ICAROB2014	 THE PROCEEDINGS OF
INTERNATIONAL CONFERENCE
ON ARTIFICIAL LIFE AND ROBOTICS

January 11-13, 2014
Compal Hall, Oita, JAPANInternational Meeting Series
Editor-in-Chief
Masanori Sugisaka
Editors: Takao Ito, Ju-Jang Lee, Yingmin Jia
ISBN 978-4-9902880-8-2	  The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

The Proceedings of the International Conference on

ARTIFICIAL LIFE AND ROBOTICS(ICAROB 2014)

January 11-13, 2014
Compal Hall Oita, Japan
Editor-in-Chief: Masanori Sugisaka
Editors: Takao Ito, Ju-Jang Lee, Yingmin Jia©ICAROB 2014

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Contents
1

Organization, etc.

1

2

Messages

6

3Time Table

104

Opening Ceremony

13

5

Technical paper index

14

6

Abstracts
6-1

IS abstracts

25

6-2

OS abstracts

27

6-3

GS abstracts

44

6-4

PS abstracts

56

7

Authors index

598

Conference rooms

65©ICAROB 2014

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

ORGANIZEDInternational Steering Committee of International Conference on Artificial Life and Robotics(ICAROB) and ALife Robotics Corporation Ltd.
CO-ORGANIZED BYIEEE Fukuoka Section (IEEE Robotics and Automation Society, Fukuoka Section, Japan)
Chinese Association for Artificial Intelligence (CAAI, P. R. China)
University of Sultan Zainal Abidin (UniSZA)ADVISORY COMMITTEE CHAIR
Moshe Kam (Drexel University, Former IEEE President, USA)ADVISORY COMMITTEE
F. Harashima (The University of Tokyo, Japan)
B. Eisenstein (Drexel University, Former IEEE President, USA)Eisenst
H. Kimura (RIKEN, Japan)
M. Tomizuka (University of California Berkeley, USA)
M. Kam (Drexel University, Former IEEE President, USA)
A. Grzech (Wroclaw University of Technology, Poland)
K. Aihara (The University of Tokyo, Japan)
D. J.G. James (Coventry University, United Kingdom)
J. Johnson (The Open University, UK)
K. Kyuma (Mitsubishi Electric Corporation, Japan)
S. Rasmussen (University of Southern Denmark, Denmark)J. M. Epstein (The Johns Hopkins University, USA)J. Świątek (Wroclaw University of Technology, Poland)
P. Kalata (Drexel University, USA)
P. Oh (Drexel University, USA)
R. Fischl (Drexel University, USA)T. Fukuda (Nagoya University, Japan)
GENERAL CHAIRS
Yingmin Jia (Beihang Univesty)
Takao Ito (Ube National College of Technology)
Ju-Jang Lee (KAIST)
VICE GENERAL CHAIRS
C. Zhang (Tinghua Univesity, P. R. China)©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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H. H. Lund (Technical University of Denmark, Denmark)J. L. Casti (International Institute for Applied Systems Analysis, Austria)
J.M. Lee (Pusan National University, Korea)
Y. G. Zhang (Academia Sinica, P. R. China)
PROGRAM CHAIRMAN
Makoto Sakamoto (University of Miyazaki, Japan)
SUB PROGRAM CHAIR
M. Oswald (The Vienna University of Technology, Austria)
HONORARY GENERAL CHAIR
M. Sugisaka (ALife Robotics Co., Ltd., Japan,
University of Sultan Zainal Abidin (UniSZA),
University of Malaysia-Peris, Malaysia)
INTERNATIONAL ORGANIZING COMMITTEE
E. Hayashi (Kyushu Institute of Technology, Japan)
H. Desa (University of Malaysia, Perlis, Malaysia)
H. Suzuki (The University of Tokyo, Japan)
H. Furutani (The University of Miyazaki)
H. Matsuno (Yamaguchi University, Japan)
J. Wang (Beijing Jiaotong University, P. R. China)
K. Shimohara (Doshisha University, Japan)
K. Ohtsu (Tokyo University of Marine Science and Technology)
K-L. Su (National Yunlin University of Science and Technology, Taiwan)
M. Kubo (National Defense Academy of Japan, Japan)
M. Obayashi (Yamaguchi University, Japan)
M. Yamashita (Kyushu University, Japan)
M. Rajiv (New Jersey Institute of Technology)
M. Rizon (University of Sultan Zainal Abidin, Malaysia)
P. Sapaty (Ukrainian Academy of Science, Ukraine)
Q. Yanbin (Harbin Institute of Technology, P. R. China)
S. Ishikawa (Kyushu Institute of Technology, Japan)
T. Kohno (The University of Tokyo, Japan)
T. Hattori (Kagawa University, Japan)
T. S. Ray (University of Oklahoma, USA)
V. Berdonosov (Komsomolsk-on-Amur State University of Technology, Russia)
Y. Yoshitomi (Kyoto Prefectural University, Japan)©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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INTERNATIONAL PROGRAM COMMITTEE
A. Nakamura (AIST, Japan)
A. Selamat(University of Technology of Malaysia(UTM), Malaysia)
B. Fu(Shanghai Jiatong University, P. R. China)
D. Ai (University of Science & Technology Beijing, China)
E. Joelianto (Bandung Institute of Technology, Indonesia)
F. Dai (Tianjin University of Science & Technology, P. R. China)
H. Ogai (Waseda University, Japan)
H. Yanagimoto (Osaka Prefecture University, Japan)
H. Umeo (Osaka Electro-Communication University, Japan)
H. Iizuka (Osaka University, Japan)
H. Zhao (Shanghai Institute of Technology, P. R. China)
H. Abbass (University of New South Wales and ADFA, Australia)
I. Tanev (Doshisha University, Japan)
J. Zhao (Beijing Jiaotong University, P. R. China)
K. E. Merrick (University of New South Wales and ADFA, Australia)
K. Kobayashi (Aichi Prefectural University)
K. Kurashige (Muroran Institute Technology, Japan)
K. Sugawara (Tohoku Gakuin University, Japan)
L. Pagliarini (Technical University of Denmark, Denmark)M. Eaton (University of Limerick, Ireland)
M. Yokomichi (The University of Miyazaki, Japan)
M. Tabuse (Kyoto Prefectural University, Japan)
M. Hatakeyama (university of Zurich, Switzerland)
M. Svinin (Kyushyu University, Japan)
M. Watanabe (Kagoshima University, Japan)
N. Mokhtar (University of Malaya, Malaysia)
P. S. Sapaty (National Academy of Sciences of Ukraine, Ukraine)
S. Ikeda (The University of Miyazaki, Japan)
S. S. Joshi (College of Engineering, UCLA, USA)
S. Omatu (Osaka Institute of Technology, Japan)
S-M Chen (National Taichung University of Education, Taiwan)
T. Zhang (Tsinghua University, P. R. China)
T. Iwamoto (Mitsubishi Electric Corporation, Advanced Technology R&D Center, Japan)
T. Katayama (the University of Miyazaki, Japan)
T. Yoshinaga (Tokuyama National College of Technology)
Y. Fan (University of California-Davis, USA)
Y. Liu (University of Aizu, Japan)
Y. I. Cho (The University of Suwon, Korea)©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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LOCAL ARRANGEMENT COMMITTEE
M. Sakamoto (University of Miyazaki, Japan)
M. Sugisaka (ALife Robotics Co., Ltd., Japan,
University of Sultan Zainal Abidin (UniSZA),
University of Malaysia-Peris, Malaysia )Takao Ito (Ube National College of Technology)
HISTORY
This symposium was founded in 1996 by the support of Science and International Affairs Bureau,
Ministry of Education, Culture, Sports, Science and Technology, Japanese Government. Since
then, this symposium has been held every year at B-Con Plaza, Beppu, Oita, Japan except in Oita,
Japan (AROB 5th ’00) and in Tokyo, Japan (AROB 6th ’01). We changed this symposium name as
The International Conference on Artificial Life and Robotics newly. This conference invites you
all.
OBJECTIVE
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 will discuss new results in the field of artificial life and robotics.
GENERAL SESSION TOPICS
GS1 Artificial intelligence & Complexity
GS3 Human-machine cooperative
systems & Human welfare robotics
GS5 Pattern recognition

GS2 Reinforcement Learning System
and genetic programming
GS4 Neural Networks
GS6 Robotics I

GS7 Robotics II
ORGANIZED SESSION TOPICS
OS1 Intelligent Control
OS3 Facial Expression Analysis, Music
Recommendation and Augmented
Reality
OS5 Analysis of Randomized Algorithms
Intelligence in Biological Systems
OS7 Intelligence in Biological Systems
OS9 Foundation of computation and its
Application©ICAROB 2014

OS2 Environment Navigation andLocalization
OS4 Applications in Kansei Engineering

OS6 Empirical research on Network and
MOT
OS8 Software Development Support
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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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COPYRIGHTS
Accepted papers will be published in the proceeding of ICAROB and 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 ARTIFICIAL LIFE, COMPLEXITY AND ROBOTICS undernegotiation. 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： icalr@alife-robotics.co.jp
Home Page：http://alife-robotics.co.jp/©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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MESSAGES
Yingmin Jia
General Chair of ICAROB

Yingmin Jia
General Chair(Professor,

It is my great pleasure to invite you to the International Conference on Artificial
Life and Robotics (ICAROB), in Oita City, Oita, Japan from Jan. 11th to 13th,2014.
ICAROB 2014 develops from the AROB that was created in 1996 by Prof.
Masanori Sugisaka and celebrated her birthday of eighteen years old in 2013.
Doubtless, new mission and big challenges in the field of artificial life androbotics 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 Oita City and wishing you enjoy
your stay in Japan.

Beihang University,
R .P. China)©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Takao Ito
General Chair of ICAROB

Takao Ito
General Chair(Professor of

Ube National

College of Technology, Japan)©ICAROB 2014

It is my great honor to invite you all to The International Conference on
Artificial Life and Robotics (ICAROB 2014).This Conference is changed as
the old symposium from the first (1996) to the Eighteenth I am pleased towelcome you to the 2013 International Conference on Artificial Life andRobotics (ICAROB) in the wonderful city of Oita, JapanThe ICAROB has long history. The former organization of the ICAROB
was developed 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
the exchange of scientific information on the study of man-made systems
that exhibit the behavioral characteristic of natural living systems including
software, hardware and/or wetware. Our conference shapes the developmentof artificial life, extending our empirical research beyond the territory
circumscribed by life-as-we-know-it and into the domain of life-as-it-could–
be. It will provide us a good place to present our new research results, goodideas, 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 23 sessions during 3 days of conference, including 3 invited
sessions.
The conference site is the Compal Hall, one of the finest congress centersin Oita. It is situated near the center of the city. You can find many fantastic
scenic spots and splendid hot-springs. Enjoy your stay and take your time tovisit the city of Oita.
I am looking forward to meeting you in Oita during 2013 ICAROB and to
sharing a most pleasant, interesting and fruitful conference

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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Ju-Jang Lee

General Chair of ICAROB

Ju-Jang Lee
General Chair(Professor, KAIST)©ICAROB 2014

The First International Conference on Artificial Life and Robotics(ICAROB) will be held in Oita City, Oita, Japan from Jan. 11th to 13th, 2014.
This year’s Conference will be held amidst the high expectation of theincreasingly important role of the new interdisciplinary paradigm of scienceand engineering 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 of appliedresearch, present and discuss the latest development of new technologies andrelevant applications.
In addition, ICAROB offers the opportunity of hearing the opinions ofwell-known leading experts in the field through the keynote sessions, provides
the bases for regional and international collaborative research, and enables toforesee the future evolution of new scientific paradigms and theories
contributed by the field of artificial life and robotics and associated researcharea. The twenty-first century will become the century of artificial life andintelligent machines in support of humankind and ICAROB is contributing
through wide technical topics of interest that support this direction.
It is a great for me as a General Chair of the 1st ICAROB 2014 to welcome
everyone to this important event. Also, I would like to extend my special
thanks to all authors and speakers for contributing their research works, theparticipants, and the organizing team of the 1st ICAROB.
I’m looking forward to meeting you at the 1st ICAROB in Oita City andwishing you all the best.

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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Masanori Sugisaka

Honorary General Chair of ICAROB

Masanori Sugisaka
Honorary General Char
(Professors,
University of Sultan Zainal
Abidin(UniSZA), Malaysia,
University of Malaysia-Perlis and President,
ALife Robotics Co., Ltd. ,Japan)©ICAROB 2014

It is my great honor to invite you all to The International Conference on
Artificial Life and Robotics (ICAROB 2014).
This Conference is changed as the old symposium from the first (1996) tothe Eigteenth(2013) anually which were organized by Oita University,
Nippon Bunri University(NBU), and ALife Robotics Corporation Ltd. under
the sponsorship of the Science and Technology Policy Bureau, the Ministryof Education, Science, Sports, and Culture (Monbusho), presently, theMinistry of Education, Culture, Sports, Science, and Technology(monkasho), Japanese Government, Japan Society for the Promotion of
Science (JSPS), The Commemorative Organization for the Japan World
Exposition (’70), Air Force Office of Scientific Research, Asian Office ofAerospace Research and Development (AFOSR/AOARD), USA. I would
like to express my sincere thanks to not only Monkasho (annually fundsupport from 1996 to 2013) but also JSPS, the Commemorative
Organization for the Japan World Exposition (’70) , Japanese companies fortheir 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 are inherited also from now. From now on,
ALife Robotics Corporation Ltd. is in charge of management. The future of
The ICAROB 2014 is brilliant from a point of view of yielding newtechnologies to human society in 21st century.
I hope that fruitful discussions and exchange of ideas between researchers
during Coferenence (ICAROB 2014) 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.

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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TIME TABLE (1/11)
1/11
9:30~

Room301

Room304

Room305

Registration

10:00-11:20

GS3 (4)GS7 (4)Chair M. Rizon

Chair J. Wang

11:20-11:40

Coffee break

11:40-12:00

Opening Ceremony (room300)

12:00-13:00

Lunch

13:00-14:00

IS (room300)
Chair J. M. Lee

14:00-14:20
14:20-16:00

Coffee breakGS1(5)

GS4(4)

Chair M. Kubo

Chair A. Selamata(will be end at 15:40)

16:00-16:20
16:20-18:00

Coffee break
Chair

GS1 Artificial intelligence & Complexity
GS2 Reinforcement learning system &Genetic programing
GS3 Human-machine cooperative systems& Human welfare robotic
GS4 Neural Networks
GS5 Pattern recognition Ⅰ
GS6 Robotics Ⅰ
GS7 Robotics Ⅱ©ICAROB 2014

GS 2(5）

GS5(5)
S. Ishikawa

Chair K. Kobayashi

OS1 Intelligent Control
OS2 Environment Navigation andLocalization
OS3 Facial Expression Analysis, Music
Recommendation and Augmented
Reality
OS4 Applications in Kansei Engineering
OS5 Analysis of Randomized Algorithms
OS6 Empirical research on Network and MOT
OS7 Intelligence in Biological Systems
OS8 Software Development Support Method
OS9 Foundation of computation and its
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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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TIME TEBLE (1/12)
1/12
9:30～
10:00-11:20

Room301

Room304

Registration
PS1(6)

OS5(3)

OS8(4)

Chair

Chair H. Furutani

Chair T. Katayama

D. Hazry(will be end at 11:00)

11:20-13:00
13:00-14:40

Lunch
PS1(6)

OS1(5)
Chair

14:40-15:00
15:00-17:00

Room305

OS3(5)
Y. JiaChair

Y. Yoshitomi

Coffee breakPS1(6)

OS2(6)

OS4(5)

Chair J. M. Lee

Chair: T. Hattori(will be end at 16:40)

18:00-20:00©ICAROB 2014

Banquet: HOTEL HOKKE CLUB OITA

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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TIME TABLE (1/13)
1/13
9:30～
10:00-12:00

Room301

Room304

Room305

Registration
GS6(5)

OS7(6)
Chair J. Nishii

Chair F. Dai(will be end at 11:40)
12:00-13:00
13:00-14:40

Lunch
OS6(5)

OS9(4)

Chair: T. Ito

Chair: M. Sakamoto(will be end at14:20)©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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The International Conference on
ARTIFICIAL LIFE AND ROBOTICS 2014
(ICAROB 2014 )January 11 (Saturday)
Room 300: 11:40-12:00

Opening Ceremony
Chair: M. Sakamoto (University of Miyazaki, Japan)
Welcome Addresses
1. General Chairman of ICAROB

Y. M. Jia (Beihang University, China)

2. General Chairman of ICAROB

T. Ito (Ube National College of Technology, Japan)

3. General Chairman of ICAROB

J. J. Lee (KAIST, Korea)

4. Honorary General Chairmanof ICAROB

M. Sugisaka( ALife Robotics Co., Ltd. Japan)

January 12 (Sunday)
HOTEL HOKKE CLUB OITA
18:00-20:00

BanquetChair: M. Oswald (The Vienna University of Technology, Austria)
Welcome Addresses
Y. M. Jia (Beihang University, China)
H.H.Lund(Denmark Technical University of Denmark,Denmark)L.Pagliarini(Denmark Technical University of Denmark,Denmark)©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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TECHNICAL PAPER INDEXJanuary 11 (Saturday)
Room 300
13:00-14:00 Invited Session
Chair: J. M. Lee(Pusan National University, Korea)
IS1-1 Playware Research – Methodological Considerations
Henrik Hautop Lund (Centre for Playware, Technical University of Denmark, Denmark)IS1-2 ALife for Real and Virtual Audio-Video Performances
Luigi Pagliarini ( Centre for Playware, Technical University of Denmark, Denmark
Academy of Fine Arts of Macerata,Italy )Henrik Hautop Lund (Centre for Playware, Technical University of Denmark, Denmark)IS1-3 Heart-pulse Biofeedback in Playful Exercise using a Wearable device and ModularInteractive Tiles
Tomoya Shimokakimoto (University of Tsukuba, Japan)
Henrik H. Lund (Technical University of Denmark, Denmark),
Kenji Suzuki (University of Tsukuba/ JST, Japan)

January 11 (Saturday)
9:30- Registration

Room 304
10:00-11:20 GS3 Human-machine cooperative system & Human welfare robotics
Chair: Mohamed Rizon (Universiti Sultan Zainal Abidin (UniSZA), Malaysia)
GS3-1 Development of Measurement System for Quantitative Evaluation of Skillfulness
of Lower Extremities
Kazunori Yamazaki, Donggun Kim, Yoshifumi Morita, Hiroyuki Ukai
(Nagoya Institute of Technology, Japan),Kenji Kozakai, Satoru Shibata, Shigenori Onishi, Akihiro Ito, Daisuke Mizuno(Sanyo Machine Works, Ltd., Japan),
Tatsuya Hirai, Haruna Takeda, Yuka Sugiura (Tanakakai Nishio Hospital, Japan)
GS3-2 Glove-Based Virtual Interaction for the Rehabilitation of Hemiparesis Stroke Patient
Khairunizam WAN, Aswad A.R, Rashidah Suhaimi, Nazrul H. ADNAN, D. Hazry,©ICAROB 2014

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Zuradzman M. Razlan, Syed Faiz Ahmed (Universiti Malaysia Perlis, Malaysia)
GS3-3 Measuring system of therapist's guiding motion for standing up training of patient withhemiplegia after stroke and analysis results of patient's motion
JungTang Wang, Kazunori Yamazaki, Yoshifumi Morita, Noritaka Sato
(Nagoya Institute of Technology, Japan)
Hirofumi Tanabe (Self-Defense Force Central Hospital, Japan)
GS3-4 Image Segmentation of Coronary Artery Plaque Using Intuitionistic Fuzzy C-Means
Algorithm
Zahra Rezaeia, Ali Selamata, Mohd Shafry Mohd Rahima,
Mohammed Rafiq Abdul Kadirb (Universiti Teknologi Malaysia, Malaysia)

14:20-16:00 GS1 Artificial intelligence & Complexity
Chair: M. Kubo (National Defense Academy of Japan)
GS1-1 Cognitive approach to Computer Go programming
N. Tenys, I. Tanev, K. Shimohara (Doshisha University, Japan)
GS1-2 Circulative Narrative Generation Based on the Mutual Transformation between
Narrative Conceptual Structures and Music in the Integrated Narrative
Generation System
Taisuke Akimoto, Takashi Ogata (Iwate Prefectural University, Japan)
GS1-3 Dynamic Analysis of Tensegrity Systems Subject to Arbitrary Joint
Constraints
Youngsu Cho, Joono Cheong (Korea University, Korea)
GS1-4 Road map generation from Smartphone data
Masao KUBO, Chau viet DAN, Hiroshi SATO, Akira NAMATAME(National Defense Academy of Japan)
GS1-5 Zigzag-Perceptually Important Points for Financial Time Series Indexing
Chawalsak Phetchanchai, Ali Selamat , Md Hafiz Selamat(University Teknologi Malaysia, Malaysia )16:20-18:00 GS5 Pattern recognition
Chair: S. Ishikawa (Kyushu Institute of Technology, Japan)
GS5-1 Recognizing a Road Environment Using Multiple-window Bag of Features
Shou Morita, Joo Kooi Tan, Hyoungseop Kim, Seiji Ishikawa(Kyushu Institute of Technology, Japan)
GS5-2 Recognized Face Tracking for CONBE Robot
Sakmongkon Chumkamon, Eiji Hayashi ( Kyushu Institute of Technology)©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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GS5-3 Eye detection Using Composite Cross-Correlation form Face Images in Varied
Illumination
Kutiba Nanaa, Mohamed Rizon, Mohd Nordin Abd Rahman(Universiti Sultan Zainal Abidin (UniSZA), Terengganu, Malaysia)
GS5-4 Development of an autonomous-drive personal robot “An object recognition systemusing BoF and SOM”
Keisuke Ito, Eiji Hayashi (Kyusyu Institute of Technology, Japan)
GS5-5 Comparison of Feature Detectors for Obstacles Detection
Shaohua Qian, Joo Kooi Tan, Hyoungseop Kim, Seiji Ishikawa
Takashi Morie, ( Kyushu Institute of Technology, Japan)
Takashi Shinomiya(Japan University of Economics, Japan)

Room 305
10:00-11:20 GS7 RoboticsⅡ
Chair: Jiwu Wang (Beijing Jiaotong University, China)
GS7-1 Decision Making System of Robots introducing a Re-construction of Emotions Based on
Their Own Experiences
Shogo Watada, Masanao Obayashi, Takashi Kuremoto, Shingo Mabu
(Yamaguchi University, Japan)
Kunikazu Kobayashi (Aichi Prefectural University, Japan)
GS7-2 Visual Servoing and Sound Localization in a Surveillance Robot
So-Yeon Park, Yeoun-Jae Kim, Ju-Jang Lee (KAIST, Korea)
GS7-3 Study on the improvement of flexibility for an industrial robot based on machine vision
Jiwu Wang, Xianwen Zhang, Weining Zhang (Beijing Jiaotong University, China)
Sugisaka Masanori(Nippon Bunri University, Japan)
GS7-4 Improved Map Generation by Addition of Gaussian Noise for Indoor SLAM using ROS
Khairul Salleh Mohamed Sahari, Barry Loh Tze Yuen
(Universiti Tenaga Nasional,Malaysia)

14:20-15:40 GS4 Neural Networks
Chair: Ali Selamat (University Teknologi Malaysia, Malaysia)
GS4-1 The recollection characteristics of Generalized MCNNusing different control methods
Shun Watanabe, Takashi Kuremoto, Shingo Mabu, Masanao Obayashi(Yamaguchi University, Japan),
Kunikazu Kobayashi (Aichi Prefectural University, Japan)
GS4-2 Revealing Terrorism Contents form Web Page Using Frequency Weighting
Techniques
Thabit Sabbah, Ali Selamat, Md Hafiz Selamat(Universiti Teknologi Malaysia, Malaysia)©ICAROB 2014

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GS4-3 Application of Self Organizing Map for analyzing of robotic arm’s action with
Consciousness-Based Architecture module
Wisanu Jitviriya, Eiji Hayashi (Kyushu Institute of Technology, Japan)
GS4-4 Study on Time Synchronization Algorithm of Wireless Sensor Networks
Based on Maximum Likelihood Estimation
Ai Dongmei, Zeng Guangping, Tu Xuyan, He Di(University of Science and Technology Beijing, China)

16:20-18:00 GS2 Reinforcement Learning System & Genetic programming
Chair: K. Kobayashi (Aichi Prefectural University, Japan)
GS2-1 Development of phrase and music search engine by humming
Kiminori Satou, Eiji Hayashi (Kyushu Institute of Technology, Japan)
GS2-2 Human Recognition based on Gait feature and Genetic Programming
Dipak Gaire Sharma, Ivan Tanev, Katsunori Shimohara (Doshisha University, Japan)
GS2-3 An Action Selection Method Using Degree of Cooperation in a Multi-agent System
Masanori Kawamura, Kunikazu Kobayashi (Aichi Prefectural University, Japan)
GS2-4 Cooperative Action Acquisition Based on Intention Estimation Method in a Multi-agent
Reinforcement Learning System
Tatsuya Tsubakimoto, Kunikazu Kobayashi (Aichi Prefectural University, Japan)
GS2-5 Integrating the Event Generation Mechanism in the Propp-based Story Generation
Mechanism into the Integrated Narrative Generation System
Shohei Imabuchi, and Takashi Ogata (Iwate Prefectural University, Japan)

January 12 (Sunday)
9:30- Registration

Room 301
10:00-17:00 PS1 Poster Session
Chair: D. Harzy (Universiti Malaysia Perlis (UniMAP), Malaysia)
PS1-1 Relationship input object position and optimal error diffusion coefficientsfor Kinoform using error diffusion method
Daisuke Kashima, Ken-ichi Tanaka ( Meiji University, Japan)
PS1-2 Optimization of dither matrix by hybrid of Genetic Algorithm and Simulated Annealing©ICAROB 2014

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Kohei Kato, Ken-ichi Tanaka (Meiji University, Japan)
PS1-3 Inverse Halftoning using Multi-Layer Feed-Forward Neural Network(P)Hiroki Hamashoji, Ken-ichi Tanaka ( Meiji University, Japan)
PS1-4 Embedding of the Confidential Image in a Dithered Color Image
Keisuke Banba and Kenichi Tanaka (Meiji University, Japan)
PS1-5 Three Dimensional Images Reconstruction on Computer Generated Holograms
the Multiple Regression Analysis
Kenta Ayabe, Ken-ichi Tanaka(Meiji University, Japan)
PS1-6 Embedding used binary number conversion into a Color image
Hiroaki Oguma, Ken-ichi Tanaka (Meiji University, Japan)

Room 304
10:00-11:00 OS5 Analysis of Randomized Algorithms
Chair: Hiroshi Furutani (University of Miyazaki, Japan)
Co-Chair: Ichihi To (University of Miyazaki, Japan )OS5-1 Markov Chain Analyses of Random Local Search and Evolutionary Algorithm
Hiroshi Furutani , Hiroki Tagami, Ichihi To, Makoto Sakamoto( University of Miyazaki, Japan)
OS5-2 Runtime Analysis of OneMax Problem in Genetic Algorithm
Ichihi To, QuinLian Ma, Makoto Sakamoto, Hiroshi Furutani(University of Miyaaki),Yu-an Zhang ( QinghaiUniversity, China)
OS5-3 A Model for Low-Frequency Burst in Subthalamic Nucleus Neuron
S. Kubota (Yamgata Univ., Japan)
J. E. Rubin (Pittsburgh Univ., USA)

13:00-14:40 OS1 Intelligent Control
Chair: Yingmin Jia (Beihang University, P.R.China )Co-Chair: Chaoli Wang (University of Shanghai for Science and Technology, China )OS1-1 Finite-Time Stabilization for Nonholonomic Chained Form Systems
Communication Delay
Hengjun Zhang, Chaoli Wang
(University of Shanghai for Science and Technology, China)

with

OS1-2 Sliding Mode Variable Structure Control for Magnetic Levitation Vehicles
Juanjuan He, Yingmin Jia( Beihang University, China)©ICAROB 2014

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OS1-3 Variable-Poled Tracking Control of a Two-Wheeled Mobile Robot using Differential Flatness
Liming Chen, Yingmin Jia (Beihang University, China)
OS1-4 Adaptive Consensus Control of Multi-Agent Systems with Large Uncertainty and Time
DelaysDongxu Zou, Weicun Zhang(University of Science and Technology Beijing, China)
OS1-5 Iterative Learning Control for Overhead Crane Systems
Xuhui Bu, Fuzhong Wang , Sanyi Li, Fashan Yu (Henan Polytechnic University, China)

15:00-17:00 OS2 Environment Navigation and Localization
Chair: J. M. Lee (Pusan National University, Korea)
OS2-1 A Hybrid Path Planning Algorithm for UGV by Combining A* and B-spline
Curve Equation
Min-Ho Kim, Hee-Mu Lee, Min-Cheol Lee(Pusan National University, Korea)
OS2-2 The actuator device design for the implementation of haptic joystickDong-hyuk Lee, Sunkyun Kang, Bo-yeon hwang, Ki-jung Kim and Jangmyung Lee(Pusan National University, Korea)
OS2-3 Design of Fuzzy Controller using Variable Fuzzy Membership Function Factors for
Inverse Ball Drive Mobile Robot
Keon-woo Jeong, Shin-nyeong Heo, Seung-Ik Hwang, Han-Dong Yoo and Jangmyung Lee(Pusan National University, Korea)
OS2-4 Error Correction of Angular Velocity for Gyroscope using Genetic
Algorithm and FIR filter
Jaeyong Kim, Hyunhak Cho, Sungshin Kim (Pusan National University, Korea)
OS2-5 Control of Robot Arm with Sterilization System for Ballast Water
Seung-Hwa Baek, Dong-Hyun Kim, Hee-Je Kim (Pusan National University, Korea)
OS2-6 Performance Study for Vehicle Infotainment System
Heung-in Park, Min-gyu Kim, Jeong Hee-In, Kang-il Park, In-uk Lee,
Dong-Ju Lee and Jangmyung Lee(Pusan National University)

Room305
10:00-11:20 OS8 Software Development Support Method

Chair: Tetsuro Katayama（University of Miyazaki,Japan）
Co-Chair: Makoto Sakamoto（University of Miyazaki,Japan）
OS8-1 Proposal of a Visualizing Method of Data Transitions to Support Debugging for Java
Programs©ICAROB 2014

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Hiroto Nakamura, Tetsuro Katayama, Hisaaki Yamaba, Naonobu Okazaki(University of Miyazaki, Japan)
Yoshihiro Kita (Kanagawa Institute of Technology, Japan)
OS8-2 Proposal of a Method to Build Markov Chain Usage Model from UML Diagrams forCommunication Delay Testing in Distributed Systems
Zhijia Zhao, Tetsuro Katayama, Hisaaki Yamaba, Naonobu Okazaki(University of Miyazaki, Japan)
Yoshihiro Kita (Kanagawa Institute of Technology, Japan)
OS8-3 Proposal of a Supporting Method for Debugging to Reproduce Java Multi-threaded
Programs by Petri-Net
Shoichiro Kitano, Tetsuro Katayama, Hisaaki Yamaba, Naonobu Okazaki(University of Miyazaki, Japan)
Yoshihiro Kita (Kanagawa Institute of Technology, Japan)
OS8-4 Proposal of a Supporting Method to Generate a Decision Table from the Formal
Specification
Kenta Nishikawa, Tetsuro Katayama, Hisaaki Yamaba, Naonobu Okazaki(University of Miyazaki, Japan)
Yoshihiro Kita (Kanagawa Institute of Technology, Japan)

13:00-14:40 OS3 Facial Expression Analysis, Music Recommendation and Augmented
Reality
Chair: Yasunari Yoshitomi (Kyoto Prefectural University, Japan )Co-Chairman: Masayoshi Tabuse (Kyoto Prefectural University, Japan)
OS3-1 Development of a Campus Guide System based on Augmented Reality
M. Tabuse, A. Tada (Kyoto Prefectural University, Japan)
OS3-2 Facial Expression Analysis While Using Video Phone
T. Asada, Y. Yoshitomi, R. Kato, M. Tabuse (Kyoto Prefectural University, Japan)
A. Tsuji, N. Kuwahara (Kyoto Institute of Technology, Japan),
J. Narumoto (Kyoto Prefectural University of Medicine)OS3-3 Method of Facial Expression Analysis Using Video Phone and Thermal Image
Y. Yoshitomi, T. Asada, R. Kato, M. Tabuse (Kyoto Prefectural University, Japan)
OS3-4 Facial Expression Recognition Using Thermal Image Processing and Efficient
Preparation of Training-data
Y. Nakanishi (ITOKI CORPORATION,Japan)
Y. Yoshitomi, T. Asada, M. Tabuse (Kyoto Prefectural University, Japan)
OS3-5 Music Recommendation System through Internet for Improving Recognition Ability Using
Collaborative Filtering and Impression Words
Y. Yoshitomi, T. Asada, R. Kato, Y. Yoshimitsu, M. Tabuse(Kyoto Prefectural University, Japan),
N. Kuwahara (Kyoto Institute of Technology, Japan)©ICAROB 2014

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J. Narumoto (Kyoto Prefectural University of Medicine)

15:00-16:40 OS4 Applications in Kansei Engineering
Chair: Tetsuo Hattori (Kagawa University, Japan)
Co-chair: Yoshiro Imai (Kagawa University, Japan)
OS4-1 Kansei Engineering based Evaluation for an e-Learning System with IP-based Network
Design and Animation
Yoshiro Imai, Chiaki Kawanishi, Tetsuo Hattori (Kagawa University, Japan)
OS4-2 Model Introduced SPRT for Structural Change Detection of Time Series (I)Yoshihide KOYAMA, Tetsuo HATTORI (Kagawa University, Japan)
Hiromichi KAWANO (NTT AT, Japan)
OS4-3 Model Introduced SPRT for Structural Change Detection of Time Series II)--- Kansei Channel Corresponding to the SPRT --Yoshihide KOYAMA, Tetsuo HATTORI (Kagawa University, Japan)
Hiromichi KAWANO (NTT AT, Japan)
OS4-4 Automated Color Image Arrangement Method and Kansei Impression
Yusuke Kawakami, Tetsuo Hattori, Yoshiro Imai, Haruna Matsushita(Kagawa University, Japan)
Hiromichi Kawano (NTT AT, Japan),
R.P.C. Janaka Rajapakse (Tainan National University of the Arts,Taiwan)
OS4-5 Novel Transistor by Opt-coupling of LED and PD and its Application to Audio Amplifier
Junichi Fujita, Tetsuo Hattori, Daisuke Sato, Kensho Okamoto(Kagawa University, Japan)

January13 (Monday)
9:30- Registration

Room 304
10:00-11:40 GS6 Robotics Ⅰ
Chair: Fengzhi Dai (Tianjin University of Science and Technology, China)
GS6-1 Study on the pose under complex multiple targets environment for
the industrial robot based on machine vision
Jiwu Wang, Xianwen Zhang, Huazhe Dou (Beijing Jiaotong University, China)
Sugisaka Masanori (Nippon Bunri University, Oita)©ICAROB 2014

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GS6-2 Research and Development of an Intelligent Robot of Medical Assistance Based on
Embedded RTOS
Yuan Li, Fengzhi Dai, Junhong Xi, Binyao Li, Guodong You(Tianjin University of Science and Technology, China)
GS6-3 High Acceleration Robotic Arm for Dynamic and Dexterous Manipulationof Deformable Object
Hiroaki Seki, Hiroki Shibata, Yoshitsugu Kamiya, Masatoshi Hikizu(Kanazawa University, Japan)
Khairul Salleh Mohamed Sahari (Universiti Tenaga Nasional, Malaysia)
GS6-4 Switching based controller algorithm design for uncertainties rejection in smoothtakeoff / landing and of quad-rotor
M. Hassan Tanveer, D. Hazry, S. Faiz Ahmed, M. Kamran Joyo, Faizan. A. Warsi, A.T. Hussain(Universiti Malaysia Perlis) (UniMAP)
GS6-5 Parameters of Gas-Liquid Flow Distribution Uniformity in Upward Multi-Pass
Channels
Zuradzman M. Razlan, R. Heng, D. Hazry, A.B. Shahriman, Khairunizam WAN,
S. Faiz Ahmed, Nazrul H. ADNAN (Universiti Malaysia Perlis, Malaysia)
M. Hirota, N. Maruyama, A. Nishimura (Mie University, Japan)
H. Hisyam (Lean Applied Pte. Ltd.,Malaysia)

13:00-14:40 OS6 Empirical research on Network and MOT
Chair: T. Ito (Ube National College of Technology)
Co-Chair: S. Matsuno (Ube National College of Technology)
OS6-1 An Empirical Examination of Inter-firm Capital Relationships in Mazda’s Yokokai using
the IDE Spatial Model
M. Sakamoto (University of Miyazaki, Japan),
S. Tagawa, T. Ito, S. Matsuno (Ube National College of Technology, Japan)
R. Mehta (New Jersey Institute of Technology, U.S.A)
V. Berdonosov (Komsomolsk-on-Amur State University of Technology, Russia)
S. Ikeda (University of Miyazaki, Japan)
OS6-2 A Comparative Study of Transactional Network between Kyohokai and Yokokai
M. Sakamoto (University of Miyazaki, Japan)
M. Hasama, T. Ito, Y. Uchida (Ube National College of Technology, Japan)
R. Mehta (New Jersey Institute of Technology, U.S.A)
Y. Park (Prefectural University of Hiroshima, Japan)
S. Ikeda (University of Miyazaki, Japan)
OS6-3 A path analytic model and measurement of the relationships between green supply
chain management implementation and corporate performance
S. Matsuno, M. Hasama, Y. Uchida, T. Ito (Ube National College of Technology, Japan)©ICAROB 2014

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OS6-4 A Study of Open Source Cloud System for Small and Medium Enterprise
Y. Uchida, S. Matsuno, T. Ito, M. Hasama (Ube National College of Technology, Japan)
M. Sakamoto (University of Miyazaki, Japan)
OS6-5 Visualization of Patent Strategies in Japanese ICT Companies Based on Text-Mining
T. Tokumitsu, T. Okada, I. Nakaoka (Ube National College of Technology, Japan)
Y. Park (Prefectural University of Hiroshima, Japan)

Room305
10:00-12:00 OS7 Intelligence in Biological Systems
Chair: Jun Nishii (Yamaguchi University, Japan)
Co-Chair: Hiroshi Matsuno (Yamaguchi University, Japan)
OS7-1 Dissipative Particle Dynamics of shape changes of vesicle
Yoshiyuki Oofuji, Naohito Urakami, Takashi Yamamoto (Yamaguchi University, Japan)
Masayuki Imai (Tohoku University, Japan)
OS7-2 Intelligent mechanisms in E. coli in processing carbon sources
Zhongyuan Tian, Hiroshi Matsuno (Yamaguchi University, Japan)
OS7-3 An autonomous propagation of ciliary metachronal wave on elastic surface ofParamecium cells.
N. Narematsu, Y. Iwadate (Yamaguchi University)OS7-4 An analysis of the synergy in a ball throwing task
Hiroshi KIMURA, Jun NISHII (Yamaguchi University, Japan)
OS7-5 A low dimensional feedback control model that exploits abundant degrees of freedom
Jun Nishii, Tohru Hamamura (Yamaguchi University, JAPAN)
OS7-6 Extension of Genetic Toggle Switch Based on the Effective Search of State Transitions
M. Sugii, A. Fauré, H. Matsuno (Yamaguchi University, Japan)

13:00-14:20 OS9 Foundation of computation and its application
Chair: Makoto Sakamoto (University of Miyazaki, Japan)
Co-Chair: Tetsuro Katayama (University of Miyazaki, Japan)
OS9-1 Hierarchy Based on Neighborhood Template about k-Neighborhood Template A-Type
Three-Dimensional Bounded Cellular Acceptor
Makoto Sakamoto, Makoto Nagatomo, Xiaoyang Feng, Tatsuma Kurogi, Tuo Zhang,Satoshi Ikeda, Masahiro Yokomichi, Hiroshi Furutani (University of Miyazaki, Japan)
Takao Ito, Yasuo Uchida (Ube National College of Technology, Japan)
Tsunehiro Yoshinaga(Tokuyama College of Technology, Japan)©ICAROB 2014

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OS9-2 Hierarchy Based on Configuration-Reader about k-Neighborhood Template A-Type
Three-Dimensional Bounded Cellular Acceptor
Makoto Sakamoto, Tuo Zhang, Tatsuma Kurogi, Makoto Nagatomo, Xiaoyang Feng,
Satoshi Ikeda, Masahiro Yokomichi, Hiroshi Furutani (University of Miyazaki, Japan)
Yasuo Uchida, Takao Ito (Ube National College of Technology, Japan)
Tsunehiro Yoshinaga (Tokuyama College of Technology, Japan)
OS9-3 Handicap of Othello GameY. Kato, S. Ikeda, M. Sakamoto (University of Miyazaki, Japan)
T. Ito ( Ube National College of Technology, Japan)
OS9-4 On Parameter Setting in Identifying the Same Languages Involved in Different
Language Data
Ren Wu (Yamaguchi Junior College, Japan)
Hiroshi Matsuno (Yamaguchi University, Japan)©ICAROB 2014

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ABSTRACTS
IS
IS1-1 Playware Research – Methodological Considerations
Henrik Hautop Lund (Centre for Playware, Technical University of Denmark, Denmark)Several sub-disciplines of engineering are driven by the researchers’ aim of providing
positive change to the society through their engineering. Based on two decades researchin developing engineering systems with a societal impact (e.g. in robotics, embodied AI,
and playware), in this paper we suggest a cyclic research method based on a mix between
participatory and experimental processes. In particular, inspiration from the action
research method applied to interdisciplinary technology development becomes aparticipatory approach characterized by rapid prototyping cycles which allow iterativetechnology specification and development together with people in their real world
environment. With the mixed research method, we suggest that there are cases, whereapproaches from the positivistic and interpretivistic epistemologies can and should bemerged. We exemplify this with health care technology and playware.

IS1-2 ALife for Real and Virtual Audio-Video Performances
Luigi Pagliarini1,2 Henrik Hautop Lund1( Centre for Playware, Technical University of Denmark, Denmark)(2 Academy of Fine Arts of Macerata, Italy)
1

MAG is an electronic art piece in which an ALife software attempts to “translate” musicalexpression into a corresponding static or animated graphical expressions. The mechanism
at the base of such “translation” is based on artificial learning techniques. MAG use
population of neural networks that can use both genetic and reinforcement learningalgorithms to evolve appropriate behavioral answers to inputs. The combination of
artificial evolution and the flows of a repeated song or different musical tunes allows thesoftware to obtain a special relationship between sound waves and the aesthetics ofconsequent graphical result. Further, we are exploring the concept of run-time creation ofboth music and graphical expression. To do that we use MusicTiles app that allows users
to remix music by connecting musical building blocks. When combining MusicTiles app
and MAG software, we provide the possibility to melt both musical expression andgraphical expression in parallel and at run-time, and therefore creating a audio-video
performance that is always unique.

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IS1-3 Heart-pulse Biofeedback in Playful Exercise using a Wearable device andModular Interactive Tiles
Tomoya Shimokakimoto1, Henrik H. Lund2, Kenji Suzuki3(1 University of Tsukuba, Japan)
2( Technical University of Denmark, Denmark)(3 University of Tsukuba/ JST, Japan)
We developed a playful biofeedback system using a wearable device andmodular interactive tiles. We suppose that patients could regulate exercise
intensity on their own through biofeedback. We propose biofeedback playsystem called “bioToys” based on exercise with the modular interactive
tiles. The system consists of a wearable device and modular interactive
tiles. We combine the two systems to provide users with heart pulse
biofeedback in playful exercise. We show that using the developed system
it is possible for the users to regulate the exercise intensity on their own
with biofeedback, and also possible to analyze exercise activity using
number of steps on the tiles and heart beat rate.

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OS1 Intelligent Control
OS1-1 Finite-Time Stabilization for Nonholonomic Chained Form Systems
with Communication Delay
Hengjun Zhang, Chaoli Wang
(University of Shanghai for Science and Technology, P.R.China)
In this paper, the problem of finite-time stabilization is developed for nonholonomic
chained form systems with communication delay in the input. The finite-time control
laws are presented by utilizing the switching control strategy and the theory of finite-timestability, which can make the states of the nonholonomic chained form systems toconverge from any non-equilibrium state to the equilibrium or a given point in a finite
time. Finally, the simulation results show the effectiveness of the proposed control
approach.

OS1-2 Sliding Mode Variable Structure Control for Magnetic Levitation Vehicles
Juanjuan He, Yingmin Jia(Beihang University, P.R.China)

This paper focuses on stability control for the levitated positioning of the magnetic
levitation vehicle system. For the nonlinear magnetic levitation system model, the output
feedback linearization method is first employed to derive a global linearization error
model. However, there exists uncertain item in the error model. To stabilize this error
model, the adaptive sliding mode variable structure control method is used to design
stability controller. Simulations show that the magnetic levitation system can be stable
and track the desired signals quickly under the proposed control scheme.

OS1-3 Variable-Poled Tracking Control of a Two-Wheeled Mobile Robotusing Differential Flatness
Liming Chen, Yingmin Jia( Beihang University, P.R.China)
This paper investigates the tracking controller design of a two-wheeled mobile robot in its
kinematic model and dynamic model. Differential flatness and linear time-varying(LTV)systems’ PD-spectral theory are used. Based on differential flatness, original system istransformed via a state prolongation and state transformation into a normal form to apply
feedback linearization. Then using PD-spectral theory, variable poles of tracking error
dynamics are assigned to realize trajectory tracking stability. Finally, simulation results
are presented to demonstrate the feasibility and effectiveness of the proposed method.

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OS1-4 Adaptive Consensus Control of Multi-Agent Systems with Large
Uncertainty and Time Delays
Dongxu Zou, Weicun Zhang(University of Science and Technology Beijing, China)
A weighted multi-model adaptive control method is proposed to achieve consensus ofmulti-agent
system with large parameter perturbation and communication delays, inwhich Hinf control is adopted to construct the controller set. Moreover a simple andeffective weighting algorithm is also presented. The simulation results demonstrate theeffectiveness of the proposed method.
Fig. 1 simplified
undirected graph

OS1-5

Iterative Learning Control for Overhead Crane Systems
Xuhui Bu, Fuzhong Wang , Sanyi Li, Fashan Yu( Henan Polytechnic University, P.R.China)

In many factories, overhead crane often transfers the same loads from one place toanother following a predefined position and speed diagrams. This unique feature offers
overhead crane an opportunity of improving its performance through learning iteratively.
In this paper, we apply the iterative learning control approach to overhead crane systems.
Based on the linearization dynamic model of overhead crane, an ILC scheme contains
both feedforward learning control part and state feedback control is proposed. By
providing a 2D formulation, the ILC design for crane systems can be transformed into the
problem of state feedback control for 2-D systems described by Roesser models. It isshown that the proposed approach can guarantee the trolley position asymptoticallyconverges to its desired profile with small swing angle. Simulations are illustrated toshow the feasibility and effectiveness of the proposed approach.

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OS2 Environment Navigation and Localization
OS2-1 A Hybrid Path Planning Algorithm for UGV by
Combining A* and B-spline Curve Equation
Min-Ho Kim, Hee-Mu Lee and Min-Cheol Lee(Pusan National University, Busan, South Korea)
This article presents a hybrid path planning algorithm to make a smooth path for an
UGV. A main theme of the proposed algorithm is to combine of the A* algorithm andthe B-spline curve equation. A* is one of the well-known path planning algorithm whichfinds the optimal path on the given map by using the heuristic cost function. However,since A* is based on the grid map, the result path consists of straight lines with only 8
directions. It's not suitable of UGV's navigation. Therefore in this paper to overcome
this issue, B-spline curve equation is proposed to make a smooth and continuous pathwith control points which are selected from the A* result path. And the optimal control
point selection algorithm is proposed to make the hybrid path. At last, to verify the
proposed algorithm, the hybrid path results are compared with A* algorithm by using adeveloped simulation program.

OS2-2 The actuator device design for the implementation of haptic joystickDong-hyuk Lee, Sun-kyun Kang, Bo-Yeon Hwang, Ki-jung Kim, Jang-myung Lee(Pusan National University, Korea)
In this paper, we designed the actuator device for haptic implementation in the general
joystick. Each linear servo actuator as pillars to support the joystick mounting plate canmove up and down. And this movement will generate the inclination of the plate. The
haptic can be implemented by the change of the inclination. The actuator device wasdesigned through the performance analysis of actuator device structure. The haptic
will represent information from obstacle cognition device by using the designed actuator
device. The haptic performance of the actuator device was verified through experiments.

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OS2-3

Design of Fuzzy Controller using Variable Fuzzy Membership
Function Factors for Inverse Ball Drive Mobile Robot
Keon-woo Jeong, Shin-nyeong Heo, Seung-Ik Hwang, Han-Dong Yoo, Jang-myung Lee(Pusan National University, Korea)

In this paper, a fuzzy controller for a inverse ball drive mobile robot is implemented tohave more stable balancing capability than the conventional control system. Fuzzy control
structure is chosen for a inverse ball drive mobile robot, and fuzzy membership function
factors for the control system are obtained for 3 specified weights using a trial-and-error
method. Next a linear Interpolation method is employed to generate fuzzy membershipfunction factors for more stable control performance when the weight is arbitrarily
selected. Through some experiments, we find that the proposed fuzzy controller using theneural network is superior to the conventional fuzzy controller.

OS2-4Error Correction of Angular Velocity for Gyroscope using Genetic
Algorithm and FIR filter
Jaeyong Kim, Hyunhak Cho, Sungshin Kim
Pusan National University, Korea

This paper is research which sensitivity of gyro sensor is optimization. Recently, theMEMS-gyroscope is usually used at mobile robots and AGV by development of MEMS
technology. However, the MEMS-gyroscope has measurement error, cumulate error, bias
drift, etc. To reduce these errors, measurement of angular velocity using
MEMES-gyroscope required high precision and exactly sensitivity. The sensitivity, is aconstant to change angular velocity form ADC value, is changed by external factors as
installed location and gradient because the MEMS-gyroscope measure angular velocity
with inertia. Therefore, this paper proposes to reduce variance of ADC value and to
optimize the sensitivity of MEMS-gyroscope using genetic algorithm. To verify
performance of proposed method, we attached MEMS-gyroscope to automatic guided
vehicle (AGV) of fork type. In experimental result, we verified that the sensitivity using
the proposed method is accurate than the sensitivity in specification.

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OS2-5

Control of Robot Arm with Sterilization System for Ballast Water
Seung-Hwa Baek, Dong-Hyun Kim and Hee-Je Kim(Pusan National University,Korea)

The inadvertent transfer of harmful aquatic organisms and pathogens in the ballast waterof ships has been determined to have caused a significant adverse impact to many of theworld’s coastal regions. Loading and discharge of ballast water is an essential part of aship’s operation, and is fundamental to maintaining safe operations under different
onditions of load. However, large vessels require thousands of tonnes of water to ensurestability and manoeuvr ability, and the environmental impacts of this can be considerable.
These impacts result from the fact that the ballast water can contain hundreds of different
species, many of which can have serious ecological, economic and public health effects
if transferred to regions where they are not native. The recognition of these effects has
made ballast management increasingly important for protection of the marine
environment. As a means to prevent, we developed a system using high voltage- highfrequency pulsed power, so called arc discharge, when this discharge happens, theelectrodes are oxidized and consumed. Regarding this point, we wanted to apply robot
arm to supply continuous electrode when it consumed by arc discharge.

OS2-6 Performance Study for Vehicle Infotainment System
Heung-in Park, Min-gyu Kim, Jeong Hee-In, Kang-il Park, In-uk Lee, Dong-Ju Lee, Jang-myung Lee(Pusan National University, Korea)
CPU performance, booting time and navigation widget are improved by the new designconcepts. These points are providing some solutions that can overcome the risks of open
architecture system in the Automotive. The improvement was verified with each of the test
results.

OS3 Facial Expression Analysis, Music Recommendation and Augmented Reality
OS3-1
Development of a Campus Guide System based on Augmented Reality
M. Tabuse, A. Tada
(Kyoto Prefectural University, Japan)
This paper presents a campus guide system based on augmented reality (AR). AR is atechnology of displaying CG overlaid on a real scene. AR is a fusion of a real world and avirtual world of human interfaces. We propose a campus guide system based onmarkerless AR. This system displays information of a university overlaid on a real sceneusing a note PC and a web camera. In addition, this system presents a variety of events,for example a university festival and a welcome party, as 3D virtual objects overlaid on areal scene. Using this system, visitors can understand a university life more deeply. We
have developed a campus guide system based on Parallel Tracking and Multiple Map
system and construct 3D virtual objects using Visual Structure from Motion System. And
we have confirmed the effectiveness of the system in our campus.

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

OS3-2 Facial Expression Analysis While Using Video Phone
T. Asada1, Y. Yoshitomi1, A. Tsuji2 , R. Kato1, M. Tabuse1, N. Kuwahara2, J. Narumoto3(1 Kyoto Prefectural University, Japan)(2 Kyoto Institute of Technology, Japan)
3( Kyoto Prefectural University of Medicine)
We have proposed a method for analyzing facial expressions of a person while speaking with use ofa video phone system (Skype). The video is analyzed using image processing software library(OpenCV) and the newly proposed feature vector of facial expression, which is extracted in themouth-part area with use of 2D-DCT performed for each domain having 8  8 pixels. The facial
expression intensity defined as the norm of difference vector between the feature vector of neutral
facial expression and that of observed one can be used for analyzing a chance of facial expression.
Combining the video signal obtained from the Skype with the sound signal obtained with the otherway, we can distinguish the facial expression with speaking from that without speaking. The
judgment of speaking is performed with a threshold of the sound intensity. Some experimental
results show the usefulness of the proposed method.

OS3-3 Method of Facial Expression Analysis Using Video Phone and Thermal
Image
Y. Yoshitomi, T. Asada, R. Kato, and M. Tabuse(Kyoto Prefectural University, Japan)
In this study, using thermal image processing, we have proposed a method for analyzing facial
expressions of a person while speaking with a video phone system (Skype). The newly
proposed feature vector of facial expression, which is extracted in the mouth-part area withuse of 2D-DCT performed for each domain having 8  8 pixels. The facial expression
intensity defined as the norm of difference vector between the feature vector of neutral facial
expression and that of observed one can be used for analyzing a chance of facial expression.
Combining the video signal obtained from the Skype with the sound signal obtained with theother way, we can distinguish the facial expression with speaking from that without speaking.
The judgment of speaking is performed with a threshold of the sound intensity. Someexperimental results show the usefulness of the proposed method.

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

OS3-4 Facial Expression Recognition Using Thermal Image Processing andEfficient Preparation of Training-data
Y. Nakanishi1, Y. Yoshitomi2, T. Asada2, M. Tabuse2(1 ITOKI CORPORATION, Japan)
2( Kyoto Prefectural University, Japan)
We investigated the influence of training data on the facial expression accuracy using thetraining data of “taro,” whose first and last vowels are /a/ and /o/, for the three intentional
facial expressions of “angry,” “sad,” and “surprised,” and the training data of 25 pairs ofvowels for the two intentional facial expressions of “happy” and “neutral.” Using the
proposed method, the facial expressions of one subject were discriminable withaccuracies of 100%, 70.0%, and 47.2% for “taro,” “koji” (the first and last vowels of
which are /o/ and /i/), and “tsubasa” (the first and last vowels of which are /u/ and /a/),respectively, for the three facial expressions of “happy,” “neutral,” and “other” when oneof the five intentional facial expressions of “angry,” “happy,” “neutral,” “sad,” and“surprised” was exhibited.

OS3-5 Music Recommendation System through Internet for Improving
Recognition Ability Using Collaborative Filtering and Impression Words
Y. Yoshitomi1, T. Asada1, R. Kato1,Y. Yoshimitsu1, M. Tabuse1, N. Kuwahara2, J. Narumoto3(1 Kyoto Prefectural University, Japan)(2Kyoto Institute of Technology, Japan)(3Kyoto Prefectural University of Medicine)
In our previously reported system, the recommendation process using collaborative filtering
was terminated when no users in the reference list have the same preference forrecommended music as that of a new user. Based on the scores for impression words, thesecond recommendation process finds the most similar music to the successfully
recommended music among music that has not yet been recommended. In the present study,
based on our previously reported system, we propose a system for music recommendation
through the Internet using a videophone system (Skype). The effectiveness of the proposedsystem is verified. The recommendation accuracy of the proposed system was 90.6% for 10
trials by five elderly subjects. The average number of recommended pieces of music for the
subjects by the proposed system was 14.9 per trial.

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

OS4 Applications in Kansei Engineering
OS4-1 Kansei Engineering based Evaluation for an e-Learning Systemwith IP-based Network Design and Animation
Yoshiro Imai, Chiaki Kawanishi, Tetsuo Hattori(Kagawa University, Japan)
An e-Learning system has been developed, which is designed for network education withfacilities of specification of IP-based network topology and demonstration of
packet-transferring animation. This system can execute on the major browsers by meansof accessing a specific Web server. It is frequently used in some lectures of university
from beginners of network to the students of information related course. Kansei
Engineering approach is utilized and applied in order to improve and evaluate the
e-Learning system.

OS4-2 Model Introduced SPRT for Structural Change Detection of Time Series(I)Yoshihide KOYAMA1, Tetsuo HATTORI1, Hiromichi KAWANO2(1Kagawa University, Japan)(2NTT AT, Japan)
Previously, we have proposed a method applying Sequential Probability Ratio Test
(SPRT) to structural change detection problem of ongoing time series data. In this paper,
we introduce a structural change model with Poisson process into the system that outputs
a set of time series data, moment by moment. Then we can calculate the value of P(Z |H1 ) that denotes the probability of observing the data Z under the condition that
H1(alternative hypothesis) is true. Also, we concretely show the theory of change
detection of time series by the model introduced SPRT.

y  2 x1  3x2  10  yy  3x1  3x2  10  

180change point tc = 70

160
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100
80
60
40
20

t

0
0

10

20

30

40

50

60

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OS4-3 Model Introduced SPRT for Structural Change Detection of Time SeriesII)
Kansei Channel Corresponding to the SPRT ---

90

100

---

Yoshihide KOYAMA1, Tetsuo HATTORI1, Hiromichi KAWANO2(1Kagawa University, Japan)(2NTT AT, Japan)
We present an application of the notions in Information Theory to Kansei Engineering
toward a mathematical methodology for the analysis in the Kansei Engineering field. In
this paper, first, we propose a definition of correlation using the mutual information inInformation Theory. Second, we explain a relation between the Bayes’ Updating using thenotion of binary channel and Sequential Probability Ratio Test (SPRT). Moreover, weshow the Channel Matrix corresponding to the SPRT where a model introduced.

x
1 P( y1 | x2 )x2

©ICAROB 2014

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y
1

y
2

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

OS4-4 Automated Color Image Arrangement Method and Kansei Impression
Yusuke Kawakami1, Tetsuo Hattori1, Yoshiro Imai1, Haruna Matsushita1,
Hiromichi Kawano2, R.P.C. Janaka Rajapakse3(1Kagawa University, Japan),(2NTT AT, Japan),3( Tainan National University of the Arts, Taiwan)
This paper proposes a new color image arrangement method using an elastic transform onsome kinds of axes. In this paper, we present the principle of our method using HMGD(Histogram Matching based on Gaussian Distribution). And we describe that the
automated method applies the HMGD to input color image only when the image has
single-peakedness in its histogram on the focused axis. We also show that the method
gives a good Kansei effect in the case of applying the HMGD onto Lightness axis.

OS4-5 Novel Transistor by Opt-coupling of LED and PD and its Application to Audio
AmplifierJunichi Fujita, Tetsuo Hattori, Daisuke Sato, Kensho Okamoto(Kagawa University, Japan)
This paper presents a simple transistor-like amplification function by the optical
coupling of LED (Light Emitting Diode) and Photodiode where the Photodiode current
by photo electromotive force is feed backed into the LED, in the same way as the case ofcommon emitter circuit of the conventional Bipolar Transistor. And also, this paper showsthat an audio amplifier can be realized in a simple form. Since the proposed audio
amplifier provides good sound with very low noise characteristic, we consider that theaudio system becomes a promising Kansei product.

Rb1
C

Rb2

Si-PD

solar cell

LED

LED

preamplifier

main amplifier

OS5 Analysis of Randomized Algorithms
OS5-1 Markov Chain Analyses of Random Local Search and Evolutionary Algorithm
Hiroshi Furutani, Hiroki Tagami, Ichihi To, Makoto Sakamoto(University of Miyazaki, Japan)
In this paper, we report the computational complexity of algorithms(1+1)EA and Random Local Search (RLS). It has been noted the close
resemblance of RLS with the coupon collector’s problem (CCP). The CCP
has a long history of probabilistic research, and many interesting results
are obtained. This study makes use of such results with somemodifications. We also show the results for (1+1)EA and RLS using
absorbing Markov chain.

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Rc2
C output
Vo

C

input

Vi

Rc1

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

OS5-2 Runtime Analysis of OneMax Problem in Genetic Algorithm
Ichihi To1, QuinLian Ma1, Makoto Sakamoto3, Hiroshi Furutani1,Yu-an Zhang2(1University of Miyazaki, Japan)(2 QinghaiUniversity, China)
Genetic algorithms (GAs) are stochastic optimization techniques that simulate thebiological evolution. Theoretical study of the evolution of the genetic algorithm is very
important for the application of GA. We have studied the effects of stochastic fluctuationin the process of GA evolution. We consider the task of estimating the hitting time of the
optimal solution in GA for OneMax problem. This task is performed by using the Markov
chain representing the behavior of population in the stationary state of evolution.

OS5-3 A Model for Low-Frequency Burst in Subthalamic Nucleus Neuron
S. Kubota1, J. E. Rubin2(1Yamgata Univ., Japan)(2Pittsburgh Univ., USA)
Bursting oscillation in the subthalamic nucleus (STN) is suggested to be linked to themotor symptoms in Parkinson’s disease. In this study, to investigate the effects ofinteractions among STN, the cortex, and globus pallidus (GP), on the burst
characteristics, we examine a model of STN neuron that contains NMDA conductances
activated through the cortical inputs and GABAergic conductances associated with GP
activities. To reproduce cortical slow oscillation consisting of two phases, the NMDA
conductances are activated by inhomogeneous Poisson spikes whose firing rate isstochastically and periodically modulated. The results show that a higher level of GABA
inhibition leads to strong burst with larger coefficient of variation of interspike intervals,
while the strengthening of NMDA activation mainly acts to increase the firing rate.

Burst response of
the STN neuron
model

OS6 Empirical research on Network and MOT
OS6-1 An Empirical Examination of Inter-firm Capital Relationships in Mazda’s
Yokokai using the IDE Spatial Model
M. Sakamoto1, S. Tagawa2, T. Ito2, S. Matsuno2, R. Mehta3, V. Berdonosov4, S. Ikeda1(1 University of Miyazaki, Japan)
2( Ube National College of Technology, Japan)(3 New Jersey Institute of Technology, U.S.A)
4( Komsomolsk-on-Amur State University of Technology, Russia
In this paper, we focused on examining the capital relationships in
Yokokai, the Mazda’s Keiretsu. Employing regression analysis, threesignificant indices, influence, degree, and efficiency, were selected fromnine indices, including others that include dyadic redundancy, dyadic
constraint, effective size, constraint, hierarchy, and density. We propose a
new approach, called IDE spatial model, to calculate the strength of theinter-firm’s relationships. In order to ascertain the rational inter-firm
relationships, network indices and corporate performance are analyzed.
This research suggests a new perspective to examine the rational
inter-firm relationship that can be used in any network organization.

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

OS6-2 A Comparative Study of Transactional Network between Kyohokai and
Yokokai
M. Sakamoto1, M. Hasama2, T. Ito2, Y. Uchida2, R. Mehta3, Y. Park4, S. Ikeda1(1 University of Miyazaki, Japan)
2( Ube National College of Technology, Japan)(3 New Jersey Institute of Technology, U.S.A)(4 Prefectural University of Hiroshima, Japan)
Consistent with the stream of research on the coordination of internal
resources and external inter-firm relationships, the purpose of this research is
to identify and contrast the best practices of Toyota to Mazda using acomparative approach. This paper reviews relevant studies of networkorganizations to focus on the differences between Kyouhokai and Yokokai.
Specifically, it makes a contribution to the literature by proposing a newperspective to identify the determinants of corporate performance and clarify
the difference among the external inter-firm’s relationships between Kyohokai
and Yokokai, thus ascertaining the rational structure of networkorganizations.

OS6-3 A path analytic model and measurement of the relationships between green
supply chain management implementation and corporate performance
S. Matsuno, M. Hasama, Y. Uchida, and T. Ito(Ube National College of Technology, Japan)
We propose and empirically test a model of the relationships between green supply chain
management (GSCM) activities and corporate performance. Five constructs, namely,environmental commitment, supplier collaboration, supplier assessment, information
sharing among suppliers, and business process improvement are used to form a structuralmodel explaining the environmental and economic performance. The model was analyzed
using the data from a survey of sample of manufacturing firms in Japan. The results
suggest that the degree of supplier collaboration has an influence on the environmental
performance directly. While, the impact of supplier assessment on the environmental
performance is mediated by the information sharing and/or business process
improvement.

Supplier
collaboration

Environmental
commitment

Supplier
assessment

OS6-4 A Study of Open Source Cloud System for Small and Medium Enterprise
1

Y. Uchida1, S. Matsuno , T. Ito1, M. Hasama1, M. Sakamoto2(1Ube National College of Technology, Japan)(2University of Miyazaki, Japan)
The use of cloud services among SMEs remains low, and R&D and technical support for
SMEs is an urgent problem. Even so, there have been almost no academic studies on therelationship between cloud computing and SME information infrastructure, or on the
future direction of this. With this background, we considered and designed a cloud systemframework suitable for SMEs, built a prototype model and investigated the ease of
implementation. Services that deliver functionality equivalent to Amazon EC2 which isrepresentative cloud service but delivered through other technologies are called EC2
clones. OpenStack is not EC2 clone, and is a typical open source project for providing
Infrastructure as a Service (IaaS) type cloud services. OpenStack was used in this study to
implement a simple cloud service model.

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Environmental
performance

Information
sharing amongsuppliers

Business process
improvement

Economic
performance

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

OS6-5 Visualization of Patent Strategies in Japanese ICT Companies Based on
Text-Mining
T. Tokumitsu1, T. Okada1, I. Nakaoka1, Y. Park2(1 Ube National College of Technology, Japan)(2 Prefectural University of Hiroshima, Japan)
We deduce that a major reason of the continual poor business performances in mostJapanese ICT companies is that they have lost their technology innovation strategies.
Especially in smartphone market, Japanese ICT companies have lagged behind
foreign companies such as Samsung and Apple. Therefore, we conduct longitudinal
research of three Japanese large ICT companies, SONY, Panasonic and Sharp, insmartphone market based on text-mining approach covering their patent information.
A visualized result of patent strategies by SONY is shown in the figure. We analyze
the other two companies, Panasonic and Sharp, in the same way, and reveal
characteristics of their patent strategies.

OS7 Intelligence in Biological Systems
OS7-1 Dissipative Particle Dynamics of shape changes of vesicle
Yoshiyuki Oofuji1, Naohito Urakami1, Takashi Yamamoto1, Masayuki Imai2(1Yamaguchi University, Japan)(2Tohoku University, Japan)

Spherical vesicles change to various shapes such as oblate type, prolate type, stomatocyte,etc., according to the osmotic pressure difference between the inner and the outer vesicle.
The shape changes of vesicles are very important to understand the living cell activities.
In the research, we investigated the process of the shape changes of vesicles by carryingout dissipative particle dynamics simulations. The shape changes of vesicles werereproduced by the variation of two parameters, the number of water molecules inside thevesicle and the difference between the number of lipid forming the inner and the outerleaflets in the vesicle. The vesicle shapes obtained in our simulations were in good
agreement with the experiments. The simulation result indicates that the vesicle shapes
are determined by two parameters.

OS7-2 Intelligent mechanisms in E. coli in processing carbon sources
Zhongyuan Tian, Hiroshi Matsuno(Yamaguchi University, Japan)
E. coli is “wise” enough to take suitable responding time, and suitable responding
behaviors, when facing different lengths or intensities of stimuli. According to the time
cost of a respond to a signal, we divide the intracellular processes into 2 levels: thecentral dogma level and the post translation level. Firstly, we constructed a systematical
network of glucose, PTS, glycogen and chemotaxis system. And this network plays afunction as a switch, which controls E coli’s motion within the first few seconds. By
using this switch, we unveiled a fact that shorter time stimuli result in the post translationlevel reactions for quicker response. But longer time stimuli will activate more time
consuming the central dogma level reactions. Secondly, different intensities of signals
result in different kinds of actions, was illustrated by ppGpp example.

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Shape changes ofvesiclesThe International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

OS7-3 An autonomous propagation of ciliary metachronal wave on elastic
surface of Paramecium cells.
N. Narematsu and Y. Iwadate(Yamaguchi University)
Ciliary movements in protozoa show metachronal coordination so as to maintain aconstant phase difference between adjacent cilia. This coordination is called as“metachronal wave”. It is now generally thought that metachronal waves arise fromhydrodynamic coupling between adjacent cilia at extracellular fluid. However, underthe breakdown the hydrodynamic coupling of ciliary movements at a restricted portion of
a Paramecium cell, metachronal waves pass over the portion. We will discuss themediator of propagation of metachronal waves.

OS7-4

An analysis of the synergy in a ball throwing task
Hiroshi KIMURA, Jun NISHII(Yamaguchi University, Japan)

To throw a ball into a given target, we must adequately select the initial state of the ball,the position, speed, and throwing direction at ball release. There are two ways in the
choice of these variables. The first solution is to learn a desired value set for these statevariables and try to adjust the variables to the desired values. Another solution is to allow
the variance of each variable in every trial but compensate for the values each other in acooperative manner so as to hit the target. The purpose of this study is to find how good
throwers select the state variables in throwing and elucidate the knack of the ballthrowing task.

OS7-5 A low dimensional feedback control model that exploits abundant degrees
of freedom
Jun Nishii, Tohru Hamamura(Yamaguchi University, JAPAN)
When animals perform skilled motor task, they often adaptively choose a solution
depending on the circumstances by utilizing abundant degrees of freedom (DOFs) of theirbody. For instance, a spinal frog can wipe an irritating stimulus off by its foot even if a
leg joint is fixed so as not to be able to bent. In this example, multiple leg joints
cooperatively work so as to control the foot position by utilizing available joints. Suchcompensative control among multiple joints is called joint synergy. We propose a simple
neural network model that realizes synergetic control that exploits abundant degrees of
freedom.

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

OS7-6 Extension of Genetic Toggle Switch Based on the Effective Search ofState Transitions
M. Sugii, A. Fauré and H. Matsuno(Yamaguchi University, Japan)

In the context of synthetic biology, artificial genetic circuits are designed in the following
way: after setting a biological target phenomenon to be investigated, reaction parameter
estimations among related molecules are conducted based on the dynamic analyses withmathematical models. Finally a system of biological reactions is developed with thesemolecules in vivo or in vitro. We propose a new procedure to effectively design amathematical model in two steps. The first step is the creation of possible networkarchitectures under a logical formalism. The second step is the creation of dynamic
models. We report an extension of a genetic toggle switch from 2-state to 3-state by our
proposed procedure.

Nullclines of
3-state toggles witch

OS8 Software Development Support Method
OS8-1 Proposal of a Visualizing Method of Data Transitions to Support
Debugging for Java Programs
Hiroto Nakamura1, Tetsuro Katayama1, Yoshihiro Kita2, Hisaaki Yamaba1,
Naonobu Okazaki1,(1University of Miyazaki, Japan)(2Kanagawa Institute of Technology, Japan)
Finding the cause of a bug needs to comprehend a flow and data transitions in executing
programs. It is difficult to grasp behavior in executing the programs whose behavior is
unexpected by a bug. We propose a visualizing method of data transitions to support
debugging for Java programs in order to improve efficiency of debugging by supportingto find the cause of a bug. We have implemented TVIS in order to show efficiency of the
proposed method. The data transitions diagram is the most characteristic function of
TVIS which shows the data transitions in executing programs as a table. It can show
visually abnormal behavior: no data renewed at all, data abnormally renewed, and so on.
Because abnormal behavior is detected in the data transitions diagram at first glance, it isuseful for programmers in finding the cause of a bug.

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

OS8-2

Proposal of a Method to Build Markov Chain Usage Model from UML
Diagrams for Communication Delay Testing in Distributed Systems
Zhijia Zhao1, Tetsuro Katayama1, Yoshihiro Kita2, Hisaaki Yamaba1, Naonobu Okazaki1(1University of Miyazaki, Japan)
2( Kanagawa Institute of Technology, Japan)

As the growth of network technology with high parallelism and high reliability of
distributed systems, they have been widely adopted in the enterprise and society. But,
how to design for testing the real-time or a communication delay of distributed systems is
not been discussed much. This paper proposes a new method to automatically build
Markov Chain Usage Model. The proposed method establishes the time points and builds
a new UML diagram called De-sequence diagram by combining the deployment diagramand sequence diagram which can test the communication delay of a distributed system toimprove its reliability. We have confirmed the usefulness of the proposed method to adapt
it to practical examples.

OS8-3 Proposal of a Supporting Method for Debugging to Reproduce
Java Multi-threaded Programs by Petri-Net
Shoichiro Kitano 1, Tetsuro Katayama1, Yoshihiro Kita2, Hisaaki Yamaba1, Naonobu Okazaki1(1University of Miyazaki, Japan)
2( Kanagawa Institute of Technology, Japan)
In multi-threaded programs, it is difficult to discover a cause of bugs. This paper proposesa supporting method using Petri-net for debugging so that programers can discover causeof bugs in multi-threaded programs written in Java language. Specifically, in order to give
reproducibility to multi-threaded programs, the proposed method generates a data file foran execution path of a multi-threaded program, and simulates the behavior of the programby Petri-net based on the data file. However, ordinal Petri-net cannot express the behaviorof multi-threaded programs written in Java completely. Therefore, we have extended
Petri-net so that we can treat with the behavior of multi-threaded Java programs. In
addition, we have confirmed validity of our method by having implemented a supporting
tool.

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

OS8-4 Proposal of a Supporting Method to Generate a Decision Table from theFormal Specification
Kenta Nishikawa1, Tetsuro Katayama1, Yoshihiro Kita2, Hisaaki Yamaba1, Naonobu Okazaki1(1University of Miyazaki, Japan)(2Kanagawa Institute of Technology, Japan)
As a means for writing specifications strictly, the formal methods are proposed. By theway, as one of the test design techniques, the decision table is proposed in the testingprocess of the software cycle. However, it takes much time and effort to extract test items
and understand contents written on specifications in designing manually the decisiontable. This paper proposes a supporting method to generate a decision table from theformal specification in order to improve efficiency of the test design with formal
methods. We have implemented a supporting tool to generate a decision table. It
automatically generates a skeleton decision table from the formal specification. By using
the tool, it is considered that the efficiency of the test design is improved.

OS9 Foundation of computation and its application
OS9-1 Hierarchy Based on Neighborhood Template about k-Neighborhood Template
A-Type Three-Dimensional Bounded Cellular Acceptor
Makoto Sakamoto1, Makoto Nagatomo1, Xiaoyang Feng1, Tatsuma Kurogi1, Tuo Zhang1, Takao Ito2,
Yasuo Uchida2, Tsunehiro Yoshinaga3, Satoshi Ikeda1,
Masahiro Yokomichi1,Hiroshi Furutani1(1University of Miyazaki, Japan)( 2Ube National College of Technology, Japan)( 3Tokuyama College of Technology, Japan)

In this paper, we introduce a new computational model, k-neighborhood template A-type
three-dimensional bounded cellular acceptor on four-dimensional tapes, and discuss somebasic properties. This model consists of a pair of a converter and a configuration-reader.
The former converts the given four-dimensional tape to three-dimensional configuration.
The latter determines whether or not the derived three-dimensional configuration is
accepted, and concludes the acceptance or non-acceptance of given four-dimensional
tape. We mainly investigate how the difference of the neighborhood template of theconverter affects the accepting powers of this computational model. It is well known thatthree-dimensional digital pictures have 6- and 26-connectedness. However, we include
the remarkable picture in neighbor.

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

OS9-2 Hierarchy Based on Configuration-Reader about k-Neighborhood Template A-Type
Three-Dimensional Bounded Cellular Acceptor
Makoto Sakamoto1, Tuo Zhang1, Tatsuma Kurogi1, Makoto Nagatomo1, Xiaoyang Feng1, Yasuo Uchida2,
Takao Ito2, Tsunehiro Yoshinaga3, Satoshi Ikeda1, Masahiro Yokomichi1, Hiroshi Furutani1(1University of Miyazaki, Japan)( 2Ube National College of Technology, Japan)( 3Tokuyama College of Technology, Japan)

Question of whether or not processing four-dimensional digital patterns is more difficult
than processing two- or three-dimensional ones is of great interest from both theoreticaland practical standpoints. Thus, the study of four-dimensional automata as thecomputational models of four-dimensional pattern processing has been meaningful. From
this point of view, we are interested in four-dimensional computational model, and we
first proposed four-dimensional automata as computational models of four-dimensional
pattern processing in 2002. In this paper, we introduce a new computational model,
k-neighborhood template A-type three-dimensional bounded cellular acceptor on
four-dimensional tapes, and mainly investigate how the difference of configuration-reader
affects the accepting powers of this model.

OS9-3 Handicap of Othello GameY. Kato1,S. Ikeda1 T. Ito2, M. Sakamoto1(1University of Miyazaki, Japan)
2( Ube National College of Technology, Japan)
In the game of Go, it is known experientially that Black (=the first move) is
advantageous. Thus, in the game of Go, White (=the defensive hand) has received ahandicap for this compensation. There is Othello game well known as the Go.
However, the handicap of White in Othello game has been hardly considered until
now. This research considered quantitatively the handicap in Othello game from aviewpoint of points. As our conclusions of Othello game is the followings.
 The advantage of the defensive hand on board of 4 × 4 is -0.641 points on
average.
 The advantage of the defensive hand on board of 4 × 6 is 0.778 points on
average.

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

OS9-4 On Parameter Setting in Identifying the Same Languages Involved in Different
Language Data
Ren Wu1, Hiroshi Matsuno2(1Yamaguchi Junior College, Japan)(2Yamaguchi University, Japan)
We have proposed a method based on tree structure and string alignment technique for
identifying the same languages involved in two language classification trees provided bydifferent linguists. Several kinds of similarity measure, such as language name similarityand language general similarity etc., and an algorithm are proposed in this method.
Several unknown parameters are used there and need to be set to constants first forcalculating the similarity values. This paper aims to determine all the values of theseparameters and then get the identification results in order to confirm that our proposed
similarity measures and the algorithm are useful and effective. We obtained better values
for the parameters throughout the experiments.

GS
GS1 Artificial intelligence & Complexity
GS1-1 Cognitive approach to Computer Go programming
N. Tenys, I. Tanev, K. Shimohara(Doshisha University, Japan)
Go (Igo) is an ancient board game that is still one of the more difficult challenges for
Artificial Intelligence research. While computers have defeated human champion in chess16 years ago, to this day best computer Go programs play only at a high amateur level.
Humans play Go using experience, heuristic values, pattern recognition and whole-board
evaluations that computers are unable to match. Today’s most successful Go programs
use Monte Carlo search algorithm, but while this approach often finds good local moves
on the board, due to its random nature it often fails to see the whole board position as wellas human does. Our approach instead is implementation of human Go professional’s“thinking” into a computer program that is, creating a system, where computer uses
algorithms and data constructs that mimic human’s mind.

©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

GS1-2 Circulative Narrative Generation Based on the Mutual Transformation
between Narrative Conceptual Structures and Music in the Integrated
Narrative Generation System
Taisuke Akimoto, Takashi Ogata(Iwate Prefectural University, Japan)
We have proposed a new framework of narrative generation which generates narratives
through the mutual transformation according to various pathways between narrative(conceptual representation) and music. This paper will continuously incorporate thisframework into the integrated narrative generation system which is a comprehensivearchitecture for our narrative generation study and develop an experimental system
combined with the integrated system. This new framework aims to apply various musicalmethods for composition and variation to narrative generation as a way to connect
different types of media. An experiment of the mutual generation between music andnarrative will show various transformation pathways or routes between music andnarrative.

GS1-3 Dynamic Analysis of Tensegrity Systems Subject to Arbitrary Joint
Constraints
Youngsu Cho and Joono Cheong(Korea Univ., Korea)
This paper provides a dynamics formulation of tensegrity systems using non-minimal
coordinates subject to arbitrary joint constraints. Advantages of non-minimal dynamics
including the simplicity of formulation and scalability are enjoyed. We start from a matrix
form of dynamics and then transform the dynamics into a vector form so that arbitrary
constraints in the joints can be handled with ease. Due to the generality of theformulation, we can create a software module that can carry out dynamic simulation ofnumerous tensegrity systems. To illustrate the effectiveness of the proposed dynamics
formulation, we show simulation results for a deployable tensegrity structure and amulti-stage tensegrity prism under various types of joint constraints such as pin, revolute,and planar types.

Figure 1. Tensegrity system

GS1-4 Road map generation from Smartphone data
Masao KUBO, Chau viet DAN, Hiroshi SATO, Akira NAMATAME(National Defense Academy of Japan)
Smartphones become widespread quickly. This type of mobile phone has a built-in GPS,
motion sensor, digital compass and large memory and battery. It is good for recording
human activity. In this paper, we introduce a method to make occupancy grid map when
the user drive outside. Usually, the vehicle behavior is analyzed by a time series ofaccelerations sensed by customized sensors installed on vehicle. The data is high quality
for understanding but it is difficult to collect data from the public. The data recorded by asmartphone is lower accuracy and the pose is unknown. Our method uses GPS data
mainly.

©ICAROB 2014

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with constraints

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

GS1-5 Zigzag-Perceptually Important Points for Financial Time Series Indexing
Chawalsak Phetchanchai, Ali Selamat , Md Hafiz Selamat(University Teknologi Malaysia, Malysia)
In this paper, we propose a zigzag based m-ary tree (ZM-Tree) to index financial timeseries. The index is done by mean of zigzag-perceptually important points (ZIPs). The
tree represents the zigzag feature of financial time series which benefits in reversal
pattern searching, and wave counting. We also propose a method of identifying ZIPs in
multi-resolution. The identification process uses the vertical distance as a measurement toevaluate the point importance. The experiments demonstrate the performance of ZM-Treein tree building, tree retrieval, tree pruning, and dimensionality reduction

p3(x3,y3)p2(x2,y2)
pd(xd,yd)
pv(xv,yv)p1(x1,y1)Fig. 2. Measurement of
PIP-ED, PIP-PD, and
PIP-VD

GS2 Reinforcement Learning System & Genetic programming
GS2-1 Development of phrase and music search engine by humming
Kiminori Satou, Eiji Hayashi(Kyushu Institute of Technology, Japan)
In recent years, the internet has become ubiquitous and is changing dramaticallyevery aspect of how people live their lives. Users not only can purchase the CD athome but also can download the music files without leaving home using the internet,
it is very easy and convenient for users who can find the lyrics. But the music’s
names are not able to be efficiently searched if users forget the music’s names. Then,enter the voice, do a search using that voice's length and pitch, find the rhythm and pitchsimilar phrases, and thought that it is possible to determine the name of the song.
Therefore, we developed a phrase and music search engine that searches a tune fromvarious tunes with one phrase of voice data.

GS2- 2 Human Recognition based on Gait feature and Genetic Programming
Dipak Gaire Sharma, Ivan Tanev, Katsunori Shimohara(Doshisha University, Japan)
Human walking has always been the curious field of research for different disciple ofSocial and Information Science. The study of human walk or human gait in association
with different behaviors and emotions has not only fascinated social science researcher,but its uniqueness has also attracted many computer scientist to work in this arena for thequest of uncovering reliable mechanism of biometric identification. In this research we
use new method for human identification based on human gait features and genetic
programming (GP) approach.

©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

GS2-3 An Action Selection Method Using Degree of Cooperation in a Multi-agent
SystemMasanori Kawamura, Kunikazu Kobayashi(Aichi Prefectural University, Japan)
In recent years, a concept of a dividual is proposed to interact properly with anotherperson. To construct a model of the dividual, the degree of cooperation is assigned to thecorresponding dividual. By introducing the degree of cooperation into multi-agent
systems, we evaluate what kind of changes appears in the agent behavior. In addition, we
propose an action selection method by introducing the degree of cooperation into thesoft-max method in multi-agent systems. Using the proposed method, we confirm
whether the cooperative action is promoted or suppressed through computer simulations.

GS2-4 Cooperative Action Acquisition Based on Intention Estimation Method in a
Multi-agent Reinforcement Learning System
Tatsuya Tsubakimoto, Kunikazu Kobayashi(Aichi Prefectural University, Japan)
In this paper, we propose a method that can acquire cooperative action to reach an
appropriate goal without controlling reward to realize cooperative action by designers.
We assume every action of other agents as a process to achieve an ultimate goal and thentreat it unimportant. In order to confirm the effectiveness of the proposed method, we
carried out computer simulations. The simulation results show that the proposed method
is superior to a standard Q-learning method and a Q-learning method with cooperation interms of the number of successful cooperation.

GS2-5 Integrating the Event Generation Mechanism in the Propp-based Story
Generation Mechanism into the Integrated Narrative Generation System
Shohei Imabuchi, Takashi Ogata(Iwate Prefectural University, Japan)
In the Propp-based story generation mechanisms and the integrated narrative
generation system which we have been developing, the former functions as amodule in the latter. These systems have used respective event generation
mechanisms to generated events which are the most important units in a narrative
structural representation. A common event generation mechanism needs to be usedin these two systems towards the complete blending. This paper will present thefirst tentative attempt as a prototype to be revised in the future.

(event Steal(type action)(ID 1)(time (time1 time2))(agent age%Man#1)(counter-agent nil)(location loc%Forest#1)(object obj%Apple#1)(instrument nil)(from age%Boy#1)(to nil))

Conceptual representation of an event“age%Man#1 steals obj%Apple#1 from
age%Boy#1 at loc%Forest#1.”©ICAROB 2014

P - 47

Relation

E1(t1→t2)

t1

Event

E2
(t2→t3)

t2

t3

State

E3
(t3→t4)t4Conceptual structure of a story

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

GS3 Human-machine cooperative systems & Human welfare robotics
GS3-1 Development of Measurement System for Quantitative Evaluation of Skillfulness ofLower Extremities
Kazunori Yamazaki1, Donggun Kim1, Yoshifumi Morita1, Hiroyuki Ukai1
Kenji Kozakai2, Satoru Shibata2, Shigenori Onishi2, Akihiro Ito2, Daisuke Mizuno2
Tatsuya Hirai3, Haruna Takeda3, Yuka Sugiura3(1Nagoya Institute of Technology, Japan),(2Sanyo Machine Works, Ltd., Japan),(3Tanakakai Nishio Hospital, Japan)
In this study, we developed a quantitative evaluation method of the skillfulness of thelower extremities. For this purpose, we used the measurement system which we havedeveloped in our previous work and redesigned the target trajectories of foot movement.
Moreover, we verified the reliability of the proposed quantitative evaluation method bycalculating the intra-subject reliability for two trials ICC(1,1) of the evaluation results.
We developed a measurement system comprising the straight course, the circular course
and the star-shaped course. Because the ICC(1,1) of the circular course and thestar-shaped course are higher than 0.7, the quantitative evaluation method of lower
extremities with the circular course and the star-shaped course has sufficient reliability.

Laptop PC（Video games）

Board
Device(PC Mouse)

GS3-2 Glove-Based Virtual Interaction for the Rehabilitation of Hemiparesis Stroke
Patient
Khairunizam WAN, Aswad A.R, Rashidah Suhaimi, Nazrul H. ADNAN, D. Hazry,
Zuradzman M. Razlan, Syed Faiz Ahmed(Universiti Malaysia Perlis, Malaysia)

This paper proposes the rehabilitation of a stroke patient by using dataglove called
GloveMAP. GloveMAP is a low cost hand glove developed by using flexible bending
sensor and accelerometer attached at the position of the fingers and wrist, respectively.
The rehabilitation exercises are conducted in the virtual environment. In the studies, 22
fundamental movements of arm are evaluated and the results are employed to design thevirtual environment. The combination of these fundamental movements focuses on
Hemiparesis sufferers which are the most common in the stroke and have the inability tomove one side of the body. The experimental results show that by combining several
fundamental arm movements, glove based-virtual interaction supports the stroke patient
do the rehabilitation by them without assisting from physiotherapist.

©ICAROB 2014

P - 48ƟFigure. 1 Performing thefundamental arm movement

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

GS3-3 Measuring system of therapist’s guiding motion for standing up training ofpatient with hemiplegia after stroke and analysis results of patient's motion
JungTang Wang1, Kazunori Yamazaki1, Yoshifumi Morita1, Noritaka Sato1, Hirofumi Tanabe2(1Nagoya Institute of Technology, Japan)(2Self-Defense Force Central Hospital, Japan)
In this paper we developed a measuring system to analyze the therapist’s guiding motion
during the standing up training of a hemiplegic stroke patient. To confirm the usefulnessof the developed measuring system, we measured the motion of a hemiplegic stroke
patient during the standing up training with/without the therapist's guiding motion. By
comparing the results of the patient's left/right weight balance during the standing up
motion with/without the therapist's guiding motion, the effectiveness of the therapist's
guiding motion was confirmed. Moreover, the measured patient's motion with/without thetherapist's guiding motion agrees with the therapist's observation. Therefore, theusefulness of the measuring system was confirmed.

6-axis force/torquesensors

acceleration/
angular velocity
sensorWii Balance Board

GS3-4 Image Segmentation of Coronary Artery Plaque Using Intuitionistic
Fuzzy C-Means Algorithm
Zahra Rezaeia, Ali Selamata, Mohd Shafry Mohd Rahima, Mohammed Rafiq Abdul Kadirb(Universiti Teknologi Malaysia)
Every year, hundreds of thousands of people die because of Coronary Heart Disease(CHD) in all over the world..Coronary Artery Disease (CAD) as a cardiovascular illness
causes blood vessels narrowing that supply blood and oxygen to the heart .
Atherosclerosis is known as the deadliest type of heart disease, which is caused by soft or“vulnerable” plaque (VP) formation in the coronary arteries. Acute Coronary Syndrome(ACS) is recognized as the first coronary atherosclerosis indicator which identifies
high-risk plaques. Intravascular ultrasound (IVUS) can be applied for characterization ofplaque and segmentation of vessel’s walls borders. Recently, Virtual Histology as a new
approach based on spectral analysis of IVUS has been introduced. In this work, weapplied a clustering method based on Intuitionistic Fuzzy C-means (IFCM) in order toautomatic segmentation of Coronary Artery plaque using VH-IVUS images.

©ICAROB 2014

P - 49Figure 1. Arterial
plaque geometry.
Cross-sectional view
[2].

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

GS4 Neural Networks
GS4-1 The recollection characteristics of Generalized MCNNusing different
control methods
Shun Watanabe1, Takashi Kuremoto1, Kunikazu Kobayashi2, Shingo Mabu1, Masanao Obayashi1(1Yamaguchi University, Japan)(2Aichi Prefectural University, Japan)
As an auto-association memory model, chaotic neural network (CNN) proposed byAdachi et al. is able to recollect multiple stored patterns dynamically. Kuremoto et al.
proposed a multi-layer chaotic neural network (MCNN) combined multiple controlled
CNN layers to realize mutual association of plural time series patterns. However,recollection simulation of MCNN was limited in a two-layer model and the recollection
characteristics concerning with the different external inputs and the control methods were
not investigated. In this paper, we extend the MCNN to be a general form (GMCNN)
with more layers and use control method using particle swarm optimization (PSO). And
the recollecting characteristics of different GMCNNs using different control methods
were invested by computer simulations.

GS4-2 Revealing Terrorism Contents form Web Page Using Frequency Weighting
Techniques
Thabit Sabbah, Ali Selamat, Md Hafiz Selamat(Universiti Teknologi Malaysia, Malaysia)
Intelligence and security informatics plays an important role in revealing terrorism in web
content. However, the extremist groups are exploiting Internet facilities intensively tostimulate violence, hatred, and terrorism. Accurate classification of web content will
increase the opportunities for the effective use of intelligence and security informatics inthe early detection of terrorist activities. In this paper, we propose a feature selection
method based on term weighting techniques TF, DF, and TF-IDF for revealing terrorism
in web content. Firstly, we compare the performance of the individual term weighting
techniques in revealing terrorism. Then, we present a method for enhancing the accuracyof classification. The method was tested with selected dataset from Dark Web Portal
Forum. The experimental results show that classification using

GS4-3

TF

DF

Our research has been focused on developing human-robot interactions, The robot need toperform a high level of intellectual activity and user compatibility. Therefore, we
considered the primary structure of a conscious human/animal action, which can be
represented by the sequence process, Perception  Motivation  Action. We have
improved a hierarchical structure model, which defines the relationship between the
consciousness field and the behavior module. This model is called Consciousness-BasedArchitecture (CBA). Furthermore, the robot should select the action itself, we haveinvestigated the application of brain-inspired technology so we introduced a Self
Organizing Map (SOM) neural network that is trained using unsupervised learning toclassify its behavior according to the motivation value in the CBA module. In this paper,
we attempt to describe the integration of a Self Organizing Map (SOM) method into CBA
module in order to classify and select autonomous behavior. We confirmed theeffectiveness of the proposed system by the experimental results.

P - 50

TF-IDF100%
0%100 300
K (# OF…
forFig. 1. Classification accuracy

individual term weighting
techniques

Application of Self Organizing Map for analyzing of robotic arm’s action
with Consciousness-Based Architecture module
Wisanu Jitviriya, Eiji Hayashi(Kyushu Institute of Technology, Japan)©ICAROB 2014

Accuracy

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

GS4-4 Study on Time Synchronization Algorithm of Wireless Sensor Networks
Based on Maximum Likelihood Estimation
Ai Dongmei, Zeng Guangping, Tu Xuyan, He Di(University of Science and Technology Beijing, China)

Wireless Sensor Network (WSN) integrates technologies of wireless communication,
sensing, network interconnection, and distribution. Comprised of a large amount of micro
sensor networks, it can collect real-time information, monitor objects within a specificrange, and send back usable information at the first time to observers. Unlike generalcomputer networks, WSNs face the issue of constraints on energy. Hence, the timesynchronization mechanism, a supporting technology for application of WSNs, isn’t
suitable when extensively applied to traditional network. This paper proposes
High-Accurate Energy-Efficient Time Synchronization (HAEE) algorithm based on
TPSN algorithm, which significantly improves the accuracy of time synchronization andreduces energy consumption, as proven by software simulation results.

.

GS5 Pattern recognition
GS5-1 Recognizing a Road Environment Using Multiple-window Bag of Features
Shou Morita, Joo Kooi Tan, Hyoungseop Kim, Seiji Ishikawa(Kyushu Institute of Technology, Japan)
In this paper, we propose a method of recognizing a road environment employing bag offeatures considering positional information. This technique works by making multiple
windows in an image. The proposed method makes a codebook in each window andcomputes histograms of local features found inside of the window. The original bag offeatures method disregards positional information in an image It is, however, important torecognition. The proposed method considers positional information in an image byintroducing multiple windows. For recognition, we employ the support vector machine.
Experimental results show satisfactory performance of the proposed method.

GS5-2 Recognized Face Tracking for CONBE Robot
Sakmongkon Chumkamon, Eiji Hayashi(Kyushu Institute of Technology)
In our research we develop the robot to combine with the consciousness and behavior
which is the conscious and behavior robot (CONBE). In this paper we present the basis
system in the CONBE robot that consist of the two main sections as the face recognition
and robot gaze tracking. This system uses the face recognition using Fisherfaces that also
refer to linear discriminant analysis (LDA). For the robot gaze tracking, we implementwith the head of the CONBE robot that has a camera, two displays used for robot eyes
and the actuators of 2 degrees of freedom. The actuators use to control the robot gaze fortracking the face which the robot can recognize. In this paper, we experiment and present
the results of the face recognition system such as frame rate, error rate; and the tracking
control system such as the tracking error and time.

©ICAROB 2014

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Multiple windows on
an image.

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

GS5-3 Eye detection Using Composite Cross-Correlation form Face Images
in Varied Illumination
Kutiba Nanaa, Mohamed Rizon, Mohd Nordin Abd Rahman(Universiti Sultan Zainal Abidin (UniSZA), Malaysia)
Facial feature detection is essential process in the field of face recognition. Template matc
h based approach is widely used to implement the issue of facial detection.However, many obstructions adversely affect in template based implementation such as
varied illumination, size, face pose and the status of eye in either open case or close case.
the objective of this paper is to detect left eye and overcome the obstruction of
low illumination in given image using composite cross-correlation. Two single template
form the proposed composite template. First single template is created from an
eye image in ordinary illumination while the second single template is created froman eye image in low illumination. The experimental results is concluded by applying our templates on the PICS Database whose varied illumination images and show thatapplying
composite templates gives a better result than applying the both single templates individu
allyGS5-4 Development of an autonomous-drive personal robot“An object recognition system using BoF and SOM”
Keisuke Ito, Eiji Hayashi (Kyusyu Institute of Technology, Japan)
In the near future, autonomous self-driving robots are expected to provide various
services in human living environments. The ability to work autonomously and accurately
recognize surrounding objects are required for the autonomous robot. In previous
research, the robot has enabled the determination of the angle and type of the object by amatching system based on the Speeded Up Robust Features (SURF). In this paper, theauthors developed the system in order to determine the type of the object by using the
Self-Organizing map (SOM) and SURF features. This system can visualize therelationship of the object and determine the type of the object.

Please attach one
typical figure in yourpaper here.

GS5-5 Comparison of Feature Detectors for Obstacles Detection
Shaohua Qian1, Joo Kooi Tan11, Hyoungseop Kim1,2Seiji Ishikawa1
Takashi Morie , Takashi Shinomiya(12Kyushu Institute of Technology, Japan)( Japan University of Economics, Japan)

Detection of obstacles in a video sequence is a basic task in autonomous collision
avoidance systems of intelligent vehicle. We have already proposed an obstacles detection
method using a video taken by a vehicle-mounted monocular camera. In this method,
accurate obstacles detection depends on whether we can accurately detect and matchfeature points. In order to improve the accuracy of obstacles detection, in this paper wemake a comparison between four most commonly used feature detectors: Harris, SIFT,
SURF and FAST detectors. The experiments are done using our obstacles detection
method. The experimental results are compared and discussed, and then we find the mostsuitable feature point detector for our obstacles detection method.

©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

GS6 Robotics Ⅰ
GS6-1 Study on the pose under complex multiple targets environment for
the industrial robot based on machine vision
Jiwu Wang 1, Xianwen
Zhang 1, Huazhe Dou 1, Sugisaka Masanori 2(1 Beijing
Jiaotong University, China)(2 Nippon Bunri University, Japan)
Effectively obtaining multiple targets pose is the key element to improve flexible
applications of industrial robots under complicated working environments. Machine
vision technology is the main method to solve this problem. In order to effectively reduce
influence of changing conditions (such as the illumination, angle and scale etc.) on target
pattern recognition, the fusion algorithms of scale invariant feature transform (SIFT) andgeometric moment, is developed in this paper. To achieve accurate identification ofmulti-targets’ pose when their position and orientation change in the two-dimensional
space, the optical-section method is used to determine the depth information of somefeature points of the target. Moreover, that data is used to correct the pose recognized by
the traditional pattern technology. Finally, using SCARA-type industrial robots as aplatform, the relative algorithms are verified with corresponding experiments.

Fig.5. The industrial robot
recognition system

GS6-2 Research and Development of an Intelligent Robot of Medical Assistance Based on
Embedded RTOS
Yuan Li, Fengzhi Dai, Junhong Xi, Binyao Li, Guodong You(Tianjin University of Science and Technology, P. R. China)
For practical purpose, this paper focuses on design and implementation of a wheeled
intelligent robot that could be applied in hospitals or nursing homes for medical
assistance. The hardware part includes two microcontrollers, MC9S12XS128 and
STC89C52, assisted by a laptop as the control center of the system, various sensors andother robotic pieces. The software part is basically achieved on CodeWarrior 5.0, KeilμVision4 and Visual C++ 6.0 with OpenCV library, porting μC/OS-II real-time operating
system to MC9S12XS128 in order for improving stability and flexibility of the system.
The robot has functions of infrared remote control, fuzzy rule-based path tracking,obstacle avoidance, real-time LCD display and voice broadcast, color recognition androbot arm grasping.

GS6-3 High Acceleration Robotic Arm for Dynamic and Dexterous Manipulation ofDeformable Object
Hiroaki Seki1, Hiroki Shibata1, Yoshitsugu Kamiya1, Masatoshi Hikizu1,
Khairul Salleh Mohamed Sahari2(1Kanazawa University, Japan)
2( Universiti Tenaga Nasional, Malaysia)
Deformable object manipulation is very important for home service robots. This paper
discusses the development of a high acceleration robotic arm capable of performing
dexterous manipulation of deformable object such as string. A mass-spring model of thestring is first developed to study the design requirement of the robot as well as todetermine the required motion of the robot to smartly manipulate the string. The
developed system comprising of the string model and robotic arm is then used tomanipulate unknown string. Based on some simple basic routine, the parameters of thestring can be approximated based on the string model. Once the parameters are known,
the string can be smartly manipulated by the robotic arm.

©ICAROB 2014

P - 53.

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

GS6-4 Switching based controller algorithm design for uncertainties rejection insmooth takeoff / landing and of quad-rotor
M. Hassan Tanveer, D. Hazry, S. Faiz Ahmed, M. Kamran Joyo, Faizan. A. Warsi(Universiti Malaysia Perlis (UniMAP) Malaysia)
This article evaluates the proposed control technique for altitude controlling of
Quad-rotor type unmanned Aerial Vehicles under different uncertainty conditions.
Smooth takeoff and landing i.e. altitude controlling of Quad-rotor UAV under perturbed
conditions such as high wind burst and system or sensor noises has been a challenging
research domain for the researchers. In this paper a Hybrid controller is purposed forbatter controlling of quad-rotor’s altitude through MPC and PID controller. This hybrid
controller works in such a way that quad-rotor’s sensor and system noises problem arehandled by MPC controller and in the occurrence of external disturbances, auto-tuned
PID controller is activated and well manage the situation. Simulations are done on
MATLAB and results shows the effectiveness of the proposed hybrid method and suggest
it as a promising way for real time implementation in altitude stabilization for quad-rotor
UAV

Proposed Hybrid Controller:

GS6-5 Parameters of Gas-Liquid Flow Distribution Uniformity in Upward Multi-Pass
Channels1
1
Zuradzman M. Razlan1,, R. Heng
, D. Hazry21, A.B. Shahriman, Khairunizam
WAN1, S. 3Faiz Ahmed1,
1
2
2
Hirota , N. Maruyama , A. Nishimura , H. Hisyam .
Nazrul H. ADNAN , M.
1

( Universiti
Malaysia Perlis, Malaysia)(2 Mie University, Japan)
3( Lean Applied Pte. Ltd., Malaysia)

The gas-liquid flow distributions in multi-pass upward parallel channels that simulate
the evaporator for an electric vehicle air-conditioner system were examined
experimentally. In this paper, the attentions are to discover the most influenced parameterto the flow distribution uniformity by using design of experiment method. Experiments
were conducted in an isothermal air-water flow system. In the mist-flow inlet, the water
distribution was insensitive to the backpressure conditions and its uniformity was
improved in comparison with that in the stratified-flow inlet. The flow distribution
uniformity for gas phase is influenced mostly by superficial air velocity, and the flow
distribution uniformity of liquid phase is mostly influence by 2-way interaction ofparameters which are flow pattern and superficial air velocity.

©ICAROB 2014

P - 54(a) Stratified-flow inlet(b) Mist-flow inlet
Fig. 1 Flow-inlet
conditions

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

GS7 Robotics Ⅱ
GS7-1 Decision Making System of Robots Introducing a Re-construction of Emotions
Based on Their Own Experiences1
Shogo Watada, Masanao1 Obayashi, Takashi Kuremoto1, Shingo Mabu1, Kunikazu Kobayashi2
1( Yamaguchi University, Japan)(2Aichi Prefectural University, Japan)

We already proposed a decision making system for autonomous robots, which is based ona Markovian emotional model proposed by Banik. In our proposed system, decision
making of the robot is designed automatically by two processes. The first is aconstruction of emotions based on learning of the input stimuli. The second is
optimization of behavior selection policy for emotions by optimization of systemparameters which define behavior selection probability of each emotion. In our above
previous studies, a construction of emotions based on learning of the input stimuli wasprocessed using learning samples of input stimuli on offline in advance. In this study, we
introduce an experiences-based reconstruction of emotions to our decision making system
as an approach to an automatic construction of decision making online learning.

GS7-2 Visual Servoing and Sound Localization in a Surveillance Robot
So-Yeon Park, Yeoun-Jae Kim and Ju-Jang Lee(KAIST, Korea)
The purpose of this paper is to explain the control and implementation details of thevisual servoing and sound localization in a tank shaped surveillance robot. With an eye in
hand camera configuration, the key point of the visual servoing is to measure the depth tothe target object. In this paper, we suggested the depth estimation technology by moving
the camera in a lateral plane. In sound localization, the hardware and software details are
fully explained to implement the sound localization technology. Especially the electronic
filter design is implemented by utilizing the genetic algorithm.

GS7-3 Study on the improvement of flexibility for an industrial robot
based on machine vision
Jiwu Wang1, Xianwen Zhang1, Weining Zhang1, Sugisaka Masanori2(1 Beijing Jiaotong University, China)(2 Nippon Bunri University,)
In order to improve the applications for an industrial robot, it is necessary to increase its
flexibility and control accuracy. Many algorithms and control strategies are developed, inwhich machine vision gets more and more attention. This is because the CCD camera canprovide more information than other sensors, which makes the whole control processsimple. But it requires that the target position should be extracted accurately and robustly.
Moreover, the frequency of image processing should be satisfied with the control
requirements. Here an industrial robot arm is designed and set up for experiment
simulation with machine vision. The target extraction method with fusion of color spaceand moment invariants is developed. The experiments results showed that the developed
image processing algorithms are robust, and the flexibility of an industrial robot can be
improved by machine vision.

©ICAROB 2014

P - 55

Fig.1. The illustration of thestructure of the arm robot

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

GS7-4 Improved Map Generation by Addition of Gaussian Noisefor Indoor SLAM using ROS
Khairul Salleh Mohamed Sahari, Barry Loh Tze Yuen
(Universiti Tenaga Nasional, Malaysia)
Rao-Blackwellized Particle Filter (RBPF) is used in this paper to solve the Simultaneous
Localization and Mapping (SLAM) problem. RBPF algorithm uses particle filter where
each particle carries an individual map of the environment. With the usage of Robot
Operating System (ROS), GMapping package was used as a basis for map generation and
SLAM. To improve the map generation, Gaussian noise was introduced to the data fromlaser range finder and also the odometry from the robot Pioneer P3AT’s pose. The
introduced algorithm was successful in decreasing the uncertainty as well as increased theknowledge of each particle in the estimation of the robot’s pose, proven through practicalexperiment. Exploration experiments were also carried out to test the performance ofP3AT based on our proposed method.

.

PS
PS1-1 Relationship input object position and optimal error diffusion coefficients for
Kinoform using error diffusion method
Daisuke Kashima. Ken-ichi Tanaka.
(Meiji Univ. Japan)
We performed simplify defining optimal error diffusion coefficients for Kinoform. In
order to define optimal error diffusion coefficients simply, we performed searching
relationship input object position and formulating the relationship to approximation. In
order to prove that approximation is true, this paper show simulation result of making
reconstructing by error diffusion method.

PS1-2 Optimization of dither matrix by hybrid of Genetic Algorithm and Simulated
Annealing
Kohei Kato, Ken-ichi Tanaka(Meiji University)
Halftoning is used by ink-jet printer, color copier and so on. Dither method is the methodof halftoning. Bayer method that is one of the dither methods is able to obtain a superior
evaluation value compare with other dither methods, though it is the decided matrix.
Several investigations of Optimization of the dither matrix are reported. However, there isno investigation from point of view of the combinational optimization problems. In thispaper, we search the optimum dither matrix by Genetic Algorithm (GA) and Simulated
Annealing (SA) and hybrid of GA and SA(GA/SA).We weigh conventional method
against these methods.

©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

PS1-3 Inverse Halftoning using Multi-Layer Feed-Forward Neural Network
Hiroki Hamashoji, Ken-ichi Tanaka(Meiji University)
In this paper, we proposed novel inverse halftoning technique, which use Multi-Layer
Feed-Forward Neural Network (MLFFNN) and Gaussian filtering. This method
reconstruct continuous tone image from any halftoning methods. As a result, a highquality image can reconstructed from binary image compared with conventional method.
Especially the proposed method image quality is good both visually and Peak
Signal-to-Noise Ratio (PSNR) value, compared with reconstructs inverse halftone images
by Look-up table (LUT) based inverse halftoning.

PS1-4 Embedding of the Confidential Image in a Dithered Color Image
Keisuke Banba, Kenichi Tanaka(Meiji University, Japan)
This paper proposed embedding information in dithered color image. We have developed
a method of encryption using computer-generated holograms (CGH) embedded indithered color image. First, confidential information is converted into a CGH. Next, theCGH data undergo two separate dithering processes in parallel: one corresponding to
CGH white pixels and one corresponding to CGH black pixels. The results from bothprocesses are used to form a dither matrix for creating the final dithered and encoded
image. In this way, confidential information can be embedded into the image. The dither
method is algorithm used for digital halftone processing. We evaluated image quality of adithered color image, and evaluation value of the image is superior to one of conventional
method.

PS1-5 Three Dimensional Images Reconstruction on Computer Generated Holograms theMultiple Regression Analysis
Kenta Ayabe, Ken-ichi Tanaka(Meiji University, Japan)
In this paper, for a Computer Generated Hologram (CGH) reconstructing athree-Dimensional (3D) object, we present the improvement of the reconstruction image,reducing of the calculation time using error diffusion method and a multiple regression
analysis. The multiple regression analysis predicts one response variable with plural
explanatory variables. The purposes of this paper are reducing calculation time andimproving reconstruction images. Therefore we move 26 pieces of images from A to Z atvarious positions. We prepare for 650 pieces of images in total and perform GA ineach.We expect diffusivity for a multiple regression analysis from the result andregenerate it..

©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

PS1-6 Embedding used binary number conversion into a Color image
Hiroaki Oguma, Ken-ichi Tanaka(Meiji University, Japan)
Digital watermarking is widely used as security of a binary format image. The purpose ofthis paper is extension to a color image from grayscale image. Then, it thought asimportant giving many embedding information to a color image. A color image is divided
into RGB, the concentration value of a pixel is made into a binary number, and last 3
digits are changed to arbitrary numbers. It embeds by carrying out by repeating this, andsecure many information. 10x19660 times of embedding are able to be performed to the
picture of 256x256 pixels. Moreover, the signal to noise ratio is about 40 db. Therefore, it
seems that there is little influence which it has on a picture.

©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

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

GS6-5

22/54

Ai

Dongmei

GS4-4

17/51

Dai

Fengzhi

GS6-2

22/53

ADNAN

Nazrul H.

GS3-2

14/48

Dan

Chau viet

GS1-4

15/45

GS6-5

22/54

Di

He

GS4-4

17/51

GS3-2

14/48

Dou

Huazhe

GS6-1

21/53

GS6-4

22/54

GS6-5

22/54

Ahmed

Syed Faiz[F]

AkimotoTaisuke

GS1-2

15/45

Fauré

A

OS7-6

23/40

A.R

Aswad

GS3-2

14/48

Feng

Xiaoyang

OS9-1

23/42

Asada

T.

OS3-2

20/32

OS9-2

24/43

OS3-3

20/32

FujitaJunichi

OS4-5

21/35

OS3-4

20/33

Furutani

Hiroshi

OS5-1

18/35

OS3-5

20/33

OS5-2

18/36

PS1-5

18/57

OS9-1

23/42

OS9-2

24/43

Ayabe

Kenta

[B]
Baek

Seung-Hwa

OS2-5

19/31

[H]

Banba

Keisuke

PS1-4

18/57

Hamamura

Tohru

OS7-5

23/39

Berdonosov

V.

OS6-1

22/36

Hamashoji

Hiroki

PS1-3

18/57

Bu

Xuhui

OS1-5

19/28

Hasama

M.

OS6-2

22/37

OS6-3

22/37

OS6-4

23/37

OS4-1

21/34

[C]
Chen

Liming

OS1-3

19/27

HattoriCho

Hyunhak

OS2-4

19/30

OS4-2

21/34

Cheong

Joono

GS1-3

15/45

OS4-3

21/34

Cho

Youngsu

GS1-3

15/45

OS4-4

21/35

Chumkamon

Sakmongkon

GS5-2

15/51

OS4-5

21/35

GS2-1

17/46

GS4-3

17/50

Hayashi[D]

Tetsuo

Eiji

D.

Hazry

GS3-2

14/48

GS5-2

15/51

D.

Hazry

GS6-4

22/54

GS5-4

16/52

©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

He

Juanjuan

OS1-2

18/27

OS1-3

19/27

Hee-In

Jeong

OS2-6

19/31

Jeong

Keon-woo

OS2-3

19/30

Heng

R.

GS6-5

22/54

Jitviriya

Wisanu

GS4-3

17/50

Heo

Shin-nyeong

OS2-3

19/30

Joyo

M.Kamran

GS6-4

22/54

HikizuMasatoshi

GS6-3

22/53

Hirai

Tatsuya

GS3-1

14/48

[K]

Hirota

M.

GS6-5

22/54

Kadirb

M. R. Abdul

GS3-4

15/49

Hisyam

H.

GS6-5

22/54

KamiyaYoshitsugu

GS6-3

22/53

Hwang

Bo-Yeon

OS2-2

19/29

Kang

Sunkyun

OS2-2

19/29

Hwang

Seung-Ik

OS2-3

19/30

Kashima

Daisuke

PS1-1

17/56

Katayama

Tetsuro

OS8-1

19/40

OS8-2

20/41

[I]
Ikeda

Satoshi

OS6-1

22/36

OS8-3

20/41

OS6-2

22/37

OS8-4

20/42

OS9-1

23/42

Kato

Kohei

PS1-2

17/56

OS9-2

24/43

Kato

R.

OS3-2

20/32

OS9-3

24/43

OS3-3

20/32

OS3-5

20/33

Imai

Masayuki

OS7-1

23/38

Imai

Yoshiro

OS4-1

21/34

Kato

Y.

OS9-3

24/43

OS4-4

21/35

Kawakami

Yusuke

OS4-4

21/35

ImabuchiShohei

GS2-5

17/47

Kawamura

Masanori

GS2-3

17/47

Ishikawa

Seiji

GS5-1

15/51

Kawanishi

Chiaki

OS4-1

21/34

GS5-5

16/52

KawanoHiromichi

OS4-2

21/34

Ito

Akihiro

GS3-1

14/48

OS4-3

21/34

Ito

Keisuke

GS5-4

16/52

OS4-4

21/35

Ito

Takao

OS6-1

22/36

Kim

Donggun

GS3-1

14/48

OS6-2

22/37

Kim

Dong-Hyun

OS2-5

19/31

OS6-3

22/37

Kim

Hee-Je

OS2-5

19/31

OS6-4

23/37

Kim

Hyoungseop

GS5-1

15/51

OS9-1

23/42

GS5-5

16/52

OS9-2

24/43

Kim

Jaeyong

OS2-4

19/30

OS9-3

24/43

Kim

Ki-jung

OS2-2

19/29

OS7-3

23/39

Kim

min gyu

OS2-6

19/31

Kim

Min-Ho

OS2-1

19/29

Kim

Sungshin

OS2-4

19/30

Kim

Yeoun-Jae

GS7-2

16/55

Iwadate

Y.

[J]
Jia

©ICAROB 2014

Yingmin

OS1-2

18/27

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

KimuraHiroshi

OS7-4

23/39

HautopKita

Yoshihiro

OS8-1

19/40

IS1-2

14/25

OS8-2

20/41

IS1-3

14/26

OS8-3

20/41

OS8-4

20/42

[M]

KitanoShoichiro

OS8-3

20/41

Ma

QuinLian

OS5-2

18/36

Kobayashi

Kunikazu

GS2-3

17/47

Mabu

Shingo

GS4-1

16/50

GS2-4

17/47

GS7-1

16/55

GS4-1

16/50

Maruyama

N.

GS6-5

22/54

GS7-1

16/55

MatsunoHiroshi

OS7-2

23/38

OS4-2

21/34

OS7-6

23/40

OS4-3

21/34

OS9-4

24/44

OS6-1

22/36

KoyamaYoshihide

Kozakai

Kenji

GS3-1

14/48

Kubo

Masao

GS1-4

15/45

OS6-3

22/37

Kubota

S.

OS5-3

18/36

OS6-4

23/37

Kuremoto

Takashi

GS4-1

16/50

Matsushita

Haruna

OS4-4

21/35

GS7-1

16/55

Mehta

R.

OS6-1

22/36

OS9-1

23/42

OS6-2

22/37

OS9-2

24/43

MizunoDaisuke

GS3-1

14/48

OS3-2

20/32

MohamedKhairul

GS6-3

22/53

OS3-5

20/33

Sahari

Salleh
GS7-4

16/56

KurogiKuwahara

TatsumaN.

Matsuno

[L]

S.

Morie

Takashi

GS5-5

16/52

Lee

Dong-hyuk

OS2-2

19/29

Morita

Shou

GS5-1

15/51

Lee

Dong-Ju

OS2-6

19/31

MoritaYoshifumi

GS3-1

14/48

Lee

Hee-Mu

OS2-1

19/29

GS3-3

15/49

Lee

In-uk

OS2-6

19/31

[N]

Lee

Jang-myung

OS2-2

19/29

Nagatomo

OS9-1

23/42

OS2-3

19/30

OS9-2

24/43

OS2-6

19/31

Nakamura

Hiroto

OS8-1

19/40

MakotoLee

Ju-Jang

GS7-2

16/55

Nakanishi

Y.

OS3-4

20/33

Lee

Min-Cheol

OS2-1

19/29

Nakaoka

I.

OS6-5

23/38

Li

Binyao

GS6-2

22/53

Namatame

Akira

GS1-4

15/45

Li

Sanyi

OS1-5

19/28

Nanaa

Kutiba

GS5-3

16/52

Li

Yuan

GS6-2

22/53

Narematsu

N.

OS7-3

23/39

Lund

Henrik

IS1-1

14/25

Narumoto

J.

OS3-2

20/32

©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Narumoto

J.

OS3-5

20/33

Nishii

Jun

OS7-4

23/39

OS7-5

23/39

Rizon
Rubin

Nishikawa

Kenta

OS8-4

20/42

Nishimura

A.

GS6-5

22/54

RazlanZuradzman.M

GS3-2

14/48

GS6-5

22/54

Mohamed

GS5-3

16/52

J. E.

OS5-3

18/36

SabbahThabit

GS4-2

16/50

Sakamoto

Makoto

OS5-1

18/35

[S]
[O]
Obayashi

Masanao

GS4-1

16/50

GS7-1

16/55

OS5-2

18/36

Oofuji

Yoshiyuki

OS7-1

23/38

OS6-1

22/36

Ogata

Takashi

GS1-2

15/45

OS6-2

22/37

GS2-5

17/47

OS6-4

23/37

Oguma

Hiroaki

PS1-6

18/58

OS9-1

23/42

Okada

T.

OS6-5

23/38

OS9-2

24/43

OkamotoKensho

OS4-5

21/35

OS9-3

24/43

OkazakiNaonobu

OS8-1

19/40

Sato

Daisuke

OS4-5

21/35

OS8-2

20/41

Sato

Hiroshi

GS1-4

15/45

OS8-3

20/41

Satou

Kiminori

GS2-1

17/46

OS8-4

20/42

Sato

Noritaka

GS3-3

15/49

GS3-1

14/48

Seki

Hiroaki

GS6-3

22/53

Selamat

Ali

GS1-5

15/46

GS3-4

15/49

GS4-2

16/50

GS1-5

15/46

GS4-2

16/50

Onishi

Shigenori[P]
Pagliarini

Luigi

IS1-2

14/25

Park

Heung-in

OS2-6

19/31

Park

Kang-il

OS2-6

19/31

Park

So-Yeon

GS7-2

16/55

Shahriman

A.B.

GS6-5

22/54

Park

Y.

OS6-2

22/37

SharmaDipak Gaire

GS2-2

17/46

OS6-5

23/38

Shibata

Hiroki

GS6-3

22/53

GS1-5

15/46

Shibata

Satoru

GS3-1

14/48

Shimokakimoto

Tomoya

IS1-3

14/26

Shimohara

Katsunori

GS1-1

15/44

GS2-2

17/46

Phetchanchai

Chawalsak

Selamat[Q]
Qian

Shaohua

GS5-5

16/52

Md Hafiz[R]
RahimaM. S. M

GS3-4

15/49

RahmanM. N. Abd

GS5-3

16/52

Rajapakse

R.P.C.Janaka

OS4-4

21/35

RashidahSuhaimi

GS3-2

14/48

©ICAROB 2014

P - 62

Shinomiya

Takashi

GS5-5

16/52

Sugii

M

OS7-6

23/40

Sugisaka

Masanori

GS6-1

21/53

GS7-3

16/55

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Sugiura

Yuka

GS3-1

14/48

OS6-4

23/37

Suzuki

Kenji

IS1-3

14/26

OS9-1

23/42

OS9-2

24/43

[T]
TabuseM.

Ukai

Hiroyuki

GS3-1

14/48

UrakamiNaohito

OS7-1

23/38

Khairunizam

GS3-2

14/48

GS6-5

22/54

OS3-1

20/31

OS3-2

20/32

OS3-3

20/32

[W]

OS3-4

20/33

WAN

OS3-5

20/33

Tada

A

OS3-1

20/31

Wang

Chaoli

OS1-1

18/27

Tagami

Hiroki

OS5-1

18/35

Wang

Fuzhong

OS1-5

19/28

Tagawa

S.

OS6-1

22/36

Wang

Jiwu

GS6-1

21/53

TakedaHaruna

GS3-1

14/48

GS7-3

16/55

Tan

Joo Kooi

GS5-1

15/51

Wang

JungTang

GS3-3

15/49

GS5-5

16/52

Warsi

Faizan. A.

GS6-4

22/54

TanabeHirofumi

GS3-3

15/49

Watada

Shogo

GS7-1

16/55

TanakaKen-ichi

PS1-1

17/56

Watanabe

Shun

GS4-1

16/50

TanakaKen-ichi

PS1-2

17/56

Wu

Ren

OS9-4

24/44

TanakaKen-ichi

PS1-3

18/57

TanakaKen-ichi

PS1-4

18/57

[X]

TanakaKen-ichi

PS1-5

18/57

Xi

Junhong

GS6-2

22/53

TanakaKen-ichi

PS1-6

18/58

Xuyan

Tu

GS4-4

17/51

Tanev

Ivan

GS1-1

15/44

GS2-2

17/46

[Y]
YamabaHisaaki

OS8-1

19/40

Tanveer

M.Hassan

GS6-4

22/54

Tenys

N.

GS1-1

15/44

OS8-2

20/41

Tian

Zhongyuan

OS7-2

23/38

OS8-3

20/41

To

Ichihi

OS5-1

18/35

OS8-4

20/42

To

Ichihi

OS5-2

18/36

Yamamoto

Takashi

OS7-1

23/38

Tokumitsu

T.

OS6-5

23/38

YamazakiKazunori

GS3-1

14/48

Tsubakimoto

Tatsuya

GS2-4

17/47

GS3-3

15/49

Tsuji

A.

OS3-2

20/32

OS9-1

23/42

OS9-2

24/43

Yokomichi[U]
Uchida©ICAROB 2014

Yasuo

OS6-2

22/37

OS6-3

22/37

P - 63MasahiroYoo

Han-Dong

OS2-3

19/30

Yoshinaga

Tsunehiro

OS9-1

23/42

OS9-2

24/43

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Yoshimitsu

Y.

OS3-5

20/33

Yoshitomi

Y.

OS3-2

20/32

OS3-3

20/32

OS3-4

20/33

OS3-5

20/33

You

Guodong

GS6-2

22/53

Yu

Fashan

OS1-5

19/28

Yuen

Barry

GS7-4

16/56

Loh

Tze

[Z]
Zahra

Rezaeia

GS3-4

15/49

Zeng

Guangping

GS4-4

17/51

Zhang

Hengjun

OS1-1

18/27

Zhang

Tuo

OS9-1

23/42

OS9-2

24/43

Zhang

Weicun

OS1-4

19/28

Zhang

Weining

GS7-3

16/55

Zhang

Xianwen

GS6-1

21/53

GS7-3

16/55

Zhang

Yu-an

OS5-2

18/36

Zhao

Zhijia

OS8-2

20/41

Zou

Dongxu

OS1-4

19/28

©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

©ICAROB 2014

P - 65

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Playware Research – Methodological Considerations
Henrik Hautop Lund
Centre for Playware, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
hhl@playware.dtu.dk
www.playware.dk
Abstract: Several sub-disciplines of engineering are driven by the researchers’ aim of providing positive change to thesociety through their engineering. These researchers are challenged by the traditional research method of experimental
research with a waterfall model which demands clearly defined project definition and functional requirements, and impose asequential processes leading to the final system evaluation, which may lead to solutions which work in the lab, but havelittle impact in the messy real world. Based on two decades research in developing engineering systems with a societal
impact (e.g. in robotics, embodied AI, and playware), in this paper we suggest a cyclic research method based on a mix
between participatory and experimental processes. In particular, inspiration from the action research method applied tointerdisciplinary technology development becomes a participatory approach characterized by rapid prototyping cycles
which allow iterative technology specification and development together with people in their real world environment. With
the mixed research method, we suggest that there are cases, where approaches from the positivistic and interpretivistic
epistemologies can and should be merged. We exemplify this with health care technology and playware.
Keywords: Playware, Research Method, Synthesis, Rapid Prototyping, Modular Technology.

etc. where the conventional engineering research method
and the waterfall model has been applied with success. In
the wild, messy real-world, there may be a need foranother research method for researchers who aim atdeveloping technological solutions that have a deep
impact on people in their daily life and on the society.

I. INTRODUCTION
Development of different research methods have often
been neglected in the engineering discipline, since thediscipline has followed the traditional natural science
research methods of performing experimental research ofa positivist nature. This involves rigor in controllingparameters keeping specific parameters constant andothers variable in empirical testing. Hence, the researchmethod arises from a reductionist belief that systems canbe described fully by its components and their interaction
with a fixed and constant environment. This “ideal” viewof the world often leads the engineer to a conventional
development approach termed as the waterfall model [1]with clearly identifiable and separated sequential
processes of 1) Project definition, 2) Functional
requirements, 3) Functional design, 4) Implementation
design, 5) System assembly, and 6) System evaluation.

Here we suggest a cyclic action research method basedon a mix between participatory and experimental
processes. In particular, inspiration from the action
research method applied to interdisciplinary technology
development becomes a participatory approach
characterized by rapid prototyping cycles which allows
iterative technology specification and development
together with people in their real world environment.

II. MIXED RESEARCH METHOD

However, engineering is also about creating and bringing
technological solutions to people and the society. Several
sub-disciplines of engineering are driven by the
researchers’ aim of providing positive change to thesociety through their engineering. These researchers arechallenged by the traditional research method. Insightfrom social science, humanities, and arts tells us thatpeople and the society may not be as rigorously
controllable as lab conditions, industrial factory halls,©ICAROB 2014

With the mixed research method, we suggest that there
are cases, where approaches from the positivistic andinterpretivistic epistemologies can and should be merged.
Health care technology is one such case. Health caretechnology aims at providing new technological solutions
for the public health care sector, e.g. for hospitals, forrehabilitation and care centres, and for care in the private
home. Developing such solutions demand a broad
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competencies, and interdisciplinary competencies todevelop health care solutions. The mixed action researchmethod is therefore particularly well suited for
performing such research in health care technology.

robotics, and tangible interfaces. Engineering researchers
perform fundamental research in these areas to develop
the basis for understanding and creating user-interactive
technological systems. To operationalise this knowledge
and develop prototypes with users, the research method
takes its point of departure from the technologicaldiscipline and the humanistic discipline investigating,understanding and exploiting these scientific fields(modern artificial intelligence, modular robotics, andtangible interaction) in combination with anunderstanding of play and play dynamics, and in moregeneral terms play culture and human motivation. The
knowledge is combined in synthesis to develop designprinciples, prototypes, and demonstrators of playware
technology, and a choice of these demonstrators are
selected for (e.g. industrial) refinement to become
modular playware technology products. The resulting
modular playware technology is systematically studied
and investigated in the messy real-world environments inorder to guide the next cycle of synthesis, demonstrator
and prototype development. Hence, the research method
is based upon the interdisciplinary research on how
playware in the form of intelligent hardware and software
creates play and playful experiences amongst the users intheir environment.

Traditional action research methods for addressing theneeds and practice in care centers and private homes areclearly different from traditional research methods inmedicine of performing experimental research e.g. effectstudies through a randomized controlled trial. However,both issues are crucial to include in research which aims
at having an impact on society. Without an iterative
participatory development, it is unlikely that users will
see the technology fit into their lives and practice,whereas it is unlikely that health authorities will accept
the technology without experimental research according
to the acknowledged protocols within the field of
medicine. There is little chance that our society will
accept technological health care solutions without the
rigorously controlled experiments that provide evidenceof effect and potential collateral effects. Hence, a mixed
research method is a necessity to ensure impact in thesociety. Indeed, it would be clearly against the aims ofaction research of creating society improvements not toinclude such rigorously controlled experiments in subprocesses of the research method, since theseexperiments are the cornerstone for the society to accept
the health care technology. Hence, in such a mixed action
research, some sub-processes in the iterations can be
performed in a participatory manner, whereas other subprocesses must be performed through controlled
experiments.
Other examples include contextualized IT training andcommunity-based rehabilitation, which by their natureare close to action research and participatory methods.
Nevertheless, in the case of contextualized IT training
there may still be a need to perform experimental
research on technology and educational outcomes inorder to obtain impact in society, and in the case ofcommunity-based rehabilitation, there is the need toperform rigorous effect studies similar to those for other
health care technologies.

Figure 1. The combination of several research disciplines insynthesis in an iterative working process

III. APPLYING THE METHOD

Within the individual cycle, the systematic study andinvestigation of prototypes and products in the messy
real-world environments can follow an experimental
research method and/or an interpretive research method.
Indeed, often it may be necessary to perform a number ofstudies with a plurality of methods, for instance in order
to be able to investigate both the technological and thepractical feasibility in the environment. There are issues

As an example of applying this mixed research method,
let us look at the research field of playware [2, 3].
Typically, playware research has a core technology
research activity focusing on research into modular
playware technology and its supporting fundamental
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regarding technological stability and robustness whichlends itself best to experimental research of a positivist
nature, whereas issues regarding human interaction may
lend themselves best to participatory research of aninterpretivist nature. The combined knowledge from thetwo research directions’ investigations will be the guidefor the next cycle of synthesis, demonstrator andprototype development. Combining and weighing theknowledge from the two research directions ischallenging and by no means a trivial task. Therefore, theresearch method demands that participants build sharedknowledge and language about the environment, andresearch focuses on how results from the two directions
are to be combined. The research method facilitates thiscombination by focusing on synthesis in the iterative
process.

For the development of solutions in the mixed researchmethod, performing such iterations is viewed as
beneficial to both understanding the environment (users,
use cases, practice, etc.) and to specify, develop, andrefine the technological solution, since the iterative
approach provides repeated observations to detect clear
patterns. Further, it is noteworthy and important thatusers and environments change with the introduction of
the technological solutions, as studied through theiterations.

IV. ITERATIVE APPROACH

Rapid prototyping is characterized by making iterationsof prototypes in a fast manner, where the quick
development of prototypes allows constant interaction
and testing in the environment (e.g. with the users). This
serves both as development of the system specifications
themselves and as grounding in reality. Since the rapid
prototyping approach recognizes that all requirements
cannot be specified a priori, in contrast to the waterfall
approach, the requirements are learned and satisfied
incrementally. Hence, small improvements are constantly
implemented and aligned with users/environment.

In order to perform these iterations to build knowledge
and to perform successful synthesis, rapid prototyping
provides a method for ensuring grounding of thetechnology development in human interaction reality andgrounding of the human interaction analyses in thetechnological reality.

The example of applying the mixed research method to
playware research points to an iterative approach inwhich knowledge is built from iterations of synthesis andapplication in the environment. Indeed, Lewin [4] (1946)
originally pointed to a spiral of steps in action research,and the research method has been known as cyclic action
research. In general, the cyclic action research involves
identification of a practical problem, making a solution,and reflections about the solution, which then leads to thenext iteration of identification, solution, and reflection,and so on. Indeed, in 1978, Susman and Evered [5]developed the action research cycle to include the fivestages of diagnosing, action planning, action taking,evaluating, and specifying learning, which are theniterated.

According to the ASTM Standard [6], in contrast to thewaterfall approach of preparing “requirements and design
documents that describe the needed system, rapid
prototyping methods concentrate on preparing a working
prototype. Users and developers learn the functional
requirements and an appropriate system design byinteracting with a series of prototypes, each of which is
rapidly produced from a starting framework or from anearlier version. A prototype can evolve into an
operational system, it can serve as an exact behavioral
specification of an operational system, or it can be used
to explore the feasibility of a new idea or design which
can be incorporated in a larger system.”
Such rapid prototyping is attractive to perform synthesis,
not only because it creates the technological prototypes
and advances, but also because it provides all participants(researchers, professionals, users) a common tool to meetaround, share, and interact with, thus creating a common
language and understanding, which is essential for theinterdisciplinary research. The concrete hands-on
experience with the prototypes in the environment
grounds abstract concepts in concrete actions, whichfacilitates the creation of a common language andFigure 2. The iterative working process used to create andcombine interdisciplinary knowledge in synthesis. There may
be parallel processes studying implementation with different
scientific methods (double arrows), and the knowledge is
combined in synthesis of the subsequent cycle.

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understanding amongst the participants. The common
understanding is further refined fast in the rapid
prototyping cycles.

ACKNOWLEDGEMENT
The author would like to thank colleagues from theCenter for Playware, DTU for experimental research,
interdisciplinary collaboration, and methodologicaldiscussions which led to the formulation of the presented
research method.

This kind of cyclic action research method proposes
flexibility in the research design to meet the real needs inthe environment. Cunningham stated this in 1976 as:“The action research process makes it highly unlikelythat the investigator will know exactly, or in advance, thedesign of the inquiry. Since every execution has to beevaluated and judged as to how effectively it meets theplan, revisions to fit new needs will be necessary. As
hypotheses are validated or invalidated by the interim
results, the problem may be redefined and the hypotheses
and research methods modified.” [7] p. 218.

REFERENCES[1] W. Royce. “Managing the Development of Large
Software Systems”, Proceedings of IEEE WESCON 26(August): 1–9, 1970.
[2] H. H. Lund, T. Klitbo, and C. Jessen, C. “Playware
Technology for Physically Activating Play”, Artificial
Life and Robotics Journal, 9:4, 165-174, 2005.
[3] H. H. Lund, and P. Marti. “Designing Modular
Robotic Playware”. In Proc. of 18th IEEE Int.
Symposium on Robot and Human Interactive
Communication (Ro-Man 2009), IEEE Press, 115-121,
2009.
[4] K. Lewin. “Action Research and Minority Problems,”
Journal of Social Issues 2 (1946): 34-46.
[5] G. I. Susman, and R. D. Evered. “An Assessment of
the Scientific Merits of Action Research”, Administrative
Science Quarterly, (23) 1978, pp. 582-603.
[6] ASTM International, “Standard Guide for Rapid
Prototyping of Information Systems”, E1340 − 05, PA,
USA, 2010.
[7] B. Cunningham. “Action Research: Toward a
Procedural Model,” Human Relations (29:3), 1976, pp.
215-238.
[8] B. Boehm. “A Spiral Model of Software
Development and Enhancement”, ACM SIGSOFT
Software Engineering Notes, ACM, 11(4):14-24, August
1986[9] B. Boehm. “A Spiral Model of Software
Development and Enhancement”, IEEE Computer, IEEE,
21(5):61-72, May 1988
[10] M. Resnick. “Sowing the Seeds for a More Creative
Society.” Learning and Leading with Technology,
December 2007[11] H. H. Lund. “Modular Playware Technology – A
Brief Historical Review”. In Proceedings of 17th
International Symposium on Artificial Life and Robotics,
ISAROB, Japan, 2012.
[12] H. H. Lund. “Anybody, Anywhere, Anytime –Robotics with Social Impact through a Building Block
Approach”. In Proceedings of IEEE Advanced Robotics
and Its Social Impact (ARSO 2011), IEEE, 2012

V. DISCUSSIONInterestingly, the spiral of steps in action research as
proposed originally by Lewin [4] seems widely to haveinfluenced other spiral models, which have become
known extensively in other engineering and scientificdisciplines such as software development [8, 9], design,and constructivist education [10]. For instance, in thecreative thinking spiral, people imagine what they want
to do, create based on their ideas, play with theircreations, share their ideas and creations with others, andreflect on their experiences—all of which leads them toimagine new ideas and new creations.
The iterative process needs to start somewhere. Susman
and Evered [5] suggest that the cycle starts with thediagnosing phase. Similarly, the imagine phase provides
the start in the creative thinking spiral [10]. This canoften be a difficult and abstract phase for researchers andparticipants. It is our belief that the process can be kickstarted in such a diagnosing or imagining phase by the
introduction of technology suitable for diagnosing,imaging and creating prototypes. Such technology canideally have a modular expression to allow any user toeasily construct, combine and create prototypes fromthose modules. Based on a deep engineering andcomputer science knowledge on modular technology andembodied artificial intelligence, it is possible to develop
technological modules that any user can easily
understand and construct with within a minute [e.g. 2, 11,12]. This provides an important hands-on involvement in
the first phase, which makes it easier for participants toground the diagnosing and imagination in both the world
reality and the technological reality.

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ALife for Real and Virtual Audio-Video Performances
Luigi Pagliarini1,21Henrik Hautop Lund1

Centre for Playware, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark2
Academy of Fine Arts of Macerata, Via Berardi, 6, 405111 Macerata, Italy
luigi@playware.dtu.dk hhl@playware.dtu.dk
www.playware.dk

Abstract

popular music with the MusicTiles app simply byconnecting musical building blocks. This creation ofmusical expression can happen as a performance (i.e.
run-time). When connecting the MusicTiles app to the
MAG software, we provide the connection and thepossibility to melt both musical expression and graphicalexpression in parallel and at run-time, and therefore
creating a audio-video performance that is always unique.

MAG (i.e.: an Italian acronym which stands for Musical
Genetic Algorithms) is an electronic art piece in which a
multifaceted software attempts to “translate” musicalexpression into a corresponding static or animatedgraphical expressions. The mechanism at the base of such“translation” consists in a quite complex and articulated
algorithm that, in short, is based on artificial learning.
Indeed, MAG implements different learning techniques
to allow artificial agents to learn about music flow bydeveloping an adaptive behaviour. In our specific case,such a technique consists of a population of neuralnetworks – one dimensional artificial agents that populate
their two dimensional artificial world, and which areserved by a simple input output control system – that canuse both genetic and reinforcement learning algorithms to
evolve appropriate behavioural answers to an
impressively large shapes of inputs, through both afitness formula based genetic pressure, and, eventually, auser-machine based feedbacks.

Introduction
In this paper we present the “MAG” [1] an application,an ALife software based on and derived from ourprevious work on the ALife models and work.
MAG (i.e. Musical Genetic Algorithm, in Italian) is anelectronic art piece that throughout an automatism
translates sound expressions into graphical ones. The
mechanism behind such “translation” is a quite complex
algorithm that, synthetically, relies on artificial learning.
Indeed, the MAG software implements a population of
artificial agents – living in an artificial world andfurnished with a simple sensory-motor system – thatgenetically evolves to coherently respond to different
inputs, thanks to the most classical selective pressure [2].
In the MAG software the agents control system is a
Perceptron [3], the simplest version of a Neural Network[4]; The world in which such perceptrons are placed is a
two dimensional grid of adaptive dimensions and with atoroidal shape; The main input which the artificialorganisms respond to is a sound wave; the artificial
agents’ answer to inputs is a behavioural one where thebehavior is traced and graphically impressed onto thecomputer screen.
The combination of artificial evolution and the executionof a song or many different songs make it possible toachieve at any software execution a special and uniquerelationship between incoming sound waves and theconsequent emerging graphical expression.
After having made all possible efforts to reach highly
aesthetical results, and once obtained such a relationshipMore closely, in the first version of MAG algorithm theagents’ control system is a perceptron; the world of theagents is a two dimensional grid that changes its
dimensions accordingly to the host-screen; the mostimportant input artificial agents get (i.e. not necessarily
the only one) is the musical wave that any given musicalfile produces, run-time; the output is the behaviouralanswer that agents produce by moving, and thereby
drawing on to a computer screen, therefore graphical.
The combination of artificial evolution and the flows of arepeated song or different musical tunes make it possible
for the software to obtain a special relationship between
sound waves and the aesthetics of consequent graphicalresults.
Further, we started to explore the concept of run-timecreation of both music and graphical expression.
Recently, we developed a software by which it ispossible to allow any user to create new song versions of©ICAROB 2014

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between music and graphics, we developed the conceptof applying the MAG algorithm to our MusicTiles
application [5, 6] - a software conceived to open up
music remixes and composition to ordinary people andprofessional users. All of that, because our final intention
is to research for a mechanism by which any kind of user
could use a tool for creativity to live performing bothmusic and graphics, sound and images.

Technology
The underlying technologies of the MAG algorithm
belong to the classic simulative logic with its roots in
Artificial Life [7], and which we already used for artisticmeans [8, 9]. Within an artificial planet (i.e. a twodimension matrix, a chessboard with a toroidal spatial
geometry) a population of artificial organisms - whose N
shapes according to the screen size – starts evolving andby doing so draws on an “electronic canvas” acontinuously-transforming image.
Artificial organisms, that are evolved thanks to a Genetic
Algorithm [10], occupy a Cartesian point of such world(i.e. are mono-dimensional) and their central nervous
system consists of a Perceptron that allows to establish a
specific relationship between a sensory input and a motor
output.
The main input we chose to stimulate such perceptrons is
a given portion (i.e. of parameterized dimensions) of asound wave, extrapolated run-time from any file or direct
sound running on the sound board. To such input, thatnumerically speaking is the main source of information
to all the Perceptrons, the user could easily add a
different input, such as the organism’s distance from one
or more landmarks. Such landmarks - which are
positioned into the world either randomly or logically(i.e. at user choice) - create a referring point(s) aroundwhich the organism can navigate or maybe, as in our
specific case, “dance”.
To these, that under all circumstances can be consideredreal inputs, we added different simulation options andtunable algorithmic parameters (see Figure 1) that enrich
the behavioral scenario and, consequently, the graphic
one, transforming each single instance of the simulation.
Indeed, as we can easily imagine observing the Control
Panel, shown in Figure 1A, there are quite many
parameters that contribute to define the specificity andevolutionary characteristics of the running simulation.
Some of them act on the agent’s general behavior - forexample the static or dynamics of the landmarks – andothers influence the aesthetical result – for example thephenotypic expressions of the agents as the tail or thecolor can be.

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A. Control Panel

B. Data Panel

Figure 1. The Control and Data Panels
All the variables shown in the Control Panel can bechanged run-time, meaning that the user can shape eachsingle simulation, making it more or less unrepeatable.
Indeed, opening up such a set of variables to users’intervention corresponds to acting on the computed
fitness gradient - since the fitness is the index by which
the adaptation coefficient of an artificial agent iscalculated. By modulating such computation we act on
the reproductive power of each single organism and,
therefore, we can change the effective history of therunning simulation. Each simulation can be monitored
through a Data Panel – see Figure 1B for an example.
Music is at the center of the MAG software, that’s is whyeach simulation starts as soon as an audio stream appears(e.g. at the beginning of a song) and produces agenerational transition as soon as silence interrupts it(e.g. at the end of the song). By generational transition
we mean that the ordinary GA’s process of bestindividual’s fitness selection and reproduction. In ouralgorithm, the reproduction routine applies both
mutations and cross-over. All of that is repeated for eachsong (i.e. a high significant discontinuity in the amplitudeof the streaming audio, or silence) and such process isreiterated up to when the user consider it sufficient, as anaesthetical result. Indeed, as the simulative process goeson, one can observe and more and more well defined
behavioral expression of the artificial agents where, mostof the times, agents learn to “dance” on the sound wavethat goes across themselves.
As shown in the Control Panel (Figure 1A), besides theinter-generational selective pressure, it is made possible
to apply to agent behavior two different forces of-6-

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Reinforcement Learning [12], since we were seeking for
a force able to change the intra-generational behavior of
our perceptrons.
The two Reinforcement Learning routines we
implemented:
A. Crash. We apply the RL each time an agent crashes
against the world borders (i.e. we might reward
agents that stand in the center of the “dance-floor”);
B. Meeting. We apply the RL each time an agent
crashes against another agent (i.e. we might reward
agents that do “not smash other agents feet”);
Both RLs are used and seem to work despite of the factthat agents are not always given the possibility of sensing
the world contours or the mates’ presence. This is also
because the two behaviors described above start to becomputed in to the Fitness Formula as soon as they are
selected as operative.
In synthesis, to summarize the whole simulation flow:
1. As soon the MAG is started, the users are allowed
to choose amongst few basic and starting
parameters (e.g. number of landmarks);
2. A population of N Perceptrons (according to screensize) is initiated;
3. As soon as the sound board is activated, agents
population is started;
4. Agents, one after the other, receive as input a
portion of run-time executed sound wave (i.e.
plotting samples, which is itself a variable)
5. For technical reasons, and since the sound process isalways faster than simulative computation, eachsingle agent is fed with a different portion of soundand, therefore, has a specific and unique input;
6. Agents calculate their behavioral reaction to soundand, eventually, other factors (e.g. distance tolandmarks);
7. Agents calculates their potential movement in the
Cartesian space and, therefore the fitness of such
(RL takes place here, eventually);
8. All agents draws their movement;
9. The simulators opens up to any user interaction,either regarding functional parameters (i.e.
Genetics, Fitness Formula, Reinforcement Learning,
Environmental Parameters, Sound Input Choice andAnalysis) and non-functional ones (i.e. GraphicalOutput, Feedback Tools, Run-Time Data
Description, Simulation Controls, etc.);
10. As soon as the sound board is deactivated (i.e. endof files or silence) agents population is submitted toa generational transition, which means selection,
reproduction and mutation;
This process is re-iterated at the users’ will.

