,  is the vector of regression
3

Data analysis

SourceAdmission Category

3.1 Analyses of Admission Scores by ANOVA

Error

The data are from the postgraduate registration andenrollment information database of a university for three
years, which totally consit of 5384 records. 29 variable
attributes are included, such as admission college, test
method and test scores after the raw data are merged andstandardized. The universities where postgraduates got
bachelor degree were divided into seven major categories,
include 985 project university, 211 project university
with graduate school, 211 project university without
graduate school, key university, ordinary university,
secondary college and this university, which in total has
seven attribute variables.
The relationship between the five factors of gender(with 2 attribute values of Male and Female respectively),
admission category (with 4 attribute values of Directional
training, Non Directional training, Self-fund Training,
Training respectively) ， register age groups (with 3
attribute values of less than 22 years old, between theages of 22 and 25, over 25 years olds respectively), thisyear’s graduates (with 2 attribute values of Yes and No
respectively), graduate university (with 7 attribute values
shown above respectively) and the postgraduates ’entrance examination scores were analyzed by ANOVA.
If the postgraduates’ entrance examination scores show
significant differences under the different values of one
attribute variable, then this variable is an important factor
to influence the postgraduates ’ entrance examination
scores.
Each factor’s homogeneity of variance was tested
by Bartlett test function in R, while the one-way ANOVA

F.D.
3

Sum of Sq.
11.85747

1.1616p-value
0.0005

Fig. 1 normalized scores of admission category

3.2 Analyses of Admission Scores by Regression
The quantitative relationship between the postgraduates’
entrance examination scores and the above threesignificant attribute variables was analyzed throughregression analysis in this paper. We established aneffective regression equation of independent variables
with statistical significance by a stepwise regression
method based on AIC information entropy. As an
example of introducint dummy variables for a discrete
attribute variable, let one’s postgraduate admission scoreis Y which could take three different values, and thus

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

139

Analyses of Postgraduates’ Entrance

three dummy variables could be introduced for registerage groups as follows:which contains 14 regression variables, and totally 15
regression coefficients to be determined. We extracted
postgraduates’ data of different department of graduate
school, then analyzed these independent variables by a
stepwise regression method based on AIC information
entropy [9] respectively. The results from the Schoolof Metallurgical and Ecological Engineering areshown in Table 2. As for postgraduate students in thisSchool, the p-values of attribute variables except 985
project university are all less than 0.05 , which showsthese attribute variables are significant factors to
influence the postgraduates’ entrance examination
scores of postgraduates. And the obtained regression
results for each school can be used to predict theirstudents’ scores, and the result are shown in Fig. 2,
from which it can be seen that the School of Foreign
Language, Computer and Community, Economy andManagement and Humanity and Law have muchhigher predictions.

1 22  25 years old
C1i  otherwise0
1 less than 22 years old
C 2i  otherwise
0

1 over 25 years old
C 3i  otherwise
0

Likewise, four dummy variables of D wereintroduced for admission category, and seven dummy
variables of E were introduced for graduate university, so
the regression equation could be represented asYi  0  1C1i  ...  3C3i  4 D1i  ...  7 D4i
 8 E1i  ...  7 E7i   iTable 2

Results of independent variables by a stepwise regression method
estimated Standard error t-value p-value
0396.4696.094
65.057 < 2e-16
985 project university
10.479
6.173
1.698
0.090
this university
13.116
4.571
2.869
0.004
211 university without graduate school
16.558
5.722
2.894
0.004
key university
16.681
4.927
3.386
0.001
ordinary university
11.789
4.323
2.727
0.007
Non Directional
10.946
3.353
3.265
0.001
less than 22 years old
15.903
5.663
2.808
0.005
between the ages of 22 and 25
11.683
4.055
2.881
0.004

4. Conclusion
Enrollment data of postgraduates of one university areprocessed by ANOVA and stepwise regression
analysis with dummy variable in this paper. A wealthof information was revealed, which can be important
guide to cultivation for postgraduates.
Analyzing the factors affecting the postgraduates’
entrance examination scores of postgraduates andprediction of the postgraduates’ entrance examination
scores of postgraduates, can provide a more scientificbasis for the development of the graduate school andachievement clarifying objectives and scientific work.

Fig. 2 Predicted scores from regression results© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan140

Ning Xiaojun, Huang Ruocheng, Liang Xiaoyi, Ai Dongmei

Acknowledgements
This paper is supported by The Postgraduate Education
and Development Grant of University of Science andTechnology Beijing.

5.
6.
7.

References
1.

2.

3.
4.

Hu Qintai, Zheng kai, Lin Nanhui (2014),
Transformation: From ”Digital Campus” to “ Intelligent
Campus”. China Audio Visual Education (1): 35-39.
Zhang Junchao (2014), Institutional Research andUniversity Management in the Age of Big Data.
Researches in Higher Education of Engineering (1): 023.
Chen Xiru (2009), Advanced Mathematical Statistics.
University of Science and Technology China Press.
Marozzi, Marco (2011), Levene type tests for the ratio of
two scales. Journal of Statistical Computation and
Simulation 81 (7): 815-826.

8.
9.

He Xiaoqun, Liu Wenqing (2007), Applied Regression
Analysis. Renmin University of China Press.
Fang Kaitai (1989), Practical Multivariate Statistical
Analysis. East China Normal University Press.
Spiegelhalter, David J., et al (2014), The devianceinformation criterion: 12 years on. Journal of the Royal
Statistical Society: Series B (Statistical Methodology)
76(3): 485-493.
Kleinbaum, et al (2014), Applied regression analysis
and other multivariable methods. Cengage Learning.
Chowdry, Haroon, et al (2013), Widening participationin higher education: analysis using linked
administrative data. Journal of the Royal StatisticalSociety: Series A (Statistics in Society), 176 (2): 431457.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan141

Clinical Evaluation of UR-System 2 for Recovery of Motor Function ofPlegic Upper Limb after Stroke
Hirofumi Tanabe, Masahiro Mitsukane

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
Norihiro Toya, Ryosuke Takeichi, Hitomi Hattori, Yoshifumi Morita

Nagoya Institute of Technology
Gokiso-cho, Showa-ku, Nagoya, Aichi 466-8555, Japan
Yoshiaki Takagi, Norio Hasegawa

Sanyo Machine Works, Ltd.
1 Oka, Okimura, Kitanagoya-shi, Aichi 481-8540, Japan

Abstract
We developed a new training system UR-System 2 for restoring motor function of the upper limb after stroke inpatients with hemiplegia. And then, we conducted clinical evaluation of the therapeutic effect of training with the
UR-System 2 in six patients. The UR-System uses Proprioceptive Neuromuscular Facilitation (PNF) to promotemuscle strength. The results show the immediate therapeutic effect of training with the UR-System 2 for restoring
the motor function of the upper limb.
Keywords: Rehabilitation, Training system, Hemiplegic stroke patient, Clinical evaluation, PNF

spastic paralysis2, 3. Since this repetitive technique isperformed manually, the therapist’s physical workload isconsiderable. Therefore, we are developing a training
system for recovery of normal movement isolated fromsynergy movement patterns by facilitating contraction ofelbow extension and forearm supination muscles.
In our previous work4, we developed a training
system (UR-System: Useful and Ultimate Rehabilitation
System) for recovery of motor function of the upper limb
after stroke in patients with hemiplegia. This system uses
PNF to promote muscle strength. Clinical evaluation of
the therapeutic effect of training with the UR-System was
performed in eight patients for two weeks. Active rangesof motion (A-ROMs) of elbow extension and forearm
supination improved after training with the UR-System.

1. Introduction
The synergy movement pattern often seen in hemiplegicpatients after stroke is one of the factors inhibiting
improvement of motor function. According to the
Brunnstrom recovery stages of hemiplegia, elbow flexion,
pronation/supination of the forearm, wrist flexion, andfinger flexion occur abnormally and simultaneously inhemiplegic patients during the early stage of recovery;
this is called the synergy movement pattern. In therecovery stage, a patient gradually evolves from asynergy movement pattern, and will be able to extend theelbow, supinate the forearm, and extend the wrist andfingers1. Proprioceptive Neuromuscular Facilitation(PNF) is an effective therapy facilitating recovery from© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

142

Hirofumi Tanabe, Masahiro Mitsukane, Norihiro Toya, Ryosuke Takeichi, Hitomi Hattori, Yoshifumi Morita, Yoshiaki Takagi, Norio Hasegawa

Moreover, the modified Ashworth scale (MAS) scores5
for elbow extension and forearm supination increased.
This means that spastic paralysis was reduced. Thisshows the effectiveness of training with the UR-System
for recovery of motor function of the upper limb.
However, the following problems persist. Since the
UR-System was not equipped with sensors that canmeasure the movement of the paralyzed forearm and theforce/torque exerted by the forearm, the therapeutic
effect was not evaluated in detail (Problem I). It wasdifficult for therapists to adjust the parameters of therubber belt to facilitate elbow extension suitable for thepatient’s condition (Problem II).
In order to solve these problems this study developed
a new training system, the UR-System 2. We evaluatedthe immediate therapeutic effect of recovery of motor
function by training with the UR-System 2.

System 2 is used in training, the rolling motion is fixed,
but is allowed when evaluating the therapeutic effect.
The brace is equipped with three potentiometers (ALPS
ELECTRIC CO. LTD., RDC803001A) and a 6-axis force
sensor (Leptrino Co. Ltd., PFS055YA251A6). The
potentiometers measure roll, yaw, and pitch angles of the
fixing plate of the brace. The 6-axis force sensor
measures the force and torque between the forearm andthe fixing plate. An elbow-pushing arm is installed on the
fixing plate to facilitate elbow extension. As the patientmoves the forearm forward, the elbow-pushing arm
pushes the elbow to facilitate contraction of a deltoid
muscle. The elbow-pushing arm is rigid and does notbend to the external force. The contact portion of theelbow-passing arm to the deltoid muscle is made byharder cushion. When the forearm is fixed to the brace,
the portions of the forearm near the wrist and the elbow
are fixed with a non-expandable bandage and an
expandable bandage, respectively. Therefore, the portionof the forearm near the elbow can leave the brace due tothe extensibility of the expandable bandage. Moreover,the patient can extend his/her elbow fully in spite of theexistence of the elbow pushing arm. If a non-expandable
bandage is not used, the patient cannot extend his/herelbow fully.
Because this system is not equipped with motors, it isextremely safe and economical. It should be noted thatthe system is used only for active exercise training.
The UR-System 2 was equipped with various
functions. The resistance display function allows
therapists to perform various types of resistance training
by changing the arm length and the resistance level. The
touch panel parameter setting function allows theparameters of the resistance patterns to be easily set bypushing buttons on the touch panel display. The
parameters consist of the magnitudes and positions of theresistance patterns. The magnitudes are selected fromamong twenty-five levels. The positions are determinedby moving the training arm to the desired position. Thisfunction provides good visibility and easy operability forthe therapist.
The resistance to the movement of the forearm andcompression of the elbow are expected to facilitate elbow
extension. These concepts are based on facilitation
elements, namely resistance to movement, compression
arthrodesis, passive movement, and manual contact in

2. UR-System 2
To solve Problem I, we introduced sensors on the brace.
To solve Problem II, we developed an elbow-pushingarm on the brace in place of the rubber belt to facilitate
elbow extension. Moreover, since the previous URSystem was over-engineered, we reconsidered thespecifications, and developed the UR-System 2. As aresult, the number of parts, weight of the mechanicalsystem, and total cost decreased.
The UR-System 2 is a force display system with one
degree of freedom. The system consists of a mechanicalsystem and a controller. The mechanical system consistsof a training arm, a powder brake (SINFONIATECHNOLOGY CO., LTD., PRB-2.5H), and a brace.
The brace was used to secure the patient's forearm to thetraining system. The patient moves the training arm
independently. The powder brake generates a resistanceforce during training. Four different resistance patterns,
i.e., step, slope, wall, and constant modes, were installedin the controller. The length of the training arm can bealtered within the range of 0.60-0.89 m. The maximum
resistance is 49 N when the length of the training arm is0.75 m.
The brace has three degrees of freedom, allowing
rolling, yawing, and pitching motions of the fixing plate.
The rolling motion can be switched between free motion
and fixed motion with a double nut. When the UR-© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan143

Clinical Evaluation of UR-System

PNF techniques. Therefore, the patient is expected toextend the elbow joint fully. Moreover, pronation andsupination of the forearm, wrist flexion, and finger
flexion are not permitted during elbow extension andflexion, because the forearm is fixed on the fixing plateof the brace, preventing a synergy movement patternfrom occurring. Therefore, isolated movement should befacilitated by using the UR-System 2.

were improved greatly in PNF-Training as comparedwith Control-training. Thus, isolated movement was
facilitated by PNF-training with the UR-System 2. The
immediate effect of restoring motor function in PNFtraining was excellent as compared with Control-training.
Moreover, it is noteworthy that motor function was
restored even in Patients A and B, who had stroke
episodes more than ten years prior to training.
Consequently, training with the UR-System 2 canimmediately improve motor function of the upper limb
after stroke in patients with hemiplegia.
Since the therapeutic effect shown in this study isimmediate, we will investigate this effect after daily
continuous PNF-training. Moreover, the workload oftherapists will decrease because the system can be used
for independent training for recovery of motor function.

3. Clinical Evaluation of Therapeutic Effect3.1. Method
We evaluated six patients who satisfied the following
conditions:1) The subject had a hemiplegic upper limb after a stroke.
2) More than one year had passed since stroke onset.
3) The subject did not have pain in training with the URSystem 2.
4) The Brunnstrom Stage was IV or V.
The post-stroke durations were 10 years in Subject A, 12
in Subject B, two in Subject C, four in Subject D, one andone-half in Subject E, and five in Subject F. We obtained
written consent from all patients.
We used two training protocols for a comparative
study. One employed training without PNF-facilitating
elements, namely resistance and compression arthrodesis,
and was referred to as Control-training. The other
employed training with PNF-facilitating elements, andwas referred to as PNF-training. PNF-training was
performed at least three days after Control-training.
The patient performed repeated training for elbow
flexion and extension with the paralyzed forearm in thesupinated position. Repeated training was performed 50
times per set for five sets. The rest time between sets was
5 minutes.
The therapeutic effects of training with the URSystem 2 were assessed using the MAS and the A-ROM
test before and after training, and were compared withthose of Control-training and PNF-training.

4. Conclusion
The immediate therapeutic effect of PNF-training with
the UR-System 2 was shown from the results of theclinical evaluation. PNF-training with the system can
immediately improve motor function of the hemiplegic
upper limb.
In a future study, we will confirm the therapeutic
effect after daily continuous PNF-training with thesystem 2 for a larger number of subjects, and develop a
training method for recovery of motor function of theshoulder and lower extremities of hemiplegic patients bymodifying the UR-System 2.
References
1. I. Safaz, B. Ylmaz, E. Yaşar, R. Alaca, Brunnstrom
recovery stage and motricity index for the evaluation of
upper extremity in stroke: Analysis for correlation andresponsiveness, Int. J. Rehabilitation Res., 32(3) (2009),
pp. 228-231.
2. J. Stein, R. Harvey, C. Winstein, G. Wittenberg, R.
Zorowitz, Stroke recovery and rehabilitation, Demos
Medical Publishing, (NY,2008).
3. K. B. Hindle, T. J. Whitcomb, W. O. Briggs, J. Hong,
Proprioceptive neuromuscular facilitation (PNF): Itsmechanisms and effects on range of motion and muscularfunction, J. Hum. Kinet., 31 (2012), pp. 105–113.
4. Y. Morita, N. Sato, H. Ukai, H. Tanabe, T. Nagao, R.
Tanemura, Y. Takagi, Y. Aoki, Clinical evaluation oftraining system for recovery of motor function after strokein patients with hemiplegia, Proc. of the 2nd Int. Conf. on
NeuroRehabilitation (ICNR2014) (2014), pp. 83-92.

3.2. Results and discussion
All subjects completed two types of training with the URSystem 2. Table 1 presents the summary of scores foroutcome measures. Table 1 shows that the A-ROMs and
MAS score in PNF-Training increased significantly after
training. Figures 2-4 show the A-ROMs and MAS score© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

144

Hirofumi Tanabe, Masahiro Mitsukane, Norihiro Toya, Ryosuke Takeichi, Hitomi Hattori, Yoshifumi Morita, Yoshiaki Takagi, Norio Hasegawa

5. Ashworth B, Preliminary trial of carisoprodal in multiple
sclerosis, Practitioner, 192 (1964), pp.540-542.

25.0
20.0
15.8

A-ROM

15.0

Controller box

10.0
5.0

1.7

0.0
Elbow pushing arm

-5.0

Brace

Control

PNF-10.0
Training arm

Fig. 2. Improvement of A-ROM of elbow extension.

Powder Brake

35.0

Fig. 1. UR-System 2.

30.0
23.3

25.0
A-ROM

Table 1. Summary of scores for outcome measures(Control: Control-training, PNF: PNF-training,
Pre: Before training, Post: After training,
Diff: Difference between the results before and after training,
Positive difference values mean improvement.)

20.0
15.0

12.5

10.0
5.0

(a) A-ROM of elbow extension.
Control
PNF
Subject
Pre Post Diff. Pre Post Diff
A
-20 -15
+5
-20
-5
+15
B
-15 -25-10 -15-5+10C-25 -25+0-25 -10 +15
D-20 -15
+5
-20
0
+20
E
-20 -15+5-20 -10 +10F-30 -15
+5
-30
-5
+25

0.0
Control

PNF

Fig. 3. Improvement of A-ROM of supination of forearm.
1.4
1.2

1.0

1.0
MAS score(b) A-ROM of supination of forearm.
Control
PNF
Subject
Pre Post Diff. Pre Post Diff.
A
65
75
+10
65
90
+25
B
40
55
+15
40
70
+30
C
55
60
+5
55
65
+10
D
35
45
+10
35
55
+20
E
40
65
+25
40
70
+30
F
65
75
+10
65
90
+25

0.8
0.6

0.3

0.4
0.2
0.0
-0.2

(c) MAS Score of elbow flexor group.
Control
PNF
Subject
Pre Post Diff. Pre Post Diff.
A
4
4
0
4
5
+1
B
4
4
0
4
5
+1
C
3
4
+1
3
4
+1
D
3
3
0
3
4
+1
E
3
4
+1
3
4
+1
F
3
3
0
3
4
+1

Control

PNF

Fig. 4. Improvement of MAS Score of elbow flexor group .

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan145

Knee Android Model Reproducing Internal-External Rotation
with Screw-Home Movement of Human Knee
Daichi Yamauchi, Sho Takei, Noritaka Sato, Yoshifumi Morita

Department of Computer Science and Engineering, Nagoya Institute of Technology,
Gokiso-cho, Showa-ku, Nagoya, Aichi 466-8555, Japan
E-mail: d.yamauchi.170@nitech.ac.jp

Abstract
This paper proposed a knee android model (Knee-AM) that can reproduce the internal-external rotation with screwhome movement (SHM) of the human knee. This rotation of the Knee-AM was realized by changing the number, thelengths and the fixing points of the nylon-cords of our previous Knee-AM. Moreover, when the anterior cruciate
ligament (ACL) was removed from the Knee-AM to imitate ligament injury, the SHM did not occur during knee
extension. The results showed the ACL played an important role for the SHM.
Keywords: Android model, Knee model, Human knee joint, Screw-home movement, Joint mechanism.

the kinematic mechanisms obtained from the use of
android models is beneficial to medical treatment.
It has been reported that the internal-external (IE)
rotation during knee extension in human can be classified
into three types3. The three types were called “Externalrotation type”, “Internal rotation at the end type” and“Internal rotation type”, respectively. In our previous
study, we proposed a Knee-AM consisting of a tibial
model, a femoral model and 10 nylon-cords imitating theligaments. The IE rotation of the Knee-AM was similar
to those of internal rotation at the end type. This type was
not majority and often occurred in the human who had ajoint laxity3.
In this paper, we aimed to propose a Knee-AM which
can reproduce the IE rotation of external rotation type.
Since most of human knee motion belongs to the IE
rotation of external rotation type, reproduction of the IE
rotation of this type in the knee-AM was important for
expanded utilization of the Knee-AM. Additionally,
since this type had external rotation occurring at the endof extension, what is referred to as the screw-homemovement (SHM), we investigated mechanism forcausing the SHM using the Knee-AM. Furthermore, we

1. Introduction
Recently, time of rehabilitation treatment is restrictedfrom increasing the number of patients with an increasein the elderly. From the background, efficiency andeffective treatments are social needs in rehabilitation
field. As a solution for the needs, we had proposed a knee
android model (Knee-AM) for supporting a treatment
planning1.
An android model of a human joint was developed to
improve medical technology in the field of rehabilitation2.
Android models are anatomical models composed of theminimum number of parts required for reproduction ofhuman movement. They can reproduce the threeelements of movement (“slide”, “roll” and “leave”) that
occur between two articulating bones. Sanaka2 found thatthe kinematic mechanisms of an android model of theindex finger had some different properties than those of
the actual movement of the index finger. The
rehabilitation based on this theory has contributed to theimprovement on the range of motion reduced after
tenorrhaphy. The study above confirmed that applying© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan146

Daichi Yamauchi, Sho Takei, Noritaka Sato, Yoshifumi Morita

also aimed to gain some knowledges of a ligament injury
from simulating the ligament injury in the Knee-AM.

the positions and the lengths of nylon-cords to reproduce
human knee motion precisely.
The tibial collateral ligament (TCL) was imitated by
one nylon-cord (Cord T). One end of Cord T was attachednear the center of medial femoral condyle (MFC) so that
Cord T did not become a loose when the flexion angle
was from 90° to 0°. The other end was attached on theposterior of the insert part of the pes anserinus.
The fibular collateral ligament (FCL) was imitated by
one nylon-cord (Cord F). One end of Cord F was also
attached near the center of lateral femoral condyle (LFC)so that Cord F did not become a loose when the flexion
angle was from 90° to 0°. The actual other end is attachedon the fibular head. However, since the Knee-AM did notinclude the fibular model, the other end was attached on
the insert part of the posterior ligament of the fibular head.
The anterior cruciate ligament (ACL) was imitated by
two nylon-cords (Cords A1 and A2, collectively referred
to as Cord A). The reason why Cord A was 2 was that the
ACL and the PCL consisted of many fibers having
different lengths4. One end of Cord A1 was attached on
the distal posterior of the LFC. The other end was
attached near the insert part of the medial meniscus.
Similarly, one end of Cord A2 was attached on theproximal posterior of the LFC. The other end was
attached on the 9 mm medial posterior from the end of
Cord A1 on the tibial model.
The posterior cruciate ligament (PCL) was imitatedby one nylon-cord (Cord P). In order to reproduce themotion between 90° of flexion angle and full extension,
we imitated the PCL, which tighten in flexion position4,
with one nylon-cord. One end of Cord P was attached on
the distal anterior of the MFC. The other end was
attached on the center posterior of the tibial plateau.

2. Knee Android Model (Knee-AM)
The Knee-AM is shown in Fig. 1. The Knee-AM imitatedthe right knee joint and consisted of a tibial model, afemoral model and five nylon-cords. In order to make theKnee-AM extend automatically, we used a drive unit.
2.1. Bone models
We used the same bone models as those in our previous
study1. The tibial and the femoral models, imitating thetibia and the femur, were made with a 3D printer out of asmooth nylon-based material. The femoral model was cut
near the knee joint to about one-fifth of its full length. An
aluminum rod was attached to the cut surface along thelong-axis of the femoral model to fix the femoral modelto the drive unit. A light weight square plate (length ofside: 150 mm) also was attached on the tibial model for
attachment of measuring makers.
2.2. Nylon-cords
We attached five inextensible nylon-cords to imitate theligaments of the knee on the basis of the motion and theanatomy of the human knee4 as shown in Fig. 2. The
nylon-cords were attached to the bone models by copper
nails. When measuring the lengths of the nylon-cords, wemeasured the lengths between the centers of two copper
nail heads. The correspondences of the five nylon-cords
to the ligaments of human knee and these lengths wereshown in Table 1. From our previous Knee-AM1, wereduced the number of nylon-cords to investigate knee
motion in the simple structure, and also we rearranged

Table 1. Correspondences of five nylon-cords to ligaments ofhuman knee and these lengths (TCL: Tibial collateral ligament,
FCL: Fibular collateral ligament, ACL: Anterior cruciate
ligament, PCL: Posterior cruciate ligament)
Name of cord
Cord T
Cord F
Cord A1
Cord A2
Cord P

Fig. 1. Knee android model (Knee-AM)

Ligament
TCL
FCL
ACL
ACL
PCL

Length [mm]
58.5
39.3
34.2
37.3
31.1

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan147

Knee Android Model Reproducing

2.3. Drive unit

3. Experiment

The drive unit and the Knee-AM are shown in Fig. 3. The
drive unit was composed of a motor, a reel fixed on the
motor shaft, a pulley, a wire and a frame. The drive unit
extended the joint of the Knee-AM by pulling a wire
connected to the tibial tuberosity. The wire was run
through the pulleys and wound onto the reel. The motor
was placed above the long-axis of the femoral model, sothat the horizontal angle of the wire with respect to thelong-axis of the femoral model was 0°. The pulley wereplaced in the position corresponding the human patella atfull extension. The femoral model was fixed on the frameby clamping the aluminum rod and the proximal part of
the femoral model. This fixing method was improved
from that of our previous study1 to fix the Knee-AM on
the drive unit firmly.

We conducted two experiments to confirm the utility of
the Knee-AM. In the experiments, we measured the
motions of the bone models with a motion capture system
VICON (Vicon Movement Systems Ltd.) and calculated
flexion angle and IE rotation angle by using the same
calculation methods as our previous study1.
3.1. Experiment method
Firstly, we tested the reproducibility of the Knee-AM for
the IE rotation of the human knee. In this test, the KneeAM was extended from 90° of extension angle to full
extension by the drive unit. To evaluate thereproducibility, we compared the IE rotation of KneeAM with that of human knee.
Secondly, we investigated the knee motion of an
ACL injury by using the Knee-AM. In this simulation,
Cords A1 and A2 were removed as the shown in Fig. 4.
The Knee-AM simulating an ACL injury is referred to as
the injured Knee-AM. The experimental procedure was
the same as that of the first experiment.
The measured data included small vibration caused
by a mechanical resonance of the Knee-AM. In order toeliminate it, we used the low-pass filter with 5Hz of
cutoff frequency.
3.2. Result(a) Tibial collateral
ligament (TCL)(b) Fibular collateral
ligament (FCL)(c) Anterior cruciate
ligament (ACL)(d) Posterior cruciate
ligament (PCL)The IE rotations of the Knee-AM and the injured KneeAM are shown in Fig. 5. In order to compare the IE
rotations of two types of Knee-AMs to those of theFig. 2. Nylon-cords imitating ligaments of humanFig. 3. Drive unit and Knee-AM© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan148

Daichi Yamauchi, Sho Takei, Noritaka Sato, Yoshifumi Morita

human knees, we used the human knee movement dataobtained from Ref. 3.
In this graph, the horizontal axis is the flexion angle,
with extension denoted in the positive direction. The
vertical axis is the IE rotation angle, with internal rotation
is denoted in the positive direction, and rotation angle isset to be zero at -90° of flexion angle. The markers showeach maximum absolute value of rotation angle between-90° and 0° of flexion angle.
Ishii et al. 3 classified IE rotation of the thirty subjects
into three types. “Type 1”, was external rotation
occurring at the end of extension, namely Screw-Home
Movement (SHM) occurring. “Type 2”, was externalrotation occurring at the beginning of extension, andinternal rotation occurring in the second half of extension.
“Type 3”, was internal rotation occurring over the entire
extension.
In the Knee-AM, the round marker is in group of theType 1, and SHM occurred. Therefore, the Knee-AM canreproduce the IE rotation with the SHM of the humanknee. On the other hand, in the injured Knee-AM, theround marker is in group of the Type 1, though the SHM
did not occur. Consequently, ACL was a necessarycomponent for occurrence of the SHM.

medial plateaus with respect to the femoral model and thetensions of the nylon-cords of the two types of KneeAMs. The motions of the lateral and the medial plateaus
and tensions of the nylon-cords investigated byobservation are shown in Table 2. In the case of the KneeAM, Cords T, F and P were weakly stretched at -90°of
flexion angle. When the Knee-AM started to extend, thelateral and the medial plateaus started to slide toward
anterior, the tension of Cord T increased until the full
extension, and Cord P became loose. Around -20° of
flexion angle, Cord A started to tighten, and the lateralplateau ceased to slide and began to roll. In contrast, themedial plateau kept sliding toward anterior. Thatdifference of the motions of the lateral and the medialplateaus causes SHM4. Additionally, Cord F tightenedwith the roll of the lateral plateau. At the full extension,
Cords T, F and A strongly tightened, and the tibial model
became fixed state on the femoral model. This means thatthe phenomenon which the knee was locked occurred in
the Knee-AM.
The motion of the Knee-AM mentioned above was inagreement with that in Ref. 4 in the following points:(i) the lateral plateau rolls larger than the medial plateau,(ii) the TCL and the FCL tighten during extension,(iii) the ACL tighten at extended position, and the PCL
tighten at flexed position.

3.3. Discussion

Rotation angle [deg]External
Internal

We considered the mechanism for causing the SHM.
Compared in Fig. 5, the two waveforms of the Knee-AM
and the injured Knee-AM show different movements
between -20° and 0° of flexion angle. Accordingly, it was
assumed that Cord A started to tighten from -20° and that
the SHM was caused by Cord A.
In order to clarify the mechanism for causing theSHM, we observed the motions of the lateral and theFlexionExtension

Flexion angle [deg]Type 1
Type 2Type 3Knee-AM
Injured Knee-AM

Fig. 5. Comparison of internal-external rotations duringextension of Knee-AM and thirty human subjects3.

Fig. 4. Injured Knee-AM© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

149

Knee Android Model Reproducing

4. Conclusion

This agreement implies that the knowledge gained using
the Knee-AM is valid.
On the other hand, in the case of the injured KneeAM, the motion of lateral plateau and the tension of Cord
F were different from those of the Knee-AM between 20° and 0° of flexion angle. The lateral plateau continued
sliding toward anterior until the full extension, and Cord
F did not strongly tighten. Thereby, the SHM did notoccur, and the tibial model did not become fixed state on
the femoral model at the full extension. This means thatthe phenomenon which the knee was locked did not occur
in the injured Knee-AM.

In this paper, we proposed a Knee-AM which canreproduce the IE rotation of external rotation type byrearranging the nylon-cords of our previous Knee-AM.
From our investigation of the mechanism for causing theSHM by using the Knee-AM, we found that the roll of
the lateral plateau tighten Cord F and that strong
tightening of Cords T, F and A fixed the tibial model on
the femoral model. In addition, from our simulation
results of the ACL injury in the Knee-AM, we found thatthe lack of the ACL did not make the SHM and thelocking phenomenon.
For future studies, we will investigate some roles ofother ligaments by some experiments. We also plan to
discuss our finding with medical personnel in regard to
medical treatments, the improvement of medical
technology, and other applications of the Knee-AM.

Table 2. Time chart of tension of nylon-cords and motion ofmedial and lateral plateaus (WT: Weakly Tight, T: Tight, ST,
Strongly Tight, L: Loose, S: Slide, R: Roll)(a) Knee-AM
Flexion angle [°]Tension of Cord T
Tension of Cord F
Tension of Cord A
Tension of Cord P

-90
WT
WT
L
WT

Motion of medial plateau
Motion of lateral plateau

S
S

↗
→
→
↘

-20
T
WT
L
L

↗
↗
↗
→

0
ST
ST
ST
L

S
S

S
S

S
R

R
R

↗
→

0
T
WT

→

L

S
S

S
S

References
1. D. Yamauchi, N. Sato and Y. Morita, An Experimental
Study on the Relationship between the Components andMovement of the Human Knee Using an Android Model Measurement of Internal-External Rotation and AnteriorPosterior Tibial Translation -, in Procs. of Int. Conf. on
Control, Automation and Systems 2015 (BEXCO, Busan,
2015), pp. 811-815.
2. K. Sanaka, Android with biological structure forsupporting medical technology, in Tokai-Section Joint
Conference on Electrical, Electronics, and Related
Engineering (Shizuoka Univ., Hamamatsu, 2013), S2-5.
(in Japanese)
3. S. Ishii and S. Yamamoto, Kinematic Analysis of Screw
Home Motion with Active Knee Extension in the NonWeight Bearing Position, in Rigakuryoho Kagaku, 23(1)(2008), pp. 11-16. (in Japanese)
4. I. A. Kapandji and E. Shiota (trasl.), Physiology of the
Joints: Volume 2 Lower Limb, 6th edn. (Ishiyaku
Publishers, Inc., Tokyo, 2010), pp. 108-129. (in Japanese)(b) Injured Knee-AM
Flexion angle [°]Tension of Cord T
Tension of Cord F
Tension of Cord A
Tension of Cord P

-90
WT
WT

↗
→

WT

↘

-20
T
WT
None
L

Motion of medial plateau
Motion of lateral plateau

S
S

S
S

S
S

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan150

Wood Species Recognition System based on Improved Basic Grey Level Aura Matrix asfeature extractor
1

Mohd Iz’aan Paiz Zamri, 1Anis Salwa Mohd Khairuddin, 1Norrima Mokhtar, 2Rubiyah Yusof

1

Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
2

Malaysia Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur Malaysia
E-mails: izanpaiz@gmail.com, anissalwa@um.edu.my
www.um.edu.my, www.fk.um.edu.my,www.mjiit.utm.my

Abstract
An automated wood species recognition system is designed to perform wood inspection at custom checkpoints inorder to avoid illegal logging. The system that includes image acquisition, feature extraction and classification is able
to classify the 52 wood species. There are 100 images taken from the each wood species is then divided into training
and testing samples for classification. In order to differentiate the wood species precisely, an effective feature
extractor is necessary to extract the most distinguished features from the wood surface. In this research, an Improved
Basic Grey Level Aura Matrix (I-BGLAM) technique is proposed to extract 136 features from the wood image. The
technique has smaller feature dimension and is rotational invariant due to the considered significant feature extractfrom the wood image. Support vector machine (SVM) is used to classify the wood species. The proposed systemshows good classification accuracy compared to previous works.
Keywords: image classification, wood texture, wood species, support vector machine, pattern recognition.

wood surface that can be determined by human naked eyewith the aid of magnifying lens. Refs. 7 and 8 develop an
automatic tropical wood species recognition system toclassify 20 wood species and 10 wood species
respectively. In both works, Grey Level Co-occurrence
Matrix (GLCM) is used as the feature extractor to extract
the features from the wood texture. After the database isincreased to 52 wood species the accuracy dropped to
50% indicating that the GLCM in the previous work is
inadequate for large wood database. This is due to theunvaried nature of macroscopic anatomy image of thewood, but it contains a visual texture - variation of
intensities which form certain repeated arrangements9.

1. Introduction
Traditional wood identification system which is based
solely on human expertise is not practical nowadays.
Several automatic wood species recognition have beendeveloped based on spectral analysis and image analysis.
The wood species identification based on the spectrum
analysis involves a lot of time, money and specialists asshown in the previous research1-6. In addition, thetechnique is not suitable to be applied in real life
applications by non-experts and is more practical to do itin the laboratory rather than in the field. On the other
hand, the image-based processing provides a simpler
method in classifying the wood species. The method
involves only the inspection of a cross section of the© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa,, Japan151

Mohd Iz’aan Paiz Zamri, Anis Salwa Mohd Khairuddin, Norrima Mokhtar, Rubiyah Yusof

In order to improve the performance of the woodspecies recognition system, a reliable and precise feature
extractor is needed. To correctly extract the woodfeatures from the wood texture and overcome thelimitations of GLCM, a new feature extractor, Improved
Basic Grey Level Aura Matrix (I-BGLAM) is proposed.
Support Vector Machine (SVM) is used as classifier toclassify the wood species based on the feature extracted
from the I-BGLAM. Several phases which consist ofimage acquisition, feature extraction and classification
are proposed as the design of the automated woodrecognition system.

homomorphic filtering is explained in more detail in Ref.
10.
2.2. I-BGLAM feature extractor
In order to overcome the limitations of the conventional
BGLAM technique, an improved BGLAM feature
extractor is proposed. Basically, the improved BGLAM
feature extractor is rotational invariant and the size offeature dimension is reduced by considering only thediscriminative features. Besides that, I-BGLAM features
are directly calculated from the grey level mapping ofimages which overcome the limitations of GLCM.
This is done by creating a new characteristic
vector which combine of the occurrence of 00 together,
the occurrences of 01 and 10 together and theoccurrences of 11 together.

2. Proposed Methodologies
The proposed wood identification system based on image
analysis consists of several parts: image acquisition,
feature extraction and classification. The illustration of
the proposed system is shown in the Fig. 1.

[ 𝟔 𝟔 𝟒 𝟒 | 𝟑 𝟖 𝟑 𝟐 | 𝟔 𝟔 𝟑 𝟓 | 𝟔 𝟑 𝟑 𝟒 ] ⇒ [ 𝟐𝟏 𝟑𝟔 𝟏𝟓]
For example,
6 + 3 + 6 + 6 = 21, 4 + 2 + 5 + 4 = 15 and 6 +4+8+ 3+6+3+3+3= 3This is how a 32 feature vector BGLAM becomes a 3
feature vector invariant to image rotation. The number of
I-BGLAM features depends only on the grey levelsegmentation as shown in Eq. (1):Fig. 1. Proposed wood identification system

Nf 

2.1. Image acquisition
In image acquisition stage, images of wood surface arecaptured by a specially designed portable camera. The
portable camera has 10 times magnification, which is thesame specification as the magnifier used in manual wood
identification process. The size of each image is 768 ×
576 pixels.
The wood images are pre-processed usinghomomorphic filters to enhance the image presentation.
Homomorphic filtering uses a linear filter to do a nonlinear mapping to a different domain and later it ismapped back to the original domain. This algorithm
reduces all unwanted illumination and reflectance on theimage. Image brightness is also normalized, thus
enhancing the contrast of the image. The algorithm ofGL  (GL  1)
2

(1)

Where Nf is the number of features and GL represents
grey level.
Consequently, the number of features becomes lessat the same time. In this research, the numbers of grey
level experimented is 2, 4, 8, 16. Finally, a grey level of16, which gives 136 features is chosen for the final
experiment because it gives the highest classification
accuracy compared to other grey levels.
2.3. Classification
The classification process is performed by training aclassifier model that works by differentiating features of
the class of observed collected data. Support Vector
Machine (SVM) is a useful tool as a learning algorithm© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa,, Japan152

Wood Species Recognition System

in analyzing and recognizing data. Previous works in
Refs. 11-17 shows that the implementation of SVM
classifier result to high classification rate.

25

30 (3000)Gabor filtersGLCM (200),
Gabor filters(80),GLCM(20)90.33

3. Results and Discussion
The experiment was conducted by using 90 training
samples and 10 test samples for each wood species and isrepeated for 30 iterations. From the result obtained(Table 1), the classification accuracy for training samples
for all set showed 100% in accuracy. The computed
training accuracy showed that SVM is able to train the
training model and identify the trained data in training
samples database accurately. The classifier managed toclassify the test samples with an average accuracy of
99.84 ± 0.23 % for 52 wood species.

4. Conclusion
The classification of features extracted from I-BGLAM
using SVM classifier shows an improvement in theclassification accuracy compared to previous proposedsystems. The features extracted from the wood species
using I-BGLAM as feature extractor are more distinct,
thus improved the accuracy of the wood recognition
system. In future, an advance wood recognition system isneeded to overcome the size of wood database and able
to differentiate the wood species that contain more likely
features with each other. A robust feature extractor thatcapable to extract the most discriminant features from thewood texture is the aim for development in future.

Table 1. Average classification accuracy
Set

Classification
accuracy (%)

Train

100

Test

99.84 ± 0.23

Acknowledgements
The authors would like to thank Malaysian Ministry ofHigher Education (MOHE) and University of Malaya forfunding this research through High Impact Research
Grant (UM.C/HIR/MOHE/ENG/16) and BKP Grant(BK047-204). The authors also would like to thankForest Research Institute of Malaysia (FRIM) for
providing us with the wood samples. The first author
would like to thank to Ministry of Higher Education(MOHE) for awarded him MyMaster scholarship.

The proposed system showed an increment in test
accuracy compared to the previous works done shown in
the Table 2.
Table 2. Previous works on wood species recognition system
based on image texture
Ref.

No of species(No ofsamples)

Texture feature(No of features)Classification
accuracy (%)

GLCM (20)Image
segmentation (5)GLCM (20)
GLCM (24),Color (18)
GLCM (24),Color (18)

95.00

7

20 (1949)

17

7(101)

18

5 (250)19

11 (347)20

22 (1270)21

25(1250)

GLCM (124)

68.40

22
23

10 (1000)
25 (500)

95.00
92.60

24

20 (2010)

GLCM (3)
GLCM (44)Gabor filtersGLCM (200)References
1. Fuentealba, C., et al. Wood products identification byinternal characteristics readings. in Industrial Technology,
2004. IEEE ICIT'04. 2004 IEEE International Conference
on. 2004. IEEE.
2. Choffel, D. Automation of wood mechanical grading:coupling of vision and microwave devices. in Photonics
East'99. 1999. International Society for Optics andPhotonics.
3. Piuri, V. and F. Scotti, Design of an automatic wood types
classification system by using fluorescence spectra.
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Evaluation the Performance of a New Quadrotor Model Based on the Arm’s Length Variation
Yasameen Kamil N.1, D. Hazry2, Khairunizam Wan3, Zuradzman M.Razlan4
1,2,3,4

School of Mechatronic Engineering, University Malaysia Perlis (uniMAP)
026000Arau, Perlis, Malaysia
1,2,3,4Centre of Excellence for Unmanned Aerial System (COEUAS), University Malaysia Perlis (uniMAP)
01000kangar, Perlis, Malaysia
E-mail: 1moonom2002@gmail.com, 2hazry@unimap.edu.my, 3khairunizam@unimap.edu.my

Abstract
The field of UAV has exceptional level of growth; in addition it is predictable to be one of the most prevalent
fields of development and research in future. The unmanned aerial vehicle (UAV)- quadrotor widely used as aservice robotic in several fields. This paper presents a new design of this miniature aerial vehicle in altitude andattitude movements, based on varying the arm’s length of quadrotor instead of varying the speed of motors to
obtain a rotation around each axes. The length of arms varying are achieved by fixing a stepper motor in each armof quadrotor to increase or decrease the length of these arms according to controller command for attitude
movement. The controller commands are accomplished by designing a PID controller with specific parameters tomaintain the stability of the quadrotor in the flight path. A MATLAB software code used to evaluate thesimulation results and demonstrate the ability of the proposed design to perform a mission.
Keywords: arm’s length; PID controller; quadrotor, trajectory tracking, UAV.

1.

payload. Major disadvantage in quadrotor is theconsumption of energy [1] this means the lower
endurance for life time.
The conventional quadrotor structure comprises offour motors which are arranged such that two motors
rotate counter clockwise in frontal and rear, and othertwo motors in the lateral rotate clockwise. This
arrangement of motors achieves stability in hover andthe total momentum of the system equiponderant [3].
Many methods have been proposed to develop the quadrotor model and validation. In [4], the structure ofquadrotor was comprised of four propellers, three ofthem were horizontally placed to control the roll andpitch whereas the fourth was vertically placed to control
the yaw rotation. . But the authors conclude that the
dynamic model was difficult to derive and cannot get

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, which has
an endless list of applications in civil, military andcommercial. Therefore, the quadrotor included the tasksof search rescue, fire monitoring in the forest, theinspection in contaminated with nuclear radiation areas,
where human pilot not desirable to be there. The
quadrotor is underactuated nonlinear system [1]. It has
exceptional advantage over helicopter and other multirotor aerial vehicle according to its simple mechanicalarrangement, weight, size, minimum cost and without
any risk to human life [2], and it has the ability to©The 2016International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan155

Yasameen Kamil Najm, Desa Hazry, Khairunizam Wan, Zuradzman M.Razlan

the accurate model. In [5], a new design for quadrotor
was presented by tilting propellers to solve the problem
of under- actuated system. In which the normally fixed
propellers are permitted to tend about the body axes ofquadrotor.
Another papers adopted the controller design toachieve the stability of quadrotor. In [1] the altitude andyaw channel are controlled by using PD controller andbackstepping based on PID are used to control theattitude. In [6] the PID controller is used for developing
the stability of pitch, roll, yaw and altitude of thevehicle. Unfortunately, the system contains sometransient overshoot because of disturbance and someother reasons like some certain of mechanical parameterand simplification of the controller. . In [7], the position
and altitude of quadrotor are controlled by using PID
controller in the condition of wind gust and conclude
that the controller worked effectively under thiscondition. In [8], the PID based on feedback
linearization combined with block control technique areused for controlling the position of quadrotor. This
technique mathematically complex may have some fault
in the mathematical model leads to a fault in the design,and the performance of the system is slow.
In this paper, a new model for quadrotor attitude is
proposed based on varying the length of arms togenerate variable torque without altering the motors’
speed. This torque has the ability to rotate the attitude
angles of quadrotor to the desired orientations andkeeping quadrotor balance. Varying the arms’ length
have been done by using stepper motor in each arm toincrease or decrease the length of arm depending oncontroller command. The PID controller is used tocontrol the altitude, attitude, and position movements
for stabilization requirement of quadrotor.
The rest of the paper is arranged as, section 2 shows theconventional quadrotor design, section 3 the proposed
quadrotor design, section 4 presents the dynamic modelof quadrotor, section 5 presents the control strategy
which include the altitude, attitude, and position
controller algorithm, section 6 included the simulation
results and finally, section 7 included the conclusion.

controlled to produce a lift force [3]. Quadrotor isunder-actuated nonlinear system with four input forces
and six output motions. Each motor produces torque andforce, the collection of all motors are produced the main
thrust, the pitch torque, the roll torque and the yaw
torques. The force generated from each motor fi is
proportional to the square of the angular velocity ω2,such that fi=k(ωi)2, where k > 0 and constant.
Finally, the front motor (m1) and the rear motor (m3)rotate counter clockwise (C.C.W). The right motor (m2)and the left (m4)are rotated clockwise (C.W). This
arrangement in motors’ rotation is to maintain balance[2]. The transitional and rotational movements of
conventional quadrotor are illustrated clearly in [9].

2. Conventional Quadrotor Design

The yaw rotational movement achieved by increasing
the arm’s length of two motors and decreasing the arm’s
length of two other motors with fixed the speed of the

3. Proposed Quadrotor Design
The quadrotor faces many problems, the main problem
is the consumption energy during flight andmaneuverability due to varying the speed of four motor.
Where, at least the speed of one motor must be
increased to perform rotational movements, this mean
lower endurance time. Herein, for roll rotational
movement about the x-axis, the arms length related tolateral motors; one of them most be increased andanother must be decreases as shown in Fig.1(c) and (d).
The other two motors in frontal and rear must be fixedat the same length while the speed of four motors will
be fixed. In the same manner, to achieve the pitchrotational movement about the y-axis, but the arms’lengths related to frontal and rear motors must bevarying, whereas the arms lengths related to lateralmotors must be fixed as shown in Fig.1 (a) and (b).

Fig. 1. Quadrotor’s motion due to the proposed design

The conventional quadrotor consist of four motors fixed
on the end of a cross frame and motors speed are© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

156

Evaluation the Performance offour motors. When the propeller rotate (C.W) then it isgenerates a torque in (C.C.W). The quadrotor rotates inthe direction of the greater torque as shown in Fig.1(e ).
This approach done by using stepper motor fixed ineach arm which produces stepping rotation, the arm’s
length varying due to the stepping rotate of the stepper
motors, when the stepper motor rotate (C.C.W) thearm’s length increased and when the stepper motor
rotate (C.W) the arm’s length decreased [10].
The stepper motor fixed inside the fixed arm at a
specific distance, the sliding arm is connected with therotate shaft of the stepper motor. Therefore, when the
stepper motor controlled to produce step rotate in C.W
the sliding arm is sliding out of the fixed arm to increase
the quadrotor total arm, if the stepper motor shaft rotate
C.C.W the sliding arm is sliding inside the fixed arm
and the total arm length will decrease.
Consequently, the increasing and decreasing in thearms length implemented according to the Eq.(1) andEq.(2). The arms that increased will have maximum
length and the arms that decreased will have theminimum length as illustrated in [10](lf + lm + L) + ∆d. (lf + lm) = maximum length(lf + lm + L) - ∆d. (lf + lm) = minimum length

Fig. 2. Quadrotor coordination

The thrust force and control torque which acting on
the quadrotor body is generated by the propellers
rotation. The dynamic equations of quadrotor in the
body frame are extracted from Newton- Euler form as in
[8].




m x  u1 (sin sin  cos sin  cos )  k1 xm y  u1 ( sin  cos  cos sin  cos )  k 2 ym z  u1 (cos cos )  mg  k3 z


 





 





 



I x   ( I y  I z )    u2  k 4 (3)I y   ( I z  I x )    u3  k 5 
I z   ( I x  I y )    u4  k6 

Where m represents the body mass of quadrotor,
I=diag(Ix, Iy, Iz) represents the moment of inertia, , x
represents the linear position, the input vectors to theconventional quadrotor are defined as in [7]
u1  b(       ) u 2  b.l ( 32   )u 3  b.l ( 42   22 )
u 4  d ( 22   42  12   32 )
2
1

(1)
(2)

Where lf is the fixed arm, lm is the diameter of the motor,( L +lf + lm) represent the normal arm, L is specificlength to ensure the freely movement in sliding arm, ∆d
the rate of change in the sliding arm

2
2

2
3
2
1

2
4

(4)

Where u1, u2, u3, and u4 are representing the input
vectors to the quadrotor system, b represents the thrustcoefficient, d represents the drag coefficient. According
to the assumptions are mentioned in [11].
The quadrotor proposed design depends on varying
the arms’ lengths of motors and fixing the angularvelocity. Therefore, the same thrust and dragcoefficients from all motors are obtained; this means(b1=b2=b3=b4), (d1=d2=d3=d4). The change in the arms
length produces variable torque around the aircraft
center of mass. Thus, the induced torque is oriented thequadrotor to the desired attitude even the rotors speed
are fixed to be constant. The total torques influential on
the conventional quadrotor rotation are expressed as in[12] whereas in the proposed design will be derived asin [10]. Then the input vectors will be expressed as:

4. Dynamic Model of Quadrotor
The motion of any rigid body in space can be
represented by rotational and translational motions [3].
Quadrotor is nonlinear system with (6_DOF) and only
four inputs which are motor speed, 3-translational and3-rotational motion as shown in Fig 2.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

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Yasameen Kamil Najm, Desa Hazry, Khairunizam Wan, Zuradzman M.Razlan

u1  b(12   22  32   42 ) u 2  b. 2 (l 2  l 4 )2
u 3  b. (l1  l3 )
2
u 4  d . (l1  l3  l 2  l 4 )

5.2. Attitude controller Algorithm
Quadrotor attitude controlled the angles of orientation
pitch, roll, and yaw. In this paper we present only the
yaw angle and only choose yaw differential equationfrom Eq. (3).

(5)

..



Where li represents the arms length related to each
motor.

The quadrotor requires a stable and robust controller
during the maneuverability and flight path [7]. In thispaper a PID controller is applied in altitude, attitude andposition.
The parameters of PID are adjusted to a specificvalue to reach steady state system. The equation of PID
defined mathematically [7] asde(t )
dt

5.3 Position controller algorithm
This controller responsible for movement in x and yaxis, this movement achieved by rolling or pitching thequadrotor, the x and y equation are considered from Eq.
(3) to achieve these movements.

(6)

where kp, ki, kd are the proportional, integral, and thederivative gain respectively [6], e(t) is the error signal
which is the difference between the desired value andthe measured value..
ux   1 (sin sin  cos sin cos 
m

..
u1
y  (sin cos  cos sin sin ) m

5.1. Altitude controller algorithm(9)Assume a small angle for pitching and rolling [11]. The
error signal to position controller will be

The quadrotor must be at desired value from the groundto maintain this distance the altitude controller is used[7]. Therefore, the quadrotor lift force must be greaterthan its weight and the earth’s gravity to keep thequadrotor at hovering and take-off. This can be done byworking on quadrotor z-axis from Eq.(3) and made x
and y axis constant.

uz  g  (cos cos ) 1m(8)

By solving the differential equation can obtain 𝜓 as
the output value, so the control design for the error
signal will be: e𝜓 =𝜓ref – 𝜓 Where 𝜓ref the desired
value and 𝜓 is the measured output signal. This appliedin Eq.(6) to get the PID controller equation. At the samemanner, other attitude angle (e.g Roll and itch) can bederived.

5. Control Strategyu (t )  k p e(t )  k i  e(t )dt  k d

u4 k6 .
 
Iz Iz

e x  xd  xe y  yd  y(10)Then, apply Eq. (10) in Eq. (6) to get the controller
input which applied in Eq. (9)

..

(7)

6. Simulation results

In the above equation, θ and ϕ must be 0 for
hovering [13] and by solving the differential equation to
obtain the value of z. The error signal in altitude is
ez=zref - z, where, zref is the desired and z is measured
output this applied in Eq. (6) to obtain the PID
controller equation.

The physical parameter used in simulated the result are
the same used in [6]: m = 2 kg, g = 9:81 m/s2, Ix = Iy=
1:25 Ns2/rad , Iz= 2:5 Ns2/rad, K1 = K2 = K3 = 0:01,
Ns/m, K4 =K5 =K6 =0:012 Ns/rad. While the chosen of
the appropriate arms length as in table 1.
Table.1 Arm’s length variation parameter

lf

lm

L
(cm)

∆d

Maximumlength(cm)Minimum© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

158

Evaluation the Performance of
(cm)

(cm)

17.5

2.5

length(cm)
5

0.25

30

20

The result shown in Fig.3 presents the performanceof quadrotor and demonstrates the trajectory tracking.
The path tracking include the take-off and landing of
quadrotor and the way point that the quadrotor tracked
are illustrated in the Fig.3.

Fig. 6. The pitch angle

Fig. 7. The y-position

Fig.3. The path followed by quadrotor

Fig. 4 illustrated the altitude and hovering position by
applying PID controller while Fig. 5 and Fig. 6
illustrated the x-position and the pitch angle
respectively, when the quadrotor oriented to the desired
x-position, the pitch angle change its state due to thisorientation. In the same manner, Fig. 7 illustrated the yposition and Fig. 8 demonstrated the roll angle whichchanges its state due to y-position.

Fig. 8. The roll angle

7.

Conclusion

In this paper, a new design of quadrotor is proposed
depends on varying the arms length instead of varying
the motors speed for performing the maneuverability.
The PID controller is used in altitude, attitude, andposition movements to control the stability of thesystem. The mathematical model of the proposed design
is modeled. The simulation results evaluated theeffectiveness of the proposed design with control
strategy to improve the system performance.

Fig.4. the z-position

References
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2. A. R. Patel, M. A. Patel, and D. R. Vyas, "Modeling andanalysis of quadrotor using sliding mode control," inFig.5. The
x-axis position© The 2016 International
Conferenceon Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

159

Yasameen Kamil Najm, Desa Hazry, Khairunizam Wan, Zuradzman M.Razlan

System Theory (SSST), 2012 44th Southeastern
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helicopters," Journal of Intelligent & Robotic Systems,
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12. A. A. Mian and W. Daobo, "Modeling and backsteppingbased nonlinear control strategy for a 6 DOF quadrotor
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261-268, 2008.
13. M. H. Tanveer, D. Hazry, S. F. Ahmed, M. K. Joyo, and F.
A. Warsi, "Design of overall Stabilized controller for
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M. K. Joyo, D. Hazry, S. Faiz Ahmed, M. H. Tanveer, F.
Warsi, and A. Hussain, "Altitude and horizontal motion
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8. H. K. Kim, T. T. Nguyen, S. J. Oh, and S. B. Kim,
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Discussion on Factors Influencing the Performance of Hospital Renovation Engineering Taking one Medical Center in Taiwan as Example
Wen-Lung Lin, Yan-Chyuan Shiau*, Ting-Chi Lai and Chen-Chung Liu

Department of Construction Management, Chung Hua University
707, WuFu Rd., Sec. 2, Hsinchu 30012, Taiwan
E-mail: E10216015@chu.edu.tw; *ycshiau@ms22.hinet.net; E10416007@chu.edu.tw; E10416003@chu.edu.tw

Abstract
The subjects of this study were to investigate the renovation engineering case of four hospital areas in Mackay
Memorial Medical Center in Taiwan. The rank of each factor’s influencing severity and influencing performance infour stages for hospital renovation engineering was summarized. The factors’ severity rank influencing the performance in the four stages were planning and design stage, construction management stage, bidding stage and caseacceptance and closing stage. The conclusions and suggestions of this study can be used as reference for future
hospital renovation projects.
Keywords: Hospital Building, Hospital Refurbishment Projects, Likert Scale, Questionnaire

1. Introduction

1.2. Health care classification in Taiwan

1.1. Background

National health insurance in Taiwan classifies medicalcare institutes into 4 levels (Fig. 1): Medical Centers of
the regional level, regional hospitals of the regional level,
local hospitals of the local level and basic health careunits of locality level [2].

A hospital building is a complex integration of versatile
professional disciplines; in addition to general building
facilities, the scope includes a steady supply of medicalgas (A.V.O) system, fast gas transfer system, safety protection for radiation therapy and nuclear medicine, provision of highly clean air quality of hospital quality (for theOR, supply room, burn center and laboratory), steady
medical power supply, safe and clean water supply forthe kidney dialysis room, negative pressure ward andpositive pressure ward, IT equipment room, medical
wastewater treatment plant, movement stream lines ofclean objects and contaminated objects, security facilitiesof psychiatric wards; all the tasks, from refurbishment
proposal, planning and design, tender invitation andawarding, construction management, acceptance inspection and case closure, to development of operation procedures, are included in this domain. Therefore, refurbishment of hospital facilities concerns a management
science that combines complexity and professionalism.

2. Hospital Refurbishment Project2.1. Planning and precautions in hospital constructions
A hospital is highly professional and widely encompassing medical care installation that takes care of birth, aging,illness and death. There are delivery rooms, nurseries,labs, mortuaries, ORs, out-patient and emergency clinics,
kitchens, canteens, post office, bank, barber shop, beauty
parlor, sewage treatment plant, incinerator, etc., it is likea small community that requires collaboration of various
professionals for achieving its perfection. The Public
Construction Commission (PCC) of the Executive Yuanrequires that when an entity undergoes a project of public© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan

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Wen-Lung Lin, Yan-Chyuan Shiau, Ting-Chi Lai, Chen-Chung Liu

hospital or health center, reference must be made to “Operation Handbook for Public Buildings of Government
Entities” [3] promulgated by PCC, in combination withcompliance to medical care requirements, for enhancing
engineering quality of the public medical care institutes.
PCC has also set forth precautions for carrying out construction projects of public medical care institutes.

ments, theories, and years of personal experience in practicing design and supervision of hospital refurbishments,
the author discusses critical constructional phases that
affect the refurbishment, aiming to improve the quality of
hospital refurbishments. The study compiles and proposes 38 performance affecting factors that may affect therefurbishment. Performance affecting factors are screened
via interview and questionnaire given to 8 experts including a supervisor, auditor, labor safety officer, architect, contractor, etc., who have participated in constructions.

MedicalCenterRegional
Hospital

3. Investigation and Analysis Against Performance
Affecting Factors
The study takes the refurbishment projects of a medical
center in Taiwan as case study subject. Four stages of
construction life cycle are concluded as Planning and
Design, Tender Invitation and Awarding, Construction
Management, and Acceptance Inspection or Case Closure.
Performance Affecting Factors of Interview results areshown in Tables 1 to 4.

Local Hospital
Basic Care Unit
(Including general practitioners
and clinics)
Figure1 Classification of Health Care Institutions
2.2. A study on the current status of hospital construction management
Hospitals have the most complicated spatial functionalities of all types of constructions. Via reference docu-

Table 1. Performance Affecting Factors at Planning and Design Stage
No.
A1

Performance Affecting Factor
Unclear investigation of function requirements of the user unit (such as power system,
air-conditioning system, etc.)

1

2

3

Expert
4 5

6

7

8

       

A2 Only CAD plotted drawings of the refurbished area are provided without actual survey of the site. 
The user unit cannot fully comprehend the design drawing, and no sufficient communication has
A3
been made.
Dimensions and space for handling movement of medical care facilities are not sufficiently conA4
sidered in the planning.
Drawings are not plotted for integration (such as architecture system, E&M System,A5
air-conditioning system, gas transfer system, etc.).
Elevators are not located based on classified transfer of contaminated objects, wastes and relevant
A6
recycling resources.
Poor design of soundproof materials of special clinic rooms or inspection units, affecting examinaA7
tion results (e.g., hearing examination, language therapy, consultation room).
UPS system for medical care facilities not isolated from that for IT system, therefore causing inA8
terferences.
A9 Space for maintenance access of facilities not considered.
A10 Improper material design and poor material quality causing maintenance difficulties.

No integrated planning given for treatment plant and space for infective wastewater and general
A11wastewater.
Number of pros for inclusion
10

Total
8

    



7

  





6

  





6

  

  

7

  





6

  





6

  





6

  
  



  

5
7

  



6

11 11 11

2

11


3

11

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan

162

Discussion on Factors Influencing

Table 2 Performance Affecting Factors at Tender Invitation and Awarding StageNo.

Performance Affecting Factor

1







7

B1 Drawings of tender invitation document not fully cross-checked.
B2 Improper definition of supplier qualification.
B3 Tenderer qualification not fully ratified prior to tender open.
B4 Inconsistencies in tender documents, drawings and specifications.
B5 Unclear inspection standards for work items, materials and facilities in tender documents.
B6 Irrational construction period.
B7 Improper price for work items.
Number of pros for inclusion

2







7

3







7

Expert
4 5

 





7 1

6 7 8
  
  










7 2 7

Total
7
8
6
6
6
6
6

Table 3 Performance Affecting Factors at Construction Management Stage
No. Performance Affecting Factor
C1 Improper quality supervision of the project executive entity.
C2 Lack of experience of outsourced supervisor; lack of experience in supervising medical care constructions.
C3 Supervising unit fails to implement construction review plan and quality plan.
C4 Contractor autonomous inspection not fully implemented.
C5 Contractor defect follow-up and remedial/preventive measures not fully implemented.
C6 Review and plotting of pipeline systems not carried out prior to construction, resulting in disorderedpipelines that affect spatial usage of the hospital.
C7 Pipelines are not identified with color code and flow direction.
C8 Air-conditioning ducts not sealed, resulting in contamination inside the duct.
C9 Emergency plugs not color coded for discriminating from general ones.
C10 Pipelines go through internal walls in the ceiling of isolated ward, damaging air-tightness of theisolated ward.
C11 Proper sealant not provided at openings where pipeline goes across fire zones.
C12 Improper location or lack of cleaning openings and vents of sewers and drainage pipelines.
C13 Insufficient firefighting facilities on the construction site.
C14 Excessive number of supporting suppliers resulting in interfacing difficulties that affect generalcompletion date of the project.
C15 Slow design change process that severely affects construction progress.
C16 Failure to control critical path operation resulting in lag of construction progress and lacking aneffective countermeasure.
Number of pros for inclusion

ExpertTotal
1 2 3 4 5 6 7 8      7        8
           

 6
 6
 7

        8
         





 6
 6
 6

   

 6

         





 6
 6
 6

     

 7

     

 7

      716 16 15 16 6

16 4

16

Table 4 Performance Affecting Factors at Acceptance Inspection or Case Closure StageNo.

Performance Affecting Factor

D1
D2
D3
D4

Failure to set up SOPs for functional test run, tests, random tests, etc.
Failure to provide equipment training for the hospital ADM and medical care staff.
Failure to perform an integrated system test.
Excessive improvement deadline for acceptance inspection.
Number of pros for inclusion

1




4

2




4

3




4

Expert
4 5 6






  
4 1 4

7 8
 

 
 
3 4

Total
7
6
7
8

Performance Affecting Factors are developed for hospitalrefurbishment projects. The study summarizes interview
and questionnaire results against 4 hospital locations asshown in Table 5. The study also compiles frequent defects seen in the refurbishing works and concludes construction Performance Affecting Factors and improve-

4. Results
Analysis of Expert Opinion Survey is carried out mainly
by using a questionnaire based on 5-point Likert Scalewith the above construction Performance Affecting Factors identified by experts. Experts curve and ranking of© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan

163

Wen-Lung Lin, Yan-Chyuan Shiau, Ting-Chi Lai, Chen-Chung Liutions. Lack of price analysis against singular work
items.
 Construction Management Stage: Contractor autonomous inspection not fully implemented. Insufficientquality supervision of the implementation entity.
 Acceptance Inspection or Case Closure Stage: Excessive improvement deadline for acceptance inspection. Failure to abide by SOPs for functional test run,tests, random tests, etc.
 Severity Ranking of Performance Affecting Factorsof these 4 stages is: Tender Invitation and Awarding
Stage, Construction Management Stage, Tender Invitation and Awarding Stage, and Acceptance Inspection or Case Closure Stage.
Comprehensive suggestions proposed by the study addressing the above problems are as follows: Strengthen communication skills of the staff at the
Planning and Design Stage. Suggest setting up a
communication platform for the execution unit anduser unit, members of which include medical carepersonnel, first-line operators and heads of units.
Special considerations must be given to movement
streamline of the medical care, medical care facilities,
and subjects requiring the service, so as to avoid differences during the acceptance inspection, and toprevent from affecting quality of medical care services.
 Tender of project must be carried out more openly
with more channels. Suggest that a contractor database be established to ensure number of participating
contractors and that a contractor assessment systembe established for quality enhancement.
 Explicit construction specifications must be set forthat the Construction Management Stage. Suggest thatthe person in charge of the project demand construction quality based on contractual requirements, andthat a construction inspection team must be organized.
 Set up SOPs for Acceptance Inspection.
6. References

ment suggestions for providing valued references to practices of clients.
Table 5. Statistics of severities of Performance Affecting
Factors in respective stages.
Study Subject
Taipei General
Hospital
Tamsui Branch
Taitong Branch
Hsinchu Branch
Total

Stage 1 Stage 2 Stage 3 Stage 4
4

3

2

1

2
4
4
14

1
3
1
8

4
2
3
11

3
1
2
7

5. Conclusions
Via review of reference documents, study of actual construction cases, questionnaires, and integration of expert
opinions, the study compiles problems in the current execution of refurbishment as follows: After discussing with user units at the planning anddesign stage, drawings have not been fully verified,resulting in confusion in the acceptance inspection,
or awkwardness of modification or re-do. Respective
systems were not integrated during system design,
resulting in construction difficulties and subsequently design changes.
 Tender information was only available at the hospital
website and bulletin boards within the hospital, suppliers were unable to acquire information from otherresources, resulting in insufficient number of tenderers, which in turn resulted in failure of tender andprolonged time for contracting.
 The person-in-charge failed to demand exact compliance to specifications, such as materials not submitted for approval prior to construction, failure to
implement inspection procedures at respective stages,etc.
 Excessive deadline for improving defects.
After analyzing properties of respective hospital areasand problems encountered during for carrying out works,
the most severe Performance Affecting Factors at respective stages are as follows: Planning and Design Stage: Poor communication
with relevant units, differences in demands and design, unclear investigation of function requirementsof the user unit (such as power system,
air-conditioning system, etc.).
 Tender Invitation and Awarding Stage: Inconsistencies in tender documents, drawings and specifica-

1. Likert, R. A Technique for the Measurement of Attitudes,
Archives of Psychology, 140, (1932) 1–55.
2. 2015 statistics of medical care institutes, Ministry of
Health and Welfare, website http://r.search.yahoo.com.
3. Operation Handbook for Public Buildings of Government
Entities,
PCC,
the
Executive
Yuan,
https://www.pcc.gov.tw© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan

164

Discussion on Land Expropriation Compensation System
Hung-Chi Liu, Yan-Chyuan Shiau*, Yao-Shan Huang and Chen-Chung Liu

Department of Construction Management, Chung Hua University
707, WuFu Rd., Sec. 2, Hsinchu, 30012, Taiwan
E-mail: E10216020@chu.edu.tw; *ycshiau@ms22.hinet.net; grace624252@gmail.com; E10416007@chu.edu.tw

Abstract
Land expropriation is a necessary means for the public construction of country. The study results show that the public in Taiwan is rather satisfied with the current land expropriation at the land market value. “Estimation of land
value by real-estate estimators” and “Compensation based on values listed on the Actual Price Registration” aremuch accepted by the public. It is also suggested that the existing land expropriation compensation shall adjust to
meet the public wish to reduce incidences of struggles and improve administrative proficiency.
Keywords: Land Expropriation Compensation, Real-estate Estimators, Compensation Standards, Construction Improvements

land expropriation, so mutual benefits can be obtained
between the government and citizens.

1. Introduction
1.1 Background

1.2 Motive and Purpose of Research

Since almost all resources of human living depend on
land supply, land expropriation is often necessary ineach country for the need of public works. Land expropriation contains four major elements, namely public
interest, legal process, compliance to necessity andneed of compensation. In case of small expropriation
facing extensive resistances and conflicts, the reasons
behind include public suspicion of owners' rights ignored during expropriation and mostly importantly,
dissatisfaction against amount of compensation fromexpropriation. In order to achieve smooth expropriationthat balances public and private interests, it is important
to discuss from method of compensation system for
expropriation, where expert's opinions are referred todraft more suitable method of land expropriation that
addresses difficulties faced under current execution of

Due to its compulsory nature and difficulty on defining
the compensation standard, land expropriation is often
criticized and opposed by landowners. Should disputes
arise from compensation on land expropriation and further cause conflicts between the government and its
people, the social cost is often much greater than the
expense of compensation on expropriation. According
to revision on Article 30 of the Land Expropriation Act
in 2012[1], the method of compensation has changed
from original context of “extra proportion of announced
current land value upon expropriation as additional
compensation” to “compensation according to current
market value upon land expropriation.” One of the prerequisites of land expropriation is realizing public interests; when damage to citizens' rights becomes necessary,reasonable measures shall be taken to minimize such

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan165

Hung-Chi Liu, Yan-Chyuan Shiau, Yao-Shan Huang , Chen-Chung Liu

loss. The purpose of this research includes the following: Compile literature to discuss unreasonable systems
and phenomenon during the operation of land expropriation.
 Use questionnaires to investigate satisfaction fromthe owners of land expropriated against compensation system based on current market value of expropriation.
 With reference to foreign study on compensation of
expropriation, domestic experts' suggestions oncompensation of expropriation are compiled forfinding an improved strategy that suits the status in
Taiwan.

cultural improvements; (3) compensation for paid land
improvements on improved farm land that is deemed
legal with possession of building license yet works terminated; (4) compensation for loss from legitimate
business; (5) relocation fees. Relevant parties of land
expropriation include the government (applicant), landowner and general public (see Fig. 1). Among them, thegeneral public pays taxes to the government. The government sets budget for expropriation listed and releases compensation to the landowner that transfers property to the government and thus completes the publicconstruction with feedback of mutual benefits to general public's use. The relationship among these threeparties is closely related and inseparable.

2. Discussion on current compensation system
for land expropriation in Taiwan
Government

Be Imposed
PeopleImposition Profits
Provide Imposition Costs

s
efit
Ben
l
a
i
S oc

es
Tax

FairnessandEfficiency

Pay

Pro
per
ty T
A dm
ran
sfer
inis
trat
ive
Imp
osit
io

n

The content of relevant regulations to land expropriation in Taiwan was mostly duplicated from the Land
Acquisition Act in Japan. To address the need of socialand economic development during the period from1980 to 1990, the government made massive investments to commence major public works and actively
acquired lands needed. Since the development of relevant public facilities induced significant boost of land
price at then, great difference occurred between current
land price announced and market price, which caused
reluctant acceptance by landowners towards compensation of expropriation. Furthermore, inconsistent compensation standard on expropriation triggered citizen
opposition, which not only affected the progress of landacquisition for public works, but also caused conflicts
between citizens and the government. After revision of
relevant regulation in Taiwan in 2011, the compensation standard in Land Expropriation Act followed requirements under Article 30: “The value of expropriated land shall be compensated based on its current market value. The value of land reserved for public facilities in the urban planning area shall be compensatedbased on the average market value of its adjoining lands
not reserved for public facilities. The market value referred to in the preceding paragraph shall be forwarded
by the municipal or county (city) competent authority to
the Land Evaluation Committee for determination.”
According to requirements under the Land Expropriation Act and actual situation, items qualified for compensation upon expropriation of land or land improvements include: (1) Compensation for land value; (2)compensation for construction improvements or agri-

General Public(Non-expropriatedPeople)Fig. 1. Relationship among the three parties in compensation
for land expropriation

3. Various compensation standards
The compensation standards for land expropriation include the following:
3.1 Compensation for land value
For land expropriated, compensation shall match current market price upon expropriation. Should an inconsistency between area reported for expropriation andarea actually used after land expropriation, correction
shall be implemented according to actual situation andcompensation for land value of increase area will becalculated with compensation standard counted 15 days
from expiration of correction notice. (Article 5, Subject
and collection rules of compensation for expropriationon land or land improvements)© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan

166

Discussion on Land Expropriation

3.2 Compensation for construction improvements
or agricultural improvements

3.4 Compensation for loss from businessLand improvements can be divided into construction
improvements and agricultural improvements. The former include buildings or construction works attached to
the land; the latter include improved crops, plants andirrigation works attached to the land. The compensationfor expropriation on land improvements is assessed byauthorities from the Special Municipality or County(City) together with relevant agency [2]. For agricultural improvements such as fruit trees, tea trees, bamboos,
flowers / trees for viewing, trees for afforestation andother crops, the Government of Special Municipality or
County shall base on “Assessment criterion of compensation for expropriation on agricultural improvements”
issued by the Ministry of Interior and refer to actual
local status and latest data announced by the agricultural authorities to set its own basis of assessment of compensation for expropriation on agricultural improvements, which is then submitted to the Standard Land
Value Determination Committee for reassessment [3].

The above-mentioned area of trading is limited to areaof land or land improvements registered or applied for
trading, and excluding the non-operational parts.
Should the main structure or facilities of operation be
expropriated, where the remaining parts cannot continue to operate, compensation shall be provided as per
requirements under Point 3) mentioned above (Point 4,Compensation criterion for business loss from land andland improvements expropriated)
3.5 Relocation fees
Upon expropriation of land or land improvements, relation fees shall be dispensed for any of the following
situations (Paragraph 1, Article 34 of Land Expropriation Act).
3.6 Compensation for tenanted farmland
In case the compulsorily purchased land is tenanted
farmland, Article 11 of the Equalization of Land Rights
Act stipulates that the government shall compensate thelessee for the expenses he has incurred in making land
improvement as well as for any crops not yet harvested.
In addition, the lessor shall pay to the lessee one thirdof the compensated land price after a deduction for land
value increment tax through the government.

3.3 Compensation for land improvement
 Improvements on building foundations include leveling or excavation, water and soil maintenance,
piping, earth retaining work, trench excavation androad construction.
 Improvements on farmland includes earthworks,
water and soil maintenance, soil improvements, aswell as constructing farm roads, irrigation works,
wind breaks, sand breaks and embankments.
 Improvements for other land developments.

3.7 Compensation for loss from adjoining land
In case the use of expropriated land affects the adjoining land and causes an inability in its original use or
reduction of original performance, the owner of suchadjoining land is entitled to request appropriate compensation from the land applicant. The amount of compensation shall not exceed the land value reduced dueto the use of expropriated land. The regulations on relevant executions include Article 216 of Land Law, TaiNei-Di-Zi Letter no. 8886374 dated 1999/7/27 and TaiNei-Di-Zi Letter no. 8911895 dated 2000/9/5 from the
Ministry of Interior.

Damages arise from the cessation or down-scaling oftrading due to expropriation shall be compensated. The
relevant terminologies are defined as follows: “Legal business” means entity that lawfully acquired relevant license needed for operation and carried out proper trades.
 “Cessation of trade” means the termination of trading activity due to full expropriation on the land or
land improvements for trading.
 “Down-scaling of trade” means reduction of original trading size due to partial expropriation on theland or land improvements for trading.

3.8 Compensation for superficies
Rules governing the compensation for superficies shallbe prescribed jointly by the central competent authority
in charge of the relevant industry and the Central Com-

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan

167

Hung-Chi Liu, Yan-Chyuan Shiau, Yao-Shan Huang , Chen-Chung Liu

petent Authority. At present, the Ministry of Transportation and Communications, Ministry of Economic Affairs and Ministry of National Defense together with the
Ministry of Interior have followed the above regulations and set the “Rules governing the compensation fortransportation / water / defense undertakings passing
through the private lands from above or underneath”(Paragraph 1 and 4, Article 57, Land Expropriation Act).
Furthermore, Article 19 of the Mass Rapid Transit Act
stipulates that the construction of a mass rapid transit
system may pass through, over or under any government or privately owned land or any improvement on
the land under work necessity.

The analysis from statistics showed that, after adding
40% of compensation on top of publicly announced
current value for land expropriation, the percentage ofvery satisfied and satisfied citizens was greater thanunsatisfied and very unsatisfied citizens. This meansthat government's change provides further protection of
citizens' properties and reduces resentment towards
expropriation, so public works can proceed smoothly.
In the case of real estate valuation, it was found outsource commissioning received greater satisfaction,
where citizens showed lack of trust when the government carried out its own assessment on market price forcompensation.
 Improvement on strategy of compensation for expropriation
It is recommended to increase real estate valuation,
where cases processed by out-sourced evaluators can bereviewed by land valuators in public sectors. Sincecompensation for expropriation falls under enforcement,
citizens may build up a grudge easily and file appeals;
thus responsibilities shall be defined precisely. The
results from statistical analysis show that citizens believe the compensation for land expropriation has notreached the principle of full compensation. Therefore,such compensation shall elevate from appropriate compensation to full compensation, where economic andnon-economic loss suffered by landowners can be included within compensation for expropriation.

3.9 Compensation for requisition
Compensation for the use of land shall be calculated
starting from the date of public announcement of requisition and paid to the landowners or the holders of superficies, servitude of real property, agricultural right,
right of permanent tenancy or the right of cultivation in
one lump sum within 15 days after the expiration of the
public announcement period. (Paragraph 5, Article 58,
Land Expropriation Act).
4. Conclusions
Since 1st September 2012, the land expropriation policy in Taiwan has been implemented according to compensation with market price, where public benefit andnecessity are in place. However, both acquirers andlandowner think improvements are still needed for theland expropriation system. The conclusions are as follows: On topic of unreasonable compensation for expropriation
In order to comply with budget of the current year, theassessment units would make assumptions and portionof citizens would perceive this as the government controlling prices, which further lead to difference between
market price recognized by the citizens and the market
price assessed by the governmental unit, hence the extensive difference on the standard of price assessment
between government expropriating land from the citizen and vice versa. When changing into national landwith greater use, the citizens deem that price shall becalculated according to the value after development.
 Citizens showed satisfaction towards compensation
at market price for land expropriation

References
1. Land Expropriation Act, Department of Land Administration, Ministry of Interior (2012)
2. Department of Land Administration, Ministry of Interior(2011), "Assessment criterion on compensation for expropriatedconstruction
improvements",
URL:
http://www.land.moi.gov.tw/law/chhtml/lawmain1.asp
3. Department of Land Administration, Ministry of Interior(2011), "Assessment criterion on compensation for expropriatedagricultural
improvements",
URL:
http://www.land.moi.gov.tw/law/pda/lawappend.asp?lid=3132© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan

168

Study on User Satisfaction of Pick Areas for Elementary Schools – Using Nanyang Elementary
School in Taichung City as Example
Chun-Feng Chang, Yan-Chyuan Shiau* and Kuan-Yin Chen

Department of Construction Management, Chung Hua University
707, WuFu Rd., Sec. 2, Hsinchu, 30012, Taiwan
E-mail: E10316003@chu.edu.tw; *ycshiau@ms22.hinet.net; jac32455@gmail.com

Abstract
This study has investigated the user satisfaction of pick areas for elementary schools and their improvement requirements. Questionnaire is adopted to discuss user satisfaction of the school environment. The improvement
strategies for pick areas were inspected for different various backgrounds. The research results have showed that
overall satisfaction ranges from general to satisfaction. Some useful suggestions are proposed. These suggestions
can be served as reference for schools and educational organizations to establish an unimpeded pick area for a safe
school commuting environment.
Keywords: Commuting Environment, Pick-up/Drop-off Area, Walkways, Satisfaction, Improvement needs

 Discuss the satisfaction and improvement needs fromparents of students in elementary schools towards current situation of commuting environment to schooland pick-up/drop-off area.
 Analyze the difference between satisfaction from parents of students in elementary schools with different
backgrounds towards current situation of commuting
environment to school and pick-up/drop-off area.
 Discuss the difference between improvement needsfrom parents of students in elementary schools withdifferent backgrounds towards current situation ofcommuting environment to school and pick-up/dropoff area, as well as providing relevant suggestions
about establishment or improvement for commuting
environment to school pick-up/drop-off area.

1. Introduction
1.1 Background
According to statistics from the Ministry of Health andWelfare in 2010, data showed that among campus accidents in elementary schools, the percentage of deaths
from traffic accidents ranked top and reached more than
one-third of total accidents. Most of the traffic accidents
involved students occurred on the way to or from schools
and mostly happened near students' home or school [1].
Over recent years, government institutions at various
levels have actively promoted improvement on overall
commuting environment to school by providing safe andcomfortable commuting environment, as well as encouraging cooperation between communities and schools,
which shall gradually integrates surrounding commuting
environment to schools, and further achieves a perfectly
smooth and safe commuting environment to school [2].

1.3 Definition of commuting environment to school andpick-up/drop-off area
The commuting environment to school mainly includes
six fundamental areas: (1)Pedestrian walkways aroundthe campus (i.e., walkways to school); (2)traffic safety
facilities including pedestrian crossings and traffic sig-

1.2 Purpose of Research
Based on the above-mentioned problems and situations,the purpose of this research includes the following:© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan169

Chun-Feng Chang, Yan-Chyuan Shiau,Kuan-Yin Chen

40-49 years old at 58.7%. In the “Education level” category under parents of students in elementary schools
sampled for investigation, the majority were 303 parents
with college degree at 47.7 %. In the “Occupation” category under parents of students in elementary schools
sampled for investigation, the majority were 170 parents
in service industry at 26.8 %. In the “Average monthly
income” category under parents of students in elementary
schools sampled for investigation, the majority were 290
parents with NTD 20k-40k at 45.7%. In the “pickup/drop-off method” category under parents of students
in elementary schools sampled for investigation, the majority were 352 parents using motorcycles at 55.4.7%. In
the “Most often used gate” category under parents of students in elementary schools sampled for investigation, themajority were 298 parents using the main gate (NanyangRoad) at 46.9%.

nals; (3)pick-up/drop-off block for parents; (4)pedestrian
walkways, land bridges and underpasses from school toresidential communities; (5)passages from school gates tocampus buildings; (6)various functional facilities andlandscapes with visual effects along above-mentioned
passages. The pick-up/drop-off area refers to the region
consisting of traffic safety facilities such as pedestrian
walkways around the campus, pedestrian crossings andtraffic signals, as well as pick-up/drop-off block for parents within the commuting environment to school. [3]2. Investigation on Research Base
Nanyang Elementary School is located on Nanyang Road,
Fengyuan District, Taichung City as a central school intownship format with numerous stores and dense population nearby. According to information provided by education notification system of the Education Bureau of Taichung City [4]: In 2014, Nanyang Elementary School had
a total of 102 classes with 2,905 students, which made it
the largest school with most students among 228 public
elementary schools in Taichung City. The main gate sits
right next to Nanyang Road, the main road towards theCity with Ziqiang Street on the east side, Tongan Street
on the north side and Huiyang Street on the west side.
The school is surrounded by roads that form a 4-way intersection at each corner. There are 6 gates in total, where
3 gates serve as entries and exits for students arriving andleaving the school. Investigation was made on traffic
safety facilities at 4 intersections around Nanyang Elementary School and its 3 gates.

3.2 Analysis on satisfaction towards status of commuting environment to school and pickup/drop-off area
The commuting environment to school and pick-up/dropoff area includes three items: the walkways to school,
pick-up/drop-off block for parents and traffic safety facilities. In this research, the 5-point Likert Scale was used
for measurements and answering. Analysis was done withdescriptive statistics such as mean and standard deviation
to show the difference of satisfaction between each question. The resultant statistics are as follows: The item with the greatest satisfaction from parents ofstudents in elementary schools to "walkways toschool" was the “adequate width”. The item with thegreatest satisfaction to "pick-up/drop-off block forparents" was “adequate control time”. The item withthe greatest satisfaction to "traffic safety facilities"
was “proper execution adopted by the school on nonmaterial control measures".
 The top three items with the greatest satisfaction fromparents of students in elementary schools to current
status of commuting environment to school and pickup/drop-off area were “proper execution adopted by
the school on non-material control measures”, “adequate establishment of pedestrian crossings on surrounding roads” and "adequate width of walkways toschool”.

3. Results of Research
3.1 Analysis on profile of participantsThis research took parents escorting students to and from
Nanyang Elementary School in Taichung City as the participants, where questionnaires were used to understandtheir satisfaction towards the current status of the commuting environment to school and pick-up/drop-off area.
In the “Gender” category under parents of students inelementary schools sampled for investigation, the majority were 453 females at 71.3 %. In the “Age” category
under parents of students in elementary schools sampled
for investigation, the majority were 373 parents between© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan

170

Study on User Satisfaction
Study on User Satisfaction

 The overall satisfaction from parents of students inelementary schools to current status of commuting
environment to school and pick-up/drop-off area was
between normal and satisfied. The item with thegreatest satisfaction was "traffic safety facilities" followed by "walkways to school" and then "pickup/drop-off block for parents".

3.4 Analysis on improvement needs for commuting environment to school and pick-up/drop-off area
The objective of this section is to discuss improvement
needs from parents of students in elementary schools withdifferent backgrounds (including gender, age, educationlevel, occupation, average monthly income, pick-up/dropoff method and the most often used gate) towards thecurrent situation of commuting environment to schooland pick-up/drop-off area, which covers items such as
walkways to school, pick-up/drop-off block for parents,
traffic safety facilities. Descriptive statistics was used foranalysis, where the number of items for improvement
needs were represented with frequency distribution andpercentage, as well as ranking the improvement needs
according to magnitude of percentage (Fig. 1 to Fig. 3).

3.3 Differential analysis of satisfaction towards the current status of item with different backgrounds
The objective of this section is to discuss the difference
between satisfaction from parents of students in elementary schools with different backgrounds (including gender,
age, education level, occupation, average monthly income, pick-up/drop-off method and the most often usedgate) towards commuting environment to school andpick-up/drop-off area. In this research, independent sample T test and One-Way ANOVA were adopted for analysis. The background acts as the independent variable
and satisfaction to commuting environment to school andpick-up/drop-off area as dependent variable. Should distinctive difference be achieved, the Scheff Method can beused afterwards to compare and verify the difference.
Based on results of satisfaction from research investigation, the following three conclusions can be summarized:
 For parents of students in elementary schools withdifferent gender, age, education level, occupation, average monthly income and pick-up/drop-off method
towards commuting environment to school and pickup/drop-off area, there is no distinctive difference insatisfaction between "walkways to school", "pickup/drop-off block for parents" and "traffic safety facilities", as well as the “overall satisfaction”.
 For parents of students in elementary schools using
different gate towards commuting environment toschool and pick-up/drop-off area, there is no distinctive difference in satisfaction to "pick-up/drop-off
block for parents"; but distinctive difference does appear in satisfaction to "walkways to school" and "traffic safety facilities", as well as the “overall satisfaction”.
 For satisfaction from parents of students in elementary schools to "traffic safety facilities", the main gate
receives the greatest satisfaction, which is followedby the back gate, and then the east gate. In terms ofsatisfaction towards “walkways to school” and “overall satisfaction”, the main gate receives greater satisfaction than the east gate.

Walkway Improvement Needs
200
150
100
50
0

Walkway Improvement Needs
200
150
100
50
0

Figure 1 Analysis on improvement needs for walkways toschool© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan

171

Chun-Feng Chang, Yan-Chyuan Shiau,Kuan-Yin Chen

Chun-Feng Chang, Yan-Chyuan Shiau, Kuan-Yin Chen

Pick-up/Drop-off Block Improvement Needs

Traffic Safety Facilities Improvement Needs
200
150
100
50
0

300
250
200
150
100
50
0

Pick-up/Drop-off Block Improvement Needs
Traffic Safety Facilities Improvement Needs
300
250
200
150
100
50
0

200
150
100
50
0

Figure 2 Analysis on improvement needs for pickup/drop-off block for parents

Figure 3 Analysis on improvement needs for traffic safety
facilities
4. Conclusions and Suggestions
4.1 Conclusions
Based on the investigation of this research, the analysis
on data of satisfaction from parents of students in elementary schools towards the current status of commuting
environment to school and pick-up/drop-off area reveals
the following: The overall satisfaction from parents of students inelementary schools towards the current status ofcommuting environment to school and pick-up/dropoff area is between normal and satisfied. The satisfaction on individual items ranks from "traffic safety facilities", "walkways to school" to "pick-up/drop-off
block for parents".
 The satisfaction from parents of students in elementary schools towards the current status of commuting
environment to school and pick-up/drop-off area does
not show distinctive difference because of difference© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan

172

Study on User Satisfaction

2. Y. J. Wang, A study on the degree of satisfaction of schoolin gender, age, education level, occupation, averagemonthly income and pick-up/drop-off method.
 For improvement needs from parents of students inelementary schools towards commuting environment
to school and pick-up/drop-off area, the "walkways toschool" shows the greatest needs for "high safety"; the"pick-up/drop-off block for parents" shows the greatest needs for "sufficient waiting space" and the "traffic safety facilities" shows the greatest needs for"clear and adequate marking of stop lines along surrounding roads".
 The suggestions made by parents of students in elementary schools for establishing perfect commuting
environment to school and pick-up/drop-off area are
ranked from “control time of vehicle passing, implement separate access for pedestrians and vehicles, enhance executions of penalties to illegal parking”, “asktraffic police to assist with traffic guidance”, “hire experts to provide suggestions of improvements” and“provide sufficient funds for improvements”.

walkways for elementary school students in Yulin County,
Master’s Thesis for the master’s program of leisure environment management, Department of Tourism Management,
Nanhua University, Chia-Yi, 2013.
3. C. A. Hsiao, A study on the safety and satisfaction of pickup area at primary school of Taoyuan City, Master’s Thesis
for the master’s program under the Department of Architecture, Fengchia University, Taichung, 2011.
4. Website of Education Bureau, Taichung City Government,
http: //www.tc.edu.tw/school/student. ,4.2 Suggestions
Based on the investigation and results of this research, thefollowing suggestions are proposed for improvements oncommuting environment to school and pick-up/drop-offarea: Suggestions for commuting environment to schooland pick-up/drop-off area(i) Enhance maintenance and management of walkways to school(ii) Increase waiting space in pick-up/drop-off block
for parents(iii) Enhance maintenance and repair of traffic safety
facilities Suggestions for education authorities(i) Hire experts in planning of commuting environment to design a suitable improvement program,as well as providing schools with sufficient funds
to establish a perfectly safe commuting environment to school and pick-up/drop-off area.
(ii) Re-plan the school districts to reduce problems
such as insufficient pick-up/drop-off space andtraffic chaos in commuting environment to school
due to excessive number of students.
6. References
1. P. J. Wang, Y. C. Wu, The safe way to school, Journal of
Healthy Cities, Vol. 4, Page 46-51, 2006.
© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan173

Study on User Satisfaction in Sport Site Facilities for Senior Students of Elementary Schools inTaichung City
Pei-Ling Lin, Yan-Chyuan Shiau* and Ling-Lin Chang

Department of Construction Management, Chung Hua University
707, WuFu Rd., Sec. 2, Hsinchu, 30012, Taiwan
E-mail: E10316007@chu.edu.tw; *ycshiau@ms22.hinet.net; lin88@sish.ntpc.edu.tw

Abstract
This study has discussed senior students’ actual use and satisfaction towards school sport field facilities in elementary schools of Taichung City. This study adopted questionnaire method and senior students in elementary schoolsof Taichung City were used as research subjects which were sampled with stratified cluster sampling method. The
obtained data were analyzed by descriptive statistics, Chi-square test, T test, one-way ANOVA, and Pearson product-moment correlation. It is expected to provide reference for relevant personnel so as to provide the ideal campus
sports environment.
Keywords: School Sports Site Facilities, Use Condition, User Satisfaction, Requirements for Improvement

teenagers between the ages of 6 to 18 in Taiwan ranked
the highest in Asia with 26.8％. In 2007, the Ministryof Education (MoE) took statistics on the height of students between Grade 4 and 6 from 2004 to 2006. The
average student height showed negative growth for 2
consecutive years. Based on MoE's statistics in 2006,
the physical fitness of students in domestic junior highschools and elementary schools fell behind neighboring
countries in Asia, namely China, Japan and Korea [2].
If the learning results of our next generation are affected by poor physical fitness and health, we will lose international competitiveness and future of the nation inlong term [3].
In order to encourage students' motivation and interest in sports and cultivate the habit of regular sports,
as well as consolidating ability and attitude of life-time
participation in physical activities, the MoE proposed
the “Get Active Quick Project” in 2007, where students
were expected to exercise a minimum of 30 minutes per
day and 210 minutes per week, so interest in sports
could be developed. This research targeted on senior
students of public elementary schools in Taichung City
for discussion on use condition of school sports site

1. Introduction
1.1 Background
Over recent years, many countries have tried to increase
the amount of student physical activities in order toenhance general nation competitiveness. Article 1 of the“National Sports Act” stipulates that: “The implementation of national sports serves to strengthen the physicalfitness of citizens, nurture national morals, expound
ethnic spirit and fulfill the lives of the general public.”[1] Good attitudes and habits towards sports shallbe cultivated from childhood as stipulated in Article 6of the “National Sports Act”: “Schools at the highschool levels and under and the first three years of fiveyear colleges should provide time slots for the daily
participation of sports activities, aside from sports classes, with weekly participation of over one hundred andfifty minutes, while students are on school premises.”
1.2 Motive and Purpose
According to the report from the International Obesity
Task Force (IOTF), the obesity rate of children and© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan174

Pei-Ling Lin, Yan-Chyuan Shiau, Ling-Lin Chang

 Questionnaire Editing
The “Questionnaire on use condition and user satisfaction from senior students of elementary schools in Taichung City towards school sports site facilities” used asa tool for this research selected topics from past questionnaires that implemented investigations on sports
site facilities. Reference was also made to domestic
regulations and literature of relevant research on school
sports site facilities, where the draft of questionnaire
was compiled according to the current state of sports
site facilities and features of elementary schools in Taichung City.
 Content of Questionnaire
The questionnaire for this research mainly consisted of
two parts with basic information in Part 1 and questionsin Part 2. The relevant contents are described as follows:(i) Basic information
These included name of the school, size of the school,
gender and grade.
(ii) Questions in questionnaire(a) Investigation on use condition of school sports
site facilities (16 questions in total)(b) Investigation on user satisfaction for school
sports site facilities (23 questions in total)(c) Investigation on requirements for improvementof school sports site facilities (4 questions in total)

facilities, user satisfaction and requirements for improvement, so adequate suggestions could be made to
the education authorities and administration unit inschools. Base on the above-mentioned background andmotive, the objectives of this research are as follow:
 Discussion on the perception of senior students ofpublic elementary schools in Taichung City towards the use condition of school sports site facilities and user satisfaction.
 Discussion on variables of different student background against difference between use condition
and user satisfaction towards sports site facilities,
as well as the degree of relevance between the two.
 Provide relevant suggestions on school sports sitefacilities for senior students of public elementary
schools in Taichung City.
2. Design and Implementation
Based on the background and purpose, this article designed the structure of the research, where credible
questionnaires were edited as a tool of investigation andobjective data were collected through sampling. The
data acquired were analyzed at the final stage and thecurrent status of the research was revealed. The research structure is shown in Fig. 1. There are a total ofthree sections in the Chapter, namely tools, participants,
data processing and analysis.

2.2 ParticipantsStudent Background
Variables

Result Variables

 Specimen Population
In this research, senior students of public elementary
schools in Taichung City acted as the specimen population. According to statistics taken by the Education Bureau of Taichung City, there are a total of 229 public
elementary schools in Taichung City excluding one
school in the process of preparation for enrollment andnot yet operational. Among these schools, there are
2,190 classes (Grade 5 and 6) and 54,620 students (bothmale and female) in total [4].
 Specimen Selection
This research adopted the stratified cluster sampling
method, which divided the elementary schools inTaichung City into four groups based on the number ofgeneral classes: below 20 classes, between 21~40
classes, between 41~60 classes and above 60 classes.
The schools with classes below 20 were categorized as
small schools and a total of 104 classes were counted.

Sports Buildings andFacilities Usage
1. School Scale
2. Gender
3. Grade

Sports Buildings andFacilities Improvement
Needs
Sports Buildings andFacilities Usage
Satisfaction

Fig. 1. The research structure of the study.
2.1 Tools
The tools used for this research were the “Questionnaireon use condition and user satisfaction from senior
students of elementary schools in Taichung City
towards school sports site facilities” edited by the
researcher himself. The process of editing and content
of questionnaire are described as follows:© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan

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Study on User Satisfaction

The schools with classes between 41~60 werecategorized as large schools and a total of 37 classes
were counted. The schools with classes above 60 werecategorized as jumbo schools and a total of 15 classes
were counted. Table 1 shows the size grouping of the
public elementary schools over each administrative
district in Taichung City. 3 schools were randomly
selected from the small, medium, large and jumbo
schools respectively to a total of 12 schools, where 2classes from senior grades of each school wererandomly selected to a total of 24 classes and 560
students for completion of the questionnaires. This
research issued 560 questionnaires in total, and 18 were
not collected, which means a collection of 542
questionnaires with 539 effective questionnaires
counted. 542 students were actually counted as
participants to the investigation with the effective rateof collection at 96%

2.3 Data Processing and Analysis
In this research, invalid questionnaires with incomplete
answers above 50% or careless answers (all answers
were ticked) were eliminated first. Coding andregistration were then implemented, where various dataprocessing and analysis were carried out with thestatistic software “IBM SPSS Statistics 22”. Eachquestion was analyzed with discriminant analysis anditem analysis, and internal consistency was acquiredwith Cronbach α. No statistics and analysis were madeto partial data that was lost but defined with SPSS
missing value. For background variables and school
sports site facilities, the use condition, user satisfaction
and requirements for improvement underwent
descriptive statistics and analysis. Chi-Square Test wasused to see if there were distinctive differences inbackground variable on senior students of elementary
schools upon differential analysis on use condition ofschool sports site facilities. T-Test and One-way
ANOVA were used to see if there were distinctive
differences in background variable on senior students ofelementary schools upon differential analysis on use
condition of school sports site facilities. Finally,
Pearson product-moment correlation coefficients [5]were used in statistics and analysis on relevance
between use condition and user satisfaction.

Table 1. Statistics on size grouping of public elementary
schools over each administrative district in Taichung City [4]Group below 20
Districtclasses
East
2
South
0
West
1
North
0
Central0
Beitun2
Xitun
4
Nantun1
Fengyuan
3
Houli4
Shengang
0
Daya
2
Tanzi
1
Waipu
4
Qingshui
7
Wuqi
3
Dajia
6
Shalu
4
Daan
4
Longjing
3
Dadu
4
Wuri
5
Dongshi9
Shigang
2
Xinshe7
Heping8
Dali
3
Wufeng9
Taiping
6
Total
104

21-40
classes
3
1
2
6
1
10
5
4
3
1
4
4
2
2
3
2
2
0
0
3
3
2
1
0
1
0
2
0
6
73

41~60
classes
1
3
1
0
0
3
3
2
2
2
1
1
2
0
0
1
2
2
0
1
0
1
0
0
0
0
6
1
2
37

above 60
classes
0
0
0
1
0
1
1
3
2
0
0
1
1
0
1
0
0
1
0
0
0
0
0
0
0
1
1
0
1
15

3. Results from Research
Based on the results from data analysis and discoveryof the research, conclusions acquired from the research
can be compiled as follows: Use condition of senior students of elementary
schools on school sports site facilities(i) Use condition(a) 61.1% of senior students of elementary
schools knew about the rules and notes related to the school sports site facilities. 85.7%
knew that outdoor facilities such as “outdoor
ballpark”, “athletic field” and “playground”
opened after school hours.
(b) 90.5% of senior students of elementary
schools had the habit of using the school
sports site facilities. Among them, 36.3%
used the facilities “after school hours”;
32.6% used the facilities “during sports classes or club activities”. 40.0% of the senior
students of elementary schools exercised© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan176

Pei-Ling Lin, Yan-Chyuan Shiau, Ling-Lin Changwith school sports site facilities “less than 30
minutes” every time and 39.8% were between “30 minutes~1 hour”.
(c) 44.8% of senior students of elementary
schools usually used the school sports sitefacilities together with classmates or clubmembers; 55.7% “played with a group” and36.6% “played with 2~3 classmates”.
(d) The top 3 school sports site facilities that
senior students of elementary schools wishedto have in future were “swimming pool”,“rock climbing field” and “table tennis court”
in sequence.
 Differences in different student background on use
condition of school sports site facilities(i) In terms of school size: There were distinctive
differences in senior students of elementary
schools on “recognition on rules of school sports
site facilities”, “average duration of every use”,“the sports facilities mostly used by PE teachers”
and “willingness on re-use”.
(ii) In terms of gender: There were distinctive differences in senior students of elementary schools
on “average frequency of use per week”, “average duration of every use”, “number of companions during sports”, “frequency of use during
lesson breaks”, “level of fun” and “willingness
on re-use”.
(iii) In terms of grade: There were distinctive differences in senior students of elementary
schools on “average frequency of use per week”
and “frequency of use during lesson breaks”.
 Requirements for improvement on school sports sitefacilities(i) A. For outdoor facilities (athletic field, outdoor
ball court, and playground): too far from classrooms, weather influence, crowded with excessive number of people, insufficient space, andinsufficient quantity.
(ii) B. For indoor facilities (gymnasium or sports
field with roof): too far from classrooms and insufficient space, insecurity due to insufficientnumber of people, insufficient lights and facilities closed.

sports, few suggestions are proposed to the education
authorities and school administrative units: As learned from school data provided by the
Education Bureau, Taichung City, the total rate ofestablishment on gymnasiums for public elementary
school in Taichung City was 48% (110 gymnasiums/229 schools) [6]. Since the rate of establishment on
small or medium size of gymnasiums or sports
ground with roof is still below the total rate ofestablishment, priority funds must be issued toincrease number of indoor facilities including
gymnasiums or sports ground with roof.
 Regardless of sports classes or hours after school,
all senior students of elementary schools were fondof ball sports; but the willingness and results were
often affected by insufficient number of ball courts
and weather. Therefore, a sufficient number ofindoor and outdoor ball courts must be provided tomeet such needs. As more idle classrooms will beavailable due to declining birthrate, these facilities
can be renovated into simple sports classrooms to
reduce the influence of inclement weather on sports.
 Regarding the rate of using school sports sitefacilities by senior students in elementary schools,
boys were higher than girls. Therefore, different
needs generated from gender differences must be
considered during the planning stage of school
sports site facilities, where facilities more suitablefor girls are provided, so the willingness of usage
can be enhanced.
5. References
1. National Sports Act, Legislative Yuan, 2013.
2. C. W. Chang, New prescription for sports learning, Parenting Magazine, Vol. 28, 2011.
3. W. C. Hsu, Relevant study on social support and sports
attitude towards elementary student sports in Hsinchu City,
Master Thesis, Department of Physical Education, National
Hsinchu University of Education, Hsinchu, 2007.
4. Education Bureau, Taichung City, School data at all levels,
enquired on November 26, 2014.
5. M. L. Wu, Practical SPSS statistics and applications: Questionnaire analysis and application statistics, Yixi Publisher,
New Taipei City, 2000.
6. J. W. Huppertz, J. A. Sidney & H. E. Richard, An application of equity theory to buyer-seller exchange situations,
Journal of Marketing Research, 15, (1978) pp. 250-260.

4. Conclusions
In order to improve user satisfaction from senior students of elementary schools on school sports site facilities, which further encourages willingness and fun in© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan

177

The Establishment of the Sustainability Performance Indicators for Wetland Ecological Project:
Using Construction Inspection Phase as Example
Ching-Mei Miao

Department of Civil Engineering, Chung Hua University, 707, WuFu Rd., Sec. 2, HsinChu 30012, Taiwan
Yan-Chyuan Shiau*, Chen-Chung Liu and Jen-Kuo Chang

Department of Construction Management, Chung Hua University, 707, WuFu Rd., Sec. 2, HsinChu 30012, Taiwan
E-mail: miaomei589@hotmail.com; *ycshiau@ms22.hinet.net; E10416003@chu.edu.tw; e10416014@chu.edu.tw

Abstract
The critical success factors of sustainable performance indicator (SPI) were investigated in this research. A wetland
water purification project of a stream in Hsinchu was used as an example in this paper. The experiment information
was integrated as feedback to correct the index library. The measurement from test results verifies the SPI fromsample project case in some facet is consistent with the results of current inspection system. The research result canbe served as reference for the sustainable development of ecological engineering achievements.
Keywords: SPI, Wetland, Sustainable Development, Ecological Engineering.

over recent years and the lowest bid of public works
awarded, the engineering sustainability could not berealized effectively. Should the techniques in construction management and performance-based contracts be
adjusted and improved, they could be used to solve theproblem of engineering unsustainability [1].

1. Introduction
1.1 Background
Over the past years, the Taiwanese Government has
invested tremendous amount of resources, manpower
and funds to build many artificial wetlands all overTaiwan with ecological engineering, and the results
proved that water quality was in fact improved. However, numerous failures of artificial wetlands with ecological engineering still occur from time to time. Afterinvestigation, it was found that the stage of acceptanceinspection was crucial to determine the success andremedy of the project as a whole. However, the problem of unsustainability during the stage of acceptanceinspection has never been taken seriously. Instead of
implementing project planning, design, construction
and maintenance with sustainable thinking as a whole,
the conventional approach of linear thinking still exists
in the managerial concepts. With additional factors suchas reduction of human resources by the government

1.2 Sustainable performance target
In order to study and draft sustainable performancetarget of artificial wetland work, relevant domestic andforeign policies on sustainable use of water resources
need to be collected and analyzed. The sustainable performance in this research is based on environmental,social and economic aspects that are capable of performing self-repair and reaching a balanced state [2].
The environmental aspect refers to harmless continuityof all species and the environment; the social aspectrefers to fair treatment and continuity to all species, andthe economical aspect refers to an economy with reasonable, balanced and sustainable benefits.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan

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Ching-Mei Miao, Yan-Chyuan Shiau, Chen-Chung Liu, Jen-Kuo Chang

2. SPI for Ecological Engineering of Wetlands

streams after initial purification, entered into the wetlands via directed channels for further purification, andthen discharged into a large river. The work content
included the main structure (inclusive of pond and tankslope), riverbanks, alleyways and access roads in thework area. Clay crystallization was adopted to constructimpervious layer of the tank base and slope; a gabion
wall was applied for riverbanks; the protective fences,
platforms and boardwalks were built with environmental friendly wood/plastic composites where earth excavation and backfilling provided balance and the surface
was paved with re-generated asphalt.

2.1 Establishment of SPIMThe SPIM (Sustainable Performance Indicators Method) is a measure of judgment integrated within level 3
quality control process of public works to determine
compliance of sustainable performance [3]. The method
is objective, clear and measurable with precise measuring process, threshold value, featured indicators of relative response time as well as properties such as preliminary indicator, process indicator, result indicator anddriving indicator. Instead of a general scoring methodof sustainable indicator evaluation mode, this researchis based on procedures of SPI establishment and referred to suggestions provided by experts to construct
the SPIM used during acceptance inspection on ecological engineering of artificial wetlands, which implements inspection operation according to inspection control chart of sustainable performance on ecological engineering of artificial wetlands and continuously sends
back the results for rectification of SPI database.

3.2 Results of actual verification
From September to October 2014, this research carriedout actual verification on SPI items completed for thewater purification works on a specific artificial wetlandin Hsinchu City. The inspection results recorded after
measurements are shown in Table 1 and 2. The resultsof the SPI inspection in this case were not ideal sinceout of the 23 items of sectional inspection indicators
according to the work progress, 14 items did not comply with the inspection indicator and a result rate of
non-compliance at 61%, which exceeded the inspection
determination standard of 40%. Therefore, this chosencase was an “unsustainable wholeness of work”. From
inspection and construction results, it was found thatthe contractor did not have concepts of sustainable
work and construction, yet still blamed the design unit
for the poor work results, hence the contractor showed
incapability in agility and flexible construction. Basedon results of SPI actual verification on the case andcompared to current public works audited, the work
performance was marked B on September 4, 2014,
which proved that results of SPI inspection on certain
phases under this research were authentic.

2.2 Inspection and control of SPI
During the stage of acceptance inspection, this research
implemented inspection and payment control on thestandard of contractual service. In a contract withsustainable performance format, the official processcontains a monthly report where each pricing with
precise results receives payment; the unofficial processcontains inspections on the standard of contractual
service carried out randomly, weekly and upon receipt
of a complaint, where SPI acts as the basis of inspection
control and payment to ensure performance of work androutine services. Penalty or reduction in payment will
be implemented should failure of compliance occur,and in case of severe violation, payment will be stopped
or the contract terminated. Accordingly, the contractor
needs to reach a minimum threshold value of service
standard under the SPI contract, or additional penalties
will be executed [4].

4. Conclusions
The description of major achievement from this research includes the following: Compile and propose objectives of sustainable artificial wetlands with ecological engineering and key
factors of success.
 Compile and propose SPIM at stage of acceptanceinspection of sustainable artificial wetlands withecological engineering.

3. Actual Verification
3.1 Case introduction
In this research, a specific artificial wetland work in
Hsinchu City, Taiwan was taken as the example. In thisproject, living sewage was discharged into specific© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan

179

The Establishment of theProvide operational experience of verified case on
SPI at stage of acceptance inspection of sustainableartificial wetlands with ecological engineering.

Table 1 Summary of SPI test results over water purification work on artificial wetlands along river in Hsinchu City
Indicator
SPI
No.

Description of compliance failure

SP01

Area of foundation subsidence

8m2 and 3.5m2 of subsidence per spot with total area of subsidence >11.5m2

SP02

Area of slope collapse at pond /riverbank

1.6m2 and1.2m2 of collapse

SP03

PotholePothole at diameter of 28cm and depth of 12cm over foundation surface

Monetary value converted fromlocal material acquisition
Amount of water flow in pond
(leak amount)

Monetary value converted from local material acquisition ＞20% of total materials purchased-

Multiple collapses and holes in pond

16 spots in
total

SP04
SP05
SP06
SP07
SP08
SP09
SP10
SP11
SP12
SP13
SP14
SP15

SP16

SP17

SP18
SP19
SP20
SP21
SP22
SP23

Test results
2 spots intotal
4 spots intotal
1 spot in
total

Odor of water inlet & discharge, temperature, turbidity, pH value, electric conductivity,
BOD5, TSS, CI–, NO2–+NO3–, NH4+, TKN, TP
Plastic bottles were found all over the site and reinforcement abandoned by the ecological
5 spots in
Site cleanness
pool
total
Toxicity measured from construc- Level of toxicity measured was ≦ legal specification. There shall be no toxic substance intion materials
the pond at all due to hazard of contamination.
Area of construction interference The moving path of construction machinery passed through the work area and riverbeds to 3 spots inaround the sitecollect local river stones at ecologically sensitive area.
total
Proportion of ecological engineer- For the ecological engineering work of stone laying by the pond, concrete was used as adhe- 2 spots ining areasive between rocks, which severely affected living environment of species in the pond.
total
Planting timing and survival rate
3 spots in
The area of plant survival over the wetland was 20%, 30%
on the wetland
total
Proportion of ecological experts The ratio of ecological experts from engineering industry /total number of construction
from engineering industry
personnel was ＞80% (most personnel and administrators have ecological expertise)
Number of air pollution incidence
1 case in
Average number of incidence /Month was ≦1
reported
total
Number of complaint on construc- Average number of incidence /Month was ≦2. The noise to surrounding areas should betion noise
reduced.
Number of citizen complaints or
Average number of incidence /Month was ≦3reports
Proportion of overall expense on 1. Inferior construction quality was found on river diversion and stone revetment at impactaccident prevention, reduction and ing side of the site. The height and number of layers constructed was insufficient.
2 spots indamage for the river and2. Inferior construction quality was found on stone revetment to riverbanks. The height and total
riverbanks
number of layers constructed for stone revetment at impacting side was insufficient.
Proportion of expense on safety of
There were no safety fences erected by the site and surrounding areas for separation. No
site and surrounding areas, as well
3 spots insafety fences were erected by personnel near the pond during construction and the personnel
as accident prevention facilities
total
were not wearing safety protections.
and measures
Proportion of expense on preservExpense on preserving historical remains was ≧1.5 times of valuating historical remains
ing historical remains
Employment ratio between minorNumber of minority employees / general employees on site (monthly average) was ≧30% ity labors and general labors
Broken and chipped finish of fences by wooden boardwalks were covered up with industrial 2 spots in
Loose chipping
tape. Concrete pieces chipped off from superstructure of the water intake well.
total
The wooden floor of observatory platform bulged to height of 2.1cm and the pavement with 8 spots in
Bulged or sunken pavement
bricks from environmental regeneration was sunken to depth of 1.8cm.
total
Percentage of local labors over
Local laborers was 5%
Sep. & Oct.
foreign labors
Proportion on amount of material The amount of material procurement locally /amount of material procurement remotely wasprocurement locally≧6 times
Water contamination in pond

Table 2 Results of SPI site test over water purification work on artificial wetlands along river in Hsinchu City
Description of inconsistency to standard
Method of site
Duration of improvement
requiredmeasurement© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan
SPI

180

Ching-Mei Miao, Yan-Chyuan Shiau, Chen-Chung Liu, Jen-Kuo Chang
Area of pond /river slope collapse

Area of collapse at 1.6m2
Pictures from inspection

Through case study, this research implemented actual
operation, testing and application to verify applicabilityof SPI at stage of acceptance inspection of sustainableartificial wetlands with ecological engineering, as wellas whether the case of verification provides sustainable
performance. The results obtained are described as follows:













It is verified that this research can be operated
practically.
Through SPI of this research, the sustainable performance of site works can be tested at speed andconvenience with simple tools, which extensively
reduces funds spent on manpower and experiments.
The results prove that certain phases and indicatorsof this research are consistent with status of level
I/II QC (Quality Control) check and audit resultson public works.
The SPI inspection from this research has indeed
driven contractors to think of more innovative
methods of work performance and realizing establishment of sustainable work gradually.
Compared to conventional inspection, the sustainable performance inspection carried out through SPIof this research can effectively test whether there issustainable performance on the project or not.
For SPI inspection implemented per current work
progress on the case actually tested, the standard
requested, method and tools of measurements, aswell as responsive allowable tolerance or TOR(Time of Reaction) set out by the indicator items
tested are all adequate in practical operation of testing.

Roller tape

Repair completed within 3 days

It is feasible and effective for verification donewith indicator method that integrates work procedures with sustainable performance, which realizes
the mode of sustainable work at stage of acceptance inspection of work for artificial wetlands
with ecological engineering.
For subsequent research, it is recommended to provide SPI that are more diversified and suitable,
where a relevant SPI study can be commenced particularly on type, functions required and construction method of artificial wetlands.

5. References
1. S. L. Liao, Establishment of sustainable indicator system,
evaluation and method of comment on water and soil resources (I, II), Presentation of NSC Project Reports No.
NSC-88-2621-Z-008-004, Taipei, 1999-2000
2. M. Munasinghe, Environmental economics and sustainable development, World Bank Publications, 1993.
3. H. K. Ku, A study on establishing evaluation criterion
for sustainable public works, Doctoral Dissertation,
Graduate Institute of Civil Engineering, National Central
University, 2010.
4. C. Labuschagne and A. C. Brent, Sustainable ProjectLife Cycle Management: Aligning project management
methodologies with the principles of sustainable development, 2004.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan181

Development of Type Control Guidelines for the Old Town District of Hsinchu City
Yi-Yin Tsai

Department of Architecture and Urban Planning, Chung Hua University, No. 707, Sec. 2, Wufu Rd., Hsinchu, Taiwan
E-mail: yiyint@gmail.com
www.chu.edu.tw

Abstract
The concept of “Building Type Control” was employed to establish hierarchical design guidelines for the urban
regeneration process. GIS was used to manage and map environmental database as visual reference tool. Existingbuilding regulations and urban design policies were reviewed to set out limitation and development vision for the
study district, type control zones were set out with control design guidelines respectively to serve as a sufficientreference tool to assist design decision making in the urban regeneration process.
Keywords: Type Control, Design Guideline, Urban Regeneration, Urban Design

more sufficiently in the design process in fulfillingthe vision and requirements of future urban
development.

understanding the historical development of the site, thecultural landscape features and changes over years; it
was clear that a set of sufficient design guidelines orregulations is necessary to ensure the quality of urban
environment through the development process. The
study attempts to employ the concept of “type control”
to formulate a set of rules in regulating features of eachindividual building, as well as the overall appearance ofbuilding streetscape. The concept of “typology” wasused to identify the development types of various
building district to set the zoning types initially. The
type control design guidelines were set out accordingly.

1.1. Research Subject

2. Method

1. Introduction
Addressing urban regeneration in an old town
district of a compact urban city is a complex task, which
involves consideration of many interconnected issues.
Consequently, the subject of urban regeneration orsustainable urban development with regard to theexisting building fabric requires a systematic controllinghierarchies and urban design guidelines to be employedThis research selected the old town district ofHsinchu City to be the research subject. Documentationof the subject background data was carried out first withon-site interviews to collect basic information of theresearcharea’sexisting
condition.
Through2.1. Typology
According to the Oxford Advanced Learners
Dictionary of Current English, the word “Typology” isexplained as to distinguish between different types of© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan182

Yi-Yin Tsai

type system [1]. In the field of architectural discourse,
in terms of typology, it is defined as a way ofunderstanding and thinking. In the eighteenth century,building typology is recognized as the method of acontinuous and uniform system for the classification
process for architectural building. According to M.
Bandini [2], the concept of typology can be summarized
as three aspects: the first one to be the method of cityreading which emphasizes the integrated nature of thecity and explore the morphology of city with thebuilding types; the second aspect is concerned with thecultural perspective represented by G. Argan which
addresses urban typology as a method consists ofvarious city architectural styles; the third aspect,directed by Quatremere de Quincy and represented by O.
M. Ungers and others, defines typology as the
production tool and production theory of architecture.
In terms of research, Johnson and Christensen [3]defined this concept as a classification system that split
something into a number of different types or kinds.
Grix [4] refers to the concept as dividing the study
objects into many types, where the attributes of each of
the type are mutually exclusive to each other but
together as a exhaustive system. Since one type only
requires one property; typology can be used in a varietyof variables under various circumstances. In typology, a
special order can be derived based on the research
purpose and the research phenomena, and this order isable to set limitation on the methods of datainterpretation. Thus, a hierarchical type control design
guidelines could be set out for the regeneration process.

Figure 1. GIS mapping of the “Style” sub-type feature
under the “Basic Element” sub-type of the existingbuilding fabric condition of the study subject.
2.3. Policy Review
Existing government regulation and policy
addressing topic of urban regeneration was firstreviewed and summarized to formulate the development
vision for the research subject in constructing the typecontrol guidelines. Both the land-use zoning control
regulations, and the urban design regulations of thethree major city in Taiwan, the Hsinchu City, theTaichung City and the Tainan City, were reviewed tounderstand the basic control regulation each city used inregulating and controlling the appearance of building
design in different urban district in order to establish aset of type control guideline for various urban district.
3. Result2.2. GIS Data Input

3.1. Type Control Guidelines

Employing the type model structure developed
through literature review, GIS software was used toconstruct the façade feature database to illustrate theexisting building condition of the study subject.
Different colors were used to represent different “subtype” or “sub-type item” of the data information. An
example of the GIS mapping of a sub-type feature isshown in Figure 1.

The old town district of Hsinchu City serves as theurban development center of the city, it hosts most of
the daily activities of the local people as well as the
major tourist attractions. It has a mixture of historicaland modern buildings, as well as a mixture of residential,commercial, civic, religious and governmental building
functions. Thus, four type control zones were set out asfollowing:(1) District I: Urban Commercial & Historical Fringe
Zone.
District I is the fringe of the historical street zone andthe newly urban commercial development area. The© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan183

Development of Type Control

district is closed to the main urban activity traffic roadand serves as a landmark for the newly developed area.
Creating new city image and connecting pedestrian
activities are the key considerations for setting up
control guidelines for the district.
(2) District II: Historical Street Zone.
This district is a historical street control zone; the oldbuildings were set to be preserved and maintained
according to its original historical appearance. The main
control guidelines were set to emphasize on the building
envelope and façade appearance type controls.
(3) District III: Art & Culture Activity Zone.
District III is designated to be the art and culture activity
control zone within the old town area of Hsinchu City as
the cultural art museum is located at the center of the
district. Since the cultural art museum has just been
listed as the City’s historical building, and the museum
was designed to host modern exhibition; the district was
set to consider both the idea of historical preservation as
well as future modern developments.
(4) District IV: Monumental & Local Business Zone.
Type IV district is classified as monumental and local
business zone due to several important existingbuildings within the periphery area; including the main
religious temple, the Cheng Huang Temple, the city’scentral vegetable market, and the traditional shopping
center which are all in need to be refurbished andpreserved under city’s urban renewal project. The local
restaurant business is also very active within this district,which serves as important tourist attraction and local
food cultural identity. Design guidelines for this district
were set to target at preservation and maintenance of thelocal identity and the sense of place.

management when dealing with both historical andmodern urban district.
Table 1. Type Control Guidelines for District I-IV
Four Type Control Zones

Main
Type

Sub-Type /Type Item

Dist
rict
I

Style

II

This research established a systematic type control
guidelines with the concept of typology to integratecity’s urban development plan with local building
regulations. The results could serve as a future reference
for Hsinchu City government as design guidelines for
specific regional planning policy to maintain thecharacteristic of local building style in the process ofurban regeneration. In addition, the urban environmental
analysis of this research could serve as a reference for
future researches in the field of environmental

IV
I
III
Geom
etry
and
Shape

II

IV

Geometry Vocabulary

4. Conclusions

Basic Elements

Envelope & Volume

III

Buildi
ng
Base

I
II
III
IV

Rooft
op

I
II
III
IV

Control Guidelines
Western style with local
material.
Traditional Chinese arcade
style.
Modern style
Traditional Chinesemonumental style
No limitation, but maintain theoverall sense of volume of thestreetscape.
Arch style arcade with straightline distributed on the building
elevation.
Building shapes shall consider
the Taiwanese traditional
temple style.
Building’s ground level shallreserve pedestrian space.
Encourage the usage ofdifferent materials, wall
treatments and volume
variations.
Rooftop machinery andequipment should be shelteredand should use similarmaterial as building itself withappropriate design.

Table 1. (Continued)© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan184

Yi-Yin TsaiColor&
Material
III

Building Height & Skyline

Composition Style

IV

Openi
ng
Ratio
Function
Confi
guration

Appearance

I
II
III
IV
I
II
III
IV
I
II
III
IV
I
II
III
IV

Landmark

I
II
IV
III

Building’s front façade color
could be and distinctive.
Building identity and harmonyof the overall streetscapeshould be emphasized.
Building’s front façade color
should mainly be brick red orstone gray.
Building materials shouldhave a traditional material
texture appearance, or use
facade tile, clear-finishedbrick, pebble stones or othersimilar materials.
Building’s main façadematerial shall consider its
permanence andsuitability to the local
climate conditions.
The building volume color
shall primarily be silvery
white, light gray or otherlight color tones.
Composition of various
color tones and shades
could be used.

LengthRatio

I

Area Ratio

II
III
IV

Size
Advertisement

II

Setback & Building Line

Geometry Vocabulary

Envelope & Volume

I

Balcony or extruded parts ofthe building shall not exceed
the building line and shall not
exceed more than 50 cm of theexternal walls.

Exterior Attachment

Elevat
ion

I
II
III
IV

Color
Location
Style

CanopyAdditional
Equipment

30-60%

I
II
III
IV
I
IV
II
III
I
II
III
IV

Arcade’s ground level shouldalign with the sidewalk, in thecase of no sidewalk, it should
be 10-20 cm higher than the
traffic road with no stairs and1/40 slope ratio.
Setback arcade without
overhead coverage shouldincorporate appropriate
plantation and greenery
Pedestrian sidewalk width
shall not be less than 2.5 m.
Arcade height shall not be lessthan 3 m.
Building façade facing street
less than 7 m should setback at
least 0.5m from the building
line.
Vertical length shall not be
more than 6 m.
Harmony with the front
elevation.
The lower part should not beless than 3m from the ground.
Front-facing style.
Side-facing style.
Rain coverage and sun
shading should not be lessthan 6 m.
Should not be placed on thefront façade.

5. Acknowledgment
The author would like to thank The Ministry of Scienceand Technology of Taiwan financially supporting thisresearch under the Contract No. NSC 102-2410-H-216010.

Residential and commercial
mixed use.
Commercial use

Align with the street.

References

Uniform skyline with proper
setback arcade area.
High-rise building withlandmark image.
Mix of building heights withlocal recognition.
Street corners.

1. A. S. Hornby, S. Wehmeier, Oxford advanced learner’s
dictionary of current English. Oxford: Oxford University
Press, 2000.
2. B. Micha, Typology as a Form of Convention, AA Files,
6, pp. 73-82, 1984.
3. B. Johnson, and L. Christensen, Educational research,Thousand Oaks, CA: Sage, 3rd ed., 2008.
4. J. Grix, The foundations of research, 2008.