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Aesthetical Outcome
The simulation leader decides if, and to what extends, tocontrol the graphical outcome of our population of
agents, leave that decision to emerge from specificexpressions of agents’ artificial genes or, since the wholeprocess can be modified run-time, both. For the graphicaloutcome we intend both the colour and the shapes of thedrawings. The MAG genes act in such a way that part ofthe phenotypic expressions of the output are controlled
by the single agent, while other ones (e.g. the backgroundcolor) might be controlled by all agents together. For
example, a single agent’s gene controls the length of its
stepping, while in each cycle the algorithm weights up
the outputs of the whole population and determines the
background color (i.e. when a specific option has been
activated by the users). Therefore, the chromatic basis of
the agents’ “drawings” can either be determined by theuser or rely on a genetic mechanism. The resulting
aesthetical outcomes perform a quite impressive range ofgraphical solution and, moreover, is capable of big
variations even within one single simulation run (seeFigure 2 for few examples).

Figure 2. Some still images from different evolutions ofdifferent songs.
Aesthetics is (or can be) modified across the time andchanges accordingly to the interactive process enacted
the users with genetic components, landmarks,
reinforcement learning and the other variables but,overall, it is influenced and transfigurated by the choiceof different music flows, that stands for the user decisionof the sound input which can be determined at any given
moment along the evolutionary paths initiated by the
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The MAG software has already found its artistic
positioning under the form of a preassembled video(Gabriele 2012) whose projection rights have been soldto different galleries and festivals, all over the world.
Despite of that, the software itself is under constanttechnical evolution, and in the early future we will
release an advanced version where the agents populationwill:
1. Incorporate three layers Neural Networks, instead of
the actual Perceptrons (i.e. to reach much more
complex and articulated movements);
2. From simple input-output organisms agents’ brain
will be furnished with a neural memory mechanism(i.e. to better follow the bit);
3. Each single population will be split into different“races”, or different subsets of populations (i.e. topush agents specialization on different frequencies ofsound analysis).

a tile to another (see Fig. 5 left) the two tiles get
synchronized and are able to play together.
To actually start the music, the user has to press theplay/stop icon, positioned at the bottom-right side of thescreen (see Fig. 3). At this point, the music will keepplaying and the remaining music tiles can be connectedto the former two enriching the music scenario of thesong running. In the same way, by dragging the tiles
around and rearranging their geometry, the user changes
the music sequence and flow.

Figure 4. Left: Connecting two tiles. Right: 2-sides connection
example.

MusicTiles Interface

Every tile has 7 music loops, one for each side (northside, east side, south side and west side) plus 3 extra
music loops which will play when a tile is connected on,
respectively, two, three or four sides. In this way, as inFig. 5 left, the Drum tile will play the East Side loop andthe Guitar tile will play the West Side loop, and as in Fig.
5 right, the Drum tile will play the 2 Sides loop, the
Guitar tile will play the West Side loop and the Choir tile
will play the North Side loop.
Likewise, as in Fig. 5 the Drum tile will play 3 Sides
loop, the Choir tile the North Side loop, the Guitar tile
will play the West Side loop, and the Bass tile the Eastone.

The MusicTiles [5] (former MagicTiles [6]) music app,derived from the RoboMusic concept [13] and that can beused by users of different musical competencies - fromprecipitants to musician to expert composers - to bothremix songs or experiment with new tunes.
The MusicTiles app that runs on iPads and iPhones, ismade of a small number (i.e. more than 3 less than 10) of
virtual music tiles. Each single tile embodies a single
instrument and each instrument-tile is capable ofexpressing seven different musical solution (i.e. loops) orvariation of the same one. Every instrument of aMusicTiles song is wrapped inside a single tile and,
therefore, we can have a tile for vocals, one for therhythm guitar, one for the bass, one for the piano, etc.
At the start, the user is presented with all the tileinstruments of a given song inside the tiles board with thetile-instruments being disconnected from each other (seeFig. 3).

Figure 5. A 3 Sides connection example.

Growing the number of instruments/tiles impressively
increase the number of possible combinations and out ofthese simple rules, one obtains an extraordinary numberof musical possibilities.

MAG and MusicTiles App Integration
Besides all of that, we are at the beginning of a process of
integration of MAG software with MusicTiles music app
to explore the concept of run-time composition of bothmusic and graphical expression. The idea is to allow any

Figure 3. MagicTiles for iPad.

Since a tile is disconnected from other tiles, it is not
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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014user to create musical expression through the MusicTiles(i.e. which can happen run-time as in a performance runtime) and, when connecting the MusicTiles app to the
MAG software, providing he/she the possibility to melt
both musical expression and graphical expression inparallel therefore creating a audio-video performance thatis always unique.

http://www.fastcompany.com/3004537/peter-gabrielslatest-app-musictiles-so-20[6] L. Pagliarini and H.H. Lund: MagicTiles. ALife forreal and virtual Robot Music. Proceedings of the 17th
International Symposium on Artificial Life and Robotics(AROB’12), ISAROB, Japan, 19-21 Jan, 2012.
[7] C. G. Langton. Artificial Life. In C. G. Langton,
editor. Artificial Life, Volume VI of SFI Studies in the
Sciences of Complexity, pages 1-47, Addison-Wesley,
Redwood City, CA, 1989.
[8] A. Spina, L. Pagliarini, Globalization: tra arte scienza
e società. In Rivista di Psicologia dell'Arte Anno XII,
n.12, 87-92. Roma, Dec. 2001.
[9] L. Pagliarini. Una Tela Viva. Rivista di Psicologia
dell’Arte, Nuova Serie Anno XXIII, n°13, Dicembre
2002. Pag. 33-36[10] M. Mitchell. An introduction to genetic algorithms.
MIT Press. 1997.
[11] A. Gabriele, Ogni Dove Edizioni, 2012
http://www.ognidove.eu/ode2/.
[12] R. S. Sutton, Temporal Credit Assignment in
Reinforcement Learning, Ph.D. thesis, University of
Massachusetts, Amherst, MA, 1984[13] H. H. Lund, and M. Ottesen. RoboMusic – A
Behavior-Based Approach, Artificial Life and Robotics
Journal, 12: 1-2, pp. 18-23, 2008.

Discussion and Conclusion
In this paper, we presented both a new application –MAG, which aims to create graphical expression byfollowing musical paths - and a previous one MusicTiles, which aims at exploring a new approach tomusic for run-time composition and/or remixing.
Through such applications any user could create andperform both Graphics and Music, on one side, by
manipulating simulation parameters and, on the other, byassembling a number of interactive virtual tiles on thescreen. After our past efforts in building physicalinterfaces, we decided to conceive a cross-media
platform to head to cross-modal and cross-sensory artistic
result. Further evolution of such an approach might leadto a combination of both virtual interface (i.e. MAG andMusicTiles) with a physical interface. In short, it seems
that we instantiated a research process in which we aretrying to combine different input and output
methodologies, systems and tools that might lead to abroader vision of software and robotic systems with anarticulated, fluid and bidirectional flow between thephysical and virtual environment. Indeed, we believe thatsuch a fluid and multifaceted representation of a single
activity may widely enhance the user immersion into a
one reality that combines multi-sensory activation oneither a physical and a virtual level.

References[1] L. Pagliarini. Algoritmi Genetici Musicali (MAG). In
Rivista di Psicologia dell'Arte, Nuova Serie, Anno
XXXIII, n° 23, Dicembre 2012. Pag. 103-108.
[2] D. E. Goldberg. Genetic Algorithm in search,
optimization and machine learning. New York, AddisonWesley. 1998.
[3] F. Rosenblatt. The Perceptron--a perceiving andrecognizing automaton. Report 85-460-1, Cornell
Aeronautical Laboratory, 1957.
[4] D. E. Rumelhart, J. L. McClelland. Parallel
Distributed
Processing.
Explorations
inthe
Microstructure of Cognition. MIT Press, Cambridge,
MA. 1986.
[5] Peter Gabriel’s Latest App is So 2.0, Fast Company, 7January 2013:©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Heart-pulse Biofeedback in Playful Exercise
using a Wearable device and Modular Interactive Tiles
Tomoya Shimokakimoto1, Henrik H. Lund2 and Kenji Suzuki1,3
1

Artificial Intelligence Laboratory, University of Tsukuba, Japan
Center for Playware, Technical University of Denmark, Denmark
3
Japan Science and Technology Agency, Japan2shimokakimoto@ai.iit.tsukuba.ac.jp, hhl@playware.dtu.dk, kenji@ieee.org
Abstract: We developed a playful biofeedback system using a wearable device and modular interactive tiles. In thisapproach we suppose that patients could regulate exercise intensity on their own through biofeedback. We propose
biofeedback play system called “bioToys” based on exercise with the modular interactive tiles. The system consists of awearable device which measures heart-pulse via ear-mounted sensor, and modular interactive tiles which are used forphysical rehabilitation exercise through playing a game. The wearable devise enables detection of heart pulse in realtime and therefore provides heart beat rate during playful activities, even if the heart pulse wave have motion artifacts.
The tiles are designed to build flexible structures and to provide immediate feedback based on the users’ physicalinteraction with the tiles. We combine the two systems to provide users with heart pulse biofeedback in playful exercise.
We show that using the developed system it is possible for the users to regulate the exercise intensity on their own with
biofeedback, and also possible to analyze exercise activity using number of steps on the tiles and heart beat rate.
Keywords: playware, biofeedback, exercise, rehabilitation

exercise with a wearable device and the modular
interactive tiles.

I. INTRODUCTION
In order to improve the health and quality of life
(QOL) of elderly people and patients with metabolic
disorders, stroke and cardiac diseases, physical training
and exercise is required in daily life. Heart Beat Rate
(HBR) is usually used as a standard for regulating theexercise intensity [1]. Thus, there are some commercial
wearable devices of measuring HBR. And some
exercise systems that control the exercise intensity
based HBR are worth consideration [2][3].
For practical implementation of wearable sensing
technologies in physical exercise, there is a need formethods to simplify measurement of bio-signals, as wellas signal processing techniques to reduce motion
artifacts in order to provide stable biofeedback. Systems
such as those in [4][5] try to take those considerations
into account.
In physical exercise, it is not easy to maintainparticipants’ motivation for joining a sport [6]. Recent
research also utilized games as means for exercise,showing a number of cases where gaming may bebeneficial for health.
In this paper, we propose the hypothesis that patients
could regulate exercise intensity on their own throughbiofeedback during playful activity, and we propose abiofeedback play system called “bioToys” based on©ICAROB 2014

II. SYSTEM CONFIGURATION
1. Wearable Device
We developed a wearable device to measure heart
pulse and generate biofeedback in real-time. The
wearable device consists of an ear-mounted sensor (earsensor, Combi Wellness Corp.), a processor(LPC1752FBD80, NXP), a light emitting diode forbiofeedback and Bluetooth module for communication
with PC. Fig. 1 shows the overview of the wearable
device for heart pulse measurement and biofeedback.
Heart pulse measurement via ear-mounted sensor isprone to noise resulting from body movements, thus it isimportant to process the heart pulse waveform in realtime to generate a stable biofeedback output. In our
device we implemented an algorithm based on linear
prediction and an adaptive template matching method to
overcome noise in the waveform [5]. The algorithm iscapable of detecting actual edges in the heart pulse
waveform in real-time, even if the pulse waveform
include motion artifacts similar to heart pulse. Thismethod utilizes human physiological features togenerate a correct cyclic pulse from noisy signals. Thisalgorithm is mainly constructed by two equations (seeequation 1 and 2). The equation 1 represents a-10-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

differential equation of linear prediction and theequation 2 means a template matching.
In equation 2, P(t) is the pulse waveform vector
measured by the A/D converter with the mean value
normalized to zero. When P includes a cyclic motion
artifact such as those resulting from locomotion or
jumping, a mere thresholding method is not adequate todistinguish such artifacts from heart pulse. The
developed algorithm, by linear prediction, avoids
detecting artifacts similar to heart pulse. In the linear
prediction method we designed a Kalman filter
according to equation 1, which is a differential equation
model for calculating an estimate time interval of theheart pulse ̂[s] depending on previous detected
time intervals. This linear prediction method is capableof approximately predicting the next heart pulse interval
according to the pattern of the heart pulse[7]. Therefore,an edge of heart pulse waveform is detected after̂[s], which is the sum of estimated time anddifference between the previous estimated time andactual detected pulse interval.
In addition, for reducing noise effect around an edgeof heart pulse waveform, this algorithm uses ideal pulse
waveform vectors for heart rates (τs[bpm]) that don’t
include noise: I(t;τs). Then, using the correlation value
between I and P; M(τs) in equation 2, when the value ofM(τ) is higher than a threshold, the system detects anedge of heart pulse waveform.
Using the linear prediction and template matching
methods, this wearable device is able to detect edges ofheart pulse and calculate HBR stably. LEDs on thedevice blink in synchronization with the detected heart
pulse, and the HBR data is transmitted to PC via
Bluetooth in real-time.
̂

1 = ̂

𝛾 ̂

−̂
−1
(0 < γ ≤ 1

𝑀1= ∑𝐼 𝑡−𝑛 𝑃 𝑡−𝑛𝑁based on modular interactive tiles. The tiles attach toeach other to form the overall system, and they aredesigned to be flexible for different arrangements. The
tiles are also motivating to the user since they provide
immediate feedback based on the users’ physicalinteraction with the tiles, which is a design principle ofmodular playware [8]. Fig. 2 shows a scene of playing
with the tiles.
Each tile comprises a sensor and transmitter forcommunicating with other tiles, has a quadratic shapemeasuring 300mm*300mm*33mm, a force sensitive
resistor (FSR) and eight RGB LEDs that emit light withequal spacing in between on a circle shape for gamefeedback.
On the circuit board of each tile there are also
connectors to mount an XBee radio communication
add-on board, including the MaxStream XBee radio
communication chip. Hence, there are two types of tiles,
those with a radio communication chip (master tiles)
and those without one (slave tiles). The master tile may
communicate with the game selector box and initiate thegames on the built platform. Every platform has to haveat least one master tile if communication is needed togame selector box or a PC. The tile becomes a mastertile only when it has XBee radio communication
connected to it.
The modular interactive tiles can be easily set up on
floor. They can be simply attached to each other as a
jigsaw puzzle, and there are no wires. The modular
interactive tiles can register whether they are placed
horizontally or vertically, and they can adapt thesoftware games to behave accordingly.
3. Real-time biofeedback for exercise system
The games on modular tiles are designed forphysical exercise. Basically when users touch or stepon the tiles, the tiles generate feedback through LEDs’
color and intensity according to the game’s rules thatare installed in tiles. We developed a playful
biofeedback system by combining a wearable device formeasurement of heart pulse rate with the modular

(1)

𝑁−1

𝑠

2

𝑖=0

2. Modular Interactive Tiles
We developed a system for playful biofeedback

Fig.1. (a) Overview of the wearable device in a sports
wrist pocket (b)the circuit board and heart pulse sensor©ICAROB 2014

Fig.2. Modular interactive tiles used for playful
physiotherapy with feet

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Fig.4. The scene in the playing using a wearable device and modular interactive tiles
interactive tiles. In this paper, we modified the games
installed in modular interactive tiles [9] for some new
purposes as will be illustrated.
The wearable device transmits the calculated HBR
to PC via Bluetooth as the wearable device detects users’heart pulse. After the PC receives data of HBR, data islogged in the PC and transmitted to the master tile
wirelessly via XBee. When the master tile receives theheart beat data, it interprets it and light the LEDs
according to the game rules. This system has six colors
for biofeedback. Fig. 3 shows an example of color
assignment based on HBR in reference to ratings ofperceived exertion scale, which is a scale of perceived
physical exertion relating to HBR [10].
For biofeedback, HBR is assigned by color and thegame level or score point. Modular interactive tiles havegames based on counting steps or touches, or based ontime constraints. For example, there are “color racegame” and “final countdown”. Color race is based on
counting steps. When the user steps on an activated orilluminated tile, user gets score. Finally when the user
acquires an adequate score the game finishes
successfully. By adding the wearable heart beat device,
the user is able to know his heart pulse and HBR andshare it with audience.
Final countdown game is based on time constraint.
In this game, all tiles are activated and the eight LEDs
on each tile count down independently. When any tilefinishes to count down it will send a “game over”
message to the master tile and the game will finish.
When the user steps on a tile, count down time in thistile will reset and the eight LEDs on this tile are lit
again. For this game, in normal case, count down speed
parameter is fixed depending on game difficulty. For
regulating the exercise, we designed a game where theuser keeps playing as long as proper HBR is observed.
In this case, we designed the countdown parameter tochange depending on the HBR. At the first level whichis under 70 bpm (color is purple), the counts downinterval is 2s. Then for the following levels (light blue,
blue, green) the countdown time decreases 0.32s
respectively. Considering the fourth level (green,
100bpm) as the desired level, count down timer will
increase for later levels (yellow and red) also by 0.32s
respectively, in order to keep the user at the desired
level.

III. EXPERIMENT
1. Regulating exercise
In order to evaluate the performance of the proposedsystem, we conducted an evaluation experiment toverify the feasibility of the proposed system as a playful
biofeedback system for regulating exercise. In thisexperiment, we used six tiles and final countdown game.
We asked four healthy male participants to exercise fortwo minutes with the proposed system and to go up to
the third level (green color level) and keep this level.
We logged heart beat pulse by 120Hz, HBR, the numberof steps and also recorded these experiments on video
for analysis.
2. Result and analysis method
Fig. 4 shows a scene during the experiment. We
confirmed that the proposed system provide color
feedback dependent on HBR during the game. Fig. 5
shows all participants’ results of this experiment. HereFig.5. The result of all participants

Fig.3. Example of a color assignment based on HBR©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

We developed a wearable device which measures
heart pulse via ear-mounted sensor, and we
implemented an algorithm for biofeedback in the realtime which detects heart pulse in intense activity. We
also developed a system for playful feedback gamebased on modular interactive tiles. Then we combined
the two systems and developed games to provide users
with heart pulse biofeedback in playful exercise.
We devised an evaluation experiment to verify thefeasibility of the proposed system for regulating
exercise. We showed that it is possible to use thedeveloped system to regulate the exercise intensity on
one’s own with biofeedback, and it is also possible toanalyze exercise activity using number of steps on thetiles and HBR.

Fig.6. The result of participant A in two minutes exercise

REFERENCES[1] Karvonen, J., and Vuorimaa, T., (1988), Heart Rateand Exercise Intensity During Sports Activities., Sports
Medicine, 5(5): 303–311.
[2] Su, S., W., and Wang, L., and Celler, B., G., andSavkin, A., V., (2005), Heart rate control during
treadmill exercise. Proc. of Annual Intl. Conf. of theIEEE EMBS,
pp. 2471–2474.
[3] ElSamahy, E. and Genedy, A. and Abbass, M.A. andGaddallah, M. (2011), A Computer–based System for
Safe Physical Fitness Evaluation, Proc. of Annual Intl.
Conf. of the IEEE BMEI, 2011, pp. 1443–1447.
[4] Watanabe, M., and Tsukamoto, S., and Hoshino, H.,(2008), A handle electrode system for measuring heart
rate while riding a bicycle, Proc. of Annual Intl. Conf.
of the IEEE BMEI, 2008, pp. 542–545.
[5] Shimokakimoto, T., and Ayuzawa, S., and Suzuki,K., (2013), Real-time Pulse Detection for Physiothrapy
and its Application toWearable Device, IPSJ Journal,
54(4): 1480–1488 (in Japanese)[6] Dishman, R.K., (1988), Exercise adherence: Its
impact on public health, Human Kinetics Publishers.
[7] Kobayashi, M., and Musha, T., (1982), 1/f
Fluctuation of Heartbeat Period, IEEE transactions on
biomedical engineering, 29(6): 456–457.
[8] Lund., H., H., and Marti, P., (2009), Designingmodular robotic playware, the IEEE Int. Workshop
Robots Human Interactive Commun Toyama, Japan.
Sep. 27-Oct. 2., IEEE Press, pp. 115–121.
[9] Lund., H., H., (2010), Modular Interactive Tiles for
Rehabilitation: Evidence and Effect, Proc. of the 10th
WSEAS Intl. Conf. on ACS, 2010, pp. 520–525.
[10] Borg, G., (1973), Perceived exertion: a note on "history" and methods., Med. Sci. Sports., 5(2): 9
0–93.

Fig.7. The result of participant B in two minutes exercise
we compare the number of steps and HBR among
participants. In this experiment, the results of participant
A and B were similar. Participant C (who exercises onregular bases) did not get tired during the experiment.
On the other hand, participant D who does not usually
exercise was tired from the point of HBR.
Then we focused on the time series of steps and
HBR. Fig. 6 and 7 show the results of two participants(A and B). The top graph shows the events of detectedheart pulse, the HBR is expressed in the color of
biofeedback. The bottom graph shows the sum value of
number of steps for each 10 seconds.
In Fig.6, when participant A reached green level, he
intentionally decreased his pace to the desired HBR. In
participant B’s case, first he overshoot to the red level
for around 20s, then he decreased his pace to keep thedesired HBR. Finally, he was able to find the proper
pace to stay between green and yellow levels.
The proposed system showed the possibility for theusers to regulate the exercise intensity during playful
activity on their own with biofeedback.

IV. CONCLUSION
In this paper we proposed a biofeedback play systemcalled “bioToys” based on exercise with modular
interactive tiles and a wearable device. We consider thatgames as a mean for exercise are beneficial for health,and for maintaining the motivation for exercise.

©ICAROB 2014-13-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Development of Measurement System for Quantitative Evaluation of
Skillfulness of Lower Extremities
Kazunori Yamazaki*, Donggun Kim*, Yoshifumi Morita*, Noritaka Sato*, Hiroyuki Ukai*,Kenji Kozakai**, Satoru Shibata**, Shigenori Onishi**, Akihiro Ito**, Daisuke Mizuno**,Tatsuya Hirai***, Haruna Takeda***, and Yuka Sugiura****Dept. of Computer Science and Engineering, Nagoya Institute of Technology, Japan** Sanyo Machine Works, Ltd., Japan*** Tanakakai Nishio Hospital, Japan
Tel : +81-52-735-5412; Fax : +81-52-735-5412
20517513@stn.nitech.ac.jp

Abstract: The final goal of our research is to develop a measurement system for quantitative evaluation of theskillfulness of lower extremities. In our previous work, we developed the measurement system of the foot movementon the floor with the sitting posture. The subject moves the training device attached to the subject's foot on the training
board. The pointer corresponding to the position of the training device is displayed on the monitor. The subject moves
the pointer along the target trajectory by moving the training device. In this study, we developed a quantitativeevaluation method of the skillfulness of the lower extremities. For this purpose, we used the measurement system
which we have developed in our previous work and redesigned the target trajectories of foot movement. Moreover, weverified the reliability of the proposed quantitative evaluation method by calculating the intra-rater reliability for two
trials ICC(1,1) of the evaluation results. We developed a measurement system comprising the straight course, thecircular course and the star-shaped course. Because the ICC(1,1) of the circular course and the star-shaped course arehigher than 0.7, the quantitative evaluation method of lower extremities with the circular course and the star-shaped
course has sufficient reliability.
Keywords: Rehabilitation system, Quantitative evaluation, Intra-rater reliability, Lower extremities, Skillfulness

the accuracy and the speed of the motion when his/herfoot is moved to follow the given target trajectory. In
this study, we developed a measurement systemcomprising three courses that have different difficultylevels about the target trajectory following in order toevaluate the skillfulness of the lower extremities.
On the other hand, the quantitative evaluation
results should have high reliability. This reliability is
Intra-rater reliability and Inter-rater reliability [1]. The
intra-rater reliability is the degree of agreement among
multiple repetitions of a diagnostic test performed by asingle rater. Moreover, the inter-rater reliability is thedegree of agreement among raters. The intra-classcorrelation (ICC) has been widely used to evaluate thereliability in rehabilitation medicine.
In this study, we developed a quantitative evaluation
method of the skillfulness of the lower extremities. For
this purpose, we used the measurement system which
we have developed in our previous work and redesigned
the target trajectories of foot movement. Moreover, weverified the reliability of the proposed quantitativeevaluation method by calculating the intra-rater
reliability for two trials ICC(1,1) of the evaluation
results.

I. INTRODUCTION
Sensorimotor
integration
functionandproprioceptive control in the lower extremities arenecessary for walking ability and skillful movement.
The elderly and patients sensorimotor integration
function and proprioceptive control has become
impaired. However, evaluation of therapeutic effect isonly done subjectively by experienced doctors ortherapists. Quantitative evaluation of therapeutic effecthas not, been established until now. Therefore, thequantitative evaluation method of therapeutic effect isdesired strongly in clinical sites of the rehabilitation.
Moreover, it is absolutely imperative that the effective
training method for improvement of walking ability andskillful movement.
Thus, the final goal of our research is to develop ameasurement system and robot system for quantitativeevaluation of the skillfulness of lower extremities. It has
been desired in clinical sites of the rehabilitation. The
skillfulness of the lower extremities is the ability toadjust the posture and the motion of the body to achieve
his/her desired action.
We think that the skillfulness can be evaluated by©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

II. Measurement system for quantitativeevaluation of skillfulness of lower extremities

2. Software
We developed three courses as the target
trajectories; the straight course, the circular course andthe star-shaped course as shown in Fig.2. The
parameters of the courses were designed so that thedifficulties of the three courses were different. The
description of the software is shown in Fig.3.
The subject moves the pointer displayed on themonitor quickly and accurately along the target
trajectory. The evaluation results of the skillfulness oflower extremities are expressed as the speed and theaccuracy. The speed t [s] is given by the travelling timeat each course. The accuracy S [pixel] is calculated byaccumulating the error between the pointer position andthe target position in each sampling time at each course.
The extension of the knee joint is mainly used，inorder to follow the target trajectory in the straightcourse. On the other hand, in the circular and star-

1. Hardware and operation procedure
We have developed a rehabilitation support system
as shown in Fig.1. The hardware consists of a laptop PC,
a training device and a training board [2]. A PC wirelessmouse is installed in the training device. The mouse isused as a low-cost position sensor. The pointercorresponding to the position of the training device isdisplayed on the monitor. And, we designed the system
so that the motion of the pointer on the monitor and the
motion of the foot on the training board might become
the same size.
In the training, the subject moves the pointerdisplayed on the monitor by moving the training device
in the sitting posture. During this the training, thesubject watches the monitor of the laptop PC and does
not watch their lower limbs directly. The subject canperform the training by playing the video gamedisplayed on the laptop PC monitor. The therapist canevaluate the motion by using the acquired data during
the training.
There are few risks of patient’s overturning because
this system is performed in the sitting posture. Moreover,there are no dangers by the runaway training device
because the system does not have any actuators.
Therefore, this system can be introduced at the earlystage of the rehabilitation because of high safety of thissystem.

(a) Straight course(b) Circular course

Laptop PC（Video games）

Trainingboard
Training
device(PC Mouse)(c) Star course
Fig.1. Measurement system for quantitative evaluationof skillfulness of lower extremities©ICAROB 2014

Fig.2. Three courses for evaluation-15-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Device position (pointer) Number of trials

Course button

AnalysisbuttonElapsed time
Evaluation
value

Targettrajectory

User
information

CourseStart position Goal position Control panel

Fig.3. Explanatory drawing of courses

information and the location to save data, the therapist
selects the course. When the trial, one course running, isfinished, if the course is desired to run continuously, thesystem counts the time of trials and displays it.
In order to check the results during/after the trials,
the system shows the spent time from the start on eachtrial and the time which is counted up when the pointeris out of the course, as the feedback to the subject.
There information can be eliminated from the control
panel, if the therapist wants. In addition, the system candisplay subject’s trajectory overlaid on the target
trajectory after the training as shown in Fig. 4(a). And,the system can also display the area which is surroundedby subject’s trajectory and the target trajectory as shownin Fig. 4(b).

III. Verified the intra-rater reliability
1. Method
In order to verify the inter-rater reliability of themeasurement system we perform experiments. The
subjects are fourteen healthy young persons. The age
was 22±2.4 years old. The height was 166±8.3 cm. The
dominant foot was right.
The subject was seated so that the distance between
the subject's eyes and the PC monitor was 0.7 [m]. The
training device was attached to the subject's left foot.
The height of the seat surface was adjusted so that theknee joint angle was 90 [deg] in the sitting position. The
subjects performed the tasks consisting of three courses.
The subjects were instructed to move the left foot along
the black line (the desired trajectory) quickly andprecisely as possible, not to move the body, and to movethe left foot smoothly on the training board. The
subjects perform the tasks two times for each task. After
finishing each task, the result was not informed to thesubject.
We used the data of three courses and two times foreach person. The ICC(1,1) of the speed t and theaccuracy S were calculated by the following equation[1]．

(a) Pointer trajectory and target trajectory(b) Area which is surrounded by subject’s trajectory
and the target trajectory
Fig. 4. Evaluation results

shaped course, the complex motion which is mixture of
the extension of the knee joint and the internal therotation of the hip joint is necessary. In addition, in thestar-shaped course, more time of switching of thedirection of the motion is necessary than in the circular
course. Therefore, each course is designed to bedifferent in the difficulty.
When the software starts, it will display a control
panel on the monitor. In the control panel, the therapist
inputs the subject’s information (user name, ID, height,
weight, BMI, dominant foot, remark) and the location
on HDD to save the data as a CSV file, at first. If not
input, only measurement data is saved to the desktop of
the PC. A time, a X & a Y coordinate of the pointer ismeasured and saved at each sampling. The sampling
frequency is 100 [Hz]. After inputting the subject’s©ICAROB 2014

ICC (1,1) =BMS − WMS
BMS + (k − 1)WMS
N

N

BMS =

-16-

∑ k ( x i − x) 2
i =1

N −1

(1)

,

WMS =

k

∑∑ ( x − x )i =1 j =1

ij

N (k − 1)i2The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Table 1. Results of BMS and WMS

where BMS is the variance between the subjects，WMS
is the variance in each subject, k is the number ofmeasurements，N is the number of subjects， x is themean value of data of N subjects and k times, x i is themean value of data of k times of Subject i, xij is the dataof Subject i and j time．In this study, k = 2 and N = 14.
The criterion methods of ICC(1,1) were reported[3][4][6]. In Ref. [3], it was reported that 0.00-0.20:
Slight, 0.21-0.40: Fair, 0.41-0.60: Moderate, 0.61-0.80:
Substantial, and 0.81-1.00: Almost perfect. On the other
hand, in Ref. [4], it was reported that more than 0.6:
Possible, more than 0.7: Normal (OK), more than 0.8:
Good, and more than 0.9: Great. From the previous
studies, if the ICC(1,1) is more than 0.7, then thereliability is defined as good.

Three evaluating courses(a)Straight course (b)Circular course (c)Star course
4.01×101

2.10×102

2.97×102[s] WMS

1.01×100

1.95×100

2.42×100

BMS

1.17×107

1.29×1086.01×107[pixel] WMS

6.79×106

5.07×106

1.01×107

S

IV. Conclusion
We developed a measurement system of theskillfulness of lower extremities with tasks of thestraight course, the circular course and the star-shaped
course. The quantitative evaluation method of lower
extremities in the circular course and the star-shaped
course has sufficient reliability.
The future work is to verify the reliability of the
proposed quantitative evaluation method to elderly
persons and subjects with reduced skillfulness of lower
extremities.

2. Results and discussion
The BMS and the WMS of the attainment time andthe accuracy in the three courses are shown in Table 1.
From these values, the ICC(1,1)s were calculated．The
ICC(1,1)s of the attainment times were 0.951, 0.982 and0.984 in the straight course, the circular course and thestar course, respectively. The ICC(1,1)s of the accuracy
S were 0.265, 0.924 and 0.711 in the straight course, thecircular course and the star-shaped course, respectively.
From these results the reliabilities of the speed t andthe accuracy S were confirmed in the circular course
and the star-shaped course. On the other, the reliabilityof the accuracy S was not confirmed in the straightcourse. The reason was examined from Table 1andEq.(1). In the case of the accuracy S in the straightcourse, the ratio of BMS and WMS was small ascompared with the other cases. This means that thevariability between the subjects was small and thevariability in each subject was large. It is guessed thatthe reason was that the task of the straight course waseasy for the healthy subjects.
As a result, it was confirmed that the developed
measurement system has the sufficient reliability in thecircular course and the star-shaped course.

©ICAROB 2014

BMSt

REFERENCES[1] Shrout PE, Fleiss JL (1979), Intraclass Correlatio
ns: Uses in Assessing Rater Reliability, Psychol Bull, Vol.86:420-428[2] Yamazaki K, Morita Y, Sato N, Ukai H, Nagay
a M, Hayashi T, Ito T , Sakai Y (2013), Quantitati
ve Evaluation System of Sensorimotor Integration F
unction and Proprioception of Lower extremities -V
erification Effectiveness for surgical patients with intramedullary spinal cord tumor-, The Japan Society
for Welfare Engineering, Vol.15, No.2: 44-49 (Artic
le in Japanese)[3] Landis JR, Koch GG (1977), The measurementof observer agreement for categorical data, Biometrics, Vol.33:159-174[4] Kuwabara Y, Saito T, Inagaki Y (1993), Evaluat
ion of intra- and inter-observer reliability, Kokyu To
Junka, Vol.41, No.10:945-952 (Article in Japanese)[5] Portney LG, Watkins MP (1993), Foundations lf
clinical research-Applications to practice, Appleton& Lange, USA:505-516-17-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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Glove-Based Virtual Interaction for the Rehabilitation of Hemiparesis Stroke
Patient
Khairuniam Wan, Aswad A.R, Rashidah Suhaimi, Nazrul H. Adnan , D. Hazry , Zuradzman M. Razlan and Syed
Faiz Ahmed
Centre of Excellence for Unmanned Aerial Systems (COEUAS),
School of Mechatronic, Universiti Malaysia Perlis,
Kampus Pauh Putra, 02600 Arau, Perlis, MALAYSIA(Tel: 604 9798333, Fax: 604 9798334)
khairunizam@unimap.edu.my
Abstract: This paper proposes the rehabilitation of a stroke patient by using dataglove called GloveMAP. GloveMAP is a low
cost hand glove developed by using flexible bending sensor and accelerometer attached at the position of the fingers and wrist,
respectively. The rehabilitation exercises are conducted in the virtual environment. In the studies, 22 fundamental movementsof arm are evaluated and the results are employed to design the virtual environment. The combination of these fundamental
movements focuses on Hemiparesis sufferers which are the most common in the stroke and have the inability to move one sideof the body. The experimental results show that by combining several fundamental arm movements, glove based-virtualinteraction supports the stroke patient do the rehabilitation by them without assisting from physiotherapist.
Keywords: Dataglove, VR interaction, Rehabilitation, Stroke.

1 INTRODUCTION

2 LITERATURE REVIEW

Strokes have been one of the major causes for long term
disability. Death cases from stroke have decreased
considerably but the survivors usually have to live withsome form of disability. Given stroke cause damage to thebrain by forming blood clot in the brain artery, thecomplexity of the brain may affect the stroke patient invarious forms.
Virtual Reality (VR) is a digital environment which canbe fully manipulated by the respective party. VR has its
various function in today’s development in medical area
which most have been used by the doctor for surgery
simulation and also for psychological therapy for the mind.
As it stand VR has serve its purpose in the development of
this project for rehabilitation of the stroke patient [1-4].
This paper proposes the impairment of the stroke patient
to rehabilitate by using VR approach. The study includes
the research in investigation of muscle activities where therehabilitation takes place.
This research paper organized as subsequent; Section 2
encompasses literature review of the related research andapproach toward VR based stroke therapy. Section 3
presents the methodologies of applied procedures. Section 4
is divided into 2 sections, first section states about
experiment setup where second section demonstrates theresults of experiments. Finally section 5 expresses theconclusions over current research.

Worldwide stroke is the second leading cause of death,
responsible for 4.4 million or equivalent to 9 % of the total
50.5 million deaths each year, and only 10 percent recoveralmost completely while some other survivor have to livewith few disability or impairment. Thus this stroke patientneeds special care and rehabilitation to gain back their bodyfunction. Most people over 55 years of age and with a malegender of family history of stroke could be one of many
vital factor risk of getting a stroke.
Nowadays exists various application of the VR whichhave make rise to the development of interfacing devices
such as dataglove, head-mounted display (HMD) andmechanical suit [3,5]. The application has various usages inthe development therapy for the disability patient such as
stroke patient. In this modern age lots of equipment havebeen develop for this VR applications. Though thistechnique requires a high cost to develop thus a lot
improvement can still be made for this VR application.
Therapy by using VR is widely used in the society byintegrating perceptions and experience within a simulation
for the phobic patient. Rehabilitation can be made easy forthe stroke patient as VR can help in attaining portable rehab
center by remotely control the virtual exercise for thepatient. Furthermore VR creates an environment where all
objects can be created digitally allowing the user or patient©ICAROB 2014-18-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

to interact with the virtual object by using external
hardware.
In this research, the dataglove called GloveMAP is usedby the stroke patient to interact with the VR environments[6-8]. The most challenging part will be the designation of
the virtual object installed in the virtual environments. The
consultations from the medical doctor and the stroke
therapist are required in order to design VR based stroke
rehabilitation environments.

2.1 Hemiparesis Stroke
Hemiparesis stroke attacks when blood flow to the brain
stopped. The oxygen and blood that supply to the brain willstop which result in the damage of the brain which canaffect the important body parts that control daily activities.
Hemiparesis sufferer will experience one side of weakness.
They often experience balance impairments which cause
difficulties to perform movements such as grabbing objects,
drinking from a glass, eating and using bathroom. The
loss of abilities of movements due to stroke depends on thebrain injury area.

3 METHODOLOGIES
Arm movement
analysis

MOCAP

2.3 Fundamental Movements in Stroke Rehab
2.3.1 Rotational Angles of Human Arm
Human arm consist of 7 DOF which are located at theshoulder, elbow and wrist. Shoulder part has 3 DOF for
pitch, yaw and roll. While the elbow only has 1 DOF whichmake it can bend up and down. Finally the wrist that has 3
DOF for bending up and down, side to side deviation andtwisting which make a total of 7 DOF for human arm [10].
Anatomical movements for hemiparesis stroke consistof several general movements that involve the shoulder,elbow, wrist and fingers. The movement carry out must be
in correct range of motion to avoid injury during
rehabilitation. Muscle is the tissues that provide movement
for human body structure and the movement are classified
by the direction of movement by the affected structures.
Movements beyond the normal limit will be added with theprefix hyper- such as hyper flexion and hyper extension.
Such movements are very important in medical field.
General anatomical motion with its opposites includes
flexion, extension, abduction, adduction, internal rotation,
external rotation, elevation and depression.

EMG#

VR Environment#Consultation from the stroke therapy required(a)Dataglove

VR Environment

Rehabilitation

2.4 Arm kinematics(b)
Fig. 1. (a) Designing the VR environment (b)Rehabilitation teraphy

Flexion and extension are the motion of adjusting angle
between two parts by bending or straightening the parts.
While, abduction and adduction are adjusting relation to
midline of the body by pulling the body structure away or
toward the midline, the abduction and adduction of thewrist are called as radial and ulnar deviation. Internal andexternal rotations are rotating the body parts inwards oroutwards. The movements of adjusting the elevation insuperior or inferior direction are elevation and depression.
Special motions for hands and feets are pronation andsupination which are the rotation of the forearm.

Fig. 1 shows the flow chart of the proposed methods.
The proposed works are divided into 2 parts, which are thedesign of the Virtual Reality (VR) environments and therehabilitation therapist [9]. Several preliminary experiments
are conducted to design the VR environments. The purpose
is to investigate the best sequence of arm movements forthe rehabilitation purpose. In the rehabilitation process, thestroke patient has an option to choose the VR environments,
which is suitable to their stage of the stroke.

©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Human arm structure shows the rotational angles ofshoulder and elbow define as yaw, pitch and roll. 3 DOF at
the shoulder define the pitch angle β which is the rotation
around y-axis for pronation and supination, yaw angle αwhich is the rotation around z-axis for abduction andadduction, while flexion and extension stand for roll angle γ
which rotate around x-axis. The DOF at the elbow must be
considered as two which are the pitch and roll angle,
however pronation and supination will not change the hand
position. A single transformation matrix can be formed by
calculating the combination of eulerian angles in the orderof Y-X-Y and Z-X-Z and the angles can be computed [11].

No.
1
2
3
4
5
6

Table 1. The location of marker placement
Location
ID Number
Shoulder
P1
Elbow
P2
Wrist
P3
Trapezium
P4
Index
P5
MiddleP6

P1

2.5 Virtual Reality (VR) software

P4
P5

Since this project needs to do the virtual rehabilitation
therapy, the proposed software is Quest3D which is aplatform of the VR system. Quest3D is the software fordeveloping real-time 3D Microsoft Windows applications.
It’s consists of only a few high level software tools andalmost all tasks are performed identical to the hardware. By
using the software development kit, users can build theirown components for Quest3D and build support for specifichardware likes dataglove. The Virtual Reality Peripheral
Network (VRPN) will be used to interface the dataglove
with the VR environment and Head Mounted Display(HMD). VRPN is a zero cost, open source tool that canhandle many VR devices.

P2

P6

P3
Fig. 3. Position of the 6 markers

In the experiments, 1 subject was selected to performthe fundamental movements of the arm as shown in Fig. 2.
The movements were captured by using the motion capturesystem Qualisys Track Manager (QTM) software. The
QTM was calibrated each time in the experiment to reduce
the tolerance of the 3D measurement. Six markers wereattached to the right arm of the subject, which was at thelocation of shoulder, elbow, wrist, and fingers (Index andmiddle) as shown in Fig. 3.

4 EXPERIMENTS

4.1 Experimental results
4.1 Experimental setupƟ

Fig. 2. The subject perform the fundamental movement(a)©ICAROB 2014-20-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

REFERENCES[1] Holden M (2005), Virtual Environments for Motor
Rehabilitation: Review. CyberPsychology and Behavior Vol.
8, No. 3, pp. 187-211[2] Forducey P. et. al (2006), Tele-Rehabilitation using
the Rutgers Master II glove following Carpal Tunnel
Release surgery.
Virtual Rehabilitation, International
Workshop, pp. 88-93[3] Deutsch J., Latonio J. and Burdea G (2001),
Rehabilitation of Musculoskeletal Injury Using the Rutgers
Ankle Haptic
Interface:Three Case Reports.
Eurohaptics’01, pp. 11-16[4] Sveistrup H., Mccomas J. and Thornton M. (2003),
Experimental Studies of Virtual Reality-Delivered
Compared to Conventional Exercise Programs for
Rehabilitation. CyberPsychology & Behavior, Vol. 6, No. 3,
pp. 245-249[5] Rose FD et. al (1996), Experimental Studies ofVirtual Reality-Delivered Compared to Conventional
Exercise Programs for Rehabilitati Virtual Reality: an
Assistive Technology in Neurological Rehabilitation.
Current Opinion in Neurology, Vol. 9, No.6, pp. 461-467[6] Nazrul H. ADNAN et. al (2013), Principal
Component Analysis For The Classification Of Fingers
Movement Data Using DataGlove “GloveMAP”.
International Journal of Computer Engineering &Technology (IJCET), Vol. 4, No.2, pp. 79-93[7] Nazrul H. ADNAN et. al (2013), PCA-based Finger
Movement and Grasping Classification using DataGlove“GloveMAP”. International Journal of Innovative
Technology and Exploring Engineering (IJITEE), Vol. 2,
No.3, pp. 66-71[8] Nazrul H. ADNAN et. al (2013), Classification Of
Finger Grasping By Using PCA Based On Best Matching
Unit (BMU) Approach. International Journal of Advanced
Research In Engineering And Technology (IJARET), Vol. 4,
No.2, pp. 92-105[9] Aswad A.R et. al (2014), Glove Based Virtual Reality(VR) Interaction for the Purpose of Rehabilitation.
Australian Journal of Basic and Applied Sciences, To be
published.
[10] A. A. Ali and A. H. Miry (2011), Human Arm
Inverse Kinematic Solution Based Geometric Relations andOptimization Algorithm. International Journal of Robotics
and Automation (IJRA), Vol. 2, No.4, pp. 245[11] Khairunizam Wan and Hideyuki Sawada (2007), 3D
Motion prediction of human upper body by tracking
reflective markers on a moving body. Malaysia Japan
International Symposium on Advanced Technology(MJISAT2007)(b)Fig. 4. Experimental results (a) Position for shoulder
abduction (b) angle of shoulder abduction
Fig. 4(a) shows the markers at the position of shoulder(P1) and elbow (P2) experienced very little movement
while the markers at the position of wrist (P3), index finger(P5) and middle finger (P6) show significant movement
which about 250 mm of the vector magnitude.

5 CONCLUSIONSThe paper proposes the rehabilitation therapy of thestroke patient by interacting dataglove with the VR
environments. Preliminary experiments are conducted toinvestigate the motion sequence of the arm by using
MOCAP and EMG sensors for the purpose of designing the
VR environments. The stroke patient does picks and place
activities of the virtual objects in the VR environments.
Based on these preliminary results, the portable stroke
rehabilitation therapy device could be developed in the
future. As the future work, the functional movements of thearm will be further investigated.

©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Measuring system of therapist’s guiding motion for standing up training ofpatient with hemiplegia after stroke
and analysis results of patient's motion
JungTang Wang *, Kazunori Yamazaki *, Noritaka Sato *, Yoshifumi Morita *, Hirofumi Tanabe***Dept. of Computer Science and Engineering, Nagoya Institute of Technology, Japan**Self-Defense Force Central Hospital, Japan
Tel : +81-52-735-5412; Fax : +81-52-735-5412
24417627@stn.nitech.ac.jp
Abstract: Although a patient with slight hemiplegia after stroke can stand up by using both his/her legs, he/she stands
up by using mainly his/her healthy leg rather than his/her paralyzed leg. If this motion is repeated as the self training,
the patient standing up motion cannot be restored to normal. Therefore, a therapist guides a patient motion to be able tostand up by using both his/her legs well in balance or by putting more weight on the paralyzed leg. The final goal of ourresearch is to develop a rehabilitation support robot of standing up training for patients with hemiplegia after stroke torestore normal standing up motion. For this purpose, we analyze the therapist’s guiding motion, and to design andmanufacture a rehabilitation support robot of standing up training on the basis of the analysis results of the therapist’s
guiding motion.
In this paper we developed a measuring system to analyze the therapist’s guiding motion during the standing up
training of a patient with hemiplegia after stroke. To confirm the usefulness of the developed measuring system, we
measured the patient's motion during the standing up training with and without the therapist's guiding motion. The
usefulness of the measuring system was confirmed by comparing the results of the patient's left/right weight balance
during the standing up motion and the therapist’s objective of guidance.
Keywords: Standing up training, Hemiplegia after stroke, Measuring system, Weight balance, Guiding motion

I. Introduction
In Japan the number of stroke patients are increasing,and 1.235 million stroke patients are living in 2012 [1].
The rehabilitation is important for the patient's motor
function to be restored to a normal condition and thenfor the patient to return to daily life. The corresponding
increase in the workload of therapists has become asocial issue. Therefore, the patients usually perform thestanding up training by themselves at present. During theself-training, the patient feels anxious about the use of aparalyzed leg in the standing up motion. For this reason,
the patient stands up by using mainly the healthy leg
rather than both the legs. In this case the patient'
standing up motion cannot be restore to normal. If thetherapist guides the patient's standing up training as
shown in Fig.1, the recovery effect is improved fornormal standing up motion. Therefore, a rehabilitation
support robot which can simulate the therapist’s guiding
motion is greatly desired. The final goal of our researchis to develop a rehabilitation support robot of standingup training for patients with hemiplegia after stroke torestore normal standing up motion. For this purpose, we
analyze both the patient's guided motion and thetherapist’s guiding motion in the patient's standing up
training. And then we design and manufacture a©ICAROB 2014

Fig.1 Standing up training guided by therapist
rehabilitation support robot of standing up training on
the basis of the analysis results of the therapist’s guiding
motion.
In this paper we developed a measuring system toanalyze the therapist’s guiding motion and the patient’s
motion guided by the therapist during the standing up
training of the patient with hemiplegia after stroke. In
order to confirm the usefulness of the developed
measuring system, we analyzed the patient's motion
during the standing up training with and without thetherapist's guiding motion.

II. Standing Up Training of Patient with
Hemiplegia after Stroke
In order to recover motor function of a paralyzed leg,standing up training is the one of important rehabilitation
training. Although a slight stroke patient can perform the-22-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014(a) Phase 1(b) Phase 2

three phases, namely Phase 1, Phase 2 and Phase 3.
Phase 1 are Phase 3 are equal to Phase I and Phase IV,
respectively. Phase 2 is composed of Phase II and PhaseIII.
In this study, we used the left/right weight balance toevaluate the ability of the patient’s standing up motion.
Let WL, WR denote the weights which are applied to theleft and the right Wii Balance Boards, respectively. Let
RL and RR denote the left and right ratios of weightbalance of the left and right legs, respectively. RL and RR
can be calculated by Eqs. (1) and (2).

(c) Phase 3

Fig.2. Three phases of standing up motion.
standing up training by himself/herself, the self-training
may not be effective in the case of repetition of abnormal
standing up motion.
2.1. Characteristic of standing up training
It was found form the therapist's interview that apatient uses mainly his/her healthy leg as compared withhis/her paralyzed leg when standing up from the sitting
position. There are two reasons. One is that the muscle
strength of the paralyzed leg is insufficient for normal
standing up motion. The other is that the patient feels
anxious for putting weight on the paralyzed leg and fearof falling down. In this case the self-training is noteffective for restoring normal standing up motion.

WL
WL  WRWRRR 
WL  WR
RL (2)

RL  RR  1(3)
The relationship between the weight balance and theratio of weight balance are shown in Table 1.

Table 1 Relationship between WB(weight balance)
and RWB(ratio of weight balance)
Balance state
NormalInclination to the right
Inclination to the left

2.2. Therapist’s guiding motion
The therapist guides the patient’s standing up motion
by pulling or pushing the patient’s waist during thestanding up training. There are two types of thetherapist's guiding motions for two training objectives.
One is the training of the paralyzed leg by putting moreweight on the paralyzed leg as compared with thehealthy leg. The other one is the training of theparalyzed leg by making the patient stand up in goodbalance with both the legs.

WB
WL=WR
WL<WR
WL>WR

RWB
RL=0.5 (RR=0.5)
RL<0.5 (RR>0.5)
RL>0.5 (RR<0.5)

III. Motion Measuring System
In standing up training the therapist guides thepatient's motion while paying attention to the stability ofthe trunk, the synchronized movement of both the legs,
and the weight balance of both the legs. In order toanalyze the patient's guided motion and therapist’s
guiding motion during the standing up training, weconstructed the measuring system on the basis of thetherapist’s interview.

2.3. Analyzing of standing up motion
In order to analyze the standing up motion, it isnecessary to divide the standing up motion on the basisof the features. In the previous research [2][3][4], it was
found that the standing up motion of healthy persons
can be divided into four phases. Phase I is the motion
from the initial position to leaving the chair. Phase II is
the motion from leaving the chair to reaching theankle’s angle to the maximum dorsiflexion. Phase III
begins after Phase II and ends when the hip angularvelocity reaches 0 deg/sec. Phase IV begins after Phase
III and ends when all motions are stable. Since thestanding up motion of a patient with hemiplegia after
stroke is different from that of a healthy person, it isdifficult to distinguish Phase II and Phase III. For thisreason, we divided the patient's standing up motion into

©ICAROB 2014(1)

3.1 Sensors for patient's guided motion
In order to measure the patient's guided motion, weused the following sensors. Three wirelessacceleration/angular velocity sensors (ATR-Promotions,
WAA-006) were used to measure the attitude and the
movement of the left and right lower limbs and thetrunk. The two sensors were mounted on the patient's
tuberositas tibiaes with the Velcro belts, and the otherone was mounted on the xiphoid process with the Velcro
belt as shown in Fig.3 (a) The pressure sensitive sensor
was attached to the seat surface of the chair to detect atiming at which the buttocks leaves the seat surface.

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

6-axis force/torquesensors

collected from the sensors and cameras insynchronization. The sampling frequency is 30Hz. The
appearance of the patient's standing up motion without
the therapist's guiding motion is shown in Fig.4. The
inclination of the trunk, the lower limbs and theforce/torque sensors are calculated from Eqs.(4) where is the angle of inclination, ay and az are the y and z
axes components of acceleration that are measured by
the acceleration/velocity sensors.

ZY

acceleration/
angular velocity
sensorX(b)

Z

Wii Balance Board

Y
X

(c)
(a)
Fig.3. Subject with sensors
The two Wii Balance Boards were used, because thetherapist has to put therapist's foot between the patient’s
legs in order to hold the patient's waist during thestanding up motion. The two Wii Balance Boards
measure the weight balance and center of pressure on
the foot.

 ay  , az 
(0    180)  tan 1 

IV. Experiments
In order to verify the usefulness of the developed
measuring system, we analyzed the standing up motionsof a healthy person and a patient with hemiplegia after
stroke with/without the therapist's guidance. For thispurpose, we measured the standing up motions by using
the developed measuring system and calculated theratios of weight balance of the standing up motion.

3.2 Sensors for therapist's guiding motion
In order to measure the therapist's guiding motion, weconstructed the equipment for measurement which isfixed to the patient's waist. The equipment formeasurement is shown in Fig.3(b). The two 6-axis
force/torque sensors (WACOH-TECH Inc., WEF6A200-4-UG5) were fixed to the equipment formeasurement to measure the therapist's force and torqueapplied to the left and right sides of the patient’s waist.
The force/torque sensors were located on the iliac crests.
The handle for the therapist to pull and push the patient
was fixed to the force/torque sensor. The two wirelessacceleration/angular velocity sensors were fixed to theforce/torque sensor to measure the attitude of theforce/torque sensor. The appearance of the patientmounting the sensors was showing in Fig.3 (a) (b) (c).

4.1. Experimental conditions
The patient with hemiplegia after stroke was a malewith paralysis of the left half of the body. The
Brunnstrom stage was IV. The patient stands up by
using mainly the healthy (right) leg. The height of theseat surface of the chair was 42 cm. The standing up
speed was not instructed in the self-standing up training.
The experiment was performed two times. The first time
was the standing up training without the therapist's
guiding motion, which means the self-standing up
training. The second time was the standing up training
with the therapist's guiding motion.

3.3 Measuring system
The two digital video cameras (Microsoft Corp.,
Kinect) were set up to capture the movement of thesagittal plane of the patient and the movement of the
diagonal from the rear of the therapist. The data were

4.2. Standing up motion without guiding motion.
The patient's self-standing up motion was measured
with the developed measuring system and analyzed. The
standing up motion was divided into three phases. The
time histories of the self-standing up motion are shownin Fig.5. The time histories of the ratios of weightbalance are shown in Fig.6. It’s seen that the patient’s
weight was inclined to the healthy side in Phase 2 andthe patient can standing well in balance in Phase 3. As
the comparison study, we measured the standing up
motion of the healthy person. The time histories of theratios of weight balance are shown in Fig.7. The ratiosof weight balance were almost equal to 0.5 during

Angular velocityof trunk

Seat leaving timing
Angle of lower thighZ
Y

Weight balance &Center of pressure of footZX

Fig.4. Measuring quantity in subject's motion©ICAROB 2014

(4)

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Angular velocitof trunk [rad/s

0.5

0

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,-0.5
Japan, January 11-13, 2014
-1
3

4

5
Time[Sec]

6

7

Pressure sensitive
sensor [v]5standing up motion. This means that the healthy person
can stand up well in balance.

Phase1

Phase2Phase32

3Time[sec]

4

5

4

Weight Balance
Ratios [%]Pressure sensitive
sensor [v]

5

3
2
1
0

Angle of left lower
thigh [deg]123Time[sec]

4

Left
Right

0.8
0.6
0.4
0.2

0

5

3

4

5
Time[Sec]6

7

Fig.8. Time histories of left/right weight balance ratioswith therapist's guiding motion.

110

90
70

50

V. CONCLUSION

30
1Angle of rightlower thigh [deg]2measuringsystem and analyzed. The time histories of1
the ratiosof weight balance are shown in Fig.8. It’s seen
0
3
4
5
6
7
Time[Sec]
that the patient’s weight was
inclined to the paralyzed
side110in Phase 2. The therapist guided the patient’s
90
standing
up motion so that the patient uses mainly the70paralyzed leg for restoration of the motor function of the50paralyzed
leg. It was confirmed that the measuring
30
results agreed
with4 the therapist’s
training
objective.
3567Time[Sec]
Phase1
Phase2Phase3
1

Angular velocityof trunk [rad/s]

123Time[sec]

4

5

The usefulness of the measuring system was confirmed
from the analysis results of patient’s standing up motion
with and without the therapist's guiding motion.
The future work is to analyize the therapist's guiding
motion and force/torque during the patient's standing up
motion, and to design and manufacture a rehabilitation
support robot of standing up training on the basis of theanalysis results of the patient's and the therapist's
motions.

110

90
70

50
30

Weight Balance Weight Balance
Ratios [%]Ratios [%]

1
2
3
4
5
Time[Sec]1
Fig.5. Time histories of standing up motion without
the
Left
0.8
therapist's guiding motion.
Right
0.6

0.4

Phase1

0.21
0
0.8

0.6

Phase2Phase3
Left

1

2

3
Time[sec]

Right5

4

REFERENCES

0.4[1] Ministry of Health, Labour and Welfare (2012),“Overview of the 2011 patient survey,”
http://www.mhlw.go.jp/toukei/saikin/hw/kanja/11/i
ndex.html.
[2] M. Schenkman, et al. (1990), “Whole-Body
Movements During Rising to Standing from
Sitting”, Physical Therapy, Vol.70, No.10, pp.638648.
[3] D. Chugo, et al. (2006), “Force Assistance System
for Standing-Up Motion”, Proc. of The 9thInternational Conference on Climbing and Walking
Robots (CLAWAR2006), Brussels, Belgium,
pp.65-70.
[4] T. Takayuki and K. Seiki (2005), “A Study ofControl Mechanism for of Standing-up Achieving
a Motion Task; Biomechanical Motion Analysis ofStanding-up”, Rigakuryoho Kagaku Vol.20, No.4,
pp.303-307.

0.2
0

1

2

3
Time[sec]

4

5

Fig.6. Time histories of left/right weight balance ratios
without the therapist's guiding motion.

Phase1

1
Weight Balance
Ratios [%]3

Angle of left lower
thigh [deg]

1.5
1
0.5
0
-0.5
-1
-1.5

4

Phase2Phase3
Left

0.8

Right

0.6
0.4

0.2
0

2

2.5

3

3.5
4
Time [Sec]

4.5

5

5.5

Fig.7. Time histories of left/right weight balance ratiosof the healthy person.

4.3. Standing up motion with therapist’s guiding
motionThe patient's standing up motion with the therapist's
guiding motion was measured with the developed©ICAROB 2014-25-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Image Segmentation of Coronary Artery Plaque Using Intuitionistic
Fuzzy C-Means Algorithm
Zahra Rezaeia,1, Ali Selamata,2, Mohd Shafry Mohd Rahima,3, Mohammed Rafiq Abdul Kadirb,4aK-Economy Research Alliance & Faculty of Computing, Universiti Teknologi MalaysiabFaculty of Biomedical Engineering & Health Sciences, Universiti Teknologi Malaysia
1

Rzahra.rezaei@gmail.com, 2aselamat@utm.my, 3shafry@utm.my, 4rafiq@biomedical.utm.my

Abstract : Every year, hundreds of thousands of people die because of Coronary Heart Disease (CHD) in all over the world.
Coronary Artery Disease (CAD) as a cardiovascular illness causes blood vessels narrowing that supply blood and oxygen to theheart . Atherosclerosis is known as the deadliest type of heart disease, which is caused by soft or “vulnerable” plaque (VP)
formation in the coronary arteries. Acute Coronary Syndrome (ACS) is recognized as the first coronary atherosclerosis indicatorwhich identifies high-risk plaques. Intravascular ultrasound (IVUS) can be applied for characterization of plaque andsegmentation of vessel’s walls borders. Recently, Virtual Histology as a new approach based on spectral analysis of IVUS has
been introduced. In this work, we applied a clustering method based on Intuitionistic Fuzzy C-means (IFCM) in order toautomatic segmentation of Coronary Artery plaque using VH-IVUS images.
Keywords – Vulnerable plaque; Intravascular ultrasound; optical coherence tomography, Fuzzy c-means
1 INTRODUCTION
Coronary Artery Disease (CAD) is the result of accumulationof plaques within the coronary arteries. The plaque is madeup of fat, cholesterol, calcium, necrotic core along withfibrous shell , flat muscle cells, foam cells and T-leukocytes
components (Fig. 1). This condition is called Atherosclerosis.
Atherosclerotic plaques affect the wall of coronary artery andresult in luminal stenosis. It is contribute to cause of deathwhen the degree of stenosis is over a significant level (75%)[1, 2]. Approximately, 60% of plaque rupturing occurring in‘‘shoulders’’ or at cap–intima junctions as a result of
circumferential stresses and the remaining 40% are awayfrom the junctions [2].

Figure 1. Arterial plaque geometry. Cross-sectional view [2].

Over time, plaque become harden or rupture where the breakstart to occur. If the plaque ruptures, a blood clot is formedon its surface. Vulnerable Plaque (VP) is likely to rupture,and leads to acute coronary events, such as unstable angina,
heart attack, myocardial infarction (MI), stroke and sudden
death. Therefore, early detection of high-risk plaque is a
critical task in medical science along with its major©ICAROB 2014-26-

influences on treatment and prevention of heart event.
Recently, characterization of VP is growing in order to acutecoronary event prevention [3]. Studies show that cardiac
events happen as a result of fat plaque burden , tiny lumen
region and thin-cap fibroatheromas (TCFA). It has been
widely demonstrated that acute coronary syndromes (ACS)
are related to rupture and acute thrombosis over a mildly
stenotic plaque, rather than to a slow growth with final
occlusion of a plaque encroaching the lumen [4]. The wideclinically practical invasive and the non-invasive imaging
techniques to identify ”VP” has been introduced. Noninvasive techniques such that : X-ray angiography, Ultrafast
Computed Tomography (UCT), nuclear scintigraphy,
Magnetic Resonance Imaging (MRI) and tnvasive
techniques:intravascularangioscopy
intravascular
thermography, spectroscopy, Intravascular Ultrasound(IVUS), IVUS-Palpography, IVUS-VH and Optical
Coherence Tomography (OCT) [1]. Virtual HistologyIntravascular Ultrasound (VH-IVUS) image has been applied
as invasive clinical technique to reliable identification,
quantification, and characterization of vulnerable plaques [1].
In addition, virtual histology (VH-IVUS) as sophisticatedtissue characterization technique using RFA, can display
tissue map as color-coded way. Plaque components in VHIVUS is classified into four basic tissue types such as :fibrous (dark-green pixels), fibro-fatty (light-green pixels),
necrotic core (red pixels), and dense calcium (white pixels)[1, 6 ,7]. Furthermore, VH-IVUS can be applied inquantitative cross-sectional lumen area, assessment of
thickness and vessel wall remodelling as well as progressionof plaques monitoring [8].The providing regional
observations to study predictors of events in the coronary
tree study or PROSPECT study organization [9] is aninternational multicentre prospective study that is responsible
for adverse conditions of coronary events by evaluatingplaque types in VH-IVUS image. Recently, optical coherent
tomography (OCT) has introduced as a new medical imaging

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

technology that have a higher resolution compare to the
IVUS[10].
This paper is organized into three sections as follows: section
2 describes literature review, section 3 defines the conceptsof vulnerable plaque and the details of segmentation method
based on Intuitionistic Fuzzy C-means (IFCM) is introduced
in section 4.

and test data set are also demonstrated. Threshold is appliedin hard decision and gradual changes were used for soft
decision in the dissimilarity values by Athanasiou et al. [16].
They have proposed a method in order to various
atherosclerotic plaque characterization using geometricalfeatures in VH-IVUH Images. They applied intensity
features, texture based features and two novel geometricalfeatures along with Random Forests classification method.

2 LITERATURE REVIEW

3 CONCEPTS OF VULNERABLE PLAQUE

Gradient vector flow (GVF) as a new method ofsegmentation for IVUS image has been proposed by Zhu etal. in [11].They used nonlinear filtering for decreasing thecritical points and variation in curves structure along with theballoon snake. Their method solves the local convergenceproblem. According to [12], multiple k-nearest neighbor(MkNN) has been proposed and applied for tissue
characterization of coronary artery plaque in IVUS image.
As a substitute for labeling k-nearest neighbor (kNN), a
distances between input vector and the prototype vectors wasused as a weighted decision. In order to perfectly
characterized lipid and fibrous tissues. They have done someactual experiments for simulated data and IVUS image. In
order to describe the luminal border, a method based onminimization of probabilistic cost function was presented in
IVUS B-mode in [13]. Mendizabal-ruiz et al. [13] usedconcept of curve lumen contour due to minimizing a costfunction. Support Vector Machine (SVM) classifier wasapplied to decide whether a pixel is blood or non-blood,
lumen or non-lumen. Their proposed method can segment
IVUS B-mode images in diverse type and frequencies. In
order to shadow the detection in IVUS image, an adaptive
threshold method was used by Basij et al. in [14]. They
improved segmentation of the classified plaque by using
active counters in order to separate the shadow region
efficiently. This method segments the hard plaques tissues
using intensity value in calcified region. The achieved
outcomes for detecting shadow regions proved that thismethod has efficiency even in segmentation of challenging
IVUS images. According to [1], a robust approach wasrecommended based on IVUS images for characterization of
the plaques. Their algorithm involves shadow detection,
feature extraction and classification. They used plaque areathat is yield from VH-IVUS images for validation of their
proposed method. Also, they applied texture analysis of both
Ex-vivo and in-vivo data to extract Dense Calcium (DC),
Necrotic Core (NC), Fibrotic Fibro-Fatty (FF) portion ofplaque. Feature extraction method such as local binary
pattern (LBP), modified Run-length (MRL) andneighbouring grey-level (NGL) were applied. In addition, theSVM and Error-Correcting Output Codes (ECOC) classifier
are used for arrangement of each pixel into one of the threeclasses (calcium, necrotic core and fibro-fatty). Anothermethod to overcome tissue characterization of coronary
artery plaque challenges was proposed in [15]. Vachkov et al.
introduced histogram-based and centre-of-gravity-based
based on general computational strategy for moving window.
The obtained similarity results of two real data sets - training

The phrase of vulnerable plaque descriptions which is related
to concept of acute cardiovascular disease was introduced 20
years before [17]. In 2010, according to the study that has
been done by PROSPECT study organization [9], predictionof vulnerable plaque is affected by three factors which are
Virtual Histology-Thin Cap Fibro Atheroma (VH-TCFA),
burden of plaque more than 70% along with an Minimal
Lumen Area (MLA) a smaller amount than 4 mm [18].
According to experts, definition of the plaque rupture has
different descriptions so it is not only related to the VP as
shown in Fig. 2.

©ICAROB 2014

Figure 2. Normal arterial in segment (A) compared with a vulnerable plaquein section (B) along the length of the vessel and arterial wall cross-sectional
image [17].

High likelihood of thrombotic complications and speedy
progression are two major signs of atherosclerotic plaques
indicate VP. Also, morphological parameters include
thickness of cap, lipid core volume , plaque color, content ofcollagen, content of lipid ,solidity, calcification and stream
pattern within coronary artery tree are indicators of VP [10].
There are two criteria in identifying plaque vulnerability.
The major one is active inflammation cells (about 26%
macrophage infiltration of fibrous cap); a thin fibrous cap
less than <100µ along with a large lipid core less than 40%of the plaque’s total area; endothelial denudation withsuperficial platelet aggregation; signs of recent rupture infissured cap; or strict coronary stenosis and eccentric lumen[17].The minor case consists of calcified nodules; larger lipid-27-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

core is indicated by yellow color; bleeding of intraplaque;dysfunction of endothelial and remodeling in positive side
[17].

(FT), Fibro-Fatty Tissue (FFT) and Necrotic Core (NC) thatis shown in Fig. 4 [17].

3.1. Virtual Histology-Intravascular ultrasound (VHIVUS)
Ultrasound is a medical imaging technique based on catheter
and is formed by ultrasound radiofrequency signal for
constructing real time image inside of vessel. These waves
produce electrical signals and send them to an external
system in order to processing tasks such as amplification,filtering, and scan conversion [9]. IVUS strategy canprovides a two-dimensional cross sectional views of image
along with assessment burden of plaque [10].Three-vessel
lumen demonstration and also characterization of plaque areprovided by IVUS. Calcified areas and dense fibrous parts
are homogeneous and have bright reflectance in gray-scale
images of IVUS. On the contrary, “soft” or “mixed” lesions
are represented by fewer echoes. The IVUS equipment has
three main component include a catheter instrument alongwith a miniaturized transducer and a console forreconstructing and demonstrating the output IVUS image(Fig. 3).

Fibrous

Fibro-fatty

Dense-Calcium

Necrotic-Core

Figure 4: VH-IVUS color code

However,VH can distribute IVUS image into differentiate
tissue components; but it is difficult to divide the plaqueowing to heterogeneous nature along with overlapping
components (Fig. 5).

Figure 5. tissue spectrum of VH illustrating four plaque component include
fibrous tissue, fibrofatty, necrotic core, and dense calcium by different color[19]

Fig. 5 demonstrates plaque components overlapping thatcauses inaccuracy difficulty in VH. Progression of disease
between fibrous and fibro-fatty area have common
characteristics in VH images as well as necrotic core anddense calcium. So, VH image analysis can detect at-risk
lesions but non-critical. VH images along with interventional
cardiologist can interpret plaque into four components andtheir related location in order to measurement thevulnerability of plaque [17].Several expert softwares havebeen implemented such as Volcano (Fig. 6).

Figure 3: Catheter designs

To improve image resolution, ultrasound should be used inhigh frequencies. Since declining saturation of tissue isrelated to increasing the frequency of ultrasound, it's
frequency should be limited to 45 MHz [8]. In order togenerating cross-sectional image, electronic systems
activated serially and use 64 transducer elements that isordered in an annular array.Electronic catheters of IVUS canbe used for characteristic lumen and wall of boundaries bydemonstrating couloirs blood flow.Lately, virtual histology(IVUS-VH) as invasive clinical technique has been proposed
based on the 20 MHz IVUS platform to assist analysis ofimage into special tissue components through identification,
quantification, and characterization of VP [9]. Plaques
components can be discriminated into three classes base ofechogenicity consist of calcified tissue with extremely echo
reflective range, fibrosis tissue that is characterized by hyper
echoic areas and thrombus or lipid-rich tissue representing
by hypo echoic regions. Whereas, tissue constituents aredifferent in hardness, VH can classify soft and hardcomponents along with the assessment’s properties of vessel
wall [10]. This method classified plaque components into
four types involves Dense Calcium (DC), Fibrotic Tissue©ICAROB 2014

Figure 6.Volcano software

Volcano’s VH-IVUS technology classifies 4 tissue types ofplaque by means of advanced, proprietary spectral analysis
methods with 93-97% of accuracy. This equipment has beenused for assessment of patient’s disease severity by image-28-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

representation. Furthermore, Volcano software has advanced
medical tools such as Lesion Risk Assessment, TransplantPatient Monitoring, Carotid Stenting, Longitudinal Stent
Placement and Distal protection [20].

as a result of acute myocardial infarction (AMI) ; but, IVUS
identified only 33% of them [9]. Recently, combination of
OCT and IVUS as two imaging technique were used forvulnerable plaques detection.High resolution of OCT image
can identify vulnerable plaque for in vivo image.
Intravascular OCT has potential to visualisation of normal
and atherosclerotic structure in coronary arteries. It seems
that in order to achieve high precision of TCFA discovery,
IVUS-VH image and OCT should be combined [9,10].

3.2 IVUS and VH limitation
IVUS technique has significant difficulties to distinguish andexact assessment plaque component especially in areas of
low contrast include fibrous tissue, fibro-fatty tissue andthrombus part. Besides,VH technique has various limitations
such as: (i) poor recognition of VH-TCFA as a result offibrous tissue that causes false detection in thrombotic
material; (ii) Misdiagnosis red halo around the white spotsand (iii) The range of its resolution is more than limitation of
VH-TCFA cap (65 μm) [18].
3.3 Virtual Histology–Thin Cap Fibroateroma (VHTCFA)
According to recent studies , thrombus formation in lumen
causes plaque rupture and led to more than 70% of acutecoronary events. TCFA is recognized as the most frequentcause of thrombus formation. This type of plaque has fat
necrotic core along with thin fibrous cap that is covered bymacrophages particles, lymphocytes along with reduced
smoothness of muscle.This circumstance causes clotting
cascade triggering and thrombus formation that can provide
adequate conditions for acute coronary syndrome (ACS).
Rupturing lipid-rich atheroma causes coronary block due to
VH-TCFA with thin fibrous layer along with necrotic core. It
is reported to be the most common cardiac basis (60%) of
myocardial infarction and death base on pathologicaltrainings. VH-TCFA has been defined by two major
principles include: (i) plaque burden more than 40%; (ii)necrotic core more than 10% that is located near the lumen
directly with no signs of fibrous tissue. We can classify VHTCFA into four categories which are VH-TCFA I, by
calcium amount less than 5%; VH-TCFA II, by more than
5% calcium; VH-TCFA III, with various layers; VH-TCFA
IV that is determined by necrotic core value more than 20%
along with calcium more than 5%, remodeling criterion
larger than 1.05, and plaque burden more than 50%. All fourtypes of classification of VH-TCFA and measurement
necrotic core are needed for identifying the degree of plaquevulnerability [18].

Figure 7 . OCT

4 SEGMENTATION
The first and essential step in plaque characterization issegmentation. Performing medical image segmentation
manually is a difficult, individual, time-consuming procedure,
and it depends on observer ability. Therefore, several
methods of automatic image segmentation have beendeveloped. These methods are categorized based on different
parameters. Segmentation algorithms such as direct detectionof the border(s) involves Edge-Tracking frameworks,
Gradient-Based approaches, methods of Active ContourBased, deformable models along with Statistical shape model,
Probabilistic-Based Techniques, graph searching, gradientdriven algorithms and clustering methods.
4.1 Intuitionistic Fuzzy C-means Algorithm (IFCM)
Dividing distinctive homogeneous groups of image ,including color and texture for classifying different patterns
called as clustering. Clustering method is based on crisp or
fuzzy. although, each pixel of a distinct clusters (i.e. 0 or 1)
in non-fuzzy or crisp clustering is belonged to one group.
Nevertheless, in fuzzy clustering method, membership value
is associated with each pixel could be a member of several
clusters. [21]. In IFCM algorithm, X is a nonempty fixed set
and A in X is an object which defined as:

3.4 IVUS and OCT
OCT image is comparable to IVUS. IVUS-VH not able toassess the thin fibrous cap due to limitation in axial
resolution (100–200 mm). However, OCT can identify the
microstructure with high-resolution imaging technique (10–20 mm). The penetration of OCT signal is limited to 1–2mm, so it cannot identify the lipid pools or amount ofcalcium behind thick fibrous caps and it produces inexact
detection (Fig. 7). IVUS can not complete thrombus
characterization precisely due to lack of resolution. In a teston patient's investigations it was revealed that OCT could
observe all intracoronary thrombus’s cases and angioscopy©ICAROB 2014

A : { x,  A ( x), A ( x)  | x  X })5(For each x  X ,  A : X  [0,1] consider as the value ofmembership function .Also, the degree of non-membershipgroup is  A : X  [0,1] . For every x  X ,  A and  A satisfy
equation 6:

0   A ( x)   A ( x)  1 )6(-29-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Unlike traditional fuzzy, The summation of  A and  A is notnecessarily had to be 1. IFCM function sums two equations:(i) conventional FCM objective function which is modified
by IFS and (ii) IFE which shows entropy of intuitionistic
fuzzy.
Minimizing objective function using IFCM is:
c

J

IFCM



n

c

u d   ei 1 k 1
*

*m

2

ik

ik

u u 
ik



ik



ik

i 1

*

1 i*a) Fibrous)7(ib) Fibro-Fatty,n  N  , k 1, N 
*

1

ik

i

k 1

*

Where the intuitionistic fuzzy membership is ik ,the kth
data in ith class.
membership and



ik

 1

u

ik

 ik

 ik

c) Dense-Calcium d) Necrotic-Coreis belong to conventional fuzzy

is Hesitation degree in this equation:
 1/ 
)8(),   0,

 (1 

u

Figure 9: IFCM with 7 cluster

ik

Yager’s intuitionistic fuzzy complement used as fallow
equation:)9(N ( x)  (1  x )1/  ,   0,

5 CONCLUSION
An arterial lumen can be threatened by plaque with lumen
narrowing or stenosis and rupturing of plaque cap. Recent
techniques, such as VH technology can identify
atheromatous plaques. However, efforts should be made to
improve its accuracy for identifying thrombotic material.
Moreover, fibrous cap thickness in VH-TCFA must be
measured carefully by increasing the resolution . In order toassess or have more accurate the coronary artery plaque,several imaging techniques have to be combined into a single
catheter. Medical image segmentation as a essential step ofidentifying plaque, persist a challenge since lack of resolution,
characteristics of imaging, motion, adjustable pathology andanatomy. Thus, suitable segmentation methods along withreliability, computation time reduction, higher accuracy, andfull computerization will be desirable for treatment of clinicalevents. In VH-IVUS segmentation, unsupervised method
such as IFCM algorithm can be applied for segmentation of
input images in order to cluster pixels and visualize four
components. Based on our experiments, performance of theclustering algorithm depends on the number of clusters thatare chosen.

Finally, intuitionistic fuzzy set equation is:A  {x,  ( x),(1   ( x) )  x X }
IFS

A

A

1/

)01(

The algorithm is implemented using MATLAB software. The
results with considering various numbers of clusters weredifferent. Fig. 8 shows the result of clustering algorithm withthree clusters. As seen in the figure, the algorithm is not able
to separate the components properly, because the number ofclusters is inappropriate.

a) VH-IVUS imageb) segmented result

AKNOWLEDGEMENTS

Figure 8: IFCM with 3 cluster

The Universiti Teknologi Malaysia (UTM) under research
grant 03H02 and Ministry of Science, Technology &Innovations Malaysia, under research grant 4S062 are hereby
acknowledged for some of the facilities utilized during thecourse of this research work.

However, increasing the number of clusters can raise theaccuracy of the IFCM algorithm. Fig. 8 shows the results byusing seven clusters which could separate the components
more accurately.

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©ICAROB 2014-30-

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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Cognitive approach to Computer Go programming
N. Tenys, I. Tanev, K. Shimohara
Doshisha University, Japan(Tel: 81-080-9608-1313)
nerijus.tenys@gmail.com

Abstract: Go (Igo / Baduk / Weiqi) is a board game that is still one of the more difficult challenges for Artificial Intelligence
research. While computers have defeated human champion in Chess 16 years ago, to this day best computer Go programs play
only at a high amateur level. Humans play Go using experience, heuristic values, pattern recognition and whole board evaluations
that computers are unable to match. Today’s most successful Go programs use Monte Carlo search algorithm, but while thisapproach often finds good local moves on the board, due to its random nature it often fails to see the whole board position as wellas human does. Our approach instead is implementation of human Go professional’s “thinking” into a computer program - that is,creating a system, where computer uses algorithms and data constructs that mimic human’s mind.
Keywords: Go programming, Artificial Intelligence, Game AI, Cognitive Programming1.

the game tree complexity is so large that, combined with thedifficulty of evaluating game positions, the same method
can not be applied successfully to Go programs.
Several other approaches were tried in Computer
Go research, like Expert Knowledge system, which is a setof low level rules compiled by Go professionals andimplemented into a system by programmers. This approach
can be very successful when rules can be defined wellenough to evaluate majority of possible situations for thesystem, however in Go, things are a lot more complicated.
There are thousands of rules created throughout thecenturies of Go play and analysis, but none of them areexhaustive, definite and unchangeable. Many rules are very
situational and can be even contradictory in different games.
That is one of the reasons why this approach wasn’t very
successful in Computer Go.
My approach is using Cognitive Psychology andComputer Science to create a distinct Software
Development model. If tested and proven successful in
Computer Go, this approach could be then applied to manyother systems. While there aren’t that many systems that are
as complicated and open ended as Go, one particularapplication for this approach would be the implementationof human-like Artificial Intelligence - the merge of
Cognitive Psychology and Computer Science is an ideal
basis for building digital human brain model.
The long term goal of this research isunderstanding and implementation of human thoughtprocess into computer programs, allowing us to build digitalbrain models that eventually might lead to a true “StrongAI” envisioned by Computer Science pioneers more than 50
years ago.

Introduction

Background
Go is an ancient board game invented in China~4000 years ago, and while easy to learn, it is very deep andcomplicated game to play at a master level, even more sofor computers.
One of the reasons why computer Go is socomplicated is that it has a lot more variations than Chess.
Chess has a game-tree complexity of about 10123 and Go~10360. For comparison of how large number that is, thenumber of atoms in the known Universe is about 1080.
Another difficulty in computer Go is the evaluation of board
positions, combined with the fact that one stone placed in
the beginning of a game can completely change theoutcome in the end game. Humans play Go using
experience, pattern recognition and whole board evaluationthat computers are unable to match.

Motivation
Computer Go is an interesting subject in itself andhas applications in the industry in the form of Computer Go
engines, teaching programs and Go analysis tools, but evenmore interesting is the approach taken to solve Go problems.
Outside of direct applications in Computer Go, this research
is a test-bed for cognitive programming model approachthat might have a lot wider application if proven successful.
Normally, game logic and similar deterministic
systems are solved using specific computer algorithms andmethodical approach to analyze each possible position andcome to the best possible solution. When that is not possible,
various heuristic methods are used to reduce the game treecomplexity by removing nodes that are unlikely to lead tosuccessful solution, like the approach widely used in
Computer Chess programs - Alpha-beta pruning algorithm,
which was used to reduce Chess game tree and allowed
computers to beat human Chess champions, however in Go©ICAROB 2014-32-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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tree, but fail to recognize overall game strategy and often
make critical long term mistakes.
Current programs keep getting better, partly
because of increasing computation speeds, but also becauseof better heuristics being applied to evaluation of the gamepositions. In a way it is approaching a complex Expert
Knowledge system that improves on computation basedalgorithm. While it is a valid approach, and someday
improvements in computing speed and heuristic evaluation
would reach the point where it could play as good as best
human players, I believe that currently however, thatpossibility is quite far off.
My approach would take the Expert Knowledge
system one step further and implement Go concepts like
Influence, Stone frameworks, Connections, Safety, Eyepotential, Sente / Gote, etc. into computer program thatwould use them to evaluate the board positions same way ahuman player does and then apply computational algorithms
only to areas that are deemed important from this heuristic
evaluation - similarly to how humans play.
As the evaluation and relative weights of theseconcepts are abstract and highly subjective, after initial
implementation and balancing, system could be improved
by applying genetic evolution to find best performing
constants and values. It could also implement a scheme
similar to Arthur Samuel’s checkers program [1], which
made groundbreaking work in early game AI
implementations and Machine Learning, by playing games
against itself (Reinforcement Learning).

Research Objective
Research goal is to write a Computer Go engine(software program) that is able to understand and use
human-like concepts in the game of Go. Success of theresearch is difficult to define, however if the engine proves
to be at least as strong at Go as mainstream programs using
Monte Carlo approach, it could be considered a success, as
it would create a unique approach to Go programming andwould have a lot of unexplored potential for improvementin the future. One of the big improvements for this approach
could be genetic programming, used to evolve constants andvalues used by the game engine.
The ultimate goal of the engine would be to one
day beat the best human Go players.

2.

Research Scheme

Approach
My approach is, in a way, a combination ofdeterministic algorithmic approach and Expert Knowledge
system, but instead of defining many low level rules todetermine all common game situations, I want to implementhuman Go professional’s “thinking” into a computer
program - that is, to create algorithmic Expert Knowledge
system, where instead of a set of rules, computer uses
algorithms and constructs that mimic human’s mind. In
programming terms, this is similar to adding anotherabstraction layer to create a program that manipulates onhigher level concepts.
This kind of approach isn’t used often, as, in manysystems, deterministic approach is enough to define thesystem and its working rules, even if they sometimes arequite complex. Another reason for the lack ofexperimentation with similar approaches is that it requires amerging of two distinct science fields - Computer Science
and Cognitive Psychology. To successfully create such
system, good analysis of human mind is required as well as
sufficient knowledge of Computer Science to translate
human thought into digital computer program’s structure
and algorithms. In the past, 20-30 years ago, this approachprobably wouldn’t have been possible due to the lack ofsupport from programming languages, but in recent years
improvements in Computer Science, such as higher level
programming languages, more advanced and intuitive
Integrated Development Environments (IDEs), expansion ofsoftware Design Patterns and similar advances, have createdpossibilities for programs to work with more complicatedand higher level concepts.

Difficulties and Issues
One of main difficulties of the research is difficulty
to verify success of the approach until it is nearly fullycompleted, as Go engine can not function properly with
only partially implemented features and play competitively
until all the main concepts are implemented. As such, a lotof preparation and mundane work is needed for it tofunction properly, for example: implementing Go rules,
capturing and calculating territories, eye recognition
algorithms, integration with User Interface and Go Text
Protocol for online play, etc.

Concepts
There is a long list of human-like concepts, rules
and gameplay traditions evolved through the centuries ofplaying Go and it would be impossible to gather andorganize them all into computer program, however, I
believe, just using main ideas from the plays of humanprofessionals, combined with strengths of digitalcomputation, can eventually achieve better results than mostof contemporary Go programs, that mostly find good local
moves, using Monte Carlo algorithm to search the game
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Japan, January 11-13, 20143.

●

Research Contents

Frameworks are formed by connected stones thatsurround certain territory - it is one of key concepts inprofessional Go play. Frameworks have many possible
values and attributes, like potential territory, safety,
influence, connection strength, etc. Much of the success ofplay in Go depends on how successful player is inextending and protecting their frameworks and as such it
should be one of most important concepts in Computer Go
also.

Go Concepts and their implementation
Main Go concepts and their currently planned
implementations for the engine:●

Frameworks

Influence●

SafetyFramework and connection safety can depend on
several factors, like nearby opponent’s stones, supportingstones, inherent connection strength, eye making potential,
etc. It is important attribute to successfully protect stone
frameworks from being captured and to use opponent’s
safety weaknesses to threaten his frameworks.
●

While at first glance a simple concept, it actually isvery hard to implement in a computer program. To this day
there does not exist an algorithm that can 100% successfullyrecognize and calculate the end result of a finished gamebetween human players. Main reason for it is difficulty ofeye recognition and estimation. Benson’s UnconditionalLife algorithm [2] can recognize unconditionally alive
groups, however it can be applied only in very limited cases.
My plan is to extend Benson’s algorithm [2] to give a
prediction of possible eyes in a framework, even if it is not
100% accurate. Like many other subjective concepts andvalues, it can be later evolved to find optimal point.

Influence is implemented using Influence Maps,
where each stone on the board “radiates” influence which
degrades with distance, and each intersection on the board
has certain influence value calculated from the influence of
nearby stones.
●

Eye Estimation●

Stone Connections

Sente / Gote

Sente and Gote are simple concepts of move’s
priority or “forcing” factor of the move - Sente move is theone that requires immediate answer from opponent. Gote
move is more passive, usually territorial or defensive move.
Human professionals always keep these concepts in mind
when making move and current Go programs are not goodat recognizing that. My idea is to implement a formula thatapplies Sente value to potential moves and estimates
possible opponent’s replies.
●

Ko fights and Ko threats

Sente move value ties directly into “Ko fights”,
where players search for a move that requires immediateanswer to make use of the Ko rule. Ko evaluation is anotherarea that is important in professional play and I feel is not
given sufficient attention in computer Go.

Stone connection strength is calculated using
influence values and algorithms to calculate possible
connection paths and potential cutting stones.
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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014
insight into this distinct approach and maybe inspire newdevelopments in similar direction in the future.

Research Evaluation
The success of the research can be measured byhaving game engine play against humans or other gameengines using Go Text Protocol through user interface oronline server play. Most precise evaluation would be letting
engine play on a live Go server, like KGS (gokgs.com), toestablish a stable strength rank or participating in Computer
Go competitions, like Computer Olympiad or UEC cup.
Partial measure of game engine’s potential can also
be estimated by a human player. Since game engine
operates on abstract human-like concepts, if computer isable to come up with the moves that a good human player
would make or is able to recreate joseki (standard Go
opening) moves without having prior knowledge about
them, it would prove that the approach was successful
enough to reach human-like play ability.

4.

References[1] Arthur Samuel (1959), Checkers Program, Machine
Learning.
[2] David B. Benson (1976), Life in the Game of Go,
Information Sciences 10, 17-29.

Progress

Current Status
Current status of the project is a working versionof computer Go engine that is integrated with Go UI and
can be played against right now. The engine is able torecognize and manipulate concepts of Influence, stone
Connections and stone Frameworks, though it is not making
full use of them yet.
Next steps in the research are calculations forvarious framework attributes, like safety, territory, numberof eyes. For that purpose work on the eye calculation isneeded by using or extending Benson’s Unconditional Life
algorithm.
After that, the rest of the concepts can be added like Sente / Gote and Ko fighting. Then, computationalalgorithms are needed to calculate specific local situations
like capturing or “cutting” stones. For that purpose, eventhough Monte Carlo algorithm is successful in otherprograms, I would like to use Alpha-beta pruning, so
successfully applied in Chess programs. The reason for thatis that search space should be reduced dramatically by theprevious heuristic evaluations to consider only areas that
program deems important.
Lastly, test results can be improved using genetic
evolution or Samuel’s reinforcement learning [1] by setting
up the Go engine to play against itself.
As mentioned previously, one of main difficultiesof this research is that results won’t be seen until all parts ofthe program are finished, but I believe there is value insuccessful completion of the project, even if the engine
doesn’t prove to be as strong as current programs, as this iscompletely new approach in Go programming and has a lotof potential to evolve in the future and be adapted in otherareas.

5. Conclusion
It is hard to draw conclusions from current results,
as the implementation of the Go engine isn’t complete yet.
The goal of the research is to explore new possibilities in
Computer Science and Artificial Intelligence programmingand it could be said, that, no matter the outcome and thestrength of play of this Computer Go program, it will give

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Circulative Narrative Generation Based on the Mutual Transformation
between Narrative Conceptual Structures and Music in the Integrated
Narrative Generation System
Taisuke Akimoto and Takashi Ogata
Iwate Prefectural University, 152-52 Sugo, Takizawa, Iwate, Japan
g236i001@s.iwate-pu.ac.jp
Abstract: We have proposed a new framework of narrative generation which generates narratives through the mutualtransformation according to various pathways between narrative (conceptual representation) and music. This paper
will continuously incorporate this framework into the integrated narrative generation system which is acomprehensive architecture for our narrative generation study and develop an experimental system combined with
the integrated system. This new framework aims to apply various musical methods for composition and variation to
narrative generation as a way to connect different types of media. An experiment of the mutual generation between
music and narrative will show various transformation pathways or routes between music and narrative.
Keywords: integrated narrative generation system; narrative generation; music generation; structural transformationfrom a surface medium, for example, will be also
possible in the latter sense.
Music has been the first main object or medium toaccomplish the concept. We have been studying narrative
generation methods through mutual transformationaccording to various pathways between narrative andmusic based on the development of some experimental
systems. The first system was presented by [5]. And themost recent system presented by [6] included eight routesof the transformation between narrative and music using
automatic composition and musical variation mechanisms.
However, the system was an independent experimental
system in which only the relationship of narrative
conceptual structures and music was simply defined.
The goal in this paper on the basis of the research is toincorporate
the
above
experimental
mutualtransformation mechanism into the integrated narrative
generation system. Music in this mechanism will not be
just the accompaniment for a narrative. We aim to use
musical knowledge & techniques and conceptual
knowledge & techniques in fusion by the medium ofmusic. Mechanisms for story & discourse and the musicalexpression mechanism are organically integrated together.
This paper will introduce the current status of thedevelopment.
There are various systems that generate thebackground music for a narrative in the area of artificial
intelligence (e.g., [7]). However, although narrative
generation systems or computational narrative models are
currently an independent study genre [8], the otherresearches do not exist to treat music from the view pointof narrative generation or the structural correspondence

I. INTRODUCTION
Issues on the similarity or correspondence relation
between narrative and music have been discussed inmusicology [1, 2, 3]. Overview, based on the studies,music itself cannot express concrete meaning as
narratives. However, a narrative and a piece of music cancommonly be composed of a sequence of events althougha temporal sequence of events in narrative is different
from a thematic sequence of events is music. We focus on
the common structures.
The major purpose of our current research isdeveloping an integrated narrative generation system toincorporate a variety of narrative generation mechanisms
into an organic architecture [4]. The system consists ofthe following three large units: 1) Story generation
mechanism generates the content of a narrative with ahierarchical conceptual structure using such knowledge
units as conceptual dictionaries and story contents
knowledge base. 2) Discourse generation mechanism
transforms a story structure into the other conceptual
structures considering how to narrate the story. 3)
Expression mechanism generates representation bysurface media including natural language, music andvisual image.
One of the design concepts of the system is circularity
or cyclicity. Although the one meaning is the ability torepeatedly generate narratives in one generation session,another meaning is the generation ability based on themutual transformation relationship between narrative
conceptual structures and the surface media expression.
The transformation to a narrative conceptual structure©ICAROB 2014

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piece of “original music” and the “variation music” in thelevel of music. The basic method of the mechanism is torelate a story structure and a discourse structure to thestructure of a piece of original music and a piece ofvariation music according to the concrete procedure
explained in the following part. The mutualtransformation is processed based on relational mappings.
The tree structure of a story is mapped to a musicaltree structure with the similar form. Fig. 1 shows the
structure of a piece of original music. Each of the internal
nodes in the musical tree is equal to “primary-secondary”
or “secondary-primary” relation between the child nodes.
The primary side is more important musical part than thesecondary side. Each of the leaf nodes, on the other hand,is corresponded to a musical event. A musical event isequal to a set of the following musical elements: one ormore motifs, the harmony, and the duration defined in thenumber of quarter notes from 1 to 16. The musical eventsat the leaf nodes are sequentially played in original music.
A motif is a basic musical unit consisting of a sequenceof notes and has the same duration with the musical event.
Each of the notes has a pitch and its duration. Each of themusical events is corresponded to an event in the story
and each of the motifs is corresponded to one of theinstances in the story.

II. BASIC METHODS OF NARRATIVE
AND MUSICAL PROCESSINGSThis section will present summaries of the integrated
narrative generation system and the musical processing.
Most of the foundational parts have been implemented
with Common Lisp incrementally. The system iscurrently operating as a unified system.
1. Narrative Structures and the Processing
A story and a discourse generated in each of themechanisms are respectively a same form of hierarchicaltree structure. The basic units are an event or event
concept and a relation. The former is a type of frame
structure. And the latter connects two or more events, or
two or more sub-structures with some events and one ormore relations. An event concept is equivalent to a casestructure with a verb concept and instances as values ofsome cases corresponded to the verb concept including
agent, object, location, and so on. For example, “(event
Eat (agent Boy) (object Apple) (location Park))” is thedescription of an event concept.
Both of structures of a story and a discourse are
generated or transformed through the application ofvarious types of techniques. We call the structuraloperational techniques associated with the story
generation mechanism and the discourse mechanism“story techniques” and “discourse techniques”
respectively. The name of “narrative techniques” is acollective term which contains story techniques, discourse
techniques and moreover techniques for the generation by
the surface expression media. Basically, for each of thestory techniques and discourse techniques, the input (ortarget) is an event or a sub-structure with some events andthe output is a newly expanded or transformed structure.
The difference is the point that story techniques basically
add a new element to the existing tree structure to expand
it, while discourse ones transform a tree structure toanother structure including the same event elements. A
variety of types of narrative techniques can be integrated
grounded on this common principle. The current versionof the system has about thirteen types of story techniquesand similarly ten types of discourse techniques.

The tree structureof an original music

ME1

A musical
event(Harmony T)((Track0 Motif1)(Track 1 Motif2)(Track2 nil)
(Track3 nil)
(Track4 Motif3)(Track5 nil)
(Track6 nil)
(Track7 nil))$primary-secondary

ME2$secondary-primary

ME3

ME4

Harmony (in functional definition){T:Tonic, S:Sub-dominant, or D:Dominant}Motif: A sequence of notes.
Each note has a pitch and a duration

V
III
I
DurationFig. 1. The structure of a piece of original music
A piece of original music is generated through theoperation of the structure and elements. The procedure of
the original music generation from a story is as follows:
1) The mechanism generates motifs associated with each
instance in the story. Each of the motifs is made with asequence of notes which are selected at random. 2) It
transforms each of the internal nodes, namely relations, inthe story into the musical internal node using a set ofrules for associating the two groups of internal nodes. 3)
It decides a harmony in each musical event using a set ofrules for deciding the harmony progression with amusical tree structure. 4) It transforms each event in thestory into a musical event by substituting each instance tothe corresponding motif.

2. Musical Structures and the Processing
The mutual relationship between a story structure anda discourse structure in the level of conceptual narrative
are corresponded to the mutual relationship between a©ICAROB 2014

$primary-secondary

Pitch

between narrative and music except for a series of ourstudy.

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Japan, January 11-13, 2014

On the other hand, the structure of a piece of variation
music is also represented as a tree structure form which istransformed from the structure of a piece of original
music using variation techniques. Each of the variation
techniques is defined as a manipulation of the musicaltree structure or the element. There are such techniques as
the move and deletion of a sub-structure of a musical tree,
the addition of a musical event created newly into amusical tree, and the modification of musical elements
like tempo, pitch and volume.

The story mechanism is always executed firstly togenerate a story by using adequate story techniques
according to the generative parameters for story. Then themethod sends the result by the previously executedmechanism to the next mechanism.
The user is necessary to input the following
information in the middle of a continual generation
process. When the discourse mechanism is executed, theuser needs to set the generative parameters and the othersettings for discourse generation. The user needs to selectone or more variation techniques to be used for themusical variation processing. For generating a new pieceof original music from an existing piece of original music,
the user is required to select one or more operational
techniques to change the original music structure.

III. A MUTUAL TRANSFORMATIONMECHANISM BETWEEN NARRATIVE
AND MUSIC
1. The System Configuration
We show in Fig. 2 the macro framework of theintegrated narrative generation system including musicalmechanisms. The musical mechanisms consist of an
original music mechanism and a variation musicmechanism. The former generates a piece of original
music from a story, a piece of variation music, or a pieceof original music. The latter, on the other hand, generates
a piece of variation music according to a piece of original
music, a piece of variation music, or a discourse structure.
We have incorporated in addition to the above amechanism for generating a discourse structure from apiece of variation music into the discourse mechanism. A
mechanism to generate a story structure from a piece oforiginal music is currently under development.
User input:
1) Generative parameters (and other setting
information) for the story generation
2) A generation pathway

2. The Example of a Generation Flow
We will present the example of a narrative generation
process including music generation by the method
described above. The initial values inputted by the user
are generative parameters such as “(macro-structure 2)(length 3) (unreality 1) (repetition 2)” and the generation
pathway is set as “S-OM-VM-D-NL”.
The result of each generation phase is shown in Fig. 3
in order. The surface representation by language of story
and discourse in the figure is English translation by handfrom Japanese language texts generated by a simple
language generation mechanism in the integrated
narrative generation system. These language texts aretransformed from each of the conceptual representation
forms. On the other hand, only first four musical eventsand the scores by hand are shown in the results oforiginal music and variation music.
Conceptual processing and musical processing in aseries of generation are mutually connected as shown in
the example. To add the function of story generation from apiece of original music, which is not implemented in thecurrent version, will contribute more free and flexible
generation since we will be able to start the system’s
generation process from a piece of original music. It means
the narrative generation from music in the extreme sense.
One of the issues to be solved in the current
implementation is that the current discourse mechanism
cannot transform musical structures into the corresponding
discourse structure in several types of musical variation
techniques, namely the modification techniques for such
musical elements as tempo, pitch, and volume because of
the theoretical problem on structure transformationconsidering the meaning in narrative and music.

Output:Generation results of each step ofstory, discourse, and expressions
Image mechanism
Natural language
mechanism

Music mechanism

Storymechanism

Story

Discourse
mechanism

Discourse

Original
music
mechanism

Original
music

Variationmusicmechanism

Variationmusic

X *Under development

Fig.2. The expanded framework by musical mechanisms of the integrated narrative generation system
The user initially inputs generative parameters for
directing the story generation. The following fourparameters are prepared: “macro-structure”, “length”,“unreality” and “repetition”. The user continuously sets
the generation pathways from the next prescribedsequences: “S-D-NL”, “S-D-NL-I”, “S-D-VM” and “SOM-VM-D-NL”. Each of the S, D, NL, I, OM and VM
mean Story, Discourse, Natural Language, Image,Original Music and Variation Music respectively.

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

A critic prohibited the killing to a man at an amusement park. The man
killed an older woman. The critic penalized the man. A runner found theolder woman. The man arrived at a tropical area. A detective investigatedthe man at the tropical area. The detective went to a clinic. The detective
found an evidence at the clinic. The man went to the amusement park.
The detective went to the amusement park. The detective arrested the
man at the amusement park.

1. Storymechanism

Story
2. Originalmusicmechanismtrack01

Original
music

2
3

b) The mechanism used four musical variation
techniques: “ellipsis”, “analepsis”, “repeating”, and“summary”. For example, the variation music isstarted from the fourth musical event in the original
music (the first three musical events in the original
music are played at the measures 5 to 7 in thevariation music). This change was by “analepsis”
which transforms order of musical events.

6

3. Variationmusicmechanismtrack01

Variation
music

2
3
6

4. Discourse
mechanism

Discoursea) Musical events composed of motifs corresponding
to instances in the story were sequenced. In the
result, one measure in the score means one musicalevent. For example, the “location” of the first four
events in the story are an “amusement park” and it iscorresponding to the repeated motif in the track3 in
the generated music.

A runner found an (dead) older woman at an amusement park. A man
arrived at a tropical area. A detective investigated the man at the tropicalarea. The detective went to a clinic. [backward: A critic prohibited thekilling to a man at an amusement park. The man killed an older woman.
The critic penalized the man.] The detective found a evidence at the clinic.
The detective arrested the man at the amusement park. [repetition: The
detective arrested the man at the amusement park.]c) Such changes as the reordering and the omission
in the music were reflected into the discourse
structure. The generated discourse has a mysterynovel like structure in which the murder scene isnarrated at the latter part.

*In the scores, track 4, 5, 7 are omitted because these tracks were blanks.

Fig.3. A generation flow example
introducing musical theories and techniques into the partof the musical processing.

V. CONCLUSION
This paper has presented the expanded framework of
the integrated narrative generation system through theexperimental system implementation. The important
point of this framework is that musical generation andvariation techniques are incorporated into the system toenable the mutual transformation between music andnarrative. The previous version of the integrated narrative
generation system basically generated a story structure, adiscourse structure and expression representations
including music in order, while introducing the mutualtransformation mechanisms enabled the system to
execute a variety of generation routes. Actually, we
showed a generation example of the mutualtransformation in which a piece of music is generated
from a narrative structure and a musical variation of thepiece causes a change of the narrative structure. We
would like to verify the abilities in the several sides of themutual transformation mechanisms between narrative andmusic after the completion.
The program of narrative structure processing has
been implemented based on relatively sound theoreticalfoundations by a kind of knowledge-based architecturewith narrative techniques and conceptual dictionaries.
The background of the musical composition and variation,on the other hand, has been weak in both sides of theory
and design. Thus, one of the next major topics is©ICAROB 2014

REFERENCES[1] Walsh R (2011), The common basis of narrative andmusic, Storyworlds: A journal of narrative studies, 3(1):49-71.
[2] Maus FE (1991), Music as narrative, Indiana theory
review, 12: 1-34.
[3] Kramer L (1991), Musical narratology: A theoreticaloutline, Indiana theory review, 12: 141-162.
[4] Akimoto T & Ogata T (2012), Macro structure andbasic methods in the integrated narrative generation
system by introducing narratological knowledge, Proc.
of 11th IEEE international conference on cognitive
informatics & cognitive computing, 253-262.
[5] Kobayashi F & Ogata T (2004), Narrative and musicas variation: Transformation of musical structure basedon narrative discourse theory, Proc. of the 9th ISAROB,
1: 170-173.
[6] Akimoto T, Endo J, & Ogata T (2012), The
expansion of paths in the mutual transformation
mechanism of music and narrative, Proc. of the 11th
IEEE international conference on cognitive informatics& cognitive computing, 230-239.
[7] Jewell MO, Nixon MX, & Prügel-Bennett A (2005),
State-based sequencing: Directing the evolution of music,
Proc. of the international computer music conference.
[8] Gervás P, Lönneker-Rodman B, Meister JC, &Peinado F (2006), Narrative models: Narratology meets
artificial intelligence, Proc. of satellite workshop:
Toward computational models of literary analysis, 5thinternational conference on language resources andevaluation, 44-51.

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Japan, January 11-13, 2014

Dynamic Analysis of Tensegrity Systems Subject to Arbitrary Joint Constr
aints
Youngsu Cho and Joono Cheong
Korea Univ., Sejong-City, S. KOREA
Tel : 82-44-860-1797; Fax : 82-44-860-1587{youngsucho, jncheong}@korea.ac.kr

Abstract: This paper provides an approach for dynamic analysis of tensegrity systems subject to arbitrary joint constraints.
Advantages of non-minimal dynamics including the simplicity of formulation and scalability are enjoyed. Due to thegenerality of the formulation, we can create a software module that can carry out dynamic simulation of numerous
tensegrity systems. To illustrate the effectiveness of the proposed dynamics formulation, we show simulation results fora deployable tensegrity structure and a multi-stage tensegrity prism under various types of joint constraints such as pin,
revolute, and planar types.
Keywords: Tensegrity, dynamics, joint constraints.

𝛾̂ = tensile force density matrix𝜆̂ = compressive force density matrix

I. INTRODUCTION
A tensegrity system is one of truss structures thatconnect many rigid bodies via tension members such as
wires and cables. Thus the tensions are the main forcesthat enable the structural stability and configuration [13]. A class k tensegrity system has as many as k bars
connected at their ends by a ball joint, but the barconnections form a topological tree (no closed loops ofbars)[4]. Examples of the static analysis include [4-9]among many others. On the contrary, dynamicists paid
little attention to tensegrity structures, perhaps due to theapparent complexity of the structures and the equations.
The papers [10,. 11] by Skelton show one of the simplest
forms of the dynamics equations using the nonminimal
coordinates.

P = A matrix of node constraints
η = [η1 , η2 ] = A matrix of transformed coordinates
This section briefly introduces the dynamics ofgeneral tensegrity systems in a matrix differential
equations using node coordinates. Reader refer to [7] forthe details. Throughout this paper, the following
assumptions on the bars and cables are made: (i) bars are
uniform in their cross section, (ii) the moment of inertia
about the bar axis is negligible (thin-bar assumption), and(iii) cable are uniform and massless.
The form of dynamics for tensegrity systems with Bbars and a cables can be written by the following form𝑁̈𝑀 + 𝑁𝐾 = 𝑊 + Ω𝑃𝑇

Previous tensegrity dynamic analysis was performed
only to those whose joint types were ball joints. However,there can be other types such as planar joints, revolutejoints, or universal joints, although the ball joints are themost common types of joint pairs in the tensegrity
systems. So, we will present a generic formulation fortensegrity dynamics with arbitrary joint types.

Where N ≜ [n1 𝑛2 ⋯ 𝑛𝑛𝑛 ] ∈ 𝑅3×𝑛𝑛𝑛 is the nodal
coordinate matrix whose columns represent the end
points of the bar; W≜ [𝑤1 𝑤2 ⋯ 𝑤𝑛𝑛 ] ∈ 𝑅3×𝑛𝑛𝑛 is theexternal force matrix whose columns represent the forcesat the corresponding nodes; M and K are defined asM=

II. PRELIMINARYNOMENCLARTURE
R = A matrix of bar’s center coordinates
T = A matrix of tension vectors
W = A matrix of external forces
Cb , Cs = Matrices of bar and cable connectivities
M = Mass matrix
K = Stiffness matrix©ICAROB 2014

1 𝑇
1
𝐶𝐵 𝑚
̂𝐶𝐵 + 𝐶𝑅𝑇 𝑚
̂𝐶𝑅
12
4

Where 𝑚̂ = 𝑑𝑖𝑎𝑔[𝑚1 , 𝑚2 , ⋯ , 𝑚𝑏 ] is the mass matrix
where 𝑚𝑖 denotes the mass of the i-th bar.
K = CST 𝛾̂𝐶𝑆 − 𝐶𝐵𝑇 𝜆̂𝐶𝐵
Where 𝛾̂ = diag[𝛾1 , 𝛾2 , ⋯ , 𝛾𝑎 ] is a force density matrix
for cables whose components are-40-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

γi = {𝑘𝑖‖𝑆𝑖 ‖ − ‖𝑆𝑖0 ‖‖𝑆𝑖 ‖0ni,1 − 𝑛𝑖,2 = 0
ni,1 − 𝑛𝑖,3 = 0⋮
ni,1 − 𝑛𝑖,𝑘 = 0

𝑖𝑓 ‖𝑆𝑖 ‖ > ‖𝑆𝑖0 ‖𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

Where ‖𝑆𝑖 ‖ is the length of the i-th cable vector, ‖𝑆𝑖0 ‖B. Planar, prismatic joint
If the i-th node can move on the ground,is its rest length, and 𝑘𝑖 is the material stiffness of the ith cable; 𝜆̂ = diag[𝜆1 , 𝜆2 , ⋯ , 𝜆𝑏 ] is the force density in

Pi,x n = ni,xPi,y n = ni,ythe bars defined as1
1 −2
λ̂ = 𝑙̂−2 𝐵𝑇 (𝑆𝛾̂𝐶𝑆 − 𝑊 − Ω𝑃𝑇 )𝐶𝐵𝑇 −𝑙̂ 𝑚̂𝐵̇ 𝑇 𝐵̇2
12

IV. Simulation Result
In this section, we present some simulation result todemonstrate the validity of the proposed analysis. We
used a tensegrity prism shown in Fig.1. We assumed thatthe masses of bars are identically to be m =1[kg] and thatthe stiffness coefficient in the cable is uniform as k=
500[N/m]. The height of stage was set to be 1/2 +√3/6 [m], and each three nodes in horizontal faces
formed an equilateral triangle with the unity side length.
This corresponded to the length of each bar equal to(1 + √3)/2[m].

III. Vector Form of Dynamics
1. Vector Notation
Dynamics using matrix form has limitation inhandling constraints. Matrix form can only define
constraint like pin joint type connection. When we apply
tensegrity system to robots and mechanisms however, it
is good to consider other joint constraints including
revolute and prismatic types. So, we present a vector
form of dynamics in this paper. Vector notation anddynamics can be defined asn = [n1T , 𝑛2𝑇 , ⋯ , 𝑛𝑛𝑇 ]𝑇
Cb = kron(CB , 𝐼3 )Cs = 𝑘𝑟𝑜𝑛(𝐶𝑆 , 𝐼3 )Cb 𝑛 = 𝑏
Cs 𝑛 = 𝑠𝑚𝑛̈ + 𝑘𝑛 = 𝑤 + 𝑝𝑇 Ω
1
1
𝑇
𝑇
λ=
̂𝐶𝑠 − 𝑤 − 𝑝𝑇 Ω) − 𝑏̇ 𝑇 𝑏̇)
𝑛𝑏 −2 (𝑏 𝐶𝑏 (𝐶𝑠 𝛾62 ∑𝑖=1 𝑙𝑖Using the proposed dynamics formulation withphysical parameters, we obtained the simulated responseof the tensegrity prism under the external forces[0,0, −1]T , applied on each node at the top face. The
proposed numerical correction algorithm was applied.
Figs. 2-4 show the responses of node n3 and n6 andtension 𝑠4 and 𝑠9 . The correctness of the simulation
results was verified by comparing with the results from acommercial package, ADAMS.
Since the formulation for the dynamic analysis was
general, we could apply for any shape and scale oftensegrity systems.

This form is derived from the matrix form of equation in
Section II.
2. Arbitrary Constraints
A. Pin-joint
If the i-th node is connected on the ground by pinjoint type, constraints condition can be written as
Pi,x n = ni,xPi,y n = ni,yPi,z n = ni,zIf a tensegrity model is class-k type, assume there are
k-1 virtual nodes and there are 3(k-1) conditions.

©ICAROB 2014

Fig.1. Tensegrity prism-41-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014[4] Pellegrino S (1993) Structural computations with thesingular value decomposition of the equilibrium matrix,
Int. Journal of Solids and Structures, 30:3025-3035[5] Connelly R and Terrell M (1996) Globally rigid
symmetric tensegrities, Structural Topology 21:59-78[6] Guest S (2006), The stiffness of prestressedframeworks: A unifying approach, Int. J. of Solids andStructures 43:842-854[7] Micheletti A and Williams WO (2007), A marchingprocedure for form-finding for tensegrity structures,
Journal of Mechanics of Materials and Structures 2:857882[8] Zhang JY and Ohsaki M (2007), Stability condition
for tensegrity structures, Int. J. of Solids and Structures
44:3875-3886[9] Kmet S and Kokorudova Z (2009), Non-linear closedform computational model of cable trusses, Int. J. of NonLinear Mechanics 44:735-744[10] Skelton RE (2005) Dynamics and control oftensegrity systems, in: Proc. Of IUTAM symposium on
Vibration Control of Nonlinear Mechanisms andStructures 309-318[11] Skelton RE (2010), Efficient models of multi-bodydynamics, in: R.Blockley and W.Shyy(ED.),
Encyclopedia of Aerospace Engineering

Fig.2. N3

Fig.3. N6(a) t 4(b) t 9Fig.4. Tension

VI. CONCLUSION
In this study, we presented a vector form of dynamics oftensegrity systems using node coordinates. The
formulation in particular allowed arbitrary types of joint
connections between nodes, which was impossible in theprevious analysis. Due to the generality of theformulation, we can apply for any shape or scale oftensegrity systems once the necessary topology matrices
are defined.

Acknowledgement
This work was supported by the Basic Science Research
Program through the National Research Foundation of
Korea(NRF) funded by the Ministry of Education,
Science and Technology (2012R1A1A2042823). And
the Human Resources Development Program forConvergence Robot Specialists supervised by the
NIPA(National IT Industry Promotion Agency, NIPA2012-H1502-12-1002)

REFERENCES[1] Pugh A (1976), An introduction to tensegrity.
University of California Press[2] Ingber DE (1998), The architecture of life, Scientific
American 48-57[3] Connelly R (1999) Tensegrity structures: why are
they stable? (in©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Road map generation from Smartphone data
Masao KUBO, Chau viet DAN, Hiroshi SATO, Akira NAMATAME
National Defense Academy of Japan, Yokosuka, Japan
Tel : 81-46-841-3810; Fax : 81-46-844-5911
masaok@nda.ac.jp

Abstract: Recently, methodologies to retrieve information for achieve a safety mobility environment from driving data have been developed actively. In this paper, we propose a method to make a safety map which revea
ls incidents that a driver takes care. Many researches discuss the relation between a driver and its control but
the driver's response against incidents is not discussed enough. The proposed method uses GPS data so our
method is applicable to any smartphones. We verify our method by experiments in our commuter environment.
Keywords: smartphone, Models of Driving Behavior, safety map, collective intelligence

I. INTRODUCTION
In this paper, a method to make a safety map fromGPS path recorded by smartphone which showsincidents to take care.
Traditionally, safety map used to describe spots
which an accident happens. Therefore, this map is madeby mainly government and public administration, forexample, Kanagawa pref. [1], Aichi pref. [2] and etc..
Recently, people try to share their own driving
experiences to achieve a more safety mobility
environment. By analyzing their driving data collectedby social network services, it is possible for thecommunity members to share their each aspects about
their mobility environment, for example, Nakajima [3],Hilton [4], Honda[5].
In this paper, we propose a method to make a map
which reveals incidents that a driver should take carefrom GPS data recorded by smartphones.

Fig.1. state transition model of a grid of IMAC.
Independent Markov Chain Occupancy Grid
Map(IMAC) by Saarinen[6] is adopted for our purpose.
IMAC is a kind of occupancy grid map (Thrun[7]) .
IMAC consists of grids which record information
around. Basically this map is adequate for representing
existence of static obstacles. However, normally there is
no static obstacles on a road while cars and pedestrians
should be dynamic obstacles, and traffic signals andstop lines should be consider as semi-static obstacles.
We expect that IMAC is very adequate forrepresentation of semi-dynamic obstacles because stateof each grid of IMAC is switched as a 2 states Markov
model.
In this section, we explain IMAC first. Next, thealgorithm to make a map of incident is proposed.

II. APPROACH
In this chapter, we propose a method to make an
occupancy grid map of incidents. In this paper, we call ageographical factor which forces a driver pays more
attention an incident. GPS path is mainly used for
because the pose of smartphone is usually unknown
beforehand. Each episode of the data is not accurate bynoise for example, included GPS signal. We expect the
proposed method can extract a plausible map from alarge collection of the episodes.

©ICAROB 2014

1. Independent Markov Chain Occupancy Grid
Map(IMAC)
Independent Markov Chain Occupancy Grid Map(I
MAC) is an occupancy grid map based on Luber[8]. Each grid has one of the states, free or occupi
ed. The state transition is described as Fig.1. Eachtransition is follows as Poisson process.

-43-The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

⎛1 − Pentry
Pentry ⎞ ⎛1 − λentry
λentry ⎞P = ⎜
⎟ = ⎜⎟
1 − Pexit ⎠ ⎝ λexit
1 − λexit ⎠⎝ Pexit
Let suppose m is a grid and the average transition
probability from occupied to free and the averagetransition probability from free to occupied are λm,exit,
λm,entry respectively.

€

λˆ m,exit = Pr ob(m = free | m = occupied)

λˆ m,entry = Pr ob(m = occuppied | m = free)€€

Saarinen[6] proposes the following estimation
method for these transition probabilities. After m isobserved in a sufficient number of times, theseprobabilities are estimated as follows.

λˆ m,exit =# events : occ.to free +1# observations when occ.+1

λˆ m,entry =# events : free to occ.+1# observations when free +1

Fig.2. the update rule when a vehicle goes forward.

€ where #observations when occ. means the numberof time when m is occupied. #events:free to occ.
€indicates the number of times when m is switched fromfree to occupied.
Saarinen[6] showed that this rule works well by
experiment in warehouse.
2. The proposed rules for making incident map
on road environment.
In this section, we explain the method to make a
map of IMAC from GPS path by estimation of status ofneighbor grids.
We suppose that a driver of vehicle takes an action
to reduce the risk to make an accident (for example,
Masuda [9]). We propose 3 rules divided into 2
categories which estimate status of grids around its
vehicle.
Fig.2 shows the first rule to estimate grid's status
which is used for when a vehicle is moving forward. Its
driving environment is discretized as grids. First vehicle
speed at time t V(t) is calculated from a pair of positionsof GPS at time t P(t) and P(t-1). If the V(t) andAzimuth(t) are stable, (Rule1) the number of times offree of all grids inside Sector(t) is added 1.

©ICAROB 2014

Fig.3. the update rule when a vehicle turns.
Fig.3 shows the second rule. When a vehicle isturning, we suppose that its driver tries to avoid
obstacles in Sector(t). Therefore, the number of times
when each grid is in Occupied added 1.
(Rule2) sector(t) ∩ sector(t +1)
Also, we suppose that there should be obstacles
when a vehicle is moving slower.
(Rule3) V (t −1) > V (t)€III. Experiments€

1. Test in “sandbox”
A. Test environment
We verify the basic performance of our method by awell organized experiment in the closed area. Vehicle's
behavior includes GPS, accelerations, and azimuth, etc
is recorded in 100 Hz by iPhone by Apple which is one-44-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014of popular smartphones. We drive about 90 minutes
around one of our academy's buildings clockwise andabout 60 episodes (=60 laps) and 527640 records are
obtained. During this experiment, we met only 1
pedestrian and no cars. Fig.4 shows the test
environment and the episodes over the Google map. In
the center of this figure, there is the building and thelines around it show the GPS path. The road around thebuilding has a single lane road on each side. The driverattempted to drive through a same location while thepaths recorded are seriously scattered. Therefore thisdata of GPS path contains a lot of noise.

about 83 m height above sea level and go down to D atabout 1 m height above sea level . Then we turn right at
D and go to the end of the right. We go to the end of theleft hand side and return to the academy. We seldom
meet a traffic congestion while we frequently stop at a
red light of traffic signals.
For simplicity we show only the result ofλm,exit inFig.6. A filled circle at each grid obtained indicates thevalue of λm,exit. The green shows the high, the red isabout middle and the blue represents a value of nearlyzero.
Simply speaking, markers where we release thebrake pedal after a large and/or long speed down tend to
be green. For example, this map shows the 6 grids
labeled by alphabets are green. There is a zebra crossing
without traffic signal at A and B. C and D are grids justbeside of traffic signal on a national road. E is the spot
where 2 lanes merge 1 lane. At these all spot, we haveto stop or take the corresponding careful driving.
Also, grids where we drive slowly become to beblue. The area of the end of the right is a residential area
and we have to drive very carefully. Also the rectangle
shape path of the left side of "F" indicates roads in a
residential district.
By this experiment, we can say our method canmake an incident map at least if there is no congestion.

Fig.4. The experiment in the closed installation

VI. CONCLUSION

B. Result of the closed area.
Fig.5 shows the map obtained. The diameter ofgreen circle at each grid indicatesλm,entry that of the redcircle indicatesλm,exit.. The area around the upper rightcorner shows the performance of our method. During
turning the corner green grids are impressive. There arelarger red circles at around where the vehicle starts toaccelerate at the end of the curve. We suppose that thesecharacteristics of the obtained map well consistent with
our expectations. We can say our method can makeincident map under noisy environment if there are noother vehicles.

In this paper, we propose a method to make a safety
map that reveals incidents which a driver takes carefrom GPS path recorded by smartphone. By the 2
simple but well conducted experiments, we showed our
method can make the map adequately.

REFERENCES[1]Kanagawa prefecture, http://www.police. pref. kanagawa.
jp/mes/mesf0152.htm[2]Aichi prefecture, http://www. pref.aichi. jp/cmsfiles/
contents/0000041/41119/sitara-jikomap.pdf[3]Nakajima Y,Makimura K,Masuko T, Applications forRoad and Urban Transport planning administration using
data on driver reaction in unforeseen circumstances by probevehicle,IBS Annual Report2005,pp81-86, 2005
[4] Hilton B.N., Horan T.A., Burkhard R., Schooley
B.,SafeRoadMaps: Communication of Location and Densityof Traffic Fatalities through Spatial Visualization and Heat
Map Analysis Information Visualization.
[5] Honda, http://safetymap.jp/[6]Saarinen1
J.,
Andreasson
H.,.
Lilienthal
A.J.,
Independent Markov chain occupancy grid maps forrepresentation of dynamic environment, 2012 IEEE/RSJ
International Conference on Intelligent Robots and Systems(IROS), pp.3489 - 3495,2012

2.Road test
Next, our method is evaluated by public roadenvironment. We drove about 20 times during from
June to July 2013 around our academy (mabori,yokosuka, kanagara prefecture, Japan). Fig.6 shows thepath and the incident map obtained. The driving route is
as follows. We start our academy (the right of A)at

©ICAROB 2014-45-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014[7] Thrun et.al., Probabilistic robotics, MIT press, 2005
[8] Luber M, Diego G., Tipaldi, and Arras K.O.. Placedependent people tracking. The International Journal of
Robotics Research, 30(3):280, 2011.

[9]Masuda, Haga, Models of Driving Behavior and Accident
Prevention,
2009,
Reliability
Engineering
Association of Japan(REAJ), Vol.31,No.3,pp223228

Fig.5. The incident map for the closed area.

Fig.6. The incident map for the commuter area of our academy.

©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Zigzag-Perceptually Important Points for Financial Time Series Indexing
Chawalsak Phetchanchai1, Ali Selamat2 , Md Hafiz Selamat3
K-Economy Research Alliance & Faculty of Computing,
University Teknologi Malaysia
81300 Skudai, Johor
1chawalsak_phe@dusit.ac.th, 2aselamat@utm.my, 3Md Hafiz Selamat

Abstract: In this paper, we propose a zigzag based m-ary tree (ZM-Tree) to index financial time series. The index is done bymean of zigzag-perceptually important points (ZIPs). The tree represents the zigzag feature of financial time series which
benefits in reversal pattern searching, and wave counting. We also propose a method of identifying ZIPs in multi-resolution.
The identification process uses the vertical distance as a measurement to evaluate the point importance. The experiments
demonstrate the performance of ZM-Tree in tree building, tree retrieval, tree pruning, and dimensionality reduction.
Keywords: Financial time series analysis, time series indexing, zigzag-perceptually important points.
1 INTRODUCTION
experiments. The last section, section 5 discusses theconclusion and recommendation for future works.

A time series is a sequence of observed data which are
generated chronologically. A financial time series is a timeseries that generated in financial activities; including stock
prices, exchange rates, fund prices, and dollar indexes etc.
Recently, there are varieties of financial time series
research areas, for examples, pattern searching [1, 8],segmentation [14], dimensionality reduction [13], clustering[12], classification [25], and prediction [15]. Due to timeseries are usually large of size, unstructured and highdimensionality; the way of locating time series of interest isa non-trivial problem. Thus, indexing those time series is
one of the possible solutions for increasing processing
performance. A financial time series illustrates its own
specific characteristics comparing to other time series datasuch as crime time series data [26]. The illustration of afinancial time series shape is typically characterized by a
few important points within multi-resolution view. These
important points are always formed in a zigzag direction. A
zigzag direction movement can be considered as a zigzag
feature which is collected by filtering out random noise [16].
A zigzag feature demonstrates past performance trends andonly the most important changes. Most of using a zigzag
feature in technical analysis is related to reversal patterns
identification, price retracement measures, and corrective
waves counting [27]. In this paper, we propose a financial
time series indexing approach. Indeed, we also propose amethod of identifying zigzag important points in multiresolution so-called Zigzag-Perceptually Important Points(ZIP) identification method. Further, these zigzag important
points are indexed by using our proposed Zigzag based Mary Tree (ZM-Tree). The rest of this paper is organized asfollows. Section 2 discusses in the previous related works.
Section 3 describes the proposed ZIP identification method
and the indexing method. Section 4 represents the©ICAROB 2014

2 RELATED WORKS
2.1 Time Series Indexing
Indexing a time series is necessary in several researches ofsimilarity searching. Faloutsos et al. [2] proposed a fast
subsequence matching technique that allows the similarity
search in between different size-time series. They extracted
a time series feature and divided it into several
subsequences by using sliding window. Each subsequenceis represented by a Minimum Bounding Rectangle (MBR)
which is further indexed and stored using ST-index. Lin etal. [17] proposed a new method based on Piecewise
Aggregate Approximation (PAA) [18, 19] so-called
Symbolic Aggregate Approximation (SAX). SAX allows
a time series of arbitrary length s to be reduced to equilength segmented time series p by using PAA, where s >> p,and then symbolize the PAA representation into a discrete
string. SAX was later improved as indexable SAX or iSAX
and indexed them for mining terabyte sized time series [20].
Roh and Yi [21] introduced a new class of time series
sequences where each observation is represented by aninterval rather than a number. They then efficiently indexed
these intervals for fast retrieval of similar interval time
sequences from large databases. However, above
approaches of indexing focus on dividing a time series into
several subsequences without interesting in important
points which are represented in financial time series.
Important points are very important and non-trivial infinancial time series analysis. Chung et al.[6] introduced theperceptually important points (PIPs) identification method-47-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

for using in patterns matching of financial applications.
Later,
Fu et al. [7] and Fu et al. [9] indexed theidentified PIPs individually. Fu et al. [7] introduced theindexing method of financial time series based on low
resolution clustering. PIPs are identified in multi-resolution
and they are grouped together by mean of its shape by using
a clustering method such as K-means clustering. The index
is produced during the clustering process. More structurally,
Fu et al.[9] proposed the method to index PIPs by means of
the tree data structure so-called specialized binary tree (SBTree) with the benefits of efficient computation of
cumulative new data points, retrievals, and access.
Unfortunately, the representation of important points in atime series is not in the zigzag form.

between the first and second PIPs or between the second
and the last PIPs. The process of locating the PIPs
continues until all the points in T are attached to a list. To
calculate the distance to the two adjacent PIPs, three data
point importance evaluation methods are proposed tomeasure this distance. Fu et al. [9] introduced threemethods for evaluating the importance of the PIPs in a timeseries, they are: Euclidean distance (PIP-ED), perpendiculardistance (PIP-PD) and vertical distance (PIP-VD) as shownin fig. 1.

p3(x3,y3)Perng et al.[22] proposed a Landmark Model which relies
on human spatial memory. Landmark Model produces azigzag compressed time series. The model collects thegreatest important points in a time series with a specificminimal distance/percentage principle (MD/PP). MD/PP
has its own intuitive meaning. For example, if a stock trader
trades once for a week (5 business days) and regards a 10%
gain or loss as significant, then MD is set to 5, and PP is set
to 10. Landmark Model represents a time series which isfiltered out the noise and retained only the significant
important points underlying the specific MD/PP. For
querying the Landmark sequence, they recommended that
the Landmark sequence is more similar to a string sequencerather than a multidimensional sequence, thus the stringindexing is more suitable than using R-tree. The similar
concepts of the important points identification can be foundin studies done by Pratt and Fink [24] and Fink and Pratt[23]. They compressed a time series by selecting some ofminima and maxima or important points of peaks andtroughs, and dropping the other points. The intuitive idea is
to discard minor fluctuations and keep major minima andmaxima. The compression rate is controlled with aparameter R, which is always greater than one; an increaseof R leads to selection of fewer points. They gave atechnique for indexing of compressed series, which
supports retrieval of series similar to a given pattern byconsidering their major inclines, which are upward anddownward segments of the series. However thesetechniques do not illustrate data structure to well-organizeand store the identified important points.

p2(x2,y2)
pd(xd,yd)
pv(xv,yv)p1(x1,y1)Fig. 1. Measurement of PIP-ED, PIP-PD, and PIP-VD.

PIP-ED calculates the sum of the Euclidean distance of thetest point to its adjacent important points. PIP-PD calculates
the perpendicular distance between the test point and theline connecting the two adjacent PIPs. Finally, PIP-VD
calculates the vertical distance between the test point andthe line connecting the two adjacent PIPs.

3 THE PROPOSED METHOD
In this section, we propose the method of zigzagperceptually important point identification method, thealgorithm of constructing the ZM-Tree from ZIPs and also
the algorithm of accessing, updating, and retrieving ZIPs
from ZM-Tree.
3.1 Zigzag-Perceptually Important Point Identification
Method

2.2 Perceptually Important Points Identification

For a time series T = {t1, t2, …, tm}; where m is a number oftime instances, the main idea of ZIP algorithm is to aim tocollect the important points that influence the zigzag shapeof the time series. These important points are collectedfrom a time series by evaluating the distance of the point to
its adjacent important points. The selected data point
importance evaluation is based on the vertical distance(VD) [9].
However, in our case, we make somemodification of VD measurement for utilizing evaluating
their zigzag states by removing the absolute function
according to retain the sign (+ or −) of the VD for later
evaluating their zigzag turning signals (ZTS).

The concept of PIP identification [6] is based on the data
point importance. The importance is defined by thedomination of a data point on the shape of the time series.
A data point that has a greater domination to overall series
shape is thought as more importance. For a given timeseries T, all the data points, t1,t2,…,tm in T will go throughthe PIP identification process. Initially, the first two PIPs
are collected from the first and the last points of T. The
next PIP will be the point in T with the greatest distance tothe first two PIPs. The fourth PIP will then be the point in Twith the greatest distance to its two adjacent PIPs, either©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

The modified function depicts as follow:

For representing of the ZIP identification process, we
illustrate step-by-step of the method graphically. The
empirical example is a synthetic data sequence with 17 data
points. Algorithm 1 depicts the steps of ZIP identificationfrom the first step until the last step. Table 1 shows the
ZIP list ordered by their importance level collected from the
ZIP identification process of algorithm 1.

(1)where (x1,y1) and (x2,y2) are coordinates of points at thestart and end of a segment or p1 and p2 respectively, p3(x3,y3) is a point which we try to find its VD comparing to p1
and p2 . Finally, pv(xv,yv) , a projection of p3 on the line
connecting between p1 and p2. The algorithm of identifying
ZIPs is illustrated as follows.

4 TIME SERIES INDEXING
In this section, we describe the indexing technique offinancial time series based on ZIP identification. The ZIPs
which collected from the ZIP identification method areused to construct the tree data structure. The constructed
tree is a variety of m-ary tree; so-called Zigzag based M-ary
Tree (ZM-Tree). ZM-Tree follows the M-ary Treeconstraints with some additional constraints. Section 4.1,
we describe the structure of ZM-Tree. Section 4.2, we
introduce the ZM-Tree building algorithm.
Finally,section 4.3, we propose some basic tree operations.

Algorithm ZIP_Identification( T )1.
Choose the first and the last point of the time series T; p1,
pm as the first two ZIPs, add to S;
2.
Z ← Find the next ZIP
3. IF Z is found THEN
4.
Determine ZTS of the Z;
5.
Add the ZIP and its Z to S;
6.
Loop_cont=TRUE
7.
WHILE Loop_cont
8.
C ← Collect all available segments in T
9.
IF C is not empty THEN10.
sm ← Count a number of segments inC
11.
FOR i = 1:sm:i=i+1
12.
seg ← Read seg i13.
Remove seg i from C
14.
IF seg is not marked as ‘N’ THEN
15.
Z1 ← Find the ZIP of seg
16.
IF Z1 is found THEN
17.
Determine ZTS of Z1,
18. IF ZTS of Z1 equals to one of its adjacent ZIP THEN19.
Z2 ← Find the ZIP of this su
b segment of seg
20.
IF Z2 is found TH
EN
21.
Determine ZTS of Z2
22.
IF ZTSof Z2 equals to one of its adjacent ZIP THEN
23.
Delete Z2, Z1
24.
Mark seg as ‘N’
25.
ELSE
26.
Add Z1, Z2 and their ZTS to S sequentially
27.
END IF
28.
ELSE
29.
Delete Z1
30.
Mark seg as ‘N’31.
END IF
32.
ELSE
33.
Add Z1 to S
34.
Mark seg as ‘N’35.
END IF36.
END IF
37.
END FOR
38.
ELSE
39.
Loop_cont = FALSE
40.
END IF
41. END WHILE
42. END IF
END_ZIP_Identification

Algorithm 1.

4.1 ZM-Tree data structure
The proposed ZM-tree is a variety of the incomplete m-ary
tree with m = 3 where m is a number of available children
for each node. ZM-tree is an ordered tree in which:(1) The nodes hold between 1 to 2 distinct keys.
(2) The keys in each node are sorted.
(3) A node with k elements has k+1 sub trees, where thesub trees may be empty.
(4) The ith sub tree of a node [v1, ..., vk], 0 < i < k, may
hold only values v in the range vi < v < vi+1 (v0 is assumed toequal - infinity, and vk+1 is assumed to equal + infinity).
The additional constraints and information to the m-ary tree
are added to ZM-Tree. The root contains exactly two keysof the ZIPs from level zero important points. And, eachother node has one or two key(s) depending on a number of
retrieved ZIPs on that iteration level. Information storing ineach key comprise of ZIP, and ZTS (placed over the key
block).
4.2. A node structure and contents
There are three available types of node in ZM-Tree;, asingle key node, a double keys node, and a root node.
Firstly, a single key node is a node that contains only one
key while holds available two children at left and right subtree. A double keys node is a node that contains twoordered keys. It holds available three children at left,
middle and right sub-tree. Lastly, a root node, it is a special
kind of a double key node but it holds only available one
child at the middle key while the left and the right keys are
set to null. For considering which node is a single key nodeor a double keys node, this depends on the collected ZIPs of
a sub-segment at the specific level.

ZIP identification process

Each node contains a key or keys depending on the kind ofthat node. However, each key also attaches some necessary
information, for examples, key’s ZTS, and its VD. ZTS is

3.2 An example of ZIP identification©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

placed on the top of the correspondent key of each node
except the root node which there is no ZTS. VD is stored ina key structure moreover a key value. VD is necessary fortree pruning or tree retrieval activities.

Algorithm Seek_position( root, newKey)
1 cur ← root ;2 prev ← null;
3 REPEAT WHILE cur < > null
4 BEGIN5
prev ← cur;6
IF (cur is root) THEN
7
IF (cur.key(1) is null) THEN
8cur ← cur.key(1);9ELSE IF (cur.key(2) is null) THEN

4.3. Tree Building
To build a ZM-Tree, firstly all ZIPs are needed to collectand kept in a ZIPs list sequentially. The root of the tree iscreated by reading the first two ZIPs from the ZIPs list andstoring them in the root node. Further, all other ZIPs areread and put to the appropriated nodes and positions. The
algorithm of creating a ZM-Tree is illustrated as follows:

10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26

Algorithm Insert( root, newKey)
1
cur ← root of the tree ;2 prev ← null;3
IF (root is null) THEN
4root ← create a new TreeNode(newKey);
5 ELSE6
pos ← seek_position(root, newKey);7
IF( pos is the tree root) THEN
8
IF (newKey.zip < pos.key(1).zip) THEN
9
pos.key(1) ← pos.key(1);10pos.key(2) ← newKey;
11
ELSE
12
pos.key(2) ← newKey;
13
END IF
14 ELSE15
IF (pos.key(2) is null) THEN
16
IF (pos.key(2).level equals to newKey.level)
THEN
17
IF (newKey.zip < pos.key(1).zip) THEN
18
pos.key(2) ← pos.key(1);19pos.key(1) ← newKey;
20
ELSE
21
pos.key(2) ← newKey;
22
END IF
23
ELSE
24
IF (newKey.zip < pos.key(1).zip) THEN
25
pos.left ← new TreeNode(newKey);
26
ELSE
27
pos.right ← new TreeNode(newKey);28
END IF
29
END IF
30
ELSE
31IF (newKey.zip < pos.key(1).zip) THEN
32
pos.left ← new TreeNode(newKey);

26
27

ELSE
IF (cur.key(2) is null) THEN
IF (cur.key(2).level equals to newKey.level) THEN
cur ← cur.key(2);ELSE
IF (newKey.zip < cur.key(1).zip) THEN
cur ← cur.left;
ELSE
cur ← cur.right;
END IF
END IFELSE
IF (newKey.zip < cur.key(1).zip) THEN
cur ← cur.left;
ELSE IF (newKey.zip < cur.key(2).zip) THEN28
cur ← cur.middle;
29
ELSE
30
cur ← cur.right;
31
END IF
32
END IF
33
END IF
34 END
35 RETURN prev;
End Seek_position

Algorithm 3. Seek a position in a ZM-Tree for inserting a new
key

In order to create a ZM-tree, we start with creating the rootof the tree. The root node contains two elements.
Theoretically, in the case, the root must hold three sub trees,
but however, the left and the right sub trees are set to benull due to the node containing the elements which theirvalues larger than the last point in time series and the nodecontaining the elements which their values smaller than thefirst point in the time series are not available. The
remaining sub tree is the middle sub tree between twoelements of the root. Children nodes may comprise of one
or two elements ascending ordered from the left to the right.
The creation of a tree can be done recursively. The next
importance level is considered as the children of the current
importance level. The conclusion of the recursion ofcreating the ZM-tree can be illustrated in algorithm 3, andan example of the ZM-Tree from ZIPs of table 1 isdemonstrated in fig. 2.

33
ELSE IF (newKey.zip < pos.key(2).zip)
THEN
34
pos.middle ← new TreeNode(newKey);
35
ELSE
36
pos.right ← new TreeNode(newKey);37
END IF38
END IF
39
END IF
40 END IF
41 RETURN root;
End Insert
Algorithm 2.
Insert a new key to the ZM-Tree©ICAROB 2014cur ← cur.key(2);ELSEcur ← cur.middle;
END IF-50-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Table 1. ZIP list from ZIP identification process.

5 EXPERIMENTAL RESULTS

level

ZIP

y

VD

ZTS

0
0
1
2
2
2
3
3
3
3
3
3
4
4

1
17
9
6
15
13
2
3
7
8
10
11
4
5

0.50
0.00
1.00
0.21
0.53
0.41
0.18
0.46
0.80
0.63
0.86

n/a
n/a
0.75
-0.60
0.28
-0.28
0.26
-0.27
0.33
-0.27
0.16

0.71
0.25
0.39

-0.22
-0.13
0.16

n/a
n/a
−
+
−
+
+
−
−
+
−
+
+
−

In this section, we evaluate our methods of ZIP
identification, indexing, and time series dimensionality
reduction. The evaluation experiments are implemented
with Java programming language performing on IBM PC
compatible (Windows XP Professional, CPU Intel Pentium
IV 1.7 GHz, RAM 512 MB). Our dataset is the daily close
price stock index time series of Stock Exchange of Thailand(SET) collected during Jan 4, 1999 and July 31, 2006.
Measuring of the indexing performance, we experiment on
the dataset by measuring the CPU cost against the number
of ZIPs. The varieties of number of ZIPs to build the tree
tend to show that the increasing of the number of ZIPs
causes the increasing of time to build the tree linearly, as
can be seen in figure 3.
level
1

0

17

–

1

9

4.4 Basic Tree operationsa) Retrieval

To retrieve a time series from the ZM-tree, data points are
retrieved according to their importance. The retrieval
process starts from the root to their leaves recursively. The
steps of retrieval process depicts as follow:
 Firstly, the first two important points which stored in theroot are retrieved and put in the sorted heap.
 The children nodes are determined their important levels
and VDs. Children nodes are retrieved ordered from left
to right. Hence, nodes comprise of one or two keys, inthis case, all keys in a node must be retrieved at the sametime.
 The retrieval can be done recursively until the maximum
number of required ZIPs is reached or until the last nodeis retrieved.

–

+

6

13

15

+

–

–

+

+

–

2

3

7

8

10

11

+

–

4

5

2

3
4

Fig. 2. ZM-tree data structure of retrieved important points inTable 1 .
110
100
90

time(ms)

80
70
60
50
40
30

b) Pruning

20
10

In this study, we propose a method of financial time series
dimensionality reduction based on ZIPs retrieval from ZMTree. Our method is based on the idea that the time series
always represent their shape in a zigzag movement. All
points between the two important points which are closelylaid on the strait line between these two points are removed
according to their less importance. Additionally, thepruning technique proposed in [6] is adapted for this study.
Firstly, we attempt to remove the less significant points ofthe time series by considering their VD. The nodes with atleast one element have smaller absolute VD values than thethreshold γ are removed. The meaning of this condition isthat the corresponding nodes and their ascendants have lessfluctuation. However, the threshold γ can be appropriatedassigned by users.

©ICAROB 2014

+

20

30

40

50

60

number of ZIPs

Fig. 3. CPU cost for building the index against number of ZIPs.

For the tree accessing, we measure two factors; one is thetime usage against the number of ZIPs retrievals, andanother one is the time usage against the tree-height level
retrievals. Figure 4 demonstrates the CPU cost (time)against the number of retrieved ZIPs. The increasing of
number of retrieved ZIPs causes the increasing of CPU costlinearly. While the figure 5 shows the CPU cost against thetree-height level retrievals in the same way.

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 20141

0.22
SB-Tree
0.2

0.9

ZM-Tree
0.8

0.18
0.7

0.16
RMSE

RMSE

0.6

0.14

0.5

0.12

0.4

0.1

0.3
0.2

0.08

0.1

0.06
0
0.05

0.04
2

4

6

8

10

12

14

16

18

20

0.1

0.2

22

0.3

0.4

0.5

Pruning threshold(γ)

Numbers of important points

Fig. 4. Comparison of dimensionality reduction errors
between ZM-Tree and SB-Tree.

Fig. 6. Tree pruning thresholds and their errors.
0.22
SB-Tree
0.2

ZM-Tree

0.18

Finally, we experiment our study on the task ofdimensionality reduction using the tree pruning approach.
The comparison of errors of the dimensionality reduction
found that the cost of dimensionality reduction in ZM-Treeand SB-Tree approaches are very high when using a very
few number of important points and the errors are dropped
quickly when the number of important points are increased.
The graph shows slightly decline when the numbers ofimportant points are more than twelve. This can be seen in
fig. 7, and the examples of reconstructed time series of theretrieved important points comparing to their original timeseries are illustrated in figure 8a for ZM-Tree, and figure 8b
for SB-Tree.

RMSE

0.16
0.14
0.12
0.1
0.08
0.06
0.04
2

4

6

8

10

12

14

16

18

20

22

Numbers of important points

Fig. 7. Comparison of dimensionality reduction errors between
ZM-Tree and SB-Tree.

1
0.9
0.8
0.7

45

0.6

price

40
35

0.4
0.3
0.2

30

time( ms )

0.5

0.1
0

25

0

200

400

600

800

1000

1200

1400

1600

1800

2000

time

20

Fig. 8a - ZM-Tree

15
10

1
0.9

5
1

2

3

4

5

6

0.8

number of retrieved ZIPs
0.7

Fig. 4. CPU cost of tree retrieval against numbers of retrieved
price

0.6

ZIPs.

0.5
0.4
0.3
0.2
0.1

350
0
0

200

400

600

800

1000

1200

1400

1600

1800

2000

time

300

accessing time(ms)250

Fig. 8b - SB-Tree200

Fig. 8. Comparison of dimensionality reduction errors for (a) ZM150

Tree and (b) SB-Tree.