Local temple.
Local civic building.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Conventional Center, Okinawa, Japan185

Applying Fuzzy Sliding Mode Control on Electrowetting on Dielectric System

Arsit Boonyaprapasorn

Department of Mechanical Engineering, Chulachomklao Royal Military Academy,
Nakhon-Nayok, 26001, Thailand
Eakkachai Pengwang

Institute of Field Robotics (FIBO), King Mongkut’s University of Technology Thonburi
Bangkok, 10140, Thailand
Thavida Maneewarn

Institute of Field Robotics (FIBO), King Mongkut’s University of Technology Thonburi
Bangkok, 10140, Thailand
E-mail: urarl.urarl9@gmail.com, eakkachai@fibo.kmutt.ac.th, praew@fibo.kmutt.ac.th
www.crma.ac.th

Abstract
In Lab-on-a-chip (LOC) devices, an electrowetting on dielectric (EWOD) method can be performed to create, cut,mix, and transport droplets. Moreover, the micro assembly process of micro parts can be manipulated by the
EWOD system. High accuracy of droplet motion in both applictions is required. Based on the simplified model, thefeedback control such as sliding mode control can be applied so that the droplet motion can achieve a high accuracy
performance under disturbances. However, the sliding mode control often has chattering problem. Thus, in thisstudy, the fuzzy sliding mode control was applied to manipulate the droplet in the EWOD system. The study was
conducted via simulation in MATLAB environment. The result showed that the proposed control method could
provide an accurate motion control with lower chattering compared to that of classical sliding mode control.
Keywords: Electrowetting on Dielectric (EWOD), Digital microfluidic system, Sliding mode control, Fuzzy logic.

generated inside the droplet as shown in Fig.1 1,2. Thisphenomena is able to support the required operations in
Lab-on-a-chip such as mixing, creating, cutting, andtransporting1,2. Many of applications in Lab-on-a-chip
can be seen from Refs. 1-5. Recently, some interesting
EWOD systems have been presented, for example, Shenet al.6 developed the digital micro fluidic system withmicro heater for the single-nucleotide polymorphism(SNPs) detection. Lai et al.7 proposed biomedical
detection using EWOD system with multi electrode dot
array architecture (MEDA). Moreover, transporting thesmall particle or a part using the droplet as a carrier can

1. Introduction
The electrowetting on dielectric (EWOD) systems havedrawn interests from engineers and scientists especiallyin two main applications; Lab-on-a-chip and microassembly including micro robots and micro conveyors.
The principle of the EWOD systems can be explainedby utilizing the change in interfacial tension induced
from the electrical voltage 1,2. The droplet that is appliedby the electrical voltage will have variation ingeometries from the change in contact angle 1,2. This
allows the droplet to move by the pressure difference© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan186

Arsit Boonyaprapasorn, Eakkachai Pengwang, Thavida Maneewarn

be implemented by EWOD principle8,9. This application
also includes using EWOD as a micro actuator10-12.
Another related area is the feedback control applying
on EWOD system such as previous works in Refs. 1318. For example, Shih et al.18 employed the feedback
control for the movement of the droplet in the EWOD
system. According to Bhattacharjee and Najjaran13,14,
the droplet motion and operations in the EWOD system
can be improved by applying feedback control. The
possibility of using feedback control for EWOD system
was also presented in Ref. 14.
Among various choices of control laws, the sliding
mode control (SMC) has been applied to achieve theaccuracy of the droplet motion in EWOD system15,16.
The principle and capability of the control method canbe found from the Refs. 19-21. However, one of themain drawbacks of this control method is the chattering
issue19-23. In order to reduce this effect, the fuzzy logic
can be combined with the sliding mode control aspresented in Refs. 21-23. Therefore, the main focus of
this study was to apply the fuzzy sliding mode control(FSMC) to the EWOD system. The control system wassimulated to demonstrate the performance of the droplet
motion control. Also, the simulation results of the
FSMC method were compared to those of SMC method
to evaluate the capability to reduce the chattering
phenomena.
This paper is organized in following sections. The
mathematical model of the EWOD system is presented
in section 2. The controller design is demonstrated in
section 3. In section 4, the simulation results anddiscussions are provided. Finally, the conclusion of thestudy is stated in section 5.

2. Mathematical Model of EWOD System
2.1. Equation of motion
The motion of the micro droplet in the EWOD system
can be modeled as the one degree of freedom lumped
mass model based on the Newton’s law. The model is
explained by previous researchers in Refs. 13-14 and24-26. In this work, this mathematical model is used in
the design procedures of this motion control.
For a mathematical model of EWOD system, thedroplet is considered as the moving rigid body under thesummation of driving force, threshold of driving forces,
and resistive forces as shown in Fig. 2. Thus, themathematical model describing the behavior of thedroplet motion can be expressed in (1)13,14,24-26.

md 2x
 Fdr  Fthresh  Fd  Fc  Ff
dt 2

(1)

where the displacement and the mass of the droplet are
represented by x and m respectively. The driving force( Fdr ) is the electrical force induced from the applied
voltage and the corresponding threshold ( Fthresh ) is theminimum force needed to move the droplet from therest1,13,14,24-27. The terms Fd , F f , and Fc represent
resistive forces. These forces are listed and denoted as
fellows: (i) Fd is the shear force acting on the droplet on
top and bottom of the droplet 1,13,14,24-26. (ii) F f is thedrag force acting on the droplet from the filler fluid
13,14,24-26. (iii) Fc is the contact line friction force acting
at the contact line on the top and bottom of thedroplet13,14,24-26. The resistive forces can be expressed interms of velocity of droplet ( U  x ), fluid properties,
and geometries of the droplet as shown in (2) and (3).
The geometric properties of droplet related to theresistive forces are explained in the following content.
(i) the radius of the droplet on the hydrophobic layer isassumed to be 5% overlapping with the length of theelectrode 13,14 and denoted by a 1,13,14., and (ii) the gap
between the bottom plate and the top plate, defining theheight of the droplet 13,14, is denoted by H. Relevant
fluid properties are the viscosity of the droplet and thecoefficient of contact line of friction. These arerepresented by  d and  , respectively13,14,24-26.

Fig. 1. Droplet movement via applying electrical field 1,17.

 6 U 
Fd   d   2 a 2 
 H 

(2)

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan187

Applying Fuzzy Sliding ModeFc  U n  4a ( xr ) under the fuzzy sliding mode control. The diagramof control can be shown in Fig. 3.
The controller design consists of two parts. The
design of sliding mode controller based on an equivalent
control is in section 3.1. In section 3.2, the fuzzy rules
are defined for further investigations.

(3)
The exponent term ( n ) is in the range from 0 to 2 13,14,2426.
According to Ref. 24, in the case of using air as afiller fluid, the drag force ( F f ) can be neglected.
Readers can obtain more details of all forces in Eq. (1) –(3) from Refs. 1, 13, 14, and 24-27.

3.1. Sliding mode control
The concept of sliding mode control based on an
equivalent control is based on Refs. 19-23. The
procedure of sliding mode controller based on theequivalent control (from Refs. 19 and 21-23) arepresented below. In this approach, the control input u isdefined as shown in (5) 19,21-23.
(5)u  ueq  usw
where u eq is an equivalent control and usw is aswitching control.
The corresponding sliding surface22, 23 or switching
function21 ( s ) can be expressed in term of tracking error
as in (6) 19,21-23.
(6)s( x, t )  c1e  c2 e
where e  xr  x1 and e  xr  x2 . The derivative of
surface function is calculated as (7) 19,21-23.
s( x, t )  c1e  c2 e

Fig 2. Moving droplet in EWOD system13,14,17.

2.2. State space representation
In order to design the control law, the state space
representing the EWOD system needed to be formulated.
The state variables in this system are the displacement( x ) and velocity ( x ) of the droplet. Thus, the statevectorofthesystemisdefined
as
15-17
,
the
state
space
x  [ x1 x2 ]T  [ x x]
representation can be formulated as (4)15-17.
.

x1  x2.

(4)
x 2  f  x, t   g ( x, t )u (t )  d (t ) y  x1 c1e  ( xr  x2 )s( x, t )  c1e  ( f ( x, t )  g ( x, t )u(t )  xr )(7)

When d (t )  0 , the equivalent control ( u eq ) is
determined by solving for u (t ) in (7) such thats( x, t )  0 19,21-23.

c1e  ( f ( x, t )  g ( x, t )u(t )  xr )  0

1
where g  x, t  m1(c1e  xr  f ( x, t ))(8)g ( x, t )In order to handle with the bounded disturbanced (t ) , the switching control ( usw ) is designed below.
The Lyapunov function can be selected in the termof sliding surface ( s ) as (9) 19,21-23.
1(9)V  s22
and the derivative is calculated as (10) 19,21-23(10)
V  ss
The control usw is defined as (11) 19,21-23.
ueq 

1  6d x2 [ 2a 2   x2 n (4a)] .
m  H u (t ) is a control input representing Fdr and y is theand f  x, t  output. The bounded disturbance signal is denoted asd (t ) and d (t )  M for M  0 15,16,21.
3. Fuzzy Sliding Mode Controller Design
The fuzzy sliding mode control (FSMC) based on an
equivalent control is employed to achieve the accuracyof motion droplet in EWOD. Physically, the droplet iscontrolled to move from one electrode to the next one
accurately along the desired periodic reference signal

1
K sgn( s)g ( x, t )where K   M ,   0 19,21-23.
usw 

(11)

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

188

Arsit Boonyaprapasorn, Eakkachai Pengwang, Thavida Maneewarn

The stability of the method is proved as follows.
Based on the control input in (8) and (11), it can beshown that V  ss  0 19,21-23 as below.
1
V  ss( x, t )  s[c1e  f ( x, t )  g ( x, t )((c1e  xr
g ( x, t )inputs. In simulation, the Runge-Kutta method was used
for integration from initial time ( t  0 s) to final time
( t  0.5 s) with the sampling time ( 0.00001 s).

1
K sgn( s ))  xr  d (t )]g ( x, t ) s[ K sgn( s)  d (t )]The simulation example of the EWOD system has twogroups of parameters; the fluid properties and thegeometry of the component of EWOD system. All
necessary parameters with numerical values are
summarized in this section. The dimensions of the
EWOD system are as follows. First, the gap of the top
and bottom surfaces was H  300 106 m 13,14. Second,
the length electrode and the gap between them wereL  1600 106 m and l  70 106 m as Ref. 28. The
fluid properties used in simulation were as follows: (i)density of the droplet was d  998 kg / m3 29, (ii) the
contact angle of the droplet on the hydrophobic surface
was   110 24, (iii) coefficient of contact line frictionwas   0.08 Ns / m 2 24, (iv) the radius of the droplet,a  8.8 104 m , defined under the overlap condition asa  0.5L  0.05L 13,14, and (v) the value of an exponent
term was n  2 . After the primary parameters were
defined, the mass of the droplet ( m ) was
7.7863  10 7 kg , that was calculated from d and a 1.
The value of threshold force was assumed as
Fthresh  8  106 N 24. The disturbance signal was
assumed to be the Gaussian function as (15) 21.

4.1. Simulation example

 f ( x, t ))  s( M  )sgn( s)  sd (t )   s  0.

(12)

It is noted that the stability is satisfied by the control
input (5) as V  0 19,21-23. According to f ( x, t ) andg ( x, t ) in (4), the control EWOD system can track thereference signal accurately.
3.2. Fuzzy rules
In order to reduce the chattering problem caused byswitching control ( usw ), the fuzzy rules is used to assign
the value of switching gain (  ). This switching gain isdepended on the magnitude of the disturbance 21,23.
When   1 in the case of SMC method, the control
input in (5) becomes (13) for the case of FSMC 21,23.
(13)u  ueq  usw
In the fuzzy system,  is a fuzzy output derived fromthe switching function input ( s ). The range of  and s
are defined to be from -1 to 121. The fuzzy rules aredefined for the sliding mode control in (14) as presented
in Ref. 21.
If s  0 , then   0 

If s  0 , then   0 (14)
If s  0 , then   0  ( t  md )22(15)
d (t )  Ae 2 sd
where A  3000 106 , md  0.15 , and sd  0.02.
Most of the numerical values in this simulation wereused before in our previous work 15-17. Assuming thatdroplet was at rest at the initial time, the initialcondition for the state variables in this simulation was
defined as x1 (0)  0 m and x2 (0)  0 m/s 15-16.

Fig 3. The Fuzzy sliding mode control applying on EWOD
system 15,16.

4.2. Sinusoidal reference signal
The sinusoidal function was the reference signal
representing the periodic motion of the droplet
travelling between two adjacent electrodes as a mixing
process in Lab-on-a-chip1,2,15.
xr (t )  ( L  l )sin(t )(16)
The amplitude of the signal is a distance between
two adjacent electrodes, including the gap between twoelectrodes 15. The frequency of the signal was assumed
to be   20 rad/s.

4. Simulation Results and Discussions
The simulation was performed in MATLAB
environment to evaluate the feasibility of fuzzy sliding
mode control (FSMC) based on an equivalent control
for the droplet motion in EWOD system. In addition,
the chattering in the control input was considered. Thus,
the simulation results of both FSMC and SMC methods
were compared especially the chattering in the control© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan189

Dropletdisplacement:x1(t)Applying Fuzzy Sliding Mode

4.3. Control parameter

Tracking
error:e(t)The control parameters consisted of constant terms in(6) and (11), which were selected as c1  50 , c2  1 andK  15.6 . The parameter  in (13) is defined by fuzzy
rules. The membership function of the fuzzy systemcorresponding to  and s are shown in Fig. 3(a) and3(b) respectively. The terms of N , Z , and P are
negative, zero, and positive values for s and  in fuzzy

N

Control
input:u(t)x1 (t)

x 10

0.05

0.1

0.15

0.2

0.25
t (sec)

0.3

0.35

0.4

0.45

0.5

0.05

0.1

0.15

0.2

0.25
t (sec)

0.3

0.35

0.4

0.45

0.5

0.05

0.1

0.15

0.2

0.25
t (sec)

0.3

0.35

0.4

0.45

0.5

-4

0
-5
0
x 10

-5

0
-5
0

x 10

-5

SMC Control Input

FSMC Control Input

4

0.6
0.4
0.2

-0.8

-0.6

-0.4

-0.2

N

Degree of membership: mu

1

0

0.2

s

0.4

0.6

0.8

1

Z

3
2
1
0

P

0.8

-1

0.6

-2
0

0.4

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

t (sec)0.2

Fig. 5. Control input signal of FSMC (Pink) and SMC (Blue)methods.

0
-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

mu

(b)
Fig. 3. Fuzzy’s Rule: (a) membership function for s (b)membership function for  .

5.

The displacements of the droplet, tracking errors, andcontrol inputs corresponding to the FSMC and SMC
method based on an equivalent control are shown in Fig.
4(a) and 4(b) respectively. In Fig. 4, when employing
the FSMC method, the control system could track thesinusoidal reference signal accurately as the SMC
method. However, the tracking error of the FSMC
method converges to zero slower than that of the SMC
method. Additionally, the chattering in the control inputof the FSMC method was less than that of the SMC
method as shown clearly in Fig. 5.
-3

xr(t)

Conclusions

This study illustrated that the FSMC method could
provide a good accuracy in tracking the periodical
signal under the disturbance. Thus, the droplet in the
EWOD system was able to track the periodic signal
accurately by using the FSMC method under the
disturbance signal with lower chattering compared tothat of the SMC method. The reduction of the chattering
was important for this method, especially in theimplementation of practical control system. Therefore,
the fuzzy sliding mode control based on an equivalent
control was successfully applied for the micro droplet
motion in this EWOD.

4.4. Results of simulation example

Dropletdisplacement:
x1(t)

5

P

(a)

Tracking
error:
e(t)

-2
0

5

-1

x 10

xr(t)

0.8

0

2

-3

(b)
Fig. 4. The displacement, tracking errors, and control inputs;(a) Fuzzy sliding mode control (b) Sliding mode control.

Control input:u(t)Degree of membership: s

Z

x 10

0

5

rules21. Moreover, a centroid of area method was usedin defuzzification.
1

2

x1(t)

6.

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© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan191

Development of Micro-Permanent Magnet Synchronous Reluctance Generator for TPMS on
Smart Robots
Chun-Chieh Chang, Cheng-Tang Pan, Shao-Yu Wang, An-Yun Yang, Gu-Xuan Lin, Yu-Jen Wang

National Sun Yat-sen University, Kaohsiung, 80424, Taiwan
panct@mail.nsysu.edu.tw
Roger Chenglung Lee, Ting-Hung Chung

Naroller Electronics, Kaohsiung, 80424, Taiwan
335i2007@gmail.com

Abstract
Synchronous Reluctance Generator (SynRG) is a stability and robust motor with simple structure and low cost. In
this paper, the SynRG which embedded magnet was used in tire pressure monitoring system (TPMS). The slot-pole
ratio was analyzed to obtain greater generating capacity, minimize cogging torque and torque ripple. A wide
selection of surface permanent magnet generator (SPMG) was compare with PM-SynRG. The results show that thegenerating capacity of PM-SynRG was more than 3 V with high degree of stability in the operation.
Keywords: Magnet synchronous reluctance generator, Tire pressure monitoring system

structure. In the experiment, a wide selection of surface
permanent magnet generator (SPMG) was compare with
PM-SynRG to confirm the performance. Figure 1 shows
the schematic of the winding structure and the rotor
structures. There are plenty of magnet materials where
the selection of magnet material needs to be considered
carefully. The NdFeBr was selected because of themagnet undesired demagnetization [10]. The ratio of
the slot and pole and the winding distribution were also
analyzed in this paper. In addition to a larger amount of
power, the cogging torque and torque ripple wasconcerned.

1. Introduction
Synchronous reluctance generators (SynRG) havebeen widely used in industries such as energy,
integration - starter generator (ISG), and wind turbines[1-3]. In general, an optimization characteristics ofSynRG are defined as the maximum induced voltage,
the minimum cogging torque, the maximum efficiency,
and the maximum power factor. There are many studies
on SynRG in a wind turbine power generating system[4-7]. SynRG has a robust and low price due to its
simple rotor structures. In addition, SynRG is applies tovariable speed operation, because of its low noise. In themost popular hybrid electric vehicle (HEV), there aremany studies of SynRG to replace permanent magnet
generator (PMG) [8,9]. The permanent magnet
synchronous reluctance generator (PM-SynRG) wasapplied to micro-generator due to low cost, highefficiency and stability. Especially in the smaller size ofthe generator, a large torque ripple easily leads to the
obstruction of rotation.
In this paper, the PM-SynRG which embedded
magnet was used in tire pressure monitoring system(TPMS). It has a stable generating capacity and robust

(a)

(b)
statormagnetrotor
coil

Figure 1 Schematic diagram of (a) SPMG and (b) PMSynRG© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

192

Chun-Chieh Chang, Cheng-Tang Pan, Shao-Yu Wang, An-Yun Yang, Gu-Xuan Lin, Yu-Jen Wang, Roger Chenglung Lee, Ting-Hung Chungaxis respectively and φm is the flux linkage originatingfrom the magnets.
The torque is formed as

2. Experimental Methods
2.1 Design specifications
Finite element method (FEM) was used for themagnetic circuit analysis, including the B-EMF, B-EMF
waveform, torque, and torque ripple. In the structuraldesign, as shown in table 1, the number of coils wasconsidered to the size of generator, and the 400 turns
was calculated. An air gap between the rotor and thestator was set at 0.55 mm, and rotational speed is 500
rpm.
The critical properties of permanent magnets forpermanent magnet generators are high BH energy. The
commercial magnet, Nd2Fe14B, have coercive force
approaching 890 (kA/m). To operate the PMG withpermanently excited PM poles at variable power factors
is concerned.

T3  m i sq  ( L d  L q )i sq i sd 
2 

where p is the number of pole pairs.

2.3 Winding design
There are a lot of feasible combinations of stator androtor pole numbers in multiphase PM machines, viz.,N s  k 1m , k1  1, 2...

(4)
N r  N s  k2 , k2  1, 2...
(5)where Ns and Nr are the number of stator and rotor
poles, respectively, and m is the number of phases. k1 is
an integer number when m is an even number, but k1
should be an even number when m is an odd number,since the number of stator poles must be even, which isdifferent from the conventional fractional-slot PM
machines [12].

Table 1 Specification List
Item(Unit )ValueType of machines
SPMG
SynRG
Stator poles numbers
12, 24
Rotor poles numbers
4, 8, 10
Stator outer radius(mm)50
Stator inner radius(mm)28.6Airgap length(mm)0.55
Axial length(mm)5Turns per coil
400
Magnet remanence(T)1.23
Coercive force of890
PM(kA/m)Rated speed(rpm)500
Load Resistance(ohm)100

2.3.1 Calculation of the span θs of per slot is

N P 180
NS

The equations for the permanent magnet synchronousmachine in the d-q-components were shown below [11],sq  RS i sq el L d i sd el m(2)120  q  360

S

(7)

Where q = 1,2, ..., (𝑁𝑝/2 -1), Phase offset of slot pitch
K0. When unable to find a suitable integer K0, thatmeans that the selected number of poles and stator slots
feasible combination, and to be re-selected.
The 120° means that each of phase is a gap of 120°
degrees; q * 360 / "∅" s means to find in all phases of acoil, and it could sense the same voltage waveform in across slot pitch

2.2 Permanent magnet synchronous machine

(1)

(6)

2.3.2 Calculation of the phase offset 120 ° E that therequired number of slots (K0) is
Ko sd  RS i sq el L d i sd

(3)where µsd and usq are the stator voltage in d and q axis,
Rs is the armature phase resistance, ωel is the electrical
speed, Ld is the d-axis inductance, Lq is the q-axis
inductance, isd and isq are the stator current in d and q

2.3.3 Select the number of slots spans of coil S*

180
S *  max[ fix(
)]

S

(8)

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan193

Development of Micro-Permanent Magnet

The fix (.) is the maximum of two numbers in the
integer part. All the slots are numbered along thedirection of rotation of the generator.

smaller than SPMG, the generating capacity of micro
generators was reached that more than 3 V.

2.4 Calculation of the torque ripple
Back electromotive force (B-EMF) waveform wasanalyzed above. When the generator was rotated, thetorque was produced. The torque ripple was calculated
in each of slot, poles of combination as defined in (9):T -T
Trip (%)  max min 100%
Tavg

(9)

where Tmax is the maximum torque, Tmin is the torqueminimum, and Tavg is average torque.

Figure 2 B-EMF of comparison of two types ofgenerators

3. Simulation Analysis
FEM was used for the magnetic circuit analysis,
including the B-EMF, B-EMF waveform, torque, andtorque ripple.
3.1 Analysis of Back Electromotive Force(BEMF)
Figure 2 shows that the generating capacity of
SPMG is greater than PM-SynRG, because the flux of
the SPMG was no obstacle in rotor. In the study, when
the number of poles was increased gradually, thegenerating capacity of PM-SynRG was decreased; when
the number of poles gradually was increased, thegenerating capacity of SPMG was increased. On theother hand, the number of slots was increased,
generating capacity of PM-SynRG are also increased.
The result above in the poles 10 poles, the 12 and 24
slots generating capacity were almost the same. Whenthe number of slots of SPMG is increased to 24 slots,
power generating capacity is not significantly increased.
Figure 3 shows a B-EMF waveform diagram, B-EMF
waveform was observed to exhibit a concave or flat
shape of the peak, when the number of slots with pole
number is less. The reason was that when the number ofslots and poles was small, each slot of each phase coilwhich induced B-EMF was different. When all are
combined, B-EMF will form a non-sinusoidal shape.
Generator was stabilized and enhanced the efficiency ofthe operation due to the output is close to the sine wave.
Although the generating capacity of PM-SynRG is

Figure 3 B-EMF waveform of comparison of two typesof generators
3.2 Torque Analysis
Figure 4 shows that SPMG torque magnitudes werelarger than the PM-SynRG and the gap was even greaterwith the increase of the number of poles. The reason
was the structure of PM-SynRG had a high degree ofstability during operation. The torque magnitudes of
PM-SynRG were gradually reduced with the increase of
the number of poles; the torque magnitudes of SPMG
were gradually increased with the increase of thenumber of poles. Figure 5 shows a waveform diagramof torque and it was found that SPMG torque ripple wasgreater than the PM-SynRG. Both torque ripples were
smaller with the increase of the number of slots andpoles. From the above analysis, torque and torque rippleof PM-SynRG were less than SPMG. In particular, thegenerating capacity was less than 4V and the trend
increasingly obvious.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan194

Chun-Chieh Chang, Cheng-Tang Pan, Shao-Yu Wang, An-Yun Yang, Gu-Xuan Lin, Yu-Jen Wang, Roger Chenglung Lee, Ting-Hung Chung

3.

Erko Lepa and Aleksander Kilk, Analysis andPrototyping of a Low-Speed Small Scale
Permanent Magnet Generator for Wind Power
Applications, Electric Power Quality and Supply
Reliability Conference (PQ), 2012.
4. S. Morimoto, H. Kato, M. Sanada and Y. Takeda,“Output Maximization Control for Wind
Generating System with Interior Permanent Magnet
Synchronous Generator”, Proceedings of IEEE –IAS Annual Meeting, Tampa, FL, October 4-8,
2006, pp. 503-510.
5. S. Morimoto, H. Nakayama, M. Sanada and Y.
Takeda, “ Sensorless Output Maximization Control
for Variable-Speed Wind Generating System using
IPMSG”, IEEE Transactions on Industry
Applications, Vol. 41, No. 1, 2005, pp. 60-67.
6. Dept. of Electr. & Comput. Eng., Texas A&M
Univ., College Station, TX, “Maximum output
power control of permanent magnet-assisted
synchronous reluctance generator”, ICEM, 2008.
7. Urase, K., Tokyo, Kiyota, K., Sugimoto, H., Chiba,
Design of a Switched Reluctance Generator
Competitive with the IPM Generator in HybridElectrical Vehicles, Electrical Machines andSystems (ICEMS), 2012.
8. Poopak Roshanfekr, Sonja Lundmark, Torbjörn
Thiringer, and Mikael Alatalo, “A Synchronous
Reluctance Generator for a Wind ApplicationCompared with an Interior Mounted Permanent
MagnetSynchronous
Generator”,
Power
Electronics, Machines and Drives (PEMD), 2014.
9. Dept. of Electr. & Comput. Eng., Texas A&M
Univ., College Station, TX, “Optimal design andcomparison of stator winding configurations inpermanent magnet assisted synchronous reluctancegenerator”, Conference: Electric Machines andDrives Conference, 2009.
10. M.
Katter,“Angular
DependenceofDemagnetization Stability of Sintered Nd-Fe-B
Magnets,” IEEE Transactions on Magnetics, Vol.
41, 2005, pp. 3853-3855.
11. J. T. Chen, and Z. Q. Zhu, “Winding configurations
and optimal stator and rotor pole combination offlux-switching PM brushless AC machines,” IEEE
Trans. Energy Conversion, vol. 25, no. 2, 2010, pp.
293-302.
12. D. Ishak, Z. Q. Zhu, and D. Howe, “Comparison of
PM brushless motors, with either all or alternative
wound teeth,” IEEE Trans. Energy Convers., vol.
21, no. 1, 2006, pp. 95-103.

Figure 4 Torque of comparison of two types ofgenerators

Figure 5 Torque ripple of comparison of two types ofgenerators
4. Conclusions
In the paper, the PM-SynRG was applied to the TPMS
to achieve a stable and robust power generating
structure. The ratios of slot-pole number were analyzed
to obtain greater power generating capacity anddecrease cogging torque and torque ripple. The results
show that generating capacity of PM-SynRG was morethan 3 V. In the torque magnitude and torque ripple,
PM-SynRG was less about 3 times than SPMG withhigh stability. For micro tire pressure detectors required
performance, PM-SynRG was achieved the requirement
for generating capacity and has a high degree ofstability in the operation.

References
1.

2.

S. Tokunaga, K. Kesamaru. “FEM simulation ofnovel small wind turbine generating system withsynchronous reluctance generator”, Electrical
Machines and Systems (ICEMS), 2011.
M.A. Rahman,” Advances of Interior Permanent
Magnet (IPM) Wind Generators“, Electrical
Machines and Systems, 2008, pp. 2228 - 2233.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

195

Study of Micro-Flux-Switching Permanent-Magnet Generator for TPMS on Smart Robots
An-Yun Yang, Cheng-Tang Pan, Shao-Yu Wang, Chun-Chieh Chang, Gu-Xuan Lin, Yu-Jen Wang

Department of Mechanical and Electromechanical Engineering, National Sun Yat-sen University,
No. 70, Lienhai Rd., Kaohsiung 80424, Taiwan
panct@mail.nsysu.edu.tw
Roger Chenglung Lee, Ting-Hung Chung

Naroller Electronics, Kaohsiung, 80424, Taiwan
335i2007@gmail.com

Abstract
Flux-Switching Permanent-Magnet Generator (FSPMG) with high flux density and high efficiency was designed due
to its double salient structures. This study presents a micro FSPMG which was applied to tire pressure monitoring
system (TPMS) on the smart robots. In this paper, finite element method (FEM) was utilized to simulate the magnetic
properties of generator and then the flux and voltage output was obtained. The magnetic material used in this study
was ferrite magnet which has the advantage of lower-cost.
Keywords: Generator, Flux-Switching, Tire pressure monitoring system, Finite element method.

on tire pressure monitoring system (TPMS) with stableelectromotive force (EMF) was discussed.

1. Introduction
In automotive and aerospace fields, high efficiency
machines are necessary nowadays. The axial fieldpermanent magnet (PM) machine was concerned [1-3].
However, when the rotor temperature raise, the magnets
inside the rotor on the conventional PM machine isirreversible demagnetize and mechanical damage. In
1995, the flux-switching PM (FSPM) machine was first
proposed [4]. The feature of FSPM was that magnets inthe stator with double salient structure were able to avoid
the problem above [5]. FSPM machines are also hightorque density with appropriate control [6-11]. The
FSPM machines tend to investigate on FSPM motors sofar, only few study mention about FSPM generator(FSPMG). In this study, finite element method (FEM)
was utilized to simulate two types of FSPM generators
properties and compared with the surface permanent
magnet (SPM). Meanwhile, the FSPM generators apply

2. Design of generator
2.1. Steps of winding design
There are many combinations of slot-pole ratio,
feasible winding poles formula was shown in Eq. (1)and (2) [12].
𝑁𝑠 = 𝑘1 𝑚(1)
𝑁𝑟 = 𝑁𝑠 ± 𝑘2
(2)
Where Ns is slots of stator, Nr is poles of rotor, m isphase number.
If the motor is multi-teeth, feasible winding poles
formula was shown in Eq. (3) and (4) [13].
𝑁𝑟 = 2𝑁𝑠 ± 1
𝑁𝑟 = 𝑁𝑠 (2𝑛 − 1) ± 1

(3)
(4)

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

196

An-Yun Yang, Cheng-Tang Pan, Shao-Yu Wang, Chun-Chieh Chang, Gu-Xuan Lin, Roger Chenglung Lee, Ting-Hung Chung, Yu-Jen Wang

The FEM was used to analyze a three-phase FSPMG.
The schematic of 12/10-pole FSPMG with a doublysalient structure and 6/17-pole FSPMG with multi teeth
were shown in Fig. 1.
(a)

(b)

12/10

multi-teeth
FSPM
6/17
25mm
15mm

12/10

0.58
mm

0.13mm0.5mmType of machines

FSPM

Slot-pole ratio
Stator outer radius
Stator inner radius
Airgap length
Turns per coil
Rated speed
Resistance
Magnetmagnitude

Fig. 1. (a)The structure of 12/10-pole FSPM generator(b) The structure of 6/17-pole FSPM generator

𝐾0 =

𝑁𝑝

400
500rpm100 ohm-253000 A/m

3. Results and Discussion
The equivalent circuit of the generator was shown in
Fig. 2. The circuit was including inductance andresistance with 100 ohm. The inductance was regarded as
input.

Calculation of the span in every slot , the slot of phaseoffset and the slot numbers of span were shown in Eq. (5),(6) and (7) [14].
𝜃𝑠 =

SPM

(5)

𝑁𝑠

120°+𝑞×360°

(6)

𝜃𝑠

∗

𝑆 = max⁡[𝑓𝑖𝑥 (180
𝜃𝑠Fig. 2. The equivalent circuit) , 1](7)
3.1. Flux distributions in FSPM, multi-teeth
FSPM and SPM

Where the 𝜃𝑠 and the 𝐾0 are all integer.

Three generators were investigated by FEM. The
flux distributions were shown in Fig. 3. From the flux
distribution, the density of flux in the stator back-iron islower than that in the stator teeth due to leakage flux.

2.2. Winding Factor
Eq. (8) was calculated the winding factor [15],
𝑄𝑣𝑎

𝐾𝑑 =

sin⁡( 2 )

𝑣𝛼
)
2

𝑄𝑠𝑖𝑛(

(8)

where Q is the number of EMF vectors per phase, α
is the angle between two adjacent vectors, and v is theorder of harmonic. Stator slots, rotor poles and thenumber of phases can decide winding factor.
The winding factors of the FSPM machines withdifferent combination of stator pole and rotor pole
numbers cause different results. Table 1 shows thedesigns specifications including traditional SPM, FSPM
and multi-teeth FSPM.

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

Fig. 3 (a)-(c) FSPM flux distributions from 0 s to 0.01s

Table 1. FSPM, multi-teeth FSPM and SPM specification© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

197

Study of Micro-Flux-Switching Permanent-Magnet(d)-(f) multi-teeth FSPM flux distributions from 0s to
0.01s, (g)-(i) SPM flux distributions from 0s to 0.01s
3.2. Flux-linkage in FSPM, multi-teeth FSPM and
SPM
Fig. 4 show that three generators flux-linkage were
almost sinusoidal. The FSPM is between ±0.0121 Wb,
the multi-teeth FSPM is between ±0.0006 Wb and the
SPM is between ±0.0096 Wb

(a)

(a)

(b)

(b)

(c)

Fig. 5 The EMF of (a) FSPM generator, (b) multi-teeth
FSPM generator and (c) SPM generator.
3.4. Torque in FSPM, multi-teeth FSPM and SPM
Fig. 6 shows the torque of three generators. After 6ms,
the torque of FSPM is about -7 mN-m and the SPM isabout -1.79 mN-m. The average torque multi-teeth FSPM
is about -53.7 N-m.

(c)Fig. 4 The flux-linkage of (a) FSPM generator, (b) multiteeth FSPM generator and (c) SPM generator.
3.3. EMF in FSPM, multi-teeth FSPM and SPM
Fig. 5 shows the EMF of three generators were almost
sinusoidal. The FSPM is between ±3.6 V, the multiteeth FSPM is between ±0.58 V and the SPM is between±2.5 V.
(a)© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

198

An-Yun Yang, Cheng-Tang Pan, Shao-Yu Wang, Chun-Chieh Chang, Gu-Xuan Lin, Roger Chenglung Lee, Ting-Hung Chung, Yu-Jen Wang

4. S. E. Rauch and L. J. Johnson, “Design principles of theflux switch alternator,” AIEE Trans., vol. 74, no. 3, pp.
1261–1268, 1955.
5. E. Hoang, A. H. Ben-Ahmed, and J. Lucidarme,“Switching flux permanent magnet polyphased
synchronous machines,” in Proc. 7th Eur. Conf. PowerElectron and Applicat, 1997, vol. 3, pp. 903–303.
6. E. Hoang, A. H. Ben-Ahmed, and J. Lucidarme,“Switching flux permanent magnet polyphased
synchronous machines,” in Proc. 7th Eur. Conf. PowerElectron. and Applicat, 1997, vol. 3, pp. 903–303
7. H. Wei, C. Ming, Z. Q. Zhu, and D. Howe, “Study on static
characteristics of novel flux-switching doubly salient PM
machine,” in Proc. CSEE, 2006, vol. 26, no. 13, pp. 129–
134.
8. Z. Q. Zhu, Y. Pang, and D. Howe, “Analysis ofelectromagnetic performance of flux-switching permanent
magnet machines by non-linear adaptive lumped
parameter magnetic circuit model,” IEEE Trans. Magn.,
vol. 41, no. 11, pp. 4277–4287, 2005.
9. W. Hua, M. Cheng, and G. Zhang, “A novel hybrid
excitation fluxswitching motor for hybrid vehicles,” IEEE
Trans. Magn., vol. 45, no.10, pp. 4728–4731, Oct. 2009.
10. W. Hua, M. Cheng, Z. Q. Zhu, and D. Howe, “Analysis
and optimization of back-EMF waveform of a fluxswitching permanent magnet motor,” IEEE Trans. Energy
Convers., vol. 23, no. 3, pp. 727–733, Sep. 2008.
11. J. T. Chen, Z. Q. Zhu, and D. Howe, “Stator and rotor polecombinations for multi-tooth flux-switching permanentmagnet brushless ac machines,” IEEE Trans. Magn, vol.
44, no. 12, pp. 4659–4667, Dec. 2008.
12. Z. Q. Zhu, and J. T. Chen, “Advanced flux-switchingpermanent magnet brushless machines,” IEEE Trans.
Magn., vol. 46, no. 6, pp. 1447-1453, Jun. 2010.
13. Z. Q. Zhu, J. T. Chen, Y. Pang, D. Howe, S. Iwasaki, and
R. Deodhar, “ Analysis of a novel multi-tooth fluxswitching PM brushless AC machine for high torquedirect-drive applications,” IEEE Trans. Magn., vol. 44, no.
11, pp. 4313-431, Nov. 2008.
14. R. L. Owen, Z. Q. Zhu, A. S. Thomas, G. W. Jewell, andD. Howe, “ Fault-tolerant flux-switching permanent
magnet brushless AC machines, ” IEEE Trans. Ind. Appl.,
vol. 45, no. 6, pp. 1971–1981, Oct. 2009.
15. N. Bianchi, M. D. Pr´e, L. Alberti, and E. Fornasiero,
Theory and Design of Fractional-Slot PM Machines.
Padova, Italy: CLEUP, 2007.

(b)

(c)

Fig. 6 The torque of (a) FSPM generator, (b) multi-teeth
FSPM generator and (c) SPM generator.
4. Conclusion
A FSPM generator was proposed because of better
power generation than traditional SPM. The ripple of
FSPM is larger than SPM when generator, stable
operation. Due to the Magnetic circuit of FSPM iscomplicated, it still can be improved by structure design.
The power generation of FSPM is up to 3.6V. It was
improved about 44% than traditional SPM. Also thefrequency of EMF in FSPM is higher than SPM, it means
the FSPM can save more energy than SPM in the sametime. In multi-teeth FSPM, the ripple is obvious at first,
but it was tended to be stable after 18 ms.
5. References
1. Y. Chen, P. Pillay, and A. Khan, “PM wind generator
topologies,”IEEE Trans. Ind. Appl., vol. 41, no. 6, pp.
1619–1626, 2005.
2. A. Parviainen, J. Pyrhönen, and P. Konthanen, “Axial flux
permanent magnet generator with concentrated wind
power applications”, in IEEE Int. Conf. Electric Machines
Drives, San Antonio, TX, USA, 2005, pp. 1187–1191.
3. S. Huang, J. Luo, F. Leonardi, and T. A. Lipo, “A
comparison of power density for axial flux machines basedon general purpose sizing equations,” IEEE Trans. Energy
Convers., vol. 14, no. 2, pp. 185–192, 1999.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan199

MEMS Microrobot Controlled by Mounted Neural Networks IC with Two Types Actuators
Kei Iwata, Hirozumi Oku, Yuki Okane, Yohei Asano, Masaki Tatani, Yuki Ishihara, Kazuki Sugita,

Graduate school of Precision Machinery Engineering College of Science and Technology, Nihon University,
7-24-1, Narashinodai, Funabashi, Chiba, 274-8501, Japan
Satohiro Chiba, Satoko Ono, Mizuki Abe, Minami Takato, Ken Saito, Fumio Uchikoba

Departmet of Precision Machinery Engineering College of Science and Technology, Nihon University,
7-24-1, Narashinodai, Funabashi, Chiba, 274-8501, Japan
E-mail: takato@eme.cst.nihon-u.ac.jp, kensaito@eme.cst.nihon-u.ac.jp, uchikoba@eme.cst.nihon-u.ac.jp

Abstract
We report the hexapod microrobot controlled by the bare chip IC of hardware neural networks. MEMS technology
is used for fabrication of the microrobot. Actuators of the robots are used lead zirconium titanate (PZT) and shapememory alloy (SMA). As the result, PZT type is realized the walking motion by bare chip IC. Moreover, SMA typecan be succeeded the hexapod walking locomotion with mounted bare chip IC. The walking speed is 2.4mm / minand the step width was 0.083mm.
Keywords: Microrobot, Artificial Neural network, MEMS, PZT, SMA.

The robots that introduce the model of the neuralnetwork as the control system are reported3. However,the small size robot controlled by neural networks israre. Therefore, robots that are perfectly mimicked theinsects have not been realized.
Previously, we reported on an insect-type MEMS
microrobot that fits within 5-mm-cubed driven by apulsed hardware neural network (P-HNN)4. However,the P-HNN circuit, which was constructed from discreet
components, was found to be too big to be mounted on
the microrobot. We then developed a P-HNN integrated
circuit (IC) using a bare chip IC and peripheral
amplifying circuits, but the resulting control system wasstill too big5. To solve the problem, the bare chip IC for
SMA-type microrobot was newly developed6.
In this paper, we report on the development of insecttype microrobots controlled by bare chip IC of artificial
neural network. Actuators of the microrobots are usedpiezoelectric element composed of lead zirconium
titanate (PZT) and artificial muscle wire composed of

1. Introduction
Insects have small sophisticated motion mechanisms
controlled by flexible brain neural networks. Therefore,
fully understanding how insects move is among theultimate goals of microrobotic studie.
Many researches that mimic the flexible motion
mechanism of insects have been reported 1. Especially,walking is a typical movement mechanism of insects.
The robot mimicking walking mechanism by using legs
has been reported2. However, most cases of them arelarge body compared with the insects. Realization of thesmall walking mechanism as large as the insect isdifficult in conventional process. Therefore, the demandfor Micro Electro Mechanical system (MEMS)
technology that can create miniature components withhigh degrees of accuracy is increasing2. In addition, thebig feature of the insects is flexible thinking circuit.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan200

Kei Iwata, Hirozumi Oku, Yuki Okane, Yohei Asano, Masaki Tatani, Yuki Ishihara, Kazuki Sugita, Satohiro Chiba, Satoko Ono, Mizuki Abe, Minami
Takato, Ken Saito, Fumio Uchikoba

shape memory alloy (SMA). The bare chip ICs for eachtype of microrobot were newly developed.

Fig. 2. Mechanism of the PZT-type rotary actuator.

2. Mechanism

SMA which shrinks at high temperatures and expands at
low temperatures is used for the rotary actuator. The
SMA displacement can be changed by applying
electrical energy to create heat. We used 0.05 mm
diameter wire to create the SMA with a standard coildiameter of 0.2 mm. A standard drive current from 50 to
120 mA was used, and a maximum kinetic displacementof 50 % of the original length was achieved. Figure 3
shows the design of SMA-type rotary actuator.

2.2. SMA-type microrobot

Insect-type microrobot has hexapod legs like insects andfalls within 5-mm-cubic. The designs of the two-type
microrobot are shown in figure 1. The components ofthe microrobot are the frame, rotary actuator, and thelink mechanisms. PZT and SMA are easy to downsize
because the material itself has the function of theactuator. Almost all of the robot’s parts are fabricatedfrom silicon wafer using MEMS machining technology.

Fig. 3. Mechanism of the SMA -type actuator.

Four SMAs and a ground (GND) wire are connected to
the rotor parts. Rotational motion of the actuator isgenerated by passing an electric current through the
SMA in rotation.

Fig. 1. Microrobot designs.

2.1. PZT-type microrobot
2.3. Walking mechanism

The PZT distorts by applying a voltage. In this study, amultilayer piezoelectric element composed of PZT isused for driving low voltage. The PZT has advantage oflow electricity consumption and fast response time.
However, it is difficult to use PZT as direct driven
actuator because of small displacement. Therefore, theimpact mechanism is adopted for the actuator of the
microrobot. The design of the PZT-type actuator, which
has side, end, and height dimensions of 1.0, 4.4, and 3.2mm, respectively, is shown in figure 2. The
displacements of a multilayer piezoelectric element are
110nm at applied 6V. The impact head is attached to the
PZT and adjusted to contact to the impact head. The
PZT impacts the rotor to generate rotary motion.

Insects perform a stable hexapod walking by grounding
the three legs. In this paper, link mechanisms are
adopted by realizing hexapod walking like insects.
Rotary motion of the actuator is converted into walking
motion by link mechanisms via a shaft shown in figure
4. The locomotion pattern uses a 180° phase shift oneach side.

Fig. 4. Walking mechanism of the microrobot.

3. Control system
Insects function by using the neural networks in theirown brains. In this study, we mimic those neural circuits© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan201

MEMS Microrobot Controlled byusing an analog system that allows the microrobot tofunction in an insect-like manner.
Synaptic model

3.1 Cell body model
The PZT-type microrobot is controlled by the cell bodymodel shown in figure 6. The cell body model has
refractory period, analog characteristic of the output
pulse and time varying negative resistance characteristic.
Driving frequencies of the PZT-type microrobot is 25
kHz. The amplifier circuit is added to obtain the output
voltage 20V.
icin

MC4

CG

MC2
MC1
MC3

Cell body model

CIS V
DD
MIS2
MIS3

VISin
MIS4

Vcout

CG
MC4

MC2
MC1
MC3

MIS5

Vcout

CM

VA

Fig. 8. Circuit diagram of the neuron.

4. Results

CM

The bare chip IC of the neural network made it possible
for the control system to be mounted on the microrobot.
Figure 9 shows the fabricated microrobot mounted thebare chip IC. The side, the end, and height dimensionsof the fabricated PZT-type microrobot were 4.0, 4.6,
and 3.6 mm, respectively. The weight of the robot was
0.04 g. The length and the width dimensions of thecontrol circuit were 2.0, 3.0mm, respectively. The side,
the end, and height dimensions of the SMA-type
microrobot were 4.0, 2.7, and 2.5 mm, respectively. The
weight of the robot was 0.02 g. The length, width, andweight of the control circuit were 4.0mm, 4.0mm, and0.059g, respectively.

VA

Fig. 6. Cell body model circuit diagram.

3.2 CPG model
It is well known that locomotion rhythms of livingorganisms are generated by central pattern generator(CPG). The SMA -type microrobot is controlled by the
CPG model. CPG model can be formed by inhibitory
synaptic connection of four neurons shown in Figure 7.

Fig. 9. Fabricated microrobot mounted control system. (a)PZT-type (b) Artificial muscle wire-type.

Fig. 7. Artificial neuron circuit connection diagram.

Figure 8 shows the circuit diagram of the neuron
composed the cell body model and inhibitory synaptic
model. The inhibitory synaptic model has
spatiotemporal summation characteristics similar toloving organisms. This sums the pulse waveforms
outputted by other bonded cell body models.

PZT-type microrobot could generate the walking motion
by the bare chip IC. However, the robot could notachieve locomotion by itself. Because, the bare chip IC
is insufficient of the output voltage for moving legs.
However, the bare chip IC for the PZT-type microrobot© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan202

Kei Iwata, Hirozumi Oku, Yuki Okane, Yohei Asano, Masaki Tatani, Yuki Ishihara, Kazuki Sugita, Satohiro Chiba, Satoko Ono, Mizuki Abe, Minami
Takato, Ken Saito, Fumio Uchikoba

Acknowledgements

could drive the rotary actuator. Figure 10 shows therotary motion of 1 s intervals.

The MEMS microrobot fabrication was supportedby the Research Center for Micro Functional
Devices, Nihon University. This study was
supported by Nihon University College of Scienceand Technology Project Research and JSPS
KAKENHI (25420226). We appreciate the above
support. The very large scale integration (VLSI)
chip used in this study was fabricated by Digital
Technology, Inc. This work was supported by the
VLSI Design and Education Center (VDEC) of the
University of Tokyo in collaboration with Synopsys,Inc., Cadence Design Systems, Inc. and Mentor
Graphics, Inc.

Fig. 10. Rotational motion of PZT-type actuator.

On the other hand, hexapod walking locomotion of the
SMA-type microrobot, as shown in figure 11, wassuccessfully achieved. The walking speed was 2.4
mm/min and the step size was 0.083 mm. Power sourceof the control circuit is externally connected.

References
1. A. T. Baisch, P. S. Sreetharan and R. J. Wood,
Biologically-inspired locomotion of a 2g hexapod robot,
in Proc. 2010 Int. Conf. on Intelliigent Robots andsystems, (Taipei, Republic of China, 2010), pp. 53605365.
2. B. R. Donald, C. G. Levey, C. D. McGray, I. Paprotny
and D. Rus, An Untethered, Electrostatic, Globally
Controllable
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Uchikoba, Piezo Impact Type MEMS Rotary Actuatorand Application to Millimeter Size AI Controlled Robot,
in Proc. 2013 Int. Ultrasonic Symp., (Prague, Czech
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Microrobot with Pulse-Type Hardware Neural Networks
Integrated Circuit, in Handbook of Research on
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18-35.
6. K. Saito, Y. Ishihara, K. Sugita, Y. 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.
Advanced Intelligent Mechatronics, (Busan, Korea, 2015),
pp. 1055-1060.

Fig. 11 Walking locomotion of the microrobot controlled by
the bare chip IC. The figure show microrobot transitions at 10s intervals.

5. Conclusion
In this paper, control system of the neural network was
mounted the fabricated insect-type microrobot. It waspossible to reduce a control system within a 4mm
square by using a bare chip IC. In the PZT-type
microrobot, the bare chip IC could actuate the rotaryactuator. Moreover, hexapod walking locomotion of the
SMA-type microrobot was achieved. The walking speed
was 2.4 mm/min and the step size was 0.083 mm. The
only external wire connections to the microrobot are to
the power source.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan203

Social Expression of Pet Robot Based on Artificial Consciousness and Biologically Inspired
On-line Topological Method
Sakmongkon Chumkamon and Eiji Hayashi

Department of Mechanical Information Science and Technology, Kyushu Institute of Technology
680-4 Kawazu, Iizuka, Fukuoka, Japan, 820-5202
Tel: +819 0 9499 2408
E-mail: s.chumkamon.jp@ieee.org, haya@mse.kyutech.ac.jp

Abstract
The social robot becomes important to the future world of pervasive computing, where the robot currently facilitates
our life. The social behavior and natural action of the robot is one of the most needed function for emerging future
realistic human-like robot. Our paper proposes the artificial topological consciousness based on a pet robot using theartificial neurotransmitter and motivation. Since, the significant is cross-creature communication to friendly
companionship. This system focuses on three points. The first, the organization of the behavior and emotion model
regarding the phylogenetic. The second, the method of the robot that can have empathy with user expression. The
third, how the robot can socially perform its expression to human using biologically inspired topological on-line
method for encouragement or being delighted by its own emotion and the human expression. We believe the artificial
consciousness based on complexity level and the robot social expression enhance the user affinity by the experiment.
Keywords: Consciousness-Based Architecture, Cross-Creature Communication, EQ.

have an affinity therefore it is needed to having empathy
skill and need its comprehensible expression in order tohuman understands robot feeling as Fig 1.

1. Introduction
The personal robot in the boom of the robot market recent
years, the long-standing goal of the robotics research isthe autonomous behavior, having artificial cognitive andmake a human delighted without the conflict. The
complexity of the natural behavior, perception systems,
empathy, sympathy and a cognitive stream of the robotthat exists and cooperate with human is the challenge toarchive the personal robot. The personal robot also
improves living better for elderly people or a child by
being a friend or companionship. The crucial ability ofthese robots to naturally interaction learn form andaccordingly cooperate with human.
The traditional robot stands aloof slightly from utilizing
nature cognitive process of the organism with the robot.
Accordingly, the behavior and mentation of robot would
not have a strategy of intrinsic movement and capabilityof communication with human. Due to the robot does not

I feelhappy.

You seemsorrowful.
I sympathize withyour sorrow.
I do not feel
good.

Fig. 1. The proposed concept of the companionship robot basedon the social-emotional intelligence.

We have been striving to investigate and develop the
personal robot and the related know-how deliberated
with the biologically inspired consciousness, the social© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan204

Sakmongkon Chumkamon, Eiji Hayashi

intelligence (SI) and perception system in order to serve
the organism-like consciousness and social-emotional
intelligence (SEI) of personal robot come along thenatural behavior and affinity [1]. In this paper, wedevelop conscious behavior robot named by “CONBE”.
This robot includes two manipulators and one facerespecting to semi-humanoid. In this paper, we propose
the system of consciously behavior robot along with SI
using topological adaptive resonance theory (TopoART)[2] as shown in Fig 2. The system implements concerning
to organism behavior and emotion by phylogenetic
illustrated by consciousness based architecture (CBA).
Because of this concept, that significantly enable therobot self-awareness, which emerge naturally social
interaction with human, and being a companion
technically. The major contributions are the crossemotion expression from the robot toward human andcross facial emotion expression comprehension.
Moreover, the robot’s expression would interact withuser by deliberation of robot emotion and user’s
emotional expression based on SI likes human being insociety. Like human way, given example, when you are
happy, however, your friends around you, they are
feeling sad from some manner, and then you cannot
express your actual emotion as happy. Therefore, you
might sometimes express neutral or pretend to be sad as
sympathy way.

into the head and each hand for animal world. The robot
can express the eyes by two display as shown in Fig. 1.

Motivation

Social Emotional
Intelligence

Environmental
stimulus

Facial expression
recognition

Neurotransmittercreation using
artificial dopamine

Robot emotional
creation

Motivation
Intensity

Emotional
regulation using
TopoART-R

Animal action by
the manipulator

Eye’s Expression
moduleHuman world

Animal world

CBA based on animal behavior

Fig. 2.The overview of the proposed methodology.

3. Robot Biologically Inspired Consciousness
with Emotional Intelligence3.1. Animal behavior and emotion of the robot
We developed the system to mimic the biologicalconsciousness embodied to a robot. The system iscomposed of the animal world perception that
represented by the color of interested object, which
includes like-red, most favorite green and dislike-blue.
For the emotion system, the artificial dopamine is used toinduce the emotional motivation and behavior. There is
the separated dopamine divided by each object. Each
dopamine also performs a long-term memory as arobot experience in behavior process. For the creation of
motivation, the intensity is calculated with the total
dopamine. Therefore ,The system could naturally
perform the creature-like motivation. The waveform
pattern is calculated using a second-order of linear
differential discretized by Runge-Kutta method [3].

2. Configuration of Conscious Behavior Robot
In this research, we contribute human-robot interaction
based on the upper humanoid-like CONBE robot. It is todevelop the artificial consciousness. For the proposed ofenhancing the natural behavior in friendly way. The
hardware of element of CONBE robot consists of two
manipulators and one head as shown in Fig 1.
In the robot construction, the arms were assembled asseven degrees of freedom (DOF) included gripper. Dueto the determination of angle for each joint of actuator, it
is complicated to move to the target position. We thendivided the seven DOF arm relating to human arm, where
each part represent to be a shoulder, an elbow, a wrist anda finger. In the section of head robot, that was constructedby two DOF actuators for rotating in left-right and updown where was equipped with a web camera and two
displays. For the vision system, the camera is embedded

3.2. Emotional Intelligence
EQ is the ability to identify, use, understand and manage
emotion in positive way in the society of human. EQ
encompasses self-awareness, self-management, social
awareness and relationship management. Therefore, EQ
skill is important for the companion robot to crosscommunicate between robot and human and maintain the© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan205

Social Expression of Pet

relationship among human. Moreover, in sociality, when
the robot exists with the human, it has to consider on theetiquette in a society. Therefore, the robot is needed tohave EQ skill.

method to carry out the stable learning and fast therefore
we decide to utilize the enhanced ART view by
TopoART [2]. TopoART is the fast-online learning andstable, which improve the ART by using the topology
learning with addition sub-network in different level ofdetail. Therefore, this method is more suitable for realworld information.
For the robot SI topology is shown in the Fig 3. xF0
represents to input data which encompass the robot
emotional parameter er, human emotional parameter eh,
motivation value of robot mr, object parameter vector
from the robot perception system oi and emotional
expression (for training) ee. y(t) denote to the socialemotional expression depend on the user expression androbot’s emotion as shown in Fig. 3.

3.2.1 Facial expression recognition (FER)
To archive the ability of EQ, the robot is needed tocomprehend the social emotion from user or that means
the social awareness in EQ. In this paper, FER systemcomposed of the facial feature extraction by theconstrained local model (CLM), and emotional
expression classification by Hidden Markov Model(HMM) to process FER [4]. Afterward, the output of
FER will feed to emotional intelligence regulation
system to consider the proper SI expression to user.
Emotional expressiony  t   y F 2b (t ) interactingwith human

3.2.3 Robot’s eye expression for social interaction

TopoART-R b
Prediction ART network
F2b

b

Robot eye is the major medium for interaction. The
expression includes the emotion of surprise, pleasure orhappiness, hope, neutral, fear, sadness, disgust and anger.
The eyes were implemented, as the 3D virtual eye
regarding human’s eye along with the nature expression
where the eye’s expression is shown in Fig. 4.

F1b

F0m

m F 0  t   [m1i , m2i , m3i , m4i , m1d ]t
TopoART-R a
Attention ART network F2αz Fj 2  t 
aFig. 4.The robot eye’s expression a) pleasure b) hope c) neutrald) fear e) sadness f) disgust g) anger h) surprise.

F1α

x F1 t 

4. Experimental Results

F0

In this section, we demonstrate the robot behavior,emotion generation and social expression with human by
the robot face. The experimental configuration is basedon the CONBE robot, which is set the heightapproximated by human height. For the SI interaction, we
firstly extract the facial feature parameter using CLM topredict the emotion expression by HMM. Afterward, thehuman emotion and robot emotion are used to consider
the suitable social expression using the TopoART. The
emotional expression is deliberated with the emotional
sharing between human and robot, which the expressionis acceptable, and not let human unsatisfied. The top of
Fig. 5 shows each parameter for the experiment thex F 0  t   [er , eh , mr , oi , ee ]t
Fig. 3. The SI using TopoART-R.

3.2.2 SI regulation using TopoART-R
Because the Adaptive Resonance theory (ART) is closely
related to the cognitive and neural theory of the brain
instinct learning categorizing recognition and prediction
and the topology relates among consciousness process,expectation, attention, resonance, and synchronization
between unsupervised and supervised learning [5].
Because the real world information is dynamic andvariant, this system also needs the on-line incremental© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan206

Sakmongkon Chumkamon, Eiji Hayashi

emotional motivation based on the neurotransmitter
dopamine of each object, which is recognized byovertime. The bottom is observed the robot’s emotion,
human’s emotion and the expression throughout the task
period. Fig. 6 shows the capture images of FER and therobot’s expression on each time from T0 to T5.
T0

7

T1

T2

T3

5
3

of SI. On T3, robot is hope but human disgust robot thenexpresses fear. On T4 robot is sad but human is happy,
the robot then expresses neutral and mask its sadness due
to SI. On T5, robot is sad and human is angry then robot
expresses disgust due to the emotion sharing.
5. Conclusion

T4
T5
Green Dopamine
Red Dopamine
Blue Dopamine
Motivation

As the demonstration and the proposed system, the
CONBE robot with the emotional intelligence by sharing
the emotion from the user could enhance the capabilityof the human affinity, which is strongly important for
personal robot. For our further inspiration, we also expect
this system can emerge the social ability to the creature
animal due to recent research from the remote animal
control [6] which the research proof the nearly future wecan connect and control the animal brain. Consequently,
if we embed this artificial social emotion intelligence tothe animal in near future the animal might communicate
with EQ skill likes human that should make the natural
cross-communication creature between animal andhuman.

-3

0.1
2.2
4.3
6.4
8.5
10.6
12.7
14.8
16.9
19
21.1
23.2
25.3
27.4
29.5
31.6
33.7
35.8
37.9
40
42.1
44.2
46.3
48.4
50.5
52.6
54.7

1
-1

-5
-7

0.1
2.2
4.3
6.4
8.5
10.6
12.7
14.8
16.9
19
21.1
23.2
25.3
27.4
29.5
31.6
33.7
35.8
37.9
40
42.1
44.2
46.3
48.4
50.5
52.6
54.7

Surprise
Happiness
Hope
NeutralFear
Sadness
DisgustAnger

Robot Emotion

User Emotion

Emotion of Expression

Fig. 5. The robot’s motivation and emotional expression
T0

T3

T1

T4

T2

References
1. S. Chumkamon and E. Hayashi, 'ConBe robot: The
development of self-perception and expression in face-toface interaction', 2014 Joint 7th International Conference
on Soft Computing and Intelligent Systems (SCIS) and15th International Symposium on Advanced Intelligent
Systems (ISIS), 2014.
2. M. Tscherepanow, 'An Extended TopoART Network for
the Stable On-line Learning of Regression Functions',
Neural Information Processing, pp. 562-571, 2011.
3. S.Chumkamon, K.Masato, and E.Hayashi. "Facial
Expression of Social Interaction Based on Emotional
Motivation of Animal Robot" In IEEE International
Conference on Systems, Man, and Cybernetics, pp. 185190, 2015.
4. S. Chumkamon and E. Hayashi, 'Facial expression
recognition using constrained local models and Hidden
Markov models with consciousness-based architecture',
Proceedings of the 2013 IEEE/SICE International
Symposium on System Integration, 2013.
5. S. Grossberg, 'Adaptive Resonance Theory: How a brain
learns to consciously attend, learn, and recognize achanging world', Neural Networks, vol. 37, pp. 1-47, 2013.
6. S. Talwar, S. Xu, E. Hawley, S. Weiss, K. Moxon and J.
Chapin, 'Behavioural neuroscience: Rat navigation guided
by remote control', Nature, vol. 417, no. 6884, pp. 37-38,
2002.

T5

Fig.6 Robot eye’s expression and human’s expression.

When robot starts, it feels neutral and expresses neutral
according to the motivation, when the favorite red object
is recognized the motivation increased. On T0, the robot
feels hope according to increased motivation and humanexpresses neutral, then it expresses hope due toencouraging human feeling. On T1, robot feels surprised
because the most favorite green is recognized suddenly
and human expresses hope then robot expresses surprise.
On T2, robot is happy but human expresses sad thenrobot expresses neutral and masks its happiness because© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

207

Anthropomorphic robot modelling with virtual height inverted pendulum approach in
Simulink: step length and robot height influence on walking stability.
Ramil Khusainov, Ilya Afanasyev and Evgeni MagidIntelligent Robotic Systems Laboratory, Innopolis University, Russian Federation
E-mail: {r.khusainov, i.afanasyev, e.magid}@innopolis.ru
http://university.innopolis.ru/en/research/robotics/lirs/

Abstract
Humanoid stable walking is a complex task due the high number of degrees of freedom, system nonlinearity andrelatively small size of robot footprint. Biped robots tend to fall down as walking speed increases or when the terrain
conditions change. This paper presents dynamically stable walking modelling of Russian humanoid AR-601M in
Simulink environment with virtual height inverted pendulum model (VHIPM), an effective and simple trajectory
generation method based on inverted pendulum model (IPM). This algorithm adjusts height of the center of mass in
IPM model to reduce ZMP error and guarantees stable locomotion up to some critical speed. We investigate influenceof the step length and step period on walking stability. Maximum torque values in leg joints are estimated in order toverify if such trajectories are attainable by robot motors. We demonstrate that the robot model is capable to achievesignificant walking speeds on flat surfaces using this method.
Keywords: bipedal robot, dynamic stability, Simulink, AR-601M robot

model does not consider full body dynamics. This causes
ZMP errors, which may become critical for a given
walking speed. To deal with this problem, the authors in
Ref. 4 suggested a novel simplified robot model which
was called a Virtual Height Inverted Pendulum Model(VHIPM): the authors experimentally demonstrated4 that
the ZMP error could be significantly reduced by varying
the height of Center of Mass (CoM) in LIPM model.
This paper suggests the VHIPM application formodeling of Russian anthropomorphic robot AR-601M
in MATLAB/Simulink environment. We find maximal
speed of AR-601M stable walking and next calculate
torques in the robot joints to provide such locomotion.
The rest of the paper is organized as following. Section 2
presents the robot and its Simulink model. Section 3
describes the process of walking trajectory generation,
followed by simulation results within Section 4. Finally,
we conclude in Section 5.

1. Introduction
Humanoid robot design and locomotion are currently
among of the most exciting and challenging research
topics in robotics. Over the last decades, a number ofsuccessful approaches for stable bipedal robot walking
have been developed.1 The multi-functionality and highflexibility of anthropomorphic design, abilities of ahumanoid robot to replace a human in various practicaloperations and human-robot interaction scenarios
provide good motivation for such research activities.
To ensure walking stability, the majority of modern
humanoid robots employ analytical methods, which are
based on locomotion dynamics and usually apply someparticular stability constraints. One of the most common
stability constraints is the Zero Moment Point (ZMP).2 In
order to simplify equations deriving, ZMP criteria could
be applied for walking reference trajectory generation
together with a simplified robot model, such as Linear
Inverted Pendulum Model (LIPM).3 However, LIPM

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

208

Ramil Khusainov, Ilya Afanasyev, Evgeni Magid

where kn, bn, bt and μ are contact force parameters. The
details of the contact model can be found in Ref. 6 which
presents the previous work of the authors.

2. The Biped Model
We have constructed 41 degrees of freedom (DoF) modelof AR-601M robot; however, only 12 joints (6 DoF ineach pedipulator) are active during the robot locomotion.
Three joint axes are in the hip, two joints are at the ankle
and one in the knee. Mass and size parameters of therobot pedipulators are given in Table 1. Total mass of therobot is 65 kg. Figure 1 demonstrates the robot simulationin MATLAB/Simulink environment.

3. Walking Trajectory Generation
Robot locomotion could be considered as a repetition ofa single step motion7. We calculated walking trajectoryunder the following assumptions, which are widely
applied in experimental approaches for biped walking8:(i)
The swing foot is parallel to the ground(ii)
The upper body is always kept upright(iii)
CoM of the robot model is moving at theconstant height(iv)
The swing foot is moving in a cycloid
trajectory and its coordinates can be
described with the following equations:

Table 1. Mass and size parameters of the pedipulators.
Link
Size parameters (mm)
Mass (kg)ThighLength : 280
7.5
Shank
Length : 280
6.9
Foot
L×W×H:
3.2
254×160×106

𝜋𝑡𝑦(𝑡) = −𝑆 cos( )
𝑇

(3)
2𝜋𝑡

𝑧(𝑡) = 0.5𝐻 (1 − cos( ))𝑇
𝑥(𝑡) = 𝑥0(4)(5)

where S is a single step length, H is a step height, T is a
step period, x0 is a distance between two feet in x
direction. In Ref. 4 authors showed that equations for
CoM trajectory can be expressed as:𝑥̈ 𝐶𝑜𝑀 −𝑦̈ 𝐶𝑜𝑀 −

𝑔
𝛼𝑧𝐶𝑜𝑀
𝑔
𝛽𝑧𝐶𝑜𝑀

𝑥𝐶𝑜𝑀 = 0(6)

𝑦𝐶𝑜𝑀 = 0(7)where coefficients α and β are found experimentally to
reduce ZMP error. Therefore, we have following
trajectories for CoM point of the robot:
Fig. 1. A snapshot from animation window in
MATLAB/Simulink environment

𝑥(𝑡) = 𝐶1 𝑒 −𝑤1 𝑡 + 𝐶2 𝑒 𝑤1𝑡 , 𝑤1 = √𝑦(𝑡) = 𝐷1 𝑒 −𝑤2 𝑡 + 𝐷2 𝑒 𝑤2 𝑡 , 𝑤2 = √Complete 3-D simulation of multibody dynamics was
performed using variable step period implicit solver(ODE 23t*) of MATLAB SimMechanics Toolbox. We
modelled the robot feet ground contact with nonlinear
spring-damper method5, where normal and friction forces
are calculated according to Eq. (1) – Eq. (2) respectively:
𝐹𝑛 = 𝑘𝑛 𝑧 + 𝑏𝑛 𝑧𝑧̇
𝐹𝑡 = 𝑏𝑡 𝑧𝑥̇ , 𝐹𝑡 ≤ 𝜇𝐹𝑛

𝑔
𝛼𝑧𝐶𝑜𝑀
𝑔

𝛽𝑧𝐶𝑜𝑀

(8)
(9)

where C1, C2, D1 and D2 coefficients are found frominitial and final values of CoM coordinates. After wedefine translational and rotational coordinates for the
body and the swing foot, the joint angle trajectories are
obtained from inverse kinematics problem solution.
Such trajectory assumes that robot has initial velocity.
To move robot from resting state we first calculate

(1)
(2)

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

209

Anthropomorphic robot modelling withThis section considers a walking trajectory which is
described with the listed in Table 2 parameters.

x-coordinate optimal value of coefficient α is equal to 1,
for y-coordinate optimal β should lie within [0.7, 0.8]
interval. Walking simulation with these coefficients
showed that such optimization of trajectories allows toincrease maximal step length from 0.95 m to 1.1 m andto achieve a maximal speed of 1.1 m/s accordingly while
keeping stable walking for at least 100 sequential steps.

Table 2. Simulation parameters
Parameter
Value
Step height
0.1 m
Step period
0.5 sec
CoM height0.75 mStep length
0.1 m – max. value

Fig. 3. ZMP x coordinate

trajectory with a short step length and then gradually
increase it (to maximal value) until ZMP errors become
critical and the robot falls down.
4. Simulation Results

First, we estimated maximal speed of the robot modelthat could be achieved using LIPM method. We define
robot’s walking trial as “stable” if the robot successfully
executes a sequence of 100 steps. After each stable
walking trial we increase step length by 0.05 m and verify
robot’s walking again. The process of step increase wasrepeated until the robot failed to execute a successful
stable walking trial.
Simulations demonstrated that maximum step length
at which the robot could perform stable walking is 0.95m, which corresponds to 0.95 m/s walking speed. CoM
forward velocity value is shown in Fig 2.

Fig. 4. ZMP y coordinate

Fig. 5. Dependence of ZMP x coordinate error on alphaFig. 2. Robot velocity in LIPM method
Figures 3-4 demonstrate ZMP coordinates for a stepof 0.95 m length. Support foot center corresponds to (0,0)
coordinate. According to the calculated values, we haveapproximately 2.5 cm error for X coordinate and 10 cm
error for Y coordinate just after a single step.
Next, we vary α and β coefficients in Eq. (6) and (7)around 1 in order to minimize ZMP errors. Figures 5-6
show the dependence of maximal error of ZMP
coordinate on α for x-coordinate and on β for
y-coordinate. It was empirically detected that for

Fig. 6. Dependence of ZMP y coordinate error on beta
Finally, we estimated the peak torque value in ankle
and knee joints that the robot motors should generate inorder to afford the robot locomotion according to thecalculated trajectory. Figures 7 and 8 show the calculated
torque values for the ankle and the knee joints
respectively.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

210

Ramil Khusainov, Ilya Afanasyev, Evgeni Magid

limited capabilities of its joint motors. However, theknowledge of the theoretically possible speed within asimulation and the robot motors specifications give agood hint for the real achievable speed. As a part of our
future work, we are going to implement the presented inthis paper algorithms on AR601-M robot and verify thatit could perform stable locomotion and achieve thecalculated through simulations peak walking speed.

Fig. 7. Torque value in ankle joint

6. Acknowledgements
This research has been supported by Russian Ministry of
Education and Science as a part of ”Scientific andTechnological Research and Development Program ofRussian Federation for 2014-2020 years” (agreement
14.609.21.0004,
research
grant
ID
RFMEFI60914X0004) and by ”Android Technics”
company, the industrial partner of the research.

Fig. 8. Torque value in knee joint
The maximal value is approximately 100 N*m for the
ankle and 80 N*m for the knee joint. With regard to thetechnical specifications of real AR601-M robot, its
motors can generate maximal values of 50 N*m for theknee and 20 N*m for the ankle joint. Unfortunately, thismeans that such high maximal speeds could not be
achieved for the real robot. Therefore, we are limited touse trajectories with lower maximum speed, which are
dynamically stable according to simulations.
5.

References
1. J. Wright, I. Jordanov, Intelligent approaches inlocomotion, in International Joint Conference on Neuralnetworks, pp. 1-8, 2012.
2. M. Vukobratovich, B. Borovac, Zero-moment point thirty five years of its life, in International Journal of
Humanoid Robotics, vol. 01, pp.157-173, 2004.
3. S. Kajita, F. Kahehiro, K. Kaneko, K. Fujiwara, K. Harada,
K. Yokoi, H. Hirukawa, Biped walking pattern generation
using preview control of the zero-moment-point, inProceedings of IEEE International Conference on
Robotics and Automation, vol.2, pp. 1620 - 1626, 2003.
4. T. Ha, C.-H. Choi, An effective trajectory generation
method for bipedal walking in Journal of Robotics andAutonomous Systems, vol.55(10), pp.795–810, 2007.
5. K.H Hunt., F.R.E. Crossley, Coefficient of restitution
interpreted as damping in vibroimpact in ASME Journal ofApplied Mechanics, vol.42(2), pp.440-445, 1975.
6. R. Khusainov, I. Shimchik, I. Afanasyev, E. Magid,
Toward a human-like locomotion: Modelling dynamicallystable locomotion of an anthropomorphic robot in
Simulink environment, in 12th International Conferenceon Informatics in Control, Automation and Robotics, vol.2,
pp. 141-148, 2015.
7. J. Denk, G. Schmidt, Synthesis of walking primitive
databases for biped robots in 3-D environments, in IEEE
International Conference on Robotics and Automation.
vol.1, pp.1343-1349, 2003.
8. A. Yonemura, Y. Nakajima, A.R. Hirakawa, A.
Kawamura, Experimental approach for the biped walking
robot MARI-1, in 6th International Workshop on
Advanced Motion Control, pp.548–553, 2000.

Conclusions

This paper presents a biped walking robot AR601-M
trajectory reference generation algorithm based on ZMP
approach with Linear Inverted Pendulum Model (LIPM)
and Virtual Height Inverted Pendulum Model (VHIPM)
methods.
Simulations
in
MATLAB/Simulink
environment indicated significantly better performanceof VHIPM method when compared with LIPM method.
We investigated in depth the influence of the step lengthand step period on walking stability. In general, proper
coefficients selection with a help of simulation for eachparticular robot model affords to achieve maximal
walking speed for stable locomotion. The resulting
torque values in the knee and the ankle joints could be
calculated for such maximal walking speed and next
verified against the technical capabilities of the real robot
joint motors. Unfortunately, in our case the maximal
speed of stable locomotion that was successfully
demonstrated in the flat surface walking simulation
turned out to be unfeasible for AR601-M robot due to the© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan211

A low cost genetic algorithm based control scheme for wheelchair control in hospitalenvironment
Karam Dad

Department of Mechanical Engineering, Pusan National University, 635-4Jangjeon 2 dong, Geumjeong-gu, Busan, 609735,South Korea
Muhammad Jawad Khan

Department of Mechanical Engineering, Pusan National University, 635-4Jangjeon 2 dong, Geumjeong-gu, Busan, 609735,South Korea
Wang Jie

Department of Mechanical Engineering, Pusan National University, 635-4Jangjeon 2 dong, Geumjeong-gu, Busan, 609735,South Korea
Min Cheol Lee

School of Mechanical Engineering, Pusan National University, 635-4Jangjeon 2 dong, Geumjeong-gu, Busan, 609-735,
South Korea
E-mail: karam@pusan.ac.kr, mclee@pusan.ac.kr

Abstract
In this paper, a control strategy to operate a wheelchair in a hospital is proposed to assist patients. The strategy
is tested using a mobile robot that is navigated in a hospital using line following scheme. The mobile robot is
operated using both genetic algorithm and A* algorithm for a thorough comparison of the control scheme. The
comparison of the results revealed that genetic algorithm is a better solution in controlling the wheelchair in a
hospital environment.
Keywords: Wheelchair, Patients, Mobile Robot, Genetic Algorithm, A* Algorithm.

paper proposes an offline scheme for navigation of theautomated wheel chair.

1. Introduction
Automated wheel chairs are characterized as mobile
robots due to the involvement of motion planning andcontrol schemes [1]. The automation schemes in wheel
chairs are sub-divided into two major parts i.e. (i) control
schemes and (ii) navigational schemes [2]. Both are used
in interpretation of human gestures for assistance via
control through sensors. The navigational schemes arefurther sub divided into offline and online schemes [3],where the offline algorithms plan the course of actions
before starting the path, while online algorithms plan thepath of the robot while executing the path. Control
schemes provide the disturbances which are encountered
due to the irregularities in the environment [4]. This

2. Proposed Methodology
The main emphasis of this research is the implementationof path planning algorithms on a real time robot. Several
strategies are involved in developing a real-time pathfollowing mechanism. The components used in thisresearch are given below:
 BeInMotion® SDK Motor Control Kit
 Accelerometer Arduino UNO
A brief description of the methodology used for
implementation of the optimal path planning in a static© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan212

Karam Dad, Muhammad Jawad Khan, Wang Jie, Min Cheol Lee

environment is given in this section. This methodology is
divided into three significant parts:
 Optimal Path Planning for known environment Communication of optimum path to the Mobile
Robot Evaluation of optimal path tracking by the
Mobilefrom two sensors, accelerometer and rotary encoders, isreceived and evaluated [6]. The mobile robot is firstturned to the direction of next path point and then it
travels the desired distance in a straight line. To force the
Mobile Robot to follow a straight path Proportional
Integral and Derivative (PID) control is proposed. The
Mobile Robot stops when the designated distance in that
direction is travelled and then turns to the next path point
and repeats the process. The sensors inform that theplanned distance has been travelled.

2.1. Optimal Path Planning for known
environment
Optimal path planning algorithm convergence cost is
higher than the ordinary algorithms; therefore Field
programmable Gate Arrays (FPGA’s) are generally used
for implementing these algorithms, as they possesscomparatively more computational power than
microcontrollers [5]. But the use of an FPGA for optimal
path evaluation disturbs the scalability process of themethodology. For example, consider a case where wewant to change the path planning algorithm. In that casea retransform of new path planning algorithm for the
FPGA may require extensive amount of work. As thisresearch emphasis on creating a scalable method which
can be used with different path planning algorithms,
therefore, a more generalized approach is proposed. As
the environment is completely static the path is, therefore,
to be evaluated offline. It may be more advantageous if
the path is preplanned and then the coordinates are fed tothe mobile robot controller. For this a control scheme toevaluate the optimal path beforehand using computer is
proposed. The details are given in the next section.

3. Implementation of Proposed Methodology
The proposed methodology is shown in the Figure 1.
Flow Chart for Proposed Methodology as a flow chart
which is implemented in three parts as following: Optimal Path Evaluation Mechanism
 Data Transfer Mechanism
 Path Following Mechanism.
3.1. Optimal Path evaluation mechanism
This station performs the function of evaluating the
optimal path from the desired algorithm. It consists of a
PC only. In this research the optimal path has been foundfrom Genetic Algorithm and A* algorithm but as it has
been already discussed that the methodology is scalable
so any other path planning algorithm can be used to findthe optimum path. The Genetic Algorithm and A*
algorithm takes the input environment and generates thepath points of the optimum path. The station saves thesepath point coordinates for future operations.

2.2. Communication of optimum path to themobile robot

3.2. Data transfer mechanism

Once the optimum path is planned the new pathcoordinates are to be communicated to the Mobile Robotfor further processing. Parallel communication may
require many lines and is not generally preferred while
communicating with the PC is done through serial
communication.

This station performs the function of data transfer
between the PC and the mobile robot. The path points
which were evaluated in the first step are seriallytransferred to the mobile robot through this mechanism.
3.3. Path following mechanism

2.3. Evaluation of optimal path tracking by themobile robot

The path following mechanism includes motion of therobot from starting to the end point along the given pathfollowing the fed points on the path. This consists of bothforward and turning of the robot along the path until
arrives to the goal point [7].

The Mobile Robot receives the path points from the PC
and starts path execution command it follows the desired
path and passes through each path point. In order tolocalize the mobile robot in the given environment data

4.

Path Planning© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan213

A low cost genetic

The algorithms used for static environments in this study
are following: A* Algorithm
 Genetic Algorithm
4.1. A* Algorithm
A* Algorithm finds the most cost efficient path startingfrom initial node to one of the destination nodes, given
there are one or more destination nodes. As A* always
prefers a path with the lowest expected total cost which
could be distance or time or any other parameter, while
keeping an already sorted priority line up of pathsegments that could be alternately used along the way [8].
Two functions combine to make the cost function, bothof which are as follow
 Cost Function of Past Path, this is the cost
between starting and current node and isgenerally represented by g(x) Cost Function of Future Path, this is apermissible "heuristic approximation" of thecost from current position to the destination andis represented by h(x).
Each node is evaluated by adding the heuristic function
h(x) and g(x) i.e. the cost of reaching the present node
being evaluated from the start node.
F(x) = g(x) + h(x)Fig. 1. Implementation
Methodology

Blockdiagramof

Proposed Decode chromosomes
 Evaluate fitness of each chromosome
 Select mates for reproduction
 Carry out reproduction
 Carry out mutation
 Check for convergence If not converged go to step (iii) Print converged solution
GA’s work on the principle of survival of the fittest. Thismeans all the good points which give maximum value forthe objective function are permissible to continue in thenext generation and the points which does not give
maximum values for the objective function are thrownaway from our calculations. GAs maximizes a given
objective function, so it is necessary to convert aminimization problem into maximization problem before
applying the technique of GA. Fitness function is defined
as follows:(1)

F(n) = f(n)for a problem of maximization (2)F(n) = 1/(1+f (n)) for a problem of minimization (3)4.2. Genetic Algorithm
Genetic algorithm is based on the principle of genetics
and natural selection. It comes under the class of metaheuristic techniques which are based on the generalexperiences [9]. It works on the principle of survival of
the fittest. In search space it searches the optimal points
intelligently by using statistical evolutionary methods.
Evolutionary methods are based on natural selection
where the fittest chromosomes survive, reproduce andmutate so that the resulting chromosomes in successive
generations are increasingly competitive. This natural
selection process is coded mathematically to generate an
initial population of chromosomes and use selection
procedures to converge to an optimal configuration [10].
The computation can proceed in parallel rather than inseries thus resulting in high computational speed-ups. A
detailed pseudo-code for the GAs are as follows: Define GA parameters
 Generate initial population

Equation (2) is used for the maximization of the problem
whereas equation (3) is used to minimize the costfunction. In path planning application, path distance isused to evaluate the fitness value of each chromosome. If
the distance to the goal or path length of a certain
chromosome is small, the chromosome is better than theother chromosomes in the current generation. The fitness
function for path length can be defined as.
𝐹𝑡 (n) = 1/d (n)(4)
Whereas d(n) defines the path length for the specificchromosome.
5. Experiments
Experiment were tested in a mobile robot instead of awheelchair since a automatic wheel chair is an© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan214

Karam Dad, Muhammad Jawad Khan, Wang Jie, Min Cheol Lee

5.2. Tests Designed

autonomous mobile robot that is used for thetransportation of patients in a hospital.

The test for the mobile robot consisted of a sample which
is given in Figure 2. The black lines present the linkages
possible while blue boxes represent the obstacles. The
start and stop points are also mentioned by “Start” and“Goal” respectively. Starting point is 0 and the goal pointis 15. Each point is defined as the change in direction.
There are many paths to reach the goal

5.1. Testing Procedure
First the offline navigational algorithm is run on acomputer. The computer computes the optimum path andevaluates the path points for that particular path. It thenserially transmits the coordinates to a host controller(Arduino UNO in this case) mounted on the mobile robot.
The Arduino UNO board communicates with both theaccelerometer and the computer. Firstly, it receives thecoordinates from the computer and transmits it to the
FPGA mounted on the mobile robot one by one.
Secondly it acquires the acceleration values continuously
from the accelerometer, applies Simpson’s rule fordouble integration of the acceleration and acquires the
distance covered and then transmits it to the Altera
Cyclone IV E FPGA mounted on the mobile robot [11].
The Altera Cyclone IV E FPGA communicates with the
Altera UNO board to get both the data fromaccelerometer and computer. It further evaluates the dataacquired from the IR sensors mounted on the mobile
robot base to apply the proportional integral derivative(PID) control which ensures that the mobile robot moves
in a straight line. The IR sensors are also used for thecomputation of distance covered. The effective distancecovered is the result of multiplication of the readings of
distance covered from both sensors (accelerometer andIR sensor) with their respective weights and adding them.

6. Results and Discussion
The main objective of the research was the navigation ofa Mobile Robot (BeInMotion Motor Control Kit) based
upon the optimal path provided by an offline path
planning algorithm.
6.1. Implementation of Optimized Solution
For the implementation of the path the environment
under test was practically created. The createdenvironment is shown in Figure 3. Figure shows the robot
in given environment while navigating through the
obstacles it starts from 0 and reaches to goal point 15
according to path generated by genetic algorithm. It firststarts from 0 and reaches point 15 following the pathcreated by points 0,4,6,7,9,15 respectively. Robot moves
in two steps firstly it calculates the turning angle and thenit moves forward and reaches desired point and thisprocess is repeated for every movement from point topoint.

Fig. 3. Implemented Environment details
The computed path was implemented on the Mobile
robot and with the help of genetic algorithm we achieved
less errors and increased accuracy with each trial. The
average percentage error observed from theimplementation of genetic algorithm was 1.54% which
can be easily reduced further by using better quality

Fig. 2. Test Environment Designed© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan215

A low cost genetic

navigational sensors. By improving the quality of thesensors, more importantly the accelerometer we canachieve greater accuracy

conference on Evolutionary computation. The Third
conference on parallel problem solving from nature, 1994).
11. http://www.arrownac.com/solutions/beinmotion/

7. Conclusion
The results show that this technique can be used fortransporting patients from one place to another in a
hospital within a controlled environment where thedestination point has been loaded. The result weresatisfactory and can further be improved by use of acontrol scheme alongside the presented scheme.
Acknowledgements
This section should come before the References. Funding
information may also be included here.
References
1. J. Borenstein, B. Everett, and L. Feng. Navigating Mobile
Robots: Systems and Techniques. A. K. Peters, 1996.
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Mobile Robots," IEEE Tran. System, Man, andCybernetics, Special Issue on Unmanned Systems andVehicles, December, pp. 1437-1443. 1990,
3. J. Czyzowicz , E. Kranakis , D. Krizanc , L. Narayanan ,and J. Opatrny, Optimal online and offline algorithms for
robot-assisted restoration of barrier coverage, arXiv:
1410.6726v1 [cs.DS] 24 oct 2014
4. Mnif, F. & Touati, F. , An Adaptive Control Scheme for
Nonholonomic Mobile Robot with Parametric Uncertanity,
pp. 059 - 063, International Journal of Advanced Robotic
Systems, Volume 2, Number1 (2005), ISSN 1729-8806
5. Shilpa Kale and S. S. Shriramwar, FPGA-based Controller
for a Mobile Robot, (IJCSIS) International Journal ofComputer Science and Information Security, Vol. 3, No. 1,
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6. J. Borestein, H. R. Everett, L. Feng: Where Am I? Sensors
and Methods for Mobile Robot Positioning, University ofMichigan, Michigan 1996.
7. Karam Dad Kallu, Muhammad Usman Rafique and Dr
Zafar Ullah Koreshi, Development of a path following
autonomously navigating mobile robot with parallel
processing using FPGA, 1st International Young
Engineers Convention (IYEC-2014) on April 18-20, 2014
8. Delling, D. and Sanders, P. and Schultes, D. and Wagner,
D., Engineering route planning algorithm (Algorithm oflarge and complex networks, 2009).
9. Peter E. Hart, Nils J. Nilsson and Bertram Raphael, “A
Formal Basis for the Heuristic Determination of Minimum
Cost Paths,” IEEE Transactions on Systems Science andCybernetics, pp. 100-107, July. 1968.
10. Eiben, A. E. et al., Genetic algorithm with multi-parents
recombination (PPSN III: Proceedings of the International© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan216

Simultaneous Localization and Mapping (SLAM) algorithm base on EKF and SPKF.
ZolghadrJavad, Yuanli Cai,Yekkehfallah Majid

School of electronic and information engineering, Xi’an Jiaotong University, Xi’an 710049,Chinae-mail addresses: Javad.zolghadr@gmail.com, ylicai@mail.xjtu.edu.cn, Majid.fallah@mail.xjtu.edu.cn,

Abstract
Simultaneous Localization and Mapping (SLAM) is the problem in which a sensor-enabled mobile robot incrementallybuilds a map for an unknown environment, while localizing itself within this map. The Kalman Filter’s linearized error
propagation can result in big errors and instability in the SLAM problem. One approach to reduce this situation is usingof iteration in Extended Kalman Filter (EKF) and Sigma Point Kalman Filter (SPKF). We will show that therecapitulate versions of kalman filters can improve the estimation accuracy and robustness of these filters beside oflinear error propagation. Simulation results are presented to validate this improvement of state estimate convergencethrough repetitive linearization of the nonlinear model in EKF and SPKF for SLAM algorithms. Results of thisevaluation are introduced by computer simulations and verified by offline implementation of the SLAM algorithm onmobile robot in MRL Robotic Lab.
Keywords: Extended Kalman Filter, Sigma Point Kalman Filter, SLAM, instability, Mobile Robot,Nonlinear
Estimation.

confirmed in [4],[6],[7] with carefully designatedexperiments. Another serious potential drawback of
EKF-SLAM is the derivation of the jacobian matrices,which is really a bothersome process.
Some researchers applied these new extensions of the
Kalman filters family called Sigma Point Kalman Filters(SPKF) to solve the SLAM problem [8],[9]. In spite ofsuperiority of the SPKF over the EKF, We will study
the future of SPKF over EKF.
In this paper, we want to investigate SLAM with SPKF
and show this method robustness. The rest of the paper
is structured as follows. Section II summarizes the EKF
based SLAM. The we will briefly describe the SPKF
based SLAM in section III. In section, IV theformulation of the EKF and SPKF approaches with their
iterated versions in section V. Section VI concludes thepaper.

1. Introduction
Simulation Localization and Mapping (SLAM) has the
problem of incrementally building a spatial consistent
map from noisy sensor data gathered by a robot andtracking robot pose with the built map[1][2].In the past
decade, the SLAM related research has received anincreasing and extensive attention in theory andapplication level. Due to the mutual dependence ofrobot pose and the map, the noise of robot pose arise theuncertainty of map estimation and vice versa. Therefore,
this problem requires a solution in a high dimensional
space.
Numerous method have been used to address the SLAM
problem, an overview of which is presented in [3],[4].
One of the most popular approaches to the SLAM
problem are the extended Kalman filter (EKF-SLAM).
The effectiveness of the EKF approach lies on the factthat it holds a fully correlated posterior over featuremaps and vehicle poses [3]. Due to the inherent nonlinearity of the SLAM problem, it applies the Kalmanfilter framework to nonlinear Gaussian systems, byemploying the first-order Taylor expansion toapproximate the non-liner models. However, thisapproximation treatment often introduces large errors in
the estimate of the states and can lead filter todivergence [5].These serious drawbacks have been

2. EKF SOLUTION FOR THE SLAM
In this section we summarize the basic EKF SLAM
equations to point out the steps in which linearized
approximations are introduced. We subsequently
analyze their influence in the consistency of the solution
obtained. In the standard EKF-based approach to SLAM,©The 2016International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

217

ZolghadrJavad, Yuanli Cai, Yekkehfallah Majidv(k  1)  zi (k  1)  z i (k  1)

(4)

Moreover, corresponding matrix:
S (k  1)   x h.B (k  1). x hT  R(k  1). (5)III.
UpdatePoster distribution’s covariance can be same as follows:

X ( k  1)  X ( k  1)  W( k  1) . v( k  1)
B (k  1)  B (k  1)  W .S (k  1).WT(6)

Fig.1. Robot Position

Kalman gain:
The Extended Kalman Filter estimates the mean andcovariance of the posterior probability distributionfunction (PDF) of the random state variable X. Let^W(k  1)  B (k  1). X hT .S1 (k  1)

(7)

IV.
Consolidation
After new feature is calculated, estimation must shownat last part of consoled state vector:X ( k ) denote the mean of the posterior PDF at time k.
The corresponding covariance matrix of the posterior

X i (k  1)  gi ( X V (k ), z(k)).

distribution is B ( k ) . The process model for vehicle
and features can be written as:(8)gi = convert function for polar observation z(k) to a

 X v (k  1)   f v ( X v (k ), u (k  1))  v(k  1) 
 X (k  1)    X (k )  0 (1)

 m
  m
 

global Cartesian feature location.
3. SPKF SOLUTION FOR SLAM

Where f v is the motion model of the Robot, u(k) is

Mathematic framework of SPKF Based Slam will
discuss in this section, in section 2 in this paper
assumptions made for SLAM problem, we define a newconsolidation state vector that include the original statevector X(k) and process noise. Block diagonal matrix of
B(k) and process noise covariance matrix Q(k) is newcovariance matrix:

control input and v k( ) represents process noise that iszero mean white with covariance Q(k). The EKF fuses
the odomtery measurements with a sequence ofobservations from the external sensors with thefollowing observation model:z (k )  h( X (k ))  w(k )Xka  [ XkVk ]T , Bka   k
B
0

(2)

w (k) is zero mean white observation noise with thecovariance matrix R(k). Four fundamental stages of the
EKF based SLAM are briefly written as follows:I.
Prediction a     0 ,  i   Bka ,  i   Bka ,  (10)sigma point around the mean. We can use cholesky
factorization for calculate more efficient the covariancematrix square root. According to [10],[11] these sigma
points pass through the process model and transformed
sigma points will be calculated:z (k  1)  h( X (k  1)),II.

 is a scaling parameter that controls the spread ofB  (k  1)   x f (k ).B  (k ). x f T (k )  Q(k ).

(9)Sigma point at the time k will be:

X (k  1)  f ( X (k ), u (k  1)),


0
Qk 

(3)

Observation

ix (k  1)  f ( ix (k ), u(k ), iv (k )),(11)© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

218

Simultaneous Localization and Mapping

For the original consolidation state vector the ixsigma points set and i

After Zk 1 detected the consolidation process easly( k ) isv

performed with the following equations:( k ) is sigma point set forprocess noise. The associated covariance matrix andpredicted state estimate can be computed as follows:Pk1
 
 0

AUG
k 1

P

2N

 k 1   wimix,k 1

G
XkAUG1   gi ( XiAU),
, k 1

i 0


k 1

B

2N 2N

  w ( i 0 j 0

c
ij



x
i , k 1)(xTj, k 1

)

(12)

i 0

2N 2N


AUG  T
Bk1   wijc (X iAUG
(18)
.k 1 ).( X j .k 1 )i 0 j 0Zi (k  1)  h( ix (k  1)) (13)mc

In above equations, the parameters wi and wi j are

Corresponding covariance matrices and predictedmeasurement can obtain:

scalar positive valued weights.
In following, we illustrated our results which we
implemented on MATLAB:

2N



2N

U
X k 1   wimiAG, k 1

The transformed sigma points pass through themeasurement model and samples of predicted are:Z k 1   wim Zi,k 1

0 
Rk 1 

(14)

i 0

Robot Pose Error

Cross covariance matrix between state and measurement

B

X-Error (m)


ZZ

0.5

and covariance matrix and measurement error

-0.5

-1

with assuming correct data are:

i

50

100

150

200

250

150

200

250

150

200

250

Time(sec)
Y-Error (m)w { i 0

0

1B( k  1) 
2N

EKFSLAMSPKFSLAM0( k  1)  Xˆ ( k  1) } { iAUG ( k  1)  Xˆ ( k  1) }T

AUG 
i

0.5

0

-0.5

0

50

100

Time(sec)



0.05

2N

Phi-Error (rad)v k  1  z k  1  z i ,k  1 '
2N


BZZ
 R    wicj (zi,k 1) ( zj,k 1)T, k 1

0
-0.05
-0.1
-0.15

0

50

100

Time(sec)i 0 j 0

Fig.2. Comparison of vehicle pose estimate
errors for EKF (Blue line) vs.SPKF (Red
Line)

2N 2N


c


T
BXZ
(15)
, k 1  R   wij ( i , k 1 )( z j, k 1 )0.2

i 0 j 0

EKFSLAMSPKFSLAM

0.18



Trace of Robot Pose Covariance Matrix

With observation noise additively the update state
estimate and corresponding covariance matrix can becomputed:X k 1  X k 1  Kk 1.v k 1,

k 1


k 1


ZZ .k 1B  B  Kk 1.B

.K

T
k 1

(16)

0.16

0.14

0.12

0.1

0.08

0.06

0.04

0.02

Kalam gain will be:

0

0

50

100

150

200

250

Time(sec)_1
Kk 1  BXZ(17).k 1.[BZZ .k 1 ]Fig. 3.State Covariance of Robot for KLF ad
SPKF© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

219

ZolghadrJavad, Yuanli Cai, Yekkehfallah Majid

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Defense Sensing, Simulation and Controls, Multi Sensor Fusion,
Tracking and Resource Management II, SPIE, 1997.
[11] R. van der Merwe. “Sigma-Point Kalman Filters for Probabilistic
Inference in Dynamic State-Space Models.” PhD thesis, OGI Schoolof Science & Engineering, Oregon Health & Science University, April
2004[12]Won-Seok Choi, Jeong-Gwan Kang, and Se-Young Oh, Member,
IEEEMeasurement Noise Estimator Assisted Extended Kalman Filter
forSLAM Problem,2009 St. Louis, USA

Fig.4. Comparing Robot position with EKF and SPKF

Mean Square
Error (MSE)x R statey R state R state

EKF-SLAM

0/622

0/1180

0/002

SPKF-SLAM

0/00770/01200/0003

Table 1. Comparing the Mean Square errors for EKF
and SPKF

4.CONCLUSION
In this paper, we investigated the EKF and SPKF
frameworks to improve the estimation accuracy
in simultaneous localization and mapping. All of
simulation results consistently verified better
performance and more accurate states estimate of
the SPKF than EKF approaches. After
Comparing the Errors and the Position vector of
the Robot with two different optimizations,
SPKF had significant better result than EKF.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan220

An accurate method for the extraction of structured light stripe

Jiwu Wang

School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University
Beijing 100044, China
Yaodong Li, Zhijing Jian

School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University
Beijing 100044, China
Masanori Sugisaka

Alife Robotics Corporation Ltd, Japan and Open University, United Kingdom
E-mail: jwwang@bjtu.edu.cn; ms@alife-robotics.co.jp

Abstract
In order to obtain the highest measurement accuracy in the On-line measurement of rail profile with a line
structured light based on machine vision, the extraction of structured light stripe is a necessary step. An accurate
extraction method is proposed for the structural light stripe extraction in practical applications. The noise in thecaptured image can be removed with region segmentation method. The structured light stripe is separated andextracted accurately based on the characteristics of the structure light stripe in the binary image. The method wasverified in laboratory conditions. Experiment results show that the method can effectively solve the problem ofreal-time and accurate extraction of structured light stripe.
Keywords: Structured light; region segmentation; light stripe

the specified region, so as to achieve the accurate
extraction of structural light stripe.

1. Introduction
The On-line measurement of rail profile with a line
structured light based on machine vision need collectand manipulate the captured image to obtain the
structured light stripe. The structured light stripe stands
for the measured rail profile. By comparing theextracted contour lines with the standard rail profile, theon-line measurement of the rail profile can beaccomplished. In order to ensure the accuracy ofmeasurement, the accurate extraction of structured light1
stripe becomes very important. The traditional method
includes the threshold method and the extreme value
method. Those are simple, but low precision. In light of
the above problems, an accurate method for the
extraction of structured light stripe is proposed. The
noise in the captured image can be removed with region
segmentation method. According to the characteristicsof the structure light stripe, the target is extracted from

2. Measurement principle
Two image acquisition devices A and B are arrangedon the upper and lower sides of the rail, as shown in
Fig.1.The image acquisition device is composed of aline structured light emitter and an industrial camera
Because of the projection relationship, the stripe ofthe light can represent the rail profile. So the outline
dimension of the rail can be calculated by thecollection and analysis of the line structured light
stripe.

©The 2016International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan221

Jiwu Wang, Yaodong Li, Zhijing Jian, Masanori Sugisaka

Fig.3.Binary image

Fig.1.Measurement method

Fig.4. remove the noise

3.1 Regional intelligent segmentation

3.2 Identifying Geometric FeatureFirst of all, the line structure light contour line iscollected, shown in Fig.2. And then binarization isshown in Fig.2. The calibration of the connected regionof the image is carried out, and the result is to separate2
the discontinuous regions . Since the characteristics ofthe spot is obvious, it can be easily removed. Only theline structured light contour lines and a small part of thenoise was left, as shown in Fig.3.

Illumination conditions and mirror reflection and otherfactors affecting the contour extraction of line structured
light. In order to eliminate these effects, the structurallight area is studied as a whole object. It can be used todirectly extract the light area of the line structure bypattern recognition technology. In this area, the outerboundary and inner boundary of the line structure light
contour are calculated. By analyzing the characteristicsof the shape, the size of the structure and the luminancegradient of the binary image, we can determine theposition of the structure light stripe. Through continuous
scanning of the structured light stripe of along the X
direction and the Y direction coordinate values inascending order ， structured light stripe of the outerboundary and inner boundary can be accurately
extracted. Because of the method is completely
combined with the imaging characteristics of thestructured light on the rail tracks surface which can
reduce the extraction of target range and reduce theinfluence of interferents, thus improving the reliabilityof line structured light contour extraction, stability andaccuracy.

Fig.2. Structured light profile© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan222

An accurate method for

3. Analysis

stable. By comparing with common edge detection
operator such as Roberts operator, Canny operator,
Sobel operator, the common edge detection operator has
no pertinence in the binary image and can’t effectively
remove the noise in the image. And the result is a closed
circle which can’t distinguish the inner and outer edges,
as shown in Fig.6. The method used in this paper caneffectively eliminate the noise according to thegeometrical features of the stripe and can extract theedge and the outer edge respectively. The innerboundary or outer boundary is used as the extraction
result. The extraction result is used to measure the rail3
profile .
4. Conclusions
By using the method of region segmentation andgeometric feature extraction, the line structured light
stripe can be extracted. Compared with the traditionaledge detection operator, it can effectively eliminate theinterference of the noise, and can extract the edge andthe outer edge of the structured light stripe. It effectively
solves the problem of real-time extraction of structured
light stripe, which is convenient for the following
contour matching.

Fig.5. Fringe extraction results

References

Fig.6. Edge detection operator

1.

Qiucheng Sun and Renyun Liu, An extraction method oflaser stripe centre based on Legendre moment,International Journal for Light and Electron
Optics,Volume 127, Issue 2, January 2016, Pages 912–
915.

2.

Qiucheng Sun and Jian Chen,A robust method to extracta laser stripe centre based on grey level moment, Optics
and Lasers in Engineering, Volume 67, April 2015,
Pages 122-127.

3.

J. Cheng, S.G. Zheng, X.Y. Wu,Structured light-based
shape measurement system,Signal Process, 93 (6)(2013), pp. 1435–1444.

Line structured light stripe extraction results are shownin Fig.5. The accurate extraction of line structured light
can be achieved by using region segmentation andgeometrical feature recognition technology. The outeredges and inner edges of two structured light stripes are
extracted respectively as shown in the Fig.5. It can beseen that the extraction of the stripe is continuous and© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan223

Feature Acquisition From Facial Expression Image Using Convolutional Neural Networks
Taiki Nishime
Graduate school of Information Engineering, University of Ryukyus
Nishihara, Okinawa, Japan
Satoshi Endo, Koji Yamada, Naruaki Toma, Yuhei Akamine
School of Information Engineering, University of Ryukyus
Nishihara, Okinawa, Japantaiki_one@eva.ie.u-ryukyu.ac.jp, endo@ie.u-ryukyu.ac.jp, koji@ie.u-ryukyu.ac.jp
tnal@ie.u-ryukyu.ac.jp, yuhei@ie.u-ryukyu.ac.jp

Abstract
In this study, we carried out the facial expression recognition from facial expression dataset using Convolutional
Neural Networks (CNN). In addition, we analyzed intermediate outputs of CNN. As a result, we have obtained aemotion recognition score of about 58%; two emotions (Happiness, Surprise) recognition score was about 70%. We
also confirmed that specific unit of intermediate layer have learned the feature about Happiness. This paper details
these experiments and investigations regarding the influence of CNN learning from facial expression.
Keywords: facial expression recognition, convolutional neural networks, deep learning, feature learningwith resolve each expression into several Action Units(AUs). FACS labels approaches need to learn from FACS
manual and test. As of now, FACS label can only be given
by experts or trained individuals. As a results, not everybody using easily FACS labels to facial expression recognition.
The previous studies on facial expression recognition can
be classified into two categories: FACS based method [2]or feature learning method [3]. In FACS based method,they first extracted feature from AUs, then they recognizedfacial expression from facial images using a these extracted feature and Support Vector Machine. In contrast tofeature learning method [3], recognized facial expression
using Convolutional Neural Networks (CNN) [4].

1. Introduction
Facial expression recognition is important to non verbal
communications among the people. Now, opportunities tocommunicate using either voice or text is increasing because of developing mobile phones and Internets. Thus, it
is considered that communication method via some devises has increased than face to face communication.
“UNMASKING THE FACE” by Paul Ekman and W.V.
Friesen [1] described that facial expressions have a closely
connection with the emotions. For this reason, It is naturalto think that we can recognized your happiness if you smiling. Mainstream approaches in facial expression recognition use Facial Action Coding System (FACS) labels.
FACS was designed to help facial expression recognition© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan224

Taiki Nishime, Satoshi Endo, Koji Yamada, Naruaki Toma, Yuhei Akamine

Fig 1. CNN structure: consist of three convolutional layers, three pooling layers, two fully connected layers, and output layer

3.2. Preprocessing

But, this study is not discussed in CNN model that has finished learning, and there was no argument about learning
the feature of CNN.
In this study, firstly, we carried out facial expression
recognition using CNN. In addition, we analyze the intermediate outputs of CNN that obtained from facial expression images.

We preprocess the data using Global Contrast Normalization (GCN) and ZCA whitening [5]. In GCN, subtract bymean and divide by dispersion for each dataset images. By
GCN preprocessing, if the value range of the input is normalized from -2 to 2, and can be aligned to that range, even
if there is a different axis scales. Natural image is characterized by strong correlation with neighboring pixels. ZCA
whitening has function to erase such correlation.

2. Convolutional Neural Networks
Convolutional Neural Networks is a type of feed-forward
artificial neural networks that consist of convolutional layers, pooling layers, fully connected layers and output layer.
Convolutional layers compute product-sum of image andweight. Pooling layers compute the max value of a particular feature over a region of the image. These convolutional layer and pooling layer were repeated for every suchlayer. Fully connected layers applied at the end of or after
repeated each layer. Fully connected layers is the same as
regular multilayer perceptron. By propagating these eachlayer, CNN was feature extracted from input images.

3.3. CNN settings
Fig 1. shows CNN model that used in this experiment. Arrows in Fig 1. show weights, and number of under eachboxes show unit number. The number of input units setting
to same as number of input image pixels. The number of
output unit setting to same as number facial expression
classes. As the facial expression recognition result, using
the maximum value in output layer units.
3.4. Result
The results of this experiments shown in Table. 2. We haveobtained an recognition score 57.02%; Happiness and Surprise facial expression recognition score was about 70%.
In contrast, Fear, Sad and Neutral score was below 52%.
Also these recognitions from only image data is seemed tobe difficult. We have obtained an Disgust recognition score0% because of Disgust data was less then other facial expression data.

3. Experiments
3.1. FER-2013 Dataset
We have selected Facial Expression Recognition 2013
(FER-2013) dataset [6]. FER-2013 was created by Pierre
Luc Carrier. This dataset was created using the Google image search API to search for images of faces that match a
set of 184 emotion-related keywords like “blissful”, “enraged” etc. Each images included dataset is cropped
around a face, and cropped images were then resized to
48x48 pixels and converted to grayscale. Table. 1. present
the details of the dataset. Facial expression We focused on
are Angry(An), Disgust(Di), Fear(Fe), Happiness(Ha),
Sadness(Sa), Surprise(Su) and Neutral(Ne).

Table 2. Confusion matrix of facial expression recognition
Table 1. Detail of FER-2013 dataset© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

225

Feature Acquisition From Facial

Fig 2. Visualized the 25-dimensional intermediate output ofrecognized Happiness. The 25-dimensional output wascompressed to 2-dimension output by Principal Components Analysis. Plotting first and third principal components. 48.959% is contribution rate, it is show that 48.959%
information remained by PCA. Labeled by k-means.

3.5. Analyzing the intermediate outputs
As mentioned before, CNN was extracted feature at eachlayers, and these result of feature extraction was influenceon the recognition. Thus, it seems that recognition accuracy is higher, feature extraction is better result. From thisreason, we focused on Happiness, and analyzing the intermediate output values. By this analyzing, we reveal thatextracted feature from Happiness data.
In this experiment, there are image that recognized 682
Happiness images in 895 labeled images. We are focusedon the Happiness images and intermediate output values
previous layer for the output layer. Result of compressedintermediate output to two dimension is shown in Fig 2.
From Fig 2, It can be seen that some clusters are overlapped with each other like cluster 1 and cluster 10, but
independent cluster like clusters 5 and cluster 6 is also exists. As a result, we investigated the difference in the distinguishable clusters 5 and cluster 6, we examine the extracted features from the result of this investigation. Examination method is simple, we checked that whether the intermediate outputs connoting a pattern.
All of 25 units intermediate output of cluster 5 and cluster
6 are shown in Fig 3, and also examples of image and intermediate outputs include in cluster 5 and cluster 6 areshown in Fig 4. As a results, It can be seen that the intermediate outputs of cluster 6 was bigger than cluster 5. As

Fig 3. Intermidiate outputs of all 25 units. Upper line graphis shown intermediate output of cluster 5, lower line graph isshown intermediate output of cluster 6. Red bodal line isaverage value of 25 units. Other lines represent one input reconstructed Happiness facial image.

far as we have confirmed detail of outputs in cluster 6, alldata was Happiness face, and is commonly mouth was
open, as shown in Fig 4. On the other hand, cluster 5 included Happiness face both opened mouth and closed
mouth. Most of cluster 5 data was Happiness face withclosed mouth. We focus on the intermediate output of each
unit in data of cluster 6 (Fig 3), for example, it can be seenthat the output value of 21th unit is large. Also in Fig 4, theHappiness face with opened mouth and closed mouth weredifference in 21th unit output value. In Fig 4, it is selected
some example from cluster 5 and cluster 6. It was also observed that this result of 21th unit output value was thesame tendency as Happiness face with opened mouth inother cluster. In these results, the 21th unit output have closure connected in shape of mouth open. We conclude that

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan226

Taiki Nishime, Satoshi Endo, Koji Yamada, Naruaki Toma, Yuhei Akamine

References

CNN have learned the feature of facial expression aboutmouth.

[1] Paul Ekman, W.V. Frisen, “UNMASKING THE FACE”(1975)[2] Hiroki NOMIYA, Teruhisa HOCHIN, “Facial Expression
Recognition using Feature Extraction based on Estimation of Useful Features” (2011)[3] VICTOR-EMIL NEAGOE, ANDREI-PETRU BRAR,
NICUSEBE, PAUL ROBITU, “A Deep Learning Approach for Subject Independent Emotion Recognition
from Facial Expressions”, Recent Advances in Image,
Audio and Signal Processing, pp.93-98 (2013)[4] “Convolutional Neural Networks (LeNet) – DeepLearning 0.1 documentation”, LISA Lab. (2013)[5] Alex Krizhevsky, “Learning Multiple Layers of Featuresfrom Tiny Images”, 2009.
[6] Goodfellow, Ian J, .et al. “Challenges in Representation
Learning: A report on three machine learning contest”
Neural Information Processing. Springer Berlin Heidelberg, 2013.

3.6. Future work
In this paper, We carried out the emotion recognition fromfacial expression image using a CNN. As a result, we haveobtained an average emotion recognition score of 58%;
two emotions (Happiness, Surprise) recognition score wasabout 70%. From the result of the analyzing intermediate
outputs of CNN, we confirmed specific intermediate outputs was likely to be reflected in shape of mouth. In thisexperiments, though using the simple method that analyze
the intermediate outputs pattern, it is considered to effective method that analyzing the extracted feature from input
data. In the future work, we will try to analyze larger intermediate outputs than in this experiment. And we revealed
that method of feature extracted of deep layer.

Fig 4. Example of All of 25 units intermediate
output of cluster 5 and 6.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan227

Estimating Age on Twitter Using Self-Training Semi-Supervised SVM
Tatsuyuki Iju

Graduate School of Information Engineering, University of the Ryukyus
1 Senbaru, Nishihara-cho, Nakagami-gun, Okinawa, Japan
Satoshi Endo, Koji Yamada Naruaki Toma, Yuhei Akamine

School of Information Engineering, University of the Ryukyus
1 Senbaru, Nishihara-cho, Nakagami-gun, Okinawa, Jaapn
E-mail: k148582@ie.u-ryukyu.ac.jp Endo@ie.u-ryukyu.ac.jp
http://www.u-ryukyu.ac.jp/en/

Abstract
The estimation methods for Twitter user’s attributes typically require a vast amount of labeled data. Therefore, anefficient way is to tag the unlabeled data and add it to the set. We applied the self-training SVM as a semi-supervised
method for age estimation and introduced Plat scaling as the unlabeled data selection criterion in the self-training
process. We show how the performance of the self-training SVM varies when the amount of training data and theselection criterion values are changed.
Keywords: Twitter, Age, Semi-supervised learning, Self-training, SVM, Plat scaling

experiments and present and analyze the results. Finally,
we describe our findings and the extension of this work.

1. Introduction
Nowadays, the use of Twitter as a social activity sensor
has become popular trendy. Although it is more
efficient to consider attribute differences such as user
age and gender for analysis, users rarely share theirpersonal information to the public. Therefore, a variety
of methods for estimating Twitter user’s attributes has
been studied[1][2][3]. However, these methods require avast amount of labeled data. Since collecting labeleddata is typically a high cost work, the estimation method
is efficient when unlabeled data is labeled and used asadditional data. We investigate a method for buildingclassifier by using self-training SVM, which is acombination of semi-supervised method, self-training
and SVM. In this study, we formulate age estimation asa binary classification problem where user is labeled asunder 30 or over 30. The method for user vectorization
is simple bag-of-words model and all the tweets treatedas data were Japanese tweets. In section 2, we describe
self-training SVMs. In section3, we describe the2.

Self-training SVM algorithm

We describe about the method of building our classifierby using a self-training SVM. Self-training is a simple
semi-supervised learning algorithm, with examples ofapplications started by Scudder[4]. The standard
approach for self-training is as follows.
i.
By Using underlying learning algorithm, train aclassifier from the labeled data set.
ii.
Label a part of the unlabeled data set according to
the classifier, and retrain it, with the newly labeleddata as an additional training set.
We construct our classifier for Twitter user’s age
estimation by using a self-training SVM, in which theunderlying learning algorithm is SVM. Furthermore, we
introduce Plat scaling as a criterion for selecting users
appropriately from the unlabeled set in order to be© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

228

Tatsuyuki Iju,Satoshi Endo, Koji Yamada Naruaki Toma, Yuhei Akaminelabeled. It is expected that poor quality data is filtered out
by that criterion. Plat scaling[5] is a method used formodeling a function that returns posterior probability
𝑃(𝐴𝑔𝑒|𝑋) in which user 𝑋 is in the 𝐴𝑔𝑒 class, according
to classifier’s the decision function.
The steps of the self-training SVM algorithm where
sample set {𝑋𝑖 , 𝑖 = 1, … , 𝑁𝐼 } and sample set {𝑋𝑢 , 𝑢 =1, … , 𝑁𝑈 } belongs to training set 𝐹𝐼 and unlabeled set 𝐹𝑈 ,respectively.
i. Using 𝐹𝐼 , we train a SVM and obtain probability
𝑃(𝐴𝑔𝑒𝑙 |𝑋𝑢 ) ∈ {𝑃(𝐴𝑔𝑒𝑙 |𝑋𝑢 ), 𝑙 = 1, … , 𝑁𝐴 } thatsample 𝑋𝑢 belongs to 𝐴𝑔𝑒𝑙 by Plat scaling.
ii. Define 𝐹𝑢∗ containing all 𝑋𝑢 with at least one of
the 𝑃(𝐴𝑔𝑒𝑙 |𝑋𝑢 ) ≥ 𝑡ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑 . Furthermore,
Define sample in 𝐹𝑈 ∗ as 𝑋𝑢∗ .
iii. Define new𝐹𝐼 as 𝐹𝐼 = 𝐹𝐼 + 𝐹𝑈∗ . The label of 𝑋𝑢∗
is predicted as the 𝐴𝑔𝑒𝑙 for which 𝑃(𝐴𝑔𝑒𝑙 |𝑋𝑢∗ )is highest. Furthermore, Define new 𝐹𝑈 as 𝐹𝑈 =𝐹𝑈 − 𝐹𝑈 ∗ .
iv. Repeat i. ~ iii. until 𝐹𝑈 ∗ cannot be defined.

performance with the test set. Additionally, in order toprovide the baselines for these classifiers, we built otherclassifiers by using normal SVM for each of four training
set arrays and measured their performance the same way,
we did for the self-training SVM ones. The performancemeasurement was done by the five-fold cross validation.
The number of users in the test set was 480 and thenumber of users in the unlabeled set was 1200, In
addition, the number of users from under 30 and over 30
classes were balanced in both the training and test sets.
The way for user vectorization was simple bag-of-words
model, at the time of classifier training and user’s age
prediction. Prior to proceeding the experiments, it wasnecessary to set hyper parameters of SVM and featuresin a bag-of-words representation. Therefore, we
performed a grid-search, set the kernel and cost
parameters of SVM as linear and 1000 respectively. As
for the features, we set the 158 top-ranked words by 𝜒 2score which appeared in user tweets from the training set.
Results

3.2.

Table 1: Results for the normal SVM
Number of the users in training set
76

256

376

Age classPrecision

Under 30
0.613

Over 30
0.792

Under 30
0.638Over 30
0.825

Under 30
0.647

Over 30
0.834

RecallF measureMean
F measure

0.882
0.723

0.441
0.563

0.896
0.745

0.492
0.616

0.898
0.752

0.509
0.632

0.643

0.680

0.692

Experiments

3.
3.1.

Experimental contents and settings

We defined age as two classes, under 30 and over 30. Thisdefinition is the same as Rao’s research[1]. we carried out
some experiments in order to evaluate the performanceof the Self-training SVM for the Twitter user’s age
estimation. By using self-training SVM, we built aclassifier for each of the nine training set arrays (threesets of different number of users containing 76, 256 and376 with three possible selection criterions of 0.5, 0.7 and0.9 respectively). Then, we measure the classifier’s© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

229

Estimating Age on Twitter

Table2: Results for the training set with 76 number of users
Unlabeled data selection criterion

Age classPrecision
RecallF measureMean
F measure

0.5

0.7

0.9

Under 30
Over 30
0.614
0.792
0.882
0.442
0.723
0.563
0.643

Under 30
Over 30
0.610
0.793
0.888
0.430
0.722
0.554
0.639

Under 30
Over 30
0.625
0.791
0.901
0.473
0.728
0.590
0.659

Table3: Results for the training set with 256 number of users
Unlabeled data selection criterion

Age classPrecision
RecallF measureMean
F measure

0.5

0.7

0.9

Under 30
Over 30
0.638
0.825
0.896
0.492
0.745
0.616
0.680

Under 30
Over 30
0.630
0.818
0.895
0.473
0.739
0.599
0.669

Under 30
Over 30
0.645
0.808
0.876
0.512
0.742
0.629
0.686

Table4: Results for the training set with 376 number of users
Unlabeled data selection criterion

Age classPrecision
RecallF measureMean
F measure

0.5

0.7

0.9

Under 30
Over 30
0.647
0.834
0.898
0.510
0.752
0.632
0.692

Under 30
Over 30
0.649
0.825
0.891
0.516
0.750
0.634
0.692

Under 30
Over 30
0.657
0.829
0.890
0.534
0.756
0.650
0.703

First, we describe about the results for the normal SVMs.
Table 1 shows the performance of the normal SVM
classifiers. From Table 1, we can assure that theclassifiers can improve their performance as the size ofthe set is increased, at least when the size of the given
training set is in the 76 to 376 range. According to thisresult, as well as for self-training SVM, it is expected that
performance can be improved by utilizing unlabeled set
with the training set size within the same range size.
Additionally, regardless of the training set size, it wasobserved that precision is high and recall is low for theunder 30 class and, oppositely the precision is low andrecall is high for the over 30 class. F measure was betterfor the under 30 class than for over 30 one. It is, basically,that the under 30 class user was easier to predict than theover 30 class one. As for the results about self-training
SVM, Table 2, 3, 4, and correspond to the prediction
results from classifiers built from 76, 256 and 376 users
training sets, respectively. The classifier with the highest
improvement from baseline was the one with 76 users
training set with a selection criterion of 0.9 (improvement
was 0.032 point for the over30 class recall). Classifier
from the 376 users with a selection criterion of 0.9 was
the second highest (improvement was 0.025 point for theover 30 class recall). Although the recall improvementfor over 30 class is remarkable, precision for the over 30
class and both precision and recall for the under 30 classsubtly improve or diminish as the mean F measure© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan230

Tatsuyuki Iju,Satoshi Endo, Koji Yamada Naruaki Toma, Yuhei Akamine

Table 5: Results for the training set 76 number of users (labeling)
Unlabeled data selection criterion
0.5
Age classPrecision
RecallF measureMean
F measure
Labeling rate

Under 30
0.621
1.0
0.766

Over 30
0

0.7

0.9

Under 30
Over 30
0.722
0.699
0.890
0.425
0.796
0.524
0.660

Under 30
Over 30
0.752
0.705
0.90
0.440
0.820
0.537
0.678

0.97

0.83

1.0

merely improves. Table 5 shows the labeling accuracy forthe 76 users set self-training and ratio of the labeled set
for all the unlabeled set. Precision, F and mean F
measures for the over 30 class were not defined, because
the entire unlabeled set is filtered and eventually labeled
as the under 30 class. We could observe better labelingaccuracy for higher selection criterions and, in contrast,
for bigger selection criterions the amount of labeled datafrom the unlabeled set is reduced. However, as indicatedby Table 2 through Table 4, performance was better for aselection criterion of 0.5 than 0.7 for all of classifiers
excluding the 376 users training set, although
performance for a selection criterion of 0.9 was best for
all classifiers. This result is inconsistent with the fact thatthe labeling error is more frequent for selection criterionof 0.5 than 0.7 as indicated in Table 5. For that reason, it
is implicated that users are easier to predict if selectedfrom a self-training process with a selection criterion of
0.7 than a 0.5 one, since the classifier with a selection
criterion of 0.7 is more strongly affected by labeling error
than a 0.5 one. In addition, it is inferable that, for aselection criterion of 0.9, the labeling error rate is sosmall that the classifier achieves in improving its
performance.
4.

baseline for training set with a size of 76 and 376
respectively with a selection criterion of 0.9. For future
works, we will investigate the relation between theselection criterion and performance of self-training SVM
as well as explore the way to improve them.
References
1. Rao, Delip, et al. "Classifying latent user attributes intwitter." Proceedings of the 2nd international workshop on
Search and mining user-generated contents. ACM, 2010.
2. Burger, John D., et al. "Discriminating gender on Twitter."Proceedings of the Conference on Empirical Methods in
Natural Language Processing. Association for
Computational Linguistics, 2011.
3. Pennacchiotti, Marco, and Ana-Maria Popescu.
"Democrats, republicans and starbucks afficionados: userclassification in twitter." Proceedings of the 17th ACM
SIGKDD international conference on Knowledge
discovery and data mining. ACM, 2011.
4. Scudder, Henry J. "Probability of error of some adaptive
pattern-recognition machines." Information Theory, IEEE
Transactions on 11.3 (1965): 363-371.
5. Platt, John. "Probabilistic outputs for support vector
machines and comparisons to regularized likelihood
methods." Advances in large margin classifiers 10.3
(1999): 61-74.

Conclusions

In order to evaluate self-training SVM for Twitter user’s
age estimation, we construct a classifier by for each of
the twelve training set arrays of (three sets of different
users containing 76, 256 and 376 with three possible
selection criterion of 0.5, 0.7 and 0.9 respectively). Then
we evaluate the performance of the classifiers with test
set. As a result, in the recall for the over 30 class, it wasobserved a 0.032 and 0.025 point of improvement from© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan231

Interactive Musical Editing System to Support Human Errors
and Offer Personal Preference for an Automatic Piano
Kenji Tsunenari

Department of Mechanical Information Science and Technology, Kyushu Institute of Technology
680-4, Kawazu, Iizuka-City, Fukuoka, 820-8502, Japan
Email: ktsune@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
E-mail: haya@ mmcs.mse.kyutech.ac.jp

Abstract
We have developed a system that allows a piano to perform automatically. In order to play music in the manner of alive pianist, we must add expression to the piano’s performance. In the case of music, there are often 1,000 or morenotes in the score, requiring that an editor spend a huge amount of time to edit. Therefore, we have developed aninteractive musical editing system that utilizes a database to edit music more efficiently.
Keywords: Automatic Piano, Knowledge Database, Computer Music, Music Interface

Moreover, in the case of piano music, there will often be1000 or more notes in the score of even a short piece ofmusic, and the editor must spend a huge amount of timeto accurately simulate an actual emotionally expressive
performance that a highly skilled pianist could give.
Moreover, a highly skilled pianist is able to play anunfamiliar piece of music by sight, even if theperformance is not completely in accord with anintended specific musical interpretation. Current
computing systems cannot perform a new piece ofmusic by sight, and thus they cannot simulate a humanpianist’s musical expression. Therefore, we havedeveloped an interactive musical editing system that

1. Introduction
We have developed a system that allows a piano toperform automatically. In this system, 90 actuators havebeen installed on the keys and pedals of a grand piano.
These actuators execute key strokes and pedal
movements to govern the piano’s performance. (SeeFig.1)In order to develop an automatic piano that plays
music in the manner of a live pianist, we have to add
expression to the piano’s performance. Essentially,variations in tempo, dynamics, and so on are needed inorder to arrange the respective tones in a desired way.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan232

Interactive Musical Editing Systemutilizes a database in order to edit music more
efficiently 1.
In a current research, MIDI data regarding theperformances of highly skilled pianists has been
analyzed in order to observe the stylistic tendencies of
their performances. The results showed that phrases
having similar patterns in the same composition were
performed in the similar style.
We developed a system that searches for similarphrases throughout a musical score and infers the styleof the performance. Here, we proposed a method using
Dynamic Programming (DP) matching as a way tosearch for similar phrases. In our interactive musicalediting system, we have created the Score Databasewhich contains information regarding a musical score.
This database contains a field called “Note Value,” inwhich data indicates the type of note--e.g., a quarter
note, a triplet, and so on. This system converts notes
into character strings using the “Note Value” data.

process with regards to similar phrases using the bestalignment and database.
In this paper, we describe the results of searching forsimilar phrases using DP matching and inferring forthem using DP matching and database.
2. Searching for Similar Phrases
As a result of the analysis, it was found that phrasesof the same pattern existing in the same tune are
performed in a similar expression. This time, we used
DP matching to search for similar phrases.
DP matching is a technique used widely in the fieldof speech recognition, bioinformatics and so on. It has afeature that can calculate the similarity between two
words that are different in a number of characters fromeach other.
In Fig.2, the route of minimum cost in each point istaken, and the route with the lowest cost is assumed
finally to be the optimal path. The cost at that time isdefined as the distance between patterns. In this system,
this distance is handled as a threshold to judge whether
the phrases are similar to each other.
For example, if the cost moves up or to the right,
then it is increased by 1. If it moves to the upper right,
then it does not increase. Also, if the characters do notcorrespond in each point, then the cost is increased by 5.