100

50

6 CONCLUSIONS

0
1

2

3

4

5

6

7

tree heightThis study, we propose the methods of financial time series
indexing by considering their zigzag shape movement. The
methods consist of two major algorithms; firstly, theidentification of important points so-called Zigzag-

Fig. 5. CPU cost of the tree retrievals against the tree-height level.

©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

13. A. Lendasse et al.,Dimensionality reduction oftechnical indicators for the prediction of financial timeseries application to the BEL20 Market Index.,
European Journal of Economic and Social Systems
15(2) (2001) 31-48.

perceptually Important Points(ZIPs) identification method,
secondly, the indexing method by applying the variety ofMulti-way search tree so-called ZIP-based M-ary tree (ZMTree). This tree data structure illustrates significantly theirbenefits in dimensionality reduction comparing to SB-Tree.
Further, the experiments should conduct to the several sizesof the time series and several datasets. This to comparing its
clearly performance in various conditions.

14. T.-C. Fu, F.-L. Chung, C.-M. Ng, Financial time series
segmentation based on Specialized Binary Tree
representation. In Proc of DMIN, 2006, pp. 3-9.
15. W. Huang, Yoshiteru, Nakamori, S.-Y. Wang,
Forecasting stock market movement direction withsupport vector machine. Computer and Operation
Research 32(10)(2005) 2513-2522.

AKNOWLEDGEMENTS
The Universiti Teknologi Malaysia (UTM) under research
grant 03H02 and Ministry of Science, Technology &Innovations Malaysia, under research grant 4S062 arehereby acknowledged for some of the facilities utilized
during the course of this research work.

16. Zigzag. StockCharts.com, accessed Feb. 20, 2010.
17.

18. B. Yi, C. Faloutos, Fast time series indexing forarbitrary Lp norms. In: Proc. of VLDB, 2000, pp. 385394.

REFERENCES
1. C.D. Kirkpatrick, J.R. Dahlquist, Technical Analysis:
The Complete Resource for Financial Market
Technicians, Financial times press, 2007.

19. E. Keogh et al., Dimensionality reduction for fastsimilarity search in large time series databases. Journalof Knowledge and Information System 3(3)(2000),
263-286.

2. R. Agrawal, C. Faloutsos, A. Swami, Efficientsimilarity search in sequence databases, In: 4th
Conference on Foundations of Data Organization and
Algorithms (1993) 69-84.

20. J. Shieh, E. Keogh. iSAX: indexing and mining
terabyte sized time series. In: Proc. of ACM SIGKDD,
2008, pp. 623-631.

3. K.-P. Chan, and A.-C. Fu, Efficient time series
matching by wavelets. In: Proc. of DE., 1999, pp. 126.

21. J.-W. Roh, B.-K. Yi, Efficient indexing of interval timeseries sequences. Information Processing Letters 109(2008) 1-12.

4. E., Keogh, K. Chakrabarti, and M. Pazzani,
Dimensionality reduction for fast similarity search inlarge time series databases. Journal of Knowledge andInformation Systems 3(3) (2001) 263-286.

22. C.-S. Perng, H. Wang, S.-R. Zhang, D.-S. Park,
Landmarks: a new model for similarity based pattern
querying in time series database. In: Proc. of ICDE,2000.

5. E. Keogh, and P. Smyth, A probabilistic approach to
fast pattern matching in time series databases, In: Proc.
of KDD, 1997, pp. 24–30.

23. E. Fink, B. Pratt, Indexing of compressed time series,
Data Mining in Time Series Databases (2003) 51–78.

6. F.L. Chung, et al. Flexible time series pattern matchingbased on perceptually important points. In: Proc. of
IJCAI-01, 2001, pp. 1–7.

24. B. Pratt, E. Fink, Search for patterns in compressed
time series, Image and Graphics 2 (1)(2002) 89–106.

7. C. Fu-Lai, et al., An evolutionary approach to patternbased time series segmentation. Evolutionary
Computation, IEEE Transactions on 8(5) (2004) 471489.

25. Z. Zeng, H. Yan, Supervised classification of shareprice trends. Information Sciences 178 (2008) 39433956.
26. W. Gorr, A. Olligschlaeger, and Y. Thompson, Shortterm forecasting of crime. International Journal of
Forecasting 19 (4) (2003) 579-594.

8. T.-C Fu, F.-L. Chung, R. Luk, C.-M. Ng, Stock market
time series pattern matching: Template-based vs. rulebased approaches. Engineering Applications ofArtificial Intelligence 20 (2007) 347-364.

R. D. Edwords, J. Magee, W.H.C. Bassetti.
Technical Analysis of Stock Trends 9th Edition. CRC
Press, 2007

27.

9. T.-C Fu et al., Representing financial time series basedon data point importance. Engineering Applications ofArtificial Intelligence 21(2) (2008) 277-300.
10. D. Bao. A generalized model for financial time series
representation and prediction, Applied Intelligence
29(1) (2008) 1-11.
11. D. Bao, Y. Zehong, Intelligent stock trading system byturning point confirming and probabilistic reasoning.
Expert Systems with Applications 34(1) (2008) 620627.
12. W. Ohta, An analysis of intraday patterns in priceclustering on the Tokyo Stock Exchange., Journal of
Banking & Finance 30(3) (2006) 1023-1039.

©ICAROB 2014

J. Lin, E. Keogh, S. Lonardi, and B. Chiu, “A
Symbolic Representation of Time Series, with
Implications for Streaming Algorithms”, In: Proc. of
ACM SIGMOD, 2003, pp.2-11.

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Recognizing a Road Environment Using Multiple-window Bag of Features
S. Morita, J.K. Tan, H. Kim and S. Ishikawa
Kyushu Institute of Technology
Tobata Kitakyushu 804-8550, Japan
Tel : 81-93-884-3183
e-mail: morita@ss10.cntl.kyutech.ac.jp, {etheltan, kim, ishikawa}@cntl.kyutech.ac.jp

Abstract: The idea of Bag of Features (BoF) is recently often employed for general object recognition. But, as it does
not take positional relations of detected features into account, the recognition rate is still not very high for practical use.
This paper proposes a method of describing the feature of an object by the BoF representation which considers
positional information of the features. Although the original BoF representation is applied to an entire image, theproposed method employs multiple windows on an image. The BoF representation is applied to each of the windows to
represent an object in the image interested for recognition. The performance of the proposed method is shown
experimentally.
Keywords: Object recognition, bag of features, multiple-windows, VLAD, computer vision.

background of the image.
The present paper proposes a method of describing
the feature of an object by BoF representation which
considers positional information of the features.
Although the original BoF representation is applied toan entire image, the proposed method employs multiple
overlapping windows on an image. The BoF
representation is applied to each of the windows to
represent an object in an image. In this way, thepositional information among obtained BoFs isemployed for recognizing an object interested.

I. INTRODUCTION
The future of a mankind will be more and more
complicated and will definitely need the help of an
intelligent robot. Then the robot must be equipped witha strong ability of object recognition. On the other hand,a hand-held camera and a wearable computer system
which can recognize every object around a blind person
may help him/her a lot in living a daily life safely aswell as conveniently. Such a system again needs to havea strong ability of object recognition. Various techniquesof object recognition have been developed to date. But
such techniques normally employ the features
depending solely on the objects interested. The featurescommon to every object should be considered todevelop a general objects recognition method.
General objects recognition has been paid muchattention among computer vision researchers recently. A
well-known general object recognition technique is theidea of Bag of Features (BoF) [1]. It is a point-based
feature description method and describes every object
using a visual word dictionary. But it describes an object
as a set of feature points without taking positional
information into account. The positional information, or
to know how feature points distribute on an object, isactually important information for its recognition. An
idea of spatial pyramid matching (SPM) [2] is proposedin order to take positional information of feature points
into account. But it is not very effective, since themethod segments an image into 2n by 2n regions with nooverlap some of which may contain only the©ICAROB 2014

II. BOF AND VLAD
The idea of BoF is overviewed in the first placefollowed by giving the concept of VLAD (Vector of
Locally Aggregated Descriptors) [3] proposed asanother representation of BoF.
Given an object image, the SIFT operator [4] isapplied to the image to derive a number of featurepoints on the object. The point is described by a 128dimensional vector. It is then projected into a 128dimensional feature space. A number of object images
are respectively transformed into the feature space eachas a set of feature points. The feature space thencontains a large number of the feature points, to whichclustering is applied to make some hundreds or
thousands of prominent classes. Let the number of theclass in the feature space by M. A class Ci is represented
by a feature vector vi (i=1,2,...,M). The feature space isthen defined as a visual word dictionary (VWD) by the-54-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

set V={vi| i= 1,2,...,M}: Vector vi is referred to a visual
word (VW) within the dictionary.
An object image is then described using the VWD.
Given an object image, the SIFT operator is applied tothe image and the feature points are extracted from theobject. Once they are projected into the VWD, they
distribute around the VWs which represent the object.
Let the number of the feature points distributing arounda VW vi be denoted by fi. This is actually the frequency
of a histogram of the chosen VWs. The object is thencharacterized by a M-dimensional feature vector
w=(f1, f2, ..., fM).

Fig. 1. Multiple-windows set on an object image.

(1)

This is called Bag of Features (BoF). An object isfinally identified by the BoF w.
Instead of using the frequency of the VWs, anotherdescription of an object [3] is proposed using a VWD(Vector of Locally Aggregated Descriptors). If a featurepoint extracted from an object image by SIFT is denoted
by x, the VLAD feature vector is defined bywi = ∑ ( x − vi ) .

(iii) Combining (i) and (ii): Arranging windows
randomly under the condition that the spots where there
are many feature points have priority in the arrangement.
Among the above three strategies, (ii) is natural andreasonable, since the present object recognition isfeature-points-based recognition. It is, however,important to take some randomness into account toescape from over-learning against training images. Thisis the reason why (iii) is considered. (i) is conducted forthe comparison with (ii) and (iii).
The size of the window and the randomness in thewindows placement is determined experimentally.
Let the number of the visual words in a window Wk(k=1,2,...,K) be denoted by Mk. In the original idea of
BoF, frequency in the BoF histogram is employed forthe components of feature vector wk. Instead of using
the histogram, the present method introduces VLAD for
describing a BoF. The magnitude of the VLAD wkm at
visual word vkm (m=1,2,...,Mk) in window Wk becomeslarge, if many feature points distribute close to vkm in abiased way. After all, the overall feature vector w is
defined by(2)v ( x ) =i

After all, the VLAD expression provides a 128Mdimensional feature vector of the form
w=(w1, w2, ...,wM).

(3)

The magnitude of the component wi depends largely on
the feature points distributing around VW vi.

III. MULTIPLE-WINDOW BOF
The proposed method puts K mutually overlapping
windows (W1, W2, ...,WK) on an image in order toconsider positional relation among extracted featurepoints, which is a strategy different from the original
BoF [1]. It also differs from SPM [2] in the overlap of
the windows. The idea of multiple windows is shown in
Fig. 1. The proposed method also introduces VLAD for
describing BoF. This means to put more emphasis on
the VWs which have many feature points aroundthemselves than the frequency description.
Locating windows on an image has three variations:(i) Random location; Windows are randomly located on
an image;(ii) Considering feature points distribution: Arranging
windows more at the spots where many feature points
distribute;©ICAROB 2014

w=(w1,w2, ..., wk, ...,wK),
wk=(wk1,wk2, ..., wMk).

(4a)
(4b)

The dimension of the feature vector w is therefore 128M
(M=M1+M2+...+MK).
The recognition strategy employs a nonlinear SVM
based on one-versus-rest classification.

IV. EXPERIMENTAL RESULTS
An experiment was conducted using the images inan road environment. The employed objects forrecognition are a pedestrian, a traffic signal, a car and abicycle (See Fig. 2). They are all principal objects in the-55-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

road environment. The number of images used for the
training of a SVM is 800; 100 with each object and 400
negative images. On the other hand, the number ofimages used for test is 500; 100 with each object and
100 negative images. Used PC has a 3.40 GHz CPU
with 8 GB memories.
The first experiment, Exp_1, was done to examine
the performance of VLAD. The original method and
SPM employing a frequency histogram for BoF
representation were compared to those employing
VLAD for BoF representation. The result is given in
Table 1.
In the second experiment Exp_2, the proposed
method employing multiple windows is examined its
performance with respect to the three cases of windows
placement explained in the former section; (i) placementat random, P_R, (ii) placement considering featurepoints distribution, P_FPD, and (iii) placement
considering feature points distribution and randomness,
P_FPD&R.
The experimental result is shown in Table 2. In
Exp_2, the number of VWs is parameterized and it
varies from 50 to 500 per window.
As seen in Table 2, the recognition rate is the
maximum when P_FPD&R is adopted for windows
placement and 200 VWs are employed with every
window. The third experiment, Exp_3, was conductedunder the employment of multiple windows with
P_FPD&R, 200 VWs with each window, and VLAD
expression for BoF. The result is shown in Table 3.

Table 1. Result of Exp_1: Original BoF & SPM
without/with VLAD.
Methods
OO-BoF
SPM
SPM
*
BoF
+VLAD
+VLAD
Rec rate [%]**

63.0

67.4

69.8

72.8

* Original BoF** Recognition rate

Table 2. Result of Exp_2: Multiple window BoF.
P_R
No. VWs / window
50
100
200
500
Recogn' rate [%]
68.6
72.8
69.4
68.4
P_FPD
No. VWs / window
50
100
200
500
Recogn' rate [%]
71.0
69.4
69.4
68.8
P_FPD&R
No. VWs / window
50
100
200
500
Recogn' rate [%]
71.8
73.0
73.2
67.6

Table 3. Result of Exp_3: Multiple window BoF
with VLAD.
Proposed method
Recognition rate [%]

(a)

74.8

(b)

Fig. 3. The windows employed in Exp_2 and Exp_3: (a)Case (ii), (b) case (iii).
(a)

The number of used windows in Exp_2 and Exp_3
is 10 whose size is approximately 1/3 of the entire
image. They are arranged as shown in Fig. 3, in whichwindows are placed where many feature points exist in(a), whereas randomness is considered in addition to thefeature points distribution in (b). These windows
placements are kept unchanged through Exp_2 andExp_3.

(b)
Fig. 2. Objects for recognition: (a) Positive samples; apedestrian, a traffic signal, a car and a bicycle, (b)negative samples.
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Table 4. Average recognition rate with respect to threestrategies of windows placement.
P_R
P_FPD
P_FPD&R
Ave. rec. rate*
69.8
69.7
71.4
*Average recognition rate [%]In the employment of BoF, various weights could beconsidered including frequency of the SIFT featurepoints [1], VLAD [3], TF-IDF [5] and weighted BoF [6].
But they still give not very high recognition rates togeneral objects. One may need to improve this in someway.

V. DISCUSSION
VI. CONCLUSIONIn Exp_1, the positive performance of the VLAD
was recognized as shown in Table 1. The idea of VLAD
is to put emphasis on the VW which is characteristic toa particular object more than frequency, and it worked
affirmatively in the recognition of the 10 objects
employed in the experiment.
On the other hand, in Exp_2, the maximum
recognition rate was 73.2% when strategy (iii) in section
III was employed as windows location and 200 visual
words were used with each of the 10 windows. When asingle window, an image itself, is employed, which is
the original way of using the BoF, the recognition rate is
63.0% as seen in Table 1. Although SPM is a multiple
window method, the recognition rate is worse, 69.8%,
than the proposed method which is more flexible inwindows placement than SPM. This fact indicates theeffectiveness of the proposed use of multiple windows
in the BoF-based object recognition.
Finally, the proposed method, employing multiple
windows and VLAD expression, achieved 74.8% of therecognition rate as given in Table 3. This is the best
result at the moment.
As for the three strategies of windows placement,
strategy (iii) seems to act better than the other two,
which is seen in Table 4. It shows average recognition
rates with respect to each windows placement in Table 2.
It may, however, be necessary to perform moreexperiments to make the superiority certain, since thedifference is not very large.

©ICAROB 2014

In this paper, multiple window bag of features was
proposed which took positional relation of the featurepoints on an object into account. For BoF representation,
the vector of locally aggregated descriptors, VLAD, wasalso employed for recognizing ten familiar objects in a
traffic environment. By effective placement of themultiple windows on an image, 74.8% of recognition
rate was achieved, which is satisfactory for generalobject recognition. However, the research should becontinued to raise the recognition rate more in order to
put the method into practical use.

REFERENCES[1] Csurka G, Dance CR, Fan L, Willamowski J, Bray C(2004), Visual categorization with bags of keypoints,
Proc. ECCV: Learning in Computer Vision, 1-22.
[2] Lazebnik S (2006), Beyond bags of features: Spatial
pyramid matching for recognizing natural scene
categories, Proc. IEEE Computer Vision and Pattern
Recognition, 2169-2178.
[3] Jégou H, et al. (2010), Aggregating local descriptors
into a compact image representation”, Proc. CVPR.
[4] Lowe DG (2004), Distinctive image features fromscale-invariant keypoints, International Journal ofComputer Vision, 60(2):91-110.
[5] Frakes WB, Baeza-Yates R (1992). Information
Retrieval: Data Structures & Algorithms. Prentice-Hall,Engelwood Clifs.
[6] Manabe T, Tan JK, Kim H, Ishikawa S, Recognizing
indoor objects by weighted bag of features, 2014 IEEE
Tensymp. (submitted)-57-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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Recognized Face Tracking for CONBE Robot
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: m-san@mmcs.mse.kyutech.ac.jp, haya@mse.kyutech.ac.jp

Abstract: In our research, we develop the robot to combine with the consciousness and behavior which is the conscious
and behavior robot (CONBE). In this paper we present the basis system in the CONBE robot that consist of the twomain sections as the face recognition and robot gaze tracking. This system uses the face recognition using Fisherfacesthat also refer to linear discriminant analysis (LDA). For the robot gaze tracking, we implement with the head of the
CONBE robot that has a camera, two displays used for robot eyes and the actuators of 2 degrees of freedom. The
actuators use to control the robot gaze for tracking the human face which the robot can recognize. In this paper, we
experiment and present the results of the face recognition system and the tracking control system such as the tracking
error and time.
Keywords: Consciousness-Based Architecture, Fisherfaces, Face recognition, Cognitive robot, face tracking.

vertical and horizontal axis. We then calculate thedistances of the angular errors for each rotation in yaw
axis and pitch axis and command the target angle to theactuators to tracking object. As previously mentioned,
this research studies the face recognition that is one ofrecognition models in our CBA, and studies the part of
the robot behavior that is an action of following gaze.
This system imitates the animal character in the case of
the animal seeing some people. It could recognize thepersons and interested in them, it then acts somebehavior such as a gaze following in our case. Our
system imitating the animal system, due to our attempt
is to increase the function of the robot.

I. INTRODUCTION
An autonomous robot requires efficient operation of
their action. Decades, many good approaches have been
proposed for controlling the autonomous robot under
their condition such the development of the self-aware
robot. Due to a robot with self-awareness has a realistic
action to operate under the diversity situation morefunctional than a system without self-awareness. In thispaper, we develop a conscious behavior robot (CONBEI) that imitates a human head. The mechanism of a head
CONBE robot has 2 degrees of freedom that areorthogonal to each other in Cartesian coordinate. In ourlaboratory, we develop the robot to have theconsciousness and behavior as the mention and propose
the consciousness-based Architecture (CBA) system asa hierarchical structure model base on the mechanistic
expressed model of animal consciousness and behavior[1]. The motivational action of animal is derived fromrecognition and comprehension respectively. Therefore,
in this research, we focus on developing the recognitionof the robot to be a basis of CBA system. We develop
the face recognition with the CONBE robot to tracking
face which is recognized. In face recognition, we use
Fisher-faces algorithm that also refers to Linear
Discriminant Analysis (LDA), use a video camera as an
input instant image [2]. For hardware of the head robot,
we use Dynamixel actuators that are controlled via
RS485 bus to control the rotation of yaw and pitch axisof the head. For controlling, in the instant image, we
determine the distances between the center of the
tracking object and the center of camera image in©ICAROB 2014

Fig.1. Consciousness-based architecture
The robot would operate more than following thecommand from human such as when the robot combines
CBA system; it can introspect to act some suitable-58-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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behavior when the robot can recognize the user beingsad, happy and so forth. Moreover, this robot would be
more attractive for human or user with this proposed.

USB video capturedeviceCVBS

TTL

TTL/RS232
convertor

RS232

USB

USB toSerial port

USB

CMOS Camera

USB
RS485

Yaw rotation

RS485

USB/RS485
convertor

Pitch rotation

Fig.3. Connection diagram of the system.
Obtain image from
CameraAnd Initialize robot

Fig.2. Head robot.

II. ROBOT SYSTEM OVERVIEW
The system based on CBA system combining withrobot mechanism and software that we develop toarchive face recognition and tracking face which isrecognized. This system consist of two main such as
hardware and software system.

Face Detection

Haar classifiercascade profile

Eye detection

Face Preprocessing
Face training
data

Face recognition
using FisherFaces
Determine theposition error of theface and calculate indegree to rotate

1. Hardware
For head robot, we designed and constructed thestructure with the acrylic plastic. The head robot also
assembles with two actuators to rotate in Cartesian
coordinate which the first actuator operates as a robot
neck which base Dynamixel RX-64 and the second
actuator for up down rotating which base Dynamixel
DX-117. These actuators are DC gear motor with plastic
body which are controlled by packet commandcontaining with an identity number of the actuator via
serial RS485 multi drop and yield the feedback such as
position of rotation degree, temperature, load, Input
voltage, speed and so forth. For obtaining the image toprocess, we use embedded CMOS camera based on
SONY FCB-H11 which support full HD (1080i highdefinition) video format with; NTSL and PAL. The
camera model is equipped with ×10 optical auto focus
zoom lens and the ×12 digital zoom. The camera
module can control the features of the camera with
VISCA protocol via serial port. In this paper, we use
this camera in PAL mode connecting with composite
video channel and convert composite video channel toUSB by USB video capture device. All the device in the
robot system is controlled by a laptop which base CPU
i7-2630QM, RAM 4GB.

©ICAROB 2014

Haar classifiercascade profile

Send command to
the actuators

Fig.4. Software flow diagram.
2. Software
The system software has two major such facerecognition and control which are implemented by C++
language in a laptop. For the image processing library,
this software use OpenCV library. At the beginningsoftware from the face recognition, the system would
capture the instant image from the camera thenoperating with human face detection using Haar featuresand Haar classifier cascade profile to determine the faceposition [3]. The software also detects the human eye
due to obtain the definite position to provide the face
preprocessing image due to the face recognition isvulnerable with some difference in lighting condition,
face orientation, face expression, and so forth. For eye
detection, it is used to crop the input image to show
only the approximate eye region. We then perform thepreprocessing
image
combininggeometricaltransformation and crop; separate histogram
equalization for left and right side; and smoothing-59-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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III. EXPERIMENT

image using a bilateral filter; and elliptical mask toremain hair and background from the face image.
Eventually we perform the face recognition using
Fisherfaces algorithm with our training data and facelabel. Fisherfaces is the popular algorithm for facerecognition using LDA which is to improve thecapability of classification from PCA which is the coreof the Eigenfaces method that is a powerful method torepresent data. The simple concept of LDA is that thesame classes should group closely together whereas
different classes are more distance [4]. In this section
we provide the environment for recognized facetracking. For the next section, we would experiment thissystem to apprehend the performance and other specificof this system.

In the system that we already provided, we
experiment this system divided in two main sections;the first is face detection and recognition; and thesecond is the tracking system or the robot gazefollowing the recognized face. The face detection is
implemented using local base pattern feature cascadeclassifier with the restricted condition of the smallest
size image as 25×25 pixels which can detect the face.
For the eye detection, we used Viola-Jones object
detection framework with Haar feature-based cascadeclassifier that is an effective and popular object
detection method [3]. We experiment the performanceof the face and eye detection in different condition withsome results as shown in Fig. 5. The face detection also
is implemented to incorporate with the robot toexperiment for the robot face tracking. The robot facetracking perform the experiment for the robot tracking
performance as shown in Fig 6. In this experiment, wetest the robot tracking the face while the human moving
and facing the camera, the first we move from left toright side and return, the second we move up and down
to test. The distance between the human while testing isaround 1.5 meters. The experiment finds out theperformance and the characteristic system such as theaverage frame rate as 16 frames per second, the average
error as 5, 15.6 pixels, the minimum error as -272, -83
pixels and the maximum value as 228, 159 pixels inhorizontal and vertical axis respectively.
The face recognition algorithm test with the laptop
and the camera in the robot incorporating with the
tracking control. In the experiment, 6 members of ourlab have contributed the sample videos to construct theface recognition algorithm. The training program
acquired the image by capturing each contributed
videos. During the video recording using the camera in
the head robot, the member would face the camera firstthen turn left, right and move up down his face in frontof the camera slightly. Every image would perform theface detection using LBP-based cascade classifier
profile and preprocessing image to preparing fortraining in Fisherfaces algorithm to construct the facerecognition model [2], [5]. The training image size after
resize and preprocessing would be 120×120 pixels.
Fig. 7 illustrates the example of the experimental
results face recognition and tracking. There are two
member lab testing the system, left side image which is
the recognized face then the robot would track the faceFig.5. Example image of face and eye detection indifferent case; the rectangle is the face region and thecircle is the position of the left and right eye.

Center error (pixel)

300

Horizontal

200

Vertical

100
0
0

5

10

15

20

25

30

-100
-200

-300

Time (s)Fig.6. Distance error between the image center andtracking object center in horizontal and vertical.

©ICAROB 2014-60-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014detection and 13 frames per second for face recognition
incorporating with robot face tracking. For the facedetection and tracking the robot is able to track the facefollowing pretty fast but in the recognized face tracking,the robot following pretty slow as the face recognition
algorithm processing time and depending on the
difficulty of the facial similarity. Because while testing
the recognized face tracking, this system could notrecognize the face every frame as the image is a blur
image from the movement reason.
As the results, we first proceed the system toimprove the robust face recognition in real time as wellas possible. We then develop the facial express
recognition to recognize the emotion expressing on theface to fulfill the part of the CBA operating with theface.

whereas the right side image which is the unknown facethen the robot would not track the face. Fig 8 shows theresults of the recognized face tracking such as the errorof the center image between the center of the faceregion and the center of the camera image in horizontal
axis and vertical axis. In this condition we move theface on the right, left, up and down to find out thecharacteristic and performance of the tracking control.
In this experiment, we find out the average frame rate at
13 frames per second, a minimum error as -121, -59
pixels, a maximum error as 158, 84 pixels, and theaverage error as 2.7, 13.8 pixels in horizontal andvertical respectively.

ACKNOWLEDGEMENT
We would like to thank the members of our lab who
contributed the image along with for testing the systemand thank Mr. Masato Koike from Department of
Mechanical Information Science and Technology,
Kyushu Institute of technology who contributed thesample source code for the robot eye expression.
Fig.7. Example images in the robot tracking ofrecognized face. The left side show the recognized facetracking and the right side show unknown face.

REFERENCES[1] Hayashi E, Ueyama K, Yoshida M (2011),
Autonomous motion selection via consciousness-basedarchitecture. In Ubiquitous Robots and AmbientIntelligence (URAI), 2011 8th International Conferenceon, pp. 401-402[2] Belhumeur P. N., Hespanha J. P., Kriegman D. J.
(1997), Eigenfaces vs. fisherfaces: Recognition usingclass specific linear projection. In Pattern Analysis andMachine Intelligence, IEEE Transactions on, pp. 711720.
[3] Viola P., Jones M. (2001), Rapid object detection
using a boosted cascade of simple features. In Computer
Vision and Pattern Recognition, Proceedings of the2001 IEEE Computer Society Conference on Vol. 1, pp.
511-518[4] Turk M, Pentland A (1991), Eigenfaces forrecognition. In Journal of cognitive neuroscience, pp.
71-86.
[5] He DC, Wang L. (1990), Texture Unit, Texture
Spectrum, and Texture Analysis. In Geoscience andRemote Sensing, IEEE Transactions on, vol. 28, pp. 509- 512.

170

Horizontal
Center error (pixel)

120

Vertical

70
20
-30 0

2.5

5

7.5

10

12.5

15

17.5

20

22.5

25

-80
-130

Time(s)Fig.8. Distance error between the image center andtracking object center in horizontal and vertical.

IV. CONCLUSION
This paper presents the face recognition combining
with image tracking by CONBE robot for an
experimental personal robot in our concept to develop
the CBA. In the proposed visual tracking and the systemprocesses around 16 frames per second for face

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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Eye detection Using Composite Cross-Correlation form Face Images in Varied llumination
Kutiba Nanaa1, Mohamed Rizon 2 and Mohd Nordin Abd Rahman1
1

2School of Computer Science, Faculty of Informatics and Computing,
School of Manufacturing Technology, Faculty of Design and Engineering Technology,
Universiti Sultan Zainal Abidin (UniSZA), Terengganu, Malaysia

Abstract — Facial feature detection is essential process
in the field of face recognition. Template match based
approach is widely used to implement the issue offacial detection. However, many obstructions adversely
affect in template based implementation such as varied
illumination, size, face pose and the status of eye ineither open case or close case. the objective of thispaper is to detect left eye and overcome the obstructionof low illumination in given image using composite
cross-correlation. Two single templates form the
proposed composite template. First single template is
created from an eye image in ordinary illumination
while the second single template is created from an eye
image in low illumination. The experimental results is
concluded by applying our templates on the PICS
Database whose varied illumination images and showthat applying composite templates gives a better result
than applying the both single templates individually.
INTRODUCTION
Template match based approaches are widely used
for finding small parts of an image which match agiven template image. In the field of facial featuresdetection, many methods have been proposed based on
template match such as deformable template whichlinks edges, peaks and valleys to find the best place ofeye. However, in creating templates verse, several
obstructions that may adversely affect on the result of
template match based approches such as how much eye
open is, the variety in sizes and illuminations between
eye templates and the eyes in the given face images,
and the pose of face in the given image. In the fact, thebehavior of creating eye templates plays a fundamentalrole in the performance and result of applying templatematch based approaches.
However, Dong and Wu have proposed a templatematch method to detect the eye whether it is fully open
or semi open in the paper aimed to determine thefatigue driver by compute the distance of the eyelids
during driving [1] while Bhoi and Mohanty have
employed match template to detect the eye in bothcases open eye and close eye regardless the prior [2]In order to overcome the obstruction of verious
sizes between the eye in template and the eye in given
image, Peng et al. have proposed a method to expect
the eye size in the given image to resize the eye
template [3]. Multi-scale templates is also proposed by
Florea et al., to detect the eye in deferent size of given
images in the study aimed to analyze the upper facial
expressions [4].
©ICAROB 2014

Furthermore, in order to overcome the obstructionof verious poses between the eye in template and theeye in given image, Chen et al., have proposed amethod to detect the human face in deferent poses. In
the fact, they created the face template from only halfface. By this way, the implementation of the method
has be accelerated and it has gaven the same results forthe frontal face images and has given batter results inthe case of deference in face poses. On the same
criterion, creating eye template from only half part ofeye overcome the obstruction of face poses assuggested by Nanaa et al., in composite crosscorrelation [5].
One of the ideas that have been realized by creatinga template of from only the iris [6]. Kuronuma andSuzuki has employed this template to detect the pupil
in vehicle operations [7]. and it has achieved goodresult because of the ease of preparation template dueto the regular circular shape of iris.
In our paper we present a method to overcome thevarious of illumination between eye templates and thegiven image. We employed composite cross correlation
which have been presented in [5] in order combine theimpact of ordinary and low illimunation templates incomposite eye templates.
PROPOSED METHOD
In general, detecting eye form given image I byusing cross-correlation requires an eye template for eye
T of size of q  p. as shown in [2]. Both of templateand given image is converted into gray images. crosscorrelation is defined as in Equation (1).
(1)

Where:-62-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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In this correlation matrix,. the element that contains
the maximum correlation value refers to the center ofthe eye in the face image I. However, Elements’ value
in correlation matrix locate in the range [-1,1]. In order
to increase the contrast in the correlation matrex weapply Equation (2) .

whose a low corrolation with the template while thebright pixels shows the point whose a high correlation
with the template. In composite cross-correlation image
Co(I, [T1, T2, we noticed that the eye position has muchbrightness than same positionin single compositecorrelation images(2)
Where:

In our methodology we used an eye template inordinary illumination for PICS database. From
incorrect detection result due to low illumination weused the eye in the worst detection result as new
template and combine this templates together to form
composite cross correlation as presented in [5].
Equation (3) defined the composite cross correlation
for k number of templates.
(3)
Where:In accordance with our templates, we rewrite theformula of cross correlation in Equation (1) for theordinary illumination template T1 and low illumination
templatT2 considering to the Equality of templates’ size
as defined in Equation (4).
4Where:

Fig. 1.Using of Ordinary and low Illumination eye template incomposite cross correlation.

EXPEIMENTAL RESULTS

Fig. 1 demonstrates the use of the composite templates
by applying two eye templates on a given image I, T1 is
eye template in ordinary illumination and T2 is eye
template in low illumination. The cross correlation isshown as Co(I,[T1]) and Co(I,[T2]) for T1 and T2,
respectively. The composite cross correlation for both
templates is shown as Co(I, [T1, T2]). However, dark
pixels in cross-correlation image shows the point©ICAROB 2014

. The experimental results has been implemented
applied on PICS database [8], a well-known freedatabase of faces using two eye templates, ordinary
and low illumination templates to form composite left
eye templates [T1,T2]. However Fig. 1 shows the
templates of T1 and T2. PICS Database includes 90
individual faces in various illuminations. Eachindividual face has a number of images between 1 to 18.
The size of images is varied from 336480 to 624544.
However, the results demonstrate that the ordinary
illumination template exactly detect the eye in theordinary given images while low illumination template- 63 -

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014
produce a good results in low illumination given
images.
Furthermore, applying composite templates
produces the correct detection from detection resultsthat produced by applying single template (T1 or T2) ,individually. Amazingly the composite
template
recorrect the detection result although the failure in
detection by both of single templates T1 and T2, (Seethe forth row in Fig. 2.
Generally, applying singal templates is the same
method used in [2] and it gives 90% as detection rate
on PICS database while it gives 98% as detection rateby using our proposed compisite template on the same
database.

Fig. 2 shows samples of the results in varied
illumination. From left to right, first column shows agiven image. Second column shows the correlation
results of ordinary illumination template T1. Third
column shows the detection results of applying T1.
The detection result refers to the point whose themuch brightness value in correlation result (second
column ). Forth column shows the correlation resultsof low illumination template T2 and its detection
results is shown in fifth column . Correlation
produced form the composite [T1,T2] is shown insixth column and in the last column is the final left
eye detection by applying the composite templates.

Fig. 2. sample of template match based appoache to detect the left eye by using ordinary illumination eye template, low illumination eye
template and composite eye template
CONCLUSION
In this paper we demonstrate eye detection by using©ICAROB 2014

cross correlations. We have presented the previous-64-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014
study used template match based method and theimprovement to overcome the obstructions in variedsize, varied orientation and varied status of eye in bothcases (open and close). The objective in this paper is to
overcome the obstruction of varied illumination byusing composite template in order to detect the eye invaried illumination conditions.
The proposed composite template is compound of
two single templates, an eye template in ordinary
illumination and another eye template in low
illuminations. The experimental results show that
applying composite template produces a correct eye
detection that produced from applying the appropriate
templates and it re-correct the detection in the case of
failure of both detection that produced by applying
single templates. Comparing the results, using our
proposed composite template rises the eye detection in
PICS Database form 90% up to 98%.
REFERENCES[1] W. Dong and X. Wu, "Fatigue detection based on
the distance of eyelid," VLSI Design and Video
Technology, 2005. Proceedings of 2005 IEEE
International Workshop on, pp. 365-368, 2005.
[2] N. Bhoi and M.N. Mohanty, "Template matchingbased eye detection in facial image," Int. J. Of
Computer Applications, vol. 12, pp. 15-18, 2010.
[3] K. Peng and L. Chen, “A Roust Algorithm for Eye
Detection on Grav Intensity Face without
Spectacles,” JCS&T, Vol. 5 No. 3, 2005.
[4] L. Florea, C. Florea, C. Vertan and B. Ionescu,"Upper face description by comparative analysis ofgray-scale image pairs," Signals, Circuits andSystems (ISSCS), 2011 10th International
Symposium on, pp.1-4, 2011.
[5] K. Nanaa, M. Rizon, M. N. A. Rahman, A.,
Almejrad, A. Z. A. Aziz and S. B. Mohamed,. “EyeDetection Using Composite Cross-Correlation,”
American Journal of Applied Sciences, vol. 10, no.
11, pp. 1448-1456, 2013.
[6] T. Kawaguchi and M. Rizon,”Iris detection using
intensity and edge information,” Pattern
Recognition, vol. 36, no. 2, pp. 549-562, 2003.
[7] K. Kuronuma, S. Suzuki and H. Igarashi,“Estimation Method of Awareness Level Using An
Iconic Model for Support Robots,” In The First
IFToMM Asian Conference on Mechanism andMachine Science, vol. 21, 2010.
[8] PICS: hltp://pics.psych.stir.ac.uk.

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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Development of an autonomous-drive personal robot“An object recognition system using Self-Organizing Map”
Keisuke Ito and Eiji Hayashi
Department of Mechanical Information Science and Technology
Faculty of Computer Science and Systems Engineering,
Kyushu Institute of technology, 680-4, Kawazu ,Iizuka, Fukuoka 820-8502, Japan
Tel : 0948-29-7793
ito@mmcs.mse.kyutech.ac.jp

Abstract: We have been developing an autonomous personal robot, it's able to perform practical tasks in a humanenvironment based on information derived from the CCD camera images and the Laser Range Sensor (LRS), It is very
important that the robot be able to move autonomously in a human environment, and to select a specific target object
from among many objects. Thus, in this paper, we developed the system in order to determine and decide the type of an
object using Bag of Features (BoF) and Self-Organizing Map (SOM). The BoF method can extract local features andcreate a one-dimensional histogram, the SOM can cluster the input data and create a neural network map by using onedimensional histogram data of BoF. Because of these methods are able to create a neural network and determine thetype of the object using histogram of local features. In addition, we focus on recognition of plastic bottle, evaluate its
performance and discuss our experimental results.
Keywords: Personal robot, Image processing, Object recognition, Self-Organizing Map

unsupervised learning with a template matchingalgorithm. Therefore, in this paper, we developed thesystem in order to determine the type of an object byusing BoF and SOM. This system can visualize therelationship of the object and determine the type of theobject.

I. INTRODUCTION
In the near future, the autonomous self-driving
robots are expected to provide various services inhuman living environments. For this to occur, the robots
will need to gain a grasp in human environments.
Therefore, the systems to provide environmental
recognition based on image information are being
widely studied. However, it is very difficult to recognize
all driving environments from image information only;
so far, no prospects for such a system have emerged.
The robot’s system is composed of an autonomous run
system for movement and an object recognition system
for the recognition and grasping of an object. The object
recognition system is composed an object-recognition
processing part and a location-information acquisition
processing part, both of which use the monocularcamera and the LRS. In this research, we focus on theobject recognition process by using a CCD camera andspecial recognition of plastic bottle. In previous
research, we developed object recognition system using
Speed-Up Robust Features (SURF) that can progress therobust extraction with the bottle features. As a result, therecent robot can determine the angle of an object andoperate with the target object from the image provided
by the CCD camera. In order to build the robots acting
more autonomously, the system need to combine an©ICAROB 2014

Laser Range Sensor (LRS)A CCD camera

Fig.1 Our developed robot

II. SYSTEM OF AUTONOMOUS ROBOTThe figure 1 shows the appearance of our robot, thathas a drive mechanism consisting of two front and two
back wheels. The front wheels are attached to themotors which means that the wheels on either side are
driven independently, while the back wheels function as-66-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

castor wheels. This method has the advantage ofallowing a small turning radius. In addition, to acquire
image information, both a single CCD camera withapproximately 2,000,000 pixels and an LRS are
installed on the head of the robot and can be rotated toall sides by two motors. DC servo motors are used forthe robot’s drive mechanism, position and speed control
are achieved by the control system of the drive
mechanism. The robot also has two arms and hands
equipped with sensors, which enable it to respond to thevarious demands of humans. Finally, an installedwireless LAN can provide remote control for humans.
All devices are controlled by a personal computer, andlead-acid batteries are used as power sources in the
robot’s system.

Fig.2 Flowchart of process
Speed-Up Robust Features (SURF) which is a standard
local feature extraction and K-means method which is astandard data clustering method. SURF can extract local
features quickly and accurate. K-means method cancluster feature data made from SURF and calculate
visual words quickly. BoF stores two data, visual words
and result of BoF. In the case of the estimation kind of
object, by using the same visual word, this system canestimate kind of object. The processing algorithm is asfollows.

III. OBJECT RECOGNIZE SYSTEM
1. Outline
This system is composed of BoF and SOM process
as shown in Fig.2. These processes permit in the system
for learning and determining the objects by using
learning data. We explain the database learning system.
First, the system load an image data, extract a local
feature point and calculate one-dimensional histogramusing BoF method. Second, The SOM create an output
layer using output data from BoF process. Finally, thissystem store both BoF data and SOM data. We explain
decision process. Next, I explain object recognition
process by using created BoF data. First, this system
acquires object image data and calculates onedimensional histogram using same Visual Words createdby learning process. Second, this system compares
histogram, calculates the Euclidean distance and selects
SOM’s node which is most nearest against input data.
At the end, this system decides the kind of object byusing nearest input data. We will present the algorithmof processing below. We think about design recognitionof plastic bottle.

B. Learning algorithm of BoF method
Step1: Load images and Extract features
The images are loaded from database and extract
local features from each image and gather all featuresby using SURF.
Step2: Clustering features
By using K-means method, cluster all local featuresof the input images and create visual words.
Step3: Create a one-dimensional histogram
By using Visual words and the SURF method in eac
h database image, each feature in images vote most simi
lar visual words and create a one-dimensional histogram.
Step4: Store the hitogram data into the database
all
oneSavedimensional histogram data and Visual words as Extensi
ve markup Language (xml) data.

2. Bag of Features (BoF)

C. Decide the kind of an object by BoF algorithm
Step1: Extract features
Load images from database and extract local
features. Load the BoF database, visual book and onedimensional histogram.
Step2: Create one-dimensional histogram
By using visual words which are same learning

A. Outline
Bag of Features (BoF) is a kind of expression of
local features of brightness gradient[1]. BoF can convert
all local features in the image created to onedimensional histogram by using the center of gravity,
which features called visual words. BoF method uses

©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014mi

Output layer

CocaCola_zero (2) Fanta_grape (3) Fanta_orange (4)Ayataka (0)CocaCola (1)Lemon (5)Namacha-old (6)Namacha-new (7)Oi-otya (8)Oi-otya-genmai (9) Oi-otya-koi (10)Soken-bitya (11)U-roncha (12)

mi1 mi2 mi3 minxj1 xj2 xj3

xjn

Input layer

Fig.3 Structure of SOM
process and doing vote of nearest vote, create a onedimensional histogram.
Step3: Compare a histogram and the decision object
By calculating the Euclidean distance between input
histogram and database histogram in order to decide akind of object.

Fig.4 Types of plastic bottles

x  mc  min x  mi

3. Self Organizing Map (SOM)(1)

A. Outline
A Self Organizing Map (SOM) is unsupervisedmachine learning method developed by Teuvo Kohonen[2]. The SOM has 2-layer neural network composed of
the input layer and the output layer. The SOM cancalculate feature by using the Euclidean distance
between input data and output data and be placed close
to the network. This characteristic allows a mappingfrom high dimensional feature vector to low
dimensional layer without impairing the relationship.
We will present the algorithm of SOM processing as
below.

Step4: Adjusting the weights
The connecting weight vectors of all neurons are
updated by Equation (2). hc,i (t ) is the neighborhood
function that will decrease in time. The most commonlyused neighborhood function is the Gaussian function
which is represented by Equation (3) .

B. Learning Algorithm of SOM method
Step1: Initialize network
Set the learning processing times T , the number of
output layer’s neuron i and the initial learning rate
init . The connection weights between the input layer
x and the output layer m are initialized with small
random values .
Step2: Present the input vector
The database histogram is used to load into the input
layer. In this case, we use one-dimensional histogram as
input data.
Step3: Calculate the Euclidean distanceCalculate the Euclidean distance between the input
data and the output layer’s neurons by Equation (1).
And find the winner unit c (Best Matching Unit, BMU)
which has the minimum Euclidean distance.

Where, r  ri  rc is calculated between unit i andwinner unit c and  (t ) is called the neighborhood
radius. In addition,  (t ) is the decay of the learning
rate is calculated each iteration using the follow
Equation (4).

©ICAROB 2014

mi (t  1)  mi (t )   (t )hc,i (t )[ x(t )  mi (t )](2) r  r 2

hc,i (t )  exp  i c22 (t ) (3)

T t 
 t 

 (t )  init (4)

Step5: Repeated learning
The time t increases to t+1. If t < T then go to step 2;otherwise stop the training.
Step6: Decide class of object
Using the average data of each class, decide class of
output neuron. The class which is the minimum
Euclidean distance in neuron class.

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Japan, January 11-13, 2014

IV. EXPERIMENTAL RESULTS
1. Simulation of the classification by SOM
A. Outline
In this simulation, we evaluated the effectiveness ofthe development model when evolving a SOM. We
confirmed that a reference vector depended on an input
value. Table 1 shows each parameter used in the SOM
network part of the proposed model.
Table 1. Parameters used to classify simulations.
Number of visual words
Output layer’s size
Initial learning rate
Learning number

300
15 x 15
0.5
1000

In this system, the database has 13 classes, the numberof each class assigned to the types of plastic bottles asshown in Fig.4. In each class has 12 images because weuse those picture each 30 degrees.
B. Result and discussion
The classification on the map shows in Fig.5, which
is derived 13 types of plastic bottles by dividing thecells on the map based on unsupervised learning. In the
SOM learning, the similar node gets together in the nearlocation on the map. This result shows SOM’s
characteristic that similar object gather near. However,collections of class don’t gather near. For instance,class1 and class 2 (Coca Cola and Coca Cola Zero) andclass 8, class 9 and class 10 (Oi-otya, Oi-otya genmai
and Oi-otya-koi). We consider it occurred this result that
latest database has bias of input data. For instance, tea is
8 kinds. So we need to increase database object andconsider the best setting of BoF and the SOM.

Fig.5 The classification on the map
B. Result and discussion
This result shows that recognition system at Table2
that can recognition difference of between similar
objects. For this system more accurate, to store several
objects and to detect features which can divide
conclusively the type of object are necessary. So, weneed to consider adding database object and setting
parameter of BoF and SOM system.

V. CONCLUSION
We propose the object recognition system using the
Self-Organizing Map and the Bag of Features. This syst
e m can extract local feature and create a onedimensional histogram by using visual words which isthe center of gravity features. The SOM can cluster
input a one-dimensional histogram and create a neuralnetwork map by using of the data. We have shown
experimentally this advantage. In order to improve theperformance of the system, we will add input data andconsider the best setting of BoF and SOM.

2. Experiment of object recognition system
A. Outline
We try to experiment and evaluate the accuracy ofthis object type determination system. Regarding theexperiment’s condition of BoF and the SOM learning,
we use the same condition as experiment 1. The input
data has 3 objects such as Oi-otya (8), Oi-oyta-koi (9)and Lemon (5), and 6 images per object.
Table 2. Result of object recognition
Input object

The recognition rate [%]Oi-otya (8)66 %Oi-otya-koi (9)Lemon (5)

100 %
66 %

©ICAROB 2014

REFERENCES[1] Keiji Y., et al., CVIM tutorial series computer
vision leading-edge guide 2(Shinjuku,Tokyo,Japan:
Advanced Communication Media CO.,LTD., 2011) ,p.85-110.
[2] Kohonen, T., Self-Organizing Map, trans. Heizou,
T., et al., (Bunkyo, Tokyo, Japan: Springer-Verlag
Tokyo, 1998), p.102-171.

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Japan, January 11-13, 2014

Comparison of Feature Detectors for Obstacles Detection
Shaohua Qian1, Joo Kooi Tan1, Hyoungseop Kim1, Seiji Ishikawa1, Takashi Morie2 and Takashi Shinomiya3
1

Department of Mechanical & Control Engineering, Kyushu Institute of Technology
Sensuicho 1-1, Tobata, Kitakyushu, 804-8550, Japan{qian,etheltan,ishikawa}@ss10.cntl.kyutech.ac.jp; kim@cntl.kyutech.ac.jp
23School of Brain Science & Engineering, Kyushu Institute of Technology
Hibikino 2-4, Wakamatsu, Kitakyushu, 804-8550, Japan
morie@brain.kyutech.ac.jp

Department of Business Administration, Japan University of Economics
Sakuragaokacho24-5, Shibuya-ku, Tokyo, Japant.shinomiya@aa.cyberhome.ne.jp

Abstract: We have already proposed an obstacles detection method using a video taken by a vehicle-mounted
monocular camera. In this method, correct obstacles detection depends on whether we can accurately detect and matchfeature points. In order to improve the accuracy of obstacles detection, in this paper, we make a comparison between
four most commonly used feature detectors; Harris, SIFT, SURF and FAST detectors. The experiments are done using
our obstacles detection method. The experimental results are compared and discussed, and then we find the mostsuitable feature point detector for our obstacles detection method.

Keywords: Feature detectors, Harris, SIFT, SURF, FAST, car vision.

the same scene. This is an important problem incomputer vision with applications in object detection,
object recognition and structure from motion. While
feature points detection and matching has been studied
extensively for various applications, our interest is toreliably match two images in real time for camera
motion estimation. Accurate feature points detection andmatching can improve the accuracy of 2D and 3D
objects classification, and improve the accuracy of
obstacles detection ultimately.
Currently the most commonly used feature detectors
are Harris, SIFT, SURF and FAST detectors. In thispaper, we will make a comparison between these four
different feature detectors. The experiment will be doneusing the 2D and 3D objects classification method [2].
The experimental results will be compared anddiscussed, and then we find the most suitable featurepoint detector for our obstacles detection method.
The structure of this paper is as follows. First fourfeature detector methods are overviewed in section 2.
Experimental results are shown in section 3. Finally the
paper is concluded in section 4.

I. INTRODUCTION
Detection of obstacles in a video sequence is a basic
task in autonomous collision avoidance systems ofintelligent vehicle. Accurate obstacles detection will
improve the performance of obstacles tracking,recognition, classification and motion analysis. We havealready proposed an obstacles detection method using avideo taken by a vehicle-mounted monocular camera [1].
But this method detects 2D and 3D objects
simultaneously. Since these 2D objects are notdangerous to driving, they will reduce the accuracy ofdetection if they are detected as obstacles. In order not
to detect these 2D objects, we also have proposed amethod for classifying 2D objects and 3D objects [2]. In
this 2D and 3D objects classification method, correctclassification depends on whether we can accurately
estimate the camera motion parameters: Using thecamera parameters, the method calculates thecoordinates of 3D points in the world coordinate systemusing two corresponding feature points in two
consecutive images. The first step of the camera motion
estimation is corresponding feature points detection in
two consecutive images: It involves the feature points
detection and matching.
Feature points detection and matching is the task ofestablishing correspondences between two images of©ICAROB 2014

II. FEATURE DETECTORS
1. Harris
The Harris detector [3] is based on the local autocorrelation function of a signal: The local auto--70-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

principal curvature can be computed from theeigenvalues of the Hessian matrix.
C. Orientation assignment
By assigning a dominant orientation to each key
point, the key point descriptor can be represented
relative to this orientation and therefore achieve
invariance to image rotation. An orientation histogram
is formed from the gradient orientations of sample
points within a region around the key point. The peaksin this orientation histogram are the dominantorientations. If there are multiple dominant orientations,
new key points are added at the same location and scaleas the original key points.
D. Descriptor
This step is to compute a descriptor for each key
point. The descriptor is formed from a vector containing
the values of all the orientation histogram entries.
Usually, for one key point, we use a 4*4 array oforientation histograms with 8 orientation bins in each todescribe it. Therefore, the descriptor of each key point is
a 4*4*8 = 128 element feature vector.

correlation function measures the local changes of thesignal with small windows shifted by a small
displacement in different directions.
Let E (u , v ) be the change of intensity for thesmall shift [u , v ] :E(u, v) = ∑w(x, y)[I (x + u, y + v) − I (x, y)]2

(1)

x, y

where w( x, y ) is a smooth circular window function,for example a Gaussian.
This change can be concisely written asu 
E (u , v) = [u v]M  
v 

(2)

where M is a 2*2 matrix computed from image
derivatives:

 I x2M = ∑ w( x, y ) x, yI x I y

IxIy 
I y2 

(3)Let λ1 , λ2 be the eigenvalues of matrix M. Then,if λ1 and λ2 have large positive values, a corner isfound at the position (x,y).

3. SURFSURF or Speeded Up Robust Features [5] isdeveloped to speed up the feature detecting processusing SIFT as a basic algorithm.
A. Detector
The detector is based on the Hessian matrix. It uses
a very basic approximation of the Hessian matrix which
relies on integral images to reduce the computation time.
Therefore it is called the 'Fast-Hessian' detector.
Given a point X = ( x, y ) in an image I, theHessian matrix H = ( X , σ ) at X with a scale σ isdefined as follows

2. SIFT
SIFT or Scale Invariant Feature Transform [4] is afeature detector which is invariant to image rotation andscale. SIFT consists of four main steps to find thefeatures.
A. Scale-space extrema detection
This step is to find the salient points in the input
image. First the Difference of Gaussian images are
created by building an image pyramid of Gaussianblurred image of the input image with different scales.
Then in these DoG images the extrema are found bylooking for each pixel in eight neighborhood pixels of
the current scale image and in nine neighborhood pixels
in the neighboring scale images. If the pixel is a minimaor maxima, the pixel is a candidate key point.
B. Key point localization
Once a key point candidate has been found in theprevious step, the next step is to eliminate the low
contrast key points and the key points which have astrong edge response. For low contrast key points, we
can eliminate them using the Taylor expansion of the
Difference of Gaussian scale space function. For the key
points which have a strong edge response, we caneliminate them using the principal curvature. The©ICAROB 2014

 Lxx ( X , σ ) L xy ( X , σ ) H ( X ,σ ) =  Lxy ( X , σ ) L yy ( X , σ )(4)

where L xx ( X , σ ) is the convolution of the Gaussian
second order derivative with the original image I at
point X, and similarly for L xy ( X , σ ) andL yy ( X , σ ) . The Gaussian second order derivative
approximation is further approximated using box filters.
The approximations are denoted by D xx , D xy andD yy . The approximation determinant of the Hessian
matrix is given bydet( H approx ) = D xx D yy − (0.9 D xy ) 2

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(5)

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

candidate pixel is a corner. This is measured by theentropy of the positive and negative cornerclassification responses based on this pixel. The process
is applied recursively on all three subsets and terminates
when the entropy of a subset is zero. The decision tree
resulting from this partitioning is then converted into Ccode, creating a long string of nested if-then-else
statements which is compiled and used as a cornerdetector.
C. Non-maximal suppression
Finally non-maximal suppression is applied on thesum of the absolute difference between the pixels in thecircle and the center pixel.

The approximated determinant of the Hessian matrix
represents the blob response in the image at location X.
These responses are stored in a blob response map.
A pyramid is created by applying the box filters
which have different sizes on the original image.
The localization of the key point is done by finding
the local maxima over a 3*3*3 neighborhood. The
maxima of the approximated determinant of the Hessian
matrix are then interpolated in the scale and image
space.
B. Descriptor
The descriptor is based on the neighborhood of thelocation of the key point. A square region is centered at
the key point, and oriented along the dominantorientation. The size of the window is 20. This region issplit up in 4*4 sub-regions. For each sub-region, Haar
wavelet responses d x and d y are computed inhorizontal and vertical directions. The wavelet
responses are summed up over each sub-region to forma first set of the feature vector. Also added to this vector
are the sums of the absolute values of the responses.
Therefore, each sub-region has a four-dimensionaldescription vector of the formv = (∑ d x , ∑ d y , ∑ d x ,∑ d y )III. EXPERIMENTAL RESULTS
In this section, we carry out 2D and 3D objects
classification, which is described in [2], using these fourfeature detectors. For the experimentations we use twodifferent image sets. The four feature detectors are alltested with those image sets. The results from theseexperiments contain information about the detection
time, number of detected feature points and theprecision of 2D and 3D classification.
First, we analyze the detection time and the numberof detected feature points. Figure 1 shows the detection
time and Fig. 2 gives the number of detected featurepoints. From these two figures, we see the FAST
detector can find a lot of feature points, and also costthe least time. It seems the FAST detector is the best
detector in these four detectors.
Next, we evaluate the precision of 2D and 3D
objects classification. The result of the evaluation isshown in Fig. 3. From Fig. 3, we see SIFT and SURF
are the best detectors for this 2D and 3D objects
classification method. So why can the FAST detector(6)This results in total vector length of 4*4*4 = 64. At last,
the descriptor is converted into a unit vector.
4. FAST
FAST or Features from Accelerated Segment Test [6]is built on the SUSAN detector. The most promisingadvantage of FAST corner detector is its computational
efficiency.
A. Segment test detector
FAST corner detector uses a circle of 16 pixels
around the corner candidate p to classify whether acandidate point p is actually a corner. Each pixel in thecircle is labeled from integer number 1 to 16 clockwise.
If a set of N contiguous pixels in the circle are allbrighter than the intensity of candidate pixel p plus athreshold value t or all darker than the intensity ofcandidate pixel p minus threshold value t, then p isclassified as a corner. N is chosen to be 12 because it
admits a high-speed test which can be used to exclude avery large number of non-corners: the test examines
only the four pixels at the four compass directions.
B. Machine learning of a corner detector
The ID3 algorithm is used to select the pixels whichyield the most information about whether or not the©ICAROB 2014

Detection time
250

Time (ms)

200
150
100
50
0
Harris

SIFT SURF
Detectors

Experiment 1

Experiment 2

Fig.1. The detection time.

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FAST

Number of feature points

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014detected feature points. Fig.4 (d) shows the detectedfeature points using FAST detector. In this result, thedetected feature points are concentrated in the upper
part of the image: Just a few are located in the roadregion. But in the 2D and 3D classification method, weneed to use the feature points which are located in theroad region to estimate the parameters of the road plane.
So if we use the FAST detector, we cannot estimate theequation of the road plane accurately, this results in the
low classification precision.
According to the precision of 2D and 3Dclassification (shown in Fig. 3), although SIFT and
SURF have almost the same precision, according to thedetection time (shown in Fig. 1), it’s easy to get thisconclusion: The SURF detector is the most suitablefeature detector for our obstacles detection method.

Number of detected featurepoints
800
700
600
500
400
300
200
100
0
Harris

SIFT SURF FAST
Detectors
Experiment 1 Experiment 2

Fig.2. The number of detected feature points.

Precision （×100%
100%））

Precision of 2D and 3Dclassification

IV. CONCLUSION
In this paper, we performed comparison among fourmost commonly used feature detectors; Harris, SIFT,
SURF and FAST detectors. The experiments are doneusing our 2D and 3D objects classification method [2].
The experimental results are compared in different ways,
and we find that the SURF detector is the most suitablefeature point detector for our 2D and 3D objects
classification method from the points of the detection
time and the precision of 2D and 3D classification. This
will also improve the accuracy of obstacles detection(described in [1]) ultimately.

1
0.95
0.9
0.85
0.8
0.75
0.7
Harris

SIFT

SURF

FAST

Detectors
Experiment 1 Experiment 2

Fig.3. The precision of 2D and 3D classification.

(a)

(b)

(c)

(d)

REFERENCES[1] Qian S, Tan JK, Kim H, Ishikawa S, Morie T,
Shinomiya T(2013), Road region estimation andobstacles extraction using a monocular camera.
International Journal of Innovative Computing,Information and Control, 9(9): 3561-3572.
[2] Qian S, Tan JK, Kim H, Ishikawa S, Morie T,
Shinomiya T (2013), Classifying 2D and 3D objects ona road employing the road plane. Proc. SICE Annual
Conference 2013, 1689-1692.
[3] Harris C, Stephens M (1988), A combined cornerand edge detector. Proc. Alvey Vision Conference, 147151.
[4] Lowe DG (2004), Distinctive image features fromscale-invariant keypoints. International Journal ofComputer Vision, 60(2): 91-110.
[5] Bay H, Tuytelaars T, Gool LV (2006), SURF:
Speeded up robust features. Proc. the European
Conference on Computer Vision, 404-417.
[6] Rosten E, Drummond T (2006), Machine learningfor high-speed corner detection. Proc. the European
Conference on Computer Vision, 430-443.

Fig.4. The detected feature points. (a) Harris detector, (b)SIFT detector, (c) SURF detector, (d) FAST detector.
find the most feature points, but get the lowest precision?
We can find this reason in Fig. 4. Figure 4 shows the©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

A Decision Making System of Robots Introducing a Re-constructionof Emotions Based on Their Own Experiences
Shogo Watada1, Masanao Obayashi2, Takashi Kuremoto3, Shingo Mabu4 and Kunikazu Kobayashi5
1-4

Yamaguchi University, Japan, 5Aichi Prefectural University, Japan｛1t003we, 2m.obayas, 3wu, 4mabu}@yamaguchi-u.ac.jp, 5kobayashi@ist.aichi-pu.ac.jp
Abstract: We already proposed a decision making system for autonomous robots, which is based on a Markovian
emotional model proposed by Banik. In our proposed system, decision making of the robot is designed automaticallyby two processes. The first is a construction of emotions based on learning of the input stimuli. The second is
optimization of behavior selection policy for emotions by optimization of system parameters which define behavior
selection probability of each emotion. In our above previous studies, a construction of emotions based on learning of
the input stimuli was processed using learning samples of input stimuli on offline in advance. In this study, we
introduce an experiences-based reconstruction of emotions to our decision making system as an approach to an
automatic construction of decision making online learning.
Keywords: Emotion model, Decision-making, Optimization problem.
task for the robot is to maximize floor areas which therobot has passed. In the simulation, the result showedthat additional learning of input stimuli makes
unevenness of frequency of use of each node in the
SOM decreases and it also makes more efficientassociations between input stimuli and emotional states.
This paper is organized as follows. Section 2
describes the Markovian emotional model proposed by
Banik. In Section 3, the proposed behavior selection
system is described. In Section 4, the simulation resultsof the proposed method are presented. Finally, Section 5summarizes the effectiveness of the proposed method
and future works.

I. INTRODUCTION
There exists much literature on the study which
focuses on the concept of emotions that animals have,for developing autonomous systems in various fields1.
We have already proposed a decision making system forautonomous robots, which is based on a Markovian
emotional model proposed by Banik2. The Markovian
emotional model is a probabilistic model that consists ofa finite state machine1.
In our proposed system, a decision making systemof the robot is designed automatically by two processes.
The first is a construction of emotions based on learningof input stimuli. We proposed use of a SOM (Selforganizing map) to associate input stimuli with internalparameters that represent emotional states. The second
is optimization of behavior selection policy foremotions by optimization of system parameters which
define behavior selection probability of each emotion.
In our previous studies, a construction of emotions
based on learning of the input stimuli was designed only
using learning-samples of input stimuli on offline in
pre-learning. Therefore, it made useless emotional areas
corresponding to the unrealistic input stimuli in the
SOM.
In this study, we introduce an experiences-based
reconstruction of emotions in our decision making
system as an approach to an automatic construction ofdecision making online learning. That is, we thoughtthat more effective associations with input stimuli andparameters decided by considering actual frequency andmeasurement values of input stimuli is achieved byonline learning.
We confirmed the usefulness of our method bycomputer simulation for an E-puck mobile robot. The©ICAROB 2014

Ⅱ. The Markovian emotional model
The Markovian emotional model referenced in thispaper is a probabilistic model that consists of a finite
state machine1. It consists of four basic emotions: joy,anger, fear and sadness. Emotional states are presented
Y as shown in Eq.1.

Y  [ y joy

yangerysadness y fear ](1)

The emotional state transition at time k is given by Eq.2.
Yk 1  CYk(2)
Here C is the emotional state transition matrix, which
can be expressed as Eq.3. PA/B is the probability oftransition from state B to state A. q/  is a priorprobability of emotion transition, which should be set inadvance (See Eq. 4). These values P/  can be changed
online by the influence of emotion-inducing factors: α,β, γ and δ for joy, anger, fear, and sadness, respectively.
These factors vary depending on the internal state andinformation given by certain other robots and have therole to stimulate each emotion. For example, the-74-The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

probability of state transition from joy to other state
expressed by Eq.4.
Pjoy / anger
Pjoy / sad
Pjoy / fear  Pjoy / joyP
anger/ joy Panger/ anger Panger/ sad Panger/ fear C Pfear / joy Pfear / anger Pfear / sad Pfear / fear Psad / anger Psad / sad
Psad / fear  Psad / joymeans transpose of a vector.

X  xdrive

Pfear / joy  q fear / joy  (    )q fear / joy(4)

Pjoy / joy  1.0  ( Panger/ joy  Psad / joy  Pfear / joy )Ⅲ. Proposed behavior selection system(6)

2. Clustering of input stimuli in Cognition module
In our proposed system, we used a SOM in the
Cognition module in order to cluster input stimuli suchthat similar input stimuli correspond with similaremotion-inducing factors. In this method, we regard theposition of each node in the SOM as value of emotioninducing factors. Since it is considered that groups ofsimilar input vectors are compressed in the output layer
as weight vectors after learning, the value of emotioninducing factors is uniquely determined to a stimulation
input vector if we consider that the two-dimensional
output layer is a two-dimensional map consisting of
emotional inducing factors.
The structure of the Cognition module in this study
is shown in Fig.2. Each node in the input layer of the
SOM corresponds to N input stimuli. The Cognition
module constitutes the association between emotions
and input stimuli by clustering of input stimuli to the
output layer. Then, after learning, the Cognition module
can convert input stimuli into emotion-inducing factors
by determining a winner node and normalizing theposition of it into range from 0 to 1.
In previous studies, learning of the SOM wasprocessed only using learning-samples of input stimuli
in advance. Therefore, it generated useless emotional
areas corresponding to the unrealistic input stimuli in
the SOM. In this study, we propose that the association
between input stimuli and emotion-inducing factors
becomes more efficient by additional learning ofrealistic input stimuli on online during the task.

1. The function of each module
The function of each module is as follows; theCognition module transforms input stimuli Z such as
sensor values into emotion-inducing factors α, β, γ and δ.
The Emotion module is a Markovian emotional model
proposed by Banik, and this module updates emotional
states Y using α, β, γ and δ. The Behavior-selection
module determines the behavior selection probability
vector X using Y by Eq.5.
(5)

Elements of X are selection probability of each behaviorof the robot, for example, X is defined such as Eq.6. T

3. Optimization of behavior policy by GA
In our Behavior-selection module, the policy of
behavior selection depends on transition matrix A. So, it
becomes possible to make various methods for behavior
selection by adjusting parameters being included in A.
In this work, we use GA for setting A. GA is a meta-

Z: Measurement Y: Emotional state
C: Emotional state transition matrix
A: Behavioral state transition matrix
s: Situation Behavior state α, β, γ, δ: Emotion-inducing factors

Fig.1. A proposed behavior selection system©ICAROB 2014



xBack T

Finally, the Behavior-making module generates thecontrol input u based on X taking into account states of
the robot.

We have already proposed a decision making
system of a robot, which is using a Markovian
emotional model proposed by Banik ． The overall
structure of the system can be shown in Fig.1. The
system consists of four modules: Cognition, Emotion,
Behavior-selection, and Behavioral-making. Z is input
stimuli, α, β, γ and δ are emotion-inducing factors, Xk is
a behavior selection probability vector at time k, s is
robot states and u is a control input for the robot.

X k 1  AYk 1xrightA is a transition matrix consists of probabilities
Pbehavior/emotion which means a probability of transition
from emotion to behavior, and represented by such as
Eq.7.
 PDrive/ joy PDrive/ anger PDrive/ sad PDrive/ fear P
PLeft / anger PLeft / sad PLeft / fear 
Left / joy
 (7)
A
 PRight/ joy PRight/ anger PRight/ sad PRight/ fear  PBack / joy PBack / anger PBack / sad PBack / fear (3)Panger/ joy  qanger/ joy  (    )qanger/ joy
Psad / joy  qsad / joy  (   )qsad / joy

xleft

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Fig.2. New cognition module proposed in this paper.

Fig.3. Environments used in simulations.

Table 1. Input stimuli of the robot.

heuristic algorithms for searching the approximate
solution. The detail explanation about GA is omitted.

Ⅳ. Simulation
For evaluating our system, we simulated the
problem of maximizing areas that the robot passed. Insuch as house cleaning robots, it is demanded thatavoiding obstacles using own sensors, robots pass morelarge areas in the environment. In this work, we showed
our proposed system generate a behavior policy
automatically that a two-wheel mobile robot passes
larger areas in fixed time. The simulation for an e-puckrobot was carried out using a Webots simulator whichmake possible to simulate in a very realistic way.

z0

The average distance value of four sensors on front (see Fig.4).

z1

The average distance value of two sensors on right (see Fig.4).

z2

The average distance value of two sensors on left (see Fig.4).

z3

The average distance value of two sensors on rear (see Fig.4).

z4

The total moving distance of robot from 12 seconds ago.

z5

The total rotation angle of robot from 12 seconds ago.

Table 2. Parameters setting of SOM and GA.
SOM
GA
Number of learning

500

Number of Individuals

24

Number of nodes

30×30

Final Generation

50

Learning coefficient η

0.0001σ(0) *

24.0

The limited timein one environment

600
(s)

* The neighborhood function: σ(𝑠𝑡𝑒𝑝) = 𝜎(0)(1.0 − 𝑠𝑡𝑒𝑝/𝑚𝑎𝑥𝑆𝑡𝑒𝑝)

1. Simulation setting
The simulation was executed through four
environments with different structures which are shownin Fig.3. Each environment is a square of 1 m on a side,and there are walls or obstacles (brighter areas in Fig.3).
The e-puck is equipped with 8 infrared sensors whichmeasure proximity of obstacles in a range of 4 cm. We
defined four behaviors of the robot; straight, left, rightand back, and defined the motor torques of left and rightwheels on each behavior as follows: straight: [100,100]; left [-50,50]; right: [50, -50]; back: [-100,-100].
The Behavior-making module calculates output torques
by weighted syntheses of torques with rate of eachbehavior probability in the probability vector X.
The number of input stimuli is 6, and there areshown in Table1. The pre-learning of SOM usedlearning samples which were generated randomly basedon prior knowledge of devices such as the range of thesensor.
On the basic setting of simulation, the pre-learningof the input stimuli runs at first. Then, the decision
making policy is generated by optimization of thetransition matrix A using GA. In the GA, eachindividual executes the task in all 4 environments, andthe fitness value of the individual is determined as thesum of above 4 areas the robot passed. The setting of
the SOM and the GA are shown in Table 2.

©ICAROB 2014

Fig.4. Sensors of e-puck

2. Simulation result
In this simulation, the additional learning runs while
the generation is young in the process of GA after prelearning. Altogether, the SOM is updated on online
using input stimuli which are occurred in first 10
generations of 50 generations by using input stimuli
which occur in the task. Since updating of the SOM isrunning every steps, the total number of updates is576,000．
Since the position of each node of the SOM
correspond to values of emotion-inducing factors, it iseasy to understand the relationship between input
stimuli and emotions by making the emotional map.
Fig.5 shows emotional map made from the SOM after
the additional learning. The input stimuli are
represented in each circle on the map. A distance toobstacle is represented as size of an ellipse, andmagnitudes of a moving distance and a rotation angle
are represented as the length of the up arrow and thehorizontal direction of the arrow, respectively. For
example, Joy and Anger correspond to situations of
input stimuli such that there are no obstacles around therobot, and these situations occur frequently during thetask. In the pre-learning, SOM learns equally all
situations such as the robot is surrounded by wall.
However, input stimuli corresponding to the situation ofgoing straight or turning left are more often. This result-76-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014shows that the SOM reflects realistic input stimuli byadditional learning.
Observing behaviors of the robot using final
parameters in GA, the behavior policy were such as
follows: go straight bending slightly the left when there
are no obstacles around the robot; turn left when
there’re obstacles in front or right side of the robot; turnright when there’re obstacles in left size of the robot. In
our method, the behavior of are mentioned is explainedby parameters of A shown in Eq.8. For example, inprobabilities of transition from Joy which correspond toa free space according to Fig.5, Pdri/joy, 0.64, and Plef/joy,
0.23, are higher than others. Therefore, the robot wentstraight bending slightly the left when the robot is in the
open space.
PDri / ang
PLef / ang
PRig / ang
PBac/ ang

PDri / sad
PLef / sadPRig / sad
PBac/ sad

PDri / fea  0.64
PLef / fea  0.23
 
PRig / fea  0.12
PBac/ fea  0.01

0.44 0.00 0.34
0.16 0.80 0.12
 (8)0.21 0.00 0.42

0.19 0.20 0.10

11000
Fitness

 PDri / joyP
Lef / joyA 
 PRig / joy PBac/ joy

Fig.5. Emotional map.
12000

9000
8000

Additional learning
Only Prior learning

7000
6000

Next, the result using additional learning wascompared with the result using only pre-learning. Fig.6
shows transitions of fitness values at two results in GA,
and the vertical dashed line on 10 steps means thegeneration which additional learning finishes. It showsthat the fitness value of the system using additional
learning is higher than the other.
Fig.7 shows histograms of referenced number as awinner node in each system where white nodes werereferenced frequently. In the case of only prior learning,areas of white nodes are concentrated, and it means thatemotion-inducing factors changes little. On the other
hand, in the case of additional learning, areas of whitenodes are spread because that useless node areas of
SOM decrease by additional learning. The performanceof decision making process of the robot is improved by
the more efficient association between emotioninducing factors and input stimuli.

0

10

20
30
Generation
Fig.6. Transition of fitness value.

40

Fig.7. The histogram of the number of times each node is selected.

decreases useless emotional areas, it improved decision
making of the robot. In addition, it was impossible toexplain the decision-making policy of robot by
analyzing generated the SOM and parameters of A.
For further work, we will attempt to consider amethod which optimizes parameter setting on online
and robot tasks that is possible to evaluate the system on
online instead of additive learning of using GA.

V. CONCLUSION
In our previous studies, a construction of emotions
was processed only using learning-samples of input
stimuli on offline. Therefore, it generated uselessemotional areas in the SOM.
In this work, we proposed reconstruction ofemotions by additional learning input stimuli obtained
during the task. Then, associations between input
stimuli and parameters more effective by additional
learning, using actual input stimuli were made.
The proposed method was evaluated by a simulationof a problem which maximizes areas a robot passed.
The result showed that since an additional learning©ICAROB 2014

10000

REFERENCES[1] Sajal Chandra, Banik, Keigo Watanabe, Kiyotaka
Izumi (2008), Improvement of group performance ofjob distributed mobile robots by an emotionally biased
control system”, Artificial Life Robotics(12), pp.245249[2] Shogo Watada, Masanao Obayashi, Takashi
Kuremoto, Kunikazu Kobayashi, Shingo Mabu (2013),
Behavior selection method of robots based on a
Markovian emotional model (in Japanese). IEEJ
Transactions on Electronics, C, Vol.134, No.1

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Visual Servoing and Sound Localization in a Surveillance Robot
So-Yeon Park, Yeoun-Jae Kim, and Ju-Jang Lee
Electrical and Electronic Engineering KAIST, Eu-Seong Gu Daehak-Ro 291 KAIST EE 4231
Tel : 82-42-350-5432; Fax : 82-42-350-5432
e-mail address: arashi@kaist.ac.kr, lethkim@kaist.ac.kr, jjlee@ee.kaist.ac.kr

Abstract: The purpose of this paper is to explain the control and implementation details of the visual servoing andsound localization in a tank shaped surveillance robot. With an eye in hand camera configuration, the key point of thevisual servoing is to measure the depth to the target object. In this paper, we suggested the depth estimation technology
by moving the camera in a lateral plane. In sound localization, the hardware and software details are fully explained to
implement the sound localization technology. Especially the electronic filter design is implemented by utilizing thegenetic algorithm.
Keywords: visual servoing, sound localization, surveillance, robot.

implemented. The visual servoing without pre-depth
information is explained in section 3 and In section 4,
the detailed explanation of sound localization techniquein the surveillance robot is presented. Conclusion isdrawn in section 5.

I. INTRODUCTION
The visual servoing technique can be divided into
two categories. One is image based visual servoing(IBVS) and the other is position based visual servoing(PBVS). IBVS uses the image information directly forthe visual servoing and PBVS extracts 3D position datafrom the image information and performs the visual
servoing technique by the 3D position data. Most of theeye in hand shaped robot has only an eye camera on its
manipulator, so, IBVS technique is used in this kind ofrobot. The main concern of IBVS technique is how to
extract the depth information [1]. In most cases, authors
deal with interaction matrix for IBVS and combined
IBVS control with plant control algorithm. In this paper,
we suggested a simple depth estimation algorithm
without combining IBVS with plant control algorithm.
The suggested algorithm is verified by experiment of
the surveillance robot.
Comparing with the visual servoing, soundlocalization is not so popular technique. However, many
authors researched this area [2]. The humans have only
two ears but in robot, with two microphones it isdifficult to precisely measure the direction of the soundsource. Usually three or more microphone array isneeded. In this paper, we implemented the state of theart sound localization technique with three microphonearray on the surveillance robot. The detailed hardware
and software implementation is explained later andexperimental results shows that the sound localization
technique is well implemented on the surveillance robot.
The rest of paper is organized as follows. Section 2
explains the surveillance robot in which the visual
servoing and sound localization functionality is©ICAROB 2014

II. SURVEILLANCE ROBOTThe surveillance robot with which the visual servoi
ng and sound localization functionality is implement
ed is a tank shaped robot which performs surveilla
nce work in normal time and rescue humans in em
ergency time. The caterpillar is implemented in thisrobot instead of a wheel for its movability in outdo
or environment and robustness against an external s
hock. The length of the tank robot is about 1.5m and the width is about 1m. It has a manipulator whi
ch has a 5 DOF(Degree of Freedom) for picking u
p and investigating a suspicious object. The robot has two cameras. One camera is mounted at the topof the tank body for watching distance scenery. The other camera is mounted at the bottom of the manipulator gripper for visual servoing purpose. Overal
l appearance of the surveillance robot is in Fig. 1.

Figure 1 The surveillance robot-overall view

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.III. VISUAL SERVOING WITHOUT PRE- X tcarg et 
Dtcarg et   Yt carg et  c  Z t arg et 

DEPTH INFORMATIONThe camera at the end-effector of the surveillancerobot is a single lens camera, not a stereo camera. For
this reason, with the vision signal from the single lenscamera, the surveillance robot cannot determine the 3D
position of the target object. Therefore some algorithm
to determine the depth information is needed. We
divided the visual servoing movement into two parts.
First part is to move the manipulator in lateral direction
to determine the depth length and the second part is to
move the manipulator forward to the target object withthis determined depth information.

(c  u ) Z c
X
 u
f
(
c

v) Z cYt carg et  v
f

(3)

c
t arg et

Z tcarg et  0
As in Fig.2, the principal pointof the camera (black dot) moves in the reverse directionof the target object (blue dot). Therefore, the moving
distance of the camera with respect to camera framecis computed as in equation (4)Dcamera

1. Image-based visual servoing
In order to place end-effector of the manipulator
near the target, the position of the target object must be
determined by using the visual information of thecamera.
(u  cu ) Z c
Xc f(v  cv ) Z c
(1)
Yc 
f

c
 X camera



c
c
Dcamera   Ycamera  Z c camera (u  cu ) Z cf(v  cv ) Z ccYcamera Yt carg et fc
X camera X tcarg et (4)c
Z camera0

In the equation (1), u and v are the coordinates of thetarget object in pixel units and cu and cv are theprincipal point in pixel units. X c , Yc , Z c is 3-D
coordinates of the object with respect to the camera
frame. The 3-D coordinates of the object with respect tothe camera frame can be transformed to the 3-D
coordinates of the object with respect to the inertial
frame by equation (2).

Then, the moving distance of the camera with respect to0
the base frame Dcamera can be computed as inequation (5)0c Dcamera 0 1 2 3 c 4  Dcamera


A
A
A
A
A
A


 (5)
1
2
3
c
4
5 
1

1


2. Estimating the depth
As mentioned previously, the objective of the visual
servoing is to move the end-effector of the manipulator
near to the target object. That is, the coordinates of the

X0 
Xc 
Y 
 
 0   0 A 1 A 2 A 3 A c A 4 A Yc 
1
2
3
c
4
5
 Z0 
 Zc 
 
 
1 
1 

(2)

center of target object (u,v) expressed in pixel units
should be located at principal points ( cu , cv ) at goal
position. Therefore, moving distance of the target objectcwith respect to camera frame Dt arg et can becomputed as equation (3) when the manipulator endeffector moves in lateral direction.

Figure 2. Base and camera (end effector) frame
As mentioned previously, sinceZ c cannot measured

directly by single lens camera, it must be estimated. In
our research, moving repetitively the end-effector inlateral direction and returning to the initial position, we
estimate the Z c .
As seen in Fig. 3, the lateral movement of the target©ICAROB 2014

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Japan, January 11-13, 2014

point depends on estimated Z c . ( u1 , v1 ) is the position

recommended. In this paper, we used 30ms duration.
Framing can be presented as the multiplication of theinfinite signal with a rectangular window as followsof the target object in the image plane before the lateral
movement of the manipulator and ( u2 , v2 ) is theT x(t ), | t  nT |xn (t )  x(t ) wrec tan gle (t , n)  2
 0, otherwise

position of the target object in the image plane after thelateral movement of the manipulator. As can be seen in
Fig. 3 (a) when the estimated value is smaller than theactual value, the location of the target object before andafter moving the manipulator is at the same quadrant in
the image plane. However, when the estimated value isbigger than the actual value, the location of the target
object before and after moving the manipulator is at thedifferent quadrant in the image plane as in Fig. 3 (c). If
the estimated value matches the actual value, thelocation of the target object is just at the principal point
as can be seen in Fig 3 (b). Therefore, the control policyof Z c estimation is as follows.
-If target object before and after moving is located ina different quadrant, the estimated Z c is bigger than

Where T stands for time duration. After extracting frame,
the signal is converted from time-domain to frequencydomain using Fourier transform (FT) as follows.

X( f )  

x(t )e j 2 ft dt (7)When we change the framed signal into frequency
domain, there exists a signal leakage because therectangular window mentioned above has an infinite
spectrum. Therefore in order to reduce error due to thefinite duration processing, weighting function whichfalls sharply as frequency bin increases has beensuggested. Hann weighting, one of the most widely usedwindow, is used in this paper.
t
0.5(1  cos ), | t | T
(8)
wHann (t )  T
0, | t | >T

the actual value. And it should be decreased.
-Otherwise the estimated Z c is smaller than theactual value. And it should be increased.

Moreover, most of meaningful sound, that is, audible
sound lies between 20Hz and 20kHz in frequency band.
Therefore in order to reduce possible noise andconcentrate on audible sound, c-weighting is also usedin the project.

(a)
(b)
(c)
Figure 3 Lateral movement of the end-effector

IV. SOUND LOCALIZATION ALGORITHM
For the sound localization, a hardware capturing thesound signal and an algorithm calculating the directionof arrival is needed. As for the hardware, it can belargely divided into three groups, microphone array,
analog circuit and digital data acquisition device.
Regarding to the algorithm, it can be largely divided
into two part, signal processing and direction of thearrival estimation. As for the algorithm used in thesound localization, it can be divided into two part,signal processing and direction of arrival (DOA)
estimation. Theoretical background for the algorithm
can be obtained in [3].

Gc ( s) 

5.918 109 s 2( s  129.4)2 ( s  76655)2(9)

When estimating direction of arrival, voice activity
detector (VAD) detects the presence of voice in a mixed
signal consisting of voice plus noise. Its application
range can vary from simple binary classification, “noiseonly” or “voice present”, for entire frame to presence
probability for every frequency bin. VAD itself can be
usefully applied but it can be included in most of noise
suppression. VAD detects presence of voice based on anassumption that noise changes more slowly than voice.
When capturing the signal through the microphone, thecaptured signal has signal x(t) and noise d(t) at the sametime. Since signal and noise are unobservable we haveto estimate original signal through observed signal andsome a priori-knowledge.
In noise suppression, noise reduction process can beconsidered as an application of a time-varying, nonnegative, real-valued gain H to very frequency bin of

1. Signal processing
In signal processing, a sound signal obtained from the
hardware part, which went through some editing, goes
through further process. It is transformed into frequency
domain, filtered by more sophisticated filter. Because
ideal infinite time signal cannot be used in the real-time
application, time domain signal must be chopped up
appropriately. Frame duration between 10 and 50ms is©ICAROB 2014

(6)

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observed signal in order to get the estimation of original
signal. While the noise filters included in the hardware
part passes or stops the signal according to designed
pass band and stop band frequencies, noise suppression
learns background noise in a working environment. For
example, if the noise filter from hardware is set to passfrequency band between 100Hz and 10 kHz, it cannot
filter the siren signal which mainly lies in 1 kHz. But
noise suppression can learn siren signal as noise andremove the appropriate frequency bins. In noise
suppression, at first, time-domain signal x(t) passes
through framing, weighting, and fast FT (FFT). Thenconverted frequency-domain signal is divided into
phase component and magnitude component. Phase
component is kept during the noise suppression.
Meanwhile after magnitude component passes throughper-frame soft VAD, rough noise model is built. Again
magnitude passes through per-bin soft VAD. Then
precision noise model is built. And finally suppression
rule is made based on rough model and noise isremoved. Finally if needed, magnitude and phase gettogether and after inverse FFT, estimated time-domain
signal can be obtained.

and microphones. Therefore
stands for timedifference microphone j based on microphone I as
follows.

dij  M j S  M i S

We divided 2-dimensional space into 3 areas as shownin Figure 4.

Figure 4. Microphone deployment

V. CONCLUSION
In this paper, we implemented visual servoing without
depth information and sound localization algorithm in asurveillance robot.

2. Direction of arrival estimation
In this paper, we use an interaural time difference inorder to estimate sound orientation. It is caused by thedifference in distance where the sound must travel toreach each microphone. Theoretically time difference
between two microphones is represented as follows.

d sin c

ACKNOWLEDGEMENT
This work was supported by the Industrial Strategic
technology
development
program,
10045252,"Development of Robot task intelligence technologythat can perform task more than 80% in inexperiencesituation through autonomous knowledge acquisition
and adaptation knowledge application" funded By the
Ministry of Trade, industry & Energy (MI, Korea)(10)

Meanwhile time difference in two microphones can bepractically measured using cross-correlation function of
two signals. Many of the methods which have beensuggested so far, and a generalized cross-correlation
using phase transform (PHAT) weighting is used in the
project as follows.
R12 (t )  iFFT [ PHAT  X 1 ( f ) X 2H ( f )](11)1
 PHAT ( f ) 
G X1 X 2 ( f )REFERENCES[1] H. Fujimoto (2003), “Visual servoing of 6 dofmanipulator by multirate control with depth
identification”, Proceedings of 42nd IEEE Conferenceon Decision and Control, pp. 5408-5413.
[2] K. Nakadai, H. G. Okuno, and H. Kitano (2002),“Real-time sound source localization and separation for
robot audition”, Proceedings IEEE International
Conference on Spoken Language Processing, pp. 193196[3] Ivan Tashev (2009), Sound Capture and Processing,John Wiley and Sons.

PHAT weighting eliminates magnitude and gives equal
weight to the phases in each frequency been. Therefore
it can work in a reverberant environment.
As for the microphone array used in the project, there
exist three pairs of time difference between microphone
0 and 1, between 1 and 2, and between 2 and 0. Timedifference means distance difference between source©ICAROB 2014

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Study on the improvement of flexibility for an industrial robot basedon machine vision
Jiwu Wang 1, Xianwen Zhang 1, Weining Zhang 1, Sugisaka Masanori 2
1 Department of Mechanical and Electrical Engineering, Beijing Jiaotong University, Beijing, China, 100044
2 Nippon Bunri University, Oita 870-0397, Japan
1 jwwang@bjtu.edu.cn
2 sugisaka3@mocha.ocn.ne.jp

Abstract: In order to improve the applications for an industrial robot, it is necessary to increase its flexibility andcontrol accuracy. Many algorithms and control strategies are developed, in which machine vision gets more and more
attention. This is because the CCD camera can provide more information than other sensors, which makes the wholecontrol process simple. But it requires that the target position should be extracted accurately and robustly. Moreover,the frequency of image processing should be satisfied with the control requirements. Here an industrial robot arm isdesigned and set up for experiment simulation with machine vision. The target extraction method with fusion of color
space and moment invariants is developed. The experiments results showed that the developed image processing
algorithms are robust, and the flexibility of an industrial robot can be improved by machine vision.
Keywords: Arm robot, moment invariants, image processing.

provide multiple target positions in a captured image.
Moreover, it can calculate the end position of the robot
arm in a captured image if necessary. In a word, with a
CCD camera, the previous problems for the arm robot
can be solved[3] .
In order to get robust and reliable image processing
results, the lighting conditions and image processing
efficiency should be considered carefully. In this paper,
considering the practical applications, the fusion
technique of true color space and moment invariant isapplied in the pattern recognition[4].
The structure of the arm robot is shown as figure 1.
A is the CCD camera, B is the control box, C is a step
motor to control the upper arm, D is a step motor tcontrol the forearm, E is a step motor to control thehand, F is a support just like the shoulder, G is the upper
arm, H is the forearm. The length of the upper arm andforearm is 400mm. The height between the camera andground is 700mm.
The relationship among all hard wares is
illustrated in Figure 2. The CCD camera is used
to determine the target position. Three motors(C, D, E) is controlled by the PC through motion
control card. One control process is: 1) After the
CCD camera detect one target, the target will be
extracted by the image processing algorithms in
PC, and its position will be calculated. 2) Basedon the determined target position, three motors will
run together and make the hand reach the target.

I. INTRODUCTION
With the development of new technology, therequirements of product quality and production
efficiency get higher, and experience workers can not be
trained in short time and their number are not enough.
With more and more production lines are used inmodern companies, many automation machines are
required to operate like a skill worker. Compared with aworker, the robot seldom makes mistakes if its working
conditions are not changed. And its efficiency andreliability is higher than a worker. Especially in thedanger or high working intensity environment, it ismore appropriate[1]. Then various robots are applied in
the most advanced company.
Generally, most robots can only be used for some
simple, repeating and single jobs, which is not
appropriate to the further applications for its high cost.
On the other hand, the repeat positioning accuracy can
not be satisfied if a robot arm is used for a longer time[2].
Although some feedback or sensor fusion technology isadopted, the effective solutions are not enough. .
With the development of the CCD camera
technology, machine vision gets more attention. The
CCD camera not only works like human being eyes, butalso can provide more information than other sensors.
With the pattern recognition, the CCD camera can©ICAROB 2014

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3) The arm go to its original position, and one
movement cycle is performed.

is more difficult to determine an appropriate color
measure range suitable for different lighting conditions.
This will bring some mistakes.
In order to reduce the influence of different lighting
conditions, a color model in the chromatic color space
was built[6]. Chromatic colors, also known as purecolors in the absence of luminance, are defined by anormalization of the triple components (R, G, B). The
process to build guideline color model is as follows:
1) Take common RGB color images of the target
under different lighting conditions as sample
images, and extract the typical RGB target
colors manually from these sample images as
sample colors.
2) Transform these sample colors from theFig.1. The illustration of the structure of the arm
robot

3)

RGB color space to the chromatic color
space by a normalization process. Here color green is redundant because r  g  b  1
Calculate the mean values and covariance ofsample colors in the chromatic color space.

where, x is a pair of normalization components (r, b)
for each pixel in the chromatic color space, x is themean value and C is the corresponding
covariance.
4) The possibility of the target color for one.Fig.2. The illustration of the relationship about all
hard wares
The developed software system is shown infigure 3r  R /( R  G  B)b  B /( R  G  B)
pixel in the detected image can becalculated as follows

x

x n
n

 ( x  x )( x  x )

C n

n 1

When we use this color model to extract target, we shou
ld firstly transform each pixel from the RGB color repre
sentation to the chromatic representation. Then a pair ofchromaticcolor
valuesforeachpixel is input the color model to decide if thatpixel has the same color as the target [7]. Then thegeometric features of the targets calculated with

Fig.3.The illustration of the developed softwareinterface

II. Target extraction method

Px  exp[0.5( x  x )C 1 ( x  x )]moment invariants, is used for further verification about
the extracted regions

In order to extract targets from the captured image at
a higher speed, the color based target detection method
is developed. At the same time, to reduce recognition
errors, the moment invariants are applied to separate theprevious extracted objects again[5].
Although the triple components (R, G, B) in acommon RGB image can be used to extract the target, it©ICAROB 2014

T

III. Multiple targets extraction andposition control experiments
The experiments are performed by two steps: First,
determine the multiple target positions by CCD camera.
Based on the developed image processing method, the-83-The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

IV. CONCLUSIONS

sample targets with different colors were verified using
the arm robot system[8]. The original image is shown infigure 3, the extracted results is shown in figure 4.

In this paper, an arm robot and the position control
system based on CCD camera is developed. By fusionof color based target detection and moment invariants,
the target can be extracted efficiently and its position
can be calculated. Then the robot can reach target
position accurately.
Later, the further research, such as error analysis etc.,
will be carried on.

a)The extraction of two purple marks

REFERENCES[1] Masanori Sugisaka, Xin Wang, Intelligent control
of a mobile vehicle using on-line learning, Systemscience, Vol. 25, 1999: 41-50.
[2] Masanori Sugisaka, Xin Wang, Ju-Jung Lee,Intelligent control strategy for a mobile vehicle,
Applied mathematics and computation, 1998: 9198.
[3] S. O. Belkasim, M. Shridhar, M. Ahamadi, Patternrecognition with moment invariants: a comparative
study and view results, Pattern recognition, Vol.24,
No.12,1991: 1117-1138.
[4] Alireza Khotanzad, Yaw Huahong, Rotation
invariant image recognition using features selected
via a systematic method, Pattern recognition,
Vol.23, No.10,1990: 1089-1101.
[5] Hagan, M.T., H.B.Demuth, and M.H.Beale, Neuralnetwork design, Boston, Ma: PWS publishing,
1996[6] Moller, M.F., A scale conjugate gradient algorithm
for fast supervised learning, Neural networks, Vol.
6, 1993: 525-533[7] Riedmiller, M., and H.Braun, A direct adaptive
method for faster backpropagation learning: The
RPROP algorithm, Preceeding of the IEEE
international conference on neural networks, 1993
[8] Vogl, T.P., J.K.Mangis, etc., Accelerating theconvergence of the backpropogation method,
Biological cybernetics, vol.59, 1988:256-264a) The extraction of two purple marksb)The extraction of green markb) The extraction of green markFig.4 Illustration of multiple targets extraction based onimage processing
After the target position is obtained, the robot will
move its arm to the target position. Figure 5 is an
example to show the arm robot move to the above of
one blue target.

Fig.5. The illustration of the arm robot performing
target moving operation©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Improved Map Generation by Addition of Gaussian Noise for Indoor
SLAM using ROS
Khairul Salleh Mohamed Sahari and Barry Loh Tze Yuen
Centre for Advanced Mechatronics and Robotics, College of Engineering, Universiti Tenaga Nasional,
Jalan IKRAM-UNITEN, 43000, Kajang, Selangor, Malaysia
Tel : 60-3-89212020 Fax : 60-3-89212116
khairuls@uniten.edu.my

Abstract: Rao-Blackwellized Particle Filter (RBPF) is used in this paper to solve the Simultaneous Localization and
Mapping (SLAM) problem. RBPF algorithm uses particle filter where each particle carries an individual map of theenvironment. With the usage of Robot Operating System (ROS), GMapping package was used as a basis for map
generation and SLAM. To improve the map generation, Gaussian noise was introduced to the data from laser range
finder and also the odometry from the robot Pioneer P3AT’s pose. The introduced algorithm was successful indecreasing the uncertainty as well as increased the knowledge of each particle in the estimation of the robot’s pose,proven through practical experiment. Exploration experiments were also carried out to test the performance of P3AT
based on our proposed method.
Keywords: SLAM, ROS, Gaussian Noise, map generation, exploration.

very common. Zaman et al. [3] presented their findingsof utilizing open-sourced system, Robot Operating
System (ROS) for mapping, localization andautonomous navigation. Gong et al. [4] also presented
their research on utilization of ROS for the purpose ofobject localization, using RFID and laser scans. Despite
being open-sourced and free, there are also drawbacks.
Hence this paper is produced to test the reliability
and precision of already programmed SLAM algorithms
found in open-sourced software ROS [5] using acommon research robot. Based on the results obtained
from experimentations, improvements were carried out
to further enhance the SLAM and map generation. By
studying the algorithms written by the open-sourced
community, a portion of the code was found that theaddition of a Gaussian type noise will implement theusage of Kalman filter to improve the knowledge of theparticle filters of the map. Besides that, exploration
experiments were carried out on the robot to further test
the reliability and performance of the proposed
improvement. To achieve this objective, a Hokuyo
URG-04LX laser range finder was used together with a
Pioneer 3-AT equipped with wheel encoders only. A
common laptop was used to provide the algorithms anddata collection throughout the process.

I. INTRODUCTION
Over the years, the rapid development of technology
has contributed to the development of robots. The
production of many new sensors including laser range
finders and Kinect camera provide more precise,reliable and faster readings.
Robots equipped with sensors allow them to learn
about their surrounding environment. This enables therobot to conduct various tasks that include map
generation, localization and exploration, indoor services
like moving from one room to another, gripping andpicking up objects. Moreover, robots should be able toconduct Simultaneous Localization and Mapping(SLAM) to achieve even greater results.
SLAM, as the name suggests, allows the robot to
conduct mapping of the environment while localizing
itself in the map being created on-the-fly. An addition to
SLAM algorithm is to utilize more than one sensor for
more accurate results. Castellanos et al. [1] presented a
2D version of SPmodel, a probabilistic representation
model and EKF integration system for the usage ofmulti-sensor fusion. Results obtained proved that fusing
two sensor data improved the localization of mobile
robots even more accurately. Besides that, Arras et al.
[2] proved that using feature extraction and the Kalmanfilter to fuse data from a laser range finder andmonocular camera increased the precision of
localization significantly. On the other hand, utilizationof open-sourced systems and software are becoming©ICAROB 2014

II. CURRENT WORKIn this research, a ROS-based control system wasused for the Pioneer 3-AT robot for localization and-85-

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distribution, the better is the performance of the filter.
Montemarlo et al. [7] presented RBPF that uses a
Gaussian approximation of the improved proposal in thecontext of landmark-based SLAM. However, Grisetti etal. [6] found that GMapping uses the same principle ofGaussian approximation of improved proposal but in thecontext of dense grid maps.
The Gaussian computes for each particle using a
Kalman filter that estimates the pose of the robot. This
technique is utilized whenever the map is represented
by a set of features and the errors affecting the featuredetection is assumed to be Gaussian. From theunderstanding, to fully utilize the Kalman filter, aGaussian distribution or also known as normal
distribution type of noise was added to both the laser
range finder sensor data and the wheel encoders’ sensor
data. The Box-Muller transform was used to add a
Gaussian distribution noise to the sensor data. In [8],Box and Muller mentioned that the Box-Mullertransform is a pseudo-random number sampling method
to generate pairs of standard, independent, normally
distributed random numbers when provided uniformly
distributed random numbers. The Box-Muller transform
can be expressed in two different forms, the basic andpolar form. The polar form calculations were adapted in
the source codes due to its advantages of being arguably
faster and cheaper to the performance of the CPU.
The navigation stack provides a costmap that is built
around the mobile robot as it moves to provide
information of any nearby obstacles. The navigation
stack also provides Dijkstra algorithm, Trajectory
Rollout and Dynamic Window Approach for local andglobal path planning, and obstacle avoidance.
The exploration stack implements a frontier-based
entropy approach with loop closing abilities for further
stability. The exploration node scans the environment
around the current robot’s position for reachable empty
spaces within the map. Nearest goals will be selected
while unreachable areas are ignored. The exploration
node work very closely with the costmap created by thenavigation stack.
Throughout the experiments, the configuration files
for all the packages were tweaked to provide
improvements and enhance the results obtained.

creation of maps in unexplored controlled anduncontrolled indoor environments.
1. ROS setup for Pioneer 3-AT
The two sensors used in this research are the
Hokuyo URG-04LX, and the wheel encoders of themobile robot. Linux (Ubuntu 12.04 LTS) based
computer system equipped with ROS was used tocontrol the robot. For manned maneuvers of the robot, akeyboard was used. Both the laser and the mobile robot
are connected to the computer through USB ports.
Based on ROS, the packages and stacks used in thisresearch
are
the
P2OS,
Hokuyo_node,
SLAM_Gmapping, Navigation stack and the
Exploration stack. The P2OS and Hokuyo_node
package serve as the driver to establish connection anddata transfer between the laser range finder and themobile robot to the computer. Not only that, the P2OS
package provide a 3D model of the Pioneer 3-AT thatcan be viewed in the RVIZ Visualization tool and also
allows the movement of the mobile robot to becontrolled by a joystick or a keyboard.
SLAM_Gmapping package provides a highly
efficient Rao-Blackwellized particle filter (RBPF) tocreate occupancy grid maps. The RBPF algorithm not
only incorporates data fusion between the laser androbot’s pose, but also solves the SLAM problem.
However, the utilization of particles to carry individual
map of the environment possess a big threat to theperformance of the computer. This is because large
computations have to be conducted. Grisetti et al. [6]proposed adaptive techniques to be included in the
GMapping algorithm to reduce the number of particles
in a RBPF for learning the occupancy grid maps.
Gmapping also computes an accurate proposal
distribution, considering not only the movement of therobot, but also the most recent observation. This
produces drastic decrease of the uncertainty in the
robot’s pose, in the prediction step of the filter. Resampling operations were also carried out selectively
which reduces the problem of particle depletion.
Following up the algorithm of Gmapping, individual
importance weightage is assigned to each particle basedon the significance of the sampling principle. The
weightage allows resampling that performs particle
filtering when the target distribution differs from the
proposal distribution. Instinctively, the more efficient
proposal distribution approximation of the target©ICAROB 2014

2. Experiments
Two experiments were carried out in different typesof environments. The map area, as shown in Figure 1, is-86-The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014a self-created controlled environment. It is an empty
room, filled with cardboard boxes, with a dimension of
7m x 5m. The purpose of the controlled environment isto ensure a safe and high performance working
automatic robot system before proceeding with more
complex situations. Figure 2 shows the map generated
which only took about 90 seconds.

many issues faced and observations made that will be
discussed further in the next section.

Figure 3: Map generated for uncontrolled environment
without addition of Gaussian noise

Figure 1: Self-created controlled environment

Figure 4: Map generated for uncontrolled environment
with addition of Gaussian noise
Figure 2: Map generated of the controlled environment
The other experiment was carried out in an
uncontrolled environment, that is the South Wing cornerof the College of Engineering, UNITEN with total
estimated dimensions of about 41m x 25m. During thisexperiment, to prove that the map generation will be
increased further, the mobile robot was manually driven
twice. Once without the addition of Gaussian noise to
the sensor data, and another time with the addition ofGaussian noise. Few parameters like the configurations,path taken by the mobile robot and the speed of themobile robot were kept constant throughout the entire
experiment to obtain results with integrity. Figures 3
and 4 show the map generated without addition ofGaussian noise and addition of Gaussian noise,respectively.
For the final experiment, the mobile robot was
equipped to utilize the exploration stack in the same
uncontrolled environment. During this experiment, themobile robot was allowed to be independent in
conducting path planning and obstacle avoidance. The
entire map generation took about 35 minutes and shownin Figure 5. Throughout all the experiments, there were

©ICAROB 2014

Figure 5: Map generated for uncontrolled environment
with addition of Gaussian noise and exploration stack

III. DISCUSSION
1. Observations
In smaller environments, the mobile robot is able tonavigate around smoothly and generate accurate maps.
This is mainly due to the fact that in that short period oftime, not many errors were accumulated by the wheel
encoders. Not only that, the laser range finder ispowerful enough for the small environments. However,in bigger environments like the uncontrolledenvironment, the map generated tends to have a lower
resolution and is inconsistent. As the mobile robot-87-

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Japan, January 11-13, 2014

C. Surface difference and inclination
As shown in Figure 7, the difference in surface typeand its inclination caused a disturbance in the sensor
data of the laser range finder, generating maps withstructural irregularities. Besides the laser range finder,
the wheel encoders also produce a much higher error
count whenever the mobile robot is navigating throughthis area. Moreover, the main plug hole covers which
are not properly fitted a shown in Figure 7 (right) also
contributed to inconsistency in the map generated.

continues to navigate and generate map of its
surrounding environments, the wheel encoders tend toaccumulate more and more errors, mainly due toslippage. Hence, to counter the accumulating errors, thesensor data from the wheel encoders were fused
together with sensor data from the laser range finder.
However, as shown in Figure 3, the effort still may not
be enough as the map generated is not very satisfactory.
With the addition of Gaussian noise to both the sensor
data, the map generation improved quite significantly
due to the usage of Kalman filter.
On the other hand, Figure 5 displays one of the bestmaps generated. The exploration stack from ROS
implements a frontier-based exploration algorithm,
which also includes loop closing methods. It formulates
the entropy of the environment as the mobile robot isnavigating. Higher entropy generated signifies a higher
uncertainty of the knowledge of the environment. This
allows the mobile robot to recalculate and navigate
through the most optimal path with low entropy togenerate the most accurate map.

Figure 6: Chair with a wider lower part

2. Issues
Despite providing the algorithms and hardware for
optimal map generation, there were still some factorsthat were affecting the results. Most of these factors
involve the aspects related to both the environment andthe mobile robot. These issues include:

Figure 7: Left picture shows the different surface types
and inclination. Right picture shows the main plug holecoversA. Height and orientation of the laser range finder
The laser range finder was placed 20cm above theground and onto the platform of the mobile robot. The
laser range finder also scans the environment at a plane
parallel to the ground. Due to that, there were certain
occasions whereby the mobile robot could not navigate
away from an obstacle as the obstacle’s height was
below the scanning plane of the laser. Furthermore, thelaser range finder has a blind spot at the back and would
not be able to detect obstacles behind the robot.

D. Overbearing dynamic obstacles
While conducting the exploration experiment, there
may be times whereby the mobile robot may stop for along period of time before being able to navigate itself.
When there are too many people walking pass by in theroom, the costmap generated by the navigation stacktends to get very congested and not being refreshedquickly. The mobile robot will then assume that there
are many obstacles very close to it in all directions.

B. Objects with wider lower parts
Certain objects in the environment like chairs andtables affect the navigation of the mobile robot. The
mobile robot tends to have the perception that the path
planned is clear. However, the chair in Figure 6 has its
frame sticking out and the mobile robot has a very highchance of actually colliding with it.

©ICAROB 2014

IV. CONCLUSION
Maps of the environment are very important to themobile robot. With accurate maps, the mobile robot may
be able to conduct proper path planning, obstacle
avoidance and precise localizations.

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(SIECPC), 2011 Saudi International, pp. 1-5, 2011.
[4] S. Gong, H. Liu, Y. Hu, J. Zhang, “ROS-based
object localization using RFID and laser scan,”Information and Automation (ICIA), 2012 International
Conference on, PP. 406-411, 2012.
[5] M. Quigley, K. Conley, B. P. Gerkey, J. Faust, T.
Foote, J. Leibs, R. Wheeler and A. Y. Ng, “Ros: an
open-source robot operating system,” in ICRA
Workshop on Open Source Software, 2009.
[6] G. Grisetti, C. Stachniss and W. Burgard, “Improved
techniques for grid mapping with rao-blackwellized
particle filters,” Robotics, IEEE Transactions on, vol. 23,
no. 1, pp. 34-46, 2007.
[7] M. Monteerlo, S. Thrun, D. Roller and B. Wegbreit,“Fastslam 2.0: An improved particle filtering algorithm
for simultaneous localization and mapping that provably
converges,” in Proceedings of the 18th international
joint conference on Artifical Intelligence, IJCAI’03,(San Francisco, CA, USA), pp 1151-1156, Morgan
Kaufmann Publishers Inc., 2003.
[8] G. E. P. Box and M. E. Muller, “A note on thegeneration of random normal deviates,” The Annals of
Mathematical Statistics, vol. 29, pp. 610-611, 1958.

The GMapping node from ROS utilizes the RBPF
SLAM. Not only that, it also uses the EKF for multisensor fusion to reduce the amount of errors
accumulated by the sensors. Based on the algorithm, it
detects that if the sensor data contains error of Gaussian
distribution, a Kalman Filter is used to compute eachparticle that estimates the robot’s pose. Given thelinearity and advantages of Kalman Filter, theestimation would be more accurate to correct the
localization. In turn, it will generate more precise andconsistent maps as the particles contain more valuable
information than before. Hence, two nodes were written
in C++ language to perform addition of uniform
distribution values using the Box-Muller transform toboth the laser range finder sensor data and the wheel
encoders sensor data. Experiments were then carried out
to prove the improvement of the map generated which
turned out to be successful as shown in Figures 4 and 5.
However, this improvement is only limited toapplications using RBPF-based grid-mapping algorithm
and the usage of GMapping in ROS. Further
implementations in the future may include a visualsensor for calculating depths to enhance further theknowledge of the particles and reduce the accumulated
errors. Not only that, it is also recommended to look for
improvements in the costmap generated, reliability ofthe sensor data, and even various mapping strategies
that could be implemented.

ACKNOWLEDGMENT
The authors would like to thank Ministry of
Education, Malaysia for the funding of this work.

REFERENCES[1] J. Neira, J.Horn, J. Tardos and G. Schmidt,“Multisensor mobile robot localization”, in Robotics
andAutomoation,
1996.
Proceedings.
1996
International Conference on, vol. 1, pp. 673-679, 1996.
[2] K. Arras, N. Tomatis and R. Siegwart, “Multisensor
on-the-fly localization using laser and vision,” inIntelligent Robots and Systems, 2000. (IROS 2000).
Proceedings, 2000 IEEE/RSJ International Conferenceon, vol. 1, pp. 462-467, 2000.
[3] S. Zaman, W. Slany and G. Steinbauer, “ROS-based
mapping, localization and autonomous navigation usinga Pioneer 3-DX robot and their relevant issues”,
Electronics, Communications and Photonics Conference©ICAROB 2014-89-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

The recollection characteristics of generalized MCNN
using different control methods

Shun Watanabe1, Takashi Kuremoto1, Kunikazu Kobayashi2, Shingo Mabu1, and Masanao Obayashi1
1Graduate School of Science and Engineering, Yamaguchi University, Yamaguchi, Japan
Tel: +81-836-85-9005; e-mail { s005we, wu, mabu, m.obayas }@yamaguchi-u.ac.jp
2
School of Information Science and Technology, Aichi Prefectural University, Aichi, Japan
Tel: +81-561-64-1111; e-mail kobayashi@ist.aichi-pu.ac.jp
Abstract: As an auto-association memory model, a chaotic neural network (CNN) proposed by Adachi et al. isable to recollect multiple stored patterns dynamically. Kuremoto et al. proposed a multi-layer chaotic neuralnetwork (MCNN) combined multiple CNN layers to realize mutual association of plural time series patterns.
However, recollection simulation of MCNN was limited in a two-layer model, the recollection characteristics c
oncerning with the different external inputs, and the control methods were not investigated. In this paper, we
extend the MCNN to be a general from (GMCNN) with more layers and use particle swarm optimization (PSO) to select internal parameters of CNNs to improve the recollection performance of GMCNN. The recollecti
ng characteristics of different GMCNNs using different methods to decide internal parameters were invested b
y computer simulations of multiple time series pattern association.
Keywords: chaotic neural network, association memory, time-series pattern, particle swarm optimization (PSO)

I. INTRODUCTION

External inputs
Outputs ofanother CNN

Hopfield model has characteristics of a transfer of
network energy to a stable state and an ability torecollect a stored pattern stably [1]. On other hand, achaotic neural network (CNN) proposed by Aihara et al.
is able to recollect multiple stored patterns dynamicallybecause CNN consists of neurons which internal states
perform unstably and chaotically [2, 3]. Generally,
chaotic neural network models proposed in the 1990s
are known as their recollection characteristics of“chaotic itinerancy”, which means stored patterns arerecollected one by one in aperiodic states [2-4]. CNNs
are applied to not only dynamic association memory [3]but also optimization [5] and complex computing [6].
Additionally, Kuremoto et al. have combined multiple
CNNs to be a multi-layer chaotic neural network(MCNN) [7-11]. MCNN is able to recollect mutuallymultiple time series patterns by controlling the unstablebehaviors of CNN. Control methods for MCNN are
proposed by various approaches, e.g., by observing theamount of dynamical state changes [9, 10], or upper andlower limit of recalling times [12,13], and metaheuristics (e.g., genetic algorithm, particle swarm
optimization (PSO) ) [14]. MCNN has been applied to amathematical model of hippocampus to realize atransform of long-term memory [8-11]. However, inthese works, the number of layers in MCNN was
limited to two layers. So we modified MCNN to be a
generalized MCNN (GMCNN) and have investigated©ICAROB 2014

Outputs(a)External inputs

Outputs(b)

Fig. 1 Association memory models: (a) a chaotic neuralnetwork (CNN) [2, 3], (b) a generalized multi-layer
chaotic neural network (GMCNN) [7-11].
recollect characteristics of GMCNN using direct control
method (DC) [15]. However, characteristics of GMCNN
using other control methods was not investigated andcompared.
In this paper, a meta-heuristics control method using
PSO (PSOC) for GMCNN was proposed.
Computational simulations of 2, 3, and 4 layers
GMCNN were reported and the recollection
characteristics of GMCNNs using DC [15] and PSOC
were compared by the simulation results.

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state, x n (t )  x1 n (t ),, x Nn (t ) is an output of CNN,n
R (t ) is a propositional function (nth CNN layer
performs as "association layer" if R n (t ) is fulfilled,
otherwise the layer performs as "display layer"). f 
is a output function of the neuron:

II. ASSOCIATION MEMORY MODEL
In this section, a generalized multi-layer chaotic
neural network (GMCNN) which consists of pluralchaotic nerual networks (CNNs) of Aihara et al. [7-11]
and its storage/recollection methods of multiple timeseries patterns are introduced.

f y 2. Storage process of time series patterns
GMCNN stores each partial pattern of plural timeseries patterns into the multiple CNN layers alternately.
Hebbian learning rule [16] for storing the plural timeseries patterns are defined as follows:g t l   t l mod M  1



w

n,m
ij

n,m

wij

j

N

(1)

j

y i (t  1)   i (t  1)n

  i (t  1)   i (t  1)
n



n

 f yi (t  1)
xi (t  1)  n xi (t )nn if R (t ) ,(4)

0 if n  g t l ,t l   0 if n  m  i  j , t , mothrewise
 ij l



 wij

n,m



 wij

n,m

tl 

(5)

3. Network energy
GMCNN has a characteristic of transfer network
energy (NE) to a local minimal state when the network
outputs its stored pattern (also called "recollection") [1]
because GMCNN store the patterns by Hebbian learning
rule (which is also used in Hopfield network [1], CNN[2,3], and MCNN [8-11]). In this paper, the equation of
NE is defined as a follow:n(2)otherwise

where, t is an association time, n, m  1,, M  is
CNN layer number, i, j  1,, N  is neuron numberof CNN layer, M is the number of CNN layers in
GMCNN, N is the number of the neurons of one CNN
layer,  i n (t ),  i n (t ),  i n (t ) are internal states for
feedback inputs from self-layer, refractoriness andinputs from another layers respectively, wijn,m is aconnection weight from jth neuron of mth layer to ith
neuron of nth layer, k1 n , k 2n , k 4n are decay parameters
for feedack inputs from self-layer, refractoriness andinputs from another layers respectively, k 3 n is a
refractory scaling parameter, ai n is a summation ofthreshold and external input,  is a rate ofeffectiveness from another layers, yi n (t ) is a internal©ICAROB 2014

   where, t l is a storage time, χt l   1 t l ,,  N t l  isa partial pattern of the time series patterns at t l , g t l 
is the layer number to store a pattern,  m t l  is astorage time for mth layer at t l ,  is floor function,
 is a storage rate, wijn,m t l  is a correction mass of
the weight, Eq.(5) is an equation to renew state at t l .

 i n (t  1)  k 2n  i n (t )  k 3 n xi n (t )  ai nm n ij t l , m   2  i t l   1 2  i  m  1

Nn t  m  1
  m 1
 M
 m t l   M  l

 i n (t  1)  k1 n  i n (t )   wijn,n x jn (t )M(3)where,  is a steepness parameter. k n  k1 n ,, k 4n 
are called internal parameters in this paper.

1. Generalized multi-layer chaotic neural network
GMCNN is structured by interconnecting chaotic
neurons of CNN layers which have same network
structures [15]. In Fig.1 (a), a CNN is shown and in
Fig.1 (b) a GMCNN which has multiple CNN layers isshown. The chaotic neurons in CNN layer connect toeach other as complete connected recurrent network as
same as Hopfield network [1]. CNN layers in Fig.1 (b)connect to each other and play either role of "display
layer" or "association layer". "display layer" means astate of the layer keeps static. "association layer" means
the layer output patterns dynamically. GMCNN is able
to recollect multiple time series patterns by switching
the roles between CNN layers. The dynamics of thechaotic neuron of CNN layer in GMCNN is defined asfollows:

 i n (t  1)  k 4n  i n (t )   wijn ,m x jm (t )1
1  ey 

E (t )  

1 M N n ,m nmwij xi (t ) x j (t )
2 n,m i , j(6)III. CONTROL METHODS
The meta-heuristics control method using PSO(PSOC) is proposed for controlling the state of each
CNN layer and switching the roles of “display layer”
and “association layer” for GMCNN. The controlled theparameter of CNN layer are k n . The propositional-91-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

E t , u p ( s)   E (t )  E (t  1) k n '( t ) u ( s )function R n (t ) of Eq.(5) is defined as n  n(t ) inorder to make only one CNN layer as “association
layer” at all times. n(t ) is the layer number as“association layer” at time t and is defined a follow:
n(t ) mod M  1 if Q(t ) ,(7)n(t  1)  

n
(
t
)
otherwise
where, Q(t ) is a propositional function for switching
the role of CNN layer to be “display layer” or“association layer” at time t. The output of GMCNN for
observing the output of “association layer” is defined asa follow:

1 if
z i (t )  0xin ( t )(t )  0.5 ,F u p ( s)   max E t , u p ( s) ,0

v pd ( s  1)  u pd ( s)

 c1 r1 uˆ pd ( s)  u pd ( s) 
 c r u~ ( s)  u ( s ) 
2 2

pd

min

, ud

max

min
d

, vd

max




(8)

range [a, b] , u dmin and udmax are the minimum value
and the maximum value in a d-dimensional range
udmin , udmax  , v pd (s) is a velocity of d  1,, D thp  1,, P th particle at step s,
is the NE of Eq.(6) in “association layer”
renewed by Eq.(1)-(2) in a case that parametersu p (s)  u p1 (s),, u pD (s) of pth particle at step s areparameter ofE (t  1) k n '( t ) upapplied to k n '(t ) of the layer at time t, F u p  is thefitness of pth particle,  is an inertia coefficient,
c1 ,c2 are coefficients for local and global search, r1 , r2
are uniform random numbers in a range [0,1] ,~~~uˆ p (s)  uˆ p1 (s),, uˆ pD (s) and u(s)  u1 (s),, u D (s)are the best parameters in all past of pth particles and allparticles and defined as follows:uˆ p ( s)  arg max F u p ( s' ) u p ( s ')| 0 s ' s~( s)  arg max F uˆ ( s) 
u
p


(12)

u p ( s )| 0 p  P

IV. COMPUTATIONAL SIMULATIONSIn this section, the storage / recollection simulationsof GMCNN using DC [15] and PSOC and their results
are reported. The neurons of all CNN layers arenstimulated by the external input ai
in eachsimulation. And recollections of partial patterns of timeseries patterns are observed as the output of GMCNN.
The external input is defined as a follow:(9)

Step 2 x n(t ) (t  1) is calculated by Eq. (1)-(2) using
given by u p (s) . The fitness of pth particle iskdefined as follows:ain '( t )©ICAROB 2014

pd

Step 4 The particles are reevaluated by Eq.(10).
Step 5 This process is repeated from Step 3 if an
iteration step s fulfills s  s max . Otherwise, this process
ends and “association layer” is renewed by Eq. (1)-(2)~( s) is applied to k n '(t )( t  t  1 ) after u( k n'(t )  u~(s) ). Q(t ) of Eq. (17) is defined as
E(t )  E(t  1) .
Where, U (a, b) is an uniform random number in a

1. Particle swarm optimization control method (PSOC)
Particle swarm optimization (PSO) is a metaheuristic method base on an exploratory behavior of agroup of birds or fishes proposed by Kennedy andEberhart [17, 18]. An exploration of optimal parameters
by PSO is run according to a joint ownership parameterin a search space. Here, PSO is used to find the optimal
parameters of each CNN layer in GMCNN. In [14],Kuremoto et al. used PSO to find the optimal
parameters of MCNN, however, to evaluate fitness ofeach particle, it is used stored patterns during recalling
process. To eliminate this supposition, the stability ofNE given by Eq.(6) can be used to evaluate the fitnessof each particle. An algorithm for the control method
using PSO named “PSOC" is shown as follow:[Algorithm for PSOC]
Step 1 Initializations of the particles are defined asfollows:v ( s)  U v

d

(11)

u pd ( s  1)  u pd ( s)  v pd ( s  1)

The direct control method (DC) [15] controls“association layer” in GMCNN by switching theinternal parameters k n '(t ) to different parameters fromthe amount of decrease of state changes of the layer.
However, the switching parameters were set arbitrarily.
In this paper, optimal parameters are found by a metaheuristic method described in the next subsection andthey are used to “association layer” to control the layer.

u pd ( s)  U u d(10)

Step 3 Equations for renewing the particles aredefined as follows:otherwise

s0

p

-92-

n

 ce 2ˆ i  1The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Table 2 Parameter setting of each simulation enviro
nment.

Table 1 Parameters setting.
Name
Number of neurons
in a CNN

Symbol

Value

N

100

Initial internal states

i n (0),  i n (0), i n (0)0Initial connection
weights

wij

n, m

0

Initial patterns

xi (0)

nRandom pattern

Steepness parameter
Dimensionof parameters
Number of particle
Maximum step
Search space range
Search velocity
range
Inertia coefficient
Search coefficients
Initial association
layer



0.015

D

4

P

10
20

s

max

u

max

u

min

{0,0,0,0}v

max{1,1,20,1}v

min

c1 ,c2{-1,-1,-20,-1}
1
1n' (0)1{1,1,20,1}(13)©ICAROB 2014

Rate ofeffectivenessLearningrate



Scalingcoefficient
ce

Env.1
Env.2
Env.3

2
3
4

1
0.5
1/3

0.2
0.3
0.4

6
9
12



(14)

Where, r0,1 is a binary pattern randomized with 0 or
1, χ (t l )  0.5 means no pattern can be observed(because  ij t l , m  0 in Eq. (4)). Eq. (13) gives 2time series patterns ( 0  t l  4 , 7  t l  11 ) which areconsisted of 5 random binary patterns. An interval
between these time series patterns are 2 from storage
time tl  5 to t l  6 . On the other hand, Eq.(14) gives
3 time series patterns consisted of 5 random binary
patterns which intervals between these time series
patterns are 2 respectively ( t l  5,6 , t l  12,13 ).
In computational simulations, recollection processesof the stored plural time series patterns were observed
from t  1 to t  100 in Env.1-Env.3 and in
GMCNNs using DC [15] and PSOC. The recollection



The numberof layer
M

processes were repeated 100 times (as 100 samples) toevaluate a performance of GMCNN by each average
stored pattern retrieval times in Env.1-Env.3.
Fig. 2 shows storage processes and recollection
processes of GMCNN with 3-CNN layers (Env.2). The
plural time series patterns stored in GMCNN is thepatterns of Eq.(13). Fig. 2 (a) is using DC. Fig. 2 (b) isusing PSOC. The external input to GMCNN was arandom pattern as same as the stored pattern at thestorage time tl  2 given by Eq.(13). The left columnof Fig.2 (a)-(b) shows the storage processes of GMCNN
for storing 2 time series pattern χ (0),, χ (11) ofEq.(13) by Eq.(4)-(5). The right column of Fig.2 (a)-(b)shows the recollections of the stored patterns( χ(0),, χ(4) and χ (7),, χ (11) ) on an above tenlines and the role of “association layer” on an under
three lines in GMCNN with 3-CNN layers from theassociation time t  1 to t  100 . From Fig.2, we canconfirm that the switching of roles of CNNs worked
constantly and χ (2) , χ (3) and χ (4) were recollectedin different association times. And PSOC resulted morerole switching times of 3-CNN layers than DC,
comparing the role switching times of DC and PSOC (Where, c e is a scaling coefficient for external input,χˆ  ˆ1 ,, ˆ N  is an external input pattern given toeach CNN layer.
Table 1 shows parameter setting in all simulations.
Table 2 shows parameter setting in 3 kind of thesimulation environments: Env.1 with 2 CNN layers,
Env.2 with 3 CNN layers and Env.3 with 4 CNN layers.
In this paper, the number of kinds of the time series
patterns stored in GMCNN is 2 defined as follows:r 0,1 if 0  t l  4χ (t l )  r 0,1 if 7  t l  11
 0.5
otherwiser 0,1 if 0  t l  4
r 0,1 if 7  t  11l
χ (t l )  


r
0
,
1
if
14

t
l  18 0.5otherwise

Environme
nt number(a) DC(b) PSOC

Fig. 2 Examples of GMCNNs which stored the tim
es series patterns of Eq. (13) in Env.2 and givenχ (2) of Eq. (13) as external input.

-93-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014deviation (that is the recollection of the stored pattern is
expected and it is called “expectancy” in this paper).
Height of the “expectancy” indicates higher recollection
performance of GMCNN. GMCNN using PSOC
showed higher expectancy, comparing GMCNNs using
DC in Table 3(a) and PSOC in Table 3(b).
Total retrieval frequencies of all stored patterns, total
switching frequencies and expectancies for eachexternal input pattern in Table 3 and other simulations
are illustrated in Fig. 3. Fig. 3 (a) is in a case of 2 timeseries patterns given by Eq. (13) in 2-layer GMCNN(Env.1). Fig. 3 (b) and (c) is in 3-layer GMCNN and in
4-layer GMCNN. Fig. 3 (d)-(f) is in a case of 3 timeseries patterns given by Eq. (14) in 2, 3 and 4-layer
GMCNN. The better performance of GMCNN using
PSOC than DC (marked with ★ ) is concluded,
comparing the total retrieval frequencies, total switchingfrequencies and expectancies in respective external
input patterns. Therefore, PSOC proposed in this paper
is suggested preferentially as the control method of theswitching the role of CNN layers as “display layer” and“association” in GMCNN.

Table 3 The average retrieval frequencies of the part of the stored time series patterns, the average of
their totals, the average total switching frequencies
and the expectancies of the number of the patterns
recollected for an association time from t=1 to 100(and their standard deviations) in GMCNN which
store Eq. (13) in Env.2 (in 100 sample).
(a) DCStoredpattern

External input pattern: χ̂

χ (0)

χ (1)

χ (2)

χ (3)

0.00
0.00
0.00
18.89
(2.15) (0.00) (0.00) (0.00)
0.00
0.00
6.22
20.31
χ (1)
(4.28) (5.81) (0.00) (0.00)
0.26
31.56 19.66 23.50
χ (2)(6.19) (8.51) (8.61) (2.59)
0.00
20.63 16.75 28.75
χ (3)(0.00) (5.09) (9.95) (8.71)
0.13
0.89
17.82 22.62
χ (4)(0.93) (1.56) (7.01) (13.88)
0.00
0.00
0.00
0.00
χ (7 )(0.00) (0.00) (0.00) (0.00)
0.00
0.00
0.00
0.00
χ (8)
(0.00) (0.00) (0.00) (0.00)
0.00
0.00
0.00
0.00
χ (9)
(0.00) (0.00) (0.00) (0.00)
0.00
0.00
0.09
0.06
χ (10)(0.00) (0.00) (0.90) (0.42)
0.00
0.00
0.85
0.43
χ (11)(0.00) (0.00) (3.93) (2.54)
Total
56.80 61.49 59.01 52.12
retrieval (8.46) (12.31) (17.39) (20.34)
Total
34.07 26.15 28.06 32.98
switching (3.51) (3.29) (3.46) (4.43)
expectancy
3
3
3
2

χ (0)

χ (7 )

χ (8)

χ (9)

χ (10) random

0.00
0.00
0.00
0.05
(0.00) (0.00) (0.00) (0.50)
0.00
0.00
0.00
0.00
(0.00) (0.00) (0.00) (0.00)
0.00
0.00
0.00
0.00
(0.00) (0.00) (0.00) (0.00)
0.00
0.09
0.00
0.38
(0.00) (0.63) (0.00) (2.35)
0.00
0.10
0.00
0.09
(0.00) (0.70) (0.00) (0.63)
0.00
0.00
0.00
21.92
(5.08) (0.00) (0.00) (0.00)
0.00
0.00
5.88
22.54
(4.39) (7.39) (0.00) (0.00)
0.00
24.73 12.96 17.43(6.05) (7.41) (1.80) (0.00)
0.02
13.87 24.27 28.70(0.20) (5.49) (11.57) (8.34)
0.26
1.26
29.30 22.97(1.02) (2.07) (3.56) (14.46)
52.81 50.82 71.00 52.19(11.30) (14.38) (11.81) (19.70)
32.31 26.30 29.30 32.93(3.22) (3.21) (3.22) (4.40)
3
3
3
2

0.00
(0.00)
0.01(0.10)
0.61(1.77)
1.29(2.58)
1.68(3.51)
0.00(0.00)
0.00(0.00)
0.50(2.63)
0.39(1.60)
2.70(5.20)
7.18(8.71)
23.11(3.67)
0(b) PSOCStoredpattern

External input pattern: χ̂

χ (0)

13.82
(6.70)
13.14
χ (1)(7.60)
21.72
χ (2)(2.31)
9.97
χ (3)(6.53)
7.49
χ (4)(6.29)
0.00
χ (7 )(0.00)0.00
χ (8)(0.00)0.00
χ (9)(0.00)0.01
χ (10)(0.10)
0.00
χ (11)(0.00)
Total
66.15
retrieval (10.98)
Total
62.87
switching (5.39)
expectancy
5

χ (0)

χ (3)

χ (7 )

χ (8)

χ (9)

χ (10) random

V. CONCLUSION

0.25
0.22
0.00
(0.90) (0.70) (0.00)
0.33
0.00
14.56
(6.97) (1.00) (0.00)
17.29 17.51 13.19(7.71) (9.79) (10.83)
17.40 15.74 22.94(7.54) (10.00) (6.20)
6.71
15.68 20.39(5.60) (9.82) (7.11)
0.00
0.00
0.00
(0.00) (0.00) (0.00)
0.00
0.02
0.20
(0.00) (0.20) (1.32)
1.08
3.27
0.52
(3.67) (6.96) (2.55)
1.22
3.24
0.63
(3.99) (6.95) (2.84)
1.24
3.42
1.46
(4.02) (7.12) (3.98)
59.75 59.43 59.33(21.49) (24.16) (20.26)
62.66 65.94 64.43(5.48) (6.09) (6.27)
4
3
3

0.22
(0.61)
0.15(0.74)
1.56(4.82)
1.89(4.97)
1.70(4.61)
4.93(3.19)
3.68(3.18)
16.59(7.18)
13.55(6.65)
12.79(6.45)
57.06(17.61)59.13(4.93)
5

0.14(0.79)
0.29(0.98)
1.38(4.39)
1.90(4.78)
1.69(4.45)
0.04(0.31)
8.61(6.40)
14.39(8.26)
14.63(8.34)
8.18(7.05)
51.25(23.35)60.29(5.24)
4

0.00(0.00)
0.00(0.00)
0.12(1.19)
0.15(1.49)
0.12(1.19)
0.00(0.00)
0.00(0.00)
26.88(3.13)
25.88(3.24)
26.10(3.26)
79.25(6.27)
73.83(4.97)
3

0.38
0.45(1.14) (1.27)
0.33
0.65
(1.11) (1.67)
3.73
5.18
(7.31) (8.55)
4.69
5.57
(7.87) (8.39)
4.00
5.20
(7.41) (8.28)
0.00
0.00
(0.00) (0.00)
0.00
0.11
(0.00) (0.82)
8.63
4.29
(10.03) (7.98)
4.38
14.14
(10.03) (8.12)
4.67
12.53
(10.18) (8.42)
48.43 30.50(26.10) (29.43)
61.37 58.12(6.19) (5.73)
20

In this paper, we proposed the control method using
PSO as meta-heuristics which finds and uses the
optimal parameters in GMCNN. In the computational
simulations using 3 kinds of parameter setting, 2 kindsof time series patterns, different external input patterns
and 2 kinds of the control methods (DC and PSOC), thebetter performance of GMCNN using PSOC wasconfirmed by comparing the processing results of DC
and PSOC. The highest expectancy is in 3-layer
GMCNN. As future works, GMCNN is expected to be
adopted into long term memory models and the limbic
model of the brain.

χ (1)

χ (2)

REFERENCES

in Fig.2).
To investigate the relationship between the external
input and the output of GMCNN (retrieval patterns), on
columns in Table 3, we show the average retrieval
frequencies of 10 stored patterns ( χ (0),, χ (4) andχ (7),, χ (11) ) in GMCNNs with Env.2 given
respective 9 kinds of the external input patterns( χ (0),, χ (3) , χ (7),, χ (10) and a non-stored
random pattern). In Table 3, above data in cells mean
the average retrieval times, and under data inparentheses are deviations. Bold number in Table 3means the average retrieval frequency is larger than its©ICAROB 2014

[1] Hopfeld JJ (1984) Neurons with graded response havecollective computational properties like those of two-state
neurons. Proc. Natl. Acad. Sci. U.S.A. 81:3088-3092[2] Aihara K, Tanaka T, Toyoda M (1990) Chaotic neuralnetwork. Phys. Lett. A 144(6-7):333-340[3] Adachi M, Aihara K (1997) Associative dynamics in achaotic neural network. Neural Networks 10(1):83-98[4] Kaneko K (1990) Clustering coding, switching,hierarchical ordering, and control in a network of chaotic
elements. Physica D 41:137-172[5] Hasegawa M, Ikeguchi T, Aihara K (2002) Solving large
scale traveling salesman problems by chaotic neurodynamics.
Neural Networks 15(2):271-283[6] Horio Y, Ando H, AiharaK (2009) Fundamental
technology for complex computational systems. IEICE
Fundamentals Review 3(2):34-44-94-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014(a) A case of 2 time series patterns in Eq.(13) for a 2-layer GMCNN (Env.1)(b) The case of 2 time series patterns in Eq.(13) for a 3-layer GMCNN (Env.2) in Table 3(c) A case of 2 time series patterns in Eq.(13) for a 4-layer GMCNN (Env.3)(d) A case of 3 time series patterns in Eq.(14) for a 2-layer GMCNN (Env.1)(e) A case of 3 time series patterns in Eq.(14) for a 3-layer GMCNN (Env.2)(f) A case of 3 time series patterns in Eq.(14) for a 4-layer GMCNN (Env.3)

Fig. 3 The average total retrieval frequencies, the average total switching frequencies and the expectancies of
GMCNNs (for an association time from t=1 to 100 in 100 samples) given by different parameter control methods: DC and PSOC; “” denotes that over one of total retrieval, total switching and “expectancy” in the GM
CNN using PSOC are higher than in one using DC.
[7] Kuremoto T, Eto T, Obayashi M et al. (2005) A multilayer[12] He GG, Cao ZT, Chen HP et al. (2003) Controlling chaos
chaotic neural network for associative memory. Proc. of SICE
in a neural network based on the phase space constraint. Int. J.
Annual Conf.
Mod Phys B 7(22-24):4209-4214[8] Kuremoto T, Eto T, Kobayashi K et al. (2005) A chaotic[13] He GG, Shrimali MD, Aihara K (2008) Threshold control
model of hippocampus-neocortex. Lect. Notes Comput. Sci.
of chaotic neural network. Neural Networks 21:114-121
3610:439-448[14] Kuremoto K, Watanabe S, Kobayashi K et al. (2011)[9] Kuremoto T, Eto T, Kobayashi K et al. (2007) A
Dynamical recollection of interconnected neural networks using
hippocampus-neocotex model for chaotic association. inmetaheuristics (in Japanese). IEEJ Trans. EIS 131(8):1475-1484
Trends in Neural Computation (Eds: K. Chen and L. Wang),[15] Watanabe S, Kuremoto T, Kobayashi K et al. (2013) The
Studies in Computational Intelligence 35:111-133
recollection characteristics of a generalized MCNN. Proc. of[10] Kuremoto T, Ohata T, Kobayashi K et al. (2009) A
SICE Annual Conf. 1375-1380
dynamic associative memory system by adopting an amygdala[16] Hebb DO (1949) The organization of behavior. New
model. AROB 13(2):478-482
York: Wiley[11] Kuremoto T, Ohata T, Kobayashi K et al. (2009) A[17] Kennedy J, Eberhart RC, Shi Y (1995) Particle swarm
functional model of limbic system of brain. Lect. Notes
optimization. Proc. of the IEEE Int. Conf. on NeuralComput. Sci. 5819:135-146
Networks :1942-1948[18] Kennedy J, Eberhart RC (2001) Swarm intelligence.
Morgan Kaufmann Publishers Inc. San Francisco, CA, USA©ICAROB 2014

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Japan, January 11-13, 2014

Revealing Terrorism Contents form Web Page Using Frequency Weighting
Techniques
Thabit Sabbah1, Ali Selamat2 and Md Hafiz Selamat3
1,2,3

K-Economy Research Alliance & Faculty of Computing,
Universiti Teknologi Malaysia, Malaysia
12

sosthabit2@Live.utm.my
aselamat@utm.my, 3mhafiz@utm.my

Abstract: Intelligence and security informatics plays an important role in revealing terrorism in web content. However, theextremist groups are exploiting Internet facilities intensively to stimulate violence, hatred, and terrorism. Accurateclassification of web content will increase the opportunities for the effective use of intelligence and security informatics in theearly detection of terrorist activities. In this paper, we propose a feature selection method based on term weighting techniques
TF, DF, and TF-IDF for revealing terrorism in web content. Firstly, we compare the performance of the individual term
weighting techniques in revealing terrorism. Then, we present a method for enhancing the accuracy of classification. The
method was tested with selected dataset from Dark Web Portal Forum. The experimental results show that classification using
the proposed method is more accurate than the individual technique based classification.
Keywords: Classification, Term Weighting, Terrorism Detection, TF, IDF, TF-IDF.
methods, the text is represented as vectors of weighted
features (words). TF-IDF term weighting algorithm is themost commonly weighting scheme used to determine thesignificant words in the text [3]. In some other cases, text isrepresented statistically base on lexical, syntactic, domain
specific bag of words, and n-grams [5-10] features.
However, the inability of statistical methods to understandthe semantic meanings of text written by human [3] cause
the low performance of these methods in text classification.
In general, the performance of TF-IDF and other statisticalmethods is not sufficient [3, 11, 12]. So that some othertechniques are proposed to overcome the deficiency inusing individual statistical methods. Techniques proposedby [3, 13, 14] utilize the use of knowledge base tools thatprovide the conceptual hierarchal interconnections such as
WordNet and Wikipedia to measure the semantic relations
between features (concepts) in order to achieve higher
performance.
In this research we compare the performance ofindividual term weighting techniques such as Term
Frequency (TF), Document Frequency (DF), and Term
Frequency Inverse Document Frequency TF-IDF as afeature selection methods, and propose a method forenhancing the accuracy of dark web detection.

1 INTRODUCTION
The Internet facilities such as anonymity, cheap
development and maintenance environment and the huge
potential audience are misused by the extremist groups toincite terrorism, market their ideas, and seek for the support
and fund rising[1]. The hidden and covert part of the web
used by terrorists or extremist groups online is referred as
the Dark Web (DW)[2]. September 11 2001 (9/11) attacks
stimulated the information technology researchers' to study
and analyze different types of DW content such as text,
video, images, etc., in order to reveal the potential terrorism.
However, text content is the largest in web data [3], and in
DW collection [4], so that many text content analysis based
techniques are proposed to detect the terrorism on web,section 2 discusses some of these techniques.
The rest of this paper is organized as follows: section 2
reviews the related work in terrorism detection and term
weighting techniques used in this study. In section 3 wecompare the performance of individual term weighting
techniques in terrorism detection. Then, we present our
proposed feature selection method in section 4. Whilesection 5 discusses the experimental results, and finally, we
conclude this study in section 5.

2 RELATED WORKS

2.2 Term Weighting Techniques
Term weighting techniques such as Term Frequency(TF) is used widely [11] in Information Retrieval and web
classification domain, besides the DF and TF-IDF. However,there are many other term weighting algorithms proposed

2.1 Terrorism Detection
Over the recent years, text analysis has been used
intensively in detecting potential terrorism on the web
based on statistical methods [3]. Commonly, in statistical©ICAROB 2014

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ERM WEIGHTING PERFORMANCE

and used in the domain [15, 16]. Here is a brief descriptionof the techniques used in this study.
i) Term Frequency (TF)
TF is concerned with the normalized frequency of
certain term in the document. Larger TF value indicates themore significance of the term. tf of term t in document d isdenoted as tft,d , and it is defined as in equation (1) [17]:tf t ,d 

N t ,d  fr n

To compare the performance of individual term
weighting techniques in terrorism detection, we firstly
downloaded thousands of Arabic web pages from the Dark
Web Portal Forum (DWPF). DWPF is the largest collectionof crawled terrorist related documents [4]. The data on
DWPF is collected from 17 Arabic forums, and many otherforums in other languages such as English, German, French,and Russian languages[4]. However, in this research we
focus on Arabic web pages. An expert labeled the
documents into two classes (Dark and non-Dark), a
collection of 1000 documents (500 from each class) was
selected as the dataset for our experiments. Then, thedocuments preprocessed and represented in the Vector
Space Model (VSM) form, based on term weighting
techniques included in this study. The preprocessing
included removing the stop words, stemming, and filtering
the text against non-Arabic characters, numbers, andsymbols. Finally, the classification is performed using
Support Vector Machine (SVM) classifier based on Top K
feature sets for each term weighting technique. In thisexperiment, we use feature sets consist of the top 100, 200,
300, and 400 features. Following are the results of thisexperiment.

(1)

2

t ,d

t 1

where the denominator is known as the Euclidean normof the documents d in which frt,d is the raw frequency of tth
term in document d, and n is the number of distinctive
terms in document d.
ii) Document Frequency (DF)DF weight of a term t (denoted by DFt) represents thenumber of documents within the collection in which theterm t is found. DF is expressed by equations (2):D

DFt   df t ,d

(2)

d 1

where D is the total number of documents in the collecti
on, and dft,d is a function defined as in equation (3):

1 t  d
df t ,d  
0 t  d

Accuracy
TF

(3)

DF

TF-IDF100%90%iii) Term Frequency- Inverse Document Frequency (TFIDF)
TF-IDF reflects the assumption that less frequent termin the collection is the most term significant, and vise-versa.
TF-IDF is used by many information retrieval applications[18]. TF-IDF of a term t in document d (denoted by TFIDFt,d) is the dot product of the term frequency (TF) and theinverse document frequency (IDF) of the term, as inequation (4):

TF  IDF  TFt ,d .IDFt

80%
70%
60%
50%
40%
30%
20%
10%
0%
100

N
DFt

3 COMPARISON OF INDIVIDUAL©ICAROB 2014

300

400

K (# OF FEATURES)(4)

Fig. 1. Classification accuracy for individual term
weighting techniques

Where IDFt is the inverse document frequency, which
defined as in equation (5):IDFt  log

200

As we can see from Fig. 1, the accuracy of classification
based on TF-IDF is the least for all feature sets withmaximum value near to 81% for top 400 features. However,the classification accuracies based on TF and DF arecomparable for almost all feature sets except for Top 300

(5)

T

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feature set. The highest accuracy was achieved by using thefeature set of top 400 feature based on DF term weighting
technique.

of features in the base feature sets which equal to 200, 300,
and 400), as shown in Table 2.
Table 1 Number of features and accuracy achieved by
combinations of two sets

4 PROPOSED METHODDF
TF-IDF# of features
100 200 300 400 100 200 300 400
In base set
90.71
92.18
100
132
148
94.23
92.28
200
360
318
TF
95.80
93.16
300
535
466
96.28
93.45
400
704
539
91.69
100
177
93.35
200
401
DF
95.21
300
600
95.50
400
799

4.1 Method Design
Our proposed method aims to enhance the accuracy ofterrorism detection by combining the feature sets ofindividual term weighting techniques. In this study, we use
three different term weighting techniques: TF, DF, and TFIDF, so the combinations will consists of two sets and threesets combinations. The combination of two and three sets isdefined as in equations (6-8):

TpTF,
First, let S  TpDF,TpTF  IDF 



(6)

And

A, B, C  S , where A  B  C , thenC( A, B)  A  B  {x : x  AorB}

In エ ラ ー ! 参 照 元 が 見 つ か り ま せ ん 。 , theaccuracy (in bold lines) varies from one combination toanother, as well the number of features in the combination
set. The highest accuracy achieved is 96.28% by combining
the set of top 400 features generated by TF techniques withthe corresponding set generated by DF technique, and thenumber of features in the combined set is 704 features. Our
experiment included only the combination of feature setsthat has the same number of features as seen in エラー!
参照元が見つかりません。.

(7)

and

C ( A, B, C )  A  B  C
 {x : x  AorBorC}

(8)

Where
{TpTF} is the Top K features based on TF,{TpDF} is the Top K features based on DF,{TpTF-IDF} is the Top K features based on TF-IDF,
C(A,B) is the combination of two sets,And
C(A,B,C) is the combination of three sets.

Table 2 Number of features and accuracy achieved by
combinations of three sets

Based on the previous definitions, the following
combinations are generated:

Combination
TF_DF_TFIDF# of features in base set 100
200
300
400
Accuracy
91.89 93.16 95.60
96.48# of Features
180
478
526
897

TF_DF, TF_TF-IDF, DF_TF-IDF, and TF_DF_TF-IDF.
4.2 Experiments and Results
To test and evaluate our method we conducted theexperiment based on the same dataset and parameters of theindividual term weighting techniques comparison,considering top 100, 200, 300, and 400 features.

In Table 2, it seen that the highest accuracy achieved is96.48% with 897 features when combining the feature sets
generated based on top 400 features for each term
weighting technique.
Last step was joining the results in Fig. 1 with theresults in in エラー! 参照元が見つかりません。 andTable 2 (i.e. the accuracies and number of features based on
individual term weighting technique with the results basedon combining two and three sets respectively). From laststep, we get Fig. 2 that compares the results of our proposed
method to the results of classical method that depend on
individual term weighting technique in feature selection.

エ ラ ー ! 参 照 元 が 見 つ か りま せ ん 。 shows thenumber of features and the classification accuracy for all
possible combinations of two feature sets for all parameters.
However, there is only one possible combination basedon three sets which is (TF_DF_TF-IDF), which generates
only three sets according to different K values (i.e. number©ICAROB 2014-98-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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900

100%

800

90%

700

80%

# of Features

60%

500

50%
400

40%

300

Accuracy

70%

600

30%

TF-DF-TFIDF_400

TF-TFIDF_400

DF-TFIDF_400

TF-DF_400

DF_400

TFIDF_400

TF_400DF-TFIDF_300

TF-DF-TFIDF_300

TF-TFIDF_300

TFIDF_300

TF-DF_300

DF_300

TF_300

TF-DF-TFIDF_200

DF-TFIDF_200

TF-DF_200

TF-TFIDF_200

TFIDF_200

TF_200

DF_200

TF-DF-TFIDF_100

DF-TFIDF_100

0%

TF-DF_100

0
TF-TFIDF_100

10%
TFIDF_100

100
TF_10020%

DF_100200

Compination# of features

Accuracy

Fig. 2. Performance of proposed method versus classical method in terms of accuracy and number of features
achieved in this research is 96.48%. The results shown by
this research are very encouraging because the higher
accuracy could be achieved in relative to the fewer numberof features. Moreover, the results clearly show that highaccuracies (>95%) could be achieved by combining lesssmall feature sets that are generated using different basic
term weighting techniques.

From Fig. 2, It can be seen that the largest number offeatures among different combinations was around 900
features (in the TF-DF-TFIDF combination), and theaccuracy at that point was the maximum (96.48%).
Moreover, it can be seen, on one hand, that the lowest
accuracy was recorded in the case of using the feature set of
Top 300 features generated by the DF technique
individually. On the other hand, it also noticed that theaccuracy (96.29%) achieved based on Top 400 featuresgenerated by DF technique was almost comparable to the
maximum accuracy achieved by the combination-based
method. In addition, it is notable that the lowest accuracies
are related to feature sets based on TF-IDF technique, in
addition to the minimum accuracy mentioned above.

5 CONCLUSION
In this paper, we compared the performance of theindividual term weighting techniques TF, DF, and TF-IDF
in revealing terrorism content. Then, we present a method
for enhancing the accuracy of classification by combining
the Top K feature sets generated individual techniques. The
method was tested and evaluated on a selected dataset
downloaded from the Dark Web Portal Forum. The results
show that higher classification accuracy is achievable bycombining different individual feature sets. As mentioned
before, in this research, we only combine feature sets of thesame sizes, so, our future work will focus on finding thepoint of equilibrium in which the highest accuracy could beachieved with less number of features.

4 DISCUSSION
This research proposes a feature selection method basedon the combination of feature sets generated by different
classical term weighting techniques. By comparing theclassification accuracy achieved in this research to theaccuracies reported in [3, 12], we find that the highest
achieved accuracy by our proposed method was greater.
The accuracies reported in mentioned studies were 94% and91.67% correspondingly, while the maximum accuracy©ICAROB 2014

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ACM: Sheffield, United Kingdom. p. 468-469.
[13] Hwang, M., C. Choi, and P. Kim, Automaticenrichment of semantic relation network and its application
to word sense disambiguation. IEEE Transactions on
Knowledge and Data Engineering, 2011. 23(6): p. 845-858.
[14] Choi, D. and P. Kim, Automatic Image Annotation
Using Semantic Text Analysis, in Multidisciplinary
Research and Practice for Information Systems, G.
Quirchmayr, et al., Editors. 2012, Springer Berlin
Heidelberg. p. 479-487.
[15] Yang, Y. and J.O. Pedersen, A Comparative Study on
Feature Selection in Text Categorization, in Proceedings ofthe Fourteenth International Conference on Machine
Learning1997, Morgan Kaufmann Publishers Inc. p. 412420.
[16] Crestani, F., et al., Short queries, natural language
and spoken document retrieval: Experiments at Glasgow
University, 1998.
[17] Salton, G. and C. Buckley, Term-weighting
approaches in automatic text retrieval. Information
Processing & Management, 1988. 24(5): p. 513-523.
[18] Chiang, D.-A., et al., The Chinese text
categorization system with association rule and category
priority. Expert Systems with Applications, 2008. 35(1–2):
p. 102-110.

AKNOWLEDGEMENTS
The Universiti Teknologi Malaysia (UTM) under research
grant 03H02 and Ministry of Science, Technology &Innovations Malaysia, under research grant 4S062 arehereby acknowledged for some of the facilities utilized
during the course of this research work.

REFERENCES[1] Abbasi, A. and H. Chen. Affect intensity analysis ofdark web forums. in ISI 2007: 2007 IEEE Intelligence andSecurity Informatics, May 23, 2007 - May 24, 2007. 2007.
New Brunswick, NJ, United states: Inst. of Elec. and Elec.
Eng. Computer Society.
[2] Zhou, Y., et al., Exploring the dark side of the web:collection and analysis of u.s. extremist online forums, inProceedings of the 4th IEEE international conference onIntelligence and Security Informatics2006, Springer-Verlag:
San Diego, CA. p. 621-626.
[3] Choi, D., et al., Text analysis for detecting terrorismrelated articles on the web. Journal of Network andComputer Applications, 2013(0).
[4] Fu, T., A. Abbasi, and H. Chen, A focused crawler fordark web forums. Journal of the American Society for
Information Science and Technology, 2010. 61(6): p. 12131231.
[5] Abbasi, A. and H. Chen, Writeprints: A stylometricapproach to identity-level identification and similarity
detection in cyberspace. ACM Transactions on Information
Systems (TOIS), 2008. 26(2): p. 7.
[6] Abbasi, A. and H. Chen, Applying Authorship
Analysis to Extremist-Group Web Forum Messages. IEEE
Intelligent Systems, 2005. 20(5): p. 67-75.
[7] Zheng, R., et al., A framework for authorship
identification of online messages: Writing‐style featuresand classification techniques. Journal of the American
Society for Information Science and Technology, 2006.
57(3): p. 378-393.
[8] Huang, C., T. Fu, and H. Chen, Text-based video
content classification for online video-sharing sites. J. Am.
Soc. Inf. Sci. Technol., 2010. 61(5): p. 891-906.
[9] Tianjun, F., H. Chun-Neng, and C. Hsinchun.
Identification of extremist videos in online video sharing
sites. in Intelligence and Security Informatics, 2009. ISI '09.
IEEE International Conference on. 2009.
[10] Choi, D., et al., Building knowledge domain n-grammodel for mobile devices. Information, 2011. 14(11): p.
3583-3590.
[11] Ran, L. and G. Xianjiu. An Improved Algorithm to
Term Weighting in Text Classification. in Multimedia
Technology (ICMT), 2010 International Conference on.
2010.
[12] Greevy, E. and A.F. Smeaton, Classifying racist
texts using a support vector machine, in Proceedings of the27th annual international ACM SIGIR conference on
Research and development in information retrieval2004,©ICAROB 2014-100-

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Application of Self Organizing Map for analyzing robotic arm’s action
with Consciousness-Based Architecture module
Wisanu Jitviriya and Eiji Hayashi
Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
Tel : 81-97-597-7760; Fax : 81-97-597-7760
wisanu@mmcs.mse.kyutech.ac.jp, haya@mse.kyutech.ac.jp

Abstract: Our research has been focused on developing human-robot interactions, The robot need to perform a highlevel of intellectual activity and user compatibility. Therefore, we considered the primary structure of a conscious
human/animal action, which can be represented by the sequence process, Perception  Motivation  Action. We have
improved a hierarchical structure model, which defines the relationship between the consciousness field and the
behavior module. This model is called Consciousness-Based Architecture (CBA). Furthermore, the robot should selectthe action itself, we have investigated the application of brain-inspired technology so we introduced a Self Organizing
Map (SOM) neural network that is trained using unsupervised learning to classify its behavior according to themotivation value in the CBA module. In this paper, we attempt to describe the integration of a Self Organizing Map(SOM) method into CBA module in order to classify and select autonomous behavior. We confirmed the effectivenessof the proposed system by the experimental results.
Keywords: Consciousness-Based Architecture (CBA), Human-robot interactions, Self Organizing Map (SOM)

I. INTRODUCTION
Nowadays, the rapid development of hightechnology has produced non-industrial robots such as
robotic pets for kids, home robots, therapeutic robots
and service robots. These robots are designed forentertainment, guide in museums, education, welfare
and other purposes. The necessary challenge in thedevelopment of these robots is a high level ofintellectual ability and user compatibility. “Usercompatibility” in the future robot is an important forease of use, robot friendliness, human-like capricious
behavior and the robots are more attractive to people if
their movements are not endlessly repeated. However,so far the development of the robots has met withproblems in the recognition system, memorizing thesituation, the action selection and communication etc.,
when the robots interact with human. Therefore, ourresearch has been focused on developing the robot thathas process of the actions and consciousness resemblingthat of a human or an animal which is able to enhance
the liveliness and the likelihood of their interactions
with users [1]-[3]. We have constructed a hierarchicalstructure model, which defines the relationship between
the consciousness field and behavior module in the
robot. This model is called Consciousness-BasedArchitecture (CBA). We have improved the integrationof a Self Organizing Map (SOM), one kind of
unsupervised artificial neural networks (ANNs)
proposed by Kohonen [4]-[6], and based on the neuron
organization and decision-making process in the humanbrain into CBA module. In order to realize a practicalrobot that can autonomously choose its behavior andadapt to its environment.
In this paper proposes an unsupervised classification
method or Self Organizing Map (SOM) combined with
CBA module for use with a robotic arm. First, we©ICAROB 2014

Fig.1 System Structure of Conbe-I
Joint 3

3

2

Joint 4

Joint 2

Joint 7

7

6

Elbow
T4

T3

Joint 5

Joint 6

O5
T5

O7

1
5

Wrist

O6
T6

Joint 1

4

Fingers

O4
O3

T2

O2

T1

O1

ShoulderzyxFig.2 Degree of freedom in the robotic arm andthe robotic arm is divided into 4 parts
showed the outline of the proposed overview systemand described an autonomous behavior system of aconscious behavior robot (Conbe-I), consisting of arecognition of the external situation using a web camera,
calculating the naturally occurring dopamine anddetermining the robot’s motivation, selecting an action
using Self Organizing Map combined with CBA moduleand the drive of the actuator which accepted an action.
Next, we described the SOM context that consists of theclassification as “liking” behavior and “disliking”
behavior when the robot recognizes the green and blue
object, respectively, and the normalization process for
preparing the input data to be suitable for training.
Finally, the kinds of actions such as look around,-101-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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interest, be alert, approach, catch the object and avoid
were classified using a weight matrix derived by
dividing the cells on the map based on unsupervised
learning, the robot could select an action by finding the
maximum similarity of the each action.

Flowchart for Autonomous Behavior System
The main system

SOM-CBA module

Recognize the external situation
using a web camera

Initialize all the neurons with random
values and data normalization

Calculating the naturally occurring dopamineand Determine the robot’s motivation

Calculate the distance between the input
pattern and weights of each neuron. And
select the nearest neurons as winner(BMU)

II. SYSTEM STRUCTION OF CONBE-I
The figure 1 shows the appearance of a conscious
behavior robot (Conbe-I), the robotic arm is divided
into 2 parts, an arm and a hand. The arm part has 6
levels of flexibility: the shoulder (Joint1, Joint2), theelbow (Joint3, Joint4) and the wrist (Joint5, Joint6). The
hand part has 3 fingers and one level of flexibility. So
thus the robotic arm has a total of 7 degrees of freedom
as shown in Fig.2 and a small web camera equipped at
the palm of the robotic arm which is able to recognize
an external situation. The web camera and the multiple
actuators are controlled using the RS-485 serial data
communication.

Action selection according to the
SOM-CBA module

Update each neuron aroundthe winner neuron

The drive of the motor which
accepted an action

Max iterations

No

Yes

Fig.3 The flowchart of autonomous behavior system

III. AUTONOMOUS BEHAVIOR SYSTEM
McCarthy has indicated that a robot will need toconsider and introspect in order to operate in thecommon sense world, it will need to have aconsciousness and introspective knowledges and some
philosophy [7]-[8], so we describe the overview systemby the flowchart of the autonomous behavior system isshown in Fig.3 and the details of each item aredescribed below.

Fig.4 Simplified image

Motivation model

Positive

Postive dopamine

time
The total of dopamine

Negative

1. Recognize an external situation by a web camera
In this research, The images were simplified by
divided into three color groups: green, blue and fleshcolor, which were distinguished and recognition interms of the shape, size and center-of-gravity position
as shown in Fig.4. The robot could recognize theposition and its distance from the target object in orderto calculate the naturally occurring dopamine model.

Fig.5 Motivation model
Where, y (t ) is the naturally occurring dopamine, x(t ) is
the input data from image recognition, n is the naturalangular frequency,  is the damping factor and T is
the time constant.

2. Calculating the naturally occurring dopamine anddetermining the robot’s motivation
When humans and animals take various actions,changes occur in the dopamine in the brain. Dopamine
affects brain processes that control movement, emotion
response, and ability to experience pleasure and pain, so
we used the typical pattern of naturally occurring
dopamine for determining the robot’s motivation. The
waveform of naturally occurring dopamine divided into
the raise and fall parts, which are given by Equation (1)and (2) respectively.
y ''(t )  2n y '(t )  n2 y(t )  n2 x(t )  0(1)

Ty '(t )  y (t )  x(t )(2)©ICAROB 2014

Negative dopamine

The total of dopamine is calculated by the sum of
their positive (the green objects) and negative values(the blue objects), then the total of dopamine isconsidered as the input variable for determining therobot’s motivation as shown in Fig.5 and expressed by a
2nd order system of linear difference equation as shownin Equation (3).
motivation ''(t )  2nmotivation '(t )n2motivation(t )  n2u (t )(3)Here, motivation(t ) is the robot’s motivation andu (t ) is the total of naturally occurring dopamine.

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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 (t ) 0  e x pt T

3. Choose an action using Self Organizing Map
method with CBA module
In this section, we are aiming for an integrated
development the autonomous behavior system thatcombines a sample the Self Organizing Map and CBA
module in order to classify and select its behavior
correspond to its environment and the details are
explained below.
A.Self Organizing Map algorithm

w

(
j

,w2
w1 j

)
, w nj, ...

(6)

B.Combine the Self Organizing Map with CBA module
CBA with Self-Organizing Map Module
Motivation

External
situation
Recognize:
Color
Shape
Distancen

R

Level 3
Level 2

Level 1j

Behavior
SOM++

SearchApproach
EscapeGaze
Open-mouthed
Stand

Motor
control

Wake
Look
Rest
Sleep

Fig.7 Combine SOM into CBA module
Our previous research, the CBA model decides on
the conscious level that the robot must strongly consider
and select the behavior corresponding to the conscious
level. In this paper, the CBA has been developed asshown in Fig.7, the CBA model has a 3-level hierarchyin the motivation module that connects with a SOM
module for clustering actions in a a 3-level hierarchy inthe behavior module.

x  ( x1, x2 ,..., xn )  R n
Input layer

Output space

Fig.6 A basic rectangular SOM topology
The basic Self Organizing Map (SOM) consists of
the input layer and the output layer which is fully
connected with the input layer by the adjusted weights.
After learning of the SOM, the weight vector of aparticular cell defines the center of a certain class of the
input. In Fig.6 a typical two-dimensional rectangular
SOM is shown. The input vector x  ( x1, x2 ,..., xn )  R n
is connected to cell j and the respective connection has
na weight w j  ( w1 j , w2 j ,..., wnj )  R . The learning of
the SOM is performed in the following steps:
Step 1. Initially, we assign to the weight vectors
w j (0) random values, and we normalize the values ofw j (0) in [0,1].
Step 2. Present an input vector x to the network,
then we compute the cell j that is closest to it by meansof the Euclidean distance and then find the winner ofunit c (Best Matching Unit: BMU) which has theminimum Euclidean distance as expressed by Equation(4).

Uc  min x j  w j (t )IV. EXPERIMENTAL RESULTS
1. Classification results
The actions of the robotic arm are divided into two
characteristic behaviors according to the value of the
motivation and the effect of surface color on the object
recognition. For instance, when the robot sees a green
object (Liking behavior), the motivation value rises andthe robot looks around and interests the target object
after that the robot attempts to approach or catch the
Look around

Interest the green object
Approach to
the green object
Grasp the green object

Fig.8 “Liking behavior”: recognize the green object(4)
Look around

Step 3. The adaptation process adjusts the weightvector of cell j and the weight vectors of its neighboring
cells by Equation (5).

Be alert tothe blue object

w j (t  1)  w j (t )   (t )   x(t )  w j (t ) Avoid the blue object(5)

Step 4. The time t increases to t+1, if t < T then go tostep 2; otherwise stop the training.

Fig.9 “Disliking behavior”: recognize the blue object
object as shown in Fig, 8. On the other hand, when the
robot recognizes a blue object (Disliking behavior), themotivation value drops and the robot should be alert andWhere, t is the time of iteration, T is the predefined
number of iterations and  (t ) is the learning rate which isdecreasing function of time by Equation (6).

©ICAROB 2014

Action-103-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Look around(Green)Interest(Green)CatchApproach

Motivation level

Be alert(Flesh)Avoid(Blue)

Interest(Flesh)Look around

Look around + Interest

Be alert

Approach(Green)Avoid

Be alert(Blue)

T1 T2

T3

T4

T5

Time(s)

T6

T7

T8

Fig.11 Transition of the motivation
Catch the ball(Green)the green ball at T1, then the value of motivation
increased steadily and the robot selected the action
between looking around behavior and an interesting
behavior. From T2-T3, it was selected “Approach”
action and it began to approach the green ball andopened the fingers after that the robot came closeenough to the green ball to catch it in a time interval(T3-T4), but on the other hand, the blue ball was shownin order to decrease the robot’s motivation and the
motivation became to the negative value. In the timeinterval from T5 to T8 that shown the disliking behavior
that consists of “Look around”, “Alertness” and“Avoidance” respectively.

Look around(Blue)

Fig.10 The classification on the map
avoid the object as shown in Fig, 9. In addition, when
the robot recognized the flesh color, the actions are
clustered around interested behavior and alert behavior.
In this paper, the Min-Max Normalization technique isused to scale the input sample in the range of 0 and 1
depending on its relative size. The parameters of SOM
as shown in Table 1.
Table 1. Parameters of SOM
Value
Moti_flag

True of False

Moti_value

0 to 1

Description
The positive motivation
value is True = 1, and thenegative value is False = 0.
The motivation valueGreen

0 to 1

Pixel of green colorBlue

0 to 1

Pixel of blue color

Flesh

0 to 1

Pixel of flesh color

t

1000

The number of learning

η0

0.35

Map size

20 x 20

The initial learning rate
The rectangular SOM
topology size

V. CONCLUSION
In this paper, we present the sample application of
Self Organizing Map (SOM) for analyzing andclustering the robotic arm’s action. From theexperimental results, 9 motions of the robotic arm could
be classified on the map of SOM and the robotic arm
could autonomously select its behavior and adapt to its
surrounding environment. In the future study, ourresearch will continue to develop the system of Conbe-Isuch as it becomes possible to create an emotional
system and apply the memorization system based on an
artificial neural network into the robot’s system.

REFERENCES

To make it easy to understand, Fig.10 shown the all
actions of the robotic arm which were classified using aweight matrix derived by dividing the cells on the map
based on unsupervised learning SOM. For instance, if
the input data was Moti_flag = 1, Moti_value = 0.5,
Green = 0.8, Blue = 0 and Flesh = 0.1404. The robot
recognized the green object and the target object wasnear the robot’s hand, when the system finished the
SOM learning. The blue cells were shown in Fig.10,
which were the winner node of each behavior and thegreen cell was the response action from SOM learning,
so in this state the robot autonomously selected the“Catch the object” action.

[1]
[2]

[3]
[4]
[5]
[6]

2. Verification experiment
We let the robotic arm recognized a green ball
(Liking) and a blue ball (Disliking). We observed theaction of the Conbe-I until it took a green ball from thehuman’s hand. The transition of the motivation is shownin Fig.11. After the Conbe-I was started it recognized©ICAROB 2014

[7]
[8]

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N. Goto and E. Hayashi, “Design of Robotic Behavior that
imitates animal consciousness,” Journal of Artificial Life andRobotics Vol.12, Springer, pp. 97-101, 2008.
Eiji Hayashi, Takahiro Yamasaki and Koichiro Kuroki,“Autonomous Behavior System Combing Motivation with
Consciousness Using Dopamine,” IEEE International
Symposium on Computational Intelligence in Robotics andAutomation (CIRA2009), pp.126-131.
Motoki Yoshida, Eiji Hayashi, “Design of robotic behavior that
imitates animal consciousness: Construction of the userrecognition systems,” AROB 17th 2012, Beppu, Oita, JapanT. Kohonen, “Self-Organizing Maps,” Series in Information
Sciences, Vol. 30, Springer, Heidelberg. Second ed., 1995.
T. Kohonen, “The self-organizing map,” Neurocomputing,
Vol.21, pp.1-6, 1998.
T. Kohonen, “Essentials of the self-organizing map,” Neural
Networks, Vol. 37, pp.52-65, 2013.
McCarthy J (1995) Proceedings of the fourteenth international
joint conference on Artifi cial Intelligence, IJCAI 95, pp.2041–
2044.
McCarthy J (1996) Making robots conscious of their mental
states. In: Muggleton S (ed) Machine intelligence, vol. 15,
Oxford University Press, Oxford, pp. 3–17.

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Study on Time Synchronization Algorithm of Wireless Sensor Networks
Based on Maximum Likelihood Estimation
Dong-Mei Ai1), Guang-Ping Zeng2), Di He2), Xu-Yan Tu2)1)2)

Mathematics Laboratory, University of Science and Technology Beijing, China

School of Computer and Communication Engineering, University of Science and Technology Beijing, China
aidongmei@ustb.edu.cn

Abstract: Time synchronization, a supporting technology for application of WSNs, is critical to the design andapplication of WSNs. Unlike general computer networks, WSNs face the issue of constraints on energy. Hence, the timesynchronization mechanism isn’t suitable when extensively applied to traditional network. This paper proposes
High-Accurate Energy-Efficient Time Synchronization (HAEE) algorithm based on TPSN algorithm, whichsignificantly improves the accuracy of time synchronization and reduces energy consumption, as proven by software
simulation results.
Keywords: Wireless Sensor Networks, time synchronization, maximum likelihood estimation, high accuracy, lowconsumption

I.

INTRODUCTION

Sensing technology is considered to be the essential
technology in obtaining information. Along with theprogress of modern sensing technologies, outmoded
simple means of acquisition of information graduallydeveloped towards integration, miniaturization, andnetworking. These changes accelerate the developmentof Wireless Sensor Network (WSN), which integrates
technologies of wireless communication, sensing,
network interconnection, and distribution. Comprised ofa large amount of micro sensor networks, it can collectreal-time information, monitor objects within a specificrange, and send back usable information immediately toobservers for examination and analysis. WSN has a huge
potential for application in military safety, environmental
monitoring, traffic management, medication, intelligent
furniture, etc.
As the supporting technology for WSNs, timesynchronization algorithm directly decides on theprospect of WSNs. Unlike traditional networks, WSNs
need to deploy a huge number of sensor nodes. It
requires cheap and small sizes sensor nodes, so theenergy consumption must be lowered.
This paper is a study on several synchronization
algorithms and compares their pros and cons. It uses themathematical method of Maximum Likelihood
Estimation (MLE) to improve TPSN synchronization©ICAROB 2014

algorithm, which effectively reduces energy consumption
while improving accuracy of synchronization. It also
describes the building of a software simulation andexperiment platform. The simulation result proves theadvantages of this improved algorithm over TPSN
algorithm.

II. Classic algorithm of time synchronization of
WSNs
1.

RBS algorithm
Reference Broadcast Synchronization (RBS) [1-3]algorithm was advanced by Elson et al. The basic idea of
RBS algorithm is that nodes compare their receipt time,
according to reference broadcast sent by a reference node,with each other by exchanging message, as shown in
Figure.1. Then, each node calculates its clock offset withall others through the offset matrix [1] (Formula 1). Next,
all nodes that have received reference broadcast from thesame reference node achieve time synchronization, i.e.
subnet synchronization.
∀ i, j ∈[1, n] : offset[i, j ] =1 m(T - Ti ,k )m k =1 j ,k∑(1)

In RBS algorithm, the features of broadcast channelof wireless data link are used. However, it results inexcessive time of message exchange. Simple networkwith n nodes needs o(n 2 ) times of message exchange. As
the number of nodes increase, the time complexity of
RBS algorithm increases rapidly and results in high-105-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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energy consumption.

layer discovery stage, leader offset solution stage andnode synchronization stage, as shown in Figure.2.

Figure.2. HAEE algorithm framework

Figure.1. RBS synchronization algorithm message
transmission

1.

2.

TPSN algorithm
Saurabh Ganeriwal advanced the Timing-Syn
Protocol for Sensor Networks (TPSN) [4] synchronization
mechanism in November 2003. TPSN adopts thebidirectional paired message synchronization mechanism,
through which two modes obtain the offset between eachby message exchange. The entire algorithm includes thelayer discovery stage where all nodes are divided tolayers, and the node synchronization stage, wheresynchronization between nodes is achieved.
TPSN algorithm significantly increases the energy
consumption for time synchronization due tocomplicated calculation and the use of flooding
broadcast hierarchical tree. Once the root node becomes
invalid during synchronization in TPSN algorithm, it
needs to choose the root node again before proceeding to
the layer discovery stage and node synchronization stage,a process that weakens robustness of the algorithm while
increasing the work of calculation and energy
consumption [5-7].

III. HAEE algorithm
Clock offset between nodes includes two parts:
clock-phase offset and clock-frequency offset [8]. Most of
the existing time synchronization mechanisms consider
only the effect of clock-phase offset on clock
synchronization and neglect the effect of clock-frequency
offset. In fact, clock-frequency offset, i.e. drift of clock
frequency, continually increases the time difference
between nodes. So the effect of clock frequency offset
should not be neglected. This paper introduces aninnovative HAEE algorithm, which introduces the theoryof MLE to calibrate clock-phase offset andclock-frequency offset between nodes based on original
bidirectional paired message synchronization mechanism[9-11].

HAEE algorithm is divided into three stages, namely,©ICAROB 2014

Layer discovery stageDuring the layer discovery stage, every node is
assigned a layer number. Besides, each node should havethe information of the lower-layer nodes. All sub-nodes
in each node build into a group. The upper-layer noderandomly assigns one of its sub-nodes as the leader node
(Figure.3). After several flooding broadcast, the entire
network shall grow to a hierarchical topology.

Figure.3. Node topology
2.

Leader offset solution stage
The time synchronization between the root node andleader node of first layer nodes is based on bidirectional
paired message synchronization mechanism during thisstage. The mathematical method of MLE, an importantmathematical statistics method whose basic idea is tobuild a mathematical model based on the density
function and to estimate the possible maximum values, isintroduced to weaken the effect of indefinite delay andclock frequency offset on clock synchronization.
As shown in Figure.4, node A represents the root
node and node B as the leader node of the first layer. θ1
represents the time difference between nodes A and B atmoment T1,1 which is kind of clock-phase offset.
Meanwhile, θ 2 represents the clock-frequency offset
between nodes A and B, which leads in causing
increasingly bigger clock offset between two nodes. To
simplify the calculation, T1,1 is assumed to be the timebenchmark, i.e. T1,1 = 0. The following equation can be
deduced from the relationship between T1,i and T2, i .
T2,i = T1,i + d + X i + [θ1 + (θ 2 - 1) • (T1,i + d + X i )]

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(2)

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Where d + X i represents the transmission delay from

Assuming random time sequence{X i }iN=1 andtime T1,i to T2, i , d is the definite part and X i is the{Yi }iN=1 as normal distribution with mean  and theindefinite part; θ1 + (θ 2 - 1) • (T1,i + d + X i ) represents the sum ofvariance of δ 2 , then P and R is random variables with theclock-phase offset 1 and clock-frequency offset during

mean of 0 and the variance of 2δ 2 , we can conclude thatthe time benchmark T1,i

as follows,

and T2, i , which is theaccumulative effects between node A and node B.

f P , R ( p, r ) =

-

1
4πδ

2

1

e 4δ

2(p

2

+r 2 )(12)

Using the maximum likelihood estimation method, we
can get the clock phase offset and clock frequency offset
as follows,
∧

θ1 =

∧

·
U·
i - Vi
2

θ2 =

(13)

D2 2 + D3 2
D1 • D2 + D3 • D4

(14)

Where，
Dk = Tk , N - Tk ,1 , k = 1,2,3,4

Figure.4. Time synchronization process between root
node and first layer’s leader nodeU·i =Similarly, following equations can be respectively
deduced(3)
T3,i = T4,i - d - Yi + [θ1 + (θ 2 - 1) • (T4,i - d - Yi )]T2,1 = T1,1 + d + X 1 + [θ1 + (θ 2 - 1) • (T1,1 + d + X 1 )](4)

T3,1 = T1,1 - d - Y1 + [θ1 + (θ 2 - 1) • (T4,1 - d - Y1 )](5)

Next, following equations can be deduced from (2) (4) and (3) - (5):(6)
T2,i - T2,1 = T1,i - T1,1 + X i - X1 + (θ2 - 1)(T1,i - T1,1 + X i - X1)
T4,i - T4,1 = T3,i - T3,1 + Yi - Y1 + (θ2 - 1)[T3,i - T3,1 - (Yi - Y1)]

(7)

Then, let(8)

Dk ⇔Tk ,i - Tk ,1
P ⇔X i - X 1

R ⇔Yi - Y1

(9)

The following equations can be deduced fromequation (2) and (3), using equation (7), (8) and (9)D2
P=- D1 = θ 2· • D2 - D1
θ2

R = D4 -D3= D4 - θ 2· • D3
θ2

(10)
(11)

When θ 2· = 1

θ2

We previously assume that X and Y are the random
portion of the transmission delay during the packet
transfer process, then the complexity of WSN network
data link allows us to see P and R as the accumulation of
the independent and identically distributed random delay,
and according to the central limit theorem, we canconclude that the probability density function of P and R
is close to normal random variable.

©ICAROB 2014∧
1 N
(T - θ T )N - 1 i = 2 2,i 2 1,i

∑

V·
i =

1 N ∧
(θ T - T )N - 1 i = 2 2 4,i 3,i∑N represents the number of package switching during
the node synchronization phase, and the bigger thenumber is, the more accurate synchronization we can get,So after correction the time for Node B will be：(15)
TB· = TB • θ2 + θ1
Leader offset solution stage can accurately calculate
the clock phase and frequency of leader phase, but thefrequent exchange of messages makes it relatively
energy-intensive. However, as mentioned at the beginningof this chapter, this stage may cost lots of energy, but for
the entire algorithm, the energy consumption actuallysave lots of trouble in other stages. The detailed reason
will be described in the next section.
3.

Node synchronization stage
After the leader node solution stage, because of thetime offset having been obtained, the difficulty andenergy consumption of the node synchronization stage,
which includes the self-calibration of leader node andreference synchronization of node synchronization, aresignificantly reduced,
1) Self-calibration of leader nodeIn the previous section, clock-phase offset andclock-frequency offset are calculated through themodified bidirectional paired message synchronization
mechanism. The clock-frequency offset of the node may
be regarded as constant within a period. Thus,
self-calibration may be implemented based on the“constant” nature of leader node frequency offset, and-107-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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time may be calibrated without communication withother nodes.
At the moment g (t N1,i ) , the leader node N1 carries
thout i synchronization and calibrates its local time to:t N1,i = θ2 • g (t N1,i ) - (θ2 - 1) • g (t N1,i -1 )Whereg (t N1,i )

represents

local

(16)
time

of

last

synchronization, and g (t N1,i ) represents local time ofcurrent synchronization.
2) Reference synchronization of member nodes
The leader node can maintain extremely high time
accuracy in the remaining process of synchronization
through self-calibration, a process that makes at least one
node in each group of WSNs to have accurate local time
and that significantly simplifies the time synchronizationof member nodes. In this scenario, reference broadcast
time synchronization mechanism, rather than existingtime synchronization mechanisms, is more suitable.
Taking the upper layer node as the reference node tosynchronize the time between member nodes and leader
node is simple, easy to implement, effective andenergy-efficient.
As shown in Figure.5, where BS represents root node.
N1 and N2 represent the leader nodes of the first layer
and the member node of the first layer respectively. At
first, BS broadcasts at the moment T1 , while the leader

error in different layers, and energy consumption. The
simulation results are indicated below.
As shown by Figure.6, it is synchronized once every
500ms, and the time offset is reset every 500ms for thesimulation. The figure gives rise to the conclusion that
HAEE algorithm proposed by this paper improves thesynchronization accuracy in comparison with TPSN
algorithm. HAEE algorithm considers the clockfrequency offset and performs better in approximation ofstandard time, thus achieves more accurate
synchronization time.

Figure.6. Comparison of Errors of First Layer Nodes at
Different Moments in HAEE and TPSN
2.

Analysis of node error in different layers
Figure.7 shows that the error is gradually amplified
as the number of node layers increases. HAEE algorithm
has relatively small error, with less divergence than
TPSN algorithm.

node N1 receives the broadcasted message at the
moment T2 and records the local time T2 . Similarly,
the node N2 does at T2·. The self-calibration ability ofleader node N1 makes that local time of N1 regarded as
the standard time. The time offset of N2 at this moment
could be represented by its time offset with N1:
θ = T2· - T2

（17）

Figure.7. Comparison of error of nodes in different
layers in HAEE and TPSN algorithms

Figure.5. Reference Synchronization of Member Nodes

IV. Simulation and Comparison
1.

Analysis of node error in first layerThis study adopts NS-2[12] network simulation
software to compare and simulate HAEE and TPSN
algorithms in analysis of node error in first layer, node

©ICAROB 2014

3.

Energy consumption analysis
As shown in Figure.8, HAEE algorithm does moremessage forwarding in early synchronizations, thereby
causinghighenergyconsumption.
Asthesynchronization goes, HAEE algorithm graduallydemonstrates the energy efficiency features in node-108-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014synchronization in comparison with TPSN algorithm,

Figure.8. Comparison of energy consumption of HAEEand TPSN algorithms

V. Conclusions
Time synchronization of node is a critical issue to
WSNs. Accurate time synchronization will play an activerole in the conduct of applied studies on WSNs in the
future. This paper proposes HAEE algorithm whichrefers to broadcast synchronization mechanism, is a
modification of TPSN algorithm. HAEE algorithm
considers both clock-phase offset and clock-frequency
offset, and proves its ability to guarantee synchronization
accuracy through simulation analysis. Self-calibration ofleader node and reference synchronization of member
nodes significantly reduces energy consumption andmeets the requirements low energy-consumption.
However, the leader offset solution stage entails highenergy consumption due to frequent message exchange.
Therefore, HAEE algorithm needs further improvement.

References[1] J.Elson, D.Estrin (2001). Time Synchronization in
Wireless Sensor Network [M]. In Proceedings of the
15th International Parallel & Distributed Processing©ICAROB 2014

Symposium (IPDPS-01)[2] WANG Fuqiang, Zeng Peng, ZHANG Xiaoling,
LIANG Wei, et al (2011). Survey on Time
Synchronization in Wireless Sensor Network. Computer
Engineering 37 (22): 70-73[3] J.Elson, K.Romer (2002). Wireless Sensor Networks:
A New Regime for Time Synchronization [J]. Proceedingof the First Workshop on Hot Topics in Networks(HotNets-I): 28-29[4] David L. Mills (1992). Network Time Protocol(Version 3), Specification Implementation and Analysis.
RFC 1305: 31-106[5] Tao Zhiyong, Hu Ming (2012). Time synchronization
algorithms on hierarchical structure in wireless sensor
network. Journal of Computer Application 32 (6):1513-1515, 1551
[6] Yang Peng, Yang Minghui (2011). Energy-EfficientTime Synchronization Algorithm based on Improved
TPSN Algorithm. Industrial Control Computer 24 (2):41-42
[7] Tao Zhiyong, Hu Ming, Fang Ning (2012).
Cluster-Based Time Synchronization Algorithm for
Wireless Sensor Network. Computer Measurement &Control 20 (7)[8] Miao Yonggang (2009). Study on Application of
Mathematical Method in Wireless Sensor Network.
Zhejiang University of Technology[9] Chaudhari, Q.M. E. Serpedin and K. Qaraqe (2008).
On Maximum Likelihood Estimation of Clock Offset andSkew in Networks with Exponential Delays. IEEE
Transaction on Signal Processing 56 (4)[10] Leng, M (2010). On Clock Synchronization
Algorithms for Wireless Sensor Networks Under
Unknown Delay. Vehicular Technology 59 (1): 182-190[11] H.S. Abdel-Ghaffar (2002). Analysis ofsynchronization algorithm with time-out control over
networks with exponentially symmetric delays, IEEE
Transactions on Communications 50 (10): 1652-1661[12] Xu Leiming, Pang Bo, Zhao Yao (2003). NS and
AnalogSimulation.
People’s
PostandTelecommunication Publishing Press-109-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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Development of phrase and music search engine by humming
Kiminori Sato and Eiji Hayashi

Department of Mechanical Information Science and Technology
Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology;
680-4, Kawazu, Iizuka-City, Fukuoka Prefecture, Japan;
Tel : 0948-29-7793;
e-mail address: sato@mmcs.mse.kyutech.ac.jp,haya@mse.kyutech.ac.jp;

Abstract: In recent years, the internet has become ubiquitous and is changing dramatically every aspect of how people
live their lives. Users not only can purchase the CD at home but also can download the music files without leaving
home using the internet, it is very easy and convenient for users who can find the lyrics. But the music’s names are notable to be efficiently searched if users forget the music’s names. If one could enter a sample of the voice, do a searchusing that voice's length and pitch, find the rhythm and a similarly pitched phrase, it may be possible to determine thename of the song. Therefore, we developed a phrase and music search engine. It searches for a tune from among various
tunes, using one phrase of voice data. For the purpose of studying the relationship between tempo and pitch changes
related to the frequency of the input voice data, we developed a method using dynamic programming (DP) matching as
a way to search for similar phrases. This system converts notes into character strings, runs DP matching using thestrings, and calculates the degree of disagreement between these strings. We then use these calculations as an index todetermine whether the strings resemble each other.
Keywords: automatic piano, knowledge database, computer music, DP matching

I. INTRODUCTION
The internet has significantly changed most people’s
environments. It is possible to search for and obtain
information about nearly any subject, and it is possible topurchase many things on the internet.
Music purchases are no exception. Previously, CDs
were purchased or rented from stores, but now they canbe purchased or rented from home. This is convenient for
consumers. However, even if the singer, a part of themusical phrase and the release date are known, a songcannot be accessed if one does not remember the nameof the song.
On the other hand, related to music performance, wehave developed pedal-powered equipment and typing
equipment. The problem of fast repetitive typing with thesame key and complaints about performance have beensolved, and a system in which piano playing takes place
automatically has been developed. That system can makeuse of data when you play piano with MIDI functions
and edited data of loudness, the length of the sound,
tempo, etc [1]. In addition, studies were conducted two
years ago at time of the development of an interactive
musical editing system. That system made it possible toperform efficient editing of large music data files. The
automatic piano performs the music of the large musicaldata files. Finding the desired song requires atime-consuming search for the correct musical data. So,
we thought it would be useful to have a music
information search engine. The system makes anefficient search for the desired song names and musicaldata, which can reduce the burden on the user.
In this research we developed a system that uses DP
matching to search the entire score to look for a match to©ICAROB 2014a given phrase. This system converts notes into character
strings and calculates the degree of disagreement
between these strings during searching. We use thesecalculations as an index to determine whether the strings
resemble each other.
In this paper, we describe the results of searching forsimilar phrases using DP matching.

Figure 1.1: View of the automatic piano

II. Musical Editing Support System
2.1 System Architecture
The structure of the system is shown in Figure 2.1.
The user edits music via the user’s interface on a

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Figure 2.1: Structure of the editing
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computer display. The user can also access a databasethat has musical grammar, the user’s preferences, and soon. As a result, editorial work is reduced and efficient
editing becomes possible.

III. Phrase and music search engine

2.2 Performance information
The score is read by the scanner. Scanned data isconverted to MusicXML format by the KAWAI Score
Maker FX5 program. A database of performance
information containing notes, scales, and symbols ofmusic data is created based on the MusicXML data. The
created database will be subject to search.
2.2.1 Format of Performance Information
The parameters of performance information are
shown in Tables 1 and 2. The automatic piano that wehave 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 to divide themusic 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”.

3.1 System summary
We developed a phrase and music search engine. It
searches for one tune from among various tunes using
one phrase of voice data. The voice is first inputted; thenthe interval between the sounds of the inputted voice datais converted back to notes according to the tempo of themusic. The system then searches for sequence similarity
to the phrase rhythm and notes.
In addition, the pitch of the voice input data wasanalyzed and pitch changes were calculated. Pitchchanges are indicated by the ratio of the pitch of a soundto the pitch of the previous one. By using the change inpitch, we hoped to improve the retrieval accuracy of oursystem.
3.3 Processing that inputs voice
Voice is input in the shape of waves, as shown in
Figure 3.1. The vertical axis of Figure 3.1 shows
loudness, and the horizontal axis shows time. By settinga threshold on the vertical axis, the presence of voice canbe recognized. The interval of this sound is measured as
the time. In other words, as shown in Figure 3.1, theinterval of sound is the period between the initial
iterative peak and the next sound iterative peak.
Interval of soundTable 2.1: Format of the note information

Figure 3.1: Result of sound input
3.4 Converting notes
Measurements of the interval of sound are converted
to notes based on the velocity symbol, which appears at
the top of the score and represents the number of beats
per minute. For example, the indication in Figure 3.2means 120 quarter note beats per minute. In other words,
the length of a quarter note would be 0.5 seconds. Theinterval of sound is converted into note by using thelength of a quarter note.

Table 2.2: Format of the pedal information=120

Velocity symbol

Figure 3.2: Velocity symbol of score
3.5 Frequency analysis of voice data
Pitch is determined by the frequency of the sound,and pitch changes can be determined by the transition of
frequency. The voice data frequency analysis isperformed using the FFT (fast Fourier transform)
algorithm.

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Frequency analysis of the voice waveform isconducted for each fixed intervals. And, in one interval,
the highest power spectrum frequency is recorded as themain frequency.

IV. 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. In the present study,
we used DP matching to search for similar phrases.
4.1 DP matching
DP matching is a technique used widely in the fieldof speech recognition, bioinformatics and so on. It cancalculate the similarity between two words that are
different from each other in a number of characters.
In Figure 4.1, the route of minimum cost in eachpoint is taken, and the route with the lowest cost is
assumed finally to be the optimal path. The cost at thattime is defined as the distance between patterns. In thissystem, 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, thenit is increased by 1. If it moves to the uppermost level on
the right, then it does not increase. Also, if the characters
do not correspond in each point, then the cost isincreased by 5.

Figure 4.2: Patterning of notes g (i  1, j )
g (i, j )  min  g (i, j )   d (i, j ) g (i, j  1)
Figure 4.3: Patterning of frequency
Figure 4.1: DP matching
4.2 Searching with DP matching
In this section we describe a method of searchingwith DP matching. We had to convert a musical score
into character strings (a Note Pattern) before searchingfor similar phrases. This process is explained below.
4.2.1 Patterning
Each note is expressed by three hexadecimal
numbers and patterned using only the digits in the firsttwo decimal places, as shown in Figure 4.2 However, themeanings of these two digits vary. Therefore, to avoid
confusion, the second digit is distinguished by applying
the letter of the alphabet (V from G).
In addition, the sound frequency obtained in Section
3.5 is made into a pattern. First, the system determines
the frequency ratio of two successive sounds, and thenthe ratio of the frequency is patterned by size, as shownin Figure 4.3©ICAROB 2014

4.2.2 The method of searching
A flow chart of a search for a music phrase similar to
an input phrase is shown in Figure 4.4.
First, the data of patterned notes column is matched.
The phrases with little distance between patterns aresearched. Music containing those phrases becomes asearch result candidate.
Next, the data of patterned sound frequency ratio
are matched. Candidates of the search results are
narrowed by matching of the frequency ratio.
The search is performed song by song through the
database. When the search engine has processed one
song, it then proceeds to the next song, continuing tolook for the phrase. When the song changes, searchengine is change the tempo of the song. And, searchphrase is patterned again. After pattern matching in eachof two, search result of the lowest distance between
patterns is outputted.

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VI. CONCLUSIONStart

We designed methods of searching for similarphrases using DP matching and combined thesefunctions into a single system.
In the similar phrase search, the system was able tofind similar phrases using DP matching in a short time,and it was even possible to find phrases whose
resemblance might not be immediately apparent.
In the phrase and music search engine, we developeda system to search for a tune by inputting the data of avoice performing the tune. The interval between thesounds of the inputted voice data is converted to a note
according to the tempo of the music. Using thistechnique, we can search for sequence similarity to aphrase’s rhythm and notes. In addition, the search using
the patterns of frequency ratio became possible. The
search using the patterns of frequency ratio could
improve the retrieval accuracy.
In this study, we were able to perform similar phrase
searches and searching for a tune by voice. In our future
research we will perform evaluations with different
pieces of music and will evaluate the existing system.

Voice
input
Frequency and notes column acquisition
Patterning
Reading the performance information
Search phrase
No

Search the last song finished?
Yes

Candidate of search result selects
Reading performance information of selected music
Search phrase
No

Search the last song finished?
Yes

Outputs the result of low distance between patterns
End

Figure 4.4: The flow of the similar phrase search

V. Retrieval experiment and consideration
We searched for music using the phrase and music
search engine. The phrase beginning in bar 5 of
"AGEHACYOU" is sung. The music is searched basedon the inputted voice data.
First, the data of the patterned notes column ismatched. The system output phrases with a distance of
30 or less. Next, the data of the patterned output phrase
frequency ratio is matched. Table 5.1&5.2 summarizes
the results of the matching.
A glance at the average distance between patterns(Total) of Table 5.1 will reveal that the phrase of"AGEHACYOU" is lower than the phrase of the othermusic. And a glance at the minimum distance between
patterns (Total) of Table 5.2 will reveal that the phrase of"AGEHACYOU" is lower than the phrase of the othermusic.
Therefore, the input voice is most similar to the phrase of"AGEHA".

REFERENCES[1] Hayashi, E. et al, “Behavior of piano-action in a grand piano.I”,
Journal of acoustical Society of America, Vol.105, pp.3534-3544,
1999.
[2] Hayashi, E., et al, Interactive musical editing system for supporting
human errors and offering personal preferences for an automatic
piano, Proc. of the 7th International Symposium on Artificial Lifeand Robotics, Vol. 2, pp. 513-516, 2002.
[3] Hikisaka, Y., Hayashi, E., et al, Interactive musical editing system
for supporting human error and offering personal preferences for an
automatic piano –Method of searching for similar phrases with DP
matching and inferring performance expression-, Proc. of the 12th
International Symposium on Artificial Life and Robotics, GS4-3,
2007.

Table 5.1: Search result (average)
Name of Music

Distance between
patterns

Distance between
patterns

Distance between
patterns(Note column)(Frequency)(Total)AGEHACYOU

28.43

57.4

85.83

TENTAIKANSOKU

29.5

72

101.5

Table 5.2: Search result (minimum)
Name of Music

Distance between
patterns

Distance between
patterns

Distance between
patterns(Note column)(Frequency)(Total)AGEHACYOU

26

47

73

TENTAIKANSOKU

29

70

99

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Japan, January 11-13, 2014

Human Recognition based on Gait Features and Genetic Programming
Dipak Gaire Sharma, Ivan Tanev and Kasunori Shimohara
Graduate School of Science and Engineering, Doshisha University
Kyotanabe City, Kyoto, 610-0394, Japan
Telephone : +81-774-65-6200, Fax : +81-774-65-6800{sharma2013@sil.doshisha.ac.jp, itanev@mail.doshisha.ac.jp, kshimoha@mail.doshisha.ac.jp}

Abstract: Human walking has always been the curious field of research for different disciple of Social and Information
Science. The study of human walk or human gait in association with different behaviors and emotions has not only
fascinated social science researchers, but its uniqueness has also attracted many computer scientists to work in thisarena for the quest of uncovering reliable mechanisms of biometric identification. In this research, we used a novel
method for human identification based on inferring the relationship between the human gait features via genetic
programming. Moreover, we focus on generating a unique numerical signature that is similar for different locomotion
gaits of a particular individual but different across different individuals
Keywords: Human Identification, Biometrics, Genetic Programming, Human Gaits, Nature Inspired Computing

I.

This paper is the finding in a research of gait feature
recognition where genetic programming (GP) is used togenerate unique signature among different individuals.
Although there are variety of genetic programming
approaches available, in this case XML-based Genetic
Programming [5] approach was deployed. The study onhuman gait analysis using a genetic programming
approach is relatively an understudied problem.
Therefore, our study will also be the milestone towards
the analysis of efficiency and reliability of GP in case ofhuman gait analysis and human recognition.

INTRODUCTION

Analysis of human behavior involves different
methods to identify or recognize human. It is done by
the visual analysis where different characteristics arestudied to authenticate the people [1]. This approach is
gaining substantial amount of interest because it is also
driven by the task of automated human identification
which is very crucial in different fields that are sensitive
to the issues of security. There are different methods
that are implemented for this task, and among them gait
recognition is a new dimension unlike gaitclassification. Gait is a rhythmic physical movement ofbody parts. As analysis of gait does not require a person
to face the sensors directly, it can be done without any
notice by the subject, because of this feature, it has been
the important dimension of research in computer vision
among different security agencies throughout the world.
Human locomotion is said to be unique. It is uniquein a sense that, human being holds distinctive ability todetermine their close friend and family easily. It wasproven by Johannson[2] in 1973 by the experiment
where he attached light markers to the subject, andpeople were asked to identify the subject based on those
attached markers. It is also unique because many animal
behavior such as walking or swimming require rhythmic
contraction of muscles which is generated by the signals
from the neurons called Central Pattern Generator(CPG) [3] [4]. These CPG are responsible for generating
rhythm and shapes the pattern of the motor neurons.
Basically each and every individual have unique
walking pattern, therefore, it can be considered as oneof the most reliable source of human recognition.

©ICAROB 2014

II. PREVIOUS WORK
Human gaits recognition and human recognition by
motion is one of the active topics in the field of
Computer Research. Many existing approaches focus on
analyzing of motion using the video frame and using thedifferent processing techniques [6] [7] [8]. We can find vast
research done in this field using different dimensions.
However, some are focused on hand limb gait features
while other are based on merging the features and mostuse classifiers from the gait classification record such as
Back Propagation Neural Network Algorithm, Fisher
Distance, Support Vector Machine and K-Nearest
Neighbor. If we analyze different research that has been
done so far, we could find that most of the research arefocused on the classification of gait features but none ofthese research focuses of finding the relationshipbetween the several gaits of individuals. Therefore, thisresearch is concerned on generating a unique numericalsignature which is similar for different motion gaits of aparticular individual but different across different-114-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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(Eq.1)(Eq.2)

III. COMPUTATIONAL APPROACH(Eq.3)

This study involves different computational steps
and methodologies that were implemented to achieve
the experimental result of this research, these steps
includes;…
(Eq.4)

1. Data Acquisition
The initial step involves the technique of identifying
a person while in motion and generates the skeleton
frame from the captured data. For this task, a device
from Microsoft called Kinect™ for Windows® is used.
This device has the ability to detect human and generateskeleton with 20 different joints. To interface thisdevice, we developed an application that could track aperson. A sample snapshot of this application is shownin Figure 1.

3. Feature Extraction
The third step is basically concerned with featurefinding. These features are calculated from the dataset
which are previously obtained after performing
mathematical model. For this task we made some basic
calculation and extracted the values of major features ofhuman gaits. These include the angle between joints, theangular displacement, velocity, distance between joints,
rate of change in distance, etc. These features arecalculated for few selected joints from arms, legs, hipand shoulder. As the features are calculated frame byframe, they were reduced to a single value by
considering the average of each feature. As a whole,
total of 30 features of dataset were evaluated and stored
in database to use as an offline source. We used thesefeatures as the terminal set in genetic programming.
4. Genetic Programming
The fourth step involves implementation of genetic
programming framework. Since our problem isconcerned with finding relationship between humangaits which is not known in advance as well as does notpossess any predefined method of performing theoperation, the use of genetic programming is the best
technique to implement for this task. For the purpose ofevolving the association between gaits feature, we used
our XML-based Genetic Programming framework [5](XGP). Beside this, the communication with different
sub-systems and calculation of fitness value from fitness
evaluator is also performed in this phase. A sample
snapshot of the XGP applied for the recognition ofhuman gaits is shown in Figure 2. The main parametersof XGP are show in Table 1.

Fig.1: Application Interface for analyzing the gaits to
identify the major joints that are used to determine thefeatures
2. Smoothing Parameters
The next step comprises of method to generate thenumerical dataset for the skeleton previously generated
where joint coordinates are accessed from the device
and are stored in a data file. Since data from theKinect™ are not consistent for our work, the task ofsmoothing those dataset are required, which is doneusing the mathematical model known as sliding window
average method, as shown in Equations 1, 2, 3 and 4.
This method is important because it help to exclude theextreme parameters and helps to increase the probabilityof minimizing noise level in our dataset.

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IV. EXPERIMENTAL RESULTS
We implemented the system with Intel Core 2 Quad
processors each with 2.5GHz speed, and 2GByte of-115-

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physical memory. We acquired the data about thefeature of human gaits in the Socio Informatics
Laboratory of Doshisha University. Data were collected
for one individual at the initial experimental test, where
the individual was asked to make three different gait
movements starting from (i) normal walk, (ii) slow walkand (iii) fast walk, respectively. The motion detection
sensor was placed perpendicular to the walking
direction at the distance of 3 to 4 meters.
The experimental results of the fitness convergenceof 20 independent runs of XGP are shown in Figure 3.
Table 2 shows the values of the signatures of the threehuman gaits, obtained from a sample best-of-run genetic
program. Notice that despite the fact that these threegaits have different values of their respective features;the values of the tree signatures are very close. The
obtained fitness value is very small too, and it is equal
to 2.

Table 1: Main Parameters of XGPParameter

Value

Terminal Set(i) Variables v_0,v_1,v_2,......,v_30
representing the features of thehuman gait, and (ii) a random
integer constant within the range[0..10]{+, -, *, /}Function Set
Population
Size
Selection
Ratio
Mutation
Rate
Selection
MethodFitness Value

Bloat control
Termination
Criteria

100 genetic programs
10%2%
Binary Tournament Selection
Quadratic deviation of threedifferent gaits scaled up 10000
times. Lower fitness values
correspond to better solutions
Parsimony pressure(Fitness value<=2) or(# Generations = 100)

100000Fitness Value

10000
1000
100
10
1
0.1

No. of Generations

Fig.3: Fitness convergence characteristics of 20
independent runs of XGP. The dashed line represents
the convergence of the average fitness value.

Fig.2: XGP applied for recognition of human gaits: GP
Manager (top) manages the population of genetic
programs, and the fitness evaluation subsystem (bottom),which calculates the signatures for three gaits of thesame person, and measures the quadratic deviation ofthese three signatures.

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1 3 5 7 9 11 13 15 17 19 21 23 25

Table 2: The values of signatures of three human gaits,
obtained from sample best of run genetic program(fitness value=2).
Gait
Normal Walk
Slow Walk
Fast Walk-116-

Value of the Signature
17.0000869346814
17.000062104833
16.9998314813823

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V. CONCLUSION
The characteristics shown in Fig.3 illustrate how thefitness improves as the evolution progresses; theimprovement is dramatic and the average fitness
decreases from the initial value of around 7000 toaround 8, which is almost 1000 times. The result isobtained relatively quickly within about 20 generations,which correspond to about 4 hours of runtime. Such an
impressive performance of XGP could be explained by
the fact that so far we tried to evolve very close
signatures of three different gaits of a single person, andwe have yet to evolve similarly close to each other threesignatures of the gait of another person. Moreover, thesignatures of the gait of both persons should be
distinguishably different. We anticipate that the additionof additional person(s) in the gait identification andclassification task would significantly increase thecomputational effort of XGP.
The future work includes experiments with multiple
individuals and researching on the methods to evolvethe distinguishable signatures of their respective gaits.

REFERENCES[1]L. Wang, W.M. Hu, and T.N. Tan, “Recent
Developments in Human Motion Analysis,” Pattern
Recognition, vol. 36[2]Johannson, G.: Visual Perception of Biological
Motion and A Model For Its Analysis. Perception andPsychophysics.
[3]Jacques Duysens, Henry W.A.A. Van de Crommert,
Neural control of locomotion; Part 1: The Central
Pattern Generator from cats to humans.
[4]Levitan, Kaczmarek, The Neuron Cell and Molecular
Biology,Third Edition, Oxford[5]I.Tanev and K.Shimohara, XML-based Genetic
Programming
Framework:
DesignPhilosophy,
Implementation, and Applications, Artificial Life andRobotics, Vol.15, No.4.
[6]Hu Ng, Hau-Lee Tong, Wooi-Haw Tan, Timothy-Van
Yap, Pei-Fen Chong, Junaida Abduallah. Human
Identification Based on Extracted Gait Feature[7]Liang Wang, Tieniu Tan, Huazhong Ning, andWeiming Hu.
Silhoutte Analysis-Based
Gait
Recognition for Human Identification.
[8]Nikolaos Gkalelis, Anastasios Tefas, Ioannis Pital.
Human Identification from Human Movements.
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An Action Selection Method Using Degree of Cooperation in a Multi-agent
SystemMasanori Kawamura, Kunikazu Kobayashi
Aichi Prefectural University
1522-3 Ibaragabasama, Nagakute, Aichi 480-1198, Japan.
Tel: :+81-561-64-1111; Fax: +81-561-64-1108
kobayashi@ist.aichi-pu.ac.jp

Abstract: In recent years, a concept of a dividual is proposed to interact properly with another person. To construct a model of the dividual, the degree of cooperation is assigned to the corresponding dividual. By introducing the degree of cooperation into multi-agent systems,
we evaluate what kind of changes appears in the agent behavior. In addition, we propose an action selection method by introducing thedegree of cooperation into the soft-max method in multi-agent systems. Using the proposed method, we confirm whether the cooperative
action is promoted or suppressed through computer simulations.

Keywords: Multi-agent system, Reinforcement leaning, Cooperative action, Dividual, Degree of cooperation,I.

INTRODUCTION

In the human society, a person act cooperatively by taking
some kinds of communication such as gesture, language,
and eye contact. In recent years, a concept of a dividual is
proposed by Hirano to interact properly with another person [1]. At present, by introducing the above concept intoa multi-agent system [2]–[4], we construct a model of thedividual to realize cooperative behavior.
In this paper, we treat a diﬀerence of how to interact withanother person, which characterizes the dividual model.
When a self-agent recognized the other agent, a dividualis formed in the self-agent. At the same time, the degree ofcooperation proposed in this paper is assigned to the corresponding dividual.
By introducing the measure into multi-agent systems, weevaluate what kind of changes appears in the agent behavior. In addition, in this paper, we propose an action selection method by introducing the degree of cooperation
into the soft-max method in multi-agent systems. Using
the proposed method, we confirm whether the cooperative
action is promoted or suppressed through computer simulations.
In the simulations, we prepare for two agents and one
goal, two agents act in a discrete grid world. The agents acquire a route to the goal using the Q-learning method [5],which is one of the representative reinforcement learning
methods [6] [7]. In the proposed method, the agent identifies the direction of the other agent such as the right, left,
top, or bottom direction. Then the agent reflects the degree of cooperation in its action probability. To evaluate
the degree of cooperation, we prepare two levels of the degree. The performance of the proposed method is evaluated by the distance between agents in every step at the last
episode. From the simulation results, when the degree ofcooperation of two agents is both high, the agents becameclose at few steps from the beginning and arrived at thegoal with keeping the distance. On the other hand, when

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the degree of the cooperation is low, the agents did not become close in all the steps and did not reach the goal. As aresult, it is revealed that the cooperative action is promoted
or suppressed by the degree of cooperation. In addition, it
is clarified that the agent gave priority to cooperation overarriving to a goal.
In section II, an action selection method using degree ofcooperation is proposed. In section III, the performance ofthe proposed method is evaluated through computer simulations. In section IV, we give a summary of this paper anddescribe future problems.

II.

PROPOSED METHODFirst of all, we define a problem. Then, we explain a concept of a dividual and the degree of cooperation. After that,
we proposed an action selection method using the degreeof cooperation.
1. PROBLEM DEFINITION
We treat a problem that plural agents arrive the same goalin a grid world in Fig.1.
2. DIVIDUAL
A person usually communicate by using how to contact
other person properly. For example, you face your boss, I
bet you communicate politely with him/her. But you face agood friend, I bet you communicate friendly with him/her.
Although we give a simple example, a person may change
a way to interact with other person according to sex, nationality, relationship with him/her. This concept is named
as a dividual by Hirano [1].
A diﬀerent dividual interacts with the other person, i.e.
families, friends, and acquaintances. The number of dividual therefore corresponds to that of other persons who interact with. When you communicate with person A and become A’s acquaintance, A’s dividual is constructed in yourself. Similary, when you communicate with person B andbecome B’s acquaintance, B’s dividual is also constructed

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the self-agent can select an action out of moving the right,
left, top, or bottom. The proposed method realizes that the
self-agent tends to select an action toward the other agent
as a0 out of available actions by reflecting the degree ofcooperation on the probability of selecting a0 .
In the proposed mehod, we use the soft-max action selection method. But, our approach apply to ϵ-greedy method.
The proposed action selection method is defined as Eq.(1).
exp (Q(s, a) g(c)),· · · · · · · · · · · · · · · (1)b∈A exp (Q(s, b) g(c))π(a|s) = ∑Fig. 1.

where A is a set of available actions. The degree of cooperation is introduced as a function g(c). In proposed method,g(c) is defined as Eq.(2).
{2c (a = ao )g(c) =.· · · · · · · · · · · · · · · · · · ·(2)1 (otherwise)

An example of field.

in yourself. It is suggested that a set of your dividuals may
characterize a human personality.
In this paper, when a dividual is created, its degree of cooperation is defined so as to cooperate with other person.
3. DEGREE OF COOPERATIONIn this paper, the degree of cooperation c which corresponds to its dividual is defined. The c takes a scalar value
and fall within the range of 0 ≤ c ≤ 1. When a dividual
is firstly created, a social dividual is defined as a default
dividual and grown by interacting each other. The degreeof cooperation for the social dividual is defined as 0.5. If
c > 0.5 and c < 0.5, the degree of cooperation is regarded
as high and low cooperation, respectively. The high andlow degrees of cooperation promotes and suppress the cooperation.
4. ACTION SELECTION METHOD USING DEGREE OF COOPERATIONIn this section, we propose an action selection method
using the degree of cooperation in order to cooperate withothers. In the proposed method, we identify the directionof the other agent and realize the cooperation by reflecting the degree of cooperation toward its direction. This is
explained using Fig.2.

Fig. 2.

Relationship between two agents.

In Fig.2, there is a goal in the top direction and a cooperative partner in the right direction. Let us assume that©ICAROB 2014

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We assume that the degree of cooperation for the social dividual is set to 0.5. The value of function g(c) is calculated
as g(0.5) = 1 so that the probability for the social dividual does not change at all. This corresponds to not considering cooperation with a stranger. On the other hand,when the degree of cooperation is larger than 0.5, it becomes g(c) > 1 and the cooperation is promoted. Whenthe degree of cooperation is smaller than 0.5, it becomesg(c) < 1 and the cooperation is suppressed. Therefore,
function g(c) is set as Eq.(2).

III.

COMPUTER SIMULATIONS

The proposed method is evaluate using the 10 × 10 fieldin Fig.1. In the field, the black surrounding is wall, there
are two agents A1 and A2 and one goal G. The agents canselect an action out of moving the right, left, top, or bottom
and their aim is moving towards the goal.
The number of episodes is set as 10, 000 and the maximum number of steps is limited to 100. The performance isevaluated by the distance between two agents at every step.
In case of a cooperative agent, the degree of cooperation isset as 0.8 and in case of a non-cooperative agent, it is set
as 0.2. In this simulation, agents can only move four directions, i.e. up, down, right, and left. The minimum numbersof steps toward the goal for agents A1 and A2 are 8 and 11
steps, respectively.
Firstly, the transition of the distance between two agents
whose degrees of cooperation are both high is shown in
Fig.3. The image of an action sequence in the field is also
illustrated in Fig.4. In these figures, the degree of cooperation from A1 to A2 is denoted by C(A1 ，A2 ).
As a result, two agents approach each other and movetoward the goal while keeping close distance. Therefore,
it followed that they cooperated when each other’s degreeof cooperation was high. In addition, considered minimum
step of agent A2 was 11 steps, it is thought that they arrived
at a goal after they drop in a little by they give priority tocooperate.
Secondly, we show the transition of the distance between
two agents when the degree of cooperation from A1 to A2
is high but that from A2 to A1 is low in Fig.5. The image ofThe International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Fig. 3. Transition of distance between agents whenC(A1 ，A2 ) = 0.8 and C(A2 ，A1 ) = 0.8.

Fig. 7. Transition of distance between agents whenC(A1 ，A2 ) = 0.2 and C(A2 ，A1 ) = 0.8.

Fig. 4. An action image when both C(A1 ，A2 ) andC(A2 ，A1 ) are high.

Fig. 8. An action image when C(A1 ，A2 ) is low andC(A2 ，A1 ) is high.

Fig. 5. Transition of distance between agents whenC(A1 ，A2 ) = 0.8 and C(A2 ，A1 ) = 0.2.

Fig. 9. Transition of distance between agents whenC(A1 ，A2 ) = 0.2 and C(A2 ，A1 ) = 0.2.

Fig. 6. An action image when C(A1 ，A2 ) is high andC(A2 ，A1 ) is low.

Fig. 10. An action image when both C(A1 ，A2 ) andC(A2 ，A1 ) are low.

©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

an action sequence in the field is also illustrated in Fig.6.
As a result, the distance between agents does not decrease rapidly but become gradually close toward the goal.
Therefore, when C(A1 ，A2 ) is high and C(A2 ，A1 ) is low,
cooperative action is slightly suppressed. In addition, as
considering the number of steps for A2 , it takes 24 steps
until it arrives at the goal. This shows that A1 gives firstpriority to cooperate with A2 than to arrive at the goal andA2 gives first priority not to cooperate with A1 than to arrives at the goal.
Thirdly, we show the transition of the distance between
two agents when the degree of cooperation from A1 to A2
is low but that from A2 to A1 is high in Fig.7. The image ofan action sequence in the field is also illustrated in Fig.8.
As a result, the distance between agents does not also decrease rapidly but become gradually close toward the goal.
Therefore, when C(A1 ，A2 ) is low and C(A2 ，A1 ) is high,cooperative action is slightly suppressed. In addition, as
considering the number of steps for A2 , it takes 15 steps
until it arrives at the goal. This number of steps is a little
bit smaller than the above case, i.e. C(A1 ，A2 ) = 0.8 andC(A2 ，A1 ) = 0.2 because the goal position is closer to A1
than A2 . This shows that A2 gives first priority not to cooperate with A1 than to arrives at the goal and A1 gives firstpriority to cooperate with A2 than to arrive at the goal.
Finally, we show that the transition of the distance between two agents whose degrees of cooperation are both
low is shown in Fig.9. The image of an action sequence inthe field is also illustrated in Fig.10.
As a result, the distance between agents does not decrease at all. This shows that both agents give first priority
not to cooperate with each other than to arrives at the goal.

IV.

CONCLUSION

In this paper, we focused on the concept of the dividual andintroduced the degree of cooperation. Then, we proposed
the action selection method based on the soft-max method
using the degree of cooperation. Through computer simulations, it was verified that two agents with high degree ofcooperation approached each other and arrived at the goal.
On the other hand, it was clear that two agents with low
degree of cooperation did not approach each other and arrive at the goal neither. In addition, when one agent withhigh degree of cooperation and the other agent with low degree of cooperation, it was shown that the distance between
agents became gradually low and finally agents arrived at
the goal.

ACKNOWLEDGMENTS
This work was partly supported by Grant-in-Aid for Scientific Research (No.23500181) from JSPS, Japan.

REFERENCES[1]

Hirano K (2012), Who am I?: From individual
to dividual. Kodansha shinsho (in Japanese)©ICAROB 2014

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[2]

[3]

[4]
[5]
[6]
[7]

Stone P and Veloso M (2000), Multiagent Systems: A Survey from a Machine Learning Perspective. Autonomous Robots, 8:345–383
Ohuchi A, Yamamoto M, and Kawamura H(2003), Basics and Applications of Multi-agent
Systems. Corona Publishing (in Japanese)
Takadama K (2003), Multi-agent Learning.
Corona Publishing (in Japanese)
Watkins CJCH and Dayan P (1992), Q-learning.
Machine Learning, 8:279-292
Sutton RS and Barto AG (1998), Reinforcement
Learning: An introduction. MIT press
Kaelbling LP, Littman ML, and Moore AP(1996), Reinforcement Learning: A Survey.
Journal of Artificial Intelligence Research,
4:237–285

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Cooperative Action Acquisition Based on Intention Estimation Method in a
Multi-agent Reinforcement Learning System
Tatsuya Tsubakimoto, Kunikazu Kobayashi
Aichi Prefectural University
1522-3 Ibaragabasama, Nagakute, Aichi 480-1198, Japan.
Tel: :+81-561-64-1111; Fax: +81-561-64-1108
kobayashi@ist.aichi-pu.ac.jp

Abstract: In this paper, we propose a method that can acquire cooperative action to reach an appropriate goal without controlling reward
to realize cooperative action by designers. We assume every action of other agents as a process to achieve an ultimate goal and then treat
it unimportant. In order to confirm the eﬀectiveness of the proposed method, we carried out computer simulations. The simulation results
show that the proposed method is superior to a standard Q-learning method and a Q-learning method with cooperation in terms of thenumber of successful cooperation.

Keywords: Multi-agent system, Reinforcement learning, Cooperative action, Reward interpretation, Intention estimationI.

INTRODUCTION

Current machines such as robots and computers can only
be operated according to predesigned actions by human.
In other words, it is impossible for machines to think andact by itself like human. Machines have therefore low flexibility to cope with little environmental changes. To solve
the above problem, reinforcement learning has been proposed as a method for getting autonomous actions [1] [2].
In reinforcement learning, a learning agent like robots canperceive environmental information by itself and select anappropriate action. As a result, it can autonomously acquire an optimal action sequence through given reward or
punishment from an environment. Q-learning is one ofrepresentative reinforcement learning methods. Q-learning
is originally designed for a single-agent system but is recently introduced to a multi-agent system [3]–[5].
In this paper, our goal is the acquisition of cooperative action by Q-learning in multi-agent environment. Nagayuki et al. presented a policy estimation method which
can estimate the other’s action to be taken based on the observed information about the other’s action sequence [6].
The policy estimation method however is required to store
all the state and action sequences and then has state explosion problem.
In this paper, we assume every action of other agentsas a process to achieve an ultimate goal and then treat it
unimportant. The important thing is the intention of otheragents, namely their goals. Then, we try to acquire cooperative action by intention estimation. Consequently, we use
a small amount of storage capacity. We propose a methodthat can acquire cooperative action to reach an appropriate goal without controlling reward to realize cooperative
action by designers. To accomplish this, we introduce theconcept of “reward interpretation”. The reward interpretation is a method that can increase or decrease the reward given by an environment through the interpretationof agent itself and reflect it to Q-values. Then, we use two©ICAROB 2014

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important variables in this method. One is a goal priority.
This corresponds to an estimate of priority which goal theother agent reaches. The other is a goal value. This meansa value of each goal that the agent estimates. The system
flows is summarized as follows.
The agent calculates the goal priority at each step andthen updates goal value by using goal priority. When it
reaches a goal, it estimates the other’s intention using thegoal value corresponding to the goal. By repeating thisprocess, it gradually acquires cooperative action sequence.
In addition, we try to acquire an eﬀective policy adapted tothe environment in minimal learning steps. In order to confirm the eﬀectiveness of the proposed method, we carriedout computer simulations. In the simulations, we prepares
plural agents and plural goals, and aim to acquire the cooperative action. In the simulations, we compare three methods, namely the standard Q-learning method, a Q-learning
method designed to give reward only when all agent act
cooperatively, and the proposed method. The simulation
results show that the proposed method is superior to theother two methods in terms of the number of successful
cooperation.
In section II, a reinforcement learning method based on
intention estimation is proposed. In section III, the performance of the proposed method is evaluated through computer simulations. In section IV, we give a summary of thispaper and describe future problems.

II.

PROPOSED METHOD

In this section, we propose an intention estimation method.
An important concept in the proposed method is reward
interpretation. In the standard reinforcement learning suchas Q-learning, an agent directly receives rewards from anenvironment. An agent incorporating intention estimation,
however, receives rewards through this reward interpretation.

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

By interpreting rewards, the agent can easily acquire cooperative actions. The reward interpretation requires twovariables, i.e. a goal priority and a goal value. The goal priority denotes an estimate that the other agent prefers whichgoal. The goal value represents an estimated value that the
self-agent considers how much the goal has value.
1. Estimation of other agent’s Intention
In this paper, we propose two measures, i.e. a goal priority and a goal value. Firstly, a self-agent estimates a goal
priority. The goal priority GPt,g,k represents a measure thatthe self-agent predict for other agent k to arrive to goal g
at time t by observing its action. Note that there are plural
goals. As calculating GPt,g,k , the distance d and the angular θ between directions of agent a and goal g as shown in
Fig.1.

Fig. 1.
goal.

Positional relation between an agent and a

When the self-agent reaches goal g at time t, the reward
interpretation is conducted by the goal value defined inEq.(6) as soon as the self-agent receives a reward rg froman environment.
rt ← rt × 2 GVt,g .· · · · · · · · · · · · · · · · · · · · · · · · · · · · · (6)The agent updates the Q-values by interpreting the reward and acquires the cooperative action. GVt,g denotes agoal value for goal g at time t. If the self-agent assumes
that the goal value is worth at a maximum, i.e. GVt,g = 1,
the agent interprets the reward as double, i.e. 2rt . On theother hand, If the self-agent assumes that the goal value is
not worth at all, i.e. GVt,g = 0, the agent interprets the
reward as 0.0. If GVt,g = 0.5, the self-agent interprets the
reward as standard.
3. Proposed Algorithm
The proposed algorithm is described as follows.
( 1 ) Let the present time be t. The self-agent calculates GPt,g,k for all the combination of goal g andother agent k. Then it calculates GVt,g for all the
combination of g.
( 2 ) The self-agent determines action a according toit’s policy π and executes a.
( 3 ) The state st transits to the next state st+1 after executing a. If the self-agent reaches the goal, it getsa reward and then interprets it.
( 4 ) The self-agent updates Q(s, a) by the Q-learning
method. Return to the first step after t ← t + 1 until
a terminal condition is satisfied.

GPt,g,k is calculated by Eq.(1).
()
GPt,g,k = α Dirt,g,k + β Dist,g,k + Dirt,g,k Dist,g,kIII.

/ (α + β + 1) , · · · · · · · · · · · · · · · · · · · · · · · (1)1.

where α and β are weighting parameters and generally set
to 1.0. GPt,g,k has a normalized value as 0 ≤ GPt,g,k ≤ 1.
Dirt,g,k and Dist,g,k are defined by Eq.(2) and Eq.(3), respectively.

SIMULATION

Problem Setting• We prepare a two-dimensional grid field (20×20) with
two agents and two goals shown in Fig:2. The field issurrounded by the black wall. Two agents and two
goals place randomly.

Dirt,g,k = 0.5 (cos θ + 1), · · · · · · · · · · · · · · · · · · · · · · (2)Dist,g,k = d−1 , · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · (3)where Dirt,g,k and Dist,g,k have a normalized value as 0 ≤Dirt,g,k ≤ 1 and 0 ≤ Dist,g,k ≤ 1, respectively.
Secondly, the self-agent calculates a goal value. The
goal value GVt,g represents a measure that the self-agent
considers how much the goal has value. GVt,g is updatedby Eq.(4).
−−→GVt,g = (1 − η) GVt−1,g + η e(GPt,g ), · · · · · · · · · · · (4)−−→GPt,g = (GPt,g,1 , GPt,g,2 , · · · , GPt,g,n ),· · · · · · · · · (5)where η is a learning rate, e(·) is an evaluation function defined by a designer and n is the number of other agents.
−−→
GPt,g is a vector which contains all of goal values GPt,g,k
except for the self-agent. GVt,g has a normalized value as
0 ≤ GVt,g ≤ 1. Note that GVt,g reflects intention which
estimated by other agent’s action.
2. Reward Interpretation
As introducing the reward interpretation, the self-agent
can adjust a reward value given from an environment.

©ICAROB 2014

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Fig. 2.

A simulation field

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

• The agents have to haul a heavy box, which only one
agent cannot carry. In this simulation, the positions of
two boxes correspond to those of two goals. The aim
is for two agents to reach the same goal by acquiring
cooperative action.
• An agent can move up, down, left or right to an adjacent cell.
• One episode is defined that two agents are placed at
the initial position and reach the same goal.
• If two agents cannot reach the same goal until 300
steps, terminate the episode and start a new episode.
• If an agent reaches one of two goals, the agent stops
moving and learning despite whether cooperation succeeds or not. If both two agents reach the goal, an
episode terminates and the agents replace to their initial positions.
• One trial is defined for 10, 000 episodes.
• An agent gets 20 when it reaches a goal, −0.1 when
it bumps into a wall, and −0.01 otherwise as a reward
from an environment.
• An agent can completely observe the other agent’s position and action.
2. Parameter Setting
For Q-learning, a learning rate is 0.1, a discount rate is
0.9. A temperature parameter T is scheduled by the following equation.
T=

Fig. 3.

Simulation results

T max,
1 − exp{a(t + ϕ)}

where T max is the maximum temperature, a is a gain, t is atime (episode), and ϕ is an initial phase. In this simulation,
we set that T = 20, a = 0.01, ϕ = 500.
An agent needs information on goal positions for intention estimation method. An agent can get the information
only when it reach a goal and store it. At an initial state,
an agent cannot calculate a goal value because of no information on goal positions. An agent start calculating agoal value once it find a goal. An agent with the proposed method therefore behaves an agent with a normal
Q-learning until it find a goal.
A function e(·) set as an average function E[·] because
two agents reach the same goal. We set α = 1 and β = 1
for calculating a goal priority because distance and direction has same importance. Then we set learning rate η as
0.3.
3. Evaluation Method
The standard Q-learning and a Q-learning with cooperation are used for performance evaluation. The Q-learningwith cooperation is assumed that two agents get reward if
and only if they arrive at the same goal. In other words, it
is assumed that two agents do not get reward if they arriveat the diﬀerent goal.
The aim of this simulation is for two agents to reach thesame goal. Successful episode is assumed that two agents
reach same goal, we count it every 10 episodes. The maximum number of success is 10. We evaluate the successful
number of cooperation.
4. Simulation Results
We show simulation results for three methods, i.e. thestandard Q-learning, Q-learning with cooperation, and the©ICAROB 2014

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Fig. 4. A placement that two agents are hard tocooperate(case1)

Fig. 5. A placement that two agents are hard tocooperate(case2)

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

proposed method in Fig.3. This simulation aim to acquire
cooperative action in a multi-agent system. We conducted
the simulation for 20 times. Figure 3 shows averaged results for 20 trial.
5.

ACKNOWLEDGMENTS
This work was partly supported by Grant-in-Aid for Scientific Research (No.23500181) from JSPS, Japan.

Discussion

REFERENCES

As seen in Fig.3, there are no significant diﬀerence
among three methods at the beginning. The proposed
method and Q-learning with cooperation show a good performance after 600 episodes. This is because the standard Q-learning has no cooperative function. The proposed
method is slightly better than Q-learning with cooperation.
We found two types of initial placements for evoking uncooperative action in the proposed method. One is an initial placement which two goals are very close as shown in
Fig.4. Because an agent judge two goal values as almost
same, it tends to select the closest goal. The other is an initial placement which two agents and two goals are far away
as shown in Fig.5. An agent tends to reach the closest goal
because another goal is far away from it. An agent therefore cannot update Q-value adequately. It is shown that an
agent acquire cooperative action for an initial placement
which two distances between agents and between goals arealmost same as depicted in Fig.2.
In Q-learning with cooperation, a designer has to decideto give a reward whether cooperation succeeds or not at every step. In the proposed method, we do not need the above
design.
Furthermore, memory capacity required by the proposed
method is much smaller than that for the policy estimation
method. In this simulation, required memory is (400 × 4) +(1 × 2) = 1, 602 for the intention estimation method (proposed method) and (400 × 4 × 41 ) + (400 × 4) = 8, 000
for the policy estimation method. The diﬀerence becomesmuch significant as increasing the number of agents.

IV.

CONCLUSION

We have proposed the new method for autonomously acquiring cooperative action in a multi-agent system. At thesame time, the proposed method solved a memory problem
in the policy estimation method.
Besides, we compared three methods, i.e. the proposed
method, Q-learning, and Q-learning with cooperation ina multi-agent system in order to evaluate the proposed
method. The aim of this simulation is to acquiring cooperative action. We have shown that the proposed method canacquire cooperative action without changing reward by adesigner. Finally, we confirmed that the proposed method
is superior to the standard Q-learning and the Q-learning
method with cooperation.
In this paper, we assume that an environment can be fully
perceived by other agents. As future developments, we will
extend it to partially observable environment and the agent
cooperative even if the agent has limited sight. Furthermore, we try to treat more complicated environments.

©ICAROB 2014

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[1]
[2]

[3]

[4]

[5]
[6]

Sutton RS and Barto AG (1998), Reinforcement
learning: An introduction. MIT press
Kaelbling LP, Littman ML, and Moore AP(1996), Reinforcement Learning: A Survey.
Journal of Artificial Intelligence Research,
4:237–285
Stone P and Veloso M (2000), Multiagent Systems: A Survey from a Machine Learning Perspective. Autonomous Robots, 8:345–383
Ohuchi A, Yamamoto M, and Kawamura H(2003), Basics and Applications of Multi-agent
Systems. Corona Publishing (in Japanese)
Takadama K (2003), Multi-agent Learning.
Corona Publishing (in Japanese)
Nagayuki Y and Ito M (2003), A Reinforcement Learning Method with the Inference of
the Other Agent’s Policy for 2-Player Stochastic
Games. IEICE Transactions on Information and
Systems, J86-D-I:821–829 (in Japanese)

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Integrating the Event Generation Mechanism in the Propp-based Story
Generation Mechanism into the Integrated Narrative Generation System
Shohei Imabuchi, and Takashi Ogata
Iwate Prefectural University, Takizawa, Iwate, Japan
Tel : 019-694-2000
g231k005@s.iwate-pu.ac.jp, t-ogata@iwate-pu.ac.jp
Abstract: In the Propp-based story generation mechanisms and the integrated narrative generation system which wehave been developing, the former functions as a module in the latter. These systems have used respective event
generation mechanisms to generated events which are the most important units in a narrative structural representation.
A common event generation mechanism needs to be used in these two systems towards the complete blending. This
paper will present the first tentative attempt as a prototype to be revised in the future.
Keywords: narrative generation; story generation; integrated narrative generation system; Propp-based story generation mechanism; event generation mechanism.

Propp’s theory. The event generation mechanism in the
Propp-based mechanism, however, has not beenconnected fully organically to the integrated narrative
generation system. An event means one of the mostfundamental elements in each of the narrative structures
to be generated by the Propp-based mechanism and theintegrated narrative generation system.
The event description forms in the two systems arebasically same, but mechanisms for the respective event
generation mechanisms are not unified formally since thePropp-based event generation mechanism has somespecial techniques to generate stories with the style of a
Russian folk tales and is comparatively simplified. The
Propp-based event generation mechanism, for example,
has a function to set seven types of characters including
hero, villain, victim, helper, dispatcher, donor and false
hero in the Russian fairy tales into an agent in an event,while the event generation mechanism in the integrated
system does not have the function. This paper considers
the two event generation mechanisms in detail tosynthesize them.
Gervás [7] describes that many of story generation
systems using Propp’s theory aim at general story
generation mechanisms beyond the description of Proppeven though the most important and essential part in story
generation study using Propp’s theory should be
conducted on the basis of the valuable insight inaccordance with the corpus of Propp. We have also been
insisting that most of the systems using Propp’ theory
focus on only the element of the “function” to bementioned below, but the theory includes many
productive and theoretical ideas to contribute to story
generation in various sides. One of our goals of thePropp-based
story
generation
mechanismisimplementing a story generation system introducing avariety of elements of Propp’s theory as comprehensively
as possible, while another goal is integrating this special

I. INTRODUCTION
One of the current major goals in our study isdeveloping an integrated narrative generation system
based on artificial intelligence, cognitive science andlitery theories. The system aims to organically combine avariety of mechanisms or modules grounded on ourprevious fundamental and individual research results into
one organic mechanism. We have described its ultimate
philosophy and overall design [1], and introduced thecurrent status of its actual implementation [2]. A
significant characteristic of this system’s research is thepoint that it adopts an interdisciplinary principle withsuch literary theories and narratology as Genette [3],Jauss [3] and inter-textuality. Propp’s theory on“morphology of the folktales” [4] to be described in thispaper is also equal to one of the literary theories relevant
to some significant functions for the narrative generation.
We have been considering the following two types ofapproaches for the literary theory by Propp: the first isdeveloping a story generation mechasnism using Propp’s
theory as comprehensively as possible (“Propp-based
story generation mechanism”) and the second approach is
its introduction as one of the modules into the integrated
narrative generation system [5].
As described above, the Propp-based story generation
mechanism can function independently to generatestories with mainly the structural style of a Russian fairy
tale which is the material of Propp’s theory. On the other
hand, we have been addressing the issue of its use as one
module of the integrated narrative generation system [6].
For example, we have introduced the Propp-based
mechanism into the integrated system as a set oftechniques to generate macro structures of stories, anddefined a story content grammar relating to theconceptual dictionaries based on our original
interpretation and formalization of an essential part of©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

approach into the framework of a more generalizedarchitecture of the integrated narrative generation system.
We simultaneously pursue specialty and generality.

structure and a case structure of an event within the story
structure. The story generation mechanism generates
story structures having a sequence of events according tostructural operation techniques using the dictionaries andthe other narrative knowledge. We call a procedure for
expanding the tree structure of a story using the story
knowledge base, which stores concrete narrativefragments or structures corresponded to the material for
narrative generation for such various types of narrative
relations as scripts and causal relations, a narrativetechnique for story generation or directly a story
technique. The input to a story technique is an event, thewhole of a story structure or a sub-structure in a story
structure and the output is chiefly the expanded story
structure that is one of the possible various structures.
They can treat from micro story structures to macro ones.

II. AN OVERVIEW OF THE INTEGRATEDNARRATIVE GENERATION SYSTEM
The integrated narrative generation system consists ofthe following three types of generation phases: story,
discourse and expression. The system architecture, on theother hand, has the following parts from the type ofprocessing: narrative techniques, knowledge bases, andconceptual dictionaries. For the conceptual dictionaries[8], we have been developing systems for verb concepts,
noun concepts, adjective concepts and adjective verb
concepts with the respective hierarchical structures fromhigher concepts to lower concepts according to a “is-a”
relation. We differentiate a word’s meaning or conceptfrom the word itself as the fundamental policy ofdeveloping the conceptual dictionaries. In particular, one
or more meanings related to a noun are described in thenoun conceptual dictionaries as one or more noun
concepts, and the word to be used normally and thenotations for these concepts are described in anotherlinguistic dictionary. The noun conceptual dictionary
contains 115765 terminal concepts and 5808 intermediateconcepts in the current version. The verb conceptual
dictionary similarly has 12174 terminal concepts and 36
intermediate concepts. A terminal verb concept in thehierarchical structure has the following information: abasic sentence pattern for natural language generation,
one or more case structures and constraints for each of
the cases. A case structure defines several cases for nouns
required in the verb. In addition, knowledge for managing
relations among events and mutual relationships between
events & states are composed on the basis of theconceptual dictionaries for verb and noun concepts. The
input information by a user to the first story generation
phase is several parameters including length, unreality
and repetiton. The system selects one or more adequate
story techniques according to the parameters in order toexpand a story tree structure.
Although the Propp-based story generation
mechanism can be used as an independent story
generation system, its integration into the story
generation phase in the integrated narrative generation
system contributes to increase the diversity and flexibility
in story generation. A story is described, in the story
generation phase, as a conceptual representation form
including a temporal sequence of events at the bottom
level in a hierarchical tree structure with several types ofrelations as the intermediate nodes. Each of the events is
described as a specific case structure which has a verb
concept and the necessary instantiated noun concepts
linked to the conceptual dictionaries. Fig. 1 shows a story©ICAROB 2014

(event Steal(type action)(ID 1)(time (time1 time2))(agent age%Man#1)(counter-agent nil)(location loc%Forest#1)(object obj%Apple#1)(instrument nil)(from age%Boy#1)(to nil))

Conceptual representation of an event“age%Man#1 steals obj%Apple#1 from
age%Boy#1 at loc%Forest#1.”

Relation

E1(t1→t2)

t1

Event

E2
(t2→t3)

t2

t3

State

E3
(t3→t4)t4Conceptual structure of a story

Fig.1. Structures of a story and an event

III. PROPP-BASED STORY GENERATIONSYSTEM
1. The overview
Propp’s theory proposed the concept of a “function”,
which means an action seen from the result and theprinciple of the sequential arrangement of thirty one“functions” as the most important theoretical idea. The“function” of “villainy”, for example, means that acharacter causes the damage of a different character
through a concrete action. However, there is a variety of
specific actions to actualize it since the “function” meansa collective definition to abstractly define many concrete
and real actions. We call more specified actions for
actualizing each of the “functions” “sub-functions” on thebasic of hierarchical concept. Propp described a variety ofexamples corresponded to the level of sub-functions. The“Propp-based story content grammar” based on theframework of Propp’s theory with “functions” and subfunctions is the central part in our Propp-based story
generation mechanism.
We describe the Propp-based story generation
mechanism. A story as the output information isequivalent to a sequence of events bound hierarchicallyusing “story relations” including “causal relation”,“continuation relation”, etc. The form of an event as oneof the basic components in a story takes a frame-127-

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under “interdiction (γl)” as “If Baba Jaga comes, don’t
you say anything, and be silent.” There is a asset of “Pairof sub-functions” as another significant elements. Thiswas defined according to the description on mutual
relations between two sub-functions in Propp’s theory.

representation, which is formed by a verb concept and thecorresponding case elements for such noun concepts asagent, object and location. The verb concepts and noun
concepts are associated with each of the corresponding
elements in the conceptual dictionaries. The structure
generation mechanism and the event concept generation
mechanism are respectively corresponded to generative
mechanisms, while the Propp-based story content
grammar and the conceptual dictionaries are equivalent to
the knowledge parts to be referred by the generative
mechanisms. Since these generative parts and the parts tobe referred by them are mutually independent, theredundancy of processing lessens.

IV. COMBINING THE EVENT GENERATION MECHANISM WITH THE INTEGRATED NARRATIVE GENERATION SYSTEM
In the Propp-based story generation mechanism,
when the structure generation mechanism reaches to thelowest level in the story content grammar, the event
generation mechanism is executed to make the event
structure based on the format of the case structure
described in the story content grammar. On the other
hand, in a story generation mechanism in the integrated
narrative generation system, when a story techniquerefers to the corresponding unit in the story content
knowledge base, the generation mechanism is executed tomake the event structure based on the format of the casestructure described in the story content knowledge base.
The story knowledge base in the integrated narrative
generation system stores concrete knowledge for story
generation, in particular a variety of sequences of events
described in the form of case structure corresponded tothe types of relations such as “cause-effect” and “script”.
The story content grammar in the Propp-based story
generation mechanism will be also a part of the story
knowledge base in the future. Two types of event
generation mechanism have been independently prepared
for the Propp-based mechanism and the integrated systemsince both of the description forms are different eachother. To develop a common event generation mechanismis the goal for incorporating the Propp-based mechanism
into the integrated system completely. We will describe
previous four types of procedures according to thedifferent types of case structure’s description in the
Propp-based mechanism and the tentative revision in the
integrated narrative generation system.
When the generation of a story structure according to
the story content grammar in the Propp-based story
generation mechanism reaches to the level of event
generation, the event generation mechanism is executed
to make the case structure. The mechanism inserts
specified instances into each of the cases based on the
difference of the second term in each element of the casestructure. Fig.3 shows an original case structure and atransformed instance by the following techniques in theevent generation mechanism.
(1) When one of the seven types of roles of actors is
described in the second term of a case such as “!hero”,
the event generation mechanism sets the instance of theactor, we call “agent” in the system, based on thepredefined or inputted correspondence knowledge such

2. Propp-based Story Content Grammar
The story content grammar [5, 6] that is a new styleof the reorganization of Propp’s theory, which gradually
generates a macro story with the structure of the style of a
Russian fairy tale from the highest level to the lowest
level, consists of the following five hierarchical levels.
The actual description by Common Lisp is shown in Fig.
2. The elements including “reserve portion”, “beginning”,
etc. in the level 1 and 2 are defined based on thedescription of structures of Russian fairy tales in [4]. The
levels of “functions” and sub-functions were faithfully
defined from the description of the analysis of 100 tales.
The role of these hierarchical levels is to make thestructural part of a story which is a tree structure withrelations as the intermediate nodes. Events in the level 5,on the other hand, are generated by the event generation
mechanism associating with the above structure
generation mechanism. An event particularly forms acase structure including a verb concept and thecorresponding noun concepts as noted above. We havedefined by hand patterns of these case structures based on
examples corresponded to sub-functions in [4]. For
example, we have formally defined a case structure of“(interdict 1 (agent human) (object outgo@out)(to !hero))” based on an example in the sub-functions(setq *Propp-level1-list*‘ ((Russian folktale(00_Preliminary-part P-Problem P-Trial P-Solution))))(setq *Propp-level2-list*‘ ((P-Problem (Reserve-portion Beginning))(P-Trial (OR (Reserve-trial Battle-and-victory)(Reserve-trial Task-and-solution))) …))(setq *Propp-level3-list*‘ ((Reserve-portion (OR (01_Absentation 02_Interdiction
03_Violation) …))(setq *Propp-level4-list*‘ ((01_Absentation (OR bet-1_Going-out1 bet-2_Deathbet-3_Going-out2))(02_Interdiction (OR gam-1_Interdiction
gam-2_Command-or-proposal)) …))(setq *Propp-level5-list*‘ ((alp-1_Preliminary-part ((Preliminary-part (1))))(bet-1_Going-out1 ((Go-out (agent Parents)(object Business))))(bet-2_Death ((Dead (agent Parents)))) …))

Fig.2. A part of the description of the Propp-based
story content grammar©ICAROB 2014

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Case structure(tell(agent demon_and_ghost)(counter-agent !hero)(object “event 1”)(location !loc1))

knowledge base is a set with two or more events and theelements in the events need to have a mutual-dependence
relation. One of the issues is that the number in the
Propp-based mechanism is not many, while the number inthe story knowledge base will be very many.

Generated event(event tell(agent age%vampire#1) (2)(counter-agent age%Ivan#1) (1)(object (event soak
(3)(agent age%Ivan#1)(object obj%steam_bath#1”)))(location loc%house#1)) (4)V. CONCLUSION

Insertion of an instance or an event

Fig.3. The correspondence between a case structure
and the actgually generated event

This paper described both of the overviews of our
Propp-based story generation system and an integrated
narrative generation system, and presented ways forincorporating the Propp-based system in the level of
event generation mechanism into the integrated system
towards the complete blending. The basic policy was that
we incorporate the special mechanisms in the Proppbased system into more generalized mechanisms in the
integrated system, however a part of the mechanisms
such as actors’ roles in the Propp-based system will beconsidered to be arranged in the integrated system too.

as “Ivan: hero”, “Baba-Yaga: donar”, etc. For
incorporating it into the integrated narrative generation
system, when an agent as a Propp-based agent including
hero, heroin etc. appears in a story generation process, thesystem describes the label such as “hero” in the attribute
frame. An attribute frame is a data set associated with anoun concept and the noun concept is embodied byinserting instances or the other various labels into it.
(2) When the value of a case in an event is a name ofa noun concept in which a specified instance of an agent,
an object or a location should be inserted, the event
generation mechanism sets an instance made from a noun
concept in the noun conceptual dictionary selected within
the range of the constraint. The name of the noun concept
specifies the constraint in the current design. For example,
if the name is “demon_and_ghost”, the values to beinserted is selected in the range under the name in thenoun conceptual dictionary. On the other hand, when thename is described in the style such as “horse@beast”, itwill be used as the value directly.
(3) When an event concept itself should be the valueof a case (object case in particular) in an event recursively,
the event generation mechanism expands the description
such as “event 1” to a case structure different from thecase structure in which the expanded event is included.
The previous mechanism has used predefined sets of casestructures to be inserted in the position based on
examples described by Propp’s theory. The examples
have not three or more nests. The integrated narrative
generation system uses a recursive function for dealing
with this type of processing based on a predefined list ofnested events. To achieve more general recursive
processing will be a future issue.
(4) When one or more events have a mutualdependence relation, the mechanism processes a special
operation. In particular, when the value of the agent,
object or location in a case structure must be equal to thevalue of the other case structure for the consistency
among events, the mechanism uses a same value for theone or more elements and designates the range which thevalues can take in the noun conceptual dictionary. This isrelated to the “pairs of sub-functions”. The integrated
narrative generation system prepares a procedure for treat
this processing with reference to the story knowledge
base in section II. Each of many data stored in the story©ICAROB 2014

REFERENCES[1] Ogata T and Kanai A (2010), An Introduction to
Informatics of Narratology (in Japanese). Gakubunsha.
[2] Akimoto T and Ogata T (2012), Macro Structure
and Basic Methods in the Integrated Narrative
Generation System by Introducing Narratological
Knowledge. Proc. of the 11th IEEE International
Conference on Cognitive Informatics & Cognitive
Computing: 253-262.
[3] Akimoto T and Ogata T (2012), A NarratologicalApproach for Narrative Discourse: Implementation and
Evaluation of the System based on Genette and Jauss.
Proc. of the 34th Annual Confer-ence of the Cognitive
Science Society: 1272-1277.
[4] Propp V (1968), Morphology of the Folktale.
University of Texas Press.
[5] Imabuchi S and Ogata T (2012), A Story Generation
System based on Propp Theory: As a Mechanism in an
Integrated Narrative Generation System. Lecture Notes
in Artificial Intelligence, vol.7614: 312-321.
[6] Imabuchi S and Ogata T (2013), Methods for
Generalizing the Propp-based Story Generation
Mechanism.
LectureNotes
in
Computer
Science/Lecture Notes in Information Systems andApplications, incl. Internet/Web, and HCI, vol.8210:
333-344.
[7] Gervás P (2013), Propp’s Morphology of the Folk
Tale as a Grammar for Generation. Workshop on
Computational Models of Narrative, a satellite
workshop of CogSci 2013: The 35th meeting of the
Cognitive Science Society: 106-122.
[8] Oishi K, Kurisawa Y, Kamada M, Fukuda I,
Akimoto T and Ogata T (2012), Building Conceptual
Dictionary for Providing Common Knowledge in the
Integrated Narrative Generation System. Proc. of the34th Annual Meeting of the Cognitive Science Society:2126-2131.

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Japan, January 11-13, 2014

Relationship input object positionand optimal error diffusion coefficientsfor Kinoform using error diffusion method
Daisuke Kashima. Dept. of Electronics and Bioinformatics, Meiji Univ.
E-mail: ce11021@meiji.ac.jp
Ken-ichi Tanaka. Dept. of Network Design, Meiji Univ.
E-mail: tanaken@meiji.ac.jp
Abstract: In order to improve quality of reconstructed image from Kinoform, we investigated about separation object
and noise.
It’s considered that taking error diffusion method is good for separation object and noise from Kinoform. And it’sconsidered that optimal error diffusion coefficients by Genetic Algorithm (GA) exist. Now, first we searched
relationship input object position and optimal error diffusion coefficients. Second we derive relational expression withmultiple linear regression analysis. Third we perform estimating optimal error diffusion coefficients for various input
object position by derived relational expression. Therefore we report about efficacy of deriving relational expression of
optimal error diffusion coefficients and input object position for Kinoform using error diffusion method.
Keywords: Kinoform, Computer-Generated-Hologram (CGH), error diffusion method, GA, multiple linear regression
analysis.
calculated optimal error diffusion coefficients by GA.
We report efficacy of deriving relational expression of
optimal error diffusion coefficients and input object
position for Kinoform using error diffusion method.

I. INTRODUCTION
CGH is made in order to reconstruct three-dimensional
image of object or virtual abject having difficult in
irradiating a laser directly. It’s known that thereconstructed image from Kinoform is brighter than thereconstructed image from CGH [1-3]. In order topractical use Kinoform, it’s necessary to develop simple
calculation method that high-quality reconstructed
image is obtained.
In process of making Kinoform, factor to be negative
impact most in reconstructed image is quantization ofwave surface calculated for Hologram. In order tomitigating this impact, Kinoform was performed tocompose Kinoform with repetition algorithm [4] or
combination optimization problem [5]. However, thecomposition took a long time. In contrast, error
diffusion method can separate object and noise fromreconstructed image by one quantization. Therefore,
error diffusion method is regarded as a good method to
obtain high-quality reconstructed image by low cost
processing.
In error diffusion method, it’s considered that selectionof error diffusion coefficients can be optimized bysearching such as GA [6]. However, GA has a problem
to take a long time for processing.
In this paper, we derived relational expression by
searching relationship input object position and optimalerror diffusion coefficients with multiple linear
regression analysis. This analysis used data that is©ICAROB 2014

II. ERROR DIFFUSION METHODFOR KINOFORM
In this paper, we perform error diffusion method
expanded using complex to quantize complex value into
complex value.
(1) Scaling complex value
Before quantization of complex value, scaling is
performed as follows.
𝐹(𝑢, 𝑣)
𝐹1 (𝑢, 𝑣) =(1)
max |𝐹(𝑢, 𝑣)|(2) Quantization of complex value
Complex value is quantized as follows.
𝐹2 (𝑢, 𝑣) = exp{𝑗 arg{𝐹1′ (𝑢, 𝑣) − 𝐹1 (𝑢, 𝑣)}}
(2)
Quantization is performed by raster scanning.
(3) Calculation and diffusion of quantization error
Quantization error as 𝑠(𝑢, 𝑣) is generated.
𝑠(𝑢, 𝑣) = 𝐹2 (𝑢, 𝑣) − (𝐹1′ (𝑢, 𝑣) − 𝐹1 (𝑢, 𝑣))(3)This 𝑠(𝑢, 𝑣) is diffused to adjacent and not quantized
pixels with weight. The s (𝑢, 𝑣) is diffused to 4
directions such as Fig.1 Then, weight a, b, c, d are
regarded as complex and relationship as follows.
|𝑎| + |𝑏| + |𝑐| + |𝑑| = 1(4)
We make a direction of “𝑎” of Fig.1 agree with adirection of scanning.
The 𝐹1′ (𝑢, 𝑣) is quantized by diffusion of quantization
error as follows.

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𝐹1′ (𝑢 + 1, 𝑣) = 𝐹1′ (𝑢 + 1, 𝑣) + 𝑎 ∙ 𝑠(𝑢, 𝑣)(5)𝐹1′ (𝑢 + 1, 𝑣 + 1) = 𝐹1′ (𝑢 + 1, 𝑣 + 1) + 𝑏 ∙ 𝑠(𝑢, 𝑣) (6)𝐹1′ (𝑢, 𝑣 + 1) = 𝐹1′ (𝑢, 𝑣 + 1) + 𝑐 ∙ 𝑠(𝑢, 𝑣)(7)
𝐹1′ (𝑢 − 1, 𝑣 + 1) = 𝐹1′ (𝑢 − 1, 𝑣 + 1) + 𝑑 ∙ 𝑠(𝑢, 𝑣) (8)In these 4 expressions, they are forms by the procedure
language.

IV. SIMULATION RESULTIn this paper, we simulated reconstructing image from
Kinoform and evaluating quality of reconstructing
image. Now, quality is regarded as better if evaluation
value is lower. Then, we set following:
Kinoform length: 𝑁 = 512.
Input object form: A white square.
Input object length: 96.
Input object central position: (𝑖, 𝑗)
Evaluation form: Square
Evaluation length: 128
Evaluation central position: (𝑖, 𝑗)
Approximate order: n
Number of samples: p

𝑎

𝑑

𝑐

𝑏

Fig.1 Diffusion coefficients
I. Comparison by approximate order
We simulated reconstructing image with relational
expression from calculated optimal error diffusion
coefficients. Fig.2 (a)-(g) are shown as results for GA
and changing n from 1 to 7.
As shown in Fig.2, the reconstructed image using thegreater approximate order is nearly equal to thereconstructed image using GA. And as shown in Table1,
quality of reconstructed image using the greaterapproximate order tends to improve.

III. DERIVINGRELATIONAL EXPRESSION
We derive relational expressions of input object
position (𝑥, 𝑦) and diffusion coefficients 𝑎, 𝑏, 𝑐, 𝑑. It’s
considered that relational expressions are composed ofn-ordered approximation as follows.
𝑛𝑘𝑎 = ∑ ∑ 𝑎𝑘𝑟 𝑥 𝑘−𝑟 𝑦 𝑟(9)

𝑘=0 𝑟=0
𝑛
𝑘
𝑘−𝑟 𝑟

𝑦

(10)

𝑐 = ∑ ∑ 𝑐𝑘𝑟 𝑥 𝑘−𝑟 𝑦 𝑟(11)𝑏 = ∑ ∑ 𝑏𝑘𝑟 𝑥

II Comparison by number of samples
We simulated reconstructing image with relational
expression from 49 or 98 of samples as p. Samples areused to derive relational expression. Fig.3 (a),(b) are
shown as results of reconstructed image.
As shown in Fig.3, these don’t have many changes.
However, as shown in Table2, p has better 49 than 98.
Large number of samples isn’t better at all. It’s
considered that quality of reconstructed image is better
when relational expression is used for considered input
object position.

𝑘=0 𝑟=0𝑛𝑘

𝑘=0 𝑟=0𝑛𝑘𝑑 = ∑ ∑ 𝑑𝑘𝑟 𝑥 𝑘−𝑟 𝑦 𝑟(12)

𝑘=0 𝑟=0

However, input object central position 𝑥 and 𝑦 takevalues from -1/2 to 1/2. This is to retain generality whena number of pixels of Kinoform change. On a
𝑁 − pixel/side Kinoform, if input object central
position by pixels is regarded as (𝑖, 𝑗), 𝑥 and 𝑦 isderived as follows.
𝑖 − 𝑁/2𝑥=(13)
𝑁𝑗 − 𝑁/2𝑦=(14)𝑁Now, we derive coefficients of relational expressions
with multiple linear regression analysis. For that,
response variable and predictor variable is defined from
Eq. (9)-(12) as follows.
Response variable: Optimal error diffusion coefficients
which was calculated beforehand by GA.
Predictor variable: Variables with combination from
input object position(𝑥, 𝑦).

©ICAROB 2014

III Estimating for various input position
We simulated reconstructing image for various input
object position with relational expression from 49 or 98
of samples as p and 7 of approximate order as n. Fig.4(a)-(c) are shown as results of reconstructed image.
As shown in Fig.4, (b) is shown not to separate input
object and noise very well. On the other hand, (c) isshown to separate input object and noise a little. And
position of noise of (c) is similar to (a) than (b).

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Fig.3 Comparison of reconstructed image
by changing number of samples(i = 353, j = 243)
Table2 Comparison of evaluation value
by changing number of samples(i = 353, j = 243)(c) n=3(d) n=4(e) n=5(f) n=6

Number of samples Evaluation valuep=49
0.001576671
p=98
0.001767945(a) GA(g) n=7(h) GA

Fig.2 Comparison of reconstructed image
by changing approximate order(i = 353, j = 243)(b) p=49

Fig.4 Comparison of reconstructed image
by using estimated optimal error diffusion coefficients(i = 125, j = 160)

Table1 Comparison of evaluation value
by changing approximate order(i = 353, j = 243)

Table2 Comparison of evaluation value
by using estimated optimal error diffusion coefficients(i = 125, j = 160)

Deriving method Evaluation value
n=1
0.005529698
n=20.004612562
n=30.001690110
n=40.001600949
n=50.001808101
n=60.001615804
n=70.001576671
GA0.001471379©ICAROB 2014(c) p=98

Deriving method Evaluation valueGA0.001573104
p=49
0.033828547
p=98
0.002430123-132-

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V. CONCLUSION
In this paper, we performed to improve reconstructing
image by deriving relational expression of input object
position and optimal error diffusion coefficients from
Kinoform. As a result, we suggest follows:
1. Better quality of reconstructed image is obtained byrelational expression with the greater approximate
order.
2. It’s suitable for estimating by relational expression
with larger number of samples.
In order to improve quality of reconstructed image
from Kinoform, these have obviousness about efficacy
of deriving relational expression of input object positionand optimal error diffusion coefficients.
In this paper, only one input object is used. So we havea future task for various input object to confirm efficacy.

REFERENCES[1] S. Weissbach, F.
Wyrowski and O.
Bryngdahl :”Digital phase holograms: coding andquantization with an error diffusion concept”, Opt.
Comunn., 72, pp.37-41 (1989)[2] L. B. Lesen. P. M. Hirxch and Jr J. A. Jordan. :”The
Kinoform: A new wavefront binary phase only filters”,
Appl. Opt. 27, 18, pp.3785-3790 (1988)[3] F. Mok, H. K. Disp Liu and D. Psaltis :”Real-time
computer generated hologram by means of liquid crystal
television”, Opt. Lett., 11,11,pp748-750 (1986)[4] Hiroshi Akahori :”Detection of the Complex
Amplitude Distribution of a Kinoform Using an
Iterative Fourier-Transform Algorithm”, Journal ofTrans., IEICE.,J78-C-1, 9, pp.400-408 (1995)[5] Ken-ichi Tanaka and Yuta Shimizu :”Search of
Optimal Error Diffusion Coefficient for Kinoform”,
Journal of IIEEJ., Vol.53, No.3, pp.1-6 (1999)©ICAROB 2014

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Japan, January 11-13, 2014

Optimization of dither matrix by hybrid of
Genetic Algorithm and Simulated Annealing (P)Kohei Kato
Department of Electronics and Bioinformatics, Meiji University
ce21019@meiji.ac.jp
Ken-ichi Tanaka
Department of Network Design, Meiji University
tanaken@isc.meiji.ac.jp
Abstract: Halftoning is used by ink-jet printer, color copier and so on. Dither method is the method of halftoning. Bayer
method that is one of the dither methods is able to obtain a superior evaluation value compare with other dither
methods, though it is the decided matrix. Several investigations of Optimization of the dither matrix are reported.
However, there is no investigation from point of view of the combinational optimization problems. In this paper, wesearch the optimum dither matrix by Genetic Algorithm (GA) and Simulated Annealing (SA) and hybrid of GA andSA(GA/SA).We weigh conventional method against these methods.
Keywords: Dither matrix, Genetic Algorithm, Simulated annealing, Hybrid.

In this paper, the individual are dither matrix（4-4）
and（8-8）. Elements of individual are threshold levelof gray level from 0 to 255. The threshold level is as
below.
I[x][y]=(2*i+1)*8, I[x][y]=(2*i+1)*2
I: element of the matrix
i: numerical number from 0 to 16 or from 0 to 64.
The same number is not set in the same individual.
In this study, the algorithm is as below[step1] Initialization sets up the parameter such as
number of individual the number of generation,
crossover rate and mutation rate.
[step2] Initial individual is created by the number set
up. Initial individual of element is putted in
random[step3] Evaluation value of fitness to all individual iscalculated.
[step4] Selection: Selection is to decide theindividuals of the next generation by the method ofRoulette Tournament.
[step5] Crossover: Crossover is performed if uniform
random number is lower than crossover rate
[step6] Mutation: Mutation is performed if uniform
random number is lower than mutation rate. Twoelements of the individual which is selected at
random are exchanged.
[step7] It is repeat from step[3] to step[6] until thenumber of generation.

I. INTRODUCTION
Halftoning is used by ink-jet printer, color copierand so on. Dither method is the method of halftoning.
Bayer method that is one of the dither methods is able toobtain a superior evaluation value compare with otherdither methods, though it is the decided matrix. Several
investigations of Optimization of the dither matrix arereported. However, there is no investigation from pointof view of the combinational optimization problems. In
this paper, we search the optimum dither matrix by
Genetic Algorithm (GA) and Simulated Annealing (SA).
GA is superior to search for the state of global search.
SA is superior to search for the state of local search.
So we introduce hybrid of GA and SA (GA/SA) for
halftoning. By GA/SA, the optimum dither matrix is
obtained without converging in a local solution. In thispaper, we research dither matrix (4-4) and (8-8).

II. Genetic Algorithm（GA）
GA is the method of combinatorial optimization. GA
is the method of global search by individual group ofsolution candidate. Each individual is given fitness that
calculated by cost function. The individual which has
high fitness is selected. The individual which does nothave high fitness is went out. The selected individual is
performed crossover and mutation. This process is
continued by decided generation. In the end optimum
solution is the individual which has highest fitness.

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[step8] In the final generation, the individual which
has the highest fitness is the optimal solution. andoutput the evaluation value ,and dither matrix.
Ⅱ.ⅠCost function
Cost function E is the addition of cost E𝑚 and costE𝑐 . Cost E𝑚 is evaluated value of gray level. Cost E𝑐
is Evaluated value of contrast. Equation of cost function
E is as below.
E = 0.2 × Em + 0.8 × Ec
Fitness is given by
f = 1⁄𝐸
Ⅱ.Ⅱselection
In this paper, the method of Roulette tournament
selection is used. The method of Roulette selection is toselect the individual in probability P. P is defined by𝑓(𝐼𝑖 )
P= 𝑁∑𝑗=1 𝑓(𝑓𝑖 )[step3] solution of cost function E to all individual iscalculated.
[step4] Accordingtoacceptanceprobability
P ,whether a new matrix is accepted is decided. If∆E ≤ 0 is less than 0, a new matrix is accepted. If∆E ≥ 0 is higher than 0, P is given as below∆𝐸P=𝑇
If P is less than uniform random number, a new matrix
is accepted.
[step5] repeat from[2] to [4] enough and decrease
temperature. T is given by[step6] until T ≤ 𝑇𝑚𝑖𝑛 ,repeat from step[2] to step[5].
Ⅲ.Ⅰ Set up parameters
Parameters are as below
𝑇0 =0.15904, 𝑇𝑚𝑖𝑛 = 4 ∗ 10−6 (4-4)
𝑇0 =0.114719, 𝑇𝑚𝑖𝑛 = 4 ∗ 10−6 (8-8)
This value are given by
𝑇0 = ∆𝑓𝑚𝑖𝑛 + (∆𝑓𝑚𝑎𝑥 − 𝑓𝑚𝑖𝑛 )⁄10𝑓𝑚𝑖𝑛 and 𝑓𝑚𝑎𝑥 are determined by multiple experimentpreliminarily. In this preliminary experiment, thetemperature is divided temperature 1 degree into 32
parts from 1000 degree. Maintaining the constanttemperature, SA is performed. the initial temperature is
obtained by the result. A temperature reduction rate α is0.99.The termination condition is that the temperature islower than 𝑇𝑚𝑖𝑛 , or a new dither matrix is hardly
accepted.

The method of Tournament selection is as below.
1) select individual at random as many as the numberof tournament size2) select the individual that has the highest fitness.
In this paper, tournament size is 2.Roulette tournament
selection is the method of combining the two methods.
Ⅱ.ⅢCrossover
Perform crossover for the two individual which is
selected at random. In this paper, cycle crossover is used.
Cycle crossover: Select position X at random. The
element of individual A which of position is X is justinherited. Element of individual A same as theelement of individual B is inherited. This process repeat
until the position which has already been selected is
selected.
The elements that are not selected is inherited in theorder of appearance of elements of individual B.
Ⅱ.ⅣMutation
The elements of the individual which is selected at
random are exchanged in order to converge in a local
solution.

IV. GA/SA
The problem of GA and SA is as below
GA: non-good at local search. Around the optimal
solution, calculation cost for the improvement of thesolution rise so much.
SA: to set up initial temperature is very difficult. If
initial temperature is low, the optimum solution
converges in a local solution. If initial temperature ishigh, it takes a long time.
In this study, at first, the dither matrix is searched by
GA. In the next, the dither matrix that gotten by GA is
set to initial dither matrix of SA. The optimum
combination is searched by SA using the matrix. In thisway, the problem is expected to improve by hybrid of
GA/SA. In GA process, if a solution becomes theequilibrium, break off operation of GA. The problem of
GA is improved by this way. In SA process, the initial
temperature can be set low temperature, because it isbegun with optimum solution neighborhood. The

III. Simulated Annealing（SA）
This section, we describe SA. The algorithm of SA is
below[step1] initialization sets up parameter such as an
initial temperature 𝑇0 , and temperature 𝑇𝑚𝑖𝑛 ,temperature reduction rate α and initial dither
matrix which is decided at random.
[step2] A new matrix is generated how to exchange
two elements of the dither matrix.

©ICAROB 2014-135-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

optimum solution is obtained without converging in a
local solution by using hybrid.
Table 1 .the result by GA
crossover rate
mutation
rate

0.2
0.15
0.1
0.05
0.01

crossover rate
mutation
rate

0.2
0.15
0.1
0.05
0.01

0.7
101.721101.7392
101.7433
101.7775
101.82760.7
101.7457
101.7648
101.7624
101.7262
101.9554

4*4
0.75
0.8
101.7632 101.8071
101.8115 101.7428
101.7301 101.7555
101.7708 101.7575
101.7686 101.8071
8*8
0.75
0.8
101.6455 101.6949
101.7865 101.7078
101.7496 101.7684
101.7087 101.7228
102.2744 101.7651

0.9
101.7337
101.7034
101.7392
101.7326
101.7387

0.95
101.7603
101.6948
101.7136
101.7898
101.7760.85
101.6934
101.797101.6936
101.7403
101.9025

0.9
101.6934
101.797101.6936
101.7403
101.9025

0.95
101.7245
101.7226
101.6878
101.7577
102.128

Table.4 shows the result that is obtained by Bayer, GA,
SA and GA/SA. The result by GA/SA is better
evaluation value than bayer method, GA and SA （4-4）
and (8-8). Fig.4,5 show the image and dither matrix
obtained by GA/SA. By GA/SA, the processing time ismuch earlier because beginning with optimum solution
neighborhood is able to set low parameters, T and α.
This is because the problem of SA and GA are able tobe improved.

V. Simulation
Ⅴ.Ⅰ Conventional method
In this section, the result that is conventional method,
Bayer method, is shown. Fig.1 shows original image.
Fig.2,3 shows Bayer matrix and the image that is
obtained by Bayer method (4-4) and (8-8). Evaluation
value E is 101.8021(4-4) and 101.9177(8-8)
Ⅴ.Ⅱ GA
Table.1. shows the results obtained by GA.
The results are the average obtained by simulating
several times. According to parameters, the results isgreatly varies.

VI. CONCLUSION
The result of GA, SA and GA/SA is better
evaluation than Bayer method. The result obtained by
GA/SA is the best evaluation value. By using GA/SA,
the optimum solution is obtained in the parameters thatare not able to obtain it by only GA. So it is notnecessary to set up detailed parameters. The processing
time by using GA/SA is much earlier than GA and SA.
These show the effectiveness of GA/SA

Ⅴ.Ⅲ
SA
Table.2 shows the results obtained by SA. In (4-4), theresult is same as by GA. In (8-8), the result obtained by
SA is better value than GA. The result is improved byincreasing temperature reduction and the number ofannealing. In this paper, the optimum solution is
obtained in parameter that α is 0.99 and the number ofannealing is 2000.

𝑇0
0.15904
0.15904

Ⅴ.Ⅳ GA/SAWe set the parameters of GA that the best value isobtained by only GA. The initial temperature sets it that
acceptance probability of a minimum change for theworse solution becomes 90%. The temperature
reduction α is 0.9 and the number of annealing is 3000.
These parameters are obtained from a result of various
simulation. Table.3 shows the result.
Optimum
solution is obtained even if α is lowed until 0.9.
Ⅴ.Ⅴ Comparison of 3 methods©ICAROB 2014

0.85
101.6875
101.681101.7229
101.7548
101.8321

𝑇0
0.114719
0.114719-136-

Table 2. The result by SA
4*4
α
an
E
0.9
2000 101.681
0.992000 101.681
8*8
α
an
E
0.9
2000 101.6352
0.992000 101.5754

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Table 3. The result by GA/SA
4*4
crossover mutation
𝑇0
α
an
rate
rate
0.026775
0.9
2000
0.85
0.15
0.026775
0.99
2000
0.007508
0.9
2000
0.85
0.1
0.007508
0.99
2000
8*8
0.026775
0.9
3000
0.85
0.15
0.026775
0.99
2000
0.007508
0.9
3000
0.85
0.1
0.007508
0.99
2000

E
101.6585
101.6585
101.6585
101.6585
101.5678
101.5781
101.6071
101.5881

Table 4. Compares the methods
bayer
GA
SA
GA/SA
101.6585
E(4*4)
101.8021 101.681 101.681
E(8*8)
101.9177 101.6552 101.5754 101.5678
CPUTIM(4*4)
31 476755 4424220 1498078
CPUTIM(8*8)
37 1563145 16473800 2892149

Fig. 1 Original image

Fig. 2 Bayer method(4-4)

Fig. 3 Bayer method(8-8)

Fig. 4GA/SA(4-4)

Fig. 5GA/SA(8-8)©ICAROB 2014

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Table 5 Optimum dither matrix (4-4)
7 12 4 13
11 0 8 2
5 14 6 15
9 3 10 1
Table 6 Optimum dither matrix (8-8)
56 36 9 43 35 50 10 30
40 17 24 53 12 19 37 2
54 6 57 31 4 61 46 22
63 27 41 47 23 58 29 7
38 11 18 3 34 16 39 13
51 48 32 62 49 8 60 33
26 1 42 15 25 44 20 5
14 52 21 55 0 28 59 45

REFERENCES[1] Toshiharu Nagao”evaluation image processing“ publication shoukoudou Inc.(2002).
[2] Ken-ichi Tanaka."Color Image Halftoning Using
Simulated Annealing"publication the journal of theInstitute of Image Information and Television
Engineers. VOL.61,pp. 828-837( 2007)1[3] Yoshito Abe "A New Method of Designing a
Dither Matrix"IEICE Trans.Fundamentals,VOL.
E85-A ,NO.7, pp.1702-1709 (2002).
[4] Subtours in Complete Subtour Exchange
Crossover and the Behavior Property"Institute ofElectronics,Information,and
Comunication
Engineers,Vol. J81-D-I,No.2,pp213--218(1998)[5] Yutaka Shirai,Naofumi Matumoto"A Hybrid
Method for Block Layout Design based on
Metaheuristics" Japan Society of MechanicalEngineers,Vol. 63, No. 611, pp.2337--2344 (1997).

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Inverse Halftoning using Multi-Layer Feed-Forward Neural Network (P)Hiroki Hamashoji
Department of Electronics and Bioinformatics, Meiji University
ce21063@meiji.ac.jp
Ken-ichi Tanaka
Department of Network Design, Meiji University

Abstract: In this paper, we proposed novel inverse halftoning technique, which use Multi-Layer Feed-Forward Neural
Network (MLFFNN) and Gaussian filtering. This method reconstruct continuous tone image from any halftoning
methods. As a result, a high quality image can reconstructed from binary image compared with conventional method.
Especially the proposed method image quality is good both visually and Peak Signal-to-Noise Ratio (PSNR) value,compared with reconstructs inverse halftone images by Look-up table (LUT) based inverse halftoning.
Keywords: inverse halftoning, neural network, image processing, halftoning

We consider inverse halftoning as a nonlinear
problem. MLFFNN has function approximation ability.
Therefore we applied MLFFNN to inverse halftoning.
We used a low-path filter to remove a noise, because animage provided by MLFFNN has some noise.
The proposed MLFFNN inverse halftoning method
input 7x7 halftone image pixels to reconstruct center
pixels. We use Gaussian filtering to the image processedby MLFFNN. We use back propagation algorithm fortraining MLFFNN. When the neural network learn, theneural network is made to learn the original continuous
tone image as a teacher signal. Thus the neural network
can be made to function as reconstruct continuous tone
image from binary image.
More over this paper examine some parameters suchas the number of hidden nodes.

I. INTRODUCTION
Halftoning is the techniques that convert continuous
tone image into a binary image. It is used in printing
device and image display, which cannot display
continuous tone image. Human visual system acts as
low-pass filter, and halftoning binary image have highfrequency information. Binary images are inconvenient
for image processing such as the scaling, and image
analysis. Therefore the inverse halftoning are required.
Inverse halftoning is a technique that reconstructing
continuous tone images from binary images.
As the conventional halftoning methods, various
algorithms have been proposed ordered dither[1], theerror diffusion[2], Direct Binary Search (DBS), Genetic
Algorithm (GA)[3] and so on. Besides, various
algorithms have been proposed to inverse halftoning.
However some of these algorithms are only for halftone
images produced by error diffusion method, because theerror diffusion method is widely used. Above alldeconvolution linear model of error diffusion halftoning(inverse error diffusion method) are known producesvery good results.
Look-up table (LUT)[4] based inverse halftoning
method can be processed regardless half-toneprocessing method. However LUT method is not better
than inverse error diffusion method.
In this paper, we proposed novel inverse halftoning
technique, which use Multi-Layer Feed-Forward Neural
Network (MLFFNN) and Gaussian filtering. Thismethod reconstruct continuous tone image from any
halftoning methods.

©ICAROB 2014

II. MLFFNN INVERSE HALFTONING
1. CONFIGURATION OF NEURAL NETWORKWe regard inverse halftoning as reconstruction
processing from the neighborhood of the target pixel ofthe halftoning image. As neural network has function
approximation ability, neural network can estimate thesuitable function for inverse halftoning. In order to
output center pixel, 7x7 pixels are input into MLFFNN.
MLFFNN consist an input layer, two hidden layer, andan output layer. This network is shown in Fig 1. Linerfunction is used to the output layer activation function,and sigmoid function is used to the hidden layer
activation function.

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0.1, we stopped the training. The training rules are
written as:
(𝑖)

(𝑖)

∆𝑤𝑡 = −𝜂 𝑡 ∇𝑓(𝑤𝑡 )
(𝑖)

(𝑖−1)

𝑤𝑡 = 𝑤𝑡

(5)

(𝑖)

+ ∆𝑤𝑡

(6)

1
(𝑖)
∑ 𝑤𝑡
𝑁

(7)

3𝑡
3𝑡
exp⁡(−
)
100
100

(8)

𝑁

𝑤𝑡 =

𝑖=1

where.
η𝑡 =and teacher signal of the t epoch
𝑡𝑠𝑖𝑔𝑛𝑎𝑙 𝑡 = 𝑡𝑠𝑖𝑔𝑛𝑎𝑙 + 0.05 × 𝑟𝑎𝑛𝑑 ∗ exp⁡(−

Fig.1. Structure of MLFFNN
Output y is expressed as follows:

(1)

i=0

where.
nh2j = f(∑ h1i × w2ji )III. EXPERIMENTAL RESULT(2)
1. EXAMINATION OF THE PARAMETER
We used 8bit, 768x512 images for training of thei=0mh1j = f(∑ xi × w1ji )(9)Where 𝑁 is the number of training data. Weights were
initialized with uniform random numbers of -0.2 to 0.2,
and rand is uniform random number of -0.5 to 0.5.
These parameters were decided by a preliminary
experiment.

ny = ∑ h2i × w3i

1
)
120

(3)

i=0

f(x) =

1
1 + exp⁡(−𝑥)(4)

Where 𝑛 is the number of hidden elements, 𝑥𝑚 , ℎ1𝑛 ,ℎ2𝑛 is the bias, and 𝑤1𝑗𝑖 , 𝑤2𝑗𝑖 , 𝑤3𝑗𝑖 ⁡are the weight
between the elements.
2. LEARNING OF THE NEURAL NETWORKMLFFNN trained by back propagation, and thesquare error between output signal and the teacher
signal was minimized.
Neural network has a problem called the over-fitting.
This is a problem that generalization ability became
worse by the excessive fit to the training data. To avoid
over-fitting, we use α function[5] for a learning rate.
Furthermore, we mixed noises into a teacher signal.
Online training is used. The online training biased totraining data of near the end. Therefore we took theaverage of the weights when the neural network learned
all the training data. Furthermore, neural network is
input the training data in random order in order to avoid
bias in the learning. When the learning rate was under©ICAROB 2014

Fig.2 The image that was used to training of the
MLFFNN-139-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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MLFFNN. The image which used for learning is shownin Fig 2. These images are Kodak Lossless True Color
Image Suite. We tried the reconstruction of the halftone
image which processed by Floyd-Steinberg error
diffusion method. Lenna and pepper image are used forevaluation.

Fig.4 Inverse halftone image processed by M
LFFNN
Fig.3. Variation of PSNR for the test image andthe training image.

Fig.5 Enlarged inverse halftone image processed by MLFFNN(left), MLFFNN + Gaussian(right)
solution for the test data, and the over-fitting is
suppressed.
Table 1 is the PSNR for the number of hidden
elements. From Table 1, a number of hidden layer
elements improve PSNR. However, computational costalso increases. Thus it is necessary to set appropriately
the number of hidden layer.
Fig 4 is the PSNR for the number of training data.
Even less training data, MLFFNN have some
performance. Besides when MLFFNN used a lot of
training data, performance is improved as much as whena number of hidden layer elements used. When using
more 7 images for the training data, performance isdeteriorating. This can be improved because this arise
from discontinued learning when the learning rate is cut
to 0.1. A lot of training data increase the time fortraining. However the training process required only

Fig.4. PSNR for the number of the training i
mage.
Table 1. PSNR (dB) values for the number of hidd
en layer elements.
Number of elements

Lenna

Pepper

5

30.97

29.98

10

31.53

30.63

15

31.79

30.84

20

31.93

31.02

25

32.00

31.07

30

32.05

31.16

35

32.10

31.14

40

32.11

31.17

To confirm over-fitting whether can be suppressed,
the graph of PSNR for the test data and training data isshown in Fig 3. There is not the aggravation of the©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Table 4. PSNR (dB) values for restoration from ord
ered dither image(4x4 bayer dither Matrix).

once, and the time processed inverse halftoning is notchange.
From the above, elements of hidden layer set the 25
and 7 images are used to train the MLFFNN. Fig 4, 5 isinverese halftone image processed by proposed
MLFFNN. This image has some noises in flat area.
Gaussian filter is used to remove these noises. Standard
deviations σ is set so that a square error minimized
between training image and image processed by
MLFFNN.

Inverse halftoning method

Lenna

Pepper

Proposed

29.0

28.7

GA(Furuya and Mori)[9]

26.6

25.9

LMS-MMSE(Change et al.)[10]28.2

Only Gaussian filter(σ=1.5)

28.2

27.7

2. COMPARISON
The comparison between the proposed method andconventional method is showed in Table 2. The
proposed method obtained better results than GA and
LUT. Furthermore, equivalent results are obtained incomparison with corresponding to only the error
diffusion method. From this result, it is thought that
MLFFNN can estimate the suitable function for inverse
halftoning.
Then, we tried to reconstruct the image that has beenprocessed by the ordered dither method and GA.
MLFFNN is trained by the image shown in Fig 2 andthe image processed by each halftoning method. In thehalftoning using GA, we use the MSE and the Gaussian
filter to the evaluation. However the proposed method is
becoming better results than the Gaussian filter. Fromtable 3, the proposed method is good results in inverse
halftoning for ordered dithering.

Table 2. PSNR (dB) values for restoration from Flo
yd-Steinberg error diffusion.
Inverse halftoning method

Lenna

Pepper

Proposed

32.5

31.5

Wavelet (Xiong et al.)[6]

31.7

31.0

POCS-Wavelet(Bozkurt and Ceting)[7]

32.2

30.9

Anisortropic LPA-ICI(Foi et al.)[8]

32.4

31.6

LUT(Mese and Vaidyanathan)[4]31.0GA(Furuya and Mori)[9]

31.4

28.8

LMS-MMSE(Change et al.)[10]

31.4

31.2

Fig.6 Floyd-Steinberg error diffusion image inverse halftoning by Proposed method. Lenna(upper,
PSNR=32.54), Pepper(under, PSNR=31.50)

Table 3. PSNR (dB) values for restoration from G
A.
Inverse halftoning method

Lenna

Pepper

IV. CONCLUSION

Proposed

33.3

31.4

Only Gaussian filter(σ=1.5)

32.1

31.2

In this paper the novel inverse halftoning method
using combination of MLFFNN and a Gaussian filter is
proposed.

©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

The proposed method, neural network calculates thesuitable weights for the inverse halftoning from a pair ofhalftone image and the original image. Therefore, it ispossible to perform inverse halftoning without
considering the computational model of the halftoning.
Thus proposed method can optimal inverse halftoning
even the halftone image that result changed by an initialvalue like DBS method and GA. In addition, it was able
to reconstruct image from processed by the error
diffusion that was equivalent to the method using alinear model of the error diffusion.
Multiple parameters such as the initial weight widthand learning rate with in the neural network, thoseparameters influence it mutually and affect a result. As
future problem, there is the adjustment of parameters fora neural network further performance.

REFERENCES[1]B. E. Bayer, An optimum method for two-level
rendition of continuous-tone pictures. ICC Conf. Rec.26
pp11-15 (1973)[2]R. W. Floyd and L. Steinberg, An Adaptive
Algorithm for Spatial Greyscale. Proc Society for
Information Display vol. 17-2:75-77 (1976)[3] E Myodo, H Aguirre and Kiyoshi Tanaka, Improved
Image Halftoning Technique Using GAs with
Concurrent Inter-block Evaluation. GECCO 2003
Lecture Notes in Computer Science Vol 2724, pp22642276 (2003)[4]M Mese and P.P. Vaidyanathan, Look Up
Table(LUT) Method for Inverse Halftoning. IEEE Trans.
Signal Processing 10-10:1566-1578 (2001)[5]T Takahashi and M Adachi, Nonlinear Scheduling of
the Learning Rate in an Actor-Critic Reinforcement
Learning (in Japanese). Technical Report of IEICE 1318 (2004)[6]Z Xiong, M.T. Orchard, and K. Ramchandran(1999),
Inverse halftoning using wavelets. IEEE Trans. Signal
Processing 8:1479-1482[7]Bozkurt Unal G and A.E. Çetin, Restoration ofErrorDiffused images using Projection onto Convex
Sets. IEEE Trans. Image Processing (2001)[8]A. Foi et al, Inverse Halftoning Based on The
Anisotropic LPA-ICI Deconvolution, TICSP Workshop
on SMMSP2004 (2004)[9]T Furuya and K Mori, Restoration of Halftone Image
Using Genetic Algorithms (in Japanese). T.SICE 457:361-368 (2009)[10]P Chang, C. Yu and T Lee, Hybrid LMS-MMSE
Inverse Halftoning Technique
IEEE Transaction on image processing VOL10-1(2001)

Fig.7 Halftone image processed by GA(upper), and inverse halftone image processed by propose
d method(under, PSNR=33.28).

Fig.8 Bayer ordered dither image inverse halft
oning by proposed method(PSNR=28.71).

©ICAROB 2014

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Japan, January 11-13, 2014

Embedding used binary number conversion into a Color image (P)Hiroaki Oguma
Department of Electronics and Bioinformatics, Meiji University
1-1-1, Higashi-Mita, Tama, Kawasaki, Japan
ce31016@meiji.ac.jp
Ken-ichi Tanaka
Department of Network Design, Meiji University
4-21-1, Nakano, Nakano-ku, Tokyo, Japantanaken@isc.meiji.ac.jp
Abstract: Digital watermarking is widely used as security of a binary format image. The purpose of this paper isextension to a color image from grayscale image. Then, it thought as important giving many embedding information toa color image. A color image is divided into RGB, the concentration value of a pixel is made into a binary number, andlast 3 digits are changed to arbitrary numbers. It embeds by carrying out by repeating this, and secure many information.
10x19660 times of embedding are able to be performed to the picture of 256x256 pixels. Moreover, the signal to noise
ratio is about 40dB. Therefore, it seems that there is little influence which it has on a picture.
Keywords: Digital watermarking, Binary number, RGB,

watermarking, and partial cutoff etc. are moreoveredited, it is also possible to take out the embedded
information.
As for a binary format image, many security methods
exist focusing on a document picture. Although how tochange between characters, and the method of putting adesign into a background are famous, they also have
many problems, there being little amount of informationof a watermark, or the use range being restrictive [3][4].
The previous techniques is performed thedigital-watermarking method for a color image,therefore still now [5]. In this paper, the technique ofembedding with emphasis on giving many information
to a color image is considered.

I. Introduction
In recent years, in addition to rapid osmosis of theInternet, it is becoming the environment where
everyone can circulate easily the digital information
which led the network by the spread of personal digitalassistants.
In connection with this, practical use we which gathers
information according to its purpose is performed out of
the information which various people built.
On the other hand, as for the digital contents whichhave value as social wealth, the kind of media also
spreads from numerical text to a still picture, a sound,and video by progress of an information technology, anda sale of the movie and musical piece using a network
also came to be made.
However, since it has the feature that a duplicate iseasy and does not deteriorate, digital information is,when circulating worthy information, the structure
which prevents unjust use and protects intellectual
property rights is indispensable [1].
After generating of the illegal use of a work, detection,
discernment,
and
proof
are
enabledanddigital-watermarking technology is mentioned as arepresentative case of the technology in which thisexpects deterrence of an illegal use indirectly [2].
Digital watermarking is technology which embeds
watermark information, as not perceived by man atdigital contents, such as a picture, an image, and asound.
Even if ID information and copyright information areembedded at the contents themselves if it is digital©ICAROB 2014

II. Experimental
A color image is divided into a RGB ingredient and theconcentration value of a pixel is acquired. The acquired
concentration value is changed into a binary number,and the beforehand fixed embedding information bit isput into last 3 digits portion one after another. Many
embedding information is secured by performing theabove processing to all the pixels of RGB each color.
The bit value sequence of 0 and 1 used as the contents
embedded as a sample and picture figure 1 of 256x256
were prepared. Here, the bit value sequence of 10 digitsof (0,1,0,1,0,1,0,1,0,1) was used.

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Fig.1 Original picture image

Fig.2 After embedding

Table.1 MSE and the signal to noise ratio in embedding picture
Name
Parrots
Girl
PepperEarth
Aerial

Embed
19660
19660
19660
19660
19660

Extranction
19660
19660
19660
19660
19660

Chenge
65360
65434
65432
65415
65412

MSE
7.784
7.437
7.574
7.416
7.559

S/N[dB]
39.2188
39.4168
39.3376
39.4291
39.3462

Average

19660

19660

65410.6

7.554

39.3497

About Fig. 2, it separates into each color of RGB andreads, after acquiring a concentration value, binary
number conversion is carried out, and lower 3 figure isrecorded. It is checked whether it is in agreement withthe bit value sequence which connected and embedded
the bit value sequence which is read one by one andrecorded.
Moreover, in the pictures I and K of picture size m×n,
the maximum pixel value which a picture can take wasdefined as MAX, and the mean squared error (MSE)and the peak signal-to-noise ratio (S/N) were calculated
from the formula (1) and (2).

It separates into each color of RGB, Fig. 1 is shown,
and a concentration value is acquired per pixel from theupper left to the lower right.
The acquired numerical value is changed into a binary
number, and it replaces in the bit value sequence which
was preparing the last 3 digits. The numerical value
after replacing is re-changed into a decimal number, andit stores in the pixel of a basis, and moves to thefollowing pixel. In the next replacement, the 4th to 6th
three of the prepared bit value sequence is replaced.
This processing is performed one after another andembedding is performed to all the pixels. Afterembedding is completed to the last, the separated RGB
ingredient is compounded and it returns to a color image.
The picture after embedding is shown in Fig. 2. The
signal to noise ratio of Fig. 2 was investigated.

𝑚−1 𝑛−1

1
MSE =∑ ∑[𝐼(𝑖, 𝑗) − 𝐾(𝑖, 𝑗)]2
𝑚𝑛
S

=1

10

[

]

The calculation result to some pictures is shown in
Table 1.

©ICAROB 2014

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(1)

𝑖=0 𝑗=02( )The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

III. CONCLUSIONIEEE Transactions on Image Processing, Vol.6, No.12,
p-p1673-1687, (1997).
[4] Jack T.Brassil, Steven Low, Nicholas
F.Maxemchuk, Lawrence O’Gorman, Electronic
marking and identification techniques to discourage
document copying, IEEE Journal on Selected Areas inCommunications, Vol.13, No.8, p-p1495-1504, (1995).
[5] Go Tanaka, Noriaki Suetake, and Eiji Uchino, HighCapacity Data Embedding Method for Palette-Based
Images, Technical Report of the Institute of Electronics,
Information and Communication Engineers, SIS, asmart information media system, Vol.108, No.461,
p-p13-16, (2009).
[6] Hiroyuki Kitayama, C++Builder6 component
practical use guide & practice programming Vol.6, a cut
system, pp203-352, (2004).
[7] Yasushi Yamanaka, Hiroshi Fujii, Digital Image
Scrambling Method for Information Distribution,
Information Processing Society of Japan, Vol.38, No.10,
p-p1945-1955, (1997).
[8] Yasushi Abe, Koichi Inoue, Digital Watermarking
for Bi-Level image, Ricoh Technical Report, No.26,
pp8-16, (2000).
[9]Naohisa
Komatsu,
Ken-ichiTanaka,digital-watermarking technology : the security of digitalcontents, Tokyo Denki University Press, (2004).

It succeeded in repeating the bit value sequence of 10
digits of (0,1,0,1,0,1,0,1,0,1)
19660 times, andembedding it to the picture of 256x256. Moreover,extraction was also successful same. Since the signal tonoise ratio of the embedded picture was about 40 dB, it
can be said to be that there are little change and sense ofincongruity before and behind embedding. In the
viewpoint of the quantity of the embedded bit value
sequence, the technique of this paper has secured
sufficient amount of information to use for digitalwatermarking.

REFERENCES[1] Norihiko Sakurai, Yutaka Kidawara, Yoichi
Takashima, Noburou Taniguchi, and Koji Namba, A
Survey on Copyright Protection Technologies for
Network Distribution of Digital Information,
Information Processing Society of Japan, Vol.42, No.
SIG 15(TOD 12), p-p63-76, (2001)[2]Ryoichi Sasaki, Hiroshi Yoshida, Consideration on
Copyright and Illegal Copy Countermeasures under IT
Revolution, Information Processing Society of Japan,
CSEC, Vol.13, No.7, p-p37-42, (2001).
[3] Cox IJ, Kilian J, Leighton FT, and Shamoon T,
Secure spread spectrum watermarking for multimedia,©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Three Dimensional Images Reconstruction on Computer Generated Holograms
the Multiple Regression Analysis (Poster)
Kenta Ayabe
Dept. of Electronics and Bioinformatics Meiji University
4-21-1, Nakano, Nakano-ku, Tokyo,Japan
E-mail; ce21002@meiji.ac.jp
Ken-ichi Tanaka
Dept. of Network Design Meiji University
E-mail;tanaken@meiji.ac.jp
Abstract: In this paper, for a Computer Generated Hologram (CGH) reconstructing a threeDimensional (3D) object, we present the improvement of the reconstruction image, reducing of the calculation ti
me using error diffusion method and a multiple regression analysis. The multiple regression analysis predicts one response variable with plural explanatory variables. The purposes of this paper are reducing calculation time and improving reconstruction images. Therefore we move 26 pieces of images from A to Z at various positions. W
e prepare for 650 pieces of images in total and perform GA in each.We expect diffusivity for a multiple regression analysis from the result and regenerate it.
Keywords:Three Dimensional Image Reconstruction ,Computer Generated Hologram
Multiple Regression Analysis, Genetic Algorithm, Diffusion CoefficientI.

ducing of the calculation time using error diffusion method and a multiple regression analysis,
provided CGH is used in this paper and the fault type.