Fig.1. The automatic piano.
Fig.2. DP Matching

In addition, the system computes DP matching using
the character strings and calculates the degree of
disagreement between these strings. It uses an index tojudge the resemblance between the strings. For its
method of inferring performance expression, it uses thebest alignment for DP matching, which enables it toexpress the best correspondence between notes. In orderto edit music more efficiently, we must consider
dynamic marking, beat and so on and we createddatabase contains them. We developed an inferring

3. Musical Editing Support System
3.1. System Architecture
The structure of the system is shown in Fig.3, Theuser edits music via the user’s interface on a computer
display. The user can also access a database that hasmusical grammar, the user’s preferences, and so on. As
a result, edition work is reduced and efficient editing
becomes possible.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan233

Kenji Tsunenari, Eiji Hayashi

Fig.4. Automatic translation with database
Fig.3. Structure of the editing system

3.3.1 Musical Rules Database
3.2. Format of Performance Information

This database contains the architecture of musicalgramar necessary to interpret symbols in musicalnotation. It is composed of five tables containing“Dynamics marks”, “Articulation marks”, “Symbol of
Changing Dynamics or Changing Tempo” (symbol that
affects the speed of a note or the increase or decrease of
the volume), “Time signature”, and “Tempo marks”.
Analyzing a music symbol according to its usage allows
efficient information processing by the system.

The automatic piano that we have developed uses a
music data structure that is similar to MIDI. We defined
performance information, dividing it into two
categories: the notes and the pedals. The note
information is comprised of the six parameters involvedin producing a tone: “Key” (note), “Velo” (velocity),“Gate”, “Step”, “Bar” and “Time”. “Velo” is the
dynamics, given by the value of 1-127. “Gate” is theduration of the note in milliseconds. “Step” is theinterval of time between notes, and it also exhibits
tempo. “Bar” is the vertical line placed on the staff todivide the music into measures.
The pedal information is comprised of four parameters:“Key” (indicating the kind of pedal: “Damper” or“Shifting”), “Velo” (the pedaling quantity), “Time” (theduration for which the pedal is applied)”, and “Bar”.

3.3.2 Score Database
This database has symbols including time signatures,notes, rests and so on in standard musical notation.
Symbols were pulled together in order of bars, and bar
symbols were arranged in a time series. Performance
expression in itself is only information such as pitch,strength, and length and concerns only the enumerationof a sound. Because the identification of each sound isdifficult, editing of the performance expression isdifficult. By adding the Score Database’s information to
performance expression, we can connect each note to its
enumeration. In doing so, it becomes easy to edit eachphrase. This database consists of three tables, the“Element table” (showing the position of the note andthe composition of the chord), the “Symbol table”(showing the position of the music symbol) and the“Same table” (showing the position of the repetition of
the phrase). The Element table contains the field “NoteValue”. Data in this field indicates the type of note, e.g.,a quarter note, a triplet, and so on. “Note Value” isexpressed by three hexadecimal numbers, which are

3.3. Editing Support Process with Database
Our system can automatically apply a roughperformance expression using Musical Rules Databaseand Score Database. (See Fig.4)
In addition, the system has Preference Database,which sores the editing characteristic of the user.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

234

Interactive Musical Editing System

the “Oppitz Data” more than it resembles the “Existing
result”

shown in Fig.5.

(a) Input phrase(b)Similar phrase
Fig.6. Phrases for inferring
Table 1. Results of inference
StepRate
No. Existing result

New result

Oppitz data

1
2
3
4
5

0.8962
1.21150.91840.9144
0.97120.7596
1.29810.82400.6992
0.69231.00001.2115
1.16481.04401.1500Fig.5. Note Value

3.4. Beat DatabaseWe created Beat Database to edit music more
efficiently. Beat Database is stored data which isGerhard Oppitz’s performance of beat expression.
Using it, only step is inferred to change tempo.

Fig.7. Results of inference

5. Conclusion
4. Inferring Result

We developed an interactive musical editing system
to edit music more efficiently. This system is composedof a "Searching system" and an "Inferring system”. The
Inferring system infers the emotional expression in a
performance of similar phrases by referring to the
databases automatically, greatly reducing the timeneeded for editing. Moreover, the interactive musicalediting system provides edited phrases that resemble the"Pianist Data," which are the actual emotional
expressions in a performance by a skilled human pianist.

In Fig.6a is an input phrase used to search similarphrase for comparative experiments. Fig.6b is searchedsimilar phrase. It is inferred two methods and results are
outputted. First method, similar phrase is inferred, not touse Beat Database. The second method, Beat Databaseis used. The two results and data that performed bypianist in the similar phrase are compared to confirm
improvement of the result.
The results of the experiment are shown Table1 andFig.7. The results provide three values: the “Existing
result” which don’t use Beat Database, the “New result”
which use Beat Database and the ”Oppiz data” which isthe actual emotional expression in a performance by a
Oppitz in the similar phrase. “No.” means note number.
“No.1” means the first note in the phrases.
We have consistently found that the emotional
expression represented by the “New result” resembles

References
1. E. Hayashi et al, Behavior of piano-action in a grandpiano.I, Journal of acoustical Society of America,
Vol.105, pp.3534-3544, 1999.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan235

Geometric parameters measurement of wheel tread based on line structured light
Jiwu Wang

School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, China
Zhijing Jian, Yaodong Li

School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, China
Masanori Sugisaka

Alife Robotics Corporation Ltd, Japan and Open University, United Kingdom
E-mail: jwwang@bjtu.edu.cn; ms@alife-robotics.co.jp
www.bjtu.edu.cn

Abstract
The positioning precision is important in the processing of geometric parameters measurement technology of wheel
tread. In this paper, a new method with no-contact measurement based on line structured light is given to solve thisproblem. Here traditional mechanical locating method is used as a rough reference. Moreover, some digital image
processing techniques are developed for each of the key dimensions of measurement. According to the feature points(particularly, the key points that have not been worn) and lines extracted effectively on the wheel tread, precise
positioning is realized. In this paper, the experiments show that the proposed method is feasible to implement noncontact measurement of the wheel tread, wheel flange thickness and rim width. It also ensures the accuracy of
positioning and analyses the factors of measurement error.
Keywords: no-contact measurement, line structured light, wheelset tread, image segmentation

At present, the wheelset detection method can be roughly
divided into the contact and non-contact measurement.
The traditional methods still depend on the special
mechanic tools like artificial calipers. Albeit its simple
principle, there are the problem of high labor intensity,
low efficiency and low accuracy[2]. With thedevelopment of science and technology, non-contact
measurement appears and is widely applied.
In this paper, the experimental model is established andthe method with no-contact measurement is adopted
which is based on line structure light. The problem, led
by the precision of positioning between laser device andwheelset, is attempted to solve. The geometric

1. Introduction
With the rapid development of rail traffic, the issues oftrain safety are becoming increasingly prominent. A
railway wheelset, which is indispensable and the mostimportant component, bears all the static and dynamic
load from the vehicle. Since it directly contacts with therail, gradually and inescapably, the wheelset is worn
down by the interactions between wheel and rail, whichcause the undermining of critical dimensions. Moreover,the safety and operation quality are seriously affected.
Hence, it is necessary to measure geometric parametersof train wheelset tread periodically[1].

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

236

Jiwu Wang, Zhijing Jian, Yaodong Li, Chao Yang, Masanori Sugisaka

parameters, measured by using CCD to grab images andprocessing them with visual C++ and OpenCV, areflange height, flange width and wheel diameter.
2. Experimental principle
This part, the definition of wheelset’s parameters andexperimental principle are intuitively introduced.
2.1. Definition of wheelset’s parameters
It is necessary to illuminate the definition of wheelset’s
parameters.

Fig. 2. one wheel of wheelset

2.2. Experimental system and principle
Fig. 3 shows the experimental device. The camera A andthe camera C are used to positioning and the camera B isused to grab images of tread. Lasers and cameras are
relatively fixed and calibrated.

Fig. 1. the critical parameters

Fig. 1 shows the critical parameters of wheelset. the left
is the medial surface of wheel rim (ostensibly, it is a linein the image). There is a point on the tread and the
distance is 70mm to the medial surface of wheel rim, and
it is customarily called the base point. Tread is thecontacting area between wheelset and railway. Flangebase line is the horizontal line 12mm above the base point.
Flange thickness H is the horizontal thickness along
flange base line. Flange height h is the height between
flange base line and flange top. Rim width L is the
distance between the medial surface and the lateralsurface of wheelset[3].
Fig. 2 shows the rolling circle passing the base point on
the tread. Wheel diameter D is the average diameter ofrolling circle. In this paper, the key parameters H, h andD, are mainly concerned.

Fig. 3. The experimental device

The traditional method based on mechanism is regarded
as slow and subjective to a great extent in the practicalmeasurement. In order to ensure the light plane
perpendicular to the tread, the more accurate method is
proposed.
The geometric parameters of wheel tread are measured
through laser scanning method in which linear lightsource is projected on the surface of work piece [4]. Laser
vertical incidence and vertical imaging is applied to themeasuring system.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

237

Geometric parameters measurement ofcenter of camera and the center of axis is h1, and to thebase point is h2, so the wheel dimeter D, as Fig.2 shows,can be got:
D = 2(h1 − h2 )The flange width H and flange height h can be obtained
after image processing. The key to implement themeasurement as follows:
1) the collocation of the cameras and the linear structured
light source.
2) calibration method
3) the extraction accuracy of light stripe.
3. Image processing
As Fig.6 shows, there are lots of noises. In order toremove noises, image segmentation is adopted. The
principle of image segmentation is to separate out regionsof an image corresponding to objects which we want toanalyze. In this paper, this separation is based on thevariation of intensity between the object pixels and thebackground pixels. To differentiate the pixels, we areinterested in from the rest (which will be eventually
rejected), we perform a comparison of each pixel
intensity value with respect to a threshold.
Filtering is perhaps the most fundamental operation ofimage processing and computer vision. In the broadest
sense of the term “filtering”, the value of the filteredimage at a given location is a function of the values of the
input image in a small neighborhood of the same location.
In the practical processing, sometimes, the filters do not
only dissolve the noise, but also smooth away the edges.
To avoid this, at certain extent at least, the bilateral filter
is recommended. In an analogous way as the Gaussian
filter, the bilateral filter also considers the neighboring
pixels with weights assigned to each of them. These
weights have two components, the first of which is thesame weighting used by the Gaussian filter. The second
component takes into account the difference in intensity
between the neighboring pixels and the evaluated one.
Finally, the processing result is showed in the Fig.7. As
it shows, image segmentation works well and bilateralfilter makes a great contribution. Then, according to theresult, we can get what we want to.

Fig.4. triangulation measurement

Fig.5. Diagram of positioning

According to Fig.4, θ is the angle between QO and PO,
the height of point H on the work piece can be calculated
as follows formula:(𝑂𝐼 − 𝑓)∆sinα
OH =𝑓𝑠𝑖𝑛θ + ∆sinαcosθ
In the above formula, f is the focal distance. The point on
the surface of measured objects a three-dimensional
coordinate reflected in the CCD’s image plane, which
presents as two-dimensional image, so the threedimensional coordinates can be reconstructed by using
the optical coordinate.
As Fig.5 shows, when the light is perpendicular to theaxis, the distance between the highest point of axis andthe optical center of camera can be obtained. If the
distance AC is equal to BD, it can ensure two axes
parallel. Besides, when AC or BD is minimum, thepositioning is realized. Then we can use image
processing to easily get the geometric parameters ofwheelset. we assume that the distance between the optical© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan238

Jiwu Wang, Zhijing Jian, Yaodong Li, Chao Yang, Masanori Sugisaka

5. Conclusions
The method based on line structured light with no-contact
measurement is proposed. Some digital image processing
techniques, such as image segmentation and bilateralfilter which not only dissolve the noise, but also reserve
the edges, are used to position and measure. The error
also is analyzed in order to get higher accuracy.
Eventually, the experiments show that the proposed
method turn out to be feasible to implement non-contact
measurement of the wheel tread, wheel flange thickness
and rim width.
Fig.6. Wheel tread images: The initial image from camera.

References
1. Xing Z, Chen Y, Wang X, et al. Online detection system
for wheel-set size of rail vehicle based on 2D laser
displacement sensors[J]. Optik - International Journal forLight and Electron Optics, 2016,127(4):1695-1702.
2. Zhang W, Zhang Y, Li J, et al. The defects recognition ofwheel tread based on linear CCD[C]// Nondestructive
Evaluation/Testing (FENDT), 2014 IEEE Far East Forumon. IEEE, 2014:302-307.[Z].
3. YP Luom,LI Xue lei,F Wang,Y Yang. Measurement ofwheel tread parameters with linear structured
light[J].JOURNAL OF RAILWAY SCIENCE AND
ENGINEERING,2005,2(3):7577.DOI:10.3969/j.issn.1672-7029.2005.03.014. [Z].
4. Gomez E, Giménez J G, Alonso A. Method for the
reduction of measurement errors associated to the wheel
rotation in railway dynamometric wheelsets[J]. MECHSYST SIGNAL PR, 2011,25(8):3062-3077.

Fig.7. Wheel tread images after pre-processing

4. Error Analysis
In the process of measuring the wheel geometry
parameters, the measurement precision is influenced bymany factors. Usually, sensor position error, light angle
error, the extraction accuracy of line structure light stripe,and so on.
All in all, errors include systematic errors and random
errors. System error can be obtained by the structure
parameters of the measurement accurate systemcalibration. Besides, it can be eliminated with thecomparison between stand wheelset and measured result.
Random error is mainly caused by laser sensor
measurement error and the extracted line structured light
stripe center, which can’t be completely eliminated.
For the running heads for the authors names, please apply
the following rules:© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan239

Study on the ORB algorithm in the application of Monocular SLAM
Jiwu Wang1

School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University
Beijing 100044, China1
Shunkai Zheng1

School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University
Beijing 100044, China1
Sugisaka Masanori2

Alife Robotics Corporation Ltd, Japan and Open University, United Kingdom2
E-mail: jwwang@bjtu.edu.cn1; ms@alife-robotics.co.jp2

Abstract
In view of reducing the accumulative error, we perform loop closing based on PTAM in our Monocular SLAM.As
this method relies on extracting natural environment features, we chose ORB algorithm as the feature extraction
and matching. We demonstrate that ORB features have enough recognition power to enable place recognition fromsevere viewpoint change and they are so fast to extract and match (without needing multi-threading or GPU
acceleration) that enable real time accurate tracking and mapping. Through to outdoor scene experiment, we
validate the algorithm performance.
Keywords: SLAM; ORB; Monocular Vision；Loop closing

unknown and multi view geometry is necessary to solve
the problem. Stereo and RGB-D cameras have a range
in which they can recover the depth but in certain
situations such as sunny day in the outdoor they are not
not practical and monocular techniques are stillnecessary.
In this work we focus on ORB and loop closing, two
open problems that are essential for real SLAM
applications.
ORB features are oriented multi-scale FAST corners
with a 256 bits descriptor associated2. As binaryfeatures, they are extremely fast to compute and match,while they are highly invariant to viewpoint. This
allows matching them from wide baselines, boosting theaccuracy of BA.
Loop closing is the task of detecting when a robot is

1. Introduction
Monocular SLAM has been developed from the initial
filtering approaches to the most modern key framebased SLAM systems. The key frame-based SLAM
system is more useful and accurate in the large
environment. One of the most representative keyframebased systems is Parallel Tracking and Mapping,
PTAM1.The map points of PTAM correspond to FAST
corners matched by patch correlation. This makes thepoints only useful for tracking but not for place
recognition. In fact PTAM does not detect large loops,
and the relocalization is based on the correlation of low
resolution thumbnails of the keyframes, yielding a low
invariance to viewpoint.
Using just one camera is a more complex problem
because the depth of observed features in the image is© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan240

Jiwu Wang, Shunkai Zheng, Sugisaka Masanori

Revisiting a previously mapped area, in order to correct
the error accumulated in the robot trajectory duringexploration 3. So, how to determine whether a newframe in the image sequence has occurred? One is basedon the robot's position, which is adjacent to the previous
position; the two is the appearance of the image, which
is similar to the previous key frame. In this paper, wechoose the last one.

scaling, illumination and processing speed. Therefore
design the following experiment to compare the ORB
features extraction performance.
1. Generating and matching method of test data:
Taking image sequence Fig2 as original image thatphotoed in Beijing Jiaotong University mechanicalbuilding Z706

2. System Overview
The system is composed of three main tasks: tracking,local mapping and loop closing that run in parallel in a
multi-core machine
Fig 1 shows a scheme of the different building blocksof the system
Cameracalibration

Image frame

ORB Feature
extraction andmatching

Systeminitialization

Fig2 Left: original frame Right: next frame of originalframe
Then using the affine transformation of the specified
argument and mathematical model including Gaussian
blur, rotation, scaling, illumination for Right frame togenerate the sequences，Analog cameras in thecomplex scene pictures taken in the process of moving,close to the performance of the real data
2.Correct judgment of matching points：
Use RANSAC find out the Homography Matrix
between the generated sequences and original frame,then the sequences frame of the feature points projection
to original frame, calculate the size of the projection
error, if less than the threshold value of set (thisexperiment using threshold for 2), argues that the pointis the right match, otherwise it is a false matching points.
The relation between the calculated is:

PnP imageinversion

Key frame
extraction

Prune keyfram

Local Bundle
Adjustment

Update thelocal area

BOW process

Loop closing

Fig.1. System Overview

2.1. Tracking
The map initialization is performed finding a planar
scenes or an essential matrix from two near frames in an
automatic process. Once an initial map exists, thetracking estimates the camera pose with every incoming
frame. Next we describe each step of the tracking

ratioMatch correctMatch
100% （2-1）
Match

2.1.1 ORB extraction and tracking
The first step is to extract ORB features in the frame.
At first a scale image pyramid is computed and FAST
key points are detected at each level, then only a subsetof these key points will be described. If tracking was
successful in the last frame, the tracking tries to get afirst estimation of the camera pose from the last frame.
Each ORB from the last frame, which has a map point
associated, searches a match in the current frame in asmall area around its previous position.
In the large scale environment, feature extraction andmatching are influenced by Gaussian blur, rotation,

Fig3 Gaussian blur

Fig4 Rotation Angle (0-360, per 20)© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

241

Study on the ORB

bag-of-words model is to extract features from theimage, the feature clustering (K-means) to the
vocabulary tree, get the image of the visual word
description vector, then calculate the similarity between
the image and the matching strategy, complete closedloop detection.

Fig5 Scaling (0.25-1.95 per 0.1)

7

5

2
3

6

4

1

KeyFrame

Fig6 Additional brightness values
Tab1 Processing time
Algorithm

Average time per
Frame(MS)Average time
Per Key point(MS)

BRISK
FREAK
ORB
SURF

58.3473845.55132.272244.560.201757
0.282354
0.267296
0.61098

Vocabulary
Tree
Bag-of-words Vector

Bag-of-words Vector
Similarity
calculation
Matching
strategy

Fig 3 show that the feature matching performance of
ORB under the condition of fuzzy is the best, it cangreatly improve the stability of the algorithm; Fig4
show that ORB is the most stable for rotation .Fig 5andFig6 show that the several feature extraction algorithms
are similar to the change of the scale and brightness in acertain range.
Tab1 show that extracting all the feature points of thewhole image, the speed of ORB is the fastest and 17
times faster than SURF，for every feature point of theimage, ORB is 3 times faster than SURF which is theslowest.
The above results and analysis show that the excellent
performance of the ORB feature extraction algorithm.
We demonstrate that ORB features have enoughrecognition power to enable place recognition fromsevere viewpoint change and they are so fast to extract
and match (without needing multi-threading or GPU
acceleration) that enable real time accurate tracking andmapping.

Loop Closing
or notFig7 loop closing detection based on bag-of-words model

TF-IDF (Frequency Inverse Document FrequencyTerm) model is used to evaluate the similarity of images.
The similarity between two bag of words vectors v1 andv2 can described as 4:s(v1 , v2 )  1 

1 v1 v2

2 v1 v2（2-1）

The loop closing thread takes K t , the last key
frame processed by the local mapping, and tries todetect and close loops. The steps are next described.
 At first we compute the similarity between the bagof words vector of K t and all its neighbors  K c in the covisibility graph and retain the lowest
score S min ;

2.2. Loop Closing

In the DBoW2 database which save all the key
frames to find the key frames whose score is notless than S min , the key framesisFig7 is a loop closing detection of visual SLAM
based on the bag-of-words .A node in the map
corresponding to the position of the robot, represented
by 1 - 7, each node with a key frame scene at thelocation of the image description. The main idea of theK s  , K s  cannot include the K c  ;By step 2 to find the key frame K t 1 associatedwith  K s 1 ,and the key frame K t  2 associatedwith  K s  2  ,if there is a key frame K p in the© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

242

Jiwu Wang, Shunkai Zheng, Sugisaka Masanori

K s  and similar to the key frame in the

3.2. Experiment on our Machine buildingK s 1 and K s 2  in the covisibility graph, takeExperiments on the image sequences generated by theroad of Mechanical Laboratory of Beijing Jiaotong
University, The actual map is as follows:

K p as a candidate key frame;


If K t and K p is supported by enough inliers, theloop with K t is accepted.

Fig10 the map of Mechanical Laboratory

The walking path is ABCDA，get the real map byscale transformation and the comparison between thetwo map：
Fig8 Use loop closing to modify the map（Laboratory inBeijing Jiaotong University）

3. Experiment and Conclusion
3.1. Experiment on public data sets
The odometry benchmark from the KITTI dataset5
contains sequences from a car driven around a
residential area with accurate ground truth from GPS
and a Velodyne laser scanner. This is a very challenging
dataset for monocular vision due to fast rotations, areas
with lot of foliage, which make more difficult dataassociation, and relatively high car speed, being thesequences recorded at 10 fps.

Fig11 Left: Generated map of points and key frame Right: thecomparison between the two map

From the above results, the results are more accurate.
In the process of the experiment, the characteristics ofthe corner are easy to be lost, and the error of the
distance is inevitable estimated. In the future, we focus
on solving the robustness of the map initialization andthe processing that easy to lost at the corner.
References
1.

G. Klein and D.Murray.Parallel tracking and mapping for small
AR workspaces. In International Symposium on Mixed and2.

Fig9 Left: points and key frame trajectory Right: map after
scale change

3.

Tab2 Results of the KITTI dataset

Sequence
KITTI 05

Dimension( m m )478x425

KFs
850

RMSE
(m)
8.23

4.

5.

The result of Sequences 05 of the KITTI dataset isshown as Tab2：The RMSE of this experiment is
8.23m.

Augmented Reality (ISMAR), 2007...
E. Rublee, V. Rabaud, K. Konolige, and G. Bradski.ORB: anefficient alternative to SIFT or SURF. In IEEE International
Conference on Computer Vision (ICCV),2011

B. Williams, M. Cummins, J. Neira, P. Newman, I. Reid, and J.
D.Tard´os, “A comparison of loop closing techniques inmonocular SLAM,” Robotics and Autonomous Systems, vol.
57, no. 12, pp. 1188–1197, 2009.
R. Mur-Artal and J. D. Tard´os, “Fast relocalisation and loop
closing in keyframe-based SLAM,” in IEEE International
Conference on Robotics and Automation (ICRA), Hong Kong,
China, June 2014, pp. 846–853.
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meetsrobotics: The KITTI dataset,” The International Journal of
Robotics Research,vol. 32, no. 11, pp. 1231–1237, 2013.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan243

Affective Human Computer Interaction
Kaoru Sumi

Future University Hakodate,
Hakodate, Hokkaido, Japan

E-mail: kaoru.sumi@acm.org

Abstract
This paper introduces a study of spoken dialogue agent systems using emotional expressions as affective human
computer interaction. The paper describes an experiment investigating the effect that the expression and words ofthe agent have on people, introduces a spoken agent for customer services using expressive facial expressions and a
spoken agent for mental care using expressive facial expressions and positive psychology as application systems for
affective human computer interaction, and presents a discussion and a conclusion.
Keywords: Affective computing, facial expression, human computer interaction

Our research group has developed intelligent dialoguesystem that proactively interacts with a user according
to the user’s circumstances. As a first step, we examined
how to react with the user under several emotionalsituations through the experiment.
We performed an experiment to evaluate how the facial
expressions of an agent and the words used by the agent
affected users during agent-user interaction.
In the past, ELIZA [3] was developed to imitate a
Rogerian psychotherapist, and was an early example ofprimitive natural language processing. SimCoach [4] isa spoken dialogue agent system used in the mental careof returned soldiers. It displays a speaking counselor
agent and analyses the psychology of the user using aquestion and answer technique.
The paper describes an experiment investigating theeffect that the expression and words of the agent haveon people, introduces a spoken agent for customer
services using expressive facial expressions and a

1. Introduction
According to Media Equation [1], people treat
computers, television, and new media as real people andplaces, thereby making the users uncomfortable if anagent behaves in a disagreeable manner. In the field of
persuasion technology research [2] it is said that if auser recognizes the presence of something in a computer,he or she will respond to it according to the normalsocial rules. However, there are still many things that
we do not know about how an agent’s response affects auser during their interaction.
In the development of intelligent systems, it is important
to consider how best a feeling of affinity with thesystem and show the presence of the system that has
human-like
intelligent
functionssuchasrecommendation or persuasion. Therefore, evaluating
the interpersonal impressions conveyed by agents isvery important.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

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Kaoru Sumi

spoken agent for mental care using expressive facial
expressions and positive psychology[5][6] asapplication systems for affective human computerinteractions, presents a discussion and a conclusion.

For example, the words and facial expressions were“joy” when the user’s emotion was “joy” for the control
group. It is very interesting that when the user’s emotion
was “joy”, the agent’s words for “joy” with facial
expressions of “surprise”, “sadness”, or “fright” weremost favorable. On the other hand, when the user’s
emotion was “fright”, the agent’s words for “fright”
with facial expressions of “disgust” or “sadness” werethe most favorable.
These facial expressions were recognized as theemotion conveyed by the words and were more
empathetic and somewhat meaningful emotions. For
example, when the user’s emotion was “joy”, theagent’s words of “joy” with facial expressions of“surprise” or “fright” might have been recognized as theagent being exaggeratedly surprised at the “joy”
scenario. When the user’s emotion was “joy”, theagent’s words of “joy ” with facial expressions of“sadness” might have been recognized as the agent
being highly pleased from the heart at the “joy” scenario.
When the user’s emotion was “fright”, the agent’s
words of “fright” with facial expressions of “sadness”
might have been recognized as the agent grieving
deeply at the user’s “fright” scenario. When the user’s
emotion was “fright”, the agent’s words of “fright” withfacial expressions of “disgust” might have beenrecognized as the agent feeling deep hate at the user’s“fright” scenario.
Through these observations, we concluded that there isa rule for facial expressions: in a certain scenario,
synchronizing foreseen emotion of the user caused by
the situation will make a favorable impression. For
example, when the user has the emotion of “ joy”,
he/she wants someone to be surprised or highly pleased.
Then, showing surprised or highly pleased face
expression make the user feels favorable impression.
When the user has the emotion of “fright”, he/she wants
someone to grieve deeply or disgust. Then, showing
grieved or disgust face expression make the user feels
favorable impression.
Users want the agent to ooze synchronized theirforeseen emotion by hearing the news instead of simply
showing synchronized reaction according to emotion atpresent time.

2. Experiment investigating impressions andbehavior change caused by replies from theagent
We conducted an experiment to examine how theimpression that the user gets from the agent’s answer isaffected by the combination of facial and word
expressions. It was intended to clarify the impressionthat the agent gave the user by answering wheninteracting with the user in an emotion-arousing
scenario.
We chose six kinds of feelings. From the total of 216
combinations, covering multiple feelings that the user
felt (6 patterns) and the facial expressions for theagent’s interaction with the user (6 patterns) and word
expressions used by the agent (6 patterns), we selected
96 patterns in this experiment. These covered 16
patterns in each feeling: empathetic words andconsistent facial expressions, nonempathetic words andconsistent facial expressions, word consistent and facialinconsistent, and word inconsistent and facial consistent.
This is because the conditions of nonempathetic andboth inconsistent word and facial expressions arenonsensical in normal communication. This is thecondition where the word and facial expressions areinconsistent, which is the condition for double bindcommunication, but it can be considered as either wordor facial being empathetic to the user. The case ofnonempathetic condition and inconsistent word andfacial expressions can be considered as pathological.
A total of 1236 people, 568 male and 668 female (AV.
38.0, SD 11.5), were assigned 96 contents. More thanten users were assigned to each content [7].
As a result, people are easy to be persuaded when theagent was favorable impression. This result is very
natural; however, the combination of favorable wasdifferent from our prediction. First, we predicted thatwords and facial expressions reflected on theemotions aroused by the scenario would lead to themost favorable impression, so we set these data as thecontrol group. In fact, there were more favorable
impressions than those obtained for the control group.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

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Affective Human Computer Interaction

3. A Spoken Agent
Customer Services

Systemfor

Learning

Using our system, a user can talk to the spoken agent,and the spoken agent teaches the user how to interact
with customers. The spoken agent is displayed on thescreen and sometimes the system displays lines in ascene which the user should practice, along with anappropriate facial expression. The system judges
whether the user has spoken the lines appropriately bycomparing them with the speech recognition systemusing AIML templates. The system judges whether theuser’s facial expression is appropriate or not bycomparing it with the facial expression recognition
system.

As an application system for affective human computerinteraction, this first spoken agent system consists of a
3D spoken agent display system, a speech recognition
system, a speech synthesis system, a dialogue control
system and a facial expression recognition system. Thissystem is for providing educational training in
hospitality through dialogue with a spoken agent.
This system focuses on the Japanese style of servicemindedness, which is typified by paying attention toindividual customers [8][9].
The 3D spoken agent display system displays a 3D
spoken agent, which can show the facial expressions of“"smile", "laugh", "anger", "sadness", "disgust", "fright",
and "surprise”. The system superimposes the mouth
shapes of the vowels onto each facial expression, lipsynching with the sounds. Fluid movement of the facial
expressions and lip-synching is made possible using
Microsoft XNA morphing technology. Our system uses
Google speech API as the speech recognition system,
and AITalk as the Japanese speech synthesis system. As
a dialogue control system, we revised Artificial
Intelligence Markup Language (AIML [10]) which isbased on Extensible Markup Language (XML) to use
the Japanese language. Using templates, we can express
a dialogue freely.
<pattern>* tell me your name *</pattern><template>My name is Ayaka</template>
For example, if there is a question “Tell me your name?”from a user, then the system answers, “My name is
Ayaka,” in this case. The important pattern here is “*tellme your name*”, so the system can answer the sentence“Please tell me your name” or “Could you tell me yourname?” because these sentences include the phrase “tellme your name.” When we analyze Japanese language,
we have to add spaces between words because there areno spaces in Japanese sentences. We use MeCab, which
is a fast and customizable Japanese morphologicalanalyzer, to add spaces between words. As the facial
expression recognition system, this system uses thefacial recognition application of brain wave measuring
equipment called “Emotive EPOC”. It recognizes theintensity of facial expressions digitally.

4. A Spoken Agent System for Mental Care using
Expressive Facial Expressions and Positive
Psychology
We developed a spoken agent system for mental care of
hikikomori (a Japanese term to refer to the phenomenonof reclusive adolescents or young adults who withdraw
from social life) persons improving previous system(Figure 1). An increasing number of people are in needof mental care, because associated social issues include
problems of bullying in schools, mental disability andhikikomori or social withdrawal of young people,
suicide of young people, people being pressured to quit
work in middle age, and PTSD (posttraumatic stressdisorder) of the victim. According to the result of theexperiment of first spoken agent system, it was revealed
we must improve interface of the system, especially for

Figure 1. A spoken agent system for mental care
© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

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Kaoru Sumi

5. Discussion

the attachment of Emotiv. We improved a spoken agent
system. The system consists of a 3D spoken agent
display system, a dialogue control system, Japaneseword spacing system, a speech recognition system, aspeech synthesis system, and a facial/gesture expression
recognition system. The improved points are as follows.
1) KINECT sensor recognizes a user’s facial
expressions and gestures, 2) An agent displays gestures ,3) The facial expression/gestures can be controlled inintensity digitally, 4) An agent can be controlled its size,
direction and position, 5) A background of the system ischangeable , and 6) An agent blinks its eyes.
Interactive talks using gestures are enabled byrecognizing a user’s motion from the skeleton using a
KINECT sensor. In addition, a simple facial expression
is enabled by recognition of a user’s face using
KINECT sensor without an Emotiv. The system can beswitched between a KINECT and/or an Emotiv. An
agent displays gestures of “nodding”, “nodding no”,
“waving her hand”, “bowing her head” and “bowing herhead deeply”. An agent can be controlled its size (fullbody/ upper body/ face or parentage of full body ) ,direction and position. The background of the system
can be changed using the dialogue editor. The agent
blinks its eyes for making the agent more human.
Because, according to the experiment, there are many
comments that the agent should blink its eyes.

Even if a partner is not a human being, we are
persuaded by an agent with superficial information suchas facial expressions or words. Media Equation [1] andPersuasive Technology [2] researches reveal the same.
From the result of the first experiment in section 2,
facial expressions and words are very important because
people are easy to be persuaded when the agent wasfavorable impression. It is thought that an agent canmake in-depth contact with the user by foreseeing theuser’s emotion and empathizing with the user.
Using the results, we have developed spoken agent
systems for learning customer services and mental careusing expressive facial expressions. These are working
well as the system for an agent as an instructor or a
guidance counselor using affective human interfaces.
According to the interviews, users felt the brain wave
measuring equipment and the speech recognition andsynthesis system were novelties, and the system’s
specialty was that its use was facilitated throughdialogues and it was highly motivating. The brain wave
measuring equipment and the speech recognition andsynthesis system used in our system were highly
motivating; however the subjects were divided overtheir use, and some felt them to be awkward to use.
Some of them made comments on the speech
recognition system such as “It was difficult to recognize
words”, “It was difficult”, etc. Consideration should begiven to improving the system’s ease of use by
developing or using a speech recognition system ofgreater accuracy. Also, regarding the brain wave
measuring equipment, some made comments on it suchas “It was hard to wear” and “It was painful”, so it could
be considered that it was not easy to accept.
Consideration should be given to improving the humaninterface, including using image processing and theheadset concurrently.

In addition, this system offers the function of answering
positively when a user speaks negatively, for creating a
positive mood. This system answers with positive words
to a user’s negative words using Negapo Dictionary [11],which is a dictionary for translate negative words topositive words. Such counseling is provided in a field ofpositive psychology. For example, such as follows istalked.
The agent: How was your day? The user: I went to myuniversity.
The agent: What kind of thing happened?
The user: I get frustrated because only a friend might be
good.
The agent: I felt sorry for you. (Facial expression is“Sad”) I think you should be able to
make the feeling of frustrated with a motivation.
(Positive words from Negapo Dic.)

According to the interviews we must improve theinterface of the system, especially for the attachment of
the brain wave measuring equipment. Then wedeveloped next version of the system for mental care of
hikikomori social withdrawal persons or using KINECT,© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

247

Affective Human Computer Interaction

6. Martin E. P. Seligman: Using the New Positive
Psychology to Realize Your Potential for Lasting
Fulfillment, Atria Books (2004)
7. Kaoru Sumi and Mizue Nagata: Evaluating a Virtual
Agent as Persuasive Technology, Psychology of
Persuasion, Janos Csapó and Andor Magyar eds., Nova
Science Publishers.2010.
8. Kaoru Sumi, Ryuji Ebata: A Character Agent System for
Promoting Service-Minded Communication, Intelligent
Virtual Agents, Lecture Notes in Computer Science,
LNAI8108, pp.438, Springer (2013.8).
9. Kaoru Sumi, Ryuji Ebata: Human Agent Interaction for
Learning Service-Minded Communication, iHAI2013,
1st international conference on Human-AgentInteraction, (2013.8).
10. AIML: http://www.alicebot.org/documentation/
11. Negapo Dictionary, Syufuno-Tomo-Sya (2012). In
Japanese.

which recognizes the facial expression/gestures of theuser.
I think this system is helpful for mental care of
hikikomori social withdrawal persons if it can be usedeasily in house. Because even hikikomori or social
withdrawal persons are at home and stay in front of acomputer most of the time. I think that supporting thesepeople by a personal computer is a key to solution.
Study of a spoken dialogue agent system needs to beinvestigated further including emotions, technology anddesign domains. Our research group is going to advanceour own studies of a spoken agent system using
superficial information in the future.
6. Conclusion
This paper introduces an experiment and the spoken
agent system developed according to the results. We
evaluated the first version of the system and revised.
Even if a partner is not a human being, we are perceived
by an agent with superficial information such as facial
expressions or words.
7. Acknowledgements
This work was supported in part by JSPS KAKENHI
Grant-in-Aid for Scientific Research on Innovative
Areas Numbers 22118503.
References
1. Reeves, Byron and Clifford Nass The Media Equation:
How People Treat Computers, Television, and New
Media Like Real People and Places, Cambridge
University Press, 1996.
2. B.J.Fogg: Persuasive Technology –Using Computers toChange What We Think and Do-, Elsevier, 2003.
3. Weizenbaum, Joseph: "ELIZA—A Computer Program
For the Study of Natural Language Communication
Between Man And Machine", Communications of theACM 9 (1): 36–45, (January 1966).
4. A. Rizzo, K. Sagae , E. Forbell, J. Kim, B. Lange, J. G.
Buckwalter, J. Williams, T. D. Parsons, P. Kenny, David
Traum, J. Difede, B. O. Rothbaum: SimCoach: an
intelligent virtual human system for providing
healthcare
information
andsupport,The
Interservice/Industry Training, Simulation & Education
Conference (I/ITSEC), 2011.
5. Christopher Peterson : A Primer in Positive Psychology ,Oxford University Press;© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

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8

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4

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7

8

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8

9

10

1

2

3

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So-Youn Park, Ju-Jang Lee

250© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

A Study on Differential

mode
spread251© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

So-Youn Park, Ju-Jang Lee

252© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

A Study on Differential

253© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

Image Encryption Implementation Based on Fractional-order Chen System
Hongyan Jia

Department of Automation, Tianjin University of Science and Technology,
1038 Dagunanlu Road, Hexi District, Tianjin300222, PR China
Qinghe Wang

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 this paper, based on the fractional-order Chen system, a kind of double encryption algorithm method is adopted
to realize the image encryption. The method mainly refers to the transformation of the pixel position and thetransformation of pixel value. The effectiveness of the double encryption method is verified by encryption anddecryption of some typical images. The experimental results show that this method not only has the ideal effect ofimage encryption and decryption, but also possesses a better guarantee on the image security. That is, thisencryption method is practical and feasible.
Keywords: fractional-order; Chen system; pixel; image encryption

algorithm will be achieved as follows. Step 1, encrypts
the image by transforming the pixel position; Step 2,
encrypts the image by transforming the pixel value; Step
3, decrypts the image by corresponding inverse
transformation. All these transform are done by utilizing
chaotic sequence from fractional-order Chen system.
That is, a three-dimension chaotic sequence is firstly
obtained by analyzing and computing fractional-order
Chen system. Then any one of the three sequences canbe selected as a position transform sequence in step 1,
and subsequently all the three sequences will be selected
as value transform sequences in step 2. And thus someencryption sequences for an image are gained byutilizing these chaotic sequences. Next, based on thecorresponding inverse transformation, the image will bealso decrypted by these chaotic sequences. At last, thisencryption method is verified to be valid by encrypting
and decrypting some images. All the experimental
results show this method is effective and practical.

1. Introduction
With the rapid development of information technology,
the issue on network information security has attracted
more and more attention in recent years. Especially forimages, a kind of important information carrier, it would
be very necessary to store and transmit with a highdegree of security [1]. Therefore, image encryption
technology has increasingly become one of research
focus in the field of information security [2].
Image encryption technology is an effective method
to protect the transmission of digital images [3]. So far,many kinds of image encryption technology have beenreported, such as text encryption technology [4], quadtree image encryption technology [5], chaotic
encryption technology [6], and image encryption
technology based on DNA [7], and so on. As one of
Image encryption technology, chaotic encryption
technology has increasingly highlighted its advantage
because of the development of chaos theory.
In this paper, a kind of double encryption algorithm
is adopted to realize the image encryption. The© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan254

Hongyan Jia, Qinghe Wang

2. Fractional-order Chen systemcorresponding transformation, the value results ofchaotic sequences are consistent with those of the image.
Then the numerical encryption algorithm using XOR is
used to encrypt the pixel value after image scrambling.
And thus three components of the encrypted image canbe obtained. Finally, three components of the encrypted
image is combined together, and getting the encrypted
image information.

The fractional-order Chen system is described asfollows:
dα x
dtα
dα y
dtα
dα z

= a(y − x)
= (c − a)x − xz + cy

(1)

{ dtα = xy − bz
where system parameters a,b,c ∈ R, 0 < 𝛼 < 1 refer tothe fractional order. When fixingα = 0.9, a = 35,b =3 , c = 28 , the system will show a typical chaotic
attractor. Under the above conditions, setting an initialvalue of the system, a three-dimension chaotic sequencecan be obtained when utilizing four-order Runge-Kutta
algorithm to solve the system. Here, system parameters,
the fractional order, and initial value are all used as keys.

4. Experimental Results
This paper selects two color images named Lena andXuexiao as the research object. The size of image Lenais 512 ∗ 512 , and the size of image Xuexiao is 256 ∗
256. The experiments have been done by MATLAB
2012, with the initial values of state variables
being (0.1,0.2,0.3) . The image of Lena and its threecomponent diagrams of R, G, B are shown in Fig.1 (a),(b), (c), and (d), respectively. The encrypted images oftricolor component after the pixel scrambling operation
are shown in Fig.2 (a), (b), and (c), respectively. The
encrypted image of Lena after pixel scrambling and theencrypted image of Lena after pixel value transform aregiven in Fig.3(a) and (b) , respectively. The decrypted
images of Lena are shown in Fig.4 (a), (b), and (c),respectively. The encrypted image of Xuexiao and thedecrypted image of Xuexiao are shown in Fig.5 andFig.6, respectively. The experimental results show thatthe encrypted image is completely different from theoriginal image, while the decrypted image and theoriginal image are identical. It shows that the encryption
method is practical and effective, and has goodencryption effect.

3. Image Encryption Algorithm
3.1 Pixel position scrambling
In this paper, the sequence x in three-dimension chaotic
sequence is used as the pixel position transformsequence. The sequence x is firstly sorted by ascendingorder. Generally speaking, the image matrix stored incomputer includes three components, R component, G
component and B component. These components are
all m ∗ n matrix. Therefore, in order to complete
encryption operation, R component, G component and
B component of the image matrix need to be converted
into one-dimension array, R-value, G-value and B-value,
respectively. Then, the sort operations for R-value arecarried out by using the sorted sequence x , andsubsequently scrambling encrypted image for R
component can be obtained. With the same method, thescrambling encrypted image for G component or B
component can also be obtained.

5. Performance Analysis
5.1 Histogram analysis
Generally speaking, histogram can clearly reflect the
distribution of the quality characteristics of the image[8], so it is convenient for people to correctly evaluate
the overall quality condition of image. Here because thehistogram of Lena image is same as one of Xuexiao
image, the histogram of Lena image is selected toillustrate the pixel distribution at each color of theimage. Histograms for R components, G components,
and B components, before and after encryption of Lena
are shown in Fig.7 (a), (b), and (c), respectively. It canbe seen that the distribution of the encrypted histogram

3.2 Pixel value transform
Although the length of chaotic sequence can be selected
to be same as each component of the image, every valueof chaotic sequence is not consistent with the image
storage value. In general, the storage value of image isan integer between 0 and 255. So it is necessary for the
chaotic sequences used in encryption to carry out
numerical transformation such as numerical expansion
and limitation domain. After completing the© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa Japan255

Image Encryption Implementation Basedis very uniform, which indicates that the ability to resist
statistical attack for the encryption method is good.

Fig.5 The encrypted image of Xuexiao

Fig.6 The decrypted image of Xuexiao
Fig.1

Image
diagrams

Lena

and

its

three

component

5.2 Correlation analysis
In this paper, the correlation coefficient among adjacentpixels of the image is shown in Table 1. As can be seenfrom the table, the correlation coefficient of the original
image is close to one, which indicates that the adjacentpixels are highly correlated; The correlation coefficientof the encrypted image is close to zero, which
illustrates there is little correlation among adjacentpixels. All these show the pixels of the encrypted image
are randomly distributed. The encrypted image caneffectively resist the attacks of the relevant statisticaltechniques, and its security is good.

Fig.2 The encrypted images of tricolor component after
the pixel scrambling operation

Fig.3 The encrypted image of Lena

Fig.4 The decrypted image of Lena
Fig.7 Histogram before and after encryption© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa Japan256

Hongyan Jia, Qinghe Wang

Table 1 Correlation CoefficientLenamap, Computer and Electrical Engineering. 38(5) (2012)
1240-1248.
8. Zhang Yonghong, Zhang Bo, Algorithm of image
encrypting based on logistic chaotic system, application
research of Computer. 32(6) (2015) 1770-1773
9. Zhang Jian, Fang Dongxin, Image encryption technology
applied chaotic maps index and DNA coding, Computer
Engineering and Design. 36(3) (2015) 613-618.

XuexiaoR
G

Original
Image
0.98663
0.985

Encrypted
Image
0.00077537
0.0012447

Original
Image
0.91771
0.91698

Encrypted
Image
0.00020332
0.0057597

B

0.96870.00068893

0.93997

0.0036447

6. Conclusion
This paper adopts an effective method to implement theimage encryption operation. The method is based on the
chaotic sequence generated by the fractional–order
Chen system, and combines the double encrypted
technique including the pixels position scrambling ofimage and the pixels value transform of image. The
experimental results and performance analysis show that
this method not only has good encryption effect, butalso has high security.
Acknowledgements
This work was supported in part by the Young
Scientists Fund of the National Natural Science
Foundation of China (Grant No. 11202148), the Natural
Science Foundation of China(Grant No. 61174094).
References
1. Dong W, Zhang L, Shi G, Li X, Non-Locally centralized
sparse representation for image restoration, IEEE
Transactions on Image Processing. 22(4) (2013) 16201630.
2. Liao X, Lai S, Zhou Q, A novel image encryption
algorithm based on self-adaptive wave transmission,
Signal Processing. 90(9) (2010) 2714-2722.
3. Jin Bing, Qi Lilei, JIA Yuzhen, Research of chaos crossencryption algorithm based on Logistic mapping,Computer & Digital Engineering. 39(1) (2011) 93-94.
4. Wang Xiao, Zhao Dong. Double image encryption
method with resistance against the specific attack basedon asymmetric algorithm, Optics Express. 20(11) (2012)
11994-12003.
5. ZHANG Xiaoyan, WANG Chao, SUN Zhiren, et al. An
image encryption scheme based on sequential CA, Optics
and Precision Engineering. 16(9) (2008) 1781-1786.
6. Nooshin B, Youset F, Karim A, A robust hybrid method
for image encryption based on Hopfield neural network,
Computer & Electrical Engineering. 38(2) (2012) 356369.
7. Liu Lili, Zhang Qiang, Wei Xiaopeng, A RGB image
encryption algorithm based on DNA encoding and chaos© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa Japan

257

Mobile Camera based Motion Segmentation by Image Resizing
Chunyu Yu

Department of Electrical Engineering and Computer Science
Syracuse University, Syracuse, NY 13244-1240
Email: cyu07@syr.edu

Abstract
The need of detecting moving object like human and vehicle by mobile camera is increasing in commerce and industry.
In this paper, an image-resize methodology which can abstract motion segmentation and detect moving object frommoving background is proposed. First, edges images are computed. Then movement vector between frame images
are computed and the relative background motion is compensated. By adjusting the parameters of resize algorithm,
human liked object or vehicle liked object can be segmented separately and the segmentation can be used for further
detection. Experiments have been performed under three different environments for human detection and vehicle
detection.
Keywords: mobile camera, motion segmentation, edge-based alignment, object detection.

combines Expectation Maximization and extendedMarkov Random Field [7]. Another method is wavelets
based. The main idea is to analysis the difference part’s
frequency. For instance, [8] presented a motion
segmentation based on Galilean wavelets. In 1980 Hornand Schunch first proposed a new method named opticalflow [9]. After that, many researchers made improvementon it such as J. Zhang, F. Shi, J. Wang, and Y. Liu [10].
In this paper, an image resizing based motion
segmentation method is proposed. Unlike othercomputational expensive method, this algorithm only
relies on a new edge alignment method and fast image
resizing according to object morphology. First, edges in
two consecutive frame images are detected. Then the newedge-based alignment method is adopted to find the
motion vector between two images. Motion
compensation is performed before motion segmentation.
Then the image is resized according to object
morphology. After filtering pixels lower than thresholdvalue, object is stand out of the background. The
experiment was performed by MATLAB 2014a on i52450m and 6GB RAM in three different scenarios for

1. Introduction
Real-time human detection is a key study field incomputer vision. This technique is widely used in
automotive engineering, traffic monitoring, robotics,
human-computer interaction, security, military and manyother applications. In literature, there are a lot of
researchers focused on stationary camera based object
detection. Main technique used is image difference tofind the difference of two image pixel by pixel. However,when camera is moving during the recording, the wholebackground is changing. To resolve this problem, [1-4]developed different algorithms to against the lightchanges.
Another widely used method is statistical theory whichclassifies pixels into background and foreground. C.
Rasmussen and G. D. Hager combine Maximum A
posteriori Probability (MAP) with a Probabilistic Data
Association Filter [5]. N. Vaswani, A. Tannenbaum, andA. Yezzi unified geometric active contours and level sets
within Particle Filter [6] and R. Stolkin, A. Greig, M.
Hodgetts, and J. Gilby, proposed an algorithm which© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

258

Chunyu Yu

human liked object detection and vehicle liked object
detection. The results illustrate that the accuracy rate canreach as high as 93.04%, and the execution time ishighly speeded up (approximately 0.82s per frame).
The paper is organized as follows. The proposed
method for motion segmentation and object detection is
described in section 2 and section 3 presents theexperiment results and the conclusion part is in section 4.

Table 1 Algorithm for finding relative motion direction
and distancefor 1 to 8 directionsfor 1 to 10 pixels
m[][]=abs(tailored(f1,-direction,pixel)–tailored(f2,directionpixel))

direction = minima(m).direction
distance = minima(m).pixel
According to the direction and movement distance,
tailor pixels from two images respectively. After that,beside the people, backgrounds are almost in the same
position in two images (say, reach the global minima).

2. Proposed Method
2.1 Finding global minima
Take human liked object detection as an example. Twoconsecutive frame images f1 and f2 are loaded first. By
using Canny edge detector, two binary edge images are
obtained. Ideally, the camera is stable and thebackground is not moved, so in the difference image (f2f1), only moving object is visible and background isoffset. Virtually, the camera may slide a very small
distance in a direction, so the background and people are
moved simultaneously when two consecutive images arecompared. Therefore, eliminate the movement ofbackground is essential.
Assume people are most likely appear in the central
area of an image, when detecting background movement,
only edges at the margin of images are compared.
Specifically, fill zero into the central area of two images
and find out the relative movement between the
background in 8 directions (vertical, horizontal anddiagonal) (Fig.1). By the algorithm in table 1, relative
motion direction and distance are achieved. Base on
experiments, the movement between two frames isalways 3 to 6 pixels, so 10 pixels are sufficient forsearching in our algorithm.

2.2 Resizing image algorithm
Even global minima is reached, virtually the edges in
two images cannot be overlapped exactly. As a result,when difference image (f2-f1) is calculated, backgroundedges of two images can hardly offset each other. The
background may become even more complicated thanhuman outline. As a result, the human cannot stand outof background (Fig.2).

Fig. 2 Difference image acquired without resizing
algorithm
To resolve the problem, an image resizing algorithm isadopted. Table 2 is the matrix of yellow rectangle in Fig.
2 which is achieved without dilating images. Table 3 isthe result acquired after dilating images. By compare twotables, we can notice that the number of pixels which
value is 1 is decreased and they are more scattered inTable 3. In terms of the whole image, dilating images
results to more mottled background.

Fig. 1 One of eight directions of relative movement
between two images© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

259

Mobile Camera based Motion

Table 2 Difference image acquired without dilating(1-value pixels: 15)
391
392
393
394
395
396
397
398
399
400

258

259

260

261

262

263

264

265

266

267

0
1
1
0
0
0
0
0
0
0

0
1
1
0
0
0
0
0
0
0

0
1
1
0
0
0
0
0
0
0

0
1
1
0
0
0
0
0
0
0

0
1
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

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
1
1
0

0
0
0
0
0
1
0
0
1
1

Fig. 3 Difference image acquired by resizing algorithm
The following step are dilating image, filling image
hole and eroding image. The largest block in the image isthe object.

Table 3 Difference image acquired after dilating (1value pixels: 13)
391
392
393
394
395
396
397
398
399
400

258

259

260

261

262

263

264

265

266

267

1
0
0
1
0
0
0
0
0
0

1
0
0
1
0
0
0
0
0
0

1
0
0
1
0
0
0
0
0
0

1
0
0
1
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
0
0
0
0
0
1
1
0
0

0
0
0
0
1
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

As moving human most likely look like vertical
rectangles, they are more robust than noise (remained
background) in terms of vertical image resizing.
Therefore, based on the result in Table 3, the image isresized in vertical direction and then restored. After thatpixels’ value are between -1 and 1.
Table 4 Matrix after image resizing
391
392
393
394
395
396
397
398
399
400

258
0.31
0.34
0.31
0.25
0.19
0.14
0.09
0.03
-0.00
-0.02

259
0.31
0.34
0.31
0.25
0.19
0.14
0.09
0.03
-0.00
-0.02

260
0.31
0.34
0.31
0.25
0.19
0.14
0.09
0.03
-0.00
-0.02

261
0.31
0.34
0.31
0.25
0.19
0.14
0.09
0.03
-0.00
-0.02

262
0.23
0.23
0.16
0.08
0.01
-0.02
-0.02
-0.01
-0.00
0.00

263
0.23
0.23
0.17
0.08
0.01
-0.02
-0.05
-0.05
-0.04
-0.00

264
0.00
-0.00
-0.02
-0.04
-0.04
-0.00
0.07
0.17
0.25
0.26

265
-0.04
-0.00
0.07
0.17
0.25
0.29
0.31
0.31
0.29
0.25

266
0.01
0.04
0.11
0.18
0.23
0.23
0.16
0.08
0.00
-0.02

267
0.00
0.00
0.00
0.00
0.00
-0.00
-0.01
-0.02
-0.02
0.01

Fig. 4 segmentation achieved by resizing algorithm(sinister) versus without resizing algorithm (dexter)
The following assumptions are made in proposed
algorithm. Firstly, there must be sufficient edges in themargin of the images as they are used to find motion
vector. Secondly, there must be sufficient movement of
the object. If the object is stationary, it will be considered
as background. Finally, the object should large enough interms of the image scale as the largest block is selected
as object in the final step.

Due to the distortion, mottled background faded
dramatically compared to human outline. It can beeliminated by deleting pixels which value is lower thanspecific threshold value. According to the environmentand object, 0.5 is adopted in this experiment. Therefore,
all the pixels in Table 4 will be 0. Compare to Fig. 2, theroad line in Fig. 3 is eliminated (yellow rectangle) andhuman is out stand of background.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan260

Chunyu Yu

3. Experiment Results

References
1. A. Cavallaro, O. Steiger, and T. Ebrahimi, “Tracking
Video Objects in Cluttered Background,” IEEE
Transactions on Circuits and Systems for Video
Technology, vol. 15, no. 4, pp. 575–584, 2005.
2. F.-H. Cheng and Y.-L. Chen, “Real time multiple objects
tracking and identification based on discrete wavelet
transform,” Pattern Recognition, vol. 39, no. 6, pp. 1126–
1139, 2006.
3. R. Li, S. Yu, and X. Yang, “Efficient spatio-temporalsegmentation for extracting moving objects in video
sequences,” IEEE Transactions on Consumer Electronics,
vol. 53, no. 3, pp. 1161–1167, Aug. 2007.
4. A. Colombari, A. Fusiello, and V. Murino, “Segmentationand tracking of multiple video objects,” Pattern
Recognition, vol. 40, no. 4, pp. 1307–1317, 2007.
5. C. Rasmussen and G. D. Hager, “Probabilistic dataassociation methods for tracking complex visual objects,”
IEEE Transactions on Pattern Analysis and MachineIntelligence, vol. 23, no. 6, pp. 560–576, 2001.
6. N. Vaswani, A. Tannenbaum, and A. Yezzi, “Tracking
deforming objects using particle filtering for geometricactive contours,” IEEE Transactions on Pattern Analysis
and Machine Intelligence, vol. 29, no. 8, pp. 1470–1475,
2007.
7. R. Stolkin, A. Greig, M. Hodgetts, and J. Gilby, “An em/emrf algorithm for adaptive model based tracking in
extremely poor visibility.” Image and Vision Computing,vol. 26, no. 4, pp. 480–495, 2008.
8. M. Kong, J.-P. Leduc, B. Ghosh, and V. Wickerhauser,“Spatio-temporal continuous wavelet transforms formotion-based segmentation in real image sequences,”
Proceedings of the International Conference on Image
Processing, vol. 2, pp. 662–666 vol.2, 4-7 Oct 1998.
9. B. K. Horn and B. G. Schunck, “Determining optical flow,”
Cambridge, MA, USA, Tech. Rep., 1980.
10. J. Zhang, F. Shi, J. Wang, and Y. Liu, “3d motion
segmentation from straight-line optical flow,” inMultimedia Content Analysis and Mining, 2007, pp. 85–
94
11. Y. Zheng, “Detection of moving people with mobile
cameras by fast motion segmentation”, 2013 SeventhInternational Conference on Distributed Smart Cameras(ICDSC), pp.1-6, Oct. 29-Nov. 1 2013.

Without loss of generality, the experiment was performed
to test vertical resizing and horizontal resizing in threedifferent scenarios: (1) human walk indoor, (2) humanwalk outdoor and (3) human walk outdoor with moving
vehicle in background. The dataset of Zheng et al.’s
experiment was adopted [11].
3.1. Human walk indoor without moving object
231 frames of a video were tested in this experiment. Fig.
5 as part of results illustrates that the movement
segmentation is highly accurate and almost overlaps withthe human body. Thus the segmentation will decrease therunning time largely for further HOG detection. Even
though illumination sometimes disturbs the detection, theoverall successful detection rate for whole frames is77.56%.
3.2. Human walk outdoor without moving vehicle
In this experiment, 273 frames were tested and theaccurate detection rate is 93.04%. Similar to the resultsof indoor experiment, the segmentation part is highly
overlapped with the human body (Fig. 6). Human walkoutdoor with moving vehicle in the background3.3. Human walk outdoor with moving vehicle inthe background
100 frames were tested for human and vehicle. The
detection rate is 86% and 83.1% respectively. As shownin Fig.7 and Fig.8, the moving vehicle can be
distinguished from stopped bus.
4. Conclusions
In this paper, a fast and simple motion segmentation
method for moving background is proposed. Zheng etal.’s dataset was tested in the experiment. The results
show that the proposed method is highly accurate
especially in complex environment. In the future, priorknowledge will be considered. The position of yellow
box can be limited in a range according to its position inprior frame. Thus the algorithm can be more robust to fast
light changing. Due to pretty accurate segmentation
region, further detection such as HOG in this region canbe speed up largely.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan261

Mobile Camera based Motion

Fig. 5 Indoor sequence. The green line outlines the largest block and the yellow box indicates the possible
human region

Fig. 6 Outdoor sequence. The green line outlines the largest block and yellow box indicates the possible humanregion© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan262

Chunyu Yu

Fig. 7 Outdoor sequence with moving car in background. The green line outlines the largest block and yellow
box indicates the possible human region

Fig. 8 Outdoor sequence with moving car in background. The green line outlines the largest block and yellow box
indicates the possible vehicle region.
© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

263

Synchronization of the Fractional-order Permanent Magnet Synchronous Motor
Wei Xue

Department of Automation , Tianjin University of Science and Technology,
1038 Dagunanlu Road, Hexi Distric, Tianjin 300222, PR China
Xue Li

Department of Automation , Tianjin University of Science and Technology,
1038 Dagunanlu Road, Hexi Distric, Tianjin 300222, PR China
E-mail:xuewei@tust.edu.cn,lixueTJKJ@163.com
www.tust.edu.cn

Abstract
In this paper, the chaotic synchronization of the fractional-order permanent magnet synchronous motor isinvestigated. The presented control scheme is simple and flexible, and it is suitable both for design and implementin practice. According to the stability theory of fractional-order linear system, the chaotic synchronization of thedrive system and the response system is achieved. Adopting the nonlinear feedback method, a nonlinear feedback
controller in theoretical analysis is designed. The obtained numerical simulation results agree with that oftheoretical analysis, which can further demonstrate the feasibility and effectiveness of the proposed synchronization
scheme.
Keywords:Chaos; Fractional-order; Permanent magnet synchronous motor; Nonlinear feedback synchronization;
Numerical simulation.

order Liu system,3 fractional-order Lü system,4 andfractional-order generalized augmented Lü system .5
The chaotic behavior of PMSM is harmful in mostcases. However, the chaotic behavior of PMSM can also
be useful in some applications, such as in industrial
mixing process, the chaotic motion itself gives theextension and folding characteristics of good mixing. It
is of practical significance to improve the efficiency ofindustrial mixing. Therefore, it is necessary to study thechaos synchronization of fractional-order PMSM.
Recently, many synchronization approaches to thechaotic system have been proposed such as linear
feedback synchronization,6 adaptive synchronization,7
impulse synchronization,8-9 projective synchronization,10
active synchronization,11 generalized synchronization,12
sliding mode control,13 and lag synchronization.14
Adopting the nonlinear feedback method, a nonlinear
feedback controller in theoretical analysis is designed.
And the synchronization of the fractional-order PMSM
is achieved according to the stability theory of

1. Introduction
Since permanent magnet synchronous motor (PMSM)
has excellent properties such as simple structure, hightorque-to-weight ratio, low manufacturing cost and hightorque-to-inertia ratio, it is used in the industrial
applications more widely. Studies have shown that,when the motor works under certain parameters andoperating conditions, the chaotic behavior will occur,
such as intermittent ripples of torque, the lowperformance property speed control, irregular current
system noise and so on. The study of the integer-orderchaotic model of the PMSM is comprehensive. For
PMSM, the fractional-order chaotic model can reflect its
chaotic characteristics more accurately. Some
investigation of fractional-order PMSM are still in its
infancy. Many scholars have researched on fractionalorder chaotic systems, such as fractional-order Rössler
system,1
fractional-order Chen system,2 fractional-© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan264

Wei Xue, Xue Li

fractional-order linear system. The obtained numericalsimulation results agree with the theoretical analysis
results, which can further demonstrate the feasibility
and effectiveness of the proposed synchronization
scheme.

In order to observe the nonlinear feedback
synchronization of PMSM, the response systems can be
defined as follow:

 d  y1    y1  y2 y3  u1
 dt
 d  y2    y2  y1 y3  y3  u2
 dt d y3   ( y  y )  u
2
3
3


 dt

2. Synchronization of fractional-order PMSM
2.1. General nonlinear feedback synchronization
schemeHere, the fractional-order chaotic system is considered
as follow:

where ud x
 Ax  f (x)dtx  ( x1, x2 , , xn )  R
Tn

2.2. Design of synchronization controller
The drive system(3) can be described as system(1) in
the form of:is the statevariable,f ( x)  ( f1 ( x), f 2 ( x),0  1 0  x2 x3 d x 





0

1

x


x
x
1 3
dt 



 0   
 0 





, f n ( x))T  R n1

is continuous nonlinear vector function. The system(1)can be used as a driving system, and the corresponding
response system is given by:

d y
(2)
 By  g ( y )  udtnnwhere   (0,1] , B  R
is the matrix of systemparameters,y  ( y1 , y2 , (u1 , u2 , u3 ) is the nonlinear controller tobe designed for PMSM with the same parametersand  in spite of the differences in initial conditions.

(1)
n n
where   (0,1] , A  R
is the matrix of systemparameters,

T

(4)

(5)
Similarly, the response system(4) is written in the formof system(2) as follow:  1 0 0    y2 y3  d y 
  0  1   y    y1 y3   u.
dt
 0     0  , yn )T  Rn is the statevariable, g ( y)  ( g1 ( y), g2 ( y),, gn ( y))T  Rn1

is the state variable and u  (u1 , u2 ,, un )T is the(6)Here, the nonlinear feedback method to realize thesynchronization of PMSM is adopted. And controlleru  f ( x)  g ( y)  Ke can be selected as the following
form:

controller to be determined in order to realize nonlinear
feedback synchronization.
Let the error vector be e = y - x  (e1 , e2 , , en )T , andthe appropriate controller u should be designed to makelim e  0 .

 x2 x3   y2 y3  u  f ( x)  g ( y )  Ke    x1 x3     y1 y3   Ke
 0   0 

 


t

The chaotic model of PMSM can be expressed as thefollowing form15:

 d  x1
    x1  x2 x3
 dt
d x2
(3)
    x2  x1 x3  x3
 dt
 d x3   ( x  x )
2
3
 dt

where   (0,1] is the fractional order,  >0 and  >0

(7)
Where e  y  x  (e1 , e2 , e3 ) .
T

From Eq.(5), Eq.(6), and Eq.(7), the error system isobtained, which can be expressed as:

0  1 0de   0  1  e  Ke
dt
 0   are constants.
© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

265

(8)

Synchronization of the Fractional-

0 0 0K  0 0  
0  0In this section, we conduct the numerical simulation of
the synchronization via Matlab, taking the 2.85-order
PMSM as an example. The system parameters arechosen to be = 0.95, =11, and  =90, and in time
steps of 0.01. The initial states of the drive system andresponse system are assumed as x1 (0) = 0.8, x2 (0) =5, x3 (0) = 1.3, y1 (0) = 3, y2 (0) = 6, and y3 (0) = 1,

(9)

From Eq.(8) and Eq.(9), the error system is obtained asfollow:0  1 0de   0  1 0 edt
 0 0  respectively. It can be observed that the drive systemand the response system are in asymptotic
synchronization in Fig.2. In addition, the curves ofsynchronization error is obtained as shown in Fig.3. It’s
clearly showed the synchronization error vector
converge to zero as time goes to infinity. The obtained
numerical simulation results agree with theoreticalanalysis results.

(10)

From Eq.(10), the eigenvalues are 1  1 , 2  1 , and3   . Because  >0, all eigenvalues i (i  1,2,3)
are negative, that is | arg(i ) |






. According to
2

160
x1
140

Ref.[16], it will be asymptotical stability of the origin of
the error system, which means that lim e  0 .

y1

(a)

120

y

1

100

t

80
60

Therefore, the goal of synchronizing the fractional-order
PMSM is achieved.

40
20
0

0

1

2

3

4

5
x1

6

7

8

9

10

3. Numerical simulation
From system(3), the phenomenon of chaos can beobserved when  =0.95, therefore we will concentrateon the 2.85-order system in the following part. By
adopting the time domain method based on predictorcorrector to conduct the numerical simulation, thechaotic attractors of system(3) is obtained. When
 =0.95,  =11, and  =90, and phase portraits are shownin Fig.1:

100
x2
y2

(b)

y

2

50

0

-50

0

1

2

3

4

5
x2

6

7

8

9

10

50

40

x3
30

40

30
20

30

10

0

x

2

x

3

10

y3

(c)

20

0

-10
-10

-30
40

-20

20

y

3

-20

20

120
100

0

80

-20

60
-40

x2

10

-30

40

-40
50

x1

60

70

80

90

100

110

0

120

x1

-10

25

25

20

20

15

15

10

10

3

0

-5

-5

-10

-10

-15

-15

-20
-25
50

0

1

2

3

4

5
x3

6

7

8

9

10

Fig.2. Variation of state variables with time: (a) ( x1 , y1 ) ;(b) ( x2 , y2 ) ; (c) ( x3 , y3 ) .

5

0

x

x

3

5

-20

-20

60

70

80

90
x1

100

110

120

-25
-40

-30

-20

-10

0
x2

10

20

30

40

Fig.1. Phase portraits of system(3) with  =0.95,  =11, and =90: (a) x1  x2  x3 ;(b) x1  x2 (c) x1  x3 ;(d) x2  x3 .
© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

266

Wei Xue, Xue Li
0.2

6.

References

1.

Li C G, Chen G R. Chaos and hyperchaos in thefractional order Rössler equations. Physics Letters A.
2004, 341: 55-61.
Li C P, Peng G J. Chaos in Chen’s system with afractional order. Chaos, Solitons and Fractals. 2004, 22:443-450.
Daftardar-Gejji V, Bhalekar S. Chaos in fractional
ordered Liu system. Computers and Mathematics with
Applications. 2010, 59(3): 1117-1127.
Lu J G. Chaotic dynamics of the fractional order Lüsystem and its synchronization. Physics Letters A. 2006,
354(4): 305-311.
Xue W, Xu J K, Cang S J, et al. Synchronization of thefractional-order generalized augmented Lü system andits circuit implementation. Chinese Physics B. 2014,23(6): 060501.
Liu F, Ren Y, Shan X M, et al. A linear feedback
synchronization theorem for a class of chaotic system.
Chaos, Solitons and Fractals. 2002, 13(4): 723-730.
Wang Y W, Guan Z H, Wen X J. Adaptive
synchronization for Chen chaotic system with fully
unknown parameters. Chaos, Solutions and Fractals.
2004, 19(4): 899-903.
Ren Q S, Zhao J Y. Impulsive synchronization of
coupled chaotic systems via adaptive-feedback approach.
Physics Letters A. 2006, 356(3): 226-230.
Mohammad H, Mahsa D. Impulsive synchronization ofChen’s hyperchaotic systems. Physics Letters A. 2006,
355(4, 5): 342-347.
Meng J, Wang X Y. Generalized projective
synchronization of a class of delayed neural networks.
Physics Letters A. 2008, 22(3): 181-190.
Li G H. An active synchronization for two modified
chua circuits. Chinese Physics B. 2005, 14(3): 472-475.
Shih Y L, Zheng M G. Generalized synchronization of
chaotic systems with different order by fuzzy logic
constant controller. Expert Systems with Applications.
2011, 38(3): 2302-2310.
Yau H T, Yan J J. Desgn of sliding mode controller for
Lorenz chaotic system with nonlinear input. Chaos,
Solitons and Fractals. 2004, 19(4): 891-898.
Shahverdiev E M, Sivaprakasam S, Shore K A. Lag
synchronization in time-delayed systems. Physics
Letters A. 2002, 292(6): 320-324.
Xue W , Li Y L , Cang S J , Jia H Y , Wang Z H.
Chaotic behavior and circuit implementation of afractional-order permanent magnet synchronous motor
model. Journal of the Franklin Institute. 2015, 352:
2887-2898.
Matignon D. Stability results for fractional differential
equations with application to control processing. In
Computational Engineering in Systems and Application
multiconference, Lille. 1996, 2: 963-968.

0.18
0.16
0.14

e

1

0.12
0.1

2.

0.08
0.06
0.04
0.02
0

0

1

2

3

4

5
t

6

7

8

9

3.

10

0.4
0.35

4.

0.3

e

2

0.25
0.2

5.

0.15
0.1
0.05
0

0

1

2

3

4

5
t

6

7

8

9

10

6.

0.1

0.08

7.

e

3

0.06

0.04

0.02

0

-0.02

8.
0

1

2

3

4

5
t

6

7

8

9

10

Fig.3 Synchronization errors versus time t (a) e1  t ;9.

(b) e2  t ;(c) e3  t .

10.

4. Conclusion
In this paper, a general nonlinear feedback chaotic
synchronization scheme for fractional-order PMSM is
introduced. By analyzing phase portraits of fractionalorder PMSM, we have found there are abundant
dynamical behaviors in the fractional-order PMSM.
Moreover, the synchronization problem of thefractional-order PMSM has been investigated. Based on
the nonlinear feedback method, the synchronization
controller to achieve synchronization between the drive
system and the response system is designed. The
obtained numerical simulation results agree withtheoretical analysis, which can further demonstrate thefeasibility and effectiveness of the proposedsynchronization scheme.

11.

5. Acknowledgement

16.

12.

13.

14.

15.

This work is supported by the Young Scientists Fund of
the National Natural Science Foundation of China(Grant No.11202148).

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

267

Analog Circuit Implementation and Full State Observation of Chua’s Circuit
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
Dongcheng Tan

College of Electronic Information and Automation, Tianjin University of Science & Technology
Tianjin, 300222, China
Yongjun Wu

College of Electronic Information and Automation, Tianjin University of Science & Technology
Tianjin, 300222, China
E-mail: spots@163.com
www.tust.edu.cn

Abstract
In this paper, an inductance simulator and a nonlinear resistor, which are constructed by operational amplifiers andresistors, are applied to complete the analog circuit implementation of Chua’s circuit as well as improve theaccuracy of circuit parameters. For the observation of all variables, the state variable z, which represents the
product of the linear resistance and the inductor current in Chua’s circuit, should be observed even if it is not an
actual measurable physical quantity. It is found that z can be obtained via scaling of the voltage of the resistor in the
inductance simulator. The real chaotic curves generated from the analog Chua’s circuit are displayed on theoscilloscope clearly and correctly. It demonstrates that the realization of Chua’s circuit is correct and available forother research such as chaos synchronization of Chua’s circuits.
Keywords: Chua’s circuit; inductance simulator; circuit implementation; scaling; full state observation

resistance R and the inductor current iL. In this paper,
Chua’s circuit will be implemented by an inductancesimulator and a piecewise linear resistor consisting ofsix resistors and two operational amplifiers, as shown in
Ref. 8. Moreover, the variable z will be obtained via
scaling of the voltage of the resistor in the inductancesimulator to show the chaotic attractors of Chua’s
circuit completely.

1. Introduction
Chua's circuit was introduced in 1983 by Leon Ong
Chua.1 It is a three-dimensional autonomous electronic
circuit that exhibits classic chaos behavior. Because
Chua’s circuit has rich chaotic dynamics and a simple
circuit structure, it has been one of the best chaotic
circuits for experimental observation and study.2-6
In Ref. 7 and Ref. 8, hybrid realizations of Chua’s
circuit, combining the circuit topologies proposed for
nonlinear resistor and inductor element, are presented.
However, there is no study on how to observe the statevariable z=RiL which represents the product of the

2. Chua’s Circuit
Chua's circuit consists of two capacitors, one
inductor, one linear resistor and one nonlinear resistor
named Chua’s diode, as shown in Fig. 1. The inductance,© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

268

Hong Niu, Dongcheng Tan, Yongjun Wu

resistance, and capacitances respectively are L = 21.83
mH, R = 1.875 kΩ, C1 = 10 nF and C2 = 100 nF.

equivalent internal resistance, and its inductance is easy
to adjust.
Assume that the currents through the resistors R1, R2,
R3, R4 and the capacitor C5 respectively are i1, i2, i3, i4
and i5, whose reference directions are all from top tobottom. When the operational amplifiers U1A and U1B
are being operated in linear mode, it is obtained thati2  i3 , i4  i5 , i1R1  i2 R 2 , i3R 3  i5 1 jC5  .
Fig. 1. Chua’s circuit

By solving the equations above, the relationshipbetween the currents i1 and i4 is expressed as

The dynamic equation of Chua’s circuit isrepresented asi4   j R1R 3C5 R 2  i1.

x    y  x  h  x   ,y  x  y  z,z    y.

According to the definition of two-terminal network
impedance, the equivalent impedance of the inductancesimulator is

Where α = C2/C1 = 10, β = (R2C2)/L = 16.1, andZ  i4 R 4 i1  j R1R 3R 4C5 R 2  L  21.83mH.

h  x   m1 x 

1
 m0  m1   x  E  x  E  .
2

As a result, the desired inductance is obtainable byadjusting the resistance of the resistor R4.

Where E=1.1, m0 = -1.4205, and m1 = -0.7671.
Let the initial values of Chua’s circuit be (x0, y0, z0)= (0.1, 0.2, 0.5), then the Lyapunov exponents are λ1 =0.3193 > 0, λ2 = -0.0001 ≈ 0 and λ3 = -3.1683 < 0. It
indicates that the system is chaotic. The simulation
phase portraits of Chua’s circuit are shown in Fig.
3(a1)-(a3).

3.2. Chua’s diode
Chua’s diode consists of two operational amplifiers
U2A, U2B and six resistors, as shown in the right dottedbox of Fig. 2. The resistances are R6 = R7 = 22 kΩ, R8 =3.3 kΩ, R9 = R10 = 220 Ω and R11 = 2.2 kΩ. The design
procedure of Chua’s diode’s equivalent circuit can befound in detail in Ref. 6.

3. Implementation of Chua’s Circuit

3.3. Realization of Chua’s circuit

3.1. Inductance simulator

Chua’s circuit is implemented by replacing the ordinary
inductor and Chua’s diode shown in Fig. 1 with theinductance simulator and the equivalent circuit of
Chua’s diode. The whole analog Chua’s circuit ispresented in Fig. 2.

Because the internal resistance of an ordinary inductor
is too large and the inductance is difficult to alter, it is
not suitable to put an ordinary inductor in a chaotic
circuit which is very sensitive to the parameters. Hence,an inductance simulator should be used to construct
Chua’s circuit instead of an ordinary inductor.6-8 The
equivalent circuit of the inductance simulator is shownin the left dashed box of Fig. 2, where R1 = R2 = R3 = 1
kΩ, R4 = 2.183 kΩ and C5 = 10 nF. The operational
amplifiers used in this paper are TL082CP and their
supply voltages are ±9V, so that their output saturation
voltage is 8.3V. The inductance simulator has no4.

Phase Portraits of Chua’s Circuit

4.1. Observation of state variable z
For the observation of all variables of Chua’s circuit, thestate variable z = RiL should be observed even if it is not
an actual measurable physical quantity. The observation
circuit of z consists of four operational amplifiers U3A,
U3B, U4A, U4B and some related resistors, as shown in© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

269

Analog Circuit Implementation and

4.2. Comparison of simulation and experimentphase portraits

Fig. 2. The resistances are R12 = R13 = R14 = R15 = R16 =R18 = 10 kΩ, and the resistance of the resistor R17 isvariable.
The relationship between the voltage uR1 and thestate variable z can be described asiL  i1  z  RiL  

The experiment phase portraits of Chua’s circuit are
shown in Fig. 3(b1)-(b3). The scales of x, y and z are 2
V/Div, 0.5 V/Div and 2 V/Div, respectively. By
comparing figures (b1)-(b3) with (a1)-(a3), it can be
confirmed that here is a good qualitative agreement
between the numerical simulation and the experimental
realization. It demonstrates that the realization ofChua’s circuit is correct and available for other researchsuch as chaos synchronization of Chua’s circuits.

RR
uR1   17 uR1.
R1
R16

Thus, the design procedure of the observation circuit
can be divided into three parts. Firstly, U3A and U3B
are two input voltage followers used to get thepotentials of both ends of R1. Secondly, U4A and theresistors R12-R15 constitute the differential input
proportion operational circuit used to work out thevoltage uR1. Finally, the state variable z can be obtained
from the output of the inverse proportion operational
circuit consisting of U4B and the resistors R16 and R17.
Furthermore, the operational amplifiers U3A and U3B
can also insulate the main Chua’s circuit from impact of
the observation circuit.
It is found that some peak (valley) values of z arelarger than the output saturation voltage of theoperational amplifier by numerical simulation, such thatthe cut-off (saturation) distortion will occur whenobserving the experiment phase portraits of x-z and y-z
on the oscilloscope. Hence, scaling of z should be
performed. Adjust the resistance of the resistor R17 to
R17 = 14.77 kΩ, then the distortions will no longer occur.
Consequently, the experiment value of z is about 0.8
times its simulation value. For a convenient comparison,
the simulation value of z is adjusted to 0.8 times its
original value in Fig. 3(a2)-(a3).

5. Conclusions
The hybrid analog circuit implementation of Chua’s
circuit, which exploits the circuit topologies for the
inductor element and nonlinear resistor, is proposed inthis paper. The observation circuit for the directly
nonmeasurable variable z is also designed forcomprehensive investigation of the chaotic attractors ofChua’s circuit. It is necessary to observe all chaotic statevariables, especially for full state chaos synchronization.
For example, in synchronization physical test of two
identical three-dimensional chaotic circuits, theassumption that all three states would synchronize as
long as two of them synchronized may be incorrect, sothat all of the states generated from the circuits should
be investigated to verify the synchronization phenomena.
Therefore, the study in this paper has some engineeringsignificance.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

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271

Hong Niu, Dongcheng Tan, Yongjun Wu

Fig. 2. Analog Chua’s circuit

Fig. 3. Comparison of phase portraits: Simulation-Experiment: (a1)-(b1) x-y; (a2)-(b2) x-z; (a3)-(b3) y-z

References

circuit and the design methods of these circuits, Acta
Physica Sinica 63(20) (2014) 95–102.
6. S. Yu, Chaotic Systems and Chaotic Circuits: Principle,
Design and Its Application in Communications (XiDian
University Publishing House, Xi’an, 2011).
7. R. Kiliç, A comparative study on realization of Chua’s
circuit: Hybrid realizations of Chua’s circuit combining
the circuit topologies proposed for Chua’s diode andinductor elements, International Journal of Bifurcation
and Chaos 13(6) (2003) 1475–1493.
8. H. Liu, F. Jiang, B. Xia and L. Yue, The experimentstudy of multiscroll chaotic circuit based on a stimulated
inductor circuit, Journal of Northeast Normal University(Natural Science Edition) 39(2) (2007) 55–59.

1. T. Matsumoto, A chaotic attractor from Chua's circuit,
IEEE Trans. on Circuits and Systems 31(12) (1984)
1055–1058.
2. Z. Yang, J. Zhang, Y. Ma, Y. Bai and S. Ma, Design andrealization of Chua's circuit based on current conveyers,
Acta Physica Sinica 59(5) (2010) 3007–3016.
3. B. Bao, W. Hu, J. Xu, Z. Liu and L. Zou, Analysis andimplementation of memristor chaotic circuit, Acta
Physica Sinica 60(12) (2011) 63–70.
4. E. Lv and S. Huang, The equivalent circuit design ofChua's circuit and application thereof, Chinese Journal of
Electron Devices 37(5) (2014) 891–895.
5. X. Zhang, H. Sun, J. Zhao, J. Liu, Y. Ma and T. Han,
Equivalent circuit in function and topology to Chua’s© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan272

Research on Visualized Rescue Robot
Yuanli Yue, Fengzhi Dai *, Qijia Kang, Pengfei Xie

College of Electronic Information and Automation, Tianjin University of Science and Technology, China
E-mail *: daifz@tust.edu.cn

Abstract
Aimed at the shortcomings of the low efficiency and the limitation of the search and rescue robot, this paper
presents a visualized search and rescue robot based on Arduino platform. The infrared detector for searching humanbody is used to detect lives, and the camera can transmit the information back to the mobile phone or computer
terminal in real-time. The track wheel improves the capability of shuttle and over obstacle, and can easily cope withcomplex terrain. The mounded manipulator can complete many missions, such as clean up the road block anddeliver essential supplies to small space. It can replace humans to carry out the task into the dangerous environmentand search-and-rescue person into the confined space.
Keywords: visualized, Arduino, track, search-and-rescue

environment is uncertain or serious. So we have higher
requirements for the robot. Search and rescue robots
generally have the following advantages 4-5:
1. Walking through the ruins to detect signs of life
freely with small size and flexible operation.
2. Having strong adaptability in various
environment, which can arrive at the high temperatureand high pressure place where the rescue workers
cannot arrive in.
3. Being equipped with other functional sensors
and detection equipment, such as infrared detector forhuman body and cameras.
4. Using lithium-ion batteries with low power
consumption and strong battery life, so it can work for along time.

1. Introduction
With the rapid development of science and technology,
the application of search and rescue robots become
wider 1. By virtue of superior mobility and well adapt
ability in various environments, search and rescue robot
have already been paid more and more attention. The
search and rescue robot is experiencing rapid
development, such as earthquake rescue robots, it is arobot used to search for survivors in the rubble of theunderground shopping mall after the earthquake, whichis equipped with a color camera, thermography andcommunication system 2. The robot can replace therescue workers into the dangerous environment, thenimplement the rescue work and transport the necessary
supplies for the trapped persons. Therefore, it isimportant to develop a rescue robot that can replace orpartially replace the relief workers into the unknown
environment 3.
Search and rescue robot is different from thehousehold robot or industrial robot. Its working

2. Research at home and abroad
Since 1980s, the concept of search and rescue robot has
been put forward, then, it gradually plays an
irreplaceable role in the rescue activities. The study ofsearch and rescue robots has several stages. One©The 2016International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan273

Yuanli Yue, Fengzhi Dai, Qijia Kang, Pengfei Xie

landmark event is the explosion of the Federal Buildingin Alfred, Oklahoma in 1995.It is the first time that the
search and rescue robots appeared in public 6. In 2001,
911 event, which is a case of search and rescue robot
actual maneuver. The following agencies to participate
in the rescue activities: The SOLEM system in FosterMilker, the VGTV system in Tolon and the Microtac
system in Inuktun. At the same time, many problems ofsearch and rescue robots are exposed, including the
choice of the control strategy and the problem of water
proofness 7.
Since the 911 event, countries step up the pace of
research on the rescue robots and set off a new wave ofresearch. Then, research on search and rescue robots
have a huge leap. Not only in theoretical and practicalaspects, people have made big progress, but also
published a lot of research results. And in appearance,
functionality and performance, robots are becoming
more and more perfect. Then, with the development of
the technology and science, the crawler robots, snakelike robots and spider-shaped robots have been appeared.
Owing to the frequent earthquake and tsunami,
Japan’s search and rescue robot has been at the forefrontof the world 8. The Japanese spider-shaped robot, by
mimicking the spider’s movement, the multiple foots
are coordinate with each other to complete a variety ofactions, or jump over the obstacles. Better than crawler
robots or snake-like robots, spider-shaped robots has agreater ability to adapt to the complex environment andto through the narrow space. In a word, it can quickly
and efficiently complete the search and rescue mission.
The "Asterisk" was developed by Professor Nii Kenzoat the Osaka University in Japan 9. Not only theAsterisk’s six foots can move on different ground, butalso can it be used as a hand to move on the ceiling.
Thus, it has a good prospect in the future.
Because of the complexity and the unknown
environment, the robot must have better performance.
And in order to better completing the search and rescue
work, it should have an outside perception, by which it
can work fine and transmit the current information in
real-time with harsh environment. Therefore, this paperon the basis of studying both at home and abroad, then
combined with the advantages and disadvantages of thepractical application, we studied a new visualized rescue
robot that has a greater ability to adapt to the complex
environment and more powerful rescue capability 10.

3. The overall scheme design of search andrescue robots
Firstly, search and rescue robots enter the rescue scene.
Second, gathering the information of the environment.
Finally, using the camera to transmit images to mobile
phone or computer terminal in real-time by wirelesstechnology. Not only the search and rescue robots
should have the ability of adapting to changeable
environment, but also need the following functions: Collecting parameters in spot environment:
Search and rescue robots arrive at the accident scene
to implement missions, which requires robots gathering
information accurately and efficiently. The mainfunctions are as follows: capturing motion video,
infrared detector for human body and informationtransmission in real-time.
 Excellent overstepping capability:
Overstepping capability is an important index forsearch and rescue robot. When a rescue robot
encounters obstacles, it can clear obstacles bymechanical arm. If the obstacle is big, it can also crosswith a mechanical arm as main origin of force.
 Feedback capability in real-time
Visualization is a major feature of rescue robots,
which can send the location information back to theoutside world. Therefore, relief workers can get
environment information and physical condition of thetrapped personnel in real-time. In a word, it can provide
more valuable information for the rescuer.
The overall scheme design of search and rescue
robots as shown in the Fig.1:

Fig.1 The overall scheme design of search and rescue
robots© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan274

Research on Visualized Rescue

4.

5.1. Main control chip

Design of mechanical structure

The control circuit choose the ATmega128-AU as themain chip, which is used to control and deal with avariety of information from sensors. Its voltage is 3.3-5
V and it has 16M high speed crystal. At the same time,
which can provide a high clock frequency. The main
control chip have a high processing speed, so it canprocess the data from sensors more timely and more
quickly.
ATmega128 in ATMEL is a CMOS 8 bit
microcontroller based on the AVR with low power andhigh performance. The structure of the AVR is theHarvard, with independent data and program
bus.ATmega128 contains EEPROM of 4K bytes, which
can meet the needs of the program. The EEPROM as aseparate department of data space, which reads andwrites processes in bytes. At the same time, the
EEPROM's life is at least 100000 times. The point of
ATmega128 SCM and main control chips shown in the
Fig 3.

The mechanical structure design of search and rescue
robots includes the design of the car chassis and thedesign of the manipulator. In the mechanical design
process, the main part is car chassis, which determines
maneuvering capability and it is important foroverstepping obstacles.
Through analyzing the rescue robot chassis, we canconclude that the design is turned up, in the front of the
car tracks, with an oblique angle (as shown in Fig 2.),
this design can greatly enhance the ability to climb
obstacles.

Fig 2. Crawler design of search and rescue robot
The design uses a rhombus frame structure. At thesame time，the material selected guarantees the frame's
strength as well as lower weight. Thus, in the main
bearing part, installing reinforced steel plate to enhancestrength. The height of overcoming obstacles isdetermined by the position of the robot's center ofgravity and the length of the track, therefore, we
lengthen the length of the track in the design of crawler
structure and using symmetrical structure in the designof car body. The crawler mechanical structure has many
advantages, such as gripping firmly, improving theability of climbing and having good ability to overcome
obstacles.
5.

Fig 3. Main control chip
5.2. Motor selection
The motor provides power for the whole search andrescue robot. Through the motor mutual cooperation, it
can complete a variety of different actions. As for motor
selection, large torque motor can provide enough powerin the progress of moving ahead. Therefore, the robot
use 37GB-500 permanent-magnet direct current speed
reduction motor, which rated voltage of 1.5-36 V ,rated
speed of 5-3000 (rpm) and rated torque of 0.02-30 (NM).

The circuit control unit

Circuit control unit includes the following sections：main control chip，driving motor, sensing scheme andremote signal transmission module. The sensing scheme
includes a variety of sensing modules. For example,
rotating machine arm function module, visual operation
function module and the human infrared sensor module
for detecting life characteristics. Finally, all thesemodules consist function platform of search and rescue
robots.

Fig 4. DC geared motor© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan275

Yuanli Yue, Fengzhi Dai, Qijia Kang, Pengfei Xie

6.

Introduction of functional modules

temperature. When the external alternating infrared
radiation shines on pyroelectric crystal, the temperatureof the crystal surface will have a significant change, so astrong external electric field is formed. Instead, whenthe external alternating infrared radiation does not shine
on pyroelectric crystal, the temperature of the crystal
surface will not have a significant change, so a strong
external electric field is not formed. The characteristicof pyroelectric crystal can be applied to detect
alternating infrared, but cannot detect the constantinfrared. Pyroelectric crystal is generally produce
electrical charge, so it needs to be converted into
voltage and then put into use. Pyroelectric infrared
sensors can detect whether the life exists, because thethickness of the filter is generally 14 ~ 8 m, it can cover
the body's infrared. Thus, the sensors of infrared
detector for human body achieve the effect, detecting
human infrared. The sensors of infrared detector forhuman body as shown in the Fig 6.

The sensor parameter gathering and human detection
are very important aspects in the field of search andrescue robot. With a variety of auxiliary function
modules, search and rescue robot can detect the location
information and life characteristics of the trapped
personnel, by which the rescue work can complete inreal time. The data-collection of a single sensor islimited, so that it only gets partial environment
information and does not let rescue workers get enoughinformation of the trapped people. Thus, integrating
multiple sensors into one piece instead of a single
sensor, which has several advantages: enhancing
detecting precision, reducing the costs of getting
information, improving the intelligence level of searchand rescue robots and completing the task in the shortesttime.

Fig 5. The overall structure figure

Fig 6. The sensors of infrared detector for human body

6.1. The module of infrared detector for human body
6.2. Rotating mechanical arm
The visualized rescue robot can search and rescue
trapped person in dangerous areas or in ruins, therefore,
it need to carry the module of infrared detector forhuman body. In nature, all the objects will appear thephenomenon of infrared spectrum, so long as its
absolute temperature exceeds -273℃.Such as: human
body, stones, mountains, etc. But they have different
wavelengths in the infrared spectrum. According to thisnatural phenomenon, it can be applied to the search andrescue work. From other sources: Normal bodytemperature is 36~37 ℃ and human body products
wavelength of infrared spectrum is 9~10μm. Thus, inthe rescue, pyroelectric infrared detector can be used toshorten the search and rescue time and increase theefficiency of search and rescue.
Pyroelectric effect is a polarization phenomenon
that pyroelectric crystal will changes with the change of

Search and rescue robot has a six-degree of freedom
Rotating mechanical arm, which is an important part forsearch and rescue robot. When a small obstacle is in
front of the robot, it can easily pass it by the rotating
mechanical arm. If the obstacle is too big, themechanical arm helps the crawler to climb. Thus, it has
a stronger adaptability in complex environment. The
rotating mechanical arm is made up of upper arm, lower
arm and rotary frame. Upper arm and lower arm are
mounted on the rotating frame by two large torque servo
to drive, which has enough strength to carry goods. And
when the lower arm moves, there’s no need for
additional arm operation, handling or cleaning operation
can be controlled by a mechanical linkage firmware.
The advantage of it is vastly simplifying operational.
And the rotating frame is a support part of the wholemechanical arm system, on one side of it is equipped© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

276

Research on Visualized Rescue

with a high torque servo for rotating run the entire
system.
Some parameters of the rotating mechanical are as
follows: The rotating angle can reach on 180 degrees,
the gyration radius is 355mm, the maximum height is
460mm (the gripper closed), the maximum of the
opening in front of the gripper is 55mm and the widest
range of the clipped goods is 98mm.Rotating
mechanical arm is shown in the Fig.7.

clarity in the image transmission when the disasterscene is in poor light.
(3) The camera have the TF card slot in which
video and image information can be saved in the rescue
and relief operations. Therefore, it is convenient to
optimize path in the progress of follow-up succor.
(4) The visual angle rotates 360 degrees and thecamera rotates 80 degrees to ensure video capture in
narrow space.

Fig 7. Rotating mechanical arm

Fig 8. Video camera

6.3. The function of visualization

7.

Search and rescue robot has a function of visualization.
In the upper part of the robot was equipped with two
small geared motors that control the camera to move up
and down or left and right, which is a major video
capture device. The function of visualization is theprimary mean to providing the environment information
for rescuer in real-time, which we can see theimportance of it. The situation of collapsed may be inpoor light, thus, using lights may cause secondary
damage to the affected people. In order to avoid thissituation, the infrared camera is been selected, whichwith the function of wireless transmission. The model ofthe camera is SP-H01W (as shown in Fig 8), which
wide-angle is 3.6mm and effective distance is 10m.And
it has video recording function, which provides valuable
data for the disaster relief.
The camera has the following features:(1) The module of wireless transmission optimizes
the H.264 lossless compression arithmetic, this method
can decrease the transmission data and time in effect,
which ensures high picture quality.
(2) With infrared light, the camera automaticallyswitches between modes of day and night by the IRCUT. In this way, it can guarantee the accuracy and

The realization of the overall function

7.1. Selection of rescue route
Since we have finished the construction of Hardware
and software platform of search and rescue robot, thelast step is to process information from the robot andplan the path to reach to the scene. The information
processing style is based on fuzzy reasoning, at thesame time, the path planning based on the combinationof artificial potential field method and grid method.
Ultimately, through the test and analysis of the test
results, it can be improved that the search and rescue
robot is feasible.
Grid method has some disadvantages. First, theintensity of dividing the grip is difficult to control. Then,in order to accurately represent the location of therobots and obstacles, a large amount of storage space is
occupied by much smaller grid granularity. But on thecontrary, if the grid size is too large, it will make therobot path planning very inaccurate.
The artificial potential field method raised by
Khatibis a virtual method, its basic idea is that we
consider the motion of the robot in surrounding
environment as a motion in a artificial gravitational field.
The target point exerts a gravitational pull, on thecontrary, the obstacle exerts repulsion to the mobile© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

277

Yuanli Yue, Fengzhi Dai, Qijia Kang, Pengfei Xie

Acknowledgements

robot. Finally, the movement of the mobile robot is
controlled by the resultant force. In the process ofsearch and rescue, to avoid crashing into obstacles andto approach the target gradually, the method is generallysmooth and safe. But at the same time, the method has adefect of locally optimum.
The problem of path planning based on the
combination of artificial potential field method and grid
method. It is obvious that the two can learn from eachother. Not only solving the problem of path planninginaccurately caused by grid granularity dividing, butalso can we find the exact target coordinates by theartificial potential field method.

The research is partly supported by the Research Fundfor the Doctoral Program of Higher Education of China(20131208110005).
References
1. Guohua Xu, Min Tan. The development and tendency ofmobile robots. Robot Technique and Application, 2001,(3): 7-14.
2. You He, Guohong Wang, Dajin Lu. Multi-sensor
information fusion (Continued). Infrared and Laser
Engineering, 1999, 28(2): 10-15.
3. Yinghui Huo, Lianming Zhang, A path planningalgorithm for mobile robot. Techniques of automation
and applications, 2003, 22(5): 8-10.
4. Lei Li, Tao Ye, etc. The research and future research ofmobile robot technology. Robot, 2002, 24(5): 475-480.
5. Xiaodong Zhuang, Qingchun Meng. The robot pathsearch method based on fuzzy concept in dynamic
environment. Robot, 2001, 23(5): 397-399.
6. Jun Wang, Xinhan Huang. Obstacle avoidance control of
two wheeled mobile robot based on neural network
learning. Robot, 1996, 18(5): 292-297.
7. Shiyong Li. The theory of fuzzy control and intelligent
control. Harbin Institute of Technology Press, 1999
8. Qingzhong Li, etc. A path planning method for mobile
robot based on genetic algorithm. Pattern Recognition
and Artificial Intelligence. 2002, 15(2): 161-165.
9. Zhiwen Wang, Ge Guo. The present situation andprospect of mobile robot navigation technology. Robot,
2003, 25(5): 470-474.
10. Leize Jin, Zhenjun Du, Kaijia. The research on
simulation of path planning for mobile robot based on
potential field method. Computer Engineering andApplications. 2007, 43(24): 226-228.

7.2. Testing
By the test of the search and rescue robot, the following
data is obtained. The maximum height of the barriercrossing is 120 mm, the maximum angle of climbing is
38, the maximum range of detection is a circle of radius
1, and the maximum distance of transmission is 22m.
Through the above analysis of the data, which canbasically meet the needs of rescue.
8.

Conclusion

On the basis of analyzing current robot, a kind ofvisualized rescue robot was designed. The robot has
more perfect functions better than current rescue robot.
On the one hand, the design of walking manner is basedon the crawler structure with an oblique angle, andcooperate with rotating mechanical arm, which improve
the ability of climbing. On the other hand, using camera
and the technology of wireless transmission to transmit
the image information to the control terminal in realtime. At the same time, with the sensors of infrared
detector for human body, the rescue robot can detect theaffected person on a wide range, through these functions,the search and rescue robot can achieve timely rescue.
In the design, the final step is test, which include theability of climbing, the accuracy of infrared detector forhuman body and the relationship between wirelesstransmission distance and obstacle. Finally, the test
result shows that the visualized search and rescue robot
have a good ability to overcome obstacles and canbasically meet the needs of rescue.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan278

Development of Training Instrument for Upper Limb Muscle Rehabilitation
Qijia Kang, Fengzhi Dai *, Yuanli Yue, Bo Liu, Hongtao Zhang

College of Electronic Information and Automation, Tianjin University of Science and Technology, China
E-mail *: daifz@tust.edu.cn

Abstract
This equipment can work in multiple degrees. By the single joint and composite motion, it imitates human upper
limb movement. Composite motion can be multiple degrees of freedom, so that the equipment can fit with thephysiological structure of human body well. Upper limb strength can be recovered efficiently and the muscle
contraction can also be well preserved as expected. Obtaining vibration module, it enhances the effect of recovering.
Wireless transceiver module makes the wireless monitoring and data wireless transmission be a feasible way. It isconnected with the computer, and the recover information of patients could be gathered, analyzed and recorded incomputer as well. Date can also be transmitted by the internet, so as to offer the recover information to medical
institution.
Keywords: multiple degrees, efficiently, recovering, muscle contraction, training instrument

paralysis or half paralyzed. The symptoms are thatupper limbs are not controlled by the brain, upper limb
activity was blocked, and even lost independent living
skills. It can cause muscle belly shortening in a shorttime, then will harden the soft tissues which are near themuscles and joints and make the joint contracture, overtime can lead to loss of muscle contraction function andmuscle atrophy. It was called disuse atrophy. It willproduce adverse effect on the digestive system andcardiovascular system and worsen the condition. From
what has been mentioned above，we can come to theconclusion that it is important to develop the equipmentthat it can help patients with muscle rehabilitation 1.
Rehabilitation training not only can effectively
alleviate the exacerbation of illness. Insisting for a longtime to arm rehabilitation exercise, the patients also can
obtain good effect, such as make muscle cross-sectional
area increase, the enlargement of muscle fibers. Then
can enhance the shoulder muscle strength and muscular
endurance and prevent muscle contracture around

1. Introduction
With the rapid development of economy, people's living
standards have improved significantly since the 21st
century. So modern people pay more attention to theimprovement of life quality. High incidence of diseases
such as apoplectic hemiplegia is seriously threatening
the health of human body. How to deal with those ills
has become a problem to be solved.
The symptoms of apoplectic hemiplegia mainly
embodies in the patient's physical activity, which is thatthe limbs can't be controlled by the brain. Due to theseverity of the disease, the symptoms will show in avariety of sides. The limbs of the most serious cases arecompletely unable to motion. Some patients only showthat physical activity becomes awkward and action
becomes inconvenient. The physical activity of others
performances worse and worse and due to the untimely
recovery treatment, the illness becomes more serious.
The clinical manifestations of this kind of patients are©The 2016International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan279

Qijia Kang, Fengzhi Dai, Yuanli Yue, Bo Liu, Hongtao Zhang

shoulder joint 2. Muscles exercise can speed up theupper limb blood flow, and indirectly enhances the
cardiovascular function, meanwhile, patients can getsome relief in psychological and spiritual aspects.
Rehabilitation exercise can be divided into the passive
motion, the massage movement, the help active
movement, the active movement, the resistancemovement and so on. The research shows that theguidance of proper upper limb movements and auxiliary
recovery can restore upper limb movement, strengthen
the upper body strength, repair the damaged motor
function and prevent malformation, paralysis andatrophy of upper limbs. Thus, it can play a positive
recovery effect.
Upper limb rehabilitation equipment is a kind ofmodern device which can help paralyzed patients toavoid to lose function of upper limbs and help upper
limbs muscle rehabilitation. But this kind of auxiliary
recovery equipment research is still in its infancy. There
aren't finished products on sale on the market. And mostof instrument developed by the researchers at home andabroad has its limitations. There is a lot of difficulty
which hasn't been overcame in technical and functional
aspects 3-4. Problems are mainly manifested in thefollowing aspects:(1) Safety performance. Safety is the primarypremise. Safety should be considered firstly in the
rehabilitation process. It includes that the limitation ofhuman upper limb movement should be considered. For
example, in order to avoid the harmless to human body
health, it should be taken into account whenrehabilitation equipment is in action group.
(2) The man-machine comfort. For complete relax inthe recovery process can help enhance recovery effect,
it requires equipment should have good comfort andcan't make the patient produces psychological resistance.
(3) Portability. Because the existing recovery area ofsome hospital is too large, mobility is not convenient,and restricted by regional recovery process, Patientsneed spend too much time and energy on treatment.
Those require that the recovery equipment should be insmall shape, easy to move, recover at home and savetime and effort.
(4) Working time. Because the recovery equipment
need motor to drive the human upper limb and costmore power, the arm recovery equipment need havelong life battery and ensure the recovery time meet therequirements.
2. Research of the field
Many experts from elite schools domestic and overseas
have done a lot of researches about the upper limb
rehabilitation equipment 5. Such as the six degree of
freedom robot ROBOTHERAPIST designed by
Takehito Kiuchi team from Osaka University. The
whole robot use external traction structure, throughmeasuring the hand position of patients, it couple backtraining information, patients can also have access to
movement information through screen with the virtual
reality technology. This robot can make patients recoverpreferably, and add more interest into recover training.
But the cost of manufacture is too high to popularize topublic, it’s more suitable to deploy in professional
places such as hospital.
Helparm rehabilitation equipment produced by
Kinsman company 6, use pulley winded by wire rope tomotive patient upper limb to achieve rehabilitation
training. With simple structure, each side of patients canbe trained partly, it has strong controllability, whichmake it easy to achieve rehabilitation training at homeor in community. The defect is it’s hard to control andcan only provide several movements, which can’t makea full recovery.
3. Design of mechanical structure
Upper limb auxiliary recovery equipment should havesufficient strength, and can withstand the pressure ofhuman arm and pulling force. In the aspect of material
selection, use the high strength aluminum plate, and use
organic glass in upper limbs fixed place. Organic glass
plate is bent into a radian. The radian which accords
with human body upper limb physiological radianmakes patients without the feeling of discomfort aftertighten the arm. Recovery device based on a chair. It
gives patients a more comfortable experience. In orderto make patients in rehabilitation has the best effect,mechanical structure was designed into the shape ofhuman upper limb exoskeleton to making it easier for
upper limb when fixed.
The designed machinery structure considered theactual situation of different patients. In order to meet thedifferent needs, can adjust the length of the forearm and© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan280

Development of training instrument

the big arm with more holes on the fittings. There also
design the adjustable artifacts, which can slightly adjustin the design of the shoulder. A rough adjustment is
controlled by a set of three cross slide guide rail.
Stepper motor provides power supply, which by remotecontrol can automatically adjust. The design meets thedifferent patients demand, making it to have a wider
range of use.
The hardware structure of the mechanical structureparts takes the design of the stationary. For small land
occupation, the whole hardware of device fixed on theseat. On the design of some adjustable parts innovateboldly, by adjust the spacing and density of drilling holeto achieve the adjustment of dimension. 3 d figure of theoverall and local figure as shown in figure 2-1, 2-2 and
2-3.Material selection is 6 mm thick aluminum plate.
The density of aluminum plate ρ = 2700 (kg/m^3).
Take the person of weight 75 kg, height 75 cm as an
example by check data. The weight of Human upper
limb a single arm materials is about 3.5 kg 7. Throughthe data calculation and finite element analysis, 6 mm
thick aluminum plate can support the weight of thehuman upper limb and meet the required strength
requirements. Three-dimensional structure are shown in
Fig 1.

Need low speed large torque in the rehabilitation
process .Large torque servo provide tangential force to
do twist motion, generate bigger torque in a smaller
space and provide convenience for shoulder structure
design .The large steering engine is used to complete thetraining of rehabilitation.
4.2. Construction of circuit system
In the aspect of electric system's control: Master control
chip is Freescale 32-bit Kinetis (ARM Cortex - the M4)
micro controller .It has the advantages of highperformance, low cost and low power consumption .The
steering gear control panel is used on the control andexecution of the action. The steering gear control panel
can provide more accurate rotation Angle. Realize thecommunication between the servo control board and thestm32 chip. In the servo control board keep several setsof action in advance. When the steering gear control
panel and stm32 chip make communication each other,draw on the inner recovery process according to thedemand of the patients. There have set a few buttons on
the back of arm recovery equipment. Family membersof patients or doctors can choose different rehabilitation
movement patterns according to the needs. The functionof control system as shown in Fig 2.

Fig 2. The function of control system
4.3. Limit size of upper limb

Fig 1. Three-dimensional structure

The upper limb rehabilitation equipment is designed tohelp patients recover in a safe and comfortable way, as aresult, the extreme dimension is necessary while
designing in case injury may happen during training.
Through consulting data the author found: In the human
body, the limit of the physiological structure of theupper limb of is 80°~90°.When the degree is based on
the upright standing human body with arm dropping, theangle of lifting arm upward is 170°and extending arm

4. Research on human upper limb rehabilitationequipment
4.1. Review of circuit system
In the aspect of software: Synthetically analyzing thestrength needed by the human upper limbs andcalculating the steering engine power and torque need
output pulse signal to control steering gear rotation.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan281

Qijia Kang, Fengzhi Dai, Yuanli Yue, Bo Liu, Hongtao Zhang

4.5. Current and voltage

backward is 40°. The range of swing arms left and rightis 70°to 60°.In order to prevent accident that do harm to
the patient during the recovery process, we must set
aside scope of safety margin during compile programof rehabilitation action. Setting the security range(should not exceed the limit angle) is as follows: therange of lifting arm upward is 0°~130° and extendingarm backward is 0°~10° and the range of swing arms
left and right is 0°~30°. It must fit with thephysiological characteristics of human body at the sametime, and refer to the dimension of human upper limb
and range of motion of the body.

At the same time, Aiming at different voltage
requirements of chip and motor in the system, adopt thepower supply method of different lines with different
voltage. In the aspect of power supply, high-power,
multi-voltage output switching power supply as thepower supply equipment, chip with 5V voltage of power
supply, motor power supply with 12V voltage. those not
only meet the requirements of the voltage of the motor,
make the motor has enough energy to drive the upper
limb movement, but also can satisfy the requirement of
the chip safe voltage[8].Equipment as shown in Fig 4.

4.4. Realize alarm function
Upper limb rehabilitation auxiliary equipment also has
the function of ultralimit alarming. When the duty cycle
adjusts the output, it may be result steering rotational
angle is too large from adjusting the duty cycle is toolarge. Thus, it will lead to accidents if it is beyond thescope of the human upper limb. According to the humanupper limb medical data, we pre-configured angularrange of human upper limb movement in the mechanicaldesign and program. In the recovery process, if theequipment will exceed the limited data, the angle
monitor will detect the change. As the same time, it will
send the feedback to the main control chip, and thentrigger the alarm to prevent the action. It makes theequipment keep in a safe range and prevent damage topatients. In a word, the mode of automatic alarm andreset has a protective effect on patients and enhances
safety performance. The main function as shown in Fig
3.

Fig 4. Equipment
4.6. LCD and data transmission
Upper limb rehabilitation system is equipped with liquid
crystal display, which not only can display the current
patterns, exercise and other information, but also showthat the rehabilitation training time and rehabilitation
action group. The design so as to patients and theirfamily members can better understand the current stateof motion and reasonably arrange rehabilitation exercisegroup, training time and intensity of training. LCD
shown in Fig 5.

Fig 5. LCD shown

Fig 3. The main function© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan282

Development of training instrument

sagittal axis and vertical axis. The plane divides thehuman body into left and right two parts.
The shoulder joint motion is defined into three kinds:
flexion and extension movement, outreach andadduction movement and extorsion and intorsion
movement by Human anatomy 9.
(1) Flexion and extension movement: Arms movearound the coronary axis in the sagittal plane. Forward
is flexion. Backward is extension.
(2) Outreach and adduction movement: Arms movearound sagittal axis in the coronal plane. Away from thetrunk is outreach. Near the trunk is adduction.
(3) Extorsion and intorsion movement: Arms movearound the vertical axis movement in the horizontal
plane. Away from the trunk is extorsion. Near the trunk
is intorsion.
Upper limb movement is divided into two categories
in accordance with degree of freedom of the movement.
(1) Single joint movement
Single joint movement is only a single joint training,such as the elbow bend and stretch. It is a training
methods which is adopted in the early stages of therehabilitation training, or to improve the strength of aparticular muscle group. The way of movement is
simple. The purpose is clearer. It can strengthen theimpression of the nervous system to specific actions andpromote the rapid recovery of neural function.
(2) Compound movement
Compound movement is the most popular way inrehabilitation exercise. Patients' muscle groups must be
achieved through multiple joints movement, and multijoint muscle in the majority in the joints and muscles.
So the compound training must be conducted:
compound movement can exercise the coordination ofmuscle, and the control of multiple muscle groups.
Compound movement mainly adopt the way of a
specific task to achieve, such as take water glass todrink. This series of composite action, must passthrough joint movement to achieve. At the same time,treat patients from the psychological and improve therehabilitation confidence of patients. It must be very
careful when composite motion do, avoid muscle
damage.
For normal human upper limb movement, the brain
controls the upper limb different muscle contraction inorder to realize the upper limb movement, thencomplete a series of actions. Upper auxiliary

The equipment is different from the traditional device.
It is equipped with vibration module, in the recovery
process, the vibrating plate is attached to the patient's
arm by the control unit to control it. It can stimulate thepatient's muscles, blood circulation and peripheral nerve,at the same time, combined with the recovery design ofprofessional doctors. Then you can achieve better andfaster recovery of upper limb motor function.
The wireless transceiver module, which is introducedin the device, can realize wireless monitoring andwireless transmission of motion data. By the module,
the doctor can monitor remotely the patient's motion
state, and change the program in real time. The
computer terminal equipment can summarize andanalyze the recovery information of patients, and canrecord the recovery of patients, which helps medical
workers to analyze and improve the action group. It
provides the basis for the record of the rehabilitation
data and provides the basis for the future dataprocessing, by the Internet, it can provide rehabilitation
data for medical institutions.
5. Medical foundation of upper lamb rehabilitation
The movement of the human body shoulder joint isaccomplished by movement of the arm movement. To
design a reasonable shoulder joint rehabilitation training
plan, need to analyze the mode of Human arm motion.
According to the theory of the human anatomy, when
the body vertically stands on the ground, call the axis
which is perpendicular to the ground and through the
body vertical axis, call the axis which through the bodyof the front and rear and is perpendicular to vertical axis
sagittal axis, call the axis which through the body and isperpendicular to the above two axes coronal axis. The
human body is divided into three planes by the humananatomy. They are defined as: Horizontal plane (cross section): It parallels to theground and through the sagittal axial and coronalaxis. The plane divides the human body into Upperand lower two parts.
 Coronal plane (frontal plane): It is perpendicular tothe sagittal axis and through the coronary axis andvertical axis. The plane divides the human body
into front and back two parts.
 Sagittal plane (longitudinal section): It isperpendicular to coronal axis and through the© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan283

Qijia Kang, Fengzhi Dai, Yuanli Yue, Bo Liu, Hongtao Zhang

rehabilitation equipment according to the human upper
limbs motion common action to drive the upper limb
muscle contraction and expansion. Action group isdivided into different movement patterns: the horizontal
motion model, the vertical movement patterns andstretching model. Different patterns train different
muscle tissue. This can get better effect.

of meeting the structure and strength requirement,
greatly reduced the quality of ontology and control the
manufacturing cost. Equipment test result and the effectof human movement experiment of upper limbs torestore movement basically corresponds. It shows thatthe design of the upper limbs restoration equipment
structure design is reasonable. In the design of the upper
limbs restoration movement groups, can achieve theeffect of upper limbs to restore, feel safe andcomfortable and provide certain help for upper limb
disorders.

6. Conclusion
Upper secondary recovery equipment is a kind of forupper limb movement disorder patients recovery
equipment. The recovery equipment based on theanalysis of the human body of medical knowledge,mechanical structure design, and the construction of thecircuit system, combined with the test data and so on
comes out the conclusion. Through mode selection for
designed recovery actions in advance group then moves
to restore, restored information real-time display on the
LCD screen.
The innovation points of this paper is to design theadjustable mechanical structure and recovery of realtime display. This device can replace physical therapy
for patients with rehabilitation massage in some degree,achieve the effect of upper limb rehabilitation. At thesame time, consider the characteristics of less occupied
area, easy to move and portable. Therefore therehabilitation equipment made into the shape of a chair.
In order to reduce the cost of production, adopt standard
parts as far as possible in the meantime. with 24V safe
voltage，make it safe and reliable to use.
Synthetically consider arm recovery equipment
simplicity, aesthetics, and security of human body
recovery and comfort of the design. Ultimately
determines the design of upper limbs restorationequipment:(1) From a security perspective come out the extreme
dimension of human upper limb. With alarm device tomonitor the data.
(2) From the point to achieve the best recovery effect,
design different action group to implement the recovery
of upper limb different muscles.
(3) From the humanistic perspective, the display
screen shows the currently executing movement patterns
and can be adjusted and chosen according to need.
In this paper, the design of upper limbs restorationequipment adopted a large number of aluminum and its
standard fittings in structural design. Under the premiseAcknowledgements
The research is partly supported by the Research Fundfor the Doctoral Program of Higher Education of China(20131208110005).
References
1. Yonglian Zhu, Ruihua Wang, Dinghua Fang,
Neurorehabilitation, BeiJing, People's Military Medical
Publisher, 2001
2. Ming Yang, Research on rehabilitation training device ofelbow join. Wu Han, Huazhong University of Scienceand Technology, 2008
3. Yuchuan Hu, Linhong Ji. Discussion on the design of
rehabilitation training robot for upper limb rehabilitationfrom the medical point of view. Chinese Journal of
Clinical Rehabilitation, 8(34): 7754-7756
4. Hesheng Wang, Yunhui Liu, Dongxiang Zhou. Adaptive
Visual Servoing Using Point and Line Features With an
Uncalibrated Eye-in-Hand Camera, IEEE TRANSACTIONS ON ROBOTICS VOL. 24, NO. 4, AUGUST
2008
5. Feng Cai. Design and research of shoulder joint
rehabilitation training robot. Dalian, Dalian Jiaotong
University,2012
6. Dequan Guo, Hui Ju,Yuqin Yao. Research of
Manipulator Motion Planning Algorithm Based on
Vision. 2009 Sixth International Conference on Fuzzy
Systems and Knowledge Discovery. 2009
7. GB1000-88.Human dimensions of Chinese adults.
Beijing: China Standards Press,1988
8. Zhiyan Jin. Rehabilitation nursing of stroke patients withhemiplegia. Journal of Clinical Nursing, 2006, 4: 5-2.
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© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan284

Haptic system with fuzzy controller for extended control of Teleoperation mine detector
wheeled robots
Yekkehfallah Majid

School of electronic and information engineering, Xi’an Jiaotong University, Xi’an 710049,China
Yuanli Cai ,Guao Yang, Naebi Ahmad, Zolghadr Javad

School of electronic and information engineering, Xi’an Jiaotong University, Xi’an 710049,China
Majid.fallah@stu.xjtu.edu.cn, ylicai@mail.xjtu.edu.cn, gayang@mail.xjtu.edu.cn, ahmad.naebi@stu.xjtu.edu.cn,
javad.zolghadr@gmail.com

Abstract
In the tele-operation robots one of the big challenges is operator’s error in urgent situations that can make big problem
for robot and other systems which working beside the robot. This paper proposes an approach of haptic system andfuzzy logic system with implementation on tele-operation deminer robot, in order to reduce operator’s error throughfusion of haptic system and fuzzy logic controller, The haptic system convey vibration to user’s hand and fuzzycontroller, controlled rate of the vibration in different situation to user hand and controlled the speed of the robot.
Keywords: Haptic system, fuzzy logic, deminer robot, vibrotactile, magnetic field.

This approach presents a fuzzy logic system (FLS)
based force feedback generation method for deminer
robots in teleoperation manipulators deminer robot. The
approach utilizes the MDS’s output and position of arm
to generate desire vibrotactile using FLS.
In this paper, we investigated problems of previous
work on deminer robot such as safety controlling,
communication time delays, communication fail, andoperator’s error. The structure of this paper is in thefollowing: in section 2 we described completely previous
work on demine robot, in section 3 we explained new
approach in detail. In section4 where we implemented
FLS and haptic system in MATLAB fuzzy tools, while
in section 5 we concluded result of implementation insection 4.

1. Introduction
Tasks of deminer robot is detecting mine in landmine andmarks it with tools mounted on the robot (fig. 1) andrescue robots [1].Currently robots controlled by humanoperators [2] with visual display (fig. 2) and operators arelimited in their interact with the robot and because ofdifficult situation and the low threshold for dealing on
demine tasks. Minimizing operator’s error while care forhigh level of accuracy and speed is difficult also if
operator can’t clearly hear or see data from mine detector
sensor (MDS) in operator GUI (fig.2) or for some otherproblem as communication delay between operator androbot so the robot will enter to hazardous region. For
these reasons we used vibrotactile feedback according to
the level of MDS’s output to inform user when robot isnear dangerous touch with environment.
Accordingly, we investigated the use of a force threedimensional joystick for applying forces and vibrations
on user hand. This joystick connected to operator
computer in (fig. 5) the joystick haptic device also used
to control the robot speed and robot navigation [3].

2. Background2.1. Deminer robot
Basically mobile deminer teleoperation robot uses for
detection mine in landmine with MDS mounted on© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

285

Yekkehfallah Majid, Yuanli Cai ,Guao Yang, Naebi Ahmad, Zolghadr Javadthe robot and user controls this robot by joystick, butuser just send command to robot by joystick andmonitoring robot is through visual display (fig. 2) andsound without any repulsive feedback from MDS inurgent situations.

2.3. Electromagnetic induction
As we mentioned before, in this section there are twoparts first one is transmitter and second one is receiver
we simulated this two coils with FEMM (fig. 4)[4][5].FEMM is one of power full software that uses fordesigning and analyzing magnetic problems[6][7].

Fig. 1: photographs of the deminer robot.

Fig.4: color flux density plot of two coils with mine.
we added a mine in analyzed region, most of flux passed
through mine because reluctance of mine is smaller than air
therefore most flux passed through low reluctance and least flux
passed through receiver coil and caused least EMF induced inreceiver coil.

3. Haptic system and fuzzy logic controller
3.1 New approach
Our solutions for those problems we mentioned inprevious section (2.1) are haptic system and fuzzy logic
controller. Haptic system solved feedback problem wesuggest two ways for using haptic system in this project
the first one is vibrating electric motors in joystick andsecond suggestion is haptic force three-dimensional
joystick (fig.5) actually this joystick is a haptic system
contains the vibration electric motors and joystick(fig.5) .Haptic increases human sensitivity fromenvironment and user’s hand can feel environment
through vibrotactile feedback.
The level of vibrotactile with respect to different
situations as MDS sense, robot speed and robot’s arm
status is variable, for controlling level of vibrations andvelocity of robot we used fuzzy logic controller.

Fig. 2: operator GUI which contained: camera monitoring,robot controller, mine illustrates, network monitoring, robot’s
batteries status illustrates, laptop’s battery status illustrates,
motors statues indicators.

This console developed in windows and this is agraphical consol between operator and robot, this console
installed in operator computer (fig. 5)
2.2. Mine Detector sensor (MDS)MDS work on the principle of transmitting amagnetic field and analyzing a return signal from thetarget and environment. These systems may use a single
coil as both transmitter and receiver, or they may have
two or even three coils working together. In this project
we used two coils first coil is transmitter and second one
is receiver signal fig. 3.

Fig 3: Overall hardware of mine detector sensor© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan286

Haptic system with fuzzy

for arm status as : Down position(DP),obstacle avoidance
position(OAP),rest time position(RTP) in last portions
relatedtovibrotactile
signalas:veryslow(VS),slow(S),medium(M),fast(F), very fast (VF)and nothing (NOP).
Since one input divided to five fuzzy sets and another one
divided to three fuzzy sets, thus fifteen fuzzy rules forvibrotactile controller must determined. Following theoutput signal of MDS directly related to distance between
coil and mine in the ground therefore when the robot is
far from the mine sensed a mine with very low level andfuzzy logic gets the level of the MDS and give very slow
vibrotactile to haptic system and haptic system transfer
vibration to user hand and user feels mine throughvibration.

Fig. 5: overall framework of operator side controller.
In new approach we added force three dimensional haptic
joystick in this part.

(a)Fig. 6: overall framework of deminer robot.

4. Implementation(b)

Trap membership function used for MDS inputs andoutputs in two FLSs, (fig. 8) and (fig. 10).the rule basedin this work is shown in Table 1 and Table 2The
commonly used Madman’s min-max implication
function was utilized. Finally for defuzzification we used
centroid technique because it’s very accurate .The control
surface generated by the rule base and the given fuzzy
sets is depicted in (fig. 9) and (fig.11). Detail of fuzzy
logic system could find in literature [8] [9].

(c)Fig. 7: (a) membership functions of MDS. (b)Membership functions of arm status. (c) Membershipfunctions of vibrotactile.

4.1. Fuzzy logic system for dealing with haptic
systemTable1: Rule table of the vibrotactile controller with respect
to MDS output and arm status.

Respectively, they are decomposed into five fuzzy
partitions for output ,such as very low (VL),low(L),medium(M), high(H),very high(VH), and three partition

Fig. 8: The output surface of the implemented fuzzy
vibrotactile, generator.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

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Yekkehfallah Majid, Yuanli Cai ,Guao Yang, Naebi Ahmad, Zolghadr Javad

The results shows which fuzzy logic controller and haptic
system, significantly reduced operator’s fault and also
fuzzy logic can prevent dangerous contact between the
robot and mine when network connection drops, with thismethod operator drives robot with confidence because
this system warned the operator in urgent situation.

Three-dimensional curve that represents mappingfrom MDS and arm to level of the vibration, because
this curve represents a two-input and one output case.
As this curve is obvious when the arm is in down
position and MDS level is increased therefore
vibrotactile corresponding with MDS’s level increases.

6. ACKNOWLEDGMENT
This work supported in part by grant from the XI’AN
JIOTONG UNIVERSITY and MRL laboratory of QIAU.

4.2. Fuzzy logic system for velocity controller
This section is similar to section (4.1) inputs of FLS aresame as last one just output is different.

References[1] Sibert, John, et al. "Vibrotactile feedback for enhanced
control of urban search and rescue robots." Proceedings ofthe IEEE International Workshop on Safety, Security andRescue Robotics. 2006. Page1[2] Smith, F. M., D. K. Backman, and Stephen C. Jacobsen.

Fig9: Membership function of velocity.

[3]

[4]
[5]

Fig 10: The output surface of the implemented fuzzy velocity[6]controller.

Three-dimensional curve that represents mappingfrom MDS and arm to robot velocity, because thiscurve represents, a two-input and one output case. As
this curve is obvious when the arm is in down position
and MDS level is increasing in other side velocity withrespect to MDS’s level reduces.

[7]
[8]

[9]

5. CONCLUSION"Telerobotic
manipulator
forhazardous
environments." Journal of Robotic Systems 9.2 (1992):
251-260.
Pantelios, Michael, et al. "Haptics technology in
Educational Applications, a Case Study." JDIM 2.4(2004): 171-178.
Meeker, David. "FEMM 4.2 Electrostatics Tutorial1."(2006).
Jin, Jian-Ming. The finite element method inelectromagnetics. John Wiley & Sons, 2014.
Turner, Robert. "Gradient coil design: a review ofmethods." Magnetic Resonance Imaging 11.7 (1993): 903920.
Singh, Isha, and Rajul Mishra. "Analysis of Magnetic
Field of Air Cored Solenoid using FEMM."
Mizumoto, Masaharu, and Hans-Jürgen Zimmermann.
"Comparison of fuzzy reasoning methods." Fuzzy sets andsystems 8.3 (1982): 253-283.
Iancu, Ion. A Mamdani type fuzzy logic controller.
INTECH Open Access Publisher, 2012.

This paper presented a dynamic, real-time fuzzy logic
based force feedback control for deminer robot inremotely operated robot manipulators. The presented
method utilizes distance from the mine to the robot, itneeds consider two items first one is mine distance torobot and second one is arm’s position with respect tothese items new approach generates force feedback andcontrol speed of the robot. The presented method was
implemented on a deminer robot and for simulate andillustrates result of vibrotactile data and velocity
controller we used MATLAB.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

288

Effects of System Parameters and Controlled Torque on
The Dynamics of Rigid-Flexible Robotic Manipulator
Sachindra Mahto

Mechanical Engineering Department
NERIST, Itanagar, Arunachal Pradesh-791109, India
E-mail: smh@nerist.ac.in
www.nerist.ac.in

Abstract
This work illustrates the effects of various system parameters on the dynamics of flexible link of revolute-jointed
rigid-flexible manipulator. Flexible link is considered as a Euler-Bernoulli beam and finite element based on
Langrange approach is employed for dynamic analysis. A comparative study is carried out for comparative
dynamic response for the variation of system parameters and controlled torque excitation.
Keywords: Rigid-flexible revolute manipulator, Euler-Bernoulli beam, Shape optimization, Finite element method,
Parametric study

the gravity, payload, and the coupled vibration between
the first and second arm. Usoro et al. [3] developed
finite element models to describe the deflection of aplanar multi-link model. Ower and Vegte [4] used a
Lagrangian approach to model the planar motion of amanipulator consisting of two flexible links and tworotary joints. Benati and Morro [5] developed a
Lagrangian approach for the dynamics of chain withflexible links..
Bayo [6] used FEM to deal with multi-link
flexible manipulator considering Timoshenko beam
theory and including nonlinear Coriolis and centrifugal
effects for the elastic behavior. Jonker [7] presented anonlinear finite element based formulation for analyzing
the dynamic behavior of flexible manipulators. De Lucaand Siciliano [8] presented closed-form equations of
motion for planar flexible multi-link robot arm. Morrisand Madani [9] studied the accurate modeling based on
Lagrange-Euler formulation of a two link flexible
manipulators.
Dogan and Iftar [10] carried out the modelingand control of two-link robot manipulator whose first

1. Introduction
In the last few decades, dynamics and control of theflexible manipulators have received a considerable
research attention. Flexible robotics systems haveseveral advantages over the convention systems.
However, due to lightweight of flexible systems,
stiffness is low and there is a serious problem ofvibration. Sometimes, to retain the advantages of theflexible system, some of the links are made flexible andsome are rigid. Still the dynamic behaviour
improvement, optimal design and proper control
strategy is the research interest for flexible manipulator.
Most of the researchers contributed their works
in different ways of dynamic modeling and control
aspects of flexible robotic manipulators. Sunada andDubowsky [1] developed a lumped parameter FEM
model for analyzing the complete behavior of industrial
robotic manipulator with complex-shape flexible links.
Fakuda and Arakawa [2] studied the modeling anddynamic characteristics of two-link flexible roboticarms and controlled the vibration by taking into account©The 2016International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan289

Sachidra Mahto

link is rigid and second link is flexible. Everett et al.
[11] showed that it is possible to design a two-link
flexible manipulator that has nearly position invariant
first natural frequency with wide separation between thefirst two natural frequencies, to have its behaviour likerigid manipulator. Chen [12] developed a linearized
dynamic model for multi-link planar flexible
manipulator. Yang et al. [13] studied the tip trajectory
tracking control for flexible multi-link manipulator
using Lagrangian assumed mode method. Zhang et al.
[14] derived a partial differential equation model for a
flexible two-link manipulator using Hamilton's principle.
Some researchers presented their works onshape optimization of static/rotating beams. Karihaloo
and Niordson [15] determined the optimum tapering of
a cantilever beam carrying an end mass to maximize
fundamental frequency. Wang [6] addressed optimum
design of a single link manipulator to maximize its
fundamental frequency. Yoo et al. [17] used theassumed mode method for dynamic modelling ofrotating flexible manipulator for modal analysis andshape optimization to increase the fundamental
frequency of the beam. Dixit et al. [18] presented afinite element model of single link robotic manipulator
for revolute as well as prismatic joint. They used SQP
for optimizing beam shapes under different optimization
conditions.
From the above survey, it is observed that there
is no much research contribution for the effects ofsystem parameters in the dynamics of rigid-flexible
robotic manipulator. In this paper, the dynamic behaviorof robotic manipulator is presented for the variation ofsystem parameters and improvement through control
strategy.
2.

treated as a Euler-Bernoulli beam and gravity force isneglected.
Consider a point P in the ith element on the
manipulator at a distance ' x ' from the elbow hub. The
point P attains the position P with respect to inertial
frame of reference (XOY) after having rigid body
motion θ1(t) and θ2(t) of shoulder and elbow joint
respectively and flexural deflection w ( x, t ). Flexural
deflection w ( x, t ) of point P is approximated in finite
element technique using Hermitian shape functions as
w  x, t   N1w2i 1  N 2 w2i  N 3 w2i 1  N 4 w2i  2   N  W  , (1)where  N    N1 N 2 N 3 N 4 and

W    w2i 1 w2i
T

w2 i 1

w2 i  2  .

Fig. 1. (a) Configuration of rigid-flexible manipulator,(b) Typical ithelement with six dof

In FEM formulation, the manipulator is divided
into finite number of elements with each element havingth
six degrees of freedom. Detail of i element of the link
is shown in Fig. 1(b), where θ1 and θ2 are the hub
rotation of shoulder and elbow joints respectively, w2i-1,
w2i and w2i+1, w2i+2 are the transverse deflection andslope at the first and second node of the element
respectively. The position vector of P with respect toinertial system XOY is given by Eq. 2. For theprediction of approximate dynamic behaviour of the
optimized beams, smaller excitation torque isconsidered for linearization of system modelling. Global
position vector of the point P under smaller angularand flexural displacement is given by

Modeling and Solution Technique

The finite element formulation has been adopted here as
described by Usoro et al. [3]. Fig. 1(a) shows rigidflexible manipulator comprised of two links, two joints
and tip load. The links are clamped on the joints.
Shoulder joint (joint 1) is located at the origin of XOY
represents the stationary co-ordinate frame and elbow
joint (joint 2) is located at the origin of X1O1Y1 andX2O1Y2 which represent the moving co-ordinate frames.
manipulator is considered slender. Flexible link is© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

290

Effects of System Parametersa  L1  b  (i  1) h  xX  
r  op   Y   ( a  L1 )1  ((i  1)h  x  b)(1   2 )   N  W  ,

1 
1
  


1 h  w
T 1h'' T ''
Vi   EI  2  dx  W   EI  N   N  dx W  .
  
2 0  x 
20

(2)
In finite element method, variables are converted into
nodal variables.
r  op  f 1 , 2 , w2 i 1 , w2 i , w2 i 1 , w2 i  2  .
(3)Thus, elemental stiffness matrix is given byhKie  EI   N ''  T  N ''  dx

LetZ  1  2

w2 i 1 w2 i  2  ,w2 i 1 w2 i

2

(9)

0

t



 r 

T(4)

 Z  Z .



0
0

EI 0
Kie  3 
h 0
0

0

then

absolute velocity of the point P of the flexible link is
obtained as

r

2

e

 

0
0
0



0
0
12

0
0
6h

0
0
12

0 
0 

6h
2

.

2



0 6h 4h 6h 2h 
0 12 6h 12 6 h 
2

0

6h

2h

2

(10)

6h 4h 

2.1 Kinetic Energy Computation of the Link Element
2.3 Lagrange’s Equation of Motion in Discretized
Form

th

The Kinetic energy of the i element of the link isgiven by r T r 
e 1h(5)
Ki   m2  dx .
20 t t 
We haveTTrr
T  r   r Z(6) Z   Z  Z .
t t
Substituting Eq. 10 in Eq. 9, we haveT r   r  
e 1 T hKi  Z   m2
    dx  Z , (7)20  Z   Z  First link is rigid and posses kinetic energy only.
However, being second link flexible it posses bothkinetic and potential energy. The kinetic energy and thepotential energy of the system are obtained bycomputing energy of rigid link and the energy of eachelement of the flexible link and then summing over all
the elements. The global mass matrix and global
stiffness matrix can be obtained as1
T1TT   q   M  q  and V   q   K  q  (11)22respectively.
Here q  1  2 w1 w2 . . w2 n 1 w2 n  2  isand the elemental mass matrix is given byT r   r e h
M i   m2
 Z    Z dx
0
 M11
M 21

e  M 31
Mi 
M 41

M12
M 22
M 32
M 42

 M 51 M 52

M 61 M 62

M13
M 23

M14
M 24
Pi ( 4X4)

the global nodal vector. The Lagrangian of the system is

M15
M 25

given by L = T - V and then Lagrange’s equations ofM16 
M 26 


.





motion of this dynamic system may be written as  L L Fq ,
t  q  q

(8)

(12)

where Fq is the generalized force vector. Due tomodelling linearization, global mass and stiffness
matrices are constant and equation of motion ofundamped system is expressed as(13)
M q   K q  F .

All the constants of the above matrix in Eq. 8 are
obtained by proper integration.

Global load vector {F} and global nodal displacement

2.2 Elastic Potential Energy of the Link Element

vector {q} for ' n ' number of finite elements are givenTby {F }   1  2 0 0.....0 0  .

The potential energy of the ith element of link due toelastic deformation is given by© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

291

Sachidra Mahto

3. Optimization Procedurelink lengths in its natural frequencies, static tip
deflection, hub angles, and dynamic response areplotted in Fig. 2 to Fig. 5. Natural frequencies of theflexible links decrease with increase of its length andvice versa. Static link deflection increases with increaseof its length. Hub angles increase with decrease of its
length and vice-versa. Residual vibration increases withincrease of length of the flexible link.

To retain and optimize the advantage, upper limit of the
optimized mass (M*) is constraint to the prescribed mass
(M). In the rigid-flexible robotic system, flexible link is
considered for the shape optimization. X = [d1 d2 ….....
dn]T is the design vector with di indicating diameter ofthe ith finite element of link 2.
Table 1. Different Optimization Problems
Optimization Problem/Objective
Constraint
Maximization of fundamental beam
M* -M≤0frequency
Permissible Bound : XUB < X<XLB

Minimum and maximum diameter of the beam elements(X) are denoted by XLB , XUB respectively. The
MATLAB function “fmincon” employing sequential
quadratic programming (SQP) technique is used forconstrained optimization of nonlinear function.
4. Results and Discussion

Fig. 2. Beam frequencies of flexible link for different link
lengths

Modeling of this system is highly complex andnonlinear in nature. However, linearized model ispreferred and considered in this work to predict its
approximate dynamic behavior. Structural dimensionsof this revolute-jointed rigid-flexible robotic
manipulator for numerical experiments are taken as
lengths 0.75m and 0.75m, diameters 0.03m and 0.01m,
mass densities 2710 kg/m3 and 2710 kg/m3 , hub radii
0.02m and 0.01m, hub inertias 0.03 kgm2 and 0.03 kgm2
for first and second link respectively. Young's modulus
7.11x1010 N/m2 and mass of the motor 0.1 kg is
considered.
Excitation torque τ=τmsin2πt Nm is considered
for both the joints for 2 seconds action. Torqueamplitude 3.0 Nm and 1.0 Nm are considered forshoulder and elbow joints respectively. Static load of
1N is considered at the tip of the manipulator forcomparative static beam deflections. The ration of
payload (Mp) to the mass of second link (M2) is denotedby µ2; ratio of motor mass (Mm) to mass of the first link(L1) is denoted by µ1 and ratio of the second link length(L2) to first link length (L1) is denoted by (L*).

Fig. 3. Static deflection of flexible link due to 1N force at tip
for different link lengths

4.1 Effects of Different Link Length
Fig. 4. Hub angles due to excitation torques for different link
lengths

As the second link is flexible, its length is varied withrespect to the first link length. Effects of variation of© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan292

Effects of System Parameters

Fig. 7. Dynamic flexible tip deflection for different payloads
Fig. 5. Vibration residual of the tip of flexible link for
different link lengths

4.2 Effects of Different Payload, Motor Mass and HubInertia
Robotic system means to take load and due to flexible
links, there is always flexural vibration. Changes in the
dynamics of the manipulator due to the change of
payload, motor mass and hub inertia are plotted in Fig. 6
to Fig. 11. Hub angle decrease and residual vibration offlexible link tip increases with the increase of payload
and vice-versa. By increasing the motor mass which isacting as a payload for link 1 has significant effect onshoulder joint angle for a given set of torques but lesseffect on elbow joint, however residual vibration
increases. By increasing the hub-inertia of the shoulder
joint, hub angles of shoulder joint decreases and elbow
joint increase for a given set of applied torque and very
less effect on the residual vibration of the flexible linktip.

Fig. 8. Hub angles for different motor mass for set of applied
torquesFig. 9. Dynamic flexible tip deflection for different motor
mass

Fig. 10. Hub angles for different hub inertia for set of applied
torquesFig. 6. Hub angles for different payloads for set of applied
torques© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan293

Sachidra Mahto

Fig. 11. Dynamic flexible tip deflection for different
hub inertia
Fig. 14. Comparison of natural frequencies

4.3 Effects of Mass Distribution of Link
Optimal design has a great importance in engineering
applications. Shape optimization is done as per the
optimization problem (Table 1) to increase thefundamental frequency and the optimized shape of theflexible link is shown in Fig. 12, where the mass of thelink is re-distributed. It is observed that mass isconcentrated more towards the root side in shape
optimization. Static link deflection due to 1N force at tip,natural frequencies of the optimized flexible link areplotted in Fig. 13 and Fig. 14 and its dynamics (hubangles and residual vibration) are plotted in Fig. 15 andFig. 16 respectively for set of excitation torque. It isobserved that parameters viz., static tip deflection,
natural frequencies, hub angle and residual vibration are
improved.

Fig. 15. Comparison of joint angles for set of applied torques

Fig. 16. Comparison of tip residuals for set of applied torques

4.4 Effects of Controlled Torque
Yigit [19] presented the position and derivative (PD)control torque for single link revolute-jointed flexible
manipulator as given below(14)
 PD   K p (   f )  Kv ( ).

Fig. 12. Optimized shape of flexible link

The feedback gains K p and K v depend upon theequivalent rigid system parameters and are expressed as
1

3

2

2

(15)

K v  2( J h  mL  M p L ) f n .

3

2

(16)

K p  ( J h  mL  M p L ) f n ,
3

and
1

3

Fig. 13. Static beam deflection due to 1 N force at the tip of
optimized link
© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan294

Effects of System Parameters

where 'm' is the mass per unit length, 'L' is the link
length, 'Jh' , 'θf' is the final angular position, and 'fn ' isthe fundamental frequency. PD controller is able tostabilize the system but vibration of the flexible beam
can not be controlled. Ge et al. [20] extended the workof Yagit [19] and introduced energy-based robust (EBR)
control law and added the nonlinear deflection feedback
to improve the performance of the PD controller byadding the nonlinear control term as given by

5. Conclusion
In this work, finite element analysis of revolute-jointed
rigid-flexible manipulator has been performed throughlinear modeling. Classical nonlinear optimization isused to solve the constrained shape optimization. From
the numerical experiments, it is observed that there is agreat role of system parameters (link lengths, payloads,
hub-inertia, link shape) in its system dynamics andshould be analyzed properly during the design. It is also
observed that controlled torque improves the system
dynamics further.

t EBR   PD  K f y ( L, t )   ( ) y ( L,  )d ,

(17)

0

where K f is the gain constant of robust control,  isthe dummy variable,  is the hub angular accelerationand y is the deflection at the tips. Above control law isalso applicable for multi-link robotic revolute jointed
systems.
Elbow angle due to the controlled torque isplotted in Fig. 17 and residual vibration is shown in Fig.
18. It is observed that hub angle is achieved faster bycontrolled torque and tip vibration is lesser than thesinusoidal excitation.

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P.B. Usoro, R. Nadira and S.S. Mahil, S.S., A finite
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Fig. 17. Comparison of elbow angles for different excitation

Fig. 18. Comparison of dynamic tip response for different
excitation© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

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©The 2016International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

297

Modeling of Mobile Manipulator and Adaptive Super-Twisting Backstepping Control
Seong-Ik Han

Dept. of Electronic Eng., Pusan National University, Jangjeon-dong, Geumjeong-gu
Busan, 609-735, Korea Republic
Deok-Su Kim

Dept. of Electronic Eng., Pusan National University, Jangjeon-dong, Geumjeong-gu
Busan, 609-735, Korea Republic
Hyun-Uk Ha

Dept. of Electronic Eng., Pusan National University, Jangjeon-dong, Geumjeong-gu
Busan, 609-735, Korea Republic
Jang-Myung Lee
Dept. of Electronic Eng., Pusan National University, Jangjeon-dong, Geumjeong-gu
Busan, 609-735, Korea Republic
E-mail: skhan@pusan.ac.kr, deoksu1696@pusan.ac.kr, hyunuk.ha@gmail.com, jmlee@pusan.ac.kr
www.pusan.ac.kr

Abstract
A mobile manipulator with three wheels and three DOF links is modeled by using Euler-Lagrange method andvector orientated constraint conditions. In this modeling process, the conventional complex nonholonomic
constraint transformation need not be considered in mobile robot system and then much simpler dynamic model canbe obtained. Next, the super-twisting sliding mode control is combined with nonlinear backstepping control to
obtain the systematic nonlinear controller design, fast response speed, and improved robustness to uncertainty due
to dynamic coupling and disturbance. Simulation and experiment were carried out to prove the effectiveness of the
proposed control methodology.
Keywords: Mobile-manipulator, Super-twisting sliding mode control, Backsteppping control.

nonlinearities is overcome via adopting step-by-steprecursive process.
However, although a controller designed using thistheorem guarantees the infinite-time stability of aclosed-loop system, it has drawbacks such as a slow
convergence rate and reduced robustness to uncertainty.
On the other hand, systems with finite-time settlingtime design possess attractive features such as
improved robustness and disturbance rejection
properties [7], In this paper, terminal backstepping
control based multi-agent consensus control for EulerLagrange system with one-leader and multi-followers isdeveloped.

1. Introduction
In recent years, there has been a great interest for
researches of multi-agent systems, whose applications
include spacecraft, mobile robots, sensor networks, etc.
Interesting research directions are containment control,
consensus, formation, and flocking control [1]. These
problems focus on two cases, namely, the case that
there does not exist a leader and the case where there
exists a leader. The coordinate tracking problems to
track a single leader have been investigate for followers
with single-integrator, double-integrator,high-order
dynamics, nonlinear or Euler-Lagrange dynamics [2-5].
Linear control theory and variable structure control
methods in most researches are used. On the other hand,
there are few examples that use the backstepping
control technique [6] for nonlinear or Euler-Lagrange
multi-agent system. In this method, the problem of
unmatched uncertainty and neglecting the efficient

2. Background and Preliminaries© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

298

Seongik Han, Deoksu Kim, Hyunuk Ha, Jangmyung Lee

2.1. Description of the Mobile-Manipulator

11
m1 (l1s1 ) 2 and I 2  m2 (l1s1  r2 s12 ) 2 .
3
3
The potential energy is obtained as follows:V  m1 gr1c1  m2 g (l1c1  r2 c12 ) .
(2)The dynamics are obtained using the Euler-Lagrangian
method. From the tire dynamics, we obtain(3)
I zwl   l ,where I1 withI zwr   r ,the kinematic(4)
relationsx  ( xr  xl ) / 2 ,x  ( xr  xl ) / 2 , r  xr / R , l  xl / R , l  xl / R ,Fig. 1 Schematic diagram of the two-link and twowheeled mobile manipulator.
The following variables are selected to describe thetwo-link and two-wheeled mobile manipulator shownin Fig. 1:  r , l , and  2 are the torques of two wheels
and the joint2;  r and l are the rotation angles of theright and left wheels of the mobile platform,
respectively; x and  are the forward distance and therotation
angle
of
the
mobileplatform,
respectively; 1 and  2 are the rotation angle of the links
1 and 2, respectively; m p  5kg ,mw  0.58kg ,m1  2kg , and m2  0.5kg are the masses of the mobile
platform, wheel, link 1, and link 2, respectively;
I p , I1 , and I 2 are the moment of inertia of the mobile
platform, link 1, and link 2 with respect to y axis,
respectively; I1 , I2 , and I w are the moment of inertia
of link 1, link 2, and each wheel with respect tothe z and z1 axis, respectively; d  0.145 m is the
distance between point P and the wheels; R  0.08 m is
the radius of the wheels; l1  0.25 m and l2  0.2 m are
the lengths of link 1 and link 2;
and r1 and r2 are thedistances between the joints and the center of mass of
the links.
The generalized coordinated is selected asq  [ x,  , 1 , 2 , l , r ]T . The total kinematic energy
is given as,

T1
1(m p  m1  m2  2mw ) x 2  ( I p  I w  I1  I 2 ) 2
2
2
1
1
 (m1r12  I1 )12  m2 [r1212  r22 (1   2 )2
2
2
I 
1
1
2r1r2 (12  12 )c2 ]  I2 22   mw  zw2  xr2
2
2
R 
I 
1
  mw  zw2  xl2 ,
2
R 

(1)

and r  xr / R , which is obtained from the assumptionthat a slip does not occur between tire and contact
surface. Therefore, we obtain IFl  l   zw2  M l  xl ,
(5)
R R


r

I

  zw2  M r  xr .
(6)
R R

Under the assumption of no sideslip, the relationships
Fr 

of M r  M l  M w , x  ( xl  xr ) / 2 ,   R(l  r ) / 2d 

( xl  xr ) / 2d , and   R(l  r ) / 2d  ( xl  xr ) / 2dgive
  I
Fl  Fr  l  r   zw2  M w  ( xl  xr )R R R
Fl  Fr 

l
R



r

I

 2  zw2  M w  x ,
R
R


(7)

l

 I

 r   zw2  M w  ( xl  xr )R R R


l


I

 r  2d  zw2  M w   .
(8)
R R
R

Finally, the reduced generalized coordinate is givenas qr  [ x,  , 1 , 2 ]T . The derived dynamic equation is
written as
M (qr )qr  C (qr , qr )qr  G(qr )  B(qr ) ,(9)
Where,
0 m13 m14 
 m11
 0 m
0
0 
22
,
M (qr )  
 m31
0 m33 m34  m41 0 m43 m44 
0
0
0 0

0 0

l
l
eq1
eq 2

,
C (qr , qr )  0 0 m2 r1r2 2 s 2 m2 r1r2 2 s 2 0
0 0 m2 r1r21s 2
T1/ R d / R 0 0 
B(qr )  1/ R d / R 0 0  , and   [ l , r ,  2 ]T .
 0
0
0 1 

© The 2016 International Conference on Artificial Life and Robotics (ICAROB
2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan299

Modeling of Mobile Manipulator

By denoting v and m as suffixes of the mobile
platform and manipulator, respectively, the coordinates
qr can be decomposed as q  [qvT , qm ]T , qv  [ x,  ,1 ]T , and qm   2 . Thus, (51) can be written as
follows:
 Mv
M
 mv

M vm   qv   Cv
M m   qm  Cmv

Cvm   qv   Gv Cm   qm  Gm    B 0    v   dv    v(10)  .
 dm   0 Bm   m 
The state space model of the mobile platform of (10)can be expressed as follows:x1  x2 ,x2   M v1  Cv x2  Gv  Fdv   M v1Bv u2
yv  x1 ,(11)

where x1  qv , x2  qv , Fdv  M vm qm Cvm qm   dv ,and u2   v . The state space model of the manipulator
from (10) can be expressed as follows:x3  x4 ,x4   M m1  Cm qm  Gm  Fdm   M m1Bmu4 ,ym  x3 ,(12)

where, x3   2 , x4   2 ,Fdm  M mv qv  Cmv qv  dm , and u4   m . The state variables of the mobile

manipulator system are selected as x1  [ x1,1 , x2,1 , x3,1 ]T [ x,  ,1 ]T , x2  [ x1,2 , x2,2 , x3,2 ]T ,

 [ x,  ,1 ]T ,x4  x4,2   2 . The control

x3  x4,1   2 , andobjective is that the outputs track the desired commandin finite-time, and the tilting angle, 1 , should bemaintained stably in the vertical position.
3.

Design of Finite-Time Controller Design andStability Analysis

3.1 Design of a STA backstepping controller for amobile platform
The auxiliary tracking error and new states are definedas
zv1  qv  qvd ,(13)

 v1,1  kv1 zv1  zv1

1/2

sign( zv1 ) ,t v1,2   v1,2  [kv21 zv1  (3 / 2)kv1 zv1
0

 v1sign( zv1 )]d .

(14)
1/2

sign( zv1 )(18)

 v 2,1  kv 2 zv 2  zv 2

1/2

sign( zv 2 ) ,t

 v 2,2   v 2,2  [kv22 zv 2  (3 / 2)kv 2 zv 2
0

(19)
1/2

sign( zv 2 ) v 2 sign( zv 2 )]d ,If we select the virtual control as follows: v1    v1,1 v1,1  qvd   v1,2 ,(21)(21) can be expressed as v1,1   v1,1v1 v1,1  v1 v1,2  v1 zv 2 .

(22)

(20)

 v1  [ v1,1  v1,2 ]T v1 Av1 v1  v1Bz1 ,   v1,1
where Av1     v1,2

(23)

1
z 
and Bz1   v 2  . Defining the

0
 0 

Lyapunov function candidate Vv1  (1/ 2) vT1Pv1 v1 ,Vv1  v1 vT1Qv1 v1  v1 v1 v1 zv 2 ,(24)

where  v1  (cv1  4 v21 ) v1,1  2 v1 v1,2 ; Pv1 is a positive
definite matrix defined with constants cv1  0 ,  v1  0 ,c  4 v21 2 v1 
Pv1   v1
 and is a solution of an1  2 v1
algebraic
Lypunov
equation(ALE)
AvT1Pv1  Pv1 Av1  2Qv1 , and a positive definite
symmetric matrix Qv1 is defined as v1,1 (cv1  4 v21 )  4v1,2 v1 (cv1  4 v21 )  2 v1v1,1  v1,2 
Qv1  .
4 v1
(cv1  4 v21 )  2 v1v1,1  v1,2

Defining the state vector as  v 2  [ v 2,1  v 2,2 ]T and
one can then obtain that v 2,1  v 2 [ M v1Cv qv  v  gv v )  v1 ] , v 2,2  v 2  v 2,2 v 2,1 ,

(25)
(26)

If we select the control law  v as


  
 v  gv   v 2,1 v 2,1  M v1Cv qv  ˆv  v1 v1 v1 zv 2   v1  , (27)v 2 v 2 v 2

Defining the following Lyapunov function candidate asVv 2  Vv1  (1/ 2) vT2 Pv 2 v 2  (1/ 2v ) vT v , (28)where v  0 is constant. The time derivative of Vv 2 iswritten as
1 T
Vv 2  Vv1   vT2 Pv 2 v 2  v v v
2

(15)

 v1,1  v1 ( xv 2  qvd ) ,(16)

 v1,2  v1v1,2 v1,1 ,

(17)

 1

zv 2  xv 2  v1 ,  vi viT Qvi vi  v1 v1 zv 2 v1 v1 zv 2
i 1



1
 vT  v 2 v 2 v 2  ˆ v  ,
v 


(29)

where v1  kv1   v zv1 v . Define the virtual error

where  v 2  (cv 2  4 v22 ) v 2,1  2 v 2 v 2,2 . If we selectsurface and new states as

an adaptive law as© The 2016 International Conference on Artificial Life and Robotics (ICAROB
2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan300

Seongik Han, Deoksu Kim, Hyunuk Ha, Jangmyung Lee

ˆ v  v v 2 v 2 v 2  v ˆv  ,(30)

where   v  0 are constants, (50) can be written as:2

Vv 2   vi viT Qvi vi v vT ˆ vi 1

2

  v vT v

i 1

2

  vi viT Qvi vi   v vT v
2. (31)

3.2. Design of a STA backstepping controller for amobile platform
The auxiliary tracking error and new states aredefined as:
zm1  qm  qmd ,(32)

zm2  xm2   m1 , m1,1  km1 zm1  zm1

(33)
1/2

sign( zm1 ) ,

t

(34)

 m1,2    m1,2  [km21 zm1  (3 / 2)km1 zm1

1/2

0 m1sign( zm1 )]d , m2,1  km2 zm2  zm2

1/2

(35)

sign( zm 2 ) ,t

 m 2,2    m 2,2  [km2 2 zm 2  (3 / 2)km 2 zm 2

sign( zm1 )

(36)
1/2

0

sign( zm 2 ) m2 sign( zm2 )]d .
(37)
We select the virtual control, the control law, andadaptive law as follows: m1    m1,1 m1,1  qmd   m1,2 ,(38)
 m  g m1  M m1 (Cm qm  Gm )   m 2,1 m 2,1 m1 m1 m1
zm 2  ˆ m   m1  , m 2 m 2 m 2
ˆm   m vm m2 m2  m ˆm  .

(39)
(69)

4. Simulation Example
To validate the proposed control scheme, the following
group of one leader indexed by 0 and four followers
indexed by 1, 2, 3, and 4, respectively as shown in Fig.
1. The strict feedback state equations of each agent are
expressed as:

Fig. 2. Simulation result for proposed algorithm. (a) Mobile
platforms tracking performance, (b) Pitch control of mobile
platform, (c) Yaw control of mobile platform (d)Manipulator’s position control(Sinusoidal input).

Above figure 2 illustrates simulation result for vehicle’s
tracking performance, pitch and yaw control, andmanipulator’s sinusoidal postion control.
5. Conclusion
A terminal backstepping control scheme to guarantee
the fast error convergence and small tracking error
performance for a multi-agent Euler-Lagrange system
is developed in this paper. A virtual finite-time error
surface is defined to design a virtual control. The finitetime convergence is proved by the finite-time stability
analysis of Lyapunov function. Simulation for one-link
manipulator agents confirms the theoretical proposal.
Acknowledgements
This work was supported by the National Research
Foundation of Korea(NRF) grant funded by the Korea
government(MSIP) (NRF-2015R1A2A2A01004457).

© The 2016 International Conference on Artificial Life and Robotics (ICAROB
2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan
301

Self-tuned Local Feedback Gain Based Decentralized Fault Tolerant Controlof Reconfigurable Manipulators
Bo Zhao

The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation,
Chinese Academy of Sciences, Beijing 100190, China
Bo Dong, Yan Li

Department of Control Science and Engineering, Changchun University of Technology,
Changchun 130012, China
Fumitoshi Matsuno

Department of Mechanical Engineering and Science, Graduate School of Engineering, Kyoto University,
Kyoto Daigaku-Katsura, Nishikyo-ku, Kyoto 615-8540, Japan
Yuanchun Li*

Department of Control Science and Engineering, Changchun University of Technology,
Changchun 130012, China (Corresponding Author)
E-mail: zhaobo@ia.ac.cn, dongbo@ccut.edu.cn,liyandianqi@ccut.edu.cn, matsuno@me.kyoto-u.ac.jp, liyc@ccut.edu.cn

Abstract
This paper investigates the decentralized fault tolerant control (DFTC) scheme based on self-tuned local feedback
gain to against partial loss of actuator effectiveness of reconfigurable manipulators. Consider the entire system as aset of interconnected subsystems due to its modularity property, the decentralized control method is proposed by
employing two neural networks for the system fault-free. For the subsystem suffers to partial loss of actuator
effectiveness, the self-tuned local feedback gain is added to the proposed decentralized control method to guarantee
the control performance. Finally, a simulation example is provided to demonstrate the effectiveness of the present
DFTC scheme. The main contributions of this method are: i) The fault tolerant control structure is simple since it is
no need to be redesigned in the presence of partial loss of actuator effectiveness; ii) The actuator fault can behandled in its local subsystem, it implies that the performance degradation of the faulty subsystem can not affect thefault-free subsystems.
Keywords: Fault tolerant control; Decentralized control; Neural networks; Partial loss of effectiveness;
Reconfigurable manipulators.

occur after working for a long time. They bringperformance degradation or system damaged if they can
not be repaired timely.
As the modularity property of reconfigurable
manipulators, the decentralized control architecture ismore feasible than the centralized one. Zhu et al.3
proposed a decentralized adaptive fuzzy sliding mode
control scheme so that the system was guaranteed to bestable joint by joint. K. Sato et al.4 investigated a

1. Introduction
Reconfigurable manipulators consist of a set of joint andlink modules in standard size and interface1-2. By adding
or removing the modules, they can change theirconfigurations and degree of freedoms (DOF) to adapt
different tasks. Hence, the flexible structure serves
plenty of potential applications. However, failures ofactuators, sensors or other components will inevitably© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan302

Bo Zhao, Bo Dong, Yan Li, Fomitoshi Matsuno, Yuanchun Li

effectiveness factor of the ith subsystem with 0  i  1
holds, which presents the degree of fault.

decentralized control scheme in which phasic and tonic
control were well coordinated, this scheme obtained
success on a snake-like robot. Liu et al.5 presented a
DFTC based on an observer for fault detection ofmodular reconfigurable robot with joint torque sensing.
Motivated by it, Yuan et al.6 developed an independent
joint power efficiency estimation-based healthmonitoring and fault detection method. Zhao et al.7
investigated an active DFTC strategy for reconfigurable
manipulators based on local joint information and an
unknown input observer. They also8 proposed an active
DFTC scheme based on signal reconstruction to against
the sensor fault in reconfigurable manipulators.
This paper presents a self-tuned local feedback gain
based FTC scheme for reconfigurable manipulators withpartial loss of actuator effectiveness. For the fault-freesystem, a decentralized neural network control isdeveloped to stabilize the closed-loop reconfigurable
manipulator system. By adding a self-tuned local
feedback gain, the trajectory tracking can be guaranteed
to the acceptable level though the fault occurs. The
simulation of 2-DOF reconfigurable manipulator showsthe effectiveness of the proposed method.

Let xi   xi1 , xi 2   qi , qi  , (1) can be expressed asT xi1  xi 2
Si :  xi 2  fi (qi , qi )  gi (qi ) i ui  hi (q, q, q)y  xi1
 i

gi (qi )  M i1 (qi )hi (q, q, q)   M i1 (qi ) Zi (q, q, q)
The control objective is to design a DFTC scheme
for the faulty subsystem of reconfigurable manipulators(1) to make the joint displacements to follow their
desired trajectories.
3. Decentralized Fault Tolerant Controller
Design3.1. Decentralized control design for the fault-freesystemAssumption 1. The desired trajectories are bounded asqidqid  q A

For the development of the decentralized control, eachjoint is considered as a subsystem of the entire
manipulator system interconnected by coupling torques.
And then the dynamical model of the ith subsystemsuffers to partial loss of actuator effectiveness can beformulated in joint space as
M i (qi )qi  Ci (qi , qi )qi  Gi (qi )  Zi (q, q, q)  i ui (1)where q A  0 is a known constant.
Define the tracking error as
ei1  xi1  xi1d
Introducing a first order filter assi  i ei1  ei1

Assumption 2. The interconnected term hi (q, q, q) is
norm-bounded as3nhi (q, q, q)   dij S j2where d ij  0 and S j  1  s j  s j .
For the system (1) in fault-free, i.e., i  1 ,differentiating (5) and using (4), one hassi  i ei1  ei1(7) i ei1  f i (qi , qi )  g i (qi )ui  hi (q, q, q )  qid

inertia matrix, Ci (qi , qi ) is the Coriolis and centripetal
force, Gi (qi ) is the gravity term, Zi (q, q, q) is theinterconnected term, and ui is the applied joint torquerespectively.

i

is

(6)

j 1

, n , qi , qi , qi are the vectors of joint

subsystem,(5)

where i  0 is a known constant.

displacement, velocity and acceleration, M i (qi ) is the

ith

(4)

where xi1d is the desired displacement of the ith joint.

 n
Z i (q, q, q)    M ij (q)q j   M ii (q )  M i (qi )  qi  j 1, j  in   Cij (q, q)q j  Cii (q, q)  Ci (qi , qi )  qi  j 1, j  i Gi (q )  Gi (qi ) 

the

(3)

qid

with

of

(2)

where xi and yi are the state and the output of thesubsystem Si , respectively, andfi (qi , qi )  M i1 (qi )  Ci (qi , qi )qi  Gi (qi ) 

2. Problem Statement

where i  1, 2,

Tthe© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan303

Self-tuned Local Feedback Gain

where the nonlinear terms fi (qi , qi ) and gi (qi ) areapproximated by employing two ideal radial basis
function (RBF) neural networks asfi (qi , qi ,WifT )  WifT  if (qi , qi )   if ,  if  1 (8)gi (qi ,WigT )  WigT  ig (qi )   ig ,  ig   2

 if

vectors,

 ig

and

are

their

Proof. Define i  i  ˆi and i  i  ˆi , and selectthe Lyapunov candidate function as1
1
1
Vi  siT si  WifT  if1Wif  WigT  ig1Wig
2
2
2
(21)
1
1
  iT  i1 i  iT  i1i
2
2Taking its time derivative and noticing (7), we haveˆ if and ˆ ig are the estimation of  if and  ig , we havefˆ (q , q ,Wˆ T )  Wˆ T ˆ (q , q )
(8)
i

i

if

if

if

i

i

gˆ i (qi ,WˆigT )  WˆigT ˆ ig (qi )(9)Let Wif  Wif  Wˆif , Wig  Wig  Wˆig be the weight
estimation errors, we havef (q , q ,W )  fˆ (q , q ,Wˆ )
i

i

i

if

i

i

i

Vi  siT si  WifT if1Wˆif  WigT ig1Wˆig

if W ˆ if (qi , qi )  WifT  if (qi , qi )   if
Tif

gi (qi ,Wig )  gˆ i (qi ,Wˆig )  iT i1ˆi  iT i1ˆi(10)

 siT (i ei1  fi  gi ui  hi  qid )  WifT if1Wˆif(11)

 WigT ˆ ig (qi )  WigT  ig (qi )   ig

 ig () are defined as

i

i i

i

i

i

i

i

i

 if (qi , qi )   if (qi , qi )  ˆ if (qi , qi )(12)

 ig (qi )   ig (qi )  ˆ ig (qi )(13) W  Wˆig    ˆ    ˆii  W  if (qi , qi )  W  ig (qi )ui   if   ig ui
T
ig

(14)

i

i

  e ˆ sgn( s )
i i1

i

i

if



if

T
i

if

if

1
i i

(23)

According to assumptions 2-3, and using (17)-(19),
we have

(15)

i

i

1
i i

 WigT ig1Wˆig   iT i1ˆi  iT i1ˆinVi  ki si2  siT  dij S j  ˆi si Si   iT i1ˆi

The decentralized controller can be designed asu   k s  fˆ (q , q ,Wˆ T )  ˆ sgn( s ) S  q
i

i

T
i

 ˆi sgn( si ) Si  ˆi sgn( si ))  WifT if1Wˆifwhere i  0 is a known constant.

i i

i

1
ig

 siT (ki si  WifT ˆ if  WigT ˆ ig ui  i  hi

Assumption 3. The whole approximated error i isnorm-bounded as

i  ii

T
ig

Define the whole approximated error as

i ˆ sgn( s ) S  ˆ sgn( s ))  W T  1Wˆ

T
if

(22)

 WigT ig1Wˆig   iT i1ˆi  iT i1ˆi
Substituting (16) into (22), one can obtainV  sT (k s  f  fˆ  ( g  gˆ )u  hwhere the output errors of the basis functions  if () and

i

(20)

Theorem 1. For the system (1) in fault-free, consider
the assumptions 1-3, the decentralized control law (16)with the adaptive updated laws (17)-(20) can guarantee
the tracking errors of the closed-loop system converge
to zero asymptotically.

approximation errors, 1 and  2 are known positive
constants, respectively.
Let Wˆif and Wˆig be the estimation of Wif and Wig ,iˆi  i si(9)the hidden layer to the output layer,  if and  ig are thefunction(19)

where  if ,  ig ,  i and  i are positive constants.

where Wif and Wig are the ideal weight vectors frombasis

ˆi  i si Sii

gˆ i (qi , WˆigT )

i

(24)

j 1

id

Then,

(16)

where ki  0 is a known constant, Wˆif , Wˆig , ˆi and ˆiare updated byWˆif  if siˆ if (qi , qi )(17)

Wˆig  ig siˆ ig (qi , qi )ui(18)© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan304

Bo Zhao, Bo Dong, Yan Li, Fomitoshi Matsuno, Yuanchun Linscheme proposed in section 3.1, the DFTC can beachieved.
The decentralized fault tolerant controller isdesigned asui  ˆ i1ui(32)

V  Vi
i 1
n

n

i 1

j 1

  (ki si2  siT  dij S j  ˆi si Si   iT i1ˆi )nn   ki si2   iT i1ˆi )  max{dij } si  S jij
i 1 
i 1
j 1
ˆ s S

(25)

n

i

i

i

where ˆ i can be updated by the following adaptive law:ˆi  i  ˆi1 siT gˆ i ui
(33)

where  i   0 is a known constant.

Noticing si  s j  Si  S j , and using Chebyshev
Theorem 2. Consider the system (1) with partial loss ofactuator effectiveness, the proposed self-tuned local
feedback gain based DFTC scheme can guarantee all
the variables of the closed-loop system to be uniformly
ultimately bounded (UUB).

inequality, we havennn

 s  S  n s S
i

i 1

j 1

j

i 1

i

j

(26)

Thus we havenV   (ki si2  ˆi si Si   iT i1ˆi )i 1n

 n max{dij } si Si
ij

Proof. Define i  i  ˆi , and select the Lyapunov
candidate function as1
Vif  Vi  iT i1 i(34)
2
So its time derivative is
Vif  Vi  siT iT gˆ i ˆi1ui  iT gi ˆi1ui  iT Ti  ˆi(35)
 ki si2  iT siT gˆ i ˆi1ui  Ti  ˆi  siT iT gi ˆi1ui

(27)

i 1

Let   n max{dij } , so we have
ij



n

V   (ki si2   iT si Si   iT i1ˆi )

(28)

i 1

Substituting (20) into (28), we have







(29)

Assuming that s  gi ˆ u  if , where  if  0 is

So we can know V (t )  V (0) , which implies si isa known constant. Substituting (33) into (35), we have(36)
Vif  ki si2  ifTin

V   (ki si2 )  0
i 1

n

i 1

Lyapunov direct method, the tracking error is UUB.

  k s dt   Vdt  V (0)  V (t )
0

i 1

t

2
i i

0

(30)

4.

Because V (0) is bounded, and V (t ) is bounded frombelow and is non-increasing with time, so the following
result is obtained:t nlim   ki si2 dt  t  01ii

Thus Vif  0 as long as si  if ki . According tobounded. Integrating  ki si2 with respect to time
t n

T
i

Simulation StudyIn this section, a 2-DOF reconfigurable manipulator(Configuration a in reference 3, as well as the dynamic
model, the desired trajectories) is employed to verify theeffectiveness of the proposed DFTC scheme.
The actuator failure is injected into joint 1, and theeffectiveness factor is chosen as
1, t  4 s
1  
(37)
0.8, 4s  t  10s

(31)

i 1

It implies that si  L2 . From (7), si is bounded for all
time, so according to Barbalat’s Lemma, it concludes
that lim si (t )  0 , so the joint tracking error ei will alsot 

converge to zero asymptotically.

The control parameters are set as i  5 , ki  10 ,
 if  0.002 ,  ig  0.002 , i  500 ,  i  400 and

3.2. Self-tuning local feedback gain based DFTC

 i   2000 .

Through observing the system (1) with actuator failure,
if we can estimate the effectiveness factor  i in realtime, and then adding it to the decentralized control© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan305

Self-tuned Local Feedback Gainloss of actuator effectiveness, only a self-tuned local
feedback gain is added to guarantee the system stable.
This control architecture is simple since it is no need toredesign the fault tolerant controller once the systemsuffers to the partial loss of actuator effectiveness.
Meanwhile, the fault is handled only in its local
subsystem, rather than the entire system.

Joint 1 Position(rad)1
Desired trajectory
Actual trajectory

0.5
0
-0.5
-1

0

1

2

3

4

5
6
Time(sec)

7

8

9

10

Joint 2 Position(rad)1
Desired trajectory
Actual trajectory

0.5

Acknowledgements

0This work is supported in part by the National
Natural Science Foundation of China under grant61374051, and in part by the Scientific andTechnological Development Plan Project in Jilin
Province of China under grant 20150520112JH.

-0.5
-1

0

1

2

3

4

5
6
Time(sec)

7

8

9

10

Joint 2 position error(rad)Joint 1 position error(rad)Fig.1. Tracking curves

References

0.2
0

1. M. Yim, K. Roufas, D. Duff, et al. Modular
reconfigurable robots in space applications, Autonomous
Robots, 14(2-3) (2003) 225-237.
2. C. Paradis, H. Brown and P. Khosla. A rapidly
deployable manipulator system. Rob Auton Syst.
21(1997) 289–304.
3. M. Zhu, Y. Li, Decentralized adaptive fuzzy sliding mode
control for reconfigurable modular manipulators,
International Journal of Robust and Nonlinear Control,
20(4) (2010) 472-488.
4. T. Sato, T. Kano and A. Ishiguro, A decentralized control
scheme for an effective coordination of phasic and tonic
control in a snake-like robot, Bioinspiration &Biomimetics. 7(1) (2012) 016005.
5. S. Abdul, G. Liu, Decentralised fault tolerance and fault
detection of modular and reconfigurable robots with joint
torque sensing, in Proc. 2008 IEEE Int. Conf. Robotics
and Automation, pp. 3520-3526.
6. J. Yuan, G. Liu and B. Wu, Power efficiency estimationbased health monitoring and fault detection of modular
and reconfigurable robot, IEEE Transactions on
Industrial Electronics, 58(10)(2011) 4880-4887.
7. B. Zhao, Y. Li, Local joint information based active fault
tolerant control for reconfigurable manipulator,
Nonlinear Dyn. 77(2014) 859-876.
8. B. Zhao, Y. Li, Signal reconstruction based active
decentralized fault tolerant control for reconfigurable
manipulators, Acta Sinica Automatica. 40(9)(2014)
1942-1950.

-0.2
-0.4
-0.6

0

1

2

3

4

5
6
Time(sec)

7

8

9

10

0

1

2

3

4

5
6
Time(sec)

7

8

9

10

0.2
0
-0.2
-0.4
-0.6
-0.8

Fig.2. Tracking error curves

Fig.1 illustrates the trajectory tracking curves byusing the proposed DFTC scheme, from where we canobserve that the trajectory tracking is successfully
guaranteed though the fault occurs after t  4s .
Meanwhile, the fault-free subsystem’s control
performance is not affected by the faulty one. The
tracking errors in Fig.2 demonstrate this conclusion.
Hence, the proposed self-tuned local feedback gain
based DFTC is effective to deal with partial loss ofactuator effectiveness.
5. Conclusions
A self-tuned local feedback gain based DFTC scheme isdeveloped to deal with the partial loss of actuator
effectiveness of reconfigurable manipulators. The
decentralized neural network controller is designed forthe fault-free system. Once the system suffers to partial© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan306

Study on Decentralized Integral Nested Sliding Mode Control for Constrained
Reconfigurable Manipulator with Harmonic Drive Transmission
Bo Dong1, Zeyu Dong1, Bo Zhao2, Yan li1, Fumitoshi Matsuno3 and Yuanchun Li1,*
1

Department of Control Engineering, Changchun University of Technology
Changchun 130012, China
2
The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation,
Chinese Academy of Sciences, Beijing 100190, China
3
Department of Mechanical Engineering and Science
Graduate School of Engineering, Kyoto University, Japan
E-mail:1liyc@ccut.edu.cn(*Corresponding Author) 2zhaobo@ia.ac.cn 3matsuno@me.kyoto-u.ac.jp

Abstract
This paper addresses the problems of trajectory tracking control of a constrained reconfigurable manipulator withharmonic drive transmission under a decentralized integral nested sliding mode control (INSMC) method, and ahigh-performance control is achieved without using force/torque sensor. The dynamic model of the constrained
reconfigurable manipulator is formulated with a nonlinear harmonic drive model. Based on only local dynamic
information of each module, a decentralized integral nested sliding mode control method is proposed to reduce thechattering effect of the controller and compensating the model uncertainties. Finally, simulations are performed fora constrained 2-DOF reconfigurable manipulator to study the effectiveness of the proposed method.
Keywords: Reconfigurable manipulator, harmonic drive, decentralized control, sliding mode control

1.

system. Some researchers have studied the decentralized
control method for reconfigurable man-ipulator7-9, andcombining it with the sliding mode control method 10, 11.
However, the main drawback for these control strategies
is the so-called “chattering effect”, which will beaggravated due to harmonic drive based reconfigurable
manipulator is with the joint flexibility. Therefore, an
excellent control approach for reconfigurable
manipulator should be able to compensate the model
uncertainty accurately and reduce the chattering effectof the controller.
In this paper, a decentralized integral nested sliding
mode control method is proposed for constrained
reconfigurable manipulator with harmonic drive
transmission. The dynamic model of the constrained
reconfigurable manipulator is formulated with anonlinear harmonic drive model. An integral nested
sliding surface12, which is designed by employing a
pseudo-sliding surface block, is implemented to reducethe chattering effect of the controller. Model
uncertainties, including unmodeled subsystem dynamics,
friction modeling error and the interconnected dynamic
coupling, are compensated by using a variable gain
super twisting algorithm (VGSTA) based decentralized

Introduction

Reconfigurable manipulator consists of the manipulator
modules that incorporate power electronics, computing
systems, sensors and actuators. These modules can beserially connected with standard electromechanicalinterfaces and assembled to desirable configuration forsatisfying the requirements of various tasks. Besides,
reconfigurable manipulator needs an appropriate control
system to provide accuracy and stability.
Harmonic drives have been widely used inmanipulator design due to the excellent properties suchas high reduction ratio, compact size, light weight, andcoaxial assembly. A typical harmonic drive includes awave generator, a circular spline, and a flexspline
placed in between. Numerous studies have been carriedout on analytical description of harmonic drives based
systems1-5. However, in conventional methods, thekinematic error6 between the measured and expected
flexspline output is always neglected and this may leadto a local torque ripple, which is introduced in harmonic
harmonic drive should be considered when formulating
the dynamic model of reconfigurable manipulator.
Decentralized control is a particularly promisingconcept in implementing reconfigurable manipulator© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan307

Bo Dong, Zeyu Dong, Bo Zhao, Yan li, Fumitoshi Matsuno, Yuanchun Li xi1  xi 2
Si :  xi 2  Fri (i ,i )   i (i ,i )  hi (i ,i ,i )  bi ui (4) yi  xi1
where xi is the state vector and yi is the output ofcontroller. Finally, simulations are performed for aconstrained 2-DOF reconfigurable manipulator toillustrate the effectiveness of the proposed method.
2.

Problem Formulation

Si . bi   I mi  i  
1

According to the dynamic model of a n-DOF
reconfigurable manipulator which is formulated as a

i (i ,i ) Imi  i   fci  i 
1

Fri (i ,i ) Imi  i  fi (i ,i )
1

i 1

T
I mi  ii  fi (i ,i )  I mi  zmiz j ji 1 j 1

j 1

j  2 k 1i 1i 1 j 1

j 1

j  2 k 1

 fci
 i
i

 i1 j 1 T
(5)
 Imi  zmi  z k  z j k j 
1 
j 2 k 1hi (i ,i ,i ) Imi  i 
i 1


T
z j j  ui  Imi  zmi

j 1


where  ui is the output torque ripple that is considered
as a model uncertain term and will be compensated in
the next section.

(1)

T
T
where I mi  zmi z j j  I mi  zmi  z k  z j  k j is the

dynamic coupling term, I mi is the moment of inertiaof the rotor about the axis of rotation,  i is the motor
output torque, zmi and z i are the unity vectors along
the axis of rotation of the ith rotor and joint
respectively,  fci is the certain part of the estimatedjoint torque using only position measurements5, andcan be represented as followingsgn( wi )1
c  fci  tan  c f k f 0  i (1  e)   (2)cf
 i cw kw0



w

, i (i ,i ) , Fri (i ,i ) andhi (i ,i ,i ) is defined assynthesis of interconnected subsystems7, the dynamic
model of ith subsystem of reconfigurable manipulator
is given as follows

T I mi  zmi
 z k  z j  k j 3.

Control Design

In this section, a decentralized integral nested sliding
mode control approach is proposed based on VGSTA.
Assumption 1. Desired trajectory yir1 (t ) is bounded
and second order derivable.
Assumption 2. The interconnected dynamic coupling
term hi i ,i ,i is bounded, and satisfywiwhere k f 0 , kw0 , c f and cw are the constants to beh  , ,   g   gn(6)determined, sgn() is the normal sign function,  i is
the torsional angle of the harmonic drive, which is
obtained by combining the measured link side withthe motor side position measurements, and the wave
generator torque  wi can be approximated by thewhere gi 0 is a positive constant, g ij is a smooth
Lipschitz function. Define the tracking position error
and its time derivative as
ei1  xi1  yir1 (t ), ei1  xi 2  yir1 (t )(7)motor torque command. Note that the term fi (i ,i )in (1) is the frictional torque term which is considered
as a function of the joint position and velocity13, andgiven as follows

Define the pseudo-sliding surface si1 for the first
block assi1  ei1  zi1 , zi1 (0)  ei1 (0)(8)
and the derivative of si1 can be given as follows



i



fi (i ,i )  b fii  f ci  f si e(  f ii ) sgn(i )  f i (i ,i ) (3)
2

i

i

i0

j 1

ij

si1  ei1  zi1  xi 2  yir1 (t )  zi1(9)z
In (8) and (9), i1 is an integral variable which will
be defined later. It is of importance to introduce thesecond block of sliding surface, defined as follows
si 2  ei 2  zi 2 , zi 2 (0)  ei 2 (0)(10)
where zi 2 is an integral variable to be determined, andei 2 , which is used to design the second block of sliding
surface, can be defined asei 2  xi 2  yir 2 (t )(11)where yir 2 (t ) is defined aswhere f ci denotes the Coulomb friction-relatedparameter; f si denotes the static friction-relatedparameter; f i is a position parameter about theStribeck effect, b fi is the viscous friction coefficient,f i (i ,i ) is the position dependency of friction andfriction modeling errors. Let xi  [ xi1 , xi 2 ]T  [i ,i ]Tfor i  1, 2, , n . According to (1), the following state
equation is obtained:© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

308

Study on Decentralized Integral

yir 2 (t )  yir 2,0 (t )  yir 2,1 (t )where Y (i ) can be defined as follows

(12)

ˆ 2
ˆ 2
Y (i )  i ,sgn(i ),e(  f ii ) sgn(i ), fˆsii2 e(  f ii ) sgn(i ) (25)In order to incorporate variable parametric model
uncertainty compensation, we can designTF i  bˆ fi  b fi , fˆci  f ci , fˆsi  f si , fˆ i  f i   Fci  Fvi (26)In (12), yir 2,0 (t ) is the nominal part of the control
design, and yir 2,1 (t ) is used to design the control law for
the purpose of compensating the unmodeled subsystem
dynamics.
Substituting (11) into (10), one can obtain thatxi 2  si 2  yir 2 (t )  zi 2
(13)
Combining (9) with (13), the derivative of thepseudo-sliding surface si1 can be rewritten assi1  si 2  zi 2  yir 2,0 (t )  yir 2,1 (t )  yir1 (t )  zi1(14)

where Fci and Fvi are constant and variable unknown
vector respectively. And Fvi is bounded as
Fvni  ni , n  1, 2,3, 4

(27)
ˆ
ˆ
ˆ
ˆ
In (25), b fi , f ci , f si , f i are the normal friction

Choose zi1 as the form ofzi1    si 2  zi 2  yir 2,0 (t ) (15)parameters, u i10 is the term designed to compensate theThen, combining (10), (14), with (15), the derivativesof i1 and zi1 can be represented assi1  yir 2,1 (t )  yir1 (t ), zi1  ei 2  yir 2,0 (t )(16)nonparametric uncertainty f i ( , ) in (3). The terms
u i20 and u i30 are designed to compensate for theparameter uncertainty Fci and Fvi , respectively. The
friction compensation is of the same form for all the
joints, and hence, for the ith joint, the compensators, u i10

With the initial condition zi1 (0)  ei1 (0) , define
yir 2,0 (t ) and yir 2,1 (t ) as
yir 2,1 (t )   1 sigm(1 , si1 )yir 2,0 (t )  k1ei1 ,(17)

u i20 , u i30 are defined bywhere k1 ,  1 and 1 are positive constants to be
determined. sigm() is a sigmoid function which can be
defined as follows
sigm(1 , si1 )  tanh(1si1 )(18)Then, we can obtain the time derivative of si 2 as0iu    ni
n
3
i0



where k 2 and  2 are positive constants to be
determined. For the purpose of compensating thedifferent terms of the derivative of the sliding surface,
we can design the control law as the form ofui  ui 0  ui1  ui 2 . So that (21) can be rewritten as(23)

dynamic coupling term hi  , ,

First, design the control law ui 0 to compensate thesgn( )u Y ( )u u  (24)
i

1
i0

i

2
i0

(28)

 
i
n

i
pn

 which is represented

as the following formhi  , ,  gi1 (i , t )  gi 2 (i , t )effect of joint friction Fri (i ,i ) .

in  k  k  e  k sat ( s , si 2 ) 
ui1  bi1  1 2 i1 3(29)2   1   2  1  tanh (1 si1 )  
where  s and k3 are positive constants to be determined.
sat () is a saturation function. which is defined asfollowssgn( si 2 ) si 2   ssat ( s , si 2 )  (30)
si 2   s
 si 2  s
Third, based on the VGSTA theory8, 14, design thecontrol law ui 2 to compensate the interconnected

zi 2 k2 ei1 21 1tanh2 (1si1 )1sigm(1 ,si1 ) yir1 (t ) (22)ui 0 bˆfii  fˆci  fˆsi eipn

the terms of i (i ,i ) , yir 2 (t ) , and zi 2

where zi 2 is chosen as(  fˆ ii2 )ini

si 2 i (i ,i ) Fri (i ,i )hi  , , bi ui  yir 2 (t ) zi 2 (21)i   ,   in
 pn

i
n

uncertainty bounds.  i ,  pn are parameters to be
determined.
Second, design the control law ui1 to compensate

(20)



si 2   i

TSubstituting (4) into (19), we can rewrite si 2 as

 hi  , ,  bi ui 2  yir 2 (t )  zi 2iwhere  i  Y  i  si 2 , and  fi ,  ni are the parametric

si 2  Fri (i ,i )  bi ui 0   i (i ,i )  bi ui1

si 2

t

(19)
According to (17), one can obtain that yir 2 (t ) isrepresented as
yir 2 (t ) k1ei1 11 1 tanh2 (1si1 ) 1sigm(1 ,si1 )  yir1 (t )



si 2   i ,   fi

ui20  k3  Y (i )T si 2 dt

si 2  ei 2  zi 2  xi 2  yir 2 (t )  zi 2



si 2
si 2

ui10    fi



(31)

where gi1 (i , t ) and gi 2 (i , t ) are bounded by

3i0© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan309

Bo Dong, Zeyu Dong, Bo Zhao, Yan li, Fumitoshi Matsuno, Yuanchun Li

g i1 ( i , t )   i1 i , t  i1 ( si 2 )dg i 2 ( i , t )  i 2 i , t  i 2 ( si 2 )dtthat including the conventional one-order sliding mode
controller (SMC) and the proposed integral nested
sliding mode controller (INSMC). The simulation
results are shown in the following figures. Fig. 1 shown
the joint position error curves and the control torquecurves of two different configurations under the SMC.
Fig. 2 shown the joint position error curves and thecontrol torque curves of configurations A and B underthe INSMC. Fig. 3 shown the constrained force andtrajectory tracking curves of the end-effector.
From these figure we can conclude that the
chattering effects in both the error curves and thecontrol torque curves are relatively obvious under theconventional SMC method. However, the tracking
performance of the joint positions is improved under the
INSMC method because the model uncertainty has been
compensated accurately. The chattering effect can be
reduced in a short time interval by using the proposed
integral nested sliding surface design, and the desired
trajectory of end-effector can be tracked accurately
under the proposed INSMC method.

(32)

where  i1 i , t   0 and  i 2 i , t   0 are

known

continuous functions. 1 ( si 2 ) and 2 ( si 2 ) are defined asi1 ( si 2 )  si 2 1 2 sgn( si 2 ) i 3 (t ) si 2

(33)
1
3
12

i 2 ( si 2 )  sgn( si 2 )  i 3 (t ) si 2 sgn( si 2 ) i23 (t ) si 222where  i 3 (t ) is a positive gain to be determined.
Substituting (31) into (23), one can obtain thatsi 2  Fci i ,i  bi ui 0   i i ,i  bi ui1(34)
 gi1 (i , t )  gi 2 (i , t )  bi ui 2  yir 2 (t )  zi 2









Design the control law ui 2 to compensate the intercomnected dynamic coupling term, shown as follows

tui 2  bi1  i1 (t )i1 ( si 2 )    i 2 (t )i 2 ( si 2 )dt
0



(35)

where the variable gains  i1 (t ) and i 2 (t ) are chosen as
2
 1 2 2 i1   i 2   2 2 i 2 1  i1 (t )  4 2   (36)v    2      4 2 


2
i1
v
2 
 2

2

 i 2 (t )  v  4 2  2 2 i1 (t )where  , v ,  2 are positive constants to be
determined. Therefore, combine (24), (29) with (35), thedecentralized integral nested sliding mode control law
ui for the ith joint can be defined as follows
  bˆ fii  Y (i )  ui20  ui30   ui10    k1  k2  ei1 
ui  b  bi ˆ 2 )(f   fˆci  fˆsi e  i i sgn(i )   (37)2   1   2  1  tanh (1 si1 )   k3 sat ( s , si 2 ) t  i1 (t )i1 ( si 2 )    i 2 (t )i 2 ( si 2 )dt0Theorem. Given a constrained n-DOF reconfigurable
manipulator, with the dynamic model as formulated in(1), and the model uncertainties that existed in (5). The
closed-loop system is stable under the decentralized
integral nested sliding mode control law defined by (37).
Proof. Refer to Ref. 12.
1
i

4.





Fig. 1. Position error and control torque curves of configurable
A and B under SMC

Fig. 2. Position error and control torque curves of configurable
A and B under INSMC

Simulations

To study the effectiveness of the proposed decentralized
control approach, in this paper, two different
configurations of the constrained reconfigurable
manipulator with harmonic drive transmission are used
to conduct the simulations. The dynamic model and theparameters of controller are chosen as Ref. 12.
To verify the effectiveness and precision of the
proposed control method, in this paper, the simulations
are conducted with two different controllers respectively,

Fig. 3. Constrained force and trajectory tracking curves of
end-effector© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

310

Study on Decentralized Integral

5.

9. G. Liu, S. Abdul, and A. A. Goldenberg, “Distributed
control of modular and reconfigurable robot with torquesensing,” Robotica, vol. 26, no. 1, pp. 75-84, 2008.
10. M. C. Zhu and Y. C. Li, “Decentralized adaptive fuzzy
sliding mode control for reconfigurable modular
manipulators,” International Journal of Robust andNonlinear Control, vol. 20, no. 4, pp. 472-488, 2010.
11. M. C. Zhu and Y. C Li, “Decentralized adaptive sliding
mode control for reconfigurable manipulators using fuzzy
logic,” Journal of Jilin University Engineering andTechnology Edition, vol. 39, no. 1, pp. 170-176, 2009.
12. B. Dong and Y. C. Li, “Decentralized integral nested
sliding mode control for time varying constrained
modular and reconfigurable robot” Advances in
Mechanical Engineering 2015. doi:10.1155/2014/317127.
13. G. Liu, A. A. Goldenberg and Y. Zhang, “Precise slow
motion control of a direct-drive robot arm with velocity
estimation and friction compensation,” Mechatronics vol.
14, no. 7, pp. 821–834, Sep. 2004.
14. T. Gonzalez, J. A. Moreno and L. Fridman, “Variable
gain super-twisting sliding mode control,” IEEE
Transactions on Automatic Control, vol. 57, no. 8, pp.
2100-2105, 2012.

Conclusion

This paper studies on constrained reconfigurable manipulator with harmonic drive transmission, and addresses
the problems of trajectory tracking control without
using force/torque sensor. The dynamic model is formulated based on a nonlinear harmonic drive model. A
decentralized INSMC method based on VGSTA is
proposed to reduce the chattering effect and compensating the model uncertainties. Finally, simulations are
performed to study the effectiveness of the proposed
method.
Acknowledgment
This work is financially supported by: National Natural
Science Foundation of China (Grant No. 61374051).
References
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© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan311

A Number of Mobile Manipulator Control for Moving an Object
by using Cooperative Control
Deok-Su Kim

Dep. of Electronic Eng., Pusan National University, Jangjeon-dong, Geumjeong-gu
Busan, 609-735, Korea Republic
Dong-Eon Kim

Dep. of Electronic Eng., Pusan National University, Jangjeon-dong, Geumjeong-gu
Busan, 609-735, Korea Republic
Seong-Ik Han

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: deoksu1696@pusan.ac.kr, hyunuk.ha@gmail.com, dongeon1696@pusan.ac.kr, jmlee@pusan.ac.kr
www.pusan.ac.kr

Abstract
In this paper, we propose a method of cooperative control of a three mobile manipulator for moving an object.
These robots go to desired position independently by using encoder data and inverse kinematics and after arrivingin the position, they grasp and lift the object. For the carrying operation, the lifting operation is implemented by using the manipulator mounted on the top of the mobile robots cooperatively. In this system, master-slave mode isused for finding each position of robots and for coordinative control among the robots. During the moving operation, a trajectory planning has been kept constantly. The real cooperative carrying motions was implemented tocheck the possibility of the master-slave mode control based on the mobile manipulator’s kinematics.
Keywords: Cooperative Control, Kinematics, Mobile Manipulator, Master-Slave.

plan of a robot to cooperation of a number of robots.[2]the cooperation system with robots can solve impossible problems that can’t do with performance of eachrobots.[3] to achieve goal in cooperative robotic systems,
a strategy for the cooperation between robots is essential.

1. Introduction
Due to rapid growth of the intelligent robot industry inthe modern society, role of robot has been increased. so
it is used in ordinary life as well as industries[1]. recent
studies of mobile robot is extended from single action© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan312

Deok-Su Kim, Dong-Eon Kim, Seong-Ik Han, Jang-Myung Lee

recently a theory of intelligent communities make itpossible that solving complex missions by serving flexible way.[4] cooperative robot system is classified behavior control technology, situation recognizing technology, networking technology, clustering technology,
and in this paper, we want to propose the overall system about cooperative robot. situation, and action is
given, so we just investigated for moving and communicating method.
In this paper, we studied cooperative control for carrying an object by using three mobile manipulator. We suggest method of cooperative control for moving an object by using three mobile manipulator. and we explain
the position control by using encoder value and trajectory plan after that we conclude this paper.

Fig. 3. Schematic description of a mobile part.

1
 m2 [ x  r22 s2 c1  r2 (  1 )c2 s1 ]2
2
1
 m2 [ y  r22 s2 s1  r2 (  1 )c2 c1 ]2
2
1
 I z 2 [(  1 )2   22 ]2
1
 m3[ x  l22 s2 c1  l2 (  1 )c2 s1  r3 (2  3 )s23c121
r3 (  1 )c23 s1 ]2  m3 [ y  l22 s2 s12l2 (  1 )c2c1  r3 (2  3 )s23 s1  r3 (  1 )c23c1 ]2

Fig. 1. Mobile robot.

1
2
The potential energy is obtained as follows:V  m2 gr2 s2 m3 g (l2 s2  r3 s23 )And we use Bumblebee stereo camera to detecting object and get depth information of it. We use triangulation
algorithm which refers to the process of determining apoint in 3D space given its projections over two or more images. Now, we recognize our mobile robot`s velocity, torque, object`s position and depth information.
We propose an approach to control the communication
among 3 mobile robots. In our system, 3 mobile robots
which take role of clients are worked under the control
of a computer server. Status data coming from mobile
robot, which contains the information about robot`s
position, orientation and end-effector`s posture, is transferred to computer. In server term, after collecting datafrom 3 robots, the programed calculation process is operated. The result of this process is the next status datafor 3 mobile robots, is then sent to all slaves by using
wireless communication. Our mobile robot system is set
22r3 (  1 )c23c1 ]2  I z 3[(  1 )  (2  3 ) ] .

2. Cooperative Control System
The robot in Fig. 1was made for experiment. for position control it use two motors and it was controlled byEmbedded Computer. The manipulator is composed ofthree-wheeled and three-linked mobile manipulator system.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan313

A Number of Mobile

up with the architecture of sever & multi-client, whichis include one computer and three autonomous mobile
robots. Computer takes a role of server device, which isin charge of receiving the status of each mobile robot,
and then, after calculation process, it send back thecommand to each client (mobile robot). In term of mobile robot, after sending the status containing the information about position of the platform and the angler of
each link in end-effector, it will wait until receive thecommand from server. Depend on the data coming fromcomputer, mobile robot will move to next position. In
mobile robot, we use embedded system to control wholeprocess. The device using in our experiment is IEC1000
series industrial embedded computer & HMI, which is
supported to use exter nal HNS wir eless card.
Schematic description of a mobile part(Fig.3), the dynamic equations of mobile robot is describe as the following equation:
𝑥𝑜̇
𝑦𝑜̇
𝜃̇ =
𝜃𝑟̇
[ 𝜃𝑙̇ ][𝑟𝑟

2𝑙(𝑙𝑐𝑜𝑠𝜃−𝑑𝑠𝑖𝑛𝜃)𝑟

2𝑙(𝑙𝑐𝑜𝑠𝜃+𝑑𝑠𝑖𝑛𝜃)𝑟

2𝑙(𝑙𝑠𝑖𝑛𝜃+𝑑𝑠𝑖𝑛𝜃)𝑟

2𝑙(𝑙𝑠𝑖𝑛𝜃−𝑑𝑐𝑜𝑠𝜃)
𝑟

2𝑙

2𝑙

1
1

 𝑞𝑣 = [𝑥𝑜

𝑦𝑜

−

𝜃

𝜃𝑟

0
0

𝜃̇
. [ 𝑟]𝜃𝑙̇

WAN or internet. In client-Server module, it allows toshare information and resource among the systems likefiles, disk space, processors and peripherals which cancollaborate and deliver message inevitably between
many processors. Therefore, The mechanism which get
the other machine`s network address while communicating between network. A server (computer) maintains
the communication with the three clients. Therefore, itis needed to apply several way to separate the mobile
robot`s connection time between server. Therefore, inthis case, multi-thread is applied for server programming. The key of the multi-thread create the main server
socket object in the main program. As each client connects to the server, the main program creates a separate
thread to handle the communication.
Many methods have been suggested for the control
of autonomous mobile robot like behavior-based approach, virtual structural approach. To control the threemulti mobile robots we used leading-follow approachand virtual architecture. Behavioral approach use thedistance between the robot to control the other action
like evasion, maintaining the position and the movement.
It is mainly used when lots of robots search the space, itis easy to derive a control strategy when a plurality oftasks are given. Virtual structure approach is the methodthat as robots moving keep the charge point to maintainthe specific structure. This method defines a desired
virtual structure and convert the virtual structural
movement to the path that robots move. Finally, leading-follow approach is a way to maintain the overall
position by keeping the robot along a desired distanceand angle tracking robots. In this paper, we decide thesequence of operations and make different behavior
depending in the order of the conditions. The driving
method will be different according to the robot is holding a object or not. Also, when lifting the object the
movement of the robot is changing. It divided into three
conditions to have a driving and the operation patterns
for each situation. 1. The case that the robot does notholding an object. First, the mobile robot need the finding action to find the object. It use the bumblebee camera to identify the location through the object recognition. In addition, the starting position of the mobile robot is fixed point. So the first step is to access a placethat exist the object to lift. The mobile robot that knows
the target location and the start location is easily accessible to the target position. 2. When lifting the object.
The robot knows the coordinates of the current location.
To lift an object we set the manipulator movement by
inverse kinematics analysis. We consider the approach(1)]

𝜃𝑙 ]𝑇 : The generalized

coordinates of the mobile platform.
Where

𝑥𝑜 𝑦𝑜
:The position of mobile robot.
𝜃 : The rotation angler of mobile robot’s platform.
𝜃𝑟 𝜃𝑙 : The rotation angler of left wheel and right wheel, respectively.
 r : The radius of the wheels.
 2l : The distance between 2 wheels.
From eq.(1), if the desired velocity vector of the left
and right wheel is given, the translational velocity, withrespect to x-axis and y-axis respectively, and angularvelocity of the platform is also specified. Consequently,
after sending the status data (platform`s position anddirection, end-effector`s posture) to computer, mobile
robot is waiting for the command coming from computer, which contains the information about desired velocity of the left and right wheel. Client`s processor controls
all other parts based on the data coming from server.
Client-Server (CS) mode is an information sharing
mode which is widely used in information system. The
core of CS system structure is the distribution of tasklevel application between client and server. In the C/S
system Clients and servers can be linked through LAN,© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan314

Deok-Su Kim, Dong-Eon Kim, Seong-Ik Han, Jang-Myung Lee

distance of the mobile robot to the inverse kinematics
analysis. 3. When mobbing with the holding object.
After lift the object, three mobile robots should move inthe same direction at the same time. Additionally, thedistance and angle between the robot to be held constant.
Three robot is composed of a single Master and two
Slave robot. Master robot determine the each situation
and receive communication information with the slave
robot. A control algorithm of a robot can be seen from
Figure 3.
Start

Location SelectGripper Flag
check

Fig. 4. Mobile Robots in formation based on the Vision.
Leader-Follower

three mobile robot system,. By applying structure ofmulti-thread server, computer can handle the communication with three mobile robots without collision. We
deal with the multiple robot coordination control systemusing inverse kinematics analysis.

Manipulator
Flag Check

Virtual Structure

Close

Acknowledgements
Fig. 3. Algorithm for swarm robots.

This work was supported by the National Research Fou
ndation of Korea(NRF) grant funded by the Korea
government(MSIP) (NRF-2015R1A2A2A01004457).
This research was supported by the MOTIE (Ministry ofTrade, Industry & Energy), Korea, under the Industry
Convergence Liaison Robotics Creative Graduates Education Program supervised by the KIAT (N0001126).
References

The first three robots must go to find their target position. If all three robot reaches the correct position, therobot takes an object with stretched arms. At that time
the Slave robot sends the signal to determine the objects
caught to the Master robot. When Master robot noticethat three robots grab the object it will lift the object on.
This algorithm inform to master robot that slave robot
holding all of the objects and when master robot is holding the object is lifted on it will drive with maintaining
the present distance and angle based on position ofleader robot. Each of the conditions will be noticed using camera. Each robot is controlled by Embedded
Computer. We can confirm that driving works well byusing PD controller in the Encoder value.

1. [1] M.-H. Tak, Y.-J. Cho, and Y.H. Joo, "Behavior Control for Cooperative Exploration for Swarm Robots",
Proceedings of KIIS Fall Conference, vol. 21, no.2, Korea, pp.169-170, Dec, 2011.
2. [2] D.-W. Lee, and K.B. Sim, "Study on Cooperative
Behaviors and Cooperative Control in Collective Autonomous Mobile Robots", Proceedings of Institute of Electronics and Information Engineers Conference, Korea,
pp.68-71, Jan, 1997.
3. [3] H.-C. Kim, J.-S. Kim, Y.-K. Ji, and J.-H. Park, "PathPlanning of Swarm Mobile Robots Using Firefly Algorithm", Journal of Institute of Control, Robotics and Systems, vol. 19, no.5, pp.435-441, May, 2013.
4. [4] J.-S. Kim, D.-H. Yeom, Y.-H. Joo, and J.B. Park,"Hierarchical Control of Multiple Mobile Robots", Proceedings of KIEE Summer Conference, Pyeongchanggun, Korea, pp.1890-1891, Jul, 2011.

Conclution
In this paper, we proposed the method of navigation of
Mobile Robot using recognition and distance information with Bumblebee stereo camera. And controllingthe communication which use Wifi-comm based on thesocket programing applying wireless communication in© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan315

Adaptive Control of Discrete-Time Systems Using
Multiple Fixed and One Adaptive Identification Models
Yuzhen Zhang

School of Automation and Electrical Engineering, University of Science and Technology Beijing, 30# Xueyuan Road,
Haidian District, Beijing, China
Qing Li

School of Automation and Electrical Engineering, University of Science and Technology Beijing, 30# Xueyuan Road,
Haidian District, Beijing, China
Weicun Zhang

School of Automation and Electrical Engineering, University of Science and Technology Beijing, 30# Xueyuan Road,
Haidian District, Beijing, China
E-mail: luxuhuiluxuhui@163.com, ymjia@buaa.edu.cn
www.buaa.edu.cn

Abstract
The adaptive control of a linear time-invariant discrete-time system using multiple models is considered in thispaper. Based on the prediction errors of a finite number of fixed and one adaptive identification models, a newweighting algorithm is proposed for improving system performance. The principal contributions of the paper are
the proofs of global stability and the convergence of the overall system. Computer simulation results are included tocomplement the theoretical results.
Keywords: discrete-time; multiple model adaptive control; weighting algorithm; stability; convergence.

The earliest research on weighted multiple model
adaptive control (WMMAC) appeared in 1970’s.2, 3 Inthat system, posterior possibility evaluator (PPE)
algorithm played the key role. Later, K. S. Narendra put
forward the concept of indirect multi-model adaptive
control.4 The basic idea is that multiple fixed models are
established off-line to cover uncertainty plant. At thesame time, one or two adaptive identification models
are present for global stability and the convergence ofthe overall system.4 The scheme is designed easily.

1. Introduction
The control of dynamical systems with large
uncertainties is of great interest. Such problem arises
when there are large parameter variations. So, anapproach gains development: the use of multiple models
to identify the unknown plant. In any time, the closest
model will be identified according to the performanceindex.1 Then, the corresponding controller is used tocontrol the system.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa, Japan

316

Yuzhen Zhang, Qing Li, Weicun Zhang

Therefore it has been widely used in recent years. Thisis also one basis of the algorithm in this paper.
In this paper, we made efforts from two aspects toimprove the stability of WMMAC. On one side, a newalgorithm for calculating controller weights is proposed.
On the other side, a finite number of fixed and one
adaptive identification models are designed.
It is worth noting that this paper is focused on thelinear time-invariant discrete-time system because mostpractical systems are controlled by computers that are
discrete in nature. 3

A( z 1 )  1  a1 z 1  a2 z 2    ana z  na

(2)

B( z 1 )  b0  b1 z 1  b2 z 2    bnb z  nb

(3)

y (k ) and u(k ) are the output and the input. The  (k )is a zero-mean white noise with constant variance.
The plant P also can be written asy (k )   T (k )   (k )

(4)

where

 (k )  [ y(k  1), ,  y(k  na ), u(k  d ), , u(k  d  nb )] (5)  [a1 , , an , b0 , , bn ]2. Description of Weighted Multiple Model
Adaptive Controlacorresponded to the fixed model; C is the local
controller corresponded to the adaptive identification
model; pi (k ) is a weight for its local controller; p (k )is the weight for the adaptive identification controller.
Through the algorithm, local controller weights are
known. Then, the weighed sum u(k ) is obtained by the
local controller weights.
yr(k) +

.
.
.

Ci
.
.
.

-

(6)

  {M i , M , i  1, 2, , N} is the model set that
includes a finite number of fixed and one adaptive
identification models in this system. For every fixedmodel, the corresponded controller is designed by poleplacement control. At the same time, the one adaptive
identification model is designed by indirect poleassignment self-tuning control. The forgetting factor
recursive extended least square is used to estimatedparameter 5. That is

In order to analyze system clearly, a concise block
diagram is shown in Fig.1. The simplified block
diagram shows a general discrete-time WMMAC
system. The details of every local control strategy andweighting algorithm are omitted.
The meanings of symbols are shown: P is theuncertainty plant; Ci is a local controller, which is

C1

b


ˆ(k )  ˆ(k  1)  K ( k )[ y ( k )  ˆ T ( k )ˆ( k  1)]
P(k  1)ˆ (k )(7)
 K (k ) ˆ   T (k ) P(k  1)ˆ (k )1T
 P (k )  [ I  K (k )ˆ (k )]P(k  1)



p1(k)
ω(k)

pi(k)

∑

u(k)

P

where

y (k )

ˆ (k )  [ y (k  1), ,  y (k  na ), u (k  d ), , u (k  d  nb )]
(8)
ˆ
ˆ
ˆ T
 (k )  [aˆ1 , , aˆna , b0 , , bnb ]

pn(k)

Cn
pθ(k)

For each model, including the adaptive identification
model certainly, its output ymi (k ) is used to define theoutput error. That isCθ

Fig. 1. Concise block diagram of a general WMMAC systemei (k )  y(k )  ymi (k )Consider the following discrete-time dynamical plant
P , the mathematical model isA( z ) y (k )  z B( z )u (k )   (k )
1

d

1

(9)

which is used to calculate the weight for the local
controller. As shown in Fig.1, the global control u(k ) is
obtained by

(1)

where

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa, Japan317

Adaptive Control of Discrete-TimeN 1u(k )   pi (k )ui (k )G ( s) 

(10)

i 1

1
N 1

(11)

li(k )   

1 k 2
 ei ( j )k j 1(12)

k4  1.3 . The four corresponded controllers aredesigned by pole placement control. At the same time,
the one adaptive identification model is designed byindirect pole assignment self-tuning control. The
forgetting factor recursive extended least square is used
to estimated parameter.
The controller 1 iswhere   0 , which is a small constant to avoidli(k )  0
 (k )  min{li(k )}
lmin
i

(17)

For simplicity, four fixed local models are used as theuncertainty of k , that is, k1  0.7 , k2  0.8 , k3  1.2 ,In this paper, the algorithm to calculate weight is
proposed, that is
p i (0)  li (0) ks  3s  2
2(1  4.3670 z 1  4.4817 z 2 ) y (k ) q 1 (0.1473z 1  0.2428 z 2 )u (k )   (k )

(13)

(18)

The controller 2 isl  (k )li (k )  minli (k  1)li(k )l (k )pi (k )  N 1i
 l j (k )(1  4.3670 z 1  4.4817 z 2 ) y (k )(14) q 1 (0.1683z 1  0.2775 z 2 )u (k )   (k )(19)

The controller 3 is(15)

(1  4.3670 z 1  4.4817 z 2 ) y (k )j 1 q 1 (0.2525 z 1  0.4163z 2 )u (k )   (k )This algorithm is very simple in calculation. The
results and their proofs regarding its convergencecharacter are presented in Ref. 3 and 6-8.

(20)

The controller 4 is
(1  4.3670 z 1  4.4817 z 2 ) y (k ) q 1 (0.2735 z 1  0.4510 z 2 )u (k )   (k )(21)

3. Simulation Studies
The controller 5 is designed according to the adaptive
identification model by indirect pole assignment selftuning control. The forgetting factor recursive extended
least square is used to estimated parameter. The initialvalue of  is 0.001 ones(na  nb  1,1) ; the initialvalue of P is 106  eye(na  nb  1) . The forgetting
factor  is 1. The calculation in detail is shown informula (7) and (8).
The expected closed-loop characteristic polynomial iscorresponds to the characteristic polynomial of thefollowing continuous-time second-order system

In the previous section, the algorithm for calculating
controller weights is presented. And a finite number of
fixed and one adaptive identification models are
discussed. In this section, the results of computer
simulations will be presented. Using a single example,
the effectiveness of the adaptive control of discrete-timesystems with multiple fixed and one adaptive
identification models, is discussed in detail.
Consider an uncertain discrete-time plant(1  a1 z 1  a2 z 2 ) y (k )  z 1 (b0  b1 z 1 )u (k )   (k ) (16)n 2
s 2  2n s  n 2

where  (k ) is a zero-mean white noise with constantvariance. This discrete-time model can be obtained by
the simple continuous-time in (17) with sample time
ts  0.5s and the zero order hold.

(22)

where  is 0.7, n is 1.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa, Japan

318

Yuzhen Zhang, Qing Li, Weicun Zhang

4. Conclusions

The true model of plant is not included in the four fixedmodel, which k is 1. The simulation results are shownin Fig.2 to Fig.4.

In this paper, a finite number of fixed and one adaptive
identification models are designed. At the same time,
the new algorithm for calculating controller weights is
proposed. The weighted value can converge quickly.
The controller designed according to the adaptive
identification model performs well when the true one is
not included in the fixed models, which improves thestability of WMMAC.
Acknowledgements
This work is supported by the National Basic Re-search
Program of China (973 Program: 2012CB821200,
2012CB821201) and the NSFC (61134005, 60921001,
61327807).

Fig. 2. Parameter estimation of the adaptive identification
model

References
1. K. S. Narendra and X. Cheng, Adaptive control of
discrete-time systems using multiple models, IEEE
Transactions on Automatic Control 45(9) (2000) 16691686.
2. D. G. Lainiotis, Partitioning: a unifying framework foradaptive systems - I: Estimation - II: Control,
Proceedings of the IEEE 64(8) (1976) 1126–1143 and
1182–1197.
3. W. Zhang, Stable weighted multiple model adaptive
control: discrete-time stochastic plant, International
Journal of Adaptive Control and Signal Processing 27(7)(2013) 562-581.
4. F. Li, Robust multiple model adaptive control base on
dynamic optimization model bank, Master Thesis, South
China University of Technology, 2004.
5. Z. Pang and H. Cui, System identification and adaptive
control MATLAB simulation (Beihang University Press,
Beijing, 2009), pp. 175-189.
6. W. Zhang, T. Chu, L. Wang, A new theoreticalframework for self-tuning control, International Journalof Information Technology 11(11) (2005) 123–139.
7. W. Zhang, On the stability and convergence of selftuning control - virtual equivalent system approach,International Journal of Control 83(5) (2010) 879–896.
8. W. Zhang, Stability of weighted multiple model adaptive
control, Journal of Control Theory and Applications
29(12) (2012) 1658-1660.

Fig. 3. Controller weight signals

Fig. 4. Output, reference and control signals

As shown in Fig. 3, if the true model of plant is notincluded in the four fixed models, controller 5 designed
according to the adaptive identification model is chosen
rapidly in a good performance.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa, Japan319

Adaptive Polynomial Regression and Its Application to Gene Selection of Rat Liver
Regeneration
Juntao Li

Henan Engineering Laboratory for Big Data Statistical Analysis and Optimal Control, School of Mathematics andInformation Science, Henan Normal University ,46 Jianshe Road, Xinxiang, 453007, P.R. China
Yimin Cao

Henan Engineering Laboratory for Big Data Statistical Analysis and Optimal Control, School of Mathematics andInformation Science, Henan Normal University ,46 Jianshe Road, Xinxiang, 453007, P.R. China
Xiaoyu Wang

Henan Engineering Laboratory for Big Data Statistical Analysis and Optimal Control, School of Mathematics andInformation Science, Henan Normal University ,46 Jianshe Road, Xinxiang, 453007, P.R. China
Cunshuan Xu

State Key Laboratory Cultivation Base for Cell Differentiation Regulation, Henan Normal University, 46 Jianshe Road,
Xinxiang, 453007, P.R. China
E-mail: juntaolimail@126.com, cao_yimin@126.com, 49207543@qq.com, cellkeylab@126.com
www.henannu.edu.cn

Abstract
To deal with multi-class classification problem for gene expression data, this paper proposed an adaptive
polynomial regression by incorporating multi-class adaptive elastic net penalty into polynomial likelihood loss. The
adaptive polynomial regression was proved to adaptively select relevant genes in groups in performing multi-classclassification. The proposed method was successfully applied to gene expression data for rat liver regeneration andthe relevant genes were selected. The pathway relationships among the selected genes were also provided to verify
their biological rationality.
Keywords: multi-class classification, polynomial regression, gene selection, rat liver regeneration

required and each is decided by the coefficients
corresponding to the different gene groups. Hence, it isa challenge to select the proper genes for the microarray
multi-class classification problem.
To deal with the aforementioned problem, some newstatistical learning methods have been proposed. For

1 Introduction

Statistical machine learning methods, e.g., support
vector machine, lasso and their extensions,1-3 have beensuccessfully applied to the microarray classification andgene selection. Note that multiple decision functions are© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa, Japan320

Juntao Li, Yimin Ca, Xiaoyu Wang, Cunshuan Xu(1)
Although this method can select genes in groups
during performing classification, it can not adaptively
identify the important gene in the selected groups. This
paper is devoted to solving the problem.

example, Ref. 4 proposed L1-norm multi-class support
vector machine (MSVM), Ref. 5 proposed the adaptive
sup-norm MSVM. In particular, by using the initial
estimator, Ref. 6 proposed an adaptive MSVM which
can adaptively select the related genes.
Recently, the polynomial regression have attracted
much attention.7-9 Ref. 7 proposed variational bayesianpolynomial probit regression model and provide the R
package. Ref. 8 proposed the regularized polynomial
regression and applied it to multiple alignments ofprotein sequences. Ref. 9 proposed the multinomial
regression and proved its performance of the grouped
gene selection. However, these polynomial regression
methods can not adaptively select the related genes.
Motivated by Ref. 6, this paper present a newpolynomial regression method by introducing the multiclass adaptive elastic net penalty. The adaptive gene
selection performance is analyzed and experimentresults on the gene expression data for rat liver
regeneration are provided which demonstrate theeffectiveness of the proposed method.

3 Main Result
3.1 Adaptive Polynomial Regression Model
Similar to Ref. 6, let V =  vik nk  v1T ; v2T ; ; vnT ; denotethe index matrix, where viT represents vector code
corresponding to label yi .By introducing multi-classadaptive elastic net penalty, the adaptive polynomial
regression can be represented byK 1 n Kb  wT x   K p
min    yik  bk  wkT xi   log  e k k i    t j 2 wkj2  1 wkj b , w  n i 1  k 1
k 1
 k 1 j 1

s.t. 1T b  0 , 1T w j  0,  j  1,2, ,p 

(2)
where

 x , y  ,  x , y  , ,  x , y  ,
1

1

2

2

n



n



is sample label. Similar to Ref 6, Let Y   y1 , , yn T ,X   x1; x2 ;Suppose; xn   x1 , x 2 ,

that

the



x p  , x( j )   x1 j ,predictorsx( j ) , j  1,

xnj  .
T,pt j  w j  1, wkj .

 i 1 xij2n(3)

wkj and t j are the estimated contributions of the j th
gene to the k th classifier and the whole classifier.
Compared with the regularized polynomial regression(1), the weight t j  j  1, 2, , p  can be viewed asleverage vector which controls shrinkage of parameters
adaptively. Substituting the constraint condition into
objective function, the constrained optimization
problem (2) can be transformed into unconstrained
optimization problem. The altered objective function isshown as follows:
1 n   K 1
 K 1   K
L (1 , 2 ,b, w)     yiK  bk '    wk '  xi    yik (b k  wkT x i )n i 1   k 1
 k 1   k 1

K  3 represents the number of classes,
xi  xi1, xi 2 , xip is the input vector and yi  1, 2, K 

where i 1 xij vikn

2 Problem description
Given the microarray setarestandardized, the response has zero mean value. The
objective of this paper is to perform classification andselect genes by building the following classifier K 1b   K 1 w x   K 1T'   k 1 k '  i     log   ebK  wK xi  e k 1 k
 k 1 

  x   arg max f k  x  .
k 1,2, , K

For the sake of convenience, we adopt the notations inRef. 9. f k  x   bk  wkT x,  k  1, 2, , K  be the k thp
K 1 p K 1  2  t j wk2j  2  t j   wk ' j 
k 1 j 1
j 1  k 1
decision function, wk   wk1, , wkp T be the k th row
vector of matrix w , w j   w1 j , , wKj T be the j th K 1 p

p

K 1

j 1

j 1

k 1

2

 1  t j wk j  1  t j  wk ' jcolumn vector of matrix w . By using the multi elastic
net penalty and the polynomial likelihoods loss, Ref. 9
proposed the regularized polynomial regression

k(4)
3.2 Gene Selection performance

K
K p
K p
 1 n Kb  wT x  
min    yik  bk  wkT xi   log  e k k i   2  wkj2  1  wkj  b , w  n i 1  k 1
k 1
k 1 j 1
k 1 j 1TT
s.t. 1 b  0 , 1 w j  0,  j  1, 2, , p Note that the parameters corresponding to the j th genefor each classifiers is assigned the same weight t j in
© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa, Japan321

Adaptive Polynomial Regression andadaptive polynomial regression. Hence, the weighted L1
norm penalty can shrink the parameters corresponding
to the non-significant genes converge to 0 adaptively.
Meanwhile, it can also reduce shrinkage error of theparameters corresponding to important genes andemerge sparsity. In anther words, the adaptive
polynomial regression can select the relevant genes ingroups adaptively by estimating their contribution
to K classifiers. This performance of gene selection can
by described by the following Theorem.

same overall importance to the all K classifiers, i.e.
|| w( m) ||1 || w(l ) ||1 . In other words, adaptive polynomial
regression can adaptively identify the important gene in
the selected gene groups. According the terms in Ref.6,
we call it the adaptive grouping gene selection p.
Different from Ref.6, the pathwise coordinate descentalgorithm (PCD) presented in Ref. 10 can be improved
to solve the adaptive polynomial regression model.
4 Experiment
To demonstrate the performance of the proposed model,
we apply the adaptive polynomial regression to the genechip data of rat liver regeneration. This data set is
produced by the State Key Laboratory Cultivation Basefor Cell Differentiation Regulation, Henan Normal
University, which is available in the NCBI databasewith accession number: GSE55434.
Note that rat liver regeneration process has different
physiological activity at every moment. Hence, we treat
it as 9-classes classification problem which has 81
sample points, where each class has 9 sample points andeach point contains 24618 genes. In our experiments, 54
sample points are used to train the regression model andthe rest 27 for testing. In order to guarantee the balanceof different classes, we take 6 samples out of each classfor training and the rest 3 for testing. Fig.1 (a) shows thetenfold cross-validation error of the adaptive polynomial
regression on operation group training set. From Fig.1(a)we can see that when ln   1 , i.e.   0.3678794 thecross-validation error is the least. Thus, we choose theoptimal   0.220036243 and determine the solution ofadaptive polynomial regression model. Then we use theobtained coefficients to predict on test set and get 100%classification accuracy. Further, we determine 196
relevant genes using nonzero coefficients.
In order to eliminate the operation error and theinfluence of other noisy points, we also perform
experiments on sham operation group data. Fig.1 (b)shows the tenfold cross-validation error of the adaptive
polynomial regression model on sham operation grouptraining set. Similar to the case of operation group data,
we choose   0.1826835 and obtain 83 genes. By
eliminating the genes existing in sham operation groupfrom 196 genes in operation group, the rest 113 genes
are considered to be the relevant genes of rat liver

Theorem 1. Given the sample set (X, Y) and regularized
ˆ wparameters (1 , 2 ) Let b,
ˆ be the solution of adaptive.

polynomial regression (2),x( m )  R n , x(l )  R n be thecolumns of X corresponding to w
ˆ ( m) , wˆ (l ) . If predictorswˆ ( m ) and wˆ ( l ) are standardized, thenˆ (m)  w
ˆ (l )w

2

2 K n 1
 tm xim  tl1 xil
n2 i 1



(5)

holds.
Proof. Construct the following coefficient matrices
tl
 tm
'
 t  t wˆ km  t  t wˆ kl , j  m, l**ˆb  b wkj'   m lml
 wˆ ' ,j '  m, l
 kj
ˆ w)
Note that (b,ˆ be the solution of adaptive polynomial
regression. Hence, we have(6)ˆ0  L (1 , 2 , b* , w * )  L (1 , 2 , bˆ, w)
Following the similar procedure in Ref. 6, we havewˆ ( m )  wˆ ( l )2

2 K n 1
 tm xim  tl1xil
n2 i 1

(7)

Let   x(Tm ) x(l )   n xim xil ,   2tmtl tm2  tl2 .By somei 1

simple calculations, we have
2 K 12 K
tm x( m )  tl1 x(l ) 
1
n2
n2

n tm1 x( m )  tl1 x( l )

2 K
n2

tm2  tl2  1  



2

(8)

When K  2 , it can yield thatwˆ (1m )  wˆ (1l ) 

2
2(1   )n2Note that   1 if and only if tm  tl . Hence, theadaptive polynomial regression will assign the same
coefficients to the corresponding genes only if x( m) andx(l ) are highly correlated and these genes show the© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa, Japan

322

Juntao Li, Yimin Ca, Xiaoyu Wang, Cunshuan Xu

regeneration. To verify the biological rationality, we
conduct pathway relationships among the selected genes
by using the pathway studio 8. Fig.2 shows there exists
two gene pathways.

polynomial regression was proposed in this paper. The
proposed model can adaptively select genes in groups.
PCD algorithm can be improved to solve the proposedmodel. The proposed method was successfully applied
to the gene expression data for rat liver regeneration andthe relevant genes were selected. Furthermore, thepathway relationships among the selected genes wereanalyzed by using the pathway studio 8, which verifies
their biological rationality.
Acknowledgements
This work is supported by Natural Science Foundationof China (61203293), Program for Science andTechnology Innovation Talents in Universities ofHenan Province (13HASTIT040).

(a)References
1. I. Guyon, et al., Gene selection for cancer classification
using support vector machine, Machine Learning 46(1)(2002) 389-422.
2. L. Wang, et al., Hybrid huberized support vector machinefor microarray classification and gene selection,
Bioinformatics 24(3) (2008) 412-419.
3. R. Tibshirani, Regression shrinkage and selection via thelasso, Journal of the Royal Statistical Society, Series B(Statistical Methodology) 58(1) (1996) 267-288.
4. L. Wang and X. Shen, On L1-norm multi-class support
vector machine: method and theory, Journal of American
Statistical Association 102(478) (2007) 583-594.
5. H. H. Zhang, et al., Variable selection for themulticategory SVM via adaptive sup-norm regularization,
Electronic Journal of Statistics 2(2008) 149-167.
6. J. Li, et al., Adaptive Multi-class Support Vector
Machine for Microarray Classification and Gene
Selection, ICROS-SICE International Joint Conference,(Japan, Fukuoka, 2009), pp. 2658-2663.
7. L. Nicola and M. Girolami, vbmp: Variational bayesian
multinomial
probitregressionformulti-classclassification in R, Bioinformatics 24(1) (2008) 135-136.
8. J. Sreekumar et al., Correlated mutations via regularized
multinomial regression, BMC Bioinformatics 12(22)(2011) 444-456.
9. L. Chen, et al., Multinomial regression with elastic net
penalty and its grouping effect in gene selection, Abstract
and Applied Analysis 7(1) (2014) 558-577.
10. J. Friedman, et al., Regularization path for generalized
linear models via coordinate descent, Journal of
Statistical Software 33(1) (2010) 1-22.

(b)

Fig.1, (a)   0.5 tenfold cross-validation error of theadaptive polynomial regression on operation group training
set. (b)   0.5 tenfold cross-validation error of theadaptive polynomial regression on shame operation grouptraining set.

Fig.2, Regulative relation of relevant genes of rat liver
Regeneration.

5 Conclusion

By combining polynomial likelihood loss and the multiclass adaptive elastic net penalty, an adaptive© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa, Japan323

Adaptive Consensus via Dynamic Feedback Control for Lipschitz Nonlinear Multi-Agent
Systems
Lin Li

Department of Control Science and Engineering, University of Shanghai for Science and Technology, 516 JunGong
Road, Shanghai, 200093, P.R.China
Heyang Wang

Department of Control Science and Engineering, University of Shanghai for Science and Technology, 516 JunGong
Road, Shanghai, 200093, P.R.China
E-mail: lilin0211 @163.com, wangheyang2015@163.com
www.usst.edu.cn

Abstract
This paper deals with the consensus problem for multi-agent systems with fixed topologies. The agents aredescribed by Lipschitz nonlinear systems. Only output information of each agent can be obtained from its neighbor
agents. A distributed adaptive consensus algorithm via dynamic output feedback is proposed, in which the couplingweights between adjacent agents are time-varying and satisfy some designed adaptive laws. Provided examples areincluded to demonstrate the effectiveness of the proposed consensus algorithm.
Keywords: consensus, multi-agent systems, adaptive protocol, dynamic output feedback, Lipschitz nonlinear

state-feedback under a restrictive assumption that all theagents’ states can be measured, while the global
information, for example the eigenvalue of the Laplacematrix, is used. But we know that in many realapplications, the full states of the agents cannot be got,
and the global information of the MASs may be
unknown. Recently, an adaptive protocol was proposed
to solve consensus for general linear MASs,7 where thestates of agents were supposed to be known. The
observer-based protocol was investigated. 8
In this paper, the consensus problem of MASs with
Lipschitz nonlinear dynamics under a fixed undirectedconnected topology is in focus. A distributed output
feedback protocol is proposed, in which the output
errors of the agents and the controller states' errors are

1. Introduction
With the rapid development of science and technology,
the distributed coordinated control problem for multiagent systems (MASs) has received increasing attention.
Consensus is an important and fundamental issue forcoordinated control of the MASs, and has received more
and more attention due to its broader range applications,such as flocking control, tracking control, unmanned
aerial vehicle formation, underwater robot coordinatedsearch and rescue, etc. Over the past decade, researchon consensus problem has achieved numerous
meaningful results.1-6 Above the existing works thatabout consensus control for MASs are based on static© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

324

Lin Li, Heyang Wanglimxi (t )  x j (t )  0, i, j  1, 2, , Nt 

used to be the controller input. And in this designed
protocol, the coupling weights between agents satisfy
some designed adaptive law, in this way, the global
information are avoided. Sufficient condition for the
existence of this protocol is derived. Finally, numericalsimulation shows the effectiveness of our obtained
method.

Denote1 N
ei (t )  xi (t )   x j (t )N j 1andei (t )  xi (t ) 

2. Problem Formulation

1 N
 x j (t ).
N j 1

represented by a weighted connected undirected

Then, it follows form (1) and (3) that
1 N
ei (t )  Aei (t )  f ( xi )   f ( x j )+BCo eoi
N j 1graph G   V,Ε,Α . Hereinto, V represents the set ofeoi (t )  Ao eoi (t )  f ( xoi ) Thecommunication

nodes with V={v1 ,v2 ,topology

amongagentsisvN } , Ε is the set of undirected

1 N
 f ( xoj )+Bo C  j cij (t )aij (ei (t )  e j (t ))N j 1edges, and Α=[aij ] is the weighted adjacency matrix

 Bo C  j cij (t )aij (eoi (t )  eoj (t ))with aii  0, aij  0 . An undirected edge aij is denoted

Choose the adaptive law described as follows

nodes vi is defined by D (i )   j aij . Laplace matrix is(5)defined as L  [lij ]    A ,   diag{Din (1), Din ( N )} .
Consider a multi-agent system consisting of
N agents, and the dynamic of the ith agent is

f limt ei (t )  e j (t )  0 and limt eoi (t )  eoj (t )  0 .

by the pair of nodes (vi , v j )  E .The in-degree ofcij (t )  aij [(eoi (t )  eoj (t ))T QBo C (ei (t )  e j (t ))(eoi (t )  eoj (t ))T QBo C (eoi (t )  eoj (t ))], i, j  1, 2,in

xi (t )  Axi (t )  f ( xi )+Bui (t )yi (t )  Cxi (t )i  1, 2,consensus problem of system (1) is equal to theasymptotical stability of the error system (4). In thefollowing, we will focus on the asymptotical stability ofsystem (4).

Njui (t )  Co xoi (t )i  1, 2,NI

We can get that limt xi (t )  x j (t )  0 . Then, the(1)where xi (t ) is the state, yi (t ) is the measured output,
and ui (t ) is the control input of the ith agent. A, B and
C are known real matrices. The nonlinear functionf ( xi ) satisfies the following Lipschitz condition:f ( x)  f ( y )   x  y(2)where   0 is the known Lipschitz constant.
For system (1), since some states cannot be measured
in many cases, consider the following distributed
consensus protocolxoi (t )  Ao xoi (t )  f ( xoi )+Bo  cij (t )aij
[ yi (t )  y j (t )  C ( xoi (t )  xoj (t ))](4)

3. Main Result
Theorem 1. Given positive scalar  , if there exist a
constant   0 , positive definite matrices P, R, andthree matrices Vi (i  1, 2,3) such that the following LMI
holds
V2
P
0
0
0 
 (1,1) BV3
 *
(2, 2) V2
0
P
R
CT 

 *
*
I
0
0
0
0 


*
*
I
0
0
0 0
 *
 *
*
*
*
I
0
0 


*
*
*
*
I
0 
 *
 *
*
*
*
*
*
  I 


(3)

N

where xoi (t ) is the state, Ao , Bo and Co are the gain
matrices to be determined, cij (t ) is the time-varying
coupling weight of agent i and agent j satisfyingcij (0)  c ji (0) .

(6)where (1,1)  AP  PAT   2 I , (2, 2)  V1 +V1T . The signal* denotes the symmetry part of a symmetry matrix.
Then, under the protocol (3) with the adaptive law (5),the consensus of multi-agent system (1) can be solved.
And the gain matrices of this protocol can be taken as:

The objective of this paper is to design a distributed
consensus protocol (3) with an adaptive law about thecij (t ) for the group of agents such that the states ofsystem (1) achieve consensus asymptotically. That is,for any initial states xi (0) , the following is satisfied© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan325

Adaptive Consensus via Dynamic

Ao  R 1V1 P 1 , Bo  R 1V2 , Co  V3 P 1e  [e1T (t ), e2T (t ),.
Proof:For system (4), consider the following
Lyapunov-Krasovskii functional candidate
2NN
N N (c (t )   )ij
V (t )   eiT (t ) P 1ei (t )   eoiT (t )Qeoi (t )  
i 1
i 1
i 1 j 1
2(7)
where  is some appropriate constant. Taking the time
derivative of Lyapunov functional V (t ) along the
trajectory of system (4), we getN

eo  [eoT1 (t ), eoT2 (t ),

N

T
, eoN
(t )]T  [eT , eoT ]TCombing (5), (9) and (10) in to (8) yields
V (t )   T 

(11)

where
12 

   11,
 *  22 
11  I N  ( P 1 A  AT P 1   2 ( P 1 ) 2  I )N

V (t )  2 eiT (t ) P 1ei (t )  2 eoiT (t )Qeoi (t )i 1, eNT (t )]T12  I N  ( P 1 BCo )   L  QBo C

i 1

N

  (cij (t )   )cij (t ).

 22  I N  (QAo  AoT Q   2 Q 2  I )  2 L  QBo C

i 1 j 1

The graph G is connected, zero is a simple eigenvalue of
Laplace matrix L , and all the other eigenvalues are
positive. Then, there exists a unitary matrix
U  R NN such that U T LU  L  diag{0, 1 , 2 , N } ,where i (i  2,3, , N ) are positive eigenvalues of L .
Let   diag{U T  I N ,U T  I N } , we have

From (4) and (5), we have1 N
 f (xj )i 1
N j 1N
1 N+BCo eoi ]  2 eoiT (t )Q[ Ao eoi (t )  f ( xoi )   f ( xoj )(8)i 1
N j 1
  Bo C  j cij (t )aij (ei (t )  e j (t )  eoi (t )  eoj (t ))]N

V (t )  2 eiT (t ) P 1[ Aei (t )  f ( xi ) V (t )   T  (12)
If the inequality (6) is satisfied, choose  suchthat  2  2 , and let R  PQ , it is easy to getNthat V (t )  0 . Hence, V (t ) is bounded. By using the
LaSalle’s Invariance Principle, the error system (4) is
asymptotically stable. Then, the consensus problem ofsystem (1) is solved. This completes the proof.

Since cij (t )  c ji (t ) , we getN2 eoiT (t )QBo C  j cij (t )aij (eoi (t )  eoj (t ))
i 1

(9)

N

  j cij (t )aij (eoi (t )  eoj (t ))T QBo C (eoi (t )  eoj (t ))
i 1

1 N
1 N
 xi (t ), xo   xoi (t ) , we have
N j1
N j 1N
1
1 N
f ( xi )   f ( x j )  f ( xi )  f ( x )  f ( x )   f ( x j )N j 1
N j 1N
1
1 N
f ( xoi )   f ( xoj )  f ( xoi )  f ( xo )  f ( xo )   f ( xoj )N j 1
N j 1NN
In addition, because i1 ei (t )  0, i1 eoi (t )  0 , we get
Denote x 

4. Simulation Example
A simulation example is illustrated to show theeffectiveness of our results. Consider an undirected
graph G , the communication topology is the same as Fig.
18, in which there are eight agents. The elements ofadjacency matrix A satisfy aij  a ji  1 with (vi , v j )  E .
Consider system (1) with1 N
 f ( x j )]  0i 1
N j 1N
1 N
2 eoiT (t )Q[ f ( xo )   f ( xoj )]  0i 1
N j 1N

2 eiT (t ) P 1[ f ( x )  0.9 0 
 0.1
0.5 0.2
A, B    ,C  

,
2
 0
0.2
 0 0.3

Then, it follows from the Lipschitz condition (2) thatf ( xi )  0 0.1sin( xi 2 ) 

1 N
 f ( x j )]  eiT (t )(  2 ( P 1 ) 2  I )ei (t )N j 1N
12eoiT (t )Q[ f ( xoi )   f ( xoj )]  eoiT (t )(  2 Q 2  I )eoi (t )N j 1T

2eiT (t ) P 1[ f ( xi ) It is easy to see that the nonlinear dynamicf ( xi ) satisfies the condition (2) with   0.1 . SupposeTthe initial states of system (1) are x1 (0)  5 1 ,x2 (0)  0 1 , x3 (0)  9 1 , x4 (0)  5 1 ,TTTx6 (0)   2 0 , x7 (0)  1.8 1 , x8 (0)   6 2 .

(10)

T

Let

T

T

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

326

Lin Li, Heyang Wang

consensus control problem of system (1) under the
distributed adaptive protocol (3) can be asymptoticallysolved. Curves of coupling weights are given in Fig.2.

By solving the inequality (6), we can obtain the
dynamic output feedback gain matrices are 0.2040
Ao  diag(0.2, 0.2), Bo  000.2040 

5. Conclusion

Co  1.3945 1.9870

The state consensus problem for multi agent systems
with Lipschitz nonlinear dynamics has been investigated.
Under that consideration of the unmeasured agent state,a distributed dynamic output feedback protocol withtime varying coupling weights was designed. The
coupling weights satisfy the designed adaptive law. A
simulation example shows that the consensus problem
under the designed distributed adaptive protocol can beasymptotically solved.
Acknowledgements
This paper is supported by the National Natural Science
Foundation (61203143), and the Hujiang Foundation ofChina (C14002).
References
1. T. Li, M.Y. Fu, L.H. Xie, J.F. Zhang, Distributed
consensus with limited communication data rate, IEEE
Transaction on Automatic Control 56(2) ( 2011) 279-291.
2. Z.W. Liu, et al, Consensus of multi-agent networks witha periodic sampled communication via impulsive
algorithms using position-only measurement, IEEE
Transaction on Automatic Control 57(10) ( 2012 ) 26392643.
3. M. Ji and M. Egerstedt, Distributed coordination control
of multi-agent systems while preserving connectedness,
IEEE Transactions on Robotics 23(4) (2007) 693-703.
4. R. Olfati-Saber and R.M. Murry, Consensus problems in
networks of agents with switching topology and timedelays, IEEE Transaction on Automatic Control 49(9)(2004) 1520-1533.
5. W. Ren and E.M. Atkins, Distributed multi-vehicle
coordinated control via local information exchange,
International Journal of Robust and Nonlinear Control
17(1011) (2007) 1002-1033.
6. P. Lin, Y. Jia, Distributed robust H  consensus control
in directed networks of agents with time-delay, Systems& Control Letters 57(8) (2008) 643-653.
7. J. Hu, et al., Consensus of nonlinear multi-agent systems
with observer-based protocols, Systems & Control Letters72(10) (2014) 71-79.
8. Z. Li, et al., Consensus of multi-agent systems withgeneral linear and Lipschitz nonlinear dynamics using
distributed adaptive protocols, IEEE Transactions on
Automatic Control 58(7) (2013) 1786-1791.

Fig. 1. The states of multi-agent system (1)Fig. 2. The coupling weights cij (t )The state trajectories of nonlinear multi-agent system(1) are shown in Fig. 1. From Fig.1, we can see that the© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

327

Iterative Learning Based Thrust Ripple Suppression for PMLSM
Caixia Gao

School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454000 P.R.China
Fuzhong Wang

School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454000 P.R.China
Ziyi Fu

School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454000 P.R.China
gcx@hpu.edu.cn; wangfzh@hpu.edu.cn; fzy@hpu.edu.cn

Abstract
A fuzzy-PID-based control strategy was provided to deal with thrust ripple of multi-segment primary Permanent
Magnet Linear Synchronous Motor. First these thrust ripple suppression model is proposed. The iterative learningalgorithm is then synthesized with the velocity regulation for current compensation. Simulation results verify thestrong suppression of thrust ripple even with periodic disturbances.
Keywords: multi-segment primary Permanent Magnet Linear Synchronous Motor， thrust ripple, iterative learning
control, feedforward compensation.

system run out of synchronization, the rotor and cage
motor will decline dramatically due to the gravity,
causing serious accidents. Therefore, the application of
new control strategy is necessary to limit the force
ripple and avoid the accident.
Many experts and scholars have done a lot of workin the thrust fluctuation suppression of linear motor.
Their methods can be grouped into two kinds: One is to
reduce the force ripple by optimizing the motor
structure. The other is to compensate and restrain theforce ripple by algorithmic control.
Based on its characteristics, this paper builds themold of force ripple for PMLSM. It realizes theadvanced control of the force ripple through thefeedforward compensation of PMLSM.

1. Introduction
PMLSM vertical hoisting system consists of multisegment primary armature windings, rotor and lifting
container. Primary armature windings are evenly placedin the fixed frame (hoist Guide); the no-salient pole typerotor consists of permanent magnet and the rest. The car
platform moves vertically with the rotor. Without thetrouble of mechanical transmission device it employs
the segmented power supply mode and does not need
any electricity in the whole process, it is energy -saving,and efficient. Due to its significant economic and social
benefits, more and more attention is being paid to
PMLSM. However, in the actual operation process, thepermanent magnet will be disturbed inevitably, if the
disturbance can not be solved in time, it will make the© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

328

Caixia Gao, Fuzhong Wang, Ziyi Fu

2. The basic structure and working principle ofsegmented PMLSM hoisting systemof the end, the differences of magnetic permeancebetween the core and the groove produce the cogging
effect, which is the same as that in rotary motor. That is
cogging force; the frequent transformation of the motor
stator armature winding during the operation willchange the parameters of the motor caused by thechange of the air gap.
The equation for the thrust is:(1)
F (t )  kiq (t )As shown in figure 1, the segmented PMLSM
hoisting system uses the double hidden pole. By rolling
pulley, it moves up and down on the track. The lengthof the track of the rotor is equal to the total length of thetable and the longitudinal length of the interval so as tomake the coupling field area of the primary and thepermanent magnet unchanged in the process of
longitudinal motion of the rotor.
The air gap magnetic field will be generated when
the three-phase windings of PMLSM are connected tothe three sine alternating current. If the longitudinal end
effect caused by the opening of the two ends is notconsidered, the air gap magnetic field can be regarded
as the sine wave distributed along the straight line.
When the three-phase current changes with time, the air
gap magnetic field will move along a straight line in thesequence of A、B、C (or A、C、B) , which is called
the travelling magnetic field. The interaction of
permanent magnet magnetic field and the travellingmagnetic field generated by the armature winding willproduce electromagnetic force. With the force, the rotor
will do linear movement along the direction of thetravelling magnetic field because of the fixed stator
armature winding Group.

..

.

F (t )  M x  f s (t )  f m ( x)  f g ( x)  f q (t )(2)

In the above equations： F (t ) is the thrust, f s (t ) is the
carrying capacity, f m ( x) is force of friction, f g ( x) is
ripple thrust, and f q (t ) is other disturbance. Since the
Double-side non-salient pole mode is adopted, the forceof friction is ignored in this study; x is the position of
the motor.
The ripple thrust caused by frequent switching of
the motor stator armature winding and the unevendensity of the air-gap field caused by slot effect and end
effect bear direct relationship with the motion positionof the permanent linear motor. And the mathematicalmodel for the ripple thrust is:

f g ( x)  Ag sin( wx   ) Ag1 cos wx  Ag 2 sin wx(3)

In formula (3), Ag is the amplitude of the ripple thrust;Ag1 and Ag 2 are the constants; w is the angularvelocity of the constant displacement;  is the initial
phase angle. Ag , Ag1 , Ag 2 , w,  are all related to thestructure of the linear motor.
4.
Fig.1 Double-side non-salient pole PMLSM

Suppression strategy of force ripple ofsegmented permanent magnet synchronous
motor

Force ripple Suppression strategy principle control
is shown in figure2. The systematical control includes amaster controller and a feedforward controller based on
iterative learning control. Influenced by the force ripple
caused by the side effect of the linear motor, thecontrolling effect would not be good if the master
controller is applied. Therefore, iterative learning
controller is used to predict the force ripple andcompensate the main control system so as to reduce the3. The analysis of segmented permanent magnet
linear motor
The thrust wave is generated in the process of thesegmented permanent magnet linear synchronous motor.
The main reasons of the production of thrust fluctuation
are: the breaking of the end core, which produces theunique end effect of the linear motor, that is, the force© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

329

Iterative Learning Based Thrust

influence of the force ripple on the performance of thelinear motor control.

When the vertical motion of the linear motor is repeated,
the relationship between the ripple thrust and theposition (time) of the permanent magnet linear motor
remains the same. Therefore, this paper uses an iterative
learning controller to compensate for the ripple thrustand eliminate the thrust fluctuation.
The operation principle of the iterative learning
controller is as following: after k times iteration andbefore the next target position, the controller finds out
the offset current in Memory 2 via lookup table. And
linear motor, compensates the thrust ripple and stores
then it will send the current to the major loop of thefq Expectationvalue
iqk 1
Memorizer 2x

Iterative
learning
controller

Memorizer 1

fk

Memorizer 3

Disturbance

Table Lookup
Moduleiqkv

Thrust ripple
observer+-

Velocity
controller

iqk

s
K

vs

PMLSM

Fig.2 Iterative learning based thrust ripple suppression
diagram for PMLSM

iqk in Memory 3；Afterwards it will store f k sent by

4.1. Force ripple observer

thrust observer in Memory 1 and learn iq (t ) ink

The working principle of the force ripple observeris as following: according to the actual position and the
speed of the linear motor, it can calculate the thrust of
the push and then put it into the memory, and then to the
Iterative learning controller.
According to the analysis of force angle
characteristics of permanent magnet linear motor, therelationship of the speed between thrust power andAngular (position) can be obtained.

F (t ) 

3E0U s3E 2 Rsin(   )  0 2 s
Z
Z


 
  x  t  x   t
3E0U s3E02 Rsf k  kiq (t ) sin( xk  k t ) k Zk Z 2

Memory 3 and the difference value between thecontroller-predicted thrust ripple f k and expectedthrust ripple f q ，namely, ek (t )  f k  f q . As theoffset current after k+1 times iteration, the newlyk 1obtained offset current iq(t ) will do the above

operations again and realize feedforward compensation
effect.
The learning scheme is the iterative learning control
scheme. This paper uses PID as the learning scheme,
namely, the last control information and current errors
are used as correction terms.
Applied in study, the designed learning scheme of theiterative learning is：

(4)

(5)

iqk 1 (t )  iqk (t )   p ek (t )(6)t i  ek ( )d   D
0

In formula (4),(5) and (6),   arctan Rs / X T ,dek (t )dt(7)

Repeated interactive learning results in thrust ripple
approaching zero as close as possible.

f k is the dynamic thrust after K times iteration; iq (t )is the given electric current; xk and  k are positionand speed after k times iteration; Z is the synchronous

5. Simulink Simulation
In order to analyze the control effect of thecontroller designed in this paper, the iterative learning
based thrust ripple suppression system for PMLSM issimulated through the platform of Matlab or Simulink,
and a disturbing force with its amplitude and mean at
6N is added according to the actual interference
condition of the linear motion actuator.
The parameters of the permanent magnet linear
motor are set as follows ： the weight of the active cellis 20kg; the number of the active cell pole- pairs is 2;

impedance， E0 is the no-load electric potential of the
motor， U s is the imposed voltage on the linear motor
stator armature winding.
4.2. Iterative learning controller
Through the formula 3, it can be seen that there is adirect relationship between the ripple thrust and theposition (time) of the permanent magnet linear motor.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan330

Caixia Gao, Fuzhong Wang, Ziyi Fu

Thrust /N

the polar distance of permanent magnet is 30mm，thethrust coefficient is 11.45N/A; the armature resistanceof the active cell is 1.30Ω; and its inductance is3
5.30mH with coefficient potential as 1.05 10(Wb.s)/m.
The following result is concluded from the stimulation：
600
500
400
300
200
100
0

0.2

0.4

0.6

0.8

Time/sFig.3 The thrust curve under different iteration times

References
1. M. Sun, Z. Li. Characteristic Models and Iterative
Learning Control Methods of PMLSM Servo-system,
Science Technology and Engineering, 12(13) (2012)
3126-3133,.
2. J. Yang, et al., Disturbance rejection for PMLSM basedon iterative learning control, Journal of Shenyang
University of Technology, 32(1) (2010) 6-10.
3. Z. MA and Y. Nan, Position control for Permanent
Magnet Linear motor using Iterative Learning Control,
Small & Special Electrical Machines, 4(3) (2008), 49-51.
4. K. Tan, S. Zhao, Adaptive force ripple suppression iniron-corepermanent magnet linear motors, Proceedings ofthe 2002 IEEE International Symposium on Intelligent
Control, (Canada, Vancouver, 2002), pp. 27-30.

Thrust errors 103 N

From figure.3 it is loaded at 0.3s.The solid line is
the given thrust curve, the dense dotted line is the thrust
curve after 20 times iteration and the sparse dotted line
is it after 10 times iteration.
120
100
80
60
40
20
0

5

10

15

20

25

Iteration times

Fig.4 Maximum thrust error under different iteration
times

From figure.5，the maximum thrust error after 10times iteration is 40×10-3N, which is reduced to10×
10-3 N after 20 times iteration. So more iterations can
reduce the thrust error.
6. Conclusions(1)This paper employs the control strategy of a
combination of the iterative learning controller and thethrust fluctuation observer to control the thrust
fluctuation in advance.
(2)The thrust fluctuation compensation strategy takes
the impact of the thrust fluctuations into consideration.
The results of the simulation show that the control
strategy has strong power to suppress the thrust
fluctuation so as to improve the stability of the linear
motor.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

331

Adaptive Sliding Mode Control for A 2 DOF Magnetic Levitation Systemwith Uncertain Parameters
Meng Duan

The Seventh Research Division, Beihang University (BUAA)
Beijing 100191, China
Yingmin Jia

The Seventh Research Division, Beihang University (BUAA)
Beijing 100191, China
E-mail: duanmeng90@126.com; ymjia@buaa.edu.cn
www.buaa.edu.cn

Abstract
This paper investigates the stability control for a 2 DOF magnetic levitation system with uncertain parameters andexternal disturbance. The electromagnetic forces between the magnets and coils are obtained by numerical calculation.
Then, an adaptive sliding mode controller is proposed to deal with the parameter uncertainties in the system matrices.
The robust stability of the closed-loop system is proved by Lyapunov stability theory. Simulation results are presented
to verify the effectiveness of the proposed control strategy.
Keywords: adaptive sliding mode control, magnetic levitation, numerical calculation, uncertain input

end, Ref. 5 proposed a novel single-deck planar maglev
stage, its electromagnetic forces and torques are analyzed
by the ANSOFT software. Refs. 6 also used numericalcalculation method to obtain accurate electromagnetic
forces and torques, and large rotation ranges have beenrealized.
Inspired by those works, this paper mainly focus on
the problem of stability control for a 2-DOF magnetic
levitation system subjects to parameter uncertainties. The
contributions include that(i) In the modeling process of electromagnetic forces,
the errors between the real position and preset one aredescribed as the uncertainties of control matrix.
(ii) Based on the sliding mode method, an adaptive
controller is designed to guarantee the robust stabilityof the closed-loop system which has uncertain
parameters and external disturbances.