INTRODUCTION

There is the Three-Dimensional(3D) reconstruction ofan object having difficulty in irradiating a virtual object
and laser beam as one of the making purposes of thecomputer generated Hologram.It is one of the crucial
issue to develop a method of the hologram patternindication that can get high quality reconstruction image
with the number of few pixels to be of practicalapplication a CGH.
In the CGH had as one’s objective for image
reconstruction, the improvement of the reconstruction
image will have an important meaning.
Although it is made with the use of repetition algorithm
for reconstruction image improvement conventionally,
this is improvement of the reconstruction image by
optimizing phase to give an input object and is only theindirect improvement method. Therefore a superior
reconstruction image can search for the CGH that it isdirectly when we consider the reconstruction image
improvement of the CGH to be a kind of combination
optimization problem.We apply the genetic algorithm(GA) that is one of the method of solution to a CGH andperform reconstruction image improvement.GA isalgorithm for optimization and a search.
GA can demand the most suitable error diffusion
coefficient.It is relations with fitness, individual,
diffusion coefficient in GA. And the process of choice,crossing-over, the mutation is important.
In this conventional method, the reconstruction images
are improved than we reconstruct only in the error diffusion method, however length of the calculation time is a
fault. In this paper, we playeda Computer Generated Hologram (CGH) reconstructing
a three-Dimensional (3D) object. We
present the improvement of the reconstruction image, re©ICAROB 2014

II. To the operation of CGH of GA
Here, We describe the computer-generated hologram.
When creating on a computer two-dimensional
amplitude hologram, CGH assumes the reference lightsuitable for calculating the hologram pattern. Since allprocessing is performed in the computer, the following
advantages can be mentioned.
・It is not required for the imaging laser.
・It is not required, such as vibration damping real-time
holography.
・It's less noise due to the light-receiving element.
・It can be done by computer generated simulation
regeneration process is possible.
A.To the operation of a 3D-CGH reconstruction
Here, using GA, where it describes a method for thesynthesis of 3D- CGH of fault type. In this paper, weapplied GA to a search of the diffusion coefficient.
The following shows the flow of reconstruction[Step1]Initialization: Bit string A - D is encoded as
an individual in GA to show it in Fig.1. We
shall take an integer of -128~+127 each, andbit string A~D has following Eq.(1) between
diffusion coefficient A~D and bit string A~D
relation having 8[Step2] Evaluation: We evaluate the reconstruction
image from CGH by diffusion coefficient
provided from each individual.

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

a 
A
 
 
1
b 
B 
c   | A |  | B |  | C |  | D | C 
 
 
d 
 D
 
 

(1)

[Step3]Selection:
We evaluate the reconstruction
image from CGH by diffusion coefficient
provided from each individual.
[Step4] Crossover: Fig.2 shows the operation of theFig.4 Optical system
And r(u,v) the complex amplitude in the image pla
ne f-d_2 from the focal plane away and H(u,v) thecomplex amplitude in the phase plane that is recor
ded as a hologram.The diffraction method uses Fres
nel diffraction integral calculus and calculates as follows, provided, N is the sample point, L is size ofthe hologram pattern and k,l,m,s is an integer to ta
ke a value of N-1 out of 0. ω is defined by the fo
llowing equation.

crossover. First of all, We pick out at
random under the condition that does
not allow duplicates the two individuals.
Then, We select the operator having 32
bits at random. About the eachindividual, and to replace the bit
position where the bit of the operator isequal to 1 , as shown in Fig .2.

N 1 N 1r (m, n)    H ' (k , l )[Step5] Mutation: We take out one individual optionallyand decide an operator at random. Fig.3 isshown mutatetion, it is assumed thatobtained by the exclusive OR of theoperator and the individual.
[Step6] It is repeat from [Step2] to [Step5] until thek 0 l o (d 1   ) L2 2
2
 exp 
(
k

l

2
2
 j d 1 N


(2)

 2

 exp (km  kn) jN
number of generation.[3]B. Application of multiple regression analysis
The multiple regression analysis predicts one response
variable with plural explanatory variables. The response
variable is the quantity made the object of the forecast.
It is necessary to collect a large number of sets of dataof the result and the corresponding data and activities
operation.The explanatory variable is a variable toexplain response variable.The experiments were carriedout using a linear regression model, such as Eq.(3).

Fig.1 Indivisual.

ya ・x
a0a・
1 x1
p
p

(3)

y：response variable x1 ~ x p ,:explanatory variables

Fig.2 Crossover.

a0 :constant term a1 ~ a p :regression coefficientp:the number of explanation variables.
n

Q   {Y i  (a0  a1 X i1  a 2 X i 2)}2

(4)

i 1

Fig.3 Mutation.
he experiment was carried out by computer generated si
mulation.The computer generated simulation assume touse an optical system to show in Fig.4
and carries out it by a diffraction calculation the wave
surface recorded to the CGH.[1][2]©ICAROB 2014

To determine the coefficients which minimize the Q
shown by the Eq.(4).
This method called a least square method, coefficientsobtained is called the least squares estimate.
We set 0 in partially differentiated by a0 , a1 , a 2
Eq.(4).

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014n
 Q 2{Y i  (a 0  a1 X i1  a 2 X i 2)}i 1
  a0 Qn 2 X i1{Y i  (a 0  a1 X i1  a 2 X i 2)}i 1
  a1 Qn 2 X i 2 {Y i  (a 0  a1 X i1  a 2 X i 2)}
i 1
  a 2

sion coefficient by using multiple regression analysis. R
egression coefficients are as follows.
Table.1.Diffusion coefficient that led in
the multiple regression analysis(5)

Relation obtained when the Y , X 1 , X 2 the averagevalue of the variable Y , X 1 , X 2 to Eq.(5).

a0  Y  a1 X 1  a 2 X 2(6)And, change, covariation between the independent
variableAnd, covariation of independent variable and dependent
variable
Organize by substituting.
This is called a normal equation

It was an image similar in comparison with the results
Fig.6. Reconstruction time took less than one second by
one image.

a1 , a 2 partial regression coefficient is determined bysolving the equation.
In this paper, response variables are error diffusion coeff
icients and predictor variables are input object positionand optimal error diffusion coefficients calculated byGA.
Ⅲ EXPERIMENTAL RESULTThe original images which we used for an experiment
26 pieces of images from A to Z.
Create images total of 25 sheets is displayed at theposition of the (32, 32), (0,0), (0,8), ..., "A" and "a.
We prepare for 650 pieces of images in total andperform GA in each.GA is reproduced number 200
individuals, selection probability 0.2,
mutationprobability 0.01.
The setting in the optical system assumes wavelength
λ=632.8nm of the light source (He-Ne laser), focus
distance f=300(mm) of the lens, it size L=10(mm) of thehologram pattern.When we use in this paper, it
determines a cost function like Eq.(7)and decides tomake all a cost function the same, and to reproduce each
image.
E  E1  E2 

f  r
2

2



f  f

f



r  r

r

(a)d2=300mm(b)d2=330mm
Fig.4 Original image

2(7)

We are shown in Fig.4 part of the original image using
the experiment.
Fig.5 is result when using the iterative method. Rasterscan is used. It is spreading a little result. However, it
is not completely diffusion
Fig.6 is result when using the GA method. This is reconstructed cleanly compared with Fig.5.
However, Reproducing time of one image it takes three
and a half hours. Fig.7
is one that has been reproduced by predicting the diffu©ICAROB 2014(a)d2=300mm(b)d2=330mm
Fig.5 Reconstructded images(iterative method)-148-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Ⅴ

REFERRNCES[1] Ken-ichi Tanaka,Yoshimasa Sakamoto,and Teruo
Shimomura,”Effect of Cost Function on Computer
Generated Hologram Using Simulated
Annerling”,IEICE,C-I,Electronics, J80-C-1(2),100104,1997.
[2] Miki Mitsunori,Hiroyasu Tomoyuki,and Ono
Keiko,”Simulated Annealing with Advanced
Adaptive Neighborhood”IPS,44(1),1-6,2003[3] Ken- ichi Tanaka,” Genetic Algorithm Based
Method of Estimating Optimal Error-Diffusion
for Computer-Generated Holograms(Special
Section Three-Dimensional Image Information
Media) ITE , 54(3), 394-401, 2000(a)d2=300mm(b)d2=330mm
Fig.6 Reconstructded images(GA)(a)d2=300mm(b)d2=330mm
Fig.7 Reconstructded images(multiple regression analysis)
Ⅳ CONCLUSION
In this paper, we applied GA to the CGH of the fault
type and the effect and time reduction of reconstruction
image improved diffraction calculation was examinedfrom the standpoint of lowering the temperature setting.
When applying a multiple regression analysis, it waspossible to greatly improve the reconstruction time
image is not changed substantially. Also, when you set
the diffusion coefficient, in this paper, Again and again
conducted an experiment, we have to grasp the trend of
the diffusion coefficient.

©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Embedding used binary number conversion into a Color image (P)Hiroaki Oguma
Department of Electronics and Bioinformatics, Meiji University
1-1-1, Higashi-Mita, Tama, Kawasaki, Japan
ce31016@meiji.ac.jp
Ken-ichi Tanaka
Department of Network Design, Meiji University
4-21-1, Nakano, Nakano-ku, Tokyo, Japantanaken@isc.meiji.ac.jp
Abstract: Digital watermarking is widely used as security of a binary format image. The purpose of this paper isextension to a color image from grayscale image. Then, it thought as important giving many embedding information toa color image. A color image is divided into RGB, the concentration value of a pixel is made into a binary number, andlast 3 digits are changed to arbitrary numbers. It embeds by carrying out by repeating this, and secure many information.
10x19660 times of embedding are able to be performed to the picture of 256x256 pixels. Moreover, the signal to noise
ratio is about 40dB. Therefore, it seems that there is little influence which it has on a picture.
Keywords: Digital watermarking, Binary number, RGB,

watermarking, and partial cutoff etc. are moreoveredited, it is also possible to take out the embedded
information.
As for a binary format image, many security methods
exist focusing on a document picture. Although how tochange between characters, and the method of putting adesign into a background are famous, they also have
many problems, there being little amount of informationof a watermark, or the use range being restrictive [3][4].
The previous techniques is performed thedigital-watermarking method for a color image,therefore still now [5]. In this paper, the technique ofembedding with emphasis on giving many information
to a color image is considered.

I. Introduction
In recent years, in addition to rapid osmosis of theInternet, it is becoming the environment where
everyone can circulate easily the digital information
which led the network by the spread of personal digitalassistants.
In connection with this, practical use we which gathers
information according to its purpose is performed out of
the information which various people built.
On the other hand, as for the digital contents whichhave value as social wealth, the kind of media also
spreads from numerical text to a still picture, a sound,and video by progress of an information technology, anda sale of the movie and musical piece using a network
also came to be made.
However, since it has the feature that a duplicate iseasy and does not deteriorate, digital information is,when circulating worthy information, the structure
which prevents unjust use and protects intellectual
property rights is indispensable [1].
After generating of the illegal use of a work, detection,
discernment,
and
proof
are
enabledanddigital-watermarking technology is mentioned as arepresentative case of the technology in which thisexpects deterrence of an illegal use indirectly [2].
Digital watermarking is technology which embeds
watermark information, as not perceived by man atdigital contents, such as a picture, an image, and asound.
Even if ID information and copyright information areembedded at the contents themselves if it is digital©ICAROB 2014

II. Experimental
A color image is divided into a RGB ingredient and theconcentration value of a pixel is acquired. The acquired
concentration value is changed into a binary number,and the beforehand fixed embedding information bit isput into last 3 digits portion one after another. Many
embedding information is secured by performing theabove processing to all the pixels of RGB each color.
The bit value sequence of 0 and 1 used as the contents
embedded as a sample and picture figure 1 of 256x256
were prepared. Here, the bit value sequence of 10 digitsof (0,1,0,1,0,1,0,1,0,1) was used.

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Fig.1 Original picture image

Fig.2 After embedding

Table.1 MSE and the signal to noise ratio in embedding picture
Name
Parrots
Girl
PepperEarth
Aerial

Embed
19660
19660
19660
19660
19660

Extranction
19660
19660
19660
19660
19660

Chenge
65360
65434
65432
65415
65412

MSE
7.784
7.437
7.574
7.416
7.559

S/N[dB]
39.2188
39.4168
39.3376
39.4291
39.3462

Average

19660

19660

65410.6

7.554

39.3497

About Fig. 2, it separates into each color of RGB andreads, after acquiring a concentration value, binary
number conversion is carried out, and lower 3 figure isrecorded. It is checked whether it is in agreement withthe bit value sequence which connected and embedded
the bit value sequence which is read one by one andrecorded.
Moreover, in the pictures I and K of picture size m×n,
the maximum pixel value which a picture can take wasdefined as MAX, and the mean squared error (MSE)and the peak signal-to-noise ratio (S/N) were calculated
from the formula (1) and (2).

It separates into each color of RGB, Fig. 1 is shown,
and a concentration value is acquired per pixel from theupper left to the lower right.
The acquired numerical value is changed into a binary
number, and it replaces in the bit value sequence which
was preparing the last 3 digits. The numerical value
after replacing is re-changed into a decimal number, andit stores in the pixel of a basis, and moves to thefollowing pixel. In the next replacement, the 4th to 6th
three of the prepared bit value sequence is replaced.
This processing is performed one after another andembedding is performed to all the pixels. Afterembedding is completed to the last, the separated RGB
ingredient is compounded and it returns to a color image.
The picture after embedding is shown in Fig. 2. The
signal to noise ratio of Fig. 2 was investigated.

𝑚−1 𝑛−1

1
MSE =∑ ∑[𝐼(𝑖, 𝑗) − 𝐾(𝑖, 𝑗)]2
𝑚𝑛
S

=1

10

[

]

The calculation result to some pictures is shown in
Table 1.

©ICAROB 2014

-151-

(1)

𝑖=0 𝑗=02( )The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

III. CONCLUSIONIEEE Transactions on Image Processing, Vol.6, No.12,
p-p1673-1687, (1997).
[4] Jack T.Brassil, Steven Low, Nicholas
F.Maxemchuk, Lawrence O’Gorman, Electronic
marking and identification techniques to discourage
document copying, IEEE Journal on Selected Areas inCommunications, Vol.13, No.8, p-p1495-1504, (1995).
[5] Go Tanaka, Noriaki Suetake, and Eiji Uchino, HighCapacity Data Embedding Method for Palette-Based
Images, Technical Report of the Institute of Electronics,
Information and Communication Engineers, SIS, asmart information media system, Vol.108, No.461,
p-p13-16, (2009).
[6] Hiroyuki Kitayama, C++Builder6 component
practical use guide & practice programming Vol.6, a cut
system, pp203-352, (2004).
[7] Yasushi Yamanaka, Hiroshi Fujii, Digital Image
Scrambling Method for Information Distribution,
Information Processing Society of Japan, Vol.38, No.10,
p-p1945-1955, (1997).
[8] Yasushi Abe, Koichi Inoue, Digital Watermarking
for Bi-Level image, Ricoh Technical Report, No.26,
pp8-16, (2000).
[9]Naohisa
Komatsu,
Ken-ichiTanaka,digital-watermarking technology : the security of digitalcontents, Tokyo Denki University Press, (2004).

It succeeded in repeating the bit value sequence of 10
digits of (0,1,0,1,0,1,0,1,0,1)
19660 times, andembedding it to the picture of 256x256. Moreover,extraction was also successful same. Since the signal tonoise ratio of the embedded picture was about 40 dB, it
can be said to be that there are little change and sense ofincongruity before and behind embedding. In the
viewpoint of the quantity of the embedded bit value
sequence, the technique of this paper has secured
sufficient amount of information to use for digitalwatermarking.

REFERENCES[1] Norihiko Sakurai, Yutaka Kidawara, Yoichi
Takashima, Noburou Taniguchi, and Koji Namba, A
Survey on Copyright Protection Technologies for
Network Distribution of Digital Information,
Information Processing Society of Japan, Vol.42, No.
SIG 15(TOD 12), p-p63-76, (2001)[2]Ryoichi Sasaki, Hiroshi Yoshida, Consideration on
Copyright and Illegal Copy Countermeasures under IT
Revolution, Information Processing Society of Japan,
CSEC, Vol.13, No.7, p-p37-42, (2001).
[3] Cox IJ, Kilian J, Leighton FT, and Shamoon T,
Secure spread spectrum watermarking for multimedia,©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014                 ½     ½  ¾      ¿   
½

¾



                   

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Japan, January 11-13, 2014       ! / (       !
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Random Local Search
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©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Runtime Analysis of OneMax Problem in Genetic Algorithm
Ichihi To1 , QuinLian Ma2 , Makoto Sakamoto3 , Hiroshi Furutani3 , and Yu-an Zhang4
1

2

Graduate School of Engineering, University of Miyazaki, Japan
Interdisciplinary Graduate School of Agriculture and Engineering, University of Miyazaki, Japan
3
Faculty of Engineering, University of Miyazaki, Japan
4
Department of Computer Science and Technology, Qinghai University, Chinae-mail: furutani@cs.miyazaki-u.ac.jp

Abstract: Genetic algorithms (GAs) are stochastic optimization techniques that simulate the biological evolution. Theoretical
study of the evolution of GA is very important for the application of GA. We have studied the effects of stochastic fluctuation in
the process of GA evolution. A mathematical study is carried out for GA on OneMax function within the framework of Markov
chain model. We obtain the steady state solution, which represents a distribution of the first order schema frequency. This
paper treats the task of estimating hitting time of the optimum solution for OneMax problem, and study the effect of mutation
probability on the hitting time.
Keywords: genetic algorithms, schema theory, OneMax problem, Wrihgt-Fisher model, Markov model
2 MATHEMATICAL MODEL

1 INTRODUCTION

2.1 OneMax Model
We treat the evolution process of a population with N individuals. The individuals are represented by binary stringsof length ℓ, and there are n = 2ℓ genotypes,

We consider the probability that a population includes the
optimum solution by applying Markov chain model. We
call this probability as the success probability of GA. We
obtained the analytical form of the transition matrix P of
Markov chain, which represents the evolution of populationin OneMax problem. We found that GA for the OneMax
problem is equivalent to the asymmetric mutation model.
There are rich studies of the asymmetry mutation model, andwe can apply these results to the OneMax problem in GA.
The relation between the convergence time and success probability was studied analytically and experimentally. In theanalysis, we applied the Wright-Fisher model.

i = < i(ℓ), · · · , i(1) >,We use the relative frequency xi (t) for describing the evolutionxi (t) = Ni (t)/N,
where Ni (t) is the number of individuals of genotype i at
generation t.
The relative frequencies satisfy the normalization conditionn−1∑
xi (t) = 1.

This paper treats the task of estimating hitting time of the
optimum solution in GA for OneMax problem. This task is
performed by using the Markov chain representing the behavior of population in the stationary state of evolution. The
transition matrix of the stationary state is calculated by using the original transition matrix P . By dividing the states topopulations which include the optimum solution and populations which include no optimum ones, we calculate the transition matrix. By doing these transformation, we find thatthe hitting time in the stationary state can be represented approximately by using the success probability, mutation rate
and bit length. We use this theoretical result for the analysis of real GA calculations, and study the effect of successprobability on the hitting time of GA.

©ICAROB 2014

i(k) ∈ {0, 1}.

i=0

The average fitness f¯(t) of the population at generation t isf¯(t) =

n−1∑

fi xi (t).

(1)

i=0

The OneMax fitness function fi is defined as
fi =

ℓ
∑

i(k).

(2)

k=1

Thus the string of all ones < 1, 1, . . . , 1 > is the optimum
solution of this function.

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

2.2 Linkage Equilibrium
We derive here the evolution equation for the first orderschema frequency. To do this, we introduce the notion of
linkage equilibrium. Linkage means the correlation between
the different loci in a chromosome, and if there is somecorrelation we call this state as linkage disequilibrium [1].
Crossover and mutation have the effect of making the population in linkage equilibrium.
The distribution of a population in linkage equilibrium isgiven byℓ∏xi (t) =hi(k) (t),(3)

3.2 Experiment
We examined the effect of crossover by numerical calculation for OneMax problem. The length of string is L = 20,
population size N = 100 and mutation rate pm = 0.02. We
appled roulette selection. We averaged the results obtainedby repeating 1000 same calculations. The initial state is randomly generated with p(1) = 1/ℓ. We compared three typesof calculations; without crossover, one-point crossover, anduniform crossover. We investigated the change of evolution
rate by crossover.
Figures 1 and 2 show values of average and variance of fitness.

k=1

where hi(k) (t) is a frequency of the first order schema at position k, and i =< i(ℓ), . . . , i(1) >. We also use the notation(k)(k)
of h0 and h1 for the first order schema frequencies of bit
0 and bit 1, respectively.
2.3 Evolution Equation of the First Order Schema
In the deterministic schema theory, the evolution of thefirst order schema in linkage equilibrium is given by [2, 3].
The relative frequency of the first order schema at position k
is determined by
(k)

(k)

h1 (t + 1) = ah1 (t) + b,(4)

where constants a and b are1
a = (1 − ) (1 − 2pm ),
ℓ

b =

1
(1 − 2pm ) + pm .
ℓFig. 1. Average fitness

3 ANALYSIS OF EVOLUTION RATE
3.1 Fisher’s Theorem
According to Fisher’s ”Fundamental Theorem of Natural
Selection”[3], evolution rate becomes faster if variance of fitness becomes bigger. The change ∆f (t) of average fitness
f (t) at generation t is∆f (t) = f (t + 1) − f (t).
Variance of average fitness V AR(f) is∑
V AR(f ) =fi2 xi (t) − f (t)2 .

(5)

(6)

i

From eqs.(5) and (6),we have∆f (t) =1
V AR(f ).
f (t)Fig. 2. Variance of fitness

We call v(t) ≡ ∆f (t) as evolution rate

We find that Fisher’s theorem explains the results of Figs
1 and 2.

v(t) ≡ ∆f (t) = f (t + 1) − f (t).

3.3 Linkage Analysis
The linkage disequilibrium coefficient is

If we ignore mutation, evolution rate v(t) is proportional tothe variance of average fitness. We note that variance of fitness must be increased in order to promote the evolution.

©ICAROB 2014

′

(11)

(1)

D[m, m ] = pm,m′ − p(1)m pm′ .

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

(11)

(1)

Here, pm is the probability that the m-th bit is 1, and pm,m′

4 ANALYSIS OF CONVERGENCE

is the probability that the m-th bit and the m -th bit are 1.
Using Walsh function and Walsh conversion, the variance isgiven by

4.1 Markov ModelIn this study,we use the concept of Markov chain. Markov
chain is a stochastic process that the transition only dependon the previous state.
One of the Markov model in population genetics is a
Wright-Fisher model. Wright and Fisher put forward theirevolution equation by considering the finite individuals [1].
The Wright-Fisher model treats chromosomes having one locus and two alleles, corresponding to the GA of ℓ = 1 withgenotypes k ∈ {0, 1} [4]. The number of the first genotype
1 takes the values of′

V AR(f ) = VA + VI ,

(8)

where
VA =

L
∑

(1)
p(1)
m (1 − pm ),

(9)

m=0

VI = 2∑′

D[m, m ].

(10)

′

m<m

(1)

Since VA depends only on pm , which is the function of one
bit, it is not affected directly by crossover. On the other hand,
VI dependents on the correlation of two loci. Therefore, thevalue varies by crossover. If correlation of two loci is small,the absolute value of VI is small. In addition,because
VI ≤ 0,N1 = 0, 1, . . . , N,
and that of the genotype 0 is given by N0 = N − N1 .
We analyze evolution processes by taking into account theeffect of random sampling, and consider the fitness proportionate selection. If there are N1 = i copies of the genotype
1 at the current generation t, the probability P (j|i) of N1
taking the value of j at the next generation t + 1 is given by
the binomial distribution
( )
N
N −j
Pi,j = P (j|i) =pji (1 − pi )
(12)
j

(11)

variance increases with weaker correlation.
3.4 Analysis of Variance
We used the same conditions as the experiment of section
3.2, and analyzed the variance of Fig.2 by linkage analysis.
Figure 3 shows the change of VA and VI with generation.

i) + b,
N
where i is the number of genotype 1 at the current generationt, and j is the number of genotype 1 at the next generationt + 1.
The probability P (j|i) specifies the process of random
sampling, and the future behavior of the process only depends on its current frequencies [4]. Thus this process is a
Markov chain. Let µi (t) be the probability that the population is in the state of N1 = i at generation t.
In the following, we use the vector notationpi = ay + b = a(µT = (µ0 , µ1 , . . . , µN ),with the normalization conditionN∑µi (t) = 1.

(13)

i=0

Fig. 3. Change of VA and VI with generation

Then the evolution process is described byµj (t + 1) =N∑µi (t) Pi,j .

(14)

i=0

3.5 Analysis Result

The evolution equation is given in the vector form

From this calculation,we found that the variance of fitness is influenced by crossover. Crossover breaks the correlation between two loci, and a population will be in linkage
equilibrium. In the case of uniform crossover, we found thatthe variance of fitness is the largest, and evolution rate is thefastest.

©ICAROB 2014µ(t + 1)T = µ(t)T P,(15)

where P is a matrix of the size (N + 1) × (N + 1). We know
the eigenvalues of this matrix [5],λ0 = 1,-159-

λ1 = a,λ2 = a2 (1 − 1/N ), . . . .

(16)

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

It should be noted that the second largest eigenvalue λ1 does
not depend on the population size N . We denote the left andright eigenvectors
uTi P = λi uTi ,P vi = λi vi ,4.2 Experiment
We compared results of the theoretical prediction with GA
experiments. Crossover is uniform crossover with crossover
rate = 1. Mutation rate is pm = 0.02 for each bit. Selection
is roulette selection. The length of string is L = 20, andpopulation size N = 100. We averaged the results obtainedby repeating 10000 calculations. The initial state is randomly
generated with p(1) = 1/ℓ.

(0 ≤ i ≤ N ).

These eigenvectors satisfy the orthogonality conditionuTi · vj = 0(i ̸= j).

(17)

The explicit form of the right eigenvector v0 is given by
v0 = (1, 1, . . . , 1)T ,(18)

and all elements of the left eigenvector u0 are positive. We
adopt the normalization conditions
N∑|ui | = 1,

uTi · vi = 1.

i=0We consider the GA under positive mutation rate pm > 0.
In this case, all elements of the transition matrix P are positive, and the Markov chain of schema evolution is irreducible
and aperiodic. The Markov chain theory states that an irreducible and aperiodic Markov chain converges to the stationary distribution π
lim µ(t) = π,Fig. 4. Frequencies of the first order schema

(19)

t→∞

and all elements πi are positive [5].
The initial distribution of the first order schema can be
expanded in terms of the left eigenvectorsµ(t = 0)T =N∑

Ci uTi .

Figure 4 is frequencies of the first order schema calculatedby the theoretical prediction and GA experiments.
4.3 Total Variation Distance
The total variation distance between the stationary distribution and the first order schema at generation t is definedbyN
1∑
T V (t) =|µi (t) − πi |.
(24)
2 i=0

(20)

i=0

Since ui is eigenvector, and from equation (15), we haveµ(t)T =N∑

λti Ci uTi .

(21)

Figure 5 is the total variation distance TV between the stationary distribution and the first order schema. We found thatthe first order schema converges to the stationary distributionπ.

i=0Multiplying the right eigenvector v0 from right sideµ(t)T · v0 =N∑

λti Ci uTi · v0 = λt0 C0 = C0 ,i=0

5 APPEARANCE OF OPTIMUM SOLUTION

and from equations(13) and (18), we have C0 = 1. At larget, µ(t) is approximately given byµ(t) ≈ u0 + at C1 u1 ,5.1 Theory
The distribution of appearance time of optimum solution
at generation t after the convergence time Tc is(22)

and u0 is the stationary distribution π.
We treat the convergence time Tc of Markov chain. The
theoretical estimation of the convergence time is given by
Tc (eigenvalue) = min {a < 0.05},ttϕ(t) = exp(−λm t).

Here, t is time after Tc .
Markov chain theory for the steady state predicts that thedeath rate λm is(23)

where a is the second largest eigenvalue of the transition matrix P .

©ICAROB 2014(25)

λm = 1 − (1 − pm )L .

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

that the appearance of the optimal solution can be predictedby using mutation rate pm .

REFERENCES[1] J. W. J. Ewens (2004), Mathematical Population Genetics. I. Theoretical Introduction, Second Edition.
Springer-Verlag, New York.
[2] H. Furutani, S. Katayama, M. Sakamoto, M. Ito,(2007),
Stochastic Analysis of Schema Distribution in a Multiplicative Landscape, Artificial Life and Robotics
11:101–104[3] H. Furutani (2003), Schema Analysis of OneMax Problem –Evolution Equation for First Order Schemata. inFoundations of Genetic Algorithms 7, Morgan Kaufmann, San Francisco, 9–26

Fig. 5. Convergence of the first order schema[4] J. F. Crow and M. Kimura (1970), An Introduction to
Population Genetics Theory, Harper and Row, NewYork.

5.2 Experiment
We carried out experiments by setting parameters of experiment 4.2. We examined the distribution of the emergenceof the optimum solution and the convergence time Tc of thestationary distribution. Figure 6 is the distribution of the hitting time for optimum solution after convergence time Tc andthe theoretical prediction.

[5] W. Y. Tan (2002), Stochastic Models with Applications
to Genetics, Cancers, AIDS and Other Biomedical Systems, World Scientific, Singapore.

Fig. 6. Hitting time

If the mutation rate is less than 0.02, we found that theexperimental results is less than the theoretical prediction.
6 SUMMARY
We studied the distribution of the hitting time of the optimum solution. To this end, we separated the task to twoparts. One is the process to converge the chain into stationary state, another is the appearance of the optimum solution
in a population. We examined the distribution of the optimum solution after convergence of Markov chain. We found©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

A Model for Low-Frequency Bursts in Subthalamic Nucleus Neurons
Shigeru Kubota*1, Jonathan E. Rubin*2
1

Graduate School of Science and Engineering, Yamagata University,
4-3-16 Jonan, Yonezawa, Yamagata, 992-8510, Japan2
Department of Mathematics, University of Pittsburgh,
301 Thackeray Hall, Pittsburgh, PA 15260, USA(Tel: +81-238-26-3766, Fax: +81-238-26-3299)
kubota@yz.yamagata-u.ac.jp

Abstract: Burst firing in subthalamic nucleus (STN) has been suggested to be highly associated with the motor symptoms in
Parkinson’s disease, which result from a loss of dopamine. Although it is clinically very important to clarify the mechanism
underlying the bursting dynamics, complex interactions between STN and other brain areas make it difficult to understand. In
anesthetized rats, STN neurons exhibit low-frequency (  1 Hz) bursts, which are synchronous with cortical slow oscillations
and are significantly strengthened by dopamine depletion. To reproduce these low-frequency bursts, we examine a
conductance-based model of an STN neuron that includes NMDA-type glutamatergic inputs reflecting cortical oscillations. In
addition, the neuron model contains GABAergic inhibitory inputs, which are assumed to result from the activities of globus
pallidus (GP). We show that the STN neuron model can reproduce low-frequency bursts synchronized with cortical activity, inthe presence of GABAergic inhibition. In addition, we demonstrate that increased GABA activity leads to enhanced burstinesswhereas increased NMDA conductances mainly augment STN firing rate. The induction of burst firing additionally decreases
the coherence between STN and cortical activities. These results may give insights into how the complicated interactions
between the STN, cortex, and GP can modulate the dynamics of bursting oscillations in the basal ganglia.
Keywords: Subthalamic nucleus, cortex, globus pallidus, Parkinson’s disease, burst oscillations
in STN is considerably modulated by GABA inhibition
originating from GP interneurons, in addition to oscillatory
inputs from the cortex.
In this study, we simulate a model STN neuron to study
how STN bursts depend on cortical inputs, mediated by
NMDA currents, and GABAergic inhibition. In the model,
synaptic NMDA currents are activated by slow corticaloscillations consisting of active phases of high-frequency
activity alternating with inactive quiescent phases, as
observed in experiments [6-8]. We examine the changes inburst properties that arise with changes in the magnitudes ofboth the NMDA and GABA conductances. We show thatenhanced GABAergic inhibition leads to stronger burstinesswhile enhanced NMDA activation mainly acts to increase
STN firing rate. In addition, the occurrence of strong bursts
is correlated with a decrease in coherence between the STN
and cortical activities. These results may provide insights
on how the complex interaction between STN and otherassociated brain areas modulates dynamical properties ofbursting oscillations in the basal ganglia.

1 INTRODUCTION
The subthalamic nucleus (STN) is a key component of
the basal ganglia, a group of subcortical brain nuclei
involved in motor control [1]. Bursting oscillations in the
STN are suggested to be highly associated with motor
disabilities in Parkinson’s disease [2, 3]. Although it isclinically important to clarify the mechanisms underlying
bursting dynamics, complex interactions between STN andother brain regions complicate efforts to do so.
Experiments using anesthetized rats have shown thatSTN neurons exhibit low-frequency bursts that aresynchronized with cortical slow-wave activity (  1 Hz) [4,5]. Cortical ablation abolished these burst oscillations andproduced nearly regular firing in STN. This observation
implies that the cortical inputs to STN, which aretransmitted through the hyperdirect corticosubthalamic
pathway, play a critical role in maintaining the oscillatorycomponent of STN activity. Furthermore, in the case ofpharmacological dopamine depletion, a condition thatmimics a parkinsonian state, the firing rate of STN neurons
was significantly increased to more than twice its originalvalue [5]. The loss of dopamine also evoked low-frequency
oscillations in the GABAergic interneurons in globus
pallidus (GP), which are reciprocally connected with STN[5]. These data may suggest that the low-frequency bursting©ICAROB 2014

2 METHODS
2.1 STN neuron model
We constructed a conductance-based model STN
neuron including synaptic NMDA and GABA currents. The-162-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014high-frequency spiking. In the subsequent phase, which werefer to as the inactive phase, hyperpolarization rapidly
suppresses action potential generation. To model the abruptchange between the two phases, we divided time into theintervals corresponding to the active and inactive phases,
occurring alternately. We define TA and TI to be the time

NMDA conductance is stochastically activated by thearrival of cortical inputs, while the GABA conductance has
a temporally constant magnitude. The membrane potential
V of the model neuron is described asdVC  I leak  I Na  I K  I T  I Ca  I AHP
dt(1) I DIC  I NMDA  I GABA ,intervals for the active and inactive phases, respectively.
Then, the values of TA and TI are determined as follows:

where I leak is a leak current; I Na and I K are voltagegated Na+ and K+ currents, respectively, which are required
for action potential generation; I T is a low threshold T-

TA 

xA
(1  0.2 y A ) ,fc(6)

type Ca2+ current; I Ca is a high-threshold Ca2+ current; andTI (1  x A )(1  0.2 y I ) ,
fc

(7)

I AHP

is

2+

a

Ca -activatedK+

current.

I DIC

is

a

where x A = 0.4 is the fraction of each cortical period

depolarization-activated inward current (DIC), which isactivated by the intracellular Ca2+ entering through NMDA
receptors (NMDARs) [9]. I NMDA and I GABA are thecorresponding to the active phase, and f c = 0.7 Hz is themean frequency of the slow cortical oscillations [5]. y A

NMDA and GABA currents, respectively. The active
currents other than I NMDA and I GABA were taken fromand y I are random variables obtained independently foreach cycle from a standard Gaussian distribution.
We also assumed that NMDA activation times ( t exc
injprevious models of an STN neuron [9, 10]. The NMDA
current is described as(2)
I NMDA  g N mN hN (V  VNMDA ) ,
where

gN

is the peak conductance, VNMDA

Eq. 4) are determined by an inhomogeneous Poisson point
process with phase-dependent (firing) rates. The frequency
r of the NMDA activation is described as
 rA (1  0.2 yr ,1 ), (active phase)(8)r rI (1  0.2 yr ,2 ), (inactive phase)where yr ,1 and yr ,2 are random variables taken fromis theassociated reversal potential, and mN and hN are theactivation and inactivation variables, respectively. The
variable mN is governed by a pair of first-order equations
as follows [11]:dmNm
  mN x N (1  mN )  NN ,
m
dt
dx N
xN .
  xN (1  x N )  ( t  t excj ) NxdtjGaussian distribution. rA = 30 Hz and rI = 3 Hz are the(3)mean activation frequencies for the active and inactive
phases, respectively.

(4)

3 RESULTS

Here, t exc
denotes the arrival time of the j th cortical inputj

We examined the firing dynamics of the STN neuronmodel driven by low-frequency cortical oscillatory activity.
As shown in Fig. 1, burst firing occurred in the presence ofGABAergic inhibition, which can produce slow membrane
oscillations underlying bursting [1,9,10]. Higher STN firing
rates were found around the times when the active phasesof the cortical oscillations occurred (Fig. 1). This result
agrees well with the observation that STN bursts occur
synchronously with slow cortical oscillations [4, 5].
The increased activation frequency of cortical inputs
evoked NMDA activation (Figs. 1A and 1B). While theoverall high levels of NMDA activation presumably helped
support burst firing [9], STN burst onset occurred near thetrough of NMDA activation, which built up throughout theperiod of cortical and STN bursting (Fig. 1C). That is, since
NMDA current evolves slowly, STN activity during bursts(see below).  mN and  xN are the time constants toregulate the decay and rise time constants of NMDA
activation. The GABA current follows(5)
I GABA  gG (V  VGABA ) ,where gG is the conductance and VG is the reversal
potential. The parameters used in the model are selected toreproduce various firing characteristic of STN neurons andthe voltage-dependent properties of active currents [9, 10].
2.2 Slow cortical oscillation
Experimental findings show that during sleep and deep
anesthesia, cortical neurons exhibit low-frequency
oscillations (  1 Hz) with each cycle composed of twophases [6-8]. In one phase, which we refer to as the active
phase, the depolarization of membrane potential induces©ICAROB 2014

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Fig. 1 An example of the response of the STN
neuron model for g N = 30 and gG = 2 mS/cm2.
The time courses of the firing rate r of cortical
inputs (A), the activation variable mN of the NMDA
current (B), and the membrane voltage V (C).
The dashed lines show the center of the time
period of the active phase of cortical oscillations.
in the model did not depend on the precise timing ofcortical firing.
We also investigated the effects of changing the
conductances of GABA and NMDA currents (Fig. 2).
Enhancing GABA conductance considerably increased thecoefficient of variation (CV) of interspike intervals (ISIs)(Fig. 2B), suggesting that GABAergic inhibition plays a
key role in the strengthening of burstiness in our model. On
the other hand, the increase in NMDA conductances wasfound to mainly augment the firing rate (Fig. 2A).
Interestingly, the induction of burstiness by turning on
GABAergic inhibition also tended to decrease thecoherence between the cortical inputs and STN firing (Fig.
2C). This result indicates that the enhanced burst firing may
prevent the STN neuron from abruptly tracking the change
in the cortical activity phases. The depletion of dopamine, a
key feature of Parkinson’s disease, is known to enhance
GABA-mediated outward currents in STN [12]. Based on
our results, in the case of dopamine depletion, higher levelsof GABA inhibition could suppress the ability of STN totrack cortical activity, which may be harmful for
coordinating activities between the cortex and basal ganglia.

©ICAROB 2014

Fig. 2 Changes in the firing rate (A), the coefficientof variation (CV) of interspike intervals (ISIs) (B),and the coherence between the STN and corticalactivities at 0.7 Hz (C) as function of GABA
conductance gG . The NMDA peak conductance was2
g N = 15 (dotted) or 30 (solid) mS/cm .

4 CONCLUSIONSIn this study, we constructed a model of an STN neuron
influenced by cortical slow-wave activity, which
reproduced bursting oscillations. The bursting in the model
was found to depend not only on NMDA associated withcortical inputs but also on GABAergic inhibition.
Because NMDA is a slow current, STN firing times during
bursts were not strongly linked to cortical firing times.
Indeed, we observed that STN firing during bursts was lesscoherent than during regular firing (in the absence of-164-

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GABAergic inputs) and became more irregular withincreases in GABA conductance (Fig 2B). These results
may suggest a useful framework for how the complex
interactions among the STN, cortex and GP shape network
activity patterns, particularly in conditions of dopamine
depletion when oscillations and bursting are enhanced.

REFERENCES[1] Bevan MD, Magill PJ, Terman D, Bolam JP, Wilson CJ(2002), Move to the rhythm: oscillations in thesubthalamic nucleus-external globus pallidus network.
Trends in Neuroscience, 25:525–531[2] Brown P, Oliviero A, Mazzone P, Insola A, Tonali P, Di
Lazzaro V (2001), Dopamine dependency of oscillations
between subthalamic nucleus and pallidum in
Parkinson’s disease, Journal of Neuroscience, 21: 10331038[3] Levy R, Lang AE, Dostrovsky JO, Pahapill P, Romas J,Saint-Cyr J, Hutchison WD, Lozano AM (2001),
Lidocaine and muscimol injections in subthalamic
nucleus reverse parkinsonian symptoms, Brain 124:
2105-2118[4] Magill PJ, Bolam JP, Bevan MD (2000), Relationship ofactivity in the subthalamic nucleus-globus pallidus
network to cortical electroencephalogram, Journal of
Neuroscience, 20: 820-833[5] Magill PJ, Bolam JP, Bevan MD (2001), Dopamine
regulates the impact of the cerebral cortex on thesubthalamic
nucleus-globus
pallidus
network,
Neuroscience, 106: 313-330[6] Amazica F, Steriade M (1995), Short- and long-range
neuronal synchronization of the slow (<1 Hz) corticaloscillation, Journal of Neurophysiology, 73: 20-38[7] Steriade M, Amzica F, Contreras D (1996),
Synchronization of fast (30-40 Hz) spontaneous corticalrhythms during brain activation, Journal of
Neuroscience, 16: 392-417[8] Steriade M, Contreras D, Amzica F, Timofeev I (1996),
Synchronization of fast (30-40 Hz) spontaneous
oscillations in intrathalamic and thalamocortical
networks, Journal of Neuroscience, 16: 2788-2808[9] Kubota S, Rubin JE (2011), NMDA-induced burst firingin a model subthalamic nucleus neuron, Journal of
Neurophysiology, 106: 527-537[10] Terman D, Rubin JE, Yew AC, Wilson CJ (2002),
activity patterns in a model for the subthalamopallidal
network of the basal ganglia, Journal of Neuroscience,
22: 2963-2976[11] Wang XJ (1999), Synaptic basis of cortical persistentactivity: the importance of NMDA receptors to working
memory, Journal of Neuroscience, 19: 9587-9603[12] Shen KZ, Johnson SW (2005), Dopamine depletion
alters responses to glutamate and GABA in the rat
subthalamic nucleus, Neuroreport, 16: 171-174©ICAROB 2014

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Japan, January 11-13, 2014

Finite-time Stabilization for Nonholonomic Chained Form Systems
with Communication Delay
Hengjun Zhang and Chaoli Wang
Department of Control Science and Engineering, University of Shanghai for Science and Technology,
Shanghai, 200093, PRC(E-mail: rory110@163.com; clclwang@163.com)

Abstract: In this paper, the problem of finite-time stabilization is developed for nonholonomic chained form systems
with communication delay in the input. The finite-time control laws are presented by utilizing the switching control
strategy and the theory of finite-time stability, which can make the states of the nonholonomic chained form systems toconverge from any non-equilibrium state to the equilibrium or a given point in a finite time. Finally, the simulation
results show the effectiveness of the proposed control approach.
Keywords: Finite-time stabilization, nonholonomic chained systems, communication delay.

agent systems with input and communication delays is
studied based on the frequency-domain analysis in [8].
And the literature [9] presents a control strategy forstabilization of nonholonomic control systems withstrongly nonlinear uncertainties and time delay.
In order to get the better convergence speed andhigher steady state precision, many researchers havepublished literature on the finite-time stabilization. And
three kinds of global finite-time feedback stabilization
methods have been presented as follow: homogeneous
properties, backstepping constructive approach andsliding mode technology. A finite-time controller is
proposed in [10] for a class of high-order nonlinear by
applying homogeneous properties of systems andbackstepping constructive approach. The literature [11]considers the finite-time stability of homogeneous
systems and gives Lyapunov stability theory of finitetime. Recently, based on backstepping constructive
approach, finite-time stabilization is discussed in [12]for a class uncertain nonlinear systems. And the sliding
mode technology is investigated in [13]. The distributed
control laws in [14] are designed such that multiple nonholonomic mobile agents can be all in agreement within
finite time. In [15], the problem of finite-time stability
and stabilization for retarded-type functional differential
equations is considered.
In this paper, we introduce a class of chained form
nonholonomic systems with communication delay in the
input and then study the problem of finite-timestabilization for the concerned nonholonomic systems.
A global finite-time stabilization controller is presented
by using switching control strategy, and making that the
all states converge to a given point or the equilibrium
point in a finite time.
The paper is organized as follows. In Section II, we
will give a formalization of the problem considered inthis article and some preliminary lemmas. Section III
states our main results including controller design andstability analysis. In Section IV, the simulation results
carried out to validate the theoretical framework.

I. INTRODUCTION
The control and stabilization of nonholonomic
systems have received considerable attention during thelast few years. Nonholonomic systems with nonholonomic or non-integrable constraints are frequently
used to describe the practical control systems. The
representative examples of such systems are mobile
robots, car-like vehicles, n-level trailer systems and soon. See the survey paper [1] for more details andreferences therein for more examples. So it is importantfrom the view point of applications to study
nonholonomic control systems. From a theorem due to
Brockett [2], the nonholonomic system cannot bestabilized at an equilibrium point by pure smooth (oreven continuous) state feedback controller.
Therefore, the stabilization problem of nonholonomic systems is still extremely challenging which
has attracted a continuously increasing attention in thecontrol community. To overcome this problem, manymethods have been proposed, such as continuous timevarying feedback control laws [3], hybrid feedback
control laws [4] and discontinuous feedback control
laws [5] and so on. Moreover, the robust stabilization
problem has been widely researched by using thesevalid approaches.
However, most models considered are in ideal cases
without delays. From a practical point of view, delays ina control system can create significant obstacles to thestabilization problem and potentially degrade theperformance of the closed-loop system. It is well known
that guaranteeing the stability of a control system withtime delay is a challenging problem [6]. The
communication delay in the input is related toprocessing and connecting time for the packets arriving
and which also occur when actuators and controllers areconnected by networks. So it is very important to study
the delay effect on nonholonomic systems. In [7], theclassical set-point control problem is studied for rigid
robots with input-output communication delays in theclosed-loop system. The consensus problem for multi-©ICAROB 2014

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Finally, the conclusions of the paper are given in
Section V.

ky  t   x  t    LiA, Bi ut ,II. PROBLEM STATEMENT

where

A class of chained form nonholonomic systems with
communication delay in the input to be considered inthis paper is described by

and

input

ut (s)  u(t  s) :[ i ,0]  R m for everys [ i ,0] and0

L(iA, Bi )ut   e A(  i  s ) Biu (t  s)ds,

 x0  u1  t   1  ,
(1) xi  xi 1u1  t   1  , i  1, , n  1
 xn  u2  t   2  ,where xi (i  0,1, , n) and u j ( j  1, 2) are the

state

(4)

i 0

 i

then from (2) and (3), we can havey  t   Ay  t   Bu  t  ,(5)kB   e A i Bi .

with

i 0

of systems respectively, andwith both  j ( j  1, 2) being

Lemma 2.2 If the system (5) is finite-time stable
by a feedback control law

positive constants.
For convenience, some symbols are given in thefollowing at first:(6)

u j  u j (t   j )u t   k t  f  y t  ,k (t ) bounded and f : Rn  Rn continuous
such that f (0)  0 and there exists a function  ofclass  such that ‖f ( x‖) m   ‖‖( x n ) , then thewithsig   x   x sign  x  ,where   0 ,x denotes the absolute value of real
number x and sign denotes a sign function.

system (3) is finite-time stable by the feedback
controllerku  t   k  t  f  x  t    LiA, Bi ut  .
i 0



Next, we will introduce the following lemmas thatare needed for our controllers later.
Lemma 2.1[11] Consider the following nonlinear
systemy t   f  y t 

where

V:

neighborhood

R

n

of

originNow we consider the finite-time stabilization of theintegrator systems with delay in the input described by(2) xi  t   xi 1  t  , i  1,
 xn  t   u  t    ,is continuous on an openf  0   0 . The

and

x0 

x  t , x0  ofx  t , x0  \ 0 forsystem (2) is defined withx(t )  Ax(t )  Bu(t   )

0

A


0

Moreover, if R , the origin is globally
finite-time stable equilibrium.
We consider the systemn kn sig n  yn  ,

(9)

where

sig   y   y sign  y  , y  t   x  t   L A, B ut ,


nn

and

to the paper [15], we know that if let©ICAROB 2014

, kn )  R n such thatpolynomialu  k1sig 1  y1  t  0, (3),x(t )  R , u(t )  R , A  Rnmand hi are positive constants. AccordingBi  Rwheremk  (k1 ,stable under the continuous feedback control law

i 0

n

0

0
0

 
 , B   .

 0

 
0
1

s n  kn s n1   k2 s  k1 is
Hurwitz, there exists   (0,1) such that for all  (1   ,1) , the system (8) is globally finite-timethex  t   Ax  t    Bi u  t   i ,

01

Lemma 2.3 Let

x T

k

1

According to the Lemma 2.2 and the literature [15],it is not difficult for us to get the following lemma.

t  0, T  and satisfies lim x  t , x0   0 , andx  t , x0   0 , if t  T .

(8)

with

\ 0 , there is a settling

time T  0 such that every solution, n 1

which can be denoted in short byequilibrium x  0 of the system is finite-time stableif:(i) it is asymptotically stable, in, an open ;neighborhood of origin ,with(ii) it is finite-time convergent in, that is , for
any initial condition

(7)

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B  e A B , 1 ,,  n satisfy

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014 i 1   i i 1  2 i 1   i  , 2  i  n(10) n   ,  n 1  1.


 xi  xi , i  1, , n xn  u2  t   2  .
Based on Lemma 2.3, the control law

III. CONTROLLER DESIGN

u2  k1sig 1  y1  t   

In this section, the main results will be presented.
Based on the switching control strategy, the finite-time
controllers u1 and u2 are designed to make that all

wherestates of the systems (1) can converge to theequilibrium point in a finite time. To solve the finitetime stabilization problem, we divide the systems (1)into a first-order subsystem

x0  u  t  1  ,the subsystem (17) globally finite-time stable.
Substituting the transformation (16) into the control
law (18), the states x1 , x2 , , xn and u2 canconverge to the equilibrium point in a finite timeT1   2 via the control law (13).

, n 1(12)

When t  T1   2 , the states

Theorem 3.1 The systems (1) can be finite-time, kn )  R n such that thes n  kn s n1  kn sign y t  ,

1
2
(20)
x0  t  .
2Taking the time derivative of V ( x0 ) along (11)

(13)

n

yi  t   ci 1 xi  t   L 2A, B c n1u2 , B  e A 2 B,

2

0

1

10  2

 2V10  2

(21).

T2  | x0 (t ) |10 1  0 

time

via

the

switching

control law (14). Hence, the states, xn , u1 , and u2 can converge to the

T  T1  T2  1   2 .

This completes the proof of Theorem 3.1.



IV. SIMULATIONIn this section, we will give an illustrative exampleof Theorem 3.1. Consider the following nonholonomic
chained form systems with communication delay x0  u1  t   1  ,
(22)
 x1  x2u1  t   1  , x2  u2  t   2  .
Choosing 1   2  0.5 and the initial values ofsystem (22) are ( x0 , x1 , x2 )  (2, 1, 2) . Selectingc  1 , k1  2 , k2  3 and  0  0.5 ,  2  0.4 ,by the formula (10), we have 1  0.25 .

Consider the following transformation(16)

With the help of transformation (16), the subsystem(15) can be expressed as©ICAROB 2014  x01 0

1  x t   equilibrium point after time2
u1   sig 0  x0  1  x0  t   1  ,
(14)

u

0.

 2
Proof: When t  T1   2 , because of u1  c , so
the n -order subsystem (12) can be written as xi  cxi 1 , i  1, , n  1(15) xn  u2  t   2  .

,n

1 0

settling

x0 , x1 ,with the matrix A and B are same as in the Lemma
2.3.
When t  T1   2 , we also havei 1 xi  c xi , i  1,n 1
u2  c u2 .

V   x0

So the subsystem (11) can be stabilized within the

where



, xn haveV  x0  t   

switching control laws:
When t  T1   2 , we have
u1  c,
1u2  k1sig  y1  t   

x1 , x2 ,reached on the equilibrium point, thus, we only consider
the first order subsystem (11).
Construct a Lyapunov function

 k2 s  k1 is Hurwitz.
And choosing the proper parametersc  0 and
0 , 1 , ,  n   0,1 , then take the following
polynomial

yi  t   xi  t   L 2A, B u2 , B  e A 2 B, (19)A and B are the same as in the Lemma 2.3, and1 , ,  n satisfy the conditions (10). It can renderand a n -order subsystemstabilizable if let k  (k1 , kn sig n  yn  t   , (18)

the

(11)


 xi  xi 1u1  t  1  , i  1,
 xn  u2  t   2  .

(17)

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REFERENCES[1] I. Kolmanovsky, N. H. McClamroch (1995),
Developments in nonholonomic control systems. IEEE
Contr. Syst. Mag. 15(6):20-36.
[2] R. W. Brockett (1983), Asymptotic stability andfeedback stabilization. in Proc. R. W. Brockett, R. S.
Millman, H. J. Sussmann (Eds.), Differential GeometricControl Theory, Birkhauser, Boston. 181-208.
[3] Y. P. Tian, S. Li (2002), Exponential stabilization ofnonholonomic dynamic systems by smooth timevarying control. Automatica. 38(7):1139-1146.
[4] A. P. Aguiar, A. Pascoal (2000), Stabilization of theextended nonholonomic double integrator via logic
based hybridcontrol. in Proc. of 6th international IFAC
symposium on robot control, ViennaCAustria. 208-213.
[5] M. C. Laiou, A. Astolfi (1999), Discontinuous
control of high order generalized chained systems.
Systems Control Letters. 37:309-322.
[6] J. P. Richard (2003), Time-delay systems: An
overview of some recent advancesand open problems.
Automatica. 39(10):1667-1694.
[7] Y. C. Liu, N. Chopra (2012), Control of robotic
manipulators under input/output communication delays:
theory and experiments. IEEE Transactions on Robotics.
28(3):742-751.
[8] Y. P, Tian, C. L. Liu (2008), Consensus of multiagent systems with diverse input and communication
delays. IEEE Transactions on Automatic Control.
53(9):2122-2128.
[9] Y. Y. Wu, Y. Q. Wu (2010), Robust stabilization ofdelayed nonholonomic systems with strong nonlinear
drifts. Nonlinear Analysis: Real World Applications.
11(5):3620-3627.
[10] Y. G. Hong (2001), Finite-time stabilization andstabilizability of a class of controllable systems.
Systems Control Letters. 46(4):231-236.
[11] S. P. Bhat, D. S. Bernstein (1997), Finite-timestability of homogeneous systems. in Proc. of the 1997
American Control Conference. Albuquerque. New
Mexico: American Automatic Control Council. 25132514.
[12] X. Q. Huang, W. Lin, B. Yang (2005), Global
finite-time stabilization of a class uncertain nonlinear
systems. Automatica. 41(5):881-888.
[13] Y. G. Hong, G. W. Yang, D. Z. Cheng, et al. (2004),
Finite- time convergent control using terminal sliding
mode. Journal of Control Theory and Applications.
2(1):69-74．
[14] J. K. Wang, Z. H. Qiu, G. S. Zhang et al. (2012),
Finite time consensus for multiple non-holonomic
agents with communication delay. in Proc. of the 31st
Chinese Control Conference. 6267-6272.
[15] E. Moulaya, M. Dambrineb, N. Yeganefarc et al.
(2008), Finite-time stability and stabilization of timedelay systems. Systems Control Letters. 57(7):561-566.

Fig.1. The state response of system (22)Fig.2. The response of controllers u1 and u2
From Fig. 1, it is easy to see that the state variablesof systems can be driven to the equilibrium point in a
finite time. As shown in Fig. 2, the control inputs u1
and u2 can also converge to the origin.

V. CONCLUSION
In this paper, a globally finite-time stabilization
problem is presented for nonholonomic chained form
systems with communication delays in the input. Byutilizing finite-time stability theory and switching
control strategy, the nonholonomic chained systems canbe divided into different subsystems, then thecontrollers of subsystems are given with different
methods such that each state of the systems canconverge to the equilibrium point in a finite time.
Finally, a simulation example is provided to verify thatthe designed controllers are effective.

ACKNOWLEDGMENT
This paper was partially supported by The National
Natural Science Foundation (61374040); Key Disciplineof Shanghai (S30501); Scientific Innovation program(13ZZ115); Graduate Innovation program of Shanghai(54-13-302-102).

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Sliding Mode Variable Structure Control for Magnetic Levitation Vehicles
Juanjuan He and Yingmin Jia
The Seventh Research Division and the Department of Systems and Control, Beihang University (BUAA),
Beijing 100191, China(E-mails: hejuanjuan@smss.buaa.edu.cn; ymjia@buaa.edu.cn).
Abstract: This paper focuses on stability control for the levitated positioning of the magnetic levitation vehicle system. For
the nonlinear magnetic levitation system model, the output feedback linearization method is first employed to derive a global
linearization error model. However, there exists uncertain item in the error model. To stabilize this error model, the adaptive
sliding mode variable structure control method is used. Simulations show that the magnetic levitation system can be stability andtrack the desired signals quickly under the proposed control scheme.
Keywords: adaptive sliding mode variable structure control, magnetic levitation vehicle system, nonlinear model, output
feedback linearization
1 INTRODUCTION

nally, a series of simulation experiments are shown in this paper, which is used to demonstrate the effects of output feedback linearization method and adaptive sliding mode variable
structure controller.
This paper is organized in five sections. In the next section, model analysis of magnetic levitation vehicle system isformulated, and output feedback linearization is proposed.
Section 3 is related to the controller design. Simulation results are presented in section 4. Finally, in section 5, someconcluding remarks are outlined.

As the smart transportation establishing becomes a hot research issue in recent years, how to improve the operating
efficiency and convenience of the entire city is becoming increasingly important. Therefore, it is necessary to establish a
new high-speed and efficient means of transport. Up to 500km/h of speed, the maglev train can greatly improve the transport capability. Furthermore, magnetic levitation technology has seen a rapid development since the 20th century, andmade the interesting in the field of real-life applications [1],such as transportation systems [2,3], wind tunnel levitation[4], magnetic bearing systems and antivibration table. However, the design of maglev system controller still presents
plenty of formidable challenges [5] including the nonlinear
solenoid model, instability, and inevitably uncertain parameters. Hence, a proper controlling mode should be designed
for the suspension system to guarantee the robust stability ofthe entire system. For traditional methods, nonlinear magnetic levitation vehicle system model is linearized at one equilibrium point [6], which leads to the fact that the design ofthe controller seriously depends on the selected equilibrium
point. So this type of linearization cannot be applied to thesituation that the gap is a large range of variation. Thus if the
traditional linearization method is applied to this situation, it
may lead to the performance degradation or instability of thesystem. Hence, a new solution should be proposed.

2 MODEL ANALYSIS OF THE MAGNETIC

LEVITATION VEHICLE SYSTEM
2.1 Nonlinear Solenoid Model Analysis
The schematic diagram of the magnetic levitation system is depicted in Fig.1. For the sake of achieving the goalof high-precision positioning, a complex magnetic levitation
system vehicle model needs to be analyzed thoroughly. Before deriving the model, we make the reasonable assumptionthat the air-gap flux leakage is zero. Then, the idealized nonlinear solenoid model can be calculated asF=(1)where F is the electromagnetic force between the coil andsuspension; the constant µ0 = 4π × 10−7 H/m is the absolute
magnetic permeability; N is the coil turn; A is the magnetic
cross section; i(t) is the coil current, and x(t) is the levitation
height between coil and suspension.
The coil model is given by

In this paper, latest control method is applied in allusion
to the magnetic levitation planner. Firstly, output feedback
linearization technique is applied to the nonlinear magnetic levitation vehicle system model, which derives a global
linearization error model. However, the simplified model isunstable and includes uncertain factors, so the adaptive sliding mode variable structure controller design method is used
here, which makes the linearization error system stable. Fi-©ICAROB 2014

µ0 N 2 Ai2 (t)4x2 (t)d ψ (x, i)(2)
dt
where u is control voltage, and R ,ψ are circuitous resistanceand flux linkage respectively.
u(t) = Ri(t) +-170-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Rx2

Sx x2

where K = xx31 ,B = m1 , and C = − x13 − 2x2 3 3 .
1Obviously, when x1 , x3 ̸= 0,the K ̸= 0. In physics system,x j , j ∈ (1, 3) , which are central levitation height of the suspension and coil current respectivelythus their product could
not be zero, so the decoupling matrix K is invertible.
Thus, the decoupling control inputs can be obtained as
U = K −1 (Uc −C)(7)where Uc = v .
Then the linearized system model can be arrived at by applying Eq.(7) to Eq.(6) and finally obtained as follows
Fig. 1. Schematic diagram of magnetic levitation systemŸ = BUc =2.2 System Dynamics
The complete model of the maglev system can be derived
by using Newton′ s Law as∑ ⃗F = mẍ(t)DESIGN(3)

The error output vector is defined as E = Y −Yd , yD whereis the desired position and y is the current position. Then,
Eq.(8) can be rewritten as
...
... 1
E = v − Yd +W
m

(4)

(9)

where W denotes the system’s uncertainty and is assumed
bounded ∥W ∥ ≤ P. Here, we assume that P is unknown.
In order to design a controller which possesses a better ability to gain high robustness and self-tuning property, twoadvanced control methods have been integrated. In this paper, we will introduce the controller design and provide thestability analysis.
Based on the model Eq.(9), which is compactly reexpressed aswhere m is mass of the suspension; g is acceleration of gravity; and A is magnetic area.
2.3 Analysis of Nonlinear Model
Let the states be chosen as x1 = x, x2 = ẋ, x3 = i. u is
the input voltage of the system changing from 0 to 5 volts,
and X = [x1 , x2 , x3 ]T is the state vector. Thus, the state-spacemodel of the magnetic levitation vehicle system can be written as
Ẋ(t) = F(X) + G(X)U(t)(5)
Y (t) = x1 ,ż1 = Ėż2 = Ë...
...
ż3 = E = BUc − Y d +W

where S = µ0 AN
2 , and F(X), G(X),U(t) are shown as follows


x2
( )2


x3
S

F(X) =  2m x1 − g x2 x3
2x1 − S Rx1 x3
2(10)

Furthermore, we define sliding surface variable S as
S = z3 + Σ1 z2 + Σ2 z1(11)where Σ1 ,Σ2 are positive constants. In this paper, we try to
regulate the state Z = [z1 , z2 , z3 ]T to zero, in other words, thecurrent output will achieve the goal of precise tracking of positioning. In the context of SMC, asymptotical convergenceof the variable S to zero will apparently imply asymptoticalconvergence of Z as well to zero. To validate this, we willneed to investigate the dynamics of the sliding surface variable S as follows


0
G(x) =  0  ,U(t) = u(t).
2S x1
2.4 Linearization of System Model
For the purpose of the afterward controller design, thefeedback linearization is applied to the system model shownas Eq.(5) in this subsection. As a result, the third derivativeof system outputs can be finally obtained as follows...
(6)
Y = B(C + KU)©ICAROB 2014

(8)

3 ADAPTIVE SLIDING MODE CONTROLLER

Firstly, to obtain the system dynamics, we substitute Eq.(1) and Eq.(2) into Eq.(3). As a result, the dynamic equations can be rewritten asµ0 N 2 Ai2mẍ(t) =− mg
4x2 (t)1v
mṠ = ż3 + Σ1 z3 + Σ2 z2...
= BUc − Y d +W + Σ1 z3 + Σ2 z2

-171-

(12)

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

4 SIMULATION RESULTS

In addition to the SMC, an adaptive controller is applied
for estimating the parameters of the system online while simultaneously controlling the system. After we have the estimates of the system parameters, then these estimates can be
adopted to the control command in Eq.(12) to form appropriate SMC with boundary layer as...
Uc = B−1 [−k1 S − P̂sgn(S) + Y d − Σ1 z3 − Σ2 z2 ]In this section, a number of typical simulation results arepresented, including the transient and the steady-state responses in different situations. The simulation results areprovided to demonstrate the performance of the developed
magnetic levitation system with controller presented in section 3. Based on these results, we will make some conclusions which are important for the future work in this research.
In order to demonstrate the controlling performance ofthe magnetic levitation vehicle system more effectively, theblock diagram of the system as shown in Fig.2 is constructedin this section. Base on the simulation block diagram, serials
experiments are performed.

(13)

where k1 is a positive constant , P̂ is the estimate of P, andsgn(·) is the symbolic function.
Thus, substituting Eq.(13) into Eq.(12), we obtain...
Ṡ = (Σ1 z3 + Σ2 z2 − Y d +W ) + B[B−1 (−k1 S...
(14)− P̂sgn(S) + Y d − Σ1 z3 − Σ2 z2 )]= −k1 S − P̂sgn(S) +W
By appropriate gains k1 , P̂, Σ1 , and Σ2 , we can ensure
the convergence of S. Hence, the state Z converges to zero,
which means the tracking error E to zero, and meanwhile
maintain the estimation error Pe converging to zero.
Stability analysis: we define a Lyapunov function candidate V , which is a positive definite function
1
1 1 e2
V = ST S +
P
2
2ρ

(15)

where the estimation error is defined as Pe = P − P̂, P̂ is theestimates of P , and ρ is positive.
The time derivative of the Lyapunov candidate function V
can be found to beV̇ = ST Ṡ +Fig. 2. Block diagram of magnetic levitation system

1 e ė
PPρ[] 1= ST −k1 S − P̂sgn(S) +W − PeP̂˙
ρ1≤ −ST k1 S − P̂∥S∥ + ∥S∥P − PeP̂˙
ρ(16)

Using the adaptive control theory to establish bounds ofparameter estimates in the presence of modeling error terms,
the adaptive laws is devised as
Pė = −P̂˙ = −ρ ∥S∥(17)

After substituting Eq.(17) into Eq.(16), we can geteV̇ ≤ −ST k1 S − P̂∥S∥ + ∥S∥P − P∥S∥
= −ST k1 S − P̂∥S∥ + P̂∥S∥(18)< −ST k1 S < 0
where k1 > 0.
According to Lyapunov stability theory, the tracking error E(t) will converge to zero. In other words, it obviously
achieves the goal of precise tracking of positioning.

©ICAROB 2014

Fig. 3. Tracking response performance of central levitation height
along the x-axis with uncertainty bound-172-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

height can track the desire signal x(t) = 0.5sint + 1. Fromthe position error curve shown in Fig.3, we can see that theerror can be controlled within 0.03cm. The simulation result,as shown in Fig.4, demonstrates the ball can keep balance atthe equilibrium point. The simulation results for repeating a
0.2cm step-train response in x-axis, as shown in Fig.5, indicates that the effective tracking performance of the proposedmagnetic levitation vehicle system.
5 CONCLUSIONSThis paper does a research on the suspension control problem of the magnetic levitation vehicle system, which in nature, is served as strongly coupling, nonlinear and instability. The related analysis had been applied in to the maglev
system. The linearization method and adaptive sliding mode
variable structure can stable the system effectively.
6 ACKNOWLEDGMENTS
This work was supported by the National Basic Research Program of China(973 Program: 2012CB821200,
2012CB821201) and the NSFC(61134005, 60921001,
61327807).
Fig. 4. Simulation results for central levitation height

REFERENCES

holding at 2cm with uncertainty bound[1] D. Chao, Y. Kato, and D. Spilman(1993), ASliding Mode andClassical Control of Magnetic Levitation Systems, IEEE Control Syst. Mag.,42-48[2] B. V. Jayawant, P.K.Sinha, and D.G.Aylwin(1976), Feedback Control Sytem for D.C. Electromagnets in PassengerCarrying Vehicles, Int. J. Control,11:627-639[3] M. Proise(1993), System Concept Definition of the Grumman Superconducting Electromagnetics Suspension (EMS) Maglev Design, Maglev 93 Conf. Argonne National
Laboratory,11:19-21[4] E.E. Covert, M. Vlajinac, T. Stephens, and M. Finston(1973),
Magnetic Balance and Suspention System for use with Wind
Tunnels, Progress in Aerospace Science,11: 27-107[5] M.Y. Chen, M.J. Wang, and L.C. Fu(2008), Modeling andcontroller design of a maglev guiding system for applicationin precision positioning, Industrial Electronics, IEEE Transactions on, IEEE,11: 4-14
[6] H.X. Chen, Z.Q. Long, and W.S. Chang(2006), Fault tolerant
control research for high-speed maglev system with sensor
failure,Control and Automation, IEEE, 11:2281-2285

Fig. 5. Simulation results for repeating a 0.2cm
step-train response in x-axis

The tracking response performance of magnetic levitation
vehicle system is shown in Fig.3, where the central levitation©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Variable-poled tracking control of a two-wheeled mobile robot using differential
flatness
Liming Chen and Yingmin Jia
The Seventh Research Division and the Department of Systems and Control, Beihang University (BUAA),
Beijing 100191, China(E-mails: clmtest@126.com; ymjia@buaa.edu.cn).
Abstract: This paper investigates the tracking controller design of a two-wheeled mobile robot in its kinematic model anddynamic model. Differential flatness and linear time-varying(LTV) systems’ PD-spectral theory are used. Based on differential
flatness, original system is transformed via a state prolongation and state transformation into a normal form to apply feedback
linearization. Then using PD-spectral theory, variable poles of tracking error dynamics are assigned to realize trajectory tracking
stability. Finally, simulation results are presented to demonstrate the feasibility and effectiveness of the proposed method.
Keywords: mobile robot, nonlinear control, differential flatness
1 INTRODUCTION[10], which can be seen as a natural extension of the conventional eigenvalue-eigenvector theory for LTI systems. Afterthe state transformation based on differential flatness, the application of PD-spectral theory becomes much easier. By assigning time-varying ”poles”, more generalized tracking error dynamics can be obtained. And the poles can be changed
at any time as we want.
The rest of this paper is organized as follows: In section
2, the kinematic model and dynamic model of a two-wheeledmobile robot are derived and analyzed with differential flatness. Section 3 presents the design of control law in bothmodels. Simulation results are shown in section 4. Finally,
concluding remarks are given in section 5.

Wheeled mobile robots have been proven to be one of themost active areas of research since they are much useful invarieties of applications ranging from industrial settings, tomilitary systems, to home robotics, etc. One of the mainlines of research is the trajectory tracking problem, whichis concerned with driving a mobile robot as close as possible to a desire explicit trajectory. And the tracking control
approaches include backstepping [1], sliding mode control[2], linearization [3], neural network-based control [4], fuzzycontrol [5] and differential flatness-based control [6-8] whichis also used in this paper.
Differential flatness has been introduced by Fliess et al[9]. It is a very useful tool for nonlinear controller design.
Roughly speaking, a system is differentially flat, if there exists variables of the same dimension as inputs, called flat
output, such that states and inputs can be algebraically expressed in terms of flat output and its derivatives. Moreover,this mapping is invertible, and the system is equivalent to alinear one. If the desire trajectory of flat output is given, thenby performing a feedback linearization and designing a timeinvariant controller for the linearized system around the desire trajectory, stable tracking error dynamics are achieved.
Kinematic model and dynamic model of a two-wheeled mobile robot have been proven to be differentially flat by choosing the center position of the wheel axle of the robot as theflat output. In [7,8], controllers are designed in this scheme.
However, in these controllers, the parameters of the tracking
error dynamics are constant, which means that the convergence speed of tracking error is fixed, and this brings limitation for this method.

2 MODELSROBOT WITH DIFFERENTIAL FLATNESS
2.1 Kinematic modelFig. 1 shows that the robot’s configuration in Cartesian
coordinates is given by q = [x, y, θ ]T , where (x, y) is thecoordinates of the center of the wheel axle and θ is theheading angle of the robot. With the assumption of noslip condition at the wheel contact points, the velocity of
the wheel centers are parallel to the heading orientation andthe nonholonomic constraint is given by C(q)q̇ = 0, whereC(q) = [sinθ , −cosθ , 0]. Then using this constrain equation,
the kinematic model of the mobile robot can be written asq̇ = S(q)v


(1)


cos θ 0where S(q) =  sin θ 0 , v = [v, ω ]T is the vector of the0
1heading speed v and turning speed ω of the mobile robot.

In this paper, the use of PD-spectral theory of LTV systems is proposed for tracking controller design. The PDspectral theory of LTV systems has been developed by Zhu©ICAROB 2014

OF TWO-WHEELED MOBILE-174-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Y

where ux and uy are parameters to be designed, and substituting (5) into the system equations (4), one gets

v
Z
b

d

T

ż1 = ż1 ,z̈1 = uxż2 = ż2 ,z̈2 = uy

(6)

Actually, if only the states of the original system (2),[x, y, v, θ ]T , can be estimated, using the relationship of statetransformation, the inputs (5) can be rewritten as( x, y )u1 = ux cos θ + uy sin θ , u2 = (uy cos θ − ux sin θ )/v (7)X

2.2 Dynamic modelFig. 1. The configuration of a two-wheeled mobile robot with no slip.

By ignoring the mass of the wheels, the equations of motion can be derived using Euler-Lagrange method as

To outline how the kinematic model of the two-wheeledmobile robot is differentially flat, we need to select suitableflat outputs and express all state variables and inputs in termsof the flat outputs and their derivatives. The dimensions offlat outputs should be equal to that of the inputs. Here, we
can choose the center position of the wheel axle (x, y) as theflat outputs (z1 , z2 ).
With the chosen flat outputs, the states can be expressed
as x = z1 , y = z2 , θ = arctan żż12 And the two inputs can be√
1 ż2written as v = ż21 + ż22 , ω = ż1 żz̈22 −z̈
It can be noticed that+ż2
1

M(q)q̈ + C̄(q, q̇)q̇ = E(q)τ −CT (q)λ
where



]
[
m
0
−dm sin θ
τr
M=
0
m
dm cos θ  , τ =τl−dm sin θ dm cos θ
d2m + I

0 0 −dmθ̇ cos θ
cos θ cos θ
1C̄ =  0 0 −dmθ̇ sin θ  , E =  sin θ sin θ 
r
0 0
0
b
−b
(9)
Here, m is the robot mass, I the moment of inertia of the robot
about its center of mass, d the distance between the center ofmass and the center of the wheel axle, r the wheel radius,b half distance between the two wheels, τl and τr the motor
torques on the wheels, and λ the constraint force.
By differentiating (1), one gets q̈ = Ṡv + Sv̇. Then by
substituting q̈ into (8), pre-multiplying by ST , and using theproperty ST CT = 0, one can have2

the expression of state θ contains the first order derivativesof both the flat outputs, ż1 and ż2 . According to differential
flatness theory, the system (1) need to be extended to 4 dimensions. Apply one prolongation of v by considering it as
an additional state, then the extended system is given byẋ = v cos θv̇ = u1ẏ = v sin θθ̇ = u2(2)

where u1 and u2 = ω are new inputs of the extended system.
The new inputs can be calculated asż1 z̈2 − z̈1 ż2ż1 z̈1 + ż2 z̈2, u2 =
u1 = √ż21 + ż2222
ż1 + ż2v̇ = −(ST MS)−1 ST (M Ṡ + C̄S)v + (ST MS)−1 ST E τ(3)The extended system (2) can be transformed via a state transformation [x, y, v, θ ]T ←→ [z1 , z2 , ż1 , ż2 ]T into a normal form
√
ż2
ż2
ż1 = ż1 , z̈1 = u1 cos(arctan ) − u2 ż21 + ż22 sin(arctan )
ż1
ż1
√
ż2
ż2
2
2
ż2 = ż2 , z̈2 = u1 sin(arctan ) + u2 ż1 + ż2 cos(arctan )
ż1
ż1
(4)
Since the expressions of new inputs u1 and u2 in (3) are derived from the system equations (2), the state transformation
ensures that (3) satisfies the equations (4). By replacing z̈1
and z̈2 in (3) with ux and uy , respectively:ż1 ux + ż2 uyż1 uy − ux ż2
u1 = √, u2 =ż21 + ż22
ż21 + ż22

©ICAROB 2014

(8)

(5)

-175-

(10)

This can finallyas v̇ = A(v)+Bτ], where A(v) =] be calculated
[
[
1
1
2
dω
mr
mr
,
B
=
. After introb
ωv− r(d 2bm+I)
− ddm
2 m+I
r(d 2 m+I)
ducing an input transformation ū = [ū1 , ū2 ]T = A(v) + Bτ ,the dynamic model can be written asq̇ = S(q)v,v̇ = ū(11)Here, we can also choose the center position of the wheel
axle (x, y) as the flat outputs (z1 , z2 ) and then all state variables and inputs can be expressed in terms of the flat outputs
and their derivatives. The expression of state ω contains thesecond order derivatives of both the flat outputs, z̈1 and z̈2 ,which means that the system (11) need to be extended to 6
dimensions so that it can be transformed into a normal form

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

via a state transformation. On applying one prolongation ofū1 , the extended system is given byẋ = v cos θv̇ = ū1ū˙1 = ũ1ẏ = v sin θθ̇ = ω

ω̇ = ũ2

3.2

If the desired trajectory of flat outputs (z1 , z2 ) are given by (z1d (t), z2d (t)), tracking error can be introduced as
e = [e1 , e11 , e2 , e21 ]T = [z1 − z1d , ż1 − ż1d , z2 − z2d , ż2 − ż2d ]TUsing (6), parameters ux and uy can be designed as(12)

where ũ1 and ũ2 = ū2 are new inputs of the extended system. The state transformation can be written as[x, y, v, θ , ū1 , ω ]T ←→ [z1 , z2 , ż1 , ż2 , z̈1 , z̈2 ]T . Now, we calculate the third order derivatives of the flat outputs directly using (12) as[ ... ]
[
]
z1
ũ1
=C+D
(13)
...
z2
ũ2

ux = z̈1d − α1x (t)e1 − α2x (t)e11
uy = z̈2d − α1y (t)e2 − α2y (t)e21

ë1 + α2x (t)ė1 + α1x (t)e1 = 0ë2 + α2y (t)ė2 + α1y (t)e2 = 0]−v sin θv cos θ
(14)
...
...
By replacing z 1 and z 2 with ūx and ūy , respectively, thisyields
[
]
[
]
ũ1
ūx
= D−1 (
−C)
(15)
ũ2
ūy
3 DESIGN OF CONTROL LAW
3.1 Introduction of PD-spectral theory [10]Consider SISO LTV systems represented by the nth-orderscalar LTV dynamical systems of the form:(16)

Control law of dynamic model...
ūx = z 1d − α1x (t)e1 − α2x (t)e11 − α3x (t)e12...
ūy = z 2d − α1y (t)e2 − α2y (t)e21 − α3y (t)e22

(17)

(21)

By designing appropriate PD-eigenvalues ρ1 , ρ2 and ρ3 ,SD-eigenvalues can be calculated as λ1 = ρ1 , λ2 =
ρ2 + V̇V22 , λ3 = ρ3 + V̇V33 − V̇V22 where V2 = ρ2 − ρ1 , V3 =


1
1
1
, and then control gains
det 
ρ1
ρ2
ρ3
2
2
2
ρ1 + ρ̇1 ρ2 + ρ̇2 ρ3 + ρ̇3
are obtained by α1x (t) = −λ3 λ2 λ1 + λ3 λ̇1 + λ̇2 λ1 + λ2 λ̇1 −
λ̈1 , α2x (t) = λ3 λ2 + λ3 λ1 + λ2 λ1 − λ̇2 − 2λ̇1 , α3x (t) = −λ3 −λ2 − λ1 . α1y (t), α2y (t) and α3y (t) can be designed in thesame way.

(18)

where a collection {λk (t)}nk=1 is called a series Dspectrum(SD-spectrum) for Dα and an n-parameter family {ρk (t) = λ1,k (t)}nk=1 is called a parallel D-spectrum(PDspectrum) for Dα , where λ1,k (t) are n particular solutions forλ1 (t) satisfying some nonlinear independent constrains. Ac∫
tually, {yk (t) = exp( ρk (t)dt)}nk=1 constitutes a fundamental
set of solutions to Dα {y} = 0.
The solution to Dα {y} = 0 is uniformly asymptoticallystable if(i) all PD-eigenvalues are of polynomial order or slower, that
is, an integer m > 0 exists such that lim ρtkm(t) = 0;t→∞(ii) the extended means of real parts of PD-eigenvalues, that∫is em(Reρk (t)) = lim T1 tt00 +T Reρk (t)dt, are all negative.

4 SIMULATION RESULTThe desired trajectory is given by z1d (t) = 3.75 ×−4 3
2d (t) = −3 × 10 t +−32
9 × 10 t (m), over t = [0, 20].
In the kinematic model, initial states are set as[x(0), y(0), v(0), θ (0)] = [−0.1m, 0.1m, 0.05m/s, −0.5rad].
10−4t 3 − 1.125 × 10−2t 2 + 0.15t(m), z

T →∞©ICAROB 2014

ρ2 ρ̇1ρ̇2 −ρ̇1
ρ1 ρ2 + ρ1 ρ̇ρ22 −−ρ1 , α2x (t) = −λ2 − λ1 = −ρ1 − ρ2 − ρ2 −ρ1 .
α1y (t) and α2y (t) can be designed in the same way.

Similarly, if the desired trajectory of flat outputs are given by (z1d (t), z2d (t)) and tracking error e =[e1 , e11 , e12 , e2 , e21 , e22 ]T = [z1 − z1d , ż1 − ż1d , z̈1 − z̈1d , z2 −z2d , ż2 − ż2d , z̈2 − z̈2d ]T , then parameters ūx and ūy can be designed aswhere δ = d/dt is the derivative operator. The factorizationof SPDO can be represented as
Dα = (δ − λn (t)) · · · (δ − λ2 (t))(δ − λ1 (t))(20)

PD-spectral theory is used to design these time-varying control gains to ensure the error dynamics to be uniformly
asymptotically stable.
First appropriate time-varying PD-eigenvalues which satisfy (i) and (ii) are designed as ρ1 and ρ2 . Then corresponding SD-eigenvalues λ1 and λ2 can be calculated as λ1 = ρ1 ,
ρ̇1
λ2 = ρ2 + ρ̇ρ22 −−ρ1 . Using (18), we obtain α1x (t) = λ2 λ1 − λ̇1 =3.3

It can be conveniently represented as Dα {y} = 0 using thescalar polynomial differential operator (SPDO)
Dα = δ n + αn (t)δ n−1 + · · · + α2 (t)δ + α1 (t)(19)

which yields

where
[
]
[
−2ū1 ω sin θ − vω 2 cos θ
cos θ
,
D
=
C=
2ū1 ω cos θ − vω 2 sin θ
sin θy(n) + αn (t)y(n−1) + · · · + α2 (t)ẏ + α1 (t)y = 0

Control law of kinematic model-176-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

desired trajectory
tracking trajectory

1.2

desired trajectory
tracking trajectory

1.2

1

1

0.8

0.8

y(m)

y(m)

0.25t)i, ρ2 = −(1 + 0.25t) − (1 + 0.25t)i, ρ3 = −2 − 0.25t.
Fig. 2(b) shows the tracking trajectory and Fig. 4 depicts the
tracking errors and the transformed inputs ū1 and ū2 .

(b) 1.4(a) 1.4

0.6

0.6

0.4

0.4

0.2

0.2

0
−0.5

0
−0.5

0

0.5

1

1.5

5
0

x(m)

0.5

1

In this paper, we have presented a novel method for trajectory tracking control of a two-wheeled mobile robot in its
kinematic model and dynamic model. Based on differential
flatness, the systems can be transformed into normal forms
to use feedback linearization. The application of PD-spectral
theory ensures the stability of the tracking error dynamics
and establishes adjustable poles to change as we want. The
simulation results confirm the effectiveness of the proposed
control scheme.

1.5

x(m)

Fig. 2. Desired trajectory and tracking trajectory: (a)Kinematic model.
(b)Dynamic model.
(b)

(a)

0.1

0.05

0.08
0

e (m)

0.04

2

1

e (m)

0.06
−0.05

CONCLUSIONS

0.02
−0.1
0
−0.15

0

5

10

15

−0.02

20

0

5

(c)

15

20

15

20

6 ACKNOWLEDGMENTS
This work was supported by the National Basic Research Program of China(973 Program: 2012CB821200,
2012CB821201) and the NSFC(61134005, 60921001,
61327807).

(d)
0.25

1.5

0.2

1

ω(rad/s)v(m/s)

10

t(s)

t(s)

0.15

0.5

0.1

0.05

0

0

5

10

15

−0.5

20

0

5

t(s)

10

REFERENCESt(s)Fig. 3. Kinematic model: (a)Tracking error e1 . (b)Tracking error e2 .
(c)Heading speed v. (d)Turning speed ω .
(b)

(a)

0.05

0.1
0.08

0

e2(m)

e1(m)

0.06
−0.05

0.04
0.02

−0.1
0
−0.15

0

5

10

15

−0.02

20

0

5

t(s)
(c)

(d)

0.3

2

u2(rad/s )u1(m/s2)

15

20

15

20

5
4

0.2
0.1
0
−0.1
−0.2

10

t(s)

3
2
1
0

0

5

10

t(s)

15

20

−1

0

5

10

t(s)

Fig. 4. Dynamic model: (a)Tracking error e1 . (b)Tracking error e2 .
(c)Transformed input ū1 . (d)Transformed input ū2 .

It should be pointed out that the additional state v is in
the controller. PD-eigenvalues are selected as ρ1 = −(1 +0.25t) + (1 + 0.25t)i, ρ2 = −(1 + 0.25t) − (1 + 0.25t)i. Fig.
2(a) shows that the mobile robot gradually converges to thedesired trajectory and finally moves along it. Fig. 3 demonstrates the tracking errors e1 and e2 which converge to zeroand the control inputs v and ω .
In the dynamic model,[x(0), y(0), v(0), θ (0), ū1 (0), ω (0)] =[−0.1m, 0.1m, 0.05m/s, −0.5rad, 0, 0]. Controller is designed with PD-eigenvalues ρ1 = −(1 + 0.25t) + (1 +©ICAROB 2014

-177-

[1] R. Fierro and F. V. Lewis (1997), Control of a nonholonimic mobile robot: backstepping kinematics into dynamics, J.
Robot. Syst., 14(3): 149-163[2] J. M. Yang and J. H. Kim (1999), Sliding mode control for
trajectory tracking of nonholonomic wheeled mobile robots,
IEEE Trans. Robot. Autom., 15(3): 578-587[3] D. H. Kim and J. H. Oh (1999), Tracking control of a twowheeled mobile robot using input-output linearization, Contr.
Eng. Pract., 7(3): 369-373[4] V. Boquete, R. Garcia, R. Barea, and M. Mazo (1999), Neuralcontrol of the movements of a wheelchair, J. Intell. Robot.
Syst., 25(3): 213-226[5] T. Das and I. N. Kar (2006), Design and implementation ofan adaptive fuzzy logic-based controller for wheeled mobile
robots, IEEE Trans. Contr. Syst. Technol., 14(3): 501-510[6] Chun-Hsu Ko and Sunil K. Agrawal (2010), Walk-assist
robot: a novel approach to gain selection of a braking controller using differential Flatness, American Control Conference, 2799-2804[7] Chin Pei Tang (2009), Differential flatness-based kinematicand dynamic control of a differentially driven wheeled mobile robot, in Proc. IEEE Int. Conference on Robotics andBiomimetics, 2267-2272[8] Ji-Chul Ryu and Sunil K. Agrawal (2008), Differential
flatness-based robust control of a two-wheeled mobile robot
in the presence of slip, in Proc. DSCC ASME Dynamic Systems and Control Conference, 1-7[9] M. Fliess, J. Lévine, Ph. Martin, and P. Rouchon (1995), Flatness and defect of non-linear systems: introduction theory andexamples, International Journal of Control, 61(6): 1327-1361[10] J. Zhu (1995), A unified spectral theory for linear timevarying systems–progress and challenges, in Proc. IEEE Conference on Decision and Control, 2540-2546

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Adaptive Consensus Control of Multi-Agent Systems with
Large Uncertainty and Time Delays
Dongxu Zou1a, Weicun Zhang 1b
1.

School of Automation and Electrical Engineering,

University of Science and Technology Beijing, Beijing 100083, Chinaadongxu_zou@126.com, b weicunzhang@263.net

Abstract. A weighted multi-model adaptive control method is proposed to achieve consensus of multi-agent system with

large parameter perturbation and communication delays, in which H control is adopted to construct the controller set.
Moreover a simple and effective weighting algorithm is also presented. The simulation results demonstrate the effectivenessof the proposed method.
Keywords: Multi-agent system; weighted MMAC; Hcontrol

appropriate

1. Introduction

In (2),

thatbelongs

A0 , B10 , B20 are constant matrices andA(t ), B1 (t ), B2 (t ) are time-varying matrices.
E and Fi , i  1, 2,3 are constant matrices withappropriate dimensions and (t ) is time-varyingT
uncertain matrix satisfying (t ) (t )  I . It is also
assumed that ( A0 , B20 ) is stabilizable.
A  A0  A  t  , B1  B10  B1 (t )B2  B20  B2 (t )(2) A(t ) B1 (t ) B2 (t )  E   t   F1 F2 F3 
The consensus of multi-agent system is that the statesof agents satisfy (3) under control protocol ui (t ) .

lim  xi  t   x j  t    0, i, j  N
t 

(3)

3. Weighted multi-model adaptive control
Weighted multi-model adaptive controller is made upof the following sections.
3.1 Model Set. The model set is designed as  {M i | i  1, 2, , n} considering uncertainty ofConsider a second-order multi-agent system
consisting of n identical agents with the ith one modeledby as the form of (1).

©ICAROB 2014

disturbanceare supposed to follow the forms of (2).

2. Uncertain Multi-Agent Systems

xi (t )  [ xi1 (t ), xi 2 (t )]T , i (t )  L2 (0, ]

external

to L2 (0,  ] . ui (t ) denotes the control protocol.
If system matrices A, B1 , B2 are uncertain, then they

During the past decade, the study of decentralized
control has been focused on multi-agent systems, such as
flocking or swarming behaviors [1-3], multi-robot
formation control [4,5], and path planning [6] and so on.
The consensus problem, the most important andfundamental issue in the cooperation control ofmulti-agent system, is of theoretical value and practicalsignificance. For a multi-agent system, consensus meansthat the states of all agents tend to be identical
asymptotically under given protocols, based on thecommunication networks.
Recently, some researchers have solved theconsensus problem of multi-agent systems with
time-varying external disturbances and random
communication delays [7]. In [8], model uncertainty was
taken into consideration, and a robust H∞ controller was
designed.
In this paper, the consensus problem is considered for
multi-agent systems with large parameter perturbation.
External disturbances and communication delays are also
taken into account. Weighted MMAC (WMMAC)
shows its priority of system with large uncertainty.
Finally, simulation results indicate that under the
proposed protocol, multi-agent systems with large
parameter perturbation reach the desired consensus
performance in the presence of communication delays.

xi (t )  Axi (t )  B1i (t )  B2ui (t ),xi (t ) is the state of agent i, while i (t ) represents

plant parameters and structures. Ω represents a model set
with element M i . In fact, it relies on multiple models toapproach the uncertain system. Obviously, performanceof control system depends on the method of model set
and the amount of models directly.
3.2 Controller Set. The controller set C is shown as
the form of C  {Ci | i  1, 2, , n} , in which

(1)

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

  i  T i  i B2Q
B1

T T
 P
0
 i Q B2
 B1T
0 2 Id  i B2Q dB1
 d  iP00 i  A0 P   i B2Qelements are up to corresponding models. In other words,
the controller Ci is designed for model M i .
In this paper, robust H method is selected ascontrol strategy. Consider the multi-agent system (1), theoutput is supposed to follow (4). We can reformulate thesystem (1) as the form of (5) that satisfies (6) with thehelp of Kronecker product.

zi (t )  xi (t ) 

1 n
 x j (t ), i  1,..., nn j 1(4)x(t )  ( I n  A) x(t )  ( I n  B1 ) (t )  ( I n  B2 )u (t )z (t )  ( Lc  I m ) x(t )(5)x(t )  [ x1 (t )T , x2 (t )T ,..., xn (t )T ]T  R mn
1

Lc   Lc ij   R
 

dB1T
 P
0

(9)
If LMI (9) is feasible for the maximum andminimum eigenvalues of all the connected topologicalgraphs, the feedback matrix can be defined by K  QP 1 .
3.3 Weighting Algorithm. The method based on
probability-weighted is that the weighted sum ofcontroller set is taken as the global control input. The
weight of controller can be calculated by partition
theorem.
Suppose the control output of controller

ith model is yi  k  .
Let the output difference between ith model and plant n  1 n , i  j, Lc ij  1 n , i  jbe ei  k   y  k   yi  k  . Then the weighting
algorithm can be designed as follows.
First, weights of all models are initialized to 1/N, if
there are N models in set totally as (10).

According to the neighbors’ states, the protocol of agent
i can be designed as (7) to achieve the above conditions.

ui  t   K  aij  t   xi  t  d  t    x j  t  d  t  jNi  t (7)
Substitute control protocol (7) into system (5), resultin the closed-loop system (8).

x  t    I n  A x  t    L  B2 K  x  t  d  t    I n  B1    t ，z  t    Lc  I m  x  t 

pi  0   ri  0   1 , i  1, 2,..., NN(10)Then, it is conductive to improve the anti-jamming
capability by selecting the cumulative mean squared error
as the performance index shown in (11).

li  k   1 

1 k 2
 ei  k 
k j 1(11)

Finally, the computational process of weights isdenoted by (12). This method is much simpler thanprobability-weighted method. The performance indexes
are forced to compete at every step in order to make theweights converge.

(8)
Finally, the consensus problem of multi-agent system
is converted to calculate the feedback matrix K.
Under control protocol (7), system (1) achievesconsensus with a given H∞ index γ, if there are a scalar
α>0, a positive define matrix P  Rmm and a matrix

lmin  k   min li  k i 1,..., N

Q  Rm2 m that satisfies the LMI (9).

l k 
ri  k   minri  k  1li  k pi  k  ri  k  r k Ni 1 i

Simulation results©ICAROB 2014

T2

input is u  k  , the system output is y  k  , the output of(6)

z (t )  [ z1 (t )T , z2 (t )T ,..., zn (t )T ]T  R mnnnd  i Q B
T

Ci is ui  k  , the weight is pi  k  , the global control

 (t )  [1 (t )T , 2 (t )T ,..., n (t )T ]T  R m nu (t )  [u1 (t )T , u2 (t )T ,..., un (t )T ]T  R m2 n

P

0
0 0

0
 I 

d T i

-179-

(12)

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

In this section, we will illustrate the consensus ofmulti-agent system under the method of WMMAC.
Considering a multi-agent system of four agents, theparameter matrices are designed as (13).

The simulation results are shown in figures 2-3, in
which Fig. 2 represents the trajectory of state x1, x2, andFig. 3 represents the energy relationship between thesystem output z (t ) and the external disturbance  (t )

 0 10
1 0 
A0  
, B10    , B20  ,2 1 
1 
0 2
(13)
0
0

A(t )   , B1 (t )  0, B2 (t )  0t
6 1  e  0 
The communication delay is assumed to be constant
0.05. The external disturbances are supposed to beband-limited white noises shown as (14).

  t   1  t  2  t  3  t  4  t  T  2  t  1.5  t  1.2  t  1.8  t  

Fig. 2(a) State curve of x1

T(14)
For simplicity, the H∞ performance index is chosenas  =1 , the constant scalar  =0.1 ,  =1 and all thenonzero weighting factors of adjacency matrix are
assumed to be 1. The simplified undirected interaction
graph is shown in Fig. 1.

Fig. 2(b) State curve of x2
Fig. 1 simplified undirected graphWe can get the maximum and minimum nonzero

eigenvalues  i  0.5858,  i  4 by calculating
the Laplace matrices corresponding to the network graphshown in Fig. 1.
It is assumed that there are four models in the model
set which is shown as (15).

0 1
0 1
0 1
0 1
A1  , A2  , A3  , A4  2 1 
 4 1
6 1
8 1
(15)
According to the LMI (9), the feedback matrices Ki
can be determined as (16) with the help of Matlab LMI
toolbox.

If the system states are asymptotically stable, theconsensus of multi-agent system is all up to thesteady-state values of each agent. Therefore, the states of
agents are designed to be divergent in order to display
the consensus of multi-agent system significantly.
It is easy to say that the multi-agent system achievesconsensus according to Fig.2
When the state matrix A changes to 2.2365 0.3528  1.4127 0.2621 
K1  
, K2   0.1269 2.7761 
 0.4986 2.3349
 2.0759 0.9033  2.4147 1.3164 
K3  
, K4  
 1.4598 3.0850
 1.9986 2.9659
(16)©ICAROB 2014

Fig. 3 Energy of z (t ) and  (t )

 0 1
A

7.5 1

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

the weights of all models are displayed in
Fig.4.

[2] Tanner H G, Jadbabaie A and Pappas G J. Flocking
in fixed and switching networks. IEEE Transactions on
Automatic Control, 2007, 52(5):863-868.
[3] Su H S, Wang X F and Yang W. Flocking in
multi-agent systems with multiple virtual leaders. Asian
Journal of Control, 2008, 10(2):238-245.
[4] Egerstedt M and Hu X. Formation control with
virtual leaders and reduced communications. IEEE
Transactions on Robotics and Automation, 2001,
17(16):947-951.

Fig.4 weights of models[5] Olfati-Saber R and Murray R M. Distributed
structural stabilization and tracking for formations ofdynamic multi-agents. In Proceedings of the 41th IEEE
Conference on Decision and Control, 2002, 209-215.

In Fig.4, the weight of the 4th model is convergent to
1. Then the controlled quantity of the 4th model ischosen to be the global control input of the multi-agent
system.

[6] F. Lian, Cooperative path planning of dynamical
multi-agent systems using differential flatness approach,International Journal of Control, Automation, andSystems, 2008, 6(3): 401-412.

Conclusions
By applying the weighted MMAC methodology, we
have addressed the adaptive consensus problem ofmulti-agent system with large parameter uncertainty andcommunication delays. According to the states’
differences between the plant and each model, we canobtain the weight of each local controller with theweighting algorithm mentioned. Finally, the controller
corresponding to the closest model to the plant will bechosen as the right controller.

[7] W Ren, R W, Beard and D B Kingston, Multi-agent
Kalman consensus with relative uncertainty, Proc. of theAmerican Control Conference, 2005. 1865-1870.
[8] Y. Liu, Y. Jia, Robust H∞ consensus control of
uncertain multi-agent systems with time delays,
International Journal of Control, Automation, andSystems, 2011, 9(6): 1086-1094.

References[1] Olfati-Saber R. Flocking for multi-agent dynamic
systems: algorithms and theory. IEEE Transactions on
Automatic Control, 2006, 51(3):401-420.

©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Iterative learning control for overhead crane systems
Bu Xuhui*, Wang Fuzhong, Li Sanyi, Yu Fashan
School of Electrical Engineering & Automation, Henan Polytechnic University, Jiaozuo, China.
Corresponding email: buxuhui@gmail.com
Abstract: In many factories, overhead crane often transfers the same loads from one place to another following apredefined position and speed diagrams. This unique feature offers overhead crane an opportunity of improving its
performance through learning iteratively. In this paper, we apply the iterative learning control approach to overheadcrane systems. Based on the linearization dynamic model of overhead crane, an ILC scheme contains both feedforward
learning control part and state feedback control is proposed. By providing a 2D formulation, the ILC design for cranesystems can be transformed into the problem of state feedback control for 2-D systems described by Roesser models. It
is shown that the proposed approach can guarantee the trolley position asymptotically converges to its desired profile
with small swing angle. Simulations are illustrated to show the feasibility and effectiveness of the proposed approach.
Keywords: iterative learning control; overhead crane; position tracking: convergenceoriginal work in [8], the general area of ILC has been
the subject to intense research effort and widely appliedin many fields [9, 10]. In this paper, an ILC design
approach based 2-D system theory for overhead cranesystems is presented. By providing a 2D system of thelearning process, it is shown that the design problem ofan ILC law for overhead crane can be transformed
straightforwardly into feedback control problem of 2-D
systems described by Roesser models.
This paper is organized as follows. Section 2
introduces overhead crane dynamics model and problem
formulation. Section 3 presents an ILC design approachfor overhead crane system by using 2D system theory.
Section 4 is the simulation results, and Section 5 gives
conclusions.

I. INTRODUCTION
Overhead crane can transport a load from one place
to another with high load capacity, and it is widely usedin factory, wharf, etc. The purpose of control overheadcrane is to transport load to the designated placefollowing desired position and speed with small rope
swing. Because high positioning accuracy, small swing
angle and high safety are required, operating an
overhead traveling crane is a hard work. Various
controllers have been considered for overhead cranesystems.
PID controller is widely used for overhead cranesystems in practical systems [1-3]. However, it cannot
deal with nonlinear dynamic effectively. In [4], anenergy-based approach is proposed for overhead cranesystems, and it can use nonlinear terms to improve thetransient performance and repress the load swing angle
effectively. However, it also results in sensitive to thechanges of the parameters. To overcome these shortages,
many other control strategies such as fuzzy logic control[5], adaptive fuzzy control [6] and adaptive sliding
mode fuzzy control [7], are proposed for crane systems.
Although these control strategies can obtain better
tracking performance, the fuzzy rules are difficult to bebuilt.
On the other hand, the normal operation process of acrane from the start place to final place is characterizedby high repetition pattern. In many factories, loads withsame mass and size are often transported from one place
to another. In this progress, overhead crane operates
according to their desired position or speed diagrams
strictly, and their operating environments and control
tasks also remain unchanged. For each operation, thetrolley starts and ends at the same positions, and thesystem dynamic is almost repetitive due to the same
mass and size of loads. The desired position or speed
profile for overhead crane is predefined, and the trolley
is controlled to follow that position or speed profile ateach operation. Hence, we can improve tracking
performance through learning iteratively or repetitively.
Iterative learning control (ILC) is an effective
technique for systems with repetitive nature. Since the©ICAROB 2014

M

x

F

θ

m

Fig. 1 Sketch of an overhead crane

II. CRANE SYSTEM DYNAMICS
As shown in Fig. 1, the overhead crane system
contains a trolley, a rope and a load. The volume of theload can be ignored and the mass of the load is m . The
mass of rope can be ignored compared with the loadmass, and it also is assumed inflexible rod with a lengthof l . The trolley moves on a straight line with a massof M . Then, using the Lagrange’s equation of motion,
the dynamic model of overhead crane can be given as[11]:
x + mlθ cos θ − mlθ 2 sin θ = F⎪⎧( M + m) 
,
(1)
⎨ 
x cos θ + g sin θ = 0⎪⎩lθ + 
where x, θ , F denote the trolley position, the rope
swing angle, and the driving force applied to the trolley,-182-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

respectively. Parameter g = 9.8m / s 2 is the gravity
acceleration.
We first transform continuous-time differential
equations to discrete-time difference equations here to
make it easier to apply in overhead crane system. The
sampling period is chosen as h . By using Euler
Formula, (1) can be discretized as⎧ x1 (t + 1) = x1 (t ) + hx2 (t )⎪⎪ x2 (t + 1) = x2 (t ) + mgh x3 (t ) + h u (t )
⎪
M
M
, (2)
⎨ x (t + 1) = x (t ) + hx (t )
3
4
⎪ 3
⎪
( M + m ) ghhx3 (t ) −u (t )⎪ x4 (t + 1) = x4 (t ) −Ml
Ml⎩
where x1 = x, x2 = x , x3 = θ , x4 = θ and u = F .
Definex(t ) = [ x1 , x2 , x3 , x4 ] ,Tmeasurable.
Remark 1: For each operation, overhead crane often
start at the same position with the initial velocity is 0,
and the initial rope swing angle and angular velocity arealso 0, which means assumption 1 is satisfied.
Assumption 2 means that task assigned for control
should be feasible. In practical system, the trolley
position and the rope swing angle can be measured by
selecting appropriate sensors. Hence, the Assumption 3
is satisfied.
In this paper, the iterative learning control law foroverhead crane is constructed as follows:u (t , k + 1) = u (t , k ) + K1 [ x(t , k + 1) − x(t , k ) ](5)+ K 2 e(t + 1, k ),where e(t , k ) = x d (t ) − x(t , k ) is the tracking error.
The system (4) and (5) is essentially a 2D system.
We can use the 2D analysis approach to ILC to derive
an expression.
Define Δu (t , k ) = u (t , k + 1) − u (t , k ) , from (5), wehave⎡η (t , k ) ⎤Δu (t − 1, k ) = K ⎢(6)⎥,⎣ e(t , k ) ⎦then (2) can be

described asx(t + 1) = Ax(t ) + Bu (t ),

(3)

where

0
0⎤
⎡1 h
⎡ 0 ⎤
⎢
⎥
⎢ h ⎥
mgh
⎢0 1
⎢
⎥
0⎥
⎢
⎥
M
⎢ M ⎥
.
A=⎢
, B=⎢
0 0
1
h⎥
0 ⎥
⎢
⎥
⎢
⎥
⎢
( M + m ) gh ⎥⎢ h ⎥0⎥
⎢0 1 −⎢⎣ − Ml ⎥⎦Ml⎣⎦
When the overhead crane is operated during a finite
time interval t ∈ [0, T ] repetitively, the system (3) canbe described asx(t + 1, k ) = Ax(t , k ) + Bu (t , k ),(4)
where k denotes iteration number.
In practical system, the desired trajectory x d (t ) of
overhead crane is predefined. Hence, the control
objective is to seek the input signal u (t , k ) that drivesx(t , k ) to track x d (t ) when k → ∞ .

where η (t , k ) = x(t − 1, k + 1) − x(t − 1, k ), K = [ K1 K 2 ] .
Using (3), we can obtaine(t , k + 1) − e(t , k ) = x(t , k ) − x(t , k + 1)
(7)
= − Aη (t , k ) − BΔu (t − 1, k ),and
η (t + 1, k ) = x(t , k + 1) − x(t , k )(8)= Aη (t , k ) + BΔu (t − 1, k ).
Equ. (7) and (8) can be rewritten as a class of 2D
Roesser system as follows.
⎡η (t + 1, k ) ⎤⎡η (t , k ) ⎤⎢ e(t , k + 1) ⎥ = A ⎢ e(t , k ) ⎥ + B Δu (t − 1, k ), (9)
⎣
⎦
⎣
⎦
where
⎡ A 0⎤⎡B⎤A=⎢, B = ⎢ ⎥.
⎥⎣− A I ⎦⎣− B ⎦
Substituting (6) into (9), we can present the ILC
system as a class of 2D systems as follows⎡η (t + 1, k ) ⎤⎡η (t , k ) ⎤⎡η (t , k ) ⎤⎢ e(t , k + 1) ⎥ = ( A + BK ) ⎢ e(t , k ) ⎥ = Ac ⎢ e(t , k ) ⎥ , (10)
⎣
⎦
⎣
⎦
⎣
⎦

III. ILC LAW DESIGNFor overhead crane system (4), we can give thefollowing assumptions.
Assumption 1: The reinitialization condition issatisfied throughout the repeated iterations, i.e.,x(0, k ) = 0,
for all k , where x(0, k ) = [ x(0, k ), x (0, k ), θ (0, k ),θ(0, k )]T and x(0, k ) is initial position, x (0, k ) is

where Ac = A + BK ,In this case, the problem of ILC design for overheadcrane system can be transformed as the problem ofdesign a feedback controller for 2-D system (9) suchthat the resulting closed-loop system (10) is
asymptotically stable.
Remark 2: To this end, ILC design for overheadcrane systems can be transformed the problem of
feedback control for 2-D system. Hence, the existing
ILC designs [12,13] can be used, even considering
complex crane models with uncertainties, time-delays or
H ∞ performance. In this paper, we give a novel design
different from the existing methods [12,13].

initial velocity, θ (0, k ) is initial rope swing angle andθ(0, k ) is initial rope swing angular velocity.
Assumption 2: For a desired trajectory x d (t ) , there
exists such an appropriate control ud (t ) , which candrive the overhead crane to track their desired
trajectory for system (4) over the whole finite interval.
Assumption 3: The position and velocity of thetrolley and the rope swing angle and velocity are all©ICAROB 2014

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Japan, January 11-13, 2014

andWe first give the following lemmas.
Lemma 1. If there exists a positive definite block
diagonal matrix⎡Q⎤
Q = Q T = Qh ⊕ Qv = ⎢ h
⎥,
Q
v⎦
⎣
satisfying
AcT QAc -Q < 0,then 2-D discrete closed-loop system (10) is
asymptotically stable.
Lemma 2. Given a symmetric matrix Θ ∈ R n×n andtwo matrices M , N of column dimension n , there
exists a W such that the following condition holds
Θ+MWN T +NW T M T < 0,
if and only if the following projection inequalities withrespect to W are satisfied
M ⊥T ΘM ⊥ < 0, N ⊥T ΘN ⊥ < 0,

⎡ -2 P 0 ⎤(15)
N ⊥T ΘN ⊥ = ⎢⎥ < 0,⎣ 0 -P ⎦Note that the condition (14) and (15), Using Schur
Complement we have
PAcT P-1 Ac P-P < 0.
(16)
Thus, multiplying the right and the left of condition(16) by P −1 and taking Q = P−1 in the resulting
inequality we obtain
AcT QAc -Q < 0,
Using Lemma 1, it is obvious that the asymptotic
stability of closed-loop system (10) is guaranteed. This
completes the proof.
Using theorem 1 and Ac = A + BK , the synthesis offeedback controllers can be described to find ( K , P, S )such that the following matrix inequality holds
1
⎡ 1
⎤
T
T
T
S +S T -P ⎥⎢ - 2 ( S +S ) S ( A +BK )2⎢⎥-P-( A +BK ) S ⎥ < 0. (17)⎢ ( A +BK ) S⎢1⎥⎢ S T +S -P - S T ( A +BK )T- S -S T ⎥⎣2⎦where M ⊥ and N ⊥ denote arbitrary bases of thenullspaces of M and N , respectively.
Theorem 1: Consider the 2-D discrete-time system(10). If there exist a block-diagonal matrix
P = diag (Ph ,Pv ) with Ph ∈ R n×n and Pv ∈ Rl ×l andmatrices S such that
1
⎡ 1
⎤
T
T T
S +S T -P ⎥⎢ - 2 ( S +S ) S Ac
2
⎢
⎥
(11)
-P
- Ac S ⎥ < 0,
⎢ Ac S⎢1⎥⎢ S T +S -P - S T AcT- S -S T ⎥⎣2⎦then the closed-loop system (10) is asymptoticallystable.
Proof. Matrix inequality (11) can be rewritten
as follows:⎡ 0 0 - P ⎤ ⎡ 12 I ⎤⎢ 0 - P 0 ⎥ + ⎢- A ⎥ S - I⎢⎥ ⎢ c⎥ [⎢⎣- P 0 0 ⎥⎦ ⎢⎣ - I ⎥⎦Let M T = ⎡⎣ 12 I

- AcT⎡- I ⎤
0 I ] + ⎢⎢ 0 ⎥⎥ S T ⎡⎣ 12 I⎢⎣ I ⎥⎦- I ⎤⎦ , N T = [ - I− AcT

0 I ],

It is difficult to solve the matrix equation of inequality(17) for ( K , P, S ) because it is non-convex. In this case,
we introduce a new variable Y = KS , then (17) leads
to a convex sufficient condition in terms of LMI.
Theorem 2: Consider the 2-D system (10), if there
exist a block-diagonal matrix a positive definite block
diagonal matrix P = diag (Ph ,Pv ) with Ph ∈ R n×n andPv ∈ Rl ×l , S , Y such that
1
⎡ 1
⎤
T
T
S +S T -P ⎥⎢ - 2 ( S +S ) ( AS +BY )2⎢⎥-P-( AS +BY ) ⎥ < 0, (18)⎢ AS +BY⎢1⎥⎢ S T +S -P -( AS +BY )T- S -S T ⎥⎣2⎦then 2-D discrete closed-loop system (10) is
asymptotically stable. In this situation, a suitable control
law for (6) is given by K = YS -1 .
Remark 3: Compared to the existing designs [12,13],
the proposed design approach with the simple linear
matrix inequality (18) obtains less computation, andthe LMI condition also provides a direct approach todetermine learning gains.

− I ⎤⎦ < 0.

(12)

⎡ 0 0 -P ⎤
Θ = ⎢⎢ 0 - P 0 ⎥⎥ ,⎢⎣ - P 0 0 ⎥⎦and select the orthogonal complements of M andN ( M ⊥ and N ⊥ respectively) as:⎡I⎢
M⊥ = ⎢ 0⎢⎣ 12 I0 ⎤⎡ I 0⎤⎥I ⎥ , N ⊥ = ⎢⎢0 I ⎥⎥ .
⎢⎣ I 0 ⎥⎦- AcT ⎥⎦
Applying Lemma 2 we can obtain:
M ⊥T ΘM ⊥ < 0, N ⊥T ΘN ⊥ < 0,that is⎡ - P PAcT ⎤M ⊥T ΘM ⊥ = ⎢⎥ < 0,⎣ Ac P - P ⎦©ICAROB 2014

IV. SIMULATIONS STUDY
In this section, a simulation is performed for an
overhead crane system to verify our conclusions. The
sample time is chosen as h = 0.01s , and the systemparameters are given as follows:
M = 5kg , m = 10kg , l = 2m , g = 9.8m / s 2 .
The desired position of the crane gantry is selected as

(13)

(14)

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Japan, January 11-13, 2014

⎧ 0.1t 2⎪
xd (t ) = ⎨ t − 2.5⎪−12.5 + 3t − 0.1t 2
⎩

0≤t≤5

learning gains can be obtained by solving linear matrix
inequalities. The proposed controllers can guarantee theasymptotic convergence of crane position to its desired
profile with small swing angle on the whole operation
time interval.

5 < t ≤ 10 ,10 < t ≤ 15

For the initial state, it is assumed thatx1 (0, k ) = x2 (0, k ) = x3 (0, k ) = x4 (0, k ) = 0 for all k .
The ILC law is applied by adopting the zero initial
control input u (t , 0) = 0 for all t . Using overheadcrane system parameters in (3), the 2D system matrix in(9) can be calculated.
Using Theorem 2, with the help of Matlab LMI
toolbox, the LMI (18) is feasible and a gain matrix K
is also derived. The results of simulation are shown in
Fig. 2-Fig 4. Fig. 2 gives system output at the 10th, 30th ,100th iterations and the desired output trajectory
respectively. Fig. 3 gives the max position tracking
error on the iteration domain. Fig. 4 gives the rope
swing angle at 100th iteration. It can be observed that byusing the proposed iterative learning control law, we canguarantee the trolley position converges to desired
trajectory for all t ∈ [0,15] , and the maximum swing
angle is smaller than 0.035 radian (about 2 degree).
Obviously, the rope swing angle by the ILC controller ismuch smaller than other controllers [4-7].

ACKNOWLEDGEMENT
This work is supported by the Program of NSFC (No.
61203065, 61240049), the program of Key Young
Teacher of Henan Polytechnic University. The Doctoral
Fund Program of Henan Polytechnic University(B2012-003).

REFERENCES[1] J. Collado, R. Lozano, I. Fantoni. Control of convey-crane
based on passivity. in Proc. Amer. Control Conf., Chicago,
IL, Jun. 2000, 1260-1264.
[2] B. Kiss, J. Levine, P. Mullhaupt. A simple output feedback
PD controller for nonlinear cranes. in Proc. IEEE Conf.
Decision Control, Dec. 2000, pp. 5097-5101.
[3] H. H. Lee. A new approach for the anti-swing control ofoverhead cranes with high-speed load hoisting. Int. J.
Control, 2003, 76(15): 1493-1499[4] Y. Fang, W. E. Dixon, D. M. Dawson, E. Zergeroglu.
Nonlinear coupling control laws for an underactuatedoverhead crane system. IEEE/ASME Trans. Mechatronics,
2003, 8(5): 418-423[5] K. A. F. Moustafa. Fuzzy control of flexible cable
overhead cranes with load hoisting. Trans. Inst. Meas.
Control, 2006, 28(4):372-386.
[6] J. Q. Yi, N. Yubazaki, K. Hirot. Anti-swing andpositioning control of overhead traveling crane.
Information Sciences. 2003, 155(1-2) :19-42[7] D. Liu, J. Yi, D. Zhao, W. Wang. Adaptive sliding mode
fuzzy control for a two-dimensional overhead crane.
Mechatronics, 2005, 15(5): 505–522[8] S. Arimoto, S. Kawamura, F. Miyazaki. Bettering
operation of robots by learning. J. of Robotic Systems,
1984, 1(2):123-140.
[9] D. A. Bristow, M. Tharayil, A. G. Alleyne. A survey of
iterative learning control: A learning-based method forhigh-performance tracking control. IEEE Control Syst.
Mag., 2006, 26(3): 96-114[10] H. S. Ahn, Y. Chen, K. L. Moore. Iterative learning
control: Brief survey and categorization. IEEE Transactions
on Systems, Man, and Cybernetics-Part C: Applications
and Reviews, 2007, 37(6): 1099-1121[11] B. Vikramaditya, R. Rajamani. Nonlinear control of atrolley crane system. In: Proceedings of the American
control conference, Chicago, IL. 2000. 1032-1036.
[12] E. Rogers, K. Galkowski, A. Gramacki, J. Gramacki, D.
H. Owens. Stability and controllability of a class of 2-D
linear systems with dynamic boundary conditions. IEEE
Trans. Circuits Syst. I: Fundam. Theory Appl., 2002,
49(2): 181-195[13] J. Shi, F. Gao, T. J. Wu. Robust design of integrated
feedback and iterative learning control of a batch process
based on a 2D Roesser system. J. Process Control, 2005,
15(8): 907-924.

12
desired trajectory
10th iteration
30th iteration
100th iteration

10

position(m)

8

6

4

2

0

0

5

10

15

time(s)Fig. 2 Position tracking profiles
10
9

The max tracking error

8
7
6
5
4
3
2
1
0

0

10

20

30

40
50
60
iteration number

70

80

90

100

Fig. 3 Position tracking errors on iteration domain.
0.05
0.04
0.03

swing angle(deg)

0.02
0.01
0
-0.01
-0.02
-0.03
-0.04
-0.05

0

5

10

15

time(s)Fig. 4 Rope swing angle at 100th iteration

VI. CONCLUSION
In this paper, the problem of iterative learning control
for overhead crane systems has been investigated.
Basing on 2D analysis of the learning process, the©ICAROB 2014

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Japan, January 11-13, 2014

A Hybrid Path Planning Algorithm for UGVby Combining A* and B-spline Curve Equation
Min-Ho Kim, Hee-Mu Lee and Min-Cheol Lee
Pusan National University, Busan, South Korea
Tel : 81-051-510-3081; Fax : 81-051-512-9835
Xho1995@gmail.com, slm1023@nate.com, mclee@pusan.ac.kr

Abstract: This article presents a hybrid path planning algorithm to make a smooth path for an UGV. A main theme ofthe proposed algorithm is to combine of the A* algorithm and the B-spline curve equation. A* is one of the well-known
path planning algorithm which finds the optimal path on the given map by using the heuristic cost function. However,since A* is based on the grid map, the result path consists of straight lines with only 8 directions. It's not suitable of
UGV's navigation. Therefore in this paper to overcome this issue, B-spline curve equation is proposed to make asmooth and continuous path with control points which are selected from the A* result path. And the optimal control
point selection algorithm is proposed to make the hybrid path. At last, to verify the proposed algorithm, the hybrid path
results are compared with A* algorithm by using a developed simulation program.
Keywords: UGV, Path Planning, A* Algorithm, B-spline Curve Equation

In this hybrid path planning algorithm, the optimal
control point selection algorithm is proposed. In thenext chapter, we will explain the theory of A* algorithm
and the B-spline curve equation respectively to plan thepath. And we will show the A* and B-spline curve
results by using the developed program. We developed
the A* simulation program and the continuous pathgeneration algorithm using B-spline interpolation
equation in windows environment. [4], [5], [6] Next, theoptimal control point selection algorithm will be
explained. At last, to verify the proposed algorithm, we
have developed the hybrid path simulation program andthe path results are compared with A* algorithm toevaluate the proposed algorithm.

I. INTRODUCTION
After the development of fusion technology ofmechanics, electronics and IT, there are a lot of
researches about the autonomous vehicle. For example,
the grand challenge has held in USA by DARPA and
Google has tested an autonomous vehicle on the realroad environment. [1], [2] For this kind of vehicle tomove automatically, it needs the reference input pointthat the vehicle can follow, and this input point comesfrom a path planning algorithm. Therefore, researcheson the path planning algorithm have increased.
Among these, A* is one of the well-known path
planning algorithm developed by Peter Hart, Nils
Nilsson, Bertram Raphael in 1968. [3] It is the global
path planning algorithm that finds the optimal path on
the given map using the heuristic cost function. Usually
the map of A* algorithm consists of several grid cells,
and then each cell has a cost value to be compared andevaluated to find the optimal path.
However, due to the grid map, A* algorithm gives
the vehicle some reference points on the discontinuous
path. Actually the result path consists of straight lines
with only 8 directions. It's not suitable of real UGV'snavigation. In this paper, therefore to overcome thisissue, B-spline curve equation is proposed to make asmooth and continuous path with control points which
are selected from the result path of A* algorithm. Using
A* algorithm, the center points of selected grid cells
become the way points.

©ICAROB 2014

II. A* ALGORITHM
1. A* Algorithm and Cost Function
Before introducing the hybrid path planningalgorithm, first we look around the normal A* algorithm.
The A* algorithm uses the cost function like as; [3]F (n)  g (n)  h(n)(1)

In this equation, g(n) is the cost value from the start
position to current position on the map. And h(n) is theheuristic cost value from the current position to the goal
position. F(n) is the total cost value to be compared andevaluated for optimal path planning.

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014(0  i  k ) 0
ti   i  k  1( k  i  n)n  k  2 (n  i  n  k )

Fig.1. shows one of the result path of A* algorithm.
[7] The path consists of several discontinuous lines, so
if we want to use the result path into the real UGV, it
should stop at the each corners to change the heading
angle. In addition, a real UGV can move to any
direction, the simple A* algorithm may not be effective
because of the path direction limit of the A* path.

(2)

Using the above blending function, B-spline curve
can be defined as;nΡ(u )   Pi Ni ,k (u ) (tk 1  u  tn 1 )

(3)

i 0

Where, each Pi means a pre-defined control point.
Since a blending function is difficult to express as apolynomial, Cox and de Boor suggested an algorithm todivide control points. According to their algorithm, theequation for the curve can be written as the following
function P(u ) as;lΡ(u )   Pi N i ,k (u )(4)i l  k 1

Combining equation (2) and (4), the following
relation can be derived.
Fig.1. A Path generated with A* Algorithm

l

P N

Ρ(u ) i l  k  2

1
ii , k 1(u )III. THE B-SPLINE EQUATIONu  tiu  ti P Pi  1 
 Pi 1ti  k 1  ti
 ti  k 1  ti 

1. The B-spline Curve Equation
To specify a path, a B-spline curve was used [8], [9],[10]. The B-spline curve can be generated by a blending
function, which connects and makes the vertices of apolygon smooth. Cox and de Boor suggested a blending
function shown as in equation (1);N i ,k (u ) (u  ti ) N i ,k 1 (u )ti k 1  ti

1 ti  u  ti 1
N i ,1 (u )  others
0



P1

PP

In this case, the point i divides the line i i 1 .
Repeating this process, the following relation can beobtained.

(ti k  u ) N i 1,k 1 (u )

Ρ(u ) ti k  ti 1
(1)

l

P N

r
i
i l  k  r 1

i ,k r(u )u  tiu  ti  r 1
P 
Pir 1  1 
 Pi 1ti  k r  ti
 ti  k r  ti 

(6)

r
i

t

Where, i is called as knot value, which is aparameter that makes blending function nonzero. The
knot value can be divided as periodic knots and nonperiodic knots. In this paper, non-periodic knots shownin equation (2) were used.

©ICAROB 2014

(5)

1
i

Pr

The point i is called as de Boor. Plugging (k-1) to r,
the following relation can be obtained.

Ρ(u)  Ρ lk 1

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(7)

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Following the above sequence, a curve P(u ) couldP
be obtained from the given points i . Fig. 2 shows acurve path obtained from the B-spline curve equation.
In the fig. 2 the dark points are the control points whichmake the B-spline curve. Therefore, to build a hybrid
path, a control point selection algorithm is needed.

In the Fig. 3, black points are the selected points tobe the control points of B-spline curve, by using thosepoints the hybrid path is generated.

Fig.4. A B-spline Curve Path with Selected Points
Fig. 4 shows the hybrid path result. In this Fig. theblack lines mean an A* path, and the red curve means ahybrid path using B-spline curve equation. B-spline
curve are always in the polygon of the control points,
the hybrid path will not collide the obstacles.

Fig.2. The Path Generated with B-spline curve equation

IV. HYBRID PATH PLANNING ALGORITHM
V. SIMULATION & RESULT
1. Control Point Selection Algorithm
The hybrid path is the continuous curved path which
is made by combining the A* path points and the Bspline curve equation. To make this path, some points
on the A* path nodes should be selected as the control
points of B-spline curve equation. In this paper, we
proposed a control point selection algorithm such as;
1.
2.
3.
4.
5.

1. Path Tracking Simulation
To evaluate the proposed algorithm, we developed
the path tracking algorithm. By using this program, thetracking result is compared with the A* path. In thispaper, we build a 32x24 grid map with 1m x 1m gridsize like as in Fig. 5., and the maximum vehicle speed islimited in 0.6m/s due to the limit of a real mobile UGVrobot. [6]Select the start point on the A* path as a firstcontrol point
Go to the next node point on the A* path
If there isn’t any obstacle between two points,
go to the next node
If there is an obstacle, then select the previous
node as a next control point
Repeat 2~4 routine, until the goal point

Fig.5. A* and Hybrid Path Tracking Simulation
2. The Result
The result is shown as table 1. In this table, total
distance and tracking time is reduced within the hybrid
path. Especially, while total distance is reduced as 5.5%,
Fig.3. The Selected Points with the Proposed Algorithm©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

tracking time is reduced as 34.5%. It means, on the
hybrid path, the UGV has no need to reduce the speed in
the corner. Therefore, the hybrid path is more effective
for the real UGV’s navigation.

REFERENCES[1] Wikipedia (2013), DARPA Grand Challenge, http://
en.wikipedia.org/wiki/DARPA_Grand_Challenge[2] Wikipedia (2013), Google driverless car, http://
en.wikipedia.org/wiki/Google_car[3] P. E. Hart, N. J. Nilsson and B. Raphael (1968), A
Formal Basis for the Heuristic Determination of
Minimum Cost Paths. IEEE transactions of systems
science and cybernetics, Vol. ssc-4, No.2:100-107[4] M. H. Kim and M. C. Lee(2010), A Path Generation
Method for Path Tracking Algorithms that use theAugmented Reality, International Conference onControl, Automation and Systems:1487-1490[5] M. H. Kim, H. M. Lee, Y. Wei and M. C. Lee (2012),
A Study of New Path Planning Algorithm Using
Extended A* Algorithm with Survivability, Intelligent
Robotics and Applications, Vol. 7508(59):608-617[6] C. B. Noh, M. H. Kim and M. C. Lee (2013), Path
Planning for the Shortest Driving Time Considering
UGV Driving Characteristic and Driving Time and Its
Driving Algorithm, Journal of Korea Robotics Society,Vol.8(1):043-050[7] M. H. Kim, C. B. Noh, J. H. Heo and M. C. Lee(2013), A Study of Path Planning Algorithm Based on
the Survival Probability, Intelligent Autonomous
Systems 12, Vol.193:755-763[8] M. G. Cox (1972), The Numerical Evaluation of Bspline, J. of Approx. Theory, Vol. 6:95-108[9] C. de Boor (1997), On Calculation with B-spline, J.
of Approx. Theory, Vol. 6:76-78[10] K. W. Lee (1999), Principles of CAD/CAM/CAE
systems, Addson Wesley

Table 1. A* and Hybrid Path Tracking Result
A*
HybridImproved
Total Distance (m)

36.5

34.5

5.5 %

Tracking Time (s)

95.4

62.5

34.5 %

VI. CONCLUSION
In this paper, we suggested the hybrid path planningalgorithm by combining the A* algorithm and the Bspline curve equation. For this we proposed the control
point select algorithm. With this algorithm, thecontinuous path can be generated with the control points
which are from the A* algorithm. To evaluate theproposed algorithm, we developed the path planningand tracking simulation program. And the result shows
the hybrid path is effective for the real UGV’snavigation.

VII. ACKNOWLEDGMENTS
This research was supported by the MOTIE(The
Ministry of Trade, Industry and Energy), Korea, underthe Human Resources Development Program forSpecial Environment Navigation/Localization National
Robotics Research Center support program supervisedby the NIPA(National IT Industry Promotion Agency)."(H1502-13-1001)©ICAROB 2014

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The actuator device design for the implementation of haptic joystickDong-hyuk Lee, Sun-kyun Kang, Bo-Yeon Hwang, Ki-jung Kim, and Jang-myung Lee
Dep. of Electronic Engineering, Pusan National University, Korea
Tel : 82-51-510-7379; Fax : 82-51-514-1693{ldh0917, sunkyun7379, boyeon1696, kijung7379, jmlee}@pusan.ac.kr

Abstract: In this paper, we designed the actuator device for haptic implementation in the general joystick. Each linear
servo actuator as pillars to support the joystick mounting plate can move up and down. And this movement will
generate the inclination of the plate. The haptic can be implemented by the change of the inclination. The actuator
device was designed through the performance analysis of actuator device structure. The haptic will represent
information from obstacle cognition device by using the designed actuator device. The haptic performance of theactuator device was verified through experiments.
Keywords: Haptic Device, Actuator Device, Tele-operation, Ultrasonic sensor, Haptic Joystick, Linear actuator control

implementation of the haptic device, user can be aware of thesituation.

I. INTRODUCTION
Recently, with the development of robotics industry, needin the field of remote control is being magnified. This remotecontrol can be used in a hazardous environment that human isdifficult to access directly and determine the current situation,such as the scene of the accident, cleaning the inside of thenuclear reactor and the space exploration[1]. Tele-operation, asa field of the control that users perceive and determine theirenvironment at the outside by using a variety of sensor
information attached to the robot, the technology of receiving
feedback exactly from sensor information is essential. Amongthem, the research that users can take action and grasp theinformation by using the haptic joystick is in progress actively[2]. The haptic device is an essential element for the
implementation of the haptic. However, the current developed
haptic devices were expensive products and were produced bya haptic joystick. So, it was difficult to integrate easily into a
general joystick. In this paper, a haptic joystick that a common
joystick equipped with a haptic device consisting of a linear
servo actuator was developed, and basic research of verifying
the performance of haptic device is proceed.
In this paper, haptic device that grafts haptic system onto
general joystick was designed. Haptic device was composedof linear servo actuator used as a pillar to support the joystick
mounting plate, and was designed to be the optimal structureusing MATLAB simulation. We implement the haptic systemusing the change of the mounting plate’s slope caused by alinear servo actuator’s up-and-down motion. To verify theperformance of the designed haptic device, the haptic joystickand obstacle cognition device was constructed Tele-operation
system. Obstacle cognition device obtains precise location
information of obstacles by superposing the ultrasonic sensor.
By using the obtained obstacle information, through the©ICAROB 2014

II. Haptic device design
1. Actuator device design

Fig.1. Linear servo actuator
Figure 1 shows a linear servo actuator used to design
a haptic device. In this paper, the purpose is a
development of a haptic device using a linear servo
actuator. Linear servo actuator is a device that an inside
DC motor converts the rotary motion into the linear
motion through the ball screw, which enables the linear
motion. In this paper, using the linear servo actuator as apillar of the joystick holder plate, the linear movement
haptic device was designed.

Fig.2. Joystick coordinate system and rotating
coordinate system of haptic device
Figure 2 shows joystick coordinate system androtating coordinate system of haptic device. In thispaper, through the linear motion of a linear servo
actuator, the haptic devices has the rotary movement of-190-

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the joystick mounting plate was developed. And throughthe rotary movement, users could get the obstacle
avoidance information. Like Figure 2, joystick fixing
plate has movement of 2 degree of freedom throughrotation of Roll and pitch.

using Bluetooth. Obstacle cognition device estimate thedistance and the angle with obstacles using ultrasonicsensor, the haptic is realized by controlling haptic
device using the measured data [3].

2. MATLAB simulation
Square mounting plate Triangle mounting plate
Θ:10
error:40
error:66
error Θ:9

10

10

5

5

Z

Z
0

0

-5

Fig.4. Entire system

-5

-10
10
Y

10
0

0 X
-10 -10-10
10

2. Haptic joystick configuration

10
Y 0

0

X

-10 -10

Fig.3. D.O.F of haptic device simulation
Table 1. Parameters of Figure 3
Slope of haptic deviceThe slope of the haptic device, theerror first occurrence of an error
The haptic device is rotated, theerrornumber of angles that cannot be
expressed

Fig.5. Haptic joystick
Figure 5 is proposed haptic joystick in this paper.
Haptic joystick is configured on haptic joystick andjoystick controller. Haptic joystick is configured toattach haptic device designed on the basis of Chapter 2
at joystick. Haptic device was designed by considering
to implement haptic joystick combined with a variety ofdevices.

Figure 3 is simulation result for optimizing degreeof freedom of haptic device. In this paper, haptic device
is designed optimally through simulation using
MATLAB. Haptic device is simulated considering two
kinds of structure (Triangles and squares). Parameter of
figure 3 is equal to table1.
Impossible to express the number of angles were
measured during rotation according inclination of haptic
device. considering encoder resolution of miniature
linear servo actuators to be used for haptic device
design, rotation is measured by increasing change ofinclination from 0° to 10° of 1° and rotation is measured
from 0° to 360° of 10°. In result of simulation
impossible to express the angles are more found intriangle structure than square structure. Accordingly,
triangle structure is more proper structure than square as
haptic device.

3. Obstacles cognition device configuration
A. Obstacles cognition device

Fig.6. Obstacles cognition device
Figure 6 is obstacles cognition device. Obstacles
cognition device was configured by equipped withultrasonic wave sensor to mobile robot. Three ultrasonicsensors was equipped with the front of the mobile robot,
it recognizes obstacles using ultrasonic sensor. Througharrangement of such ultrasonic sensor identify location
information of obstacle in surround environment ofmobile robot and it is delivered to joystick controller.

III. Haptic Interface
1. Entire system Configuration
Figure 4 is entire system configuration. Haptic
joystick and obstacle cognition device transmit data

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sensor provides a distance of obstacle which is based on
the vertex of its own each, but it has to be changed to a
distance of obstacle which is based on the benchmark
O of nested ultrasonic for the implementation of
avoidance and obstacle detection.
The obstacle detection range of the nested ultrasonicsensor is determined by Equation (1) in consideration of
the obstacle detection square area shown in Figure 9.

B. Superposition of ultrasonic sensor to estimate theposition of obstacles

Fig.7. Position uncertainty of ultrasonic sensor
As shown in figure 7 (a) and (b), distance toobstacles in beam width is can measured, but difficulty
is to know the exact position of the obstacle, this calls
position uncertainty of ultrasonic sensor [4]. In order toreduce the uncertainty of locations, we nested threeultrasonic sensors to be able to recognize more precisely
the location of obstacles [5].

o,min  o  o,max(1)

o,min  OD  d(2)

o,max  OI  r  s,max

(3)

Here,

o,min in equation (2) is distance from the center ofthe nested ultrasonic sensor ring to the vertex of the
obstacle detection square area. o ,max in equation (3)is the distance of the obstacle on the basis of the centerof ultrasonic sensor ring corresponding to the maximum
detection distance of the ultrasonic sensor  s ,max [6].

IV. Experiments and Discussion
1. The experimental environment
Figure 10 is experimental environment for the
performance analysis about the proposed haptic device.
This experiment was carried out in the second floor
lobby, Samsung University-Industrial Alliance Buildingin Pusan University. And two cases were assumed for
performance analysis of the haptic device.

Fig.8. Position uncertainty reduction due to beam
overlap

Fig.9. Three adjacent overlapped ultrasonic sensor
Figure 8 shows the ultrasonic sensors S L , SC andS R which have cross beam width three nested on thecircle radius is not zero. Across the full detection area ofthe ultrasonic sensor in the center SC is divided threesmall areas (LC, C, RC) because of nested beam widthof the ultrasonic sensor. The combination of ultrasonicsensors for detecting obstacles depending on the relative
position of the obstacle in the beam width of theultrasonic sensor is changed. As a result, It is possible tosubdivide zones obstacle is present through combinationof ultrasonic sensors for detecting obstacles. Thus it
becomes possible to reduce the uncertainty of theposition of the unique ultrasonic sensor. Ultrasonic©ICAROB 2014(a) Experiment 1(b) Experiment 2
Fig.10. The experimental environment
2. The experimental results
Figure 11 is a performance analysis of the haptic
device in the experiment 1. In case of that the obstacle
is present on the left side of the obstacles cognition
device, the performance of the haptic device is analyzed
in accordance with situation that it approaches to the
obstacle. In section ⓛ, the obstacles cognition device
recognizes the position of the obstacle through the-192-The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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according to the situation. In section ③, the obstacles
cognition device recognized the only obstacle in the
middle through passing the obstacle on the left.

nesting of the ultrasonic sensor, and haptic device wasdelivered degree of risk and direction to avoid to theuser according to the situation. In section ②, the haptic
device operates in accordance with the situation
obstacles cognition device passes the obstacle closed to
the left side of it.

V. CONCLUSION
In this paper, it is proposed that haptic device using
linear servo actuator. Haptic device was designed to be theoptimal structure using MATLAB simulation. And it wasverified the performance of the haptic device throughexperiments on a variety of situations. In the future, it isplan to research an algorithm for traveling of the mobile
robot based on the position information of the obstacle
the user recognized.

Acknowledgements"This research was supported by the MKE(The
Ministry of Knowledge Economy), Korea, under the
Human Resources Development Program for Special
Environment
Navigation/Localization
National
Robotics Research Center support program supervisedby the NIPA(National IT Industry Promotion Agency)."(NIPA-2012-H1502-12-1002)

Fig.11. The performance analysis depending on
recognition of obstacle (Experiment 1)“This research was financially supported by the
Ministry of Trade, Industry&Energy(MOTIE), Korea
Institute for Advancement of Technology(KIAT)
through the Inter-ER Cooperation Projects.”

REFERENCES[1] H. S. Kim and H. J. Kang (2005), Design of Remote
Manipulator Control System using PHANToM Device.
Power Electronics Conference 2005: 595-597[2] H. Kim and J. H. Ryu (2012), The Effect ofAsynchronous Haptic and Video Feedback on
Teleoperation and a Comment for Improving the
Performance. Institute of Control, Robotics and Systems
18(2): 156-160[3] A. K. Ko, J. Y. Choi, H. C. Kim and J. M. Lee(2007), A Haptic Interface Using a Force-Feedback
Joystick. Institute of Control, Robotics and Systems
13(12): 1207-1212[4] Y. Choi, W. Choi, and J. Song (2011), Obstacle
avoidance of a mobile robot using low-cost ultrasonicsensors with wide beam angle. Institute of Control,
Robotics and Systems 17(2): 79-87[5] S. Kim and H. Kim (2012), Comparative analysis on
performance indices of obstacle detection for an
overlapped ultrasonic sensor ring. Institute of Control,
Robotics and Systems 18(4): 321-327[6] S. Kim and S. Lee (2009), Positional uncertainty
reduction of overlapped ultrasonic sensor ring for
efficient mobile robot obstacle detection. Institute of
Signal Processing and Systems 10(3): 198-206

Fig.12. The performance analysis depending on
recognition of obstacle (Experiment 2)
Figure 12 is a performance analysis of the haptic
device in the experiment 2. Depending on the casewhere an obstacle is present in two locations left andcenter of the obstacles cognition device, in the samemanner as described above and analyzed theperformance of the haptic device. In section ⓛ, theobstacles cognition device recognizes first obstacles
present on the left side and haptic device was delivered
degree of risk and direction to avoid to the user
according to the situation. In section ②, the obstacles
cognition device accurately recognizes the position of
the two obstacles and haptic device was delivered
degree of risk and direction to avoid to the user

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Design of Fuzzy Controller using Variable Fuzzy Membership
Function Factors for Inverse Ball Drive Mobile Robot
Keon-woo Jeong1, Shin-nyeong Heo, Seung-Ik Hwang, Han-Dong Yoo and Jang-myung Lee2
1

Department of Electrics Engineering, Pusan National University, Busan, 616-111, Korea
Tel : +82-51-510-7379; Fax: +82-051-514-1693
E-mail: asie4u2@pusan.ac.kr
2
Department of Electrics Engineering, Pusan National University, Busan, 616-111, Korea
Tel : +81-51-510-2378; Fax: +82-051-514-1693
E-mail: jmlee@pusan.ac.kr
Abstract: In this paper, a fuzzy controller for a inverse ball drive mobile robot is implemented to have more stablebalancing capability than the conventional control system. Fuzzy control structure is chosen for a inverse ball drive
mobile robot, and fuzzy membership function factors for the control system are obtained for 3 specified weights using a
trial-and-error method. Next a linear Interpolation method is employed to generate fuzzy membership function factors
for more stable control performance when the weight is arbitrarily selected. Through some experiments, we find thatthe proposed fuzzy controller using the neural network is superior to the conventional fuzzy controller.
Keywords:

Inverse ball drive mobile robot, fuzzy.

I. INTRODUCTION

II. SYSTEM MODELING

Recently, the interest of humen-robot interaction(HRI) is growing and many robot engineers areresearching about communication ability of mobile
robot with person and the path planning has been
carried out. If there is one problem, mobile robot's
rotation in particular place or driving to particular angle
are difficult caused by the narrow space at real indoor
environments. So, the new form of mobile robot that
can take the place of it is required. Among the several
attempts, the Inverse ball drive mobile robot meets theserequirements. In this paper, the algorithm that based on
general mamdani-style fuzzy, it converts to the values ofthe elements in the appropriate purge affiliation function
depending on the load to force on the robot is proposed.
When a robot comes to perform the task, its total weightis added by luggage weight. According to the changing
of load, the total weight affects the balance [1]. As amethod to complement this problem, a control system toobtain the value of the appropriate elements dependingon the weight of the user by using a linear interpolation
to obtain the value of an element belongs function of the
fuzzy control system is provided. In order to confirm
the performance of the control system, it is comparedwith typical of the fuzzy control system and theperformance of the fuzzy control system proposed byusing the MATLAB and Simulink.

The section aims to describe the system structure
and physical configuration of the designed inverse balldrive mobile robot, and then derive its dynamic modelusing Lagrangian mechanics. The modeling process isbased on a simplification assumption that the inverseball drive mobile robot is constructed by two major
components: the body and the ball, as Fig. 1 shows.

Fig.1. Inverse ball drive mobile robot

Fig.2. Simplified diagrams of the simplified robot©ICAROB 2014

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III. CONTROLLER DESIGN

Consider the uncertainty term of the systemparameter, the Euler-Lagrange equations are written by
the following matrix form [2, 3].
M x 0 (qx )qx  Cx 0 (qx , qx )qx  Gx 0 (qx )  Dx 0 (qx )  U x

1. Introduction to control system
The simulation model used in Figure 3 was designedthat stated in the previous section is based on the
inverse ball drive mobile robot using various parameters.

(8) 0  x Twhere U x is the lumped of uncertainty defined asTU x  M x qx  Cx qx  Gx (qx )  Dx  U1 U 2  and the
lumped of uncertainty is assumed to be bounded witha finite value. By defining the following state vector
X X   xx1

xx 2

xx 4  , that is xx1   x , xx 2  x ,T

xx 3

xx 3   x and xx 4  x one obtains the subsequent
nonlinear state equations
 xx 3 x 
 x4 

Xx  
0
U 1   
1  
 M x0 ( xX )           
U 2   u x (9)  C x 0 ( x X , x X ) x X  Gx 0 ( x X )

Fig.3 System Configuration  x 0 xx 4   x 0 sgn( xx 4 ) 
where ux   x . Equation (9) represents the SIMO
dynamic equation of the robot in the median sagittal
plane.
Similar to the previous derivation, the EulerLagrange equations of the robot in the median coronalplane can be derived and governed by the following
matrix form

 

0

M y 0 ( q y ) q y  C y 0 ( q y , q y ) q y  G y 0 ( q y )  Dy 0 ( q y )  U y 0  y 

T

Position and velocity of the main body attached tothe motor encoder can know its value from. It can findangle and angular rate for balancing from IMU sensor.
Figure 4’s controller was designed for balancing angle
and angular velocity from the inverse ball drive mobile
robot. Controller Input m is the weight of the main body,pi is angle of the main body, delta pi value is the angularvelocity of the main body, and delta u value is motor
drive input voltage value.

(10)

where U y  M y q y  C y q y  Gy (q y )  Dy  U1 U 2 

TWith the four state variables X Y   x y1

xy 2

xy 3

xy 4

,
T

that is x y1   y , x y 2   y , x y 3   y and x y 4   y one
obtains the subsequent nonlinear state equations
 xy3 x 
 y4 

XY  


0
U






1
1
 M y 0 ( xY )            
U 2   u x   C y 0 ( xY , xY ) xY  G y 0 ( xY )(11)

Fig.4. Block diagram of the controller
2. Fuzzy controller
Through a process of normalization, input of the
Fuzzy controller comes output. When output isnormalization, it is represented by linguistic rules of“IF-THEN form”. Final output value comes fromdefuzzification. In this process, L, S and Z in Fuzzy
controllers input membership function means Positive
Z-axis in the direction of the robot when the size ofbalancing. N, Z and P means Positive X-axis in the
direction of the robot when look into the front. In
addition, L, M, S and Z in output membership function
mean the strength of the output. N, P and Z mean
Positive X-axis in the direction of the wheel. Table 1 isrepresented to those membership functions. The graphin Figure 5 shows fuzzy rule table [4].

  y 0 x y 4   y 0 sgn( x y 4 ) 
Similarly, equation (11) represents the SIMO
dynamic equation of the robot in the median coronal
plane

 

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Table 1. Fuzzy rule table
x

LN

SN

Z

SP

LP

N

LP

MP

Z

SN

MN

Z

LP

SP

Z

SN

LN

P

MP

SP

Z

MN

LN

y

We applying it to the main body and analyze theinverse ball drive mobile robot’s position and angle.
Figure 6 is the inverse ball drive mobile robot designed
by Simulink program and Figure 7 is the inverse balldrive mobile robot designed by CATIA for 3D visual
simulation. It can be measured performance ofcontroller depending on the weight. Finally, we get thevalue of angle and angular rate from inverse ball drive
mobile robot. It was designed for transmit in real-time
controller. According to this robot’s weight, we haveapplied with controller that 3 specimen values candetermine fuzzy membership element value and using
fuzzy membership functions for the controller element
values tuned by the linear interpolation method. We justdecide random value 4kg, 7kg and 8.1kg that can know
the difference in performance instead of specimen value
3kg, 6kg, 9kg for the simulation graph.