1. Introduction
In recent years, magnetic levitation technology
becomes more and more important in the modern
industry, like high precision transportation, because
magnetic levitation technology can eliminate the friction,
vibrations and wear caused by contact. And thesemachines have to move in multiple degrees of freedom(DOF). In Ref. 1-2, two kinds of planar motors using
Halbach permanent magnet arrays which can move in alarge horizontal plane have been proposed. Ref. 3 designed a long stroke 6 DOFs direct drive machine, the moverwas constructed by symmetric linear magnet arrays andcontrol currents generated by manufactured PCBs.
However, large rotation ranges are limited by the
distribution of magnetic field in the above mentioned
technologies, forces and torques in six DOFs can not becalculated sufficiently accurately in real time.4 To this

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

332

Meng Duan, Yingmin Jia

This paper is organized in six sections. Section 2
describes electromagnetic forces numerical calculation
model; Section 3 analyses the uncertain parameters ofsystem dynamic model; Section 4 presents the controller
design. Simulation results are presented in section 5. And
the conclusions are given in section 6.

total forces can be calculated simply by adding the forces
generated by each coil. The forces can be written as :ˆF   bˆi Ii  BI

Fig. 1. Magnetic levitation system

In this paper, we investigate a 2 DOFs magnetic
levitation system that consists of one disk magnet andtwo cylindrical coils shown in Fig.1, Assumed that thelevitated magnet can only move through x-axis and z-axis
but can not rotate.
Generally, electromagnetic forces are calculated by
Lorentz's formula at a fixed relative position between themagnet and coils. The total forces are calculated as thevolume integrals:
J ｴ Bm dV =IAa ｴ Bm dV

F = ( BˆO + D Bˆ ) I

where bˆ 

(4)where Bˆo is the nominal part of B̂ .
3. Model Analysis
The dynamic model of the magnetic levitation system
can be derived by using Newton’s Law as follow:
Fx   f xi  mx

(1)

Fz   f zi  mz  mg

where J is the vector of the current density of coils, I
is the coil current, A is the cross-sectional area of thecoil, a is the direction of the current, Bm is the vector ofmagnetic field density generated by the magnet and it is
the function of the relative position of the magnet and thecoils, V is the volume of coils. From Eq. (1) we can getthat the electromagnetic forces are proportional to themagnitude of coil currents at a fixed relative position. So
we can write Eq. (1) as follow:

ˆ
F = bI(3)

where I  [ I1 , I 2 ]T , Bˆ  [bˆ1 , bˆ2 ]T .
Because the forces generated by each coil current areproportional and independent, a numerical model can beestablished that only includes one magnet and one coil,the electromagnetic forces are calculated in this model oneach preset position along x-axis and z-axis with a unit
ampere of current to get bˆi , then use Eq. (3) to get F .
But the preset positions in numerical calculation
model are discrete, when the magnet moves on thepositions that have not been numerical calculated, we can
not find the matrix B̂ . In Ref. 6, B. Peter uses nearestneighbor positions to replace real positions, so the B̂ is
not certain at some positions, in this paper, the uncertainties of B̂ are introduced as D B̂ , obviously, it isbounded, so the electromagnetic forces can be written as:

2. Electromagnetic Force ModelF=i  1, 2

i  1, 2(5)where m is the mass of magnet, g is acceleration ofgravity. Choose X  [ x1 , x2 , x3 , x4 ]  [ x, x, z, z] , apply Eq.
(4) to Eq. (5), the dynamic model can be expressed asfollow:
X  f ( X )  ( B  B) I(6)

where f ( X )  [ x2 , 0, x4 ,  g ]T , I  [ I1 , I 2 ]T , B  Bˆ / m ,TT 0 bˆ11 0 bˆ21 
 0 b11 0 b21 B /m
 .
0 bˆ12 0 bˆ22 0 b12 0 b22 (2)1
a  Bm dV .
A Simultaneously consider the uncertain parameters ofsystem dynamic model and external disturbances, write
dynamical model as follow:

In practice , the impact between coils can be ignored,
so the forces generated by each coil are independent, theX& = fo ( X ) + D f ( X ) + ( BO + D B) I + w(7)© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan333

Adaptive Sliding Mode Controlof P̂1 and P̂2 which satisfy W1 ｣ Pd1 , W2 ｣ Pd 2
respectively.
Choose the adaption error as:

where f o ( X ) is the nominal part of f ( X ) , D f ( X ) is theuncertain part of f ( X ) , w is external disturbance,
assumed that both of them are bounded.

 Pˆ  P   P 
P   1 d1    1  .
 Pˆ2  Pd 2   P2 

4. Adaptive Sliding Mode Controller Design
Define the desired state as X d  [ xd 1 , xd 2 , xd 3 , xd 4 ]T ，
the tracking error can be written as:
T T
2

T
E= 鴿
・e1 , e= Xd - X

Define a Lyapunov candidate function as:

(8)

V

where e1  [ E (1), E (2)] , e2  [ E (3), E (4)] .
T

T

T


V  sT s  PT  1
0(9)

where c1  [c11 , c12 ]T , c2  [c21 , c22 ]T . The constant c11 , c12 ,c21 , c21 are chosen to be positive to make sure that thepolynomial c11  c12 and c11  c12 is Hurwitz.
Differentiating s yields:
s&= cT f ( X ) - cT X&d + cT Bo I + W ( X , I )TT Pˆ sgn( s1 )   Pˆ1  Pd 1   s1 sgn( s1 ) =s (W ( X , I )   1 )  
ˆˆ P2 sgn( s2 )   P2  Pd 2   s2 sgn( s2 ) =s1W1  s2W2  s1 Pd 1  s2 Pd 2  0.

Because V  L2 , V is bounded, according to Barbalat Lemma, V would converge to zero, so the tracking
error E would converge to zero. In order to alleviate theinput chattering caused by the discontinuous termsgn (si ) in Eq. (14), the saturation function sat (si / f ) isused to replace sgn (si ) ,7 where f is constant.

(11)

5. Simulation Results(12)

In this paper, the electromagnetic force model is calculated by Radia which is a free 3D magnetostatics
computation software package developed by ESRF andrunning in Mathematica. The numerical calculation
model established in Radia is shown in Fig. 2. The coil in

where
(13)

 Pˆ sgn( s1 ) 
I s  (cT Bo ) 1 Pˆ sgn( s )  (cT Bo ) 1  1
ˆ P2 sgn( s2 ) 

(14)

0
P
 2 

T(10)

I o  (cT Bo ) 1 (cT f ( X )  cT X d )

(17)

T

in practice, the input coil currents are bounded, we canguarantee that W ( X , I ) is bounded.
we design the control input I to be:

I  Io  I s0P.
 2 

  ( Pˆ  P )  sT (cT f ( X )  cT X d  cT Bo I  W ( X , I ))   1 1 d 1  P  2 ( Pˆ2  Pd 2 ) 鴿
c1 0
where c = ・, W ( X , I ) is the lumped uncertain
・0 c2
・parameters and external disturbance:
W ( X , I )  W1 ,W2   cT f ( X )  cT BI  cT w


1 T
1
s s  PT  1
2
2
0

The time derivative of the Lyapunov candidate
function can be found to be:

Because the levitated magnet moves along two axes,
the sliding-surface function can be introduced as:TTs = [ s1 , s2 ]T = 鴿・c1 e1 , c2 e2(16)

and P̂1 and P̂2 are adjustable parameters, and the adaptive
law is:11Pˆ  
s1 ,
s2 
2
 1


T

(15)

where r 1 and r 2 are the adaptation gain and both are
positive. Assume that Pd 1 and Pd 2 are the terminal solution

Fig. 2. Numerical calculation model.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan334

Meng Duan, Yingmin Jia

The position tracking response performance of the 2DOF magnetic levitation system is shown in Fig. 4,
where the levitated magnet can track the desire positionx = 32 mm and z = 15 mm. From the tracking curve
shown in Fig. 4, we can see that the magnet can reach andkeep balance at the desired position with small currents.
The trajectory tracking response performance isshown in Fig. 5, from the simulation results, we can seethat the levitated magnet can track the desired trajectories
x = 6sin t + 22 mm and z = 6sin(0.6t ) + 9 mm.

Fig. 3. Vibration of electromagnetic forces along x-axis withdifferent air gaps

6. Conclusions
In this paper, electromagnetic forces obtained by themethod of numerical calculation, which brings theuncertainties to the control matrix, to deal with this, anadaptive sliding model controller has been developed andapplied to the 2-D maglev system, the simulations
confirmed the efficacy of the proposed controllers .

Fig. 4. Position tracking response performance.

Acknowledgements
This work was supported by the National Basic Research Program of China (973 Program: 2012CB821200,
2012CB821201) and the NSFC (61134005, 60921001,
61327807).
References

Fig. 5. Trajectory tracking response performance

1. H.-S. Cho and H.-K. Jung, Analysis and design ofsynchronous permanent-magnet planar motors, IEEE T
ENERGY CONVER. 17(4)(2002)492 -499.
2. J. W. Jansen, Magnetically levitated planar actuator withmoving magnets, IEEE T IND APPL. 46(5)(2008)11081115.
3. X. Lu and I.-U.-R. Usman, 6D direct-drive technology forplanar motion stages, CIRP ANN-MANUF TECHN.
61(1)(2012)359 -362.
4. M. B. Khamesee and E. Shameli, Regulation technique fora large gap magnetic field for 3D non-contact
manipulation, Mechatronics. 15(2005)1073 -1087.
5. Y.-C. Lai, Y.-L. Lee and J.-Y. Yen, Design and servo
control of a single-deck planar maglev stage, IEEE T
MAGN. 43(6)(2007)2600 -2602.
6. B. Peter and D. Michael, Magnetic levitation over largetranslation and rotation ranges in all directions, IEEEASME T MECH. 18(1)(2013) 44 -52.
7. Y.-J. Huang, T. C. Kuo and S.-H. Chang , Adaptive
sliding-mode control for nonlinear systems with uncertain
parameters, IEEE T SYST MAN CY B. 38(2)(2008)534 539.

the numerical calculation model is 26mm in height,
20mm in the inside diameter and 44mm in the outside
diameter. The current density in the coil is 0.97A/mm2.
The magnet is cylindrical with 50mm in diameter and10mm in height, its material is NdFeB and the remnant
magnetization is 1.22T. The calculation sampling interval
is 1mm along z-axis and x-axis. Figure. 3 shows thatvibration of electromagnetic forces when the magnet
moves along x-axis with different air gaps.
In this section, two typical simulation results which
including the trajectory and position tracking arepresented. In both two simulations, D f ( X ) is supposedT

to be 鴿・0.3sin t ( x1 + x2 ), 0, 0.2 cos t ( x1 + x2 ), 0 , w isT

designed as [0,sin t ,0,sin t ] ，and the parameters of thecontroller are designed as: c1  c2  [0.5,1] , r 1 = r 2 = 0.5 ,Pˆ (0) = 30 , Pˆ (0) = 70 , f = 0.1 .
1

2© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan335

Undershoot Reduction in Discrete-Time ADRC of NMP Plant by Parameters Optimization
Tong Wu

School of Automation and Electrical Engineering,
University of Science and Technology Beijing,#30 Xueyuan Rd,Haidian District,
Beijing,100083,P. R. China
Weicun Zhang

School of Automation and Electrical Engineering,
University of Science and Technology Beijing, #30 Xueyuan Rd,Haidian District,
Beijing,100083,P. R. China
E-mail: luxuhuiluxuhui@163.com, ymjia@buaa.edu.cn
www.buaa.edu.cn

Abstract
Undershoot phenomena caused by unstable zeros of non-minimum-phase (NMP) plant is difficult to deal with inactive disturbance rejection control (ADRC). This paper proposes a new method to reduce undershoot by
optimizing the controller parameters of discrete-time ADRC system. Simulation results are given to verify theeffectiveness of the proposed scheme.
Keywords: ADRC, NMP System, Discrete Control, Parameters Optimization

the linearized ADRC and gives a bandwidthparameterization method to solve the linear ADRC
tuning problem. 3 The number of ADRC parameters tobe tuned can be decreased to two in this method whichlargely simplifies the task of ADRC design.
The discrete control technology as an emerging
technology has been widely applied in control system.
Various discrete techniques have been proposed to solve
control problems. 4, 5 In This paper, a discrete ADRC isdesigned to achieve the control task for NMP system
which having the undershoot problem due to theunstable zeros.6, 7 A modified method of using
appropriate non-linear function for parameters

1. Introduction
Active disturbance rejection control (ADRC), first
proposed by Han Jingqing, as a novel control design
method has been applied to solve various control
problems since 2000s1. The ADRC technique, including
Extended State Observer (ESO), mentioned in Han’s
book2 is a kind of non-linear technique which shows
great advantages in dynamic performance, robust
performance, and disturbance rejection performance.
But it confronts with much trouble in finding
appropriate tuning method to deal with ADRC
parameters for different plants. Gao Zhiqiang focuses on© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

336

Tong Wu, Weicum Zhang


1 h


  0 1



0 0


h2 
 h2 

 
2 
2

 
h  ,   b  h  ,

 

 

0
1 
 



3  3 (   1) 2 (   5) H  1 0 0 , Lp  
 (7)
2h



(   1)3   h2

optimization to reduce undershoot will be discussed in
the following sections. For simplicity, the subject to be
discussed in this paper is the second order system.

2.

Discrete Active Disturbance Rejection
Control

2.1. Discrete Extended State Observer
The linear extended state observer (LESO) 3 can beconstructed asz  Az  Bu  L( y  yˆ )(1)yˆ  Czwhere  e  hwhere b is a constant.

0 1 00,A  0 0 1  B  b0  ,0 0 0 0 
 1 
C  1 0 0 , L    2    3 

2.2. Discrete ADRC control law
The parameterized control law can be constructed as3, 8(9)
u0 (k )  k p (r (k )  z1 (k ))  k d z2 (k )(2)Using 0 -Parameterization method mentioned in Ref. 3,
the observer gain vector L can be get as(3)
1  30 ,  2  30 2 ,  3  03

u (k ) (10)0  2 ~ 5c , k p  c 2 , kd  2c(11)

3. Optimization of ADRC Parameters
Step Response
0.7

f

0.6

where

e

0.5

 1 hAhAt  e ,   (  e dt ) B, H  C , Lp    2  (5)0 3 h is the sample period. And the estimator gain vector
L p is determined by the equation

i

g

j

k

h

0.4

Amplitude

0.3

0.2

a

0.1

0

b

-0.1

d

-0.2

-0.3

3

u 0 ( k )  z3 ( k )
b

where

where 0 is the bandwidth of the observer. And thevalue of b0 falls in the range from 20 to 100. The
extended state observer can be discretized as8, 9
z (k  1)  z (k )  u (k )  L p ( y (k )  yˆ (k ))(4)yˆ(k )  Hz (k )

 ( z)  zI  (  Lp H )  ( z   )

(8)

0

c
0

2

4

6

8

10

12

14

Time (sec)(6)
Figure 1 step response of NMP system

Combine equation (2) and (5), and solving (6) for L p
yields8

For the controller design, we need a bigger proportional
gain at a, c, f, h and j, and a smaller one at b, e, g, i andk in Fig. 1. A bigger differential gain between a and b, cand d, f and g, h and i and j and k, and a smaller one
between b and c, e and f, g and h and i and j in Fig. 1 10.
Generally this is not realizable where controller© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

337

Undershoot Reduction in Discrete-Timeparameters are found being fixed. But we can choose
appropriate non-linear functions to optimize theproportional gain and differential gain of the controller
as a solution to this problem10.
We assume yd as the derivative of the output, yd 2 as
the second derivative of the output，and e(k ) as theerror between the reference and the output of the system.
Using non-linear function, controller parameters can beconstructed ase(k )  r  z1 (k )(12)
z1 (k )  z1 (k  1)
(13)
yd ( k ) hy (k )  yd (k  1)
(14)
yd 2 ( k )  dh2(15)
K p  K pr  (1  yd)e  e  yd
K dr
Kd 
(16)
2 sign ( yd ) sign ( yd 2 ) e ( k )(1  e)parameters of discrete ESO are chosen asu ( k )  z3 ( k )u (k )  0b
1 0.005 1.25 105 1.25 105 
0.005  ,   b   0.005  ,(21)   0 1
0 0


1 
0



 1.2 H  1 0 0 , Lp   86.8 (22) 2436.6 
where b  120 .
Finally the controller can be designed as (23) to control
augmented system where r is the setpoint. The valueof c is 25 rad/sec .
u0 (k )  K p (r  z1 (k ))  K d z2 (k )K p  c 2  (1 

Kd 

where Kpr, Kdr is the original proportional gain andoriginal differential gain of the controller which can be
settled using the method mentioned in (9) and (11). And
z1 (k ) is the state of the extended observer which is used
to estimate the output of the plant.

2)e yd  e yd

2

(1  ec
2 sign ( yd ) sign ( yd 2 ) e ( k )

(23)

)

The result for the simulation mentioned above is shownin figure 2. The dash line represents the step response ofregular discrete ADRC and the solid line represents thestep response of modified discrete ADRC. Intuitively,
the undershoot of modified discrete ADRC is lower thanthat of regular discrete ADRC.

4. Design Example
A simulation of NMP system is used to demonstrate thecontrol design procedure and its resulting performance.
The plant is modeled ass  2(17)
G (s)  2s  40 s  400
Its corresponding z transfer function and difference
equation is shown as (18) and (19)
0.004501z  0.004546(18)
G( z) z 2  1.81z  0.8187
y(k )  1.81y(k  1)  0.8187 y(k  2)
(19)
0.004501u (k  1)  0.004546u (k  2)
The sample period h is taken as 0.005s. The discrete
extended state observer can be constructed asz (k  1)  z (k )  u (k )  L p ( y (k )  yˆ (k ))(20)yˆ(k )  Hz (k )

1
0.8
0.6
0.4
0.2
0
-0.2

modified discrete ADRC
regular discrete ADRC

-0.4
0

1

2

3

4

5

6

7

8

9

10

Figure 2 Step response of regular discrete ADRC andmodified discrete ADRC

5. Conclusion Remarks

As is mentioned in Ref. 2, the extended state is fed backto cancel the model uncertainty. 0 is selected as 100
rad/sec
andusing
bandwidth-parameterization,

In this paper, a method of ADRC parametersoptimization using nonlinear function has been
discussed. It is shown that the modified discrete ADRC

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan338

Tong Wu, Weicum Zhang

has smaller undershoot than the regular one. Simulation
result has been shown to verify the effectiveness of the
proposed scheme.

Acknowledgements
The author would like to thank the anonymous
reviewers for their constructive and insightfulcomments for further improving the quality of thiswork. This work was supported by the Major State
Basic Research Development Program (973 Program)(No. 2012CB821200).

References
1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

Z. Gao, On the Foundation of Active DisturbanceRejection Control, Control Theory & Applications,30(12) (2013) 1498-1510.
J. Han, Active Disturbance Rejection Control Techniquethe technique for estimating and compensating theuncertainties (National Defense Industry Press, Beijing,2008).
Z. Gao, Scaling and Bandwidth- Parameterization BasedController Tuning, American Control Conference, (USA,
Denver, 2003), pp. 4989-4996.
D. V. Stallard, Discrete Optimal Terminal Control, with
Application to Missile Guidance, IEEE Transactions on
Automatic Control, 18(4) (1973) 373-377.
D. Zhou and C. Gao, Variable Structure Control ofDiscrete System with Time-delay, Systems and Control
in Aerospace and Astronautics, (China, Shenzhen, 2008),
pp. 1-5.
S. Zhao and Z. Gao, Active Disturbance Rejection
Control for Non-minimum Phase Systems, ChineseControl Conference, (China, Beijing, 2010), pp. 60666070.
M. Tang and P. Wu. Control of Non-minimum Phase
Servo System Based on Auto Disturbance Rejection,
Radar Science and Technology, 11(3) (2013) 335-340.
R. Miklosovic, A. Radke, Z. Gao, Discrete
Implementation Generalization of the Extended State
Observer, American Control Conference, (USA,
Minneapolis, 2006), pp. 2209-2214.
C. Zhang, J. Zhu, Y. Gao, Active Disturbance Rejection
Controller Based on Deadbeat Observer Design and
Performance Analysis, Control Theory & Applications,32(1) (2015) 29-34.
D. Zhang, K. Liu, B. Fang, Non-minimum Phase System
Control Based on Nonlinear PID, Microcomputer
Development, 14(12) (2004) 4-6.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan339

Development of a Tool to Keep Consistency between a Model and a Source Codein Software Development Using MDA.
Yuuki Kikkawa*, Tetsuro Katayama*, Yoshihiro Kita†,
Hisaaki Yamaba*, Kentaro Aburada‡ and Naonobu Okazaki**University of Miyazaki, Japan,†Tokyo University of Technology, Japan‡Oita National College of Technology, Japankikkawa@earth.cs.miyazaki-u.ac.jp, kat@cs.miyazaki-u.ac.jp, kitayshr@stf.teu.ac.jp,
yamaba@cs.miyazaki-u.ac.jp, aburada@oita-ct.ac.jp, oka@cs.miyazaki-u.ac.jp

Abstract
This study improves efficiency of software development using MDA. This paper develops the tool that keeps
consistency between a model and a source code in software development using MDA. The tool has two functions:(i) Generating a source code and (ii) Keeping consistency between a model and a source code. A simple ATM
example is used in order to confirm effectiveness of the tool. The tool can reduce time and effort to keep
consistency between models and a source code.
Keywords: MDA (Model Driven Architecture), Extended Activity Diagram, Activity diagram, Detail specification.

modification tool of PIM to keep consistency with PSM
is researched.4
Also, there is no consistency if a developer edits theoriginal model. A developer can keep consistency if a
MDA tool generates models from the edited models
again. Here, some MDA tools can generate a complete
model from models including detail specification. A
framework that generates the executable source codefrom a class diagram and a state machines diagram isresearched.5 However, MDA tools cannot generatecomplete models from abstract models because thesemodels do not have detail specification of a system. The
developer must modify generated models to fit themodified original models or generate a new model fromthe modified models with a MDA tool and then add thedetail specification to the new model by hand again.
This study improves efficiency of softwaredevelopment using MDA. We proposed a modification
method of a source code to correspond with a modified
model in MDA.6 However, we did not implement themethod. In this paper, we develop the tool that keeps

1. Introduction
MDA (Model Driven Architecture) is a concept ofsoftware development.1 MDA defines five models:business model, requirement model, platform
independent model (PIM), platform specific model(PSM), and physics model. Each model has different
abstraction level. A developer defines models andgenerates a less abstract model by softwaredevelopment in MDA. Here, MDA tools are used togenerate a less abstract model. A developer uses UML(Unified Modeling Language)2 for modeling PIM and
PSM.
Before generation of a less abstract model, adeveloper must define generation rules of high abstract
model. A method to support the definition of ageneration rule is researched.3
One of the MDA's problems is how to keep
consistency between the original model and an edited
model which is generated from the original. A© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan340

Yuuki Kikkawa, Tetsuro Katayama, Yoshihiro Kita, Hisaaki Yamaba, Kentaro Aburada, Naonobu Okazaki

consistency between a model and a source code insoftware development using MDA. This paper
introduces the tool and describes how we implement thetool.
2. The Developed Tool
This chapter describes functions and an overview of thetool. Here, the tool treats with activity diagram of UML
and programming language C++.
2.1. Functions

Fig. 1. Functions of the tool.

The tool has two functions: (i) generating a source code,(ii) keeping consistency between a model and a sourcecode. (ii) consists of 3 steps. Fig. 1. shows each function.
An input of (i) is an activity diagram, and an outputof (i) is the source code based on the activity diagram.
Inputs of (ii) are an unmodified activity diagram, themodified activity diagram, and the source code
including the detail specification. An output of (ii) is asource code which is consistent with the modified
activity diagram and includes the detail specification.
In executing (ii), the tool generates an EAD(Extended Activity Diagram) as intermediate data. An
EAD is a format of the data added a part of the sourcecode which has the detail specification to the activity
diagram.

Fig. 2. Overview of the tool.

3.1. Generating a source code
The tool generates a source code from the activity
diagram. The steps to generate a source code are shown
as below.
(i) Acquire the function name
The tool generates a skeleton of source code. The
function name is the activity name that is describedin the activity diagram. Here, the type and theparameter of the function are void.
(ii) Select the initial node
The tool selects the initial node.
(iii) Implement the function
The tool executes the process as below dependingon the type of the selected node.
 Call activity node
Write the name of the call activity node to thesource code.
 Decision node
Write an if-statement to the source code.
Conditions of if-statement are guard conditions of
this node.

2.2. Overview
Fig. 2 shows an overview of the tool. The tool consistsof 4 parts shown below.
(i) Menu bar.
(ii) Mode select panel.
(iii) Multipurpose panel.
(iv) Paint panel.
A developer can execute a function of the tool by (i).
By using (ii), a developer can select an operation hewants to do on (iv). (iii) assists the selected operation on(iv). (iv) shows activity diagram.
3. Implementation
This chapter describes how to implement each function.
Here, the tool sets node ID and edge ID in the node andthe edge that the developer describes. Node ID and edge
ID are unique number in a diagram.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan341

Development of a Tool3.3. Modifying the EAD

Activity final nodeFinish the generation of the source code.
 Other than the above
Do nothing.
(iv) Select another node
The tool reselects the node connected by theoutgoing edge of the selected node and go to (iii).

At the second step of the function which keeps
consistency between a model and a source code, the toolmodifies the EAD to correspond with the modified
activity diagram. The steps to modify the EAD are
shown as below.
(i) Add an edge adjacent to a platform specific node.
(a) Select a platform independent edge from an
EAD.
(b) If selected edge connects a platform specificnode to a platform independent node and theplatform independent node does not exist in themodified activity diagram, execute the process
shown as below.
1.
Select a node in the modified activity
diagram which has the same node ID as theselected node.
2.
Select a next node of the current selected
node in the modified activity diagram.
3.
Select a node in the EAD which has thesame node ID as the selected node in themodified activity diagram.
4.
Make an edge connected with the first
selected node and the second selected nodein the EAD.
(c) If the selected edge connects a platform
independent node to a platform specific nodeand the platform independent node does notexist in the modified activity diagram, executethe process shown as below.
1.
Select a node in the modified activity
diagram which has the same node ID as theselected node.
2.
Select a previous node of the current
selected node in the modified activity
diagram.
3.
Select a node in the EAD which has thesame node ID as the selected node in themodified activity diagram.
4.
Make an edge connected with the first
selected node and the second selected nodein the EAD.
(d) If there is an unselected node, select anunselected node and go to (b).
(ii) Delete edges and nodes.
(a) Select a node or edge from the EAD.

3.2. Generating an EAD
The tool keeps consistency between a model and asource code. At the first step of the function, the tool
generates an EAD. The steps to generate an EAD are
shown as below.
(i) Select the first line of the source code added thedetail specification.
 We call the selected source code “LOS (line of
selected)”(ii) Generate and extract a line of source code from theactivity diagram.
 Generation rule of a source code is same as thefunction (i) of the tool.
(iii) If (ii) and the LOS are not same character string,
execute the process shown as below.
(a) Write the LOS to the activity diagram.
(b) Encircle lines written in (a) as a node.
(c) Select the original node of the generated sourcecode in (ii).
(d) Connect the incoming edge for the selected
node to the node generated in (b).
(e) Make an edge connected with the selected nodeand the node generated in (b).
(f) Change the LOS to the next line of the current
LOS.
(g) Go to (iii).
(iv) Change the LOS to the next line of the current LOS.
(v) Go to (ii).
Later, we call an encircle node a platform specificnode. We define a node other than a platform specificnode as a platform independent node. We define anedge connected with a platform specific node as aplatform specific edge. We define an edge other than aplatform specific edge as a platform independent edge.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan342

Yuuki Kikkawa, Tetsuro Katayama, Yoshihiro Kita, Hisaaki Yamaba, Kentaro Aburada, Naonobu Okazaki

Fig. 4. The generated source code.

password checking, this system executes a withdrawal
process or a depositing process by a user’s input. Fig. 3
shows the activity diagram of the ATM system.
The tool generates a source code from the activity
diagram. Fig. 4 shows a generated source code.
The developer adds the detail specification to thegenerated source code in order to execute it. Fig. 5.
shows the source code added the detail specification.
Suppose a case that the requirement of specificationis changed, after adding the detail specification. The
developer needs to modify the activity diagram bydeleting and adding some elements of the activity
diagram. Fig. 6 shows a modified activity diagram. The
developer deletes the password checking process tosequence of deposit.
The tool generates a new source code from the sourcecode including the detail specification, the modified
activity diagram, and original activity diagram. Fig. 7
shows a generated source code. The source code
includes the detail specification.

Fig. 3. The activity diagram of the ATM system.

(b) If the modified activity diagram does notconsist of a node or edge which has the same
ID as a selected node or an edge, delete theselected node or edge.
(c) If there is an unselected node or edge in the
EAD, select an unselected node or edge and goto (b).
(iii) Add edges or nodes.
(a) Select a node or edge from the modified
activity diagram.
(b) If the EAD does not consist of a node or edge
which has the same ID as a selected node oredge, add the selected node or edge to EAD.
(c) If there is an unselected node or edge in themodified activity diagram, select an unselected
node or edge and go to (b).
3.4. Generating the source code from the EAD
The tool generates a source code from the EAD. The
steps to generate a source code are same as section 3.1.
If the tool selects a platform specific node, the tool
writes its name to the source code.

5. Discussion
MDA Tools such as EA7 (Enterprise Architecture) cangenerate a skeleton of a source code from a class
diagram. In addition, EA can generate a source codefrom an activity diagram or a state machine diagram.
However, the source code generated by EA does nothave the detail specification. It takes time and effort thatthe developer adds the detail specification to the sourcecode generated from the modified activity diagram.

4. Application Example
We use a simple ATM as an example to confirm
effectiveness of the tool. This ATM system executes
password checking process. If a user successes© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan343

Development of a Tool

Fig. 5. The source code added the detail specification.

Fig. 7. The source code corresponded with the modified
activity diagram.

Future issues are as follows.
Improvement of the tool to treat with otherstatements except if-statement.
 Improvement of the tool to treat with programming
language Java.
References

1. MDA(ModelDrivenArchitecture),
http://www.omg.org/mda (accessed November 30, 2016).
2. UML(Unified
Modeling
Language),
http://www.omg.org/spec/UML/2.4.1(accessed
November 30, 2016).
3. D. Lopes, S. Hammoudi, J. Bézivin, F. Jouault: Mapping
Specification in MDA: From Theory to Practice,
Interoperability of Enterprise Software and Applications,(Springer-Verlag London Ltd 2006), pp.253-264,
4. Lionel C. Briand, Yvan Labiche, Tao Yue: Automatedtraceability analysis for UML model refinements,
Information and Software Technology, Vol. 51 (2009),
Issue2, pp. 512-527.
5. A. Derezinska,
Code Generation and Execution
Framework for UML 2.0 Classes and State Machines,
IMCSIT, (2008), pp. 517-524.
6. Yuuki Kikkawa, Tetsuro Katayama, Yoshihiro Kita,
Hisaaki Yamaba, Kentaro Aburada and Naonobu
Okazaki, Proposal of a Modification Method of a Source
Code to Correspond with a Modified Model in MDA,International Conference on Artificial Life and Robotics,(2015), pp. 384-387.
7. EnterpriseArchitect,
http://www.sparxsystems.jp(accessed November 30, 2016).

Fig. 6. The modified activity diagram.

The developed tool can generate a source code
including the detail specification. The tool can reducetime and effort to add the detail specification to thesource code generated from the modified activity
diagram. Moreover, it can reduce time and effort tokeep consistency between models and a source code
after the requirement specification is modified.
Therefore, the tool is useful for efficiency of softwaredevelopment using MDA.
6. Conclusion
This paper develops the tool that keeps consistency
between a model and a source code in softwaredevelopment using MDA. The tool can generate thesource code that corresponds with the modified activity
diagram and has information about the detailspecification.
We have confirmed that the tool can generate asource code including the detail specification from theoriginal activity diagram, the modified activity diagram,and the original source code. Therefore, the tool isuseful for efficiency of software development.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan344

Necessary spaces for seven-way four-dimensional Turing machines
to simulate four-dimensional one-marker automata

Makoto Nagatomo, Shinnosuke Yano, Makoto Sakamoto, Satoshi Ikeda, and Hiroshi Furutani

Faculty of Engineering, University of Miyazaki,
1-1 Gakuen Kibanadai Nishi, Miyazaki, Miyazaki 889-2192, Japan
E-mail: je.suis.makoto@gmail.com, shinchandx@ezweb.ne.jp, sakamoto@cs.miyazaki-u.ac.jp,
bisu@cs.miyazaki-u.ac.jp, furutani@cs.miyazaki-u.ac.jp
Takao Ito and Tsutomu Ito

Institute of Engineering, Hiroshima University, 4-1, Kagamiyama 1-chome
Higashi-Hiroshima, Hiroshima 739-8527, Japan
E-mail: itotakao@horoshima-u.ac.jp, 0va71-2538f211n@ezweb.ne.jp
Yasuo Uchida

Department of Business Administration, Ube National College of Technology, Tokiwadai
Ube, Yamaguchi 755-8555, Japan
E-mail:uchida@ube-k.ac.jp
Tsunehiro Yoshinaga

Department of Computer Science & Electronic Engineering,
National Institute of Technology, Tokuyama College, Gakuendai
Shunan, Yamaguchi 745-8585, Japan
E-mail:yosinaga@tokuyama.ac.jp

Abstract
We think that recently, due to the advances in many application areas such as motion image processing, computer
animation, and so on, it is very useful for analyzing computational complexity of multi-dimensional information
processing to explicate the properties of four-dimensional automata, i.e., three-dimensional automata with the timeaxis. As far as we know, there is no investigation about four-dimensional automata. Then, in 2002, we first introduced
four-dimensional finite automata in the world. In 2003, we investigated four-dimensional alternating Turing machines.
In 2015, we show the sufficient spaces for four-dimensional Turing machines to simulate four-dimensional onemarker automata. In this paper, we continue the investigations, and deal with the necessary spaces for fourdimensional Turing machines to simulate four-dimensional one-marker automata.
Keywords: computational complexity, finite automaton, lower bounds, marker, simulation, Turing machine.

can make marks on its input with the restriction that only
a bounded number of these marks can exist at any giventime[1].
As is well known among the researchers of automata
theory, one-dimensional one-marker automata areequivalent to ordinary finite state automata. In otherwords, there is no need of working space usage for oneway Turing machines to simulate one-marker automata,as well as finite state automata.

1. Introduction
An improvement of picture recognizability of the finite
automaton is the reason why the marker automaton was
introduced. That is, a two-dimensional one-marker
automaton can recognize connected pictures. This
automaton has been widely investigated in the two- or
three-dimensional case [2]. A multi-marker automaton isa finite automaton which keeps marks as ‘pebbles’ in thefinite control, and cannot rewrite any input symbols but© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

345

Makoto Nagatomo, Shinnosuke Yano, Makoto Sakamoto, Satoshi Ikeda, Hiroshi Furutani, Takao Ito and Tsutomu Ito, Yasuo Uchida, Tsunehiro
Yoshinaga

In the two-dimensional case, the following facts are
known : the necessary and sufficient space for three-way
two-dimensional deterministic Turing machines TR2DTM’s to simulate two-dimensional deterministic(nondeterministic) finite automata 2-DFA’s (2-NFA’s) is
mlogm (m2) and the corresponding space for three-way
two-dimensional nondeterministic Turing machines
TR2-NTM’s is m (m), whereas the necessary andsufficient space for three-way two-dimensional
deterministic Turing machines TR2-DTM’s to simulate
two-dimensional deterministic (nondeterministic) onemarker automata 2-DMA1’s (2-NMA1’s) is 2mlogm (2𝑚 2 )and the corresponding space for TR2-NTM’s is mlogm(m2), where m is the number of columns of twodimensional rectangular input tapes.
In the three-dimensional case, the following facts are
known : the necessary and sufficient space for five-way
three-dimensional deterministic Turing machines FV3DTM’s to simulate three-dimensional deterministic(nondeterministic) finite automata 3-DFA’s (3-NFA’s) is
m2logm (m3) and the corresponding space for five-way
three-dimensional nondeterministic Turing machines
FV3-NTM’s is m2 (m2), whereas the necessary andsufficient space for five-way three-dimensional
deterministic Turing machines FV3-DTM’s to simulate
three-dimensional deterministic (nondeterministic) one2 2
marker automata 3-DMA1’s (3-NMA1’s) is 2lmloglm (2𝑙 𝑚 )and the corresponding space for FV3-NTM’s is lmloglm(l2m2), where l(m) is the number of rows (columns) oneach plane of three-dimensional rectangular input tapes.
In the four-dimensional case, we showed the sufficientspaces for four-dimensional Turing machines to simulate
four-dimensional one-marker automata [3]. In this paper,
we continue the investigations, and deal with thenecessary spaces for four-dimensional Turing machines
to simulate four-dimensional one-marker automata.

Definition 2.1. A four-dimensional nondeterministic
one-marker automaton (4-NMA1) is defined by the 6tuple 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 (# ∉ Σ is the boundary
symbol);(5) {+,－} is the pair of signs of presence and absenceof the marker ; and(6) δ : (𝑄 × {+, −}) × ((Σ ∪ {#}) × {+, −}) ↦ ×
2𝑄×{+,−} × (( Σ ∪ {#} ) × {+, −} ) × {east, west,
south, north, up, down, future, past, no move} is thenext-move function, satisfying the following : For any q,q’∈Q, any a, a’ ∈ Σ, any u, u’, v, v’∈ {+, −}, and any d∈{east, west, south, north, up, down, future, past,no move}, if ((q’, u’), (a’, v’),d) ∈ 𝛿((q,v), (a,v)) then𝑎 = 𝑎′ and (𝑢, 𝑣, 𝑢′ , 𝑣 ′ ) ∈ {(+, −, +, −),(+, −, −, +), (−, +, −, +), (−, +, +, −), (−, −, −, −)}.
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 exist according to the sign 𝑢 = + or
𝑢 = −, respectively.
Therefore, the restrictions on 𝛿 imply the following
conditions. (i) When holding the marker, M can put it
down or keep on holding. (ii) When not holding themarker, and ① if the marker exists on the current cell,
M can pick it up or leave it there, or ② if the marker
does not exist on the current cell, M cannot create a newmarker any more.
Definition 2.2. Let Σ be the input alphabet of 4-NMA1
M. An extended input tape 𝑥̃ of M is any fourdimensional tape over Σ × {+, −} such that for some𝑥 ∈ Σ (4) ,(i) for each 𝑗(1 ≤ 𝑗 ≤ 4), 𝑙𝑗 (𝑥̃) = 𝑙𝑗 (𝑥),(ii) for each 𝑖1 (1 ≤ 𝑖1 ≤ 𝑙1 (𝑥̃)), 𝑖2 (1 ≤ 𝑖2 ≤ 𝑙2 (𝑥̃)),𝑖3 (1 ≤ 𝑖3 ≤ 𝑙3 (𝑥̃)) , and 𝑖4 (1 ≤ 𝑖4 ≤ 𝑙4 (𝑥̃)) , 𝑥̃(𝑖1 , 𝑖2 ,
𝑖3 , 𝑖4 )=𝑥((𝑖1 , 𝑖2 , 𝑖3 , 𝑖4 ), 𝑢) for some 𝑢 ∈ {+, −}.
Definition 2.3. A configuration of 4-NMA1 M = (Q, q0, F,
Σ, { +, - }, δ) is a pair of an element of ((Σ ∪ {#}) ×(4){+, −})
and an element of 𝐶𝑀 = (𝐍 ∪ {0})(4) ×(𝑄 × {+, −}). The first component of a configuration𝑐 = (𝑥̃, ((𝑖1 , 𝑖2 , 𝑖3 , 𝑖4 ), (𝑞, 𝑢))) represents the extended
input tape of M. The second component (𝑖1 , 𝑖2 , 𝑖3 , 𝑖4 ) of

2. Preliminaries
An ordinary finite automaton cannot rewrite any
symbols on input tape, but a marker automaton can makea mark on the input tape. We can think of the mark as a‘pebble’ that M puts down in a specified position. If M
has already put down the mark, and wants to put it down
elsewhere, M must first go to the position of the mark andpick it up. Formally, we define it as follows.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

346

Necessary spaces for seven-way

Definition 3.1. Let x be in Σ (4) (Σ is a finite set ofsymbols) and l1(x)=l, l2(x)=m, l3(x)=n. For each j(1 ≤ j ≤ Q[l4(x)/lmn]) (where Q[l4(x)/lmn] denotes thequotient when l4(x) is divided by lmn),x[(1, 1, 1, (j-1)lmn+1), (l, m, n, jlmn)]
is called the jth (l, m, n)-block of x. We say that the tapex has exactly k (l, m, n)-blocks if l4(x)=klmn, where k isa positive integer.
Definition 3.2. Let (l1, m1, n1), (l2, m2, n2), … , be asequence of points (i.e., pairs of three natural numbers),
and let {(li, mi, ni)} denote this sequence. We call asequence {(l1, m1, n1)} the regular sequence of points if(li, mi, ni)≠(lj, mj, nj) for i ≠ j.
Lemma 3.1. Let T1={x ∈ {0, 1}(4) | ∃ l ≥ 1, ∃ m ≥ 1, ∃n≥1[l1(x)=l and l2(x)=m and l3(x)=n and (each plane of x
contains exactly one ‘1’) and ∃d≥2 [(x has exactly d (l,m, n)-blocks, i.e., l4(x)=dlmn) and (the last (l, m, n)-blockis equal to some other (l, m, n)-block)]]}. Then,(1) T1 ∈L[4-DMA1], but(2) T1  L[SV4-DTM(2L(l, m, n))] (so, T1  L[SV4-NTM(L(l,m, n))]) for any function L(l, m, n) such thatlimi→∞[L(li, mi, ni)/(liminiloglimini)]=0. for someregular sequence of points {(li, mi, ni)}.
Proof: (1): We construct a 4-DMA1 M accepting T1 as
follows. Given an input x with l1(x) = l, l2(x) = m, andl3(x) = n, M first checks, by sweeping plane by plane, thateach plane of x contains exactly one ‘1’, and M thenchecks, by making a zigzag of 45°-direction from top
plane to bottom plane, that x has exactly d (l, m, n)-blocks
for some integer d≥2. After that, M tests by utilizing its
own marker whether the last (l, m, n)-block is identical tosome other (l, m, n)-block. M then finds the ‘1’ positionon the plane and move up vertically from this position. In
this course, each time M meets a ‘1’ position, it checks
whether or not there is a marker on the plane (containing
the ‘1’ position).
(i):If there is a marker on the plane, M knows that thekth planes of the hth and the last (l, m, n)-blocks are
identical, and so M then tries to check whether the next(k+1)th planes of the hth and the last (l, m, n)-blocks are
identical.
(ii):If there is no marker on the plane, M goes back tothe ‘1’ position on the plane, and vertically moves up
again to find the next ‘1’ position.
In this case, if M eventually encounters the top boundary,
M knows that the kth planes of the hth and the last (l, m,n)-blocks are different (thus, the hth (l, m, n)-block is not𝑐 represents the input head position. The third
component (𝑞, 𝑢) of 𝑐 represents the extended state.
An element of 𝐶𝑀 is called a semi-configuration of M. If
q is the state associated with configuration c, then c issaid to be an accepting configuration if q is an accepting
state. The initial configuration of M on input x is
𝐼𝑀 (𝑥) = (𝑥 − , ((1,1,1,1), (𝑞0 , +))), where 𝑥 − is the
special extended input tape of M such that𝑥 − (𝑖1 , 𝑖2 , 𝑖3 , 𝑖4 ) = (𝑥(𝑖1 , 𝑖2 , 𝑖3 , 𝑖4 ), −) for each
𝑖1 , 𝑖2 , 𝑖3 , 𝑖4 (1 ≤ 𝑖1 ≤ 𝑙1 (𝑥 − ), 1 ≤ 𝑖2 ≤ 𝑙2 (𝑥 − ), 1 ≤ 𝑖3 ≤
𝑙3 (𝑥 − ), 1 ≤ 𝑖4 ≤ 𝑙4 (𝑥 − )). If M moves deterministically,
we call M a four-dimensional deterministic one-marker
automaton (4-DMA1).
Definition 2.4. Given a 4-NMA1 M = (Q, q0, F, Σ, {+, -},δ), we write 𝑐├ 𝑀 𝑐 ′ and say 𝑐′ is a successor of c if
configuration 𝑐′ follows from configuration 𝑐 in one
step of M, according to the transition rules 𝛿 . ├ ∗𝑀denotes the reflexive transitive closure of ├ 𝑀 . The
relation ├ 𝑀 is not necessarily single-valued, because 𝛿
is not. A computation path of M on 𝑥 is a sequence𝑐0 ├ 𝑀 𝑐1 ├ 𝑀 … ├ 𝑀 𝑐𝑛 (𝑛 ≥ 0) , where 𝑐0 = 𝐼𝑀 (𝑥) . An
accepting computation path of M on x is a computation
path of M on x which ends in an accepting configuration.
We say that M accepts x if there is an accepting
computation path of M on input x.
Let L(l, m, n) : N3 ↦ R be a function. A seven-way
four-dimensional Turing machine M is said to be L(l, m,n) space-bounded if for each l, m, n≥ 1 and for each x
with l1(x) = l, l2(x) = m, and l3(x) = n, if x is accepted byM, then there is an accepting computation path of M on x
in which M uses no more than L(l, m, n) cells of thestorage tape. We denote an L(l, m, n) space-bounded SV4DTM (SV4-NTM) by SV4-DTM(L(l, m, n)) (SV4NTM(L(l, m, n))).
Definition 2.5. For any four-dimensional automaton M
with input alphabet Σ , define T(M) = {x ∈ Σ (4) | M
accepts x}. Furthermore, for each X ∈ {D,N}, define
L[4-XMA1] = {T | T = T(M) for some 4-XMA1},
L[SV4-XTM(S(l,m,n))] = {T | T = T(M) for some SV4XTM(S(l,m,n)) M}, andL[SV4-XTM(L(l,m))] = {T | T = T(M) for some SV4XTM((l,m)) M}.

3. Necessary spaces
In this section, we investigate the necessary spaces (i.e.,lower bounds) for seven-way Turing machines tosimulate one-marker automata.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan347

Makoto Nagatomo, Shinnosuke Yano, Makoto Sakamoto, Satoshi Ikeda, Hiroshi Furutani, Takao Ito and Tsutomu Ito, Yasuo Uchida, Tsunehiro
Yoshinaga

identical to the last (l, m, n)-block), and so M then tries tocheck whether the next (h+1)th (l, m, n)-block is identicalto the last (l, m, n)-block.
In this way, M enters an accepting state just when it
finds out some (l, m, n)-block, each of whose planes is
identical to the corresponding plane of the last (l, m, n)block. It will be obvious that T(M)= T1.
(2):Suppose to the contrary that there exists an SV4DTM(2L(l, m, n)) M accepting T1, where L(l, m, n) is afunction such that

By using the same technique as in the proof of
Theorem 3.1, we can get as follows.
Theorem 3.2. To simulate 4-NMA1’s,(1) SV4-NTM’s require Ω(l2m2n2) space, and(2) SV4-DTM’s require 2Ω(l2m2n2) space.
4. Conclusion
In this paper, we showed the necessary spaces for fourdimensional Turing machines to simulate fourdimensional one-marker automata. It will be interesting
to investigate how much space is necessary and sufficientfor seven-way four-dimensional deterministic or
nondeterministic Turing machines to simulate fourdimensional ‘alternating’ one-marker automata.

limi→∞[L(li, mi, ni)/(liminiloglimini)]=0.
For some regular sequence of points {(li, mi, ni)}. Let s
and t be the numbers of states in the finite control andstorage tape symbols of M, respectively. We assume
without loss of generality that if M accepts an input, thenM enters an accepting state on the bottom boundary. For
each l≥1, m≥1, n≥1, letV(l, m, n)= {x∈T1 |l1(x)=l and l2(x)=m and l3(x)=n and (xhas exactly ((lmn)lmn+1) (l, m, n)-blocks)}. For eachx∈V(l, m, n), let B(x)={b∈{0, 1}(4) |∃h(1≤h≤(lmn)lmn) [bis the hth (l, m, n)-block of x] }, and let S(l, m, n)= {B(x)| x∈ 𝑉(l, m, n)}. Note that for each x∈ 𝑉(l, m, n), there isa sequence of configurations of M which leads M to an
accepting state. Let conf(x) be the semi-configuration justafter M leaves the second-to-last (l, m, n)-block of x. Then,we get following proposition.

References
1. M. Blum and C. Hewitt, “Automata on a two-dimensionaltape”, IEEE Symposium on Switching and Automata
Theory, pp.155-160 (1967).
2. M. Sakamoto, “Three-dimensional alternating Turingmachines”, Ph.D. Thesis, Yamaguchi University (1999).
3. M.Nagatomo, M.Sakamoto, H.Susaki, T. Zhang, S.Ikeda,
H.Furutani, T.Ito, Y.Uchida, and T.Yoshinaga, “Sufficientspaces for seven-way four-dimensional Turing machines
to simulate four-dimensional one-marker automata”,
Proceedings of the International Conference on Artificial
Life and Robotics（ICAROB2015）, Horuto Hall, Oita,
Japan，OS6-1，pp.340-343(CD-ROM) (2015).

Proposition 3.1. For any two tapes x, y∈V(l, m, n), if B(x)≠ B(y), then conf(x) ≠ conf(y).
Proof of Lemma 3.1(continued): There are at mostE (l, m, n)=(l+2)(m+2)(n+2)s2L(l, m, n)t2L(l, m, n)different semi-configurations of M just when M enters thelast (l, m, n)-block of tapes in V(l, m, n). On the otherhand,|S(l, m, n)|=2r-1 (r=(lmn)lmn).
Thus, from the assumption concerning the function L(l,m, n), it follows that there exists a point (li, mi, ni) suchthat |S(li, mi, ni)|＞E(li, mi, ni). For such (li, mi, ni), there
exist two tapes x, y in V(li, mi, ni) such that B(x) ≠ B(y)and conf(x) ≠conf(y). This contradicts Proposition 3.1.
This completes the proof of (2).
□From Lemma 3.1, we can conclude as follows.
Theorem 3.1. To simulate 4-DMA1’s,(1) SV4-NTM’s require Ω(lmnloglmn) space, and(2) SV4-DTM’s require 2Ω(lmnloglmn) space.
© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

348

Analysis for 4×12 board of 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
Satoshi Ikeda

Department of Computer Science and System Engineering, Miyazaki University,
Japan, bisu@cs.miyazaki-u.ac.jp

Abstract
More than 20 years has passed since J. Feinstein (1993) found by using computer that perfect play on 6×6 board of
Othello gives 16-20 loss for the first player. During this time, computers have improved drastically, however, standard
8×8 board remain unsolved. In our previous paper, we were able to obtain the perfect plays on 6×6 board and 4×10
board. In this paper, we challenge the unsolved problem of 4×12 board by dividing it into about 100 small distinct
sub-problems.
Keywords: combinatorial theory, combinatorial optimization, perfect play, rectangular Othello(the sequence obtained in this case is called perfect play).
In 1993, Joel Feinstein reported that perfect play on 6×6
board of Othello gives 16-20 loss for the first player4(Fig.
1); we confirmed that the sequence reported by him is oneof the perfect plays5. Othello on standard 8×8 board has
not been solved, since the number of positions is toolarge; according to some strong programs, it seems
theory of becoming draw is strong6. In our previous
paper7, we were able to obtain some perfect plays on 6×6
board and 4×10 board. In this paper, we challenge theunsolved problem of 4×12 board by dividing it into about
100 small distinct sub-problems.

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, tournament is held in Japan, the UnitedStates 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 bothplayers choose constantly the best move, these areclassified into win, loss or draw game for the first player3© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan349

Yuki Takeshita, Satoshi Ikeda, Makoto Sakamoto, Takao Itoprogram. This is because end-game routine of gameprogram is the perfect analysis exactly, and evaluation
function in the middle-game routine is available for theordering of the search in perfect analysis.
In addition, end-game routine is classified into
solving for WLD (win/loss/draw) score and solving forexact score. Both are the perfect analysis, but there is adifference in the evaluation of the end. For an exact score
solve, the end is evaluated with the piece difference. Thisroutine can find best one move. For WLD score solve,
the end is evaluated in three ways win, loss, and draw. It
generates relatively many pruning (Alpha-Beta Pruning8)
if the range of the evaluation value is small. Therefore,
an execution time for the exact score is estimated to be
several times of the execution time for the WLD score.

Fig. 1. Feinstein’s perfect play.

2. OthelloFirst of all, we will introduce the rules of Othello. The
game always begins with the setup as shown in Fig. 2,
however, since we deal with 4×12 board of Othello in thispaper, the disposition such as shown in Fig. 3 is thestarting position. One player uses the black side of thepieces (circular chips), 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 his opponent’s the pieces, he flips outflanked
pieces to his color. To outflank means to place the pieceon the board so that his opponent’s rows of the piece arebordered 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 not allowed. The
game ends when neither player can make any valid move.
The winner is the player who has more pieces than his
opponent.

4. Perfect analysis for 4×2n board of Othello
Table 1 is the results that we analyzed from the starting
position for 4×2n board of Othello. See Table 1, we seethat rectangular Othello has the feature that gives the firstplayer an advantage according to extended board size.
Therefore, we suppose that there is a strong possibility
that first move wins in 4×12 board of Othello.
Table 1. The analysis results for 4×2n board of Othello.

4×4
4×6
4×8
4×10

WinnerPerfect Play

White
Black
Black
Black

B: 03, W: 11
B: 20, W: 04
B: 28, W: 00
B: 39, W: 00

5. Estimate the number of the final positions
See Fig. 4, it is changes in the number of the final
positions for 4×2n board of Othello. By Fig. 4, thenumber of the final positions in 4×12 board seems to beabout 1016. However, in 4×12 board, since the white isoften wiped out in the middle, it is assumed to be 1015.
Therefore, since execution time in 4×10 board is six days,
we suppose that execution time in 4×12 board is about
six thousand days. In this paper, by dividing it into about
100 distinct sub-problems, we solve each sub-problem in
4×12 board of Othello within sixty days.

Fig. 2. Othello’s setup.

Fig. 3. The starting position on 4×12 board of Othello.

3. Computer Othello
For perfect analysis of strategy board game such as
Othello, it is useful to refer thinking routines of game© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan350

Analysis for 4×12 board

figures 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. 4. Changes in the number of the final positions for
4×2n board of Othello.

Fig. 7. Depth 6.

6. Perfect Analysis with the fixed moves
We found an initial position of 4×12 board as shown Fig.
5, which fixed from move 1 to move 6 in 4×12 board.
This position gives win for the first player. This positionis also the sequence of perfect play which is consistent
with 4×8 board and 4×10 board. This is the reason why
we choose this sequence. See Fig. 6, it is perfect playunder starting position Fig. 6. If this sequence is the best
move for both players, it shows that 4×12 board of
Othello gives win for the first player. Therefore, it isnecessary to check some positions which branched fromwhite turn. Concretely, they are move 2, move 4 andmove 6.

Fig. 8. Depth 4.

Fig. 9. Depth 2.

Table 2 shows a part of the position that is fixed frommove 1 to move 6 in 4×12 board of Othello. These are
currently analyzing sub-problems for this board. 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 thecoordinates in Fig. 10. For further details on the subproblems, refer to Appendix Table 3.

Fig. 5. A fixed position from move 1 to move 6.

Table 2. Currently checking problems for each position.
Depth 6(α)(β)

Fig. 6. Perfect play fixed from move 1 to move 6 in 4×12
board. The black wins 48-0. PASS is 28, 30, and 40.

7. Experiments
We use six computers with quad-core to solve the subproblems for 4×12 board. Our solver has the exact scoreas evaluation value for perfect analysis.
Fig. 7, Fig. 8 and Fig. 9 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 these

Depth 4(γ)e4g1(γ)e4h2(γ)e4f4(γ)f4g1(δ)h4h1(δ)h4h2(δ)h4h3(ε)h3f4(ε)h3h4(ε)h4e2(ε)h4e4(ε)h4g4(ε)h1g1

Depth 2(ζ)f4e1d1g1(ζ)f4e1d1h2(ζ)f4g1h1h2(η)h1e1d1e3(η)h1e1d1f4(η)h1e1d1h2(η)h1e3g4h3(η)h1e3h4h3© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan351

Yuki Takeshita, Satoshi Ikeda, Makoto Sakamoto, Takao Ito

8. D. E. Knuth and R. W. Moore: “An Analysis of AlphaBeta Pruning,” Artificial Intelligence, Vol. 6, pp. 293–326(1975).

Appendix
Table 3. The other problems fixed to move 6 on 4×12 board.
Fig. 10. Coordinates on 4×12 board.

Depth 4(γ)e2g1(γ)e2h1

Depth 2(ζ)f4e1e2g1(ζ)f4e1d3d4(ζ)f4g1d3e4(ζ)f4g1h2e1(η)h1e1h3h2(η)h1e1h3h4(γ)g4h1(ζ)f4e1d3g1(ζ)f4g1h2e4(η)h1e1h3i1(γ)g4h2(γ)g4h2(ζ)f4e1d3h2(ζ)f4e1g4g1(ζ)f4g1h2g4(ζ)f4g1h2h3(η)h1e3d4d3(η)h1e3d4e1(γ)g4h3(ζ)f4e1g4h2(ζ)f4g1h2h4(η)h1e3e4e1(γ)g4h4(ζ)f4e1g4h3(ζ)f4g1h2i2(η)h1e3f4e1(γ)h4g1(ζ)f4e1g4h4(ζ)f4g1h2i3(γ)h4h1(ζ)f4e1h2g1(ζ)f4g1h3e1(γ)h4h3(ζ)f4e1h2h3(ζ)f4g1h3g4(δ)e2g1(ζ)f4e1h2h4(ζ)f4g1h3h2

9. Future work(δ)e2h1(ζ)f4e1h2i2(ζ)f4g1h3h4(δ)e4g1(ζ)f4e1h3g1(ζ)f4g1h3i3

The next challenge is the analysis for 6×8 board of
Othello. It is necessary to estimate the size of game tree
in the 6×8 board accurately. After that, we will
implement the distributed processing for transferring to
the cloud servers.

(δ)e4h2(ζ)f4e1h3h2(η)h1e1e2e3(δ)e4e3(ζ)f4e1h3h4(η)h1e1e2i1(δ)e4d4(ζ)f4e1h3i3(η)h1e1e3d3(δ)g4h1(ζ)f4g1d2d3(η)h1e1e3i1(δ)g4h2(ζ)f4g1d2d4(η)h1e1g4f4(δ)g4h3(ζ)f4g1d2e1(η)h1e1g4h2(δ)g4h4(ζ)f4g1d3c3(η)h1e1g4h4(ε)h1e3(ζ)f4g1d3d4(η)h1e1g4i1(ε)h1f4(ζ)f4g1d3e1(η)h1e1h3f4

8. Consideration(ζ)f4e1d1g1, (γ)e4g1 and (η)h1e1d1f4 in Table 2 havealready finished. Both results are the black win 48-0.
When all of the execution given in Table 2 are over, if
their results are the black win, it is proven that the firstplayer wins in 4×12 board of Othello. Moreover, if all ofthem are complete victories of the first player, it is proventhat a sequence given in Fig. 6 is one of the perfect plays
on 4×12 board of Othello.

References.
1. Japan Othello Association: Nihon osero renmei kyougi ruru, [online]www.othello.gr.jp/r_info/rule.html (Retrieved
10 December. 2015)
2. J. V. Neumann and O. Morgenstern: Theory of Games andEconomic Behavior, Princeton University Press (1944).
3. J. Schaeffer, N. Burch, Y. Björnsson, A. Kishimoto, M. M
üller, R. Lake, P. Lu and S. 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 1
December. 2015), pp. 6–8 (1993).
5. Y. Takeshita, S. Ikeda, M. Sakamoto and T. Ito: “Perfect
Analysis in miniature Othello,” Proceedings of the 2015
International Conference on Artificial life and Robotics,
pp.39 (2015).
6. B. Rose: Othello - A Minute to Learn… A Lifetime to
Master, [online]www.ffothello.org/livres/othello-bookBrian-Rose.pdf (Retrieved 1 December. 2015) (2005).
7. Y. Takeshita, S. Ikeda, M. Sakamoto and T. Ito: “Perfect
Play in Miniature Othello,” Proceedings of the Ninth
International Conference on Genetic and Evolutionary
Computing, pp.281-290 (2015).

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan352

A space lower-bound technique for four-dimensional
alternating Turing machines
Makoto Nagatomo, Shinnosuke Yano, Makoto Sakamoto, Satoshi Ikeda and Hiroshi Furutani

Faculty of Engineering, University of Miyazaki,
1-1 Gakuen Kibanadai Nishi, Miyazaki, Miyazaki 889-2192, Japan
E-mail: je.suis.makoto@gmail.com, shinchandx@ezweb.ne.jp, sakamoto@cs.miyazaki-u.ac.jp,
bisu@cs.miyazaki-u.ac.jp, furutani@cs.miyazaki-u.ac.jp
Takao Ito, Tsutomu Ito

Institute of Engineering, Hiroshima University, 4-1, Kagamiyama 1-chome
Higashi-Hiroshima, Hiroshima 739-8527, Japan
E-mail: itotakao@horoshima-u.ac.jp, 0va71-2538f211n@ezweb.ne.jp
Yasuo Uchida

Department of Business Administration, Ube National College of Technology, Tokiwadai
Ube, Yamaguchi 755-8555, Japan
E-mail:uchida@ube-k.ac.jp
Tsunehiro Yoshinaga

Department of Computer Science & Electronic Engineering,
National Institute of Technology, Tokuyama College, Gakuendai
Shunan, Yamaguchi 745-8585, Japan
E-mail:yosinaga@tokuyama.ac.jp

Abstract
Alternating Turing machines were introduced in 1981 as a generalization of nondeterministic Turing machines andas a mechanism to model parallel computation. On the other hand, we have no enough techniques which we can showthat some concrete four-dimensional language is not accepted by any space-bounded four-dimensional alternating
Turing machines. The main purpose of this paper is to present a technique which we can show that some fourdimensional language is not accepted by any space-bounded four-dimensional alternating Turing machines.
Concretely speaking, we show that the set of all four-dimensional input tapes over {0,l}, which each top half part isequal to each bottom half part, is not accepted by any L(m) space-bounded four-dimensional alternating Turingmachines for any function L(m) smaller than log m.
Keywords: Alternation, Complexity, Computation Tree, Configuration, Four-Dimension, Turing machine

1. Introduction and Preliminaries

accepted by any space-bounded four-dimensional
alternating Turing machines [1, 2]. The main purpose ofthis paper is to present a technique which we can show

We have no enough techniques which we can showthat some concrete four-dimensional language is not© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan353

Makoto Nagatomo, Shinnosuke Yano, Makoto Sakamoto, Satoshi Ikeda, Hiroshi Furutani, Takao Ito, Tsutomu Ito, Yasuo Uchida, Tsunehiro Yoshinaga

the next-move relation δ. Note that the machine cannot
write the blank symbol. If the input head falls off the
input tape, or if the storage head falls off the storage tape(by moving left), then the machine M can make no furthermove.
A configuration of a 4-ATM M = (Q, q0, U, F, Σ, Γ, δ)
is a pair of an element of Σ（４）and an element of CM =(NU{0})3 x SM , where SM =Q x (Γ-{B})* x N and N
denotes the set of all the positive integers. The firstcomponent x of a configuration c = (x, ((il, i2, i3, i4), (q,α, j))) represents the input to M. The second component(il, i2, i3, i4) of c represents the input-head position. The
third component (q, α, j) of c represents the state of thefinite control, nonblank contents of the storage tape, andthe storage-head position. An element of CM is called asemi-configuration of M and an element of SM is calleda storage state of M. If q is the state associated withconfiguration c, then c is said to be a universal(existential, accepting) configuration if q is a universal(existential, accepting) state. The initial configuration of
M on input x is IM (x) = (x, (1, 1, 1, 1), (q0, λ, 1)), where
λ is the null string.
Given M = (Q, q0, U, F, Σ, Γ, δ) , we write c ┠M c'
and say c' is a successor of c if configuration c' follows
from configuration c in one step of M, according to thetransition rules δ. The relation ┠M is not necessarily
single-valued, because δ is not. A computation path of
M on x is a sequence. C0 ┠M Cl ┠M …┠M Cn (n ≥ 0),
where C0 = IM (x). A computation tree of M is a finite,nonempty labeled tree with the following properties: (1)Each node v of the tree is labeled with a configurationl(v), (2) If v is an internal node (a nonleaf) of the tree,l(v) is universal and {c | l(v)┠M c} = {cl,…, ck}, then v
has exactly k children v1,…,vk such that l(vi) =ci (1≤ i ≤k), and (3) If v is an internal node of the tree and l(v) isexistential, then v has exactly one child u such that l(v)┠M l(u). A computation tree of M on input x is acomputation tree of M whose root is labeled with IM (x).
An accepting computation tree of M on x is acomputation tree of M on x whose leaves are all labeledwith accepting configurations. We say that M accepts x
if there is an accepting computation tree of M on input
x. Define T(M) = {x ∈Σ（４）| M accepts x}.
In this paper, we shall concentrate on investigating theproperties of 4-ATM's whose each side-length of eachfour-dimensional input tape is equivalent and whose
storage tapes are bounded (in length) to use.

that some four-dimensional language is not accepted byany space-bounded four-dimensional alternating Turingmachines. Concretely speaking, we show that the set of
all four-dimensional input tapes over {0,l}, which each
top half part is equal to each bottom half part, is not
accepted by any L(m) space-bounded four-dimensional
alternating Turing machines for any function L(m) suchthat limm→∞[L(m)/log m] = 0. We let each side-length ofeach four-dimensional input tape of these automata beequivalent in order to increase the theoretical interest.
Let Σ be a finite set of symbols. A three-dimensional
tape over Σ is a four-dimensional rectangular array ofelements of Σ. The set of all four-dimensional tapes over
Σ is denoted by Σ（４）. Given a tape x∈Σ（４）, for each
integer j( 1  j  4 ), we let m j (x) be the length of x
along the jth axis. The set of all x   with l1(x)=m1,l2(x)=m2, l3(x)=m3, and l4(x)=m4 denoted by Σ(m1,m2,m3,m4).
If 1 ≤ij≤lj(x) for each j (1  j  4) , let x(i1,i2,i3,i4) denotethe symbol in x with coordinates (i1,i2,i3,i4).
Furthermore, we define x[(i1,i2,i3,i4),(i1’,i2’,i3’,i4’)], when
1  i j  i' j  l j ( x) for each integer j (1  j  4) , as the fourdimensional tape y satisfying the following (i) and (ii):(i) for each j(1≤j≤4), lj(y)=i’j-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+i21, r3+i3-1, r4+i4-1). (We call x[(i1, i2, i3, i4), (i’1, i’2, i’3,i’4)] x[(i1, i2, i3, i4), (i’1, i’2, i’3, i’4)]-segment of x.);A four-dimensional alternating Turing machine (4ATM) M is defined by the 7-tuple M = (Q, q0, U, F, Σ, Γ,δ), where (1) Q is a finite set of states; (2) q0 Q is theinitial state; (3) U  Q is the set of universal states; (4)F  Q is the set of accepting states; (5) Σ is a finite input
alphabet (#  Σ is the boundary symbol); (6) Γ is a finite
storage-tape alphabet (B  Γ is the boundary symbol),and (7) δ  (Q x (Σ∪{#}) x Γ) x (Q x (Γ - {B}) x {east,
west, south, north, up, down, past, future, no move} x{right, left, no move}) is the next-move relation.
A state q in Q - U is said to be existential. The machine
M has a read-only four-dimensional input tape withboundary symbols # 's and one semi-infinite storage tape,initially blank. Of course, M has a finite control, an input
head, and a storage-tape head. A position is assigned toeach cell of the read-only input tape and to each cell of
the storage tape. A step of M consists of reading one
symbol from each tape, writing a symbol on the storagetape, moving the input and storage heads in specified
directions, and entering a new state, in accordance with© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan354

A space lower-bound technique

Let L(m) : N → N be a function with one variable m.
With each 4-ATM M we associate a space complexity
function SPACE that takes configurations to natural
numbers. That is, for each configuration c = (x, ((il, i2,
i3, i4), (q, α, j))), let SPACE(c) =|α|. We say that M is
L(m) space-bounded if for all m ≥ 1 and for each x with
l1(x) = l2(x) = l3(x) = l4(x) = m, if x is accepted by M,
then there is an accepting computation tree of M on
input x such that for each node v of the tree,
SPACE(l(v)) ≤ L(m). We denote an L (m) spacebounded 4-ATM by 4-ATM (L (m)). L [4-ATM (L (m))]= {T | T= T (M) for some 4-ATM (L (m)) M}.

=l(vb) (i.e., the labels of va and vb are identical). For eachia (1 ≤ia ≤ ji), let dis(ia) = min{d(v) | v ∈ P(ia)} and letn(ia) be the leftmost node among those nodes v in P(ia)such that d(v) = dis(ia). Further, let N'(i) = N(i) - {n(l),n(2), …, n(ji)}. By removing from Tr all the subtrees
whose roots are included in N'(i), we make the new Tr.
4. If the height of Tr (i.e., the length of the longest
path of Tr ) is less than or equal to i, then we let t'(x)=Tr. Otherwise, we let i = i + 1 and go to step 3.
end[Example 1] Let x ∈ V(m) and t(x) be a tree. Here,suppose that nodes A and D have the same label, nodes
B and C have the same label, and other nodes each havedifferent labels. [From the preceding assumption (ii)concerning t(x), identical.] Then, t'(x) is a tree. That is,t'(x) is obtained from t(x) by moving the subtree withnodes C and D as the roots from t(x).
It is easily seen that for each x in V (m), all the nodes oft'(x) have labels different from one another, and the setof all the paths from root of t'(x) to the leaves of t'(x)represents necessary and sufficient accepting
computations of M on x. From t'(x), we now define anextended crossing sequence (ECS) at the boundary
between the top and bottom halves of x. The concept of
ECS was first introduced in [3]. We relabel each node v
of t'(x), as follows. (We denote this new labeling by l'.)For each node v of t'(x), let f(v) denote the father nodeof v. Then, for each node v of t'(x), where x ∈ V(m), letif, for some storage states (q, α, j) and (q', α', j'),(i) l(f(v)) = (x, (il , i2, i3, m), (q', α', j')) andl(v) = (x, (il , i2, i3, m+1), (q, α, j)) , or(ii) l(f(v)) = (x, (il , i2, i3, m+1), (q', α', j')) andl(v) = (x, (il , i2, i3, m), (q, α, j)) , thenl'(v) = ((il , i2, i3), (q, α, j))elsel'(v) = *.
That is, if the movement of M from f (v) to v represents
the action of crossing the boundary between the top andbottom halves of x, then v is newly labeled by (il , i2, i3),(q, α, j)), where (q, α, j) is the storage state componentof l(v). Otherwise, v is newly labeled by *. From the
newly labeled t'(x), we extract those nodes v such thatl'(rn) = *, and by using these nodes, we construct a treet"(x) satisfying the following condition:(A) For any node v of t"(x), nodes v1, v2, …,vS arechildren of v if and only if v1, v2, …,vS are descendantsof v in t'(x) and l'(u) = * for each node u on the path

2. Main Result[Theorem 1] Let T={x ∈{0, 1}(4) |∃m ≤ 1 [l1(x) = l2(x)= l3(x) = l4(x) =2m & x[(l, 1, 1, 1), (2m, 2m, 2m, m)] =x[(l, 1, 1, m + 1), (2m, 2m, 2m, 2m)]]). Then, T  L [4ATM (L (m))] for any L (m) = o (log m).
(Proof) Suppose that there exists a 4-ATM (L (m)) M
accepting T, where L (m) = o (log m). We assume,
without loss of generality, that M moves a storage-tapehead after changing its state and writing a new symbolon the storage tape, and moves an input head finally.
For each m ≤ 1, let V (m) = {x ∈T | l1(x) = l2(x) = l3(x)= l4(x) =2m}. For each x in V (m), let t(x) be one fixed
accepting computation tree of M on x such that eachnode v of the tree satisfies SPACE(l(v)) ≤ L(2m), wherefor each node v of t(x), l(v) represents the label of v.
Without loss of generality, we assume that for any t(x);(i) any two different nodes on any path of t(x) arelabeled by different configurations, and,(ii) if any different nodes of t(x) have the same label,then the subtrees [of t(x)] with these nodes as the roots
are identical.
For each x in V(m), let t(m), which we call the reduced
accepting computation tree of M on x, be a tree obtained
from t(x) by the following procedure [for each node v oft(x), we denote by d(v) the length of the path from theroot of t(x) to v (i.e., the number of edges from the rootof t(x) to v)]:
Begin
1. Tr = t(x)2. i = 1
3. Let N (i) ≜ {v | v is node of Tr and d(v) ≤i}. Divide
N(i) as follows: N(i) = P(l) ⋃ P(2) ⋃…⋃ P(ji), where: (1)if ia = ib(l≤ ia, ib ≤ ji), then P(ia) ∩ P(ib) = φ, and (2)for each ia(1 ≤ ia ≤ ji) and for each va, Vb ∈ P(ia), l(va)© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

355

Makoto Nagatomo, Shinnosuke Yano, Makoto Sakamoto, Satoshi Ikeda, Hiroshi Furutani, Takao Ito, Tsutomu Ito, Yasuo Uchida, Tsunehiro Yoshinaga

from v to each vi. In general, there can be two or moresuch trees t"(x). Let these trees be t1"(x),…, tn"(x). For
each node v of each ti"(x) (l ≤ i ≤ n), we now define anelement of ECS (EECS) inductively as follows: Letl'(v) = ((il , i2, i3), (q, α, j)).
(1) If v is a leaf, then [((il , i2, i3), (q, α, j))] is an EECSof v.
(2) If v has only one child v1 and Q1 = [((il1 , i21, i31),(q1, α1, j1))P] is an EECS of v1, then [((il , i2, i3), (q, α, j))((il1 , i21, i31), (q1, α1, j1))P] is an EECS of v.
(3) If v has d(≥2) children v1, …, vd and Q1, …, Qd are
EECS's of v1, …, vd, respectively, then [((il , i2, i3), (q, α,j)) Qσ(1)…Qσ(d)] is an EECS of v for any permutation σ :{1, …, d}→{1, …, d}.
(4) An EECS of v is defined only by the precedingstatements (1), (2), and (3).
Now, let Q1, …, Qn be EECS's of the root nodes oft1"(x),…, tn"(x), respectively. Then, for any permutation
σ : {1, …, n}→{l, …, n}, we call Qσ(1), …, Qσ(n) an ECSof x. As is easily seen from the definitions, there can be
two or more EECS's of each node v of each t"(x), andthere can be two or more ECS's of x. Let Q1 and Q2 be
any two EECS's. If the following condition (B) is
satisfied, we say Q1 and Q2 are equivalent and write Q1≡Q2:(B) Let Q1 = [((il1 , i21, i31), (q1, α1, j1)) …((iln , i2n, i3n),(qn, αn, jn)) P1 …PS], Q2= [((i’l1 , i’21, i’31), (q’1, α’1, j’1))…((i’ln’ , i’2n’, i’3n’), (q’n’, α’n’, j’n’)) P’1 …P’S’]. Then n= n', s = s', and ((ilk , i2k, i3k), (qk, αk, jk))= ((i’lk , i’2k,i’3k), (q’k, α’k, j’k)) for each k (1 ≤ k ≤ n), and there
exists a permutation σ : {1, …, s} →{1, …, s} such that
Pi ≡P’σ(i) for each i(1 ≤i ≤s), where n, s ≥0, and ((il , i2,i3), (q, α, j))'s and ((i’l , i’2, i’3), (q’, α’, j’))'s are pairs(coordinates along the fourth axis, storage state), andfurther P, P' are EECS's.
Let Q = Q1 …Qn, Q’ = Q’1 …Q’n be any two ECS's.
We say that Q and Q' are equivalent if n = n' and there
exists a permutation σ : {1, …, n} →{1, …, n } suchthat Qi≡Q’σ(i) for each i (1 ≤i ≤n). [As is easily seenfrom the definition, any two ECS's of x are equivalent
for any x in V(m).] For any ECS Q, the length of Q is thenumber of pairs (coordinates along the fourth axis,
storage state) in Q, and is denoted by | Q |. For eachm≥1, let E(m) = {Q | Q is an ECS of x for some x inV(m)}. Then, the following two propositions must hold :[Proposition 1] | E(m) | = Z(m)dZ(m), where Z(m) = (2m+ 2)3rL(2m)sL(2m), r and s are the numbers of states (ofthe finite control) and storage-tape symbols of M, and d
is a positive constant.
[Proposition 2] Let x and y be any two different tapes inV(m), and let Qx and Qy be any ECS's of x and y,
respectively. Then, Qx and Qy are not equivalent.
Clearly, |V(m) |=28t (t=m4) . Because L(m)=o(log m), it
follows from Proposition 1 that |V(m) | > | E(m) | forlarge m. For such a large m, there must exist two
different tapes x, y ∈V (m) such that some ECS of x andsome ECS of y are equivalent, which contradicts
Proposition 2. This completes the proof.
□
3. Conclusion
In this paper, we presented a technique which we canshow that a four-dimensional language is not acceptedby any space-bounded alternating Turing machines. It
will be interesting to investigate infinite space hierarchy
properties of the classes of sets accepted by 4-ATM'swith spaces of size smaller than log m.
References[1] A.K.Chandra, D.C.kozen, and L.J.Stockmeyer:
Alternation, J.ACM, Vol. 28, No. 1 pp.114-133 (1981).
[2] M.Sakamoto, K.Inoue, and I.Takanami: A note on
three-dimensional alternating Turing machines withspace smaller than log m, Inform. Sci., ELSEVIER, Vol.
72, pp. 225-249 (1993).
[3] H.Yamamoto and S.Noguchi: Time-and-leaf bounded 1-tape alternating Turing machines, The Trans. of
IECE, J68-D, pp.1719-1726 (1985).

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan356

Socialized Multi-Agent System Rendezvous via Networks of Networks
Yunzhong Song

College of Electrical Engineering and Automation, Henan Polytechnic University, 2001 Century Avenue, Jiaozuo
454003, P.R.China
Ziyi Fu

College of Electrical Engineering and Automation, Henan Polytechnic University, 2001 Century Avenue, Jiaozuo
454003, P.R.China
Fuzhong Wang

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

Abstract
Networks of networks based rendezvous of leader-follower multi-agent system, where the rendezvous actors
including the target object, the leader agents, the informed follower agents and the isolated follower agents, wastoughed upon here. To be specific, humanized system inspired strategies were investigated, and results
demonstrated that the mixed strategy, could be the most prior policy in balancing of the convergent speed and theutmost task of success of rendezvous.
Keywords: networks of networks, leader-follower multi-agent network, rendezvous problems, the democracy
strategy, the autarchy strategy, the mixed strategy

a heterogeneous network. Ref. 4 provided sufficientconditions for rendezvous in first order leader-follower
network; Ref. 5 studied the distributed tracking control
In recent years, the distributed control of multi-agentof the leader-follower directed network with noisy
network has been paid more attentions from different
measurement. Ref. 6 investigated the consensus
disciplines. For instance, Ref. 1 analyzed the issue ofproblems in leader-follower network with fixed
how the ratio of leaders to followers affects connectivity.
topology and switching topology.
Ref. 2 discussed the consensus problems in undirected
Although the consensus research of multi-agent
networks of agents with fixed topology and switching
networks
and leader-follower multi-agent networks
topology. In Ref. 3, the direct control laws were used inprovided valuable results, there still have been some
© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa, Japan
1. Introduction

357

Yunzhong Song, Ziyi Fu, Fuzhong Wang

obvious limitations. For instance, the realization
mechanism of the swarm has been considered rarely.
But in actual society, the swarm members can interact
with each other in various styles, for example, thefollowers may be kept in the group or be left alone
according to the different utmost objectives; and as thesame way, the leaders may also play with the followers
in the unselfish ways or in the selfish ways, that can beboiled down to the costs and the capabilities of theleaders. Lately, the networks of networks, like Ref.7
which paid much more attention to the coupling among
the networks, was much more powerful in modeling thereal world systems, where interdependence of networks
and the effect of this interdependence on the structuraland functional behavior of the coupled system wereconsidered into detail. So, it is necessary to be clarified
that whether the networks of networks form mechanism
can be verified feasible or not in leader-follower multiagent systems. The already existing results including thedifferent layers built according to the different
relationship among agents, for example, one layer canbe used to express the relationship from the spatial
location of the agents, that is, if they are near each otherenough in spatial physical distance, they can haveconnections with each other; and the another layer canbe assigned to express their social relationships, such as
friendships, and classmates, that is if they are friends orhave been classmates before, they can be categorized
into one layer, and the connection strength can be
determined with their intimateness. The style we deal
with networks of networks here can enrich the results of
networks of networks and it can be referred further. In
this paper, the three strategies namely the democracy
strategy, the autarchy strategy and the mixed strategy
would be used in rendezvous problem of leader-follower
network. Specific to leader-follower multi-agent
network, the democracy strategy means that the motionof agent is affected by the other ones located within thesensing zone of the agent. And the autarchy strategy issuch a strategy, in which the motion of leader is notaffected by any agents, while follower only gets theinfluence of the initial nearest leader within its sensing
zone. The mixed strategy is composed of the autarchy
one and the democracy one. In the mixed strategy, all
the leaders and some followers which are affected byleader initially take the autarchy strategy, while theother followers which are called isolated followers takethe democracy strategy.
Here, we must note that in case of brevity and thelimited space of the paper, only the democracy strategy
is covered.
2. Preliminaries
Consider N agents evolving in R2. We use single
integrator agents whose motions obey the model:
xij  uij , i    {1, , N }, j    {1, , M }，(1)
Where the subscript denotes the order number of theagents in the corresponding layer, whereas thesuperscript denotes the order number of the layer thatagents belong to. So, here in (1), N and M denote thebiggest order number of intra-layer and inter-layer. And
the rest can be done in the same manner. In case of
convenience, the node order number and its
corresponding expression in networks of networks styleis listed in Table 1.
TABLE 1 THE NODE ORDER NUMBER AND ITS CORRESPONDING
EXPRESSION IN NETWORKS OF NETWORKS STYLE

V1

Followers
V2
V3

V4

V5

Leaders
V6
V7

V8

Target
V9
V10

x13

x23

x43

x14

x53

x22

x32

x33

x12

x11

Let g={V, E} describe the group topology, thatconsists of a set of vertices, V={1,...,N}, representing
the team members, and a set of edges, E={(i, j)V×V｜iN} representing the active inter-agent communication
links. If g is an undirected graph, there is a line between
node i and j, representing the impact between them ismutual. If g is a directed graph, the line (i, j) is directed,
which consists of starting point i and finishing point j,
representing node i makes an impact on node j.
According to the different functions of the agentsin leader-follower network, the agents can be divided
into two categories, i.e. leaders and followers. The
leaders were capable of knowing the details of the
global task that is to come to a given target, while thefollowers just need to keep in touch with leaders toreach the target. The agents belong either to the subset
of leaders, Nl, or to the subset of followers, Nf , where
Nl∪Nf =N, Nl∩Nf = and the number of agents in each
set is given by |Nl|=Nl and |Nf|=Nf respectively. Due to© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa, Japan358

Socialized Multi-Agent System Rendezvous

limited sensing capability of the sensors, each agent has
a limited sensing zone of radius Δ＞0. At any given
time, the set of agents located within the sensing zone ofagent iN are referred to as the neighbours of agent i,
Followers

Leadersother numbers, where agents from 1 to 5 belong tofollower agents, whereas 6 to 9 belong to leader agentsand node 10 is the target. Without loss of generality, we
assume that the target is the origin of coordinates andf(δj)= βδj, β=0.5. The initial configuration of each agent
can be seen in Tab. 2.

Target

1

2

3

4

5

6

7

8

9

10

x

27.5

28

28

28

32

17

21.5

22

21

0

y

0

-.2

-4

4

-1

-2

-1

.5

1

0

TABLE 2 INITIAL VALUES OF THE SIMULATION WORK

Ni={jN: |xi－xj|≤Δ}. Each agent has knowledge of therelative coordinates to its neighbouring agents, butcannot detect or communicate with agents outside its
sensing zone. Note that both g and Ni are time-varying.
The difference between leaders and followers is thatleaders are aware of the location of the target and cancontrol group but the followers cannot.
We start by introducing a notation for the distance
between two arbitrary agents i and j. Letl ,kk

In the democracy strategy, the motion of agent isaffected by the other ones located within the sensing
zone of the agent. If agent i has an impact on j, agent i isalso affected by j. In other words, interactions among
each agent are undirected. The topology of group is thus
described by undirected graph g1={V1, E1}.
The dynamics for an arbitrary follower agent iNf are
j
j
l
j j
l
(4)
xi  
xi  xk   N i xi xkx  x  x  x   0

 i , j   j ,i  xi  x j 
k ,l3. Results and Analysis

l

k

l

i

j

T

k

l

i

j

j

k N ijk N iSince we are considering a physical system we canassume that xk(t) is a continuous function for any agent
kN. The time derivative  ij is not directly defined when

 ik, ,jl  0 so here we shall instead consider the time
derivative of ( i , j )k ,l 2d ( i , j )
k ,l

dt

2



 2 i , j  i , j  2 xi  x j
k ,l

k ,l

k

l

 x  x 
T

k

l

i

j

(2)

To describe the relationship between leaders andtarget, the target attraction function is defined asd  xjll f  j  l ,  j  0F  xj , d   j 0,l j  0.
Fig. 1, simulation results in the democracy strategy.
(a)Topological structure (b) Position rendezvous

l

j

l

Where xi denotes the ith agent is in the jth layer, andxkl denotes the kth neighbor of xij , which is in the lthlayer of the networks. We can conclude that the motionsof followers are decided by their neighbours.
Considering the situation of two dimensions, thedynamics can be looked as the resultant vectors ofthemselves and their neighbors, where the directions of
all the agents are the same corresponding to theresultant vectors, and the speeds of the swarm motion
members can also be decided by the norm of theresultant vectors. For an arbitrary leader agent jNl, thedynamics are described by(3)

where d is the location of the target and define lj | xlj  d | representing the agent xlj ’s distance tolthe target. At any given position x j  d , the directionlof F x j , d is towards the target and the magnitude isldecided by the continuous scalar function f  j  0 .
In the following analysis, the leader-follower
multi-agent network that consists of four leaders andfive followers is illustrated. The agents are tagged withnumbers, starting at 1 at first, followed with 2, 3 and the





 

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa, Japan359

Yunzhong Song, Ziyi Fu, Fuzhong Wang

xlj   N lj xlj   xkm  F  xlj , d 
kNlj

l

connections among them can be viewed as theinterdependency of the networks of networks, and wehave examined and discussed the convergent
characteristics of the democracy strategy. The
simulation of leader-follower multi-agent network
which consisted of four leaders and five followers
demonstrated the potential usage of our suggested
scheme.

(5)mand x j and xk can be defined accordingly. By contrast,
the dynamics for leaders are the same as the followers insome way, except for the impact from the target, whichis the leaders’ feature. The simulation results in the
democracy strategy can be seen in Fig.1.In the processof simulation, the distances between the agents from theleader group and the follower group and the target agent
are used to measure the rendezvous time indirectly,
where all the distances are turned into be zero isregarded as the network rendezvous. It is not difficult torecognize that during the democracy strategy, theinformation flow among the different layers is fully
utilized, and the enjoyment of the overall information
via the shared cooperation realized win-win policy of
the agents. That kinds of profit can only be available
depend on the interdependency among the networks of
networks. In order to elucidate the democracy in theways of networks of networks further, the paradigm for
democracy of networks of networks is illustrated as
Figure 2.

Fig. 2, the paradigm for democracy of networks of networks

Acknowledgements
This work is partially supported by NSFC Grant(61340041 and 61374079) and the Project-sponsored by
SRF for ROCS, SEM to Yunzhong Song.
References
1. A, Ji,Rahmani, M. Mesbahi, et al., Controllability of multiagent systems from a graph-theoretic perspective, SIAM
Journal on Control and Optimization, 48(1) (2009) 162-186.
2. R. Olfati-Saber, R. M. Murray, Consensus problems in
networks of agents with switching topology and time-delays,
IEEE Transactions on Automatic Control, 49(9) (2004) 15201533.
3. M. Zavlanos, G. Pappas, Distributed connectivity control ofmobile networks, IEEE Transaction on Robotics, 24(6) (2008)
1416-1428.
4. T. Gustavi, D. V. Dimarogonas, M. Egerstedt, et al.,Sufficient conditions for connectivity maintenance andrendezvous in leader-follower networks, Automatica, 46(1)(2010) 133-139.
5. J. Hu, G. Feng, Distributed tracking control of leaderfollower multi-agent systems under noisy measurement,
Automatica, 46(8) (2010) 1382-1387.
6. W. Ni, D. Cheng, Leader-following consensus of multi-agent
systems under fixed and switching topologies, Systems &Control Letters, 59(3-4) (2010) 209-217.
7. R. Lu, W. Yu, J. Lü and A. Xue, Synchronization on complex
networks of networks, IEEE Transactions on Neural
Networks and Learning Systems, 25(11) (2014) 2110-2118.

4. Conclusion
In this paper, according to the paradigm of networksof networks we turned the leader-follower agent
systems into four different layers, and each of theselayers can be equivalent to one sub-network, the© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa, Japan360

Global sensor selection for maneuvering target tracking in clutter

Wenling Li

The Seventh Research Division, Beihang University
37 Xueyuan Road, Haidian District, Beijing, 100191, China
Yingmin Jia

The Seventh Research Division, Beihang University
37 Xueyuan Road, Haidian District, Beijing, 100191, China
E-mail: lwlmath@buaa.edu.cn, ymjia@buaa.edu.cn

Abstract
This paper studies the problem of sensor selection for maneuvering target tracking in the cluttered environment. By
modeling the target dynamics as jump Markov linear systems, a decentralized tracking algorithm is developed byapplying the extended Kalman filter and the probabilistic data association technique. A cost function that minimizes
the expected filtered mean square position error is utilized and a sensor selection scheme is proposed. A numericalexample is provided to illustrate the effectiveness of the proposed approach.
Keywords: Sensor selection, Jump Markov system, Extended Kalman filter, Maneuvering target tracking, Clutter

selection method that minimizes the expected filteredmean square position error have been studied forbearing-only tracking. Especially, two strategies
including the `add one sensor node at a time' and the`simplex' have been developed for selecting the best
subset of active sensors.6 These results are extended topropose local sensor selection schemes which do not
require the knowledge of all the sensors locations in the
networks.
In the target tracking community, the single model
approach is used mainly for targets with fixed kinematic
behaviors while for targets with multiple kinematics
behaviors (i.e., maneuvering targets), the multiple
model tracking algorithm is preferred.7 The problem of
sensor selection for maneuvering target tracking has
been investigated in Ref. 8 and 9. To our knowledge,
little research has been done to deal with the problem of

1. Introduction
Sensor or node selection has been found a wide range ofapplications in diverse civilian and military areas such1
as sensor placement for structures, chemical plant2
3,4
control and wireless networks. In recent years the
problem of sensor selection for target tracking has been
investigated. As pointed out in Ref. 5, the main idea
beyond sensor selection is to optimize a cost functionthat represents the localization accuracy constrained by
the number of active sensors. A cost function thatminimizes the geometrical dilution of precision (GDOP)
is used to derive the sensor selection scheme where thebest three sensors are selected. Based on the GDOP
measure, an adaptive sensor selection strategy has been
proposed in which the signal power measurements areused. By combining with the prior information, which is
not accounted for the GDOP measure, global sensor© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

361

Wenling Li, Yingmin Jia

sensor selection for target tracking in the clutteredenvironment.
In this paper, we consider the problem of sensor
selection for maneuvering target tracking in the
cluttered environment. By using the global knowledgeof all sensors locations, a cost function that minimizes
the expected filtered mean square localization error isdeveloped to select a given number of active sensors. As
the cost function is derived based on the decentralized
structure, we investigate how to derive the decentralized
fusion formula by applying the basic extended Kalmanfilter (EKF) and the PDA technique so that the clutter
information can be combined into the cost function.
Meanwhile, we have to circumvent the difficulty
encountered in multiple model decentralized fusion.10
By minimizing the cost function, a sensor selection
scheme is proposed based on the `add one sensor node
at a time' strategy. Simulations are provided to illustrate
the effectiveness of the proposed algorithm.

originated power measurement from sensor s can bemodeled in a logarithmic form

2. Problem formulation

Decentralized fusion of EKF-PDA

In this paper, we consider a two-dimensional (2-D)
target-observer scenario. The target dynamics ismodeled as the following coordinated turn model

Assume that the target dynamics
measurement model can be represented by

zk , s  K  10 log10 (rk ,s )  vk , s

rk , s  ( xk  xs ) 2  ( yk  ys ) 2，s  1,2,, N
where K and   [ 2,5] denote the transmission power
and the path loss exponent, respectively. They are
dependent on the radio environment, the antennacharacteristics and so on. ( xs , ys ) represents the positionof sensor s and rk , s is the relative distance between
sensor s and the target. N is the number of sensors.

vk ,s is zero-mean white Gaussian with variance Rk ,s .
Our aim is to find the set of active sensors that
optimizes the cost function. As the clutter is considered,
we should investigate how to combine the clutter
information into the cost function.
3.SENSOR SELECTION FOR MANEUVERINGTARGET TRACKING IN CLUTTER

thexk 1   k xk  wk
zk ,s  hs ( xk )  vk ,s , s  1,2,, Nsin(T )1 0
 1cos( T ) sin(T ) 
0 1 1cos( T )


x w
xk 1 k
0 0 cos(T ) sin(T )  k
0 0 sin(T )T cos(T ) 
where xk  ( xk , yk , xk , y k ) denotes the target state.
( xk , yk ) and ( x k , y k ) represent the target position andvelocity components, respectively.  denotes the turnrate and T is the sampling time period.  k is zero-mean
white Gaussian noise with covariance Q k .

where  k and the covariance  k of wk are derived by
the best-fitting Gaussian approximation. hs is thenonlinear measurement function of sensor s . By
combining the EKF and the PDA technique, a single
forward run of the tracking algorithm proceeds asfollows for each sensor s.
First of all, the predicted mean and covariance are
obtained by using a standard Kalman filterxˆks|k 1   k 1 xˆks 1|k 1

Pks|k 1   k 1 Pks1|k 1Tk 1   k 1Then, the predicted measurement and the innovation
covariance can be derived as

To formulate the target dynamics into the frameworkof jump Markov systems, we assume that at any time
the target motion obeys one of M dynamic behavior
models, which can be described by the above
coordinated turn model with different turn rate. The
switching between models is governed by a timehomogeneous first-order Markov chain rk with knownandzˆks|k 1  hs ( xˆks|k 1 )vki , s  zki , s  zˆki |k 1
S k ,s  H k ,s Pks|k 1H kT, s  Rk ,stransition probability  ij  P rk  j rk 1  i .

where H k , s is the Jacobian matrix of the nonlinear

A radio receiver that provides the signal power
measurement is used throughout this paper. The target-function hs evaluated at xˆ k 1|k 1 .
s

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan362

Global sensor selection for

After receiving the measurements Z k ,s by sensor s, z k , s~
S k , s  ( S k,1s  S k,1s Bk , s S k,1s ) -1

is validated if

 S k , s  S k , s S k,1s ( S k,1s S k , s S k,1s  Bk,1s ) 1 S k,1s S k , si[ zki ,s  zˆks|k 1 ]T S k,1s [ zki ,s  zˆks|k 1 ]  

 S k , s  ( Bk,1s  S k,1s ) 1

where  is the gate threshold.
By assuming that the number of clutter in the validated
region is assumed to be Poisson distributed, aparametric PDA technique is used to generate theassociation event probability
 k0, s  ki ,s 

Therefore,

On the other hand, we have~
K k , s vk , s  K k , s ( ~z k , s  zˆks|k 1 )  0

which leads to

bk , sbk , s   j 1 d
mk , 2

bk , sbk , s   j 1 d
mk , 2jk ,s~zk ,s  [ I  ( Bk,1s  S k,1s ) 1 S k,1s ]vk , s  zˆks|k 1jk ,sBased on the above EKF-PDA tracking algorithm, we
can obtain the decentralized fusion formula. We assume
that each sensor knows its position and they share thecommon prior information. Suppose each active sensor
have obtained estimates xˆk 1|k 1 and Pk 1|k 1 at time

, i  1,, mk , sWhered ki , s  exp( 0.5[vki ,s ]T S k,1s vki , s )1  PD PG
PD
PD is the detection probability, PG is the probability thatstep k-1, the filtered estimates can be derived by eachsensor via communicating with other active sensorsxˆk|k 1  k 1 xˆk 1|k 1

bk ,s   | 2S k ,s |1/ 2

the measurement originated from the target falls into thevalidated region, and  is the spatial density of clutter.
Finally, the updated estimates can be computed byxˆks|k  xˆks|k 1  K k , s vk , s
Pˆ s  Pˆ s  K ( S  B ) K T
k |k 1

k |k

k ,s

~
Rk , s  Rk , s  ( Bk,1s  S k,1s ) 1

k ,s

k ,s

xˆk |k

Pk |k 1   k Pk 1|k 1Tk   k 1~
 P [ P 1 xˆ

H T R 1 [~z  h ( xˆk |kk |k 1 k |k 1



sN a

k ,s

k ,s

k ,s

s

k |k 1)  H k , s xˆk |k 1 ]]~Pk|k1  Pk|k11   H k,1s Rk,1s H k , s
sN a

k ,s

where
Sensor selection

mk , 2

vk , s    ki , s vki , s

When the signal power measurement is applied, theupdated covariance for the decentralized fusion of EKFPDA can be rewritten as J ,0
Jf  Jp  
0,0 

i 1

K k ,s  P

sk |k 1

T
k ,s

H S

1
k ,s

mk , 2

Bk , s   k0,s Sk ,s    ki , s vki ,s [vki , s ]T  vk ,s vkT,s
i 1

1

In order to develop the decentralized fusion formula, wewould like to rewrite the updated mean and covarianceas the same form of the usual Kalman filter, i.e.,1

Where J f  Pk |k , J p  Pk |k 1 and~xˆ  xˆ K k ,s (~z k , s  zˆks|k 1 )~ ~ ~Pks|k  Pks|k 1  K k , s S k , s K kT, scos2 k , s , sin k , s cosk , s ~J  (10 log10 e) 2  Rk,1s rk, s2 

2
sN a
sin k , s cosk , s , sin k ,s Note that~~
K k , s  Pks|k 1 H kT, s S k,1s~~
S k , s  H k , s Pks|k 1 H kT, s  Rk , swhere (rk ,s , k , s ) is the position of sensor s relative to

s
k |k

s
k |k 1

H k ,s  10 log10 e rk,1s cosk ,s , rk,1s sin k ,s ,0,0

the predicted target position in polar coordinates.
The aim of sensor selection is to search for theminimum expected mean square posterior error which
can be expressed in terms of the posterior Fisher
information matrix. Specifically, the cost function to beminimized for sensor selection is

It can be observed that our aim is to define ~z k , s and~
Rk , s .To this end, we have~~ ~ ~K k , s S k , s K kT, s  Pks|k 1 H kT, s S k,1s H k , s Pks|k 1~
 K k , s S k , s S k,1s S k , s K kT, s

 ( N a )  [ J f 1 ]1,1  [ J f 1 ]2, 2© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

363

Wenling Li, Yingmin Jia

Where [ A]i , j denotes the (i,j)th element of the matrix A
 (N a ) 

tr{J p }  tr{J }| Jp  J |

tr{J p }  tr{J }| J p |  | J |  J kd

where ‘tr’ represents the trace of the matrix, and~J p  [ J p ]1:2,1:2  [ J p ]1:2,3:4 ([ J p ]3:4,3:4 ) 1[ J p ]3:4,1:2~~
J kd  (10 log10 e) 2  [[ J p ]1,1 Rk,1s rk,2s sin 2 k , s
sN a

~
~
~
~
 [ J p ]2, 2 Rk,1s rk, s2 cos2 k , s  2[ J p ]1, 2 Rk,1s rk, s2 sin k , s cosk , s ]A practical strategy called as ‘add one sensor node at atime’. For this strategy, an initial active set isestablished by using the exhaustive search, which
includes two active sensor nodes and requires
evaluating  ( N a ) over N ( N  1) / 2 configurations. Then

Fig.1 RMSE in position of the proposed filter.

REFERENCES
1.

D. Kammer, Sensor placement for on-orbit modal
identification and correlation of large space structures,
Journal of Guidance, Control, and Dynamics, 14(2)(1991) 251-259.
2. K. Kookos and J. Perkins, A systematic method for
optimum sensor selection in inferential control systems,
Industrial Engineering Chemistry Research, 38(11)(1999) 4299-4308.
3. F. Zhao and L. Guibas, Wireless Sensor Networks: An
Information Processing Approach. (Morgan Kaufmann,
San Mateo, 2004).
4. H. Rowaihy, et al., A survey of sensor selection schemesin wireless sensor networks, Proceedings of the SPIE,(2007), 6562-65621.
5. L. M. Kaplan, Global node selection for localization in a
distributed sensor network, IEEE Transactions on
Aerospace and Electronic Systems, 42(1) (2006) 113-135.
6. L. M. Kaplan, Local node selection for localization in a
distributed sensor network, IEEE Transactions on
Aerospace and Electronic Systems, 42(1) (2006) 136-146.
7. X. R. Li and V. P. Jilkov, Survey of maneuvering target
tracking. Part V: multiple-model methods, IEEE
Transactions on Aerospace and Electronic Systems, 41(4)(2005) 1255-1321.
8. Q. Le, et al., Multiple-mode Kalman filtering with node
selection using bearings-only measurements, Proceedingsof the 36th IEEE Southeastern Symposium on System
Theory, (USA ,Atlanta, 2004), pp.185-189.
9. S. Zhang et al., IMM filter based sensor scheduling for
maneuvering target tracking in wireless sensor networks,
Proceedings of the 3rd International Conference onIntelligent Sensors, Sensor Networks and Information,(Melbourne, Qld, 2007), pp. 287-292.
10. W. L. Li and Y. M. Jia, Distributed interacting multiple
model H∞filtering fusion for multiplatform maneuveringtarget tracking in clutter, Signal Processing, 90(5) (2010)
1655-1668.

the rest of sensor nodes are added to the initial active set
one at a time by minimizing  ( N a ) .
4.SIMULATIONS
Three models corresponding to different turn rates areused. The sampling period is taken to be 1 s for target
motion model. The network consists of 30 sensors
randomly scattered over a field. The sensing range foreach sensor is 25 m. For the signal power measurements,
the transmission power is taken to be 9 dBm and thepath loss exponent is 3. The variance of themeasurement noise is 0.2 for all sensors. The detection
probability and the gate probability are set to 0.997 and0.999, respectively. The gate threshold is 16 and thespatial density of clutter is 0.1.
In the simulations, the root mean square error (RMSE)
in position is used for performance comparison. The
RMSE in position of the proposed algorithm withdifferent numbers of active sensors is shown in Fig.1. It
can be observed that the tracking performance isimproved as the number of active sensors is used andthe best performance is achieved when all the active
sensors in the sensing range are utilized.
5.CONCLUSION
In this paper, we propose a global sensor selection
scheme for tracking maneuvering target in the clutteredenvironment. A formulation of PDA for decentralized
fusion is derived which helps the definition of the costfunction for sensor selection. This is implemented bydeveloping novel measurement vector and noise
covariance matrix combining the clutter information.
Simulation results are provided to verify that the
proposed algorithm.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan364

Attitude/Position Estimation of Rigid-Body using Inertial and Vision Sensors
Shihao Sun

The Seventh Research Division, Beihang University (BUAA),
37 XueYuan Road, Beijing, 100191, P.R. China
Yingmin Jia

The Seventh Research Division, Beihang University (BUAA),
37 XueYuan Road, Beijing, 100191, P.R. China
E-mail: jxcrssh@126.com, ymjia@buaa.edu.cn
www.buaa.edu.cn

Abstract
This paper is concerned with the attitude/position estimation of a rigid-body using inertial and vision sensors. By
employing the Newton-Euler method, a kinematic model is developed for the rigid-body by treating the inertial
measurements as inputs. Based on the coordinate transformation, a nonlinear visual observation model is proposedby using the image coordinates of feature points as observations. Then the Extended Kalman filter (EKF) is utilized
to estimate the attitude/position recursively. The effectiveness of the proposed algorithm is evaluated by simulation.
Keywords: attitude and position estimation, EKF, inertial sensor, vision sensor.

accelerate. Therefore, an underlying dynamic model forthe motion of rigid-body is needed for accurate
estimation when we use vision sensor alone.
Motivated by the discussions above, the combinationof the vision and inertial sensors has been recognized asa promising choice for accurate attitude and position
estimation. For example, rigid body pose estimation
using inertial sensors and a monocular camera isconsidered in Ref. 5, and it is shown how rotation
estimation can be decoupled from position estimation. In
Ref. 6, Chen studied the problem of the pose estimationof robotic end-effector with inertial and SE(3) measurements. Among these literature, the measurements ofvision sensor are the actual attitude and position that canbe obtained by using machine vision algorithms like PNP,
stereo-vision. Although the observation model is linear insuch way, the expression of observation noises iscomplicated according to the complicated machine visionalgorithms and costs huge computations. An alternative

1. Introduction
Accurate attitude/position estimation of rigid-body hasreceived considerable attention in the past decades. Thisis partly due to the fact that it is generally required inmany typical applications such as estimating the motionof a robot end-effector1, navigation for small unmanned
aerial vehicle2, spacecraft relative navigation in
rendezvous3 etc. The widely used way of obtaining
position and orientation is using an inertial measurement
unit (IMU) as the navigation sensor, which consists of a
tri-axis gyroscope and a tri-axis accelerometer. However,integration over a long time period may lead tounbounded estimation errors if noises, offsets, scaleerrors and uncertainty in navigation model are present.
Using vision as a standalone sensor for attitude/position
estimation is also quite a standard way4, because of theability to sense the actual attitude/position without
accumulative errors. However, vision sensors can only
sense the actual position but not the velocity and© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan365

Shihao Sun, Yingmin Jia

constants in each sampling interval kT ,  k  1T  withsampling period Ts .
The state transition can be approximated by:

way is applying the image coordinates of feature points
as observations directly.
In this paper, we attempt to estimate the orientationof a rigid-body using inertial and vision sensors. The
inertial measurements are treated as inputs fordeveloping the kinematic model, and a novel non-linear
observation model is proposed by using the image
coordinates of feature points. The EKF is utilized toaddress the nonlinear filtering problem. Simulation
results are provided to evaluate the performance of the
proposed algorithm.

sBpe  k  1  B pe  k   Ts B ve  k  Bve  k  1  B ve  k   Ts B ae  k 

s

Ts2 B
ae  k 2Φ  k  1 = Φ  k  + TsT Φ  k   E ωe  k (4)

2.2. Transition models using IMU measurements
The IMU in Fig. 1 consists of a tri-axis gyroscope and a
tri-axis accelerometer that output:
ωimu = I ωi  I mω2. Problem Statement

aimu  I ai  I g  I ma(5)where ωimu and aimu are IMU measurements, mω and ma
are measurement noises, and g is the gravity vector.
According to the rigid kinematic theorem, the angularvelocity and acceleration relations between point pi andpe are as follows:
E

Fig. 1. System configuration and coordinate frames

B

In this paper, we consider the case in which the motionof a rigid-body (noted as effector) is controlled by the
ABB-120 robot as shown in Fig.1. The effector isequipped with a strap-down IMU and four infrared LED
feature points. The vision sensor is a camera equippedwith an infrared filter, which is fixed at a certain locationin the base frame.

(6)
where RI is a constant orientation matrix referred as the
IMU frame attitude observed in the effector frame; I rei isthe position vector of pe relative to pi in IMU frame; I αi isthe angular acceleration of the IMU, according to theassumption that E ωe is constant in each sampling interval,then I αi  k   0 ; B RE is the orientation matrix referred as
the end-effector attitude observed in the base frame,
which is given by

As shown in Fig. 1. Denote B pe  R 3 as the effector
position in the base frame, evolving as:B(1)
pe  B ve B ve  B ae

B

cos  cos RE   sin  cos   sin  sin  cos   cos  sin  sin 
cos  cos   sin  sin  sin 
cos  sin 

sin  sin   cos  sin  cos  ; cos  sin   sin  sin  cos  cos  cos and S    is the cross product operator transforms a vectorc  c c c  to a skew-symmetric matrix

where B ve  R 3 and B ae  R 3 are referred as the translational

velocity and acceleration of SCP in the base frame,
respectively.
Denote Euler (3-2-1) rotation angles Φ =    
as the effector attitude observed in the base frame,according to attitude kinematics:
Φ = T  Φ  E ωe(2)E3
where ωe  R is the angular velocity of the effector as
viewed in the effector frame and

T

1

2

3

 0
S  c    c3
 c2Tcos  cos   sin  cos  tan  ae  B RE E RI  I ai  S  I αi  I rei  S  I ωi  S  I ωi  I rei E

2.1. Effector Kinematics

0 sin  cos T  Φ   0cos 
1 sin  tan ωe  E RI I ωi

c 3
0
c1

c2 
c1  .
0 Given (5) and (6) at the sampling interval, we haveEωe  k   E RI ωimu  k   E RI I mω  k 

Bae  k   B RE  k  E RI aimu  k   B RE  k  E RI S  ωimu  k   S  ωimu  k   I rei  B g

 





 B RE  k  E RI S S  ωimu  k   I rei  S  ωimu  k   S  I rei  I mω  k 
 B RE  k  E RI S  I mω  k   S  I mω  k   I rei  B ma

(7)
Then the state transition equation (4) can be rewritten(3)asX  k  1  φ  X  k  , U  k   Γ  X  k  , U  k  W  k 

Discretization of equations (1) and (2) is needed todesign a Kalman Filter. Assume that B a e and E ωe are(8)© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

366

Attitude/Position Estimation of Rigid-Body

where X  k    B pe  k  B ve  k  Φ  k  , U  k   ωimu  k  aimu  k T1) Prediction. Denote Xˆ  k  1 k  P  k  1 k  Zˆ  k  1 k  as
the one step predictions of estimation, covariance matrix
and observation at the time k  1 .
Then as to (8), they can be obtained(12)Xˆ  k  1 k   φ  Xˆ  k k  ,U  k TiW  k    mω  k  ma  k  S  mω  k   S  mω  k   rei  , CovW  k  ,W  j   Qk kj
I

I

I

I



I

T



Ts2 B ˆ
B

B
E
I
B
 pe  k   Ts ve  k   2 RE  k  RI aimu  k   S  ωimu  k   S  ωimu  k   rei  g 




B
φ  X  k  ve  k   Ts B Rˆ E  k  E RI aimu  k   S  ωimu  k   S  ωimu  k   I rei  B g
 





Φ  k  + TsT Φ  k   E RI ωimu  k 











 

2
 Ts 2 BEII  2 Ts RE  k  RI S S  ωimu  k   rei  S  ωimu  k   S  rei Γ  X  k     Ts B RE  k  E RI S S  ωimu  k   I rei  S  ωimu  k   S  I rei 
TsT Φ  k   E RI




 





 Γ  Xˆ  k k  , U  k  Qk Γ T  Xˆ  k k  , U  k   T2 T2 R  k  R  .

P  k  1 k   Ψ k  1 k  P  k k Ψ T k  1 k 

2

s

2

s

B

E

E

Ts
0

I


Ts B RE  k  E RI 


0


where Ψ  k  1 k  C

rbc   B RE
1  0Bpe   E pi  
1  1 i  1 4 (9)Cvi  f y C

piy
piz

(10)

where f x and f y are pixel magnification factors,  u0 , v0 
denotes the image coordinate of the camera's principal
point, and  ui , vi  is the coordinate of the feature point pi
in the image plane. In view of (9) and (10), denote Z i  k 
as the image coordinate  u  k  , v  k   at time k , then theobservation model of camera can be written as(11)
Z  k   h  X  k  , p   V (k) i  1 4
i

i

ki

3

4

h  X  k  1 , E pi X  k  1X  k  1  Xˆ  k  1 k 1.

As stated in section 2, the effector is controlled by the
robot, in which the revolute joints are programmed asiwhere hi is a 2-dim function derived by replacing C pi inequation (10) using equation (9), V (k) is themeasurement
noise
and
its
covariance
is
CovV  k  ,V  j   R  .
i2

4. Simulation results

i

i

1

P  k  1 k  1   I  K  k  1 H  k  1 P  k  1 k  (17)

E

i

4

Then the state estimate Xˆ  k  1 k  1 and its
estimation error covariance matrix P  k  1 k  1 at timek  1 can be computed byXˆ  k  1 k  1  Xˆ  k  1 k  + K  k  1  Z  k  1  Zˆ  k  1 k  (16)

 v0

i

3

where O  k  1   H  k  1 P  k  1 k  H T  k  1  diag  Rki i1 4 H  k  1 i 1 42

T

T

According to the camera pinhole model with the
projective geometry, we obtainCpui  f x C ix  u0
piz

(14)

where Zˆ   Zˆ Zˆ Zˆ Zˆ  and h   h h h h  represent
four feature points.
2) Observation. As the feature points are infrared LED
and the camera is equipped with an infrared filter, we caneasy obtain the coordinates Z i  k  1 of the feature point
pi in the image plane at the time k  1 .
3) Update. When the new image is obtained at time k  1 ,the filter can be computed as(15)
K  k  1  P  k  1 k  H  k  1 O  k  1
1

C

(13)

X  k   Xˆ  k k TThe rigid body in Fig. 1 contains four feature points notedT
as E pi   E pix E piy E piz  , i  1 4 , and the relation between
the fixed camera and the base frame are noted as theorientation matrix C RB and the translation vector C rbc . By
using the homogeneous transform matrices between theframes of Base, Effector and Camera, we obtainCX  k Zˆ  k  1 k   h  Xˆ  k  1 k  , E pi 

2.3. Observation models of Camera

 pi   RB 
 1   0

φ  X  k  , U  k 

q=


180

40 10 5 60 8 10cos t   rad  t  0,100  .
 100 And the robot forward kinematics with nominal D-H
parameters is used to provide the attitude /position of theeffector relative to base frame, which serve as the real.
The sampling period of IMU and camera are both set as
0.1s , ignored the different sampling rate of the sensors.
The noises of the IMU and camera sensors are assumed
22
as mω ~ N  0,0.1  ma ~ N  0,2.5  V ~ N  0,5  . The plots
in Fig.2 and Fig.3 show the estimate results based on
EKF and transition models only use IMU, respectively.

kj

3. Estimation based on EKF
Since the state transition model (8) and the observation
model (11) are both nonlinear, the EKF is used to update
the effector motion state estimate Xˆ  k k  and its
estimation error covariance matrix P  k k  .
The EKF is implemented as follows:2© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan367

Shihao Sun, Yingmin Jia

5. Conclusion

The simulation results suggest the effectiveness of the
pro-posed method.

This paper investigated the attitude/position estimationof a rigid-body by using a measurement system consistof an inertial sensor and a vision sensor, and an EKF isapplied to fuse these measurements. The motion states
are propagated in time using the inertial measurements
processed through the Newton-Euler equations, and the
pixels coordinates of feature points are used as
observation directly, which is different from using
position and attitude coordinates in the camera frame.
Simulation results suggest that the performance of the
EKF can be improved by using the pixels coordinates offeature points as observations than calculating position
and attitude coordinates through PNP algorithm. Future
work would focus on the on-line tracking estimation
experiments.

Fig. 2.Estimate of Effector Motion Based on EKF

Fig. 3. Estimate of Effector Motion Use IMU Only

The measure model Z  k   X  k   V  k  , which canbe obtained by using the PNP algorithm (refer tosolvepnp function in OpenCV), is also applied in EKF tocompare against the proposed observation models noted
as Pixels Measure. To illustrate the performance of theobservation models, the root mean square error (RMSE)
in position and attitude are shown and the simulation
results are derived from 100 Monte Carlo runs. The
RMSE in position and attitude are shown in Fig. 4. The
simulation results suggest that the performance of the
EKF can be improved by using the image coordinates(Pixels) of feature points as observations.

Acknowledgements
This work is supported by the National Basic Research
Program of China (973 Program: 2012CB821200,
2012CB821201) and the NSFC (61134005, 61221061,
61327807, 61520106010).
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Fig. 4. The RMSE in position and attitude

Notice from (7) that the IMU’s acceleration
measurement is affected by the effector acceleration andthe gravity, and the effector acceleration is much smaller
than gravity in this simulation. Thus, it is very sensitive
to noise, and lead to the estimate errors of position use
IMU only are very large as shown in Fig.3.
Because of the complex computation, the measurement noises covariance matrices in measurement modelobtained by PNP algorithm are not calculated based on
the pixels noises of the camera, it is set based on our
experience in this simulation. Besides, the errors of theattitude measure are shown in Fig.5. It’s clear from thefigure that the error is non-Gaussian.

Fig. 5. The errors of the attitude measure© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan368

Modeling and Control of a Suspended Gravity Compensation Systemwith Rigid-Flexible Coupling
Jiao Jia

The Seventh Research Division, Beihang University (BUAA)
Beijing, 100191, China
Yingmin Jia

The Seventh Research Division, Beihang University (BUAA)
Beijing, 100191, China
Shihao Sun

The Seventh Research Division, Beihang University (BUAA)
Beijing, 100191, China
E-mail: annebuaa@hotmail.com; ymjia@buaa.edu.cn; jxcrssh@126.com
www.buaa.edu.cn

Abstract
In this paper, the suspension gravity compensation system (SGCS) is modeled and the corresponding controller isdesigned. The system is a servo platform consisting of three sub-systems by which a micro-gravity environment canbe established. Especially, a novel zero stiffness suspension sub-system and a creative unconstrained structure areproposed. The system model is deduced based on Lagrange equation. A PID controller is designed by using the
feedback linearization approach. Simulation results show the effectiveness of the proposed method.
Keywords: gravity compensation, rigid-flexible coupling, Lagrange method, feedback linearization, PID

There are two kinds of suspension compensation
methods passive suspension compensation5 and active
suspension compensation.6-7 The active suspension
method performs much better than the passive one. The
compensation accuracy of the suspend system depends
on the structure and controller of the vertical direction
sub-system greatly. Usually, the wire rope is one of thenecessary parts of the vertical direction sub-system.
However, when a force which is opposite to or greaterthan the object gravity functions on the object, the rope
will be invalid. To solve this problem, a spring is
combined with rope, but the compensation accuracy isaffected by the rope reversing. In this paper, we develop
a new means that combines a spring buffer unit with the

1. Introduction
One of the vital differences between the spaceenvironment and the ground laboratory environment isthat the space is micro-gravity. In order to represent the
spacecraft motion in space on the ground and improve
the fidelity of ground verification of guidance andcontrol system, the micro-gravity environment should
be built. Up to now, the gravity compensation methods
have been developed such as weight loss, neutral
buoyancy, air-bearing and suspension system1-5 .There
are both advantages and disadvantages of theseapproaches. Among them the suspension method5-7
becomes an economical, practical and reliability method.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

369

Jiao Jia, Yingmin Jia, Shihao Suntransmission unit to avoid the shortage of the wire rope.
Besides, there are rarely papers or researches thatconcern how to guarantee the object rotation freedoms
when it is suspended to the compensation platform. In
this paper, a creative mechanical structure is presented.
2. System Design
The platform is a servo system consisting of a levelservo sub-system (LSS), a zero stiffness suspension subsystem (ZSSS), an unconstrained suspension sub-system(USS) and a circuit control section (CCS). The LSS canfollow the object’s horizontal movement and maintainthe ZSSS vertically. The ZSSS could compensate thegravity of the object suspended to the USS and assure
its gravity wouldn’t affect its movement. The USS holds
the object and such that the object would rotate freely.
Note that they work cooperatively to create a microgravy environment.
As described in Fig.1.the object connects to theplatform through the USS. The USS is composed oftwelve rolling bearings and their fixations, whichguarantees the equivalent suspension point coincides
with the object centroid. Hence, the object can keepbalance at any random attitude. The USS links to thespring buffer. The spring buffer attaches to the ZSSS.
Besides, the ZSSS includes universal joint, Z direction
mechanism and motor 3. The dual-axis tilt sensor isfixed to a spring buffer through an aluminum panel. The
spring buffer and the tension sensor are connected by
the universal joint. Therefore, when the object moves,
the spring buffer swings around the universal joint. The
dual-axis tilt sensor can measure the swing angle. The
tension sensor connects to the Z direction mechanism. Z
direction mechanism transmits by a rack and a pinion
driven by motor 3. The ZSSS installs on the LSS
specifically on the Y direction mechanism. In addition,
The LSS comprises motor 2, the X direction mechanismand motor 1. The Y direction mechanism mounts on the
X direction mechanism and they are driven by motor 2
and motor 1 respectively. The X direction mechanism
fixes on the supporting frame.
The USS consists of twelve bearings and their
fixations as presented in Fig.2. The bearings areclassified into yaw bearings, roll bearings and pitch
bearings according to their corresponding support
rotation of the object.

Fig.1.Structure and coordinate system of SGCS

The CCS contains the dual-axis tilt sensor, thetension sensor, drive control cards, an acquisition card
and a PC. The working principle of the platform is asfollows. When the object moves along the horizontal
plane the dual-axis tilt sensor can measure the swing
angles. Then the motor 1 and motor 2 can drive the X
and Y direction mechanism to eliminate the swing angle
to maintain the spring buffer vertically. When the object
moves vertically the length of the spring will vary. The
tension sensor will detect the variation and then the
motor 3 actuates the Z direction mechanism to eliminate
the variation. So the vertical movement of the object
wouldn’t be affected by its own gravity. The object is ina micro-gravity environment.

Fig.2. Structure of USS

3. Model Building
The SGCS coordinate system is shown in Fig.1.right.
Among it, the spring buffer and the application of USS
are equivalent to a spring and a rope linked to thecentroid of the object respectively. The meaning of eachpart is as below.
m0 , the load of LSS,m1 , m2 , the mass of the pinion and rack resp.,M , the mass of object, , the rotation angle of the pinionQ  XYZ , static coordinate systemO , centroid of m0
O1, O2 , O4 , centroids of m1 , m2 , M resp.,O3 , centroid of the universal joint,Fx , Fy , motor 1&2 driving forces on X&YTx , motor 3 output torque on the pinion© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan370

Modeling and Control ofF4 x , F4 y , F4 z , the object driving force
T4 x ,T4 y ,T4 z , the object driving torque ,wing angle between spring buffer & vertical x ,  y , orthogonal decomposition of O  xyz , coordinate moves with O ,Oi  xi yi zi , i  3,4 , coordinates move with O3 & O4 ,O3  x31 y31z31 , coordinate rotate along the buffer.

Substitute Eq. (1) into Eq. (2) and defineM  m0 y
M  m0 x
1m  m1  m2  M , x , y 
2MM
m2  Mmk1
  ,2 ,  ,  
M
M
M
M
2
2
2
y
  x   y , x  x x   y  yFrom the definition above, the coordinates ofO, O1, O2 are  x, y,0  ,  x, y  R,0  and  x, y,  R  .
And the velocity of each point is  x, y,0  ,  x, y, 0 
and  x, y,  R  .
Define l1  Mg , d  l0  l1  l Then O4 isk

The equations deduced from Eq. (2) can beformatted in a compact form0xd0  x  x 00 
0y0d   y 
y

2
0
0 R
R
0
0   2
x  y R   1 d x d  y   l 
d
0
0
d x
d2
0  x 

 
0
dy
0
d 2    y  0 d(3)
0 0 0 2 x 0 0   x   0 
 Fx 
  
F 

0 0 0 2 y 0 0   y   0  y
0 0 0 Tx 



0
0 0
 gR

  2 
y0 0 0 2 x 0 0   l    l 
 F4 z  0 0 0 2d  0 0      0 
 F4 x x x  

0 0 0 2d  y 0 0    y   0  F4 y 









 x  d sin  cos  , y  d sin  , R  0.5h  d cos  cos  
x

y

y

0

x

y

h0 is the length of the rack. O & O2 coincide at theinitial position. l is the variation length of the spring. Setl  0 , when the force of the spring is equal to Mg .
For   5 , sin    ,cos   1 , then x  d x , y  d  y , R  0.5h0  d 
The velocity of O4 is x  l   d  , y  l   d  , R  l 
x

x

y

y

The attitudes of the object will be changed when

We classify the system generalized coordinates into
two parts based on their controllability that x, y,  only
controlled by the motors and the other ones are affectedby the driving forces of the object simultaneously.

T4 x ,T4 y ,T4 z is valid, and therefore the angular velocity is
time-varying. But when we choose x, y, , l ,  x &  y assystem generalized coordinates, they are independent of
the object rotational kinetic energy. Hence, we could
ignore it when we calculate the system kinetic energy.
We define m0 as the load of LSS. The pinion andrack move along the X&Y direction mechanism at thehorizontal direction, so this part of kinetic energy is
included in 0.5m0 x vm2 0 x  0.5mm0 y vy2 ( m0 x , m0 y the load of
X, Y direction resp.).
The system kinetic energy is
1 1
11
T  ( m1 R 2 ) 2  m2 ( R ) 2  m0 x vm2 0 x
2 2
2
2
1
1
2
2
2
 mm 0 y v y2  M (vMx
 vMy
 vMz
)
2
2
Choose xOy plane as zero potential energy surface
and the system potential energy isV  m2 g R  0.5k (l  l1 )2  Mg  R  0.5h0  d 
L  T V

Lagrange equationd  L  L
 Q j ( j  1, 2,......) 
dt  q  q

Define q1   xy   , q2  l
T

x

T

 y  , andM1q1  M 2 q2  C1q1  C2 q2  G1  F1

(4)

M 2 q1  M 3 q2  C3 q1  C4 q2  G2  F2

(5)

T

Obviously, M 2 is invertible
Let Eq.  4   M1M 2T Eq.  5 . And C1  C3  0 we gain
M m q2  Cq2  G  F1  M 1 M 2 T F2
T

(6)
T

M m  M 2  M 1 M 2 M 3 , C  C2  M 1 M 2 C 4 ,G  G1  M 1 M 2 T G2

4. Controller Design
Since F2   F4 z F4 z F4 z  depends on the object
movement requirements and is uncontrollable for SGCS,
it’s regarded as interferences. So the model is a timevarying coupling model with disturbances. A PID
controller is designed by using the feedback
linearization approach.
T

(1)
(2)

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

371

Jiao Jia, Yingmin Jia, Shihao Sun

F1  F11  M m F12(7)

F11  Cq2  G(8)F12   K P q2  K I  q2  K D q2
K P   K Pz

K Py  , K I   K Iz

K Px

K D   K Dz



Fig.4. shows the system response and output error at
0.2step reference input with the same disturbance asshown in Fig.3.left. The compensation accuracy is up to
99%. The simulation results show that smaller of spring
stiffness coefficient the higher compensation accuracy.

(9)

K Iy K Ix

K Dy 

K Dx

6. Conclusions

F11 is used to counterweigh Cq2  G of Eq. (6).
Substitute Eq. (7) in Eq. (6), and we gain(10)
M m q2  M m F12  M 1 M 2 T F2

In this paper, a novel compensation platform reflecting
in the ZSSS and USS is proposed and then themathematic model based on Lagrange equation isdeveloped. Lastly, a stable and high compensation
accuracy controller is given. However, the existence ofthe sensor errors and modeling errors, we can’t
compensate the nonlinear parts. Depth study should be
conducted to find more practical controllers.

It’s easy to verify the existence of the inverse of
M m . Eq. (10) simplifies to(11)
q2  F12  d (t), d (t)   M 1 M 2 T F2
Where F12 is a PID controller. PID parameters
adjustment rules are employed to find suitable control
parameters for the system.
5. Simulation Results

Acknowledgements

Firstly, the control target is q2  0 . It means the gravityof the object is compensated completely.

This work was supported by the National Basic
Research Program of China (973 Program:
2012CB821200, 2012CB821201) and the NSFC
(61134005, 60921001, 61327807).

-3

Object Driving Input

4

1
Z Direction
X Direction
Y Direction

0.8
0.6

Output of l
Output of bitax
Output of bitay

2

0.4
0.2

Output Signal

Object Driving Signal

System Output

x 10

3

0
-0.2
-0.4

References

10

1. Y. Watanabe, et al., Microgravity experiments for avisual feedback control of a space robot capturing a target,
Proceedings of the IEEE/RSJ International Conferenceon Intelligent Robots and Systems, (USA, Piscataway,1998), pp. 1993-1998.
2. H. Sawada, et al., Micro-gravity experiment of a space
robotic arm using parabolic flight, Advanced Robotics
18(3) (2004) 247-267.
3. C. Menon, et al., Issues and solutions for testing freeflying robots, Acta Astronautica 60(12) (2007) 957-965.
4. A. Robertson, et al., Spacecraft formation flying control
design for the Orion mission, AIAA Guidance, Navigation,
and Control Conference, (USA, Reston, 1999), pp.
1562-1575.
5. D. A. Kienholz, et al., Very low frequency suspension
systems for dynamic testing, Proceedings of the 30th
Structures, Structural Dynamics and Materials, (USA,Mobile, 1989), pp. 327-336.
6. G. C. White and Y. S. Xu, Active vertical-direction
gravity compensation system, IEEE Transactions on
Instrumentation and Measurement 43(6) (1994) 786-792.
7. O. Han, et al., Gravity-offloading system for largedisplacement ground testing of spacecraft mechanisms,
Proceedings of 40th aerospace mechanisms symposium,(USA, FL, 2010), pp. 119–132.

-1

-0.6

-2
-0.8
-1

0

5

10

15
time/s

20

25

-3

30

0

5

10

15
time/s20

25

30

Fig.3. The object driving input and output of the system

The object driving input is shown in Fig.3.left whichis regarded as the system disturbance. From Fig.3.right,
the system spring variation error is less than 0.0015 m
which means the compensation accuracy is up to 99%.
System Output-32

Output of l
Output of bitax
Output of bitay

Output Error of l
Output Error of bitax
Output Error of bitay

1

0.2

0

Output Error Signal

Output Signal

System Output Error

x 10

0.3

0.25

0.15

0.1

-1
-2
-3

0.05
-4

0
-5

-0.05

0

5

10

15
time/s

20

25

30

16.2

16.3

16.4

16.5

16.6
time/s

16.7

16.8

16.9

17

Fig.4. The system response & output error of the 0.2step input

Then set q2  u(t) 0 0 . u (t) is the reference
input of the system which means when the spacecraft is
not complete weightlessness and it varies according to
the law role of the gravity effect.
T© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan372

Trajectory Tracking Control for Omnidirectional
Mobile Robots with Input Constraints
Wenhao Zheng

The Seventh Research Division and the Department of Systems and Control, Beihang University (BUAA)
Beijing, 100191, China
Yingmin Jia

The Seventh Research Division and the Department of Systems and Control, Beihang University (BUAA)
Beijing, 100191, China
E-mail: zhengwenhao@163.com, ymjia@buaa.edu.cn
www.buaa.edu.cn

Abstract
In this paper, a trajectory tracking controller based on kinematics for omnidirectional mobile robots with input
constraints is presented. The tracking error model with the control law is proved to be global asymptotic stability by
Lyapunov stability theory. The input limits can be described as an octahedron in three-dimensional space, so that aspatial vector analysis method is proposed to design time-varying feedback parameters to limit robot inputs.
Simulation results show the feasibility and effectiveness of the control strategy.
Keywords: omnidirectional mobile robot, tracking control, input constraints, time-varying feedback control.

This paper mainly focuses on the tracking control of
omnidirectional mobile robots with input constraints.
The control law is proposed with time-varying feedback
parameters to satisfy the input constraints. Then, a spatial
vector analysis method is used to design parameterswhich is devoted to find a suitable robot inputs in the
restricted area. And the tracking error system can be
global asymptotic stability with the controller. Comparing with existing results, this paper primarily contributes
to the novel solution of input constraints of omnidirectional mobile robots.
The structure of the rest paper is organized as follows.
The section 2 introduces the tracking error system. In thesection 3, the tracking controller is designed with input
constraints using the spatial vector analysis method.
Simulation results are presented to show the validity of
the control law in the section 4. In the end, the section 5summarizes the whole paper and draws the conclusion.

1. Introduction
Omnidirectional mobile robots, with the ability of
three degree-of-freedom motion in the plane, have been
widely applied in different fields of the society, which
has brought to the forefront in recent years. Many
methods about tracking control have been proposed, suchas sliding mode control1, model predictive control2, fuzzycontrol3 and their combinations. In the practical systems,
the velocities of driving motors is limited, which meansthat the inputs of the mobile robot are subject toconstraints. The control law will be affected by input
constraints. Some results about tracking control with
input constraints can be found. In Ref.4, the control
signal to a given reference system was modified to makethe error dynamics robust to the saturation constraints. In
Ref.5, the diamond-shaped input constraints wasconsidered which made the controller more effective.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

373

Wenhao Zheng, Yingmin Jia

2. Problem Statement

2.2. Input constraints

2.1. Kinematic model of omnidirectional mobile
robot

In the practical systems, the velocities of motors arelimited. Assume that four driving wheels have the samemechanical characteristics, therefore, it is obvious thatthe same input constraint viw  V (i  1, 2,3, 4) should
be satisfied, and V is the maximum wheel velocity.
Using the constraint and the equation (2), we can deduce
the equation (3) as follows:

As shown in Fig.1.(a), the four wheeled omnidirectional mobile robot is considered in this paper. The
kinematic of omnidirectional mobile robots is x   cos    y    sin    0  

 sin cos 
00  vx  
0  v y 1   

vy
vx



1
V
V
V /d

(1)

Where ( x, y ) are the robot position in Cartesian
coordinates and  is the robot orientation. The control
inputs of the robot are (vx , v y ,  ) : (vx , v y ) here denoterobot’s longitudinal velocity and transverse velocity, and represents the rotate speed of the robot. As a matter offact, the kinematic of all kinds of omnidirectional robots
including three wheeled robots or others with different
omnidirectional wheels can be expressed as equation (1).

(3)

which can be described in three-dimensional space asshown in Fig.2: the restricted zone of robot inputs is anoctahedron with the geometric representation.

Fig. 2. Input constraints area in three-dimensional space(a)2.3. Error model of trajectory tracking(b)

Assume that the reference trajectory for the omnidirectional mobile robot satisfies the kinematicFig. 1. The schematic of trajectory tracking (a) and Four-wheel
structure of the robot (b).

 xr   cos  r   yr    sin  r   0 r 

In the Fig.1.(b), the framework of omnidirectional mobile robot has been proposed. The transformation relation
between the speeds (v1w , v2 w , v3w , v4w ) of the four driving
wheels and the omnidirectional robot’s speed (vx , v y ,  )is descried as the equation (2), where d  LX  LY and LX
is the X-axis distance from each wheel to the center ofgravity (the similar definition for LY ).

 v1w  1 1 d     vx  v2 w   1 1 d   v 
 v3w  1 1 d   y      
v
1

1
d

 4w   sin  rcos  r00  vxr  
0  v yr 1  r (4)and the input constraints
vy
vx



 1 
V
V
V /d

(5)

In the equation (4) and (5), ( xr , yr , r , vxr , v yr , r ) are
the desired values for ( x, y,  , vx , v y ,  ) . Besides,  is aconstant with the condition 0    1 which guarantees
the robot has the ability to track the reference trajectory
successfully.
We can define the tracking errors as follows:(2)© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan374

Trajectory Tracking Control for xe   cos    ye     sin    0 e 

sin 
cos 
0

0  xr  x 
0  yr  y 1   r   convenient to get the conclusion that xe , ye ,e  0 ast   with Barbalat’s lemma.

(6)

3.2. Feedback parameters design with input
constraints

Combining the equation (1) and (4) with differentiating both side of (6), error model can be expressed as

Define the space vectors

xe  vxr cos  e  v yr sin  e   ye  vx
ye  vxr sin  e  v yr cos  e   xe  v y

OE   vx(7)

T

OA   vxr cos  e

 e  r  v yr cos  e

r 

T

AB   0 0 kt (vxr ye  v yr xe ) sin  e /  e TThe destination of control law design is to find theappropriate inputs (vx , v y ,  ) which are subject to the
input constraints (3) to meet the desired outcome:lim xe  0, lim ye  0, lim e  0
t 0

r 

vy

t 0

t 0

BC   k x xe

0

T

T

(8)
The controller (7) can be expressed as a space vector
OE  OA  AB  BC  CD  DE . Fig.2 shows the relationships of the vectors. According to the equation (5),3.1. Control law design without input constraints

v yr cos  e
vxr cos  er 1 
VV
V /dInspired by the tracking error model and tracking
control methods for differential-drive mobile robots, wecome up with the control law(12)

OA is in the input area.
If the conditionvx  vxr cos  e  k x xe

kt 

(9)

  r  kt (vxr ye  v yr xe ) sin  e /  e  k  e


2d V (0)  (  0, 0    1)

(13)

holds, OB is in the octahedron. We can prove this result
as follows:

Where k x , k y , k are positive time-varying parameters
and kt is positive constants. In order to ensure thecontinuity of the robot inputs, we define sin e / e  1
when e  0 . And it’s noticeable that sin e / e is bounded. The tracking errors xe , ye and e will converge tozero under the control law (9), which can be proven as
follows:
Let Lyapunov function be2 
1V   xe2  ye2  e 
2
kt 

T

DE   0 0 k  e 

3. Controller Designv y  v yr cos  e  k y ye

0 0  , CD   0 k y ye

(11)v yr cos  er  kt (vxr ye  v yr xe ) sin  e /  e
vxr cos  e


V
V
V /d

kt

v yr cos  e
vxr cos  e
r



V
V
V /d
(vxr ye  v yr xe ) sin  e /  e
V /d 1    dkt ( xe  ye )  1    dkt 4V (t )(10)

 1    2dkt V (0)  1      1

We can obtain the value of kt from equation (13)Considering the tracking error system (7) and thecontroller (9), the derivative of this Lyapunov function
can be expressed as V  kx xe2  k y ye2   k / kt e2 .

kt e2 (0)d  e4 (0)d 2  2[ xe2 (0)  ye2 (0)  2 ] 2 2
2[ xe2 (0)  ye2 (0)  2 ]dDefine a function  ( x) which has three features: ( x)  [0,1] for x  [0, ),  ( x) is non-decreasing and (0)  0, ()  1 . In order to design the time-varying

When the conditions k x , k y , k , kt  0 are satisfied, we
can infer V  0 and V (t )  V (0) . Furthermore, it’s

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

375

Wenhao Zheng, Yingmin Jia

feedback parameters k x , k y , k , we come up with a geometric method. Sign function is defined as sign( x) .
As shown in Fig.2, we can make a line through B andin parallel with vx , which goes through the constrainedboundary with a point of intersection M . TakeBC   ( xe ) BM , then, we can get the expression of k x .
And construct another line going through C and inparallel with v y , there will be another intersection N in

Fig. 4. Tracking velocity and control inputs.

the constrained boundary, then make CD  2 ( ye )CN .It

Fig.3 and Fig.4 show that the tracking errors will
converge to zero with the control law (15), and thecontrol inputs are always in the area of input constraints.

is easy to get the expression of k y . We can use the same
thought to define P and DE  3 ( e ) DP .
In the end, we can get the controller

5. Conclusionvx  vxr cos  e  1 ( xe )[ sign( xe )(V  v yr cos  e 

In this paper, the tracking control problem of omnidirectioanl mobile robot with input constraints has beensolved. A controller with time-varying feedback parameters is proposed using the spatial vector analysis method.
The simulation results show that the control law canguarantee great tracking performance and the inputs will
be located in the restricted area.

d r  kt (vxr ye  v yr xe ) sin  e /  e )  vxr cos  e ]v y  v yr cos  e   2 ( ye )[ sign( ye )(V  vx d r  kt (vxr ye  v yr xe ) sin  e /  e )  v yr cos  e ](15)  r  kt (vxr ye  v yr xe ) sin  e /  e 
 2 (  e )[ sign( e )(V  vx  v y ) / dr  kt (vxr ye  v yr xe ) sin  e /  e ]Acknowledgements
And the time-varying feedback parameters can be expressed as kx  1 ( xe )[sign( xe )(V  vyr cos e  d r  kt
(vxr ye  vyr xe )sin e / e )  vxr cose ] / xe , k y  2 ( ye )[sign( ye )(V  vx  d r  kt (vxr ye  vyr xe )sin e / e )v yr cos  e ] / ye and k  2 ( e )[sign(e )(V  vx  vy
) / d  r  kt (vxr ye  v yr xe ) sin  e /  e ] /  e .
4.

This work was supported by the National Basic Research Program of China (973 Program: 2012CB821200,
2012CB821201) and the NSFC (61134005, 60921001,
61327807).
References
1. Li, Yunqi, et al. "Sliding mode trajectory tracking control
based on dynamic model for four-wheel drive omnidirectional mobile robots." Science Mosaic (2014).
2. Zeng, Zhi Wen, et al. "Trajectory tracking based on model
predictive control for omni-directional mobile robot."Control Engineering of China (2011).
3. Hashemi, Ehsan, M. G. Jadidi, and N. G. Jadidi. "Modelbased PI–fuzzy control of four-wheeled omni-directional
mobile robots." Robot. Auton. Syst. 59.11(2011):930–942.
4. A. Leonessa, W. M. Haddad, T. Hayakawa, and Y. Morel,“Adaptive control for nonlinear uncertain systems withactuator amplitude and rate saturation constraints.” Int. J.
Adapt. Control Signal Process. 23.1(2009):73-96.
5. Chen, Xiaohan, Y. Jia, and F. Matsuno. "Tracking control
for differential-drive mobile robots with diamond-shaped
input constraints." IEEE Trans. Control Syst. Technol.
22.5(2014):1999-2006.

Simulation Results

Set the reference trajectory as xr  0.75sin(2 t / 45) ,yr  sin(4 t / 45) and  r   cos( t /16) / 4 , and theinitial robot pose is ( x(0), y(0), (0))  (0.5m,0.2m,0) .
Let V  2m / s, d  0.7m,   0.01,   1,   0.99 . Thus
kt  1.6203e-04 . The simulation results are presented in
Fig.3 and Fig.4.

Fig. 4. Tracking trajectory and tracking errors.
© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

376

Parameters tuning approach for prescribed performance function based active disturbancerejection control
Wei Wei

School of Computer and Information Engineering, Beijing Technology and Business University, 11 FuCheng Road,
100048, P.R. China
Bo Liang

School of Computer and Information Engineering, Beijing Technology and Business University, 11 FuCheng Road,
100048, P.R. China
Weijun Su

School of Computer and Information Engineering, Beijing Technology and Business University, 11 FuCheng Road,
100048, P.R. China
E-mail: weiweizdh@163.com, liangbo@st.btbu.edu.cn, swj6843@163.com
www.btbu.edu.cn

Abstract
Active disturbance rejection control (ADRC) is a control approach which needs less information of the controlledplants/processes. However, there are many parameters in the nonlinear functions utilized in ADRC, so many
parameters make the tuning of ADRC be a challenge. Prescribed performance function based ADRC is proposed
and the tuning approach is studied in this paper. Some typical controlled plants are considered in the simulations.
Numerical results are presented to support the proposed control approach and its tuning method.
Keywords: ADRC, prescribed performance function, parameter tuning

or uncertainties. Extended state observer (ESO), the key
part of ADRC, composed of the state observer and an
extended state, is designed to estimate the disturbanceand uncertainties in real time. And any control law canbe designed in order to achieve the desired performance.
Nowadays, ADRC is relatively common innumerous applications, such as superconducting RF
cavities,2 piezoelectric beam,3 nanopositioning.4 The
theoretical analysis is also given in Ref. 5, 6 and 7.
Although there are many successful applications andvalid theoretical analysis, the number of controller
parameters one has to determine is a headache forengineers. In other words, engineers are difficult to set a

1. Introduction
Active disturbance rejection control (ADRC) is acontrol approach first proposed by Prof. Han in early
1990s.1 ADRC was proposed by reconsidering theessence of control problem. Its basic idea is to make thecontrol system be more robust to disturbance anduncertainties by estimating and compensating thosefactors actively in real time.
For the idea of ADRC, standard form of any systemis the chain of integrators. The difference between themodel and standard form will be viewed as disturbance© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa, Japan

377

Wei Wei, Bo Liang, Weijun Suu  [k p ( yr  z1 )  kd z2  z3 ] b0group of applicable values for those tunable parameters.
The value of parameters depends greatly on experienceof engineers. Such problem limits ADRC’s application
more or less, even if ADRC is effective in control
engineering.
As a matter of fact, for our part, the reason for thedifficulties in tuning is that the parameters are of no
physical explanations in ADRC.
With an attempt to get an easier and a more effective
approach to fix the parameters of ADRC, Prof. Gao
propose a bandwidth-parameterization based controller
tuning approach.8 By such proposal, engineers are able
to get a clearer physical explanation for tunable
parameters, and the tuning work becomes easier.
However, such approach is based on the linear versionof ADRC, i.e. LADRC. Tunable parameters can be got
by an easier way, but the performance will be reduced
as a result of taking linear extended state observer(LESO) or LADRC.
How to get an easier tuning approach and also retain
the control performance by utilizing nonlinear function?
This is a desired goal from both practical and theoreticalview of point. In this paper, we have proposedprescribed performance function based active
disturbance rejection control (PPF-ADRC), and givenout the tuning approach. The parameters one has todetermine have clear physical explanations and thesimulation results confirm the proposed approach.
The remainder of the paper is organized as follows.
Section 2 gives out the basic structure of LADRC. PPFADRC is designed in Section 3. Simulation results andconclusions are offered in Section 4 and 5 respectively.

where yr is the desired system output, u is the control
input, k p , kd are control gains, b0 is the coefficient ofcontrol input, e(t ) is the estimation error, and e(t )  y 

z1 . z1 , z2 and z3 are outputs of LESO respectively.
z1 estimates system output y , z2 estimates y , z3 is theestimation of total disturbance and uncertainties.
1 , 2 and 3 are gains of LESO.

3. Prescribed Performance Function based
Active Disturbance Rejection Control Design
In order to improve the efficiency of LESO, in thispaper, we still take advantage of nonlinear function in
ESO, but the parameters for the nonlinear function haveclear physical explanations.
Firstly, a prescribed performance function, a positive
decreasing smooth function, is introduced.
 (t ) : R R  , and lim  (t )    0 , it can bet 

defined as(3)
 (t )  ( 0   ) exp(lt )  
where 0   , l  0 . 0 is the maximum value ofallowable estimation error,   is the maximum value ofallowable steady estimation error, l determines thedecreasing rate of  (t ) .
Then, we may define  (t ) satisfying,9 (t )   (t )   (t )(4)where 0   ,   1 are prescribed scalars. By (4), we cansee that function  (t ) is defined in a prescribed range.
For the sake of satisfying the constrained condition(4), a smooth and strictly increasing function S ( x) canbe defined,  S ( x)  (5)(6)
lim S ( x)   , lim S ( x)  

2. Linear Active Disturbance Rejection Control
Generally, LADRC is designed by linearize theestimation error, the control block diagram for 3rd-order
LADRC can be shown in Fig. 1.
yr

LADRC
1/b0

Control LawuPlants

y

x 

LESO

Fig. 1 Control block diagram for 3rd-order LADRC

The dynamics of LADRC can be described as 8
 z&1  z2  1e(t ) z&2  z3   2 e(t )  b0u
 z&   e(t )
3
 3

x 

We can introduce a transformation
 (t )   (t ) S ( x)(7)then property (4) can be described as, (t )   (t )   (t ) S ( x)   (t )(8)
If we let  (t ) be estimation error, from inequality (8),we can see clearly that estimation error will be always
within the prescribed range.
Actually, the estimation error reflects the system’sability of disturbance estimation and compensation in az3
z1, z2(2)(1)© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa, Japan378

Parameters tuning approach for

In simulations, controller parameters and the gain of
ESO in LADRC and PPF-ADRC are chosen the samevalue. Simulation results are shown in Fig. 2 and 3,
respectively. Both subfigures (e) and (f) in Fig. 2 and 3
depict the tracking error ec (t ) , i.e. the error curves

great extent. If estimation error is within a prescribedrange from the beginning, it means that ESO hasstronger power in estimation and compensation
disturbance and uncertainties. Therefore, we introduce atransformation. Let x  e(t ) , then transformation (7) canbe rewritten as(9)eo (t )   (t )S (e(t ))between y and yr .
-3

where eo (t ) is the transformed estimation error, which
satisfies property (8).
Then we use the transformed estimation error eo (t ) inerror transformation based ESO (ETESO), z&1  z2  1e(t )(10) z&2  z3   2 eo (t )  b0u
 z&   e (t )3 o 3In the design, we choose function S ( x) as exp( x)   exp( x)
S ( x) exp( x )  exp( x)

4

0.015

x 10

e

e
eo

0.01

2

0.005

0

0

-4

-0.005

e(t)

e(t)

-2

-6

-0.01

-8

-0.015

-10

-0.02

-12
-14

-0.025
0

50

100

150

-0.03

Time(s)

0

50

1.4
y
yr

1.2

y
yr

1.2

y(t)

1

0.8

y(t)

1

0.8

0.6

0.6

0.4

0.4

0.2

0

0.2

0

50

100

0

150

0

50

Time(s)

0.6

0.6
c

c

e
( s  1)3

150

e (t)

1

0.8

0.4

0.4

0.2

0.2

-0.2

0

0

50

100

150

-0.2

0

50

Time(s)Time(s)(e)(f)

Fig. 2 Comparisons of system response(for P1)

1  2s(s +1)3
For the plants shown in Table 1, we design both
PPF-ADRC and LADRC. Controller parameters chosen
approach refers to Ref. 8.
For the controller part, we take the bandwidthparameterization approach. For the ETESO part,1 ,  2 , 3 are also taken bandwidth-parameterization
approach into consideration. Parameters for error
transformation part refer to their physical explanations.
Parameter values chosen in simulations are shown in
Table 2.
Table 2 PPF-ADRC parameters


b0
o c
0
l

G2 ( s ) 

P2

1

0.8

0

5 s

100

(d)

e (t)

Table 1. Plants and its models
Plant
Model

150

1.2

1.2

With an attempt to verify the performance of PPFADRC, we have performed two simulations. Refer toRef. 10, two plants are considered.

100
Time(s)(c)

4. Simulation Results

G1 ( s ) 

150

(b)

1.4

Accordingly, PPF-ADRC can be obtained((2) and (10)).

P1

100
Time(s)

(a)

P1

3

1.6

0.3

0.291

0

0.3

1

0.8

P2

15

4

1

.18

0

1

1

0.1

Table 3. Comparisons of IAE Values(For P1)
Controller
IAE Value
LADRC

12.4144

PPF-ADRC

10.8236

From Fig. 2, we can see clearly that the response of
LADRC ((a),(c), and (e)) is inferior to the response of
PPF-ADRC((b),(d),and (f)), when control parameters
and the gains of ESO are chosen the same. Comparisonsof integral of absolute error (IAE) values are given in
Table 3.
Fig. 3 demonstrates that PPF-ADRC is also superior
to LADRC when control parameters and the gains of
ESO are chosen the same. Comparisons of IAE values
are shown in Table 4.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa, Japan

379

Wei Wei, Bo Liang, Weijun Su

In this section, typical systems including delay andunstable unit are considered. Both system response andIAE values confirm that PPF-ADRC has better
performance than LADRC. Actually, advanced designidea guarantees better performance of PPF-ADRC.

nonlinear prescribed performance function and error
transformation, PPF-ADRC not only has a relatively
easier tuning approach, but also can improve the control
performance effectively.
Acknowledgements

-3

2

x 10

0.06
e

0

This work is supported by National Natural Science
Foundation of China (61403006), Beijing Natural
Science Foundation (4132005), The Importation andDevelopment of High-Caliber Talents Project of Beijing
Municipal Institutions (YETP1449), Project of
Scientific and Technological Innovation Platform(PXM2015_014213_000063).

e
eo

0.04

-2
0.02
-4
0
e(t)

e(t)

-6
-8
-10

-0.02
-0.04

-12
-0.06
-14
-0.08

-16
-18

0

20

40

60
Time(s)

80

100

-0.1

120

0

20

40

(a)
1

1. J.Q. Han, Auto-disturbance-rejection controller and it’sapplications, Control and Decision 13(1) (1998) 19-23 (inChinese).
2. J. Vincent, et al., On active disturbance rejection based
control design for superconducting RF cavities, Nuclear
Instruments and Methods in Physics Research A, 643(1)(2011) 11-16.
3. Q.L. Zheng, et al., Active disturbance rejection control
for piezoelectric beam, Asian Journal of Control 17(1)(2015) 1-11.
4. W. Wei, et al., Compound active disturbance rejection
control for resonance damping and tracking of
nanopositioning, in Proceeding of Chinese Control
Conference, (China, Nanjing, 2014), pp. 5906-5909.
5. Q. Zheng, et al., On stability analysis of active
disturbance rejection control for nonlinear time-varying
plants with unknown dynamics, in Proceeding of the 46th
IEEE conference on decision and control, (USA, New
Orleans, 2007), pp. 12-14.
6. Y. Huang and W. Xue, Active disturbance rejection
control: methodology and theoretical analysis, ISA
Transactions 53(4) (2014) 963-976.
7. Z. Chen et al., On the stability of linear active disturbancerejection control, Acta Automatica Sinica 39(5) (2013)574-580 (in Chinese).
8. Z. Gao, Scaling and Bandwidth-Parameterization BasedController Tuning, in Proceedings of American Control
Conference, (USA, Denver, 2003), pp. 4989-4996.
9. W. Wang and C. Wen, Adaptive actuator failurecompensation control of uncertain nonlinear systems withguaranteed transient performance, Automatica 46(12)(2010) 2082-2091.
10. X. Chen, Active disturbance rejection controller tuning
and its applications to thermal processed, Master Degree
Thesis, (Tsinghua University, 2008).

0.6
y(t)

y(t)

References

y
yr

0.8

0.4
0.2

0.4
0.2

0

0

-0.2

-0.2

0

20

40

60
Time(s)

80

100

-0.4

120

0

20

40

(c)

60
Time(s)

80

100

120

80

100

120

(d)

1.4

1.4

1.2

1.2

1

1

0.8

0.8
e (t)

0.6

c

c

120

1
y
yr

0.6

e (t)

100

1.2

0.8

0.6

0.4

0.4

0.2

0.2

0
-0.2

80

(b)

1.2

-0.4

60
Time(s)

0

0

20

40

60
Time(s)

80

100

120

-0.2

0

20

40

(e)

60
Time(s)(f)

Fig. 3 Comparisons of system response(for P2)
Table 4. Comparisons of IAE Values(For P2)
Controller
IAE Value
LADRC

7.8713

PPF-ADRC

5.9112

5. Conclusion
In this paper, based on the prescribed performance
function and the idea of error transformation, we haveproposed PPF-ADRC. Parameters of PPF-ADRC arechosen according to the bandwidth-parameterization
approach and the physical explanation of prescribed
performance function. Two typical examples are taken
to confirm PPF-ADRC and its parameters tuning
method. Numerical results confirm that, by introducing© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa, Japan380

Moving Robots Lies and Their Minds with Degree of Confidence
in a Decentralized Autonomous FMS
Shizuka Tanaka

Human and Information System Division, Gifu University, 1-1, Yanagido
Gifu-shi, Gifu, 501-1193, Japan
Hidehiko Yamamoto

Department of Mechanical Engineering, Gifu Univ., 1-1, Yanagido
Gifu-shi, Gifu, 501-1193, Japan
Takayoshi Yamada

Department of Mechanical Engineering, Gifu Univ., 1-1, Yanagido
Gifu-shi, Gifu, 501-1193, Japan
E-mail:t3128020@edu.gifu-u.ac.jp, yam-h@gifu-u.ac.jp, yamat@gifu-u.ac.jp
www1.gifu-u.ac.jp/~yamlab/

Abstract
According to the previous study of a decentralized autonomous FMS using a mind, it is found that the production
outputs decreased because an agent sometimes lies. To solve this problem, we study an AGV’s lie. The study is that
AGVs recognize the lie that each AGV sends. To do this, we propose to give a degree of confidence to the mind thateach AGV has. By performing the production simulations with the degree of confidence, we improve the increase of
the production outputs.
Keywords: Decentralized Autonomous FMS, AGVs Lie, Minimum Unit of Mind, Degree of Confidence.

Specifically, we propose the method of finding lies using
the AGV’s position information. We also propose themethod of controlling the AGV's moving using a degreeof confidence.

1. Introduction
We’ve developed the computer control systems torealizeadecentralized
autonomous
flexiblemanufacturing system (FMS). To do this, it is necessary
to avoid the path interference of automated guided
vehicles (AGVs) in a decentralized autonomous FMS. To
solve this problem, we inserted an arrogant mind and amodest mind into AGVs to control AGVs’ moving.
In a decentralized autonomous FMS with minds, it isassumed that the information provided by the agents isalways correct. However, this is not the case in a realfactory, where incorrect information can sometimes besent as a result of machine failures. In this study, wedefine and find this incorrect information as a "lie" ofAGV. Then, in the decentralized autonomous FMS
including AGVs lies, we perform the moving control of
the AGVs in order not to have a low productivity.

2.

Mechanism of Decentralized Autonomous FMS

2.1. Description of Decentralized Autonomous FMS
Fig.1 shows the example of a decentralized autonomous
FMS factory. The factory floor has been divided into agrid pattern where AGV moves on the line. Then, asystem of automated factory performs the production bycarrying the product to parts of the warehouse andmachining centers (MCs). A decentralized autonomous
FMS does not have a management mechanism that
controls the whole system between agents that constitutethe system to the knowledge exchange, to determine thebehavior by coordination.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

381

Shizuka Tanaka, Hidehiko Yamamoto, Takayoshi Yamada

increased by 1, the A1 value becomes the threshold, and
the AGV is changed to a modest one.
When AGVs with a modest mind keep giving a way,the value of A2 is increased by 1. When the situation isrepeated at optional times, A2 becomes excited and asignal is sent to a load. The received load decreases thevalues of units A1 and A2 by optional values. Owing tothis, A1 and A2 are returned to normal and a modest mind
is changed to an arrogant mind. In this way, AGV avoids
the path interference by the change of heart.
Fig.1. Example of decentralized autonomous FMS

3.

2.2. Model of the Mind and Behavior of AGV

In the decentralized autonomous FMS using a mind
from previous studies, each agent transmitted theinformation, treats the received information as all correct,
works in cooperation each other. However, trouble
including short-period stop occurs in the real FMS
factory. To realize this real factory in this study, we adopt
the decentralized autonomous FMS where failure occurs.
We define the AGV as “liar” when the AGV sends a
incorrect information and "honest" when the AGV sends
a correct information. In this study, we focus on the lie
that especially takes the AGV among the lie of the agent.
AGV’s lies are defined as the following three types.
1. Although the current position is correctly taken, an
AGV sends incorrect information to other AGVs. We callthis as "USO1".
2. An AGV sometimes takes the current position for adifferent one and sends it. We call this as "USO2".
3. An AGV sometimes fails to read the current positionand does not update the information. We call this as"USO3".
The state of the USO1 is shown in Fig 3. When USO1
occurs, correct position information is saved in the AGV
that lied. However, incorrect information is saved in theother AGVs.

In the decentralized autonomous FMS, all AGVs use theposition information received from the other AGVs anddetermine each behavior autonomously. In order torealize the autonomous behavior, we give a mind to
AGVs. In the present study, in order to perform thebehavior efficiently; we give the arrogant mind and themodest mind to AGVs. AGV with arrogant mind takes
action approaching the destination forcibly. AGV withmodest mind takes the action to give way to other AGV.
This mind is expressed in mind model in Fig.2,
Minimum Unit of Mind (MUM)[1]. Then, A1 and A2
called as unit, X is load, and arrow is a stimulation vector.
The threshold is determined by the unit. If the internal
value reaches the threshold, we call it "excited". And if it
does not reach the threshold, we call it "normal”.
When a signal is sent to the unit, it sends a signal to thedirection of the arrow if it is excited, and it is not sent if
it is normal. Load has the function to change the internal
value of the unit. When the signal is sent to the load, thevalue of the unit is increased by the value of X.
Stimulation vector is a line connecting the load and theunit, and it gives a signal to the load or unit if the signal
comes. When A1 is kept excited, we call it "modest
mind", and when normal, we call it “arrogant mind”.
Path interference

A１‐X

Stay awayfrom the destination

Finding AGV’s Lies

MUM

A２
Fig.3. USO1

Fig.2. MUM

By the amount of the signal input to the unit, MUM
changes the mind to arrogant or modest frequently.
Next, we describe the internal functions of MUM. Whenthe arrogant AGV has path interference, A1 is increasedby 1. Keeping the situation of the interference and being

The state of the USO2 is shown in Fig 4. When USO2
occurs, both AGV that lied and other AGVs saveincorrect information.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan382

Moving Robots Lies andhonest AGV to work preferentially to solve this problem.
To do this, we find the AGV to lie, to distinguish honest
AGVs and liar AGVs. In order to achieve thedistinguishing, we propose the new degree of confidence
in the mind of AGV. And we adopt the method thathigher confidence AGV acts preferentially.
In this study, we define the degree of confidence as "thedegree which an AGV can correctly work without lie".
When an AGV passes through the intersection without lie,
the confidence of the mind of the AGV is increased. The
higher the confidence, the more arrogant the AGV
becomes. Because of this, the AGV does the work
preferentially.
The way to give confidence is shown as the following.
1. When the AGV is passed through the intersection
without lie, the mind of confidence is increased by 1.
2. If confidence has reached a threshold, the thresholdvalue of the unit A1 is increased by 1, and the mind sets
the confidence as 0.
The new mind model of Fig.6. is generated by adding
MUM of Fig2 to a unit C, load Y, Z, and the stimulus
vector. We define it as MUMS (MUM with Selfconfidence degree). Unit C of MUMS is called degree ofconfidence; unit C has a threshold C[T] and excitement
value C[E]. Load Y indicating by the triangle of Fig.6
sends a signal to the threshold A1[T]. Load -Z sends asignal to the excitement value C[E] of the unit C.

Fig.4. USO2

The state of the USO3 is shown in Fig 5. When USO3
occurs, the AGV that lied fails to read the current position.
The AGV does not update the location information and does
not send the information to other AGVs.

Fig.5.

USO3

We propose the three methods to find the lies.
[1] The AGVs except for the liar AGV receive theinformation from the liar AGV, compare the liar AGV’scurrent position with its previous positions and find theincorrect information. This is the method to find USO1.
When the information transmitted from the other AGV
satisfies Equation (1), the AGV that has transmitted theinformation is defined as USO1.
|(Past X-coordinate)-(Current X-coordinate)|+ |(Past Y-coordinate)-(Current Y-coordinate)| > 5 (1)[2] When the difference between the own current position
and the previous position becomes big, the situation iscalled as USO2. When the AGV’s information satisfies
Equation (2), the AGV realizes as USO2.
|(Past X-coordinate)-(Current X-coordinate)|+ |(Past Y-coordinate)-(Current Y-coordinate)| >10 (2)[3] This is the method to find the USO3. When the
AGV’s information satisfies Equation (3), the AGV
realizes as USO3|(Past X-coordinate)-(Current X-coordinate)|+|(Past Y-coordinate)-(Current Y-coordinate)| = 10 (3)A2[E]Path interference

A2[T]

-X
A1[E]

Stay away from the destination
A1[T]

MUMS
Y
C[E]

Go throughthe intersection
honestly

C[T]

Fig.6

-Z
Degree ofconfidence

MUMS

Further, an function of the MUMS is shown below.
When an AGV passes through the intersection without lie,a signal is sent to C[E]. In other words, the degree ofconfidence is increased. When C[E] continues to receive
the signal, unit C becomes excited and sends a signal to
the load Y and load -Z. Load Y increases A1[T], load -Z

4. Mind with Degree of Confidence
If there are AGVs that lie, AGV that uses the incorrect
information takes the wrong action. As a result, thenumber of production is reduced. In this study, we get the© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

383

Shizuka Tanaka, Hidehiko Yamamoto, Takayoshi Yamada

reduces C[E]. In this way, when an AGV passes throughthe intersection without lie, A1[T] is increased. When A1[T] increases, unit A1 is hard to become the excitedcondition, that is, it is easy to have an arrogant mind. On
the other hand, when an AGV passes through theintersection with lie, the signal is not sent to the unit C.
Because of it, A1[T] is not changed.

was close to the number of production with honest FMS.
Because AGVs that have the characteristics easy to
become arrogant and modest exist by confidence, thetime required path interference avoidance is reduced.
In addition, comparing 500 with 900, the number of
production for 900 is two more than 500. Table.2 showsthat the case of C[T]=500 gave a lot of stimulations to themind of AGVs. If C[T] is too small, many signals are sent
to A1[T] and A1[T] becomes too large. The larger A1[T]is, the longer the time until the change of the mind is. As
a result, the time to perform the judgment of the pathinterference avoidance becomes longer. Because of this,the magnitude of C[T] affects the ease of the change ofthe mind and the number of production.
However, even using the degree of confidence, it was
not possible to reduce the total interference number. Thisis because the interference occurs together with the lie.
Even if using the degree of confidence, it is not possible
to reduce the number of the lie itself.

5. Simulation Results and Consideration
We simulated the production results. We set AGVs lie
with a certain probability and carried out the production
simulations by using the following four conditions.
・FMS just using honest AGVs
・FMS including AGVs that lie
・FMS including AGVs that lie, and the AGVs with MUMS
(Set the threshold value C [T] shown in Fig.6 as 500)
・FMS including AGVs that lie, and the AGVs with MUMS
(Set the threshold value C [T] shown in Fig.6 as 900)
The production simulations were carried out seventimes under these conditions. Table.1 shows the averageof the output of simulation production, the total numberof collisions and the efficiency of each AGV.

6. Conclusions

Table.1. Simulation Results
Honesty Liar

Efficiency

Production
Outputs
Collision
Number
AGV1
AGV2
AGV3
AGV4
AGV5

Degree ofDegree ofConfidence(500) Confidence(900)

158.7

133.7

138.4

140.4

0

2159.92385.12236.7

0.591
0.901
0.847
0.686
0.774

0.414
0.716
0.646
0.506
0.559

0.408
0.707
0.622
0.495
0.551

0.424
0.718
0.642
0.507
0.569

References

Table.2 also shows the adopted stimulus values of thedegrees of confidence of AGVs.

1. Hidehiko YAMAMOTO, Takayoshi YAMADA and
Shinsuke KATO, AGV Mind Model and its Usage for
Decentralized Autonomous FMS by Change of Mind,
Proceeding of Third KES International Symposium, KESAMSTA 2009, Agent and Multi- Agent Systems : Technologies
and Applications, Lecture Notes in Artificial Intelligence,
Edited by Anne Hakansson, Ngoc Thanh Nguyen etc. , ISSN
0302-9743, Uppsala, Sweden, pp.744-753, June(2009),
Springer.

Table.2. Stimulus number in the degree of confidence
AGV1

AGV2

AGV3

AGV4

This study found that finding the lie and a degree ofconfidence are useful for improving the production
number in a decentralized autonomous FMS including
AGVs that lie. This study also found that the magnitudeof C[T] affects the ease of the change of the mind and thenumber of production. We can establish the foundationof the way how to handle the lie.
In this study, the threshold value of the degree ofconfidence was determined in accordance with a certain
probability of the lie happening. In the actual FMS
factory, however, the agent does not lie with a certain
probability. Therefore, it is necessary to determine thethreshold of the confidence flexibly in a future work.

AGV5

C[T]=500
C[T]=900

2
11
9
8
9
0
6
4
6
4
From Table.1, comparing honest FMS with FMS
including liar AGVs, the latter FMS reached to a bad
production results 25 products, lower than the former
FMS. Comparing the latter FMS with the FMS including
AGVs with confidence, the number of production for the
FMS with confidence was increased. The number of
production for each condition, 500 and 900, wasincreased. The number of production with confidence© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan384

Conquest Oriented Robot Knowing Its Own Availability
Sho Yamauchi

Future University Hakodate, 116-2, Kameda Nakanocho,
Hakodate-shi, Hokkaido, 041-0803, Japan,
Keiji Suzuki

Future University Hakodate, 116-2, Kameda Nakanocho,
Hakodate-shi, Hokkaido, 041-0803, Japan,
E-mail: yamauchi@fun.ac.jp,kjsuzuki@fun.ac.jp
www.fun.ac.jp

Abstract
Robot is designed for achieving specific task. However, robot is able to do much more things than it is expected byits own hardware. In this paper, concept of conquest oriented robot is proposed to know its availability systematically and autonomously. Algorithm of conquest oriented robot and its experimental result in simulation field for the
first step is shown in this paper.
Keywords: autonomous, conquest, robot, availability

robot is proposed in this paper. Base algorithm to findwhat they can do by its body is proposed for the firststep and experimental result using the algorithm isshown.

1. Introduction
Robot is designed for achieving specific task. Cleaner
robot is designed to collect dusts in room and industrial
robot is designed to assemble some kind of merchandisein factory. These tasks are designed by human and theonly thing robots do is complete the tasks. Previously,
robot is just a tool to achieve some tasks that are hardfor human or tool for automation. However, robot can
do much more things by their body even though thething is not expected to do by human. For example,
Cleaner robot with two wheels can push something aswell as cleaning. This means that the cleaner robot
might bring something heavy and might communicate
with human by pushing their hands, for example. Robot
has lots of possibility by their given body and this possibility is not limited to the purpose expected by human.
From these points of view, concept of conquest oriented

2. Concept of conquest oriented robot
The purpose of conquest oriented robot is finding, increasing and maintaining available things of the robot.
Robot has its own available event in its existing environment (Fig. 1). In other words, conquest oriented robot tries to increase event that robot can execute withhigh probability, increase the probability and maintainits events and probability.
Comparison between previous robot design and conquest oriented robot is shown in Fig. 2. Robot is designed to reach only one event, it means “task”, previously. However, if the task is changed, robot must be re-© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan385

Sho Yamauchi, Keiji Suzuki

designed to achieve new task. On the other hand, conquest oriented robot researches its own availability previously and it can change its task easily if the new task
is within its availability.
What is an advantage of conquest oriented robot? If
robot knows its own availability, robot is able to do
maximum work even though it does not know the task
preliminarily. If the task is unavailable, robot is able toknow it is unavailable by its given hardware. In thiscase, human should prepare another robot that has suitable hardware.

of robot’s action as shown in Fig. 3. Action fragment F
is defined as
.

  

F  [ x1 , x2 , x3 ,..., xn ]

 ai(1)  ( 2)  ai 
(1)
 xi      (n )   xi( 2 )  ai a xi     bi(1)  ( m)   ( 2)  xi   bi 
  


bi( nb ) 

(1)

(2)

(𝑘)

where 𝑎𝑖(𝑘), 𝑏𝑖 are k-th actuator output and sensor input
at time i respectively .

Fig. 1. Conquest oriented robot concept. Event space means a
set of all the events that might happen in current environment.
Conquest oriented robot tries to extend a set of event that isable to happen at any time.

Fig. 3. Action fragment of conquest oriented robot. Action
fragment is made of time series data of sensors and actuators.

Fig. 2. Previous robot design and conquest oriented design. In
previous robot design, the specific event is focused and robot
tries to achieve the specific task. However, conquest oriented
robot tries to find “what it can do” and maintain those things.

3. Action Fragment
Generation

andVariableUse Fourier series expansion to generate various typesof time-series action for robot. Equation is as follows.

Actiona 0 nmaxf (t )   (a n cos nt  bn sin nt ) (3)2 n 1

As a unit of robot’s available event, action fragment isintroduced. Action fragment is a part of time-series data

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

386

Conquest Oriented Robot Knowing

If coefficients 𝑎𝑛 , 𝑏𝑛 are changed periodically, various
types of wave would be generated and this wave is used
as actuator output of robot for generating various actions. To find new action fragment, robot has to detect whether the action fragment is different from all other experienced action fragments. It means that criterion to measure the difference between two action fragments is needed. For this purpose, criterion G is introduced asfollows.

G(F A ,F B ) 

AMSS (F A ,F B )smax

4. Experiment
Confirm the process of “accumulate” in simple experiment. Consider a robot in 2D physics simulation field
shown in Fig. 4. In this simulation field, a differential
wheeled robot and two objects are placed. The robot can
detect object in front of it and obtain the distance fromdetected object. It means that this robot has 1 sensor and2 actuators.

(4)

smax  AMSS (F long ,F long )(5)Flong  arg max{len(F )}(6)F{FA ,FB }where 𝑭𝑨 , 𝑭𝑩 are compared action fragments. Function
len() returns length of action fragment’s time series data.
Function AMSS() is also returns the value of AMSS between two action fragments. AMSS(Angular Metrics for
Shape Similarity)[Ref. 1] is method to compare twotime series data and calculate similarity. AMSS assume
time series data as vector and calculate similarity by cos
similarity and dynamic programing. It can be applied totime series data that are different length. In criterion G,
similarity is normalized and all the results can be compared with each other. The value of criterion G is called
similarity rate in this paper.
Robot stores new action fragment if new action fragment is different enough comparing with all the action
fragments stored already as follows.

Fig. 4. 2D physics experimental field. Red line indicates thesensor of differential wheeled robot. Two objects are also
placed.

Friction between wheels of robot and floor, objects andfloor exists. Robot starts “accumulation” in this fieldand store action fragments. All the fragments have thesame fixed length in this time. In this experiment, twotypes of action generating methods are applied. Onechanges coefficients in equation (3) periodically. Another does not change coefficients. If coefficients arechanged periodically, various types of action of robot
would be generated and number of action fragments is
supposed to increase. However, action of robot islimited, so the number of action fragment is expected tobe saturated eventually. On the other hand, if coefficien
ts in equation (3) are fixed, generated action of robot is
far more limited and number of stored action fragment
is expected to be smaller.
Experimental result is shown in Fig. 5.
Horizontal axis denote time step and vertical axis means number of action fragments. “Update” is a case thatcoefficients in equation (3) are periodically updated.

For all i, if similarity rate 𝑟𝑖 satisfies conditionri  G(F, Fi ) c acc _ l(7)then store action fragment F
where cacc _ l (0  cacc _ l  1) .
The process to store new action fragment is called “accumulate” in this paper. “Accumulate” is considered as
the first step of conquest oriented robot.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

387

Sho Yamauchi, Keiji Suzuki“Fixed” means coefficients in equation (3) are fixed.
As expected, number of action fragment saturated inboth case. Also, as we expected, the number of action
fragment in “Fixed” case is smaller than “Update”. This
result means that criterion G for similarity rate works
properly and “accumulation” phase algorithm works
properly.

References
1. Tetsuya NAKAMURA, Keishi TAKI, Hiroki NOMIYA
and Kuniaki UEHARA, AMSS: A Similarity Measure fortime Series Data, IEICE TRANSACTIONS on Information and Systems, Vol.J91-D, No.11, pp.2579-2588,
2008.

Fig. 5. Experimental result. Horizontal axis denote time steps
and Vertical axis denote number of action fragments..

5. Conclusion
The concept of conquest oriented robot that tries toknow its own availability is proposed in this paper. For
the first step, algorithm of “accumulate”, the first phaseof conquest oriented robot algorithm, is proposed.
AMSS based similarity criterion is also introduced.
“Accumulate” phase process is confirmed by experiment using differential wheeled robot in 2D physics
simulation experiment field. We develop second andthird phase of conquest oriented robot algorithm for the
future work.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan388

A Consideration on Feature Extraction for Operation Skill
Based on Control Engineering Approach
Kazushige Koiwai

Institute of Engineering, Hiroshima University,
1-4-1, Kagamiyama, Higashihiroshima City, Hiroshima, 739-8527, Japan
Yuntao Liao

Graduate School of Engineering, Hiroshima University,
1-4-1, Kagamiyama, Higashihiroshima City, Hiroshima, 739-8527, Japan
Toru Yamamoto

Faculty of Engineering, Hiroshima University,
1-4-1, Kagamiyama, Higashihiroshima City, Hiroshima, 739-8527, Japan
Takao Nanjo, Yoichiro Yamazaki, Yoshiaki Fujimoto

Global Engineering Center, KOBELCO Construction Machinery CO., LTD.,
2-2-1, Itsukaichikou, Saeki-ku, Hiroshima City, Hiroshima, Japan
E-mail: koiwaik@hiroshima-u.ac.jp, liao-yuntao@hiroshima-u.ac.jp, yama@hiroshima-u.ac.jp
http://www.hiroshima-u.ac.jp

Abstract
Social issue of reducing and aging population create difficulty in the transfer of professional skills on the field where
the human-skill is needed for operation of some equipments. In this study, the operator is considered as a kind ofcontroller that consists of CMAC-PID. CMACs calculate PID parameters to evaluate human-skill by their trajectories.
As an example, the comparison of operator skills for an excavator will be performed.
Keywords: PID Controller, Human Skill Analysis, Neural Networktransfer and educate the optimized skills. Moreover, thequantification of human skill can be applied toautomation and efficiency for the work.
As precedent studies, adaptive control of deburring
robots based on human skill models is proposed by Liu
and Asada1. And the human-skill is considered as a kindof nonlinear controller. It consists of PID controller thatinterpret trajectories of parameters as a way to evaluate
human skill2. Then neural network is utilized for thearchitecture of nonlinear controller3. As one of artificial
NNs, a cerebellar model articulation controller (CMAC)
has been proposed that advantages are a simple structure

1. Introduction
Technologies of automation or manpower-saving areinnovated in production fields. However, there are manyindustrial equipments to need human operation, i.e. theconstruction site, the crane operation and the building
demolition work. The human-skill optimized byprofessionals exists in those fields. However, the transferof skills is not processing in leading countries because of
decreasing employed population, aging of professionals,
increasing multinational workers and others. Therefore
the quantification of the human-skill is required to© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

389

Kazushige Koiwai, Yuntao Liao, Toru Yamamoto Takao Nanjo, Yoichiro Yamazaki, Yoshiaki Fujimotoand short learning time4. Moreover CMAC-PID is
proposed5 and the controller is applied to human skill
evaluation6.
In this paper, human skills are evaluated by using
CMAC-PID. As an application example, the comparisonof operator skills for an excavator will be performed. The
feature of professional skills will be extracted bytrajectories of PID parameters.
Fig. 2 The Architecture of Human skill Evaluation

2. Scheme for Evaluation of Human Skill∆𝑢(𝑡) = 𝐾𝐼 𝑒(𝑡) − 𝐾𝑃 ∆𝑦(𝑡) − 𝐾𝐷 ∆2 𝑦(𝑡)2.1. CMAC(1)

where the control error signal of 𝑒(𝑡) is defined as𝑒(𝑡): = 𝑟(𝑡) − 𝑦(𝑡) and the difference operator of ∆ isdefined as ∆≔ 1 − 𝑧 −1 . Each CMAC has some weights
to calculate PID parameters in Eq.(2).

The CMAC is a mathematical model based on a
cerebellar information processing mechanism by using
mapping function. The simple example of CMAC with
3wights, 4 labels and 2-dimensional input is shown in
Fig.1. The input signals of S (3, 6) are mapped to the setof labels M {B, F, J} and{c, g, k}. Those labels indicate
the location of weight tables and select the numbers 8, 9
and 3 form each weight. The sum of them 20 is the output
of CMAC. If the teacher signal is 14, each weight are
adjusted by the differential of the output and teachers
signal 6 into the total weight number 3.

𝐾

𝐾𝑃 (𝑡) = ∑ 𝑊𝑃,ℎ (𝑡)ℎ=1𝐾

𝐾𝐼 (𝑡) = ∑ 𝑊𝐼,ℎ (𝑡)

(2)

ℎ=1
𝐾

𝐾𝐷 (𝑡) = ∑ 𝑊𝐷,ℎ (𝑡)ℎ=1ℎ = 1,2, ⋯ , 𝐾 is defined as the number of weight tables
in each CMAC. CMACs which calculate PID parameters
has 3-demensional inputs of 𝑒(𝑡) , 𝑦(𝑡) and∆𝑦(𝑡). In
Fig.3, the diagram of off-line learning manner is shown.
CMACs learn those weights by using human skill data of𝑦 ∗ (𝑡) and 𝑢∗ (𝑡). Those weights are updated by using thesteepest descent method of the following Eq.(3). In thelearning process, the differential of teachers signal 𝑢∗ (𝑡)and the controller input signal 𝑢(𝑡) is reduced to express
the human skill by PID controller.

2.2. Architecture of Human Skill Evaluation
The architecture of human skill evaluation is shown in
Fig.2. 𝑦 ∗ (𝑡) and 𝑢∗ (𝑡) are denoted as the system output
and the system input by human operation. To evaluate thehuman skill, the human is considered as PID controller
using CMACs that tune parameters. The following
control law with the PID controller is considered:Fig. 3 The Leaning Structure of Human Skill Evaluation

Fig. 1 The Sample of the CMAC Model© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan390

A Consideration on Feature𝑛𝑒𝑤𝑜𝑙𝑑(𝑡) = 𝑊𝑃,ℎ(𝑡) − 𝑔(𝑡)
𝑊𝑃,ℎ

𝜕𝐽 1
𝜕𝐾𝑃 𝐾

𝑛𝑒𝑤
𝑜𝑙𝑑
(𝑡) = 𝑊𝐼,ℎ(𝑡) − 𝑔(𝑡)
𝑊𝐼,ℎ

𝜕𝐽 1
𝜕𝐾𝐼 𝐾

𝑛𝑒𝑤
𝑜𝑙𝑑
(𝑡) = 𝑊𝐷,ℎ(𝑡) − 𝑔(𝑡)
𝑊𝐷,ℎ

𝜕𝐽 1
𝜕𝐾𝐷 𝐾

(3)

where 𝑔(𝑡) is defined as the gradient to update weightand 𝐽 is defined as the error criterion. Following
equation are shown them.
1𝑔(𝑡) =(4)
𝑐 + 𝑎 ∙ 𝑒𝑥𝑝 (−𝑏|𝑢∗ (𝑡) − 𝑢(𝑡)|)1
𝐽(𝑡) = 𝜖(𝑡)22(5)

𝜖(𝑡) = 𝑢∗ (𝑡) − 𝑢(𝑡)(6)Fig. 4 Excavator for the Experiment(1) Obtain the data by operators. Here, those obtained
data are normalized. And the sampling time of
𝑇𝑠 should be 200ms with consideration for thehuman quickness of a response.
(2) Calculate the initial values of PID parameters by theleast squared method.
(3) Learn CMACs according to Eq.(3)-(7). Here, each
CMACs are designed as the number of weight tables
and labels are 10 both of them.
The results by a professional operator and a beginner
operator are shown in Fig.5 and Fig.6. The period of stop
motions are 3 seconds to 5seconds on the both of them.
Typically, the PID parameters have the following
properties. The proportional action shows the
responsiveness for the current information. The integral
action shows the following performance based on thepast information. In addition, the derivative action shows
the forecast in the future. The following features are
extract by the comparison of those result.
(1) The beginner has higher proportional gain 𝐾𝑃 andhigher derivative gain 𝐾𝐷 then the professional.
(2) The professional decreases the proportional gain andincreases the integrated gain and the derivative gain
when the stop motion is started.
(3) The beginner increases the proportional gain anddecreases the derivative gain and the integrated gainat the stop.
(4) The professional parameters are changed slightly.
On the other hand, the changes of beginner is bigger
than the professional relatively.
The professional changes parameters properly at theappropriate position. Increasing the derivative gain is toprevent the overshoot, and increasing the integrated gain
is to follow the target value. And the decreasing of
proportional gain keep the stability of system. That is, hekeep the stability and following performance, and predict

Appropriate positive constant is set in a, b and c. Andeach partial differential of Eq.(3) is calculated as follows:𝜕𝐽= 𝜖(𝑡)∆𝑦(𝑡)𝜕𝐾𝑃𝜕𝐽= −𝜖(𝑡)𝑒(𝑡)𝜕𝐾𝐼𝜕𝐽= 𝜖(𝑡)∆2 𝑦(𝑡)
𝜕𝐾𝐷

(7)

3. Application Example
3.1. Equipment
The proposed scheme is applied to the excavator swing
operation. The excavator which is used for experiment is
SK200-9 by Kobelco Construction Machinery in Fig.4.
Operators are accelerated by the maximum input from thestarting point where is defined as 0 degree. After
acceleration, they stop accurately at the target point thatare 90 degrees. The skill for the stop operation isevaluated.
3.2. Application result
In this experiment, the system output value of 𝑦 ∗ (𝑡) isthe angle of swing and the system input value of 𝑢∗ (𝑡) is
the pilot pressure. The following procedure is performed
to compare the professional skill and the beginner skill.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

391

Kazushige Koiwai, Yuntao Liao, Toru Yamamoto Takao Nanjo, Yoichiro Yamazaki, Yoshiaki Fujimoto
Stop motion

Stop motion

Fig. 5 Result of professional Skill Evaluation

Fig. 6 Result of Beginner Skill Evaluation

5. References

the future. On the other hand, the beginner has the
opposite reaction of all gains. He focuses to only thecurrent information to give the higher proportional gain.
And the initial values shows the stability margin. The
beginner has bigger proportional and derivative gains
than the professional. Therefore, the beginner is difficult
to control the system properly by having bigger gains.

1. S.Liu and H.Asada, Adaptive Control of Deburring Robots
Based on Human Skill Models, Proc. of IEEE Conf. on
Decision and Control, Brighton(1991), pp.348-353.
2. M.Kato, T.Yamamoto and S.Fujisawa A Skill-Based PID
Controller Using Artificial Neural Networks, Proc. of Int.
Conf. on Computational Intelligence for Modeling,
Control and Automation, Vienna(2005), pp.702-705.
3. M.Tokuda, T.Yamamoto and Y.Monden, A Design ofMultiloop PID Controllers with a Neural-Net Based
Decoupler, Asian Journal of Control, Vol.7(3), (2005)
pp.275-285
4. J.S.Albus A New Approach to Manipulator Control
Cerebellar Articulation Control (CMAC), Trans. on ASME,
J. of Dynamics Systems, Measurement, and Control,
Vol.97, No.9,(1975), pp.220-227.
5. T.Yamamoto, R.Kurozumi and S.Fujisawa A Design of
CMAC Based Intelligent PID Controllers, Artificial
Neural Networks and Neural Information Processing,
Lecture Notes in Computer Science, Vol.2714,(2003)
pp.471-478.
6. K.Koiwai, K.Kawada and T.Yamamoto, Design andExperimental Evaluation of an Intelligent PID Controller
using CMAC, Proc. of IEEE Int. Conf. on Networking,Sensing and Control, Okayama,(2009), pp740-745.

4. Conclusion
In this study, the evaluation scheme of human skill is
proposed by using CMAC-PID. In order to verify the
proposed scheme, it is applied to the excavator operationand the comparison of the beginner and professional is
carried out. The feature of operation is extracted throughtrajectories of PID parameters are considered. It is
possible to interpret operation skills from the control
engineering approach. For the future work, it will be
extended to another operation and other type of operator.
And the stochastic method will be introduced.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

392

Design of a Data-Driven Control System for a Hydraulic Excavator
Takuya Kinoshita

Graduate School of Engineering, Hirosihma University,
1-4-1, Kagamiyama, Higashihiroshima city, Hiroshima, Japan
Kazushige Koiwai

Collaborative Research Division, Institute of Engineering, Hiroshima 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
Takao Nanjo, Yoichiro Yamazaki, Yoshiaki Fujimoto

Global Engineering Center, KOBELCO Construction Machinery CO., LTD.,
2-2-1, Itsukaichikou, Saeki-ku, Hiroshima City, Hiroshima, Japan
E-mail: takuya--kinoshita@hiroshima-u.ac.jp, koiwaik@hiroshima-u.ac.jp, yama@hiroshima-u.ac.jp
http://www.hiroshima-u.ac.jp

Abstract
PID control schemes have been widely used in most industrial systems. However, it is difficult to achieve the desired
control performance for nonlinear systems such as hydraulic excavators by using fixed PID parameters. In order toovercome such a problem, data-driven PID control scheme based on database has been proposed. Moreover, datadriven scheme has a learning method in off-line by using closed-loop data. In this paper, data-driven control scheme
is applied to a hydraulic excavator to get desired control performance.
Keywords: PID controller, Data-driven controller, Hydraulic excavators, off-line learning.

is a controller for nonlinear system and it has an off-line
learning method by using closed-loop data.
In this paper, data-driven control scheme is applied to a
hydraulic excavator to improve control performance. The
effectiveness of the proposed scheme is numericallyverified by using a simulation example.

1. Introduction
In most industries, it is very important to get desired
control performance by using some control schemes.
PID control schemes1, 2 have been widely used because
control parameters have a clear physical meaning andcontrol structure is simple. However, it is very difficult
to get desired control performance for nonlinear systems
such as hydraulic excavators by using fixed PID
parameters.
In order to overcome such problem, data-driven PID
control scheme3 based on database has been proposed. It

2. Schematic Figure of a Hydraulic System
Fig.1 shows schematic of a hydraulic system4. As thesystem, the motion of system is swing operation. The
input should be the direction of flow rate for a hydraulicpump, and the output should be this torque. In the© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan393

Takuya Kinoshita, Kazushige Koiwai, Toru Yamamoto, Takao Nanjo, Yoichiro Yamazaki, Yoshiaki Fujimoto

system, the relief valve works in order to prevent
increasing hydraulic pressure. Therefore, the hydraulicsystem is a kind of time-variant system include aderivative element after the relief valve work. That why,
it is difficult to get desired control performance by using
fixed controller.
3. Schematic of Data-Driven Control System
Schematic of data-driven control system is shown in
fig.2. The current information of controlled object isstored in a database and suitable control parameters arecalculated by historical data in the database. Moreover,off-line learning method is utilized to avoid on-line
learning time cost. The specific design procedure ofdata-driven scheme is described in section 7.

Fig. 1. Schematic of a hydraulic system.

4. Controlled Object
A controlled object can be described by following
nonlinear system:
𝑦(𝑡) = 𝑓(𝜙(𝑡 − 1)),
(1)where 𝑦(𝑡) is system output, 𝑓(⋅) is function of
nonlinear and 𝜙(𝑡 − 1) is information vector. 𝜙(𝑡 − 1)is defined as follows:𝝓(𝑡 − 1) ≔ [𝑦(𝑡 − 1), ⋯ , 𝑦(𝑡 − 𝑛𝑦 ),𝑢(𝑡 − 1), ⋯ , 𝑢(𝑡 − 𝑛𝑢 )],(2)
where 𝑢(𝑡) is system input, and 𝑛𝑦 , 𝑛𝑢 are the order ofsystem output and input respectively. In data-driven
control scheme, I/O data (equation (2)) are stored in the
database.

Fig. 2. Schematic of the data-driven control system.

𝛥2 𝑢0 (𝑡) = 𝐾𝐼𝐼 𝑒0 (𝑡) + 𝐾𝐼 𝛥𝑒0 (𝑡) − 𝐾𝑃 𝛥2 𝑦0 (𝑡)− 𝐾𝐷 𝛥3 𝑦0 (𝑡)(5)Therefore, 𝑟̃ (𝑡) is derived as follows:𝑟̃ (𝑡) = [𝛥2 𝑢0 (𝑡) + 𝐾𝐼 𝑟̃ (𝑡 − 1) + 𝛥2 𝐾𝑃 𝑦0 (𝑡)+𝛥𝐾𝐼 𝑦0 (𝑡) + 𝛥3 𝑦0 (𝑡) + 𝐾𝐼𝐼 𝑦0 (𝑡)]/(𝐾𝐼𝐼 + 𝐾𝐼 ). (6)In addition, user set a desired reference model expressedby following equation:𝑧 −1 𝑃(1)𝑦̃𝑚 (𝑡) =𝑟̃ (𝑡),(7)𝑃(𝑧 −1 )where 𝑦̃𝑚 (𝑡) is reference model output and 𝑃(𝑧 −1 ) is
user-specified polynomial. In FRIT, Control parameters
are calculated to minimize difference between 𝑦̃𝑚 (𝑡)and 𝑦(𝑡).
Here, 𝑃(𝑧 −1 ) is designed based on the reference design
as follows:𝑃(𝑧 −1 ) ≔ 1 + 𝑝1 𝑧 −1 + 𝑝2 𝑧 −1
(8)

5. Control law
Hydraulic system in Fig. 1 has derivative element.
Therefore, controller with double integral element isneeded and PII 2 D controller is defined as follows:
𝛥2 𝑢(𝑡) = 𝐾𝐼𝐼 𝑒(𝑡) + 𝐾𝐼 𝛥𝑒(𝑡) − 𝐾𝑃 𝛥2 𝑦(𝑡)− 𝐾𝐷 𝛥3 𝑦(𝑡)(3)𝑒(𝑡) ≔ 𝑟(𝑡) − 𝑦(𝑡),(4)where 𝑒(𝑡) denotes control error, and 𝐾𝑃 , 𝐾𝐼 , 𝐾𝐷 and 𝐾𝐼𝐼
respectively are proportional gain, integral gain,
derivative gain and double integral gain. Furthermore,
𝛥 denotes a difference operator.

𝑝1 = −2exp (−𝜌2𝜇) cos (

√4𝜇−1
2𝜇

𝜌)

𝜌

𝑝2 = exp (− )6. Fictitious Reference Iterative Tuning: FRIT𝜇𝜌≔

FRIT5 is a scheme to calculate control parameters
directly from closed-loop data which are input 𝑢0 (𝑡),output 𝑦0 (𝑡) and 𝑒0 (𝑡) = 𝑟̃ (𝑡) − 𝑦0 (𝑡).

,

(9)

𝑇𝑠
𝜎

}
𝜇 ≔ 0.25(1 − 𝛿) + 0.51𝛿
where 𝜎 is a parameter related to the rise-time and 𝛿 is aparameter related to the damping oscillation. User set© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

394

Design of a Data-Driven

them arbitrarily. 𝜎 denotes the time when output reaches
about 60% of the step reference value. Moreover, 𝛿 is set
between 0 ≤ δ ≤ 2.0 desirably. In particular, 𝛿 = 0
indicates the response of Butterworth model and 𝛿 = 1.0
indicates the response of Binominal model.

In order to calculate effective control parameters, alearning method is needed. Therefore, an off-line
learning method is described in next section.

7. Design of a Data-Driven Controller

In this section, an off-line learning method is describedby using FRIT. At first, the number of neighbors’ data 𝑘
is selected and 𝑲𝑜𝑙𝑑 (𝑡) is calculated by equation (14)using closed-loop data 𝑢0 (𝑡) and 𝑦0 (𝑡). Next, thefollowing steepest descent method is utilized to modify
the control parameters:
𝜕𝐽(𝑡 + 1)
𝑲𝑛𝑒𝑤 (𝑡) = 𝑲𝑜𝑙𝑑 (𝑡) − 𝜼
(16)
𝜕𝑲(𝑡)
𝜼 = [𝜂𝑃 , 𝜂𝐼 , 𝜂𝐷 , 𝜂𝐼𝐼 ],where 𝜼 denotes the learning rate and 𝐽(𝑡 + 1) isdefined as following error criterion:1
𝐽(𝑡) ≔ 𝜖(𝑡)2(17)2
𝜖(𝑡) ≔ 𝑦0 (𝑡) − 𝑦̃𝑚 (𝑡),(18)
The each partial differential of equation (16) aredeveloped as follows:𝜕𝐽(𝑡 + 1)
𝜕𝐽(𝑡 + 1) 𝜕𝑦̃𝑚 (𝑡 + 1) 𝜕𝑟̃ (𝑡)=𝜕𝐾𝑃 (𝑡)𝜕𝑦̃𝑚 (𝑡 + 1) 𝜕𝑟̃ (𝑡) 𝜕𝐾𝑃 (𝑡)𝜖(𝑡 + 1)𝑃(1)𝛥2 𝑦0 (𝑡)=𝐾𝐼 + 𝐾𝐼𝐼𝜕𝐽(𝑡 + 1)
𝜕𝐽(𝑡 + 1) 𝜕𝑦̃𝑚 (𝑡 + 1) 𝜕𝑟̃ (𝑡)=𝜕𝐾𝐼 (𝑡)𝜕𝑦̃𝑚 (𝑡 + 1) 𝜕𝑟̃ (𝑡) 𝜕𝐾𝐼 (𝑡)𝜖(𝑡 + 1)𝑃(1)𝛥𝑦0 (𝑡)=𝐾𝐼 + 𝐾𝐼𝐼. (19)
𝜕𝐽(𝑡 + 1)
𝜕𝐽(𝑡 + 1) 𝜕𝑦̃𝑚 (𝑡 + 1) 𝜕𝑟̃ (𝑡)=𝜕𝐾𝐷 (𝑡)𝜕𝑦̃𝑚 (𝑡 + 1) 𝜕𝑟̃ (𝑡) 𝜕𝐾𝐷 (𝑡)𝜖(𝑡 + 1)𝑃(1)𝛥3 𝑦0 (𝑡)=𝐾𝐼 + 𝐾𝐼𝐼𝜕𝐽(𝑡 + 1)
𝜕𝐽(𝑡 + 1) 𝜕𝑦̃𝑚 (𝑡 + 1) 𝜕𝑟̃ (𝑡)=𝜕𝐾𝐼𝐼 (𝑡)𝜕𝑦̃𝑚 (𝑡 + 1) 𝜕𝑟̃ (𝑡) 𝜕𝐾𝐼𝐼 (𝑡)𝜖(𝑡 + 1)𝑃(1){𝑦0 (𝑡) − 𝑟̃ (𝑡 − 1)}=}
𝐾𝐼 + 𝐾𝐼𝐼
Therefore, control parameters can be learned off-line byusing closed-loop data in equation (16) and (19).

7.2. Off-line learning method in Data-Driven
Control scheme by using FRIT

7.1. Design procedure[STEP 1] Create an initial database.
The historical data is needed to use data-driven control
scheme. Therefore, initial database is created by using
Zieglar & Nichols: ZN method1 and Chien, Hrones &Reswick: CHR method2. The database are constructedby following information vector:
̅ (𝑗), 𝑲(𝑗)]𝝓(𝑗) ≔ [𝝓(𝑗 = 1,2, , ⋯ , 𝑁)(10)
̅𝝓(𝑗) ≔ [𝑟(𝑡 + 1), 𝑟(𝑡), 𝑦(𝑡), ⋯ , 𝑦(𝑡 − 𝑛𝑦 + 1),𝑢(𝑡 − 1), ⋯ , 𝑢(𝑡 − 𝑛𝑢 )](11)
𝑲(𝑗) ≔ [𝐾𝑃 (𝑡), 𝐾𝐼 (𝑡), 𝐾𝐷 (𝑡), 𝐾𝐼𝐼 (𝑡)], (12)where 𝑁 denotes the number of data. Since initial
control parameters are fixed, which indicates 𝑲(1) =𝑲(2) = ⋯ = 𝑲(𝑁).
[STEP 2] Calculate distance and select neighbors’ data.
̅ (𝑡) and 𝝓̅ (𝑗) is calculated byDistance between Query 𝝓using the following L1–norm with some weights:
𝑚𝑦 +𝑛𝑢+1

̅ (𝑡), 𝝓̅ (𝑗)) =
𝑑 (𝝓

∑
𝑙=1

|

̅ 𝑙 (𝑡) − 𝝓̅ 𝑙 (𝑗)𝝓|.
̅ 𝑙 (𝑚) − min𝝓̅ 𝑙 (𝑚)max𝝓(13)(𝑗 = 1,2, , ⋯ , 𝑁)
̅ 𝑙 (𝑗) denotes the 𝑙th element of query 𝝓̅ (𝑗).
𝝓̅
max𝝓𝑙 (𝑚) is a maximum 𝑙th element in database. In
̅ 𝑙 (𝑚) is a minimum 𝑙th element. In
contrast, min𝝓
addition, the number of neighbors’ data 𝑘 are selected,
which data are based on smallest distance 𝑑.
[STEP 3] Calculate control parameters.
Control parameters are calculated by using thefollowing linearly weighted average (LWA):𝑘

𝑲(t) = ∑ 𝑤𝑖 𝑲(𝑖) ,𝑖=1𝑘∑ 𝑤𝑖 = 1,(14)

8. Numerical Example

𝑖=1

where 𝑤𝑖 is the weight corresponding to the 𝑖 th
̅ (𝑖) in the selected neighbors. It is
information vector 𝝓calculated by following equation:
1/𝑑𝑖𝑤𝑖 = 𝑘.
(15)∑𝑖=1 1/𝑑𝑖

In this section, the effectiveness of the proposed scheme
is verified. Table 1 shows the user-specified parameters
included in proposed scheme.
Fig. 3 shows control results by using fixed PII 2 D control
and proposed scheme. Initial control parameters in fixed
PII 2 D controller are set as follows:© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

395

Takuya Kinoshita, Kazushige Koiwai, Toru Yamamoto, Takao Nanjo, Yoichiro Yamazaki, Yoshiaki Fujimoto

Table 1. User-specified parameters included inproposed scheme.
Damping property
Order of information vector
Learning rate : 𝑡 ≤ 4.0[s]: 𝑡 > 4.0[s]Number of neighbors’ data
Reference signal

𝛿=0
𝑛𝑢 = 4, 𝑛𝑦 = 3
𝜼 = [2 × 10−13 , 10−15 , 10−13 , 0]
𝜼 = 3 × [10−24 , 10−24 , 10−24 , 10−24 ]𝑘=3
𝑟 = 100

Torq[%]

𝜎 = 0.2

100
50
0
0

2

4

6

8

10

t[s]
150

Flow rate[%]Rise-time

fixed PII2D control
proposed scheme
Reference signal

150

K P = 1.8 × 10−6 , 𝐾𝐼 = 9.0 × 10−8 ,𝐾𝐷 = 1.8 × 10−5 , 𝐾𝐼𝐼 = 0.
(20)
In equation (20), 𝐾𝐼𝐼 equals to zero because thisparameter is for a system without derivative element
between 𝑡 = 2[s] - 4.0 [s]In this case, system output can be reached to reference
signal between 𝑡 = 2[s] - 4. 0 [s]. However, after 4.0[s],it cannot be reached to reference signal because thesystem includes derivative element.
In proposed scheme, control performance is better thanabove control result since control parameters are adjusted.
Trajectories of control parameters are show in Fig. 4. 𝐾𝐼𝐼
is adjusted largely after 𝑡 = 4[s] because system hasderivative elements.

fixed PII2D control
proposed scheme

100
50
0
0

2

4

6

8

10

t[s]

Fig. 3. Control results by fixed PII2 D control and proposed
scheme.

-6

KP

4

x 10

fixed PII2D control
proposed scheme

2
0
0

2

4

t[step]

6

8

10

-7

9. Conclusion

4

KI

This paper has proposed a data-driven control system fora hydraulic excavator. Control parameters should be
adjusted because a hydraulic excavator is nonlinear
system. In this paper, controller has been designed as
PII 2 D controller for derivative system. The effectivenessof proposed scheme has been numerically verified byusing simulation example.

x 10

fixed PII2D control
proposed scheme

2
0
0

2

4

t[step]

6

8

10

-5

KD

3

x 10

fixed PII2D control
proposed scheme

2
1
0

2

4

t[step]

6

8

10

-9

References
KII

2

1. J.G.Ziegler ans N.B.Nichols, “Optimum Settings for
Automatic Controllers”, Trans. on ASME, 64(8) (1942)
759–768.
2. K.L.Chien, J.A.Hrones and J.B.Reswick, “On theautomatic Control of Generalized Passice Systems”, Trans.
ASME, 74 (1952).
3. 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.
4. S. Okabe, “A Complete Work on Hydraulic Excavator (inJapanese)”, Japan Industrial Publishing Co., LTD (2007).
5. 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 of

x 10

fixed PII2D control
proposed scheme

1
0
0

2

4

t[step]6

8

10Fig. 4. Trajectories of control parameters corresponding to
Fig. 3.
systems, Control and Information Engineers, 17(2) (2004)
528–536.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan396

An Optimization of Spatio-Spectral Filter Bank Design for EEG Classification
Masanao Obayashi1, Takuya Geshi2, Takashi Kuremoto3 and Shingo Mabu4

Information Science and Design Engineering, Yamaguchi University,Tokiwadai 2-16-1
E-mail: {1m.obayas,2t015vk, 3wu,4mabu}@yamaguchi-u.ac.jp

Abstract
How to select the appropriate frequency band to classify EEG signal by motor imagery is discussed in this paper.
Our proposal is an improvement of the conventional Bayesian Spatio-Spectral Filter Optimization (BSSFO). Defectof BSSFO is on the way to generate the renewal particle of the filter bank, such a random number generation. To
avoid a local optimum, an evolutional update method of particles is introduced. It is shown that performance of theEEG classification ability is improved.
Keywords: spatio-spectral filter, EEG, classification, .optimization, mutual information, common spatial filter

Laplacian smoothing is applied to all the EEG signals to
reduce artifacts and noise as follows. The weight of thedata in attention electrode surrounded by a green circle is
4 and that of each of four surrounding electrodes
surrounded by red circles is -1. (See Fig.1) These
weights are changed to optimal values in Section 3 by us(Improvement 1).

1. Introduction
Recently, researches using brain computer interface(BCI) have been actively studied. To precisely identify
EEG signal, it is necessary to remove the artifact andnoise by using appropriate spatial and spectral filter,
Refs. 1-2. Furthermore, the best frequency bands
identifying EEG signals depend on individuals andmeasurement environment, Ref. 3. Bayesian SpatioSpectral Filter Optimization (BSSFO) is known as a
powerful method to solve these problems, Ref. 4.
However, BSSFO has also drawbacks that the obtained
solution by it falls into sub-optimum. To overcome thisdrawback, we propose improvements of the
preprocessing and update method of the filter bank in
Ref. 4, to result in confirming effectiveness of our
proposal.

Fig. 1 Explanation of the weights of the EEG electrodesin smoothing of the EEG signal in the extended
international 10/20 system used in this study

2. Classification System
The classification system of EEG signals proposed by K.
Suk, et al. Ref. 1 that is improved in this paper by us isshown in Fig.2. According to the flow of Fig.1, thecontents are described in the following subsections.

2.2 Spectral Filtering

The filter used in this paper is a fifth-order Butterworth
bandpass-filter. The EEG signals of all the data sets are

2.1 Preprocessing© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

397

Masanao Obayashi, Takuya Geshi, Takashi Kuremoto, Shingo Mabu

Input Data: X, Ω , K, m,

bandpass-filtered between 4 Hz and 40 Hz covering boththe   rhythm(8  14Hz) and   rhythm(14  30Hz) .

X  x i i 1 : Set of EEG signal, D: amount of trial,DΩ   i i 1 : Set of class labels, where i   1,1 ,D

2.3 Common Spatial Pattern

K : The number of particles,
m: The half number of spatial patterns to be
determined in a spatial pattern learning algorithm
Output Data：: Set of optimal particles,Bˆ  bˆ , ˆCommon Spatial Pattern (CSP) proposed by H. Ramoser,
et al. in Ref. 2 is used in this paper. CSP is applied to thesignals after bandpass filtering. CSP algorithm is forsearching for the spatial weight to multiply to the EEG
signals. The spatial weight W is gotten by solving the
optimization problem of following a generalized
eigenvalue problem:arg maxW

W T  1W ,



 

j 1

Optimization:--------------------------------------------------------------------------Initialization：
ˆ old  b old ,  old K
B



-b

(2)

old
k


 b , b 
k

k 1

k

s
k

e
k

-  kold  K1 : Weight of kth particle
while stopping criterion not satisfied do
if the first iteration thenB new  B old

where X means EEG signal matrix, and following: x1, 1  x1N  ,X  
  xC , 1  xC , N  : the number of particles

where  1 and 2 are covariance matrices of each class.
 1 and  2 are calculated as follows:1, 2  XXT trace ( XXT ) ,jWˆ  Wˆ j j 1 : Set of optimal spatio filters,(1)

W T  2W

j

(3)

else
B old  B new ( k )  0, k  1,  , K 
for k = 1 to K do
Generate a random number r   0, 1 ,uniformly distributed.
Find the largest j for which r   nj 1 n ,where N is the number of signal samples, C means thenumber of electrodes. The first and last row vectors aretaken as spatial patterns for class 1 and class 2,
respectively.
2.4 Bayesian Spatio Spectral Filter Optimization(BSSFO) ( j)   ( j)  1 ,Algorithm : BSSFO filter optimization algorithmoldb j  N (0, R ) if , ( j )  1b new  oldkotherwiseb j

Improvement 2end
end
Spectral Filtering(filter bank)z  hx

Single-trial EEGs

Spatial Filtering(filter bank)y  Wz

BSSFOPreprocessing

Feature Extraction(filter bank)f  log(var(y))

SVMsclassifierx

Posterior Estimation

Prior knowledge
Filter bank
UpdateFig. 2 Whole structure of the EEG classification system proposed by K. Suk, et al. Ref. 1© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan398

An Optimization of Spatio-Spectralfor k=1 to K do
Z k  h newXk

2.6 Classifier/* Perform a bandpass filter */A Gaussian kernel-based SVM is used in this paper. An
optimal filter bank S with the set of class-discrimination
frequency bands selected by the following rule:

Solve Wk ( ( )   ( ) )Wk  I/* Perform a CSP algorithm */ˆ  the first m and the last m column vectors in WWkk
for i = 1 to D do
ˆ  zi )] /* F  f i D */f i  log [var ( W
k

k

k

k

S   k ( k   ),where k  {1,2,, K } and  denotes a thresholdparameter that is determined empirically. The class label
is determined by the following rule:k i 1end
I (Fk ; Ω)  H (Fk )  {H (Fk |   1) }  H (Fk |   1)}
p(Fk , Ω | Bk )  exp{I (Fk ; Ω)}/* Update the weight of particles */
end



|S |
c
 
cˆ  arg max (12)   k   k (f k ) ,k1c{ , } where |S| denotes the size of the optimal filter bank S,
f k denotes the feature vector from the input signal-trialKˆ new k 1
B new  b new
k ,k
end
S   k ( k   ), k {1,2,  , K}



(11)



EEG x i , and  ck (f k ) is the result of a SVM whichclassifies the EEG into the class c, in the kth frequency
band.



ˆ  b new , ˆ newˆ }B, Wˆ  {W
j
j
j jS
jS
--------------------------------------------------------------------------

2.5 Feature Extraction

3. Improvement of the method

After spectral and spatial filtering, we get the feature
vector Fk of kth particle as follows:

We propose two improvements of the method
mentioned above.

D

Fk  fki i 1 ,

ˆ  zi )] .
fki  log [var ( Wkk

3.1 Improvement 1(4)

The first is that in the Laplacian smoothing, the weightvalue 4 of the data in attention electrode surrounded by agreen circle in Fig. 1 is changed to 5 to enhance thesignal of the attention electrode. .

Using Fk , mutual information I (Fk ; Ω) between Fk andclass label Ω is calculated as follows:
I (Fk ; Ω)  H (Fk )  {H (Fk |   1) }  H (Fk |   1)} ,(5)

3.2 Improvement 2

where H ( Fk ) and H ( Fk |   c) are defined as following
equations.
H ( Fk )  

H ( Fk |   c)  

1 D
1

 log   pˆ (f k ) ,j
1
D i 1DThe second is that the improvement as to the update
method of the particles which are for band start and end
positions of bandpass filter. Use of the update method of
the conventional method results in that particles withsemi higher amount of information are remained, as aresult, particles would be biased. For improvement,
remove the half of all particles from the lower amount of
information. Then, two particles with higher amount of
information are selected stochastically, crossing them,new particles are generated. After that, particles between
1/4 and half from highest amount of information are reinitialized shown in Fig. 3.

(6)

 1

1
i
j
 log  pˆ (f k  f k , ) , (7)Dc i s.s.ti  c  Dc j s.t .  j  c11 Di  (f k  f k ,  ) ,D i 1 a   1a 1,
 (a, ) 
exp 
d 2 d
122 (2 )  |  | 2 pˆ (f k ) 

(8)
(9)

Re-initializing of particles is to generate particles byuse of following probability density function:

where D and Dc are the total number and class c oftrials and  is the covariance matrix. The weight  k ofthe classification result weight of particle k is calculated
as follows:k 

expI (Fk ; ) .
 exp I (F j ; )jp (B ) 11
( μ ,  μ )  ( β ,  μ )
2
2

(13)

The crossing method of two particles is as follows:(10)(bks , bke ) new   1 (bks , bke ) old 1   2 (bks , bke ) old 2 ,(14)© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

399

Masanao Obayashi, Takuya Geshi, Takashi Kuremoto, Shingo Mabu

where the weights  1 ,  2 are set to 0.9 and 0.1,
respectively, in next simulation to avoid generation ofparticles that both particles become far away.

Table 3 Simulation results of both update methods ofparticles with conventional preprocessing

Amount of information

update method
highCrossing

subjects
aa

al

av

aw

ay

ave
rag
e

conve
ntional

avera
ge

40.29

87.5

56.68

64.06

49.6

61.
43

Improvement 2

avera
ge

53.57

91.61

53.67

65.54

50.36

63.
95

Re-initiali
zationFig. 3 Our proposed update method of particles

4. Computer simulation

Table 4 Simulation results of both update methods ofparticles with preprocessing with improvement 1

4.1 Data set, simulation condition and results
EEG data set used in this simulation is Data set Iva inthe BCI Competition III, in Ref. 5. The conditions totake these data is shown in Table 1. Table 2 , 3, and 4
show the simulation results that update method ofparticles of both conventional and improvement 2 arecommon, but without preprocessing, with conventional
preprocessing, and with preprocessing improvement 1,
respectively. In all cases, our proposed method issuperior to conventional one.

update method

subjects
aa

al

av

aw

ay

ave
rag
e

conve
ntional

avera
ge

72.23

98.75

60.51

677.1

55.36

72.
79

Improvement 2

avera
ge

73.3

99.64

61.99

74.91

69.88

75.
9

5. Conclusion

100 [Hz]In this paper, we intended to improve the update methodof particles of the conventional method “BSFFO” thatmean improvement of frequency bandpass filters andalso to improve the preprocessing method. As a result, it
is verified that our method is useful.

used time of EEG data

0.5[s]~2.5[s]References

the number of electrodes

118

1.

H. Ramoser, J. Muller-Gerking, and G. Pfurtscheller,“Optimal Spatial Filtering of Single Trial EEG during
Imagined Hand Movement”, IEEE Trans. Rehabilitation
Eng., vol. 8, no. 4, pp.441-446, Dec. 2000.

2.

Fikri Goksu, Nuri F. Ince, Ahmed H. Tewfik, “Greedy
solutions for the construction of sparse spatial and spatiospectral filters in brain computer interface applications”,
Neurocomputing, vol. 108, no. 2, pp.69-78, May. 2013.

3.

H. Ramoser, J. Muller-Gerking, and G. Pfurtscheller,

Table 1 Simulation condition
EEG data
the number of sampling

200

sampling rate

BSSFO
the number of particles

15

width of frequency band
The number of loop
algorithm

4 ~ 40 {Hz]10

Table 2 Simulation results of both update methods ofparticles without preprocessing

aa

al

av

aw

ay

av
era
ge

subjects
update method
conve
ntional

aver
age

70.5
4

99.8
2

60.5
6

76.2
5

56.6
7

72.
77

Improv
ement
2

aver
age

72.8
6

99.6
4

63.2
1

70.5
8

69.1
3

75.
08

“Optimal Spatial Filtering of Single Trial EEG during
Imagined Hand Movement”, IEEE Trans. Rehabilitation
Eng., vol. 8, no. 4, pp.441-446, Dec. 2000.
4.

Heung-Il Suk, Seong-Whan Lee, “A Novel Bayesian
Framework for Discriminative Feature Extraction in
Brain-Computer Interfaces”, IEEE Transactions. PatternAnalysis and Machine Intelligence, vol. 35, no. 2, pp.286299, Feb. 2013.

5.

http://bbci.de/competition/iii/desc_IVa.html© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan400

Design and Analog Circuit Implementation of a Dynamic Feedback Control System Based on
RLC Series Circuit
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
Yongjun Wu

College of Electronic Information and Automation, Tianjin University of Science & Technology
Tianjin, 300222, China
Dongcheng Tan

College of Electronic Information and Automation, Tianjin University of Science & Technology
Tianjin, 300222, China
E-mail: spots@163.com
www.tust.edu.cn

Abstract
The purposeas expected performance. The simulation results of the theoretical model and the corresponding resultsof the real analog circuit implementation are given in the paper to illustrate that the circuit can accomplish the trackof this paper is to verify that a dynamic feedback control system can be realized by a simple small analog circuit. A
normal RLC series circuit, which is an electrical two-order circuit only consisting of one resistor, one inductor andone capacitor, is taken as the controlled object, and the voltage of the capacitor is taken as the output of the system.
The engineering design method of regulator in DC drive control system is applied to design the dynamic feedback
compensator, so that the output of the system can track the given input, and the system is stabilized and h ing
function of dynamic feedback control system.
Keywords: dynamic output feedback; engineering design method; dynamic compensator; analog circuit
implementation

simple analog circuit will be implemented to achieve theinput tracking performance of the servo system.

1. Introduction
The servo system is a normal type in electric driving
automatic control system. To facilitate engineering
applications, scholars have put forward a variety ofengineering design methods to simplify the regulator
design procedure.1-5 The purpose of this paper is todesign a dynamic feedback compensator with theengineering design method and make the design
procedure separate from the complex application
background of electric drive system. In this paper, a

2. Design of Dynamic Feedback Compensator
Based on Engineering Design Method
The structure of RLC series circuit is shown in Fig. 1.
From Fig. 1, the state equation of the circuit can beformulated as© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan401

Hong Niu, Yongjun Wu, Dongcheng Tan

To ensure the safety requirements of the elements
and the performance of the closed-loop system, such asstability, accuracy and rapidity, the closed-loop systemshould be designed as a typical I system according toRef. 1, because the overshoot of the typical I system issmaller than that of the typical II system. Moreover, thetypical I system has an acceptable dynamic performancethat satisfies the control require of this paper. The openloop transfer function of the typical I system isFig. 1. RLC series circuit
ucl  uL  uR  uC  LC

d 2uC  t 
du  t  RC C uC  t  .
2
dt
dt

1

G  s   Gcl  s  G  s  Take the RLC series circuit as the controlled object,and let the voltage of the capacitor uC be the output of
the system. The transfer function of the RLC series
circuit can be presented asG s UC  s 1.
U cl  s  LCs 2  RCs  1

1
1
 s  1  RLCs  1
R




1

Gcl  s   2T1s  1T2 s  1

.

 4

From Eq. (4) and Eq. (3), the dynamic feedback
compensator Gcl(s) can be formulated as

K pi  1s  1

 1s

.

 5

Where τ1 = T1 = 10-3. Thus, the control signal ucl(t) isrepresented as

Where uC(0) = 0, and i(0) = 0.
Let L = 10 mH and C = 1 μF. To make the circuit
non-oscillatory, the resistance of the resistor R should
be R > (L/C)1/2 = 200 Ω.
Eq. (2) is transformed asG s K.
s Ts  1

ucl  t   K pi e  t  

K pi

1 

e  t  dt.

 6

Substitute Eq. (3) and Eq. (5) into Eq. (4). Then, 3

G  s   Gcl  s  G  s  

So that the engineering design method can be applied todesign the dynamic feedback compensator.
To make Eq. (3) and Eq. (2) equal, the resistanceshould be R = [1/C/(1-L)]1/2 ≈ 1 kΩ > 200 Ω, which
satisfies the non-oscillatory condition. Then, T1 = 1/R =10-3, and T2 = RLC = 10-5.

K pi  1s T2 s  1K.
s Ts  1

7

Where K = Kpi/τ1, and T = T2 = 10-5.
To guarantee the good tracking performance of thesystem, let KT = 0.5 so that the damping ratio of thesystem is ξ = 0.707. Therefore, Kpi = Kτ1 = 0.5τ1/T = 50.
From Fig. 2 and Eq. (7), the closed-loop transfer
function is formulated as s GI  s K 2.
1  GI  s  Ts  s  K 8It is easy to prove that all of the eigenvalues of Eq. (8)have the negative real parts, so the closed-loop system isstabilized.
When the given input signal is a step signal whose
amplitude is a, i.e., ui  t   a  1  t  and U i  s   a s ,Fig. 2. Block diagram of dynamic feedback control system

The block diagram of the dynamic feedback control
system is shown in Fig. 2. The engineering design
method, which is simple and can simplify the design
procedure, is often used to design the regulator for the
DC drive control system. In this paper, the engineering
design method is applied to design the dynamic
feedback compensator Gcl(s), such that the output signal
uC(t) can track the given input signal ui(t).

the steady-state error of the system (8) is

ess  limsE  s   0.
s 0

9

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

402

Design and Analog Circuit

When the given input signal is a ramp signal whose

well. It demonstrates that the dynamic feedback
compensator designed by the engineering design
method is feasible and effective.

slope is b, i.e., ui  t   b  t  1  t  and U i  s   b s , the2steady-state error of the system (8) is

ess  limsE  s   b K .
s 0

10 

4. Circuit Implementation
The analog circuit implementation of the servo system(8) is shown in Fig. 4. The type and parameters of theelements have been labelled in the figure. The real
output response waves under the same input signals of
the numerical simulation are shown in Fig. 5.

According to the output saturation voltage of theoperational amplifier, the parameters a and b should not
be too large to avoid distortion of the output signal.
Because the parameter K = Kpi/τ1 = 5×104, which is large
enough, the steady-state error of the system (8) under
the ramp signal input is also equal to 0, i.e., ess = 0.
When the given input signal is a sine signal whose
amplitude is A, i.e., ui  t   A  sin  t  andU i  s   A

 s    , the steady-state error of the22

system (8) is

ess  limsE  s   0.
s 0

11
Fig. 4. Analog circuit implementation of the servo system (8)Similarly, the amplitude of the input sine signal should
also be kept in a proper range.
3. Numerical Simulation
In this paper, there are three different given input
signals. The first one is a square wave, the second one isa triangular wave, and the third one is a sine wave, as
shown in Fig. 3(a). The corresponding output response
waves are shown in Fig. 3(b).

Fig. 5. Real waves of the input signal and the output signal:(up) input; (down) output

By comparing Fig. 5 with Fig. 3, it can be verified
that here is a good qualitative agreement between the
numerical simulation and the experimental realization.
It shows that the real analog circuit accomplishes the
purpose of tracking the given input signals.
Furthermore, the amplitude of the given input signal
is limited to the output saturation voltage of theoperational amplifier. Hence, it should not be too large,otherwise, the distortion of the output signal would
occur, and the elements would even be destroyed.

Fig. 3. Waves of the input signal and the output signal: (a)input; (b) output

The numerical simulation waves illustrate that theoutput signals can track the different given input signals© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan403

Hong Niu, Yongjun Wu, Dongcheng Tan

5. Conclusions
In this paper, a servo system is implemented by a simple
analog circuit only consisting of nineteen elements. The
numerical simulation results, as well as the experimentrealization results, prove that the output voltage of thecapacitor in the RLC series circuit tracks the different
given input signals well. The proposed circuit can beused as an example for the courses of control theory andapplication, such as the course of automatic control
theory, the course of motion control system and so on.
References
1. B. S. Chen, Electric Drive Automatic Control System(Motion Control System), 3rd edn. (China Machine Press,
Beijing, 2003).
2. G. Z. Yu, The design of regulator in engineering, Journalof Changchun Institute of Optics and Fine Mechanics
23(2) (2000) 48–51.
3. Y. F. Wen, The digital simulation of engineering designs
for a double closed loop speed-regulation system,
Modern Electric Power 17(1) (2000) 76–81.
4. X. L. Ma, Decoupling and regulator design of vector
control systems, Electric Drive 39(1) (2009) 3–6.
5. H. Dong, H. Wang and K. Y. Huang, Design of PMSM
drive system digital PI adjuster parameters, Electric
Drive 39(1) (2009) 7–10.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan404

Simulation Study on the Tracking Technology of the Maximal Power Point of the Solar
Photovoltaic Based on the Model Predictive Control

Xia Zhao

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
Masanori Sugisaka

Alife Robotics Corperation LTD, Oita, Japan
E-mail: zhl@sit.edu.cn, 156101128mail@sit.edu.cn
www.sit.edu.cn

Abstract
The solar photovoltaic battery is a new type of renewable distributed energy’s important part of the key researchdevelopment at home and abroad, but its output power is directly affected by light intensity and temperature,
voltage-current characteristics have obvious nonlinear features. Therefore, a new MPPT technology based onmodel predictive control (MPC) is proposed in this paper, the simulation results show that the method can quickly
track the maximum power point under the current environmental conditions, and improve the energy conversion
efficiency of the system.
Keywords: Photovoltaic cell; Maximum power point tracking; Solar energy; Matlab/Simulink;

Because of its affection of environment and the load, andconsideration of the input and output characteristics ofnonlinear, this paper presents the maximum power point
tracking (MPPT) principles and methods, mentions
several MPPT method, and compare the advantages anddisadvantages of the algorithm .

1. Introduction
Currently the survival of humanity is inseparable fromthe use of energy. Along with the increasing depletion of
non-renewable energy, development of renewable energy
has become the theme of today's world. Solar energy as aclean and renewable energy attracts the people's attention.
That photovoltaic cells convert solar energy into
electricity by some way is called as photovoltaic.

2. MODELING THE SOLAR CELL
Photovoltaic cell is the device that can convert the sun© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

405

Xia Zhao, Huailin Zhao, Masanori Sugisakalight into electricity using the photovoltaic effect. The
critical element of a photovoltaic cell is a semiconductor
diode that P-N junction is exposed to light. The incidenceof light on the cell is generates charge carrier thatoriginate an electric current if the cell is short-circuited [1].
Charge are generated when the energy of the incident
photon is sufficient to detach the covalent electrons of
the semiconductor, and this phenomenon depends on thesemiconductor material and the wavelength of theincident light. Basically, the PV phenomenon may bedescribed as the absorption of solar radiation, thegeneration and transport of free carriers at the p–njunction, and the collection of these electric charges at
the terminals of the photovoltaic device [2]. If the terminalof cell is connected with external load, electrons flow
through it which is cause of current in the circuit. The
commonly accepted solar cell model is a one diodemodel [3],which is shown in Fig. 1.This Photovoltaic cell
model is a nonlinear device and can be represent as acurrent source model. The output of the current source isdirectly dependent on the solar irradiance and theambient temperature. The I-V characteristics of aphotovoltaic cell is similar to diodes characteristic, andwhich is represent by the following equation [2] [3] [4]:

I pv  I pv,nom  Ki T

The circuit diagram of the photovoltaic cell.

 qV    1
aKT

 



Rsh  Rs
Rsh

(5)

I SC
 V

exp  OC   1
 aVt ,nom (6)whereand are the output current and voltage of thephotovoltaic cell, respectively, is the generated current
under a given in solution, is the reverse saturation current,is the charge of an electron (value is ), is the Boltzmann’sconstant (value is ), is the ideality factor(usually, andthe choice depends on other parameters of the I-V model.
And here is taken as 1), T is the temperature (in Kelin) of
the p-n junction of photovoltaic cell, is the number ofphotovoltaic cell for each array, is the internal series
resistance, is the internal shunt resistance of thephotovoltaic cell, is the light-generated current at thenominal situation , is the difference between actual andnominaltemperature,
is
the
short
circuitcurrent/temperature coefficient, G is the irradiation on
the device surface, is the nominal irradiation, is the band
gap energy of the semiconductor, and is the thermal
voltage of photovoltaic array at the nominal temperature .
The equation (2-2) have a wide application intheoretical analysis of photovoltaic cell. But it is notsuitable for engineering application, due to theparameters,, K, and are relative to the solar irradianceand the ambient temperature, and the value of them ishard to determined. Consider that the value of is verysmall, but is very large. So we can approximately see(𝑉𝑜 + 𝐼𝑜 𝑅𝑠 )⁄𝑅𝑠ℎ equal to zero [4]. Also there have otherparameters are very important to photovoltaic arrays,
such as Open Circuit Voltage, Open Circuit Current ,Maximum Power Voltage, Maximum Power Current, andMaximum Power. And the normalized value (work at

VI  I pv,cell  I o,cell  exp (4)I o ,nom 

IdFig. 1.

(3)

nom T   qEg  1 1 
Io  Io,nom  nom  exp    T   aK  Tnom T 
I pv,nom  I SC

Rs

Rsh

GG

(2)

3

I
Ipv

 q Vo  IRs    Vo  IR
  1  aN s KT   Rsh

I  I pv  I o exp (1)

Actually, we care more about photovoltaic arrays,
instead of a single cell. In each array, cells connected inparallel increase current and cells connected in series
provide greater output voltages. And if there haveparallel connection of cells in array, the photovoltaic andsaturation current can be expressed as 𝐼𝑝𝑣 =𝑁𝑝 𝐼𝑝𝑣,𝑐𝑒𝑙𝑙 , 𝐼𝑜 = 𝑁𝑝 𝐼𝑜,𝑐𝑒𝑙𝑙 .Also the characteristic ofphotovoltaic arrays can be mathematically described as:© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

406

Simulation Study on thestandard test condition, namely, 25 and1000 𝑤 ⁄𝑚2 ) of
them are provided by manufactures. For example, theSolarex MSX60, a typical 60W photovoltaic module, itparameter as the table 1 described [3].

Many research papers have produced with various
schemes for the MPPT in photovoltaic power generation
system. There are many methods of MPPT techniquesapplied to photovoltaic power system. Such as one cycle
control method, feedback voltage and current method,and feedback of power variation with voltage and current[5].
At now the commonly used method about the
maximum power point tracking of photovoltaic
generation are the constant voltage method (CVT), theV method (P&O) [7], and theperturbation and observation
incremental conductanceAmethod (INC) [8]. Moreover,the fuzzy logic based method
V could fond in [9] [10] [11].
Among all these methods,
A due to the poor accuracy, theconstant voltage method are not widely applied inWindustry. And the incremental conductance method (INC)
and the perturbation and observation method (P&O) areapplied widely. In the next section, we will discuss thetwo basic MPPT algorithms in detail.

Table.1.The parameters of the Solarex MSX60
photovoltaic panel
At temperature

T

25

Open Circuit Voltage

21.0

V

Short Circuit Current

3.74

A

Maximum Power Voltage

17.1

V

Maximum Power Current

3.5

A

Maximum Power

59.9

W

For the purpose of efficiency and stability, amaximum power point tracker (MPPT) is a powerelectronic DC-DC converter inserted between thephotovoltaic array and its load. By using an intelligent
algorithm, it has ability to predict and control thephotovoltaic module use the history data of solar
irradiance, so it ensure the photovoltaic array always
works at its maximum power point as the temperature,
insolation and load vary.

A.

Perturbation and Observation method(P&O)

The P&O method maintain the operating point near the
maximum power point by a small voltage change∆V called voltage disturbance signal. At the beginning, it
has to measure photovoltaic voltage and current at the
moment atmosphere condition, and to calculate theoutput power𝑃1 . And then periodically increasing (ordecreasing) the photovoltaic array voltage, that is𝑉2 =𝑉1 + ∆𝑉, and also to calculate the output power𝑃2 .The
photovoltaic power𝑃1 and𝑃2 are compared, if𝑃2 is morethan 𝑃1 , then the perturbation is correct otherwise it
should be reversed. This method has major drawback areoccasional deviation from the maximum due to its
inability to relate the change in the photovoltaic array
power to the change in the atmospheric condition. [9].
Moreover, when the MPP is reached, the P&O method
will oscillate around it in case of constant or slowly
varying atmospheric condition. In order to avoid these,
the value of V is very small. The simulation result of
this method will be analysis in the next section.

3. Maximum Power Point Tracking Algorithms
The operation of photovoltaic array are influenced bysolar radiation, temperature and load values. The output
power of photovoltaic cell decreasing with thetemperature increasing, and increasing with the solar
radiation increasing. At a given condition of irradiationand temperature, a photovoltaic cell can work at
difference operation point, but there is a unique operation
point of the photovoltaic array with maximum output
power, that is Maximum power point (MPP). Obviously,
it is necessary to take some measure to maintain thephotovoltaic array work at MPP, and it also can improvethe efficiency of photovoltaic power system. These
measures called Maximum Power Point Tracking(MPPT), which draws maximum power from thephotovoltaic array regardless of weather or loadcondition [5]. The principle of maximum power point
tracking is through adjusting the load impedance to makethe photovoltaic power generation system always work at
the near maximum power point, under different
environmental condition [6].

B.

Incremental Conductance method (INC)

According to the P − V characteristic curve, when aphotovoltaic array work at maximum power point the𝑑𝑣
slope ofP − Vcurve is zero, namely = 0. These relation𝑑𝑝

can further be written as following:dP d VI dI I V
dV
dV
dV

(7)

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

407

Xia Zhao, Huailin Zhao, Masanori Sugisaka

Model predictive control (MPC) is widely applied inindustrial control with several advantages over theconventional control technique. The main characteristicof model predictive control is predicting the future
behavior of the desired control variables until apredefined horizon in time based on the present data of
input or output. Also this control variables will be
optimized by minimizing a cost function to obtain asatisfying control performance of overall system.
This paper we consider a islanded operation
photovoltaic generation system as controlled member,which main consist of a photovoltaic array, a DC-DC
boost converter, and a MPC controller as shown in Fig.2.

Namely

dI
I

dV
V

(8)

The control strategy of the incremental conductancealgorithms can be written in the following simple
equations:dIIdI  , that is 0 , means V  Vmax , thendVVdV

V  V  V ;dI
IdI  , that is 0 , means V  Vmax , thendVVdV

V  V  V ;dI
IdP
, that is 0 , means thedVVdVphotovoltaic array work at maximum power point, andthenV = V.
Hence, the photovoltaic cell terminal voltage can be
adjusted relative to the maximum power point voltage bymeasuring the incremental (𝑑𝐼 ⁄𝑑𝑉 ) and instantaneousarray conductance (𝐼 ⁄𝑉 ) and make use of the mentioned
control strategy. In practical application, the incremental
are approximated as: dI = I − 𝐼𝑏𝑒𝑓 and dV = V − 𝑉𝑏𝑒𝑓 .
Where 𝐼𝑏𝑒𝑓 , 𝑉𝑏𝑒𝑓 are the last time measured value ofcurrent and voltage. This method can compare directly
the photovoltaic array conductance. It has better track ofany changes either in load or photocurrent rather than theperturbation and observation method, but this way of
tracking still has some oscillation which cannot be
avoided. Also the simulation result of the incremental
conductance algorithms will be discussed in next section.

Fig. 2. Control block diagram of overall islanded
photovoltaic system configuration implementing
MPC based MPPT technique

The photovoltaic array generates electric power directlyfrom solar radiation. And then the power is delivered to
DC load through a DC-DC boost converter, whose switch(IGBT) is operated by the MPC controller. The MPC
controller applied a model predictive control based
maximum power point tracking algorithm. And the statesof switch are controlled by the output (S) of MPC
controller. Due to the switch have two basic states (openand close) there have two equivalent circuits to represent
the DC-DC boost converter to analyze.
When the switch is considered as open, theequivalent circuits of the boost converter shown as Fig.3.
And we use the following equations to describe theoperation of boost converter.

4. Model Predictive Control based Maximum Power
Point Tracking
As mentioned above, conventional methods to track the
maximum power point the result are not as good as
expected, most of them have oscillation and cannot be
avoided. We want the output power of photovoltaic arrayis stable and smooth without oscillation, or the oscillationis small that can be ignored. It is needful to find a new
way of tracking to improve the stability of the output
power and the conversion efficiency of photovoltaic array.
And this paper we consider a model predictive control
based MPPT technique applied to photovoltaic module.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

408

Simulation Study on theV pv  L

dI pv

(14)

dt

dVC
1

VC
dt
RC

(15)

The discrete model of the system as following:

I pv  k  1 T 
VC  k  1  1  s VC  k  (17) RC 

Fig. 3. Equivalent circuits for boost converter when
the switch is open (S=0)

C

dI pv

 V pv  VC

(9)

dVC
V
 I pv  C
dt
R

(10)

L

dt

Vpv  k  1 

Ts
TV pv  k   s VC  k   I pv  k  (11)L
L
1
1
VC  k  1  I pv  k  
VC  k   VC  k  (12)C
RC

I pv  k  1 I  k  1  I pv  k J  I pv  k  1  Vpv  k  1 pv
Vpv  k  1  Vpv  k L I pv  k 1  I pv  k   VC  k  (13)Ts

When the switch is closed, the equivalent circuits ofthe boost converter shown as Fig.4.and the equations asfollowing:

Fig. 4.

L I pv  k  1  I pv  k  (18)Ts

Where 𝑇𝑠 is sampling frequency, make use of theequation (11), (12), (13) or (16), (17), (18), the behaviorof the controlled variable𝐼𝑝𝑣 , 𝑉𝑝𝑣 and𝑉𝑐 can be predicted
for the next sampling instant. And we can utilize thepredicted value of this control variables to distinguishwhether the photovoltaic array work at the maximum
power point. All of this variable estimated and optimized
based on the evaluation of a cost function. In this paper,
the cost function can be expressed as:

The Euler approximation is then used to obtain thediscrete model of the system:

Vpv  k  1 

Ts
V pv  k   I pv  k  (16)L

(19)
S 0,1For each sampling sequence the cost function is
calculated twice for each switching states. And the costfunction for each switch states are compared to determine
the control actions for the following time instant.
Fig.5.depicts the process of the model predictive control
based MPPT control scheme.

Equivalent circuits for boost converter when theswitch is open (S=1)© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan409

Xia Zhao, Huailin Zhao, Masanori Sugisaka

InputsI pv  k  ,Vpv  k  ,VC  k 

I pv  k  1 ,Vpv  k  1 ,VC  k  1 for

Predict

Fig. 6. light changing after time

Two switch states

The initial value of light was 700, at the second
the light increases by 200, at the third second thelight changed into 1000, and the changes of the
moments are mutations. Change of the light isshown in Figure 6.

Calculate cost function J S 0 andJ S 1
Two switch states

J S 0  J S 1
S=0

S=1

Fig. 7. current changing after time

ReturnOutput current is shown in Figure 7, since the output ofphotovoltaic solar cells is directly proportional to thelight intensity of the sun, when the sunlight changes, thecurrent of the photovoltaic cell can quickly track changesof the light; since the influence of an external circuit
device received by the output current, there is nodifference in the use of MPC and lower INC to control
the current, and all changes after the light steeply.

Fig. 5. Flow chart of MPC based MPPT controller

5. Simulation and Result
According to the mathematical model of the solar
photovoltaic module described above, build amathematical model of solar photovoltaic cells in the
Matalab/Simulink environment. Simulation were
designed based on IMC and MPC described above. In thesimulation process of Solar photovoltaic cells, assuming
the ambient temperature is maintained at 25 degrees, justconsidering the changes of the solar illumination and that
three
time
points
were
given
light
2
2
2
700𝑤 ⁄𝑚 ,900𝑤 ⁄𝑚 and 1000𝑤 ⁄𝑚 .In the simulation ,simulate the mutations of the light intensity at different
moments with three step signal observe and compare themaximum power output tracking situation in the current
environment conditions with the use of solar photovoltaic
cells under the control of MPC and INC. The simulation
results are shown in the following figure.

Fig. 8. voltage changing after time

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

410

Simulation Study on the

power point fast, at the same time overcome the shock
and other issues brought by the classic disturbanceobserver and incremental admittance MPPT technology.
The accuracy and speed of tracking maximum power
point have improved significantly.

References
1.
2.

3.

Fig. 9. output power4.

Output voltage and power as respectively shown infigure 9 and figure 9.shown as the right figure, when thelight intensity changes, whether it is the use of MPC or
INC to output voltage and power, they are able to follow
the changes in light intensity. But the MPC control
system is no shock, and the tracking speed issignificantly faster than the INC.it is more obviously inthe output power. In order to clearly demonstrate thedynamic process of system output under two different
control methods, amplified the dynamics process of thechange of the power amplification. From the enlarged
graph, MPC track the maximum power relatively stable
and the method of INC has severe shock that the effect isless good than predicted.
This paper analyzes principle when solar
photovoltaic cells work, and relationship between output
of current, voltage and power. Under certain lightintensity and ambient temperature, the output of power
and operating voltage is obviously nonlinear. It isdifficult to operate at the maximum outputting point of
power. Therefore, in order to improve the conversion rateof solar energy, solar photovoltaic cells designed thepredictive control based on the maximum power point
tracking technology. According to the solar photovoltaic
cell model, predict the output power of the PV array inthe current environment, control full-controlled device inthe converter circuit, Regulate the operating voltage ofthe solar cell to operate at maximum power point. The
simulation shows that this method can quickly adjust thesolar cell operating voltage and track the maximum

5.

6.

7.

8.

9.

10.

11.

A.Lasnier and T.G.Ang, Photovoltaic Engineering
Handbook. NewYork: Adam Hilger, 1990.
M.G, Villava, J.R, Gazoli, and E.R, Filho, Comprehensive
Approach to Modeling andSimulation of Photovoltaic
Arrays [J]. Power Electronics, IEEE Transactions, 2009,Vol.24, No.5, pp.1198-1208.
Geoff Walker. Evaluating MPPT Converter Topologies
Using a Matlab PV Model [J]. Innovation for Secure
Power, Queensland University of Technology, Brisbane,
Australia, 2000, PP. 138-143
ZENG Zhuoying and LIU Dong. Study on Cyber-Physical
System Modeling on Coordinated Control of Photovoltaic
Generation and Battery Energy Storage System [J]. Power
SystemTechnology,
Jun/2013,
Vol.37,
No.6,
pp.1506-1513.
Li Wei, Zhu Xinjian, “The Maximum Power Point
Tracking Control of a Photovoltaic Power System”
Computer Simulation, Vol. 23, No. 6, pp.239 243, June
2006.
Yang Xue; Sha Wang; Photovoltaic Cell Modeling and theMaximum Power Point Tracking Simulation; Materials for
Renewable Energy and Environment (ICMREE), 2013
International Conference on; 2013, pp.119-123
Radwan, H. ; Abdelkarem, E. ; Ahmed, M. ; Orabi,
M.;The non ideality effect of optimizing the P&O MPPT
algorithm for PV battery charger applications
telecommunications Energy Conference (INTELEC), 2011
IEEE 33rd International; 2011.
K. H. Hussein; Maximum photovoltaic power tracking: An
algorithm for rapidly changing atmospheric conditions;
Proc.Inst.Electr.Eng.—Generation,Transmission,
Distribution, vol. 142, no. 1, pp. 59–64, Jan. 1995.
Anaadhakumar,G.; Venkateshkumar,M.; Shankar, P.;
Intelligent controller based MPPT method for the
Photovoltaic Power system; Human Computer Interactions(ICHCI), 2013 International Conference; 2013, pp.1-6.
Al-Mohaya; M.A.M. Mahamad, A.K.; Saon, S.;
Implementation of Field Programmable Gate Array based
Maximum Power Point Tracking Controller of
Photovoltaic System;Power Engineering and Optimization
Conference (PEOCO), 2013 IEEE 7th International; 2013,
pp. 718-721.
Shijie Yan; Jia Yuan Lei Xu; Fuzzy Logic Control of
MPPT for Photovoltaic Power System; Fuzzy Systems andKnowledge Discovery (FSKD), 2012 9th International
Conference; 2012, pp.448-451.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

411

Analysis and unidirectionally coupled synchronization of a novel chaotic system

Xue Li

Department of Automation , Tianjin University of Science and Technology,
1038 Dagunanlu Road, Hexi Distric, Tianjin 300222, PR China
Wei Xue

Department of Automation , Tianjin University of Science and Technology,
1038 Dagunanlu Road, Hexi Distric, Tianjin 300222, PR China
E-mail:lixueTJKJ@163.com,xuewei@tust.edu.cn
www.tust.edu.cn

Abstract
In this paper, a new three-dimensional autonomous chaotic system is proposed. By means of theoretical analysis
and numerical simulation, some basic dynamical properties, such as dissipation, fractal dimension, Lyapunov
exponent spectrum and chaotic dynamical behaviors of the new chaotic system are illustrated. The obtained
results show clearly that this system is a new chaotic system. Furthermore, based on Lyapunov stability theory of
the system, unidirectionally coupled synchronization of the new three-dimensional chaotic system throughdesigning the appropriate coupling coefficient is investigated. Results of numerical simulation validate theaccuracy and effectiveness of synchronization scheme of the presented system.
Keywords: Chaos; New chaotic system; Lyapunov exponent; Unidirectionally coupled synchronization.

researched its basic dynamical characteristics, as thename of Liu chaotic system.5 In 2005, Qi reported athree-dimensional continuous quadratic autonomouschaotic system modified from the Lorenz system,6 inwhich each equation contains a single quadratic crossproduct term. In this paper, a novel three-dimensional
autonomous chaotic system is introduced. Numericalanalysis shows that the proposed chaotic attractor is a
new attractor, which is not topologically equivalent to
the original chaotic system, or the Lorenz system, or theChen system, or the Lü system, or even the Lorenz
system family.
In the past few decades, synchronization of thechaotic systems have attracted considerable interests
because of their practical applications in the fields ofimage encryption, secure communication, electrical

1. Introduction
In 1963, American mathematical meteorologist Lorenz
discovered chaos in a simple system of threeautonomous ordinary differential equations, called theLorenz system,1 a lot of work have been reported on
finding the new chaotic attactors. As the first chaotic
model, the Lorenz system has become a paradigm ofchaos research. In 1976, Rössler presented a threedimensional quadratic autonomous chaotic system. 2
Recently, there have been increasing attentions ingenerating chaotic system since Chen found a new
Lorenz-like chaotic system in1999, namely, the Chensystem.3 In 2002, Lü and Chen found a new chaotic
system, called the Lü chaotic system,4 which represents
the transition between the Lorenz system and Chensystem. In 2003, Liu created a new chaotic system and© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

412

Xue Li, Wei Xuex y z   ( a  1  d )x y z
Therefore, when a, d satisfy a  d >1, system(1) is

engineering, neural network, control processing and soon. Many synchronization approaches have been
proposed in order to synchronizing the chaotic systems.
For example, linear feedback synchronization,7 active
synchronization,8 adaptive synchronization,9 projective
synchronization,10-11 sliding mode synchronization,12
unidirectionally coupled synchronization,13 and lag
synchronization.14-15 By means of Lyapunov stability
theory of the system, unidirectionally coupledsynchronization of the new three-dimensional chaotic
system is investigated. Numerical simulation results
verify the accuracy and effectiveness of thesynchronization scheme presented in this paper.

V dissipative, and with an exponential rate:dV e ( a 1 d )t
dt

2. The new chaotic system and its basic
dynamical properties

It means that a volume element V0 is apparently
contracted by the flow into a volume element
V0e( a1d )t versus time t. That is, each volume element
containing the trajectories of this dynamical systemshrinks to zero as t   at an exponential rate.
Therefore, all the orbits of the new system(1) areultimately confined to a specific subset that has zerovolume, and the asymptotic motion of system(1) will
settle onto an attractor of the system.

2.1. The new three-dimensional chaotic system(II)The Lyapunov exponents

In this paper, a new three-dimensional autonomous
system is proposed, which can be expressed as thefollowing form: x  ay  ax  yz
(1)
 y  bx  y  xz
2
 z   dz  cy
When the parameters are fixed as a=33, b= 50, c=3, andd=15, the chaotic behavior of system(1) is obvious. By
adopting the time domain method based on predictorcorrector, the phase portrait of system(1) is shown in
Fig.1 and the three Lyapunov exponents are 5.767 3,
0.003 4, -7.823 6, respectively.

As is known, the Lyapunov exponents measure theexponent rates of the divergence and convergence of
nearby trajectories in phase space of system. Three
Lyapunov exponents of system(1) are calculated as
L1  5.7673, L2  0.0034, and L3  7.8236. The Lyapunov
exponent spectrum of the new system(1) is showed in
Fig.2, when the parameters is taken as a=33, b=50, c=3,
and d  (0,30). Thus, the Lyapunov dimension of the
new chaotic system(1) is
1 2
L L
5.7673 0.0034
DL  2  Li  2  1 L 2  2   7.8236  2.7376
L3 i 1
3
Which means the system(1) is a chaotic system since
the Lyapunov dimension of the system(1) is fractional.
10

80

z

60

5

40

Lyapunov exponents

20

0
40
20

20
10

0

0

-20
y

-10
-40

-20

x

Fig.1 Phase portrait of system(1) with a=33, b= 50, c=3,

0

-5

-10

-15

and d=15.

-20

0

5

10

15
d

20

25

30

2.2. Basic dynamical properties of the new
chaotic system

Fig.2 Lyapunov exponent diagrams of system(1) on x -axis
versus parameter d  (0,30)(I)Dissipation:

3. Unidirectionally coupled synchronization of
the new chaotic system

The dissipativity of system(1) is described as:© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

413

Analysis and unidirectionally coupled

In order to realize the unidirectionally coupled synchronization of the new chaotic system, the drive system isdefined as follow:

 x1  ax2  ax1  x2 x3
 x 2  bx1  x2  x1 x3
 x3  dx3  cx2 x2

lim e1 (t )  lim E1 ( s) 
t 

s 0

 lim
s 0

lim e2 (t )  lim E2 ( s) 
t 

s 0

and the response system can be described in the form of: y1  ay2  ay1  y2 y3  k ( y1  x1 ) y 2  by1  y2  y1 y3  k ( y2  x2 ) y 3  dy3  cy 2 y2  k ( y3  x3 )

 lim
s 0

(3)

sLy3e1
sLe1e3 
 lim
s

0
s  k 1s  k 1

lim e3 (t )  lim E3 ( s)  c lim
t 

(7)

sLy1e3 blim e1 (t )  lim
t


s

0
s  k 1s  k 1

0

Where e1  y1  x1 , e2  y2  x2 , and e3  y3  x3 .
And k is the coupling coefficient, it can make system(2)and system(3) compose a coupling system.
From Eq.(2) and Eq.(3), the error system is obtainede1  ae2  ae1  y2 e3  y3e2  e2 e3  ke1
which
can be expressed as:


e

(4)
 2 be1  e2  y1e3  y3e1  e1e3  ke2
Eq.(4)
utilizes Laplace transform, the error system is
3  cy 2 e2  cy 2 e2  ce2 e2  ke3
e3  deprovided
which
is described by:sE1 ( s)  e1 (0)  aE2 ( s)  aE1 ( s)  Ly2e3  Ly3e2  Le2 e3  kE1 ( s )sE2 ( s)  e2 (0)  bE1 ( s )  E2 ( s)  Ly1e3  Ly3e1 Le1e3  kE2 ( s)(5)
sE3 ( s)  e3 (0)  dE3 ( s )  cLy2 e2  cLy2e2  cLe2e2  kE3 ( s)From Eq.(5) , the error system is obtained as follow:

s 0

s 0

 c lims 0(8)sLy2e2 sLy2 e2  c lims 0 s  k  dsk dsLe2e2 sk d(9)

0From Eq.(7), Eq.(8), and Eq.(9), lim ei (t )  0 (i  1,2,3)t 

can be obtained. Theoretical analysis demonstrate
system(2) can synchronize system(3) completely.
4.Numerical simulation
In following, unidirectionally coupled synchronizationof the new three-dimensional chaotic system throughdesigning the appropriate coupling coefficient is
illustrated. The system parameters are chosen as a=33,
b=50, c=3, and d=15. The initial values of the drive
system(2) and the response system(3) are taken
as x1 (0)  3, x2 (0)  0, x3 (0)  2, y1 (0)  3,
y2 (0)  0.6, and y3 (0)  1.6, respectively. When the
coupling coefficient is taken as k  200, the simulation
results of state parameters are displayed in Fig.3.
Furthermore, the curves of synchronization error isdescribed in Fig.4. It’s clearly showed that
corresponding numerical simulations agree with theanalytical results.
15

10

(a)

1

5

y



 E ( s )  aE2 ( s )  e1 (0)  Ly2 e3  1sk a sk a sk aLy3e2 Le2 e3 sk a sk aLy1e3 bE1 ( s )e (0) E2 ( s )  2
s  k 1 s  k 1 s  k 1Ly3e1  Le1e3 s  k 1 s  k 1e3 (0)cLy2 e2  cLy2 e2  E3 ( s )  
s

k

d
sk d sk dcLe2 e2 sk dsLy3e2 sLe2e3  lims  k  a s 0 s  k  a

0

(2)

sLy2e3 alim e2 (t )  lim
t


s

0
sk ask a

(6)

0

-5

-10

-15
-15

-10

-5

0
x1

5

10

Based on the final value theorem of Laplacetransform, the error system is described in the form of:© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

414

15

Xue Li, Wei Xue

6. Acknowledgement

40
30

(b)

This work is supported by the Young Scientists Fund of
the National Natural Science Foundation of China(Grant No.11202148).