Fig.5. Surface graph of the fuzzy rule table
Mandani’s MAX-MIN method was used in reasoning
process and COA(center of area) method was used toderive output for defuzzification [5].

IV. SIMULATION
Simulations were run, in order to verify the validityof the proposed scheme. First of all, Tune the 3
specified weights using a trial-and-error method. These
values is the specimen value of 3 fuzzy membershipfunctions. Controller that 3 specimen values candetermine fuzzy membership element value and using
fuzzy membership functions for the controller element
values tuned by the linear interpolation method ismodeled by Simulink program.

Fig.7. 3D modeling for visual simulation
The controller is the same of the proposed at the xaxis and y-axis. So the performance is compared withthe existing controller and proposed controller at the xaxis.
First, Figure 8 shows angle trajectory of 4kg’s weight.
Simulation results of figure 8 shows well-balancing
controller compared with conventional controller andproposed controller.

Fig.8. X-axis’s angular trajectory for the weight of 4kg
Fig.6. Schematic Design with Simulink©ICAROB 2014

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ACKNOWLEDGEMENT“This research was financially supported by the
Ministry Of Trade, Industry & Energy(MOTIE), Korea
Institute for Advancement of Technology(KIAT) andDongNamInstitute
For
Regional
Program
Evaluation(IRPE) through the Leading Industry
Development for Economic Region.”

REFERENCES
Fig.9. X-axis’s angular trajectory for the weight of
7.1kg[1] Hyunwook Kim, Seul Jung, “Experimental Studies
of Controller Design for a Car-like Balancing Robot
with a Variable Mass,” KIIS, vol. 20, no. 4, pp.469-475
2010.
[2] U. Nagarajan and A. Mampetta, G. A. Kantor and R.
L. Hollis, “State Transition, Balancing, Station Keeping,and Yaw Control for a Dynamically Stable Single
Spherical Wheel Mobile Robot,” in Proc.IEEE Int. Conf.
Robot. and Autom., pp. 998-1003, 2009.
[3] U. Nagarajan, G. A. Kantor and R. L. Hollis,“Trajectory Planning and Control of an UnderactuatedDynamically Stable Single Spherical Wheeled Mobile
Robot,” in Proc. IEEE Int. Conf. Robot. and Autom., pp.
3743-3748, 2009.
[4] L. A. Zadeh, "Fuzzy sets," Information and control,
pp. 338-353, 1965.
[5] C. T. Lin and C. S. G. Lee, "Neural Fuzzy System,"Prentice Hall, pp. 142-159, 1996.

Fig.10. X-axis’s angular trajectory for the weight of
8.1kg
Figure 9, 10 shows angle trajectory of 7.1kg’s weightand 8.1kg’s weight. Increased inertia weight makes
control difficulty and it makes the more large differencein performance evident than in the case of the front. so
the proposed controller than conventional controller isfound to show better performance. Through thoseresults, using fuzzy membership functions for thecontroller element values tuned by the linear
interpolation method is better than controller that 3
specimen value can determine fuzzy membershipelement value.

V. CONCLUSION
In this paper, we figured out 3 values depending on
weight through trial and error. This is general fuzzycontroller’s fuzzy membership function element for
better performance. Then, by the linear interpolation
method help the optimal value and apply fuzzycontroller. Using simulation, we compared fuzzycontroller that has 3 standard fuzzy membership fuction
factor value and proposed controller with fuzzy
membership fuction factor value resulted from neuralnetwork. In this simulation, the proposed controller
shown good performance when 4kg, 7.1kg and 8.1kg
random weight value was input. Finally, We can risestability about general the inverse ball drive mobile
robot balancing©ICAROB 2014

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Error Correction of Angular Velocity for Gyroscope using Genetic
Algorithm and FIR filter
Jaeyong Kim*, Hyunhak Cho**, Sungshin Kim***+*Department of Electrical and Computer Engineering, Pusan National University, Korea** Department of Interdisciplinary Cooperative Course: Robot, Pusan National University, Korea***Department of Electrical Engineering, Pusan National University, Korea
Tel : 82-051-510-2367; Fax : 82-97-597-7760
arioner@pusan.ac.kr, darkruby1004@pusan.ac.kr, sskim@pusan.ac.kr

Abstract: This paper is research which sensitivity of gyro sensor is optimization. Recently, the MEMS-gyroscope isusually used at mobile robots and AGV by development of MEMS technology. However, the MEMS-gyroscope has
measurement error, cumulate error, bias drift, etc. To reduce these errors, measurement of angular velocity using
MEMES-gyroscope required high precision and exactly sensitivity. The sensitivity, is a constant to change angularvelocity form ADC value, is changed by external factors as installed location and gradient because the MEMSgyroscope measure angular velocity with inertia. Therefore, this paper proposes to reduce variance of ADC value and to
optimize the sensitivity of MEMS-gyroscope using genetic algorithm. To verify performance of proposed method, we
attached MEMS-gyroscope to automatic guided vehicle (AGV) of fork type. In experimental result, we verified that thesensitivity using the proposed method is accurate than the sensitivity in specification.
Keywords: MEMS, Gyroscope, Genetic Algorithm, Sensitivity, Optimization, Error Correction

maintenance of driving course. And if AGV should
precise drive, the user has only to install magnet under
the ground.
The magnetic-gyro use MEMS-type gyroscope
which is one of the mechanical gyroscope. It is bettersmall, low power and low cost than electrohydraulic,optical fiber, ring laser, etc.
However MEMS-type gyroscope is lower accuracyof angular velocity and performance of straight thantypically gyroscope. Therefore, the magnetic-gyroguided method is required as high accuracy ADC(analog to digital converter) and accurate sensitivity.
The error variance of ADC is larger by noise which
occurs by neighborhood environment as magnetic force,electromagnetic force. And the sensitivity of MEMStype gyroscope is changed by external factors which
installed location and slope because it measures theangular velocity using inertial.
Therefore, this paper proposes method that the error
variance of ADC is reduced by FIR-filter with raw data.
And the sensitivity of MEMS-type gyroscope is
optimized by genetic algorithm.

I. INTRODUCTION
Recently, interest about the performance of sensors
is increased by increasing interest about automation.
And then the research to improve performance ofsensors is very active. Particularly, low cost and small
size MEMS-type sensors are used in various areas by
the development of MEMS technology. MEMS-type
sensors are used in robot area. The angular velocity ofrobot is measured by MEMS-type gyroscope[1,2].
The autonomous guided vehicle (AGV) has been
important by the growth of logistics industry. The AGV
is a kind of mobile robot. It is device which
automatically transport object on given location. The
core technology used in AGV is induction system. In
induction system, there are wire guide and wirelessguide. And there is magnetic-gyro which mixed
property of wire and wireless guide. The magnetic-gyrois method which sparsely laid cylinder-type magnet
each 5m~10m under the ground and then drive magnetic
sensor and gyroscope sensor[3-5].
In this method, the AGV drive using variation ofangle from gyroscope during straight driving. And if
magnetic sensor measures magnet, the position andangle of AGV is corrected by information of magnet
and perform previously inputted work. This guided
method decrease install charges over 15~30 times forexisted wire guided method, and is easy addition or©ICAROB 2014

II. OPTIMIZATION OF SENSITIVITY
To experiment, we installed MEMS-type gyroscopein forklift AGV with laser navigation. Follow picture isforklift AGV for experiment.

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III. OPTIMIZATION OF SENSITIVITY
1. FIR Filter
The FIR filter is finite the convergence of impulse
response. FIR filter has stability because it need returnloop due to non-recursive filter. And it usually used thetransmission of waveform since shape of waveform of
input and output is maintained. This paper performs the
low-pass filter using FIR-filter from changed raw datafrom ADC.
FIR-filter reflects aspects which change slow on
input signal because it has average output signal.
However it does not reflects rapidly input signal. This issmoothing of high frequency by average operator. And
it is low pass filter since low frequency is influenced by
FIR-filter. The equation of FIR-filter is following.

.

Fig.1. forklift AGV for testing
Half-EPS for passive control is in the forklift AGV.
However it is converted into position controlled fullEPS for experiment. Fig. 2 is system configration of
forklift AGV.

y[n]  b0 x[n]  b1 x[n 1] 
y[n] 

 bN (n  N )

(1)

1
1
1
1
x[n]  x[n  1]  x[n  2]  x[n  3]
4
4
4
4
(2)

This paper used 4 order equations. x[n] is input, y[n]is output, bi is coefficient of filter, N is degree of filter.
Optimization of sensitivity using GA
The sensitivity of MEMS-type gyroscope is changed
by external factors which installed location and slope.
Therefore the sensitivity of MEMS-type gyroscope canbe optimized at place installed gyro using measured
data after installed gyro in system.
This paper proposes to optimize sensitivity of
MEMS-type gyroscope using genetic algorithm withcumulated filtered data.
The genetic algorithm used crossover, reproduce
operator and generation number repeated 100 times. The
crossover selects 2 chromosomes which are high fitness.
The reproduce pass 50% chromosome on next
generation. Equation to optimize the sensitivity of
MEMS-type gyro is following.

Fig.2. System configration
where industrial PC is used for rapid research anddevelopment. DAQ is used to speed control of motor.
The laser navigation for global localization of AGV is
installed on the top of AGV. The data of the sensors aretransferred from AVR to DSP. DSP send gathered data
through RS-232 communication to industrial PC. Table
1 is specification of installed sensors. Table 1 is
specification of sensors.
Table 1. Specification of sensors
SensorSpecification
Laser
Navigation(NAV200)
MEMS-type
Gyro(ADXRS613)©ICAROB 2014

Supply voltage
Positioning
accuracy
Angularaccuracy
Input voltage
Range
Drift

24V
±4m∼±25mm

Nf ( x)   (360  x  O  ) / N

(3)

i 0

±0.1°

where x is the sensitivity, O+ is sum of ADC when
AGV turn 360°. Fig. 3. is flowchart of algorithm inthis paper.

5V±150 °/s
±3 %

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Japan, January 11-13, 2014  (C  O )  S(5)The experiment is performed at 5times according todriving speed when steering angle of AGV is fixed each
10°among 30~60°of left and right, respectively.
Table 2 is average error on experiment at 5 times.
Table 2. Result of experimentError(unit: degree)
Steering Driving
FIR+GA:
GA:
Spec:
angle
Speed
0.02210.02510.031248cm/s
1.176
1.702
44.476

Fig.3. Flowchart of proposed algorithm

IV. EXPERIMENT AND RESULT

43 cm/s
38 cm/s30°
33 cm/s
28 cm/s
23 cm/saverage48 cm/s
43 cm/s
38 cm/s
33 cm/s40°
28 cm/s
23 cm/saverage48 cm/s
43 cm/s
38 cm/s
33 cm/s50°
28 cm/s
23 cm/saverage48 cm/s
43 cm/s
38 cm/s
33 cm/s60°
28 cm/s
23 cm/saverage
Total average

To verify performance of sensitivity optimization
proposed in this paper, we installed MEMS-type
gyroscope and laser navigation in forklift AGV. The
laser navigation is used to measure 360°turn of AGV.
The experiment performed in 840×2010cm ofspace with 19 of reflector as Fig. 4. The AGV turn
360°while steering angle is fixed.

Fig.4. Environment of experiment
The output of MEMS-type gyroscope is angularvelocity and temperature for temperature compensation
with analog. To measure raw data of MEMS-type
gyroscope, we changed analog data to digital data using
12bit-ADC. And output of raw data is changed asfollowing.

O  O  ((212 / 2)  T )  CA

2.534
1.142
1.228
0.995
1.569
1.528
6.099
7.367
1.301
1.479
2.583
1.632
3.410
1.040
1.942
1.861
2.538
0.793
1.777
1.658
10.581
9.856
8.871
4.125
7.303
5.582
7.719
3.579

43.95544.82644.77344.91944.55944.58547.53648.32444.55344.66445.35144.75945.86444.39244.95244.90245.32244.23844.84944.77050.32149.87049.25846.30948.28447.21448.54345.940As table 2, The optimized sensitivity using proposed
method was 0.0221/°/25ms, and it using GA was
0.0251V/°/25ms. The results of average error on
360° rotation are 2.500°, 3.579°, 45.940° in
proposed method, GA, specification respectively.
proposed method is lower error on turn of
360°than difference method. Because of proposed(4)

where O- and T is output of gyro and temperature,
respectively. In this paper, the factor of temperature isset 0.08 through experiment. Eq. (5) is angular velocity
using the compensated output of MEMS-type gyroscope.

©ICAROB 2014

0.338
1.740
1.654
1.889
1.310
1.351
3.182
4.440
1.578
1.402
0.306
1.250
2.026
1.836
0.942
1.022
0.351
2.082
1.106
1.834
7.628
6.908
5.932
1.224
4.376
2.668
4.789
2.500

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method considers information on environment, and getdata of low variance through FIR-filter.

V. CONCLUSION
This paper proposed optimization of sensitivity,
reducing variance of output from MEMS-type
gyroscope. The noise of outputs of MEMS-type
gyroscope should be reduced since the outputs of
MEMS-type gyroscope are influenced by external
factors. And the sensitivity should be optimized in place
where MEMS-type gyroscope is installed because
MEMS-type gyro is inertial sensor.
Therefore this paper reduced variance of MEMStype gyroscope using FIR-filter, and optimized
sensitivity using genetic algorithm.
To the experiment, we used forklift AGV. To theresult of experiment, we verified that proposed method
is lower error.

ACKNOWLEDGMENTS"This research was supported by the MOTIE(The
Ministry of Trade, Industry and Energy), Korea, underthe Human Resources Development Program forSpecial Environment Navigation/Localization National
Robotics Research Center support program supervisedby the NIPA(National IT Industry Promotion Agency)."(H1502-13-1001)

REFERENCES[1] Groover, MP (2007). Automation, production
systems, and computer-integrated manufacturing.
Prentice Hall Press[2] Pang, Y., Lopez De La Cruz, A., & Lodewijks, G.
(2008, June). Bipolar magnetic positioning system for
automated guided vehicles. In Intelligent Vehicles
Symposium, 2008 IEEE (pp. 883-888). IEEE
[3] Borenstein, J. (2000). The OmniMate: a guidewireand beacon-free AGV for highly reconfigurable
applications. International Journal of Production
Research, 38(9), 1993-2010[4] Chan, C. Y. (2002). A system review of magnetic
sensing system for ground vehicle control and guidance[5] Dash, P. K., Hasan, S., & Panigrahi, B. K. (2010).
Adaptive complex unscented Kalman filter for
frequency estimation of time-varying signals. IET
science, measurement & technology, 4(2), 93-103©ICAROB 2014

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Japan, January 11-13, 2014

Control of Robot Arm with Sterilization System for Ballast Water
Seung-Hwa Baek1, Dong-Hyun Kim2 and Hee-Je Kim3
1

Department of Electrical Enginneering, Pusan National University,
Jangjeon-dong, Geumjung-gu, Korea, bsh6845@naver.com

2

Department of Interdisciplinary Program in Robotics, Pusan National University,
Jangjeon-dong, Geumjung-gu, Korea, kikkc@naver.com
3

Department of Electrical Enginneering, Pusan National University,
Jangjeon-dong, Geumjung-gu, Korea, heeje@pusan.ac.kr

Abstract: The inadvertent transfer of harmful aquatic organisms and pathogens in the ballast water of ships has been
determined to have caused a significant adverse impact to many of the world’s coastal regions. Loading and dischargeof ballast water is an essential part of a ship’s operation, and is fundamental to maintaining safe operations under
different conditions of load. However, large vessels require thousands of tonnes of water to ensure stability andmanoeuvrability, and the environmental impacts of this can be considerable. These impacts result from the fact that theballast water can contain hundreds of different species, many of which can have serious ecological, economic andpublic health effects if transferred to regions where they are not native. The recognition of these effects has madeballast management increasingly important for protection of the marine environment. As a means to prevent, wedeveloped a system using high voltage- high frequency pulsed power, so called arc discharge, when this discharge
happens, the electrodes are oxidized and consumed. Regarding this point, we wanted to apply robot arm to supply
continuous electrode when it consumed by arc discharge.
Keywords: Ballast water treatment, Robot arm control, High voltage-high frequency, Pulse power.

entrained in the process of entering other fine marine
life the problem has spread to areas that are .
The movement of these marine organisms are notrare phenomena in the natural world represented by thevariation of species , such as red tide occurrence that
happens because of the ecological disturbance causes avery serious problem .
As a result, the IMO (International Maritime
Organization: IMO) in the 2004 Ballast Water
Management Convention, adopted ship , and dischargeof ballast water and water quality standards establishedin 2011 decided to apply the Convention in order .
In particular, the exotic marine life in the marine
environment due to its heavy losses sustained andclaims the United States, Australia, Canada, and on
several occasions over the active request and therelevant
international
conferences
International
Maritime Organization (IMO) in the February 2004 'ship ballast water and sediment management
International Convention was adopted .
In response, various methods of ballast water
treatment systems for the development andcommercialization efforts for preemption is going onaround the world , and in this way, the filtering method,

I. INTRODUCTION
In general, a cargo ship in the sea transportation of
freight interchange similar to the round-trip sailing ship ,except for so many to be one-way flight one way flights
to sail to the full load condition , then return voyage theship's balance, stability and coordination to improve
performance and bottom and left and right of the shipballast tanks (ballast tank) and install a separate tank ,here ballast (ballast water) ( freshwater or saltwater )flows into the board and will sail to the ballast
condition .
Ballast water on board ship without entering theservice , when the ship's center of gravity is at the top of
the back and forth violently shake from side to side androllover risk , can not be locked by a professional Palo
propulsive efficiency falls dramatically into problems .
The amount of water flowing into the ballast tanksof the ship size , ie, the displacement is set by reference
to the case of large vessels over 10,000 tons of seawaterflowing cases frequently appear. In this process, water
inflow , which includes a fine marine vessel docked
national and local seawater inflow and go to othercountries and regions to release the water , the water©ICAROB 2014-202-

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Japan, January 11-13, 2014

2. Circuit and system
The high-frequency, high-voltage pulse system
output circuit was made to vary the voltage andfrequency. A MC34063 for buck topology, IR2153 fordimming and a 2:200 transformer with Fly-back
topology were applied.

ultraviolet disinfection , high-temperature heating
method such as physics, chemistry, electrical processing
method and apparatus is being developed.
Ballast tank to remove the fine marine organisms tofilter water entering the inlet reduces the amount ofwater , so the processing capacity of the issues and thefilter mesh (mesh) of the complete process is limited
because the problem does not occur is . The chemicals
put into the ballast water introduced to the marine life
while the ballast tank to remove toxic chemicals
remaining in the water near the water at the time of therelease of the environmental damage caused bypollution is a problem .
In order to solve the above problems, before being
stored in the ballast tanks of the ship, or the ship's
ballast tanks, ballast water directly from the ocean
ballast water before it is discharged from the discharge
electrode arc generated by the ballast water sterilizationequipment.

The output voltage was varied up to 5kV pk-pk andthe output was adjusted to make a RMS value of 20V.
For the same applied voltage, the RMS value wasadjusted to 20V in water by applying a high frequency
and high voltage pulse of 1kV. Copper wire and needle
were weighed before and after the corrosion test, andthe surface of the needle was observed by SEM at ×500
magnification to compare the effects of DC and highvoltage pulsed power.

II. EXPERIMENTALS
1. Preparation
The degree of rust through an electrochemical
reaction of electrolyte was checked to compare theeffects of DC and high frequency, high voltage input
energy. To maintain constant electrolytes, 5 liters ofwater and 6 grams of salt were mixed, and 200mL wasused in every test. To match the amount of energy, boththe DC RMS and high voltage pulsed power, two
outputs were fixed to 20V RMS in the water. The
experimental time for each test was 10 minutes. The
surface of the copper wire and needle were observed byscanning electron microscopy (SEM). 5 liters of waterand 6 grams of salt are mixed in a test bottle and 200ml
of the resulting electrolyte was used for the corrosion
test on copper wire and needle.

3. Electrochemical Reaction
Most of the corrosion proceeds via anelectrochemical reaction involving an anode andcathode. The oxidation reaction is can be expressed as

M  M   ()The reduction reaction can be written as follows:M   ()  M 20
In this study, salt was used as electrolyte to boost
corrosion, and the electrical corrosion reaction in salt
water can be expressed as

Cu  4OH  Cu 2O  2e()  2H 2O©ICAROB 2014

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4.Results

III. CONCLUSION
The installation of ballast water management
systems (or ballast water treatment systems), designed,
reviewed, approved, installed and operated to satisfy anagreed upon ballast water discharge performancestandard has been determined by the international
marine industry to provide a more effective means toprevent, minimize and ultimately eliminate the transfer
of organisms and pathogens via vessel ballast discharge,
when compared to ballast water exchange.
Hart and the conjugate base of hydrogen peroxide as
Hoigne HO2- That can break down the ozone that can
act as initiator were found , much faster than hydroxyl
and ozone decomposition OH radicals can be generated
over the found. Artificially by adding hydrogen
peroxide to ozone at a faster rate by the decompositionof ozone OH La How to create a radical PEROXONEAOP is called. As a result of the initial ozonide radical
reactions (O3-) And HO2 - This is generated by a chain
reaction, each will generate OH radicals.
2O3 + H2O → 2OH + 3O2
Notably, hydrogen peroxide , as well as a catalyst to
generate OH radicals with OH radicals updated.
Raephal to as the scavenger is that action . Thus,
hydrogen peroxide injection than required. Rather may
cause an adverse effect on organic removal. Groundwater treatment, surface water taste and odor causingmaterial removal, etc. , but rather than using ozone toozone. By using hydrogen peroxide and ozone effective
dose to reduce the processing efficiency can be obtained.
In particular, the non-biodegradable organic substances,when applied to paper wastewater containing, as well as
COD, TOC removal is that you can get a fairly good test
results and costs©ICAROB 2014

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REFERENCES[1] Ilie Suarasan et al., "Experimental characterizationof multi-point corona
discharge devices for direct ozonization of liquids", J. of
Electrostatics,
54 207-214 (2002)[2] U. Kogelschatz et al., "Dielectric-Barrier Discharges.
Principle and Applications",
J. Phys. IV France, 7, C4-47 (1997)[3] H.M. Jones and E.E. Kunhardt, "Development ofpulsed dielectric breakdown
in liquids", J. Phys. D: Appl. Phys, 28, 178 (1995)[4] A. Abou-Ghazala, et al., "Bacterial decontaminationof water by means ofpulsed corona discharges", Pulsed Power Plasma
Science, IEEE
Conference, Las vegas (2001)[5] S. Espie et al., "Investigation Of Dissolved Ozone
Production Using Plasma
Discharges In Liquid", Pulsed Power Plasma Science,
IEEE Conference,Las vegas (2001)[6] I.V. Lisitsyn et al., "Water treatment by pulsed
streamer discharges", PulsedPower Conference, IEEE Conference (1999)[7] M. Mikula et al., "The destruction effect of a pulse
discharge in watersuspensions", Plasma Sources Sci. Tech., 6 179 (1997)©ICAROB 2014

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Japan, January 11-13, 2014

Performance Study for Vehicle Infotainment System
Heung-in Park, Min-gyu Kim, Jeong Hee-In, Kang-il Park, In-uk Lee, Dong-Ju Lee and Jangmyung Lee
Department of Electrics Engineering, Pusan National University, Busan, 616-111, Korea
Tel : 82-51-510-1696; Tel : 82-51-510-2378
heungin@gmail.com

Abstract: CPU performance, booting time and navigation widget are improved by the new design concepts. These
points are providing some solutions that can overcome the risks of open architecture system in the Automotive. The
improvement was verified with each of the test results.
Keywords: Open architecture, System performance, Booting time

I. INTRODUCTION
II. System Design

Vehicle infotainment system is getting a lot offunction and become more complex behavior with therapid development of electronic devices inside thevehicle. Just a few years ago, system functions that islimited to information offer services using thenavigation and telematics are evolving as combining
innovative technologies that have consumer IT sector
devices, such as smart phones Foreign competitors, suchas Ford Sync and GM On-Star, also improve the vehicle
merchantability through the company’s IT brands.
Therefore,
merchantability
andinnovation
characteristics of IT equipment inside the vehicle
becoming increasingly important for vehicle OEM's
brand appreciation and vehicle marketing strategy. But,
as conjunction with many external devices and homefunctions plant to vehicle environment, reliability andhow to properly design reliability and performance
condition become an important issue. For example, even
if consumer’s smart phone takes over 1 minute boottime, the situation is not so serious for a user. But, thecase that the boot of the car multimedia system takes
many time feel uncomfortable to users because of limit
user functions for essential, such as navigation system,
voice recognition, rearview camera, telematics,
Bluetooth phone functions.
In this paper, under the vehicle reliability
environmental conditions, the optimal design concept
which can stably support complex Use-Case of vehicle
infotainment system and the design content which cansecure system performance are proposed.
And, the performance improvement method about
the main optimization tuning items such as modeswitching speed, boot optimization, operation stability
which needs improvement under the limited system
resources is proposed.

©ICAROB 2014

1. Function and feature requirements
Development system is integrated infotainment
system that integrates audio, video, navigation andtelematics functions of inside the vehicle. Fromsafety/security functions to a simple convenience
functions, various complex functions are realized on asingle integrated system using a single CPU.
System architecture such as fig1 is designed in orderthat multimedia technology and a number of functions
work smoothly without disconnection
2. SW Architecture
In order to ensure vehicles reliability andperformance, As shown in Fig.2, add vehicle-specific
Automotive Framework based on Basic Architecture
Framework, classified as Automotive API list and OpenAPI for development apps downloading. Automotive
framework section is divided into service framework
parts and Native section. Service framework parts
performs role to control the main function and serviceof the same system, such as fast-boot service.
In native area, connectivity and security has been
supported such as all of the infrastructure of the system.

III. Performance improvement
As mentioned above, at the system configurationand the SW architecture-based environment of thevehicle, satisfied reliability and performance should bepremised. At the following section of this paper, a
proposed design concept for ensuring reliability and-206-

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The scheduling way provided OS distributes CPU
resources economically depending on the number ofprocess acted at the same time as Fig. 3.

performance in the environment of these vehicles ispresented.

1. Complex Use-Case design
3. Predictive control for load balancing system
First, Foreground and Background motion of system
to define complex motion Use-Case is restricted. It isprovide that motion service of foreground andbackground have performance limitation is 40 percent
and another 20 percent is defined as the Marginal. Thisbasis calculated by the foregoing process is standard forthe highest performance of CPU required when thevoice recognition behavior exists. Thus, if applying theabove-mentioned concept to application, maximum
permissible share of visible or invisible on the screen of
the application became 40%, so this concept can apply
to other applications. For instance, a navigation to guide
path showing or not showing map must satisfy suchrequirement.
If the full screen share is defined, it needs to restrict
share about a unit application. A unit application should
not exceed a maximum of 40% is defined.
After that, when each function is activated, before
and after motion are provided by motion characteristics
of function as Permanent, Temporary, Backgroundmode of the three kinds of operation characteristics
classified.
Then, specifications defined the service andfunctional priority regulations. When the trivial
behavior is acted such as listening the music, watching
the navigation in the car, the functional priority
regulation is not necessary. This regulation is requiredin an emergency like as telematics automatic incident
reporting. For example, when the car accident occurredand airbag was inflated, the accident story had to bereported preferentially to the emergency rescue center,and system resources should not be allocated for the low
priority function processing such as entertainment
function, telephone conversation.

In order to ensure the reliability and performance ofthe vehicle infotainment system, the functions ofmultiple provided by the system must be designed toproperly scheduling priorities for each function.
Also, the management criteria functional priority
for environment of the vehicle is defined as the priorityof the five like Table1.
A highest priority function is that responsible forcommunication in the vehicle and the power of thesystem. Notification function is managed by the next
higher one on AV, MEDIA, and the guidance voice ofnavigation in order that the audio output of the AV
media playing can’t be uninterrupted.
The combined operation function of normal system
for most situations would like to work at Group5 andGroup4. However, when the resources of all of the CPU
are concentrated for start-up at times system booting,the operation state of the application can be on thescreen occupied state in Background state by calling theuser interaction.
At this time, The CPU executes the call. If themethod of static priority is used, the execution of thecalled application is made that does not meet theperformance requirements. To deal with this problem,the priority is defined in advance so that the predictive
load balancing control is implemented according to the
dynamic operating conditions of application
For example, if blue-tooth connection configuration
behavior is carried to execution in the blue-tooth phone
application described in Fig.4
In the case that the priority of application iscontrolled dynamically, the switched application, which
occupies the screen, has a good UI operation
performance.
It compensates drawback of traditional method thatthe foreground operation or switching screen run slowly.
The operating system scheduler implements thehighest priority first after checking on the thread of highpriority function in a brief time so that its operating
performance is guaranteed even at CPU 100% sharing
state as Fig 5.

2. Complex behavior management
We adopt process scheduling methods which use theorder of priority provided by operating system, and highpriority function can be conducted overbearingly incomplex behavior.

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IV. FIGURES/TABLES
1. Figures

Fig.1. System Configuration

Fig.5. Secured important performance from priority
management.
2. Tables
Table1. Definition of scheduling priority

Fig.2. SW Architecture

Priorities

Description

Group 1

Power Management, Communications in
vehicle, First priority.

Group 2

AV, Media, Navigation guidance sound,High priority for loss of sound output,

Group3

Surface Flinger : System graphic
processing

Group 4

System Server, Telematics, Phonebook,
Navigation, Foreground application.

Group 5

Other Background application

Table 2. The results of CPU utilization from BT
phonebook download.
Fig.3. Operating system (GPOS) scheduling methods

ResultMax Min Ave

Initialversion

99

26

95

First
Improved
version

99

57

80

Optimized
version

99

24

75

Table 3. The result of switching performance.
Initial
VersionOptimized
versionNavigation switching

3.5 sec

0.6 sec

AV/Media modeswitching

3~5 sec0.4~0.6 sec

Rear camera
switching

4 sec

2 sec

Fig.4. Dynamic management priorities.

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2010
[4] Fabian Hueger, “Platform Independent Applications
for In-Vehicle Infotainment Systems via Integration of
CE Devices”, IEEE, 2012

VI. CONCLUSION
Both optimizing design techniques and performance
improvement that presented in this paper was confirmed
finally through the evaluation of the actual performanceand following results were identified.
(1) Table.2 shows major improvement results forcomplex behavior present in this paper. This is acomplex behavior in case of downloading 1000 ofphonebooks by being connected to cell phone andBluetooth hands-free while navigation is operating as
the background. There were a maintaining CPU 100%and a excessive waste of time during downloading
phonebooks because the initial version has a structuralproblem about the data processing. The primary
improvement was to improve the above structuralproblems. And the optimization version was to upgrade
through management of priority of process schedulingdescribed above or dispersion of load giving sleep atregular intervals in the SW logic. Through these
improvements solved the problem of ANR (Application
No Response) that frequently occurred because othercomplex behaviors are very slow while downloading
phonebook.
(2) The switching performance between different
functions such as Navigation, AV / Media, rear cameraand so on has a limit with only structural improvement
like each application initialization logic and navigation.
Achieved performance improvement results ofTable.3 by defining about priority of process schedulingand managing priority dynamically base on complex
Use-Case design described above.

ACKNOWLEDGEMENT“This research was financially supported by the
Ministry Of Trade, Industry & Energy(MOTIE), Korea
Institute for Advancement of Technology(KIAT) andDongNamInstitute
For
Regional
Program
Evaluation(IRPE) through the Leading Industry
Development for Economic Region.”

REFERENCES[1] M. Rahmani, “Novel Network Architecture for InVehicle Audio and Video Communication Continuum
Limit of Lattice Gas Fluid Dynamics”, IEEE, 2007[2] Jose Santa, “ An Embedded Service Platform
for the Vehicle Domain”, IEEE, 2007
[3] Hyeong-ju Song, Tae-yeon Kim, Ji-hoon Park, Bak
Lee, Ki-young Lim, “Inside Android”, Wiki-books,©ICAROB 2014

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Proposal of a Visualizing Method of Data Transitions to
Support Debugging for Java Programs
1

Hiroto Nakamura, 1Tetsuro Katayama, 2Yoshihiro Kita, 1Hisaaki Yamaba and 1Naonobu Okazaki
1

University of Miyazaki, 1-1 Gakuen-kibanadai nishi, Miyazaki, 889-2192 Japan
Kanagawa Institute of Technology, 1030 Shimo-ogino, Kanagawa, 243-0292 Japan
tf13006@student.miyazaki-u.ac.jp, kat@cs.miyazaki-u.ac.jp, kita@earth.cs.miyazaki-u.ac.jp,
yamaba@cs.miyazaki-u.ac.jp, oka@cs.miyazaki-u.ac.jp
2

Abstract: It takes much time to find the cause of a bug in debugging of programs. Finding the cause of a bug needs tocomprehend a flow and data transitions in executing programs. It is difficult to grasp behavior in executing the
programs whose behavior is unexpected by a bug. We propose a visualizing method of data transitions to support
debugging for Java programs in order to improve efficiency of debugging by supporting to find the cause of a bug. We
have implemented TVIS in order to confirm efficiency of the proposed method. The data transitions diagram is the mostcharacteristic function of TVIS which shows the data transitions in executing programs as a table. It can show visuallyabnormal behavior: no data renewed at all, data abnormally renewed, and so on. Because abnormal behavior is detectedin the data transitions diagram at first glance, it is useful for programmers in finding the cause of a bug. This paper
shows that the method can support to find the cause of a bug.
Keywords: programming, program slicing, visualization, debug, dynamic analysis, syntax analysis

a bug in order to confirm that TVIS can support to findthe cause of a bug.

I. INTRODUCTION
It takes much time to find the cause of a bug indebugging of programs [1]. Finding the cause of a bug
needs to comprehend a flow and data transitions inexecuting programs. It is difficult to grasp behavior in
executing the programs whose behavior is unexpectedby a bug. Additionally, many programs are complicatedfor including a number of loops and branches. In
particular, unskilled programmers need more time forthis work.
Thin slicing [2] is a technique to find the cause of
a bug, but sometimes its information is insufficient.
Thin slicing is a kind of program slicing [3], and cananalyze data transitions without superfluous information
by restricting abstraction of slice to data transitions only.
However, enough information to find the cause of a bug
cannot be gotten because states and renewals timing ofdata which is not selected as slice are not shown.
This paper proposes a visualizing method of datatransitions to support debugging for Java programs inorder to improve efficiency of debugging by supportingto find the cause of a bug. The method supports to grasp
behavior in executing programs because visualizes
renewals and states of each data.
This paper has implemented TVIS which is tool
to visualize data transitions for Java programs toconfirm efficiency of the proposed method. Mainfunctions of TVIS are the data transitions diagram, therenewal history table, and the slicing function. In
particular, the data transitions diagram is the mostcharacteristic function which shows the data transitions
in executing programs as a table. Therefore, the method
shows abnormal behavior and data renewals forunderstanding behavior of executing programs.
This paper visualizes the programs which include©ICAROB 2014

II. DATA TRANSITIONS
The data transitions in this paper show the flow ofvariable renewals in executing a program. It expresses
when and what value of each variable is renewed.
Programmers can grasp behavior of a program because
they can prefigure the behavior of each variable atarbitrary timing in the program execution by
understanding the data transitions. Hence, finding thecause of a bug becomes easier by comparing thedifference between the behavior which programmers
expect and the actual behavior, if they grasp the datatransitions of the program which includes a bug.
However, it is difficult to grasp data transitions of
the program which includes a bug. The reason is that the
program which includes a bug doesn't behave as
programmer's expectation, in addition they doesn’tknow the cause of a bug.

Fig.1. The process of visualizing data transitions-210-

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Fig.2. An example of the window of TVIS

III. VISUALIZATION

Main functions of TVIS are the data transitions
diagram, the renewal history table, and the slicing
function. Fig.2 shows an example of the window of
TVIS. The data transitions diagram is on the upper rightof the window, and the renewal history table is on thegreen area, and slices list as the result of the slicing
function is on the blue area. TVIS can support to grasp
behavior of executing programs because it shows datatransitions by using these functions.
The data transitions diagram is the mostcharacteristic function which shows renewals of eachvariable and its timing. It is a table and indicates thenumber of iterations of each loop in a lateral direction
and indicates the line number of data renewals in a
vertical direction, and shows renewal value of variables
in each loop. It is possible to understand the line and theloop where each renewal exists by using the datatransitions diagram. For example, the area which issurrounded by the red frame in Fig.2 shows that Loop1
is repeated four times in the first loop of Loop0. The
area which is surrounded by the yellow frame in Fig.2
shows that the value of variable asc is assigned into 10
in the third loop of Loop1 in the first loop of Loop0.
Hence, the data transitions diagram can show the flowof renewals of each data. Moreover, it is possible tograsp renewals and flow of the whole by using the table
form, it is easy to understand states of the other datawhen a suspicious state occurs.
In addition, TVIS shows the data transitions
arrow which shows relations between each renewal ofvariables as the red arrow on the data transitions
diagram. The relations mean ones between state of avariable and the state of other variables which is used
for the renewal of its state. After a programmer selects astate by clicking a value on the data transitions diagram,
TVIS draws a red arrow from the state which influenced
its renewal to the state which was selected. When a

In order to confirm efficiency of the proposed
method, TVIS (transitions visualization), which is tool
to support debugging, has been implemented. It canvisualize data transitions. Fig.1 shows the process ofvisualizing data transitions. The contents of the process
is as follows.

1.

2.

3.

TVIS statically analyses structure information of
the program in order to choose the point whichprobes to do dynamic analysis are inserted in.
TVIS does syntax analysis of the source code
which is visualized and analyses the result of it forinvestigating structure information of the program.
Structure information of a program is information
which includes the following data: locations ofdeclaring variables, locations of renewals, rangesof each variable scope, locations of loops, and soon.
TVIS generates the source code which includes
probes, and then outputs the result of dynamic
analysis of the program by executing it.
TVIS generates the probe file which is inserted
probes in the source code to get information ofrenewals of variables and loops from structure
information. TVIS outputs the result of dynamic
analysis by executing the probe file after
compiling it.
TVIS visualizes data transitions by using the resultof dynamic analysis and the structure informationof a program.
The result of dynamic analysis has information ineach process in executing the program: declaration
and renewals of variables, and loop. TVIS analyses
data transitions which occur in each process byusing the result of dynamic analysis, and showsthem on the window.

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Fig.3. An example A: Visualizing the program which includes a bug

Fig.4. An example B: Visualizing the program which includes another bug
suspicious state occurs, finding the cause of it becomeseasy by using the data transitions arrow.
The renewal history table shows how many
renewals of each variable in executing programs happen
and what its value is after renewals. It indicates thenumber of renewals in a lateral direction and indicates avariable name in a vertical direction. It can show
information about whether abnormalities of values ofvariables after renewals and the number of the renewals
happen.
In addition, TVIS can perform thin slicing toarbitrary states of each variable as an ancillary function.
When the value on the data transitions diagram or the
renewal history table is clicked, TVIS performs thinslicing, and shows the result of slicing on the blue areaof the window. TVIS can perform thin slicing at one
click, and it becomes easy to perform the slicing to not
only the final state but also an arbitrary state of eachvariable by using the data transitions diagram or the
renewal history table. Therefore, analysis of datatransitions can be performed without superfluous slices.

data.length-i-1". This mistake that programmers
confuse variables of similar names happens often, butthey hardly notice existence of it. The program has
returned the array which is identical with the originalarray because of this mistake.
The data transitions diagram on Fig.3 showsabnormal behavior of exchanging elements of the arrayfrom 9th line to 11th line and Loop1 which starts at the
5th line. The area which is surrounded by the red frameshows that all iterations in Loop1 repeated only twotimes and variable j as the loop counter didn't become
more than two. The correct behavior of Loop1 dwindles
the number of iterations of the loop as shown Fig.2.
Hence, we understand that the loop condition of Loop1
is suspicious.
The bubble sort program in Fig.4 contains
variables assigned to a wrong value. The variable which
is used from 7th line to 11th line on Fig.4 is not variable
i but variable j. This mistake occurred by merely
confusing variables, but it is hardly noticed because
multiple lines are wrong in succession. The program
finishes process without executing until completion ofsorting because of this mistake.
The data transitions diagram on Fig.4 showsabnormal behavior to exchange elements of the array.
The area which is surrounded by the red frame showsthat Loop1 repeated four times in the first loop of Loop0
which starts at the 3rd line. Here, if statement at the 7thline, was expected to compare all elements of the array,but it never exchanged them. Hence, we understand that
if statement at the 7th line is suspicious.
From the above two results of visualization, we

IV. EXAMPLE
Two programs which include a bug are visualized
by adapting them to TVIS, to confirm efficiency of the
proposed method. They are bubble sort programs in
Java and include different bugs.
The bubble sort program in Fig.3 sets a wrong
condition of a loop. The correct loop condition in the
5th line in Fig.3 is not "j < data.length-j-1" but "j <©ICAROB 2014

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have confirmed that programmers can get useful
information to find the cause of a bug and grasp
behavior of the program included the bug by using
visualization of data transitions of TVIS.

VI. CONCLUSION
This paper has proposed the visualizing methodof data transitions to support debugging for Java
programs in order to improve efficiency of debugging
by supporting to find the cause of a bug. We have
implemented TVIS which supports debugging andvisualizes data transitions, in order to show efficiency ofthe proposed method. We visualize programs which
include a bug by using TVIS, and show that TVIS can
support to grasp behavior of executing programs and tofind the cause of a bug.
Visualization of the proposed method can give theinformation which is not gotten by using conventional
methods for analyzing data transitions. The datatransitions diagram can show visually abnormal
behavior: abnormal data renewals and so on. Also, it iseasy to understanding states of other variables whenabnormal behavior is found.
Therefore, the proposed method can support tofind the cause of a bug by visualizing data transitions,and improve efficiency of debugging for Java programs.
The method reduces time to grasp behavior in executing
a program for programmers by showing data transitions
to them. In particular, it is useful for unskilledprogrammers.
The future issues are as follows.
 Improving the format for expressing values ofvariables.
 Introducing the localization in visualization.

V. EVALUATIONThis paper has implemented TVIS which supports
debugging for visualizing data transitions, in order toconfirm efficiency of the proposed method byvisualizing the program which includes bugs. It explains
the difference by comparing the method withconventional methods below.
Dynamic slicing [4] which includes thin slicing isa technique to analyze behavior in executing a programby extracting processes which relate to generation of a
selected state as slice. We cannot get useful information
if not we appositely decide a condition of slicing,
because slicing cannot show information which is notselected as slice. In case of Fig.3, it is difficult to get
useful information, because slice except initialization
processes is not gotten even if elements of the array are
selected as a condition of slicing. However, TVIS canshow useful information for programmers to grasp
behavior of a program, because it shows the situation ofeach variable and loop by the diagram.
Breakpoint is one of the most frequently usedmethods for debugging [5]. Breakpoint has the problem
which programmers need experience of programming todecide a location of breakpoint. Nevertheless, it is a
useful method of finding the cause of a bug, and also themethod which automatically generates breakpoint hasreported [6]. When programmers found an abnormal
process, it is difficult to understand correctly thesituation of execution with only information of a point.
TVIS becomes easy to grasp behavior of a program,when programmers found the abnormal process,because TVIS shows them to information of variables orloops which concern it. In a large program, efficiency of
TVIS may lower in comparison with breakpoint,
because information of the diagram is liable to increase.
An applicable range of programs which TVIS canvisualize is small in comparison with the tool [7] which
can visualize a large program by a diagram with a highabstraction level or tool [8] which can visualize
multithreaded programs. TVIS, which generates adiagram of low abstraction level, may make gigantic a
diagram because data to visualize even a program ofmoderate size may become huge [9]. Therefore, thevariety of programs which TVIS can visualize may havea limit. Accordingly, TVIS need reduce spaces to show
information by improving the expression format of the
diagram. An applicable range of programs which TVIS
can visualize is enlarged by focusing object to visualize
on only an important object.

©ICAROB 2014

REFERENCES[1] Roger S. Pressman (2001), Software Engineering A
Practitioner’s Approach 5thEdition, McGraw-Hill
Science[2] M. Sridharan, S. J. Fink, R. Bodik (2007), Thinslicing, In Proc. the 2007 ACM SIGPLAN Conferenceon PLDI, pp.112-122[3] Mark Weiser (1982), Programmers Use Slices When
Debugging, Communications of the ACM Vol.25, pp.
446-452[4] H Agrawal, JR Horgan (1990), Dynamic Program
Slicing, SIGPLAN Notices, Vol.25, No.6, pp.246-256.
[5] G. C. Murphy et al (2006), How Are Java Software
Developers Using the Eclipse IDE?, IEEE Software,
Vol.23, No.4, pp.76-83.
[6] Cheng Zhang et al (2013), Automated Breakpoint
Generation for Debugging, JOURNAL OF
SOFTWARE, Vol.8, No.3, pp.603-616[7] Steven P. Reiss, Guy Eddon (2005), From theConcrete to the Abstract: Visual Representations ofProgram Execution, DMS 2005, pp.315-320.
[8] Jan Lönnberg et al (2011), Java replay for
dependence-based debugging, PADTAD '11, pp.15-25[9] W. De Pauw et al (1998), Execution patterns in
object-oriented visualization, In Proc. 4th COOTS, pp.
219-234-213-

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Proposal of a Method to Build Markov Chain Usage Model from UML
Diagrams for Communication Delay Testing in Distributed Systems
Zhijia Zhao1, Tetsuro Katayama1, Yoshihiro Kita2, Hisaaki Yamaba1 and Naonobu Okazaki1
1

University of Miyazaki, 1-1 Gakuen-kibanadai nishi, Miyazaki, 889-2192 Japan
Kanagawa Institute of Technology, 1030 Shimo-ogino, Kanagawa, 243-0292 Japanzhao@earth.cs.miyazaki-u.ac.jp, kat@cs.miyazaki-u.ac.jp, kita@earth.cs.miyazaki-u.ac.jp,
yamaba@cs.miyazaki-u.ac.jp, oka@cs.miyazaki-u.ac.jp
2

Abstract: As the growth of network technology with high parallelism and high reliability of distributed systems, they
have been widely adopted in the enterprise and society. But, how to design for testing the real-time or a communication
delay of distributed systems is not been discussed much. This paper proposes a new method to automatically build
Markov Chain Usage Model. The proposed method establishes the time points and builds a new UML diagram which
can test the communication delay of a distributed system to improve its reliability. We have confirmed the usefulness ofthe proposed method to adapt it to practical examples.
Keywords: distributed systems, communication delay, Markov Chain Usage Model, UML diagram, reliability

II. METHOD OF BUILDING MARKOV CHAIN USAGE MODEL

I. INTRODUCTION
Software testing is one of the key technologies toensure software reliability. For decades, many software
testing methods have been proposed. Building thesoftware usage model becomes the basis of reliability
evaluation and reliability testing. Software testing with
Markov Chain Usage Model is an effective method to
be used by programmers and testers in web sites
development, to guarantee the software reliability.
Because UML (Unified Modeling Language) [1]has become a practical standard modeling language forobject-oriented program, many software developers
adopt UML diagrams for object-oriented development.
How to build the usage model from UML diagrams has
been proposed [2]. But, how to build a usage modelfrom UML diagrams corresponding to thecommunication delay of distributed systems has notbeen discussed much.
In this paper, we aim to improve the reliability of
distributed systems by testing the communication delay.
To achieve this aim, we propose a new method toautomatically build Markov Chain Usage Model by
establishing the time points and building a new UML
diagram. Specifically, we establish the time points andbuild a new UML diagram called De-sequence diagram
based on deployment diagram and sequence diagram.
Then, we propose an algorithm to generate the Markov
Chain Usage Model from the new UML diagram.

©ICAROB 2014

1. De-sequence diagram
For testing the communication delay of a distributed
system from UML diagram, we need to describeinteractive time and sequence between objects of systemthat distributed on each node. Sequence diagram shows
the dynamic partnership between objects, but it does notshows the layout of the distributed system, and it also
does not describe the time of messages sent. For thisreason, we combine it with deployment diagram, and set
the time point to describe interactive time and sequence
between objects. We call this diagram as De-sequence
diagram.
As shown in Fig.1, De-sequence diagram puts thesystem distributed on each node as an object, and puts
the life-line between time points as a state. It can
describe interactive time and sequence between objects
while describing dependencies of system distributed oneach node.
Specifically, we show the definitions as follows:
Definition1. Let system that we will test as: Sys =( Inst1…Instn), Sys is the system, and Inst is the instance.
Definition2. Let the instance of the system as: Inst =(Na, Node, TrPre, TrPost, Trans, Pf), Na is the name of
instance, Node is the node that instance belongs to,
TrPre is the Pre-condition of Transition, TrPost is the
Post-condition of Transition, Trans is the Transition of
the system, and Pf is the probability of the Transition.

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Definition3. Let message of the system as: Mes =(Mna, Sour, Targ), Mna is the name of the message,
Sour is the source instance of the message, and Targ is
the target instance of the message.
Before the other definitions, we propose a method as
follows to divide the system into some cases forestablishing the time points easily in Definition5:
Method1. Find a message from the beginning. If thesource instance of the message is the same as the target
instance of the next message of the first one, and thetarget instance of first message is the same as the source
instance of the second message, we call the message
and the next message pair messages. Otherwise, we callthe message single message. We construct a case with
two instances and pair messages or single message.
Construct the other cases as above.
Definition4. Let the case we constructed in method1
as C = (Ci, Mes, Inst, CPre, CPost), Ci is the number ofthe case, CPre is the Pre-condition of the case, CPost is
the Post-condition of the case, we use CPre and CPostto describe the sequence of cases, Mes and Inst has been
defined in definition2 and definition3.
Definition5. If a message let the case have atransition, we call this message CPre, the Pre-conditionof the case. If a message be sent just after the transitionof the case, we call this message CPost, the Precondition of the case.
Definition6. Let Pre-condition of Transition as:
TrPre = (Tm, Tv, La), Tm is the time mark, Tv is time
violation, and La is the label of system.
Definition7. Let time mark as: Tm = (Mna, Citj),
Mna is the name of message, and Citj is the count oftime points.
Definition8. Let time violation as: Tv = (Tc, TvH,
TvPos, Tvpro), Tc is the time constraint equation, TvH
is the time violation processing, TvPos is the Postcondition of time violation, and TvPro is the probabilityof time violation.
Definition9. Let system label as: La = (Initial,
Middle, TiVio, Final), Initial is the initial stage ofsystem, Middle is the intermediate stage of system,
TiVio is the time violation stage of system, and Final is
the final stage of system.
Definition10. Let Post-condition of Transition as:
TrPost = (Tm’).
Definition11. Let Trans as Transitions, it is a process
to satisfy the TrPost on you are satisfied with the TrPre.

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2. Definition of Markov Chain Usage Model andgeneration method
Re-define the Markov Chain Usage Model togenerate Markov Chain Usage Model from Desequence diagram for testing distributed systems.
Definition12. Let Markov Chain Usage Model as:
MC = (S, L, Trans, S0, F), S is the state, L is the statetransition label, Trans is the transition, S 0 is the initialstate, and F is the final state.
Definition13. Let state as: s ∈ S = (Sna, La), Sna
is the state name, and La is the state mark.
Definition14. Let state transition label as: l ∈ L =(Citj, Tc, Mna, Pf), Citj is the time point, Tc is the time
constraint equation, Mna is the massage name, and Pf is
the probability of state transition.
Definition15. Let state transition as: S × L → s,Trans(s, l) = v, it means state s use l(state transition
label) to get to state v.
After that we propose a method to generate Markov
Chain Usage Model from De-sequence diagram.
Method2. Choose one of cases we constructed toperform the following operations.
1. Set initial state and final state.
2. Set Pre-condition of transition to be the eventof state transition, Post-condition of transition
to be arriver state, and enter the state name.
3. Construct the state transition label like l = <Citj,
Tc, Mna, Pf>.
4. The transition of time violation accordancewith the provisions of Tv, and the probabilityof time violation is been set at 0.1 this time.
Finish other cases like this. At last, we propose amethod to combine each Markov Chain Usage Model ofcases we constructed.
Method3. First, use the CPre and CPost (defined in
Definition5) to describe sequence relationship of cases.
If the CPost of case A is the same as the CPre of case B,
that case A is the foregoing case of case B, and case B is
the immediate successor case of case A. Then, combine
them together to build Markov Chain Model for wholesystem. Take Markov Chain Usage Model initial state of
the case to be the final state of its foregoing case, andtake the final state of the case to be the initial state of its
immediate successor case. If the case does not have theforegoing case or immediate successor case, maintainthe status quo.

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III. APPLICATION EXAMPLES
We use E-commerce system and Factory automation
system as application examples to confirm theusefulness of the proposed method. We generate the test
cases with roulette test case automatic generation
method [3] based on two kinds of Markov Chain Usage
Model.

Fig.1. De-sequence diagram of E-commerce system(case1)

1. E-commerce System
E-commerce system [4] have three parts distributedin three nodes, it is SS(Supplier Server) in node1,
DS(Data Server) in node2, and DC(Data Control) in
node3. SS sent commodity query to DS, DS received
the message and let DC to query commodity
information, DC queried the commodity informationand sent it to DS, DS received the information and sent
it to SS. From node2 to node3 have three second
network delay.
We generate the test cases for E-commerce bybuilding Markov Chain Usage Model from De-sequence
diagram as follows:
1. Construct the De-sequence diagram for the Ecommerce system shown in Fig.1 use theDefinition1 to Definition11.
2. Divide the system into three cases shown in
Fig.2 use the Method1.
3. Choose the first case to generate its Markov
Chain Usage Model shown in Fig.3 use theDefinition12 to Definition15 and Method2.
4. Generate Markov Chain Usage Model for othercases like the first case.
5. Combine them together to build Markov Chain
Usage Model for whole system shown in Fig.4
use the Method3.
6. Generate test cases with roulette method (onetime to run it for time violation this time).
Then, we can have two test cases:{cc, cq, tv, tvh, cq,
qm, cm} and {cc, cq, qm, cm}. {tv, tvh, cq} in test cases
means to test the communication delay. The Markov
Chain Usage Model built by the conventional method
has only one test case:{cc, cq, qm, cm}.

(case2)(case3)
Fig.2. Cases of E-commerce system

Fig.3. Markov Chain Usage Model of case1

Fig.4. Markov Chain Usage Model of
E-commerce system
Controller), WC1(Workstation Controller1) andPC(Package Controller) are in node3. Have an orderrequest, C will sent parts query to PS, PS feedback
information to C, C is waiting until parts are enough.
Then C ask WC whether can work or not and waiting
until WC can work. After that, C sent a request to PS for
giving parts to WC. When WC finished its work, it ask
WC1 whether can work or not and waiting until WC1
can work, then WC sent commodity to WC1. WhenWC1 finished its work, it ask PC whether can work or
not and waiting until PC can work, then sent commodity
to PC. From node1 to node2 have two second network
delay, from node2 to node3 have three second network
delay, and from node1 to node3 have four second
network delay.
We use the same method with E-commerce system.
Construct De-sequence diagram for the Factory

2. Factory Automation System
Factory automation system [4] have five parts
distributed in three nodes, SS(Supplier Server) is in
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automation system shown in Fig.5, divide the system
into eight cases, generate Markov Chain Usage Modelfor each case, and combine them together to build
Markov Chain Usage Model for whole system shown in
Fig.6.
Then, we can have four test cases:{cqp, tv, tvh, cqp,
qmp, cqwc, tv, tvh, cqwc, qmwc, sr, sd, cqwc1, qmwc1,
sdwc, cqpc, qmpc, sdwc1}, {cqp, tv, tvh, cqp, qmp,
cqwc, qmwc, sr, sd, cqwc1, qmwc1, sdwc, cqpc, qmpc,
sdwc1}, {cqp, qmp, cqwc, tv, tvh, cqwc, qmwc, sr, sd,
cqwc1, qmwc1, sdwc, cqpc, qmpc, sdwc1}, and {cqp,qmp, cqwc, qmwc, sr, sd, cqwc1, qmwc1, sdwc, cqpc,
qmpc, sdwc1}. The Markov Chain Usage Model built
by the conventional method has only one test case:{cqp,qmp, cqwc, qmwc, sr, sd, cqwc1, qmwc1, sdwc, cqpc,
qmpc, sdwc1}.

automation system that the method of reference [2]cannot have. Hence, from comparing results we canknow, our proposed method can find the fault ofcommunication delay that the conventional method
cannot find. Consequently, we have confirmed that the
proposed method can improve the reliability of
distributed systems.

V. CONCLUSION
In this paper, we aim to improve the reliability of
distributed systems by testing the communication delay.
To achieve this aim, we have proposed a new method toautomatically build Markov Chain Usage Model by
establishing the time points and building a new UML
diagram for testing communication delay of a
distributed system. By adapting this method to practicalapplication examples, we have confirmed that we cantest the communication delay of distributed systems thatthe conventional method cannot test, and that the
proposed method can improve the reliability of
distributed systems.
Future issues are as follows: Get the probability of time violation.
We did not discuss the probability of time
violation, just set it at 0.1 in this paper. We
need to propose a method to get the probabilityof time violation.
 Get the Coverage of time violation
We did not discuss the coverage of time
violation, just run it one time in this paper. We
need to think the coverage of time violation tolet the test case accurate even more.

Fig.5. De-sequence diagram of
Factory automation system

Fig.6. Markov Chain Usage Model ofFactory automation system

REFERENCES[1] Grady booch, James Rumbaugh, and Ivar Jacobson,
The Unified Modeling Language User Guide Second
Edition, Addison-Wesley, 2005.
[2] Wu Caihua, Liu Juntao, Peng Shirui, Li Haihong,
Deriving Markov Chain Usage Model from UML
Model (in Chinese). Journal of Computer Research andDevelopment, 49(8): 1811-1819, 2012.
[3] Lei Hang, Chen Limin: Test Case Generation Basedon Markov Chain Usage Model (in Chinese). Journal of
University of Electronic Science and Technology ofChina, 40(5): 732-736, 2011.
[4] Hassan Gomaa, Designing Concurrent, Distributed,
and Real-Time Applications with UML, AddisonWesley, pp: 567-622, 2000.

IV. DISCUSSTION
Reference [3] is also proposed a method to build
Markov Chain Usage Model from UML diagram, but
the method does not discuss the condition of distributed
systems. In this paper, we have established the time
points to describe the interactive time and sequence
between objects and built a new UML diagram todescribe the dependencies of distributed systems. We
have got the test case included {tv, tvh, cq} in theexample of the E-commerce system, and {tv, tvh, cqp},{tv, tvh, cqwc} in the example of the Factory©ICAROB 2014

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Proposal of a Supporting Method for Debugging to Reproduce
Java Multi-threaded Programs by Petri-Net
Shoichiro Kitano1, Tetsuro Katayama1, Yoshihiro Kita2, Hisaaki Yamaba1 and Naonobu Okazaki1
1

University of Miyazaki, 1-1 Gakuen-kibanadai nishi, Miyazaki, 889-2192 Japan
Kanagawa Institute of Technology, 1030 Shimo-ogino, Kanagawa, 243-0292 Japankitano@earth.cs.miyazaki-u.ac.jp, katayama@cs.miyazaki-u.ac.jp, kita@earth.cs.miyazaki-u.ac.jp,
yamaba@cs.miyazaki-u.ac.jp, oka@cs.miyazaki-u.ac.jp
2

Abstract: It is difficult to implement the multi-threaded programs. The reason is that the behavior of each thread is nondeterministic. Also it is difficult to reproduce the situation in which an incident occurs. This paper proposes a
supporting method for debugging to reproduce Java multi-threaded programs by visualizing the behavior of the
programs with Petri-net. Moreover, we have confirmed the effectiveness of our method by implementing a tool.
Keywords: multi-threaded program, debugging, Petri-net, Java, reproducibility

Therefore, we extend Petri-net so that it can treat withthe behavior of Java multi-threaded programs.
In addition, we confirm validity of our method byimplementing a supporting tool that tests multi-threaded
programs with plural interleaving automatically andsimulates their execution path with Petri-net.

I. INTRODUCTION
In recent year, many computers are adopted multicore CPUs. For using such resources effectively, thedemand of multi-threaded programs increases.
In multi-threaded programs, it is hard work for evenexpert programmers to implement them, and easier toembed bugs than single-threaded programs[1]. Most ofsuch bugs are discovered in latter half of thedevelopment process or in executing the programs byusers. Therefore, it is difficult to fix the programs. It isnecessary to remove the bugs at the unit testing toresolve this problem.
One of the testing methods executes a program withplural interleaving by putting off a timing of executionof each thread. Hereby, we can discover potential bugs
in a multi-threaded program.
However, even if this testing method shows thatbugs exist, in multi-threaded programs, it is hard to
discover the cause of the bugs in debugging because thebehavior of multi-threaded programs is nondeterministic.
This paper proposes a supporting method fordebugging to reproduce multi-threaded programs by
Petri-net to improve efficiency of debugging work forthe multi-threaded programs written in Java language.
Specifically,
the
proposedmethodgives
reproducibility to multi-threaded programs, generates
data file for execution path of multi-threaded program,and simulates the behavior of the program by Petri-net
based on the data file.
Here, ordinal Petri-net cannot express the behaviorof multi-threaded programs written in Java completely.

©ICAROB 2014

II. SUPPORTING METHOD USING PETRINET
1. Supporting method
This section proposes a supporting method fordebugging that improves the efficiency of discovering
the cause of bugs by giving reproducibility to multithread programs. The procedure is following.
1. Generating the data file of the execution path in
the situation in which an incident occurs
The data has ID of threads executing process,executed processes, timing of processes, and ID ofgenerated threads.
2. Generating a Petri-net model from a tested Java
program
We explain the method to model Java programs by
Petri-net in the next section.
3. Simulating the behavior of Java program which
has tested
This process uses the data file and a Petri-net
model.
2. Modeling Petri-net for Java Programs
This paper models Java programs by Petri-net using
the conversion rule as follows. Fig.2 is an example of a

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Petri-net model for the program of Fig.1 which is an
example of a multi-threaded program written in Java.
 a statement is converted into a place a reserved word ‘synchronized’ is converted
into a place a state of waiting lock is converted into a place
 a beginning of the method is converted into aplace
 a transition to the next statement is converted
into a transition
 a thread is converted into a token
 a locked instance is converted into a token

A. Identifying Tokens by ID
Because of inability of Petri-net to identify theeach token which passes as the same path, we enable
Petri-net to identify each token by adding ID to tokens.
B. Expressing the Role of Tokens by Color
The modeling rule which we have described in
section II cannot make us to understand that the token
expresses thread or locked instance intuitively. Thus, wedefine a token expressing thread is a white circle and alocked instance is a black circle as shown in Fig.3. Thisdefinition makes us easy to understand the role oftokens.

Fig.3. Model for identifying a thread and alocked instance
C. Expressing Locked Instances
At present, it is hard to understand our Petri-net
when many locked instances exist in the model or many
threads share one locked instance. Also, there is aproblem not to know which thread locks which instance.
As an example of these problems, Fig.4 expresses
that there are threads numbered ID 1,3,4 which use thelocked instance numbered ID 10 and thread numbered
ID 2 which use the locked instance numbered ID 20.
However, such information is not described in the Petrinet model. As shown in Fig.5, it is impossible toconfirm the cause of waiting threads with ID3,4 when
the thread with ID1,2 have moved to the next place.
Hence, we extend a token expressing a thread todescribe ID which should be locked by each thread.
Furthermore, in order to express the state of a thread
locking an instance, we extend a token expressing athread to describe the locked token on the thread token.
This extension can express the state of releasing thelock in synchronized block happened by wait() method
which is a method of java.lang.Object class.

Fig.1. Example of a multi-threaded program written inJava

Fig.2. Petri-net modeling of Fig.1

3. Extending Petri-Net
To improve the efficiency of discovering the causeof bugs, we extend Petri-net. This extension can support
to understand the behavior of Java programs moreintuitively.
Fig.4. Example of Petri-net with ID©ICAROB 2014

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Fig.5. State of waiting for a locked instanceFig.7. Tested code

Fig.6. Extended Petri-net for Java

III. CONFIRMATION
We confirm the effectiveness of proposed supporting
method for debugging by implementing a tool.
1. The method of confirmation
As an example, we use a multi-threaded program
without the process of synchronization written in Java.
Fig.7 shows the code of the example.
This program outputs strings by three classes
succeeding java.lang.Thread class. FirstNamePrinterclass outputs “Shoichiro”, SpacePrinter class outputs “ ”,
and SurnamePrinter outputs “Kitano”. The expected
result is a string “Shoichiro Kitano”. However, becauseof this program without process of synchronization, theresult is often different from the expected result.
Fig.8 shows a part of the code generated by our toolfrom the tested code. An unexpected result is discoveredby executing the code. Furthermore, our tool cangenerate the data file of an execution path in testing thecode.
By using a data file generated by executing the code
shown in Fig.8, we have confirmed the effectiveness of
our proposed method by letting students in ourlaboratory use our tool.
©ICAROB 2014

Fig.8. A part of the generated code

Fig.9. Petri-net generated by tool-220-

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multithreaded program by plural interleaving at random.
Therefore, discovering cause of bugs is depended onusers.
On the other hand, our debugging method canreproduce the same situation in many times, because ourmethod uses the data file of an execution path that was
really executed. It makes us easy to discover the causeof bugs. Therefore, it may be said that our proposing
method is effective for removing the potential bug ofthe multi-thread program.

IV. CONCLUSION
In this paper, we have proposed supporting methodfor debugging to reproduce Java multi-threaded
programs by Petri-net. And we have extended Petri-net
to understand the behavior of Java programs more. We
think that this extension improves the efficiency of
debugging. Furthermore, we have shown theeffectiveness of our method by adapting a tool to an
example and comparing with related works and theexisting tool.
Future issues are as follows.
 Improving of the adaptation range of our tool Confirming effectiveness of extended Petri-net

Fig.10. Situation in which an incident occurs

2. Result
Fig.9 shows Petri-net generated by our tool. In Fig.9,
the label with number added to the place correspondswith the comment in Fig.7, and the place expresses
statement with corresponded the comment.
Fig.10 shows the reproduction of situation when an
incident occurs. Primarily, the place should be marked
in order of “FirstNamePrinter 1”, “SpacePrinter 1”,“SurnamePrinter 1” in this Petri-net. In Fig.10, Becauseof a token is on “SpacePrinter start run”, it realizes that
“SpacePrinter 1” is not executed and “SurnamePrinter1” has been executed earlier than “SpacePrinter 1” by
the marking of a place with “SurnamePrinter 1”. Here,
we can understand that threads are not executed in order
our expectation. Thus, we have noticed that there is a
defect in synchronized process between threads.
From this, because of reproducing the situation inwhich an incident occurs and understanding the
behavior of a program graphically, it is easy to discovera cause of a bug. Thus, we have confirmed our method
is effectively.

REFERENCES[1]J. K. Ousterhout, Why Threads Are A Bad Idea (formost purposes), Presentation given at the 1996 Usenix
Annual Technical Conference, January 1996
http://www.softpanorama.org/People/Ousterhout/Threa
ds/[2]Krishna M. Kavi, Alireza Moshtaghi, Deng-jyi
Chen(2002), Modeling Multithreaded Applications
Using Petri Nets International Journal of Parallel
Programming, Vol. 30, No. 5, October 2002: 353-371[3] Govindarajan, R, Suciu, F., Zuberek, W.M,
Timed Petri net
Models of Multithreaded
Multiprocessor Architectures, IEEE Proceedings of theSeventh International Workshop on Petri Nets andPerformance Models, June 1997: 153-162[4] Hongwei Liao, Yin Wang, Hyoun Kyu Cho, Jason
Stanley, Terence Kelly, Stéphane Lafortune, Scott
Mahlke, Spyros Reveliotis, Concurrency bugs in
multithreaded software: modeling and analysis using
Petri nets, Discrete Event Dynamic Systems, June 2013,
Vol. 23, Issue 2: 157-195[5] ConTest - A Tool for Testing Multi-threaded Java
Applications,
https://www.research.ibm.com/haifa/projects/verification/contest/

3. Comparison with Related Work and Existing Tool
As related work, some researches discover potential
bugs of multi-threaded programs by automaticallygenerated Petri-net models[2][3][4]. In these researches,
it is possible to discover the existence of bugs byanalyzing Petri-net statically. However, this approachcannot discover the cause of bugs.
There is a tool for multi-threaded programs that
discover potential bugs by executing the unit testing
automatically[5].
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Japan, January 11-13, 2014

Proposal of a Supporting Method to Generate a Decision Table from theFormal Specification
Kenta Nishikawa*, Tetsuro Katayama*, Yoshihiro Kita†, Hisaaki Yamaba* and Naonobu Okazaki**

University of Miyazaki, 1-1 Gakuen-kibanadai nishi, Miyazaki, 889-2192 Japan
Kanagawa Institute of Technology, 1030 Shimo-ogino, Kanagawa, 243-0292 Japannishikawa@earth.cs.miyazaki-u.ac.jp, kat@cs.miyazaki-u.ac.jp, kita@earth.cs.miyazaki-u.ac.jp,
yamaba@cs.miyazaki-u.ac.jp, oka@cs.miyazaki-u.ac.jp†

Abstract: In recent years, the software quality becomes more important because the system becomes large scale andhigh performance. In general, many defects are embedded in the upstream process of the software development. As one
reason of the above, specifications include ambiguous description. As a means for writing specifications strictly, formal
methods are proposed. By the way, as one of test design techniques, the decision table is proposed. However, it takes
much time and effort to extract test items and understand contents written on specifications in designing manually thedecision table. This paper proposes a supporting method to generate a decision table from the formal specification inorder to improve efficiency of the test design with formal methods. We have implemented a supporting tool to generatea decision table. It automatically generates a skeleton decision table from the formal specification. By using the tool, it
is considered that the efficiency of the test design is improved.
Keywords: formal method, VDM++, test design, decision table, automatic generation.

items of conditions and actions. However, it takes muchtime and effort to extract test items and understandcontents written on specifications in designing manually
the decision table. It is no exception even if you write
strict specifications with formal methods.
This paper proposes a supporting method to generatea decision table from the formal specification in order toimprove efficiency of the test design with formal
methods. This paper uses the formal specification
description language VDM++ which is the lightweightformal methods VDM (Vienna Development Method) towrite the formal specification. The proposed method
supports to generate a decision table by extracting the
logical relationship of the conditions and actions from a
specification written with VDM++. We have
implemented a supporting tool to generate a decisiontable. It automatically generates a skeleton decisiontable from the formal specification. By using the tool, it
is considered that the efficiency of the test design isimproved.
Here, the skeleton decision table which the tool
generates has condition items, action items, andcombinations of truth-values for the condition items.
This means that a user must add to write truth-values inaction items of the generated skeleton decision table tocomplete the decision table.

I. INTRODUCTION
In recent years, the software quality cannot be
maintained with the conventional software development
methods because the system becomes large scale andhigh performance. At the same time, effect of defects inthe system becomes one of the major social problems
with the economy and life [1].
Hence, the software quality becomes more important.
A demand for reliability and safety of the system is
becoming increasingly.
In general, many defects are embedded in the
upstream process of the software development. As one
reason of the above, each step in the softwaredevelopment process moves to the next step withspecifications
included
ambiguousdescription.
Therefore, specifications should be written strictly. As ameans for writing specifications strictly, formal methods[2] are proposed. The formal methods are a means forusing strict specifications in each step in the softwaredevelopment process. They express the system with a
specification
description
language
basedonmathematical logic. Using the formal methods canremove defects or ambiguity of the specifications. They
attract attention as a means to improve software quality.
By the way, as one of the test design techniques, thedecision table [3] is proposed in the testing process ofthe software cycle. The decision table uses a matrix
divided the logical relationships in specifications into
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Fig. 1. The flow of supporting method to generate a decision table from the formal specification

II. SUPPORTING METHOD TO GENERATEA DECISION TABLE FROM THE FORMAL
SPECIFICATION

1. Scanner/Parser
Scanner/Parser is a VDM++ analyzer for Overture
implemented by Marcel Verhoef [4]. Scanner/Parser
reads the formal specification specified by a user.
Scanner/Parser generates the analysis data (.app) fromthe formal specification.
The analysis data has analysis information (e.g.,
token, token ID, and abstract syntax tree) needed by
CA-Extractor.

Fig.1 shows the flow of the proposed method. The
proposed method consists of three parts as follows.
i. Scanner/Parser
ii. CA-Extractor
iii. SDT-Generator
We explain the flow of the proposed method as
follows.
1. A user prepares a formal specification after it isfinished the syntax checking and type checking.
2. A user runs the supporting tool to generate adecision table after the user specified a formal
specification.
3. The supporting tool to generate a decision tableruns Scanner/Parser, which uses a formal
specification specified by the user as an input.
4. Scanner/Parser generates the analysis data by
analyzing the formal specification.
5. CA-Extractor generates the extraction data
extracted conditions and actions of the formal
specification from the analysis data bygenerated Scanner/Parser.
6. SDT-Generator generates a skeleton decisiontable from the extraction data by generated
CA-Extractor, and output it as a csv file.
We explain each part as follows.
©ICAROB 2014

2. CA-Extractor
CA-Extractor extracts conditions and actions fromthe analysis data generated by Scanner/Parser, andwrites them to extraction data (.node). CA-Extractor
extracts conditions and actions only from pre-conditions
expressions, post-conditions expressions and if-thenelse expressions.
Table 1 shows extraction processes of conditions
and actions.
3. SDT-Generator
SDT-Generator generates a skeleton decision tablefrom the extraction data by generated CA-Extractor, andoutputs it as a csv file. Output format of a skeleton
decision table is adopted CSV format because it isversatile.
Table 2 shows an example of a skeleton decisiontable which SDT-Generator outputs. SDT-Generator-223-

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describes condition items and action items on thesecond column of a skeleton decision table. SDTGenerator describes truth-values in condition items on
and after the third column of a skeleton decision table.
The skeleton decision table of truth-values in action
items is empty, because the supporting tool to generate adecision table cannot generate truth-values in action
items.

1 class FizzBuzz
2 functions
3
public static checkFizzBuzz : nat->seq of char
4 checkFizzBuzz(input) ==5
if input mod 3 = 0 and input mod 5 = 0 then6
"FizzBuzz"
7
elseif input mod 3 = 0 then8
"Fizz"
9elseif input mod 5 = 0 then10
"Buzz"
11else
12"number"13
pre14
input > 0 ;15 end FizzBuzz

Table 1. Extraction processes of conditions and actions
 Extraction of conditions
 Conditions extraction processes are pre-conditions
expressions, post-conditions expressions and ifthen-else expressions.
 To extract conditions, CA-Extractor extracts
between from a start token (if, else, pre, post) to an
end token (then, ;) as a condition.
e.g., if (elseif) conditionA then → conditionA
pre conditionB ; → conditionB
 Extraction of actions
 Actions extraction processes are if-then-else
expressions.
 To extract actions, CA-Extractor extracts just after
a start token (then or else) as an action.
e.g., else actionA → actionA

Fig. 2. The formal specification of FizzBuzz

Fig. 3. A part of the skeleton decision table
incrementally, replacing any number divisible by threewith the word "fizz", and any number divisible by fivewith the word "buzz".
The formal specification of FizzBuzz in Fig.2 isdescribed a pre-condition (i.e., in the 13th and 14th line)
which means a value of the input is more than zero.
Fig.3 shows a part of the skeleton decision table
generated by the supporting tool, and it is the result ofapplying the formal specification to the supporting tool.
We have confirmed that the skeleton decision tablein Fig.3 is satisfied i and ii. Therefore, we haveconfirmed that the supporting tool to generate a decisiontable works properly.

…

…

…

…

…

…

…

Action

NAT

null

null

null

null

null

…

…

…

…

…

…

…

Table 2. An example output of the skeleton decision
table
Node
#1
#2
… #15 #16
Condition
A>0
T
T
…
F
F
Condition B=true
T
T
…
F
F

III. APPLICATION EXAMPLE
We have implemented a supporting tool to generatea decision table, in order to realize the proposed method.
It supports the test design. It automatically generates askeleton decision table from the formal specification.
We confirm that it works properly by adapting it toan example. Specifically, we confirm two items asfollows.
i. To extract conditions and actions from a formal
specification
ii. To generate truth-values in condition items
Fig.2 shows the formal specification which is used
as an application example. It is a specification of
FizzBuzz, which is described in VDM++. FizzBuzz is agroup word game. Players take turns to count©ICAROB 2014

IV. DISCUSSION
This paper has proposed a supporting method togenerate a decision table from the formal specificationin order to improve efficiency of the test design withformal methods. We have implemented a supporting
tool to generate a decision table, in order to realize theproposed method. It automatically generates a skeleton
decision table from the formal specification. By using
the tool, it is considered that the efficiency of the test
design is improved.
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We discuss our proposed method in this chapter.

established. In addition, some tools to automaticallygenerate a decision table are proposed [5, 6], but no tool
to support the test design from a formal specificationsuch as our proposed method.
By using the supporting tool to generate a decisiontable, it is considered that the efficiency of the test
design from a formal specification is improved.

1. Problems of the supporting tool to generate adecision table
We show problems of the supporting tool to generatea decision table as follows.
 Generating truth-values in action items
The supporting tool to generate a decision tablecannot automatically generate truth-values in action
items. This means that a user must add to write truthvalues in action items of the skeleton decision table, inorder to complete the decision table. If condition items
increases, it takes much time and effort to describe
manually truth-values in action items.
 Supporting to each syntax: cases expressions,for loop, and while loop, and so on
There is a limit to the formal specification
applicable to the supporting tool to generate a decisiontable. Specifically, the supporting tool does not support
to all syntax in VDM++. Hence, targets of conditions
and actions extracted from a formal specification are
pre-conditions expressions, post-conditions expressions
and if-then-else expressions.

V. CONCLUSION
This paper proposed a supporting method togenerate a decision table from the formal specificationin order to improve efficiency of the test design withformal methods. We have implemented a supporting
tool to generate a decision table, in order to realize theproposed method. The supporting tool generates askeleton decision table from the formal specification by
extracting conditions and actions of the formal
specification.
We have confirmed that it generates the skeleton
decision table and truth-value in conditions and actionsof the skeleton decision table. By using the supporting
tool to generate a decision table, it is considered that theefficiency of the test design from a formal specification
is improved.
Future issues are as follows.
 Generating truth-values in action items
 Supporting to each syntax: cases expressions,for loop, and while loop, and so on
 Comparing with a decision table described byhand

2. Evaluation of the proposed method
We confirm usefulness of the proposed method byusing the generated skeleton decision table in chapter III.
First, we have completed the decision table bydescribing truth-values in action items of the skeleton
decision table by hand. Second, we have implemented
FizzBuzz in Java from the formal specification used as
an application example in chapter III.
We have tested FizzBuzz program by using thecompleted decision table, and at the result, we havebeen able to test all combinations of conditions andactions in FizzBuzz. Therefore, we have confirmed theusefulness of the proposed method.

REFERENCES[1] John Fitzgerald, Peter Gorm Larsen, Paul Mukherjee,
Nico Plat, Marcel Verhoef (2005), Validated Designs for
Object-Oriented Systems, Springer.
[2] Shin Nakajima (2007), Formal Methods as Software
Engineering Tools (in Japanese), NII Technical Report,
NII-2007-007J.
[3] ISO 5806, Specification of single-hit decision tables.
[4] A Scanner/Parser for the Overture Toolset,
http://overturetool.hosting.west.nl/twiki/bin/view/Main/
OvertureParser/ (accessed November 29, 2013).
[5] CEGTest, http://softest.jp/tools/CEGTest/ (accessed
November 29, 2013).
[6] PictMaster, http://sourceforge.jp/projects/pictmaster/(accessed November 29, 2013).

3. Discussion about the supporting tool to generate adecision table
The supporting tool to generate a decision table
extracts conditions and actions from a formal
specification, and automatically generates a skeleton
decision table. It is able to reduce the time and effort
when testers manually design the testing.
Few researches of test design from the formal
specification are reported, and the method is not well
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Development of a Campus Guide System based on Augmented Reality
M. Tabuse and A. Tada
Graduate School of Life and Environmental Sciences, Kyoto Prefectural University
1-5 Hangi-cho, Shimogamo, Sakyo-ku, Kyoto 606-8522, Japan
Tel : 81-75-703-5453; Fax : 81-75-703-5453
e-mail:tabuse@kpu.ac.jp

Abstract: This paper presents a campus guide system based on augmented reality (AR). AR is a technology ofdisplaying CG overlaid on a real scene. AR is a fusion of a real world and a virtual world of human interfaces. We
propose a campus guide system based on markerless AR. This system displays information of a university overlaid on areal scene using a note PC and a web camera. In addition, this system presents a variety of events, for example auniversity festival and a welcome party, as 3D virtual objects overlaid on a real scene. Using this system, visitors canunderstand a university life more deeply. We have developed a campus guide system based on Parallel Tracking andMultiple Map system (PTAMM) and construct 3D virtual objects using Visual Structure from Motion System(VisualSFM). And we have confirmed the effectiveness of the system in our campus.
Keywords: Campus Guide System, Augmented Reality, AR, PTAMM, VisualSFM.

I. INTRODUCTION

II. AUGMENTED REALITY

Recently, augmented reality (AR) has been actively
researched. AR is a technology of displaying CG
overlaid on a real scene. Therefore, AR is a fusion of areal world and a virtual world of human interfaces.
There are three types of AR, AR with GPS and an
attitude sensor, marker based AR and markerless AR.
However, GPS does not indicate location information
with sufficient accuracy for AR. In marker based AR,
we must set up a particular marker and it is eyesore.
Therefore, markerless AR is the most promising.
In this paper, we propose a campus guide system
based on markerless AR. This system displays
information of a university overlaid on a real sceneusing a note PC and a web camera. In addition, thissystem presents a variety of events, for example auniversity festival and a welcome party, as 3D virtual
objects overlaid on a real scene. Using this system,
visitors can understand a university life more deeply.
We have developed a campus guide system based on
Klein’s Parallel Tracking and Multiple Map system(PTAMM) [1] and constructed 3D virtual objects using
Wu’s Visual Structure from Motion System(VisualSFM) [2]. And we have confirmed theeffectiveness of the system in our campus. First, wehave captured images of locations to guide and create
3D maps in our system. Then, we have moved along theguide course while capturing a campus and confirmedthat the system display the guide contents properly.

AR is a technology of displaying CG overlaid on areal scene. In order to realize AR, it is an importantmatter how to overlay digital information in the realenvironment. In particular, the estimate of a position
and a pose of a camera is the most important processing.
The methods to estimate a position and a pose of acamera are as follows.
(1) The method using GPS and an attitude sensor[3][4].
(2) The method using a particular marker [5].
(3) The makerless method [1].
When using GPS and an attitude sensor, the positionof the camera is estimated based on the position
information of GPS. However, it is difficult to display
CG overlaid on a real scene precisely because GPS does
not indicate location information with sufficientaccuracy for AR. When using a particular marker, AR
determines the coordinate based on the marker anddisplays CG in this coordinate. However, the markers
are eyesores in a real scene, so that it is difficult to placeparticular markers on building and streets. In markerless
AR, AR detects natural feature points everywhere in areal scene, estimates camera position and pose,
determines the coordinate and displays CG in thiscoordinate. One of the most promising and stable
markerless AR is PTAMM [1], which an extension of
PTAM (Parallel Tracking and Mapping) [6]. Thus, wedevelop a campus guide system based on PTAMM.

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III. PTAM AND PTAMM

a university overlaid on a real scene using a note PC anda web camera. In addition, this system presents a varietyof events, for example a university festival and awelcome party, as 3D virtual objects overlaid on a realscene. Using this system, visitors can understand auniversity life more deeply.

1. PTAMPTAM splits tracking and mapping into two separate
tasks, processed in parallel threads.
A. Initial 3D map
At first a camera position and pose are selected byuser and its image and FAST corner feature list become
the first keyframe. Then the camera is moved to a newposition and its image and feature list become thesecond keyframe. From these keyframe, an initial 3D
map is built using a stereo technique so that a dominant
plane defines z=0 surface in the world.
B. Tracking
Tracking thread receives images from the cameraand maintains a real-time estimate of the camera
position and pose relative to the 3D map using featurelists. The method of estimate of the camera position andpose is as follows.
1. A new image is obtained from the camera, and aprior pose estimate is generated from a motion
model.
2. 3D map feature points are projected into the image
according to the prior pose estimate.
3. The camera pose is updated from feature points
matching in the image.
C. Mapping
As the camera is moved, new images and features
are added as new keyframes whenever (a) the camera
has translated by a minimum distance from any
previous keyframe, (b) around 20 frames have passed
since the previous keyframe. Then mapping thread
expands 3D map.

1. 3D CG character
We create a 3D CD mascot character of theuniversity using 3D modeling software Metasequoia [7]shown in Figure 1. This character plays a role of guideof the university in the AR system.

Fig. 1. 3D mascot character
2. 3D virtual object
In order to construct 3D virtual objects we have used
VisualSFM [2]. VisualSFM is a GUI application for 3D
reconstruction using structure from motion (SFM). SFM
is reconstructing algorithm of 3D structure from 2D
image sequences which consist of camera images of an
object while changing the viewpoint of the camera.
In our system we have prepared 40 image of thestage of our university festival 2012. Figure 2 shows
examples of images and figure 3 shows 3D object
reconstructed by VisualSFM.

V. EXPERIMENTS

2. PTAMM
Since PTAM is a system for small-scale space, it can
not be applied to applications for multiple rooms andlarge-scale space. PTAMM is an extension of PTAM forlarge-scale space. It builds multiple maps of theenvironment. Then it selects the correct local map close
to a current camera image. The maps built by PTAMM
are stored in PC. Thus we can display particular 3D
virtual objects overlaid on the scene according to theselected maps.

We have installed the AR campus guide system intoa note PC and moved along the guide route with a PC
and a web camera while capturing images of locations
to guide and created 3D maps in our system. The guideroute is from the university main gate to a librarythrough the side of the No.1 building. Then we have
moved along the same route with the PC and a web
camera. We have verified that the system selects the 3D
maps and displays guide contents overlaid on a realscene appropriately.
In our experiments we have used a note PC(Panasonic CF-SX1; CPU : Core i5 2540, Memory :4GB), a web camera (Logicool HD Webcam C270).
For programming, we use Microsoft Visual C++ 2008,

IV. CAMPUS GUIDE SYSTEM
We have developed an AR campus guide system
based on PTAMM. This system displays information of©ICAROB 2014

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building. Then the system finds the next map, shows a
tag of the No.1 building and displays the outline of theNo.1 building in Fig. 7. Fig. 8 shows a state ofsearching the next map. In Fig. 9 the system finds thenext map and displays a related guide contents.
Fig. 10 shows the virtual object overlaid on a realscene. Creating a guide contents, we can change the sizeand orientation of a virtual object. Since AR estimates acamera poison and pose, a virtual object seems to be inactual space, shown in Fig. 11.

markerless AR software PTAMM, image processing
software library OpenCV2.4.3.

VII. CONCLUSION
In this paper we have developed an AR campus
guide system based on PTAMM. We have confirmedthat the system selects correct maps in the guide routeand displays the guide contents for each scene properly.
In our experiments we have found the following
subjects.
(1) It is difficult to create an initial map especially on
an outdoor scene.
(2) We find the feature which is easy to detect on an
outdoor scene.
(3) A 3D virtual object is made more realistic.
(4) We make simple and clear guide contents.
We will solve these subjects and develop a useful AR
guide system.

Fig. 2. Examples of images

REFERENCES[1] Castle R O, Klein G, Murray D W, Video-rate
Localization in Multiple Maps for Wearable
Augmented Reality, Proc of 12th IEEE International
Symposium on Wearable Computers (ISWC), pp.1522 (2008).
[2] Wu C, VisualSFM : A Visual Structure from Motion
System, http://ccwu.me/vsfm/[3] Sekai Camera, Tonchidot Corp.,
http://sekaicamera.com[4] Layar, Layar B.V., http://www.layar.com[5] Kato H, Billinghurst M, Marker Tracking and HMD
Calibration for a video-based Augmented Reality
Conferencing System, Proc. of 2nd International
Workshop on Augmented Reality (IWAR), pp.85-94(1999).
[6] Klein G, Murray D, Parallel Tracking and Mapping
for Small AR Workspaces, Proc. Sixth IEEE andACM International Symposium on Mixed andAugumented Reality (ISMER), pp.225-234 (2007).
[7] Metasequoia, tetraface Inc., http://www.metaseq.net.

Fig. 3. 3D virtual object

VI. Results
We have made 3D maps at (1) the main gate, (2) thebulletin board near the lecture building, (3) the street infront of No.1 building and (4) the tree in front of thelecture building. Then we have created guide contents
for each 3D map. Figs. 4 - 9 show guide contents
overlaid on a real scene. In Fig. 4 and Fig. 5 the mascot
character bows and the outline of the university isshown. In Fig. 6 we turn the camera to the No.1

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Fig. 4. AR at the main gate.

Fig. 5. AR at the bulletin board
near the lecture building.

Fig. 6. AR at No.1 building (1).

Fig. 7. AR at No.1 building (2).

Fig. 8. The state of searching the next map.

Fig. 10. The virtual stage overlaid
on a real scene.

©ICAROB 2014

Fig. 9. AR at the next map.

Fig. 11. Approach to the virtual stage.

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Facial Expression Analysis while Using Video Phone
T. Asada1, Y. Yoshitomi1, A. Tsuji2, R. Kato1, M. Tabuse1,
N. Kuwahara2, and J. Narumoto3
1: Graduate School of Life and Environmental Sciences Kyoto Prefectural University,
1-5 Nakaragi-cho, Shimogamo, Sakyo-ku, Kyoto 606-8522, Japant_asada@mei.kpu.ac.jp, yoshitomi@kpu.ac.jp, r_kato@mei.kpu.ac.jp, tabuse@kpu.ac.jp,
2: Graduate School of Science and Technology, Kyoto Institute of Technology,
Matsugasaki, Sakyo-ku, Kyoto 606-8585, Japan, diff.dim0505@gmail.com, nkuwahar@kit.ac.jp
3: Graduate School of Medical Science, Kyoto Prefectural University of Medicine,
Kajii-cho, Kawaramachi-Hirokoji, Kamigyo-ku, Kyoto 602-8566, Japan, jnaru@koto.kpu-m.ac.jp
Abstract: To improve the quality of life of elderly people living in a home or healthcare facility, especially in a ruralarea, we have been developing a method for analyzing the facial expressions of a person using a video phone system(Skype) to talk to another person. The video is recorded and analyzed by image processing software (OpenCV) and the
newly proposed feature vector of facial expression, which is extracted in the mouth area by applying 2D-DCT. 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 expression, can be used to analyze a change of facial expression. The judgment of
speaking is performed by using the intensity of the sound wave. The experimental results show the usefulness of the
proposed method. We intend to use the proposed method of facial expression analysis to develop a method for
estimating the emotions or mental state of people, particularly elderly patients.
Keywords: Facial expression analysis, Video phone, Mouth area extraction, OpenCV, and Skype.

I. INTRODUCTION

II. PROPOSED METHOD

In Japan, the average age of the population has been
increasing, and this trend is expected to continue.
Because of this trend, the number of older people withdementia and/or depression, especially those living inrural areas, is increasing very rapidly. Due to themismatch between the number of patients and thenumber of healthcare professionals, it is difficult toprovide adequate psychological assessments andsupport for all patients.
Information and communication technology (ICT) isa promising method for overcoming the difficultycaused by the lack of adequate healthcare. In Japan, thefirst inexpensive connection to the Internet became
available only recently in rural areas and high-quality
free software, such as Skype [1], is being distributed.
To improve the quality of life (QOL) of elderly
people living in a home or a healthcare facility, we
propose a method for analyzing the facial expressions ofa person using a video phone system (Skype) to speakwith another person. In the present study, the phone
video is recorded and analyzed by image processing
software (OpenCV) and the newly proposed feature
vector of facial expression, which is extracted in themouth area. Moreover, the judgment of speaking is
performed by using the intensity of the sound wave.

©ICAROB 2014

1. System overview and outline of the method
As already mentioned, the video phone is Skype [1].
VodBurner (Netralia Pty Ltd.) [2] is introduced forrecording the audio and video dialogue. Tapur [3] isalso introduced for recording the audio data.
Conversations are recorded for the analysis of facial
expression. The recorded data are analyzed by image
processing software, Open Source Computer Vision
Library (Open CV, Intel), for real-time computer vision[4] and the newly proposed feature vector of facial
expression described in this paper. The Y component
obtained from each frame in the dynamic image is used
for measuring the facial expression intensity. The
proposed method consists of (1) extraction of the moutharea, (2) measurement of facial expression intensity, and(3) judgment of utterance. In the following subsections,these three are explained in detail.
2. Extraction of mouth area from a dynamic image
First, the face area is extracted from each frame inthe dynamic image by the classifiers for a front-view
face included in OpenCV. The Haar-like featureparameters and Adaboost algorithm for learning areused as the classifiers [5]. It is assumed that the distance
between a subject and the camera generally remains
constant during a Skype conversation. However, weobserved that the size of the face area tends to increase
when the face deviates from a front-view image. In this-230-

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case, it is difficult for OpenCV to extract the face area.
Therefore, the minimum size of dynamic image within a
specified period is assumed to be the most likely front
view. In the present study, we set one second as theperiod. Next, by using OpenCV, the mouth area is
extracted for the frame selected by the face-area size
criterion described above. The mouth area is selected
because the difference between the facial expressions ofneutral and happy distinctly appear in this area.
3. Measurement of facial expression intensity
For the Y component of the frame selected by theprocessing described above, the newly proposed feature
vector of facial expression is extracted in the mouth area
by applying 2-dimensional Discrete Cosine Transform(2D-DCT) for each domain of 8  8 pixels.
The mouth area used for measuring the facial
expression intensity has n blocks of 8 × 8 pixels,
where n is obtained as n  [a / 8]  [b / 8] , a and b
denote the number of pixels of the mouth area in theface area obtained by OpenCV in the vertical andhorizontal directions, respectively, and [x] equals the
maximum integer that does not exceed x .
The high-frequency components of the 2D-DCT
coefficients tend to express a minute change in the data,
and thus result in the presence of noise. Therefore, we
select 15 low-frequency components of the 2D-DCT
coefficients, except for a direct current component, as
the feature parameters for expressing facial expression(Fig. 1). This selection of 2D-DCT coefficients is
popular among researchers in facial expression
recognition [6].
Because we do not know the combination of thespecific face location and the frequency component of
the 2D-DCT coefficients to successfully recognize afacial expression, we adopt the strategy described below.
To gather useful information from the mouth area,
we obtain the absolute value of 2D-DCT coefficients,
then we obtain the mean of the absolute value for each
2D-DCT coefficient component in the mouth area (Fig.
2). The number of 2D-DCT coefficient components is
15. Therefore, we obtain 15 values as the elements ofthe feature vector. The facial expression intensity,
defined as the norm of the difference vector between thefeature vector of the neutral facial expression and that of
the observed expression, can be used for analyzing achange of facial expression.

Fig. 1. Special frequency bands used for the analysis

Fig. 2. Schematic diagram of the DCT featureparameter calculation in the mouth area [7]4. Judgment of utterance
Combining the video signal obtained from Skypewith the sound signal, we can distinguish the facial
expression with speaking from that without speaking.
Based on the method reported in [8]–[10], the sounddata are smoothed and sampled to erase noise. The
judgment of speaking is performed by using a thresholdof the sound intensity. The threshold is determined by
the average and the standard deviation of the soundintensity when the subject does not speak in the soundenvironment where Skype is used. The thresholds for©ICAROB 2014

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the sound data values are set as x s  14 s andx s  14 s , where x s and  s express the averageand the standard deviation, respectively, of the sounddata value for one second under the condition of noutterance.
Then, every sampled data that falls withinx s  14 s , x s  14 s  are considered to be the range
of no utterance. When at least one sampled datum has avalue outside

x s  14 s , x s  14 s  , our systemjudges that the sound data contain an utterance.

a1III. EXPERIMENT
1. Condition
Two males (subjects A and B) in their 20s
participated in the experiment. Using Skype, the two
subjects held a conversation for approximately 80
seconds. The videos saved by VodBurner weretransformed into AVI files, and WAV files were saved
by Tapur. The AVI files were used for measuring thefacial expression intensity. The WAV files were used forjudgment of an utterance.

a2

a3

2. Results and discussion
Facial expression intensity changes of subjects A
and B during their conversation were recorded (Fig. 3).
The timing of utterances (Fig. 4), and the timing of noutterances (Fig. 5) are shown. In both Figs. 4 and 5, face
images and mouth images show the characteristic
timing positions for the facial expression intensity.

b1

b2

b3

Fig. 3. Facial expression intensity change of subjects A
and B during their conversation©ICAROB 2014

Fig. 4. Facial expression intensity changes (uppergraph), face images (lower left side), and mouth image(lower right side) of subjects A and B at the timing ofutterances during their conversation-232-

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Subject A expressed four local peaks of facial
expression intensity at approximately 20, 37, 47, and 71
seconds from the start point, while subject B expressed
six local peaks of facial expression intensity at
approximately 40, 45, 55, 65, 71, and 75 seconds fromthe start point (Fig. 4). For the timing of utterances
during their conversation, subject A expressed four local
peaks of facial expression intensity at approximately 18,
37, 47, and 71 seconds from the start point, while
subject B expressed five local peaks of facial expression
intensity at approximately 45, 55, 65, 71, and 75
seconds from the start point (Fig. 5). For the timing of
no utterances during their conversation, subject A
expressed three local peaks of facial expression
intensity at approximately 20, 46, and 70 seconds fromthe start point, while subject B expressed one local peakof facial expression intensity at approximately 40
seconds from the start point (Fig. 6). Subject B mainly
made an utterance during the second half of theconversation (Fig. 4), while the facial expression
intensity increased gradually for the timing of noutterances during the first half of the conversation (Fig.
5). Subject A expressed two local peaks of facial
expression intensity at approximately 18 and 37 seconds
from the start point, and subject B expressed no local
peaks of facial expression intensity when the data werelimited to the timing of utterances during the first halfof the conversation (Fig. 4). Just after the only local
peak of facial expression intensity having no utterance
at approximately 40 seconds from the start point,
subject B expressed continual peaks of facial expression
intensity with utterances.
The images of the face and mouth areas at thecharacteristic timing points show that the proposed
method can quantitatively express the facial expression(Figs. 4 and 5).

A1

A2

A3

B1

B2

IV. CONCLUSION
B3

We proposed a method for analyzing the facial
expressions of a person while speaking with a video
phone system (Skype). The recorded video is analyzed
by image processing software (OpenCV) and the newly
proposed feature vector of facial expression, which is
extracted in the mouth area by applying 2D-DCT. The
facial expression intensity, defined as the norm of thedifference vector between the feature vector of theneutral facial expression and that of the observed
expression, can be used to analyze the change of facial

Fig. 5. Facial expression intensity changes (uppergraph), face images (lower left side), and mouth images(lower right side) of subjects A and B at the timing of noutterances during their conversation©ICAROB 2014

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Master’s thesis, Dept. of Environmental Information
Graduate School of Human Environmental Sciences
Kyoto Prefectural University, pp 1-70

expression. The judgment of speaking is performed byusing the intensity of the sound wave. The experimental
results show the usefulness of the proposed method.
Acknowledgment
We would like to thank the subjects who participatedin the experiments. This research is supported by
SCOPE (122307003) of the Ministry of Internal Affairsof Communications of Japan and COI STREAM of the
Ministry of Education, Culture, Sports, Science andTechnology of Japan.

REFERENCES[1] Skype. http://www.skype.com/ Accessed 5 November
2013[2] VodBurner. http://www.vodburner.com/ Accessed 1
December 2013[3] Tapur. http://www.tapur.com/jp/ Accessed 6 December
2013[4] Open CV. http://opencv.org/ Accessed 1 December 2013[5] Viola P and Jones MJ (2001) Rapid Object Detection
Using a Boosted Cascade of Simple Features. In:
Proceedings of the 2001 IEEE Computer Society
Conference on Computer Vision and Pattern
Recognition, Vol.1, pp.511-518[6] Sakaguchi T and Morishige S(1997) Real-Time
Facial Expression Recognition Based on the 2Dimensional DCT. Trans IEICE J80-D-Ⅱ(6):15471554[7] Yoshitomi Y, Tabuse M, Asada T (2012) Facial
expression recognition using thermal image
processing. In: Carvalho VH (Ed) Image processing:methods, applications and challenges. Nova Science
Publisher, New York, pp. 57-85[8] Ikezoe F, Nakano M, Yoshitomi Y, et al (2005)
Facial expression recognition using thermal face
image automatically acquired in speaking (inJapanese). I n : Proceedings of Human Interface
Symposium 2005, Fujisawa, Kanagawa, Japan, Se
p.15-17, 2005, pp 7-12
[9] Nakano M, Yoshitomi Y, and Tabuse M (2006)
Efficient facial expression recognition using thermal
face image in speaking and its application to
analysis of individual variations (in Japanese). In:
Proceedings of Human Interface Symposium 2006,
Kurashiki, Okayama, Japan, Sep.25-28, 2006, pp
1151-1156[10]Nakano M (2008) Robust facial expression
recognition for various speakers (in Japanese).
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Method of Facial Expression Analysis Using Video Phone
and Thermal Image
Y. Yoshitomi, T. Asada, R. Kato, and M. Tabuse
Graduate School of Life and Environmental Sciences Kyoto Prefectural University,
1-5 Nakaragi-cho, Shimogamo, Sakyo-ku, Kyoto 606-8522, Japan
yoshitomi@kpu.ac.jp, {t_asada, r_kato}@mei.kpu.ac.jp,tabuse@kpu.ac.jp
Abstract: To improve the quality of life of elderly people living in a home or healthcare facility, especially in a ruralarea, we have been developing a method for analyzing the facial expressions of a person using a video phone system(Skype) to speak with another person. In the present study, we proposed a method for analyzing facial expressions of aperson using the video phone system to talk to another person. The recorded video is analyzed by thermal image
processing and the newly proposed feature vector of facial expression, which is extracted in the mouth area by applying
2D-DCT. The facial expression intensity, defined as the norm of the difference vector between the feature vector of theneutral facial expression and that of the observed expression, can be used to analyze a change of facial expression. The
judgment of utterance is performed by using the intensity of the sound wave. The experimental results show theusefulness of the proposed method. We intend to use the proposed method of facial expression analysis to develop amethod for estimating the emotions or mental state of people, especially elderly patients.
Keywords: Facial expression analysis, Video phone, Area of mouth and jaw, Thermal image, and Skype.
To improve the quality of life (QOL) of elderly
people living in a home or healthcare facility, we havebeen developing a method for analyzing the facial
expressions of a person using a video phone system tospeak with another person. In the present study, we
proposed a method for analyzing the facial expressionsof a person using the Skype video phone system.
Instead of a visible ray image, we use an IR image thatis applicable under widely varying lighting conditions.
Moreover, the judgment of utterance is performed byusing the intensity of the sound wave.

I. INTRODUCTION
In Japan, the average age of the population has been
increasing, and this trend is expected to continue.
Because of this trend, the number of older people withdementia and/or depression, especially those living inrural areas, is increasing very rapidly. Due to themismatch between the number of patients and thenumber of healthcare professionals, it is difficult toprovide adequate psychological assessments andsupport for all patients.
Information and communication technology (ICT) isa promising method for overcoming the difficultycaused by the lack of adequate healthcare. In Japan, thefirst inexpensive connection to the Internet became
available only recently in rural areas and high-quality
free software, such as Skype [1], is being distributed.
Although the mechanism for recognizing a facial
expression has received considerable attention in thefield of computer vision research, it still falls far shortof human capability, especially from the viewpoint ofrobustness under widely varying lighting conditions.
One reason for this lack of robustness is that nuances ofshade, reflection, and local darkness influence theaccuracy of facial expression recognition through theinevitable change of gray levels. To avoid this problem
and develop a method for facial expression recognition
that is applicable under widely varying lighting
conditions, we use images produced by infrared rays(IR), which reveal the thermal distribution of the face[2]-[13].

©ICAROB 2014

II. PROPOSED METHOD
1. System overview and outline of the method
As already mentioned, the video phone is Skype [1].
VodBurner (Netralia Pty Ltd.) [14] is introduced forrecording the audio and video dialogue. Tapur [15] isalso introduced for recording the audio data.
Conversations are recorded for the analysis of facial
expression. The recorded data are analyzed by thermal
image processing and the newly proposed feature vectorof facial expression described in this paper. The
proposed method consists of (1) extraction of the area ofthe mouth and jaw, (2) measurement of facial
expression intensity, and (3) judgment of utterance. In
the following subsections, these three are explained indetail.

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2. Extraction of area of mouth and jaw from a
dynamic image
The frame extracted every 0.1 second in the
dynamic image is used for thermal image processing.
Six face areas (Fig. 1) are extracted by the thermal
image processing reported in our study [11]. The area ofthe mouth and jaw is selected because the difference
between the facial expressions of neutral and happy
distinctly appears in this area.
Fig. 2. Special frequency bands used for the analysis

Fig. 1. Blocks for extracting face areas in thethermal image [11]3. Measurement of facial expression intensity
For the extracted frame, the newly proposed feature
vector of facial expression is extracted in the area of themouth and jaw by applying a two-dimensional discretecosine transform (2D-DCT) for each domain of 8  8pixels.
The high-frequency components of the 2D-DCT
coefficients tend to express a minute change in the data,
and thus result in the presence of noise. Therefore, we
select 15 low-frequency components of the 2D-DCT
coefficients, except for a direct current component, as
the feature parameters for expressing facial expression(Fig. 2). Because we do not know the combination of
the specific face location and the frequency componentof the 2D-DCT coefficients to successfully recognize afacial expression, we adopt the strategy described below.
To gather useful information from the mouth area,
we obtain the absolute value of 2D-DCT coefficients,
then we obtain the mean of the absolute value for each
2D-DCT coefficient component in the mouth area (Fig.
3). The number of 2D-DCT coefficient components is
15. Therefore, we obtain 15 values as the elements ofthe feature vector. The facial expression intensity,
defined as the norm of the difference vector between thefeature vector of the neutral facial expression and that of
the observed expression, can be used for analyzing achange of facial expression.
©ICAROB 2014

Fig. 3. Schematic diagram of the DCT featureparameter calculation in the mouth area [11]4. Judgment of utterance
Combining the video signal obtained from Skypewith the sound signal, we can distinguish the facial
expression with speaking from that without speaking.
Based on the method reported in [16]–[17], the sounddata are smoothed and sampled to erase noise. The
judgment of speaking is performed by using a thresholdof the sound intensity. The threshold is determined by
the average and the standard deviation of the soundintensity when the subject does not speak in the soundenvironment where Skype is used. The thresholds forthe sound data values are set as x s  14 s andx s  14 s , where x s and  s express the averageand the standard deviation, respectively, of the sounddata value for one second under the condition of noutterance.
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Then,

all

sampled

data

that

fall

withinx s  14 s , x s  14 s  are considered to be the range
of no utterance. When at least one sampled datum has avalue outside

x s  14 s , x s  14 s  , our systemjudges that the sound data contain an utterance.

III. EXPERIMENT
1. Condition
The thermal image was produced by a thermal video
system (Thermo Shot F30, NEC Avio Infrared
Technologies Co.). Two males (subjects A in his 50s and
B in his 20s) participated in the experiment. Using
Skype, they held a conversation for approximately 100
seconds. The videos saved by VodBurner weretransformed into AVI files, and WAV files were saved
by Tapur. The AVI files were used for measuring thefacial expression intensity. The WAV files were used forjudgment of the utterance of both subjects A and B.
2. Results and discussion
Facial expression intensity change of Subject A
during the conversation with Subject B were recorded(Fig. 4), and the subjects' utterances were classified (Fig.
5). In Fig. 5, images of the face and images of themouth and the jaw show the characteristic timing
positions for the facial expression intensity.
Subject A expressed constant changes of facial
expression intensity during utterances (Fig. 5 (a), (b)),
while he expressed drastic changes of facial expression
intensity during no utterances (Fig. 5 (c), (d)), whichcorresponded to his smile. Therefore, the big changes infacial expression intensity shown in Fig. 4 are mainly
expressed in the natural smile of Subject A.

A2

A3

A4

Fig. 5. Facial expression intensity changes (uppergraphs), images of face and images of area of mouthand jaw (lower) of subject A during the conversation
between subjects A and B

Fig. 4. Facial expression intensity change of Subject A
during the conversation with Subject B©ICAROB 2014

A1

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014[6] Nakano M, Ikezoe F, Tabuse M, et al (2009) A
study on the efficient facial expression using
thermal face image in speaking and the influence ofindividual variations on its performance (inJapanese). J IEEJ 38(2):156-163[7] Koda Y, Yoshitomi Y, Nakano M, et al (2009)
Facial expression recognition for a speaker of aphoneme of vowel using thermal image processing
and a speech recognition system. In: Proceedings of
18th IEEE International Symposium on Robot andHuman Interactive Communication, Toyama, Japan,
Sep 29-Oct 1, 2009, pp 955-960[8] Fujimura T, Yoshitomi Y, Asada T, et al (2011)
Facial expression recognition of a speaker using
front-view face judgment, vowel judgment, andthermal image processing. J Artif Life and Robotics
16(3):411-417[9] Yoshitomi Y, Asada T, Shimada K, et al (2011)
Facial expression recognition of a speaker using
vowel judgment and thermal image processing. J
Artif Life and Robotics 16(3):318–323[10]Nakanishi Y, Yoshitomi Y, Asada T, et al (2013)
Robust facial expression recognition of a speakerusing thermal image processing and updating offundamental training-data. J Artif Life and Robotics,
17(3):342-349[11]Yoshitomi Y, Tabuse M, Asada T (2012) Facial
expression recognition using thermal image
processing. In: Carvalho VH (Ed) Image processing:methods, applications and challenges. Nova Science
Publisher, New York, pp. 57-85[12]Yoshitomi Y, Tabuse M, Asada T, (2011) Vowel
judgment for facial expression recognition of a
speaker. In: Ipšić I (Ed) Speech Technologies.
InTech, Rijeka, pp. 405-424[13]Nakanishi Y, Yoshitomi Y, Asada T, et al (2014)
Facial expression recognition of a speaker using
thermal image processing and reject criteria in feature
vector space. J Artif Life and Robotics, in press[14]VodBurner. http://www.vodburner.com/ Accessed 1
December 2013[15]Tapur. http://www.tapur.com/jp/ Accessed 6 December
2013[16]Ikezoe F, Nakano M, Yoshitomi Y, et al (2005)
Facial expression recognition using thermal face
image automatically acquired in speaking (inJapanese). I n : Proceedings of Human Interface
Symposium 2005, Fujisawa, Kanagawa, Japan, Se
p.15-17, 2005, pp 7-12
[17]Nakano M, Yoshitomi Y, and Tabuse M (2006)
Efficient facial expression recognition using thermal
face image in speaking and its application to
analysis of individual variations (in Japanese). In:
Proceedings of Human Interface Symposium 2006,
Kurashiki, Okayama, Japan, Sep.25-28, 2006, pp
1151-1156

Several images of the face and images of the mouthand jaw at the characteristic timing points show that the
proposed method can quantitatively express the facial
expression (Fig. 5 lower images).
Based on the proposed method, we intend todevelop a method for estimating the emotions or mental
state of a patient.

IV. CONCLUSION
We proposed a method for analyzing the facial
expressions of a person while speaking with a video
phone system (Skype). The recorded video is analyzed
by thermal image processing and the newly proposedfeature vector of facial expression, which is extracted in
the mouth area by applying 2D-DCT. The facial
expression intensity, defined as the norm of thedifference vector between the feature vector of theneutral facial expression and that of the observed
expression, can be used to analyze the change of facial
expression. The judgment of utterance is performed byusing the intensity of the sound wave. The experimental
results show the usefulness of the proposed method.
Acknowledgment
The present study was partially supported by
KAKENHI (22300077).

REFERENCES[1] Skype. http://www.skype.com/ Accessed 5 November
2013[2] Yoshitomi Y, Kimura S, Hira E, et al (1996) Facial
expression recognition using infrared rays image
processing. In: Proceedings of the Annual Convention
IPS Japan, Osaka, Japan, Sep 4-6, 1996, 2:339-340[3] Yoshitomi Y, Miyawaki N, Tomita S, et al (1997)
Facial expression recognition using thermal image
processing and neural network. In: Proceedings of
6th IEEE International Workshop on Robot andHuman Communication, Sendai, Japan, Sep 29-Oct
1, 1997, pp 380-385[4] Yoshitomi Y, Kim SIll, Kawano T, et al (2000) Effectof sensor fusion for recognition of emotional statesusing voice, face image and thermal image of face.
In: Proceedings of 6th IEEE International Workshop
on Robot and Human Interactive Communication,
Osaka, Japan, Sep 27-29, 2000, pp 178-183[5] Ikezoe F, Ko R, Tanijiri T, et al (2004) Facial
expression recognition for speaker using thermal
image processing (in Japanese). Trans Human
Interface Soc 6(1):19-27©ICAROB 2014

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Facial Expression Recognition Using Thermal Image Processing andEfficient Preparation of Training-data
Y. Nakanishi1, Y. Yoshitomi2, T. Asada2, and M. Tabuse2

1: ITOKI CORPORATION, 1-4-12 Imafuku-higashi, Joto-ku, Osaka 536-0002, 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@kpu.ac.jp, t_asada@mei.kpu.ac.jp, tabuse@kpu.ac.jp
Abstract: In our previously developed method for facial expression recognition, we prepared training data for a pair of
the first and last vowels pronounced by the speaker. The number of pairs is 25 for the Japanese language. We
investigated the influence of training data on the facial expression accuracy using the training data of “taro,” whose
first and last vowels are /a/ and /o/, for the three intentional facial expressions of “angry,” “sad,” and “surprised,” andthe training data of 25 pairs of vowels for the two intentional facial expressions of “happy” and “neutral.” Using the
proposed method, the facial expressions of one subject were discriminable with accuracies of 100%, 70.0%, and 47.2%
for “taro,” “koji” (the first and last vowels of which are /o/ and /i/), and “tsubasa” (the first and last vowels of which are/u/ and /a/), respectively, for the three facial expressions of “happy,” “neutral,” and “other” when one of the fiveintentional facial expressions of “angry,” “happy,” “neutral,” “sad,” and “surprised” was exhibited. On the other hand,the facial expressions of one subject were discriminable with accuracies of 90.6% and 85.7% for “koji” and “tsubasa,”
respectively, when the training data were generated using “koji” and “tsubasa” as respective pronunciations.
Keywords: Facial expression recognition, Speech recognition, Vowel judgment, Thermal image processing, E
fficient gathering of training data.
concluded that updating the training data corresponding
to the facial expressions of “happy” and “neutral” ispractical. These two facial expressions are not only very
common in our daily lives, but are also easier to express
than other facial expressions. Furthermore, theclassifications of “neutral,” “happy,” and “other” areefficient for facial expression recognition under thecondition that updating the training data of facial
expressions is not performed frequently.
In our previously developed method for facial
expression recognition of a speaker, we prepared thetraining data for a pair of the first and last vowels
pronounced by the speaker [8]. The number of pairs is
25 for the Japanese language. It is time-consuming anddifficult for a subject to express facial expressions foreach pair of vowels.
In the present study, we investigate the influence of
training data on facial expression accuracy using thetraining data of “taro,” the first and last vowels of whichare /a/ and /o/, for the three intentional facial
expressions of “angry,” “sad,” and “surprised,” and thetraining data of 25 pairs of vowels for the two
intentional facial expressions of “happy” and “neutral.”I. INTRODUCTION
The present study investigates the first stage of thedevelopment of a robot that has the ability to visuallydetect human feelings or mental states. Although themechanism for recognizing facial expressions hasreceived considerable attention in the field of computer
vision research, it still falls far short of human capability,
especially from the viewpoint of robustness under
widely varying lighting conditions. One reason for thislack of robustness is that nuances of shade, reflection,and local darkness influence the accuracy of facial
expression recognition through the inevitable change ofgray levels. In order to avoid this problem and develop amethod for facial expression recognition that is
applicable under widely varying lighting conditions, weused images produced by infrared rays (IR), which
reveal the thermal distribution of the face [1]-[12]. We
adopted an utterance as the key to expressing humanfeelings or mental states because humans tend to
express feelings vocally [3]-[12]. Although several
studies on facial expression recognition using thermal
image processing have been reported (see references[1]-[17]), only our research [3]-[12] has focused on the
speaker. The present study also focuses on the speaker.
In addition, we added a judgment function of a frontview face to the proposed method for facial expression
recognition [7]. We developed a method for efficiently
updating the training data because frequent updates aretime-consuming [9]. Through experiments, we©ICAROB 2014

II. IMAGE ACQUISITION
The principle behind thermal image generation is the
Stefan-Boltzmann law, which is expressed as W  T 4 ,where  is the emissivity,  is the Stefan–
Boltzmann constant (= 5.6705  10-12 W/cm2K4), and T-239-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014is the temperature (K). For human skin,  is estimated torange from 0.98 to 0.99 [18], [19]. In the present study,
the approximate value of 1 was used as  for humanskin because the value of  for almost all substances islower than that of human skin [18]. Consequently, thehuman face region is easily extracted from an image
using the value of 1 for [1]-[12]. In principle, IR
temperature measurements do not depend on skin color[19], darkness, or lighting condition, and so the faceregion and its characteristics are easily extracted from athermal image.

1. Speech recognition and dynamic image analysis
We use a speech recognition system called Julius[20] to obtain the timing positions of the start of speech
and the first and last vowels in a WAV file [6]-[12].
Figure 2 shows an example of the waveform of theJapanese name “Taro.” The timing position of the startof speech and the timing ranges of the first vowel (/a/)
and the last vowel (/o/) are decided by Julius. Using thetiming position of the start of speech and the timing
ranges of the first and last vowels obtained from the
WAV file, three image frames are extracted from an AVI
file at the three timing positions. For the timing position
just before speaking, we use the timing position of 84
ms before the start of speech, as determined in ourpreviously study [5]. For the timing position of the firstvowel, we use the position at which the absolute valueof the amplitude of the waveform is the maximum while
speaking the vowel. For the timing position of the lastvowel, we apply the procedure used for the first vowel.

III. METHOD FOR FACIAL EXPRESSION
RECOGNITION
Figure 1 shows a flowchart of the proposed method,which consists of two modules. The first is a module forspeech recognition and dynamic image analysis, and thesecond is a module for learning and recognition. In themodule for learning and recognition, we embedded themodule for front-view face judgment [7]. The proposed
method is described in detail in our book [10].

Fig. 2. Speech waveform of “taro” and timing positions
for image frame extraction [6]2. Learning and recognition
For static images obtained from the extracted image
frames, the process of erasing the area of the glasses,
extracting the face area, and standardizing the position,size, and rotation of the face are performed according to
the method described in our previous study [5], [10].
Next, we generate difference images between the
averaged neutral face image and the target face image inthe extracted face areas in order to perform a 2D
discrete cosine transform (2D-DCT). The feature vector
is generated from the 2D-DCT coefficients according toa heuristic rule [4], [5]. The facial expression isrecognized by the nearest-neighbor criterion in thefeature vector space with the rejection domain using thetraining data just before speaking and that while
speaking the phonemes of the first and last vowels [12].
Fig. 1. Flowchart of the proposed method [12]©ICAROB 2014-240-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

VI. EXPERIMENTS

In the present study, we have investigated the influenceof training data on the facial expression recognition
accuracy. The method for facial expression recognition
using the training data of “taro” (the first and lastvowels of which are /a/ and /o/), for the three intentional
facial expressions of “angry,” “sad,” and “surprised,”
and the training data of 25 pairs of vowels for the two
intentional facial expressions of “happy” and “neutral”
were selected. The method is hereinafter referred to as
the efficient method. Then, as pronunciations, “taro,”“koji” (the first and last vowels of which are /o/ and /i/),and “tsubasa” (the first and last vowels of which are /u/and /a/), were selected when applying the efficientmethod.
For comparison with the efficient method, themethod for facial expression recognition using thetraining data of the same pronunciations as those of test
data was also applied to the data for “koji” and“tsubasa.” This method is hereinafter referred to as thereference method. Figures 3 and 4 show examples ofthermal images of the subject used for training by theefficient method and in the test, respectively.

1. Condition
The thermal image produced by the thermal video
system (Nippon Avionics TVS-700) and the soundcaptured from an Electret condenser microphone (SonyECM-23F5), as amplified by a mixer (Audio-Technica
AT-PMX5P), were transformed into a digital signal by
an A/D converter (Thomson Canopus ADVC-300) andwere input into a computer (DELL Optiplex 780, CPU:Intel Core 2 Duo E8400 3.00 GHz, main memory: 3.21
GB, and OS: Windows 7 Professional (Microsoft) with
an IEEE1394 interface board (I·O Data Device 1394PCI3/DV6). We used Visual C++ 6.0 (Microsoft) as the
programming language. In order to generate a thermal
image, we set the condition such that the thermal image
had 256 gray levels for the detected temperature range.
The temperature range for generating a thermal image
was decided so as to easily extract the face area on theimage. We saved the visual and audio information in thecomputer as a Type 2 DV-AVI file, in which the video
frame had a spatial resolution of 720×480 pixels and 8bit gray levels, and the sound was saved in a stereo
PCM format, 48 kHz and 16-bit levels.
Subject A, a male with glasses, performed inalphabetic order each of the intentional facial expressionsof “angry,” “happy,” “neutral,” “sad,” and “surprised,”
while speaking the semantically neutral utterance of eachof the Japanese first names listed in Table 1.
Table 1 Japanese first names used in the experiment [8]First vowel

Last
vowel

a
i
u
e
o

a

i

u

e

o

ayaka
kazukitakerukaede
taroshinnyahikarishigerumisae
hirokotsubasayuki
fuyu
yusukeyuto

keita
megumimegu
keisukekeikotomoyakoji
noborukozue
tomokoIn the experiment, Subject A intentionally
maintained a front-view in the AVI files, which were
saved as both training and test data. We assembled 20
samples as training data and 10 or less samples as test
data, in which all facial expressions of the subject were
judged as front-view faces by our reported method [7].
The number of test data was decided as a result of thefront-view face judgment. From one sample, we
obtained three images at the timing positions of justbefore speaking and while speaking the phonemes ofthe first and last vowels.
©ICAROB 2014

Fig. 3 Examples of thermal training images forspeaking “tsubasa” in the efficient method-241-

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Table 2 Recognition accuracies for “taro,” “koji,” and“tsubasa” as obtained when the efficient method was used

Fig. 4 Examples of thermal test images while
speaking “tsubasa”
2. Results and discussion
Tables 2 and 3 show the values of the recognition
accuracy of the facial expressions obtained using theefficient method and the reference method, respectively.
Using the efficient method, the average facial
expression accuracies were 100%, 70.0%, and 47.2%,
respectively, for “taro,” “koji,” and “tsubasa,” for thethree facial expressions of “happy,” “neutral,” and“other” when the speaker exhibited one of the fiveintentional facial expressions of “angry,” “happy,”“neutral,” “sad,” and “surprised.” On the other hand,using the reference method, the average facial
expression recognition accuracies were 90.6% and85.7% for “koji” and “tsubasa,” respectively.
Accordingly, the influence of training data on the facial
expression recognition accuracy might not be small.

Table 3 Recognition accuracies for “koji” and “tsubasa”
as obtained when the reference method was used

V. CONCLUSION
We have investigated the influence of training data on
facial expression accuracy using training data for “taro,”
the first and last vowels of which are /a/ and /o/,respectively, for the three intentional facial expressions of“angry,” “sad,” and “surprised,” and the training data of
25 pairs of vowels for the two intentional facial
expressions of “happy” and “neutral.” Using the efficient©ICAROB 2014

method, the facial expressions of one subject werediscriminable with 100%, 70.0%, and 47.2% accuracy
for “taro,” “koji” (the first and last vowels of which are/o/ and /i/), and “tsubasa” (the first and last vowels of
which are /u/ and /a/), respectively, for the three facial-242-

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thermal image processing. J Artif Life and Robotics
16(3):411-417[8] Yoshitomi Y, Asada T, Shimada K, et al (2011)
Facial expression recognition of a speaker using
vowel judgment and thermal image processing. J
Artif Life and Robotics 16(3):318–323[9] Nakanishi Y, Yoshitomi Y, Asada T, et al (2013)
Robust facial expression recognition of a speakerusing thermal image processing and updating offundamental training-data. J Artif Life and Robotics,
17(3):342-349[10]Yoshitomi Y, Tabuse M, Asada T (2012) Facial
expression recognition using thermal image
processing. In: Carvalho VH (Ed) Image processing:methods, applications and challenges. Nova Science
Publisher, New York, pp. 57-85[11]Yoshitomi Y, Tabuse M, Asada T, (2011) Vowel
judgment for facial expression recognition of a
speaker. In: Ipšić I (Ed) Speech Technologies.
InTech, Rijeka, pp. 405-424[12]Nakanishi Y, Yoshitomi Y, Asada T, et al (2014)
Facial expression recognition of a speaker using
thermal image processing and reject criteria in feature
vector space. J Artif Life and Robotics, to appear[13]Socolinsky D A, Selinger A (2002) A comparative
analysis of face recognition performance withvisible and thermal infrared imagery. In: Kasturi R,
Laurendeau D, Suen C, et al (Eds.) Proceedings of
16th International Conference on Pattern
Recognition, Quebec City, Canada ,Aug 11-15,
2002, 4, pp 217-222[14]Khan M M, Ward R D, Ingleby M (2004)
Automated classification and recognition of facial
expressions using infrared thermal imaging. In:
Proceedings of 2004 IEEE Conference on
Cybernetics and Intelligent Systems, Singapore, Dec
1-3, 2004, 1, pp 202-206[15]Khan M M, Ingleby M, Ward R D (2006)
Automated facial expression classification andaffect interpretation using infrared measurement offacial skin temperature variations. ACM Trans on
Autonomous and Adaptive Systems, 1(1):91-113[16]Jiang G, Song X, Zheng F, et al (2006) Facial
expression recognition using thermal image. In:
Proceedings of 27th Annual International Conferenceof the Engineering in Medicine and Biology Society,
Shanghai , China, Jan 17-18, 2006, pp 631-633[17]Hernández B, Olague G, Hammoud R, et al (2007)
Visual learning of texture descriptors for facial
expression recognition in thermal imagery.
Computer Vis and Image Underst 16 (2-3):258-269[18]Kuno H (1994) Infrared rays engineering (inJapanese). Tokyo, IEICE, p 22
[19]Kuno H (1994), Infrared rays engineering (inJapanese). Tokyo, IEICE, p 45
[20]Kawahara T, et al (2010) Open-Source Large
Vocabulary CSR Engine Julius. Julius rev.4.1.5.1.
http://julius.sourceforge.jp/ Accessed 12 March2013

expressions of “happy,” “neutral,” and “other” when the
speaker exhibited one of the five intentional facial
expressions of “angry,” “happy,” “neutral,” “sad,” and“surprised.” On the other hand, the facial expressions of
one subject were discriminable with accuracies of
90.6% and 85.7% for “koji” and “tsubasa,” respectively,
when the reference method was used. In order todevelop a practical method for facial expression
recognition, we intend to investigate a method for
reducing the influence of training data on facial
expression accuracy.
Acknowledgments
We would like to thank Ms. C. Shiraishi of Kyoto
Prefectural University for her valuable support during
the data analysis. The present study was supported by
KAKENHI (22300077).

REFERENCES[1] Yoshitomi Y, Kimura S, Hira E, et al (1996) Facial
expression recognition using infrared rays image
processing. In: Proceedings of the Annual Convention
IPS Japan, Osaka, Japan, Sep 4-6, 1996, 2:339-340[2] Yoshitomi Y, Miyawaki N, Tomita S, et al (1997)
Facial expression recognition using thermal image
processing and neural network. In: Proceedings of
6th IEEE International Workshop on Robot andHuman Communication, Sendai, Japan, Sep 29-Oct
1, 1997, pp 380-385[3] Yoshitomi Y, Kim SIll, Kawano T, et al (2000) Effectof sensor fusion for recognition of emotional statesusing voice, face image and thermal image of face.
In: Proceedings of 6th IEEE International Workshop
on Robot and Human Interactive Communication,
Osaka, Japan, Sep 27-29, 2000, pp 178-183[4] Ikezoe F, Ko R, Tanijiri T, et al (2004) Facial
expression recognition for speaker using thermal
image processing (in Japanese). Trans Human
Interface Soc 6(1):19-27[5] Nakano M, Ikezoe F, Tabuse M, et al (2009) A
study on the efficient facial expression using
thermal face image in speaking and the influence ofindividual variations on its performance (inJapanese). J IEEJ 38(2):156-163[6] Koda Y, Yoshitomi Y, Nakano M, et al (2009)
Facial expression recognition for a speaker of aphoneme of vowel using thermal image processing
and a speech recognition system. In: Proceedings of
18th IEEE International Symposium on Robot andHuman Interactive Communication, Toyama, Japan,
Sep 29-Oct 1, 2009, pp 955-960[7] Fujimura T, Yoshitomi Y, Asada T, et al (2011)
Facial expression recognition of a speaker using
front-view face judgment, vowel judgment, and©ICAROB 2014

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Music Recommendation System through Internet for Improving
Recognition Ability Using Collaborative Filtering and Impression Words
Y. Yoshitomi1, T. Asada1, R. Kato1, Y. Yoshimitsu1, M. Tabuse1, N. Kuwahara2, and J. Narumoto3
1: Graduate School of Life and Environmental Sciences Kyoto Prefectural University,
1-5 Nakaragi-cho, Shimogamo, Sakyo-ku, Kyoto 606-8522, Japan
yoshitomi@kpu.ac.jp, {t_asada, r_kato, y_yoshimitsu}@mei.kpu.ac.jp, tabuse@kpu.ac.jp,
2: Graduate School of Science and Technology, Kyoto Institute of Technology,
Matsugasaki, Sakyo-ku, Kyoto 606-8585, Japan, nkuwahar@kit.ac.jp
3: Graduate School of Medical Science, Kyoto Prefectural University of Medicine,
Kajii-cho, Kawaramachi-Hirokoji, Kamigyo-ku, Kyoto 602-8566, Japan, jnaru@koto.kpu-m.ac.jp

Abstract: Music therapy for improving recognition ability may be more effective if music that is liked by an individualis adopted. In our previously reported system, the recommendation process using collaborative filtering was terminatedwhen no users in the reference list have the same preference for recommended music as that of a new user. Based on
the scores for impression words, the second recommendation process finds the most similar music to the successfully
recommended music among music that has not yet been recommended. In the present study, based on our previously
reported system, we propose a system for music recommendation through the Internet using a videophone system(Skype). The effectiveness of the proposed system is verified. The recommendation accuracy of the proposed system
was 90.6% for 10 trials by five elderly subjects. The average number of recommended pieces of music for the subjects
by the proposed system was 14.9 per trial. In the future, we intend to increase the number of user evaluation scores inthe database and apply the proposed system to older individuals and/or to individuals with cognitive impairment.
Keywords: Collaborative filtering, Music recommendation, Music therapy, Impression word, Internet, Skype.
on impression words. When music that has not been
recommended to a user has the same scores, with the
exception of “0”, as that of at least one recommended
piece of music that has been evaluated highly by theuser on the three-level scale for at least five impression
words, the music is treated as having been positively
evaluated by the user. In contrast, when music that has
not been recommended to the user has the same scores,
with the exception of “0”, as that of another piece ofmusic that has been recommended to the user and has
been negatively evaluated by the user on the three-level
scale for at least seven impression words, the music istreated as having been negatively evaluated by the user.
In Fig. 1, when none of the not recommended musicreceives a positive evaluation by the user, anotherrecommendation is performed by using the subjective
estimations of all users whose subjective estimations are
stored in the database.
It is expressed by “with highest similarity” in Fig. 1that the music has the highest proportion of the same
three-level scores except “0” as that of other music
recommended to the user and given a positive
evaluation by the user among the music not yet
recommended to the user. In Fig. 1, the “set of musicwith a similarity to the recommended music based on
impression words” (MSRIW) is decided by using atleast seven pairs of impression words in the case of anegative evaluation.

I. INTRODUCTION
In Japan, the average age of the population has been
increasing, and this trend is expected to continue. This
trend is more remarkable in rural areas. Recently, music
therapy has been used to improve the recognition abilityof people, particularly older people. Music therapy may
be more effective if music that is liked by an individualis adopted. We have been developing a music
recommendation system aimed at improving recognition
ability [1]-[2].
In the present study, based on our previously
reported system [2], we propose a system for music
recommendation through the Internet using avideophone system (Skype)[3], and verify theeffectiveness of the proposed system.

II. MUSIC RECOMMENDATION METHODUSING IMPRESSION WORDS
We use ten pairs of impression words (Table 1) [4].
As an example, we show a user scores for one pair ofimpression words (quiet - busy), and the user scores theword pairs according to seven levels, which are thentransformed to three levels, as shown in Table 2 [2]. A
music database was created in which all songs in the
database were assigned scores, i (-3 ≤ i ≤ 3), for each pairof impression words evaluated by the participants. Fig. 1
shows a flowchart of the music recommendation based©ICAROB 2014

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Table 1. Pairs of impression words [4]quiet - busy

finishing the recommendation process, the database ofusers with subjective estimations of the music is
updated by adding the subjective estimations of the user
for whom the proposed system recommends music [2].
In the flowchart shown in Fig. 3, the estimation ofuser u for song mR is set to 1 when the score of user
u for song mR is 4 or 5 (“slightly like” or “favorite”),
and is set as 0 when the score is 1 to 3 (“dislike,”“slightly dislike,” or “neutral”). We used ten pairs ofimpression words (Table 1) [4]. As an example, we
showed the user scores for one pair of impression words(quiet - busy). Users scored the word pairs according toseven levels, which are then transformed to three levels,
as shown in Table 2 [1].

bracing - heavy
easy - uneasy
cheerful - gloomy
refreshing - depressing
happy - sad
comforting - harmful
calm - elevating
clean - dirty
magnificent - superficial
Table 2. Scores for pairs of impression words, quiet- busy [1]Score Three-level score
impression
32very busy-110slightly busy
0

-1

neutralslightly quiet

-2
-3

busy

quiet
1

very quiet

III. PROPOSED SYSTEM
Fig. 2 shows an overview of the proposed system. Fig.
3 shows a flowchart of our previously reported system,
which combines collaborative filtering and our music
recommendation process based on impression words.
In the proposed system, the recommendation processusing collaborative filtering is terminated when thenumber of users is zero in the reference list of users
showing exactly the same evaluation for therecommended music as that of the user up to thatmoment. Then, the recommendation process performs
by finding out the most similar music, from theviewpoints of three-level scores except “0” on
impression words, to that successfully recommended
among music not yet recommended. The proposedsystem recommends music stored in the database to user
u , as shown in Fig. 3. The recommendation processusing the proposed system is terminated when thenumber of recommended songs reaches the upper limit
K , which has been decided previously. Just before©ICAROB 2014

Fig. 1. Flowchart of music recommendation by using
impression words [1]

Fig.2. Overview of the proposed system-245-

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Table 3. List of songs used in the present study
Harugakita Teruterubozu
Mushinokoe Hiraitahiraita
TakibiNatsuwakinu
Soranbushi
Zuizuizukkorobashi
Akaikutsu Tanabata
Harunoogawa Antagatadokosa
Usagi
Natsunoomoide Muramatsuri Usagitokame
SakuraWarewauminoko Kutsuganaru Umi (Chinese character)
Hana
Umi (Hiragana) Yuuhi
Urashimatarou
Koinobori Kintarou
KakashiMomotarou
Seikurabe Akatonbo
Furusato
Hanasakajiji
Fujinoyama Yuuyakekoyake Yuki
Ushiwakamaru
Chatsumi Koujyounotsuki Oshougatsu Ureshiihinamatsuri
Kisha
Oborozukiyo
Katatsumuri Nanatsunoko
Amefuri
Tsuki
KagomeIchigatsutsuitachi
Ame
MomijiTouryanse
Dongurikorokoro

50-59 years: five subjects). We used 15 as the value for
K in the evaluations of the proposed method. In addition,
all 52 of the selected songs in the database wereassigned scores, i (-3 ≤ i ≤ 3), for each pair ofimpression words by five subjects of different ages (2029 years: three subjects, 40-49 years: one subject, 50-59
years: one subject). The subject in his fifties also
assigned the scores, s. The average score i obtained
from the five subjects for each pair of impression words
was used as the score i for the performance evaluation.
The 15 songs having three-level scores other than “0”for one impression word at most were not recommended
in the recommendation process based on impression
words because these songs did not have distinct
characteristics from the viewpoint of impression words.
Updating of the AUSE, which is the set of all users
with subjective estimations in Fig. 3, was effective in
improving the accuracy of the recommendation andincreasing the number of recommended songs [2].
Therefore, in order to improve the performance of the
proposed system, we used a face-to-face system that
updated the AUSE for 44 users before this experiment
was performed.
For programming, we used Visual C++ 6.0(Microsoft) on a PC (Dell Latitude E6599, CPU: Intel
Core 2 Duo P8700 2.54 GHz, main memory: 4.00 GB,
and OS: Windows 7, Microsoft) for the experiment. We
used a videophone system (Skype) to send music tousers over the Internet. The proposed system was set up
at Kyoto Prefectural University in Kyoto City located in
the southern part of Kyoto Prefecture in Japan. The
participants for this experiment resided in Kyotango
City in the northern part of Kyoto Prefecture, Japan.
Using Skype over the Internet, 10 older users, referred
to as users 1 through 10, of different ages (70-79 years:
three subjects, 80-89 years: six subjects, 90-99 years:
one subject) participated in the experiment on user-byuser updating for the AUSE. All of the users were

Fig. 3. Flowchart of music recommendation used in
the proposed system [2]

IV. PERFORMANCE EVALUATION
1. Conditions
Since older people tend to prefer children’s songs[5], we selected a CD described as an anthology of older
songs enjoyed by older people with dementia [6] andselected 52 songs on the CD that were also included in amusic textbook database for elementary schools [7].
The Japanese titles of the 52 songs are listed in Table 3.
In order to evaluate the music recommendation method,
all 52 of the selected songs in the database wereassigned scores, s (1 ≤ s ≤ 5), by 12 subjects of different
ages (13-19 years: one subject, 20-29 years: six subjects,©ICAROB 2014

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pieces of music for subjects by the proposed system was
14.9 per trial. In the future, we intend to increase thenumber of user evaluation scores in the database andapply the proposed system to older individuals and/or toindividuals with cognitive impairment.

female. Users 3, 4, and 8 were the same person andusers 5, 6, 7, and 10 were also the same person.
2. Results and discussion
Fig. 4 shows the performance of the proposedsystem, with the updating of the AUSE for users 1
through 10. The mean value of the number ofrecommended songs for users 1 through 10 was 14.9 pertrial. The mean value of the recommendation accuracy
for 10 trials by the users 1 through 10 was 90.6%.
The impression words used in the present study
were selected based on the linguistic method [4]. The
questionnaire was completed by 100 subjects for 80
pieces of classical music and the impression words usedin the present study were demonstrated to have very low
correlation with each other [4]. For details on theselection of impression words, see Reference 4. The
impression words used in the present study are notrelated to the acoustic features of music, such as tempo,
tonality, rhythm, and harmony.

Acknowledgment
We would like to thank all of the subjects who
participated in the experiments. The present study was
supported in part by SCOPE (122307003) of the
Ministry of Internal Affairs of Communications ofJapan.

REFERENCES[1]Koro C, Yoshitomi Y, Asada T, Yoshizaki S (2012),
Music

recommendation

aimed

at

improving

recognition ability using collaborative filtering andimpression words. In:Sugisaka M (Ed), Proceedingsof 17th International Symposium on Artificial Lifeand Robotics (AROB17th), Beppu, Oita Japan, Jan
19-21, 2012, pp 222-225[2]Yoshizaki S, Yoshitomi Y, Koro C, Asada T (2013),
Music recommendation hybrid system for improving
recognition ability using collaborative filtering andimpression words. J Artif Life and Robotics, in press.
[3]Skype.

http://www.skype.com/

Accessed5

November 2013[4]Kumamoto T, Ohta K (2002) Design of scales torepresent user's impressions of a music piece for use
in a music-retrieval system (in Japanese). IPSJ SIG
Notes, 2001-NL-147(6):35-40[5]Takahashi T (1997) Research report on songs
Fig. 4. Performance of the proposed system. Upper:
recommendation accuracy, Lower: number ofrecommended songs

familiar to people advanced in years (in Japanese). J
Japanese music therapy associate 15(1):68-75[6]Akahoshi T (2009) Good old anthology enjoyable forpeople advanced in years and troubled with dementia

V. CONCLUSION(in Japanese). Kirara shobo, Tokyo

We have proposed a system for music
recommendation over the Internet using a videophonesystem (Skype). The effect of this system was verified
in the present study. The recommendation accuracy ofthe proposed system was 90.6% for 10 trials by fiveelderly subjects. The average number of recommended©ICAROB 2014[7]Music textbook database for elementary school by
Kanagawa prefectural education center (in Japanese).
http://kjd.edu-ctr.pref.kanagawa.jp/daizai_music/
Accessed 11 March 2013

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Kansei Engineering based Evaluation for an e-Learning Systemwith IP-based Network Design and Animation
Yoshiro Imai1 , Chiaki Kawanishi12 , Tetsuo Hattori1
1

2

Graduate School of Engineering, Kagawa University, 2217-20 Hayashi-cho, Takamatsu 761-0396 Japan
Miss Kawanishi was a student of Graduate School of Engineering(Kagawa University) from April of 2011 to March of 2013.
(Tel : +81-87-864-2244)
E-mail: 1 {imai, hattori}@eng.kagawa-u.ac.jp

Abstract: An e-Learning system has been developed, which is designed for network education with facilities of specification ofIP-based network topology and demonstration of packet-transferring animation. This system can execute on the major browsers
by means of accessing a specific Web server. It is frequently used in some lectures of university from beginners of network to
the students of information related course. Kansei Engineering approach is utilized and applied in order to improve and evaluate
the e-Learning system.
Keywords: e-Learning tool for network education, Visualization of network topology and IP routing, Kansei Engineering-based
Evaluation with questionnaire

1. INTRODUCTION

internal procedures to user interface, namely structure andbehaviour of our tool. It also compares new facilities with
their old versions for clear explanation of our system.
The fourth one reports trial evaluations of our tool basedon some questionnaires in our university and shows significance of our system for understanding of network- related
subjects. And finally the last (= fifth) one concludes somesummaries and future plans for our tool prototype.

As Internet and its usage become popular and popular, students of universities, especially students belonging to faculties of sciences and technologies, must study network-related
subjects such as computer networks, Internet structures andorganization, architectures of Internet protocols, and so on.
There are some problems, however, that even such students
suffer from difficulty of practical understanding and graphical comprehension, because of invisibility of behaviour andstructure of Internet[1][2].
From our experiences, it is effective for students to understand the target subject by means of e-Learning tool withvisualization facility[3][4][5]. It is also useful for instructors to educate some invisible subjects, namely ones with abstractive concepts, with simplified and visualized models in
relatively short period.
So we have decided to design and implement some kind of
e-Learning tool prototype for rapid understanding of Internet
introductory mechanism. The educational tool is designed
and implemented as Web-based system for user convenience
with easy manipulation and smart version-up. It is very much
important for portability to implement it as Web-application
which will work in many kinds of Web browsers, especiallymajor browsers such as Internet Explorer, Google Chrome ,FireFox and so on.
This paper presents the following sections for design,
implementation and trial evaluation of our learner-centric
graphical educational tool for network study, which has beendeveloped and utilized to understand network-related subjects effectively and efficiently.
The next (= second) section introduces the related works
of Japan as well as other countries. The third one describes
design concepts for our educational tool and illustrates thedetail configuration of our graphical educational tool from©ICAROB 2014

2. AN EDUCATIONAL TOOL FOR NETWORK STUDYThis section presents an educational tool for network
study in Information Processing-related department of university. It explains an outline of the tool, characteristic facilyof it, and animation-based learning for network behaviour.
2.1 Outline of our Educational Tool
It is important to recognize network topology and data
flow between nodes in order to understand network-related
subjects in universities. As you know, for the learners, a
concept of ‘Packet’, tasks of each component and relation
between many components (address or connectivity) must
be understood approximately at the same time. Almost all
the instructors should prepare some suitable materials or environment for learners to obtain not only structure but also
behaviour of network mechanism. The former means 2or 3-dimensional relationship between network components,
while the later specifies time-series packet transfer from one
to another.
At the view point of IP-based network structure and behaviour, ‘IP Routing’ can play the important role to specify
the network structure and define packet transfer from one toanother. We consider that IP routing is one of the most useful layers to learn the computer network. That is very muchcentered in the network-related subjects. And it is very much

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suitable to start learning structure and behaviour of Network.
We had focused this IP routing mechanism and began to design an educational tool to understand IP routing in a shortperiod.
Figure1 shows graphical user interface of our developed
educational tool for network learning. The tool is invokedon user’s browser as Web application. Its user interface
is organized to provide two major sub-windows for users.
At the left-hand sub-window of this tool, users can specify the network topology by means of locating IP routers,source/destination nodes and mutual relation between themwith drawing directly connected lines. As we discussed later,
more detail structure of user-specified network topologies
will be illustrated with from Figure2 to Figure4.
At the right-hand sub window, the system automaticallygenerates an according table to define IP routing rules,
namely IP-based packet transfer between IP routers andsources/destinations which have been specified by users(learners). Learner, especially beginners of network-related
subjects, can recognize IP routing detail from the system in
the above way. In other words, learners can understand behaviour of IP routing without detail specification of the relevant IP routing rules.

4. Users can obtain time-series visualized image for practical demonstration of packet transfer in the IP networking level with suitable animating speed and other useful
information about IP routing mechanism.

Fig. 2 Expression of IP for Router and PC.
An animation function of our educational tool is todemonstrate data flow in the network in order to show packet
transfer from source component specified by user to destination one. Every packet starts from the source node specified
by user where a node with packet is focused with surroundedby red circle in Figure2 and propagation of packet is illustrated with red colored line between two nodes. That meansthat the relevant packet is now transferred from one node toanother. Figure2 also illustrates the packet transfer from an
IP router with three connections at the right-bottom to a PC
surrounded with red circle at the right-most.
With simple network allocation function by user anddemonstrative animation one for packet transfer, our educational tool can provide visualization facility to learners of
network-related subjects (= the users of our system). Basedon visualization facility, therefore, our tool can play an important role to allow learners to obtain suitable image of network with its practical structure and behaviour in a relatively
short period.

Fig. 1 Graphical user interface of Educational tool for Network study.
2.2 Visualization Facilities
A major facility of our educational tool is visualization ofinternal structure and behaviour of network. Visualization
facility is designed with the following functions;
1. Users can select a kind of node as network component,
locate it on the left-hand sub-window and configure thetarget network by means of repeat of the above manipulations.
2. Users can specify some node, i.e. PC or Server, as asource of packet and also specify other node as a destination. Figure2 shows PC icon surrounded by red circle
as a source of packet.
3. Users can instruct the system to animate the flow ofpacket transfer from a node specified as a source to another node also specified as a destination through orwithin the network designed by users themselves.

©ICAROB 2014

2.3 Animation-based Learning for Network Behaviour
The educational tool performs animated packet transfer
based on user-defined network configuration and source/ destination. Users can recognize how to realize IP routing forpacket transfer with IP routing information described in Figure3. When some packet transfer is being animated, IP routing information table can focus the relevant expression bymeans of red-colored changing. The second expression frombottom has been applied for IP routing in Figure3.
When users want to know what IP address each node has,they can only place their mouse icon on the relevant node.
The tool will provide a view of the relevant IP addresses
each node has in the target network configuration. Figure4-249-

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Q1: Whether our tool can support your understanding
packet transfer from some node to another or not ?
Q2: Whether our tool can support your recognition of relation between IP routing information and behaviour ofpacket transfer or not ?
Q3: Whether our tool can promote you to define userspecified network topology or not ?
Q4: Whether our tool’s animation functions can contribute you to learn network structure and behaviour ornot ?
Q5: Is user manipulation about network definition suitable or difficult ? (suitable: good, difficult: bad)
Q6: Is user manipulation about preparation for animation
suitable or difficult ?
Especially, the last 2 items for the questionnaire, namely
Q5 and Q6, are adopted to confirm user-customizing facilities to configure a whole network. User manipulation must
be useful and sometimes dominate GUI services so that wewant to obtain user impression about user manipulation of
our educational tool.

Fig. 3 IP Routing Information Table showing some Specification in it to be applied with red-colored changing.
shows that a mouse-icon placing node has three IP addresses,
namely, for example, 192.168.18.1 and 192.168.58.1, and
192.168.97.2 in red-colored expression. Because such a nodeis IP routers and it can connect two different sub networkwith two different interfaces. With the above functions, oureducational tool can capture users’ attention on the animation
performing. So these functions are ones of functional contributions to visualization facility and support users’ learning
relatively in a short period.

3.2 Kansei Engineering-based Evaluation
We have carried three-times questionnaires into execution
for different groups in order to obtain various results of questionnaire. These would be very much useful and suggestive
for system’s improvement. At first, we have conduct our prototype questionnaire for members of our laboratory becauseof expectation to acquire some hints to brush up the contents of questionnaire. Strictly speaking, our laboratory has
already contributed to define our questionnaire, so this conduction has no additional effects to modify our questionnaire
prototype consequently.
Table1 shows a result of 1st trial questionnaire. And wehave carried other two-times of questionnaire into execution
without modification even after performing the first questionnaire.
Table 1 A Result of 1st Trial questionnaire of Learners’ Impression for our Educational Tool (in our laboratory).

Fig. 4 Mouse-icon Placing Node(Router) with Red-Circled
Expression.

Question No
Q1
Q2
Q3
Q4
Q5
Q6

3. TRIAL EVALUATION OF OUR EDUCATIONAL TOOLThis section reports a trial evaluation of our educational
tool. We have prepared a few questionnaires of user’s expression for our educational tool. And we have applied somekinds of Kansei Engineeringapproach to results of the questionnaires

Good
3
4
1
1
1
0

Poor
1
1
0
1
0
0

Bad
0
0
1
0
0
0

Total No
6
6
6
6
6
6

The second time of questionnaire is to carry the same content of the previous questionnaire into execution for students
of information engineering department in the second year.
They have already begun to learn computer network, so thatthey are familiar with network-related subjects. The result ofquestionnaire is very good and their evaluation for our educational tool is the best of all the three times questionnaires.

3.1 Content Definition of Questionnaire of Learner’s Impression for the Tool
The items of contents in the questionnaire have been defined in order to evaluate whether our tool is suitable forlearners to understand network-related subjects. They are as
follows:©ICAROB 2014

Excellent
2
1
4
4
5
5

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It is written in Table2.
Table 2 A Result of 2nd official questionnaire of Learners’
Impression for our Educational Tool (in a real classroom).
Question No
Q1
Q2
Q3
Q4
Q5
Q6

Excellent
11
9
11
13
12
14

Good
11
13
10
6
7
5

Poor
1
1
2
4
3
2

Bad
0
0
0
0
1
2

Total No
23
23
23
23
23
23

The last one is to carry the questionnaire into execution
for students in the first year but they do not include membersof engineering faculty. Such students are some sort of beginners of Internet and their knowledge and experience about
computer network and Internet is not so much but they areinteresting in Internet through their daily digital lives. The
result of their questionnaire is in Table3. We donot recognize why the last 2 items for the questionnaire, namely Q5and Q6, are not answered by 2 students.

Fig. 5 Integration of the three results of questionnaires(2) Our tool is implemented with two-coupled modules
to provide user defining network with some kinds ofcomponents such as node and connectivity and then perform demonstrative animation for visualization of internal structure and behaviour of the target network.
(3) It is confirmed that user impression about our tool
is good and users evaluate our tool very well throughthe three-times questionnaires for many students withdifferent expertise.

Table 3 A Result of 3rd official questionnaire of Learners’
Impression for our Educational Tool (in a real classroom).
Question No
Q1
Q2
Q3
Q4
Q5
Q6

Excellent
8
6
4
3
1
1

Good
10
11
13
14
8
7

Poor
1
2
2
2
6
7

Bad
0
0
0
0
2
2

Total No
19
19
19
19
17
17

It is confirmed that the average of three results of questionnaires are good and evaluations from the relevant learners (i.e. users of our educational tool) are almost excellent or
good in Figure5. Of course, our educational tool must be improved more and more in the future, but impression of users
sounds like good and favorably-disposed toward our educational tool.

Acknowledgement
This research can be performed by kind supports and interests from all the students who have participated in theclassroom lectures.

REFERENCES
[1]

[2]

4. CONCLUSION[3]This paper describes summaries of several network- related researches, configuration of our educational tool for
network study and its evaluation through the three-times
questionnaires in our university. The tool has been designed
and implemented as Web application and useful for users tolearn several network-related subjects in university. We haveobtained following evaluation from the above execution ofquestionnaires. Our conclusion is summarized as follows:(1) Our educational tool has been developed as one of
Web applications and useful for users to learn networkrelated subjects in university.

©ICAROB 2014

[4]

[5]

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Arai, M., Tamura, N., Watanabe, H., Ogiso, C. Takei,
S.: Development and Evaluation of TCP/IP Protocol
Learning Tools (in Japanese). In: Journal of Information Processing Society Japan (JIPSJ), Vol.44, No.12,
pp.3242–3251, 2003.
Tateiwa, Y. Yasuda, T., Yokoi, S.: Development of a
System to Visualize Computer Network Behavior for
Learning to Associate LAN Construction Skills with
TCP/IP Theory, Based on Virtual Environment Software (2007). In: Journal of Information Processing Society Japan (JIPSJ), Vol.48, No.4, pp.1684–1694, 2007.
Stafford, T., F.: Understanding Motivations for Internet
Use in Distance Education. In: IEEE Transactions on
Education, Vol.48, No.2, pp.301–306, 2005.
Sivakumar, S. C., Robertson, W., Artimy, M., Aslam,N.: A Web-Based Remote Interactive Laboratory forInternetworking Education. In: IEEE Transactions on
Education, Vol.48, No.4, pp.586–598, 2005.
Snow, C., Pullen, J.M., McAndrews, P: Network Education Ware: An Open-Source Web-Based System for
Synchronous Distance Education. In: IEEE Transactions on Education, Vol.48, No.4, pp.705–712, 2005.

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Model Introduced SPRT for Structural Change Detection of Time Series(I)*Yoshihide KOYAMA, *Tetsuo HATTORI, **Hiromichi KAWANO*Graduate School of Engineering, Kagawa University / 2217-20 Hayashi, Takamatsu City, Kagawa 761-0396, Japan**NTT Advanced Technology/ Musashino-shi Nakamachi 19-18, Tokyo 180-0006, Japan(hattori@eng.kagawa-u.ac.jp)

Abstract: Previously, we have proposed a method applying Sequential Probability Ratio Test (SPRT) to structuralchange detection problem of ongoing time series data. In this paper, we introduce a structural change model with
Poisson process into the system that outputs a set of time series data, moment by moment. The model can be considered
as a kind of Hidden Markov Model. Then we can calculate the value of P(Z |H1) that denotes the probability ofobserving the data Z under the condition that H1(alternative hypothesis) is true. Also, we concretely show the theory ofchange detection of time series by the model introduced SPRT.
Keywords: Time series, Change detection, SPRT(Sequential Probability Ratio Test), Hidden Markov ModelIn this paper, we introduce a Hidden Markov Modelwith Poisson process on the structural change one andpropose a new probability ratio method for the change
detection problem.

I. INTRODUCTION
To make a prediction of ongoing time series data, wehave three stages in general ([1], [2]). First, we have tofind a prediction model that adequately represents thecharacteristics of the early time series data. Second, we
have to detect the structural change of the time series
data, as quickly and correctly as possible, when theestimated prediction model does not meet the data any
more as shown in Fig.1 ([3],[4]). Third, we have toreconstruct the next prediction model as soon as
possible after the change detection.
For the second problem, we have already proposed
an application of SPRT (sequential probability ratio test)
that has been mainly used in the field of quality control[5], [6]. And we presented the experimental results incomparison with Chow Test that is well-known standard
method for such structural change detection of timeseries data ([6], [7]).
However, in the SPRT method that we previously
proposed for the structural change detection problem,the probability of structural change occurrence is notknown beforehand. In addition, in the method, only theratio of the conditional probability is used. Then, theformulation of SPRT includes the notion that thestructure may be able to recover even after the change
occurrence. Then, there is a possibility that error
happens in the change detection using the ratio of theconditional probability and so the threshold of the SPRT
for the detection is decided based on the error
probability.

©ICAROB 2014

y = 2 x1 + 3x 2 + 10 + εyy = 3x1 + 3x 2 + 10 + ε

180change point tc = 70

160
140
120
100
80
60
40
20

t

0
0

10

20

30

40

50

60

70

80

90

100

Fig.1. Example of time series data where the truechange point tc=70.

II. SPRT
1. SPRT ([8])
The Sequential Probability Ratio Test (SPRT) is
used for testing a null hypothesis H 0 (e.g. the quality isunder pre-specified limit 1%) against hypothesis H 1 (e.g.
the quality is over pre-specified limit 1%). And it isdefined as follows:-252-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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data and the observed one. (σ s denotes astandard deviation in learning sample data at
early stage.)
Step2：Set up the null hypothesis H 0 and alternative
hypothesis H 1.
H 0 ：Change has not occurred yet.
H 1 ：Change has occurred.
Set the values α , β and compute C 1 and C 2 ,according to Equation (2). Initialize i = 0,λ0 = 1 .

Let Z 1 , Z 2 , … Z i be respectively observed timeseries data at each stage of successive events, theprobability ratio λi is computed as follows.

λi =P( Z1 | H1 ) ⋅ P( Z 2 | H1 ) ⋅ ⋅ ⋅ P( Z i | H1 )(1)
P( Z1 | H 0 ) ⋅ P( Z 2 | H 0 ) ⋅ ⋅ ⋅ P( Z i | H 0 )where P(Z | H 0 ) denotes the distribution of Z if H 0 is
true, and similarly, P(Z | H 1 ) denotes the distribution of
Z if H 1 is true.
Two positive constants C 1 and C 2 (C 1 < C 2 ) arechosen. If C 1 < λi < C 2 , the experiment is continued bytaking an additional observation. If C 2 < λi , the process
is terminated with the rejection of H 0 (acceptance of H 1 ).
If λi < C 1 , then terminate this process with theacceptance of H 0 .

C1 =

β
1− β
, C2 =1−αα

Step3: Incrementing i (i = i+1), observe the following
data y i . Evaluate the error | ε i | between the datay i and the predicted value from theaforementioned prediction expression.
Step4: Judge as to whether the data y i goes in thetolerance band or not, i.e., the ε i is less than (orequal to) the permissible error margin or not. If
it is Yes, then set λi = 1 and return to Step3.
Otherwise, advance to Step5.

(2)

where α means type I error (reject a true null hypothesis), and β means type II error (accept a null hypothesis
as true one when it is actually false).

Step5: Calculate the probability ratio λi , using thefollowing Equation (3) that is equivalent toEquation (1).

2. Procedure of SPRT ([8])
The concrete procedure of applying the SPRT
method to the structural change detection problem is
described using the notation of Fig. 2 as follows.

λi = λi −1

P (ε i | H1 )P (ε i | H0 )(3)

where, if the data y i goes out the tolerance band,( P(ε i |H 0 ), P(ε i |H 1 ) )= ( θ 0 , θ 1 ), otherwise,( P(ε i |H 0 ), P(ε i |H 1 ) )= ( (1-θ 0 ), (1-θ 1 ) ).

Fig.2. Example of time series data and single regressionline, where the true change point tc =70 ([6]).

Step6: Execution of testing.
(i) If the ratio λi is greater than C 2 (= (1-β)/α ),dismiss the null hypothesis H 0 , and adopt thealternative hypothesis H 1 , and then End.
(ii) Otherwise, if the ratio λi is less than C 1 (=β/(1-α) ), adopt the null hypothesis H 0 , anddismiss the alternative hypothesis H 1 , andthen set λi = 1 and return to Step3.
(iii) Otherwise (in the case where C1 ≤ λi ≤ C2 ),advance to Step7.

Step1: Make a prediction expression and set thetolerance band (a) (e.g. a=2σ s ) that means
permissible error margin between the predicted

Step7: Observe the following data y i incrementing i.
Evaluate the error | ε i | and judge whether thedata y i goes in the tolerance band, or not. Then,
return to Step5 (calculation of the ratio λi ).

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2. New Sequential Probability Ratio
Let a1a 2 ,…, ai ,… a n ai ∈ {IN, OUT} be a string(or symbol sequence) obtained from the observed data.
~Let θi and θi be the conditional probability that
outputs the observed data (or above symbol sequence,
Cn = a1a 2 … a n in the state S0 and S1, respectively. That~is, it means that θi ∈ {R, 1 − R} and θi ∈ {Rc , 1 − Rc } ,respectively.
And let P( a1 … a n , H 0 ) and P ( a1 … a n , H1 ) be thejoint probability of the symbol sequence Cn happens
with the event H 0 (the structural change is not occurred)and H 1 (the change is occurred), respectively.
Then, the following equations hold.

III. STRUCTURAL CHANGE MODELINTRODUCED
1. Hidden Markov Model
We present a Hidden Markov Model where thestructural change is Poisson occurrence of average γ . In
this model, once the change has occurred during theobserving period, the structure does not go back to theprevious one. The reason why we set such a model isthat we focus on the detection of the first structuralchange in the sequential processing. The concept of thestructural change model is shown in Fig.3.
Moreover, we introduce a more detailed model. Let
R be the probability of the prediction failure that thedata value goes out the tolerance zone when the
structure is unchanged. Let Rc be the probability of theunfitting when the structure change occurred. We canconsider that Rc is greater than R, i.e., Rc>R. The
detailed model is illustrated as a probabilistic finite state
automaton in Fig.4.

= (1 −γ) nθ1 …θn = (1 −γ) n

Πθ
i

(4)

P( a1 … a n , H 1 ) = P(C n , H 1 )
n

∏

n

∏θ)

~
θi + ((1 −γ)θ1 )(γ

i =1

1.0

~

i

i =3

n

=

∑

k −1

n

∏

((1 −γ) k −1･

k =1

γ : Probability of the structural change occurrence.

∏θ)θj )(γ

j =0

1.0
γ

S1
1-Rc

R

Rc
Rc

R
OUT

OUT

IN

1-Rc

1-R

R : Probability of prediction failure (OUT) when the structure is unchanged.
Rc : Probability of prediction failure (OUT) when the structural change has been occurred.
IN: The state that observed data goes into the tolerance zone.
OUT: The state that observed data goes out of the tolerance zone.
Fig.4. Proposed Hidden Markov Model.

©ICAROB 2014

i

(5)

1−γ

IN

~

i =k

(Poisson Process.)
Fig.3. Structural change model.

1-Ri∏θ) + …

S0 : State that the structure is unchanged.
S1 : State that the structural change has occurred.

S0

~

i =2
n

+ ((1 −γ) 2θ1θ2 )(γ

S1

S0

n

i =1

=γ

γ

1- γ

P( a1 … a n , H 0 ) = P(Cn , H 0 )-254-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

P(H 0 | a1 … an ) = P(H 0 | Cn ) =P(H 0 | Cn )･P (Cn )P (C n )REFERENCES

P (C n , H 0 )P (C n , H 0 )==P (C n )P (C n , H 0 ) + P (C n , H 1 )[1] C.G.E.P.Box and G.M.Jenkins (1976), Time Series
Analysis:Forecasting and Control, Prentice Hall.
[2] Peter J. Brockwell and Richard A. Davis (2003),
Introduction to Time Series and Forecasting,Springer; 2nd edition.
[3] C.Han, P.k.Willet and D.A.Abraham (1999), Somemethods to evaluate the performance of Page's test
as used to detect transient signals, IEEE Trans.
Signal processing, Vol.47, No.8, pp.2112-2127.
[4] S.D.Blostein (1991), Quickest detection of a timevarying change in distribution, IEEE Trans.
Information Theory, Vol.37, No.4, pp.1116-1122.
[5] A. Wald (1947), Sequential Analysis, John Wiley &Sons.
[6] Hiromichi Kawano, Tetsuo Hattori, Ken Nishimatsu(2008), Structural Change Point Detection Methodof Time Series Using Sequential Probability Ratio
Test－Comparison with Chow Test in the ability ofearly detection － (in Japanese), IEEJ Trans. EIS,
Vol.128，No.4，pp.583-592．
[7] Chow, G. C. (1960), Tests of Equality Between Setsof Coefficients in Two Linear Regressions,
Econometrica, Vol.28, No.3, pp.591-605.
[8] Katsunori Takeda, Tetsuo Hattori, Izumi Tetsuya,
Hiromichi Kawano (2010), Extended SPRT for
Structural Change Detection of Time Series Basedon Multiple Regression Mode, Proceedings of the
15th International Symposium on Artificial Life andRobotics (AROB 15th ‘10), pp.755-758, ISBN: 9784-9902880-4-4, Oita, Japan, Feb.
[9] Katsunori Takeda, Tetsuo Hattori, Tetsuya Izumi,
Hiromichi Kawano (2010), “Extended SPRT for
Structural Change Detection of Time Series Basedon Multiple Regression Model,” International
Journal of Artificial Life and Robotics, Springer,
ISSN:1433-5298, Vo.15, No.4, pp.417-420, 2010.
[10] Tetsuo HATTORI, Hiromichi KAWANO (2011),
Change Detection Method of Time Series as an
Optimal Stopping Problem -- Constructive Proof of
Optimal Solution Theorem --, Proc. of ICBAKE
2011 (2011 International Conference on Biometrics
and Kansei Engineering), IEEE Computer Society,ISBN 978-0-7695-4512-7, pp.100-105, Kagawa,Japan, 2011.

(6)

P (H 1 | Cn )･P (Cn )P (H 1 | a1 … a n ) = P (H 1 | Cn ) =P (C n )=P (C n , H 1 )P (C n , H 1 )=P (C n )P (C n , H 0 ) + P (C n , H 1 )(7)

New Sequential Probability Ratio (NSPR) that we
propose is represented using the aforementioned
equations as follows.

NSPR =

P(H1 | a1 … a n ) P(H1 | Cn ) P(Cn , H1 )==P(H 0 | a1 … a n ) P(H 0 | Cn ) P(Cn , H 0 )
n

k −1

n

∑ ((1 −γ) ･∏θ )(γ∏θ)
k −1

~

j

=

k =1

j =0

(1 −γ) nθ1 …θn = (1 −γ)

i

i =k
n
n

Πθ
i

i =1

(8)
When the NSPR is greater than 1.0, we regard thatthe structural change has been occurred before thepresent time.

IV. CONCLUSION
We have presented a Hidden Markov Model for the
structural change detection problem, and have proposeda New Sequential Probability Ratio method for thedetection using the occurrence probability based on the
proposed model.
We consider that this method will be more
promising than the SPRT one that we previously applied
it to the change detection problem. We will describe theexperimental results at another opportunity.

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Model Introduced SPRT for Structural Change Detection of Time Series(II)--- Kansei Channel and Bayes’ Estimation --*Yoshihide KOYAMA, *Tetsuo HATTORI, **Hiromichi KAWANO*Graduate School of Engineering, Kagawa University / 2217-20 Hayashi, Takamatsu City, Kagawa 761-0396, Japan**NTT Advanced Technology/ Musashino-shi Nakamachi 19-18, Tokyo 180-0006, Japan(hattori@eng.kagawa-u.ac.jp)
Abstract: In this paper, using the notion of a binary Channel Matrix what we call “Kansei Channel” as well known inInformation Theory, we present an equivalent relation between the SPRT and Bayes’ Updating. Moreover, we show the
relationship between the SPRT (Sequential Probability Ratio Test) and the Sequential Probability Ratio of twocorresponding conditional probabilities when a Hidden Markov Model with Poisson process is introduced as the
structural change model.
Keywords: Change detection, SPRT, Hidden Markov Model, Information Theory, Kansei Channel, Bayes’ Updating

the quality is over pre-specified limit 1%). And it isdefined as follows:
Let Z 1 , Z 2 , … Z i be respectively observed timeseries data at each stage of successive events, theprobability ratio λi is computed as follows.

I. INTRODUCTION
There are three problems in dealing with ongoingtime series data ([1]-[5]). First, we have to make a
prediction model from the early data. Second, we haveto detect the structural change of the time series data, as
quickly and correctly as possible, when the prediction
model does not meet the observing data any more. Third,
we have to remake the prediction model as soon as
possible after the detected change point. For the second
change detection problem, we previously proposed an
application of SPRT (Sequential Probability Ratio Test)
that has been mainly used in the field of quality control([5]-[9]).
In this paper, using the notion of a binary Channel
Matrix what we call “Kansei Channel” as well known inInformation Theory, we present an equivalent relation
between the SPRT and Bayes’ Updating. Moreover, we
also explain the relation between the SPRT and theSequential Probability Ratio of two conditional
probabilities when a Hidden Markov Model with
Poisson process is introduced as the never returning
structural change model [10].

λi =P( Z1 | H1 ) ⋅ P( Z 2 | H1 ) ⋅ ⋅ ⋅ P( Z i | H1 )(1)
P( Z1 | H 0 ) ⋅ P( Z 2 | H 0 ) ⋅ ⋅ ⋅ P( Z i | H 0 )where P(Z | H 0 ) denotes the distribution of Z if H 0 is
true, and similarly, P(Z | H 1 ) denotes the distribution of
Z if H 1 is true.
Two positive constants C 1 and C 2 (C 1 < C 2 ) arechosen. If C 1 < λi < C 2 , the experiment is continued bytaking an additional observation. If C 2 < λi , the process
is terminated with the rejection of H 0 (acceptance of H 1 ).
If λi < C 1 , then terminate this process with theacceptance of H 0 .

C1 =

1− β
β
, C2 =1−αα(2)where α means type I error (reject a true null hypothesis), and β means type II error (accept a null hypothesis
as true one when it is actually false).

II. SPRT AND BAYES’ UPDATING
2. Bayes’ Updating Based on Binary Channel
We regard that the observation and SPRT process
for the aforementioned structural change point detectionof time series data is essentially a kind of Bayes’
Updating by a binary channel what we call “Kansei

1. SPRT ([8], [9])The Sequential Probability Ratio Test (SPRT) is
used for testing a null hypothesis H 0 (e.g. the quality isunder pre-specified limit 1%) against hypothesis H 1 (e.g.

©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Channel” composed of a set of subjective probability as
shown in Fig.1, where input event X=(x 1 , x 2 ) and output
event Y=(y 1 , y 2 ).

P ( y1 | x2 )P( y j2 ) =∑ P ( x ) P( y | x )1iPn ( xi ) = p( xi | y jn ) =P( y 2 | x1 )y2=

Fig.1. Binary channel (Kansei Channel).

Using the conditional probability, the transition
matrix (or channel matrix) P B is represented as follows.

p ( x2 | y j1 ) =P0 ( x2 ) P( y j1 | x2 )i(i = 1,2)∏ P( y )P (x )
λn = n 2 =Pn ( x1 )

(3)

jk

(7)

P0 ( x2 )∏ P( y | x )
jk

k =1
n

P0 ( x1 )2∏ P( y | x )
jk

k =1

1

(8)
If we assume that
P0 ( x1 ) = P0 ( x2 ) =1
2Then, we obtain the same ratio as that of SPRT.
Therefore, we consider that the Bayes’ Updating basedon binary channel is a kind of generalization of SPRT.
As for the conditional probability, we can introduce
our subjective probability. In addition, we can adopt not
only the discrete probability, but also continuous density
function.

P( y j1 )P( y j1 )∑ P ( x ) P( y | x )
j1

jk

k =1
n

Then the probability ratio λn is as follows.

Let the prior probability distribution of input event X
be {P 0 (x 1 ), P 0 (x 2 )} as initial values. Let the observed
output sequence { y j1 , y j2 ,⋅ ⋅ ⋅, y jn } ( y jk = y1 or y2(k=1, 2,…, n)). Then, by the first observation of output,
the posterior probability is given by Bayes’ theorem.

p( x1 | y j1 ) =P( y jn )n

 P( y1 | x1 ) P( y 2 | x1 ) 
PB = 
 P ( y1 | x2 ) P( y 2 | x2 )

P0 ( x1 ) P( y j1 | x1 )Pn −1 ( xi ) P( y jn | xi )∏ P( y | x )

k =1

i

(6)

n

P0 ( xi )P ( y 2 | x2 )

0

i

y1

x2

where, P( y j1 ) =j2iThus, by the nth observation of output, we haveP( y1 | x1 )

x1

where,

i(4)
According to the Bayes’ Updating rule, we have thefollowing updated prior probability {P 1 (x 1 ), P 1 (x 2 )}.
i

P１( x1 ) = p( x1 | y j1 ) =P0 ( x1 ) p( y j1 | x1 )P１( x2 ) = p( x2 | y j1 ) =P0 ( x2 ) p( y j1 | x2 )III. CHANGE MODEL AND CONDITIONALPROBABILITY RATIO
1. Hidden Markov Model
We present a Hidden Markov Model where thestructural change is Poisson occurrence of average γ . In
this model, once the change has occurred during theobserving period, the structure does not go back to theprevious one. The reason why we set such a model isthat we focus on the detection of the first structuralchange in the sequential processing. The concept of thestructural change model is shown in Fig.2-4.
Moreover, we introduce a more detailed model. Let
R be the probability of the prediction failure that thedata value goes out the tolerance zone when the
structure is unchanged. Let Rc be the probability of theP ( y j1 )P ( y j1 )(5)

Similarly, by the second observation, we have

P2 ( x1 ) = p( x1 | y j2 ) =P1 ( x1 ) p( y j2 | x1 )P2 ( x2 ) = p( x2 | y j2 ) =P1 ( x2 ) p( y j2 | x2 )©ICAROB 2014

P( y j2 )P( y j2 )-257-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014~
is, it means that θi ∈ {R, 1 − R} and θi ∈ {Rc , 1 − Rc } ,respectively.
And let P( a1 … a n , H 0 ) and P ( a1 … a n , H1 ) be thejoint probability of the symbol sequence Cn happens
with the event H 0 (the structural change is not occurred)and H 1 (the change is occurred), respectively.
Then, the following equations hold.

failure when the structure change has been occurred. We
can consider that Rc is greater than R, i.e., Rc>R. The
detailed model is illustrated as a probabilistic finite state
automaton in Fig.2-4.

γ

1- γ

1.0

P( a1 … a n , H 0 ) = P(Cn , H 0 )S1

S0= (1 −γ) nθ1 …θn = (1 −γ) n

S0 : State that the structure is unchanged.
S1 : State that the structural change has occurred.

n

Πθ
i

i =1

(9)

γ : Probability of the structural change occurrence.
(Poisson Process.)P( a1 … a n , H 1 ) = P(C n , H 1 )n∏Fig.2. Structural change model.

i =1

1−γ

n

∏θ)

~
θi + ((1 −γ)θ1 )(γ

=γ

~

i

i =2
n

∏θ) + …
~

+ ((1 −γ) 2θ1θ2 )(γ

i

i =3

S0

n

1-R

=

R

k −1

∏

k =1

R

n

∏θ)
~

θj )(γ

j =0

i

i =k

(10)
OUT

IN

∑

((1 −γ) k −1･P(H 0 | a1 … an ) = P(H 0 | Cn ) =1-R=

Fig.3. Internal model of the State S0.

P(H 0 | Cn )･P(Cn )P (C n )P (C n , H 0 )P (C n , H 0 )=P (C n )P (C n , H 0 ) + P (C n , H 1 )
(11)

1.0
P (H 1 | a1 … a n ) = P (H 1 | Cn ) =

S1
1-Rc

=

Rc

P (H 1 | Cn )･P (Cn )P (C n )P (C n , H 1 )P (C n , H 1 )=P (C n )P (C n , H 0 ) + P (C n , H 1 )

Rc
(12)
OUT

IN

A New Sequential Probability Ratio (NSPR) of two
conditional probabilities is represented as follows, using
the aforementioned equations.

1-Rc
Fig.4. Internal model of the State S1.

NSPR =
2. Sequential Probability Ratio
Let a1a 2 ,…, ai ,… a n ai ∈ {IN, OUT} be a string(or symbol sequence) obtained from the observed data.
~Let θi and θi be the conditional probability that
outputs the observed data (or above symbol sequence,
Cn = a1a 2 … a n in the state S0 and S1, respectively. That

©ICAROB 2014

P(H1 | a1 …a n ) P(H1 | Cn ) P(Cn , H1 )==P(H 0 | a1 …a n ) P(H 0 | Cn ) P(Cn , H 0 )
n

∑
=

k =1

k −1

∏θ)

θj )(γ

j =0(1 −γ) n

~

i

i =k

n

Πθ
i

i =1

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n

∏

((1 −γ) k −1･(13)The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

On the contrary, there is another type of conditional
probability ratio such as used in the SPRT. We call it“Semi Sequential Probability Ratio Test (SSPRT)”.
That is,SSPRT ==REFERENCES[1] C.G.E.P.Box and G.M.Jenkins (1976), Time Series
Analysis:Forecasting and Control, Prentice Hall.
[2] Peter J. Brockwell and Richard A. Davis (2003),
Introduction to Time Series and Forecasting,Springer; 2nd edition.
[3] C.Han, P.k.Willet and D.A.Abraham (1999), Somemethods to evaluate the performance of Page's test
as used to detect transient signals, IEEE Trans.
Signal processing, Vol.47, No.8, pp.2112-2127.
[4] S.D.Blostein (1991), Quickest detection of a timevarying change in distribution, IEEE Trans.
Information Theory, Vol.37, No.4, pp.1116-1122.
[5] A. Wald (1947), Sequential Analysis, John Wiley &Sons.
[6] Hiromichi Kawano, Tetsuo Hattori, Ken Nishimatsu(2008), Structural Change Point Detection Methodof Time Series Using Sequential Probability Ratio
Test－Comparison with Chow Test in the ability ofearly detection － (in Japanese), IEEJ Trans. EIS,
Vol.128，No.4，pp.583-592．
[7] Chow, G. C. (1960), Tests of Equality Between Setsof Coefficients in Two Linear Regressions,
Econometrica, Vol.28, No.3, pp.591-605.
[8] Katsunori Takeda, Tetsuo Hattori, Izumi Tetsuya,
Hiromichi Kawano (2010), Extended SPRT for
Structural Change Detection of Time Series Basedon Multiple Regression Mode, Proceedings of the
15th International Symposium on Artificial Life andRobotics (AROB 15th ‘10), pp.755-758, ISBN: 9784-9902880-4-4, Oita, Japan, Feb.
[9] Katsunori Takeda, Tetsuo Hattori, Tetsuya Izumi,
Hiromichi Kawano (2010), “Extended SPRT for
Structural Change Detection of Time Series Basedon Multiple Regression Model,” International
Journal of Artificial Life and Robotics, Springer,
ISSN:1433-5298, Vo.15, No.4, pp.417-420, 2010.
[10] Tetsuo HATTORI, Hiromichi KAWANO (2011),
Change Detection Method of Time Series as an
Optimal Stopping Problem -- Constructive Proof of
Optimal Solution Theorem --, Proc. of ICBAKE
2011 (2011 International Conference on Biometrics
and Kansei Engineering), IEEE Computer Society,ISBN 978-0-7695-4512-7, pp.100-105, Kagawa,Japan, 2011.

P( a1 … a n | H1 ) P(Cn | H1 )=P( a1 … a n | H 0 ) P(Cn | H 0 )P(Cn , H1 ) / P(H1 )P(Cn , H 0 ) / P(H 0 ) P(H 0 )   P(Cn , H1 )   P(H 0 )  =  ⋅  ⋅ NSPR=  P(H 1 )   P(Cn , H 0 )   P(H 1 ) (14)
Let a notation S 0 k･S1l mean an event sequence
where, after the state S0 continuously occurs k times,
the state S1 continuously occurs l times. Then, theP(H 0 ) and P(H1 ) are represented as follows.

P (H 0 ) = P ( S 0 n ) = (1 −γ) n(15)P(H1 ) = P( S1n ) + P( S 0･S1n −1 ) + P( S 0 2･S1n − 2 )+ P( S 0 k･S1n − k ) + … + P( S 0 n −1･S1 )
n −1

=

∑
k =o

P( S0 k･S1n − k ) =n −1∑ (1 − γ ) ⋅ γ
k

k =o
n

= 1 − P(H 0 ) = 1 − (1 −γ)(16)Then we have the relation between the NSPR and
Semi SPRT as follows.

 1 P(H1 )  ⋅ SSPRT = 
NSPR = − 1 ⋅ SSPRTn P(H 0 )  (1 −γ)(17)When the NSPR is greater than 1.0, we regard thatthe structural change has been occurred before thepresent time.

IV. CONCLUSION
Using the notion of a binary Channel as well knownin Information Theory, we have shown that the SPRT isequivalent to the Bayes’ Updating Rule. Moreover, wehave explained the relation between the SPRT and theSequential Probability Ratio of two conditional
probabilities when a Hidden Markov Model with
Poisson process is introduced as a structural change
model.

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Automated Color Image Arrangement Method and Kansei Impression
Y. Kawakami1, T. Hattori1, Y. Imai1, H. Matsushita1, H. Kawano2 and R.P.C.J. Rajapakse3
1

Kagawa University, 2217-20, Hayashi, Takamatsu, Kagawa, 761-0396 Japan
Tel : 81-87-864-2000; Fax : 81-87-864-2032
s11d621@stmail.eng.kagawa-u.ac.jp, {hattori, imai, haruna}@eng.kagawa-u.ac.jp
2
NTT Advanced Technology Corporation, 19-18, Nakamachi, Musashino, Tokyo 180-0006, Japanhiromichi.kawano@ntt-at.co.jp
3
Tainan National University of the Arts, 66, Daci, Guantian, Tainan, 72045, Taiwan

Abstract: This paper proposes a new color image arrangement method using an elastic transform on some kinds of axes.
In this paper, we present the principle of our method using HMGD (Histogram Matching based on GaussianDistribution). And we describe that the automated method applies the HMGD to input color image only when theimage has single-peakdness in its histogram on the focused axis. We also show that the method gives a good Kansei
effect in the case of applying the HMGD onto Lightness axis.
Keywords: Automated color arrangement, elastic transform, histogram matching, HMGD, Curvatureprobabilistic density functions on real variables x and y,
respectively. The probabilistic density function (PDF) iscorresponding to histogram of gray level image.
However, the histogram is defined on discrete variable.
In addition, let y=φ(x) be a continuous and monotonous
increase function between variables x and y as shown in
Fig.1.

I. INTRODUCTION
Automated image processing for enhancement
and/or arrangement of color images has been morefamiliar to us according to the spreading of Digital
Camera, Smart Phone, DVD, etc. [1]-[3]. However, weconsider that the research on the automated arrangement
method that brings about good sensibility effect (orKansei effect) is still on the way to practical use.
In this paper, we propose a novel color image
arrangement method using an elastic transform based on
histogram on some kinds of axes [4]-[6]. As for the axes,
there are Lightness axis and principal component one
that can be obtained by Principal Component Analysis(PCA) in the RGB three-dimensional vector space thatis an attribute space of color image. Especially, we
explain the main principle of the proposed method in
the case of using HMGD (Histogram Matching basedon Gaussian Distribution). And we illustrate that themethod using the HMGD works very well for input
image that has single peak histogram on the Lightness
axis. Also in this paper, we show that a curvaturecomputation for the cumulative histogram of original
image will be effective, in order to automatically detect
whether the original image has single peak histogram ornot.

yg ( y)

φ( x0 + dx)yφ ( x0 )
0

x
x0

L

x

x0 + dxf ( x)

Fig.1. Continuous and monotonous increase function
y=φ(x) and probabilistic density functions f(x) andg(y)In addition, let value of x be the range from 0 to L.
Accordingly, variable y ranges from 0 to φ(L). Let P
mean the probability. From the above definitions andFig.1, we have Equation (1) ~ (3).

II. ELASTIC TRANSFORM BASED ON
HISTOGRAM MATCHING
1. Principle
We describe the principle of histogram based elastictransform in the following. Let f(x) and f(y) be two©ICAROB 2014

y = φ ( x)φ ( L)

x= L

P(0 ≤ x ≤ L) = ∫ f ( x)dx = 1
x =0

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(1)

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

P(0 ≤ y ≤ φ ( L)) =y =φ ( L )∫ g ( y)dy = 1

yφ ( L)(2)y = φ ( x)y =0f ( x)dx= P( x0 ≤ x ≤ x0 + dx)= P (f ( x0 ) ≤ y ≤ f ( x0 + dx))g ( y)(3)= P( y0 ≤ y ≤ y0 + dy ) = g ( y )dy
From Equation (3), we obtain Equation (4) and (5)because y 0 = f(x 0 ) and y 0 + dx = φ(x 0 + dx).

f ( x)dx = g ( y )dy = g ( y )f ' ( x)dx

f ( x) = g ( y )f ' ( x)

1/ L

(5)

x

L

x

x0 + dxf ( x)
Fig.2. Conceptual image of the transform from unif
orm distribution (PDF) to the desired one (PDF)
shown in Fig.3. For example, in the case where the HE
processing on the Lightness axis is applied, the HE
brings about image enhancement by contrast stretching.
In addition, in the case of HE on a principal component
axis in RGB space, we guess that the contrast stretching
will be done along to a certain tone of color. Fig.4
shows examples of the HE on Lightness axis and PC(Principal Component) axis. Fig.5 shows resultant
examples of the HE and HMGD (Histogram Matchingbased on Gaussian Distribution) on Lightness axis. The
HMGD processing shows a more moderate effect thanHE.
In this paper, we show that the HMGD is useful forthe automated color image arrangement.

(6)0

Since φ’(x) = Lφ(x), according to Equation (5), we
derive following Equation (7).
(7)

Therefore, we understand that if we take thetransform function as Equation (6), g(y) becomes
uniform distribution. It corresponds to the HE
processing, which means that function defined bycumulative histogram transforms the original histogram
into the uniform one.
Inversely, if we define the transform function φ(x) as
an integral of desired PDF f(x) for example, Gaussian
distribution, we can obtain the desired PDF using theφ(x) and the uniform distribution such as Equation (8)(Fig.2).
The abovementioned theory means that, if we
combine the both transform, we can obtain thetransformation from an original distribution (PDF) to adesired one. This means that an image with original
histogram can be transformed into another image withdesired histogram. We consider that it is the principle ofthe Histogram Matching (HM) [4].

Fig.3. Conceptual image of histogram based elastictransform on arbitrary axis in RGB space.

2. Elastic Transform on Axis
We can choose the abovementioned transform such
as HE (Histogram Equalization) and HM (Histogram
Matching) on arbitrary axis (for example, principal
component axis) in the color attribute (RGB) space as©ICAROB 2014

0

(4)

x

f ( x) = g ( y ) Lf ( x)yφ ( x0 )x0Thus, if we know the y=φ(x) and g(y), then we havethe f(x). Using the above equations, we derive theprinciple of HE. Let φ(x) be defined by Equation (6).

f ( x) = L ∫ f ( x)dx

φ (x0 + dx)(a) Original(b) Lightness axis(c) PC axis
Fig.4. Examples of HE on Lightness and PC axis

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Hence, we obtain the following equation (15) and
we can approximate to Equation (9).

R=(a) Original(b) HE(c) HMGD
Fig.5. Example of the results by HE and HMGD

R=

d2y
dx 2

(9)

{1 + ( ) }3
dy 2 2dx

σ2{1 + g ( x) }

3
2 2

≅

(a − x ) g (x )
σ2

(15)

The curvature R varies the sign according to thevalue of x; (x < a) R>0, (x=a) R=0, (x>a) R<0. So we
consider that a histogram with single peak shows thesame characteristic regarding the curvature R of the
cumulative histogram.
Fig.6 shows an example of the curvaturecomputation. The real curvature computation is
performed on the discrete values. We can find that there
are largely changing points in the curvature from plus
value to minus value at more than one point. So, weeasily understand that the histogram of the original
image is not a single peak type.

3. Detection of Histogram Peakedness by using
Curvature Computation
In this section, we describe how we detect thehistogram peakedness by using curvature. Let y be afunction with respect to x, the definition of the curvature
R is given by Equation (9).

( )(a − x ) g ( x)Let g(x) be a Gauss density function with variance
σ and average a. And also let g(x) be representing ahistogram of input image whose pixel values from 0 to
L. That is,2( x −a )2−
K2
g ( x) =
・e 2σ
σ 2π

(10)

In Equation (10), K means a coefficient that satisfies
following Equation (11).
( u −a )2(a) Original image

Let y=f(x) be a function representing the cumulative
histogram. Then f(x) can be represented Equation (12).
Since y = f ( x ) =∫ g (u )du , it can be described as
x

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Equation (13).

Pixel Valuexf ( x) = ∫ g (u )du =01e
σ 2π ∫0x−( u −a )2
2σ 2

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Cumulative Frequency(11)

CurvatureL −K2e 2σ du = 1∫σ 2π 0

Pixel Value(b) Histogram of image(c) Curvature of image
Fig.6. Example of the curvature computationdu (12)( x −a )2−
dyK2= g ( x) =
・e 2σ
dx
σ 2π

III. EXPERIMENTATION(13)

Fig.7(a), (b) shows an example of results by HMGD
processing on Lightness axis, where original image has
almost single peak histogram. The set of histogram andcumulative histogram (original image and HMGD
image) are shown in Fig.7(c), (d). And the curvature of
cumulative histogram of input image is shown as well in
Fig.7(e). Similarly, Fig.8 also shows that input image
has single peak histogram and that the cumulative histo-

By the same way,
( x −a )2−
d 2 y dg ( x)
K
2
 1 
=
=
・e 2σ ・ − 2 {2( x − a )}
2
dx
dx
σ 2π
 2σ 
( x −a )  K−2 (x − a )  (a − x )  (14)=・e 2σ ・ −) = g ( x・22 σ 2π

σ

 σ 


2

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Japan, January 11-13, 2014

gram of the output image clearly approaches to the
cumulative Gaussian distribution. For these cases where
the histogram of input image has almost single peak, the
HMGD works well for resulting a good feeling
impression (or Kansei effect).

IV. CONCLUSION(a) Original image
1000

65536

900

57344
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800
700

Frequency(b) HMGD image

600
500
400
300
200

Aiming at automated affective color image
arrangement, we have proposed a concept of Elastic
Transform (ET) method based on histogram and/or
histogram matching, which can be applied to somekinds of axis such as Lightness axis. And we havesuggested that HMGD (Histogram Matching based on
Gaussian Distribution) is regarded as one of the ET
method.
As for the concrete method for automated color
image arrangement, we have proposed a method thatapplies HMGD processing to input image after
detecting whether the input image has single peakhistogram or not. In this paper, we have also provided
the curvature computation for the peak detection inhistogram. And also we have shown some experimental
results. We consider that HMGD processing method
after histogram peaks detection based on the number ofcurvature change points will be useful and promising.

49152
40960
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Lightness(c) Histogram (left) and cumulative histogram(right) of original image
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Lightness(d) Histogram (left) and cumulative histogram(right) of HMGD image
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REFERENCES

100
50[1] Gonzalez CR, Woods ER, Digital Image Processing(1993). Addison-Wesley Publishing Company[2] Jahne B, Digital Image Processing --Concepts,
Algorithms, and Scientific Applications -- 4th Edition(1995). Springer[3] Umbaugh ES, Computer Vision and Image
Processing: A Practical Approach Using CVIPtools(1998). Prentice Hall PTR[4] Burger W, Burge JM, Principles of Digital Image
Processing: Fundamental Techniques (2009). Springer[5] Izumi T, Hattori T, Sugimono S, Takashima T(2009), Color Image Arrangement Using Elastic
Transform on Principal Component Axis (in Japanese).
Journal of Japan Society of Kansei Engineering, Vol.8,
No.3:667-674[6] Izumi T, Hattori T, Fujita E, Sugimono S, Kawano
H (2010), Feeling Impression and Quantities
Accompanying Calculation of Fluctuation in Color
Image (in Japanese). Journal of Japan Society ofKansei Engineering, Vol.9, No.2:243-250

0
-50
-100

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10
19
28
37
46
55
64
73
82
91
100
109
118
127
136
145
154
163
172
181
190
199
208
217
226
235
244
253

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Lightness(e) Curvature of cumulative histogram oforiginal image
Fig.7. Example of the results by HMGD(a) Original image(b) HMGD image
Fig.8. Example of the results by HMGD©ICAROB 2014

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Japan, January 11-13, 2014

Novel Transistor by Opt-coupling of LED and PD
and its Application
Junichi Fujita, Daisuke Sato, Kensho Okamoto, Tetsuo Hattori
Department of Electronics and Information Engineering, Kagawa University,
2217-20 Hayashi-cho, Takamatsu, Kagawa, Japan
Tel : 81-87-864-2292; Fax : 81-87-864-2262
s12d451@stmail.eng.kagawa-u.ac.jp, {oka, hattori}@eng.kagawa-u.ac.jp

Abstract: This paper presents a simple transistor-like amplification function by the optical coupling of LED (Light
Emitting Diode) and Photodiode where the Photodiode current by photo electromotive force is feed backed into theLED, in the same way as the case of common emitter circuit of the conventional Bipolar Junction Transistor. Furthermore, we will introduce the fact that the novel transistor becomes thyristor-like switching device under the condition.
Keywords: Transistor, Thyristor, Distar, Diristar, Opt-coupling, LED, Photodiode

under the condition. Okamoto named this device"diristar".
According to our literature search, there are only
two similar researches in the world other than us([1][2]). However no practical devices were developedin their works.

I. INTRODUCTION
It is well known that the bipolar transistor is asemiconductor device capable of amplifying a small
electric signal to a significant output level, e.g. of voltorder, in the case of analogue application. However, as amatter of facts, the transistor itself generates neither ahigh voltage nor large current. This might bemisunderstand in other way. Practically, the bipolartransistor behaves as a variable resistance element
whose resistance value between the collector and theemitter can be controlled widely from near zero ohm toalmost infinity ohm by a small input current, namely thebase current IB, passed from the base to the emitter. In
an common emitter transistor circuit, the collector isconnected to a resistor RC, and then a constant DC
voltage VCC, more than several volts, is applied
between RC and the emitter. Under such a condition, if
IB is passed from the base to the emitter then RCE,
which is the internal resistance between the collector
and the emitter, will changes drastically. This causes thechanges of the collector current IC and also VCE, theoutput voltage between C and E.
Prof. Kensho Okamoto noticed the behavior of thebipolar junction transistor as an electronicallycontrollable resistor and therefore he thought that it
might be possible to realize a completely new amplifier
device by a system combining LED and silicon
photodiode, or silicon solar cell, with positive feedback
manner. He named this device "distar".
Further-more, we will introduce the fact that thenovel transistor becomes thyristor-like switching device©ICAROB 2014

II. TRANSISTOR BY PHOTO-COUPLING
The bipolar junction transistor was invented in 1948.
There are two types for the bipolar transistor, i.e. PNP
and NPN transistors. Since the birth of transistor, theoperation of such bipolar transistors have been
explained by the behavior of electrons and positive
holes in the emitter(E), base(B) and collector(C) regions
as illustrated in Fig. 1 (a). Okamoto found that the wellknown common base transistor circuit shown in Fig. 1(a) could be realized by putting together an infrared
light-emitting diode (IR-LED) and a Si photodiode (SiPD) as shown in Fig. 1 (b).
Then Okamoto made the quasi emitter common
transistor circuit using IR-LED and Si-PD as shown in
Fig. 2 (b) in order to confirm whether the circuit
operated as the real transistor amplifier shown in Fig. 2(a). Fig. 3 (a) shows an example of the device which
consists of an IR-LED and Si-PD. The novel device wasnamed "distar". Fig. 3 (b) shows picture of actual distar
module.
Amplification factor α, which is defined as IC/IE
shown in Fig. 3(b), our first distar was about 0.34.
However, later we obtained α value more than 0.9 in abest combination of LED and Si-PD. This is a surprising-264-

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Japan, January 11-13, 2014

matter because α ≒ 0.99 is conventional transistor.
According to the transistor theory, if α=0.9 then it ispossible to build an emitter-common transistor with β=9.
This means the LED-photodiode system can be used as
an amplifier similar to the bipolar transistor.
The result is shown in Fig. 4. It is known from Fig. 4that the quasi emitter common circuit using the distar
device works as a practical voltage amplifier. We canobtain a voltage gain more than 40dB by adjusting eachvalue of RC, RB and V. However it is difficult tomanufacture a power or current amplifier using the
distar. Because, a normal photodiode does not generate
photocurrent as large as e.g. a 100 mA, which is
required to drive an 8 ohm speaker.
So, we used a Si solar cell module instead of a Si
photodiode. Thus, we succeeded in the development of
a distar power (or main) amplifier.
Fig. 5 is an audio pre-main amplifier circuit using
two sets of distars, and Fig. 6 is a hand-made practicaldistar audio amplifier. We can enjoy beautiful and noisefree music sound using the audio system [3].

Fig.4.Amplification waveform using distar in Fig.3..

Fig.5. Description of amplification behavior.

(a) Common base transistor (b) its simulation by LED
and Si photodiode.
Fig.1. Description of amplification behavior.

Fig.6. Description of amplification behavior.
(a) Common emitter circuit (b) quasi common emittercircuitFig.2. Amplification circuits.

III. AMPLIFICATION FUNCTION
We will explain about the amplification principle ofthe distar in this section. In the circuit of Fig. 7, IC does
not flow because PD is connected in the reverse
direction against voltage source V. therefore,

I C = I E = 0 (when IB = 0)

As shown in Fig. 8, Base-current IB flows throughthe LED connected between B to E when the switch
SW2 is closed. Hence(a) Composition of distar(b) Exterior view of distar
Fig.3. The distar consist of Infrared LED and Si-PD.

©ICAROB 2014

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I E = I B (when IB flows)

In Eq. (10), if α = 0.5 then β = 0.5. However, if α =0.9 then β = 9. As α exceeds 0.9, β enhances greatly e.g.
α = 0.99, β = 99.
On other hand, in case of α ≥ 1,

(2)

Then, the LED emits light and the LED light enters
into the PD, because the LED and the PD is optically
faced each other. Consequently, photovoltaic effectoccurs in PD and the photocurrent IC generates andflows through RC and the PD. The α is defined as theratio of the current flowing through the PD and thecurrent owing through the LED as shown in Eq. (3).

I
IE

α= C

I C= +∞(12)(3)

Therefore, IC is expressed as Eq. (4).

IC = α × I E(4)
Fig.7. Description of amplification principle 1.

At this time, IE = IB as shown in Eq. (2), Eq. (4) canbe expressed as Eq. (5).

IC = α × I B(5)At the next stage, the current flows through LED, i.e.
IE will increase, because IE becomes IE = IB +α IC = IB
+ α IB increased IB. And IC increases. Accordingly, to asEq.(6), the light emission intensity of the LED also
increases.
Fig.8. Description of amplification principle 2.

I C = α ⋅ I B + α (α ⋅ I B )(6)Each value of IE and IC are enhanced in chain
reacting manner and finally IC becomes

IV. VARIATION
A. Other Combination
In Fig. 9, the measurement circuit of quasi commonbase circuit using distar. The light intensity of LED andphotocurrent of PD are very important in distar anddiristar. For this reason, we have been looking for thebest conditions only infrared LED. Because, lightemission wavelength of the LED (940nm) and light
reception of the PD (960nm) is almost identical as
shown in Fig. 10.

I C = α ⋅ I B + α (α ⋅ I B ) + α (α 2 ⋅ I B ) +  (7)()

IC = I B α + α 2 + α  + (8)In equation (8), (α+α2 +α3 + ･･･) is a well-known
the infinite progression and it sum will be convert to Eq.
(9) when α is 0 ≦ α < 1,

(α + α + α + 3) = (1 −αα )
2

3

(9)
mA

Eventually, IC becomes as follows.

IC =

αI B
(1 − α )IIB

α(1 − α )

IP mA

RL

light
Vp

(10)

RP
Vr

V
LED

Ratio of IB and IC is as shown in Eq. (11).

β= C =

IL

(11)

Si-PD

α = I P / IL
Fig.9. quasi common base circuit using distar.

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Japan, January 11-13, 2014

1

0.8

0.8

0.6

0.6

0.4

0.4

0.2

0.2

0
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0.6
0.4
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300

400

500

ILED [ mA ]Fig.13. The α using distar consist of Red LED and SiPD.
Differential
Amplifier1

light sensitivity (a.u.)brightness (a.u.)

However, we made an distar using high luminous
efficiency red LED as shown in Fig. 11. Then, theelectrical characteristics of this distar was better than wethought.
The amplification factor α using distar consists of
IR-LED and PD as shown in Fig. 12. The α significantly
varies depending on the LED current ILED when ILED
less than about 200mA.
Fig. 13 shows the α using distar consists of red-LED
and PD. It is stable in larger than a few 10 mA.

V+

IN-

Red LEDIR LED
Si-PD

OUT

IN+

Push-Pull
Emitter Follower

01100

wavelength (nm)Common Emitter
AmplifierFig.10. Spectrum of light emission and light reception.

V-

Fig.14. Equivalent circuit of the Operational Amplifier.

V. CONCLUSION
We have presented a novel transistor by optcoupling of LED and PD. There are still rooms for
improvement in realizing efficient transistors by the
coupling. However, very practical and high powertransistor such as thyristor can be built by the
combination of large scale LED arrays and solar cell.
We consider that, not only the power transistors but also
switching devices based on the opt-coupling of LED
and PD, are very promising in the future.

Fig.11. The distar that consists of Red LED and Si-PD.
1.2

α (a.u.)

1
0.8
0.6
0.4
0.2

REFERENCES

0
0

100

200

300

400

500

[1] Van Zeghbroeck B.J. (1989), Photon transporttransistor: Electron Devices Meeting, pp.543-546.
[2] W.N. Cheung (1996), IEEE Journal of Quantum
Electronics{32} No.3, pp.502-506.
[3] J. Fujita (2013), Novel Audio Amplifier System
Using Opt-coupling of LED and PD, Proceedings of2013 International Conference on Biometrics andKansei Engineering (ICBAKE2013). pp.301-304,
Tokyo, Japan.
[4] M. Suzuki (1991), Authentic book Design oftransistor circuit , JAN9784789830485, P294.

ILED [ mA ]Fig.12. The α using distar consist of IR-LED and Si-PD.

B. Operational Amplifier
Now, we are challenging to make operational
amplifier using distar module as shown in Fig.14 ([4]).
And, we have confirmed that the Amplifier works aswell as the conventional operational amplifier.

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Study on the pose under complex multiple targets environment for the ind
ustrial robot based on machine vision
Jiwu Wang 1, Xianwen Zhang 1, Huazhe Dou 1, Sugisaka Masanori 2
1

Department of Mechanical and Electrical Engineering, Beijing Jiaotong University, Beijing, China, 100044
2
Nippon Bunri University, Oita 870-0397, Japan1

2

jwwang@bjtu.edu.cn
ms@alife-robotics.co.jp

Abstract: Effectively obtaining multiple targets pose is the key element to improve flexible applications of industrial
robots under complicated working environments. Machine vision technology is the main method to solve this problem.
In order to effectively reduce influence of changing conditions (such as the illumination, angle and scale etc.) on target
pattern recognition, the fusion algorithms of scale invariant feature transform (SIFT) and geometric moment, isdeveloped in this paper. To achieve accurate identification of multi-targets’ pose when their position and orientation
change in the two-dimensional space, the optical-section method is used to determine the depth information of somefeature points of the target. Moreover, that data is used to correct the pose recognized by the traditional patterntechnology. Finally, using SCARA-type industrial robots as a platform, the relative algorithms are verified withcorresponding experiments.
Keywords: machine vision; industrial robot; target recognition; image processing.

accurately. If robot want to work stably andeffectively ,the premise is keeping this two pose stable
and not changed. But in actual work conditions,especially in line of occasions, it is very difficult toguarantee the pose of workpiece not changed. Inevitably,
deviation between actual target object and ideal target
object exists, so robot with flexible to meet thisdeviation is required.
Increasing industrial robot’s flexible and improving
its repeatability is one of the problem to be solved
urgently for industrial robot development [3] Having acertain understanding of their environment is thepremise for robots possess the necessary autonomy, so
additional sensors to improve the perception on theenvironment is required. In this context, vision,
proximity, touch, and force plays a major role, in whichmachine vision is considered to be most importantsensory abilities. Object recognition and position byintroducing vision can help robot grab and place
according with wishes, improve production flexibility.

I. INTRODUCTION
Intelligent industrial robot is concentration anddepth of integration of advanced manufacturing
technology, information technology and smart
technology, is the forefront of high-end equipment andmanufacturing bases in the future, but also important
symbol of a country's industrial level[1] Industry of the
future will require a lot of robots and automatedequipment to support, robots will become a hugeindustry, it makes fundamental changes in traditional
patterns of industrial production, have a profound
impact on the development of human society.
At present, most of the traditional industrial robots
pick up and process according to fixed path, throughprior programming.The initial pose and end pose of an
object is predetermined, robots operate only according
to the regular procedure.For industrial robots, work on
the automated production line or a flexible
manufacturing system[2] the largest group of action is"grab and place".In order to accomplish this type ofoperation ,robots require to accurate positioning
information of manipulated objects: 1) pose before
being manipulated must be known, to ensure the robot
to accurately grab; 2) pose after being manipulated must
be known, to ensure the robots to complete task

©ICAROB 2014

II. Study on the fusion algorithm of SIFT andmoment invariants
SIFT algorithm (Scale-invariant feature transform)
can determine the multi-target effectively, but the-268-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

characteristics of the target[6] Determining the target
pose accurately an effectively by improving conditions
to meet the geometric moment calculation.

reference point generated in image processing is
uncertain sometimes during the target recognition[4].
Although does not affect the target recognition, it iseasy to generate unstable results. So SIFT algorithm
alone can not effectively determine the target positionand orientation.
SIFT algorithm steps as the following figure 1
shows.

Fig.2. Target determination by using SIFT

Fig.1. Illustration of SIFT algorithm steps
Moment invariants algorithm is mainly aimed at thegeometric characteristics, the selected reference point of
the target is established before recognition[5]Moment
invariants algorithm entirely rely on the target grayvalue ,which is obviously affected by the external lightand other factors.
The moment invariant definition of image f(x,y) is
as formula(1) shows. From formula (1) we can achieve
the formula of the formula for invariant image
displacement, as formula (2) shows to us.

Fig.3. Circle center recognition by using moment
invariants
Figure 1 is an application example of SIFT
algorithm. By using SIFT algorithm, we can determine
the recognized workpiece is class C from (A、B、C、
D) the four classes workpieces. Figure 2 using moment
invariants algorithm to determine the circle center of theworkpiece. The results show that we can achieve multitarget recognition and pose calculation effectively byusing this method.

(1)

In order to fully utilize the advantages of the bothalgorithms ,firstly using SIFT algorithm to determine
the identified target, then determine the area ofgeometric moment characteristics calculation in thecaptured images directly, according to the geometrical©ICAROB 2014

III. Study on the fusion algorithm of opticalsection and pattern recognition
In the process of pattern recognition, in many cases,
it is very difficult to achieve judgment of the target-269-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

effectively just rely on the characteristics of twodimensional information, so the target depth
information is required. At the same time, target depth
information combine with two dimensional geometry,improve the accuracy of target distinction.
Considering the change of the target pose in threedimensional space, only rely on geometric moment
method to determine the key points pose, the result is
not accurate, mainly due to changes in the height
direction could affect the computation of the pose. If weare to achieve the precise description of the position andorientation, height information change is essential to bereflected in the calculation of the original pose.
Concrete solution is determining the angle change inheight direction through optical-section method[7] thenuse that angle to correct the geometric calculated pose.
The illustration of optical section as the following figure
3 shows, the optical band reflect the depth informationof the target.

In this paper, for pose change in the twodimensional space, establish fusion algorithm of SIFT
and Moment invariants, overcome 1) target rotation,scaling,translation;2)imageaffine/projection
transformation;3) illumination effect; 4) target occlusion;5)clutter scenes;6) noise and other factors effect on theaccuracy of target recognition[8] The integration of theboth algorithms, meet the calculation condition of
moment invariants effectively, improving theeffectiveness of local features by using invariants,
improving calculation robustness of the target pose.
iii. For pose change in three dimensional space, itis the first time to determine part depth information of
captured target pictures through introducing opticalsection method, based on the traditional patternrecognition. Effectively complement the limit of only
using two dimension data in traditional two dimension
pattern recognition, enhance dimension and reliabilityof pattern recognition. Moreover, amending thecorresponding angle for traditional calculation results.

REFERENCES[1] Hui Qian,Zhiyong Yang,Tian Huang(2005),Key
Technology to the Vision SoftWare Development ofHigh-speed and Parallel Manipulator .10:34-37[2] K. L. Zhuang, J. Z. Wang, and J. Zhou(2011),“Application of Machine Vision in Angle Inspection ,”Equipment Manufacturing Technology,vol. 4, pp. 4-10,[3] Y. B. Wang, Y. Z. Hu, and M. Y. Lu(2006),Siemens
machine vision system and its application in automobile
engine assembly line, Automation systems engineering ,vol. 1, pp. 20-22[4] Fanyan Bu,Jieqing Tan(2011),Image matchingapproach using SIFT descriptor and image
interpolation.47(16)156-158[5] Xuhao He,Yueming Hu,Qifeng Qi(2008), Researchof Geometric Moments Invariants in Identifying
Chip.29(1)8-11[6] Richard Szeliski(2012),Computer vision:algorithms
and applications[7] Xu,Min Tan,Yuan Li(2011),Visiual measurement an
d control for robots[8] Jianchang Zhang,Jianzhang Ma,Chengjun Ding
(2008) , Object recognition based on robot vision incomplex environment.11(24)222-223

Fig. 4. Illustration of optical section

IV. CONCLUSION
In this paper, the machine vision system of industrial
robot is established, which provides the foundation
insurance for follow-up recognition work. The
recognition software system as the following figure 4
shows,which is programmed with VC++6.0 .

Fig.5. The industrial robot recognition system©ICAROB 2014

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Japan, January 11-13, 2014

Research and Development of an Intelligent Robot of Medical AssistanceBased on Embedded RTOS
Yuan Li, Fengzhi Dai, Junhong Xi, Binyao Li, Guodong You
College of Electronic Information and Automation, Tianjin University of Science and Technology, China
yuanli12139@gmail.com

Abstract: For practical purpose, this paper focuses on design and implementation of a wheeled intelligent robot that
could be applied in hospitals or nursing homes for medical assistance. The hardware part includes two microcontrollers,
MC9S12XS128 and STC89C52, assisted by a laptop as the control center of the system, various sensors and otherrobotic pieces. The software part is basically achieved on CodeWarrior 5.0, Keil μVision4 and Visual C++ 6.0 with
OpenCV library, porting μC/OS-II real-time operating system to MC9S12XS128 in order for improving stability andflexibility of the system. The robot has functions of infrared remote control, fuzzy rule-based path tracking, obstacle
avoidance, real-time LCD display and voice broadcast, color recognition and robot arm grasping.
Keywords: Robotics; Microcontroller; Embedded System; μC/OS-Ⅱ

I. INTRODUCTION
Artificial intelligence is a direction of thedevelopment of intelligent science [1]; robot and its
correlation techniques has obtained a significant progressin recent years, especially in the field of Integrated
Circuits and Sensors. In software aspects, Embedded
Real-Time Operating Systems (RTOS) have been applied
into plenty of intelligent devices such as mobile phone
because of superior portability, stability, and efficiency.
However, the development of intelligent robots for
medical assistance based on these new technologies isrelatively slow. In spite of many types of electricscooters or wheelchairs have been sold in market andused in hospitals and nursing homes, most of them has
simple and single functions without adopting advanced
intelligent control approaches [2, 3]. For this reason
introducing Embedded RTOS to solve this problem is anecessary work, and a versatile intelligent robot for
future medical assistance shown in Fig.1 was
implemented in the research of this paper. The robot isdesigned a series of functions including manual remotecontrol, independent path tracking with obstacle
avoidance, and fundamental pattern recognition. The
function of elevating under remote-control mode greatly
increases the range the robot arm can touch [4]. But mostimportantly, in the software aspect of the robot, theapplication of μC/OS-ⅡEmbedded RTOS allows all thefunctions of the robot being coordinated in a more
flexible and reasonable way. It deserves to believe that
this new type of robot will make great change andimprovement for our way and quality of life as long as it©ICAROB 2014

Fig. 1. Appearance of the robot
can be applied in hospitals, nursing homes or othercivilian fields.

II. HARDWARES AND PRINCIPALFUNCTIONS
Microcontroller MC9S12XS128 and STC89C52
cooperated with Dell Inspiron Mini10 laptop are adopted
for accomplishing all the functions of the robot. XS128
ported μC/OS-Ⅱ Embedded RTOS is in charge ofdriving wheels, elevating, path tracking, obstacle
avoidance, robot arm; C52 decodes infrared signal of
remote control through external interruption, andcontrols LCD display by parallel I/O ports and voicebroadcast by universal asynchronous receiver/transmitter; an interactive interface on the laptop basedon MFC and OpenCV library is designed for real-time
image capture, processing and color recognition. Two
microcontrollers are linked by I/O ports of each. The
laptop sends the result of color recognition by serial port
communication to XS128.
The motors of wheels and elevating platform aredriven by two MC33886 and one L298N respectively;-271-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

infrared remote control is achieved by HS0038 infrared
receiver linked to C52 so as to control the motion ofrobot and switching automatic/manual mode; a grayscale sensor with seven LED of high brightness detects
black path on the ground for path tracking function; five
obstacle-avoiding sensors installed on the head and leftedge enable the robot to avoid obstacles while tracking
path; The robot arm owns five degree of freedom driven
by two ULN2003A and ten relays; the LCD1602
installed on the head of the robot and the speechsynthesis module based on SYN6288 can display andbroadcast the robot’s running status in real time.
III. PORTING μC/OS-ⅡTO MC9S12XS128
The main program code of XS128 is compiled anddebugged upon CodeWarrior 5.0 IDE. The robot’s
functions of remote control, path tracking, obstacle
avoidance and color recognition can be managed flexibly
and efficiently under the environment of μC/OS-Ⅱ
Embedded RTOS. But before that, μC/OS-Ⅱ should be
successfully ported to microcontroller XS128.
CPU12X has a set of registers including a 16-bit
program counter (PC), a 16-bit stack pointer register(SP), a 16-bit accumulator D, 16-bit index registers X
and Y, a 16-bit condition code register, a global page
(GPAGE) register and specific resource page (RPAGE,
EPAGE and PPAGE) registers. As responding
interruptions, the hardware of XS128 will automatically
push the core registers onto the stack whose address
grows downward [5].
Among μC/OS-Ⅱ files, define above hardware
information including data types, stack data type, stack’s
growthdirection,
tasks’
schedulingfunction
OS_TASK_SW( ) and macro definition of critical sectionof code in the OS_CPU.H file; porting four assembly
functions including OSStartHighRdy ( ), OSCtxSw ( ),OSIntCtxSw ( ) and OSTickISR ( ) in the
OS_CPU_A.ASM file and six functions including
OSTaskStkInit ( ), OSTaskCreateHook ( ),OSTaskDelHook(),
OSTaskSwHook(),
OSTaskStatHook ( ) and OSTaskTickHook ( ) in the
OS_CPU_C.C file [6].
After porting three files mentioned above, make asystem clock for the RTOS by using XS128’s RTI
module. For one thing, set up the system bus frequency
to 32MHz.Then set related registers of RTI and add thetime-tick interrupt vector to the file TBDML_linker.prm.
Finally, include all header files into INCLUDES.H andconfigure and tailor the file OS_CFG.H based on needs,
retaining the functions about task and message mailbox.
©ICAROB 2014

So far, the porting of μC/OS-Ⅱ is accomplished andthe system is ready for programming task functions in
the file MAIN.C.
IV. CONTROL PROGRAM BASED ON μC/OS-Ⅱ
After introducing Embedded RTOS that can run
more than one task simultaneously to the robot softwaresystem, each program of the robot’s function can be
regarded as a task programming in the main file MAIN.C.
So there are four tasks that need run in the
μC/OS-Ⅱenvironment. According to their priorities fromhigh to low, these four tasks are: Task of Color
Recognition whose priority is 6, Task of Obstacle
Avoidance whose priority is 7, Task of Remote Control
whose priority is 8 and Task of Path Tracking whose
priority is 9. Fig. 2 clearly shows all the robot’s tasks
above and the way of switch between any two tasks ofthem.
1. Task of Color Recognition
Red/Blue color recognition is achieved by upper
computer of color recognition shown in Fig. 3. The
upper computer is programmed on Visual C++ 6.0
environment with MFC and OpenCV library. The digital
camera collects HSV value of the objects within its fieldof view and the computer calculates the area of theobjects that match the HSV range of red or blue. The
result of recognition will be sent to XS128 by serial port
for further processing.

Fig. 3. Upper computer for color recognition
This task, when it first begins, will call the function
msg2=(INT8U*)OSMboxPend(ExMbox2,0,&err2)
towait message from mailbox “ExMbox2”. Then thesystem determines whether or not the message is what
the task needs before executing following program; if themessage suggests that the object is red or blue, the color
is chosen by user, the system will order the robot arm tograsp the target object. Therefore, the task of color
recognition is in suspended state as waiting message and-272-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

it will not be woken up until the upper computer has
detected the object that has certain color the user wants
to look for.
2. Task of Obstacle Avoidance
The robot is designed to automatically bypass
obstacles under path tracking mode[4]. When this task
begins, it is suspended by calling function
msg1=(INT8U*)OSMboxPend(ExMbox1,0,&err1)
towait the message from mailbox “ExMbox1” of pathtracking task. Therefore, the task of obstacle avoidance isalso in suspended state as waiting message and the taskof remote control or path tracking is running. It will not
be woken up until there is obstacle detected in front of
the robot.
3. Task of Remote Control
This function is achieved by communication
protocol NEC, namely detecting duration of high level atreceiving ends and using the time interval between
pulses to differentiate logic “0” and “1”. An externalinterrupt service subroutine is designed in C52 fordecoding of infrared signal. Each time after a decoding,the subroutine will return an array storing the result of
decoding. Then C52 will give different orders according
to different value in the array to relative modules toimplement certain functions such as LCD display andvoice broadcast.
C52 is also able to send certain remote control
commands to XS128 through I/O ports of the two
microcontrollers. Once XS128 receives the order ofswitching operational mode from C52 as the robot isrunning under remote control mode, the system will callfunction OSTaskSuspend(OS_PRIO_SELF) to suspend
the current task and make a task switching, thereby the
Task of Path Tracking whose priority is highest in readystate will acquire the access to CPU and start running. So
the robot switches to path tracking mode.
4. Task of Path Tracking
The seven-channel gray scale sensor of the robot
can output analog voltage signals ranging from 1.2V to
4.0V in terms of different grayscale values on the ground.
Through the ATD channel of XS128, these analog
signals are transformed to digital signals. Compare each
digital value with predefined threshold in the robot
system. If the digital value is greater than the threshold,it means that the corresponding channel of gray scalesensor has detected black path. In order to move moresmoothly, Fuzzy Control Algorithm is adopted in thedesign of this robot.

©ICAROB 2014

Similar to the remote control task, the system can switch
back to remote control mode once receiving theswitching command from C52.But the different part isthat the current path tracking task is re-suspended bycalling function OSTaskResume(Task3Prio). Here,
Task3Prio = 8, which signifies the priority of remotecontrol task. Since at this time the suspended task ofremote control resumes into ready state and its priority ishigher than task of path tracking, the path tracking task isinstantly deprived of its access to CPU, making therunning mode of the robot switch back to remote control
mode.
On top of that, the path tracking task can fulfill dutyof sending message to start the task of obstacle
avoidance and the task of color recognition by mailbox
function of μC/OS-Ⅱ. Once the front obstacle-avoiding
sensor detects out obstacle or XS128’s SCI module hasreceived color information from the laptop, it will callthe function OSMboxPost(ExMbox1,(void*)&msg) or
the function OSMboxPost(ExMbox2,(void*)&ch) tosend messages.
V. CONCLUSION
This paper combines traditional robotic technology
with Embedded RTOS, designing and implementing a
new type of intelligent robot in the field of medical
assistance. After practical debugging, the robot can
implement many functions like remote control, pathtracking, obstacle avoidance, color recognition, andreal-time LCD display and voice broadcast, all of which
can be switched flexibly by the application of
μC/OS-ⅡEmbedded RTOS. Fig. 4 and Fig. 5 show the
robot tracking the black path and avoiding obstacle. As
Fig. 6 shows, the robot also can help user grasp theobject of certain color by using digital camera and robot
arm. This kind of robot could be applied into hospitals or
nursing homes in future to take care of old people orpatients with their life and lower the burden of nurses.

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Fig. 4.The robot runs under path tracking mode

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

ACKNOWLEDGMENT
This paper was supported in part by the Tianjin
NaturalScienceFoundationofChina(No.13JCZDJC29100).

REFERENCES
Shoshana L. Hardt, William J. Rapaport (1986),
Recent and Current Artificial Intelligence, The AI
Magazine Summer, pp. 91-100[2] Zhaoxia Wang, Yongxin Liu (2010), Design of roadtracing navigation control for smart car use CCD sensor,
E-Health Networking, 2010 International Conference on
Digital Ecosystems and Technologies,pp. 345-348[3] Mochizuki Tatsuya, HayashiEiji (2007), Navigation
system for a mobile robot using an omni-directional
camera, 12th International Symposium on Artificial Lifeand Robotics,pp. 450-453[4] Yuan Li, Chunyu Yu, Fengzhi Dai, et al(2012),Implementation of a Versatile Intelligent Vehicle
with Tracking and Remote Control for Hospital or
Nursing Homes, 3rdIEEE International Conference on
Software Engineering and Service Sciences, pp. 505-508[5] MC9S12XS256Reference Manual, Rev. 1.13 (2012),
Freescale SemiconductorInc[6] Jean J. Labrosse (2003), MicroC/OS-II: The
Real-Time Kernel Second Edition, CMP Books[1]

Fig. 5.The robot bypasses obstacle

Fig. 6.The robot recognizes and grasps target object

Start

OSStart( )YColor Recognition(6)N

OSMboxPend( )Obstacle detected?

Y

Obstacle Avoidance (7)N

OSMboxPend( )Remote Control(8)OSTaskResume( )OSTaskSuspend( )Path Tracking (9)Fig. 2. The state transition diagram of the robot’s tasks©ICAROB 2014

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Red/Blue detected?

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

High Acceleration Robotic Arm for Dynamic and Dexterous Manipulationof Deformable Object
Hiroaki Seki1, Hiroki Shibata1, Yoshitsugu Kamiya1, Masatoshi Hikizu1, Khairul Salleh Mohamed Sahari2
1

Faculty of Mechanical Engineering, Kanazawa University, Kakuma-machi, Kanazawa, Ishikawa, Japan
Centre for Advanced Mechatronics and Robotics, College of Engineering, Universiti Tenaga Nasional,
Jalan IKRAM-UNITEN, 43000, Kajang, Selangor, Malaysia
Tel : 81-76-2344705 Fax : 81-76-2344706{hseki, kamiya, hikizu}@t.kanazawa-u.ac.jp, khairuls@uniten.edu.my

2

Abstract: Deformable object manipulation is very important for home service robots. This paper discusses thedevelopment of a high acceleration robotic arm capable of performing dexterous manipulation of deformable object
such as string. A mass-spring model of the string is first developed to study the design requirement of the robot as wellas to determine the required motion of the robot to smartly manipulate the string. The developed system comprising of
the string model and robotic arm is then used to manipulate unknown string. Based on some simple basic routine, theparameters of the string can be approximated based on the string model. Once the parameters are known, the string canbe smartly manipulated by the robotic arm.
Keywords: high-acceleration robot, wire-driven robot, dexterous manipulation, home service robot, mass-spring system.

I. INTRODUCTION

The experiment is repeated 10 times. Figure 1 (right)
show the most distinguish results related to the path ofthe mark and the variation of the acceleration. In thiscase, the object was released at 0.19 sec and the heightd was approximately 210 mm, with the highest
acceleration of 22.3 m/s2, recorded during the moment
when the object is released.

Currently, development of home service robots has
attracted a lot of attention from robotics researchers[1]-[6]. These robots are required to handle not only
rigid objects, but soft and deformable objects as well.
However, there are still a lot of research areas to be
explored in the field of deformable object manipulation
by robot. Some manipulation examples involving
deformable objects include making a bed and clothesfolding. In clothes manipulation, when a person
manipulates clothes for folding, he or she will
somehow perform dynamic manipulation of the clothes
such as swinging the object to spread it etc. This paper
discusses the development of a robotic arm with highacceleration that will enable more dexterous
manipulation of clothes.

Figure 1: Finding approximate acceleration requirement

III. WIRE-DRIVEN MANIPULATOR
Most commercial robotic manipulators have theiractuators on the links itself, which contributes
significantly to the increase in weight. This is a major
disadvantage in getting a high acceleration from the
manipulators. Thus, in this paper, a robotic manipulator
using wire driven concept is suggested to obtain highacceleration. Instead of having the manipulators on thelinks, the manipulators are placed outside the robot. asshown in Figure 2.
Based on this concept, the following robotic
manipulator shown in Figure 3 has been designed anddeveloped. The total weight of the robot (link 1 to link

II. REQUIRED ACCELERATION
In order to design and develop a suitable manipulator,
the big question is how much acceleration is required
from the robot. A simple experiment is conducted toroughly estimate the acceleration required from therobotic manipulator. A human tosses an object of 18g
in weight vertically into the air while a high-speed
camera captures images of a marker placed at theperson’s wrist at 0.01 sec intervals (Figure 1 (left)).
The distance d is then obtained based on the release
point and also the highest point achieved by the object.

©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

3) is 1.3kg. The motor used are Maxon brushless DC
motors, with potentiometer installed on all therotational axes. Comparing our developed prototype
with a Mitsubishi RV-2SQ, which is very similar in thelength, it is lighter by more than 17kg. Table 1 shows
comparison between our developed prototype with RV2SQ in terms of the maximum angular velocity. Table
2 shows the detail specification of our developed
prototype.

The model of the prototype is shown in Figure 4. Thisis basically important to be embedded inside thesimulator which also includes the mass-spring
representation of the deformable object, which for thetime being is only limited to string.

Y S 3tk3
c3  R3l3t R2 tk2

l2
 S 2t

l1

c2

 R1t

k1

 S1t

Figure 2: Concept of wire-driven manipulatorc1

X

Figure 4: Model of robot prototype
From Figure 4, the following equations can be
determined or established. The moment of inertia foreach link can be determined as follows:
I gi 

mi li
12

2

(1)

Subsequently,
J1  ( I g1  m1l t c1 )  ( I g 2  m2l t c 2 )  ( I g 3  m3l t c 3 )
t

2

t

t

1 linkLimit [deg]2 link3 linkttttt
J i Ri  ki ( Ri   Si )  ci (Ri  Si )

±120

±160

Max ang vel [rad/s]

5

8

12

-

2

Max ang acc [rad/s ]

20

50

50

-

M. of inertia [gmm2]

6.82x107

1.29x107

1.78x105

6.92x107

Torque [mNm]

2587

1760

383

1963

Motor

EC-i40EC-i40EC-32flat

EC-i40Motor driver

EPOS2 24/5

EPOS2 24/5

EPOS2 24/2

EPOS2 24/5

Gearhead

GP32C(66)

GP32C(66)

GP32A(23)GP32C(23)

Speed ratio

97.02

66

23

73.6

©ICAROB 2014

(3)

Subsequently,
Ri t 1  Ri t

Base

±120

(2)

2

Ri -20~+120

2

Based on Newton’s law, the torque can be determined
as follows:

Table 2: Specifications of developed prototyperd2

J 3  ( I g 3  m3l t c 3 )Table 1: Maximum angular velocity comparison
Our prototype [deg/sec] RV-2SQ [deg/sec]st
1 link286
150
nd
2 link458
275
3rd link
687
450

nd

2

J 2  ( I g 2  m2l t c 2 )  ( I g 3  m3l t c 3 )Figure 3: Developed High Acceleration Manipulatorst2Ri t 1 

(4)

dt

 Ri t 1   Ri t

(5)

dt

Ri t 1  Ri t  (

ki
c
t
t
t
t
( Ri   Si )  it (Ri  Si ))  dttJi
Ji Ri t 1   Ri t  (Ri t  (

(6)

ki
c
t
t
t
t
( Ri   Si )  it (Ri  Si ))  dt )  dt (7)tJi
Ji

IV. MASS-SPRING MODEL OF STRING
To validate the performance of the developed
manipulator, and to enable dynamic manipulation on-276-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

V. EXPERIMENTAL RESULTS

any unknown deformable object, a mass-spring systemmodel of a simple string is developed. String is chosen
due to its simplicity. Figure 5 shows the mass-spring
system.

1

3

2

Experiments were conducted to check the
performance of the developed manipulator. An
unknown string with a 5mm diameter is selected asshown in Figure 6.

niF l bii-1

il F r bi  F biF r bii+1

Figure 6: Unknown string used in experiments

Figure 5: Mass-spring model of string

Since this paper is intended to report on thedevelopment of the robot manipulator, experimental
results regarding parameters determination of theunknown string is omitted.
Chosen experiment is to program the robot to makethe string form a u-shape as shown in Figure 7.
Assuming that point 0 is held by the robot gripper, theangle limitations for all mass points (70°≤θ1≤110°, 40°≤θ2≤-10°, -60°≤θ3≤-30°, -60°≤θ4≤-30°, -40°≤θ5≤10°, -110°≤θ6≤-70°) plus distance between points 1 and
6 (x) are determined. 2 seconds after the robot
manipulator stops its manipulation, and if the u-shapeis achieved, the experiment is considered successful.
The result is shown in Figures 8 to 10.

The system also considers air drag (Fci) andgravitational force (Fg), besides the elastic force (Fsi),
damping force (Fdi), elastic bending force (Fbi) andelastic damping force (Fhi) between the mass points.
The equations related to the mass-spring model are
shown as follow:Fsi k s  i  L   d i
m


  
Fdi
  s 2ks ri 1  ri  di di
m

(8)



 

(9)

 


Fbir kh  θ i  d i  d i 1  d i

m
i

 



(10)





(11)







Fbil kh  θ i  d i 1  d i 1  d im
 i 1

 

 


Fhir  h  L  kh  θ i  d i  d i 1  d i

m
i











Fhil  h  L  kh  θ i  d i 1  d i 1  d im
 i 1 Fci Cc1  ri  Cc 2  ri  ri
m



0

(12)

5

5
(14)

4


rit

h 1

h 1



 rit  rit  dt
h



h

2

4

h



(16)

4

2

2

3

3

3

Figure 7: Forming string to become u-shape
The manipulator is trained by reinforced learning on
how to smartly make a u-shape from the string. By 4th

where th is the current time step, and th+1 is the nexttime step.

©ICAROB 2014

5

(15)

h




 rit  rit  rit  dt  dt
h

1

1
1

(13)

By applying Euler Explicit Method, the velocity andsubsequently the position of the mass points can thenbe determined from the sum of the forces above.

rit

x

6

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

0.6

attempt, the manipulator can smartly perform the ushape. Figure 8 shows the simulated Bezier curve
projected for each of the link. Figure 9 shows thesimulated and actual result of the string manipulation.
Please note that camera EX-F1 with 60 fps capability(Casio) was used to capture the images of the string inreal time. Figure 10 shows the result for the wrist
movement of the manipulator. Figure 11 shows thescenes during the 3rd attempt.

0.5
0.4

0.3
0.2
Simulation
シミュレーション
実測値
Experiment

0.1

0
-0.5

-0.4

-0.3

-0.2

-0.1

0

Figure 10: Trajectory of wrist of manipulator
attempt

1st link
2nd link

3rd link

1st
2nd
3rd
4th
attempt
1st
2nd
3rd
4th

attempt
1st
2nd
3rd
4th

Figure 8: Projected Bezier curve; ω[rad/s] versus T[s]0.2 sec

0.3 sec

0.4 sec

0.5 sec

0.6 sec

0.7 sec

0.8 sec

0.9 sec

1.0 sec

VI. CONCLUSION
A wire-driven 3-link robotic manipulator is proposedand developed. The proposed robotic manipulator islighter and gives more acceleration which cannot be
obtained from normal robotic arms in the market,
making it more dexterous and flexibility. On top of
that, the combination between the robot and ourdeveloped simulator enables the robot to plan its
movement based on the prediction or solution provided

Experiment
Simulation

When u-shape is formed

Figure 9: Results after 3rd attempt©ICAROB 2014

0.1 sec

Figure 11: Scenes during string manipulation

1.06[s]

Trajectory comparison [m]0.0 sec-278-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

by the simulator, which is intelligent and costeffective.
Experimental results have confirmed the success ofthe proposed manipulator to a certain extent. However,
the manipulator still requires improvement, especiallyto further improve the transmission of power from the
motor through the wires. Further works may also
include application to clothes to check the robustnessof the manipulator as well as the simulator.

ACKNOWLEDGMENT
The authors would like to thank Ministry of Science,
Technology and Innovation, Malaysia and Kanazawa
University, Japan for funding this work.

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Robot Operated by CDMA Networks for security
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pp. 184-194, 2008.
[2] T. Taipalus and K. Kosuge, “Development ofService Robot for Fetching Objects in Home
Environment”, Proc. of IEEE Int. Conf. on
Computational Intelligence in Robotics andAutomation, pp. 451-456, 2005.
[3] A. Bicchi, “Hand for Dextrous Manipulation andRobot Grasping: A Difficult Road Toward Simplicity”,
IEEE Transaction on Robotics and Automation, vol. 1,
no. 16, pp. 622-652, 2000.
[4] K. Yamazaki and M. Inaba, “A Cloth Detection
Method Based On Image Wrinkle Feature for Daily
Assistive Robot”, IAPR Conference on Machine
Vision Applications, pp. 366-369, 2009.
[5] S. Hata, T. Hiroyasu, J. Hayash, H. Hojoh, and T.
Hamada, “Flexible handling robot system for cloth,”
Proc. of IEEE Int. Conf. on Mechatronics andAutomation, pp. 49–54, 2009.
[6] K.S.M. Sahari, H. Seki, Y. Kamiya and M. Hikizu,“Clothes Manipulation by Robot Grippers with Roller
Fingertips”, Advanced Robotics, vol. 24, no. 1-2, pp.
139-158, 2010.

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Switching based controller algorithm design for uncertainties rejection insmooth takeoff / landing and of quad-rotor
M. Hassan Tanveer, D. Hazry, S. Faiz Ahmed, M. Kamran Joyo, Faizan. A. Warsi, A.T. Hussain
Centre of Excellence For Unmanned Aerial Systems (COEUAS) Universiti Malaysia Perlis (UniMAP),
Seriab, 01000, Kangar, Perlis, Malaysia.
hassantanveer@live.com, , hazry@unimap.edu.my, syedfaiz@unimap.edu.my,sky_kamran12@hotmail.com, f_warsi128@hotmail.com, abadal@unimap.edu.my

Abstract: The proposed control technique for smooth takeoff / landing controlling of Quad-rotor type (UAV) unmanned
Aerial Vehicles under different uncertainty conditions is evaluated in this article. Takeoff / landing controlling of Quadrotor UAV under high wind burst and noises has been a challenging research domain for the researchers. In this paper aswitching based controller is proposed for better controlling of quad-rotor through NMPC and PID controller. This
switching controller works in such a way that noises problem are handled by NMPC controller and in the occurrence of
external disturbances, auto-tuned PID controller is activated. Simulation Results shows the effectiveness of the
proposed switching based method.
Keywords: Quad-rotor, Takeoff / Landing Control, Robust PID, Non Linear Model Predictive Control (NMPC).
.

structure. Moreover, showed that quad-rotor was able
to perform autonomous hovering with altitude control
and autonomous take-off and landing (Samir
Bouabdullah, 2007). In 2008, Bouadi presented
stabilizing control laws by sliding mode andbackstepping approach (H. Bouadi, 2008). This paper
focuses on proposed hybrid control technique that isbased on MPC and PID for VTOL and altitude control.
The proposed algorithm is simulated on (MATLAB).
The organization of the paper is structured as thesection 2 refers to the related work. Section 3 follows
the Kinematics and Dynamics of quad-rotor. The
proposed control technique is discussed in section 4.
Experimental results and simulations have been
provided in section 5.

I. INTRODUCTION
The demands of UAVs have influenced the researchers
to design UAVs that are capable of operating in roughsurroundings and effectively operational in complex
missions. During the last decade the UAVs capablewith vertical takeoff and landing configuration haveintensively been researched as their demands incivilian applications have increased. Copters androtorcrafts have proved to be the great solution to theunmanned aviation industry. The quad-rotor havebecome the most prominent amongst the researchers as
they are capable of maneuvering efficiently and their
VTOL capability makes them part of helicopter type
UAVs. Its advantage over other copters and rotorcrafts
is its easy structural assembling. However this type ofrotorcraft UAV carries severe issues regarding its
aerodynamic controls which is responsible of its roughflight performance and requires an effective controller.
In this article altitude controlling for quad-rotor is
focused. An innovative hybrid control technique is
proposed based on PID and MPC controllers. The PID
tackles the disturbances and MPC tackles the noisy
impact of the sensors.

II. QUADROTOR SYSTEM
These equations of motion are extracted from the
Newton Euler method. The above-mentioned method
is discussed comprehensively in the article by (M.
Kamran Joyo, 2013).

In the recent years VTOL and altitude control of quadrotor has remained an issue due to the kinematicconstraints and unstable dynamics. However some of
the control techniques have been developed in this area.
In 2013 Adaptive Hybrid Control Algorithm Design for
Quad-rotor UAV by (D. Hazry, 2013) , In 2006,
Ozguner used sliding mode and PID control for thesystem(R. Xu, 2006). In 2007, Samir Bouabdullah andRonald Siegwart used a Backstepping method for
altitude, attitude and position control of a quad-rotor,
the results of this technique showed a flexible control©ICAROB 2014

(1)

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Japan, January 11-13, 2014

Figure 2: Proposed Hybrid Controller
Similarly under normal conditions the The systemmoble conntes with Model predictive controller MPC
and it is selected for better controlling of the system
under noisy conditions, it predicts control signal insuch a way that it minimizes a define cost function
which is error signal between the output and desire
value over specified prediction horizon. MPC starts
predicting the future control action by choosing thesuitable values of control horizon M, prediction
horizon P and control-weighting factor R. Once MPC
completes its prediction process, it implements best
control action and then at the next sampling interval,
the control estimation is repeated again with the newavailable information. As a result, the performance ofthe system becomes increased.

(2)
Where ‘ ’ is the distance between the center of thequad-rotor and the center of a propeller. U1, U2, U3
and U4 are the movement vector components. Theirrelation with the propellers’ speeds comes fromaerodynamic calculus.
For takeoff / landing controlling only Z axis equation
is taken in consideration, illustrated in Equation (1),III. PROPOSED CONTROLLER DESIGNIn this Paper, a hybrid PID and MPC controller
technique is proposed for proper altitude controllabilityof quad-rotor under the disturbance and noisy
conditions. The sensors are noisy in nature andspecially during windy conditions smooth takeoff andlanding of quad-rotor are quite complex tasks .In this
proposed control method both control strategies (PID
& MPC) are utilized in such a way that it canovercomes these two majour above mentioned
problems. Figure 2 shows the block diagarm ofproposed technique. A decision making block switches
the system modle to the appropriate controller
according to the desired situation. Under thecircumstances of external distrubance the error sinal of
between desired altitude and original altitude (sensor
output) become greater then some theroshold value, inthis situation decision making block switches thequadrotor system to the PID controller which quickly
stabilize it and makes takeoff and landing operationsmooths. Decision making block working is definedin following two equations.

IV.

PID CONTROLLER DESIGN FORDISTURBANCE REJECTION

The altitude controller used to maintain the quad-rotor
distance from the ground at a desired value. For
achieving stabilized quad-rotor altitude, main
requirement is to work on quad-rotor z axis andremaining all other axis constant. So we select only Z
axis equation form equation (8), as show in following:(3)
andare constant values,
In above equationand must be considered as 0 for hovering condition.
Using Laplace Transform and taking all constants as, system will become:(4)
So the error signals e for PID control is:
=

Where

Where
is desired value and is actual output.
And PID controller will become:(5)
Kp, Ki and Kd can be selected by auto tuning method(M. Hassan Tanveer, 2013).
Let©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

This form can capture many common constraints suchas actuation limits and error boxes. Also note that theconstraint acts upon the estimated state, for that is
known. Given the assumption of a bounded state
uncertainty, this can be readily transformed to andfrom equivalent constraints on the actual state. Since
the feasibility of the optimization depends only on theestimate and not the truth, the form is appropriate here.

)

Z (s) =

Therefore close loop transfer function for the complete
system will become:(6)
Equation (6) is the controller equation, which gives
desired and controlled output response.

Subject to the constraint:

V. NMPC Controller Design for Noise

(11)

2H

Rejection
Thus the objective function for system can beformulated as:
The dynamics of the true state is given in equation (4),can be expressed as:

And the optimal solution to the optimization problemof the plant, therefore the control law for receding
horizon implementation will be:(k + 1) = Ax(k) + B
(7)

(12)

(k)

Prediction Horizon length N:

VI. SIMULATION RESULTS

x(k) =In this section, system simulation results are shown interms of time history which demonstrates the strengthof control method for the smooth takeoff / landing andaltitude control of quad-rotor. Disturbance is added insystem more or less around 100 percent of reference
input, and its effect i.e: Overshoot, Response Time andSettling Time are shown in Table 3. PID parameterschosen after auto-tune are listed in Table-2. PhysicalParameters of quad-rotor are chosen as mention in
Table-1. Initial Value of altitude z(0) is set to origin 0
position.

u(k) =and the estimation error is an additive term, applied ateach time step, the error is uniformly distributed inevery feedback.
(k + i + 1 | k) = Ax(k + i | k) + B(k + i | k)(8)x(k) = M x(k) + C u(k) is the state prediction.

Figure 3 shows the actual altitude tracking path. Figure
4 shows the noise which is added in trajectory bymeans of system and sensor noises. Figure 5 is thedisturbance which is added in system in a particulartime instance. Figure 6 compares the three different
control techniques effect under disturbance and noisy
conditions.

The optimization cost function is then become:
J(k)=

(9)
Where Q and
are weight coefficient. Evaluating
all above equations in J.
J(k)Parameter
Name
Rotational
Inertia Along Zaxis
Total Mass
Thrust ConstantDrag Constant
Arm Length=(10)
Where,H=
F=
G=©ICAROB 2014

C+
M
M+Q

SymbolValue

I

0.0013

M
B
D

0.65

Unit

0.23

Table 1:Quad-rotor Calibration Data

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 20141080.0007482940731823
0.000007482940731823
0.000844205739401302

Hybrid Altitude

Kp
Ki
Kd

6
4
2
0
0

2

4

6

8

10
Time (secs)

12

14

16

18

20

0

2

4

6

8

10
Time (secs)

12

14

16

18

20

0

2

4

6

8

10
Time (secs)

12

14

16

18

20

10

Table 2:PID Auto-tune Parameter
MPC Altitude

8
6
4
2
0

Overshoot

PID

10%

MPC

90%

Hybrid10%

Response
Time

1 sec

4 sec

10
8
PID Altitude

Controller

Settling
Time

1 sec

Fig. 6: Controllers Effect2 sec

VII. CONCLUSION
In this paper we present the efficient auto-tuned PID
controller with MPC for Disturbance and Noise
Rejection in Smooth Takeoff / Landing and Altitude
Stabilization of Quad-rotor UAV. NMPC shows its
robustness in overall simulations when system andsensor noises injects in system. Result shows that
proposed auto-tuned PID controller works very welland quickly become stable when external disturbances
are applied. From the simulation results, we canconclude that proposed switching controller works
efficiently and makes the system stable under thecircumstance where there is any kind of disturbanceand noises imposed on it.

8

Actual Path
7

6

Altitude

5

4

3

2

1

0
2

4

6

8

10
Time (secs)

12

14

16

18

2
0

Table 3: Effect of different controllers

04Offset=0 (secs)

2 sec

2 sec

6

20

Offset=0 (secs)

Fig. 3: Actual Altitude Path

VIII. REFERENCES
Noise
0.15

[1]

Amplitude

0.1

0.05

N. Guenard et al. 2006 “Control laws for the tele operation ofan unmanned aerial vehicle known as an x4-flyer,” in Proc.
(IEEE) International Conference on Intelligent Robots(IROS’06), Beijing, China.

0

[2]
-0.05

-0.1

0

2

4

6

8

10
Time (secs)

12

14

16

18

[3]

20

Offset=0 (secs)

Fig. 4: Gaussian noise added in System[4]8Disturbance7

6

Amplitude

5

[5]

4

3

2

[6]

1

0
0

2

4

6

8

10
Time (secs)

12

14

16

18

20

Offset=0 (secs)[7]

Fig. 5: Disturbance[8]©ICAROB 2014

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R. Xu and u. Ozguner, 2006. “Sliding mode control of aquad-rotor helicopter,” Proc. of the 45th IEEE conference on
decision and control, pp. 4957-496.
D.Hazry, S.Faiz Ahmed, 2013 “Adaptive Hybrid Control
Algorithm Design for attitude stabilization Of Quad-rotor
UAV”, archives Des Science Journal Vol.66, Issue.2.
Tanveer, M. H., Ahmed, S. F., Hazry, D., Warsi, F. A., & Joyo,
M. K. (2013). STABILIZED CONTROLLER DESIGN FOR
ATTITUDE AND ALTITUDE CONTROLLING OF QUADROTORUNDERDISTURBANCE
AND
NOISY
CONDITIONS. American Journal of Applied Sciences, 10(8),819.
Samir bouabdullah,2007. “Full Control of a Quad-Rotor,”
Proc of IEEE international conference on intelligent robots
and systems (iros 2007).
H Bouadi, m. Bouchoucha and m. Tadjine, 2008 “Sliding
mode control based on backstepping approach for an uav
type-quad-rotor,” international journal of applied mathematics
and computer sciences, vol. 4, no. 1, pp. 12-17.
Joyo, M. K., Ahmed, S. F., Hazry, D., Tanveer, M. H., &Warsi, F. A. (2013). Position Controller Design for Quadrotor under Perturbed Condition.
Tanveer, M. H., Ahmed, S. F., Hazry, D., Joyo, M. K., &Warsi, F. A. (2013). Disturbance And Noise Rejection
Controller

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

Parameters of Gas-Liquid Flow Distribution Uniformity in Upward Multi-Pass
Channels
Zuradzman M. Razlan1, D. Hazry1, R. Heng1, A.B. Shahriman1, Khairunizam WAN1, S. Faiz Ahmed1, Nazrul H.
ADNAN1, M. Hirota2, N. Maruyama2, A. Nishimura2, and H. Hisyam3.
1

Centre of Excellence for Unmanned Aerial Systems, Universiti Malaysia Perlis, 01000 Kangar, Perlis, Malaysia.
2
Department of Mechanical Engineering, Mie University, Tsu-city, Mie, Japan.
3
Lean Applied Pte. Ltd., 47620 Subang Jaya, Selangor, Malaysia.
zuradzman@unimap.edu.my

Abstract: The gas-liquid flow distributions in multi-pass upward parallel channels that simulate the evaporator for an electricvehicle air-conditioner system were examined experimentally. In this paper, the attentions are to discover the most influenced
parameter to the flow distribution uniformity by using design of experiment method. Experiments were conducted in an
isothermal air-water flow system. In the mist-flow inlet, the water distribution was insensitive to the backpressure conditions
and its uniformity was improved in comparison with that in the stratified-flow inlet. The flow distribution uniformity for gas
phase is influenced mostly by superficial air velocity, and the flow distribution uniformity of liquid phase is mostly influenceby 2-way interaction of parameters which are flow pattern and superficial air velocity.
Keywords: Mal-distribution, Stratified flow, Mist flow, Backpressure, Design of Experiment.
Table 1 Summary of the experimental conditions or parametersfrom Zuradzman et. al [1].

1 INTRODUCTION
In this study, we already examined experimentally thegas-liquid flow distribution characteristics in multiple
upward channels that simulate the compact evaporator usedin the small air-conditioning system, with attention to the
influences of the backpressure conditions at the branchoutlets and of flow-inlet conditions at the entrance of thedividing header on the gas-liquid distributions as reported
in Zuradzman et. al [1]. The gas-liquid flows of therefrigerant have been simulated by the air-water two-phaseflows under isothermal condition that are suitable forgrasping fundamental flow characteristics in the channel.
The distribution ratios of air and water in the branches havebeen measured under the upward parallel flow condition. It
is expected that the data of gas-liquid distributions obtained
under these specified inlet and outlet conditions are helpful
not only to understand the fundamental two-phase flow
characteristics in the multi-pass channels but also as a
database to examine the reliability of results obtained bynumerical simulations.

FluidsSuperficial air velocity at
the header entrance jg
Superficial watervelocity at the header
entrance jl
Pressure conditionat the branch outlets

2 METHODOLOGIESThis paper focus on analysis from the result of standard
deviation of gas and liquid phase (flow distribution
uniformity) has been done and reported recently byZuradzman et. al. [1] as in Fig. 1 and 2. It is important for
acknowledgement of which parameters and its interaction
among each other that influenced the most to the uniformity©ICAROB 2014

Isothermal air and water
1.0 m/s, 3.0 m/s, 5.0 m/s
0.015 m/s, 0.03 m/s,
0.045 m/s
Case A (non-uniform)
Case B (uniform)

Flow-inlet conditionat the header entrance

Stratified-flow inlet
Mist-flow inlet

Header attitude

Horizontal

Branch attitude

Upward(a) Stratified-flow inlet(b) Mist-flow inlet
Fig. 1 Standard deviations of the air distribution ratios σgof the flow distribution by using Minitab 15 statisticalsoftware program, a design of experiment (DOE) analysis
has been done to study which parameter that influenced the
most.

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Japan, January 11-13, 2014

to the SS total from table 3, a simplified Pareto chart can beplot to show clearer contribution of each parameter and its
interaction among each other to the uniformity of gas phaseflow distribution as in Fig. 3. The abscissa shows thesources which are the parameters and its interaction
combinations. The ordinate shows the percentage ofcontribution to the uniformity of the gas phase flow(a) Stratified-flow inlet(b) Mist-flow inlet
Fig. 2 Standard deviations of the water distribution ratios σlTable 2 Summary of the selected analyzed parameters andits level.
Parameter

3 ANALYSIS RESULTIn this paper, an analysis of parameters or factors that
influenced the uniformity of flow distribution is discussed.
Seems the liquid superficial velocity has been observed
clearly that has a small influence to the uniformity of flow
distributions, it has been decided to focus the analysis withthese sources; (1) Flow pattern, (2) Backpressure, (3)Superficial air velocity (4) 2-way and 3-way interactions
between parameters. This analysis is important fordiscovery the most valuable parameter that contributed tothe uniformity of the flow distribution and the best settingof the parameters level to archive the lowest σg and σl.
As in the previous session, total of 36 combinations oftest configuration with 4 parameters has been done and σgand σl has been calculated and being plot as in Fig. 1 and 2.
Superficial water velocity jl has a minor influence to theuniformity of flow distribution and by considering only 3parameters as in table 2. The σg and σl that to be analyzed isreduces to 8 combinations. By, repeating the combination 3times which the first, second and third 8 is the test data at jl= 0.015 m/s, 0.030 m/s and 0.045 m/s respectively. Total
data to be analyze are 24 combinations. By using the“Design of Experiment” method, or in mathematics andstatistics study in area of “Analysis of Variance (ANOVA)”(Brooks, 1979), the analysis has been done by using a
program called Minitab 15 statistical software.
First, the result of gas phase flow distribution
uniformity analysis is addressed. An ANOVA table as in
Table 3 has been made by using General Linear Model in
Minitab 15 software from the 24 combination results of σg.
From the table, clearly shows only backpressure and jg are
the main parameters contribute to the uniformity of gas
phase flow distribution as the P-value in the table are lessthan 0.05. P-value, calculated from F ranging from 0 to 1. It
is a hypothesis test to check whether the parameter issignificant to the contribution of the uniformity of the flow
distribution or not. (P-value < 0.05≡significant, P-value >Flow pattern at header
entrance

Stratified-flow, Mist-flow

Backpressure

Non-uniform, Uniformjg (m/s)

1.0 m/s, 5.0 m/s

Table 3 Summary of the ANOVA table for σg.
Source

DF

SS

MS

F

P-valueflow pattern

10.000043

0.000043

0.08

0.7860backpressure

1

0.035378

0.03537863.30

0.0000jg

10.04134

0.0413473.97

0.00001

0.0002410.000241

0.43

0.521010.001049

0.001049

1.88

0.19001

0.000276

0.000276

0.49

0.49201

0.000552

0.000552

0.99

0.3350Error

160.008942

0.000559

Total

23

0.08782

flow pattern*backpressure
flow pattern* jg
backpressure* jgflow pattern* backpressure* jg

0.05≡not significant)For further understanding on how large the contributionof these parameters, by calculate the percentage of SS value©ICAROB 2014

Level

Fig. 3 Pareto chart of sources (parameters) contribute to theuniformity of gas phase flow distribution-285-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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Fig. 4 Cube plot with means value of σg at eachcombination of setting level for each parameter .

Fig. 6 Cube plot with means value of σg at eachcombination of setting level for each parameter.

Table 4 Summary of the ANOVA table for σl.

distribution. From the chart, the total of contribution by all
the parameters to the uniformity of flow distribution of gas
phase is 89.82%. The other 10.18% is the experimental
error or considered unknown. From this 89.82% of
contribution, the jg with 47.07% continuing withbackpressure with 40.28% contribute the most for theuniformity of gas phase. The other parameters and all theinteraction shall be classified as not significant to thecontribution of uniformity of flow distribution.
The next analysis is to find the best combinations ofparameters and its level to create the best setting for thebest uniformity of flow distribution. By using Minitab 15
software again with design of experiment cube plot tool, theresult yield as in Fig. 4. This cube plot shows the meanvalue of σg at each combination of the 3 parameters and its
level as in Table 2. From the cube plot, the smallest meanvalue of σg is 0.02720 which is located at left, back andupper side of the plot. Thus to ensure the σg at minimum
level, the setting of each parameter should be as follow.
Backpressure should be at non-uniform condition, jg should
be set at 5.0 m/s and flow pattern should be set withstratified flow. Noted, the flow pattern is considered a notsignificant parameter as explain in table 3 and Fig. 3, thus
make the differential of σg value between mist-flow andstratified-flow in Fig. 4 is relatively small.
Parameters that contribute the most to the uniformity ofliquid phase flow distribution shall be discussed next. As
previously explained, an ANOVA table from the 24
combination of parameters experiment σl result is been
made as in Table 4. Different from gas phase ANOVA table,
this table, the P-value for 3-ways interaction shows lower
value than 0.05, meaning that it is significant to thecontribution to the uniformity of liquid phase flow
distribution. Only one of the 2-ways interaction
combinations is not significant to the uniformity of flow

Source

DF

SS

MS

F

P-valueflow pattern

1

0.0038346

0.0038346

9.80

0.0060backpressure

1

0.0039629

0.0039629

10.13

0.0060jg
flow pattern* backpressure
flow pattern* jg
backpressure* jgflow pattern* backpressure* jg

10.0013102

0.0013102

3.35

0.08601

0.0002948

0.0002948

0.75

0.39801

0.0062611

0.0062611

16.00

0.00101

0.00470.004712.01

0.00301

0.0043281

0.0043281

11.06

0.0040Error

160.006261

0.0003913

Total

23

0.0309525

Fig. 5 Pareto chart of sources (parameters) contribute to theuniformity of liquid phase flow distribution©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

distribution, i.e. flow pattern and backpressure.
Nevertheless, jg P-value is higher than 0.05, however due toits interactions among other parameters significant to theflow distribution, makes jg significant as a main factor asothers.
Fig. 5 shows a Pareto chart of contribution percentage
to the uniformity of liquid phase flow distribution by its
main parameters solely and also by its parametersinteraction among each other. From the chart, the total ofcontribution by all the parameters and its interactions to theuniformity of flow distribution of liquid phase is 79.77%.
The other 20.23% is the experimental error. The chartshows that the 2-ways interaction of flow pattern and jg isthe most influenced to the uniformity of liquid phase flow
distribution that contributes 20.23% continue withcombination of backpressure and jg with 15.18%. From thisobservation of Fig. 5, it shows that the interaction amongparameters are more important than the parameter itself tothe contribution of the uniformity of liquid phase flow
distribution.
Since interaction contributed more than the parameter
itself, from the experiment to find the best setting for eachfactor that can sustain minimizing the standard deviation ofliquid by a design of experiment cube plot tool as explain
earlier. Fig. 6 is the result of cube plot for mean value of σl.
From the figure, to archive the smallest value of σl theparameters should be set as follows. Flow pattern should be
mist flow, the backpressure should be uniform conditionand jg should be set at 5.0 m/s.

(3) Also, the best setting of level of each parameter forgetting the best uniformity of liquid phase flow
distribution are:(a) Flow pattern at header entrance shall be Mist-flow.
(b) Backpressure condition shall be set at uniform.
(c) jg shall be at 5.0 m/s.
NOMENCLATURE
jsuperficial velocity (m/s)
Mmass flow rate (g/min)Ppressure at the exit of branch (kPa)DF
degree of freedomSSsums of squaresMSmeans of squares
F
F test, equality of variance between factor levels.
P-value P-values range from 0 to 1
Greek Symbols
σstandard deviation
Subscriptsggas phase or airlliquid phase or waterii th branch (i= 1…10)

REFERENCES[1] Zuradzman Mohamad Razlan, Ryota Isobe, Yasuhiro
Mizuno, Hiroaki Goshima, Masafumi Hirota, Naoki
Maruyama, “Gas-liquid Distributions in Upward Multi-passChannels of Compact Evaporator”, 14th International Heat
Transfer Conference (IHTC14), paper no. IHTC14-22588

4 CONCLUSIONSAn analysis of variances that contribute to theuniformity of gas and liquid phase flow distribution byusing a computer program called Minitab 15 statisticalsoftware has been made. The results are summarized asfollows.
(1)
24 selected standard deviation’s data has been
analyzed by using design of experiment method throughMinitab 15 statistical software. For uniformity of gas
phase flow distribution, the main parameter that
contributed the most is jg. For uniformity of liquid phaseflow distribution, it was the 2-way interaction between
flow pattern and jg that contribute the most.
(2) The best setting of level of each parameter for getting
the best uniformity of gas phase flow distribution are:(a) jg shall be at 5.0 m/s.
(b) Backpressure condition shall be set at non-uniform.
(c) Flow pattern at header entrance however shall beeither stratified or mist-flow.

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

An Empirical Examination of Inter-firm Capital Relationships in Maz
da’s Yokokai using the IDE Spatial Model
M. Sakamoto4, S. Tagawa1, T. Ito1, S. Matsuno1, R. Mehta2, V. Berdonosov3, and S. Ikeda4

1 Dept. of Business Administration, Ube National College of Technology, Yamaguchi, Japan(Tel : 81-836-35-7115; Fax : 81-836-35-7115)(tagawa@ube-k.ac.jp, ito@ube-k.ac.jp, matsuno@ube-k.ac.jp)
2 School of Management, New Jersey Institute of Technology, U.S.A(Tel :1-973-596-6419; Fax :1-973-596-3074)(mehta@njit.edu)
3 Faculty of Computer Science, Komsomolsk-on-Amur State University of Technology, Russia(Tel :7-962-287-51-41; Fax :7-4217-535259)(berd1946@gmail.com)
4 Dept. of Computer Science and Systems Engineering, University of Miyazaki, Japan(Tel : 81-985-58-7392; Fax : 81-985-58-7392)(sakamoto@cs.miyazaki-u.ac.jp)
Abstract: Just as workflow and transactional linkages, inter-firm capital relationships are similarly known to havean impact on corporate performance. Because of the superior performance of Japanese networks known as Keiretsu,
many companies have begun to recognize the importance of capital linkages whereas other companies have placedemphasis on improving coordination of managerial resources. In this paper, we review the literature on networkorganizations with a lens focused on examining the capital relationships in Yokokai, the Mazda’s Keiretsu. Employing
regression analysis, three significant indices, influence, degree, and efficiency, were selected from nine indices,
including others that include dyadic redundancy, dyadic constraint, effective size, constraint, hierarchy, and density. We
propose a new approach, called IDE spatial model, to calculate the strength of the inter-firm’s relationships. In order toascertain the rational inter-firm relationships, network indices and corporate performance are analyzed. Therefore, thisresearch suggests a new perspective to examine the rational inter-firm relationship that can be used in any networkorganization.
Keywords: influence, degree, efficiency, the IDE spatial model, Keiretsu
approaches of Keiretsu. Section 3 introduces the IDE
spatial model. Section 4 shows the results, and discusses
the implications of the results. Finally in Section 5 weconclude by a summary of this paper.

I. INTRODUCTION
Just as workflow and transactional linkages, interfirm capital relationships are similarly known to have an
impact on corporate performance. Because of thesuperior performance of Japanese networks known as
Keiretsu, many companies have begun to recognize theimportance of capital linkages whereas other companies
have placed emphasis on improving coordination ofmanagerial resources. In this paper, we propose a new
approach, called IDE spatial model, to calculate thestrength of the inter-firm’s relationships. In order toascertain the rational inter-firm relationships, network
indices and corporate performance are analyzed.
Therefore, this research suggests a new perspective to
examine the rational inter-firm relationship that can beused in any network organization.
This paper is organized as follows. In Section 2, we
briefly review some relevant literature of quantitative©ICAROB 2014

II. BACKGROUND
One of the important factors in structural analysis, astrong tie and/or weak tie is popular way to describe thedifferent strength of a relationship.
However, quantitative approaches are required to
discover the determinants of corporate performance.
Relationships among firms based on distances between
their locations and the frequency of face-to-facecommunication among engineers in the automobile
industry has been examined [1]. Moreover, Fukuoka et
al. reported a new finding on relationships among
member firms in Nissan’s using transactions and crossshareholdings [2]. Recently Ito et al. applied graphtheory to network organization analysis, and identified-288-

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some characteristics such as centrality, size of network
as being important facets of relationships [3].
Transactional inter-firm’s relationships are the key
concepts in these studies. In a Keiretsu, capital interfirm’s relationship is known as cross shareholding. To
the best of our knowledge, only a few studies haveexamined cross shareholdings from an organizational
network vantage point; therefore this investigation
attempts to shed light on one of the most advanced
quantitative analysis using data gathered from Mazda’s
Yokokai Keiretsu.

Degree expresses a firm’s potential communication
activity. In cross shareholding network, degree includes
two categories: in-degree and out-degree, because crossshareholding networks are considered to be asymmetricorganizations. In-degree refers to a firm accept
investment from other member firms, whereas outdegree reflects a firm that only buy stocks of other firms
within the network. Degree is calculated as below.
n

C D ( pk )   a( pi, pk )i 1

i  1 , 2 ,   , n ;k  1 , 2 ,   , n

where

a ( p i , p k )  1 ; if and only if pi and pk are connected

III. METHOD

by a line
Many spatial paradigms, such as the IDCBI modeland the DEC spatial model have been developed todiscern the relationship between network indices andcorporate performance. One of the key questions of thisinvestigation is to determine if all these models are
suitable for the analysis of capital network. In order to
identify the factors that impact corporate performance,
we employed regression analysis using three significant
indices, influence, degree, and efficiency, were selectedfrom nine indices. The others six are dyadic redundancy,
dyadic constraint, effective size, constraint, hierarchy,
and density. We propose a new approach, called IDE
spatial model, to calculate the strength of the interfirm’s capital relationships.

= 0; otherwise
In this paper, percent data of inter-firm’s transactions
are collected from Yokokai.
Efficiency is another important index. Effective sizeof the network is the number of alters that ego has,minus the average number of ties that each alter has toother alters. Suppose that A has ties to three other actors.
Further, suppose that none of these three has ties to anyof the others. Thus the effective size of ego's network is
three. Alternatively, suppose that A has ties to threeothers, and that all of the others are tied to one another.
A's network size is three, but the ties are "redundant"
because A can reach all three neighbors by reaching any
one of them. The average degree of the others in thiscase is 2 (each alter is tied to two other alters).
Therefore, the effective size of the network is its actual
size, i.e., 3, reduced by its redundancy, i.e., 2, to yieldan efficient size of 1. Efficiency expresses the effective
size of ego's network by its actual size. That is, what
proportion of ego's ties to its neighborhood is “nonredundant”? Thus, efficiency tells us how much impact
ego is getting for each unit invested in using ties. A firm
can be effective without being efficient; and an actor
can be efficient without being effective.

1.

Outline of the IDE spatial model
Generally influence means one kind of power toaffect persons or events, or causing something without
any direct or discernible effort. Influence reflects thepower to influence or have an impact on other member
firms directly and indirectly in a network. Consequently,
influence will be divided into two parts: direct influenceand indirect influence. Suppose that A is the matrix ofthe direct network, and An means the indirect influence
from one firm to another firm by n steps. Then influenceis calculated as follows.

2. Data collection and visualization
In order to measure influence, degree, and efficiencyof each firm in Yokokai, cross shareholding data in the
Yokokai Keiretsu in 1997 and 2004 have been collectedfrom our interviews and the publications of the Japan
Auto Parts Industries Association and Automotive Parts
Publishing Company [4].
In 1997, 53 financial institutions, 190 parts suppliers,and 11 carmakers are included in Yokokai. The detailed
information of the numbers of financial institutions,T  A  R  A  A2  A3 ・・・ An
 A( I  A) 1
where
T: Total influence;
A: direct influence;
R: indirect influence;
I: Identity matrix.
An integral index known as centrality has many
facets that include degree, closeness, and betweenness.
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parts-supplies, and carmakers in Mazda’ crossshareholdings network in 1997 and 2004 are shown in
Table 1.
Table 1 Numbers of financial institutions, parts suppliers and carmakers in Yokokai in 1997 and 2004
1997
2004
Financial Institutions

53

36

Parts-suppliers
Carmakers

190
11

177
11

Total

254

224

In this paper, a singleton, which can be found in any
network, is defined as a firm that exists independently,
without any relationship with other firms. Therefore, asingleton generally is an isolated firm in a given
network. In 1997, 132 singletons including 26 financial
institutions, 105 parts-suppliers, and 1 carmaker havebeen found. In 2004, 1 financial institution, 120 partssuppliers, and 2 carmakers are singletons. All of thesingletons have been removed in this paper because thenumber of singletons has no any impact on thecalculation results.
The cross shareholdings relationships among thecompanies were identified through three dimensionalmodeling. A bar shows the percentage of the transaction
between each pairs of firms. The three dimensional
capital inter-firm relationships in Yokokai are depicted
in Figure 1 and 2 respectively.
The cross shareholdings relationships among thecompanies were identified through three dimensionalmodeling. A bar shows the percentage of the transaction
between each pairs of firms. The three dimensional
inter-firm capital relationships in Yokokai are shown in
Figure 1 and 2 respectively.
Compared with Figure 1, we find that theinteractivity between financial institutions and partssuppliers is lower, whereas the interactivity between
parts suppliers is higher. The reason is that theinteractive relationship among financial institutions,
suppliers and carmakers changed rapidly with thevarying of economic environment. For suppliers, thecontrol from carmakers is becoming looser than before.
Now suppliers can more easily forge a new capital
relationship with other firms.

Figure 1 Inter-firm’s capital network in 1997

Figure 2 Inter-firm’s capital network in 2004

Figure 3 Out-degree and in-degree in 1997 and 2004

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IV. RESULTS AND DISCUSSIONSThe estimation of regression coefficients in 2004 has
been calculated also. Based upon Table 2 and thecalculation of 2004, influence, degree, and efficiency
are significant. Degree is negative, while influence andefficiency are positive. Degree is an index of number ofstockholdings, and influence is an index of structural
positions, then the hypothesis of “better structural
position, higher performance” is upheld.
The number of influence is decreasing. And thenumber of degree and efficiency are increasing. It
means that the importance of structural position is still
effective, but the capital relationship with other firms
and the managing cost of investment is becoming more
and more important because of the membership keiretsu
is loosening.

1.

Centrality changes
In an asymmetric network, degree can be divided
into two parts: out-degree and in-degree. The result of
the out-degree and in-degree of the whole crossshareholdings is shown in Figure 3.
The out-degree and in-degree in 2004 are lower thanthat in 1997. Lower centrality means that much morecompanies invest their money to buy parts suppliers
they interested. In other word, parts supplier can accept
much more different companies’ investment without
permission from the top firm of their Keiretsu.
Especially the dramatically declining of in-degree
inflects the structure changes happened in Yokokai.
2.

Determinants of profits
We calculated partial correlation coefficient of thenine indices in multiple regression model, and founddensity, effective size and other 4 indices are notsignificant. Thus we removed those 6 variables and reestimated the regression model. The results in 1997 areshown in Table 1.

V. CONCLUSION AND FUTURE WORKSIn this paper, the new approach of IDE analysis is
proposed, and its validity is proved. We ascertained that(1) the centrality of Keiretsu is becoming low, (2) not all
network indices, only influence, degree, and efficiency
has an impact on profits. (3) The importance of
influence is becoming lower, and the degree andefficiency is becoming higher due to the trend in theloosening of Keiretsu. However, additional research
advocated developing our knowledge on rational capital
relationships with other parts suppliers.

TABLE 1RESULTS OF THE VARIANCE ANALYSIS
Sum ofSquares

Degree ofFreedom

Mean
SquareTotal

4.239E+11

61

Regression

2.777E+113

9.257E+10

Prob.

Residual

1.462E+1158

2.521E+09

0.000

F Test36.721

Coefficient of determination: 0.65510
Multiple correlation coefficient: 0.80938
Adjusted R-square: 0.79829
AIC: 1,521.98
DW ratio: 2.5382

Acknowledgment: This research was partially
supported by JSPS KAKENHI Grant Number 24510217.

REFERENCES

As shown in Table 1, the coefficient of determination
is 0.65510, and DW ratio is 2.5382, and the probability
is 0.000. Therefore, the regression model is significant.
The estimation of regression coefficients is shown in
Table 2.

[1]Dyer H. J. (1996) “Specialized Supplier Networks as a
Source of Competitive Advantage: Evidence from the Auto
Industry”, Strategic Management Journal, Vol. 17, 271291[2]Fukuoka S., Ito T., Passerini K. and Sakamoto M.
(2006) An Analysis between Transaction and Cross
Shareholdings in the Keiretsu of Nissan, Managing
Information in the Digital Economy Issues & Solutions,163-169, IBIMA International Conference, Bonn Germany[3]Ito T., Tagawa S., Matsuno S., Uchida Y., Sakamoto M.,
Ikeda S., Mehta R. (2013) An Analysis of Network
Structure in Mazda’s Yokokai using the DEC Spatial
Model, Proceedings of the 2013 International Conference
on Business Administration, Marketing, and Economics(MAME 2013), Recent Advances in Business
Administration, Marketing and Economics, pp.77-81,
September 28-30, 2013, Venice, Italy[4]JAPIA&APPC (1997, 2004), Japanese Automotive
Parts Industry, Automotive Parts Publishing Company,(Japanese Edition)

TABLE 2 ESTIMATION OF REGRESSION COEFFICIENTSPartialregression
coefficientStandard
coefficient
T value
Degree ofFreedom
Prob.
Correlation
coefficientPartialcorrelation
coefficient©ICAROB 2014

Influence

DegreeEfficiency

Intercept

4.17 E+7-7.300E+4

5.575E+5-5.509E+5

82.896-82.398

0.289

0.000

8.536

-8.4873.465-3.455

58

58

58

58
0.001

0.000

0.000

0.001

0.47560.470-0.0180.746-0.744

0.414

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A Comparative Study of Inter-firm Transactional Relationship between Kyohokai and Yokokai
M. Sakamoto4, M. Hasama1, T. Ito1, Y. Uchida1, R. Mehta2, Y. Park3, and S. Ikeda4
1 Dept. of Business Administration, Ube National College of Technology, Yamaguchi, Japan(Tel : 81-836-35-7115; Fax : 81-836-35-7115)(hasama@ube-k.ac.jp, ito@ube-k.ac.jp, uchida@ube-k.ac.jp)
2 School of Management, New Jersey Institute of Technology, U.S.A(Tel :1-973-596-6419; Fax :1-973-596-3074)(mehta@njit.edu)
3 Dept. of Business Administration, Prefectural University of Hiroshima, Japan(Tel : 81-82-251-9826; Fax :-)(ecventure@pu-hiroshima.ac.jp)
4 Dept. of Computer Science and Systems Engineering, University of Miyazaki, Japan(Tel : 81-985-58-7392; Fax : 81-985-58-7392)(sakamoto@cs.miyazaki-u.ac.jp)

Abstract: Although two major Japanese manufacturers, Toyota and Mazda, compete in the automotive market, theircorporate performance, respectively, is different because the internal resources and the way they coordinate interrelationships among their network partners is known to be divergent. Both these two companies have their own
collaborating organizations, Kyohokai for Toyota and Yokokai for Mazda. Kyohokai, in contrast to Yokokai, is acentral-oriented organization with strong interactive cooperation among network partners. Consistent with the stream ofresearch on the coordination of internal resources and external inter-firm relationships, the purpose of this research is to
identify and contrast the best practices of Toyota to Mazda using a comparative approach. This paper reviews relevant lliterature on network organizations to focus on the differences between Kyohokai and Yokokai. Specifically, it makes acontribution by proposing a new perspective to identify the determinants of corporate performance and clarify thedifference among the external inter-firm’s relationships between Kyohokai and Yokokai, thus ascertaining the rational
structure of network organizations.
Keywords: out-influence, in-degree, sales, the OIS model, Kyohokai, Yokokai, Keiretsu
and Yokokai, thus ascertaining the rational structure of
network organizations.
This paper is organized as follows. In Section 2, we
briefly review some relevant researches of quantitative
approaches of Keiretsu. Section 3 introduces the OIS
spatial model. Section 4 shows the results, and discusses
the implications of the results. Finally, Section 5
provides conclusions and summarizes this study.

I. INTRODUCTION
Although two major Japanese manufacturers, Toyota
and Mazda, compete in the automotive market, theircorporate performance, respectively, is different because
the internal resources and the way they coordinate interrelationships among their network partners is known tobe divergent. Both of these two companies have theirown collaborating organizations, Kyohokai for Toyota
and Yokokai for Mazda. Kyohokai, in contrast to
Yokokai, is a central-oriented organization with strong
interactive cooperation among network partners.
Consistent with the stream of research on thecoordination of internal resources and external interfirm relationships, the purpose of this research is to
identify and contrast the best practices of Toyota to
Mazda using a comparative approach. This research
makes a contribution to the literature by proposing a
new perspective to identify the determinants ofcorporate performance and clarify the difference among
the external inter-firm’s relationships between Kyohokai©ICAROB 2014

II. BACKGROUND
Relationship, one of the important factors instructural analysis, has basically been analyzed using
quantitative and qualitative approaches. Nowadays,
quantitative approach is widely used due to thedevelopment of computer technology and mathematicaltheories. Quantitative approaches are required toascertain the structure of any given network. Interorganizational relationships in the Keiretsu have been
analyzed with quantitative analysis tools such as
CONCOR and other statistical methods [1].

-292-The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014indirect influence from one node to another node by nsteps. Then, influence is calculated as follows.

Furthermore, Fukuoka et al. reported a new finding in
relationships among member firms in Nissan’s Keiretsu
using transactions and cross shareholdings [2]. Recently
Ito et al. begin to apply graph theory to networkorganization analysis, and clarifies some characteristics
such as centrality, size of network [3-9]. A comparative
study is one of the useful tools to uncover the common
characteristics and differences in successful cases. After
reviewing the relevant literature of networkorganizations, we found that only a few comparative
studies have been published. Therefore, we focus on thecommon successful factors and differences between
Kyohokai and Yokokai in this paper.

T  A  R  A  A2  A3 ・・・ An
 A( I  A) 1
where
T: Total influence;
A: direct influence;
R: indirect influence;
I: Identity matrix.
Because transaction network is asymmetric,
influence will be divided into two parts: out-influenceand in-influence.
An integral index known as centrality has many
facets that include degree, closeness, and betweenness.
Degree expresses a firm’s potential communication
activity. In cross shareholding network, degree includes
two categories: in-degree and out-degree, because crossshareholding networks are considered to be asymmetricorganizations. In-degree refers to a firm accept
investment from other member firms, whereas outdegree reflects a firm that only buy stocks of other firms
within the network. Degree is calculated as below.

III. METHOD
In order to identify the determinants of supplierrevenues, we calculated the network indices including
influence, degree, effective size, efficiency, constrain,hierarchy, and density. We found that only influence anddegree are significant. Thus, we analyzed therelationship between two significant explanatory
variables: out-influence and in-degree, and explained
variable: sales, which is one measure of corporate
performance. Therefore, we propose a new approach,known as the OIS spatial model, to identify the common
factors and also to illustrate the differences between
Kyohokai and Yokokai.

n

C D ( pk )   a( pi, pk )i 1

i  1 , 2 ,   , n ;wherea ( p i , p k )  1 ; if and only if pi and pk are connectedby a line= 0; otherwise
Like influence, degree also will be divided into outdegree and in-degree. In this paper, percent data ofinter-firm’s transactions are collected from Yokokai.

1.

Outline of the OIS model
Generally, a graph consists of a set of nodes and aset of arcs. Two nodes are connected if a path between
these two nodes. Path is one of the important concepts
in graph theory. According to graph theory, a path is asequence of nodes such that the nodes and the arc are
adjacent. A walk is a sequence of nodes and arcs suchthat the nodes and arcs are adjacent. The difference
between path and walk is that a path is a walk that does
not include any node twice, except that it its first node
might be the same as its last. For digraphs, walks cantravel arcs only in the direction of the arrows.
In general, influence means one kind of powers toaffect persons or events, or causing something without
any direct or apparent effort. Influence reflects thepower to influence or have an impact on other member
firms directly and indirectly in a network. Consequently,
influence will be divided into two parts: direct influenceand indirect influence in a network. Suppose that A is
the matrix of the direct network, and An means the©ICAROB 2014k  1 , 2 ,   , n

2. Data collection
In order to ascertain the relationship between
network indices and corporate performance, transaction
data in the keiretsu of Kyohokai and Yokokai have beendrawn from personal interviews as well as the
publications of the Japan Auto Parts Industries
Association and Automotive Parts Publishing Company[10].
In 2002, 209 parts suppliers, and 11 carmakers areincluded in Kyohokai. A singleton can be found in any
network. Moreover, a singleton is defined as the node
exists independently, without any relationship withother nodes in this paper. Therefore, a singleton is an
isolated node in a given network generally. In Kyohokai,
90 singletons included. The detailed information of the-293-The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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IV. RESULTS AND DISCUSSIONS

numbers of parts-supplies, and carmakers in Kyohokai

1.
250

Centrality analysis
The overall centrality of Kyohokai and Yokokai is
calculated as follows.

209
181

200

Table 1 Out-degree and in-degree of Kyohokai and Yokokai

119

150

84

100

Out-degree

Suppliers

50

11

11

11

11

beforeremoving
singlstonafter
removoing
singleton

beforeremoving
singlstonafter
removoing
singleton

Carmakers

0

KyohokaiIn-degree

Kyohokai (Toyota)

0.341%

23.326%

Yokokai (Mazda)

49.915%1005.184%

Form Table1, it is evident that out-degree and indegree of Yokokai are larger than of Kyohokai. Highcentrality means the suppliers purchase their parts froma couple of specific companies. It also means thatcompared with Yokokai, the companies in Kyohokai
have more chances to purchase their parts. This could
possibly be the main reason why Kyohokai’s firm ismore efficient. Consequently, the hypothesis that “thelower the centrality, the higher the performance” holds.

Yokokaiand Yokokai in 2002 are shown in Fig. 1.
Fig. 1 numbers of financial institution, parts
The transactional relationships among thecompanies were identified through graph modeling. A
tie shows the percentage of the transaction between
each pair of firms. We collected directed and weighted
data to measure out-influence and in-degree of eachfirm. The transactional network of inter-firm
relationships in Kyohokai and Yokokai could be shownas Fig. 2 and 3 respectively.

2.

Determinants of sales
We calculated partial correlation coefficient of theseven indices in multiple regression models, and foundeffective size, density and other 3 indices are notsignificant. Thus, we removed those 5 variables andreanalyzed the regression model. The results are
displayed in Table 2.
TABLE 2 RESULTS OF THE VARIANCE ANALYSIS

Total

3.16E+13

Degreeof
Freedom81

Regression

7.70E+122

Sum ofSquaresFig. 2 Transactional network in 2002 in Kyohokai

Mean
SquareF Test12.722

3.85E+12

Prob.

Residual
2.39E+1379
3.02E+11
Coefficient of determination: 0.24361
Multiple correlation coefficient: 0.493561
Adjusted R-square: 0.47378
AIC: 2,415.89
DW ratio: 2.1997

0.000

From Table 2, the coefficient of determination is0.243610, and DW ratio is 2.1997. The probability is
0.000. Therefore, the regression model is significant.
The estimated regression coefficients are shown in Table
3.
Based upon Tables 3, out-influence, and in-degree
are significant. Out-influence is negative, and in-degree
is positive. Out-influence is an index of the structural
position of selling its parts, and in-degree an index ofpurchasing parts from other firms, then the hypothesisof “more purchasing power is associated with higher
performance” is upheld.

Fig. 3 Transactional network in 2002 in Yokokai©ICAROB 2014

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TABLE 3 ESTIMATION OF REGRESSION COEFFICIENTSPartialregression
coefficientStandard
coefficient
T value
Degree ofFreedom
Prob.
CorrelationcoefficientPartialcorrelationcoefficient

OutInfluence

InDegree-3.550E+5

6.489E+3

4.45187E+5-0.1697

0.46720.000-1.7337

4.7736

2.557

79

79

79

0.08690.000

0.0125-0.1595

0.4635-0.0971

0.4732[3] T. Ito, K. Passerini, M. Sakamoto (2008) Structure
Analysis of Keiretsu of Toyota, Encyclopedia of
Networked and Virtual Organizations, pp.1542-1548, Idea
Group Publishing[4]T. Ito, C. Medlin, K. Passerini, M. Sakamoto (2009)
Influence Trust and Trade in the Keiretsu of Toyota: A
Centrality Analysis, Trust, Globalisation d Market
Expansion, Chapter 8, pp.101-118, Nova Science[5]Ito T., Matsuno S., Xia Z., Sakamoto M., and Rajiv
Mehta (2010) An Analysis of Interactive Influence in
Mazda's Yokokai Keiretsu, Artificial Life and Robotics,
Volume 15, Number 3, 249-252, Springer Japan[6]Ito T., S. Matsuno, R. Mehta, M. Sakamoto, S. Ikeda(2013), An analysis of inter-firms distances and corporate
performance using DEC spatial model, Recent Advances inEnergy & Environment Integrated Systems, Proceedings ofthe 2nd International Conference on Integrated Systems andManagement for Energy, Development, Environment and
Health (ISMAEDEH ‘13), pp.80-85, April 23-25, 2013,
Morioka City, Iwate, Japan[7]Ito T., S. Tagawa, R. Mehta, M. Sakamoto, S. Ikeda(2013), A research on inter-firms relationship using
network-based DEA –A case study of Mazda Yokokai–,
Recent Advances in Energy & Environment Integrated
Systems, Proceedings of the 2nd International Conferenceon Integrated Systems and Management for Energy,
Development, Environment and Health (ISMAEDEH ‘13),
pp.92-97, April 23-25, 2013, Morioka City, Iwate, Japan[8]Ito T., M. Hasama, R. Mehta, M. Sakamoto, S. Ikeda(2013), Measuring efficiency of firms in Yokokai using the
IDCBI spatial model, Recent Advances in Energy &Environment Integrated Systems, Proceedings of the 2nd
International Conference on Integrated Systems andManagement for Energy, Development, Environment and
Health (ISMAEDEH ‘13), pp.116-122, April 23-25, 2013,
Morioka City, Iwate, Japan[9]Ito T., Tagawa S., Matsuno S., Uchida Y., Sakamoto M.,
Ikeda S., Mehta R. (2013), An Analysis of Network
Structure in Mazda’s Yokokai using the DEC Spatial
Model, Proceedings of the 2013 International Conference
on Business Administration, Marketing, and Economics(MAME 2013), Recent Advances in Business
Administration, Marketing and Economics, pp.77-81,
September 28-30, 2013, Venice, Italy[10]JAPIA&APPC (2005) Japanese Automotive Parts
Industry, Automotive Parts Publishing Company,(Japanese Edition)

Intercept

Basically, high purchasing power means a specificfirm is highly capable of producing and sellingcomponent parts.

V. CONCLUSION AND FUTURE WORKSIn this paper, the new approach of OIS model was
proposed. This investigation revealed that (1) “lower
centrality, higher performance” linkage holds. (2) Outinfluence has a negative impact on sales while in-degree
has a positive impact on sales. However, additional
research is suggested to develop the theory andknowledge base on rational relationship with other parts
suppliers. For instance, it is necessary to clarify theindividual firm’s position in transactional network to
improve its efficiency. The linkage between structural
position of each individual firm and their performanceis also advocated.
Acknowledgment: This research was partially
supported by the Ministry of Education, Culture, Sports,
Science, and Technology, Grant-in-Aid for Exploratory
Research, 21510171, 2009.

REFERENCES[1]Lincoln R. L. and Gerlach M.L. (2004) Japan’s
Network Economy Structure, Persistence, and Change,
Cambridge University Press[2]Fukuoka S., Ito T., Passerini K. and Sakamoto M.
(2006) An Analysis between Transaction and Cross
Shareholdings in the Keiretsu of Nissan, Managing
Information in the Digital Economy Issues & Solutions,163-169, IBIMA International Conference, Bonn Germany©ICAROB 2014

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A Path Analytic Model and Measurement of the Relationships between
Green Supply Chain Management Implementation and Corporate
Performance
S. Matsuno*, M. Hasama, Y. Uchida, and T. Ito
Ube National College of Technology, 2-14-1 Tokiwadai, Ube, Yamaguchi, Japan*matsuno@ube-k.ac.jp

Abstract: This paper proposes and empirically tests a model of the relationships between green supply chain
management (GSCM) activities and corporate performance. From the literature review, we identified five constructs,
namely, environmental commitment, supplier collaboration, supplier assessment, information sharing among suppliers,and business process improvement. These explanatory variables are used to form a structural model explaining theenvironmental and economic performance. The model was analyzed using the data from a survey of sample ofmanufacturing firms in Japan. The results suggest that the degree of supplier collaboration has an influence on theenvironmental performance directly. While, the impact of supplier assessment on the environmental performance ismediated by the information sharing and/or business process improvement. And, the environmental performance has apositive relationships on the economic performance.
Keywords: Green supply chain management, Corporate performance, Empirical study, Path modeling

effectiveness of GSCM implementation. We analyze thestatus of GSCM implementation at firms in Japan on thebasis of our original questionnaire survey and also
consider the relationship between GSCM activities andperformance, focusing on the role of inter-firm
information sharing in Section 3. Section 4 presents a
structural equation model concerning factors that affect
GSCM performance and use path analysis to test thevalidity of the model. Finally in Section 5 we conclude
by a summary of this paper.

I. INTRODUCTION
Green supply chain management (GSCM) is aninter-firm
collaborative
initiative
concerning
environmental load reduction and environmental
preservation throughout the entire life cycle of a product.
Against a backdrop of an international trend oftightening regulations on specific chemical substances,as typified by the Restriction of Hazardous Substances(RoHS) Directive and Registration, Evaluation,
Authorisation and Restriction of Chemicals (REACH)
in Europe, as well as social demands for environmentalload reduction and preservation, a heightening ofenvironmental awareness among consumers, and otherfactors, many firms have engaged in GSCM activities inrecent years [1]-[8].
However, along with the pursuit of economic
efficiency throughout the entire supply chain, which is
the main focus of conventional supply chain
management, GSCM also seeks to achieveenvironmental load reduction, and many issues related
to GSCM require investigation, for example,
examination of cost effectiveness. In addition, thedevelopment of inter-firm information systems andinformation transfer format standardization are urgently
required; REACH in Europe, for instance, requires thecommunication and sharing of chemical substances
information between agents/partners in supply chains.
This paper is organized as follows. In Section 2, we
briefly review some relevant researches about the©ICAROB 2014

II. LITERATURE REVIEWA previous research concerning effects obtained
from the implementation of GSCM is the empirical
study that Zhu and Sarkis (2004) conducted targeting
281 manufacturing firms in China [7]. They usedstatistical analysis methods to test effects that GSCM
has on environmental and economic performance andwhether quality control and JIT activities have amoderator effect on performance. Although the findings
showed that GSCM implementation has a positive effecton a firm's environmental performance, they did notprovide sufficient conclusion to measure whether theeffect on economic performance is positive or negative.
Large and Thomsen (2011) tested the effect that
GSCM has on environmental and purchasing
performance [2]. After identifying factors that affect
GSCM performance on the basis of literature review,
they used survey data on purchasing firms in Germany-296-

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Table 1. Status of GSCM implementation (n = 124)
Mean S.D.

to perform structural equation modeling and tested thevalidity of their model. Their findings showed thatgreen supplier assessment and active collaboration havea positive effect on environmental performance, and thatthey are promoted according to the strength of thepurchasing department's strategic role and commitment
to environmental management. The findings also
indicated that improvement in environmental
performance has a positive effect on purchasing
performance.
In light of the abovementioned previous research,
we engage in further explorative discussion concerning
GSCM activities and performance at firms in Japan,
focusing on the role that inter-firm information sharing
plays in GSCM implementation.

We
provideinformationand 4.02 1.32
exchange opinions concerning
environmental regulations.
We cooperate concerning the 4.47 1.46
prohibition or reduction of use ofregulated chemicals contained in
raw materials and components.
We cooperate in the development of 3.50 1.21
resource-saving
andresourcerecycling manufacturing processes.
We cooperate in improving the 3.65 1.42
efficiency of distribution andtransport systems (joint distribution
or modal shifts).
We cooperate in the review of 3.87 1.18
packaging and the reuse andrecycling of containers andpackaging.
We cooperate concerning eco-design 3.49 1.24
(eco-friendly design).
We engage in joint research or joint 3.31 1.42
developmentof
eco-friendly
products or materials with business
partners.
Note: 1 = We do not engage in it at all. 6 = We engage
in it very actively.

III. DATA AND ANALYSIS
1. Outline of the survey
We conducted a mail questionnaire survey from
November 2010 to February 2011 with the research
objective of investigating "the current state and issues insupply chain greening and inter-firm collaboration".
Since ordinarily the introduction and implementation of
GSCM is likely to be affected by firm size, we targeted
Japanese manufacturers with 500 or more employees inthis survey. The number of valid responses obtained
from randomly sampled firms was 126, but detailed
results are omitted here [9][10].

organizations in charge of environmental management
are particularly advanced. It is likely that this state ofinternal management systems development shows thestrength of overall commitment to environmental
management, including the implementation of GSCM.