20

40

y

2

10

30

0
-10

20

-20

10

-40
-40

y

2

-30

0

-30

-10

7. References
-20

-10

0
x2

10

20

30

1.

40

-20
90

2.

-30

80

(c)

70
60

-40
-40

-30

-20

-10

0
x2

10

20

30

40

3.

y

3

50
40
30

4.

20
10
0
-10

0

10

20

30

40

50

60

70

80

90

5.

x3

Fig.3 Curves of state variable (a) ( x1 , y1 ) ; (b) ( x2 , y2 ) ;(c) ( x3 , y3 ) .

6.

0.12

0.1

7.

0.06

1

2

e, e , e

3

0.08

0.04

8.

0.02

0

-0.02

9.
0

0.5

1

1.5

2

2.5
t

3

3.5

4

4.5

5

Fig.4 Synchronization errors versus time t
10.

5. Conclusion
In this paper, a new three-dimensional autonomouschaotic system is proposed and investigated. Byutilizing theoretical analysis and numerical simulation,some basic dynamical properties, such as dissipation,
fractal dimension, Lyapunov exponent spectrum andchaotic dynamical behaviors of the new chaotic system
are introduced. In addition, based on Lyapunov stability
theory, unidirectionally coupled synchronization of the
new chaotic system is illustrated. Numerical simulation
are performed to validate the effectiveness of thepresented synchronization scheme. The synchronization
scheme proposed in our work can provide technicalbasis and support for the further study of the securecommunication and automatic control.

11.

12.

13.

14.

15.

Lorenz E N. Deterministic nonperiodic Flow. Journal of
the Atmosphere Science. 1963, 20(2): 130-141.
Rössler O E. An equation for continuous chaos. Physics
Letters A. 1976, 57(3): 397-398.
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 W B, Chen G R. A new chaotic system and its
generation. International Journal of Bifurcation and
Chaos. 2003, 13(1): 261-267.
Qi G Y, Du S Z, Chen G R, et al. On a 4-dimensional
chaotic system. Chaos, Solitons and Fractals. 2005, 23:
1671-82.
Yassen M T. Controlling chaos and synchronization for
new chaotic system using linear feedback control.
Chaos, Solitons and Fractals. 2005, 26: 913-920.
Li G H. An active control synchronization for two
modified chua circuits. Chinese Physics B. 2005, 14(3):472-475.
Kim J H, Park C W, Kim E, et al. Adaptive
synchronization of T-S fuzzy chaotic systems withunknown parameters. Chaos, Solitons and Fractals.
2005, 24: 1335-1361.
Meng J, Wang X Y. Generalized projective
synchronization of a class of delayed neural networks.
Physics Letters A. 2008, 22(3): 181-190.
Xu D L, Li Z. Controlled projective synchronization in
nonpartially-linear chaotic systems. Physics Letters A.
2002, 292(6): 1395-1402.
Yau H T, Yan J J. Desgn of sliding mode controller for
Lorenz chaotic system with nonlinear input. Chaos,
Solitons and Fractals. 2004, 19(4): 891-898.
Yang X S. On the existence of generalized synchronization in unidirectionally coupled systems. Computers andMathematics with Applications. 2001, 122(1): 71-79.
Shahverdiev E M, Sivaprakasam S, Shore K A. Lag
synchronization in time-delayed systems. Physics
Letters A. 2002, 292(96): 320-324.
Rosenblum M G, Pikovsky A S, Kurths J. From phaseto lag synchronization in coupled chaotic oscillators.
Physics Letters A. 1997, 78(22): 4193-4196.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan415

Fast collective photographic subject detection without pixels by an assumption about a shootand its elevation angle
Sora Tanioka

Department of Computer Science, National Defense Academy of Japan, hasirimizu 1-10-20
yokosuka, kanagawa 239-8686, Japan
Masao Kubo

Department of Computer Science, National Defense Academy of Japan, hasirimizu 1-10-20
yokosuka, kanagawa 239-8686, Japan
Hiroshi Sato

Department of Computer Science, National Defense Academy of Japan, hasirimizu 1-10-20
yokosuka, kanagawa 239-8686, Japan
E-mail: {masaok,hsato}@nda. ac. jp
www. nda. ac. jp/cs/

Abstract
In this paper a method that discovers socially attracted photographic subjects in real time is proposed. This needs
only, not pixel information of a digital photograph for this discovery, but at time of shoot, a GPS location, a bearingof the shoot and so on. This information is sufficiently lightweight so that we expect that a congestion-free photosharing wireless network service can be achieved. In this paper, a photography behavior model which considers anelevation angle of a shoot makes the accuracy better.
Keywords: Reality mining; Photograph, Event detection, Geo tagged images

request to the server while they are categorized only by
not-pixel information of digital photograph. The
information includes time to shoot, GPS and bearing ofshoot so on. According to this method, photo pixel data
congestion is unlikely because they are transmitted
sequentially. However, in the conventional method,
there is a practical problem that estimation accuracy ofthe object taken from the biased direction is worse. In
order to solve this issue in this paper, we propose amethod for estimating sterically an object by using theelevation angle (Fig.1).It is intended to improve theestimation accuracy by noting the characteristic of

1. Introduction
Interests at the present moment of others can beobserved by current Information Communication
Technology. Photograph based communication is more
vivid and secure than a text based information sharing
like Twitter. However, this communication system
usually becomes much slow because the heavy
congestion caused by transmission of hug pixel data.
Therefore this framework has not been used for a realtime information sharing system. So far researches
which attempt to improve this lower real time capability
have been proposed. It has been proposed a method oftransmitting only the best photos that match the viewing© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

416

Sora Tanioka, Masao Kubo, Hiroshi Satotilting the camera in order to capture the entire subject
into photographic frame.
This paper is organized as follows. First, related studies
are introduced. Next the algorithm by using theelevation angle information is proposed. Finally,
computer simulations perform, we verify the validity of
the algorithm.

Fig.2 (a)A case conventional methods solve. (b) A difficult
case for the conventional methods.

Fig.1 The concept of the poposed method: estimation ofsubject by a colletion of elevation angle data in non pixel
information in digital photographs.

2. Related works
Kubo et al. [1] introduces the photographer behavior
model using the angle of view by composition. Thereby
it realizes a method of automatically detecting its main
subject in each image. As shown in Fig.2(a) the methodthat adopts a planar estimation in estimating the subject
can be very accurately estimated when it is taken with awide angle to the object. However, such a examples arerare in the real environment. Also when it is taken froma narrow angle, it can be pointed out that the accuracy ishardly improved even if information is increased asshown in Fig.2 (b).

Fig.3 Subject location discovery algorithm by MAP estimation

3. The proposed system

3.2. Flow of the proposed system

3.1. Input data

Fig.3 describes the proposed algorithm. In this figure aset of photographic information is described by
C  {c1,L ,c N } . The each subject is estimated by MAP(Maximum a posteriori) estimation iteratively. Once a
subject is found, ci which seems to be a consequence ofshooting the subject is removed from C. By thisprocedure, the proposed method can discover multiple
subjects.

Here, small mobile devices such as a smart phone is our
main subject. The photographic information includes
latitude, longitude, altitude, azimuth of GPS, elevation
angle by accelerometer, and time to shoot in TIFF and
Exif data .

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

417

Fast collective photographic subject

3.5. Photographer behavior and elevation angle
Fig.4 illustrates the top 20 of the search result of“building” by Google image search. As these images tell,
the elevation angle is nearly horizontally when a photois taken from far. On the other hand, when it is takenfrom the vicinity of a building the elevation angle
becomes large in order to include the top of the buildingin the photo. Thus it was found to be estimated by theelevation angle at the time of photographing the
distance from the size and shooting point of the object.

Fig.4 Search result of ‘building+photograph’3.3. Photographer behavior model
The photographer behavior model is a probability
distribution Pshoot(cTi sj ) that a photographer at cTi
takes a photo for a given subject s j .
Ps h o(coT tis j )  Km o t i vVac to imop no(ss j i) t. i o n (1)where K mo t i v a t i onrepresents the motivation
 to take aphoto and Vcompos i 
) means the fitness of the
t(s
i jon
 composition. This consists of the elevation angle E and the bearing B.
(2)
Vc o mp o s(si j )
f (E
t i o
n )f (B)
However,in this paper, we assume that the photographsthat place a subject in the center of the frame are useful
to viewers and f (B)1.


3.4. Algorithm for estimation of subjects
First, the overview of the MAP estimation is described.

|c j |l og
 j,k   m a x { l Pos gh o(co Tt xo j,k ,  x ) ,l 0 }  l 0

(3)

x 1





For each grid s j in a given area, a weight as
Pshoot(cTi sj ) is added about C. After this weighting step,a grid of maximum weight is selected as a subject s inthis iteration. ci which seems to take a photo of this
subject is removed and s with ci is added the subject set
S. The next iteration starts with the new C. If C is empty
the algorithm stops..

Fig.5 Examples of the proposed photographer behavior modelof elevation angle

3.6. The proposed photographer behavior modelof elevation
The photographer behavior model of elevation f (E) is
proposed in this section. This f (E) is used for the Map
estimation procedure in the algorithm in 3.4.
(4
1(E  D) 2
Vc o m p o s(si jt) i of n(E) e x p(
)
 2
)
2

H
(5
D  arctan ())
2L The composition of a photograph is evaluated by anelevation angle E. We suppose that distribution of E
obeys anormal distribution. D is the best elevation
angle and H is the height of the subject and L is the
distance to the subject.σis a parameter.
Fig.5 shows examples of this f (E) .The motivation
kmot i vat i onis constant among all photographer. The yaxis indicates E(rad). The x axis shows the evaluation© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa
Convention Center, Okinawa, Japan



418

Sora Tanioka, Masao Kubo, Hiroshi Sato. There are 3 lines, namely the thin line, theVcomposi t i on
dotted line, bold line. The thin line shows a case fromfar and the bold line indicates a case from a close point.
Features of this model thing can sterically estimated.
Photographing direction of the imaging information that
could be collected by this can be accurately estimatedeven when biased. Although it is not easy to shoot at
different locations, it is easy to take by changing theangle of the camera. The more shot people at least the
number of each person is taken we can be expected to
be able to estimate the location of the subject.

4.

Experiment

By performing an experiment by computer simulations
to evaluate the performance of the proposed method.
The experiment is conducted in the configuration of
estimating the subject that can be taken only from thedirection of the difficult had been part in theconventional technique. Only one subject with theheight of 40 meters sets at （ x,y） ＝ （0,0） . The
dataset of photographic information is generated byMetropolis-Hastings algorithm. For reduction ofmemory resources, each elevation angle is discretized
into 18 pattern from 0 degree to 85 degree.
Fig.6 shows the result of estimation of location of thesubject. The bullet at (x,y)=(0,0) indicates the subject.
The result by the traditional method is (x,y)=(4,-46)
represented by ‘+’ mark. On the other hand, the result of
the proposed method is (x,y)=(-6,8) shown by a ‘x’mark. The proposed method can improve the accuracy.

Fig.6 the result of the experiment

References
1. Y. Kubo, M. Kubo, H. Sato, M. Hirano, and A.
Namatame, Understanding Geographic Attentions of
Crowd from Photographing Information in Journal ofAdvanced Computa- tional Intelligence and Intelligent
Informatics, Vol. 17, No. 6(2013) 890-903.

5. Conclusion
Collect only the shooting information of digital photos,
we have proposed a method for estimating a subject thathas attracted the social attention. The proposed a
photographer model using elevation and improved
conventional been difficult case. Consequently, it waspossible to improve the practicability.
This work is partially supported by JSPS 15K00433.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

419

Evaluation of a safety map generated from a collection of difference of Individuals
Viet Chau Dang

Department of Computer Science, National Defense Academy of Japan, hasirimizu 1-10-20
yokosuka, kanagawa 239-8686, Japan
Masao Kubo

Department of Computer Science, National Defense Academy of Japan, hasirimizu 1-10-20
yokosuka, kanagawa 239-8686, Japan
Hiroshi Sato

Department of Computer Science, National Defense Academy of Japan, hasirimizu 1-10-20
yokosuka, kanagawa 239-8686, Japan
Akira Namatame

Department of Computer Science, National Defense Academy of Japan, hasirimizu 1-10-20
yokosuka, kanagawa 239-8686, Japan
E-mail: dangvietchau@gmain.com, {masaok,hsato,nama}@nda. ac. jp
www. nda. ac. jp/cs/

Abstract
In this paper, a method to generate traffic safety maps automatically from a collection of GPS logs of a drivers'
smartphone. So far methods that residents can update their traffic environment better by their opinion are requiredfor. The proposed method which uses a collection of individual summary not raw data achieves a consistent safety
map while their privacy is protected moderately. In this paper, the summarization method is explained.
Keywords: Traffic Safety Map; Risk Estimation; Smartphone Sensing; Collective Intelligence; Reality mining;

using data recorded by their Internavi in-vehicle unit,
along with police reports and user contributions to theirsafety map website[8]. The locations where sudden
brake occurs as risky spots are plotted automatically inthe map.

1. Introduction
In this paper, we propose a method for generating traffic
safety maps based on differences in individual
recognition of the road environment by using
smartphone data from various users.
Safety map is a kind of risk analysis result of traffic
environment. There are some works related to risk
analysis. For example, Google Live Traffic is a service
that utilizes GPS data from Android smartphones toestimate traffic jams based on average GPS speed.
Honda initiated a project for traffic safety map in 2013

Fig.1. Process for generating traffic safety map© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan420

Viet Chau Dang, Masao Kubo, Hiroshi Sato, Akira Namatame

accident. The relation between the current speed and themoving distance is described in the following braking
equation:dxx
 v 0 (1  ) n
(1)
dt
Xs

X

where s Xs is the initial distance to the incident, x x is
x = 0
current distance to the incident (x=0 when then > 0vvehicle begins to slow down),
n>0, and 0 v0 is
the initial speed. By fitting by eq(1), it can tell the
distance Xs to an incident.

Fig.2. IMAC: it supposes that each grid forms a two statesstate Markov chain, which has with two states of being:dfree or o c c u p i e (occ)free
and occupied.

2.2. Generating a traffic-incident map with IMAC

In this paper, we propose a method for integrating acollection of hazard locations based on differences indrivers’ recognition of the road environment. The
method is inspired by STEP technique of risk analysis intraffic safety field. We suppose that if every drivers havethe same recognition of a crossroad, the level of danger
will depend on one’s driving skill. By contrast, where
one driver recognizes a crossroad as passable and whereother drivers recognize it as impassable, accidents are
more likely to occur.

We explain the method to estimate incident for
generating a user'suser’s traffic -incident map [16], [17].
Saarinen's[1]6,. Saarinen et al. proposed IMAC[2], amodel, a method to describe for describing dynamic
environment byenvironments with an occupancy grid
map, , and this model is utilized to represent a traffic
incident map[1]. Inincidents. With the IMAC model, an
environment for mapping is evenly divided into grids.
Each grid is modeled as a two states-state Markov chain,
which has with two states of being: free oroc c upi e d(occ)free and occupied (see Fig.2). The
IMAC model is suitable for representing dynamic
objects, such as traffic signalsignals or incident in roadenvironment bytraffic incidents, with a grid map.
Furthermore, itIMAC is advancedused to estimate thetransition -probability parameters (  exit ,  entry ) byobserve) by observing the occurrence of the state
Occupioccupied
ed or Free
free and the transitions
between them.

To estimate the grid states from the observed events,
Saarinen proposed a method to observe for observing
two processes forin each grid in a dynamic environment(4) , (5) :(2):as in Eqwith Eqs.

2. The Proposed Method
The process for generating the proposed traffic safety
maps is depicted in Fig.1. We refer to the proposed
traffic safety map as a TimeDiff map. The map
integrates a collection of traffic-incident maps that are
generated from users’ smartphone log data (proposed in[1]). In this section, we explain the method forconstructing a user’s traffic-incident map which is theleft side of Fig. 1. Finally, we describe the proposedsummarization method for integrating these incident
maps into a global hazard map which is shown by theright of Fig. 1.

)

exit # occupied to free  1 )# free tooccupied  1, entry # occupied# free(2)2.1. Simple Braking Model(SBM)
We explain a method for generating a traffic incident
map that can be used to interpret how a driverrecognizes the road environment. The Simple Braking
Model (SBM) is used for estimation of location of ahazard spot from a log stream of a smartphone.
We assume that whenever a vehicle encounters anincident, it always decreases its speed to avoid an© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

421

Evaluation of a safety

Fig. 5. Time differences in the judgment of a road environment

Consider a scenario where two vehicles are driving
along a road. At this time, the road upon which thevehicles are traveling is recognized as Free (see Fig. 5).
As the two vehicles approach a red traffic signal, theyencounter an incident, and the road at this location isrecognized as Occupied. Consequently, both vehicles
come to a stop. At this time, both vehicles must change
their recognition of the road environment from Free to
Occupied. The TimeDiff methodTimeDiff method
focuses on the time that each vehicle changes its status.
It is clear that the occurrence of an accident depends on
the recognition of both drivers. If two vehicles change
their status at the same time, they both have the same
recognition of road environment. Thus, an accident will
not occur unless a driver makes a mistake in controllingthe vehicle. By contrast, the more difference in time
between when the recognition changes - that is, between
when one vehicle recognizes the road as Free and otherrecognizes it as Occupied - the higher the likelihood of acollision. Thus, the global TimeDiff map is based on theroad-recognition level of the users.
Let D be the set of users. We assume that all users
generate their own traffic incident map (  exit ,  entry ) byusing the update method in Eq. (2). We define thehazard level of each grid g in the global map with Eq.
(3). Suppose two users, i and 
j in D, initially report an
Occupied status. Suppose further that user i changes thisstatus to Free before user j does. Alternatively, supposethat i and j initially report a Free status, and that thestatus from user i switches to Occupied before that ofuser j. The total difference in time between such statuschanges (whether Occupied or Free) from all pairs ofusers i and j in D is calculated as follows:o
TimeDiffg    c ij E(Pdiff (i, j))  E(Pdif(i, j))(3)iD j iD

Fig. 3. Input: a log stream of smartphone

Fig. 4. Output; the user’s traffic -incident map

Fig.3 shows an example of a GPS log of a vehicle whichis the input to generate a traffic incident map. Fig.4 and
5 illustrate the traffic incident map, Fig.4 shows the
IMAC map of  exit and Fig.5 depicts the map of  entry .
Small circles indicate incidents of this driver. As thesefigures show the proposed method can detect incidents
at eachwe explain
 corner adequately. In the next section
a method to merge multiple users’ traffic incident maps.
2.3. TimeDiff method[1]This section proposes an integration method forgenerating a global map from a collection of IMAC
users’ grid maps for (  exit ,  entry ). This method is basedon the average time that users hold a different
recognition of the same road environment. We call thisthe TimeDiff
The global map (i.e., the TimeDiffmethod.
map) shows the total time for road-recognitiondifferences between users in a given area.



where E(Pdiff (i, j)) is the difference in time between
switches when each pair of users initially reports a Freeostatus, and , E(Pdif(i, j)) is the difference in time
 between switches when each pair of users initially© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016),Jan. 29-31, Okinawa Convention Center, Okinawa, Japan422

Viet Chau Dang, Masao Kubo, Hiroshi Sato, Akira Namatame

reports an Occupied status. c ij is a probability that user iand j meet.
E(Pdiff (i, j)) 



Bf
0



t  Pr ob[ X i  occ  t](1 Pr ob[ X j  occ  t])dt(4)o
E(Pdif(i, j))  t  Pr ob[ X  free  t](1 Pr ob[ X  free  t])dt
Bo



0

i

(5)

j

We suppose that this follows a Poisson process so thatthese 2 terms can be calculated briefly. For example,
when  i,exit =0.01,  j,exit =0.1, Bo=10, c ij =1, thenoE(Pdif(i, j)) =1.5505.





3. ExperimentFig. 7. Spot F

4. Conclusion
In this paper, a method for generating traffic safety
maps based on differences in individual recognition of
the road environment by using smartphone data fromvarious users is proposed.

The global hazard map (i.e., the proposed TimeDiffmap) is shown in Fig.6. In the Fig.6 the spots where
accidents occurred in the last 5 years are also illustrated.
This proposed map agrees with the accident data.

References
1. V. C. Dang, H. Sato, M. Kubo and A. Namatame,
Building Safety Road Maps Based on Difference ofJudgment of Road Users by their Smartphone,International Journal of Advanced Computer Science andApplications(IJACSA), 6(9)(2015) 15-23.
2. J. Saarinen, A. Henrik, and J. L. Achim, Independent Markov
chain occupancy grid maps for representation of dynamic
environment., in Proceedings of IEEE/RSJ International
ConferenceonIntelligent
RobotsandSystems(IROS)( 2012)3489-3495.

Fig. 6. Process for generating traffic safety map

Also spots where a field survey is conducted are shownin Fig.6 For example spot J has many pedestrians
because it is in front of a gate of a train station. Spot H
is an ext of a parking area of restaurants. Fig.7 shows
spot F. Spot F is on a straight road, but some accidents
have been occurring. As shown by Fig.7 spot F is alonga park and has a zebra crossing. The proposed safety
map can detect a zebra crossing without a signal.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

423

Endeavor to adopt GIS data on evacuation decision making model
Saori Iwanaga

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, nama@nda.ac.jp

Abstract
By Multi Agent Simulation MAS, we focused on contagion of evacuation decision making on real map assumingthat not all people evacuate at the time of disaster. Then, we found that for contagion of evacuation decision making,local neighborhood is needed and connection of sub network is needed. But, there we faced on difficulty of obtaining
realistic population data on map, because Census Bureau data consists of not position data but numbers and properties.
In this paper, we attempt to use geographic information system GIS data and population data, then we will simulate
population with heterogeneous agents and their decision making.
Keywords: Multi agent simulation, Evacuation, GIS data, Population data.

time of disaster. We supposed that all agents evacuate.
We confirm that it takes more time to complete
evacuations if psychological conditions exist at the timeof disaster. There, similar to other evacuation simulations,
we set that all people decide to evacuate. In anotherstudy4, to deal with the condition that not all people
decide to evacuate and we deal with contagion ofevacuation decision making by cascade model. We foundthat for contagion of evacuation decision making, local
neighborhood is needed and connection of sub networkis needed. In these studies, we faced on difficulty ofobtaining realistic population data on map, because
Census Bureau data consists of not position data but
numbers and properties. By Wise5, acquiring good data
is frequently a challenge in research efforts, and manytechniques have been devised in order to address thisneed. MAS incorporate theoretically boundless

1. Introduction
The Great East Japan Earthquake was a 9.0 magnitudeundersea mega thrust earthquake that occurred on Friday,
March 11, 2011. After the earthquake, since there weremany people who were unable to effectively evacuate,
damage was considered to have spread. In fact, the ratioof victims to the residents in the Great East Japan
Earthquake was lower than that in the Meiji Sanriku
Earthquake, which was an 8.2 magnitude undersea megathrust earthquake that occurred off the Pacific coast ofJapan on June 15, 1896. It is believed that various disasterprevention methods take effect1. Although according to aquestionnaire and interview, about 30% of the people
evacuate in Japan2.
In our former study3, we adopted a multi-agent
simulation that focused on psychological conditions at© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan424

Saori Iwanaga, Akira Namatame

2.2. Wise5 Method

heterogeneity, and consequently can be designed to takeextremely rich data. For extension to many cities, it isimportant to obtain realistic population data on map. In
this paper, we attempt to use geographic information
system GIS data and population data, then we will
simulate of population with heterogeneous agents andtheir decision making.

Wise used method discussed the statistical quality of thegenerated population by Barthelemy and Toint9. It
proceeds in three main successive steps: generation ofindividuals, generation of household type's joint
distributions, and generation of households by gathering
individuals. The main idea in these generation steps is touse data at the most disaggregated level possible to define
joint distributions, from which individuals andhouseholds are randomly drawn. The method also makes
explicit use of both continuous and discrete optimization
and uses the χ2 metric to estimate distances between
estimated and generated distributions. The new generator
is applied for constructing a synthetic population of
10,300,000 individuals and 4,350,000 households
localized in the 589 municipalities of Belgium.
To automatically generate reasonable housing
distributions, the set of all roads labeled “residential" by
OpenStreetMap are extracted, and roads are assigned to
Census tracts if both of their end points are located within
the tract. Going tract by tract, the number of homes in the
tract from the Census data is determined and the implied
density of housing is calculated through the relative
cumulative length of residential roads and number ofhousing units associated with the specific tract. The
process then generates houses at the given density along
all of the residential roads. After they have beenassembled, households are assigned to these generated
houses.

2. Population Synthesis and arrangement
People are arranged in houses and workers or students arearranged in their workplaces or schools. In this paper, we
focus on population arranged in houses. Almost Census
data include population and the number of homes in
Census tract, but it is difficult to find the data including
geographic locations of houses in the town.
2.1. Our method
In our former this study, we adopted 10 towns in Kurecity where my academy located and 30 shelters, as shownin Fig. 1. We follow the Ohata’s moddel6 , which
simulate of evacuation from Tsunami in Hokkaido area,
and reproduced on Multi agent simulator Artisoc7. The
area is 2.28km by 1.88km. According to the Census dataof March 31, 2013, the residents per household averaged
1.937. We set an agent as two people, which is theaverage population of a household. It is reported thatmany people evacuated by family unit8. And we set manyroads and intersections on Bing map by manual as shownin Fig. 1. And we set agents on an intersection node foreach town at the first time step. Number of agents are
10,088 and number of intersection nodes and links are
213 and 384.

2.3. Crooks10 Method
The model combines and utilizes both raster and vector
data structures into a single simulation, utilizing both
ESRI grid and shapefiles with a range of different
resolutions. These data capture information about thetarget location, an 8 by 6 km area around Haiti’s capitaland most densely populated city, Port-au-Prince. To
initialize the population of agents in a way thatrealistically parallels the distribution of individuals
throughout the city, to estimate of population distribution
over the study area, they use population counts from the2009 LandScan11 (2011). LandScan data divides theFig. 1. Town, shelters and network of roads in Kure city.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

425

Endeavor to adopt GIS

Table 1. The planning and control components.

Population
synthesis

Our method

Wise method

Crooks method

Population Census data

Census data

LandScan data

Households Population/2

Barthelemy methodology

---

Homes

Bing Map

OpenStreetMap

---

Roads

Bing Map

OpenStreetMap

OpenStreetMap

Data

Data
Arrangement of
population

Roads

ManualAutomatic

Automatic

Position Nodes

ManualAutomatic

Automatic

Homes

ManualAlong residential roads

Evenly distribute on gridworld into roughly 1 km by 1 km squares and assigns a
population count to each cell. Such a data source is usefulfor estimating baseline populations where census data is
missing. When using this data to initialize our agents,
they assume that the agents are evenly distributed
throughout the 1 km by 1 km area, excluding the parts ofthe environment that are determined from other GIS datato contain water or similar obstacles. Based on thisinformation and methodology, the simulation is
initialized with approximately 1.3 million agents.

For arrangement of population, we will adapt shapefileof road (shp), which is GIS data. Because, there are somany nodes, we thin down the data by GIS Data
Converter14. Nodes are shown in Fig.2, too. In fact, thenodes have links. We arrange agents on the nodes at firstbased on population of the 1 km by 1 km cell.

3. Discussion
Almost towns have distorted boundary, but, computer
simulation well-acquainted grid or cell data. So, toconsider arrange of population, it is easy to use LandScan
data for population synthesis. But, definition of agent
depends on the case of simulation. For evacuation ofdisaster, it is reasonable to set agent as households.
As for arrangement of population, for extension to
many cities, it is easy to use GIS data, such as
OpenStreetMap. For behavior of agents, it is good to set
agents on node not on over cell at first step, to easily walkon the road.

Fig. 2. Population and nodes in Port-au-Prince, Haiti.

4. Example

References

For population synthesis, we will adapt LandScan data,
which divides the 1 km by 1 km squares and assigns a
population count to each cell. Fig. 2 is example of modelof 8 km by 6 in Port-au-Prince, Haiti12. A cell means 1
km by 1 km square and large population area is deep incolor. We export layer file (lyr) of population to text file
by ArcGIS13 and convert to comma separated values file(csv), then we import to Multi agent simulator Artisoc7.

1. M. Ushiyama and S. Yokomaku, Great East Japan
Earthquake and Disaster Information <Special Edition>
Feature of human damage (in Japanese), Journal ofDisaster Information Studies 10 (2012) 7-13
2. H. Hirose, Why Do People Fail to Escape (in Japanese)(Shueisha, Tokyo, 2004)
3. S. Iwanaga and Y. Matsuura, Considering Psychological
Conditions in a Tsunami Evacuation Simulation,
Information Processing and Management of Uncertainty© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

426

Saori Iwanaga, Akira Namatamein Knowledge-Based Systems: series Communications in
Computer and Information Science 442 (2014) 437-446
4. S. Iwanaga and A. Namatame, Contagion of evacuation
decision making on real map, in Proc. of 8th International
Conferenceon
Bio-inspired
Information
andCommunications Technologies (ACM Digital Library,2014)
5. S. Wise, Using social media content to inform agent-basedmodels for humanitarian crisis response (George Mason
University, 2014)
6. D. Ohata, N. Takai and H. Kagami, Spatial evaluation oftsunami refuge facilities in the central Kushiro city:
Simulation of evacuation behavior from tsunami utilizing
multi-agent system Part 2 (in Japanese), Architectural
Institute of Japan 612 (2007) 87-91
7. S. Yamakage, Modeling and expanding artificial societies-The possibilities of an artificial society (Shosekikobo
Hayama Publishing, Tokyo, 2009)
8. T.E. Drabek, and K. Boggs, Families in disaster: reactions
and a relatives, Journal of Marriage and the Family (1968)
443-451
9. J. Barthelemy and Ph.L. Toint, Synthetic Population
Generation Without a Sample, Transportation Science 47(2012) 266 – 279
10. A.T. Crooks and S. Wise, GIS and agent-based models forhumanitarian assistance, Computers, Environment and
Urban Systems 4 (2013) 100–111
11. LandScan, http://www.ornl.gov/sci/landscan/, Accessed
06. Aug. 201512. Haiti
model,
http://finder.geocommons.com/overlays/20302, Accessed
06. Aug. 2015
13. ArcGIS, http://www.esri.com/, Accessed 06. Aug. 2015
14. GIS
Data
Converterfor
Artisoc,
http://mas.kke.co.jp/modules/tinyd0/index.php?id=12,
Accessed 06. Aug. 2015

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan427

Automatic Generation, Creativity, and Production of Narrative Contents
Takashi Ogata

Faculty of Software and Information Science, Iwate Prefectural University152-52, Sugo Takizawa, Iwate 020-0693, Japan
E-mail: t-ogata@iwate-pu.ac.jp

Abstract
According to the concept of this organized session, in this presentation, we will consider the study of automatic
narrative or story generation as a research field of artificial intelligence in the following levels: generation, creation,and production. Firstly, we study the technologies of narrative or story generation systems based on mainly
artificial intelligence and cognitive science. Next, the artistic and aesthetic problems of narrative creation are
considered in the relationships with the technologies and systems of narrative generation. Further, issues toward
organizational or social narrative production are presented. The survey and discussion will conduct based on topics
in this session relating to a game and advertising systems and our integrated narrative generation system.
Keywords: Multiple narrative structures model, Integrated narrative generation system (INGS), Geino information
system (GIS), Narrative generation, Narrative production, Narrative creation.

word of production is closely associated with theorganizational or social narrative making. On the other
hand, the narrative “creation” is a word in a different
level that has an artistic and aesthetic nuance.
This paper, at first, present the author’s multiple
narrative structures model as a background of thisconsideration. Next, we respectively explain “Integrated
Narrative Generation System: INGS”1 and “Geino
Information System: GIS”2 as frameworks for single
level narrative generation and multiple level narrative
production. Lastly, according the above frameworks, we
discuss the possibilities of narrative-based systems andcontents technologies based on the relationships among
narrative generation and production, future narrative
creation as the level of the artistic and aesthetic
problems of narrative.

1. Introduction
This organized session, Automatic Generation,
Creativity, and Production of Narrative Contents,
considers the study of automatic narrative or story
generation as a highly interdisciplinary research field of
information technologies, such as artificial intelligence(AI) and cognitive science, and literary theories, such as
narratology.
For the author, this issue can be focused on thefollowing levels: generation, production, and creation.
The first automatic narrative “generation” means thetechnological aspect of systems that automaticallygenerate narrative texts. In particular, it indicatesnarrative generation mechanism as a single subject. By
contrast, we call narrative generation phenomenon as acollective
andorganizational
level
narrative“production”. This differentiation is dependent upon anarrative model by multiple narrative structures that theauthor has proposed as in the following section. The

2. A Model of Multiple Narrative Structures©The 2016International Conference on Artificial Life and Robotics (ICAROB 2016),Jan. 29-31, Okinawa Convention Center, Okinawa, Japan428

Takashi Ogata

For the author, one of the basic concepts for narrative
generation is “multiplicity”. Narrative generation
systems are necessary to design as the model of multiple
narrative structures. Although this model has various
sides from the formal aspect of narrative to the aspect ofnarrative semantics, an important characteristic is thatnarrative generation is divided into the next two aspects:
narrative generation and reception process based on asingle subject and narrative production and consumption
process based on many subjects or collective authors. In
other words, narrative production (and consumption)
process contain many narrative generation (andreception) process. Narrative production andconsumption process is the field that is socially opened
and is also strongly related to documents and narratives
produced by people on WWW.
Further, there exists the narrative multiplicity among
various elements, such as story and character, in anarrative. For instance, in character of the kabuki, anactor in a work appears with the multiplicity, such as
one or several characters in the drama, a person having
actual and contemporary body, a person as an existence
having the life story with the historical inheritance, etc.
From the viewpoint of the “Expanded Literary
Theory: ELT”3 of the author, this model is partiallyrelated to the theory of polyphony by Bakhtin4 and thetheory of inter-textuality by Kristeva5. The theory by

Bakhtin calls polyphony novels such types of novels
that are not integrated by single voice of an author andthe theory by Kristeva proposed an idea that all types of
documents can respectively be exists in the networkwith many documents and the fragments including
novels and the other genres, whether or not the author isconscious the fact. Both theories amplified narrative
multiplicity in respective ways. The multiple narrative
structuresmodel comprises these
theoreticalframeworks.
3. Integrated Narrative Generation System
INGS is a developing narrative generation architecturethat corresponds to a single level narrative generation
subject. Fig. 1 shows the overview of INGS that various
narrative techniques and knowledge are organized in asynthesizing system associating some literary theories
for realizing ELT.
INGS is consisted of several components related tonarrative generation process and control, narrativetechniques for narrative structures’ generation, andnarrative knowledge including relatively large-scaledictionaries.
At first, the generation process is divided into thethree phases: story, discourse, and expression. A “story”
generated by the story generation mechanism means thecontent of a narrative or a temporal sequence of events.

OutputGeneration results by each mechanism (i.e., a story, a
discourse, a natural language, a music, and an image).

Input
A set of generative parameters

Entire control

Story content
knowledge base

Event/statetransformation
knowledge base

Conceptual
dictionaries

Story control

Discourse control
Discourse
techniques

Story
techniques

Imagedata baseStatemanagement
mechanism

Storymechanism

NaturalMusic
Imagelanguage mechanism mechanism
mechanism

Letternotation
dictionary

Discourse
mechanism

Function call

Data reference

Fig. 1. An overall architecture of INGS1© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan429

Automatic Generation, Creativity, andA story is represented by a tree structure consisting of
the three basic units: event, relation, and state. A story
structure is generated using one or more story
techniques and states associating with the events are
made according to another mechanism. Through thisprocessing, an event that is a basic element of a story
and a discourse are also generated. Each event isbasically consisted of a verb concept and some noun
and other concepts. A “discourse” transformed by thediscourse mechanism means the structural aspect of
how to narrate a story. A discourse structure and thesurface media to narrate it are distinguished andaccordingly it is described with a conceptual
representation form. “Expression” means the aspect ofnarrative representation by various surface media
including natural language, visual media, and music.
The control mechanism relating to the process, inmany parts, currently utilizes a tentative approach basedon parameters and rules for the above three phases. Anda circulative process control, which does not limit thegeneration order among the phase to fixed order, is also
adopted in the meta-level mechanism.
The generation of each tree structure of a story anda discourse is performed by using story techniques anddiscourse techniques that generate or transform from an
old tree structure to a new tree structure using
knowledge elements including conceptual dictionaries
for verb and noun concepts. The surface narrative
representation is conducted on the process.
An important design concept is “versatile” whichmeans the potential to be used and applied to any
purposes relevant to narrative generation without thelimitations of themes and genres. In the future
perspective of INGS, we aim at developing a versatilenarrative generation architecture through which diverse
narratives’ generation with various themes, genres,
structures, etc is done. It means that INGS can beapplied to various narrative production mechanisms as asingle level narrative generation subject.
4.

diverse narrative generation processes and is a multiple
narrative phenomenon. For the author’s research plan,
INGS is contained in GIS.
GIS is a model in which multiple narrative
generation and production mechanisms are conducted to
do the social level tasks of narrative. INGS and GIS
respectively correspond to narrative generationreception and narrative production-consumption. In GIS,
various levels of narrative generation processes are
driven by authors, receivers, characters, actors, andactresses. For example, a kabuki play is a collection ofmultiple narratives built around original scenarios,
authentic related histories, and the actors’ private scenes.
The construction will be connected with the plan for a
future narrative generation system comprised of several
other narrative generation systems.
Fig. 2 shows an original conceptual version of
GIS2. This idea of a conceptual model labelled GIS was
originally based on the survey and analysis of Japanesefolklore, performing arts including the kabuki, andangles of the modern entertainment business. A geinoorganization such as an entertainment agency or
promoter is a synthetic narrative productionconsumption system that aggregates a variety ofnarrative generation-reception processes of scenarios,
lives of actors and actresses, etc. GIS is acomprehensive and multiple framework in which
various narrative generation processes are driven byauthors, receivers, characters, actors and actresses,
directors, producers, etc. It is therefore one of thesystems of multiple narrative structures.

Geino Information System

The Japanese word geino has a complex meaning that
includes elements of shrine rituals, entertainment, andamusement. From the viewpoint of narrative generation
research, a geino system a whole that multiply contains© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan430

Takashi Ogata

Geino
Field
Geino
event

Receptionmechanism

Narrativeas image

Reactionmechanism

Narrative
strategymechanism

Geino
Producer
Mechanism

Geino event
sequenceLife
event
sequence

Life event

Narrative
production
mechanism

Trail
information

Narrative
interpretation
mechanism

necessary to have a history or a story through thecirculative generation, namely the level of narrative
production-consumption. In the advertisement study as
narrative in Ogata, in particular, INGS or the part as asingle advertising generation mechanism will be able to
be integrated into GIS.
Such an approach to GIS through narrative
generation application systems design and development
will be possible in a variety of systems, such as anentertainment game system9, a new narrative approachby the model of the kabuki, and so on. Further, narrative
production and consumption model in which GIS is
included will link to the world of contents businessincluding the results of narrative generation andnarrative generation function itself.
On the other hand, there is an important issue inanother direction. By the word of narrative “creation”,
we mean various values beyond only generation andproduction of narrative. In particular, the problem ofcreativity of narrative is associated to diverse values,
such as entertaining value, artistic value, literary value,
aesthetic value, managerial value, etc. For instance, acontents business company relating to narrative
production needs to sell any values through providing
any narrative contents. For the author, this problem isrelated to the problem of narrative multiplicity or
multiple narrative structures model. We have an ideathat narrative multiplicity results in narrative values.
The verification of this idea or hypothesis will be
attempted dependent upon the development and social
practice of GIS through various application systems.

Geino history

RecieverMechanism

Interpretive
image

Narrative
expansion
mechsnism

Geino
work
resource

Geino
work

Life work
resource

Life work

GeinojinGeinojinresource

Fig. 2. The original GIS model2

5. Generation, Production, and Creation of
Narrative
Although INGS is a tentative, but progressing systemthat has been developing, GIS remains conceptual
design level. The design and development of GIS forbridging narrative generation-reception by INGS andthe narrative production-consumption is planed
dependent upon the following process throughapplication systems’ development and experiment.
For example, we have developed a multimedia
narrative generation system called KOSERUBE6. It was
designed as an application using INGS inside the system,
but we do not intend to have a function through whichnarrative generation and reception process is repeated tostore a kind of history of narrative generation, namely
narrative production. By designing as such circulative
production-consumption system, we will implement a
prototype of GIS based on the KOSERUBE.
On the other hand, Kawamura7 and Ogata8 havedeveloped advertising generation systems based on eachoriginal ideas. Advertisement as a social system is

6. Conclusion
This paper presented the author’s multiple narrative
structures model as a background of this consideration
and respectively introduced INGS and GIS as
frameworks for single level narrative generation andmultiple level narrative production. Next, based on theabove frameworks, we discussed the possibilities ofnarrative-based systems and contents technologies
based on the relationships among narrative generationand production, further narrative creation as the level ofartistic and aesthetic problems of narrative.
In the future, INGS and GIS are necessary toorganically linked as an integrated system, therefore,levels of narrative generation-reception and production-© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan431

Automatic Generation, Creativity, andconsumption also need to be associated each other.
Further, the level of narrative creation should to beconsidered in the relationships the above multiple
system framework. Designing, developing, andexperimenting (including social level) INGS, GIS, andthe application system are main future works.
References
1. T. Akimoto and T. Ogata, An Information Design of
Narratology: The Use of Three Literary Theories in a
Narrative Generation System, Int. J. Visual Design 7(3)(2014), pp.31-61.
2. T. Amino, Y. Kawamura and T. Ogata, Hierarchical
Generation of Geino-Idol Stories: Toward Geino
Information System and Narrative Marketing, Proc. 17th
Congress of the International Association of Empirical
Aesthetics (2002), pp.549-552.
3. T. Ogata, Expanded Literary Theory for Automatic
Narrative Generation, Proc. Joint 7th International
Conference on Soft Computing and Intelligent Systems
and 15th International Symposium on AdvancedIntelligent Systems (2014), pp.1558-1563.
4. M. Bakhtin (M. M. Бахтин), Проблемы позтики
Достоевского, Иэд, 2е. (Москва, 1963). Transl. C.
Emerson, Problems of Dostoevsky’s Poetics. (Universityof Minnesota Press, 1984).
5. J. Kristeva, Desire in Language: A Semiotic Approach to
Literature and Art. (Columbia University Press, 1980).
6. S. Imabuchi, T. Akimoto, J. Ono, and T. Ogata, T.,
KOSERUBE: An Application System with a Propp-based
Story Grammar and Other Narrative Generation
Techniques, Proc. the 6th International Conference on
Soft Computing and Intelligent Systems & the 13th
International Symposium on Advanced Intelligent
Systems (2012), pp.248-253.
7. Y. Kawamura, An Analysis of the Rhetoric ofCommercial Film: Toward the Building of a Commercial
Film Production Support System based on Image
Rhetoric, Proc. the 2003 IEEE International Conference
on Systems, Man and Cybernetics (Washington, DC,2003), pp.993-1000.
8. Y. Zhang, J. Ono and T. Ogata, An Advertising
Rhetorical Mechanism for Single Event Combined withConceptual Dictionary in Narrative Generation System,
7th Int.l Conf. on Natural Language Processing andKnowledge Engineering (2011), pp.340-343.
9. J. Ono and T. Ogata, Architecture of a Narrative
Generation System based on a TRPG Model: The Use ofan Integrated Narrative Generation System for
Knowledge Acquisition (Preliminary Version), Proc. the
3rd Int. Symposium on Computer and Networking,(Sapporo, 2015).

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

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A Design Plan of a Game System including an Automatic Narrative Generation Mechanism:
The Entire Structure and the World Settings
Jumpei Ono

Graduate School of Software and Information Science, Iwate Prefectural University152-52, Sugo, Takizawa, Iwate 020-0693, Japan
Takashi Ogata

Faculty of Software and Information Science, Iwate Prefectural University152-52, Sugo, Takizawa, Iwate 020-0693, Japan
E-mail: g236m001@s.iwate-pu.ac.jp, t-ogata@iwate-pu.ac.jp

Abstract
In this paper, we propose a narrative generation system based on the scenario generation method in tabletop roleplaying games (TRPGs). This system uses an integrated narrative generation system (INGS) that synthesizes
various narrative generation mechanisms. The TRPG is an analog game in which one or more players (PLs) play
the role of characters within the framework of the narrative prepared by the game master (GM). A PL promotes thedevelopment of scenes, which leads to the completion of the narrative. In particular, we concentrate on the “world
setting” that consists of the rules or constricts for a narrative and also the components defined by the rules and thestage setting of the narrative. In an ordinary scenario, a GM sets a basic world setting for the narrative generation
through the interaction between the PLs. We consider preliminarily prepared scripts corresponding to various world
settings in addition to the detailed mechanism to use it.
Keywords: integrated narrative generation system, narrative generation, table-top role playing game, world setting

narrative5, story content knowledge for constituting thestructure of the narrative6, each event concept in theconceptual structure of narrative is connected in front
state and behind it by a state-event translation
knowledge base4.

1. Introduction
We design a narrative generation system based on agame format that refers to a scenario generation method
in tabletop role-playing games (TRPGs)1. This system is
an application of the integrated narrative generation
system (INGS)2-4 that was developed using the gamemethod; the proposed system generates a narrative
based on the interactions between a user and the system.
The INGS consists of a concept generation
mechanism to generate a structure of narrative, and asurface representation mechanism to represent thenarrative through sounds, words and pictures. The INGS
has noun conceptual dictionary and verb conceptual
dictionary for constructing an event concept in the

2. Overview of the TRPGThe TRPG is an analog game where a player (PL)becomes a character and plays each role within theframework of a narrative prepared by the game master(GM). This role-playing by the character reaches aconclusion as the scene develops, and as a result, a big
narrative is completed.
Further, Fig. 1 shows the relationship between theconcepts of a “scenario.” The “scenario” consists of the©The 2016International Conference on Artificial Life and Robotics (ICAROB 2016),Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

433

Jumpei Ono, Takashi Ogata“world setting” and the “scene sequence. The “scenario”
is formed during the progression of a game through theinteraction between the GM and a PL.

indicating the dynamic change in a state, and therelationships among them that can be used to connect
these events structurally. The narrative is a structure
consisting of events and relationships in the INGS. Thisis called an attribute frame. It records the properties andcharacteristics of individual elements and is connected
to an event through an ID.
In the “generation of narrative” step, the scenario
was fleshed out of the scene sequence based on theexchanges between the GM and the PL. Therefore, thispart is the core of the system.
On the basis of a scene shown by the GM, the PL
suggests a scene. The scene chain expansion utilizes thestory (story contents) generation function of the INGS.
The extended scene sequence is shown as a scenes
sequence to the PL by the above mentioned process.
When a progress condition is met in the present person,
attribute frame of a thing, the place after this processing,
GM stop that GM receives the suggestion of the scenefrom PL temporarily and show a “scene (climax)” and a“scene (ending)”.
In the “Output of a conclusive result” refers to thestage that outputs the scenario information in the final
stage, expanding the scene sequence one way or another.
For example, a novel-like sentence is thought to becreated and produced as output. In this case, the gameprocess is equivalent to a novel generation process. The
system outputs a sentence as shown in the generation
process. A structured text is obtained by compiling thesentences outputted for every scene. The user includes
more names in the scenes and completes a story bymodifying the outputted end results.

Scenario
Scene sequence
Scene

Char
acter

Scene

Thin
gs

Loca
tion

Scene

Time

Restr
ict

World setting

Fig. 1 Scenario and its components
We will now discuss the TRPG in detail. First, a“scene sequence” in the “scenario” is created based on
the actual incident or fictitious it. Next, the “scene
sequence” by the GM is modified and expanded as thegame progresses. The “world setting” includes thesetting of the game such as the place and time andconsists of the limitations or rules applicable to the“scenario.”
3. Generation Process
The generation process is divided into three steps—“preparation of necessary information,” “generation of
narrative,” and “output for a conclusive result.
In the “Preparation of necessary information” step
implies the preparations required to obtain theinformation for an initial scenario prepared by the GM.
The user inputs the information that is necessary for the
GM to generate the initial state of the world setting andthe scene sequence. Based on the “world setting”, theuser inputs the editing contents of the conceptual
dictionary, as described. The user can operate thelimitations of a verb. For example, if the user adds “acat” to the limitations of a verb concept “buy,” a cat
becomes to be able to “buy” a thing like a human.
By using the initial scene sequence, a user inputs anevent that will play a key role in the scenario; forexample, “a man obtains a treasure.” An initial scene
sequence will consist of elements. This scene will be apartial structure of a story expressed in a conceptualform. It consists of states indicating the static
information of characters, things, locations, events

4. World SettingThis section shows a conceptual dictionary in the INGS,
and describes the “world setting”.
4.1 Conceptual dictionary in the INGS
The conceptual dictionary is related to the “world
setting”. The conceptual dictionary is used forcontrolling the world setting in a designed system. The
conceptual dictionary includes a noun conceptual
dictionary, verb conceptual dictionary, and modifier
conceptual dictionary (conceptual dictionary foradjective, adjective verb, and adverb); it storesknowledge at the conceptual level as components. A© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan434

A Design Plan ofcharacteristic function in verb conceptual dictionary is
to describe the “restriction” while using the elements inevents such as “~ do ~.” To adjust this “restriction,” thesystem should be able to modify a realistic event such as“Taro eats an ice cream” into an unrealistic event suchas “The car eats an ice cream.”game. Those purpose is stored an attribute frames in thestates.
E) Things“Things” represent the tools that a character uses;
particularly these are the elements that are necessary forachieving the purpose of the character.

5.2 Elements in the World Setting

The above five elements are the “world setting”
component. When the designed system generates anarrative and progresses game, the elements is required
relationship between characters, things, locations, thatare included in the “world setting”. Concretely, there is(1) a relationship among characters, things, andlocations, (2) an ability to achieve a certain purpose, (3)a motive of each character. In addition, for (2), therestrictions in the “word setting” limit the ability of eachelement.

The “world setting” is like a stage for the progression ofa narrative. The “world setting”, define the type ofcharacters, things, locations, time, event and action thatcharacters can perform. As a similar example, Nomura
suggested the “background world”7. The “backgroundworld” consists of “space”, “existence”, “time”, and thecomponent generates “phenomenon” and “event”.
Here, we mention each element in the “world
setting”.
A) Restrictions
The “world setting” including the stage setting of thegames such as places or times, has a kind of limitation
in a “scenario.” The narrative could include “a humancannot fly in the sky without a vehicle” in the realistic“world setting.” On the other hand, “a person can fly inthe sky” in the fantastic “world setting.” In this way, therestriction is a kind of a rule for the action possibility.
B) Locations“Locations” is the space where characters or things exist
and, for the objective achievement of a character, it canbe either a beneficial or an unfavorable element. Theyshow the environment of the stage for the narrative. For
example, a character is police, climate etc. in therealistic “locations”.
C) Time“Time” is the time when characters or things exist andaffects the styles of characters or things. For example,
for the case “to light,” the character of the reality world
uses a lighter, whereas a more primitive world character
uses a firestone.
D) Characters“Characters” are all the people appearing in the
narrative, which include the characters controlled by theuser. Each character has the purpose, in the case of acharacter being controlled by a PL, and the PL defines
the purpose of the character as the game progresses;
other characters are defined by the GM at the start of the

5.3 Generation of a Narrative through the World
SettingFig.2, Fig.3 and Fig.4 show (1) a relationship among
characters, things, and locations (Fig.2), (2) an ability toachieve a certain purpose (Fig.3), (3) a motive of eachcharacter (Fig.3).

protect

Character A
grudgeCharacter B

murderCharacter C

findCharacter D
has

has

Thing αLocation X

Fig. 2 (1) Relationship among characters, things, andlocations“Character A” can search [any person]“Character A” can fight against [any person]“Character B” dies of a [disease]“Character C” can interfere with “Character A”“Character C” can fight against [any person]“Character D” can cure [disease] using “thing α” at“location X”
Fig. 3 (2) Ability to achieve a certain purpose© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

435

Jumpei Ono, Takashi Ogata“Character A” wants to help “Character B”“Character C” wants to disturb “Character A”“Character C” wants to kill “Character B”“Character D” wants money
Fig. 4 (3) Motive of each character

References
1. “Tabletop role-playing game - Wikipedia, the freeencyclopedia”,
https://en.wikipedia.org/wiki/Tabletop_roleplaying_game (Last access, 2015, July).
2. T. Ogata and A. Kanai, An Introduction of Informatics ofNarratology: on Thought and Technology of Narrative
Generation (Gakubunsha, Tokyo, 2010). (In Japanese)
3. T. Akimoto and T. Ogata, An Information Design of
Narratology: The Use of Three Literary Theories in a
Narrative Generation System, The International Journalof Visual Design, 7(3) (2014) 31-61.
4. T. Ogata, Development of the Integrated Narrative
Generation System, Proc. of the 13th Forum on
Information Technology (2), (2014) 323-330. (InJapanese)
5. T. Ogata, Building Conceptual Dictionaries for an
Integrated Narrative Generation System, Journal of
Robotics, Networking and Artificial Life, 1(4) (2015)
270-284.
6. T. Ogata and J. Ono, Language Notation Dictionaries andthe Use in an Integrated Narrative Generation System,
IEICE Technical Report, 115(69) (2015) 25-30. (InJapanese)
7. K. Nomura, Sory Sekai no Tsukurikata (How to Making a
Story World) (Shinkigensya, Tokyo, 2014). (In Japanese)

The system generates a “scene sequence” shown in
Fig. 5 by using the relationship (Fig. 3) and motive (Fig.
4). The “Character C” inserts “C interferes with A” into
behind either scene for to achieve its own purpose.
Scene 1. “B” gets a [disease]→
Scene 2. “A” searches for a doctor→
Scene 3. “A” hears the wish of “D”→
Scene 4. “A” grants the wish of “D”→
Scene 5. “D” starts the treatment of “B”→
Scene 6-a. “B” is saved, or
Scene 6-b. “B” is not saved
Fig. 5 Generated scene sequence
The system gives an appearance based on the world
setting in Fig. 2, as shown in Fig. 5. The appearances ofeach element are change based on “world setting”. For
example, in the realistic “world setting,” the “character
A” is a detective and the “character C” is a climate. In
contrast, in the fantastic “world setting,” the “character
A” is hero, “[disease]” is a curse, and the “character C”
is a devil.
5. Conclusion
In this paper, we proposed a narrative generation system
based on a game format that refers to the scenario
generation method in a TRPG. This system can support
story generation through the interactions with the user.
In the present conditions, the system design assumes
that a human being controls the PL side and generates astory accordingly. When a system computer controls the
PL side, there is a possibility of generation of a story
that is unexpected for a human user. However, in thepresent conditions, the scene for the PL side is decided
by the user and the procedure for this generation is notclear. Therefore, the scene is difficult to be designed byan automated PL. In future, we would like to automate
the process of scene generation on the PL side.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan436

A Way for using the Verb Conceptual Dictionary in an Integrated Narrative Generation
System: the Use of Co-occurrence Information on Verb Concepts
Takashi Ogata

Faculty of Software and Information Science, Iwate Prefectural University152-52, Sugo Takizawa, Iwate 020-0693, Japan
Jumpei Ono

Graduate School of Software and Information Science, Iwate Prefectural University152-52, Sugo Takizawa, Iwate 020-0693, Japan
E-mail: t-ogata@iwate-pu.ac.jp, g236m001@s.iwate-pu.ac.jp

Abstract
This study presents a method of selecting verb concepts based on analysis of co-occurrence information of verbs inmodern Japanese novels collected from the Japanese digital library, “Aozora Bunko”. We plan to incorporate theproposed method into an integrated narrative generation system (INGS) that synthesizes various mechanisms (story
generation, sentence generation, and so on) for narrative generation. We are currently developing such a system. In
this study, we provide an overview of INGS and examine mechanisms related to the proposed method. In a verb
conceptual dictionary that we have developed, as verb concepts of a variety of difficulty (comprehensibility) are
mixed, the objective in this study is to adjust difficulty of verb concepts by using co-occurrence information onverb concepts. Our hypothesis is that the original concepts of co-occurrence and the resulting concepts have a
proportional relationship. We investigate this hypothesis by incorporating proposed mechanism into an INGS.
Keywords: Co-occurrence information, Integrated narrative generation system, Verb conceptual dictionary, Verb
concept

1. Introduction

2. The Architecture of an Integrated Narrative
Generation System

This paper mainly focuses on an event generation in thestory generation mechanism and verb conceptual
dictionary in an integrated narrative generation system(INGS)1-3 (these details are mentioned in the section 2).
An event composed of a verb concept and noun
concepts. However, their concepts are selected
randomly in an event generation in the INGS. Therefore,
we previously proposed a method of the verb concept
selection based on frequency information4. Here, we
propose a method of the verb concept selection based on
co-occurrence information.

The architecture of our INGS is shown in Fig. 1. The
system is composed of a concept generation mechanism
used to generate a story structure and edit it (discourse),
and a surface representation mechanism to represent anarrative. These mechanisms use dictionaries (storedconcepts5, language notations6), story contents
knowledge base (stored knowledge for generating
structure of narrative)7, the state-event translation
knowledge base (stored knowledge for connecting
between each event and states in the conceptual
structure)3.

©The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

437

Takashi Ogata, Jumpei Ono
Conceptual expression

Input：
parameter

Story

Discoursestory techniques and story contents knowledge. The
objective of this paper is to revise this selecting method.

Surface expression

Sentence

Music

Movie

4. Acquiring Co-occurrence Information

Knowledge
Output：narrative

Dictionary(concept,
notation)

State-event
translation
knowledge base

Story contents
knowledge baseA) Used Texts and Method
We used “Aozora Bunk”, which includes 4980 texts ofprimarily Japanese novels published from 1872 to 1963,
to acquire co-occurrence data of verbs. At first, cooccurrence information of all verbs in the texts was
acquired.
The following (1) is a formula in KH Coder7 to
calculate the co-occurrence data. In the formula, Sa isequal to the total number of sentences that include verb
a in the text; Sb is the total number of sentences that
include verb b in the text; Sa ∩ Sb is the total number ofsentences including both a and b in the text; and Sall is
the total number of sentences included in the text.

Fig. 1. Architecture of INGS
3. Overview of Conceptual Dictionaries
Conceptual dictionaries have a hierarchical structurefrom higher to lower concepts.
A) Hierarchy of Noun Concepts
The noun conceptual dictionary currently contains
115,765 terminal concepts and 5,809 intermediateconcepts. Each intermediate concept has a list ofhyponymy terminal concepts, the number of depths in
the hierarchy, the super-ordinate concepts, and a list of
the hyponymy intermediate concepts.
B) Hierarchy of Verb Concepts
The verb conceptual dictionary has 11,951 terminal
concepts and 36 intermediate categories. Fig. 2. shows
the description of the verb concept “bite.” Each terminalverb concept has the following three elements: 1) a
sentence that includes the verb (“sentence-pattern” in
Fig.2.), 2) the types of required noun cases (“caseframe”), and 3) defining the range which each noun
concept in the case-frame(s) requires (“constraint”).

𝑓=|𝑆𝑎 ∩𝑆𝑏 | |S𝑏 |||𝑆𝑎 ||

⁄

S𝑎𝑙𝑙

(1)

B) Result
The co-occurrence information was corresponded to
4866 terminal verb concepts in the verb conceptual
dictionary in INGS. As an example, Table. 1 shows cooccurrence data of “bite”. Each verb concept connects
to 143.44 verb concepts in average.
Table. 1. Co-occurrence data of “bite”
Co-occurrence
Relating verb concepts
information
打 ち 上 げ る 1[shoot up], 慕 う
141.1954
1[pout], etc.
飽 き る 1[loss intent], 謀 る
70.5977
1[deceive], etc.
…<Omitted>…
1.035663 出来る 7[be able to]
1.001386 戻る 1[return]((name 齧る 2 [bite])(sentence-pattern“N1 が N2 を齧る”[N1 bite N2])
(case-cons-set((case-frame((agent N1) (object N2) …))(constraint((人[human] -死人[corpse] -人間〈人称〉[human<person>]-準人間[semi-human])(食料[food] -調味料[flavoring] -飲物・たばこ[drink・cigarette] –汁[soup])))…) …)

Fig. 2. Description of the verb concept “bite”

5. Preliminary Questionable Research
We evaluated the effectiveness in the verb concepts
selection based on the acquired co-occurrence
information.

C) Generating an Event Concept in the Story
Generation Mechanism
A story generation process expands a story structure byusing story techniques. This process includes an event
generation. An event is generated based on thedescription of the case-frame. A verb concept that isused in an event concept generation is decided based on

5.1 Preparation
Four lists of verb concepts, which were extracted fromthe stories generated by using a partial mechanism in© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan438

A Way for using

INGS, were used in the questionable research.
A) A Used Story Generation Method in INGS
The story structures were generated by using “story
contents grammar”8 and the mechanism that we havebeen developing for a part in INGS. A hierarchy of thestory contents grammar has a layer of “functions”. The
level is the most fundamental and a sequence offunctions form a basic structure of a story. And, eachfunction is extended to the lower level where one ormore verb concepts are instantiated by selected from thecandidates.
Fig. 3. shows used functions and the fixedsequence. Each of the functions is rewritten by thelower levels to reach to the level of one or more
concrete verb concepts.

concepts in one story selected randomly from the abovestories.
[Random] The mechanism generated 5 stories by
repeatedly using a verb concept randomly for the priorverb concepts and finally decided 41 verb concepts in
one story selected randomly from the above stories.
In addition, in the following cases, a verb concept
is selected randomly: 1) the first verb concept in story
generation, 2) when two or more candidates exist in theselection timing, 3) when no candidates with cooccurrence data in the selection timing.
5.2 First EvaluationA) Goal and Process
The goal is to check the relationship between the degreeof co-occurrence information and the degree ofcomprehensibility by the users. The subjects were five
students. They read four kinds of verb concepts lists(max, middle, min, and random) to select an value from
1 (easy) to 5(difficult). We set a time limit of fiveminutes for each verb list.
B) Result
As shown in Table. 2 shows the result, we could not see
clear differences among the subjects. In short, cooccurrence information of verb concepts does not
influence the comprehensibility.

<Absentation>→<Interdiction>→<Violation>→<Villainy>→<Mediation>→<Beginning counteraction> → <Departure> → <First “Function” ofdonor> → <Hero's reaction> → <Receipt of amagical agent> → <Guidance> → <Struggle> →<Blanding> → <Victory> → <Liquidation> →<Return>→<Unrecognized arrival>→<Unfoundedclaims> → <Recognition> → <Exposure> →<Transfiguration>→<Punishment>→<Wedding>Fig.3. A used sequence of functionsB) Making Verb Concept Lists
We acquired co-occurrence data using the above story
generation mechanism by the following method. Whenthe mechanism selects a concrete verb concept fromgenerally many verb concepts, it utilizes co-occurrence
data as shown in the next types.

Table. 2. Evaluation result[Max][Max] The story generation mechanism generated 5
stories by repeatedly using a verb concept that has thestrongest co-occurrence with the prior verb concept andfinally decided 39 verb concepts in one story selected
randomly from the above stories.
[Middle] The mechanism generated 5 stories by
repeatedly using a verb concept that has the medium cooccurrence with the prior verb concept and finallydecided 41 verb concepts in one story selected randomly
from the above stories.
[Min] The mechanism generated 5 stories by repeatedlyusing a verb concept that has the weakest co-occurrencewith the prior verb concept and finally decided 47 verb

A
B
C
D
E
1.23 2.54 2.87 3.08 2.82

Average
2.51[Middle] 1.29 2.73 3.00 2.80 3.10
1.26 2.50 3.02 2.85 2.65
[Min]

2.59

[Random] 1.15 2.60 2.93 2.80 2.63

2.42

2.46

This result was similar to the case of noun concept
selection based on co-occurrence information that we
previously implemented9. Also, frequency and cooccurrence are clearly different methods to adjust verb
concepts selection in INGS.
5.3 Second EvaluationA) GoalOn the other hand, in the noun concepts selection using
co-occurrence information, we confirmed another© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan439

Takashi Ogata, Jumpei Ono

method. In the method, a noun concept is associatedwith other noun concepts that have the similar degree ofco-occurrence information9. We adapt this method toverb concepts selection.
B) Process
For the above stories of max, middle, and min, we
calculated the absolute values of difference (D) between
the value of a verb concept X and the value of the next
verb concept Y. Fig. 4 shows the procedure. As the caseof noun concepts selection, we consider if D is lower, it
indicates the similar comprehensibility. However, it isnecessary to be actually investigated the consideration.
In this paper, we check whether lower D is resulted byhigher degree of co-occurrence. It enables to adjust
selecting verb concepts using co-occurrence of verb
concepts.

Evaluation ofverb X : 1.0
Evaluation ofverb Y : 2.0

have co-occurrence relationship for prior verb concept
enables to select them by similar degree ofcomprehensibility. The proposed method may useful toadjust the verb concepts selection in INGS.
In the future, we plan to precisely improve themethod to finally integrate methods of concepts
selection in event generation in INGS using frequency
and co-occurrence of words.
References
1. Ogata, T. and Kanai, A.: An Introduction of Informatics
of Narratology: On Thought and Technology of Narrative
Generation, Gakubunsha, 2010. (In Japanese)
2. Akimoto, T. and Ogata, T.: “An Information Design of
Narratology: The Use of Three Literary Theories in a
Narrative Generation System”, The International Journalof Visual Design, 7(3), 31-61, 2014.
3. Ogata, T.: “Development of the Integrated Narrative
Generation System”, Proc. of the 13th Forum on
Information Technology II, 323-330, 2014. (In Japanese)
4. Ogata, T. and Ono, J.: “Controlling the use of Semantic
Concepts in an Integrated Narrative Generation System:
The use of the Verb Frequency Information”, Proc. of
21st International Symposium on Artificial Life andRobotics, 2016. (to appear)
5. Ogata, T.: “Building Conceptual Dictionaries for an
Integrated Narrative Generation System”, Journal of
Robotics, Networking and Artificial Life, 1(4), 270-284,
2015.
6. Ogata, T. and Ono, J.: “Language Notation Dictionaries
and the Use in an Integrated Narrative Generation
System”, IEICE Technical Report, 115(69), 25-30, 2015.
(In Japanese)
7. Higuchi, K.: “Quantitative Analysis of Textual Data:
Differentiation and Coordination of Two Approaches”,
Sociological Theory and Methods, 19(1), 101-115, 2004.
8. Imabuchi, S. and Ogata, T.: “A Story Generation System
Based on Propp Theory: As a Mechanism in an
Integrated Narrative Generation System”, Advances in
Natural Language Processing, Springer, 7614, 312-321,
2012.
9. Ono, J. and Ogata, T.: “Noun Concepts Selection in
Events Based on the Co-occurrence Relation in Verb
Concepts and Noun Concepts: Mechanisms for the Story
Generation in an Integrated Narrative Generation
System”, Proc. of the 14th Forum on Information
Technology II, 239-242, 2015. (In Japanese)
10. Ono, J. and Ogata, T.: “Selecting Noun Concepts Basedon Accounting Data: As a Mechanism in the Integrated
Narrative Generation System”, IEICE Technical Report,
114(366), 49-54, 2014. (In Japanese)

D(X,Y)=| 1.0-2.0 |= 1.0

Fig. 4 Calculating DC) Result
Table 3 shows the result of calculating D. In this Table
3, we did not use verb concepts that were selected
randomly. The average of D in [Max] was smaller thanothers. If lower D is related to the similar degree ofcomprehensibility of the verb concepts, we may be able
to use this result to adjust the degree ofcomprehensibility of verb concepts.
Table 3. The result of calculating D[Max]

The number of theevaluated verb concepts
12[Middle]

15

0.76

[Min]

14

0.96

MethodAverage of D
0.43

6. Conclusion
This paper preliminarily proposed a method of a verb
concept selection by using co-occurrence information
for the conceptual event generation mechanism in INGS.
The results indicate a next design direction of the verb
concepts generation in INGS: to use verb concepts that

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan440

A Viewing Experiment on the Effects of Advertising Story
Yoji Kawamura

Faculty of Business Administration, Kinki University, 3-4-1 Kowakae
Higashi-osaka, Osaka 577-8502, Japan
E-mail: kawamura@bus.kindai.ac.jp
www.kindai.ac.jp

Abstract
This research lays out the conception and functions of developed Commercial Film Production Support System(CFPSS). The research then explains the results of a viewing experiment that utilized CFPSS. In this experiment
where commercial films of beer were utilized, the following findings were obtained: in inducing interest, theevaluation of image type of advertising story is high; in stimulating willingness to buy, the evaluation of provider
type is high; and mise-en-scène or editing attracts interest and the advertising story associated with product function
and the supporting production and distribution stimulates willingness to buy. These findings gradually clarify thefollowing creative know-how of the creator; to attract consumer interests by focusing on the stories of consumer
situations in case consumers do not aware or understand their products or services; to arouse their willingness tobuy by focusing on the products and stories of their acceptance and effects in case consumers have a certain level ofunderstanding and interests in products or services.
Keywords: Commercial Film, Advertising Story, Editing, Viewing Experiment, Interest, Willingness to Buy

effect of advertising rhetoric on viewers.3,4,5 However,these studies do not include analyses related tocommercial films.
This paper aims to extract rules to create more specificexpressions of commercial films based on a viewing
experiment using CFPSS.
This experiment aims to show how an advertising story
influences viewers using commercial films derived fromthe four types of advertising stories generated from theimage shots that were likely related to the viewer’s
associations and experiences.

1. Introduction
There are case studies into effective commercial films
with a focus on marketing and advertisement.1 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.2 However, thesestudies provide insight into advertising expressions froma particular 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 and analysis of the

2. CFPSS
Fig. 1 shows the plan of CFPSS.6 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, that

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

441

Yoji Kawamura

between beer and the viewer. The sample included 28
male and 27 female participants between from 20 to 23
years of age. The input from the viewers included such
keywords and statements as: Asahi, foam, throat, bitter, mug A glass to drink when tired, such as after working apart-time job, is the best.
 Foam, twenty, summer, baseball, green soybeans The body gets itchy after drinking a beer.
 Alcohol, throat, bitter, taste, foam
 For me, beer is not something necessary, but I think
it is necessary on social occasions.
The database was then searched for images using asummary of the viewers’ sentences as input. An
advertising story was selected from the OT, PT, CT, andIT types, and the generated image shot shown toviewers who provided their evaluations and feedback
for each.

Fig. 1. CFPSS Plan.

searches and classifies image shots based on keywords
and sentences. The system includes a function to
generate the storyboard based on the selection ofadvertising story, and playback in the order arranged in
the storyboard. The menu of advertising story is asfollows;7
 Provider story type (PT): The primary structure
expresses the provider’s story (production anddistribution, product function, effect on company).
This is the “story of the product” and “story of thecompany.” Consumer story type (CT): This primary structure
expresses consumers’ stories (consumption
situation, product acceptance, consumption effect).
This is the “story of consumption.” Overall type (OT): This structure generally
expresses both the provider’s and consumer’s
stories. This is represented as PT + CT.
 Image type (IT): This structure expresses images
related to the consumer situation, though does not
express product acceptance and consumption
effects in the film. Rather this structure represents a
product function. This category also includes
structures expressing an image that does not belong
to the consumer, provider, or product function.

3.2. Results
Table 1 summarizes the image shot composition andorder of the generated commercial film. The numbers in
the advertising story columns indicate the order of the
shots. Fig. 2 reports the viewer evaluations of interest
and willingness to buy related to the four types ofcommercial films. IT has a greatest evaluation forinterest, while PT has a greatest evaluation forwillingness to buy.
The summary below shows the consolidated contents
for films that stimulate interest and willingness to buy
for items with responses in more than two cases. The
frequency of comments is indicated in parentheses.

3. Viewing Experiment3.1. Data and Method
In this experiment, commercial films of beer were
utilized. For the study, 55 viewers provided four to fiveassociated keywords about beer and simple sentences(approximately one to two lines) about the relationshipFig. 2. Interest and willingness to buy by advertising story.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan442

A Viewing Experiment on

Table 1. Image shot composition and order of the generated commercial film.
Advertising Story
Phase
Production
andDistribution

Consumption
Situation

Advertising Story
OT
PT
CT
1
1
2
3
2
4
5
3
1
2

4

3

4
Product Function

Product Acceptance

5
6
7
8

6
7
8
9

6
7
8
9
10

Consumption Effect
Effect on Company

9
10

5

10

Abstract of Shots
IT

1
2
3
4
5
6
7
8

9
10

Introduction of the production area of the beer
Production process of the beer (low-temperature aging)Transporting the beer tank
The cook sings a song while cooking
The products are being transported on the belt conveyor
A lifesaver monitoring the sea
A man walking toward home
Appear to drink a beer, even without a glass
People talking to the camera man
The consumers asked to tour the factory
People talking at the stock exchange
People preparing meal at the poolside
Attempting to borrow a bottle opener for beer at a seaside house
Catch the copy of a product “All for Delight”
Promotion of urban scenery and the productFoam spilling from the beer mug
Canned beer breaking the ice
Consumer drinking beer in the cup
Drinking beer while socializing
Making a toast with a large number of people
Making a mysterious face
Drinking beer and recounting funny stories
Urban (overseas, China) scenery

3.2.1. Areas of Interest

3.2.2. Willingness to Buy

Primarily related to the advertising story (including
advertising concept):
 Copy added: “All for the customer’s ‘delight’!!” (4) Acceptance added: Demonstrations of an energetic
way of drinking (2) Production added: When the provider’s efforts toprovide delicious beer is apparent (3)Primarily related to mise-en-scène: Product: When the beer is widely zoomed in (2) Buddy added: Where there are many people arehaving fun (6) Scene added: Scene showing that the beer isactually consumed with a gesture, even in theabsence of a glass (14)Primarily related to editing: Connection: The plot unravels gradually (2) / Thereis a perceived association from the creation of aprofessional story extending to the consumer’s
consumption (2) Irregularity: The type of commercial message wasinitially unknown (2)Primarily related to the advertising story (including
advertising concept):
 Copy added: Deceived by the phrase “All for thecustomer’s ‘delight’!!” (7) Situation added: A scene depicting drinking in a bar,
imagining that the cuisine was easy (3) / A scene ofa man going home (2) Acceptance added: Many scenes indicating productconsumption (7) Effects added: The beer was consumed with zest(4)
 Production added: The story clearly shows the
producer’s efforts to provide delicious beer (2)Primarily related to mise-en-scène: Product: The beer overflows from the cup (2) / Thebeer package in ice representing the chill anddelight (3) Scene added: Drinking beer with a gesture isimpressive (2)© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan443

Yoji Kawamura

3.3. Discussion

4.

3.3.1. Interest and Willingness to Buy

An experiment using commercial films for beer
generated the following findings:
 The IT type created the most interest through avariety of image shots.
 The PT type stimulated the highest willingness tobuy using a variety of image shots showing the
product function and the associated production anddistribution.
 Mise-en-scène or editing attracts interest, and theadvertising story associated with product function
and the supporting production and distribution
stimulates willingness to buy.
These findings gradually clarify the relationshipbetween the commercial film techniques and theireffects and significantly contribute to the symbolizationand systematization of creator’s inherent creative knowhow.

The IT type generated a high level of interest through avariety of image shots. However, the willingness to buy
was highest for the PT type using image shots focusing
on the product’s function and production anddistribution.
Fig. 3 summarizes the results in terms of interest andwillingness to buy, or to the films’ representation
techniques (advertising story, mise-en-scène, editing).
The results indicate that mise-en-scène and editing
attracts interest, and the advertising story associatedwith the product function and illustrating production
and distribution stimulates willingness to buy.
3.3.2. Strategic Rules of Advertising
From these results, it is conceivable to set the following
strategic or tactical rules of advertising communications.
These rules are applicable to the intelligent robot thathas know-how of advertising communication between
robot and human being.
 For the unfamiliar people for products, the robot
stimulates the people’s interest by presenting thewonder or extraordinary scene and information (forexample, the environment or scene that the
products are hidden consciously, the scene where
there has been a lot of people etc.).
 The robot tries to grasp whether the people’sinterest has been evoked. If the people’s interest has
been aroused, the robot stimulates the willingness
to buy by presenting a scene of functions,
acceptance and effects of the product.

Conclusion

Acknowledgements
This work was supported by JSPS KAKENHI Grant
Number 22500102.
References
1. D. W. Stewart and D. H. Furse, Effective television
advertising: A study of 1000 commercials (Lexington
Books, Lexington, MA, 1986).
2. H. A. Laskey, E. Day and M. R. Crask, Typology of main
message strategies for television commercial, J.
Advertising 18(1) (1989) 36-41.
3. E. F. McQuarrie and D. G. Mick, Figures of rhetoric inadvertising language, J. Consumer Research 22(4) (1996)
424-438.
4. B. A. Huhmann and P. A. Albinsson, Does rhetoric
impact advertising effectiveness with liking controlled?,
European J. Marketing 46(11/12) (2012) 1476-1500.
5. I. G. Theodorakis, C. Koritos and V. Stathakopoulos,
Rhetorical maneuvers in a controversial tide: assessing
the boundaries of advertising rhetoric, J. Advertising
44(1) (2015) 14-24.
6. 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 andCybernetics , (Washington, DC., 2003), pp.993-1000.
7. 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).

Fig. 3. Areas of interest and the willingness to buy.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan444

Examination of Robotic Aerospace Engines Maintenance Supported by Augmented Reality
through Cloud Manufacturing
Mosab Alrashed

Manufacturing Department, School of Aerospace, Transport and Manufacturing, Cranfield University, CranfieldBedfordshire, MK43 0AL, UK
Yaser Yadekar

Manufacturing Department, School of Aerospace, Transport and Manufacturing, Cranfield University, CranfieldBedfordshire, MK43 0AL, UK
John Ahmet Erkoyuncu

Manufacturing Department, School of Aerospace, Transport and Manufacturing, Cranfield University, CranfieldBedfordshire, MK43 0AL, UK
Yifan Zhao

EPSRC Centre for Innovative Manufacturing in Through-life Engineering Services, CranfieldBedfordshire, MK43 0AL, UK
E-mail: m.alrashed@cranfield.ac.uk, y.m.yadekar@cranfield.ac.uk, j.a.erkoyuncu@cranfield.ac.uk ,yifan.zhao@cranfield.ac.uk
www.cranfield.ac.uk

Abstract
This Paper aims to develop a demonstration using a mobile device to apply augmented reality to allow remote
maintenance activities. This research was focused primarily on the aerospace industry studies as there was a research
gap as there are few relevant researches in the manufacturing field and accepted industry needs. The targets wereachieved by developing and designing controls in the engineering steps to create the optimum conditions foraugmented reality. The design process pursued in four stages: first, research the current practice of using augmented
reality remotely in manufacturing maintenance; second, classify maintenance remote assisting problems andweakness in augmented reality; third, design and develop software and hardware, depending on the case study for a
prototype solution to remote maintenance enhanced by the augmented reality that can be demonstrated and tested;
finally, validate the developed software and demonstration using industrial experts and authentic reports. The resultsof this project were an open source software updated IDEA 2.0 and a hardware robot, dubbed McRobot, which was
automated to conduct the inspection process and support the augmented reality requirement. Three type of evaluations
were done at the end, and all of them recorded positive feedback, validating this project for future development.
Keywords: Augmented Reality, Virtual Reality, Mixed Reality, Cloud, NDT, Remote Maintenance, Robot
Maintenance, Wearable Maintenance Gadgets.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

445

446

is a part of augmented reality, and combining the two
produces mixed reality. In Figure 1, the cross-section of
the reality egg demonstrates the relationship between AR
and VR: the egg shell represents mixed reality; the egg
white signifies augmented reality; and the yolk is virtual
reality.

1. Introduction
Maintenance is a costly process that requires time, moneyand effort. Maintenance duration is one of the main
factors that must constantly be reduced as much as
possible, as it has dire implications in terms of effort andmoney. This is particularly true in the aerospace field,
where even one minute can have a significant cost
implication. Major manufacturing and management
teams in academic and industrial institutions haveprovided solutions to the maintenance quandary. In one
such example, Toyota TM developed concepts andstrategies to reduce maintenance as much as possible interms of cost and time (Biehl et al. 2004). These days,
technological gadgets have entered the arena and use
cutting-edge technology to provide solutions tomaintenance issues. While there are many technologies
being developed for manufacturing activities, several
come particularly recommended for future work anddevelopment (Akan et al. 2011; Fuhrmann & Encarnac
n.d.; Neumann & Majoros 1998). One of the current
developing technologies that is of increasing interest isaugmented reality (AR). The focus on AR from some of
the biggest industries and firms is growing, as theseplayers are coming to realise the valuable benefits of thetechnology. Also, the use of new technologies in
information systems and advanced networks such as
Cloud Manufacturing has allowed the manufacturingindustry to apply new and complex manufacturingsystems. Cloud Manufacturing is one of the emerging
technologies that helps to exceed the limitation that been
faced in AR process. AR can be described as thecoexistence of virtual content with the real world throughthe, compositing and supplementing of a particular areain the real world using powerful technology tools(Azuma et al. 2001). To understand AR, one must also befamiliar with VR (virtual reality) and mixed-reality.
Numerous definitions are available for VR, but they are
all similar in meaning. A simple definition might be thesimulation of virtual objects in a virtual environment
through the use of a wearable gadget like glasses or
gloves. (Steuer 1992). Mixed reality (or hybrid reality, as
it was formerly known) can be defined as virtual objects
that can be enhanced in a real environment or vice versa(Pan et al. 2006; Zauner et al. 2003).Virtual reality (VR)Augmented
Reality

Mixed Reality

VirtualRealityFig. 1. Reality Egg representing theaugmented, virtual and mixed reality.

relation

between

2. Methodology
The method for this project can be broken down into
three stages. The first was a build-up stage, whereincollected data, gathered information and explored thetechnology as a research term. This phase also comprised
a literature review and industrial requirements. Byfinishing the first phase and moving to the second stage,reserved for design and development, the whole projectplan became clearer and was able to identify the gaps thatneeded to be filled. Finally, the last loop methodology
stage included the process required to build up the designthat was developed in stage two. It was beneficial to
discover that moving backward from stage three(process) to stage two (design and development) could
happened easily if updates are needed in the design ordevelopment algorithms. Moreover, the same goes forstage two (design and development) and stage one(research), if one needs to add new knowledge or correctsome misunderstanding.
2.1. First Stage: Research
A literature review had been undertaken that included thebasics of augmented reality, the reinforcement of AR,
remote maintenance in manufacturing fields (withcomparisons), the applications of AR , the benefits ofusing AR with remote support, and the scientific gapsthat AR needs to fill. Also, one of the objectives of thisproject was to understand the real technology used in© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

447

Examination of Robotic Aerospaceindustries and to evaluate its. Nuclear AMRC (Advanced
Manufacturing Research Centre) in Sheffield was visited
to review the latest projects that industrial companies aredeveloping as this science centre is one of the biggest in
the UK for developing technologies (Stuart Harrison2015).

to detect the damages to the composites materials. Thencalculated the size to enhance in the top of the material
needed to coincide the real world and acted as a real
object. Finally, tracked the points detected and sizes
calculated to provide the dynamic affecting thecomposite surface.

2.2. Second Stage: Design and Development

3.3. Image Enhancement

The project’s core goal was a successful design anddevelopment phase. This stage summaries the ideas needto build in term of the core of the requirements andobjectives by studying the process, data flow and tools
available. One can break down the design process into
two separate areas: software and hardware. The software
was designed by using the software engineering tools likestoryboard, sequence diagram and IDF0 for data flowing,
while computer engineering tools like mock-up andprototype designs were used for the hardware. Not
neglecting the application programs that need to editedwith specific details as an example of the programming
language or the framework simulating the scripts.

After developing the ability to recognise the size andlocation of the damage, the capability to enhance infrared
images became easier and more practical. The only
method that was new was calculating the size of theenhanced image related to the distance from the sample
surface. The infrared images were generated from thesame software (IDEA) after analysing the raw files thatwere taken from a previous project done at Cranfield
University.
3.4. 3D Enhancement
The same technique of using image enhancement wasused in 3D enhancement. These images were generated
from IDEA software by analysing the infrared images
analysed the RAW files together. The same method wasused to calculate the size based on the distance movingon the sample surface.

2.3. Third Stage: ProcessThis phase consists of five parts related to each otherconsecutively. They are hardware development, softwaredevelopment, experiments, analysis and validation.

4. Validation
3. Results

The three types of evaluations were used for validation.
The results support the idea of remote maintenance using
augmented reality in manufacturing as the experts andspecialists have industrials backgrounds.

3.1. IDEA 2.0
An open source software run on Matlab was updated withnew functionality to enhance the outcomes from the data
presentation algorithms with AR technology. This
software was developed originally by Multipolar Infrared
Vision –Vision Infrarouge Multipolaire: MiViM, Laval
University, Canada then it was updated at Cranfield
University under the title of IDEA (Infrared Degradation
Enhancement and Analysis).

4.1. Ultrasound, Infrared and Augmented Realty
Comparison Results
One of the methods to evaluate the results of this thisproject was to compare the result of the previous project
done on the same samples to calculate the damage sizeon the 30J damage shiny sample. The results were
calculated with different tools; ultrasound (c-scan), an
infrared (25 HZ) camera and an RGB camera (represent
the AR). The results would not be the same, as each tool
has a different accuracy, but the range result could
provide positive conclusion about the validation level forthe result and offer more ideas to build upon for future
development and recommendations.

3.2. Recognition EffectivenessAt first, to augment any multimedia type with regard to
the samples that are in the project, there was the need tofor recognise the location and calculate the size of theenhancement objects. The locations were learned using
OpenCV libraries, as it is open source and customisable© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

448

Mosab Alrashed, Yaser Yadekar, John Ahmet Erkoyuncu, Yifan Zhao

4.2. Experts and Specialists Feedback

maintenance demonstrated a more rapid process in
manufacturing workflow. The designed case study in theaerospace maintenance field pointed many further
benefits than initially estimated.

Some experts and specialists were interviewed tocorroborate the project’s outcomes. Their feedback was
separated into three parts according to IDEA output
results and their recommendations for the termination.
The experts’ positions are as follows: Lecturer in Digital Signal and Image Processing at
Cranfield University.
 Research Fellow at EPSRC Centre in Through-LifeEngineering Services at Cranfield University.
 Research Fellow in School of Aerospace, Transport andManufacturing at Cranfield University.
 Lecturer in Service Simulation and Visualisation at
Cranfield University.
 Software Development Engineer in School of
Aerospace, Transport and Manufacturing at Cranfield
University.

References
1. Akan, B. et al., 2011. Intuitive industrial robot
programming through incremental multimodal language
and augmented reality. Proceedings - IEEE International
Conference on Robotics and Automation, pp.3934–3939.
2. Azuma, R. et al., 2001. Recent Advances in Augmented
Reality. IEEE Computer Graphics and Applications,21(November), pp.1–15.
3. Biehl, M., Prater, E. & McIntyre, J.R., 2004. Remote
repair, diagnostics, and maintenance. Communications of
the ACM, 47(11), pp.100–106.
4. Fuhrmann, A. & Encarnac, L.M., The Studierstube
Augmented. , pp.33–54.
5. Neumann, U. & Majoros, a., 1998. Cognitive,
performance, and systems issues for augmented reality
applications in manufacturing and maintenance.
Proceedings. IEEE 1998 Virtual Reality Annual
International Symposium (Cat. No.98CB36180).
6. Pan, Z. et al., 2006. Virtual reality and mixed reality forvirtual learning environments. Computers and Graphics(Pergamon), 30(1), pp.20–28.
7. Steuer, J., 1992. Defining Virtual Reality: Dimensions
Determining Telepresence. Journal of Communication,
42(4), pp.73–93.
8. Stuart Harrison, 2015. Advancing UK manufacturing.
Availableat:
http://namrc.co.uk/wpcontent/uploads/2014/11/Nuclear-AMRC-brochure.pdf[Accessed September 2, 2015].
9. Zauner, J. et al., 2003. Authoring of a mixed reality
assembly instructor for hierarchical structures. The
Second IEEE and ACM International Symposium on
Mixed and Augmented Reality, 2003. Proceedings.
10. Karasic, G. & Asada, H., 2011. For Aircraft
Manufacturing and Maintenance.
11. Chirn, J.-L.C.J.-L. & McFarlane, D.C., 2000. A holoniccomponent-based
approachtoreconfigurable
manufacturing control architecture. Proceedings 11th
International Workshop on Database and Expert Systems
Applications.
12. Luk, B.L. et al., 2005. Intelligent legged climbing service
robot for remote maintenance applications in hazardous
environments. Robotics and Autonomous Systems, 53(2),pp.142–152.
13. Mesa-Barrameda, E. et al., 2014. Sensor Deployment by a
Robot in an UnknownOrthogonal Region : Achieving Full
Coverage.

4.3. Authentic Similar Projects Contrast
As is known to the authors, there have been few papers
published about robotics in manufacturing. Most of these
papers, published and developed at the end of twentiethcentury, concern the early birth of ideas in this field. The
most famous papers focus, on specific tasks or deliver anidea that the authors hope will be developed in a future
work. They all tacitly agree on the benefits of robotic
systems in many facets such as income, costs, time,quality and management (Karasic & Asada 2011; Chirn& McFarlane 2000; Luk et al. 2005; Mesa-barrameda etal. 2014).
5. Conclusions
All the requirements and qualifications needed to make aremote assistant for manufacturing using augmented
reality were realised. An Open-source hardware andsoftware were developed and available for any engineer
to customise those in such way to fit the industry area.
Remote assistance improved the quality of augmented
reality supporting. Although there are some limitations
with regard to big data and graphic process rendering
these shortcomings could be addressed using a private
cloud. The key advantage of this Cloud is the ability tocontrol the Cloud infrastructure without third party
intervention. The case study using augmented reality in© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa,, Japan449

Unit Layout Design Supporting System of Cell Assembly Machine
Using Two Robots by Reinforcement Learning
Yusaku Ikai

Department of Human Information Systems, Gifu University, Yanagido 1-1,
Gifu-shi, Gifu 501-1194, JAPAN
Hidehiko Yamamoto

Department of Mechanical Engineering, Gifu University, Yanagido 1-1
Gifu-shi, Gifu 501-1194, JAPANTakayoshi Yamada

Department of Mechanical Engineering, Gifu University, Yanagido 1-1
Gifu-shi, Gifu 501-1194, JAPANE-mail: t3128002@gifu-u.ac.jp, yam-h@gifu-u.ac.jp, yamat@gifu-u.ac.jp
http://www1.gifu-u.ac.jp/~yamlab/

Abstract
In this study, we explain the development of Design Supporting System for Cell Assembly Machine System (CAMS)
which systemizes the decision of the unit layout that composes the assembly machine using two robots. CAMS uses
Profit Sharing which is one of the Reinforcement Learning methods, determining each units layout. We apply CAMS
to the assembly of the differential gear box in automotive parts to verify its validity.
Keywords: Reinforcement Learning, Profit Sharing, Differential Gear Box, Assembly Robot

Reinforcement Learning methods. In addition, CAMS isapplied to the design of the differential gear box
assembling machine using two robots to verify its
validity.

1. Introduction
In recent years, the design of the assembly machine witha robot has been promoted. In designing such assembly
machine, the good and bad product efficiencies arechanged by the arrangement of each unit. However,
because the placement of units is determined by theexperience of engineers in many factories, it isquestionable whether the determined placement is goodor not. In order to solve this problem, we propose Design
Supporting System for Cell Assembly Machine
System(CAMS) that determines the efficient units
placement of assembly machine and the job roles of therobot using Profit Sharing which is one of the

2. Differential Gear Box Assembly Machine
We develop a system to support units arrangement designof the assembly machine of the differential gear box. The
gear box is an automobile part which has 5 units:differential case, side gear, pinion mate, jig and thestorage station. Also, this assembly machine repeats thefollowing five works using two robots as shown in Fig.1.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan450

Yusaku Ikai, Hidehiko Yamamoto, Takayoshi Yamada

Work [1]: Take the differential case and assemble it on
the jig.
Work [2]: Take the side gears and assemble it into thedifferential case.
Work [3]: Take the side gears and assemble it into thedifferential case.
Work [4]: Take the pinion mate and assemble it into thedifferential case.
Work [5]: Take the pinion mate and assemble it into thedifferential case.
Work [6]: Put the finished product on the storage station.

Differentia
l case[3]

Step1-2: Create units value database.
As shown in Fig.3, the arrangement of possible area of
each unit are divided square-shaped and each lattice are
regarded as arrangement candidate place. And Units
value database corresponding to each arrangement
candidate place is created. It is used the decision of theunits layout.

[4]
[5]
Jig

[1]

3-2 Condition making module
The condition making module carries out the following
processes.
Step1-1: Reading the learning data
The condition making module reads the informationnecessary for determining the unit arrangements and therobots job roles. The information to be read is the numberof learning, initial values of a value, a basic reward value,
one side size of an arrangement candidate place, andarrangement possible areas of each unit.

Pinionmate

Side gear[2]

learning module, the units layout and the division of work
are determined by Profit sharing.

[6]

StoragestationAssembly robots
Fig.1 Assembly machine proceed…
3. CAMS
3-1 Module configuration of the CAMSCAMS consists of two modules, (ⅰ)the conditions
making module, and (ⅱ)the learning module as shownin Fig2.

…
ω(a, 1) ω(a, 2) ω(a, 3)…

Fig.3 Each arrangement candidate place and its units
value database

CAMS
Condition making module

Step1-3: Create the robots job roles value database
As shown in Table.1, the robots job role value databaseof m-th steps is created for each robot.

・Reading of data
・Creating two types database ofvalue

Table.1 Database of value for the robots job roles
Learning module
Right robot
Left robot

・Carry out profit sharing
・Determine efficient units
layout1
ω(R, 1)
ω(L, 1)

2
ω(R, 2)
ω(L, 2)…
…
…

m
ω(R, m)ω(L, m)Step1-4: Give an initial value to databases
The two type databases created in Step1-2 and 1-3 aregiven an initial value.

Fig.2 Outline of CAMS
The condition making module reads the learning data andcreates two type databases whose values are necessary
for the learning module. The two type databases
determine the units layout and the robots job roles. In the

3-3 Learning module
Learning module using profit sharing performs thefollowing processes.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

451

Unit Layout Design Supporting

Step2-1: Create the initial value of the reference
time.
Parts are randomly placed regulation times, and its
average evaluation time is defined as the initial value ofthe reference time.

(ⅱ) Acquiring or assembling operation
It is the operation that robot moves from the approach
point to complete acquisition or assembly of the unit.
(ⅲ)Retracting operation
The operation that moves to the retract point. The
operation is carried out after (ⅱ).

Step2-2: Select units layout and robots job roles.
The units layout and the robots job roles are determinedby roulette selection. Roulette selection randomly selects
action by the ratio of the value. This probability is
calculated by Eq. (1).

p(a | s )  ( s, a )  ( s , n)Fast forward operating time is calculated by calculating
the moving distance of robot by Pythagorean theorem
and dividing it by fast forward velocity. The acquiring or
assembling operation time and the retracting operation
time are any one of the values.

(1)

Step2-4: Calculate the evaluation time
The evaluation time is calculated using each working
time that was calculated. The evaluation time is the timethat is criteria of whether giving reward or not.
Comparing TR and TL as shown in Fig.5, the longer one
is defined as the evaluation time.

s: Unita: Arrangement candidate placen: The number of arrangement candidate place
ω: Value of the arrangement candidate place
The left side of this equation indicates the probability of
selecting the a-th arrangement candidate place of the units. The denominator of the right side means the sum of thevalue of the unit s. The molecule means a value of the ath arrangement candidate place of the unit s. By theprobability based on Eq. (1), the arrangement candidate
places with higher values are likely to be located, thelower value are less likely to be located.

One cycle
TR

T4

T6

T5
t

Calculate of the evaluation time

TR: Total of 1 cycle work time of right robot
TL: Total of 1 cycle work time of left robot
Step2-5: Compare the evaluation time and thereference time
The evaluation time which is obtained at Step2-4 iscompared with the reference time which is obtained at
Step2-1. If the evaluation time is larger than the referencetime, proceed to Step2-6 ． If it is smaller than thereference time, proceed to Step2-2.

(ⅰ)

Step2-6: Compare the evaluation time and theminimum evaluation time
If the evaluation time is smaller than the minimum
evaluation time, it is defined as minimum evaluation time.
In this case, the current unit layout and the robots job
roles are recorded as the best result. If the minimum
evaluation time hasn’t been determined, the evaluation
time which is determined in Step2-4 is defined as theinitial minimum evaluation time.

Retract
point
(ⅱ)

T2
T3

Fig.5

Another unit’s
approach point

Approach
point

T1

TL =

Step2-3: Calculate the unit work time of the robot
Using the determined units layout and the robots job roles,
each working time of robots are calculated. In this study,
we define that robot repeats the three operations toperform the work. In other words, the work is divided in
the following three operations as shown in Fig.4, and aunit work time is the sum of the operation time.

(ⅰ)

=

(ⅲ)
Unit

Fig.4 Operation of robot(ⅰ) Fast-forward operation
Fast forward movement from the retract point of the unit
to the approach point.

Step2-7: Distribution of reward© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan452

Yusaku Ikai, Hidehiko Yamamoto, Takayoshi Yamada

The units value database and the robots job roles value
database which are currently selected are given reward to
update the value. The reward used the value that is readin Step1-1.

CAMS is about half of the average evaluation time,
42.86408[s], in random. Thus, we find that CAMS canstabilize determined efficient unit layout and robots job
roles. Thereby, the validity of CAMS is verified. In
addition, the unit layout of the shortest evaluation timein 10 times simulation by CAMS is shown in Fig.7. And,robots job roles is shown in Table.3.

Step2-8: Update of the reference time
The new reference time Jn + 1 is calculated by Eq.2.

J n1 T1  T2    Tn  Tn1n1

Table.2(2)

Average evaluationtime[s]Minimum evaluationtime[s]42.86408

19.3912524.84720

18.897421

Layout by
randomLayout by
CAMS

Tn: The evaluation time that is less than the referencetime in n-th times.
Step2-9 : End of learning
Learning module repeats Step2-2～2-8 regulation times.
After that, unit layout and the robots job roles of theminimum evaluation time are adopted as the mostefficient result in this simulation.

Average time of 10 times simulations

Table.3 Robots job roles by CAMS
Robot

Work1
Right

Work2
Left

Work3
Left

Work4
Right

Work5
Left

Work6
Right

4. Simulation Application

1000

Side gear

Pinionmate

500

Storage station

Differential case

Jig

Differentialcase

500 500

1000

Jig

StoragestationFig.7 Units layout by CAMS
5. Conclusion

1500

500

Pinion mate

Side gear

We applied CAMS to the following differential gear box
assembly machine to verify the goodness of CAMS.
Learning conditions of the initial value is 10, the reward
value is 10, the number of learning is 300,000 times. Unitarranged areas are set to five as shown in Fig.6, and one
side of the arrangement candidate place is 100mm.

In this paper, we have described the development of
CAMS in determining an efficient unit layout and robots
job roles of the assembly machine using two robots. Fromthe simulation results, it is found that the units layout andthe robots job roles obtained by CAMS have shorter
evaluation time than the random layout one. This makes
it possible to verify the validity of CAMS. Furthermore,
we are able to apply the reinforcement learning todecision of unit arrangement and the robots job roles.

Fig.6 Units arranged area
We ran the simulations for the assembly machine which
determined the random unit layout to compare thelearning results. Two types of the average evaluation
time and the minimum evaluation time of 10 times
simulations are shown in Table.2. CAMS obtains goodresults from both of them as shown in Table.2. In
particular, the average evaluation time, 24.84720[s], by© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

453

Virtual Input Parts Decision System of Job-Shop Production Lineby using GA with ON / OFF Gene
Junji Ito

Department of Human Information Systems, Gifu University, Yanagido 1-1,
Gifu-shi, Gifu, 501-1194, JAPAN
Hidehiko Yamamoto

Department of Human Information Systems, Gifu University, Yanagido 1-1,
Gifu-shi, Gifu, 501-1194, JAPANTakayoshi Yamada

Department of Human Information Systems, Gifu University, Yanagido 1-1,
Gifu-shi, Gifu, 501-1194, JAPANE-mail: t3128003@gifu-u.ac.jp, yam-h@gifu-u.ac.jp, yamat@gifu-u.ac.jp
http://www1.gifu-u.ac.jp/~yamlab/

Abstract
In this laboratory, priority allocation method to the machine tool that has the minimum machining time wasdeveloped. Moreover, to solve the problem of this system, to make better solutions .we have developed thedetermination system of parts using GA. If we apply GA to job shop production line, lethal gene and loss time ofthe problem occur. The problem of the lethal gene is a thing to use the concept of the ON/OFF gene, and the
problem at loss time is solved by devising the order of turning on parts to begin the next cycle even if one cycle
does not end to the last.
Keywords: Virtual Input Parts Decision System, GA, Job Shop Production, Loss time,

production conditions such that different types of parts
need different manufacturing processes. Real genes havea directive information that sends the order to work when
it is necessary and to stop when it isn’t necessary. We
express this directive information as the concept of ON /OFF (activation / deactivation) of the gene. This
expression solves the lethal gene problem when it
happens by inserting the concept of ON / OFF genes forswitching the directive information into a chromosome.
In this study, by using the concept of ON / OFF genes,
we propose the simulator to decide the best parts input
order. We call this simulator as Virtual Input Parts
Decision by ON/OFF Genes (VIPDOG).
VIPDOG has 2 configuration systems as follows:
1) The parts order decision system to indicate the parts
input and processing orders of a job shop production line.
2) The virtual production system that performs thevisualization of the working environments acquired by

1. Introduction
In this study, the research aims to improve a production
efficiency of a job-shop production line where one person
carries parts, and to make the visualization of the
production results. We already developed the existingpriority allocation method to allocate parts to the machine
tools. The method has the special characteristic that a partis allocated to the machine tool whose machining time isthe shortest. To make better solutions, we develop the
new system to find the input parts order using Genetic
Algorithm (GA)
2. Virtual Input Parts Decision System
If we apply the conventional GA to find the order to put
the parts into the machine tools in a job shop production
line, a lethal gene problem occurs because of different© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan454

Junji Ito, Hidehiko Yamamoto, Takayoshi Yamada

VIPDOG. The virtual production system can check what
will happen in the future and some experienced engineers
can discuss the problems to solve them.
2．1 VIPDOG
Locus gene used in the GA of the VIPDOG isrepresented by three elements.
ⅰ) Part nameⅱ) Process nameⅲ) Machine tool
The respective numerical value of the element in thelocus gene shows the part and the necessary process andeach machine tool specifically. In addition, Element
numerical value of the process name shows the worker's
work information ① and ②at the same time.
①The operator turns on the parts to machine tools②The operator takes out the parts from the machine toolFig.1 shows an example of one of the locus gene.

Fig.1

Fig4 Algorithm of ON / OFF switch
2．3 ON / OFF gene not to occur lethal gene
The chromosomes of the parents that produce a lethal
gene by crossover are shown in Fig5
Parent 1[A,K(1),M(1)] [A,K(0),M(1)] [B,K(2),M(1)] [C,K(1),M(2)][A,K(1),M(1)]one of the locus gene

Parent 2[B,K(2),M(1)] [B,K(0),M(1)] [A,K(1),M(1)] [D,K(1),M(3)]Fig.5 Chromosome to be parents

A of this locus indicates the part name. K (1) shows
the process name. M (1) shows the machine tool.
Numerical value of K () and M () to enter a positive
integer. K (1) shows the process 1. M (1) shows themachine tool 1. If the number of K () is a positive integer,
the gene show the work information ① at the same time.
As an example of the work information ②, it show theFig.2.
[B,K(0),M(2)]Fig.2 one of the locus gene

The chromosomes of the children after circulation
crossover are shown in Fig6.
Child 1
[A,K(1),M(1)] [B,K(0),M(1)] [B,K(2),M(1)] [D,K(1),M(3)]Child 2
[B,K(2),M(1)] [A,K(0),M(1)] [A,K(1),M(1)] [C,K(1),M(2)]Fig.6 Chromosome of children
To summarize the information of child 1 chromosome
is as follows.
1．Put the part A to machine tool 1
2．Take out the part B from the machine tool 1
3．Put the part B to the machine tool 1
4．Put the part D to the machine tool 3
In the case of 2, there is a conflict when the operator
wants to take the part B which has not being introduced
into the machine tool 1. This is the occurrence of a lethal
gene. The lethal gene problems can be solved by using
the ON / OFF genes. ON / OFF gene is the gene that has
a work information ②.That is, it was the gene with K a(0) in the locus. Inside the chromosomes, we use the
ON/OFF gene and if we turn it OFF, the gene of thechromosome is not used. It is shown in Fig.7
Parent 1[A,K(1),M(1)] [B,K(2),M(1)] [C,K(1),M(2)]Parent 2[B,K(2),M(1)] [A,K(1),M(1)] [D,K(1),M(3)]Fig.7 OFF chromosome to be parents

Only for K shown a process name, Numeric value of ()exists that become zero. At this time, it show the work
information ②.
2．2 ON /OFF gene
Genes representing the work information ② in a geneused for GA in this study is expressed by ON / OFF genewake up when needed.
The advantage of the ON / OFF gene prevents theoccurrence of a lethal gene. ON / OFF gene is locatedbetween the normal genes that represent the working
information ① as shown in Fig.3. As shown in fig.4 theswitching ON / OFF is operated before and after theevaluation of fitness.

Fig3 ON / OFF genes sandwiched between normal genes© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

455

Virtual Input Parts Decision

Fig.8 shows the chromosomes of the children after
circulation crossover.
Child 1
[A,K(1),M(1)] [B,K(2),M(1)] [D,K(1),M(3)]Child 2
[B,K(2),M(1)] [A,K(1),M(1)] [C,K(1),M(2)]Fig.8 OFF chromosome of children

operator can’t work occurs. This is because an operator
stays for a long time till the next input cycle starts. To
solve this problem, we adopt the different two machine
tools, the last machine tool of a cycle and the firstmachine tool of the next cycle. Even if one cycle is notfinished until the cycle last, the next cycle can start.
Fig.10 shows the example of reducing the loss time.

The next three cases indicate to summarize theinformation of child 1 chromosome
1．Put the part A to machine tool 1
2．Put the part B to the machine tool 1
3．Put the part D to the machine tool 3
In the second case, the operator trying to put the part B
into the machine tool. The machine tool already has thepart A. The contradiction of work that putting part B is
impossible occurs. This is the occurrence of a lethal gene.
In order to avoid the lethal gene, we adopt to watch thelethal gene and all genes before the lethal gene, andchange necessary and sleeping off gene to ON. The above
example shows in fig.8 and fig.9. We watch[B,K(2),M(1)] corresponding to a child1 lethal gene and[A,K(1),M(1)] of fig.8. If the gene that has theinformation of taking out the part A from the machine
tool 1 is between [A,K(1),M(1)] and [B,K(2),M(1)], thecontradiction of work does not occur. [A,K(0),M(1)] of
fig.9 change ON from an off gene.

Fig.10 Example of reducing loss time
3. Simulation Applications
We applied VIPDOG to the job-shop production line asshow in Fig.11 and simulated production results. The
production conditions are shown below.
1 Operator's number : One person
2 Process : 15 types
3 Parts : 5 types
4 Ratio of the target production volume : 5 types
5 The number of machine tools : four
In addition, the ratio of the target production is shownin Table 1.

Fig. 9 ON chromosome of child
The next four works indicate the meaning of child1 of
Fig.9.
1．Put the part A to machine tool 1
2．Take out the part A from the machine tool 1
3．Put the part B to the machine tool 1
4．Put the part D to the machine tool 2
The four works has no contradiction and lethal genes
do not occur．By utilizing the ON / OFF gene, even after
the crossover, it is possible to prevent the occurrence of
lethal genes and to determine the best input order.
2．4 Elimination of loss time between cycles
When determining the parts input order under theconditions that the target production ratio of parts is
adopted as one cycle, the loss time corresponding that an

Fig.11

Layout of job shop production line

Table 1

The ratio of target production© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinaawa, Japan

456

Junji Ito, Hidehiko Yamamoto, Takayoshi Yamada

The conditions of GA are as follows
1 Population size: 100 individuals.
2 The number of generations: 500 generations.
3 Use a roulette selection and elite preservation strategy.
4 Use a circular crossover.
5 The probability of a mutation: 1%.
Using the above described various production
conditions and the GA conditions, production simulationof 8 hours, 40 hours and 160 hours were carried out. We
adopted two types of VIPDOG, one includes the loss time
and the other doesn’t. The former calls VIPDOG1 andthe latter calls VIPDOG2. The production simulation
results of VIPDOG2 are shown in Table2. The
production simulation results of VIPDOG1 are shown in
Table3. The results of the production simulator that do
not use the GA compared to VIPDOG are shown in
Table4. The number of occurrence of the lethal geneshows in Table5.
Table 2

Table 5The number of lethal gene

Table 5 shows no occurrence of the lethal gene.
VIPDOG can prevent the occurrence of lethal genes. As
a result, we can have better result to use VIPDOG2. Thisis because we got the optimal solution by the GA and we
reduced loss time. One shot example of visualized
production results are shown in Fig.12.

Production simulation results of VIPDOG2

Table 3 Production simulation results of VIPDOG1(No loss time reduction)
Fig.12 Visualization of VIPDOG
4. Conclusions

Table 4

It is ascertained that VIPDOG including ON/OFF geneand not including the loss time can have good production
results. In addition, we developed the visualized
production results. The visualized system can check what
will happen in the future and experienced engineers candiscuss the problems to solve them.

Results of previous simulator without GA

References[1] Hidehiko Yamamoto, Takayoshi Yamada andMasanori Nakamura, Parts Layout Decision for
Assembly Cell-Production by GA and Virtual Factory
System, Transactions of Japan Society of MechanicalEngineers, Vol. 76, No.764, pp.210 -215, 2010
© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan457

An Evolutionary Algorithm for Making Decision Graphs for Classification Problems
Shingo Mabu

Graduate School of Science and Engineering, Yamaguchi University, Tokiwadai2-16-1
Ube, Yamaguchi 755-8611, Japan
Masanao Obayashi

Graduate School of Science and Engineering, Yamaguchi University, Tokiwadai2-16-1
Ube, Yamaguchi 755-8611, Japan
Takashi Kuremoto

Graduate School of Science and Engineering, Yamaguchi University, Tokiwadai2-16-1
Ube, Yamaguchi 755-8611, Japan
E-mail: mabu@yamaguchi-u.ac.jp, m.obayas@yamaguchi-u.ac.jp, wu@yamaguchi-u.ac.jp

Abstract
As the exponential increase of data in the world, machine learning, pattern recognition, data mining etc. are
attracting more attentions recently. Classification is one of the major research in pattern recognition and a large
number of methods have been proposed such as decision trees, neural networks (NNs), support vector machines(SVMs). In order to easily understand and analyze the reason of the classification results, decision trees are usefulcomparing to NNs and SVMs. In this paper, to enhance the classification ability of decision trees, a new
evolutionary algorithm for creating decision graphs is proposed as a superset of decision trees, where multi-root
nodes and majority voting mechanism based on Maximum a posteriori are introduced. In the performance
evaluation, it is clarified that the proposed method shows better classification ability than decision trees.
Keywords: evolutionary computation, decision graph, classification, majority vote, multi root nodes

classification and regression tree (CART)5, ID3 andC4.52. NNs, SVMs and their extended algorithms cancreate distinguished decision boundaries for accurateclassification, however, they are black box models, thus
the reason of the classification results is difficult to beshown to the end-users. On the other hand, decision
trees can visually show the rules of classification
explicitly by the tree structures, therefore, it is usefulwhen users want to know the created rules and analyze
the relationships between attributes in databases.
However, when dealing with databases with a large

1. Introduction
In the research fields of pattern recognition and machinelearning, a large number of classification algorithms
have been proposed1, such as neural networks (NNs),
support vector machines (SVMs)1 and decision trees2.
NNs and SVMs mathematically create decisionboundaries to separate classes with high accuracy andhave applied to many applications such as intrusion
detection3, prediction of stock market indexes4. As forthe decision trees, typical learning algorithms are© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

458

Shingo Mabu, Masanao Obayashi, Takashi Kuremoto

number of attributes, many rules have to be created by
one tree, then the size of the tree would become toolarge. A large tree structure would cause over-fitting to
the training data and decrease the generalization ability,
therefore, a pruning of tree is to be executed. To
enhance the generalization abilities of decision trees,
some extended algorithms have been also proposed6.
In this paper, to enhance the representation ability ofdecision tree and obtain better classification accuracy,
directed-graph based classifier with recurrent
connections and its evolutionary algorithm are proposed.
In the case of tree structure, the classification procedure
starts from the root node, selects appropriate branches at
non-terminal nodes, and finally classifies the data at thereached terminal node. Therefore, the node transition
flows from the top layer to the bottom layers. In the caseof graph structure, 1) any non-terminal nodes canbecome root nodes, and 2) the same nodes can be sharedby several rules, therefore, many rules can be created by
the smaller number of nodes and the program structure
becomes quite compact.
In addition, this paper proposes some specificmechanisms to enhance the classification abilities ofdecision graph. First, multi root nodes are introduced tocreate various kinds of rules, while the standard
decision tree uses one root node. Second, majority
voting mechanism is introduced to enhance thegeneralization ability. In the proposed method, theclassification process starts from one of the root nodes,
selects branches at non-terminal nodes, and classifies adata into a certain class (vote for a certain class) at thetransient terminal node. However, the transient terminal
node has a connection to the next non-terminal node,
therefore, the classification process continues and thesame process is repeated until the fixed number of votes
are executed. This multi-voting mechanism is expected
to show better generalization ability.
The rest of this paper is organized as follows. In
section 2, the basic structure of evolutionary decisiongraph and its gene expression is explained. In section 3,
an evolutionary algorithm of decision graphs and how toclassify data are explained. In section 4, after
benchmark problems of classification and simulation
conditions are explained, the results and analysis aredescribed. Section 5 is devoted to conclusions.

Fig. 1. Basic structure of evolutionary decision graph

2. Structure of Evolutionary Decision Graph2.1. Basic structure
The basic structure of the decision graph is shown in Fig.
1, where there are three components: root nodes,
internal nodes and transient terminal nodes. Here, root
nodes and internal nodes are called non-terminal nodes.
Root nodes have a function to start node transition anddecide the next non-terminal node to which the current
node transfers. The proposed decision graph has multiroot nodes, and the node transition starts from each root
node one by one, which results in considering various
kinds of rules to make classification. The function of
non-terminal nodes is the same as standard decision treewith multiple branches. In Fig 1, each non-terminal
node has its own attribute Ai, 𝑖 ∈ {1,2, ⋯ , 𝑛} to be
judged, and when a data is inputted to the decisiongraph, an appropriate branch is selected according to thevalue of the data. The function of transient terminal
node is to determine the class of the inputted data, andcast one vote to the class. For example, if the transientterminal node determines the class as "class 1", class 1gets one vote. After the voting, the node transition
continues depending on the connection from thetransient terminal node. The detailed mechanism ofmaking classification at each transient terminal node is
explained in section 3.2.
2.2. Gene Structure
Fig. 2 shows the gene structure of node number k in adecision graph. NFk shows a node function number:
NFk=0 is a root node, 1 is an internal node, and 2 is atransient terminal node. ATTk shows an attribute number© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan459

An Evolutionary Algorithm fordetermined) depending on the complexity of the target
problem. Second, the values of ATTk are randomly
determined. ATTk of non-terminal nodes are determinedby randomly selecting one of the attributes in a database.
ATTk of transient terminal nodes are determined by
maximum posterior probabilities explained in section
3.2. Finally, the branch connections are determined asfollows. bk1 of transient terminal nodes are determinedby randomly selecting one node number from nonterminal nodes. bk1, bk2, ..., bkn of non-terminal nodes are
determined by randomly selecting one node number
from all the nodes.

Fig. 2. Gene Structure of evolutionary decision graph3.2. Node transition for determining voting classat transient terminal nodes

Fig. 3. Flowchart of training
evolutionary decision graphandtestingphasesTo classify a data into an appropriate class, which classis assigned to each transient terminal node is very
important. In this subsection, the procedure of assigning
voting classes to each transient terminal node based on
maximum posterior probabilities is explained in detail.
First, how to execute node transition is explained
before introducing the class assignment mechanism.
When a training data (tuple) d in a database is inputted,
the node transition starts from root node 0 (node numberk=0). The attribute ATT0 of data d is examined and oneof the branches among b01, b02, ..., b0n is selected (here,
suppose b01 is selected). If the selected node b01 is a
non-terminal node, the same procedure as the root nodeis executed to determine the next node. Fig. 4 shows anexample of the branch selection, where, in Fig. 4 (a), thevalue of attribute ATTk of data d is in the range of[0,0.3], and its corresponding branch is bk1, thus theinto be judged at non-terminal node k, or a class attributeto be assigned at transient terminal node k. bk1, bk2, ...,
bkn shows the next node number connected from node k.
Transient terminal nodes have one branch to the next
node, thus only bk1 is used at each node. Non-terminal
nodes use bk1, bk2, ..., bkn depending on the possible
number of branches for ATTk.
3. Evolutionary algorithm of decision graphs
Fig. 3 shows the flow of the whole implementation ofevolutionary decision graph. In the training phase, after
initializing the individuals, voting classes at transientterminal nodes are determined by posterior probabilities,
and the classification accuracy for training data is
obtained as the fitness for evolution. Then, evolutionary
operations such as selection, crossover and mutation are
carried out. The elite individual obtained in the final
generation is picked up for the testing phase.
3.1. Initialization of an individualFirst, the numbers of root nodes, internal nodes andtransient terminal nodes are determined (NFk isFig. 4. An example of attribute division and branch decision© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan460

Shingo Mabu, Masanao Obayashi, Takashi Kuremoto

current node transfers to node bk1. Fig. 4 (b) shows thecase of binary branch where each non-terminal node
executes yes-no branch selection. After repeating thebranch selections at non-terminal nodes, if the current
node reaches a transient terminal node t (Here, t shows
the node number of transient terminal nodes), counter
Ni(t) increases by one, where i shows the class numberof training data d. The current node transfers to the next
node bk1 and continues the node transition until transientterminal nodes are visited predefined number of times,
e.g., 10. However, if the node transition visits the sametransient terminal nodes that have been already visited,
the node transition restarts from the next root node, i.e.,root node 1, to avoid the transition loop. In summary,the node transition starts from root node 0, and every
time when a transition loop occurs, the node transition
restarts from root node 1, 2, 3,... in the numerical order.
By repeating the above node transition from the firstdata to the last one, the values of counters Ni(t) of all thetransient terminal nodes can be obtained. Then, theposterior probabilities of class i at transient terminal
nodes t are calculated byNi (t)p(Ci |t) =N(t)(1)
where, N(t) is the number of data visiting node t.
Finally, voting class (ATTt in Fig. 2) at transientterminal node t is determined bythen the classification accuracy is calculated as thefitness of the individual.
3.4. Genetic Operations in Evolutionary Decision
Graphs
The individuals are evolved by selection, crossover andmutation. The genetic operations of evolutionary
decision graphs are based on Ref. 7. In crossover, twoparent individuals are selected by tournament selection
and randomly selected nodes with the probability of PC
are exchanged between the parents. In mutation, one
individual is selected by tournament selection, and theconnections, attributes in non-terminal nodes, and root
nodes are randomly changed with the probability of Pm.
Table 1. Dataset information
dataset# of data# of attributes

303
208
155
178
569

13
60
19
13
30

Heart disease
Sonar
Hepatitis
Wine
Breast cancer# of classes
2
2
2
3
2

Table 2. Simulation condition

ATTt = arg max 𝑃(𝐶𝑗 |𝑡)j(2)# of generations

2000# of individuals
crossover rate
mutation rate

503
0.1
0.02

# of nodes

200
50
50
100

root nodesinternal nodes
transient terminal nodes

3.3. Node transition for classifying data
After the procedure described in sections 3.1 and 3.2,
the classification of data d is carried out and fitness forevolution is obtained as follows.
The procedure of node transition is the same as
section 3.2 except that, at transient terminal nodes,
voting to one of the classes is carried out instead ofcounting the number of visits Ni(t). When nodetransition reaches a transient terminal node t, it casts avote to class ATTt, then the current node transfers to thenext node bt1. The node transition continues until thepredefined number of voting finishes. Finally, the classthat wins the highest votes becomes the classification
result. The above process is repeated for all the data d,

4. Simulations
The classification accuracy is evaluated using
benchmark datasets downloaded from UCI machinelearning repository8. The information of the datasets isshown in Table 1. The objective of this paper is to show
the advantage over the conventional decision tree, but
for more reference, the comparison with support vector
machine (SVM) is also implemented. The parameters of
the proposed method are set as shown in Table 2. The© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

461

An Evolutionary Algorithm forsimulations are implemented by 10-fold cross validation,and the mean classification accuracy is calculated. In
the training, some variations are given to the training
dataset to avoid over fitting. In detail, 10% of the
training data consists of the data misclassified by theelite individual in the previous generation, and the restof 90% of the training data are generated by bootstrap
sampling9. The continuous attributes in the original
datasets are binarized by the Fayyad and Irani's entropy
based method10 executed by WEKA software11 .

Table 3. Testing results for benchmark datasets
Classification accuracy [%]datasetHeart disease
Sonar
Hepatitis
Wine
Breast cancer
mean

Decision graph
84.8
82.2
66.9
94.4
94.9
84.6

Decision tree
78.5
71.2
67.1
93.8
93.1
80.7

SVM
83.8
76.0
66.5
98.3
97.7
84.4

4.1. Simulation results
function of the evolutionary algorithm to clearly
determine the decision boundaries of classes andimprove the classification accuracy.

The performance evaluation is executed using the elite
individual obtained in the last generation in the training
phase. Table 3 shows the classification accuracy
obtained by the proposed decision graph, decision tree
and SVM. From Table 3, we can see that the mean
accuracy obtained by decision graph is better thandecision tree. In addition, decision graph obtains higher
accuracy than decision tree in four datasets out of five.
Comparing the accuracy obtained by decision graphwith that by SVM, decision graph shows comparable
result, and decision graph obtains higher accuracy than
SVM in three datasets out of five. Although the mean
accuracy for the five datasets is almost the same as
SVM, decision graph can show the classification rules
to users like decision tree, therefore, when we want toknow and analyze the obtained rules, decision graph canbe an useful method. In the future, we will execute
simulations using more datasets and analyze thegenerated classification rules and characteristics of thedecision graph.

References
1.
2.
3.

4.

5.
6.
7.

5. Conclusions
This paper proposed an evolutionary algorithm forcreating decision graphs for classification problems. To
enhance the classification abilities of the decision graph,some specific mechanisms such as multi-root nodes,
unique genetic operators and majority voting weredevised. From the simulation results, it was clarified
that the proposed evolutionary algorithm improve thefitness (accuracy) of the individuals, and showed betterclassification accuracy comparing with decision tree andcomparable accuracy to SVM. In the future, we willconsider to apply impurity measures to the evaluation

8.
9.
10.

11.

C. M. Bishop, et al., Pattern recognition and machinelearning (Springer New York, 2006).
J. R. Quinlan, C4.5: programs for machine learning,(Morgan kaufmann, 1993).
S. J. Horng, M. Y. Su, Y. H. Chen, T. W. Kao, R. J.
Chen, J. L. Lai, and C. D. Perkasa, A novel intrusion
detection system based on hierarchical clustering andsupport vector machines, Expert systems with
Applications 38(1) (2011) 306–313.
E. Guresen, G. Kayakutlu, and T. U. Daim, Using
artificial neural network models in stock market index
prediction, Expert Systems with Applications, 38(8)(2011) 10389–10397.
L. Breiman, J. Friedman, C. J. Stone, and R. A. Olshen,
Classification and regression trees, (CRC press, 1984).
L. Breiman, Random forests, Machine learning, 45(1)(2001) 5–32.
S. Mabu, K. Hirasawa, and J. Hu, A graph-based
evolutionary algorithm: Genetic network programming(GNP) and its extension using reinforcement learning,
Evolutionary Computation, 15(3) (2007) 369–398.
UCI machine learning repository. Online available:archive.ics.uci.edu/ml/
Bradley Efron and Robert. J. Tibshirani, An Introduction
to the Bootstrap, (Springer, 1993).
U. M. Fayyad and K. B. Irani, Multi-interval
discretization of continuous valued attributes forclassification learning, in Proc. of the 13th International
Joint Conference on Artificial Intelligence, (Morgan
Kaufmann, 1993), pp. 1022–1027.
Waikato environment for knowledge analysis, open
source project for machine learning, Online available:
www.cs.waikato.ac.nz/ml/weka/© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan462

Improvement of Computational Efficiency of UPF by Automatic Adjustment of the Number ofParticles
Kenta Hidaka, Takuo Suzuki, and Kunikazu Kobayashi

School of Information Science and Technology, Aichi Prefectural University
1522-3 Ibaragabasama, Nagakute, Aichi 480-1198, Japan.
Tel: +81-561-76-8782, Fax: +81-561-64-1108
kobayashi@ist.aichi-pu.ac.jp
Abstract
In RoboCup Standard Platform League (SPL), the method using unscented particle filter (UPF) has been proposed
for self-localization. The UPF resolves a problem of particle filter which cannot be sampled appropriately when thelikelihood is too high or low. This filter can estimate accurate position when the more number of particles is. However,
the more, the more computation time is needed. In the present paper, we propose an automatic adjustment method
for the number of particles in UPF. The proposed method uses three kinds of feature values with respect to particles,
i.e. centroid, standard deviation, and weight. Through computer simulations, we confirmed the improvement ofcomputational efficiency of UPF．
Keywords: RoboCup, Self-localization, Unscented particle filter, Kalman filter, Particle

computational cost by changing the number of particles
and rejecting redundant particles depending on thesituation. In RoboCup SPL, there is no method which canchange the number of particles in UPF.
Yu et al. proposed an adaptive unscented particle filter,
in which relative entropy is used for changing the numberof particles[2]. Yang et al. proposed another adaptive
unscented particle filter, in which Kullbuck-Leibler
divergence (KLD) sampling is employed[3]. In contrast,
the conventional self-localization method using UPF in
RoboCup SPL uses 16 fixed particles[4]. In RoboCup
Middle Size League (MSL), the number of particles in
the conventional self-localization method using KLD
sampling is fixed as 75[4]. In the present paper, in orderto improve the computational efficiency of UPF, we
propose an automatic adjustment method for changing
the number of particles.
In Section 2, we explain fundamentals on UPF. Section 3
describes the proposed algorithm which can adaptively
change the number of particles in UPF. In Section 4, we
conduct some computer simulations and discuss theresults. Section 5 summarizes our research and gives
challenges for the future.

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. In the present paper, we treat one ofmajor research issues in RoboCup, i.e. self-localization.
In RoboCup, self-localization is required in a variety ofsituations, such as passing a ball to a teammate andintercepting a pass.
The RoboCup Standard Platform League (SPL) is aleague that all teams compete with the same standard
humanoid robot called NAO developed by Aldebaran
Robotics. The robot operates fully autonomously, that iswith no external control, neither by humans nor bycomputers. In RoboCup SPL, the robot must process all
the calculations on vision processing and decision
making using low-end CPU (Intel Atom 1.6GHz). In
addition, the robot must devote a lot of computation timeto percept a white goal and a mostly white ball in vision
processing.
In the present paper, we focus on self-localization for
reducing the computational cost. In conventional method
using UPF[1], since the number of particles is fixed during
self-localization, it is possible to reduce the© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan463

Kenta Hidaka, Takuo Suzuki, Kunikazu Kobayashi

observation noise z(t) follow from normal distributions.
UKF approximates the non-linear function f and g using
unscented transform[1].

2. Unscented Particle Filter (UPF)
UPF has a combination of unscented Kalman filter(UKF)[5] and particle filter[6]. This 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 prior [1]. In
this section, we outline particle filter and UKF briefly.

3.

Proposed System

We propose an automatic adjustment method of thenumber of particles in UKF to reduce the computationalcost. The proposed method realizes adjusting the numberof particles by two steps, i.e. reset step and increase or
decrease step as shown in Fig. 1.

2.1. Particle Filter
3.1. Reset Step

The particle filter is a type of non-parametric Bayesian
filter. This filter can apply to nonlinear state equations
with non-Gaussian probability distribution.
The filter estimates the number of particles (state) byrepeating the following three steps A, B, and C. Thisfilter can precisely estimate state as more number ofparticles. Let the number of particles be N.
A. Motion update
The odometry information ut obtained from robot
estimates the current position xt={xt[1],…,xt[i],…,xt[N]} from the previous position xt-1.
p(xt|ut, xt-1).
B. Measurement update
The weight wt is calculated from xt using
measurement zt.
wt = ηp(zt|xt),
where η is a constant.
C. Resampling
Some particles will be replaced with newly sampled
ones.
draw i with probability ∝ wt[i],add xt[i] to state set Xt.

The reset step will be applied to increase the number ofparticles when robot is fallen or kidnapped. In RoboCup
SPL, since there are so many such cases, a fallen or
kidnapped robot must recover from disordered states. We
therefore have to increase the number of particles toimprove self-localization.

2.2. Unscented Kalman Filter (UKF)

3.2. Increase or Decrease Step

The UKF is a filter that handles nonlinear state equation
as the following equation. Probability distributions canbe used when the distribution follows from normality.
This filter continues to update one state. UPF updates thethe motion update in particle filter using UKF.
x(t+1) = f(x(t)) + bv(t),y(t) = h(x(t)) + z(t),where x(t) is an n-dimensional vector, b is a constant.
Assume that both variances of system noise v(t) andIn the increase or decrease step, the number of particles
can be changed according to the estimation accuracy ofself-localization. The detailed algorithm is illustrated inFig. 2. Let X={x1,x2,…,xN}, Y={y1,y2,…,yn} and Xnew be
sets. σn is the standard deviation of the particles with aweight more than α. 𝑒1 and 𝑒2 are any positive realvalues and used in order to adjust the size of σn at time n.

Fig. 1: Outline of the proposed method.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan464

Improvement of Computational Efficiency

Fig. 3: An example of deleting a particle.

Fig. 4: An example of adding a particle.

Fig. 5: An exapmle of fixing the number of particles.
Fig. 2: Increase or decrease step in the proposed
algorithm

reduce the number of particles. On the other hand, if anyof the white circles is beyond the circle with radius e2σn,
we cannot reduce any particles as shown in Fig. 4. If all
the white circles are within the circle with radius e2σn andcentered at the centroid of the black ones, we fix thenumber of particles.

The proposed algorithm will reduce the number ofparticles if the situation as shown in Fig. 3. In thisfigure, the white and black circles correspond to theparticles with weights less than α and more than or equal
to α, respectively and the yellow circle refers to thecentroid of the black ones. If all the white circles arewithin the circle with radius e1σn and centered at thecentroid of the black ones, i.e. the yellow circle, we can

4. Simulation
In this section, we show the validity of the proposed
method through some computer simulations using© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan465

Kenta Hidaka, Takuo Suzuki, Kunikazu Kobayashi

SimRobot simulator[4]. Assume that the maximum
number of particles is 16, i.e. Nmax=16 as the same withthe conventional method[4].The values of parameters are
set as follows: Nmin=4, parameter1=1, parameter2=2,
parameter3=2, parameter4=3, and α=0.7.
Figure 6 illustrates the difference between the centroid ofparticles and true position as the number of cycles. The

proposed method are 48,340 and 43,880 times,
respectively. As a result, the computational efficiency
was improved by about 10 percent.
5.

Conclusion

In this paper, we focused on the self-localization in
RoboCup SPL and proposed the automatic adjustment
method of the number of particles in UPF. We suggested
that the number of particles could be variable by using
three values, i.e. the centroid, the standard deviation, andthe weight of particles. Through computer simulations,
we confirmed that the proposed method improved thecomputational efficiency as compared with theconventional method. As future issues, the number ofparticles and the self-localization error in the proposed
method are required to be varied in proportion, because
there is no need to increase the number of particles whenthe error is small.
Acknowledgements
This work was partly supported by President's Special
Research Fund from Aichi Prefectural University, Japan.

Fig. 6: Transition of the difference between thecentroid of particles and true position.

References
1. R. van der Merwe, A. Doucet, N. de Freitas, and E. Wan,
The Unscented Particle Filter, Proc. of NIPS, pp.584-590(2000).
2. W. Yu, J. Peng, X. Zhang, S. Li, and W. Liu, An Adaptive
Unscented Particle Filter Algorithm through Relative
Entropy for Mobile Robot Self-Localization, Mathematical
Problems in Engineering, Vol.2013, Article ID 567373(2013).
3. J. Yang, X. Yuan, and Y. Zhang, A Fast Initial Alignment
Method for SINS Used Adaptive Sample Size Unscented
Particle Filter, Proc. of International Conference on
Chemical, Material and Food Engineering, doi: 10.2991/cmfe-15.2015.157 (2015).
4. T. Röfer, T. Laue, J. Müller, D. Schüthe, A. Böckmann, D.
Jenett, S. Koralewski, F. Maaß, E. Maier, C. Siemer, A.
Tsogias, and J. Vosteen, B-Human Team Report and CodeRelease 2014 (2014).
5. S. J. Julier and J. K. Uhlmann, A New Extension of the
Kalman Filter to Nonlinear Systems, Proc. of AeroSense:
The 11th Int. Symp. On Aerospace/Defence Sensing,
Simulation and Controls, pp.182-193 (1997).
6. N. J. Gordon, D. J. Salmond, and A. F. M. Smith, Novel
Approach to Nonlinear/non-Gaussian Bayesian State
Estimation, IEE Proceedings F (Radar and Signal
Processing), Vol.140, No.2, pp.107-113 (1993).

Fig. 7: Transition of the number of particles.
estimation accuracy of the self-localization using the
proposed method is able to maintain the same level as theconventional method. However, the computational
results confirm that the self-localization error and thenumber of particles is not proportional. The number ofcycles for processing the self-localization was measured
for 10 seconds. That for the conventional method and the© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

466

Multi Objective Evolutionary Algorithms for Association Rule Mining: Advances andChallenges
Aswini Kumar Patra

Assistant Professor, Department of Computer Science & Engineering, NERIST, Itanagar, India
E-mail: akp@nerist.ac.in, aswinipatra@gmail.com

Abstract
Association rule mining is an important research area in data mining field. The challenge, posed by many methods,
is the amount of time consumed for generating frequent items sets. To overcome this, evolutionary algorithms havebeen used widely. Moreover, apart from support and confidence, there are many other metrics available to measure
the quality of association rules. That is the reason why multi-objective approach plays a crucial role. Therefore, two
methodologies namely, multi-objective and evolutionary algorithms as a combination proved to be a preferredchoice. Though numerous works have been proposed for mining association rules, use of multi-objective
evolutionary algorithms are still in its infancy stage. This paper explores the challenges and advances that havebeen made in this regard in terms of nature of algorithm, encoding mechanism, objective functions and operators.
Keywords: Association Rule Mining, Categorical, Quantitative and Fuzzy Association Rules, MOEAs,

work in two phases. First, frequent item-sets aredetected by using a measure called support count and auser-defined parameter called minimum support. Second
phase generates the rules using another user-definedparameter called minimum confidence. Evolutionary
algorithms proved beneficial as they directly generateassociation rules skipping the frequent item set
generation. Traditionally, support and confidence are
maximized to have quality rules. But there are manyother metrics available to measure the goodness of
association rules [7]. Therefore, the problem of ARM
can be posed as a multi objective optimization problem
where the goal is to find association rules while
optimizing
severalsuchgoodness
criteria
simultaneously [5].

1. Introduction
The problem of association rule mining (ARM) waspresented in 1993 and 1994 [2, 1]. The origin of
Association Rules linked to well-known market basket
analysis, where the purchase behavior of customers isanalyzed. The goal is to discover regularities amongproducts purchased in a supermarket. The rules wereformed from transactional data (e.g., point-of-sale data),
and the information codified by the rules (e.g., ifbuy(pen) and buy(paper) then buy(book) (in short, pen∧ paper ⇒ book)), can later be utilized in order totake decisions involving promotions, product placement,
etc. The idea is to discover if-then rules to infer
interesting relations between variables in large
databases. Nowadays, their use has found the way into
many different fields, including electronic commerce,
web usage mining, bioinformatics, intrusion detection,
health care environment etc.
Many classical algorithms, so far, have beendeveloped such as APRIORI, ECLAT or FP-GROWTH.[6]which are computationally expensive. These algorithms

The rest of the paper is organized as follows: Section 2
briefs about the background and preliminaries. Section 3
surveys the research progresses that have been made for
ARM involving evolutionary algorithms. Section 4 listout the challenges and difficulties and Section 5
concludes the paper.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa, Japan467

Aswini Kumar Patra

2001) and NSGAII (Non-dominated Sorting Genetic
Algorithm II; Deb et al., 2002). The researches for
ARM use some of the standard algorithms, somevariants and some non-standard (i.e. not same as above
mentioned algorithms) MOEAs as well.

2. Background and Preliminaries
2.1 Types of Association Rules: Broadly, there are two
types of association rules.
 Categorical Association Rule
 Quantitative Association Rule.
Fuzzy association rule is a variation of Quantitative
Association Rule.

2.3 Terminologies for Evolutionary Algorithms
The Evolutionary algorithms are characterized by
Encoding mechanism, objective functions, evolutionary
operators.

2. 2 Multi objective Evolutionary Algorithms
Last two decades witnessed an increased
interest in the use of GAs for multi objective
optimization. Some of the most traditional multiobjective evolutionary methods are VEGA (Vector
Evaluated Genetic Algorithm; Shaffer, 1985), MOGA(Multiple Objective Genetic Algorithm; Fonseca &Fleming, 1993), NPGA (Niched Pareto Genetic
Algorithm; Horn & Nafpliotis, 1993), and NSGA (Nondominated Sorting Genetic Algorithms; Srinivas & Deb,
1994). The newly developed evolutionary methods, thatincorporate elitist strategy, are SPEA (Strength Pareto
Evolutionary Algorithm; Zitzler & Thiele, 1999), PAES
(Pareto Archieved Evolution Strategy; Knowles &Cornes, 1999), PESA (Pareto Enveloped-based
Selection Algorithm; Knowles et al., 2000), SPEA2(Strength Pareto Evolutionary Algorithm; Zitzler et al.,average of different objectives. Second, thelexicographical approach, in which theobjectives are ranked in order of priority. Third,
the Pareto approach, which consists of as many


There
are
mainlytwo
chromosome
representation techniques. In the first approach(Pittsburgh approach), a set of possible
association rules are encoded in eachchromosome. The second method is Michigan
approach, in which each chromosome
represents exactly one rule. This approach issuitable for Association Rule Mining.
There are three different approaches that can befound to tackle the objectives in fitness
function in multi-objective problems [6]. First,
the original multi-objective problem can betransformed into a single-objective problem byusing a weighted function. It involves the useof a GA whose fitness function is the weighted
non-dominated solutions as possible andreturning the set of Pareto front to the user.
Most of the studies using multi-objective GAs
for ARM have been performed using thePareto approach. Such a front is given in Fig 1
for two objectives f1 and f2.
Generally, both standard and varied selection,
crossover and mutation operators used for
Association Rule Mining.

3. MoEAs for Association Rule Mining
A comparison, of the proposed methods in the literature,is mentioned in Table 1, in terms of underlying MOO
tool, encoding mechanism, objective functions, andevolutionary
operators.
The
innovations,
implementation details for each category of association
rules are discussed in subsections below.
3.1. Categorical Association Rule Mining
The proposed works, related to categorical association
rule mining, are discussed below.
 Ghosh et el. [8] proposed association rulemining as a multi objective problem rather thansingle objective one. The authors adopted© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa, Japan
Fig 1. Pareto Front

468

Aswini Kumar Patra

Michigan approach to represent thechromosomes (i.e. rules). Each chromosome
has length 2n, where ‘n’ is the number of items.
Each gene, in the chromosome, represents an
attribute. If the gene is 00, the attribute is in theantecedent part. . If the gene is 11, the attributeis in the consequent part. The attribute does notexists in the rule, if the gene is 01or 10. Thismethod uses a Pareto based genetic algorithm
to solve the multi-objective rule mining
problemusing
threemeasures––comprehensibility, interestingness and thepredictive accuracy(i.e. Confidence). Thisalgorithm works on a sample of the original
database, and used the random sampling
method.
in order to be able to find non frequent but
interesting rules.

In a similar approach as in [8], the work [9]uses binary encoding for the chromosomeswith a little change. The presence of an
attribute in the antecedent and consequent partare represented by bits 10 and 01, whereas
other bit combinations represent the absence of
the attribute from the rule. It uses Pareto based
co-evolutionary algorithm in order toovercome the weakness of lexicographic ordermethod. Two new measures, statisticalcorrelation and comprehensibility, along withconfidence as objection functions are proposed.
The co-evolutionary operators, used, are Pareto
neighborhood crossover operator, combination
crossover and annexing operator. The outcome
in terms of running time is compared with thatof a classical method F-growth and found to belesser.
In [10], a non-standard MOEA is used for theARM problem. Best solutions, encounteredover generations, are filled into a secondary
population called the “Pareto Archive”. In the
production process, elitism is applied in order
to allow solutions from the “Pareto archive” toparticipate to the reproduction. The classicalroulette selection based on the Pareto ranking
is used. The proposed crossover operator has
two versions depending upon the fact that theparents may share (or not) a common attribute:
Crossover by value exchange and Crossover byinsertion. Four mutation operators were used as
mentioned in Table 1. This algorithm proposes
to consider the ARM problem as a multiobjective combinatorial optimization problem

The work [11] uses NSGA II. Six different
measures (support, confidence, interest,
comprehensibility, cosine and attribute
frequency) have been considered as objectives.
Three of these measures have been taken at atime and optimized simultaneously. Because
NSGA II known to perform well when thenumber of objective functions is at most three.
Eventually, it demonstrates that this method
obtains some rules which cannot be obtainedby the traditional mining methods.



Ali Hadian et el. [12] proposed the binarychromosome encoding as in[8]. The fourmeasures
Support,Confidence,
Comprehensibility, and Interestingness areused as objectives. Cluster-Based MultiObjective Genetic Algorithm (CBMOGA) is
used which optimizes the support counting
phase by clustering the database. Clusters are
based on the number of items in eachtransaction. The benefit of CBMOGA is that it
prevents some unnecessary comparisons ascompared to MOGA. Though CBMOGA
outperforms the MOGA, the speedup highly
depends on the distribution of transactions in
the cluster tables.

The disadvantage of binary encoding scheme isthat it leads to large chromosome length. To
overcome this, an integer encoding scheme isbeing used in ARMMGA [13]. For example,
if Ai represents the ith item, then thechromosome {3 | 2 5 4 1 3} represents the rule
A2A5A4 ⇒A1A3. However, this representation
gives rise to a variable length chromosome
length, thereby in need of a specialized
crossover operator. The classical measures
support and confidence are optimized
simultaneously. It proposes a method without
taking the minimum support and confidence
into account. The promising point in thisalgorithm is that the value of the fitness
function only specifies the order ofchromosomes in population and has not anyother effect on genetic algorithm operator;
therefore, the algorithm is convergence withany usually arbitrarily fitness function.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa, Japan

469

Multi Objective Evolutionary Algorithms

Table1. Comparison of Association Rule Mining Methods using MOEAs (in Chronological Order)
Algorithm

Underlying
Tool
MOGA

Type

Encoding

Objective Functions

Categorical

Binary(Michigan)

Kaya
and
Alhajj[22],
2003
Kaya
and
Alhajj[23],2004

SPEA Variant

Fuzzy

Real-valued(membershipfunctions)

Confidence,
Comprehensibility,
Interestingness
Number of large item sets,
Time taken to find alllarge item sets

MehmetKaya[24],2005

Non-standard(Pareto based
GA)

Fuzzy

. Real-valued(membershipfunctions)

Strongness,
Interestingness,
Comprehensibility

Four point Crossover,
Mutationnotmentioned

Hu and YangLi[9], 2007

Pareto-based coevolutionary

Categorical

Binary(Michigan)

Statisticalcorrelation,
Comprehensibility

Pareto-neighbourhood
cross
over,
Combination, annexing

Khabzaui etel.[10], 2008

Non-standard

Categorical

Not mentioned

Support, Confidence, Jmeasure, Interest, Surprise

Altas
et
el.[14],2008(MODE
NAR)

MODE

Numeric

Mixed(integer+real)(Michigan)

Support, Confidence,
Comprehensibility,
Amplitude of interval

Valueexchangecrossover,Insertion
crossover,value/attributemutation,
insertion/deletion
mutation
DE/rand/1

Alhajj
and
Kaya
[25],
2008
Chen
et
el.[26], 2008

SPEA Variant

Fuzzy

MOGA

Fuzzy

Anand
et
el.[11], 2009

NSGA-II

Categorical

Real-valued(membershipfunctions)
Real-valued(membershipfunctions)
Binary(Michigan)

Number of large item sets,
Time taken to find alllarge item sets
Number of large 1-itemsets,
suitabilityofmembership functions
Combination(3 at a time)of support, Confidence,
Interest,
Comprehensibility,
Cosine,Attributefrequency

Ghosh
and
Nath[8], 2004

Evolutionary
Operators
Multi point cross over,Bit-flip mutation
Multi-point cross over,Standard
real-value
mutation

Multi-point cross over,standard
real-value
mutationMax-minarithmetic
crossover, one point
mutation
Crossovernotmentioned, bit flip
mutation.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa, Japan470

Multi Objective Evolutionary Algorithms

Table 1(Continued)

Algorithm
Ali Hadianel.[12], 2010
(CBMOGA)etUnderlying
Tool
MOGA variant

Qodmananel[13], 2011etNon-standard

Categorical

Integer(Michigan)

Support, Confidence

Martin et el.[16],2011(NSGA-IIQAR)
Mathewsetel.[27], 2011

NSGA-II

Numeric

Realvalued(Michigan)

NSGA-II

Fuzzy

Mixed(integer+real)(Michigan)

K.Y.Fung
el.[17], 2012

NSGA-II

Categoricaland numeric

Both Michigan andPittsburgh

Lift,comprehensibility,
performance(SupportxConfidence)
Temporal support, temporalconfidence, fuzzy support,
membership function width
Accuracy,Comprehensibility,
Definability.

B. Minaei-Bidgolietel[18],
2013(MOGAR)
D.Martinetel[19],
2014(QAR-CIPNSGA-II)

Non-standard

Numeric

Michigan

Confidence, Interestingness,
Comprehensibility,

NSGA-IIvariant

Numeric

MichiganInterestingness,
Comprehensibility,
Performance

M.M.Ballesterosetel[20],
2014(GarNet)
Mehrdad Almasi
etel[21],
2015(RarePEARs)

NSGA-II

Numeric

MichiganSupport, accuracy
Confidence

Non-Standard

Numeric

Michigan(integer+r
eal)

Support, Confidence, Lift,
Centrality Factor, Length ofRule andCoverageetType

Encoding

Objective Functions

Categorical

Binary(Michigan)

Support,Confidence,
Comprehensibility,
Interestingness

Evolutionary
Operators
No specific mention of
CrossoverandMutation
Order I crossover,random replacement
mutation
Multipoint crossover,standard
real-value
mutation
Modifieduniformcrossover,randomchange mutation
Two point Crossoverand bitwise Mutation,
Arithmetic Crossoverandpolynomial
Mutation
K-point Crossover, Bit
flip Mutation
Standard
Crossoverand Mutation.

andInterval
based
crossover,probabilistic mutation.
One-point crossover,standard
real-value
mutation.

chromosome encoding where each attribute has
three components. The first component
indicates whether the attribute is present orabsent in the rule, and if present, in which part
(antecedent or consequent) in the rule it is. The
second and third components indicate thelower and upper bounds of the ranges of theattribute. The first component can have integer

3.2. Quantitative Association Rule (QAR) Mining
The works related to QAR are briefed below.
 The chromosomes encode the lower and upperbounds of the intervals of the attributes
participating in a rule. In [14], where the
MODENARalgorithm
uses
such
a

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa, Japan

471

Aswini Kumar Patra

values 0, 1, or 2, which indicate the presence of
the attribute in the antecedent of the rule, thepresence of the attribute in the consequent of
the rule, and the absence of the attribute fromthe rule, respectively. The second and third
components can take real values from thecorresponding attribute ranges. It is to be notedthat as MODENAR uses differential evolution
as an optimizer and works on real-valued
chromosomes, the authors used a round-off
operator to handle the integer part of thechromosome. Support, confidence value andthe comprehensibility of the rule are
maximization objectives while the amplitudeof the intervals, which conforms the itemset
and rule, is minimization objective.
MODENAR used the standard version of thecrossover and mutation operators adopted by
the version of differential evolution called
DE/rand/1. Additionally, a rounding operator isused to round off the first part of the attributethat requires an integer (0, 1, 2) for computing
the objective function values. ARs are directly
mined without generating frequent item sets.


objective GA approach could simultaneously
consider the accuracy, comprehensibility, anddefinability of approximate rules. The
promising feature is that it uses bothcategorical and quantitative attributes andtargets to define the design profile of a product.
The proposed multi-objective GA approach
could simultaneously consider the accuracy,
comprehensibility,
anddefinabilityofapproximate rules as objectives.

The work NSGA-II-QAR [16] uses the same
encoding scheme as in [14].
The only
difference is that in this case, the first part ofthe chromosome, instead of using the values 0,
1, 2, adopts the values 0, 1, and − 1,
respectively, to denote the same meaning. In
both cases, the algorithms used a Michigan
encoding strategy. The three objectives
interestingness,
comprehensibility
andperformance are maximized. As evolutionary
operator multipoint crossover is utilized andthe two parts of the chromosome undergo two
different mutations. It uses an extension of thewell-known MOEA NSGA-II. This method
follows a database-independent approach
which does not rely upon the minimum support
and the minimum confidence thresholds. An
experimental comparison between NSGAIIQAR and MODENAR is provided in [16].

The work [18] uses the three measures;confidence,
interestingness,andcomprehensibility have been used as different
objectives for our multi objective optimization
which is amplified with genetic algorithms
approach. The method uses rough values
which are defined with upper and lower
intervals to represent a range or set of values.
Michigan approach is used for representing
rules in chromosomes. Eventually, the best
rules are obtained through Pareto optimality. It
uses K-point crossover and bit flip mutation.



QAR-CIP-NSGA-II [19] maximizes thecomprehensibility,interestingness
andperformance of the objectives in order to minea set of quantitative association rules with agood trade-off between interpretability andaccuracy. This model extends NSGA II to
perform an evolutionary learning of theintervals of the attributes and a condition
selection for each rule. It uses Michigan typeencoding fro chromosomes and as similaras[16]. It uses standard crossover and mutation.
Moreover, this proposal introduces an external
population (EP) and a restarting process to theevolutionary model in order to store all the
non-dominated rules found and improve thediversity of the rule set obtained. The results
attained over nine real world datasets haveshown how the EP and restarting process
improves the percentage of patterns covered by
the rules of the total patterns in the dataset andallows obtaining a greater number of rules thanthe classical approach.

In [17], a two-stage rule-mining approach is
proposed to generate rules with a simple
 The work GarNet[20] is a multi-objective
chromosome design in the first stage of rule
evolutionary algorithm for mining quantitativemining. In the second stage of rule mining,association rules that has been developed toentire rule sets are refined to determine
discover gene association networks. It is based
solutions considering rule interaction. For aon NSGA II. The representation of anrule-mining problem, the proposed multi© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa, Japan

472

Multi Objective Evolutionary Algorithms



individual consists of two data structures. The
upper structure includes the intervals bounds ofall the attributes of the dataset. The bottom
structure indicates the membership of an
attribute to the rule represented by anindividual. The type of each attribute can havethree values: 0 when the attribute does notbelong to the rule and 1 or 2 if it belongs to theantecedent or the consequent of the rule,
respectively. If an attribute is wanted to be
retrieved for a specific rule, it can be done bymodifying the value equal to 0 of the type by avalue equal to 1 or 2 depending on theantecedent or consequent. Support, accuracy
and Confidence are used as three objectives tobe optimized simultaneously.

Various MOEAs have been applied in different
works on fuzzy ARM. Kaya et al. [22], [23],[25] used a variant of SPEA for fuzzy rulemining. In [26], a multi objective GA (MOGA)
is used for this purpose. NSGA-II has beenutilized in [27]. But, there is no mention of any
comparison among different MOEAs for fuzzy
rule mining in any of the work..

There are two categories of chromosome
representations for fuzzy ARM [5].
1. In the first approach, a chromosome
encodes a set of fuzzy clusters that
correspond to each attribute. The goal
is to find a suitable set of fuzzyclusters that partition the range ofvalues in each attribute domain. This
representation is employed in a series
 In this paper [21], the proposed algorithmof works done by Kaya et al. in [22],(Rare-PEARs) gives a chance to each rule with[23], [25]. In these works, eachdifferent length and appearance (antecedent
chromosome represents the baseand consequent parts of rules) to be created.
values of a variable number of
Therefore, various interesting, rare or
membership functions representing
interesting and rare rules can be found. Somethe fuzzy sets for each quantitativeof these rules might be uninteresting (those that
attribute. Fuzzy sets are represented
contain frequent item sets). However, it has
by standard triangular membershipbeen tried to avoid them by Rare-PEARs. To
functions. Chromosomes use realaccomplish this goal, the method decomposesvalues. Here, a chromosome does not
the process of association rule mining into N-1
represent
association
rules.
It
sub-problems (N is the number of attributes,
representsasuitablefuzzyclusteringand each sub-problem is handled by anof the attribute domains. The evolved
independent sub-process during Rare-PEARs
fuzzy membership functions are thenexecution).
Each
sub-process
startsused as the linguistic values of theindividually with a different initial population.
corresponding attributes [5]. Fuzzy
It then explores the search space of its
association rules are mined using
corresponding sub-problem to find rules withstandard
algorithms
basedon
semi-optimal intervals for each of the attributes.
minimum support and minimumThis process is done by a new definition ofconfidence criteria. The work [26]Non-Dominated concept. Support, Confidence,
uses a similar encoding approach.
Lift, Centrality Factor, Length of Rule and2. In
secondapproach,each
Coverage are used as objectives to be
chromosome
directly
represents
optimized.
association rule. This is a kind of
Michigan approach. In [27], such an
3.3. Fuzzy Association Rules
encoding mechanism is applied to
One of the major problems of mining numericmine temporal fuzzy association rules.
association rules is that these algorithms deal with sharpA
mixed
representation,ofboundaries between consecutive intervals. Thus, theychromosomes combining integer andcannot represent smooth changes from one interval toreal values, is used. The chromosome
another, which can be easily handled by fuzzy
encodes the lower and upper bounds
association rules [5]. The OEA-based fuzzy ARMof the temporal interval in the rules astechniques have been developed in the past decade.
integers. The indices of the items
participating in the rule are also
© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa, Japan473

Aswini Kumar Patra

encoded as integers. Eventually, thereal valued parameters of thetriangular membership functions
corresponding to each item areencoded in the chromosome. Thus,
this representation induces variablelength chromosomes needing special
evolutionary operators.
since this gives higher support values as it
includes more number of items [5].

In the works [22], [23], [25], two criterias are
optimized, that is, number of large itemsets andtime spent to obtain the large itemsets. So,
there are two objectives to evolve a possible
fuzzy clustering of the numeric attributes. First
is maximizing the number of large itemsets
while the other is minimizing the time required
to obtain all large itemsets given the clustering.
After optimizing the clustering, membershipfunctions are used as linguistic values for the
fuzzy association rules extracted based onminimum support and minimum confidence
criteria. In [26], where a similar encoding
strategy is adopted as in [22], two objectivefunctions are optimized simultaneously. The
first objective function is stability of theencoded membership functions, which has twocomponents, that is, overlap factor andcoverage factor [5]. The stability is optimized
to avoid generation of too redundant and too
separated fuzzy sets for an item. The second
objective is to maximize the total number oflarge 1-itemsets for given minimum support
values. Although this paper is a consequence of
the works of Kaya et al. with modifications in
the objective functions and evolutionary
operators, no comparison has been madebetween the works to check which is better
over the other approach. In [27], Michigan
form of chromosomes the authors used for
temporal fuzzy association rule mining. So, theobjective functions are related to the
optimization of the encoded rules. It used four
objective functions, namely temporal support,
temporal confidence, fuzzy support, andmembership function widths. The first threeobjective functions are obvious; the last
objective function is used to prevent a
membership function from covering the whole
range of attribute values. Without thisobjective function, the solutions could evolve
to cover the complete range of attribute values,

References [22], [23], have used standard
multipoint crossover operations. In [25],arithmetic crossover is used. Also, they
employed standard real-value mutation. In [26],the authors used max-min arithmeticalcrossover and one-point mutation. Thiscrossover operator generates four offspring at atime out of which the two best offspring arechosen. The mutation operator is used toslightly change the center of the fuzzy set
being mutated. Whenever mutation takes placeat the center of a fuzzy membership function, it
may disrupt the order of the resulting fuzzy
membership functions. Hence, after the
mutation, these fuzzy membership functions
need rearrangement according to their center
values. In [27], for a Michigan type of
encoding, a modified uniform crossover
operator is adopted. For mutating the genes
representing the lower and upper bounds of thetime interval, the values are generated within
the endpoint range (epr) where the midpoint isthe value of the current gene (g), such that the
mutated value is a member of the set { −epr/2, . . . , g, . . . , epr/2}. This is done to
reduce the effect of random sampling of thedataset [5].
This paper [24] first introduces optimized
fuzzy association rule mining in terms of threeimportant criteria; strongness, interestingness
and comprehensibility. Then, it proposes multiobjective Genetic Algorithm (GA) based
approaches for discovering these optimized
rules. Optimization technique according togiven criterion may be one of two different
forms; The first tries to determine theappropriate fuzzy sets of quantitative attributes
in a pre-specified rule, which is also called as
certain rule. The second deals with finding
both uncertain rules and their appropriate fuzzysets.

4. Points to Ponder
There are many challenges that have impacts on theoverall performance of the proposed algorithms or
methods. Those points that are found in literature listed
out in this section.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa, Japan

474

Multi Objective Evolutionary Algorithms



5. Conclusion
The use of minimum support and confidence
thresholds has to be avoided as there is highlyThis paper surveys the evolutionary algorithms used forchance of missing out quality rules.
association rule mining and briefed about the Two major problems were encountered while
technicalities, innovations and goals of the works.
obtaining rules using MOEAs. One is invalid
Although ARM algorithms, based on MOEAs, haverules and another is non-existent rules [11].
gained popularity in recent years, their use in real-life
These two problems are reason for reduced
applications is still limited. It would be useful if future
performance of elitist MOEAs like NSGA-II toworks will be directed to the use of rule mining for geneobtain more number of good rules. The search
expressions, financial databases, text mining, andspace tends to get stuck at local Pareto optimal
bioinformatics.
solutions found so far or converge to very few
Global Pareto optimal solutions if found[11].
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 Whenever a typical integer encoding scheme
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gives rise to variable length chromosomes [13],A. Coello Coello, “A survey of multiobjective
evolutionary algorithms for data mining: Part I,” IEEEa specialized crossover operator is required.
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 Finding the optimal interval of each rule’s
February, 2014.
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 Rapid convergence damages the efficiency of
A. CoelloCoello, “A survey of multiobjective
MOEAs. Some researches [19] solve thisevolutionary algorithms for data mining: Part II,” IEEE
problem by restarting. They restart whenever it
Trans. Evolut. Comput. Vol. 18, No. 1,
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is found that the difference between two
6. M. Jes´us, J. A. G´amez, P. Gonz´alez, and J. M. Puerta,
consecutive populations is less than "α" percent.
“On the discovery of association rules by means of
To chose "α", sufficient exploration is needed.
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If this value is high, current generations may
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not have the chance to produce elite
7. P.-N. Tan, V. Kumar, and J. Srivastava, “Selecting theright interestingness measure for association patterns,” inchromosomes.
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123–133, Jun. 2004.
numeric data in [18].
9. J. Hu and X. Yang-Li, “Association rules mining using
multi-objective co-evolutionary algorithm,” in Proc. Int.
 In fuzzy association rules mining, it is not an
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easy task to know apriori the most appropriate
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© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa, Japan476

A Study on the Structural Hole of Patent Applicant Network in R&D Management
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
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
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

Abstract
The arrangement of R&D staff refers to the issue of the linkage between structural holes in a network. In order toexamine the linkages between multiple R&D projects in a company, we use the patent information as the index of
R&D capability and examine our propositions by social network analysis. In this paper we focus on top-sharedcompanies in Japanese cosmetic industry, and suppose that these companies keep their market shares due to theirsmoothly changes of business fields to cope with the threat of new comers from other industries. The analysis in
this paper include: 1) calculate the betweenness centrality, and create heat maps to visualize the change of the
betweenness centrality, 2) examine the structural hole.
Keywords: patent analysis, social network analysis, heat maps, Japanese cosmetic industry, structural holeis cosmetic industry. Although new entries never stop,
top-shared companies keep their market shares in theindustry. We suppose that if there is any feature in thesecompanies’ R&D management, and the features of
R&D management contribute these companies to keep
their growth.
In this paper, we focus on four top-shared companies
in Japanese cosmetic industry to examine their R&D
In this paper, we focus on four top-shared companies
in Japanese cosmetic industry to examine their R&D
strategies, as well as the change of core researchers intheir R&D projects by social network analysis, based on

1. Introduction
The development of new products is needed for acompany to sustain its competitive advantage, while theresults of development process are affected by the
management of R&D organization. The arrangement of
R&D staff refers to the issue of the linkage between
structural holes in a network [1]. By using the patent
data as the index of R&D capability, the R&D network
structures can be visualized and analyzed by social
network analysis. In Japan, one of the industries thatface strong competitions due to numbers of new entries© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

477

Iori Nakaoka, Yousin Park, Yunju Chenthe patent data. The analysis include: 1) calculate thebetweenness centrality, and create heat-maps tovisualize the change of the betweenness centrality, 2)
examine the structure hole. The results of these analyses
will be discussed to find out the reason why thesecompanies can cope with the new entries.
(a) Shiseido(b) Pola
Figure 1 Total sales of each company

2. Background
2.1 Structural hole and patent analysis

3. Methodology and data

A structural hole is a relationship of non-redundancy
between two contacts. The hole is a buffer, like aninsulator in an electric circuit. As a result of the holebetween them, the two contacts provide network
benefits that are in some degree additive rather thanoverlapping [2]. Brokerage across the structural holes
between groups provides a vision of options otherwise
unseen, which is the mechanism by which brokerage
becomes social capital.
Patent documents are an ample source of technicaland commercial 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 Japanese
cosmetic companies’ R&D management based on thepatent analysis.

In the following sections, the trends of R&D project
management strategies of Japanese cosmetic 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.
Then diagrams are created by these analyses.
In Japan, patent documents are archived in J-PlatPat,
and are browsed and retrieved ubiquitously. All patents
are classified according to each three classification
codes, IPC (International Patent Classification), FI (FileIndex) and F-term (File Forming Term). Both of FI and
F-term are classification codes under IPC. Especially, Fterm is given to every patent from F-term list based on
the technical items indicated in patent documents. Thus,
almost every patent has multi-F-term codes. Table 1 ismost frequently used F-terms in cosmetic industry.
Based on these F-term related to cosmetic, patents
related to cosmetic published by each company are
extracted and collected from the patent information.

2.2 The overview of cosmetic industry

Table1 F-term list of cosmetics patents
F-termContents
4C083AA NATURAL INGREDIENTS AND INGREDIENTS

Although the expansion of market scale benefits theJapanese cosmetic manufacturers to keep their growth,
the mature of cosmetic market and customer slow down
their speed of growth. The total sales of the industry
have kept flat since the beginning of 2000, however,new comers from other industries have entered one after
another after 2000. There are 12 large new comers into
cosmetic industry and their entries concentrated on
2006-2009. The shares of sales of top 5 companies in2014 were: Shiseido 37.9%, Kao 28.4%, Pola Holdings
9.5%, Kose 9.5%, Mandom 3.4%. The top 5 sharedcompanies occupied the 80% of the total sales of thecosmetic market. Figure 1 shows the sales of Shiseido
and Pola from 2008-2014. Their sales kept growing
even after 2009, which the new entries mostconcentrated.

4C083AB
4C083AC
4C083AD
4C083BB
4C083CC
4C083DD
4C083EE
4C083FFWITH
UNKNOWNSTRUCTURESANDCOMPOSITIONSINORGANIC INGREDIENTSORGANIC INGREDIENTS CHARACTERIZEDBY ELEMENTS
ORGANIC INGREDIENTS CHARACTERIZEDBY STRUCTURESFUNCTION SPECIFIC INGREDIENTSKINDS OF PRODUCTSFORMS OF PRODUCTS
EFFECTSMETHODS, DEVICES FOR MANUFACTURE© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan478

A Study on the

4. Analysis
4.1 An approach based on the number of patentpublications
As our first approach, the numbers of patentpublications associated with cosmetics in each of thecompanies are shown in Figure 2.
(a) Shiseido(b) Pola
Figure 3 Results of correspondence analysis

4.3 An approach by the social network analysis
4.3.1. R&D network
We disclose the R&D project structures of the target
companies by the social network analysis in this section.
The results are outputted as heat maps. Figure 4 (a)and (b) are the R&D networks that show Shiseido andPola’s personnel engaged in R&D (nodes) and linkagesof joint patent applicants (edge). In addition, Figure 4(c) and (d) are the heat maps that visualized thepercentage of major R & D networks occupied in theentire networks. The vertical axis in the figure shows
top 10 networks ranked by network size, and the scoresmean the percentage of personnel belonging to the top
10 networks. When the color is dark, it means that thepercentage of personnel belonging to the network is
high. We can consider that Shiseido has adopted theoverconcentration organization which emphases oncooperation because the percentage of the person who
belongs to the biggest network in all of the years is high.
On the other hand, other companies such as Pola and soon have adopted decentralized organization which
differentiated by each function.

Figure 2 The number of patents of each companies
Every company obtains related patents to a certain
extent and the number of patents of all of them kept on
decreasing during the years. Shiseido and Kao obtain
more than 200 patents every year although the rates of
R&D spending and net sales fell. However, we canconsider that R&D of Shiseido is efficiently because therate of R&D spending and net sales of Shiseido is lower
than Kao.
4.2 An approach by the text mining
We use the correspondence analysis based on text
mining to disclose the technical trends and features
based on typical words associated with cosmetic. Resultof the analysis of Shiseido is Figure 3(a), and result of
another cosmetic company Pola is Figure 3(b). The
reference data in the analysis is the numbers of each
their patent document in each year, including tocosmetic noun. These figures are based on dates ofpatent publication, and patents are applied to products inthe companies. Figure 3(a) shows that there is low R&D
continuity in Shiseido. Therefore it is considered that
Shiseido changed its contents of technology depend on
the year. On the other hand, Pola has developed similar
technology continuously because the results almost
outputted around the same place over all of the period in
Figure 3(b).

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan479

Iori Nakaoka, Yousin Park, Yunju Chen(a) Shiseido’s Network(b) Pola’s Network
Figure 5 The Degree of core rigidity of Shiseido(c) Concentration ratio of Shiseido (d) Pola
Figure 4 R&D Networks of Shiseido and Pola
Figure 6 The change of R&D area that core structuralhole personnel involved in

4.3.2. Structural hole resource of R&D
The structural hole means the gap between socialgroups. Since personnel who connect organizations play
the role as the structural hole serves to relay the
information, we use betweenness centrality to visualize
the change of the core structural holes connection
personnel in this paper. Betweenness Centrality is oneof the well-known indices that Freeman proposed in thesocial network analysis [3].
In addition, we focus on the change of R&D area thatstructural holes connection core personnel involved in
to express how the structural holes personnel develop
new technologies. As one of the example, the corerigidity of human resource and the change of R&D area
the structural holes personnel involved in Shiseido areshown in Figure 5 and 6. There is no significant change
in Shiseido’s core rigidity degree. However, it was
found that the core rigidity degree somewhat reduced inrecent years while new entrant company increased. In
addition, structural hole personnel were found to
involve in various areas at a constant level from area ofthe previous year.

5. Conclusions
There is a thesis that innovation has exploitation andexploration type [4]. From these results, the innovation
type of Shiseido's R&D strategy is an "exploration type",
and the innovation type of other companies (Pola and soon) of R&D strategy is an "exploitation type". As
further works, the patent data of the other country andthe other companies should be carefully examined. We
would like to modify our approach into moresophisticated one and to build up a more reliable
theoretical background.
References
1. R. S. Burt, Structural Holes and Good Ideas, American
Journal of Sociology, Vol. 110 (2004), pp. 349-399.
2. R. S. Burt, Structural holes: The social structure ofcompetition (Harvard University Press, Cambridge, MA,
1992).
3. L. C. Freeman, Centrality in Social Networks Conceptual
Clarification, Social Networks, vol.1 (1978-1979),
pp.215-239.
4. D. A. Levinthal, and J. G. March, The myopia of learning,
Strategic Management Journal, vol.14 (1993), pp.95–112.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

480

The TCE-RBV Framework for Information Systems Outsourcing: Empirical Testing using
Survey Data in Japan
Seigo Matsuno*

Dept. of Business Administration, National Institute of Technology, Ube College, Ube, Yamaguchi 755-8555, Japan
Tsutomu Ito

Hino Motors, Ltd., Hino, Tokyo 191-0003, Japan
Yasuo UchidaDept. of Business Administration, National Institute of Technology, Ube College, Ube, Yamaguchi 755-8555, Japan
Yoshiki Mikami

Graduate School of Engineering, Nagaoka University of Technology, Nagaoka, Niigata 940-2137, Japan
Takao Ito

Graduate School of Engineering, Hiroshima University, Higashi-Hiroshima, Hiroshima 739-8527, Japan*Corresponding author, E-mail: matsuno@ube-k.ac.jp
Tel: +81-836-35-4016, Fax: +81-836-35-4016

Abstract
This paper investigates the factors that influence the motives of make-or-buy decisions on information systems
from the viewpoints of Transaction cost economics (TCE) and the resource-based view (RBV). Using our originalquestionnaire survey data carried out in 2007 targeting Japanese firms, we analyze the relationships between therecognition related to the role of their information systems and the pattern of their make-or-buy decisions. As aresult, we make it clear that there are two cases in which TCE-motive and RBV-motive are complement orcontradict each other. And in latter case, TCE-motive tends to dominate over RBV-motive. Finally, the implications
for theory and practice are discussed.
Keywords: Information systems outsourcing, Make-or-buy decisions, Transaction Cost Economics, Resource-based
View

approaches to the outsourcing decisions. This paper
investigates the factors that influence the motives of
make-or-buy decisions on information systems (IS)from the viewpoints of TCE and the RBV.
This paper is organized as follows. In Section 2, we
briefly review some previous arguments of the IS
outsourcing. After reviewing the relevant literature on
the IS outsourcing, we analyze the relationships

1. Introduction
Up to now, two influential perspectives of outsourcing,that is, Transaction cost economics (TCE) and theresource-based view (RBV) have been both making avaluable contribution to understanding and explaining
the complexities of outsourcing. However, thediscussion has treated TCE and RBV as independent© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

481

Seigo Matsuno, Tsutomu Ito, Yasuo Uchida, Yoshiki Mikami, Takao Itobetween the recognition related to the role of theirinformation systems and the pattern of their make-orbuy decisions. The TCE-RBV framework is tested
empirically by using our original questionnaire survey
data targeting Japanese firms in Section 3. In Section 4,
we discuss the implications derived from the results ofthe statistical testing. Finally, in Section 5 we conclude
by a summary of this paper.

in the IS activities related to external vendors would
explain why such activities are performed internally.
Thus understanding of managerial resources as anadvantage for the company is called “core competencies”
in general today. Prahalad and Hamel (1990) defined
the core competence as “a combination of technologies
and production skills which is based on the company's
infinite product lines” [4]. This concept is understood
more broadly today and is recognized as their own
resources and abilities, which are hard to be imitatedand implemented by other companies, as a source of thecompany's sustainable competitive advantage.
From the viewpoint of the RBV, competitive
advantage of organizations can only be achieved
through a focus on core competencies, the managementof organizations have chosen to concentrate on what anorganization does better than anyone else while
outsourcing the rest.

2. Literature review on IS outsourcing
2.1. Transaction-based perspective
TCE posits that organizations prefer insourcing whenthe market costs are higher than internal governancecosts [1]. Markets generally lead to smaller production
costs, because of economies of scale obtained bysuppliers and/or vendors. However, markets lead tohigher transaction costs arising from three principal
attributes of transactions: asset specificity, uncertainty,
and frequency [2].
Asset specificity for the IS outsourcing has two
aspects; for business of a client company supported by
the developed information systems, and for technologies
which external vendors utilize. If the business that aclient company outsources is unusual, external vendors
have to be familiar with the business so that the asset
specificity will occur and increase on mainly humanresources. Similarly, if technology used by externalvendors is very specific, it will be difficult to use theinformation systems for other purposes on the clientcompany. Due to the investment to these unusual assets,
the transaction costs would increase.
Hence, if it is necessary to reduce the risk ofopportunism resulted from asset specificity and to
restrict the increase of transaction costs, insourcing or
quasi-outsourcing would be selected.

2.3. TCE-RBV Framework
The large number of studies of the IS outsourcing was
published in the past [5]. Especially, in the previous
study on the two patterns of the IS outsourcing, some
focus on factors of the influence on its selection. For
instance, Barthelemy and Geyer (2005) classify thefactors of the influence on the choice of either
conventional outsourcing or quasi-outsourcing into
internal and external factors [6]. They focus on testingsome hypotheses which are suggested based on the TCE
approach, but they do not refer to the RBV arguments.
The TCE and the RBV approach are treated as
independent argument so far, but each theoreticalperspective alone cannot fully describe the phenomenaof the IS outsourcing. Then there is a growing bodies ofresearch on the recognition that the TCE and the RBV
are complementary one another [7].
McIvor (2009) integrated the TCE and RBV
perspectives into a framework specifically assessing theoutsourcing decisions [8]. The TCE dimension of thisframework focuses upon the potential for opportunism
in any outsourcing decision. The RBV dimension
focuses upon a firm's resource position against potential
suppliers or competitors. This conceptual framework(see Fig. 1) suggests that for an activity in which thefirm has a weak resource position or capability, and thepotential for opportunism is low, outsourcing is the best

2.2. Resource-based perspective
The RBV of the firm posits that organizations preferinsourcing when a resource or capability is strategic so
as to enable them to sustain competitive advantage [3].
It builds upon four properties of a strategic resource:
economic value, rareness, imperfect imitability, andnon-substitutability. The RBV is important to the studyof the IS outsourcing, as superior performance achieved© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

482

The TCE-RBV Framework for

Table 1. Status of the recognition related to the roles of
ICT and information systems
Construct
Items
TCE-motive 1. Even if the company's own(α = 0.73)
information systems are outsourced,
it isn't expected to reduce the cost
concerning them.
2. When the company's own
information systems are outsourced,
it isn't expected to accumulate skills
and know-how concerning them.
3. When the company's own
information systems are outsourced,
there is a risk that it would be
dependent excessively on a specificvendor.
4. When the company's own
information systems are outsourced,
it isn't possible to correspond tointernal users' needs rapidly.
RBV-motive 1. ICT is very important to reduce the(α = 0.80)
cost of the company's products andservices.
2. ICT is very important to improve thequality of the company's products
and services.
3. ICT is closely related to andoperated
integrally
withtheexecution of the company's corebusiness.
4. ICT is an important infrastructure
which supports the execution of thecompany's core business.
Note: Each item is measured by 6-point scale. 1 = It
does not apply at all. 6 = It applies fully.

option. In the opposite case, where a firm enjoys strong
resource position and potential for opportunism is high,insourcing is the best answer. This framework implies
not only complementary but also contradictory
prescriptions of the RBV and TCE in outsourcing
decisions due to the inherent conflicts between the twotheories [9].

Fig. 1. The TCE-RBV Framework. (Adapted from
McIvor, 2009.)
3. Data and Method
3.1. Data collection
We conducted a mail questionnaire survey from July to
September 2007 with the research objective ofinvestigating “the current state and issues in IS
outsourcing and inter-firm collaboration”. Sinceordinarily the implementation of IS outsourcing is likely
to be affected by a firm size, we targeted Japanese firms
with 500 or more employees in this survey. The numberof valid response rate obtained from randomly sampledfirms was 25.9%, but detailed results are omitted here.

Here, we omit exploratory factor analysis process
for examining the validity of the assumed constructs
variables and use the value obtained by dividing thetotal of the values of the items pertaining to eachconstruct variable by the number of items. The
reliability of the measurements was calculated using the
Cronbach's α.
Then, we classified sample data into 4 groups basedon the average value of each construct variable. For
example, LOSR (Lower potential for Opportunism andSuperior Resource position) group means that the
average value of TCE-motive is lower than the averagevalue of the whole and the average value of RBVmotive is higher than the average value of the whole.

3.2. Constructs variables and operationalization
In this study, it is postulated that both ‘TCE-motive’attaching importance to the logic of TCE and ‘RBVmotive’ attaching importance to the logic of RBV affectmake-or-buy decision making on information systems.
And we assume that these two constructs consist of eachfour question items shown in Table 1. Each score (6point scale) obtained directly from each question item isregarded as an interval scale.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

483

Seigo Matsuno, Tsutomu Ito, Yasuo Uchida, Yoshiki Mikami, Takao Ito

On the contrary, HOWR (Higher potential for
Opportunism and Weaker Resource position) groupmeans that the average value of TCE-motive is higher
and the average value of RBV-motive is lower thaneach average value of the whole.

complement mutually. On the other hand, thepercentage of IS outsourcing is high by LOSR group,and the percentage of IS insourcing is high by HOWR
group. Therefore, when TCE-motive and RBV-motive
contradict each other, TCE-motive tends to relatively
dominate over RBV-motive.

4. Analysis and discussion
5. Concluding remarks

We conducted a chi-squared test between the 4 groups
mentioned above and the IS sourcing pattern(insourcing or outsourcing). Such a contingency table isshown in Table 2. The result was significant (χ2(3) =11.96, p = 0.01, Cramer's V = 0.26, power = 0.84) andtherefore the two variables are not independent. The
evaluation of effect size was medium degree and powerof the test was a desirable level sufficiently.
In Table 3, from the multiple comparison testing
among 4 groups (adjusted p-value is based on
Benjamini and Hochberg, 1995) [10], we found out thesignificant difference between each of HOSR–LOSR,HOSR–LOWR, LOSR–HOWR, LOWR–HOWR (χ2(1)= 4.92, p = 0.05; χ2(1) = 4.55, p = 0.05; χ2(1) = 5.34, p =0.05; χ2(1) = 5.00, p = 0.05, respectively).

This paper investigates the factors that influence themotives of make-or-buy decisions on information
systems from the viewpoints of TCE and the RBV. We
empirically test the TCE-RBV framework by using ouroriginal questionnaire survey data targeting Japanesefirms. As a result, we made it clear that there were twocases in which TCE-motive and RBV-motive werecomplement or contradict each other. And in latter case,
TCE-motive tends to dominate over RBV-motive.
References
1. R. Coase, The nature of the firm, Economica, 4(16)(1937), 386–405.
2. O. Williamson, Markets and Hierarchy: Analysis andAntitrust Implications (Free Press, 1975).
3. J. Barney, Firm resources and sustained competitive
advantage, J. Management, 17(1) (1991), 99–120.
4. C. Prahalad and G. Hamel, The core competence of thecorporation, Harvard Business Review, 68(3) (1990), 79–
91.
5. S. Matsuno, T. Ito, and Z. Xia, Determinants of
information systems outsourcing: An empiricalinvestigation in Japan, Artificial Life and Robotics, 14(3)(2009), 337–341.
6. J. Barthelemy and D. Geyer, An empirical investigationof IT outsourcing versus quasi-outsourcing in France andGermany, Information & Management, 42(4) (2005),
533–542.
7. O. Williamson, Strategy research: Governance andcompetence perspectives, Strategic Management J., 20(1999), 1087–1108.
8. R. McIvor, How the transaction cost and resource-based
theories of the firm inform outsourcing evaluation, J.
Operations Management, 27(1) (2009), 45–63.
9. B. Brewer, C. Wallin, and B. Ashenbaum, Outsourcing
the procurement function: Do actions and results alignwith theory?, J. Purchasing & Supply Management, 20(2014), 186–194.
10. Y. Benjamini and Y. Hochberg, Controlling the false
discovery rate: a practical and powerful approach tomultiple testing. J. Royal. Statistical. Society. Ser. B,
57(1) (1995), 289–300.

Table 2. Contingency table of the relation between the 4groups and the IS sourcing pattern
Outsourcing
Insourcing
28
31
HOSR
(0.47)(0.53)
32
13
LOSR(0.71)(0.29)
31
13
LOWR(0.70)(0.30)
14
19
HOWR(0.42)(0.58)
Note: The numerical value in each parenthesis is therelative frequency in each line.
Table 3. Multiple comparison test among 4 groups
χ2
df
adj. pHOSR – LOSR
4.92
1
0.05
HOSR – LOWR
4.55
1
0.05
HOSR – HOWR
0.06
1
0.97
LOSR – LOWR
0.00
1
1.00
LOSR – HOWR
5.34
1
0.05
LOWR – HOWR
5.00
1
0.05
In light of the above analysis, the percentage of IS
insourcing is high by HOSR group, and the percentage
of IS outsourcing is high by LOWR group conversely.
This means that TCE-motive and RBV-motive are© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

484

Momentum and Its Implications in Corporate Management
Tsutomu Ito*, Takao Ito, Katsuhiko Takahashi, Katsumi Morikawa

Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama
Higashi-Hiroshima, Hiroshima 739-8527, Japanfw.eldorado.500cuin@gmail.com
Rajiv Mehta
School of Management, New Jersey Institute of Technology University Height
Newark, NJ 07102-1982 USA
mehta@njit.edu
Sakamoto Makoto
Faculty of Engineering University of Miyazaki 1-1, Gakuenkibanadai-nishi
Miyazaki, 889-2192 JAPANsakamoto@cs.miyazaki-u.ac.jp

Abstract
The basis of corporate strategy stems from two seminal books: The Art of War, and On War written by Sun Tzu inthe late sixth century BC and Carl von Clausewitz in 1827, respectively. In contrasting these two theses with current
strategic theories, discussions on momentum, an important concept, have been sparse, although in the context ofcurrent management strategies the pivotal nature of momentum recently has been operationalized and discussed inresearch on marketing, and finance. The current manuscript reviews the literature associated with corporate strategy,
and proposes a new approach of acceleration to measure momentum based on limited cycle theory. Thus, this researchmanuscript makes a contribution to extant thought by: 1) Defining momentum, 2) Discussing the nature of therelationship between momentum to its external environment, and company scale, 3) Ascertaining the momentum
period, and 4) Proposing a four-cell model composed of momentum and company scale for judging a firm’s position.
Additionally, the relationship between momentum and the impact of 2007-2008 financial crises is addressed. Basedon the findings, the study limitations are identified and directions for further research are suggested.
Keywords: Momentum, velocity, limit cycle theory, four cell model, strategy

discussions on momentum, an important concept, havebeen sparse, although in the context of current
management strategies, the pivotal nature of momentum
recently has been operationalized and discussed inresearch on marketing, and finance. This paper reviews
the literature associated with corporate strategy topropose a new approach of acceleration for measuring

1. Introduction
The basis of corporate strategy stems from two seminal
books: The Art of War, and On War written by Sun Tzu
in the late sixth century BC and Carl von Clausewitz in
1827, respectively. In comparing these two books,
especially the Art of War, with extant strategic theories,© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan485

Tsutomu Ito, Takao Ito, Rajiv Mehta, Sakamoto Makoto, Katsuhiko Takahashi, Katsumi Morikawa.

momentum based on limited cycle theory. This researchalso attempts to shed light on the relationship between
sales revenue and momentum, and offers an explanation
why the business cycle in Japan’s automobile industry
changed to a four-year period. Furthermore, the paper
also develops a four cell model comprising momentum
and firm scale as an effective approach for improving
corporate performance.
This paper is structured as follows. Section 2 reviews theliterature focusing on the momentum and corporate
management. In section 3, the paper explicates thecalculation process of momentum and data collection.
Section 4 discusses our findings and managerial
implications based upon our calculation. The study
limitations are identified and directions for further
research are proffered in Section 5.

It is widely acknowledged that military strategy forms thebasis of corporate strategy. But in the strategic
management literature, surprisingly there is no
theoretical description nor application of momentum,
which plays an important role in the performance ofbusiness activities. An on-line browse of the term“strategy
momentum”(inJapanese)yieldedapproximately 932,000 hits which suggest that it
obviously is an important concept in corporate strategy.
Momentum expresses a force or speed of movement. In
this paper, momentum is defined as a force or impetus offirms to maintain firm’s development durability incertain period. Why is momentum becoming more andmore important? A possible explanation is that inherent
uncertainty is in-built within the dynamic, high-speed
global economy. With the high rate of innovation, thespeed of decision-making has become a condition forsuccessful performance.
In 1995, as a new method applying business to U.S. army
logistics, velocity management focused on improving the
speed and accuracy with which materials and information
flow from providers to users. Meanwhile, velocity
management is an emergent scientific field that conflates
army’ operations and business together to reflect the new
realities of the global environment. Mourier observedthat velocity management is decision making at speeds
consistent with today’s competitive environment andtechnology [2].
It is noteworthy that momentum has been the subject ofstudy in finance and marketing. Momentum in stock andsecurities is used to illustrate that their price is more
likely to keep moving in the same direction than tochange directions. In technical analysis, momentum isconsidered as an oscillator and is used to help identify
trend lines. It is the empirically observed tendency forrising asset prices to rise further, and falling prices tokeep falling. One of the important achievements infinance is that stocks with strong past performancecontinue to outperform stocks with poor past
performance in the next period with an average excess
return of about 1% per month [3].
However, to our best knowledge, this is the first work thatshows how to calculate momentum using velocity forcorporate strategy. The basic reasons why we have tocalculate momentum using limit cycle theory because theglobal economy is becoming increasingly uncertain andmost of the corporate behaviors are non-linear. This body

2. The Main Text
Momentum is a term to express power, force and speedof organizational movement. The original description of
momentum appears in Art of War written by famous
strategist Sun Tzu. Indicating the importance of
momentum at the beginning of his book, Sun Tzu
famously asserts: It is important to “look for advantages
when applying my principles and you will gather
sufficient force to take on unforeseen situations. Force istilting the balance of power to your side by gathering
advantages” to win any battle [1, 01.11-01.12]. He
continued to explain the meaning of momentum inchapter five as force. In this context, he asserts “the rushof torrential waters tossing boulders illustrates force. The
strike of a bird of prey breaking the body of its target
illustrates timing. Therefore, the force of those skilled in
warfare is overwhelming and their timing precise. Theirforce is like a drawn crossbow and their timing is like therelease of the trigger” [1, 05.08-05.10]. Furthermore, in
analyzing the relationship between the momentum andwar, Sun Tzu, posits “Those skilled in warfare seek
victory through force and do not require too much fromindividuals. Therefore, they are able to select the rightmen and exploit force. One who exploits force
commands men into battle like rolling logs and boulders.
Logs and boulders are still when on flat ground, but rollwhen on steep ground. Square shapes are still, but roundshapes roll. Therefore, those skilled in warfare use force
by making the troops in battle like boulders rolling downa steep mountain. This is force” [1, 05.16-05.18].

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Momentum and Its Implications

4. Discussions and Analysisof thought is important as it will help increase ourunderstanding and provide guidance that strategic
management practitioners can use to increaseorganizational performance. Thus, this study addresses
the paucity in the strategic management literature andmakes a contribution to the filed by being the first, initial,
investigation that illustrates how to measure momentum
using velocity, and its observed importance for corporate
strategy.

The t in Momentum equation may vary depending on
different analysis proposed. For instance, one-year
momentum is calculated when t is equal to 1, and twoyear momentum is calculated when t is equal to 2. To
understand the movement of the momentum within ashort period, one-year momentum is calculated as below.
標準

標準

標準

3. Measurement and Data Collection

標準
Momentum('05/'04)

Previous studies have showed that chaos, which is
generally described as a disorder state, is driven bydeterministic nonlinear processes, such as hydraulic flow
and astronomical phenomena. Findings in physics andbiology have discovered some examples of chaos. For
example, Tsuda et al, found a chaotic pulsation in afinger’s capillary vessels in normal subjects andpsychiatric patients, as well as cardiac chaos [4].
Additional research illustrated that chaotic movement is
not only applicable in physics and biology, but also insociology and management science. For instance,
Priesmeyer and Baik proposed a new method to discover
the pattern of chaos in 1989. They noticed thatorganization have characteristics limit cycle like thehuman heart. They indicated “Company data tend toadhere to an attractor as they collect their quarterly sales
and drive their quarterly profits. One can almost hear theorganizational heartbeat pounding out ‘Quarter 1,
Quarter 2, Quarter 3, Quarter 4’” [5]. Furthermore, Itoand Sakamoto shed light on the nature of the relationshipbetween economic development and the velocity historyof the typical companies in Japan [6].
Because most of the behaviors of the firms are nonlinear,
momentum should be calculated based on complex
systems theory. In this paper, momentum isconceptualized as a force that composed of sales andprofit movement, and is calculated based upon limit cycle
theory because of its non-linear movement.
In order to calculate and assess the impact of momentum
on corporate performance, data were collected from 35
selected companies, including 10 car makers and 25 parts
suppliers. In order to understand the impacts of theLehman shock on momentum, the data includes sales andprofit from 2003 to 2013.

Momentum('07/'06)

Toyota Motor Corp.
Mitsubishi Motors
Mazda Motor Corp.
Daihatsu Motor Co., Ltd.
Hino Motors, Ltd.
Kawasaki Heavy Industries, Ltd.
Musashi Seimitsu Industry Co., Ltd.
Jidosha Buhin Kogyo Co., Ltd.
NOK
Keihin Corporation
Aishin Seiki Co., Ltd.
Showa Corporation
Mitsuba Corporation
Aisan Industry Co., Ltd.
Nihon Plast Co., Ltd.

Momentum('09/'08)

Momentum('11/'10)

Momentum('13/'12)

Nissan Moto Co., Ltd.
Honda Motor
Suzuki Motor Corp.
Isuzu Motors Limited
Fuji Heavy Industries Ltd.
F-Tech Inc.
Nissan Shatai Co., Ltd.
Tachi-S Co., Ltd.
Shiroki Corporation
Kasai Kogyo Co., Ltd.
Ikuyo Co., Ltd.
Koito Manufacturing Co., Ltd.
Toyoda Gosei Co., Ltd.
Meiwa Corporation
Murakami Corporation

Fig. 1. One-year momentum of each firm (2005-2013).

All momentum looks very stable except the period from2007 to 2010. As a well-known fact, 2007-2008 financial
crisis resulted from subprime lending practices. The
prospects of Japan’s economy are unpredictable and thesales of new decreased. The unstable momentum could
be considered as a reaction of the financial crisis.
Therefore, two conclusions are made based on observing
Fig. 1. First, momentum is very sensitive to externalchanges. Another important fact is that momentum of
Toyota increased in 2008, decreased in 2009, andrecovered in 2010. Other car makers, such as Nissan and
Mitsubishi, reacted similarly. Second, the financial crisis
had an impact over three years on the Japanese car market.
In other words, momentum could be considered as aneffective index to calculate the environmental changes.
Next, the multiple year momentum was calculated, which
is illustrated in Fig. 2.
In Fig. 2, Toyota is seen to have the biggest value formomentum over 9-years. The second is Honda, and theleast is Mitsubishi. As time t becomes longer, momentumof a big scale company grows larger. The thirdconclusion is that the reaction is apparently pronounced
if the scale of the company is large.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

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Tsutomu Ito, Takao Ito, Rajiv Mehta, Sakamoto Makoto, Katsuhiko Takahashi, Katsumi Morikawa.

to 2nd or 4th quadrant, or further from 2nd and 4th to 1st
quadrant would be considered positive signs. All thecompanies are divided into four different groups. Our
next conclusion is that the four-cell model is appropriate
to judge a firm’s position

1500000000000

1000000000000

500000000000

01-YearMomentum2-YearMomentum

3-YearMomentum

4-YearMomentum

5-YearMomentum

6-YearMomentum

7-YearMomentum

8-YearMomentum

9-YearMomentum5. Conclusions-500000000000
Toyota Motor Corp.
Mitsubishi Motors
Mazda Motor Corp.
Daihatsu Motor Co., Ltd.
Hino Motors, Ltd.
Kawasaki Heavy Industries, Ltd.
Musashi Seimitsu Industry Co., Ltd.
Jidosha Buhin Kogyo Co., Ltd.
NOK
Keihin Corporation
Aishin Seiki Co., Ltd.
Showa Corporation
Mitsuba Corporation
Aisan Industry Co., Ltd.
Nihon Plast Co., Ltd.
Yorozu Corporation
TS Tech Co., Ltd.
TBK

Nissan Moto Co., Ltd.
Honda Motor
Suzuki Motor Corp.
Isuzu Motors Limited
Fuji Heavy Industries Ltd.
F-Tech Inc.
Nissan Shatai Co., Ltd.
Tachi-S Co., Ltd.
Shiroki Corporation
Kasai Kogyo Co., Ltd.
Ikuyo Co., Ltd.
Koito Manufacturing Co., Ltd.
Toyoda Gosei Co., Ltd.
Meiwa Corporation
Murakami Corporation
Yachiyo Industry Co., Ltd.
Akebono Brake Industry Co., Ltd.

This paper proposed a new approach for using
momentum based on the limit cycle theory after
reviewing the relevant literature. The relationshipbetween momentum and its external changes, momentum
and company scale were analyzed over various
momentum periods. Furthermore, the four-cell modelcomposed of momentum and company sales confirmedthat it is effective for judging firm’s position.
Additionally, the impact of the financial crisis was also
considered.
Sales and profit, as indices of corporate performance, isinsufficient. Additional indices, such as aggregate market
value and ROI (return on investment), should be
investigated. What’s more, in the future these models
should be tested using data drawn from other settings,
such as, information technology, the ship-building
industry for comparative research as well as replicating
the current findings.
References

Fig. 2. 1-9 year Momentum of each firm (2005-2013).

Momentum('05/'04)

Momentum('06/'05)

Momentum('07/'06)

Momentum('08/'07)

Momentum('09/'08)

Momentum('10/'09)

Momentum('11/'10)

Momentum('12/'11)

Momentum('13/'12)

1. Sun Tzu's The Art Of War, Translated by the Sonshi Group,https://www.sonshi.com/original-the-art-of-wartranslation-not-giles.html, Retrieved July 30, 2015
2. Pierre Mourier, (2001), Velocity management: ‐creatingorganizational instinct, Strategy & Leadership, Vol. 29
Issue 2, pp.24 - 28
3. Chakrabarti Gagari (2015) Time-series momentum trading
strategies in the global stock market, Business,
http://www.thefreelibrary.com/Timeseries+momentum+trading+strategies+in+the+global+sto
ck+market.-a0423234977, Retrieved October 22, 2015
4. Tsuda I., Tahara T., and Iwanaga H. (1992) Chaotic
Pulsation in Human Capillary Vessels and its Dependence
on Mental and Physical Conditions, International Journalof Bifurcation and Chaos, Vol.2, No.2, pp.313-324, World
Scientific Publishing Company.
5. Priesmeyer H. R., Kibok Baik (1989) Discovering thePatterns of Chaos, Planning Review, pp.14-21 and pp.4647, November/December 1989
6. Ito T. and Sakamoto M. (2007) A Consideration on Limit
Cycle Theory, The papers of Technical Meetings on
Information Systems, IS-07-10 ～ 15, pp. 19-23, The
Institute of Electrical Engineers of Japan (Japanese
Edition)Fig. 3. A four-cell model of momentum (‘13/’05) andSales ‘13
Based on the results, it is also possible to identify
successful companies as well as firms with impaired
performance using momentum measurements. Based on
the findings, momentum is influenced by the external
environment, company scales, and corporate
performance. Thus a four-cell model is proposed in thispaper. Comprising of momentum and scale should be
effective. The four-cell model of momentum (‘13/’05)
and sales13 is illustrated in Fig. 3.
The reference line of x axis and y axis are the median of
momentum and sales ‘13 respectively in Fig. 3. The firms
located in the 1st quadrant and the 3rd quadrant are goodfirms and poorly performing firms, respectively, because
they have appropriate relationship between momentum
and sales’13. The firm is called developing company
with high development potential if it is located in 4 thquadrant, while the firm is called problem child if it islocated in 2nd quadrant. A firm moving from 3rd quadrant© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

488

A Method for Secure Communication Using a Discrete Wavelet Transform for Sound Data
Yuji Tsuda1, Kouhei Nishimura2, Haruka Oyaizu3, Yasunari Yoshitomi2, Taro Asada2, and Masayoshi Tabuse2

1: Software Service, Inc.
Nishi-Miyahara, Yodogawa-Ku, Osaka 532-0004, Japan
2: 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
3: NHK Media Technology, Inc.
Kamiyama-cho, Shibuya-ku, Tokyo 150-0047, Japan

Abstract
We developed a method for secure communication using a discrete wavelet transform. Two users must have one
piece of music before communicating each other. The music and the original sound data are transformed into a codeusing the scaling coefficients obtained from a discrete wavelet transform. The user can produce the sound similar tothe original sound using an inverse discrete wavelet transform with the code made from the music, the informationon the deference between these two codes.
Keywords: Secure communication, Sound data processing, Wavelet transform, Coding.

Internet protocol (IP) telephone, and has a potential for
stopping frauds using a telephone.

1. Introduction
Recently, some kinds of frauds have been critical issues.
Especially, elder persons tend to be targets of fraudusing a telephone.
The fraud pretends to be a
grandchild of the elder person in talking through thetelephone, and appeals the elder person to send money,for example, through a bank budget account. When theelder person takes the fraud for a grandchild, the fraud
can get money. Even if the voice of fraud is not similar
to that of the grandchild, the elder person might sendmoney to the fraud. This is because the fraud skillfully
appeals a serious monetary trouble such as a traffic
accident to the elder person who cherishes a real
grandchild.
In the present study, we propose a method for
secure communication using a discrete wavelet
transform (DWT). The method can be used for the

2. Wavelet TransformIn this section, we briefly explain the DWT, according
to the references.1
The original sound data s (0) , which is used as theklevel-0 wavelet decomposition coefficient sequence,
where k denotes the element number in the data, aredecomposed into the elements of multi-resolution
representation (MRR) and the elements of multiresolution analysis (MRA) by repeatedly applying the
DWT. The wavelet decomposition coefficient sequences ( j ) at level j is decomposed into two waveletkdecomposition coefficient sequences at level j  1 byusing (1) and (2):© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

489

Yuji Tsuda, Kouhei Nishimura, Haruka Oyaizu, Yasunari Yoshitomi, Taro Asada, Masayoshi Tabuse

p
 q

sk( j 1) ( j)n  2k sn

(1)

( j)
n  2k sn ,(2)nwk( j 1)nwhere p k and q k denote the scaling and wavelet( j 1)

sequences, respectively, and wk

denotes thedevelopment coefficient at level j  1 . The development
coefficients at level J are obtained using (1) and (2)iteratively from j  0 to j  J  1 . Fig. 1 shows theprocess of multi-resolution analysis by DWT.
In the present study, we use the Daubechies
wavelet for the DWT, according to the reference.2 As aresult, we obtain the following relation between p k andFig. 2. Schematic diagram of the histogram of MRA scalingcoefficients.

:

qk
qk  (－1) p1 kkgroups by Th ( minus ) , and the positive scalingcoefficients ( S p in total frequency) are equally divided(3)

into two groups by Th ( plus ) . Next, the values of T 1 ,T2 ,T 3 , and T 4 , which are the parameters forcontrolling the authentication precision, are chosen tosatisfy the following conditions:
1) T 1  Th (minus )  T 2  0  T 3  Th (plus )  T 4 .
2) The value of ST 1 , which is the number of scalingcoefficients in (T 1, Th (minus )) , is equal to ST 2 ,which is the number of scaling coefficients in[Th ( minus ), T 2) , i.e., ST1  ST 2 .

Fig. 1. Multi-resolution analysis by the DWT.1

3. Proposed Method

3) The value of ST 3 , the number of scaling coefficients3.1. Codingin (T 3, Th ( plus )] , is equal to ST 4 , the number ofscaling coefficients in (Th (plus), T 4) , i.e.,
ST 3  ST 4 .

3.1.1. Phenomenon exploited in coding algorithm
for sound data4) ST 1 / S m  ST 3 / S p .

It is known that the histogram of the wavelet
coefficients of each domain of the MRR sequences has
a distribution which is centered at approximately 0
when the DWT is performed on sound data.1 In thepresent study, we have found that the histogram of thescaling coefficients of each domain of the MRA
sequences also has a distribution which is centered at
approximately 0 when the DWT is performed on sounddata. Exploiting this phenomenon, we developed asecure communication method using sound data.

In the present study, the values of both ST 1 / S m andST 3 / S p are set to 0.3, which was determined
experimentally.

3.1.3. Coding
To prepare the coding of sound data, the procedure
separates the scaling coefficients V of an MRA
sequence into five sets (hereinafter referred to as G0, G1,
G2, G3, and G4), as shown in Fig. 3, by using thefollowing criteria:• G0  {V | V  V SC ,V  T1} ,• G1  {V | V  V SC , T1  V  T 2} ,• G2  {V | V  V SC , T 2  V  T 3} ,• G3  {V | V  V SC , T 3  V  T 4} ,• G4  {V | V V SC , T 4  V } ,3.1.2. Parameter setting
As with the digital watermark (DW) techniques forimages2, 3 and digital sound4, we set the following
coding parameters.
The values of Th ( minus ) and Th ( plus ) in Fig.2 arechosen such that the non-positive scaling coefficients( S m in total frequency) are equally divided into two© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

490

A Method for Securezero for all wavelet coefficient values at every level.
S ( A) k is obtained as:
S ( A)k  {a1, a2 , a3 ,, ak } ,where V SC is the set of scaling coefficients in the sounddata file.

where ai {m0A , m1A , m2A , m3A , m4A } is an average at therange of scaling coefficients of A , which is indicatedby X i {0,1,2,3,4} obtained from A .
Then, the sound data BA is defined as having the scalingcoefficient sequence S ( B A ) k and the value of zero for
all wavelet coefficient values at every level. S ( BA ) k is
obtained as:
S ( BA ) k  {bA,1 , bA, 2 , bA,3 ,  , bA, k } ,where bA,i  {m0 , m1 , m2 , m3 , m4 } is an average at
B

B

B

B

B

the range of scaling coefficients of B , which isindicated by X i {0,1,2,3,4} obtained from A .
scaling

S ( BA ) k is obtained by replacing Yi to X i when

The scaling coefficients of an MRA sequence arecoded according to the following rules, in which Vi

Yi  X i , followed by replacing bi to b A, i , where bi isan average at the range of scaling coefficients of B ,which is indicated by Yi . Therefore, C ( BA ) k  C  Ak .
As a result, BA is expected to be similar to A .

Fig. 3. Schematic diagram for demonstrating
coefficient selection in coding sound data.

denotes one of scaling coefficients:
When Vi  G0 , symbol ci is set to be 0.
When Vi  G1 , symbol ci is set to be 1.
When Vi  G2 , symbol ci is set to be 2.

3.3. Data for communication
A sequence D1( BA ) n is defined as:
D1( BA ) n  z1, z2 ,, zn  ,When Vi  G3 , symbol ci is set to be 3.
When Vi  G4 , symbol ci is set to be 4.

where n is the number is the total number of the cases of Yi  X i , and z p  yi mod 256 , in which theThen, the representative value for each set is set as its
average value (m0, m1, m2, m3, m4), respectively. For
sound data formation, we use a code C (hereinafterreferred to as an original code), which is the sequence of
ci , and m j defined above.

integer p is increased from 1 to n one by one when
Yi  X i happens. Here, x  is the maximum integer thatis not bigger than x . Then, a sequence D2( BA ) n isdefined as:D2( BA ) n  Z1, Z 2 ,, Z n  ,where n is the number is the total number of the casesof Yi  X i , and Z p  X i , in which the integer p is

3.2. Sound data formation using code replacement
The scaling coefficient sequence for sound data A isexpressed by
S ( A) k  {x1 , x2 , x3 ,, xk } ,increased from 1 to n one by one when Yi  X i
happens.
In the communication between two users, both asender and a receiver have B as a secret key, and thesender sends D1( BA ) n and D2( BA ) n to the receiver.
Then, using the processing described in the section 3.4,
the receiver makes BA -like sound data named by BA ,which is expected to be similar to A .

where k is the number of scaling coefficient of A .
Then, a sequence
C( A) k  {X 1 , X 2 , X 3 , , X k }is given, where X i {0,1,2,3,4} is the index showing
one of five sets of scaling coefficients to which xi
belongs.
Next, the sound data A is defined as having thescaling coefficient sequence S ( A) k and the value of© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

491

Yuji Tsuda, Kouhei Nishimura, Haruka Oyaizu, Yasunari Yoshitomi, Taro Asada, Masayoshi Tabuse

A . However, BA had the noise mainly of low
frequency. After erasing the low frequency noise, thesound became more audible and the tone of voice waschanged as if the speaker had been a different person. In
the next target, we try to develop a method for
decreasing the noise with keeping the tone of voice ofthe speaker.

3.4. Sound data composition
The scaling coefficient sequence for sound data B isexpressed by
S ( B) k  { y1 , y2 , y3 ,, yk } ,where k is the number of scaling coefficient of B .
Then, a sequence
C ( B) k  {Y1 , Y2 , Y3 ,, Yk }is given, where Yi  {0,1,2,3,4} is the index showing
one of five sets of scaling coefficients to which a scalingcoefficient yi of B belongs. S ( B) k is obtained as:
S ( B) k  {b1, b2 , b3 ,, bk } ,

5. Conclusion
We developed a method for secure communication
using a discrete wavelet transform for sound data. In
this method, two users must have one piece of music,
which has a length of one second and plays a role ofsecret key, before communicating each other. The music
is beforehand transformed into a code using the scalingcoefficients obtained from a discrete wavelet transform.
The sound data are transformed into another code using
the same method as that for the music. The informationon the deference between these two codes is sent fromone user to the other. The user who receives theinformation can produce the sound similar to theoriginal sound using an inverse discrete wavelet
transform with the code made from the music, theinformation on the deference between these two codes,
and values of zero for all wavelet coefficients. The
voice produced by the proposed method was audible.



where bi {m0 , m1 , m2 , m3 , m4 } is an average at therange of scaling coefficients of B , which is indicatedby Yi {0,1,2,3,4} obtained from B .
B

B

B

B

B

A sequence D3( B) k is defined as:
D3( B) k  z B ,1 , z B , 2 ,, z B , k ,where k is the number of scaling coefficient of B , andz B , q  yq mod 256 . BA is composed as follows; 

S ( BA ) k is composed from S ( B) k by replacing bq to

mZB p when z B, q  z p happens from p  1 to n ; then ,the sound data BA is composed by IDWT using thescaling coefficient sequence S ( BA ) k and the value ofzero for all wavelet coefficient values at every level.
The receiver makes BA using D1( BA ) n and D2( BA ) n ,which are made with both A and B and are sent by thesender, in addition to B which the receiver has
beforehand. BA is expected to be similar to A .

References
1. Y. Yoshitomi, T. Asada, Y. Kinugawa, and M. Tabuse,
An authentication method for digital audio using a
discrete wavelet transform, J. Inf. Sec. 2(2) (2011) 59-68.
2. D. Inoue and Y. Yoshitomi, Watermarking using wavelet
transform and genetic algorithm for realizing hightolerance to image compression, J. IIEEJ, 38(2) (2009)
136-144.
3. M. Shino, Y. Choi, and K. Aizawa, Wavelet domain
digital watermarking based on threshold-variable
decision, Technical Report of IEICE, DSP2000-86,
100(325) (2000) 29-34. (in Japanese)
4. S. Murata, Y. Yoshitomi, and H. Ishii, Audio
watermarking using wavelet transform and gtenetic
algorithm for realizing high tolerance to MP3compression, J. Inf. Sec. 2(3) (2011) 99-112.
5. 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.

3.5. Communication of sound data having
arbitrary length
In general, the amount of recording time of A is not thesame as that of B . We use B having a unit recording
time such as one second. Then, we apply the proposed
method described in the above sections to A every unit
time of recording time of A . When the recording timeof A cannot be divisible by the unit time, additional
scaling coefficients needed for application of the
proposed method are set as 0.
4. Numerical Experiment
We applied the proposed method to several voicesounds as A using as B the music of (1) ‘Classical’,(2) ‘Hiphop’ having one second of recording time each.
The music is clipped out from the database5. In allcases of the experiment, BA was audible and similar to© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan492

An Authentication Method Using a Discrete Wavelet Transform for a Recaptured Video
Ren Fujii , Yasunari Yoshitomi, Taro Asada, 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: { r_fujii, t_asada}@mei.kpu.ac.jp, {yoshitomi, tabuse}@kpu.ac.jp
http://www2.kpu.ac.jp/ningen/infsys/English_index.html

Abstract
Recently, several digital watermarking techniques have been proposed for protecting the copyright of moving
image files by hiding data in the frequency domain. In the present study, we applied our method for authenticating amoving image, which uses a discrete wavelet transform for a static image and a method for selecting several frames
from a moving image, to a recaptured video. In contrast to digital watermarking, no additional information isinserted into the original moving image by the proposed method.
Keywords: Authentication, Moving image, Copyright protection, Recaptured video, Wavelet transform

data. The authentication is based on features extracted
by a DWT and a characteristic code.
In the present study, we will review our method3
for authenticating a digital moving image; it is acombination of our previously proposed method 2 forstatic images and a proposed method3 for selecting
several frames from a moving image. As in our method 2
for static images, no additional information is inserted
into the original moving image, and it is authenticatedby features extracted by a DWT and a characteristiccode. We present the results of an experiment in which
we evaluated the ability of our proposed authentication
method3 to recapture a moving image. We will describe
this method3 and present the results of the experiment in
the following sections.

1. Introduction
Digital watermarking is a promising method forprotecting the copyright of digital data. Several studies
have developed methods in which a digital watermark(DW) can be extracted from data, even after
compression, and the quality of the digital data remains
high after the DW has been embedded. However, athere is usually a tradeoff between these two properties.
For a useful DW, any distortion it introduces must be
imperceptible, and it must be robust to signal processing
methods, such as compression or attempts to delete it.
Both the processing rate and the complexity of DWs
have adversely affected their performance.
In order to overcome these performance issues, wedeveloped alternative authentication methods for digitalaudio1 and static images.2 These methods use a discrete
wavelet transform (DWT), and in contrast to digitalwatermarking, our method for static images2 does notinsert additional information into the original digital

2. Observed Phenomenon Underpins theAuthentication Method
It has been observed that when a DWT is applied to anatural image, in the histogram of the wavelet© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan493

Ren Fujii, Yasunari Yoshitomi, Taro Asada, Masayoshi Tabusecoefficients of the MRRs, the center of the distribution
is very close to zero.4 We exploited this phenomenon inorder to develop an authentication method for a static
image.2
3. Authentication Ratio
We set the authentication parameters as discussed below.2
In Fig. 1, Th(minus ) was chosen so that it
divides the nonpositive wavelet coefficients ( S m intotal frequency) into two equal groups, and Th ( plus )was chosen so that it divides the positive wavelet
coefficients ( S p in total frequency) into two equalFig. 1. Three sets (F, G, and H) of MRR wavelet coefficientsused for authentication.1

groups. Next, the values of the parameters T 1 − T 4 ,which control the precision of the authentication
precision, were chosen such that the following
conditions were satisfied:
1) T 1  Th(minus )  T 2  0  T 3  Th(plus )  T 4 .

Note that the value 0.5 can be chosen arbitrarily, since
the value of bi that is the bit for creating the code forthe original image file2 does not influence the method's
performance. Finally, this sequence of bi values is used

2) The value of S T 1 , the number of wavelet coefficientsin (T 1, Th(minus )) , is equal to ST 2 , the number of

to form the authentication code C′.
The authentication ratio AR (%) is defined asfollows:

wavelet coefficients in [Th( minus ),T 2) , i.e., ST 1  ST 2 .
3) The value of ST 3 , the number of wavelet coefficientsN f 1  b  b 

100in (T 3, Th(plus )] , is equal to ST 4 , the number ofwavelet coefficients in (Th(plus ),T 4) , i.e., , ST 3  ST 4 .

AR 

i

i

i

i 1

,

N

f

(1)

i

i 1

4) ST 1 / Sm  ST 3 / S p .

where N is the number of wavelet coefficients chosen
to create the authentication code for the original image
file.2 As can be seen in equation (1), neither bi nor bi

In the present study, the value of both ST 1 / S m andST 3 / S p is set to 0.25, which is the same setting used

influence the value of AR when f i  0 , which occurs

for creating the code for the original image file2. When
preparing the authentication codes, the wavelet
coefficients V  for each MRR sequence are divided
into three sets (hereinafter referred to as F, G, and H), as
shown in Fig. 1; these sets are defined as follows:• F  {V  | V  V  AC ,V   Th' (minus)}

when the corresponding Vi that is the wavelet
coefficient of the original image is not selected forcoding in the original image file.2
To use the proposed method, we need to store theflags f i and the original code C for each copyrighted
file that we want to protect. When calculating (1) inorder to authenticate an image file, we do not use theoriginal image file; instead, we use the flags f i and the• G  {V  | V  V  AC , Th' (minus)  V   Th' ( plus)}• H  {V  | V  V  AC , Th' ( plus)  V } ,where V  AC is the set of wavelet coefficients from thetarget image file that is used to create the authenticationcode.
The wavelet coefficients Vi are then classified

code C for that file.2
4. Authentication of a Moving Image
We applied our proposed authentication method3 to arecaptured digital moving image. We explain the
proposed method3 in this section.

according to the following rules with the flags f i used increating the original code C:
When f i  1 and Vi G , bi is set to 0.

When f i  1 and Vi F  H  , bi is set to 1.

4.1. Authentication code

When f i  0 , bi is set to 0.5.

We obtained 10 codes for each moving image segment,each of which had.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan494

An Authentication Method Using

This is determined for each moving image in the
database, and the one with the highest authentication
ratio is selected as the assumed original image.

Step 1: Let N total be the total number of frames in themoving image. Calculate frame No. k B , where k B is thesmallest integer not less than 0.1 N total , and calculate

5. Experimentframe No. k E , where k E is the largest integer not
greater than 0.9  N total .

We evaluated the performance of the proposed methodby conducting a computer experiment. In this section,
we present the results.

Step 2: Output the images of frames No. k B and k B1
as BMP files. For each pixel, evaluate the difference
between the grayscale level in frame No. k B and that in

5.1. Method

frame No. k B1 . Calculate the total sum S[1] ofsquares of these differences. Store the grayscale levelsof frame No. k B1 in array A1 . Set the initial conditions
as C[1] : k B  1 , count : 1 , and k :k B 2 .

The experiment was performed in the following
computational environment: Dell OptiPlex 3020; CPU:Intel Core i5-4570 3.2 GHz; 4.0 GB memory; OS:
Microsoft Windows 7 Professional. The development
language was Microsoft Visual C++ 6.0.
The experiment proceeded as follows. We obtained
77 moving image segments, provided as MPEG-1 files,
from MUS-CLE-VCD-2007.5 These were converted from
RGB components into YCrCb components, and for eachof these segments, we obtained ten codes for Y
components. We numbered the segments in the order inwhich they were recorded and then divided them into 11
groups of 7 members each. We then chose the six
segments (Nos. 24, 45, 27, 21, 93, and 91) that recorded
the middle amount of time in each group that had theshortest to the sixth shortest recording time among 11
groups. Nos. 24 and 45 were monochrome, and so wereplaced them with the two segments that had the mostsimilar recording times, Nos. 34 and 100, respectively.
These six segments were displayed on a liquid crystal
television screen of Panasonic VIERA TH-19C305, andthe moving images were recaptured by a digital video
camera (Sony Handy-cam HDR-CX7); they were thensaved as MPEG-2 files, followed by cutting uselessmargin and being saved as MPEG-4 files. Divide allframes into 10 groups for each file by the time from the
beginning, and pick up 10 frames per a group by ourpreviously proposed method3 except the first and lastgroups in the sense of time from the beginning. This
resulted in 80 frames for each recaptured moving image,and these were used for authentication of the 77 segments.
We used FFmpeg6 to output a BMP file for eachsegment; 24 bits were used for the grayscale level andthe image consisted of 256  256 pixels. For the DWT,
we used Daubechies wavelets. Based on the results ofpreliminary experiments, we used the LH components
obtained from the DWT up to level 4.2

Step 3: If k k E 2 , go to Step 8. Otherwise, go to Step 4.
Step 4: Overwrite the image of frame No. k onto thatof frame No. k B 2 in the BMP file. Store the grayscalelevels of frame No. k in array B . For each pixel,
evaluate the difference between the grayscale level inframe No. k  1 and that in frame No. k . Calculate
the total sum T of squares of these differences. Update
the value of k to k : k  1 .
Step 5: If count  10 , update the value of count tocount: count 1 , and go to Step 6. Otherwise, go to Step 7.
Step 6: Overwrite the array Acount with B, i.e.,
A count : B . Set S [count] : T , and C[count] : k  1 .
Go to Step 4.
Step 7: Calculate l  arg max S[i ] . If S[l ]  T ,i 1, 2,,10

overwrite the array Al with B, i.e., A l : B . Set

S [l ] : T , and set C[l ] : k  1 . If k k E 1, go to Step
8. Otherwise, go to Step 4.
Step 8: Output the arrays Ai (i  1,2,  ,10) as BMP
files. Obtain the codes and flags used for theauthentication codes by using the method described inRef. 2. Output the codes, the flags, C[i ](i  1,2,,10) ,and S [i ](i  1,2,  ,10) .
4.2. Authentication
We used the method described in Section 4.1 to selectframes from moving images. The codes and the flags forthe selected frames were obtained using the methods
described in Ref. 2. For a given test moving image and agiven moving image in the database, the authentication
ratio of the test moving image to the moving image inthe database is defined as the largest authentication ratioof any selected frame in the test moving image to any
selected frame in the moving image in the database.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

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Ren Fujii, Yasunari Yoshitomi, Taro Asada, Masayoshi Tabuse

Several consecutive frames in which most pixels
are black (or most are white) are sometimes inserted
into a moving image, because they can be useful for
scene transitions. A frame in such a series is neither
representative nor unique to that moving image, and if it
is selected to be coded, it could damage the ability of
the code to authenticate that moving image. Therefore,
we used only frames in which the average grayscalelevel for the Y component was in the range of 5 to 250(the full range is 0 to 255).

Fig. 2. Left: recaptured frame of moving image segment No.
93; right: frame of moving image segment No. 17 in the
database.

5.2. Results and discussion
Table 1 shows some examples of the authentication ratios
obtained in the experiment. In the six moving image
segments (Nos. 34, 100, 27, 21, 93, and 91), the averageauthentication ratio was in the range 71.6% to 74.3%
when the recaptured segment was from a different sectionof the original moving image, but it was in the range of
91.7% to 97.0% when the recaptured segment was fromthe same segment; see Table.1. When authenticating therecaptured segment No. 93 against the codes of theoriginal segment No. 17, the authentication ratio was
90.1%. This unusually high authentication ratio wascaused by a pair of similar structures, which can be seenin the frames shown in Fig. 2. An example ofauthentication by the proposed method is shown in Fig. 3.

Fig. 3. Left: recaptured frame of moving image segment No.
27; right: frame in the database with an authentication ratio of
96.2% with segment No. 27.

References
1. Y.Yoshitomi, T.Asada, Y.Kinugawa, and M.Tabuse, An
authentication method for digital audio using a discrete
wavelet transform, J. Inf. Sec. 2(2) (2011) 59-68.
2. T. Asada, Y. Yoshitomi, and M. Tabuse, A verification
method for a digital image file using a discrete wavelet
transform, J. IIEEJ, 39(6) (2010) 1088-1094. (inJapanese)
3. R. Fujii, Y. Yoshitomi, T. Asada, and M. Tabuse,
Authentication method using a discrete wavelet transform
for a digital moving image, J. Inf. Sec. to be published.
4. M. Shino, Y. Choi, and K. Aizawa, Wavelet domain
digital watermarking based on threshold-variable
decision, Technical Report of IEICE, DSP2000-86,
100(325) (2000) 29-34. (in Japanese)
5. MUSCLE-VCD-2007,
http://www.rocq.inria.fr/imedia/civrbench/index.html
Accessed 9 September 2015.
6. FFmpeg, http://ffmpeg.org/ Accessed 9 September 2015.

Table 1. Authentication ratios for recaptured moving images.
Original file No. for recaptured moving image

Average for others
For corresponding
raw file
Maximum forothersRecording time
(min:sec)

100

34

27

21

93

91

73.9

73.1

74.3

73.5

71.6

72.4

95.5

94.7

96.2

97.0

94.7

91.7

87.1

82.6

86.4

84.8

90.1

88.6

00:30

03:20

06:58

11:32

14:50

19:50
(%)

6. Conclusion
We applied our proposed authentication method3 to arecaptured digital moving image in order to evaluate theability of the method to recapture a moving image. In
contrast to digital watermarking, in the proposed
method, no additional information is inserted into theoriginal moving image. The experimental results showthat the proposed authentication method performs well.
© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

496

A System for Facial Expression Analysis of a Person While Using Video Phone
Taro Asada, Yasunari Yoshitomi, Ryota Kato, 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, r_kato}@mei.kpu.ac.jp, {yoshitomi, tabuse}@kpu.ac.jp
Jin Narumoto

Graduate School of Medical Science, Kyoto Prefectural University of Medicine,
Kajii-cho, Kawaramachi-Hirokoji, Kamigyo-ku, Kyoto 602-8566, Japan
E-mail: jnaru@koto.kpu-m.ac.jp

Abstract
We developed a method for analyzing the facial expressions of a person having a conversation on a videophone.
This was implemented on a wireless local area network, consisting of two personal computers and a router; thesubject and the operator are placed in front of each computer. The module contains a function for determining thereference frame and for locally searching for the mouth area in the image to determine the most appropriate
position for each frame.
Keywords: Facial expression analysis, Movement analysis, Mouth area, OpenCV, and Skype.

In the present study, exploiting our reported
researches1, 2, we developed a system for facial
expression analysis of a person while using video phone.

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 living in rural areas is increasing very
rapidly. Due to a mismatch between the number ofpatients and the number of healthcare professionals, it isdifficult to provide adequate psychological assessment
and support for all patients.
To improve the quality of life (QOL) of elderly
people living in a care facility or at home, we havedeveloped a method for analyzing the facial expressionsof a person who is having a conversation with anotherperson on a videophone.1, 2 The video is analyzed using
image-processing software (OpenCV)3 and a previously
proposed feature parameter (facial expression intensity)that is based on the mouth area.1, 2

2. Proposed System and Method
2.1. System overview and outline of the method
The platform includes Skype4 for the videophone, andconversations are recorded for the analysis of facial
expressions. In the recorded data, the size of the faces
are standardized, and the data are analyzed by using
OpenCV and the proposed feature parameters for facial
expressions, as outlined below. The Y component
obtained from each frame in the dynamic image is used
for analyzing the facial expressions. The proposed
method consists of (1) the size of the lower part of theface is standardized; (2) the mouth area is extracted; (3)© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

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Taro Asada, Yasunari Yoshitomi, Ryota Kato, Masayoshi Tabuse, Jin Narumoto

the facial expression intensity is measured; (4) thereference frame is selected; (5) the best position formouth area in the frame is determined; (6) utterances
are evaluated; and (7) the feature parameter for facial
expression strength is calculated. In the following
subsections, (2), (3), (4), (5) and (7) are explained indetail. On the details for (1) and (6), see Refs 1-2.
The structure of the proposed system is shown in
Fig.1. We developed a wireless local area network,
consisting of two personal computers (PCs) and one
router, in which the subject is placed in front of a PC
with a module for analyzing facial expressions, and thesubject converses via videophone with the module
operator, who is placed in front of the other PC.

mouth area; this is performed by using a 2D-DCT foreach section of 8×8 pixels.
As the feature parameters for expressing facial
expression, we selected 15 low-frequency components
from the 2D-DCT coefficients; this did not include thedirect current component. Next, we obtained the meanof the absolute value of each of these components in thearea of the mouth. In total, we obtained 15 values as
elements of the feature vector. The facial expression
intensity is defined as the norm of the difference vector,
which is a vector of the difference between the feature
vector for the neutral facial expression and that for theobserved expression, and it can be used to analyzechanges in facial expression.
The candidate of facial expression intensity
defined as the norm of the difference vector between
two feature vectors is used for selection of the reference
frame as described in the section 2.4.
2.4. Selection of the reference frame
We propose a method for automatically selecting thereference frame to be used for measuring the facial
expression intensity; this can be used for a moving
image in which the initial facial expression is neutral
and there is no utterance. The existence of an utterance
is determined by a method that we proposed in aprevious paper.1, 2 The frame prior to any utterance isused as the target in following procedure.
We calculated the sum of the norms of thedifference vectors for the feature vector calculated foreach of the candidates for the reference frame, and wedesignated the one for which this sum was minimized as
the reference frame. The facial expression intensity isthen obtained as the norm of the vector of thedifferences between the feature vector and the feature
vector of the reference frame.

Fig. 1. Structure of the proposed system.

2.2. Extraction of the mouth area
Next, by using OpenCV, the mouth area is extracted asa rectangular shape.1 The mouth area is selected because
it is where the difference between neutral and happy
facial expressions is most distinct. An example of a face
image and the extracted image of the mouth area isshown in Fig. 2.

2.5. Correction of position of mouth area
Fig. 2. Whole-face image (left), extracted image of themouth area (right).

There is no guarantee that the center of the extracted
mouth area will remain in a suitable and constantposition by eyes, and in fact, we observed that it didvary. This change in position could affect the evaluatedfacial expression intensity. Therefore, we propose a way
to decrease the influence on the facial expression
intensity due to changes in the location of the moutharea.

2.3. Measurement of facial expression intensity
For the Y component of the selected frame, the feature
vector for the facial expression is extracted for the© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

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A System for Facial

After using OpenCV to extract the mouth area, thebest position of the mouth area under the condition on
the shift of the area is found by deciding each shiftvalue for the area in the range of -3 to 3 pixels in eachof the horizontal and vertical directions, in order to givethe smallest value of facial expression intensity for theframe.

3.2. Results and discussion

2.6. Feature parameter for facial expression
strength

Fig. 3. The mouth area of subject A at the beginning ofExperiment 1 (left), and at the beginning of Experiment 2(right).

Fig. 3 shows the mouth area of subject A at the
beginning of each experiment.

In diagnosing a patient having dementia and/or
depression, it might be useful for healthcare
professionals to evaluate the strength of facial
expressions by using a simple measure.1 Moreover, it
might be more advantageous for a diagnosis of dementia
and/or depression to separately evaluate the strength of
the facial expression as a speaker and a listener.1
Therefore, we measure the feature parameter for facial
expression strength as the average of facial expression
intensity as a speaker and a listener1, if necessary.

The facial expression intensity for subject A wasrecorded during both experiments. In Experiment 1, thetiming of utterances was 11.4 seconds. During his
utterances in Experiment 1, there were four local peaksin the facial expression intensity of subject A. Theseoccurred at approximately 3, 6, 15, and 21 seconds after
the starting point (see Fig. 4). During the times whensubject A was not making utterances during Experiment
1, there were five local peaks in his facial expression
intensity; these were at approximately 3, 7, 11, 13, and23 seconds after the starting point (see Fig. 5);characteristic images of the face and mouth area areshown in Fig.5. In Experiment 2, the timing ofutterances was 12.4 seconds; there were four local peaksin the facial expression intensity for subject A; thesewere at approximately 14, 16, 23, and 26 seconds after
the starting point (see Fig. 6). In both Experiments 1 and
2, the images of the face and mouth areas at thecharacteristic timing points show that the proposed
method can quantitatively determine the facial
expression (see Figs. 5 and 6).

3. Experiments
3.1. Experimental environment

Facial expression intensity

The experiment was performed in the following
computational environment: the PC set in front ofsubject A was a Dell Vostro 3350; CPU: Intel Core i72620M 2.7 GHz; 4.0 GB memory; OS: Microsoft
Windows 7 Professional. The development language
was Microsoft Visual C++ 2008 Express Edition.
Three males (subject A in his 30s, subject B in his
20s, and subject C in his 40s; C is a psychiatrist)
participated in addition to an operator. Each experimentconsisted of a two-way videophone conversation thatlasted from 27 to 36 seconds. Experiment 1 was aconversation between subject A in Tokyo and subject B
in Kyoto, and it was conducted using Skype.
Experiment 2 took place face-to-face as an interview tosubject A by subject C. We saved the visual and audio
information as AVI files, and these were then used formeasuring the feature parameters of the facial
expressions. The size of the image frame was 640×480
pixels, and the size of the standardized lower part of theface image was set to 240×96 pixels.

35
30
25
20
15
10
5
0
0

5

10
15
Time (s)20

25

Fig. 4. Changes in the facial expression intensity for subject A
at times of utterance during Experiment 1.
© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

499

35
30
25

A2

A1

20

Facial expression intensity

Facial expression intensity

Taro Asada, Yasunari Yoshitomi, Ryota Kato, Masayoshi Tabuse, Jin Narumoto

15
10
5
0
0

5

10
15
Time (s)

20

25

A1
A2
Fig. 5. Changes in the facial expression intensity for subject A
at times of no utterance during Experiment 1 (upper graph).
Whole-face images and mouth images are shown for two
moments, A1 and A2, which are indicated on the graph (lower
images).

1
2

A4

25
20
15
10
5
0
5

10

15
Time (s)

20

25

30

A3
A4
Fig. 6. Changes in the facial expression intensity for subject A
during Experiment 2 (upper graph). Whole-face images andmouth images are shown for two moments, A3 and A4, which
are indicated on the graph (lower images).

Acknowledgements
This research was supported by COI STREAM of the
Ministry of Education, Culture, Sports, Science, andTechnology of Japan.
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, 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. Skype Web page, http://www.skype.com/ Accessed 5
November 2015.

Table 1. Feature parameter for facial expressions of subject A.
Utterance
withwithout
with & without

A3

30

0

The feature parameters for the facial expression
intensity are shown in Table 1. Note that the intensity
tended to be greater during face-to-face conversation
than during a videophone conversation.
The processing time for analyzing the facial
expressions using the proposed system was 126 s forExperiment 1 and 135 s for Experiment 2.

Experiment

35

Feature parameter

5.65
6.96
10.67

4. Conclusion
We developed a wireless local area network system,
consisting of two PCs and one router, which includes a
module for analyzing the facial expressions of a subject
engaged in a two-way videophone conversation.
The results show the usefulness of the proposed
method. As an area of future work, we intend to develop
a related method for estimating the mental state and/or
recognition ability of a patient.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

500

Emotion Recognition of a Speaker Using Facial Expression Intensity of Thermal Image andUtterance Time

Yuuki OkaNTT DATA Financial Solutions Corp.
Kandanishikicho, Chiyoda-ku, Tokyo 101-0054, Japan
Yasunari Yoshitomi, Taro Asada, 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: {yoshitomi, tabuse}@kpu.ac.jp, t_asada@mei.kpu.ac.jp}
http://www2.kpu.ac.jp/ningen/infsys/English_index.html

Abstract
Herein, we propose a method for recognizing human emotions that utilizes the standardized mean value of facial
expression intensity obtained from a thermal image and the standardized mean value of the time at utterance. In thisstudy, the emotions of one subject could be discerned with 76.5% accuracy when speaking 23 kinds of utterances
while intentionally displaying the five emotions of “anger,” “happiness,” “neutrality,” “sadness,” and “surprise.”
Keywords: Emotion recognition, Mouth and jaw area, Thermal image, Utterance judgment.

lighting conditions, we use imagery obtained from
infrared rays to describe the thermal distribution of a
subject’s face.1-3 The timing used by a robot whenattempting to recognize facial expressions is also
important because the required processing can be timeconsuming. Accordingly, we adopted utterances as the
key for expressing human feelings because humans tendto speak aloud when expressing their feelings.2, 3
In the present study, using 23 different utterance
combinations based on the first and last vowels in eachutterance, we investigated the performance of ourpreviously proposed method4 of recognizing emotions
by utilizing facial expression intensity5 and theutterance time.

1. Introduction
This research is intended to facilitate the developmentof robots that can perceive human feelings and mental
states. While various mechanisms for recognizing
human feelings from facial expressions have received
considerable attention in the field of computer visionresearch in recent years, the results achieved to date fall
far short of human capabilities. This is due to thelimited accuracy of facial expression recognition, whichis influenced by inevitable gray level changes that result
from local variations in lighting, shade, reflection, anddarkness.
To avoid this problem and to facilitate thedevelopment of a robust facial expression recognition
method that would be applicable to widely varied© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan501

Yuuki Oka, Yasunari Yoshitomi, Taro Asada, Masayoshi Tabuse

2. Evaluation Method

2.3. Utterance judgment

The proposed method consists of (1) mouth and jaw area
extraction, (2) facial expression intensity measurement, (3)utterance judgment, and (4) calculation of featureparameters for facial expression and voice.4

After sample collection, the sound data are smoothed toerase noise. Then, all sampled data that fall withinxs  14 s , xs  14 s  , where x s and  s express theaverage and the standard deviation of the sound data
value, respectively, for one second under the noutterance condition, are considered to be the range forno utterance.5 When at least one sampled datum has avalue outside xs  14 s , xs  14 s  , our system judgesthat the sound data contains an utterance.5

2.1. Extraction of mouth and jaw areas
Six facial areas extracted at 0.1 frames per second via thethermal image processing reported in our previous study3
are used in the processing of dynamic thermal imagery, as
shown in Fig. 1. The mouth and jaw area was selected
because facial expression differences between neutrality
and happiness are most distinctive in this area.5 Fig. 2
shows thermal image examples of whole face and themouth and jaw areas.

2.4. Feature parameters
In our proposed method,4 two parameters are used asfeature vector elements. One is the mean of thestandardized facial expression intensity at the time of eachutterance and for 0.3 s before and after the utterance, while
the other is the standardized time at each utterance. The
standardization used for creating feature parameters is
expressed by Eq. (1).
xi , j  xi ,(1)xi*, j 
iwhere xi*, j , x i, j , xi , and  i express the standardized
feature parameter, the measured feature parameter, theaverage, and the standard deviations of the measured
feature parameters of the training data, respectively, andi,j denote the number (1 or 2) of the feature parameterand number ( 1, 2,  , m ) of the utterance, respectively.
Then, using the Ward method, clustering is performed
separately for the training and test data to determine
major and minor clusters for each class of the emotionsin the feature vector space. Next, to recognize emotions
from the test data, the center-of-gravity coordinates ofthe major cluster for each emotion class of the training
and test data is used.

Fig. 1. Blocks for extracting partial face areas (left), and thethermal image after partial face extraction (right).2

Fig. 2. Thermal face (left) and mouth and jaw area imagery(right).5

2.2. Measurement of facial expression intensity

3. Experiments

For the extracted frames, the expression feature vectors in
the mouth and jaw area are extracted by applying a twodimensional discrete cosine transform (2D-DCT) for each8  8 pixel domain.5 To accomplish this, we begin by
selecting 15 low-frequency components of the 2D-DCT
coefficients, excluding the direct current component, as thefacial expression feature parameters.5
Next, we obtain the mean of the absolute value foreach 2D-DCT coefficient component in the mouth and jaw
area.5 A total of 15 values are obtained and used as feature
vector elements. Facial expression intensity, which isdefined as the norm of the differences between the neutral
facial expression feature and the observed expression
vectors, can then be used for analyzing facial expression
changes.5

3.1. Conditions
Thermal imagery produced by a TVS-700 thermal video
system (Nippon Avionics, Tokyo, Japan) and sounds
captured by an ECM-23F5 Electret condenser
microphone (Sony, Tokyo, Japan), as amplified by an
AT-PMX5P mixer (Audio-Technica, Tokyo, Japan),were transformed into a digital signal via an ADVC-300
analog/digital (A/D) converter (Thomson Canopus,
Kobe, Japan).
The converted signals were input into an Optiplex
780 personal computer (PC) (DELL, Round Rock, TX)
equipped with an E8400 3.00 GHz Core 2 Duo central
processing unit (CPU) (Intel, Santa Clara, CA) and 3.21

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

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Emotion Recognition of a

GB of main memory, as well as a 1394-PCI3/DV6
IEEE1394 interface board (I-O Data Device, Ishikawa,Japan).
As for software, the PC was equipped with theWindows 7 Professional operating system (OS), while
Visual C++ 6.0, and Visual C++ 2008 Express Edition,(Microsoft, Redmond, WA) were used as the
programming languages.
To generate a thermal image that would allow theface area of a subject to be easily extracted, 256 graylevels were set to cover the detectable temperature rangeof our experimental device setup. The visual and audio
information was saved in the PC as a Type 2 digital
video-audio video interleave (DV-AVI) file, in which
the video frame had a spatial resolution of 720 × 480
pixels and 8-bit gray levels, while 48 kHz and 16-bit
level sound was saved in a stereo pulse-code modulation(PCM) format.
Subject A, a male wearing eyeglasses, deliberately
performed each of the emotions of “anger,” “happiness,”“neutrality,” “sadness,” and “surprise,” while speaking
the semantically neutral utterance of each of the Japanese
first names listed in Table 1. However, all utterances of
the Japanese first name “taro” (the first and last vowelsof which are /a/ and /o/) and “tsubasa” (the first and lastvowels of which are /u/ and /a/) were excluded because
they had been used in the previous study.4

3.2. Results and discussion
Our results show that the subject’s emotion wasreflected in his thermal face image even at 0.3 s before
he began to speak.4 From a comparison with the data
analyzed at the time of utterance, we found that the timeseries facial expression intensity differences for the fivetargeted emotions became more distinct when theanalysis included the time range beginning 0.3 s before
to the subject started to speak to 0.3 s after he finishingspeaking.4
Table 2 shows the emotion recognition accuracy
values obtained using our method. As can be seen inthis table, the average emotion recognition accuracy was
76.5%, which was near 80% for “taro” and “tsubasa”
used in the previous experiment.4 Table 3 shows theemotion recognition accuracy values for each utterance
obtained using our method. The emotion recognition
accuracy for each utterance was from 60% to 100%.
In the case of “sadness,” the accuracy was 100%,
primarily because the utterance time was remarkably
longer than for other utterances, which made it easy to
distinguish. In contrast, the emotions of “anger,” and“surprise,” were confused with each other much more
often, primarily because the utterance times when
expressing these two emotions were quite similar. Fig. 3
shows two-dimensional center-of-gravity distributions
for the dominant cluster of each emotion class for boththe training and test data samples. When the nearest
neighbor rule is used, it can be seen that emotion
recognition accuracy was 100% and 60% for theutterance of “kaede” and “fuyu,” respectively.

Table 1. Japanese first names used in the experiment.

Last
vowel

a
i
u
e
o

a

i

First vowel
u

e

o

ayaka
kazukitakerukaede-

shinnyahikarishigerumisae
hirokoyuki
fuyu
yusukeyuto

keita
megumimegu
keisukekeikotomoyakoji
noborukozue
tomokoTable 2. Emotion recognition accuracy.
Input emotion
Angry Happy Neutral Sad Surprised
Angry
Happy
Recognized
Neutralemotion
Sad
Surprised

In this study’s experiment, Subject A intentionally
maintained a front view in the AVI files, which were
saved as both training and test data. In total, 15 training
data and 15 test data samples were assembled. The AVI
files were used for measuring the facial expression
intensity. WAVform Audio format (WAV) files
obtained from the AVI files were used for measuring
the utterance times.

86.96

13.04

4.35
56.51
26.09
8.7
4.35

26.09
8.7
8.7

13.04
82.61
100
4.35

56.51
(%)

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan503

Yuuki Oka, Yasunari Yoshitomi, Taro Asada, Masayoshi Tabuse

Utterance

Input emotion

Recognizedemotion foreach utterance

angry
happy
neutralsadsurprisedtraining
◇
○
△
□
×

test
◆
●
▲
■
＊

H

N

Sa

Su

ayaka

Su H

Su

Sa

Su

60

kazuki

A

Sa

N

Sa

Su

80

takeru

Su N

N

Sa

Su

60

kaede

A

H

N

Sa

Su

100

shinnya

A

N

N

Sa

A

60

hikari

A

Sa

N

Sa

Su

80

shigeru

A

H

N

Sa

A

80

misae

A

N

N

Sa

Su

80

hiroko

A

H

N

Sa

Su

100

yuki

A

H

N

Sa

A

80

fuyu

Su H

N

Sa

A

60

yusuke

A

H

H

Sa

Su

80

yuto

A Su

N

Sa

A

60

keita

A

N

N

Sa

N

60

megumi

A

H

H

Sa

A

60

megu

A

H

N

Sa

Su

100

keisuke

A

H

H

Sa

Su

80

Acknowledgements

keiko

A

N

N

Sa

Su

80

tomoya

A

N

N

Sa

Su

80

The present study was
KAKENHI (22300077).

koji

A

A

N

Sa

N

60

noboru

A

H

N

Sa

H

80

kozue

A

H

N

Sa

H

80

tomoko

A

H

N

Sa

Su

100

Standardized facial expression intensity

Standardized utterance timeA

Accuracy(%)

Standardized utterance time

Table 3. Emotion recognition accuracy for each utterance.

Standardized facial expression intensity

Fig. 3. Two-dimensional center-of-gravity distribution ofmajor cluster of training and test data; upper: “kaede”,
lower: “fuyu”.

partially

supportedbyReferences
1. Y. Yoshitomi, N. Miyawaki, S. Tomita, and S. Kimura,
Facial expression recognition using thermal image
processing and neural network, in Proc. 6th IEEE Int.
Workshop on Robot and Human Communication, (Japan,
Sendai, 1997), pp. 380–385.
2. Y. Yoshitomi, T. Asada, K. Shimada, and M. Tabuse,
Facial expression recognition of a speaker using vowel
judgment and thermal image processing, J. Artif. Life andRobotics 16(3) (2011) 318–323.
3. Y. Yoshitomi, M. Tabuse, and T. Asada, Facial
expression recognition using thermal image processing,in Image processing: methods, applications andchallenges ed. V. H. Carvalho (Nova Science Publisher,
New York, 2012), pp. 57–85.
4. Y. Yoshitomi, T. Asada, R. Kato, M. Tabuse, Facial
Expression Recognition Using Facial Expression
Intensity Characteristics of Thermal Image, J. Robotics,
Networking and Artif. Life 2(1) (2015) 5-8.
5. Y. Yoshitomi, T. Asada, R. Kato, M. Tabuse, Method of
facial expression analysis using video phone and thermal
image, J. Robotics, Networking and Artif. Life 1(1)(2014) 7-11.

A=Angry, H=Happy, N=Neutral, Sa=Sad, Su=Surprised

4. Conclusion
In a previous study, we proposed a method forrecognizing human emotions. Using that method, theemotions of one subject were discernable with 76.5%
accuracy in speaking each of 23 utterance types while
intentionally exhibiting each of the five emotions of“anger,” “happiness,” “neutrality,” “sadness,” and“surprise.”
The recorded accuracy level was near 80% for“taro” and “tsubasa” in the previous experiment.4 These
results show that the method is capable of producing
good results.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

504

Estimation of Learners' Subjective Difficulty in e-Learning Using Thermal Image Processing
Yuki Yoshimitsu and Masayoshi Tabuse

Graduate School of Life and Environmental Sciences, Kyoto Prefectural University
1-5 Hangi-cho, Shimogamo, Sakyo-ku, Kyoto 606-8522, Japan
E-mail: y_yoshimitsu@mei.kpu.ac.jp, tabuse@kpu.ac.jp

Abstract
In recent years, e-learning has been utilized as a learning system in many schools. In a conventional class, a teacher
teaches learners face-to-face, so that a teacher observes facial expressions and posture change of learners andestimates learners’ subjective difficulty of the lecture. On the other hand, e-learning is usually utilized in the selfstudy. A learner learns a teaching material using a computer by oneself, so that a teacher can't observe the state of
the learner. It is difficult for a teacher to estimate learners’ subjective difficulty. Therefore, we propose a learners’
subjective difficulty estimation system. This system captures the face of a learner in e-learning with a thermal
camera. It extracts the face region, measures the temperature changes of the nose region and the forehead region of
the learner, and estimates the subjective difficulty of the learner based on the temperature changes.
Keywords: learners’ subjective difficulty, thermal image, e-learning, temperature changes.

region and the forehead region of the learner, andestimates the subjective difficulty of the learner basedon the temperature changes.

1. Introduction
In recent years, e-learning has been utilized as alearning system in many schools. In a conventionalclass, a teacher teaches learners face-to-face, so that ateacher observes facial expressions and posture changeof learners and estimates learners’ subjective difficultyof the lecture. On the other hand, e-learning is usually
utilized in the self-study. A learner learns a teachingmaterial using a computer by oneself, so that a teacher
can't observe the state of the learner. It is difficult for ateacher to estimate learners’ subjective difficulty.
Therefore, we estimate a learners’ subjective difficulty.
Because the thermal image of the human face changes
depending on a bloodstream change with the automatic
nerve activity, the change of the thermal image of thehuman face, especially the nose region, reflect humanphysiological psychological conditions.1
Our proposed system captures the face of a learnerin e-learning with a thermal camera. It extracts the faceregion, measures the temperature changes of the nose

2. Thermal Image
Thermal image is obtained according to the StefanBoltzmann law which describes the power radiated froma black body in terms of its temperature, expressed asW  T 4 , where  is emissivity,  is the Stefan–
Boltzmann constant (=5.6705  10-12 W/cm2K4), and T
is the temperature (K). For human skin,  is estimatedas 0.98 to 0.99.2 The human face area is easily
extracted from an image by using the value of 1 for 
when a range of skin temperatures is selected to produce
a thermal image. Fig. 1 shows example of thermal
image of a male. In our study thermal image isexpressed in gray image with 256 gray levels wheregray value 0 and 255 indicate temperatures beyond the
setup minimum and maximum ones respectively.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan505

Yuki Yoshimitsu, Masayoshi Tabuse

Fig. 3. Labeling.

Fig. 1. Thermal image of a male.

3. Extraction of face region
When the setup temperatures are chosen so that thehuman skin temperature might enter between theminimum temperature and the maximum temperature,
the human region are expressed by gray values between
0 and 255 and the lower temperature background are
expressed by 0. We first operate binary image
processing to thermal image and obtain binary image,shown in Fig. 2. The human region has value 255 andthe background has 0. We apply labeling to the binary
image and have regions with more than 2000 pixels,
shown in Fig. 3. Next we scan labeling image fromy  0 to y  H (height of thermal image) for each x
and when the coordinate ( x, y ) is in the labeling region,
we set 1 on 2 dimensional array M ( x, y ) from there toy  H . We scan labeling image from x  0 to x  W(width of thermal image) and from x  W to x  0
for each y . When the coordinate ( x, y ) is in thelabeling region, we add 1 on M ( x, y ) from there toright and from there to left, respectively, shown in Fig.
4.

Fig. 4. 2 dimensional array M(x,y). White, lightgray, gray and black colors indicate the area withthe values of 3, 2, 1 and 0.

We operate the raster scan on M ( x, y ) from (0,0) to(W , H ) and find the top coordinate of the human region( xhuman , yhuman ) where the value of M ( x, y ) has 3. We
measure the maximum width Wmax of the human region
from yhuman to y-coordinate of the center of mass of thelargest labeling region. From yhuman to y-coordinate of
the bottom of the largest labeling region we calculate
the frequency distribution of the width of the region
which have 2 or 3 and determine the width of the
maximum frequency within 1.2Wmax as the facial width
W face and x-coordinate of the left and right boundaries of
the human region as left and right boundary of the faceregion xleft and xright , respectively. We find y-coordinateof 0.8Wface width of the human region downward fromyhuman and the top y-coordinate of the face region ytop is0.2W face above from there. The bottom of the face region
ybottom is W face downward from ytop , shown in Fig. 5. The
upper left and the lower right coordinates of the faceregion are ( xleft , ytop ) and ( xright , ybotoom ) . The height of faceregion is H face  Wface .

Fig. 2. Binary image.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan

506

Estimation of Learners' Subjective

Fig. 7. Template, nose region and forehead region.
Fig. 5. Face region.

5.

Finally we operate a rotation correction to thethermal image. We measure midpoints of the left andright boundaries of binary image in the face region fromytop  0.45H face to ytop  0.75H face . We estimate a roll angle
 of the face using the least-square method (Fig. 6.) androtate the thermal image by   around the center of theface region. We normalize the face region with the sizeof 400 400 pixels.

5.1. Condition
The thermal image produced by the infrared thermal
image camera (Nippon Avionics TVS-700) is output asa composite video signal, which is input to the PC (Dell
Optiplex 9010, CPU: Intel Core i7-3770 3.4GHz, main
memory: 8GB and OS: Microsoft Windows7
Professional 64bit) through the video capture device (IO DATA GV-USB2/HQ). The thermal image isexpressed in gray image with 256 gray levels and the
setup minimum and maximum temperatures are 29℃
and 39℃, respectively. The thermal image size is 704×
480 pixels. We used Microsoft Visual C++ 2013
Express as a programming language and OpenCV
2.4.11 as image processing library. Subject A is a malein his 20s.

Fig. 6. Estimation of a roll angle.

4.

Experiments

5.2. Mental workload
Subjects perform a mental calculation as a mental
workload.1,3 Fig. 8 shows a mental calculation system
which gives questions of mental calculation of double-,
triple- or quadruple-digits. The system displays anumber and the subject memorizes it and clicks theenter button. Then the system displays a different
number, the subject adds it to the memorized number,
input the answer and clicks the enter button. The system
records the number of the answers, the number of thecorrect answers and answer time for each question.

Template matching, the nose and foreheadregions

We utilize the template matching to detect the nose
region correctly. We first click the tip of the nose andobtain a template. The size of a template is
Htmp W tmp 140 280 pixels and the upper left coordinate( xclick  0.5Wtmp , yclick  0.85Htmp ) where ( xclick , yclick ) is a
iscoordinate of the tip of the nose in the face region. The( xtmp  0.35Wtmp , ytmp  0.2H tmp )nose
regionis( xtmp  0.65Wtmp , ytmp  H tmp )where ( xtmp , ytmp ) is the coordinate of the tip of the nose.
The forehead region is ( xtmp  0.35Wtmp , ytmp  40) ( xtmp  0.65Wtmp , ytmp ) , shown in Fig. 7.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan507

Yuki Yoshimitsu, Masayoshi Tabuse

Temperature (℃)

34
33
32
31
0

100

200

300
Time (sec)

400

500

600

Fig. 10. Average temperature of the nose region.

Temperature (℃)

34
33
32
31
0

100

200

300
Time (sec)

400

500

600

Fig. 11. Average temperature of the forehead region.

Time (sec)40

Fig. 8. Mental calculation system.

30
20
10

5.3. Results0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37
Question number

The change of temperatures of the face region, the nose
region and the forehead region of subject A withquadruple-digits mental calculation are shown in Fig. 9,
10, 11. Answer time for each question and time series
variation of correct and incorrect answers are shown in
Fig. 12, Fig. 13.
6.

Fig. 12. Answer time.
10
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37
Question number

Fig. 13. Time series variation of correct and incorrect
answers (1: correct answer, 0: incorrect answer).

Conclusion

We measure the change of temperatures of the faceregion, the nose region and the forehead region of a
subject while doing mental calculations.

References
1. T. Mizuno, S. Nomura, A. Nozawa, H. Asano and H. Ide,
Evaluation of the effect of intermittent mental work-loadby nasal skin temperature (in Japanese), IEICE J93-D(4)535-543 (2010).
2. H. Kuno, Infrared rays engineering (in Japanese). (IEICE,Tokyo, 1994).
3. S. Kunimasa, K. Seo, H. Shimoda and H. Ishii, The
inference method of cognitive working states while
performing mental tasks based on performance-cognitive
load model (in Japanese), The Transactions of Human
Interface Society 17(4) 395-410 (2015).

Temperature (℃)

34

33

32

31
0

100

200

300
Time (sec)

400

500

600

Fig. 9. Average temperature of the face region.

© The 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), Jan. 29-31, Okinawa Convention Center, Okinawa, Japan508