2. GSCM implementation and internal management
First, we confirmed the status of implementation of
concrete inter-firm cooperation activities in GSCM (seeTable 1). We were able to ascertain that many firms
provide information and exchange opinions concerning
environmental regulations and cooperate concerning theprohibition or reduction of use of regulated chemicals
contained in raw materials and components. In addition,
we found that some progressive firms implemented thedetailed GSCM activities cooperate concerning ecodesign and engage in joint research or joint
development of eco-friendly products or materials withbusiness partners under inter-firm cooperation andcollaboration.
Then, we confirmed the state of development ofinternal management systems for overall environmental
management. On the whole, high scores were obtained
for all questions (based on a 6-point scale), and it was
found that the introduction of EMS and development of©ICAROB 2014

3. Supplier assessment
We analyzed what sort of requirements
manufacturing firms have for suppliers concerning
implementation of GSCM and, on the other hand, whatsort of assistance they provide.
We confirmed that many firms have a variety ofrequests for suppliers. For example, request that
suppliers understand each firm’s environmental
philosophy and requiring them to observe a code of
conduct or guidelines. Also, although the scores arecomparatively low, there are firms that conduct on-siteenvironmental inspections of suppliers and engage incontinuous auditing and monitoring, and support thedevelopment of suppliers' EMS.
4. GSCM implementation and information sharing
among firms
We examined the status of information
communication/exchange activities through information-297-

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improvement. However, firms do not enjoy the
reduction in purchasing and distribution costs.

networks among firms. Since ordinarily inter-firm
information
communication/exchange
through
information networks plays a major role in the effective
operation and implementation of conventional supply
chain management, it can be expected to play a similarrole in GSCM.
Information communication/exchange between
firms via information networks occurs mainly withrespect to information about purchase orderplacement/receipt
orsuppliers/customers,
andinformation about billing, payment, and settlement. At
the
same
time,
we
found
that
detailedcommunication/exchange between firms also occurs
with respect to information about components andmeasured values of regulated chemicals.
The background to this is likely progress in
preparations for and compliance with REACH, which
went into effect in June 2007 in Europe. REACH
requires communication and sharing of chemical
substances information between agents in supply chains.
Confirmation for matters such as the presence orabsence and the content of chemical substances subject
to regulation in molded articles is required, as is suchinformation from manufacturers as applications andamount used by application for each chemical substance.
5. GSCM implementation and performance
We confirmed awareness among firms of the statusof matters such as business process improvement
accompanying GSCM implementation. And we
confirmed awareness among firms of improvements inenvironmental and economic performance resulting
from GSCM implementation.
Here, we consider quantitative reduction of various
types of waste, regulated chemicals, and the like to beenvironmental performance, and reduction of various
costs, such as purchasing and distribution costs, to beeconomic performance. Although it is of course
necessary to also examine improvement in economic
performance on the basis of financial indicators such as
sales and profit margins, it is generally difficult toeliminate from these financial indicators the impact offactors other than GSCM implementation. Accordingly,
in this paper we perform analysis in which the conceptof economic performance is limited to theabovementioned cost reductions.
On the whole, above-average scores are given forboth environmental and economic performance©ICAROB 2014

IV. PATH ANALYSIS CONCERNING GSCM
ACTIVITIES AND PERFORMANCE
In light of the above analysis, we present a structural
equation model concerning factors that affect GSCM
performance [11]. The hypothetical path model isshown in Fig. 1. The model uses the seven composite
variables that is to say, Environmental commitment (5items), Supplier collaboration (7 items), Supplierassessment (6 items), Information sharing amongsuppliers (3 items), Business process improvement (3items), and Environmental performance (5 items) as
explanatory variables and Economic performance (4items) as the dependent variable, and assumes a causal
chain at multiple stages. We assume a positive path in
all relationships between factors.
Next, we provide an overview of the path analysis.
Here, we omit the factor analysis process for examining
the validity of the assumed latent variables and use thevalue obtained by dividing the total of the values of theitems pertaining to each latent variable by the number of
items. In other words, we use composite scales torepresent the latent variables. Table 2 shows thedescriptive statistics of each variable. For each scale,
Cronbach's α is greater than 0.8, and reliability (internalconsistency) is high.
We empirically test the path model using thesevariables, performing sequential multiple linear
regression analysis using the standard partial regression
coefficient as the path coefficient.
When we took Environmental commitment,
Supplier collaboration, and Supplier assessment as
Supplier
collaboration

Environmental
commitment

Supplier
assessment

Environmental
performance

Information
sharing amongsuppliers

Business process
improvement

Economic
performanceFig.1. Hypothetical path model-298-

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Table 2. Descriptive statistics of composite variables# of
α Mean S.D.
items
Environmental commitment

5

0.93

4.75

0.99

Supplier collaboration

7

0.89

3.77

1.03

Supplier assessment

6

0.88

3.64

1.19

Information sharing amongsuppliers
Business process improvement
Environmental performance

3

0.81

2.70

1.31

3

0.87

3.34

1.05

5

0.89

3.48

1.16

Economic performance

4

0.86

3.07

1.13

REFERENCES[1] Hoejmose S, Brammer S, and Millington A (2012),
Green supply chain management: The role of trust and
top management in B2B and B2C markets. Industrial
Marketing Management 41(4):609-620[2] Large R and Thomsen C (2011), Drivers of green
supply management performance: Evidence from
Germany. Journal of Purchasing and Supply
Management 17(3):176-184[3] Lopez-Gamero M, Molina-Azorin J, and ClaverCortes E (2009), The whole relationship between
environmental variables and firm performance:
Competitive advantage and firm resources as mediatorvariables. Journal of Environmental Management90(10):3110-3121[4] Matsuno S, Hasama M, and Ito T (2011), Greeningof the supply chain and its impact on information
sharing among firms based on the transaction costeconomics (in Japanese). Transactions of the Japan
Society for Production Management 17(3):11-16[5] Testa F and Iraldo F (2010), Shadows and lights of
GSCM: determinants and effects of these practices
based on a multi-national study. Journal of Cleaner
Production 18(10-11):953-962[6] Yang S, Lu C, Haider J, and Marlow P (2013), The
effect of green supply chain management on green
performance and firm competitiveness in the context ofcontainer shipping in Taiwan. Transportation ResearchPart E: Logistics and Transportation Review 55:55-73[7] Zhu Q and Sarkis J (2004), Relationships between
operational practices and performance among early
adopters of green supply chain management practices in
Chinese manufacturing enterprises. Journal ofOperations Management 22(3):265-289[8] Zhu Q, Sarkis J, and Lai K (2012), Green supply
chain management innovation diffusion and its
relationship to organizational improvement: An
ecological modernization perspective. Journal ofEngineering and Technology Management 29(1):168185[9] Matsuno S, Mikami Y, and Ito T (2012), An analysis
of green supply chain management practices and its
performance focused on the role of information sharing
among firms (in Japanese). Transactions of the Japan
Society for Production Management 18(2):11-16[10] Matsuno S and Tokinaga S (2012), Greening of the
supply chain and its impact on corporate networks:
Analysis based upon a questionnaire survey (inJapanese). Journal of Information and Management32(4):23-30[11] Matsuno S, Hasama M, Uchida Y, and Ito T (2013),
Green supply chain management activities andcorporate performance: Evidence from Japan. Proc. of
the 1st International Conference of Production
Management, in Ho Chi Minh, Vietnam

Note: each score on a 6-point scale
explanatory variables and Information sharing amongsuppliers as the explained variable, no significant
positive path from Supplier collaboration was found.
That is to say, some non-significant paths were found in
the causal chain between factors that affect GSCM
performance. However, all other paths were significant
at the 1% or 5% level. Also, all coefficients ofdetermination were significant, and the relationships
between factors assumed in this paper were almost
supported.

V. CONCLUSION
This paper proposes and empirically tests a model ofthe relationships between GSCM activities andcorporate performance. We identified five constructs,
namely,environmental
commitment,
supplier
collaboration, supplier assessment, information sharing
among suppliers, and business process improvement.
The results suggest that the degree of suppliercollaboration has an influence on the environmental
performance directly. While, the impact of supplierassessment on the environmental performance ismediated by the information sharing and/or businessprocess improvement. And, the environmental
performance has a positive relationships on theeconomic performance.

ACKNOWLEDGMENTS
This research was partly supported by the Japan
Society for the Promotion of Science, Grant-in-Aid for
Scientific Research (C) 24530445 and authors would
like to thank the organization.
©ICAROB 2014

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Japan, January 11-13, 2014

A Study of Open Source Cloud System for Small and Medium Enterprise
Y. Uchida1, S. Matsuno1, T. Ito1, M. Hasama1, and M. Sakamoto2
1

Dept. of Business Administration, Ube National College of Technology, Ube 755-8555, Japan
Tel : 81-836-35-7567; Fax : 81-836-35-7567
uchida@ube-k.ac.jp, matsuno@ube-k.ac.jp, ito@ube-k.ac.jp, hasama@ube-k.ac.jp
2
Dept. of Computer Science and Systems Engineering, University of Miyazaki, Miyazaki 889-2192, Japan
Tel : 81-985-58-7392; Fax : 81-985-58-7392
sakamoto@cs.miyazaki-u.ac.jp

Abstract: The use of cloud services among SMEs remains low, and R&D and technical support for SMEs is an urgent
problem. Even so, there have been almost no academic studies on the relationship between cloud computing and SME
information infrastructure, or on the future direction of this. With this background, we considered and designed a cloudsystem framework suitable for SMEs, built a prototype model and investigated the ease of implementation. Services
that deliver functionality equivalent to Amazon EC2 which is representative cloud service but delivered through othertechnologies are called EC2 clones. OpenStack is not EC2 clone, and is a typical open source project for providing
Infrastructure as a Service (IaaS) type cloud services. OpenStack was used in this study to implement a simple cloudservice model.
Keywords: Cloud, Open Source, Small and Medium sized Enterprise, OpenStack

we consider and design a cloud system framework
appropriate for small to medium sized enterprise, andinvestigate and consider the issues related to operation
and administration.

I. INTRODUCTION
Today’s information systems must be able tocontinue stable operation even during crises such as therecent Great East Japan Earthquake, or can be quickly
restored to operation. This is true even for the
information infrastructure of small and medium sizedenterprise (SME). This means that an ICT department
business continuity plan (BCP) [1], designed tocontinue operations under disaster and other emergency
situations, is essential to ensure preventative measures
to minimize impact and to quickly restore the mostimportant and most essential operations.
The “Survey on the State of Cloud Usage by Smalland Medium Sized Enterprise” (2011) [2], showed thatabout 10% of SMEs “use cloud computing,”
highlighting the urgent problem for R&D andtechnological support for SMEs. Even so, there havebeen almost no academic studies on the relationshipbetween cloud computing and SME information
infrastructure, or on the future direction of this.
Previously, we worked on a research project called“Open Source Information System Research andDevelopment for Supporting the IT Strategy of Smalland Medium Sized Enterprise” [Grant-in-Aid for
Scientific Research (C)] (2008-2010), and proposed amodel open source information system to support the IT
strategy of SMEs, and together with developing a
prototype, considered usage-related problems. Based on
the our findings from that research project, in this study,©ICAROB 2014

II. CLOUD SYSTEMS
1. Cloud computing
The United States National Institute of Standards
and Technology (NIST) defines “cloud” as a model for
enabling ubiquitous, convenient, on-demand network
access to a shared pool of configurable computing
resources, that can be rapidly provisioned and released
with minimal management effort or service provider
interaction [3].
Cloud computing includes the following fivecharacteristics:
・ On Demand and Self Services
・ Broad Network Access・ Resource Pooling・ Rapid Elasticity
・ Measured Services
Services can be categorized into the following threeservice types:
・ SaaS (Software as a Service: Functionality isdelivered over the network)
・ PaaS (Platform as a Service ： Application
development environment and customization
features are delivered over the network)-300-

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・ IaaS (Infrastructure as a Service: Even virtual
machines and operating systems are delivered overthe network)

2. Cloud research trends“Above the Clouds: A Berkeley View on Cloud
Computing,” [4] a report of the Reliable Adaptive
Distributed Systems Laboratory (RAD Lab) of theUnited States, highlighted ten obstacles facing cloudcomputing, including availability of service, data lockin, and data confidentiality and auditability.
In a Gartner report called “Survey of 2,014 global
CIOs,” [5] when asked to predict when more than halfof the information processing of the computers of theircompanies will be moved to the cloud, 53% of global
respondents said “by 2015,” but for Japan, only 25%
gave the same response. Nineteen percent of global
respondents said “after 2021” or “not possible tomigrate,” while the response was 43% for Japan. Thisshows that Japan is very late to the game in areas suchas cloud research and cloud implementation.
On the subject of research trends in Japan, other than acloud and public infrastructure study by the
Information-technology Promotion Agency [6], mostare studies related to security [7].

Fig.1. Cloud systems
refers to a web service that presents computing
capabilities in the cloud and that are easily scalable. It
can be used according to the following processes.
・ Immediately launch and execute by selecting apreconfigured template image. Or, create an Amazon
Machine Image (AMI) that includes applications,libraries, data, and related settings.
・ Configure security and network access on Amazon
EC2.
・ Select the desired instance type and operating
system, and using web service APIs or the various
administration tools that are provided, start, stop andmonitor the AMI instance as much or as little asneeded.
・ For each instance, decide whether to run frommultiple locations, use a fixed IP end point, or add
robust block storage.
・ Pricing is based on actual resources consumed, suchas total time the instance was run or amount of datatransferred.
Of these processes, the AMI selection process is shownin Fig.2.

3. Cloud computing
Cloud systems can be categorized as shown in Fig.1.
In particular, depending on the cloud system service
architecture, they are configured according to thefollowing three subsystems: SaaS (Software as aService: functions are provided over the network), PaaS
(Platform as a Service: application development
environment, customization functions are provided overthe network), and IaaS (Infrastructure as a Service: evenvirtual machines and operating systems are provided
over the network). For open source cloud system
implementations, OpenStack Compute (Nova) is an
example of a resource toolset, while OpenStack Object
Storage (Swift) is an example of a cloud storage toolset.
Even SMEs can easily use these open source projects todevelop tools that provide functionality similar to(functional compatibility) Google Apps or Amazon EC.

Fig.2. AMI selection
4. Amazon EC2
Amazon EC2 is a good example of IaaS. Amazon
EC2 [8] stands for “Amazon Elastic Compute Cloud.” It©ICAROB 2014

5. OpenStack
OpenStack [9] is a representative open source to-301-

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IV. Simple cloud model by OpenStackoffer IaaS type cloud service. OpenStack is IaaS cloudcomputing project begun in 2010 by Rackspace Inc.
[10] and NASA (National Aeronautics and Space
Administration) [11]. In 2012, nonprofit organization"OpenStack Foundation" [12] was established. A
component to operate it in GUI includes OpenStack
Dashboard (horizon) to show it in Fig.3, and OpenStackmanages compute, storage, and networking resources.
The most recent version at the time of 2013-11-21 of
OpenStack is “OpenStack 2013.2 (Havana)”.

1. Prototype system
Table 1 shows the specifications of the prototype
system tested in this study.
Table 1. Prototype system specifications
Form factor
Space saving desktop
CPUIntel Core i3 processor (withvirtualization support)
Memory4GB
Hard disk
200GB
OS
CentOS 6.4（x86_64）
Linux kernel
2.6.32Cloud platform OpenStack 2013.2 (Havana)
software
2. Installation of RDO
By installing RDO, the cloud environment can beused as follows.
・ Install of Software repository# sudo yum install -y http://rdo.fedorapeople.org/ope
nstack/openstack-grizzly/rdo-release-grizzly-3.noarch.r
pm

Fig.3. OpenStack Dashboard

・ Install of the Packstack Installer# sudo yum install -y openstack-packstack

III. RDO

・ Install of OpenStack using Packstack# packstack –allinone

RDO [13] is a community of people using anddeploying OpenStack on Red Hat and Red Hat-based
platforms. Community site openstack.redhat.com of
RDO was established like Fig.4.

Now its single node OpenStack instance is up a
nd running.
・ Visit the Dashboard
Log in to the Openstack dashboard at http://localho
st/dashboard - the username is "admin".
・ Enabe SSH
Adding Port "22" on the default security group
・ Key pair
Create or impor a key pair・ Add an image"Fedora19" in the "http://cloud.fedoraproject.org/fedor
a-19.x86_64.qcow2" [14]・ Launch the instanceIn the main portion of the screen, click the "Launch" button for the "F19" image.

Fig.4. openstack.redhat.com of RDO©ICAROB 2014

・ Associate Floating IP
In the main portion of the screen, followed by the"Associate Floating IP" link for the instance you ju-302-The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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[ 4 ] Electrical Engineering and Computer Sciences(University of California at Berkeley) ,Above the Clouds: A Berkeley View of Cloud Computing . Technical Report No.UCB/EECS-200928, http://www.eecs.berkeley.edu/Pubs/TechRpts/20
09/EECS-2009-28.html[5] Gartner (2011), Worldwide Survey of 2,014 CIOs
(In Japanese). press release,
http://www.gartner.co.jp/press/html/pr2011030301.html[6] Information-technology Promotion Agency (2013),
Report – “Research on Cloud Computing and Public
Infrastructure ( i n Japanese) .
Informationtechnology Promotion Agency, Japan[ 7 ] Hori et. al (2009) , Cloud Computing Security
Research Trends ( i n Japanese) . Information
Processing Society of Japan Report CSEC“Computer Security” 2009-CSEC-47(4):1-6[ 8 ] Amazon , Amazon Elastic Compute Cloud(Amazon EC2). http://aws.amazon.com/jp/ec2/[ 9 ] The OpenStack Foundation , OpenStack Open
Source Cloud Computing Software. http://www.ope
nstack.org/[10] Rackspace, US Inc., Rackspace: The Leader inHybrid Cloud. http://www.rackspace.com/[11] National Aeronautics and Space Administration,NASA. http://www.nasa.gov/[12] The OpenStack Foundation, Foundation »OpenStack Open Source Cloud Computing Software.
http://www.openstack.org/foundation/[13] Red Hat, Inc., RDO. http://openstack.redhat.com/[14] The Fedora Project, Fedora Project Homepage.
http://fedoraproject.org/en/

・ SSH to the Instance$ ssh -l root -i my_key_pair.pem floating_ip_address
The above steps enable an instance to be presented(see Fig.5).
Fig.5. A running instance

V. CONCLUSION
We considered and designed a cloud systemframework
suitablefor
SMEs,
performed
implementation testing and tested operation. In the
future, it will be necessary to run the prototype system
and investigate and consider issues related to operation
and administration.

ACKNOWLEDGMENT
This work was supported by JSPS KAKENHI Grant
Number 24530444.

REFERENCES[1] Asian Disaster Reduction Center (2012), BCP status of the Private Sector in the APEC Region
2012. Report:1-16, http://www.adrc.asia/publicatio
ns/bcp/survey_2012.pdf[2] Information-technology Promotion Agency (2011),
Factual Investigation on Cloud Usage by Small to
Medium Enterprise .
Information-technology
Promotion Agency (in Japanese)[3] National Institute of Standards and Technology,
The NIST Definition of Cloud Computing .
http://csrc.nist.gov/publications/nistpubs/800145/SP800-145.pdf©ICAROB 2014

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A Visualization of Patent Strategies in Japanese ICT Companies
Based on Text-Mining
T. Tokumitsu1, T. Okada1, I. Nakaoka2 and Y. Park4

1 Advanced Course of Management Information Engineering,
Ube National College of Technology, Yamaguchi, Japan(tokumitsu0310@gmail.com, takuya.takuya.7391@gmail.com)
2 Dept. of Business Administration, Ube National College of Technology, Yamaguchi, Japan(Tel : 81-836-35-5130)(nakaoka@ube-k.ac.jp)
3 Dept. of Business Administration, Prefectural University of Hiroshima, Japan(Tel :81-82-251-9826)(ecventure@pu-hiroshima.ac.jp)
Abstract: Japanese ICT companies had kept strong competitive advantage until the collapse of the bubble

economy which occurred in the early 1990s. Since then, most of the companies still have been struggling withpoor performance. The companies have implemented various management reforms for the last twenty years in order torecover competitive advantages. Although effects by those attempts appeared for the short-term, recovery of thecompetitive advantage did not continue for the long-term. We propose following two big reasons for continued
slump and repeated failures in their turnaround. First, the Japanese companies lagged behind changes ofconsumer needs. Second, they had implemented wrong technology innovation strategies. Therefore, we recognize
the necessity to understand issues of technology innovation strategies of Japanese ICT companies based on patentdocuments recognized as an ample source of technical and commercial knowledge. In this paper, we disclose andanalyze patent strategy of Japanese large ICT companies, Sony, Sharp and Panasonic in smart phone market bycorrespondence analysis and self-organizing maps.
Keywords: patent strategies, visualization, F-term, correspondence analysis, self-organizing maps
market [3]. We consider that Japanese ICT companies
have implemented wrong technology innovation
strategies. Impairments of their ability of research anddevelopment are unlikely because 28 of Japanese ICT
companies are included in the world 100 majorinnovative companies in 2012, moreover Japanesemajor ICT companies such as Sony, Panasonic, Sharp,
NTT and Nidec Corporation belong in the higher rank.
Instead we hypothesize two reasons for their long-termslumps. Firstly, their technology innovation strategies
have not led to their turnaround. Secondly, their major
products did not changes from products whose needs
were decreasing such as feature phone while leading
products in ICT industry were changing to products in
new field such as smart phone. In order to test the
hypotheses, we need to compare Japanese ICT
companies and some other foreign ones in technology
innovation strategies.
As our exploratory approach, we focus on Japanese
three ICT companies, Sony, Sharp and Panasonic, andclarify their patent strategies in smart phone market.

I. INTRODUCTION
Japanese ICT companies had sustained theircompetitive advantages until the early 1990s, but mostof them have lapsed into poor business performances
caused by the collapse of the bubble economy since1991. In order to recover their competitive advantages,
they have restructured their operations such as corporate
downsizings, business reconstructions and reexaminations of long-term transactional relationships
with their cooperative firms for last twenty years.
However, the restructurings did not lead to long-term
effects on their competitive advantages. The following
three reasons are discussed. First, Japanese ICT
companies have the innovator’s dilemma by pursuingexcessive qualities [1]. Second, the structural model ofmost digital consumer products has change from theintegral architecture to the module architecture [2].
Third, they failed to capture values from their new
products developed with their hard efforts because of
the rapid price reduction in digital consumer product©ICAROB 2014

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Since Apple launched iPhone in 2007, the market
structure has dramatically changed from feature phone
to smart phone. Therefore, we visualize characteristics
and transitions of their patent strategies associated withsmart phone since 2000, and discuss the results.
This paper is organized as follows. In Section 2, we
briefly review some relevant researches of visualizationof patent analyses. Section 3 shows our exploratory
analyses by correspondence analyses and selforganizing maps and the results. Finally in Section 4 we
describe conclusion of this paper.

are browsed and retrieved ubiquitously. All patents areclassified according to each three classification codes,
IPC(International Patent Classification), FI(File Index)
and F-term(File Forming Term). Both of FI and F-term
are classification codes for segmentalizing IPC.
Especially, F-term is given to every patent from F-term
list based on the technical items indicated in patentdocuments. Thus, almost every patent has multi-F-term
code. This paper refers to patents with F-term related tosmart phone described in “The technical development
actual condition analysis investigation report about thesmart phone according to patent document analysis(patent map)” [7]. Top 20 most frequently used F-terms
in smart phone industry are shown in Table 1.

II. BACKGROUND
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.
YG. Kim et al understand advances of emerging
technologies and forecast its trend in the future in thefollowing way. First, Keywords from patent documents
related to a target technology field are extracted. Second,
patent documents with the keywords are clustered by kMeans algorithm. Third, a semantic network of
keywords is formed. Final, a Patent Map is created [4].
B. Yoon et al. note that citation analysis is subject tosome crucial drawbacks and propose a network-based
analysis, an alternative method for citation analysis.
Such new indexes as technology centrality index,
technology cycle index, and technology keyword
clusters are suggested for in-depth quantitative analysis[5].
Y.H. Tseng et al. describes a series of text mining
techniques that conforms to the analytical process usedby patent analysts. These techniques include text
segmentation, summary extraction, feature selection,
term association, cluster generation, topic identification,and information mapping [6].
However, there are few researches focusing on
management strategies based on text-mining analysis ofpatents. Therefore, this paper describes technology
innovation strategies of Japanese ICT companies basedon the analysis.

Table 1 Top 20 most frequently used F-terms
in smart phone industry
Contents

5K127

Telephone function

5K027

Telephone circuits

5K067

Mobile radio communication systems

5K201

Telephonic communication services

5K023

Telephone set structure

5K101

Telephonic communication linked with other devices

5C122

Studio devices

5B084

Information transfer between computers

5E501

Digital calculator user interface

5C164

Two-way televisions, distribution of moving picture or the
like

5K036

Display and transmission of telephone numbers

5B087

Position input by displaying

5B020

Input from keyboards or the like

5C022

Studio equipment

5K048

Selective calling equipment (for remote control andmeasurement)

5C025

Television transmitter and receiver circuitry

5C082

Control or circuits of indicating devices

5C087

Alarm systems

5B058

Card readers/writers or multifunction peripheral apparatus

5K024

Special services for exchanges 2

1.

An approach based on the number of patentpublications
As first our approach, the numbers of patentpublications associated with smart phone in each of thethree companies are shown in Figure1.

III. ANALYSIS
In this section, patents strategies of Japanese ICT
companies are analyzed. In Japan, patent documents arearchived in IPDL (Industrial Patent Digital Library), and©ICAROB 2014

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The numbers of patent publication in Sharp andPanasonic tend to increase slightly in Figure1. As
contrasted to the two companies, the number of Sony
increases steadily until 2009, since then, substantiallydecreases. The decreasing in the number of their patentpublications is caused by a decreasing in the number of
their patent applications. We consider that investment
into research and development is decreased in Sony.

Figure 3 Correspondence analysis result of Sharp

Figure１ The number of patent publications
2. An approach by the correspondence analysis
We disclose their patent strategies based on typicalwords associated with smart phone by thecorrespondence analysis. Results of the analyses ofSony, Sharp, Panasonic and the total of the threecompanies are shown in figure from 2 to 5. The
reference data in the analysis is the numbers of each
their patent document in each year, including nouns
shown in Table 1. These figures are based on dates ofpatent publication, and patents are applied to products inthe companies.
Focusing on each of their strategies, we reveal theircharacteristics and differences. In figure2, every wordcollects vertically around 2004.

Figure 4 Correspondence analysis result of Panasonic

Figure 5 Correspondence analysis result of eachcompany
Moreover, many of the words are close to each other in2007 and 2008. We deduced that it is because they
responded to changes of smart phone industry in 2007
when iPhone was launched. In figure 3 and 4, every
word is plotted horizontally. That is, Sharp andPanasonic are not likely to intensively respond to thechanges. We deduce that the two companies invested
most of their management resources in other businesses
such as TV. Compared with Sharp and Panasonic, Sonyhas close connections with more of the words in figure5.

Figure 2 Correspondence analysis result of Sony

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Therefore, we consider that Sony addressed various
technology developments for adapting to changes of themarket. In addition, we need to analyze camera from theother perspective because its word is far from the othersin each of the companies and the years.

various attempts for adapting to change of the market.
Sharp and Panasonic are likely to be exploitationcompanies utilizing their own past strategies rather thanadapting to the market changes.
Our three future works are as follows. We analyze
foreign ICT companies such as Samsung and Apple inthe same way, and compare them with the threecompanies. Furthermore, the results in this paper need
to be compared with results based on existing methods
for business analysis and their attempts conducted in thereal world.

3. An approach by self-organizing maps
As presented above, annual connections between
keywords used in contents of the only 20 F-terms andeach of the 3 companies, Sony, Sharp and Panasonicwere established by correspondence analysis. However,
above results are not comprehensive. Therefore, we
conduct analyses covering 160 words from top 100 Fterms as one of exploratory approaches. Self-organizing
maps（SOM）is used for more multidimensional data
analysis[8]. A result of the three companies is shown infigure 6. In addition, colors of nodes depend on thenumber of patent publications including “CAMERA”.
That is, the deeper used pink is, the more the number is,and the deeper used blue is, the fewer the number is.

Figure 6

REFERENCES[1] Christensen C. M. “The Innovator's Dilemma: The
Revolutionary Book That Will Change the Way You Do
Business”, HarperCollins (2003)[2] Oshika T. and Fujimoto T. “Empirical analysis ofinternational trade theory and Product Architecture
Theory”, RIETI Discussion Paper Series 06-J-015 (2006)(in Japanese)[3] Nobeoka K., Ito M. and Morita K. “Case of digitalconsumer electronics: the failure of value capture by thecommoditization”, RIETI Discussion Paper Series 06-J017 (2006) (in Japanese)[4] Kim Y.G., Suh J.H. and Park S.C. “Visualization ofpatent analysis for emerging technology”, Expert Systems
with Applications 34 (2008) 1804–1812[5] Yoon B.G. and Park Y.T. ”A text-mining-based patent
network: Analytical tool for high-technology trend”,
Journal of High Technology Management Research 15(2004) 37–50[6] Tseng Y.H., Linb C.J. and Linc Y.I. “Text mining
techniques for patent analysis”, Information Processingand Management 43 (2007) 1216–1247[7] Patent Tec “The technical development actual
condition analysis investigation report about the smart
phone according to patent document analysis (patentmap) CD-ROM version” (2012) (in Japanese)[8] T.Kohonen ”Self-Organizing Maps”, Springer (2000)

SOM Map of each companies

In figure 6, each company of the years gathers into
small area as to Sharp and Panasonic. However, each of
the years is plotted widely as to Sony. Thus, Sony has
variously changed their technology innovation strategies
in order to adapt to environment changes. Moreover,
Panasonic and Sharp since 2008 have increased theirpatent publications associated with “CAMERA”. We
consider that Sharp and Panasonic conduct incremental
innovations. That is, they have continued own previous
strategies.

V. CONCLUSION AND FUTURE WORKSIn this paper, we visualized patent strategies of large
ICT companies, Sony, Sharp and Panasonic, in Japanesesmart phone market by the correspondence analysis andSOM. From the results, we revealed the differencesamong the three companies in the market. SONY is
likely to be an exploration company implementing©ICAROB 2014

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Dissipative Particle Dynamics Simulation for Vesicle Shape Change
Yoshiyuki Oofuji1, Naohito Urakami1, Masayuki Imai2, Takashi Yamamoto1

1

Department of Physics and Information Sciences, Yamaguchi University, Yamaguchi 753-8512, Japan2
Department of Physics, Tohoku University, Sendai 980-8578
Tel: 81-83-933-5690; Fax: 81-83-933-5690
urakami@yamaguchi-u.ac.jp

Abstract: Spherical vesicles can change to various shapes such as oblate, prolate, stomatocyte-like, and starfish-like,according to the osmotic pressure difference between the inner and outer vesicles. The shape changes of vesicles arevery important for understanding the activities of living cells. In this study, we investigated the process of vesicle shapechange by carrying out dissipative particle dynamics simulations. We prepared spherical vesicles in which thedifference between the numbers of lipids forming the inner and outer leaflets (ΔN) varied. If ΔN was small, with adecrease in the number of water beads inside a vesicle (Nw), a transformation from sphere to oblate, then oblate tostomatocyte-like was observed. If ΔN was large, a transformation from sphere to prolate, then prolate to tube-likeoccurred. The vesicles shape changes were in good agreement with the experiments. To investigate the mechanism ofvesicle transformation in detail, we performed simulations by moving lipids between the inner and outer vesicle leaflets
to vary ΔN. As a result, a transformation between the prolate and oblate vesicles was observed. These results indicatedthat the shapes of vesicles were determined by ΔN and Nw.
Keywords: Vesicle Shape Change, Biomembrane, Dissipative Particle Simulation, Area Difference Model
The area difference elasticity (ADE) model [9] hasbeen suggested as a means of understanding the various
shapes of vesicles. The ADE model introduces two
important parameters: an excess area and an intrinsic
area difference. The excess area is the area-to-volume
ratio, and the intrinsic area difference is determined by
the number of lipids in the outer and inner leaflets. The
vesicle shapes are determined by minimizing the sum of
the bending energy of the membrane and the energy
resulting from the intrinsic area difference, and various
vesicle shapes were interpreted on the basis of the ADEmodel. However, it is difficult to compare thetheoretical results with the experimental results becauseof the difficulty in counting the number of lipids in the
two leaflets.
Such difficulty motivated computational studies ofvesicles. In the past decade, many computational studies
on vesicles have been reported. For instance, vesicle
formation from random state [10], formation of lipid
domains and budding process [11, 12], and fusion andfission of vesicles [13] have been reported using
dissipative particle dynamics (DPD) simulations [14,15]. In addition, the vesicle shapes consisting of triblock
copolymers [16] and the transformation of vesicle
shapes in small systems [17] were investigated by DPD
simulations. Therefore, DPD simulation is an efficientmethod of investigating the shape changes of vesicles.
In this work, we carried out DPD simulations to
reproduce the various shapes of vesicles observed in theexperiment. The transformation of vesicles from aI. INTRODUCTION
Lipid molecules are the main constituent of
biomembranes, and they can self-assemble into various
types of structures such as micelles, cylinders, andbilayers (lamellae or vesicles) in an aqueous solution.
Vesicles have a closed surface with a bilayer structure,and they have been used as a model system of
biomembranes in many experiments designed toinvestigate the various phenomena observed in living
cells, such as the formation of lipid domains [1-4] on
the membrane and the diffusion of lipids [5] in themembrane. In addition, the vesicle is a strong candidate
for the transport of drug components across the cell
membrane, and it plays an important role in cosmetic
and food materials.
One of the fascinating properties of lipid membranes
is that they form various shapes; they may be spherical,
prolate, oblate, stomatocyte-like, starfish-like, etc. [6-8].
Moreover, the vesicles change their shape according toexternal stimuli. For example, depending on the osmoticpressure difference between the inner and outer vesicles,
the water molecules move from the inside to the outside,
and a spherical vesicle thus becomes oblate or prolate.
With further reduction in the internal volume of thevesicle, an oblate vesicle becomes shaped like a
stomatocyte or starfish. In contrast, a prolate vesiclechanges shape to a tube or pear [6, 7]. In this way, thevesicles transform to various shapes.

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Lipid molecule

(a)

Water

Fig. 1 Illustrations of coarse-grained models used in our
simulation. The lipid molecule is represented by onehydrophilic head bead (H) and three hydrophobic tailbeads (T). The water molecule is represented by a singlehydrophilic bead (W).

Nw = 59,924
ΔN = 336(b)

Nw = 45,976
ΔN = 428

Nw = 30,978
ΔN = 478

Nw = 60,020
ΔN = 997

Nw = 40,023
ΔN = 970

Nw = 30,987
ΔN = 978spherical shape that accompanied a decrease in theinternal volume of the vesicle was investigated.
Moreover, the transformation of vesicles induced by theflip-flop of lipids between the outer and inner leaflets
was also explored.

Fig. 2 Shape changes from spherical vesicles, with adecrease in the number of water beads in vesicles. In theinitial spherical vesicles, (a) (Nw; ΔN) = (59,924; 336)
and (b) (60,020; 997). Water beads are not displayed forclarity.

II. SIMULATION MODELS AND METHODS

where r0 is the cutoff radius, and θij(t) is a randomvariable satisfying the following:

We used coarse-grained models for lipids and watermolecules. As shown in Fig. 1, the lipids wererepresented by one hydrophilic head bead (H) and threehydrophobic tail beads (T), and the water molecules
were represented by a single hydrophilic bead (W). All
beads have the same mass m. The numbers of lipid andwater molecules were 10,000 and 400,000, respectively.
In the DPD simulation, the position and velocity ofeach bead i are denoted by (ri , v i ) . The time evolution
is governed by Newton’s equation of motion. The forces
acting between the i-th and j-th beads are a conservative
force Fij(C), a dissipative force Fij(D), and a pairwise
random force Fij(R). These three forces are given as
follows:
Fij(C)  aij  (rij )rˆij ,Fij(D)   2 (rij )( rˆij  v ij )rˆij , ij (t )  0
 ij (t ) kl (t )  ( ik  jl   il  jk ) (t  t ).

The spring force for lipids is given by the equation:(5)
Fij(S)  C (1  ri,i 1 / b)rˆi,i 1where ri ,i 1 denotes the distance between connected
beads, b is the equilibrium bond distance, and C is theforce constant.
The parameters aij was set to 25ε for betweenhydrophilic beads (H and W) and for betweenhydrophobic beads (T), where ε is the energy unit. For
between hydrophilic and hydrophobic beads, aij was set
to 200ε. The noise parameter σ was set to 3.0 and kBT =ε. In the spring force, C = 100ε and b = 0.45r0. The
number density was 3.0r0-3. For the other simulation
parameters, the same parameters as in the reference [12]were used. Our simulations were carried out using the
COGNAC within the OCTA program [18].
In our simulations, in order to investigate the shapechanges of vesicles, we varied two parameters: thenumber of water beads inside a vesicle, Nw, and thedifference between the number of lipids, ΔN = Nout - Nin,
where Nout and Nin denoted the number of lipids in theouter and inner leaflet of a vesicle, respectively. The
parameters Nw and ΔN correspond to the excess area andthe intrinsic area difference in the ADE model. Nw was
reduced by moving the water beads inside a vesicle tothe outside, and ΔN was varied by the flip-flop of thelipids between the inner and outer leaflets.

(1)

Fij(R)   (rij ) ij (t )rˆij ,where aij is the maximum repulsion force between i-thand j-th beads, rˆij  rij / rij , rij  ri  r j , rij  rij , andv ij  v i  v j . The parameters γ and σ are related to eachother by the fluctuation-dissipative theorem: 2  2k BT ,(2)where kB and T are the Boltzmann constant and thethermostat temperature, respectively. The weightfunction ω(r) is chosen as follows:1  r / r0 r  r0(3)
 (r )  r  r0 0©ICAROB 2014

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014(a)Flip-flop

Nw = 40,023
ΔN = 970(b)

Flip-flop

Nw = 36,039
ΔN = 662

Flip-flop

Flip-flop

Nw = 36,039
ΔN = 452(a)

Nw = 36,037
ΔN = 273(b)

ΔN=1246
Nw=40,592

Relaxation(c)

Nw = 40,977
ΔN = 485

Nw = 40,974
ΔN = 1095

Nw = 40,975
ΔN = 1096(d)

Nw = 40,975
ΔN = 1096

ΔN=618
Nw=30,981

Fig. 3 Transformation of vesicle shapes by changing ΔN.
(a) Tube-like vesicle transforms to stomatocyte bymoving the lipids from the outer to the inner leaflets in a
step-by-step manner. (b) Stomatocyte vesicle transforms
to a triangular oblate shape by moving the lipids fromthe inner to the outer leaflets at the first step.

ΔN=1095
Nw=45,024

Fig. 4 Various shapes of vesicles obtained by our
simulations by changing Nw and ΔN. (a) Starfish-likeshape, (b) dumbbell, (c) torus, and (d) racket.
2. Transformation of vesicle shapes by changing ΔN
In order to confirm that the difference of ΔN
determines vesicle shape, we investigated the shapechanges caused by the flip-flop of lipids between the
inner and outer leaflets. During the simulation run, thewater beads were not moved from the inside to theoutside vesicle but there was a spontaneous transfer ofwater beads, so Nw was kept almost constant. As shownin Fig. 3(a), the lipids in the tube-like vesicle weremoved from the outer to the inner leaflets, that is, ΔNchanged from 970 to 273 in a step-by-step manner. The
tube-like vesicle became oblate and stomatocyte-shaped.
In the stomatocyte vesicle in Fig. 3(b), the lipids moved
from the inner to the outer leaflets, and ΔN changed
from 485 to 1096 at the first step. In fact, thestomatocyte-shaped vesicle changed to a triangularoblate shape. From the results, we concluded that theshapes of vesicles were determined by ΔN. The
transformation sequences obtained in the simulations
were also observed in the experiment [8], which was
carried out on the condition that the volume of vesicles
remained almost unchanged.

III. SIMULATION RESULTS
1. Transformation of vesicle shape by changing Nw
We examined the transformation of vesicle shapefrom spherical to the other shapes. In order to reproduce
the volume change of a vesicle in our simulations, 1,000
water beads inside a vesicle were selected randomly,
and transferred outside the vesicle in every 5,000 steps.
In this way, the number of water beads inside the vesicle(Nw) decreased during simulation runs. As shown in Fig.
2, we prepared two types of spherical vesicles, Nw =59,924, ΔN = 336 and Nw = 60,020, ΔN = 997. For asmall ΔN, with a decrease in Nw, the spherical vesicle
became oblate at Nw = 45,976, and stomatocyte-shaped
at Nw = 30,978 (Fig. 2 (a)). In contrast, for a large ΔN,with a decrease in Nw, the vesicle shape changed fromspherical to prolate at Nw = 40,023, and to a tube at Nw =30,987 (Fig. 2 (b)). The transformation sequenceobtained in the simulation is in good agreement with theexperiment [6, 7]. During the simulations, ΔN changed
because the spontaneous flip-flop of lipids occurred.
Moreover, the variation of Nw occurred because of the
spontaneous transfer of water beads between the insideand outside of a vesicle. However, these variations in
ΔN and Nw were small and could be neglected.
Therefore, the results show that the difference in theinitial structures of vesicles is important in determining
which branch of transformation the sphere is subject to(oblate or prolate).

©ICAROB 2014

ΔN=1243
Nw=36,026

3. Various shape changes of vesicles
In the simulations, we reproduced various shapechanges of vesicles resulting from changes in Nw and
ΔN, as shown in Fig. 4. The starfish-like vesicle wasderived from the stomatocyte vesicle by increasing ΔN.
The dumbbell-like vesicle was obtained from the prolate
vesicle by decreasing Nw and increasing ΔN. The oblate
vesicle became torus-shaped following a decrease in Nw.
The racket was obtained from a spherical vesicle by-310-

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small angle neutron scattering. Eur. Phys. J. E, 27:379389.
[4] Cicuta P, Keller SL, Veatch SL (2007) Diffusion ofliquid domains in lipid bilayer membranes. J. Phys.
Chem. B, 111:3328-3331.
[5] Scherfeld D, Kahya N, Schwille P (2003) Lipid
dynamics and domain formation in model membranes
composed of ternary mixtures of unsaturated andsaturated phosphatidylcholines and cholesterol. Biophys.
J., 85:3758-3768.
[6] Hotani H (1984) Transformation pathways ofliposomes. J. Mol. Biol. 178(1):113-120.
[7] Yanagisawa M, Imai M, Taniguchi T (2008) Shapedeformation of ternary vesicles coupled with phaseseparation. Phys. Rev. Lett. 100:148102(1-4).
[8] Sakashita A, Urakami N, Ziherl P, Imai M (2012)
Three-dimensional
analysisoflipid
vesicletransformations. Soft Matter, 8:8569-8581.
[9] Seifert U (1997) Configurations of fluid membranes
and vesicles. Adv. Phys., 46:13-137.
[10] Yamamoto S, Maruyama Y, Hyodo S (2002)
Dissipative particle dynamics study of spontaneous
vesicle formation of amphiphilic molecules. J. Chem.
Phys., 116:5842-5849.
[11] Yamamoto S, Hyodo S (2003) Budding and fission
dynamics of two-component vesicles. J. Chem. Phys.,
118:7937-7943.
[12] Laradji M, Kumar PBS (2004) Dynamics ofDomain Growth in self-assembled fluid vesicles. Phys.
Rev. Lett., 93:198105(1-4).
[13] Granfmüller A, Shillcock J, Lipowsky R (2007)
Pathway of Membrane fusion with two tensiondependent energy barriers. Phys. Rev. Lett.,
98:218101(1-4).
[14] Groot RD, Warren PB (1997) Dissipative particle
dynamics: Bridging the gap between atomistic andmesoscopic simulation. J. Chem. Phys., 107:4423-4435.
[15] Groot RD, Madden TJ (1998) Dynamic simulationof diblock copolymer microphase separation. J. Chem.
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[16] Li X, Pivkin IV, Liang H, Karniadakis GE (2009)
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J, Doi M, (2002) A general-purpose coarse-grained
molecular dynamics program. Comput. Phys. Commun.,
145:267-279.

increasing ΔN. These vesicle shapes were observed in
the experiments [6-8]. It was possible to reproduce thevarious vesicle shapes using our model for thesimulations.

IV. CONCLUSION
We carried out DPD simulations to investigate thetransformation of vesicle shapes from spherical to theother shapes with a decrease in Nw. For small ΔN, thevesicle changed from spherical to oblate. In contrast, forlarge ΔN, the vesicle changed from spherical to prolate.
With further reduction in Nw, the oblate vesicle became
stomatocyte-like, and the prolate vesicle changed to a
tube. The results were in good agreement with theexperimental results [6, 7]. To investigate themechanism of the vesicle shape changes in detail, we
carried out the simulations by moving lipids between
the inner and outer leaflets of the vesicle. We observed
the vesicle shape change from a tube to prolate, oblate,and finally, stomatocyte-like when lipids were moved
from the outer to the inner leaflet, corresponding to adecrease in ΔN. In contrast, there was a transformationfrom stomatocyte-like to triangular oblate when lipids
were moved from the inner to the outer leaflet,corresponding to an increase in ΔN. These results
indicate that the shapes of vesicles are determined by
Nw and ΔN. The vesicle shapes obtained in thesimulations were comparable with those predicted by
the ADE model. In addition, we also obtained thestarfish, dumbbell, torus, and racket shapes by varying
Nw and ΔN. In the future, we must analyze these vesicle
shapes in detail, and we will clarify the mechanism ofvarious vesicle shape changes. Information about theshape changes of vesicles would help us to understandthe motion of living cells when reacting to external
stimuli.

ACKNOWLEDGEMENTS
This work was supported by JSPS KAKENHI Grant
Number 24540437, 25247070.

REFERENCES[1] Veatch SL, Keller SL (2002) Organization in lipid
membranes containing cholesterol. Phys. Rev. Lett.,
89:268101(1-4).
[2] Veatch SL, Keller SL (2005) Miscibility phase
diagrams of giant vesicles containing sphingomyelin.
Phys. Rev. Lett., 94:148101(1-4).
[3] Masui T, Urakami N, Imai M (2008) Nano-meter–
sized domain formation in lipid membranes observed by©ICAROB 2014

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Intelligent mechanisms in E. coli in processing carbon sources
Zhongyuan Tian and Hiroshi Matsuno*

Graduate School of Science and Engineering, Yamaguchi University,
1677-1 Yoshida, 753-8512 Yamaguchi-shi, Yamaguchi, Japan
Tel/Fax : 81-83-933-5697
hmatsuno@yamaguchi-u.ac.jp

Abstract: E. coli is “wise” enough to take suitable responding time, and suitable responding behaviors, when facing
different kinds and intensities of stimulations. According to the time cost of a respond to a signal, we divide theintracellular processes into 2 levels: central dogma level and post translation level. The central dogma level includes
the whole process of a gene transcribed to an mRNA then translated to a protein. The post translation level includes
other intracellular process of non direct relation with genetic DNA or mRNA, for example protein-protein interaction,
protein-metabolite interaction, protein localization, protein motion, etc. The time consuming for an action in the central
dogma level is longer than that in the post translation level.
In this study, firstly, we constructed a systematical network of glucose, PTS, glycogen and chemotaxis system,
which was mediated by EI, HPr and ATP. This network plays a function as a switch, which controls E. coli’s motion
within the first few seconds. By using this switch, we unveiled a fact that the shorter time stimuli result in the posttranslation level reactions for quicker response. But further longer time stimuli will activate more time consuming thecentral dogma level reactions, that is this type of signals will be handled in the process of transcription or translation.
Secondly, different intensities of signals result in different kinds of actions, was illustrated by ppGpp example. A
low concentration ppGpp will cause RNAP express rRNA & table genes only; meanwhile, a high concentration ppGpp
will cause RNAP express mRNA genes only. When E. coli is not in stringent occasion, without the binding of ppGpp,
RNAP will express mRNA and other rRNA & stable genes in a proportion of 2:8.
In all, a bacterium, like an E. coli, not only can identify different kinds of signals; but also can distinguish a signal’stime-length and intensity, and then adopt different respond actions.
Keywords: chemotaxis, PTS, ATP, ppGpp, the central dogma, post translation

protein-metabolite interaction, protein localization,
protein motion, etc. The time consuming for an action in
the central dogma level is longer than that in the posttranslation level.
For E. coli, the average length of coding sequences
is 1068 bp [1], and the maximal transcription speed isabout 40-80 bp/sec [2, 3], the maximal translation speed
is about 20 aa/sec [3, 4], then the average time cost for agene expression in an optimal condition is about 40
second. But in reality, a gene’s expression may costs

I. INTRODUCTION
A human’s body will initiatively or positively takereactions via reflex arc system or deep tendon reflexes
system, facing to different kinds and intensities ofstimulations. E. coli, a bacterium, is it “wise” enough totake suitable reactions towards the variety signals? Herethe word “suitable” includes suitable responding time,and suitable responding behaviors. Our answer is “Yes”.
In E. coli, different kinds, time length or intensities ofsignals can drive suitable functioning units (e.g.
proteins) on suitable levels to make suitable reactions.
According to the time cost of a respond to a signal,
we divide the intracellular processes into 2 levels:
central dogma level and post translation level (Fig 1).
The central dogma level includes the whole process of agene transcribed to an mRNA then translated to aprotein. The post translation level includes otherintracellular process of non direct relation with genetic
DNA or mRNA, for example protein-protein interaction,©ICAROB 2014

Fig. 1 According to the time cost of intracellularaction, we divide the intracellular processes into
two levels: the central dogma level and the posttranslation level-312-

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Shorter time stimulation causes responses on thepost translation level. The analysis in this section isbased on an assumption: proteins levels are sufficient toperform their functions. In Fig. 2, glucose and ATP are
2 stimuli of outside (in the culture) and inside(intracellular) respectively. When an E. coli meets
glucose at the first time, it quickly activates the poles
located PTS (un-phosphorylated) by passing phosphategroup to glucose. Meanwhile glucose as a chemotaxis
signal has been captured by poles locatedchemoreceptors. Signals form both these 2 pathways
repress the CheA auto-phosphorylation, which at lastresult in E. coli run-forward by rotating flagella infrom several minutes to several days. And the time
consuming in post translation level is commonly much
shorter then that of central dogma level. For example,
the for a chemotaxis reaction in E. coli, only need few
seconds, or even in a sub-second time scale [5], which
includes at least 6 steps actions of the post translationlevel from a signal to a chemoreceptor, …, at last to aflagella.
In this context, examples (a) uses a systematicalmodel of PTS-Chemotaxis-glycogen to describe
different levels’ responds resulted from a same kind of
stimuli coming with different time lengths.
According to intensity of a signal, the respondshould be classified into low, mid and high intensity
cases, which is illustrated by the example (b): Using appGpp-RNAP model to explain different kinds ofactions are caused by different intensities of the stimuli.

II. Example (a): A same kind of stimuli comingwith different time lengths result in different
levels’ responds
In studies on E. coli, PTS, glycogen, and flagellum
are common objects, but always studied separately. PTS
is the transport system for an E. coli intaking cultural
glucose [6]. Glycogen is a polymer functioning as acarbohydrate intracellular storage [7]. Flagellum
together with FilM and CheY forms the E. coli motion
driver, which gives in time response to the five kinds ofchemoreceptors [8].
Intra these 3 systems, EI, HPr, EIIAGlc and ATP are
key mediators. Un-phosphorylated EI (EI) inhibits theauto-phosphorylation of CheA (P~CheA), which in turnstops the transferring of phosphate group from P~CheA
to the flagella motor CheY; this will causes flagellarotating in clock-wise (CW) direction and E. coli will
tumbling here [6]. The binding of un-phosphorylated
HPr (HPr) with GlgP (HPr::GlgP) catalyzes a quicker
glycogen decomposition, EIIAGlc regulates expression
levels of glgBXCAP, ptsHIcrr and ptsG, indirectly via
cAMP/CRP complex [9]. ATP is a necessary member of
CheA auto-phosphorylation (Equ. 1) and ADPG
synthesis (Equ. 2) [10]. We can find, despite EIIAGlc
works up to the central dogma level, all the other 3
mediators are mainly functioning in the post translationlevel.
𝐶ℎ𝑒𝐴 + 𝐴𝑇𝑃 ⇋ 𝑃~𝐶ℎ𝑒𝐴 + 𝐴𝐷𝑃(1)
𝐺1𝑃 + 𝐴𝑇𝑃    ©ICAROB 2014

𝐺𝑙𝑔𝐶

⟹

    𝐴𝐷𝑃𝐺 + 𝑃𝑃𝑖

Fig. 2 Shorter time stimulation causes responses on
the post translation level, which is realized by the
network of PTS, glycogen and chemotaxis system.
counter clock-wise (CCW) direction. Simultaneously,
poles located HPr::GlgP complex makes pole-locatedglycogen a quick decomposition to supply more
phosphate group as soon as possible, sinceglycogenolysis is a quicker process and needless energy
driven.
If then outside glucose disappears, chemoreceptors
will lose its inhibition on CheA auto-phosphorylation,
so as the phosphorylated EI (P~EI). Now, E. coli willstop run-forward, instead, turn direction (tumbling),
until catching another glucose.

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network with Ginsim [11] (Fig. 3a). And from the resultof its analysis (Fig. 3b), we can find, the system of
double signals (glucose and ATP) and double responses(Flagella and glycogen) reaches 2 stable states, ofwhich: 1000 represents there is only ATP, but noglucose and glycogen, E. coli will tumble there to
reorient; 0101 means if there is no ATP and glucose, but
only glycogen remains, the E. coli will go straight. Thismechanism can be understood as a switch of E. coli
facing shorter time stimulation. In one word, E. coli
now either tumbles there with ATP inside, or run
forward with glycogen inside. We should not forget,
afore-talked mechanism only occurs at the first few
seconds.
Longer time stimulation causes responses on thecentral dogma level. If an E. coli is emerged in a
glucose culture, then the stimulating time is longenough to activate the reaction of the central dogma
level. This process includes a series of complex
networks, such as PEIIAGlc&cAMP and FDP&Cra
subpathways, (As show in Fig. 4), which wereexplained in the paper [9].

Fig. 3 Boolean network and states transition analysis
result of the switch of ATP, glucose, glycogen andchemotaxis system.
If following the first step, the outside glucose is
there still; the un-phosphorylated CheY will continue todrive E. coli run-forward. But accompany with quick
glycogenolysis driven by EI, much more ATP is
produced in the pathway of glycolysis and TCA cycle.
Accompany with the sudden accumulation of
intracellular ATP, the glycogenesis pathway is turned
on, which is caused by glycogen content varies
inexpertly; and the CheA auto-phosphorylation process
will be returned on by the higher pressure of ATP andlower pressure of the inhibitions, which results in E. coli
tumbling-reorient.
After the aforementioned preparation period, if there
is still a glucose signal, then E. coli will “realize”: there
really has a “glucose banquet”. At this time, more
phosphate grout is transferred from PEP to PTS for
glucose uptake, ATP is in a quite flat level. E. coli runs
into the glucose now.
In all, for shorter time stimulation, when an E. colimeets glucose (outside stimulus), it quickly takes
suitable types of motion and uptake simultaneously. But
if the time length of this decision left for E. coli islimited in only a few seconds, a longer time scale would
become re-oriented by Brownian motion [5]. Whenintracellular phosphate group (ATP) lacks (insidestimuli), the corresponding flagella motion andintracellular behavior will change.
In order to clarify the relationship with glucose,
ATP, glycogen and flagella, we construct a Boolean©ICAROB 2014

Fig. 4 Regulation mechanisms inter PTS andglycogen metabolism: PEIIAGlc&cAMP pathway,
FDP&Cra pathway and HPr subcellular localization-314-

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III. Example (b): Using a ppGpp-RNAP model
to explain that different kinds of actions arecaused by different intensities of the stimuli
ppGpp is a stringent alarmone in E. coli, whosemechanism was clearly studied by Traxler et al. in theirseries papers [12–14]. Fig. 5 shows that the 3 “bands”
of ppGpp intensities result in 3 types of RNAP
functions. If there is no starvation signal in an E. coli
(ppGpp is null), RNAP will employ RpoD to transcribe
mRNA and rRNA & stable genes in a proportion of
20% and 80%. If E. coli facing only amino acidstarvation, a low concentration of ppGpp is produced, asa consequence, the low concentration ppGpp will bindto and drive RNAP to only express rRNA & stablegenes. As all nutrition starvation occurs, Highconcentration ppGpp will be produced, which together
with DksA binds to and drive RNAP employing RpoS
to transcribe mRNA only.

Fig. 5 Different intensities of ppGpp result indifferent kinds of RNAP actions.
that the shorter time stimuli results in the posttranslation level reactions. And further longer time
stimuli will activate more time consuming the central
dogma level reaction. Secondly, different intensities ofsignals result in different kinds of actions, wasillustrated by ppGpp example.
The last but not the least, we would like to say,biology can not be classified to higher or lower levels,
they only evolve towards different directions.

IV. CONCLUSION
E. coli, a bacterium, is an efficient system, whenfacing different circumstances. In this study, firstly, by
systematically constructing a network of glucose, PTS,
glycogen and chemotaxis system, we unveiled a fact

adaptation enzymes. Annu Rev Cell Dev Biol
13:457-512[9] Tian Z (In press), Identification of Key Regulators
in Glycogen Utilization in E. coli Based on the
Simulations from a Hybrid Functional Petri Net
Model. BMC Systems Bio[10] Rahimpour M (2013), GlgS, described previously
as a glycogen synthesis control protein, negatively
regulates motility and biofilm formation in
Escherichia coli. Biochemical Journal 452(3):559573[11] Naldi A (2009), Logical modelling of regulatory
networks with GINsim 2.3. Biosystems 97(2):134139[12] Traxler
MF(2006),
Guanosine
3′,5′bispyrophosphate
coordinates
globalgene
expression during glucose-lactose diauxie in
Escherichia coli. PNAS 103(7):2374-2379[13] Traxler MF (2008),The global, ppGpp-mediatedstringent response to amino acid starvation in
Escherichia
coli.
Molecular
Microbiology
68(5):1128-1148[14] Traxler MF (2011), Discretely calibrated regulatory
loops controlled by ppGpp partition gene induction
across the ‘feast to famine’ gradient in Escherichia
coli. Molecular Microbiology 79(4):830-845

REFERENCES[1] Moriyama EN (1998), Gene length and codon
usage
bias
in
Drosophilamelanogaster,
Saccharomyces cerevisiae and Escherichia coli,
Nucl. Acids Res 26(13):3188-3193[2] Dennis PP (1974), Macromolecular Composition
During Steady-State Growth of Escherichia coli B/r.
J Bacteriol 119(1):270-281[3] Neidhart FCE (1996), Escherichia coli and
Salmonella (No. Ed. 2). Blackwell Science Ltd[4] Young R (1976) Polypeptide-chain-elongation ratein Escherichia coli B/r as a function of growth rate.
Biochem J 160(2):185-194[5] Sourjik V (2010), Spatial organization in bacterial
chemotaxis. EMBO J 29(16):2724-2733[6] Deutscher J (2006), How Phosphotransferase
System-Related Protein Phosphorylation Regulates
Carbohydrate Metabolism in Bacteria, Microbiol.
Mol Biol Rev 70(4):939-1031[7] Wilson WA (2010), Regulation of glycogenmetabolism in yeast and bacteria. FEMS
Microbiology Reviews[8] Falke JJ (1997), The two-component signaling
pathway of bacterial chemotaxis: a molecular viewof signal transduction by receptors, kinases, and©ICAROB 2014

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An autonomous propagation of ciliary metachronal wave on elastic surface ofParamecium cells
N. Narematsu and Y. Iwadate
Faculty of Science, Yamaguchi University s, Yamaguchi 753-8512(Tel : 81-83-933-5760; Fax : 81-83-933-5760)(iwadate@yamaguchi-u.ac.jp)

Abstract: Ciliary movements in protozoa show metachronal coordination so as to maintain a constant phase difference
between adjacent cilia. This coordination is called as “metachronal wave”. It is now generally thought thatmetachronal waves arise from hydrodynamic coupling between adjacent cilia at extracellular fluid. However, underthe breakdown the hydrodynamic coupling of ciliary movements at a restricted portion of a Paramecium cell,metachronal waves pass over the portion. We will discuss the mediator of propagation of metachronal waves.
Keywords: cilia, ciliary movement, metachronal wave, Parmaecium

and theoretical studies [8]–[11], it has been suggested
that hydrodynamic coupling may play a role, in that anindividual cilium’s deflection can be transmitted by thesurrounding viscous fluid to a neighboring cilium whichis then induced to deflect similarly. However, is theI. INTRODUCTION
One of the most interesting features of organisms istheir generation of spatiotemporally complex regularity,
such as repeated structure of somite and stable rhythmof heartbeat, with no external signal. This autonomous
generation of regularity is not limited to multi-cellularorganisms or tissues but observed even in uni-cellar
ones.
Surface of ciliated protozoa, such as Paramecium
cells (Fig. 1), is covered with a dense array of cilia.
They swim in viscous fluid by beating cilia periodicallywith a succession of power and recovery strokes [1], [2].

hydrodynamic interaction
metachronal waves?

the

only

mediatorofFig. 2 Metachronal wave of cilia

Ciliary movements are coordinated so as to maintain aconstant phase difference between adjacent cilia. The
coordinated beat cycles of a multitude of them seems as
waves traveling on cell surface. The formation of
traveling, so-called metachronal, waves needs a highdegree of synchronization between the beating cilia [1],[3], [4].

In this paper, we show experimental and simulation
results to suggest that, in addition to hydrodynamic
coupling, cortex coupling can also play a role in the
propagation of metachronal waves.
To show this, we held one end of the Paramecium
fixed with a micropipette and then held the other end of
the Paramecium with a micropipette that is then pulled
in an oscillatory manner to induce a periodic stretchingof the Paramecium body. We then study themetachronal wave propagation in both the unstretchedand the stretched cells.

Fig. 1 Paramecium cell
It is still not well understood how the individual cilia

II. MATERIALS AND METHODS

coordinate with one another to produce such sequential
action. From several detailed observations [1], [3]–[7]©ICAROB 2014

Paramecium culture-316-

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Paramecium caudatum cells (strain G3, trichocysts
non-discharge mutant) were cultured in an infusion oftime was controlled with an electromagnetic shutter (No.
0; Copal, Tokyo, Japan).

rice straw at room temperature (20-25 oC). Prior to eachexperiment, Paramecium cells were transferred into

Cyclic stretching of a cellstandard saline medium containing 2 mM KCl, 0.5 mM
CaCl2,
0.5
mM
MgSO4,
10mM
3Morpholinopropanesulfonic acid (MOPS)-Tris (pH 7.2).

The capturing of a swimming cell was performed
according to the methods described previously [12] withslight modifications. To slow their swimming,

Anterior and posterior of a Paramecium cell were
sucked by two pipettes. Both pipettes were connected
to piezo actuators (MC-140L, MESS-TEC, Saitama,
Japan). In order to induce cyclic stretching of the cell,
sequential voltage square pulses were applied to one
actuator, which was connected to the pipette sucking theanterior of the cell. Frequency and amplitude of cell
stretching were controlled by adjustments of those of

Paramecium was dispersed in a highly viscous medium
containing 0.5% (w/v) methylcellulose, 1 mM KC1, 1
mM CaC12, and 10 mM Pipes-KOH (pH 7.0). A cellvoltage pulses. In the case when only shaking without
stretching was applied to the cell, sequential voltage
square pulses were applied to both actuators.

Cell capturingof each species was caught using a suction pipette.
The diameter of the suction pipette was adjusted toabout 35 µm. The pipette was connected to a glass
tube and a 5 ml syringe through a silicone tube. The

III. RESULTS
Metachronal waves pass through the portion of
the cell surface where the direction of ciliary
movements is artificially reversed

pipette, the glass tube and the syringe were filled withstandard saline medium. The syringe was used for
adjusting the height of the water surface in the glass
tube to control the hydrostatic pressure.

The direction of ciliary beat in Paramecium cells isreversed by a rise in intraciliary Ca2+ concentration [13],[17], [18]. We applied UV light to a small patch of cilia
on a Paramecium cell that had been previously injectedwith NP-EGTA medium (white circle in Fig. 2). UV

Photolysis of caged calcium
UV application to the restricted area was performed
according to the methods described previously [13]–[15], with slight modifications. An inverted microscope(TE300; Nikon, Tokyo, Japan) was used throughout theexperiment. A mercury short arc lamp (USH102D;
Ushio, Tokyo, Japan) was attached to the side light pathof the microscope and used as the UV light source. A
pinhole 400 m in diameter (43-5305, Coherent, Tokyo,
Japan) was arranged at the field plane conjugated to theirradiation caused reversal of the beat direction of thecilia (Fig. 3).

plane of the specimen. The UV light through the pinhole
was filtered through a band-pass filter of 270–400 nm(U340; Hoya, Tokyo, Japan) and a cutoff filter of
300nm (UV30; Hoya, Tokyo, Japan) to pass 300–400
nm UV light, as described by Funatsu et al. [16]. The
light was collected by a UV-transmitting objective lens
(CFI S Fluor 40xH, NA 1.30; Nikon, Tokyo, Japan) toform a small image of the pinhole on the plane of theFig. 3 UV irradiation to a Paramecium cell that
had been previously injected with NP-EGTA
We investigated whether or not metachronalcoordination was eliminated by the breakdown ofhydrodynamic coupling of ciliary movements at thereversed cilia. Figure 4 shows a typical kymographfrom thin image strips that recorded ciliary movement,
in which the beat direction of a local patch of cilia wasreversed by photolysis of NP-EGTA. Metachronalwaves are shown as oblique black traces from top rightto bottom left. The area of reversed cilia is indicatedspecimen. Thus, the 300–400 nm UV light was applied
to the restricted portion of the Paramecium cell, about
2min after microinjection of NP-EGTA. The application©ICAROB 2014

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with a rectangle. A dotted line was drawn on the traces
of a wave anterior to the reversed cilia. The line wasthen extended over the portion of reversed cilia to theposterior.

Fig. 5 Wave frequencies under cyclic stretchingand shaking.
A typical result is shown (Fig. 5). Fig. 5 is wave
frequencies under cyclic stretching and shaking. The
frequency decreased significantly under cyclic
stretching, although that did not change under shaking.
Whereas, wave speeds each condition were almost same(data not shown).
Fig. 4 Kymograph from thin image strips thatrecorded ciliary movement, in which the beatdirection of a local patch of cilia was reversed.

Next, we sucked both ends of a live Paramecium
cell by two micropipettes and applied a vibration with a

Discussion
It is now generally thought that metachronal wave ismediated by the viscosity of extracellular fluid. No
intracellular organelle has been proposed as a candidate
for mediating the wave.
In this study, metachronal waves pass over the portion
where the direction of ciliary movements was

frequency of 12.5 Hz to the one micropipette, which
sucked the anterior of the cell, to induced cyclic
stretching of cell body. The applied frequency of
12.5Hz is lower than that of metachronal wave of eachcell. The stretching ratio of the distance between thetips of two pipettes after the stretching was adjusted to
5.7%. If the elasticity of cell cortex was a mediator of

artificially reversed (Fig. 4). Direction of effective andrecovery stroke of ciliary beat is not completely parallel
to body axis of Paramecium cells [1]. Whereas, weobserved the wave propagation in the direction parallel
to body axis. Thus, UV application from the direction
perpendicular to the body axis should not completely
prevent the effect of the oblique water flow. There is ametachronal wave, frequency of metachronal wave
should decrease under the cyclic stretching of a low
frequency of 12.5 Hz.

possibility in which the oblique water flow may induce
the jump of the metachronal waves over the portion
where the direction of ciliary movements was
artificially reversed, although it strongly suggest thatmetachronal wave is mediated not only by the viscosityof extracellular fluid but also by something other.
Paramecium has a contractile cytoskeletal network
called the infraciliary lattice (ICL) that lies just beneath

Artificial cyclic stretching of cell body with low
frequency decrease that of metachronal wave

the cortical alveoli [19]. ICL may be a possible
candidate as an intracellular mediator of metachronal
wave.
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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014[18] Naitoh Y and Eckert R (1969), Ionic mechanisms
controlling behavioral responses of paramecium tomechanical stimulation. Science 164: 963–965[19] Garreau de Loubresse N, Keryer G, Viguès B and
Beisson J (1988), A contractile cytoskeletal network of
Paramecium: the infraciliary lattice. J Cell Sci 90: 351–
364

REFERENCES[1] Machemer H (1972), Ciliary activity and the originof metachrony in Paramecium: effects of increased
viscosity. J Exp Biol 57: 239–259[2] Sugino K and Naitoh Y (1982), Simulated crossbridge patterns corresponding to ciliary beating in
Paramecium. Nature 295: 609–611[3] Satir P (1963), Studies on cilia. The fixiation of themetachronal wave. J Cell Biol 18: 345–365[4] Aiello E and Sleigh ME (1972), The metachronal
wave of lateral cilia of Mytilus edulis. J Cell Biol 54:
493–506[5] Rompolas P, Patel-King RS and King SM (2010),
An outer arm Dynein conformational switch is required
for metachronal synchrony of motile cilia in planaria.
Mol Biol Cell 21: 3669–3679[6] Okamoto K and Nakaoka Y (1994), Reconstitutionof metachronal waves in ciliated cortical sheets of
Paramecium-asymmetry of the ciliary movements. J
Exp Biol 192: 73–81[7] Okamoto K and Nakaoka Y (1994), Reconstitutionof metachronal waves in ciliated cortical sheets of
Paramecium-wave stabilities. J Exp Biol 192: 61–72[8] Gueron S, Levit-Gurevich K, Liron N and Blum JJ,(1997), Cilia internal mechanism and metachronalcoordination as the result of hydrodynamical coupling.
Proc Natl Acad Sci USA 94: 6001–6006[9] Guirao B and Joanny J-F (2007), Spontaneous
creation of macroscopic flow and metachronal waves inan array of cilia. Biophys J 92: 1900–1917[10] Dillon RH, Fauci LJ and Yang X (2006), Spermmotility and multi ciliary beating: An integrative
mechanical Model. Comput Math Appl 52: 749–758[11] Elgeti J and Gompper G (2013), Emergence ofmetachronal waves in cilia arrays. Proc Natl Acad Sci
USA 110: 4470–4475[12] Iwadate Y, Katoh K, Asai H and Kikuyama M(1997), Simultaneous recording of cytosolic Ca2+ levels
in Didinium and Paramecium during a Didinium attack
on Paramecium. Protoplasma 200: 117–127[13] Iwadate Y (2003), Photolysis of caged calcium incilia induces ciliary reversal in Paramecium caudatum.
J Exp Biol 206: 1163–1170[14] Iwadate Y, Kikuyama M and Asai H (1999),
Photolysis of caged Ca2+ induces trichocyst discharge inParamecium caudatum, Protoplasma 206: 11–19[15] Iwadate Y and Nakaoka Y (2008), Calcium
regulates independently ciliary beat and cell contractionin Paramecium cells. Cell Calcium 44: 169–179[16] Funatsu T, Kono E and Tsukita S (1993), Timeresolved electron microscopic analysis of the behaviorof myosin heads on actin filaments after photolysis of
caged ATP. J. Cell Biol 121: 1053–1064[17] Naitoh Y and Kaneko H (1972), Reactivated tritonextracted models of paramecium: modification of ciliary
movement by calcium ions. Science 176: 523–524©ICAROB 2014

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An analysis of the synergy in a ball throwing task
Hiroshi KIMURA, and Jun NISHII
Graduate School of Science and Engineering, Yamaguchi University.
1677-1 Yoshida, Yamaguchi, 753-8512, Japan
E-mail: {kimura, nishii}@bcl.sci.yamaguchi-u.ac.jp
Abstract: Throwing a ball is a very important task in baseball. To throw a ball into a given target, we must determine theinitial state of the ball, the position, speed, and throwing direction at ball release. There are two kinds of strategies in the
choice of these variables; the first is to learn a desired value set for these state variables through leaning and try to adjust
the variables to the desired values. The second is to allow the variance of each variable in every trial but compensate
for the values each other in a cooperative manner so as to hit the target. The purpose of this study is to elucidate the
knack of the ball throwing task by observing how good thrower selects the state variables in throwing. We measuredball trajectories thrown by subjects sitting on a chair into a given target and analyzed if cooperative change among thestate variables is observed. The result shows that in good throwers the variance of the release point is suppressed but
cooperative change between the initial ball speed and the throwing direction was observed.
Keywords: ball throwing, synergy, UCM analysis, motor control, variability.

I

Introduction

Improving the ball control in throwing is a very importanttask in baseball. If we neglect the effect of air friction,
the trajectory of a ball is determined by the initial statevariables, the release point, speed, and throwing direction.
How do good throwers choose and control these state variables? There are two possibilities. The first solution is tothrow a ball by adjusting each value of the state variables
accurately to a desired value learned through learning (Fig.
1). In fact, “fix a release point” is a typical advice given
by baseball trainers to improve the ball control of pitchers.
Furthermore, Nagami reported that the variance of the ballthrowing direction largely affects the ball control in throwing [4]. This result seems to suggest that the throwing angle
should be fixed to a certain value in ball throwing. The second solution is to allow the variance of each initial statevariable but adjust them so as to compensate for the values
each other in a cooperative manner in order to throw theball into a target (Fig. 1). For instance, when the release
point is higher than a previous successful throwing, we canhit the target by adjusting the speed and throwing direction into smaller values. In fact, good pitchers and fielders
show accurate throwing from various postures, which indicates that they have acquired the technique to adjust theinitial state variables by mutual compensation among thevariables. Bernstein called such kinds of mutual compensation among abundant state variables synergy and emphasized the importance of the synergy as a source of dexterity[1]. Scholz and Schöner proposed the UCM (Uncontrolled
Manifold) analysis to evaluate the degree of synergy [6] andmany recent papers have reported that synergy is utilized inmany movement tasks [2][3][5].
The purpose of this study is to analyze the synergy inball throwing task and elucidate a knack that good throwers
have acquired through learning.

Figure 1: Two kinds of strategies in the choice of the statevariables of the ball at ball release. The first is to throwa ball by adjusting state variables accurately to a desired
value acquired through learning. The second is to allow thevariance of each initial state variables but adjust them so
as to compensate for the values each other in a cooperative
manner so as to hit the target.

method. The method is described below.

1

Experiment methods

The subjects were three men who had played baseball morethan ten years and three men who had shorter or no experience of playing baseball. The profiles of the subjects areshown in table 1. The target was a 0.2 × 0.2 m square andplaced at the height of 0.9 m and the distance of 3.5 mfrom the throwing position (Fig. 2). Subjects were fixed
their chest to the back of the chair by a belt and directed tothrow a ball by overhand throwing into the target by theirprefer ball speed. They threw a ball 40 times in total. Theinterval for every throwing was 1 minute, and they were allowed to take a break when they felt fatigue. We measured
the trajectories of the ball by a motion capture system (HiII Methods
mawari GV 200, Library co.), by 300 fps, and determined
the ball position at ball release. The initial ball speed andWe measured the ball trajectory in throwing and we ana- the throwing angle were determined by the ball positions inlyzed the synergy between the state variables by the UCM the first 5 frames from the ball release.

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2

Subjects
A
B
C
D
E
F

The UCM method

In this section, the UCM method used in the analysis of
the synergy in the ball throwing is described. There areinfinite number of choices in the initial state variables ofthe ball at ball release, the position (x, y), the speed v, andthe throwing direction θ , to hit the target (Fig. 2). In thisstudy, the UCM is defined as the manifold that expresses
the combination of the solutions in the space of the statevariables (Fig. 3).
When the state variables at ball release is D = (x, y, v, θ ),the ball height h at the target position is given by
(
)
L−x 2
1
D) = (L − x) tan θ − gh(D(1 + tan2 θ ) + y, (1)2v

Table 1: The subject’s profile
Age Baseball career (year) Height (cm)
22
10
180
21
11
167
20
10
178
22
0
173
22
0
174
22
0
178

where L is the distance between the target and throwing
position and g is the amplitude of the gravitational acceleration. A small change in the state variables ε does notaffect the height when ε satisfies
D) · ε = 0.
∇D h(D(2)

Let the state variables at the ball release in the i-th throwing
as D i = (xi , yi , vi , θ i ) and the average as D = (x, y, v, θ ). The
parallel and orthogonal components of the deviation σ i =D i − D to the UCM, σ i∥ and σ i⊥ , respectively, are given by

σ i⊥= (σ i · ε̂ε ⊥ )ε̂ε ⊥(3)σ= σ −σ

(4)

i∥

i

i⊥

D)/|∇D h(DD)| is the normal vector of thewhere ε ⊥ = ∇D h(DUCM plane. The averages, σ ∥ and σ ⊥ , are given by
Figure 2: Overview of the measurement experiment. The
target was 0.2 × 0.2 m square and placed at the height of
h = 0.9 m with the distance of L = 3.5 m from the throwing
position.

σ∥

=

1 n
∑ σ i∥
n i=1

(5)

σ⊥

=

1 n
∑ σ i⊥ .
n i=1

(6)

When σ ∥ is larger than σ ⊥ , such distribution of the statevariables suggests the existence of the synergy that suppresses the variance of the height h. To judge the existenceof synergy among the state variables synergy, we used thedegree of synergy Sh defined byσ∥ −σ⊥(7)σ∥ +σ⊥
By the definition, the degree of synergy takes the value between −1 and 1. Sh > 0 indicates the existence of synergy
and larger value of Sh means larger degree of synergy.
Sh

3

=

Correlation coefficient

We also calculated the correlation coefficient to examine
whether positive correlation between state variables andball throwing performance exists. The correlation coeffiFigure 3: Schematic view of the UCM analysis. In this cient C is given bynstudy, the UCM refers to the manifold that expresses the(pi − p)(si − s)∑combination of the state variables, the position (x, y), speed
i=1
√
C= √
,
(8)
v, and throwing direction θ at ball release, that brings thennball to the center of the target.
22(pi − p)(si − s)

∑

i=1

∑

i=1

where pi is hit probability, si is a state variable in the i-th
throwing, and p and s are the averages.

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Variance of horizontal position [m]Degree of synergy

0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
35

40

45

50

55

60

65

70

Hit probability [%]©ICAROB 2014

0.02

0.01

0
30

35

40

45

50

55

60

65

70

60

65

70

60

65

70

60

65

70

Variance of vertical position [m](a)Results and Discussion

0.04

0.03

0.02

0.01

0
30

35

40

45

50

55

Hit probability [%](b)

Variance of speed [m/s]

1.2
1
0.8
0.6
0.4
0.2
0
30

35

40

45

50

55

Hit probability [%](c)
0.2

Variance of direction [rad]In this section, we show the results of the UCM analysis
and discuss the relation between the synergy among thestate variables and the ball throwing performance.
Fig. 4 shows the relation between synergy among statevariables and the hit probability. The degrees of the synergyof all subjects were over 0.6, which indicates that the variance of the state variables are compensated each other toadjust the ball height at the target position. The correlation
between the hit probability and the degree of the synergy
was 0.56, which suggests that good throwers tend to show
large synergy.
Fig. 5 shows the relationship between the variance ofeach state variable at ball release and hit probability. As
the hit probability increases, the variances of the horizontal
and vertical ball release position decreases (Fig. 5(a)(b)),
and the correlation coefficients were −0.89 and −0.60, respectively. This result indicates that the variance of the ballrelease position tends to be smaller in better throwers.
The correlation coefficient between the variance of thestate variables, the initial ball speed and the throwing direction, and hit probability were 0.14 and −0.20, respectively (Fig. 5(c)(d)). Therefore, the correlation between
them were weak.
Our results described above have shown that in better
throwers the degree of the synergy among the state variables of the ball at ball release was higher, on the other
hand, the variance of the release position was smaller than
beginners. The variance of the initial speed and the throwing direction have only a weak relation with the hit probability. These results seem to suggest that the release point
seldom participates in the synergy among state variables,
and the synergy is exploited between the initial speed andthe throwing direction (Fig. 6).
To ascertain this hypothesis we computed the degree ofthe synergy between two state variables, the initial ball
speed and the throwing direction, that suppresses the variance of the ball height at the target position. Fig. 7 shows
the result and suggests that its defference with the synergy
shown in Fig. 4 was small and 5 % at the maximum (inthe subject whose hit probability was 57.5 %). This result

0.03

Hit probability [%]Figure 4: The relation between synergy and task performance. The horizontal axis shows the hit probability in 40
ball throwings by each subject. The vertical axis shows thedegree of the synergy among state variables at ball release.
The solid line is the regression line of the data, and the correlation coefficient is 0.56.

III

0.04

0.16
0.12
0.08
0.04
0
30

35

40

45

50

55

Hit probability [%](d)Figure 5: The relation between the variances of the statevariables and the task performance. The horizontal axis is
the hit probability. The vertical axis in (a), (b), (c), and (d)are the variance of the horizontal and vertical ball release
position, initial ball speed, throwing direction, respectively.
The solid line is the regression line for each data, and thecorrelation coefficient in (a), (b), (c), and (d) were −0.89,−0.60, 0.14, and −0.2, respectively.

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Degree of synergy

0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
35

40

45

50

55

60

65

70

Hit probability [%]Figure 6: Schematic view of the control of the initial stateof the ball at ball release by good throwers. The varianceof the release point is suppressed, and synergetic control isfound between the initial ball speed and the throwing direction.

Figure 7: The degree of the synergy between the initial
speed and the throwing direction. The circles are the data
points and the solid line is the regressive line. The diamonds and the dotted line are the same as the circles andthe line in Fig. 4.

shows that the contribution of the ball release position into
the synergy to adjust the ball height at target position is utilized.
small and the synergy is realized by adjusting two parameters, the initial ball speed and the direction.

References

IV[1] N. A. Bernstein, The coordination and regulation of
movement. Pergamon Press, London, 1967．

Conclusion

In this study, we analyzed the synergy in ball throwing task
to elucidate how good throwers determine the initial statevariables of a ball at ball release. The results have shownthat the synergy works in the selection of the initial speed
and the direction of the ball in all subjects, and the degreeof the synergy tends to increase in good throwers. On theother hand, the contribution of the ball release position to
the synergy is low and the variance of the position is smaller
in good throwers. Bernstein proposed the hypothesis thatthe degrees of freedom (DOF) of our body is frozen in theearly phase of motor learning and the DOF would be freed
as learning proceeds to obtain motor dexterity, but our results show that freezing of the DOF seems to be also utilized in a skilled task.
This result shows that the instruction mentioned in thesection 1, ”fix a release point”, is an appropriate advice toimprove the ball control. However, our result also showsthat improvement of the coordination between the initialball speed and throwing direction would be also important
technique in ball throwing, therefore, such coordination
should be taken into the practice. For example, throwing
a ball by changing the speed and angle might be effective
practice on this purpose.
In this study, we analyzed the state variables of a ball atball release, however, it is also possible to analyze the synergy that coordinates arm joint trajectories, which might
give us a cue to know how the variance of the release position is suppressed by good throwers. Furthermore, thesubjects in this study threw balls by sitting on a chair, however, ball throwing in baseball is performed by using thewhole body. In this case, the degrees of freedom of the
body involved in the throwing task increases considerably,
hence, the analyses for such movements would be a challenging task but attractive to know the knack for skilled
throwing: which variables should be precisely controlled
and how synergy of multiple degrees of freedom should be©ICAROB 2014[2] J. Feng, J. P. Scholz, and M. L. Latash, The role ofkinematic redundancy in adaptation of reaching. Experimental Brain Research, vol. 176, pp. 54-69, 2007.
[3] M. L. Latash, J. P. Scholz, and G. Schöner, Motor Control Stratigies Revealed in the Structure of Motor Variability. Motor Control and Motor Variability, vol. 31,
no. 1, pp. 26-31, 2002.
[4] T. Nagami, J. Morohoshi, K. Kanosue, Relationshipbetween ball control and movements of hand and fingers in baseball pitchers. スポーツ・アンド・ヒュー
マン・ダイナミクス講演論文集, pp. 51-55, 2009[5] J. Nishii, Y. Hashizume, S. Kaichida, H. Suenaga,
Yoshiko Tanaka, Constraint and exploitation of redundant degrees of freedom during walking. Robotics andAutonomous Systems, Vol. 60, pp. 679-684, 2012[6] J. P. Scholz, and G, Schöner, The uncontrolled manifold concept: Identifying control veriables for a functional task. Experimental Brain Research, vol. 176, pp.
54-69, 2007.

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Japan, January 11-13, 2014

A low dimensional feedback control model that exploits
abundant degrees of freedom
J. Nishii and T. Hamamura
Graduate School of Science and Engineering, Yamaguchi University
1677-1 Yoshida, 753-8512 Yamaguchi-shi, Yamaguchi, Japan
Tel: +81-83-933-5691
nishii@yamaguchi-u.ac.jp
Abstract: When animals perform skilled motor task, they often adaptively choose a solution depending on thecircumstances by utilizing abundant degrees of freedom (DOFs) of their body. For instance, a spinal frog can wipe
an irritating stimuli oﬀ by its foot even if a leg joint are fixed.In this example, multiple leg joints cooperatively
work so as to control the foot position by utilizing available joints. Such compensatory control among multiple
joints is called joint synergy. We developed a simple neural network model that realizes synergetic control that
exploits abundant degrees of freedom. The model takes an architecture of a sand-glass type neural network, which
extracts essential variables in the middle layer to perform a task from sensory signals. The neural activity ofthe middle layer is modified by a feedback control so as to bring the activity to the desired value. The activityof the middle layer aﬀects the activity of the output layer that send control signals to actuators. The modelarchitecture is very simple, however, the results of the computer simulation of a 3-link arm control reproduced
the synergetic control observed in the above example of the spinal frog.
Keywords: motor control, sand-glass type neural network, abundant degrees of freedom, Bernstein problem

1

Introduction

When humans and animals perform skilled motortask, they adaptively choose a solution depending on
the circumstances by utilizing abundant degrees of
freedom (DOFs) of their body. For instance, Latash
et al. reported that a spinal frog can wipe an irritating stimuli oﬀ by its foot even if a leg joint are fixed[1]. In this example, multiple leg joints cooperatively
work so as to control the foot position by a compensatory manner by utilizing available joints. Suchcompensatory control among multiple joints is calledjoint synergy. Bernstein, a Russian physiologist who
contributed the development of biomechanics in thefirst half of the 20th century, insisted that the ability to solve a given task by multiple ways, in otherwords, by utilizing the synergy among the abundant
DOFs, is a typical characteristics of the intelligencethat the living bodies show [2]. The purpose of thispaper is to propose a neural network model that realizes such synergetic control.

2

A low dimensional feedback control
model

In this section, we propose a low dimensional feedback control model that extracts low dimensional©ICAROB 2014

performance variables from sensory signals, and exploits an abundant degrees of freedom (Fig. 1).
The proposed model is a simple neural networkmodel that takes an architecture of a sand-glass typeneural network (SNN). The SNN is a neural networkmodel with five layers and capable of nonlinear principal component analysis when fewer number of cells
are prepared in the middle layer than those in the
input layer and the SNN learned the identity transformation [3, 4]．
In our model the SNN learns the relation between
the sensory signals from the input layer and the control signals from the output layer. The number of thecells in the middle layer is fewer than that of the input layer, hence, it is expected that the middle layer
compresses the input signals into low-dimensionalvariables. After learning each neural cell in the middle layer consists the basis to control the actuators.
Furthermore, we add a learning for some cells in the
middle layer so as that the firing patterns express
essential variables to perform a task, so called performance variables [5], from sensory signals, e.g., thefoot position in the above example of the frog frommuscle lengths. When the neural activity of the middle layer is modified by a feedback control so as tobring the performance variables to the desired values, e.g., the target foot position, the activity of the
middle layer brings the control signals from the out-

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Desired value

sensory signals

motor command

modiﬁed from Ting (2007)

Figure 2: 3-link arm model with 6 muscles.

Figure 1: A framework of the movement control by our
nervous system proposed by Ting (2007). Ting proposedthat essential framework in movement control is the extraction of some basic variables to achieve a task and the
expansion of the basic variables into muscle synergies.
Our model gives a concrete neural network model thatrealizes the Ting’s idea. (modified from [6])．

put layer to desired one which accomplish the target
task. In other words, the low-dimensional feedback
control for the middle layer will realizes the motor
control that utilizes motor synergy.

3

Simulation experiment

In order to examine the validity of our model, weapplied our control model to an arm reaching task.

3.1

Arm model

The controlled object is a 3-link arm with threepairs of muscles for each joint in a horizontal plane.
The length and mass of each link is 0.3 m and 1.5
kg, respectively ( fig. 2)．
The muscle model used in our experiment is basedon Kumamoto et al. [7] and Kambara et. al. [8], andthe muscle tension Ti exerted by the motor signal ui
for the i-th muscle Mi (i = 1, 2, · · · , 6) takes theform:
Ti = ci ui − (k0i + k1i ui )li − (b0i + b1i ui )l˙i ,(1)where k0i and k1i are damping coeﬃcients, b0i ，b1i
are viscous coeﬃcients, and ci is a constant. li ismuscle length that depends on the joint angle θj (j =i − 1 mod 2):
li = l0i − ai θj ,(2)

©ICAROB 2014

Table 1: Muscle parametersc [N]
k0 [N/m]k1 [N/m]b0 [Ns/m]
b1 [Ns/m]
l0 [m]
a [m]

M1
42
240
105
350
350
0.12
0.034

M2
42
240
105
350
350
0.12
-0.034

M3
28
160
70
350
350
0.12
0.034

M4
28
160
70
350
350
0.12
-0.034

M5
14
80
35
350
350
0.12
0.034

M6
14
80
35
350
350
0.12
-0.034

where ai is the moment arm of i-th joint and l0i is
the neutral length of the muscle Mi when θj = 0.
In other words, the first term of the right hand ofeq. (1) represents the joint torque exerted by thei-th muscle, the second term the elastic force thatis proportional to the muscle length, the third term
the viscous force. The torque of j-th joint is givenby the diﬀerence of the muscle tension around thejoint:
τj = a2j−1 T2j−1 + a2j T2j(3)
The muscle parameters are shown in the Table 1. In
the computer simulation, Webots (Cyberbotics co.)were used for the dynamical computation of the arm
movement.

3.2

Controller

The neural network used as a controller has fivelayers, and the number of cells of each layer is 6,8,3,8,
and 6 from the input layer to output layer, respectively. The network receives each muscle length li(i = 1, 2, · · · , 6) and sends motor signals ui to eachmuscle. Back-propagation method was used to train
the network and the teacher data were given as follows.
(1) The learning was instructed so as to send
the control signal to keep the postures represented by input signals. We recorded various-325-

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Japan, January 11-13, 2014

pairs of motor commands u and the muscle
length l when the arm came to rest by thegiven command, and used the data as theteacher data set to train the neural network.
The motor commands to the flexor and extensor muscle were chosen so as to satisfy the
relation u2j−1 + u2j = 1.0 (j = 1, 2, 3).
(2) One neuron in the third (middle) layer wastrained to output the y-axis value of the hand
position.
The above learning (1) and (2) were done alternatively. The input-output relation of each neuron was
1given by a sigmoidal function f (x) =1 + exp(−x)
and the output range [0.1, 0.9] of the third and thefifth layer was scaled into the range [−0.3, 0.9] [m]that expresses the y-axis value of the hand positionand [0, 1] of the motor command ui , respectively.

3.3

Simulation

The task is to bring the y-axis value of the hand
position to the desired value y d . The motor command was computed by the controller in every 0.04
second based on the input signal that expresses themuscle lengths.
The activity of the neural cell in the middle layerthat expresses y-axis value of the hand position was
modulated by the following feedback:y ← y + ϵ(y d − y),(4)where ϵ is the feedback gain and ϵ = 0.5 in thissimulation. The reaching task is judged as the end
πwhen θ̇i (i = 1, 2, 3) becomes less than[rad/s] for36
one second.

3.4

Experimental results

Fig. 3(a) shows the hand trajectory and changeof arm posture during reaching from the initial posπ π πture θ = ( , − , − ) [rad] to the desired one
444y d = 0.4 [m]. Fig. 3(b) is the arm trajectorywith the same condition in Fig. 3(a) except the
point that perturbation is applied during reaching.
The perturbation was the anti-clockwise joint torqueτ = (−6.0, −4.0, −2.0) [N·m] for one second duringarm movement. The hand overshot the target position, but reached the target afterward by the diﬀerent posture from the final posture in Fig. 3(a), i.e.,©ICAROB 2014

the proposed control model dynamically generates
reaching trajectory.
Fig. 3(c) is the results when the shoulder joint isfixed at the initial angle from the same initial posture
as in Fig. 3(a). The trajectory is much diﬀerent from
Fig. 3(a), however, the hand approached the target by using remaining DOF, knee and wrist joints.
This result shows that the proposed model realizes
the compensatory control by utilizing abundant degrees of freedom even if some DOFs are restricted
like the example of the spinal frog mentioned in the
introduction.

4

Conclusion

In this study we proposed a control model thatutilizes abundant degrees of freedom. The model architecture is a simple five-layer neural network witha feedback control for the middle layer. The resultsof the computer simulation of a 3-link arm, however,
have shown that the model can perform a synergetic
control that exploits abundant DOFs of the bodyand reproduced the example of the spinal frog, i.e.,the model brought the hand to a given target by using three joints if all joints are available, but even
if one joint is fixed, the model computed the solution to perform the given task by utilizing the otherjoints.
The important feature of this model is that the
trajectory of each DOF is not controlled as in traditional control systems in the field of engineering,instead only the essential variables, such as the hand
position, are controlled, and the control signals foreach DOF is autonomously generated in a compensatory manner so as to realize the desired value ofthe essential variables. The combination of the desired value of essential variables forms a manifold inthe space of the variables and the manifold is called
uncontrolled manifold (UCM) [9], and many recent
studies have reported that our nervous system stabilizes the UCM in many movement tasks [5, 9, 10].
Our model would be a most primitive and simplest
model that realizes the synergetic control that stabilizes the UCM.

Acknowledgement
This work was partially supported by KAKENHI(25282183).

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References[1] I. M. Gelfand and M. L. Latash. On the problemof adequate language in motor control. Motor
Control, 2(4):306–313, 1998.
0.8[2] N. A. Bernstein. The co-ordination and regulation of movements. Pergamon Press, 1967.

Y [m]

0.6

[3] B. Irie and M. Kawato. Acquisition of internal representation by multilayered perceptrons.
Electronics and Communications inJapan, Part III: Fundamental Electronic Science, 74(11):112–118, 1991.

0.4
0.2
0

-0.6

-0.4

-0.2

0

0.2

0.4

[4] M. Scholz. Validation of nonlinear pca. Neural
Processing Letters, 36(1):21–30, 8 2012.

X [m]
(a)

0.8

[5] M. L. Latash, J. P. Scholz, and G. Schöner. Toward a new theory of motor synergies. Motor
Control, 11:276–308, 2007.

Y [m]

0.6

[6] L. Ting. Dimensional reduction in sensorimotor
systems:a framework for understanding muscle
coordination of posture. Progress in Brain Res,
165:299–321, 2007.

0.4
0.2
0

-0.6

-0.4

-0.2

0

0.2

0.4

[7] M. Kumamoto, T. Oshima, T. Yamamoto. Control properties induced by the existence of antagonistic pairs of bi-articular musclesmechanical engineering model analyses. Human Movement Science, 13(5):611–634, 1994.

X [m]
(b)

0.8

Y [m]

0.6

[8] H. Kambara, K. Kim, D. Shin, M. Sato, andY. Koike. Learning and generation of goaldirected arm reaching from scratch. Neural Network, 22(4):348–61, 2009.

0.4
0.2
0

-0.6

-0.4

-0.2

0

0.2

[9] J. P. Scholz and G. Schöner. The uncontrolled
manifold concept: Identifying control variables
for a functional task. Experimental Brain Research, 126:289–306, 1999.

0.4

X [m]
(c)

Figure 3: Change of arm posture during reaching. Theπππ
initial posture (gray line) is given by θ = ( , − , − )444[rad], and the target hand position is y d = 0.4. (a) is
normal reaching (b) is the case with perturbation, and(b) is the result when shoulder angle is fixed. The startand end of the perturbation is indicated by arrows.

©ICAROB 2014[10] J. Nishii, Y. Hashizume, S. Kaichida, H. Suenaga, and Y. Tanaka. Constraint and exploitation of redundant degrees of freedom during
walking. Robotis and Autonoumos Systems,
60(5):679–684, 2012.

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Japan, January 11-13, 2014

Extension of Genetic Toggle Switch Based on the Effective Search of State
Transitions
M. Sugii1, A. Fauré2 and H. Matsuno2

1

Media and Information Technology Center, Yamaguchi University, 2-16-1 Tokiwadai, Ube-shi, Yamaguchi 7558611, Japan
2
Graduate School of Science and Engineering, Yamaguchi University, 1677-1 Yoshida, Yamaguchi-shi, Yamaguchi 7538512, Japan
Tel : 81-836-85-9916; Fax : 81-836-85-9910
manabu@yamaguchi-u.ac.jp

Abstract: We propose a new procedure to effectively design a mathematical model in two steps. The first step is thecreation of possible network architectures under a logical formalism. The second step is the creation of dynamic models,
both using a system of differential equations for the analysis of dynamics in the model and hybrid functional Petri net
as a common platform for knowledge sharing between biologists and computer scientists.
We extended a genetic toggle switch from 2-state to 3-state by our proposed procedure. Differential equations for 3state genetic toggle switch were produced by extending the ones for 2-state genetic toggle switch. Nullclines weredrawn as a three-dimensional diagram, which shows three stable states of the genetic toggle switch as three crosspoints of three nullclines.
Keywords: Synthetic biology, Artificial genetic circuit, Logical modeling, Dynamic modeling.

I. INTRODUCTION

extended method
logical model

determination of
structure information

In the context of synthetic biology, artificial genetic
circuits are designed in the following way: after setting
a biological target phenomenon to be investigated,
reaction parameter estimations among related molecules
are conducted based on the dynamic analyses withmathematical models. Finally a system of biologicalreactions is developed with these molecules in vivo orin vitro (Fig.1). Gradner et al. produced an artificial
genetic toggle switch [1]. They set a simple biologicalphenomenon and estimated the parameters from
biological information, constructing a system ofdifferential equations enabling bistable genetic circuit.
Many possible models can usually be considered for thetarget phenomenon, which motivates us todevelop an
effective method to select the best candidates. We
propose a new procedure to effectively design amathematical model in two steps and extended a genetic
toggle switch from 2-state to 3-state by our proposed
procedure.

designtarget phenomenon

realization
mathematical model

verification

biological systemverification

dynamic estimationby computing

observation
by biological experiments

Fig.1. Framework of synthetic biology
target phenomenon

structure model

state transitions

biological model

II. METHODS
1. 2-state genetic toggle switch
The key of our proposed method is to separate thedesign process of a mathematical model into two steps.
Our proposed method enables us to realize a suitable©ICAROB 2014

・ differential equations
Petri net
incorporation of ・dynamic parameters

Fig.2a. 2-state genetic toggle switch [1]-328-The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

artificial genetic circuit in more reduced time and costthan existing methods.
Fig.2 shows the 2-state genetic toggle switch [1].
This biological model consists of two genes A and B
being led by prompter regions for each. Repressors A
and B are produced from these two genes A and B,
which are initiated by two promoter regions B and A,
respectively. Repressors A and B inhibit the expressionsof promoters A and B, respectively (biological model).
This 2-state gene toggle switch is finally transformed
into either of two stable states, where either of two
genes (target phenomenon) is expressed depending on
an initial amount of repressors A and B. A kinetic modeland nullclines are given as a system of differential
equations as shown in Fig.2b.

differential equations

nullclines

Fig.2b. Differential equations of 2-state genetic
toggle switch [1]Creating structure model

2. Extension to 3-state toggle switch
3-state toggle switch has three stable states as shownin Fig.3a (target phenomenon). Among many possible
logical structure models, three models are presented in
Fig.3a (Creating structure model, Model 1, 2, 3). Statetransitions, which are produced from GINSim [2], aregiven as well (State transitions, Model 1, 2, 3). Model 3
can be eliminated from the candidates because of thecomplicated state transitions, in which more states areused than Models 1 and 2 and inappropriate cycle
behaviors are found. Possible structures of artificial
genetic circuit to be selected in this way will be sent to
the next step, where dynamic parameters areincorporated in the selected structures. Resulting
dynamical models shall be a system of differential
equations and a hybrid functional Petri net model,
which can be executed on the tools such as MATLAB
and Cell Illustrator [3], respectively.

target phenomenon

Model 2Model 3

State transitions
Model 1

3. Simulation of dynamic models
Two different types of simulations are conducted(Fig.3b, Simulation of dynamic models). One is for the
analysis of dynamic behavior of the obtained artificial
genetic circuit model on a system of differential
equations, and the other is for acquiring biologicallyimportant behavior for the designing genetic circuit invivo and/or in vitro. Experts in microbiology need to beparticipated in the process of parameter determinationof these models in order to choose appropriate values
from biological aspects.

Model 2Model 3

4. Realization by biological experiments
E.coli will be used for the realization of the artificial
genetic circuits. Selection of condition for expression©ICAROB 2014

Model 1

Fig.3a. Process of the proposed method-329-

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Japan, January 11-13, 2014

inductions and selection of expression vector need to beconsidered (Fig.3b, Creating biological model).

Simulation of dynamic models
Hybrid Petri net
 Cell Illustrator

Differential equation
 Scilab MATLAB

Visual dynamic model

Analysis of attracterand bifurcation.

III. RESULTS
We extended a genetic toggle switch from 2-state to
3-state by our proposed procedure. 3-state toggle switch
has three stable states as shown in Fig.3a (target
phenomenon) and state transition graphs were produced
using GINSim (Fig.3a, state transitions). Some of thesecandidates could be eliminated based on the complexityof state transition graph, in particular those that
produced more than 3 stable states, or displayed cyclic
behaviors. The selected candidates (Fig.4, structure)
were sent to the next step, where dynamic parameters
were incorporated in the structures. Fig.4 (statetransition graph) shows the state transition graph of theselected candidate for 3-state genetic toggle switch. All
states and transitions can be confirmed in this graph.
Differential equations for 3-state genetic toggle switch
were produced by extending the ones for 2-state genetic
toggle switch (Fig.5). The dynamic behaviors of theobtained artificial genetic toggle switch model was
analyzed on the system of these differential equations.
Nullclines of differential equations for 2-state
genetic toggle switch are shown in Fig.2b (nullclines),
showing two stable states as two cross points of two
nullclines. These two cross points of nullclines
corresponds to two stable states of 2-state genetic toggle
switch. In the case of 3-state genetic toggle switch,
nullclines were drawn as a three-dimensional diagram(Fig.6) and the diagram shows three stable states of thegenetic toggle switch as three cross points of threenullclines in Fig.6a.
Sekine et al. suggested a tunable bistable genetic
toggle switch [4]. They considered that their model canhelp to understand the design principles of the natural
diversification. A state switch from ON or OFF must be
important function on the genetic switch. We
investigated the relation between nullcline diagram andstate transition in order to consider a switching system
on 3-state genetic toggle switch.
Fig.7 shows a relation of the transition states to thethree dimensional diagram of nullclines. Three stablestate points (001, 010, 100), transition states (111, 101,
110, 011, 000), separatrix (purple line) and transition
regions are indicated in the diagram. The genetic toggle
switch can be stabilized at one of the three stable state
points in this diagram even if the genetic toggle switch©ICAROB 2014

Creating biological model
biological model

Fig.3b. Process of the proposed method
structure model

state transition graphFig.4. A structure model and state transition
graph for extension to 3-state toggle switch

Fig.5 Differential equations for 3-state genetic
toggle switch

u1
u2
u3

Fig.6a Nullclines of the 3-state genetic toggle
switch-330-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

starts from any points. For instance, the genetic toggle
switch can be stabilized at 001 stable point if it starts
from the dark gray region (red arrow) and if it starts
from light gray region, the genetic toggle switch can bestabilized at 010 stable point in the diagram (bluearrow). These state transitions correspond to the ones ofthe state transition graph in Fig.4.

VI. CONCLUSION
Our proposed method can evaluate the artificial
genetic circuits and select the best candidate before
incorporating dynamic parameters using state transition
graph. Besides the 3-state toggle switch, many
biological behaviors such as oscillations and flip-flop
can be considered as the targets for artificial genetic
circuits. In order to further evaluate of our proposed
method, these behaviors will be studied in our future
work.

REFERENCES[1] Gardner, T.S., Cantor, C.R., Collins, J.J.,
Construction of genetic toggle switch in Escherichia
coli, Nature 403:339-342, 2000.
[2] Fauré, A., Thieffry, D., Logical modeling of cell
cycle control in eukaryotes: a comparative study,
Molecular Biosystems, 5:1769-81, 2009.
[3] Cell Illustrator: http://www.cellillustrator.com/home[4] Sekine, R., Yamaura, M., Ayukawa, S., Ishimatsu,
K., Akama, S., Takinoue, M., Hagiya, M., Kiga, D.,
Tunable synthetic phenotypic diversification on
Waddington’s landscape through autonomous signaling,
PNAS,Vol.108, No.44:17969-17973,Nov 1,2011.

Fig.6b Nullcline diagrams of each differential equation of the 3-state genetic toggle switch

Fig.7. Relation to the transition states©ICAROB 2014

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Japan, January 11-13, 2014

Hierarchy Based on Neighborhood Template about k-Neighborhood
Template A-Type Three-Dimensional Bounded Cellular Acceptor
Makoto SAKAMOTO1 , Makoto NAGATOMO1 , Xiaoyang FENG1 , Tatsuma KUROGI1 ,Tuo ZHANG1 , Takao ITO2 , Yasuo UCHIDA2 , Tsunehiro YOSHINAGA3 ,Satoshi IKEDA1 , Masahiro YOKOMICHI1 , and Hiroshi FURUTANI1
1

University of Miyazaki, Miyazaki 889-2192, Japan.
Ube National College of Technology, Ube, Yamaguchi 755-8555, Japan.
3
Tokuyama College of Technology, Shunan, Yamaguchi 745-8585, Japan.
2

Abstract: Recently, due to the advance in dynamic image processing, computer animation, augmented
reality (AR), and so both, it has become increasingly apparent that the study of four-dimensional patternprocessing (three-dimensional pattern processing with time axis) should be very important. Thus, thestudy of four-dimensional automata as the computational model of four-dimensional pattern processing
has been meaningful. From this point of view, we first introduced a four-dimensional automaton in 2002.
In the multi-dimensional pattern processing, designers often use a strategy whereby features are extracted
by projecting high-dimensional space on low-dimensional space. In this paper, from this viewpoint, we
introduce a new computational model, k-neighborhood template A-type three-dimensional bounded cellular
acceptor (abbreviated as A-3BCA(k)] on four-dimensional input tapes, and discuss hierarchy based onneighborhood template about A-3BCA(k).
Keywords: cellular acceptor, computational complexity, configuration-reader, converter, four-dimension,neighbor

concludes the acceptance or non-acceptance of given
four-dimensional tape(see Fig.1). When an input four-

I. INTRODUCTION
Due to the advances in many application areas suchas computer animation, dynamic image processing, andso on, the study of four-dimensional pattern processing
has been of crucial importance. Thus, the study of fourdimensional automata as the computational models offour-dimensional pattern processing has been meaningful. From this point of view, we first proposed fourdimensional automata as computational models of fourdimensional pattern processing in 2002 [4], and investigated their several accepting powers. By the way,in the multi-dimensional pattern processing, designers
often use a strategy whereby features are extracted
by projecting high-dimensional space on low dimensional space. So, from this viewpoint, we introduce a
new computational model, k-neighborhood template Atype three-dimensional bounded cellular acceptor (abbreviated as A-3BCA(k)) on four-dimensional tapesin this paper, and discuss some basic properties. An
A-3BCA(k) consists of a pair of a converter and aconfiguration-reader. The former converts the given
four-dimensional tape to three-dimensional configuration. The latter determines whether or not the derived three-dimensional configuration is accepted, and©ICAROB 2014

Fig 1: A-3BCA(k).
dimensional tape is presented to the A-3BCA(k), a
three-dimensional cellular automaton as the converter
first reads it to the future direction at unit speed(i.e., one three-dimensional rectangular array per unittime). From this process, the four-dimensional tapeis converted to a configuration of the converter which
is a state matrix of a three-dimensional cellular automaton. Second, three-dimensional automaton as
the configuration-reader reads the configuration and-332-

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Japan, January 11-13, 2014

determines its acceptance. We say that an input
four-dimensional tape is accepted by the A-3BCA(k)if and only if the configuration is accepted by theconfiguration-reader. Therefore, the accepting powerof the A-3BCA(k) depends on how to combine the converter and the configuration-reader. An A-3DBCA(k)(A-3N BCA(k)) is called a k-neighborhood templateA-type three-dimensional deterministic bounded cellular acceptor (k-neighborhood template A-type threedimensional nondeterministic bounded cellular acceptor). This paper mainly investigates how the diﬀerenceof the neighborhood template of the converter aﬀects
the accepting powers of A-3DBCA(k) s. In general, it
is well known that two-dimensional digital pictures have4- and 8-connectedness, and three-dimensional digital
pictures have 6- and 26-connectedness. However, we
include the remarkable pixel or voxel in neighbor. In
other words, we deal with 5- and 9-connectedness inthe two-dimensional case, and 7- and 27-connectedness
in the three-dimensional case in this paper. In Addition, in our another paper, we mainly investigate how
the diﬀerence of configuration-reader aﬀects the accepting powers of A-3DBCA(k) s. When an input fourdimensional tape is presented to the A-3BCA(k), a
three-dimensional cellular automaton as the converter
first reads it to the future direction at unit speed (i.e.,
one three-dimensional rectangular array per unit time),
and a two-dimensional cellular automaton as the converter next reads a converted three-dimensional configuration downward at unit speed (i.e., one plane perunit time). From this process, the four-dimensional
tape is converted to a configuration of the converter
which is a state matrix of a two-dimensional cellular automaton. Second, two-dimensional automaton as
the configuration-reader, reads the configuration anddetermines its acceptance. We say that an input
four-dimensional tape is accepted by the A-3BCA(k)if and only if the configuration is accepted by theconfiguration-reader. Therefore, the accepting powerof the A-3BCA(k) depends on how to combine the converter and the configuration-reader. An A-3DBCA(k)(A-3N BCA(k)) is called a k-neighborhood templateA-type three-dimensional deterministic bounded cellular acceptor (k-neighborhood template A-type threedimensional nondeterministic bounded cellular acceptor).
A DA[1] (N A, DB[5], N B, DO[2], N O,
DOP [3], N OP , DP [3], N P , DT M [4], N T M ) is
called a three-dimensional deterministic finite automaton (three-dimensional nondeterministic finite automaton, deterministic three-dimensional bounded cellular
acceptor, nondeterministic three-dimensional boundedcelular acceptor, three-dimensional deterministic on-line tessellation acceptor, three-dimensional nondeterministic on-line tessellation acceptor, deterministic
three-way parallel/sequential array acceptor, nondeterministic three-way parallel/sequential array acceptor,
deterministic four-way parallrel/sequential array acceptor, nondeterministic four-way parallel/sequential array
acceptor, three-dimensional deterministic Turing machine, three-dimensional nondeterministic Turing machine). Let T (M ) be the set of four-dimensional tapes
accepted by a machine M , and let L[A-3DBCA(k)]= {T |T =T (M ) for some A-3DBCA(k) M }. L[A3N BCA(k)], etc. are defined in the same way as L[A3DBCA(k)].
∑Letbe a finite set of symbols. A four-dimensional∑
tape over ∑is a four-dimensional rectangular array ofelementsof. The setof all four-dimensional ∑
tapes
∑
∑ (4)
(4)
over
is denoted by. Given a tape x ∈,for each integer j(1 ≤ j ≤ 4), we let lj (x) be the length∑(4)of x along the jth axis. The set of all x ∈withl1 (x) = n1 , l2 (x) = n2 , l3 (x) = n3 , and l4 (x) = n4 is de∑(n1 ,n2 ,n3 ,n4 ). When 1 ≤ ij ≤ lj (x) for each
noted byj(1 ≤ j ≤ 4), let x(i1 , i2 , i3 , i4 ) denote the symbol in x
with coordinates (i1 , i2 , i3 , i4 ). Furthermore, we definex[(i1 , i2 , i3 , i4 ), (i′1 , i′2 , i′3 , i′4 )], when 1 ≤ ij ≤ i′j ≤ lj (x)for each integer j(1 ≤ j ≤ 4), as the four-dimensional
input tape y satisfying the follwing conditions:(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 + i2 − 1, r3 + i3 − 1, r4 +i4 − 1). (We call x[(i1 , i2 , i3 , i4 ), (i′1 , i′2 , i′3 , i′4 )] the[(i1 , i2 , i3 , i4 ), (i′1 , i′2 , i′3 , i′4 )]-segment of x.)
We let each sidelength of each input tape of these automata be equivalent in order to increase the theoreticalinterest.

II. MAIN RESULTS
This section investigates how the diﬀerence of theneighborhood template of converter aﬀects the accepting powers of A-3BCA(k)’s. First, we investigate thediﬀerence between the accepting powers of one-neighborand five-neighbor.
Lemma 1. Let T1 = x∈{0,1,2}(4) |∃n ≥ 1 [l1 (x) =l2 (x) = l3 (x) = l4 (x) = n & ∀i(1≤i≤n)[x[(1,1,n,i),(n,n,n,i)] = x[(1,1,n,i), (n,n,i,n)]]]. Then, (1) T1∈ L[DA-3DBCA(7)] ∩ L[DB-2DBCA(7)] ∩ L[DO2DBCA(7)], and (2) T1 ∈/ L[T M -2N BCA(1)].
Proof: (1) The proof is omitted here since it is easy

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to prove. (If necessary, see the proof of Lemma 1(1) in[6].) (2) We can show T1 ∈/ L[T M -3DBCA(1)]. by using the same technique as in the proof of theorem 1 in
[7]. Therefore, below, we show T1 ∈ L[T M -3DBCA(1)].
Suppose that there exists a T M -3DBCA(1) M = (R,B) accepting T1 , where R is a converter and B is aconfiguration-reader.
Let S be the number of states of each cell of R. For eachn ≤ 1, let
V (n) = {x∈{0,1,}(4) |l1 (x) = l2 (x) = l3 (x) = l4 (x) =n + 1 & ∀i(1≤i≤n)x[(1,1,1,i), (n + 1,n + 1,n,i)] ∈ {0}(3) ,x[(1,1,n + 1,n + 1), (n + 1,n + 1,n + 1,n + 1)] ∈ {0} (3) },W (n) = V (n) ∩ T1 .

Also, for each x ∈ V (n) and for each i(1≤i≤n), letρ(x) ≡ the configuration of R just after needing x, ρU (x)≡ [(1,1,n+1,i),(n+1,n+1,n+1,i)]-segment of ρ(x), andρD (x) ≡ the [(1,1,n + 1,i),(n + 1,n + 1,n + 1,i)]-segmentof ρ(x). Further, for each n ≥ 1, let C(n) = {ρD (x) |x∈W (n)}. Then, the following two propositions must
hold.
Proposition 1. For each i(1 ≤ i ≤ n),(i) For any two tapes x, y ∈ V (n) such that their[(1,1,1,i),(n+1,n+1,n,i)]-segment are identical, ρU (x) =ρU (y),(ii)For any two tapes x,y ∈ V (n) such that their[(1,1,1,i),(n+1,n+1,n,i)]-segments are identical, ρD (x)= ρD (y).
[proof : Since R is deterministic and is of one-neighbor,the proof is easy to see.
□]
Proposition 2. For any two diﬀerent tapes x, y ∈W (n), ρD (x) ̸= ρD (y).
[Proof : Suppose, on the contrary, that ρD (x) = ρD (y).
Consider the tapes z ∈ V (n) satisfying the following two
conditions for each i(1 ≤ i ≤ n):
1 z[(1,1,1,i), (n + 1,n + 1,n,i)] = x[(1,1,1,i),⃝(n+1,n+1,n,i)],2 z[(1,1,n+1,i), (n+1,n+1,n+1,i)] = y[(1,1,n+1,i),⃝(n + 1,n + 1,n + 1,i)].
Clearly, x ∈ W (n) ⊆ T1 . Thus x is accepted by M .
Therefore, ρ(x) is a accepted by B.
1 and ProposiOn the other hand, it follows from ⃝2tion 1 (i) that ρU (z) = ρU (x), and it follows from ⃝and Proposition 1 (ii) that ρD (z) = ρD (y). Further,from the foregoing assumption ρD (z) = ρD (y), it follows that ρD (z) = ρD (x). From ρU (z) = ρU (x) and©ICAROB 2014ρD (z) = ρD (x), it follows that ρ(z) = ρ(x).
Since ρ(x) is accepted by B, ρ(z) is also accepted by
B. Consequently, z is also accepted by M . This is acontradiction.(Note that z ∈/ T1 .)□]
Proof of Lemma 1(continued ) : As is early seen,|W (n)| = 2n(n+1)(n+1) and |C(n)| ≤ s(n+1)(n+1)
Therefore, it follows for large n that |W (n)| < |C(n)|.
Consequently, it follows for such large n that there mustbe two diﬀerent tapes x,y ∈ W (n) such that ρD (x) ̸=ρD (y). This contradicts Proposition 2.
□
Theorem 1. For each A ∈ {DA, N A, DB, N B, DO,
N O, DOP ,N OP , DP , N P , T M } and f or each X ∈{D, N }, L[A-3XBCA(1)] ⊊ L[A-3XBCA(7)].
Proof: The inclusion relarion holds immediately definitions. Further, it is easily seen from Lemma 1 and
Proposition 1 in [1] that the theorem holds.
□
We investigate the diﬀerence between the accepting powers of seven-neighborhood and twenty-sevenneighbor. As shown later in Theorem 2 and 3, diﬀerent
situations emerge depending on whether the converter
is deterministic or nondeterministic.
First, we consider the case when the converter is deterministic.
Theorem 2. For each A ∈ { DA, N A, DB, N B, DO,
N O }, L[A-3DBCA(7)] ⊊ L[A-3DBCA(27)].
Proof : The inclusion relation holds immediately fromdefinitions. Further, it is easily seen from Theorem 3
and Proposition 1 in [7] that the theorem holds.
□
We conclude this action by investigating the diﬀerence between the accepting powers of seven-neighborand twenty-seven-neighbor for the case when converter
is nondeterministic.
Theorem 3. For each A ∈ { DA, N A, DB, N B, DO,
N O, DOP , N OP , DP , N P , T M }, L[A-3DBCA(7)]= L[A-3N BCA(27)].
Proof: For each A ∈ { DA, N A, DB, N B, DO,
N O, DOP , N OP , DP , N P , T M }, it is obviousthat L[A-3N BCA(7)] ⊆ L[A-3N BCA(27)] from definitions. Below, we show that L[A-3N BCA(7)] ⊇ L[A3N BCA(27)].
a given A ∈ { DA, N A, DB, N B, DO, N O, DOP ,N OP , DP , N P , T M }, let M = (R, B) be an A3N BCA(27). We now consider the A-3N BCA(7) M ′-334-

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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= (R , B ) which acts as follows.
Suppose that a four-dimensional input tape x witheach sidelength n (n ≥ 2) is presented to M . Since
R′ has seven neighbors, each voxel of R′ can refer directly to the states of north, south, east, west, up, down,neighbor voxels, but not other neighbor voxels. Therefore, by guessing the six states of neighbor voxels whichcannot be referred to directly, and by checking whether
or not this guess is convect, each voxel of R′ simulates
the action of the corresponding voxel of R. In fact, M ′= (R′ , B ′ ) acts as follows.
(i) Action of the cnoverter R′

R′ starts to act with the same initial configuration
with one of R. That is, every voxel reading the boundary symbol is in q# , which is the boundary state of R,
and all of the other voxels are in q0 , which is the initialstate of R. Next, the seven states of neighbor voxels
of R is easily simulated by R′ , and the twenty states ofother neighbor voxels of R are guessed nondeterministically by R.
(ii) Action of the configuration-reader B ′B ′ accepts the configuration of R′ just after reading
1 ⃝,
2 ⃝
3 are
x (say, ρ(x)), if and only if the following ⃝,satisfied.
1 For each i, j, k (1 ≤ i, j, k ≤ n), no (i, j, k)-voxel⃝
enters the dead state.
2 The information guessed by any (i, j, k)-voxel on⃝
the last three-dimensional rectangular array of fourdimensional input tape is correct.
3 Let h be a mapping extracting one state, which is⃝
obtained by simulating the action of the corresponding
voxel of R, from the states stored in the state of (i, j,k)-voxel. Let h be a projection which is obtained byextending the mapping h. Then h(ρ(x)) is accepted byB.
1 and ⃝
2 in the foregoing, check whether or not R′In ⃝can correctly simulate the action of R. Therefore, it isclear thet h(ρ(x)) reflects the configuration of R just
1 and ⃝
2 are satisfying.
after reading x, if ⃝is easily seen that T (M ′ ) = T (M ) for M = (R′ , B ′ ).
Thus, L[A-3N BCA(7)] ⊇ L[A-3N BCA(27)]. This
completes the proof of the theorem.
□

In this paper, we investigated how the diﬀerence of neighborhood template of the converter affects the accepting powers of k-neighborhood template A-type three-dimensional bounded cellular acceptor(abbreviated as A-3BCA(k)). Generally speaking,when the converter is deterministic, the accepting powerof the A-3BCA(k) tends to be more powerful as thenumber of neighborhood cells of the converter increases
or the accepting power of the configuration-reader ismore powerful. However, this tendency is not always
true when the converter is nondeterministic.
We conclude this paper by giving two open problems.
(1) For each A ∈ {DOP , N OP , DP , N P , DT M ,N T M }, L[A-3DBCA(7)] ⊊ L[A-3DBCA(27)]?
(2)L[N O-3N BCA(7)] ⊊ L[DOP -3N BCA(27)]?

REFERENCES[1] M.Blum and C.Hewitt, “Automata on a twodimensional tape”, IEEE Symposium on Switchingand Automata Theory, pp. 155-160, 1967.
[2] K.Inoue and A.Nakamura, “An n-dimensional online tessellation acceptor”, The Transactions of the
IECE, Japan, J59-D(4), pp. 299-236, 1976.
[3] A.Rosenfeld and D.L.Milgram, “Parallel/sequential
array acceptor”, Information Processing Letters, 2,
pp. 43-46,1976.
[4] M.Sakamoto, H.Okabe, S.Nagami, S.Taniguchi,
T.Makino, Y.Nakama, M.Saito, M.Kono, andK.Inoue, “A note on four-dimensional finite automata”, WSEAS Transactions on Computers, Issue
5, Vol.3, pp. 1651-1656, 2004.
[5] S.Seki, “Real-time recognition of two-dimensional
tapes by cellular automata”, Information Sciences,19, pp. 179-198, 1979.
[6] H.Taniguchi, K.Fujii, K.Inoue and I.Takanami,“k-Neighborhood template A-type two-dimansionalbounded cellular acceptors (Part II)”, Technical Report No. AL82-19, IECE, Japan, 1982.
[7] H.Taniguchi, K,Inoue and I.Takanami, “kNeighborhood template A A-type two-dimensionalbounded cellular acceptor”, The Transactions of
the IECE, Japan, J69-D(3), pp. 291-301, 1986.

III. CONCLUSIONS©ICAROB 2014

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Hierarchy Based on Configuration-Reader about k-Neighborhood Template
A-Type Three-Dimensional Bounded Cellular Acceptor
Makoto SAKAMOTO1 , Tuo ZHANG1 , Tatsuma KUROGI1 , Makoto NAGATOMO1 , Xiaoyang FENG1 ,Yasuo UCHIDA2 , Takao ITO2 , Tsunehiro YOSHINAGA3 ,Satoshi IKEDA1 , Masahiro YOKOMICHI1 , and Hiroshi FURUTANI1
1

University of Miyazaki, Miyazaki, Miyazaki 889-2192, Japan.

2

Ube National College of Technology, Ube, Yamaguchi 755-8555, Japan.

3

Tokuyama College of Technology, Shunan, Yamaguchi 745-8585, Japan.

Abstract: Blum and Hewitt first proposed two-dimensional automata as computational models of twodimensional pattern processing − two-dimensional finite automata and marker automata, and investigated theirpattern recognition abilities in 1967. Since then, many researchers in this field have investigated the properties ofautomata on two- or three-dimensional tapes. On the other hand, the question of whether or not processing fourdimensional digital patterns is more difficult than processing two- or three-dimensional ones is of great interest
from both theoretical and practical standpoints. Thus, the study of four-dimensional automata as the computationalmodels of four-dimensional pattern processing has been meaningful. From this point of view, we are interested infour-dimensional computational models, In this paper, we introduce a new four-dimensional computational model,
k-neighborhood template A-type three-dimensional bounded cellular acceptor on four-dimensional input tapes,
and investigate about hierarchy based on configuration-reader about this model.
Keywords : cellular acceptor, configuration-reader, converter, finite automaton, four-dimension, on-line tessellation
acceptor, parallel/sequential array acceptor, Turing machinemines its acceptance. We say that a four-dimensional input
tape is accepted by the A-3BCA(k) if and only if the configuration is accepted by the configuration-reader. Therefore, the accepting power of the A-3BCA(k) depends
on how to combine the converter and the configurationreader. An A-3DBCA(k) (A-3N BCA(k)) is called ak-neighborhood template A-type three-dimensional deterministic bounded cellular acceptor (k-neighborhood template A-type three-dimensional nondeterministic boundedcellular acceptor). A DA[1] (N A, DB[5], N B, DO[2],N O, DOP [3], N OP , DP [3], N P , DT M [4], N T M )is called a three-dimensional deterministic finite automaton (three-dimensional nondeterministic finite automaton,
deterministic three-dimensional bounded cellular acceptor, nondeterministic three-dimensional bounded celular
acceptor, three-dimensional deterministic on-line tessellation acceptor, three-dimensional nondeterministic online tessellation acceptor, deterministic three-way parallel/sequential array acceptor, nondeterministic three-way
parallel/sequential array acceptor, deterministic three-way
parallrel/sequential array acceptor, nondeterministic fourway parallel/sequential array acceptor, three-dimensional
deterministic Turing machine, three-dimensional nondeterministic Turing machine). Let T (M ) be the set of fourdimensional tapes accepted by a machine M , and let L[A3DBCA(k)] = {T |T =T (M ) for some A-3DBCA(k) M }.

I. INTRODUCTION AND PRELIMINARIESIn 2002, we first introduced a four-dimensional automaton, and investigated some properties [4]. In general, in the multi-dimensional pattern processing, designers often use a strategy whereby features are extracted
by projecting high-dimensional space on low-dimensional
space. In this paper, from this viewpoint, we introduce
a new computational model, k-neighborhood template Atype three-dimensional bounded cellular acceptor (abbreviated as A-3BCA(k)) on four-dimensional tapes, and discuss some basic properties. An A-3BCA(k) consists of apair of a converter and a configuration-reader. The former converts the given four-dimensional tape to the threedimensional configuration and the latter determines the acceptance or nonacceptance of given four-dimensional tapewhether or not the derived three-dimensional configuration is accepted. When a four-dimensional input tape ispresented to the A-3BCA(k), a three-dimensional cellular automaton as the converter first reads it to the future
direction at unit speed (i.e., one three-dimensional rectangular array per unit time). From this process, the fourdimensional tape is converted to a configuration of the converter which is a state matrix of a three-dimensional cellular automaton. Second, three-dimensional automaton as
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L[A-3N BCA(k)], etc. are defined in the same way as
L[A-3DBCA(k)].
∑Let ∑be a finite set of symbols. A four-dimensional
tape over ∑ is a four-dimensional rectangular array of elements of. The∑
set of all four-dimensional ∑tapes over
∑
(4)
(4)
is denoted by. Given a tape x ∈, foreach integer j(1 ≤ j ≤ 4), we let lj (x) be the length∑(4)of x along the jth axis. The set of all x ∈withl1 (x) = n1 , l2 (x) = n2 , l3 (x) = n3 , and l4 (x) = n4 is∑(n1 ,n2 ,n3 ,n4 )denoted by. When 1 ≤ ij ≤ lj (x) for eachj(1 ≤ j ≤ 4), let x(i1 , i2 , i3 , i4 ) denote the symbol inx with coordinates (i1 , i2 , i3 , i4 ), as shown in Fig.1. Furthermore, we define x[(i1 , i2 , i3 , i4 ), (i′1 , i′2 , i′3 , i′4 )], when
1 ≤ ij ≤ i′j ≤ lj (x) for each integer j(1 ≤ j ≤ 4), as
the four-dimensional input tape y satisfying the follwing
conditions:(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 + i2 − 1, r3 + i3 − 1, r4 + i4 − 1).
(Wecallx[(i1 , i2 , i3 , i4 ), (i′1 , i′2 , i′3 , i′4 )][(i1 , i2 , i3 , i4 ), (i′1 , i′2 , i′3 , i′4 )]-segment of x.)

2 & (each symbol on the remaining parts is “0” or “1”)& x[(i,1,n+1,n+1), (i,n,n+1,n+1)]̸=x[(n+1,1,n+1,n+1),(n+1,n,n+1,n+1)]]]}. Then, (1) T1 ∈L[N A-3DBCA(1)],and (2) T1 ∈L[DA-3DBCA(27)].
/
Proof: It is easily seen that there exists a nondeterministic two-dimensional finite automaton accepting the set oftwo-dimensional tapes which are obtained by extracting thebottom plane from the tape contained in T1 . Therefore, (1)holds. On the other hand, the proof of (2) is similar to thatof Lemma 2(2) in [7].
□Lemma 2.
Let T2 = {x ∈ {0,1}(4) | ∃ n ≥ 1[l1 (x) = l2 (x) = l3 (x) = l4 (x) = 2n & [x(1,1,2n,2n),(2n,n,2n,2n) = x[(1,n+1,2n,2n),(2n,2n,2n,2n)]]}. Then,(1) T2 ∈ L[DOP -3DBCA(1)], and (2) T2 ∈/ L[N O3DBCA(27)].

theWe let each sidelength of each input tape of these automata
be equivalent in order to increase the theoretical interest.
x(1, 1, 1, l4 (x))
north
west
l3 (x)

up
down

east
south

x(l1 (x),l2 (x),l3 (x),l4 (x))
x(1, 1, 1, 1)
up

north
west
l3 (x)

down

east
south

࡮

࡮࡮

l1 (x)

l2 (x)

futurex(l1 (x),l2 (x),l3 (x),1)

time axis
l2 (x)

l1 (x)(the 4th axis)

Proof: (1) Note that there exists a deterministic one-way
parallel sequential array acceptor accepting the set of twodimensional tapes obtained by extracting the bottom planeof the first cube from the tape contained in T2 . It is easily
seen from this fact that (1) holds. (2) The proof is similar to that of Lemma 2(2). Suppose that there exists an
N O-3DBCA(27) M = (R, B) accepting T2 , where R is aconverter and B is a configuration-reader. Let K be the setof each cell of B ∈ N O, and |K| = s.
For each n ≥ 1, let V (n)={x{0,1}(4) | l1 (x)=l2 (x) =l3 (x) = l4 (x)= 2n & x[(1,1,1,1), (2n, 2n, 2n, 2n-1)] ∈{0}(4) }, V ′ (n) = V (n) ∩ T2 , W (n) = {w ∈ K (2) |l1 (w) = 2n & l2 (w) = 1∑
} (K (2) means the set of all twodimensional tapes over .). For each x ∈ V (n), let ρ(x)≡ the configuration of R just after reading x, ρW (x) = thewest half of ρ(x), and ρE (x) ≡ the east half of ρ(x). Further, for each x ∈ V ′ (n), let Run(x) = {z ∈ K (2) | z is arun of B on ρ(x) whose lower right corner symbol is anaccepting state of B.} and r(x) = { z[(1,n),(2n,n)] | z ∈Run(x) } ⊆ W (n). Then, the following proposition must
hold.

past

Fig. 1: Four-dimensional Input Tape.

Proposition 1. For any two different tapes x and y inV ′ (n), r(x) ∩ r(y) = ϕ.

II. MAIN RESULTS[Proof: The proof is similar to that of Proposition 4 in [8].
□]Proof of Lemma 2 (continued): As is easily seen,2|V ′ (n)| = 22n and | W (n)| ≤ s2nThis section investigates how the difference ofconfiguratitu-reader affects the accepting powers of A3BCA(k)’s. First, we start to investigate the case when
the converter is deterministic.

Therefore, it follows for large n that |V ′ (n)| > |W (n)|.
Consequently, ii follows for such large n that there mustbe two different tapes x and y in V ′ (n) such that r(x) ∩r(y) ̸= ϕ. This contradicts Proposition 1.
□

Lemma 1. Let T1 = {x∈{0,1,2}(4) |∃n ≥ 1 [l1 (x) = l2 (x)= l3 (x) = l4 (x) = n+1 & ∃i(1≤i≤n)[x(i,n+1,n+1,n+1) =©ICAROB 2014

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Lemma 3. Let T3 = {x∈{0,1}(4) | ∃n≥ 1[l1 (x) =l2 (x) = l3 (x) = l4 (x) = 2n & x[(1,1,2n,2n), (n,2n,2n,2n)]= x[(n+1,1,2n,2n), (2n,2n,2n,2n)]]}. Then, (1) T3 ∈L[DP -3DBCA(1)], and (2) T3 ∈L[N/OP -3DBCA(27)].

Lemma 4. Let T4 be the set of three-dimensional tapes
described in Lemma 1 in [7]. Then, (1) T4 ∈L[N B3DBCA(1)], and (2) T4 ∈L[DOP/-3DBCA(27)].
Proof: (1) It is easily seen that there exists a nondeterministic one-dimensional branded eellolar automaton accepting the set of two-dimensional tapes which are obtained hy
attracting the bottom plane from the tape contained in T4 .
Therefore, (1) holds. On the other hand, the proof of (2) isshown Lemma 1 in [7].
□Proof: (1) Note that there exists a deterministic two-way
parallel sequential array acceptor accepting the set of twodimensional tapes which are obtained by extracting the bottom plane of the last cube from the tape contained in T3 . It
is easily seen, from this fact, that (1) holds.
(2) Suppose that there exists an N OP -3DBCA(27) M= (R,B) accepting T3 . Let s be the number of statesof each cell of B∈N OP . For each n≥1, let V (n)= {x∈{0, 1}(4) l1 (x) = l2 (x) = l3 (x) = l4 (x) = 2n &x[(1,1,1,1), (2n,2n,2n,2n-1)]∈{0}(4) }, V ′ (n) = V (n) ∩T3 . For each x ∈ V (n), let ρ(x) ≡ the configuration of
R just after reading x, ρN (x) ≡ the nouth half of ρ(x), andρS (x) ≡ the south half of ρ(x). Furthermore, for each x ∈V ′ (n), let conf(x) ≡ the set of possible configuration of B
just after ρN (x) is read, when ρ(x) is accepted by B. (Notethat ρ(x) is accepted by B since each tape in V ′ (n) is accepted by M .) Then, the following two propositions must
hold. (The proofs are omitted here. If necessary, see proofs
of Lemmas 7 and 8 in [6].)Lemma 5. Let T5 = {x ∈ {0,1}(4) | ∃ n ≥ 1 [l1 (x)= l2 (x)=l3 (x)=l4 (x)=2n & [x(1,1,2n,2n), (n,n,2n,2n)]̸=x[(n+1,n+1,2n,2n),(2n,2n,2n,2n)]]}. Then, (1) T5 ∈L[N B-3DBCA(1)], and (2) T5 ∈/ L[N A-3DBCA(27)].
Proof: (1) It is easily seen that there exists a nondeterministic one-dimensional bounded cellular automaton accepting the set of two-dimensional tapes which are obtained byextracting the bottom plane from the tape contained in T5 .
Therefore, (1) holds. On the other hand, the proof of (2) issimilar to that of Lemma 2 in [7].
□From the foregoing lemmas, we can obtain the following theorem when the converter is deterministic.

Proposition 2. (i) For any two tapes x and y in V ′ (n)such that their [(1, 1, 2n, 2n), (n, 2n, 2n,2n)]-segments
are identical, ρN (x) = ρN (y), and (ii) For any two tapes x
and y in V ′ (n) such that their [(n + 1, 1, 2n, 2n), (2n, 2n,2n, 2n)]-segnents are identical, ρS (x) = ρS (y).

Theorem 1. For each k ∈ {1, 7, 27},(1) L[DA-3DBCA(k)] ⊊ L[NA-3DBCA(k)] ⊊L[N B-3DBCA(k)] = L[N O-3DBCA(k)] ⊊L[N OP -3DBCA(k)] ⊊ L[N P -3DBCA(k)](2) L[DB-3DBCA(k)] ⊊ L[N B-3DBCA(k)],(3) L[DO-3DBCA(k)] ⊊ L[N O-3DBCA(k)],(4) L[DB-3DBCA(k)] ⊊ L[DOP -3DBCA(k)] ⊊L[DP -3DBCA(k)], and(5) L[DO-3DBCA(k)] ⊊ L[DOP -3DBCA(k)] ⊊L[N OP -3DBCA(k)].

Proposition 3. For any two different tapes x and y inV ′ (n), conf(x) ∩ conf(y) = ϕ.
Proof of Lemma 3 (continued): As is easily seen,|V ′ (n)| = 22n2

Proof: It is clear from Proposition 1 in [7] that the inclusion relations hold. Therefore, below, we show that theproper inclusion relations held for each k ∈ {1,7,27}.
(1): It is obvious from Proposition l in [7] that L[N B3DBCA(k)] = L[N O-3DBCA(k)].
From Lemma
1, L[DA-3DBCA(k)] ⊊ L [N B-3DBCA(k)] holds,
and from Lemma 5, L [N B-3DBCA(k)] ⊊ L[N B3DBCA(k)] holds. In addition, it is obvious from
Proposition 1 in [7] that L[DOP -3DBCA(k)] ⊆ L[N P 3DBCA(k)]. It follows from this and Lemma 2 that
L[N O-3DBCA(k)] ⊊ L[N OP -3DBCA(k)] holds. Further, it is also obvious from Proposicion 1 in [7] that
L[DP -3DBCA(k)] ⊆ L[N P -3DBCA(k)]. It follows
from this and Lemma 3 that L[N OP -3DBCA(k)] ⊊L[N P -3DBCA(k)] holds.
(2) and (3) : These are easily proved from Lemma 4 andProposition 1 in [7].

Let t(n) be the total number of different configurationsof R just after reading north halves of configurations of R
just after reading tapes in V ′ (n). Clearly,t(n)≤s2n
Therefore, it follows for large n that|V ′ (n)| > t(n)Consequently, it follows for such large n that there
must be two different tapes x and y in V ′ (n) such thatconf(x)∩conf(y) ̸= ϕ. This contradicts Proposition 3. □©ICAROB 2014

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the configuration-reader affects the accepting powers of kneighborhood template A-type three-dimensional boundedcellular acceptor(abbreviated as A-3BCA(k)). As the results, we showed that when the configuration-reader isdeterministic, the A-3BCA(k) which is the converter isnondeterministic is more powerful than the A-3BCA(k)which is the converter is deterministic. However, this tendency is not always true when the configuration-reader isnondeterministic.
We conclude this paper by giving a few open problems.
(1) Accepting powers in the case of alternating version ofconfiguration-reader.
(2) Closure properties of A-3BCA(k).
(3) Recognizability of topological four-dimensional input
tapes by A-3BCA(k).

(4) and (5) : These are also easily proved from Lemmas
4,5,6 and Proposition 1 in [7].
□Next, we investigate the case when the converter is nondeterministic.
Lemma 6. For each k ∈ {1,7,27}, (1) L[N O-3N BCA(k)]⊆ L[DA-3N BCA(k)], (2) L[N O-3N BCA(k)] ⊆L[DB-3N BCA(k)], and (3) L[N O-3N BCA(k)] ⊆L[DO-3N BCA(k)].
Proof: (1) We prove only L[N O-3N BCA(1)] (The othercases are proved similarly.) Let M = (R, B) be an arbitrary
N O-3N BCA(1), and let KR and KB be the set of statesof R and B, respectively. Further, let M ′ = (R′ , B ′ )be a DO-3N BCA(1) which acts as follows for a given
four-dimensional tape x with each sidelength is n (n ≥ 1).
(i) Actions of the converter R′
At each time, each (i, j, k, l)-cell (1 ≤ i, j , k, l, ≤ n)of R′ simulates the action of the corresponding cell of R
on x at the same time. In parallel to this action, the cellselects nondeterministically a state in KB (we let q(i, j,k, l) be the state) and stores the state in its state, when thecell reads a symbol on the top plane of the first cube of x.
Here, q(i,j,k,l) is a guessed state of B which the (i, j, k,l)- cell of B will enter by reading the configuration of R
just after reading x; q(i, j, k, l) will have been stored in
the state of the cell until x is completed to read.
(ii) Actions of the configuration reader B ′For each i, j, k, l(1 ≤ i, j , k, l≤ n), let ρ(i, j, k, l) be astate in KR which the (i, j, k, l)-cell of R′ continues tosimulate the action of corresponding cell of R and enters.
B ′ accepts a configuration of R′ just after reading x if andonly if the following two conditions are satisfied.
1 For each i, j, k, l(1 ≤ i, j, k, l ≤ n), the (i, j, k,⃝l)-cell can enter q(i, j, k, l) when it reads ρ(i, j, k, l).
2⃝q(n,n,n,n)
is an accepting state of B.
It is easily seen that T (M ′ ) = T (M ) for M = (R′ ,B ′ ).
This completes the proof of the lemma.
□

REFERENCES[1] M.Blum and C.Hewitt, “Automata on a twodimensional tape”, IEEE Symposium on Switchingand Automata Theory, pp. 155-160, 1967.
[2] K.Inoue and A.Nakamura, “An n-dimensional on-line
tessellation acceptor”, The Transactions of the IECE,
Japan, J59-D(4), pp. 299-236, 1976.
[3] A.Rosenfeld and D.L.Milgram, “Parallel/sequential array acceptor”, Information Processing Letters, 2, pp.
43-46,1976.
[4] M.Sakamoto, H.Okabe, S.Nagami, S.Taniguchi,
T.Makino, Y.Nakama, M.Saito, M.Kono, and K.Inoue,“A note on four-dimensional finite automata”,
WSEAS Transactions on Computers, Issue 5, Vol.3,
pp. 1651-1656, 2004.
[5] S.Seki, “Real-time recognition of two-dimensional
tapes by cellular automata”, Information Sciences, 19,
pp. 179-198, 1979.
[6] H.Taniguchi, K.Fujii, K.Inoue and I.Takanami, “kNeighborhood template A-type two-dimansionalbounded cellular acceptors (Part II)”, Technical Report
No. AL82-19, IECE, Japan, 1982.

From Lemma 6 and from Proposition 1 in [7], we canobtain directly the following theorem when the converter
is nondeterministic.(The proof is omittied here.) It is ofgreat interest to compare the following Theorem 2 with
Theorem 1 mentioned for the deterministic case.

[7] H.Taniguchi,
K,Inoue and I.Takanami,“kNeighborhood template A A-type two-dimensionalbounded cellular acceptor”, The Transactions of the
IECE, Japan, J69-D(3), pp. 291-301, 1986.

Theorem 2. For each k ∈ {1, 7, 27}, L[DA-3N BCA(k)]= L[N A-3N BCA(k)] = L[DB-3N BCA(k)] =L[N B-3N BCA(k)] = L[DO-3N BCA(k)] = L[N O3N BCA(k)].

[8] H.Taniguchi, K.Inoue and I.Takanami, “Hierarchicalproperties of the k-neighborhood template A-type 2dimensional bounded cellular acceptor”, The Transactions of the IECE, Japan, J69-D(7), pp.1025-1034,
1986.

III. CONCLUSIONSIn this paper, we investigated how the difference of©ICAROB 2014

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Handicap of Othello Game
Graduate course in Computer Science and System Engineering, University of Miyazaki
Department of Computer Science and System Engineering, University of Miyazaki
Department of Business Administration, Ube National College of Technology
Tel : 81-985-58-7414; Fax : 81-985-58-7414
e-mail address: tf13002@student.miyazaki-u.ac.jp

Abstract: In the game of Go, it is known experientially that Black (=the first move) is advantageous. Thus, in the gameof Go, White (=the defensive hand) has received a handicap for this compensation. There is Othello game well known
as the Go. However, the handicap of White in Othello game has been hardly considered until now. This research
considered quantitatively the handicap in Othello game from a viewpoint of points. As our conclusions of Othello gameis the followings.
 The advantage of the defensive hand on board of 4 × 4 is -0.641 points on average.
 The advantage of the defensive hand on board of 4 × 6 is 0.778 points on average.
Keywords: Othello, handicap, handicap stones, first move advantage

1. Introduction

2. Method and Analysis

In the game of Go, how to put handicap has been
discussed for a long time. Its handicap is given bymeans of compensation and handicap stones as known
well.
It is known experientially that Black (= the firstmove) has a first move advantage. Thus, in the game Go,
White (= the defensive hand) has received a handicap
for this compensation. On a 19×19 goban, thispredetermined compensation for Black's first moveadvantage, called "komi", means that White may
receive somewhere on average of +6.5 points.
There are handicap stones as another handicap in thegame of Go. The rank difference within a given amateur
ranking system is one guide to how many handicap
stones should be given to make the game a more equal
contest. Generally, it is considered that one stone is 1316 points in term of points, although this figure is notconstant over levels.
There is a Reversi well known as the Go. Reversi isalso called Othello game in general. It is thought begiven a handicap similar to the Go in Othello game.
However, the discussion of quantitative handicap was
not in the game of Othello such as in the game of Go.
This research considered quantitatively the handicap
about a first move advantage and a handicap stones in
the Othello game from a viewpoint of points.
Throughout this paper, we assume that the handicap
for Black's first move advantage is compensation given
to White. Therefore a negative handicap means asituation with an advantageous to White.

In the game of Othello, regular board is 8 × 8, but in thisresearch, we use some reduced boards such as 4 × 4,
4 × 6 and so on.
In this experiment, players take a next move from all
possible choice at random each other. Firstly, we
enumerate all the situation that a game was finished,
and count the number of results according to thedifference of points of Black and White. Next, we add
all the values (obtained for each point difference) of the
product of a point difference (= Black’s – White’s) andthe number of situations to become its point difference.
Finally, we divide the values obtained above by the total
number of situations. We assume this acquired value tobe a handicap for White.
And the location with a handicap stone assumes thecircumference of a board, see Fig.1.

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
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3. Results and Discussions
3.1 Board of 4 × 4
Fig.2 shows the number of situations of each point
difference (Black’s – White’s) on 4 × 4 board.

Figure.4: The number of situations of each point
difference on 4 × 6 board
The handicap of White becomes +0.778 points, which
implies a first move advantage.
According to an exhaustive search of 4 × 6 board, we
found that Black can win. The fact that the value of thehandicap for White of 4 × 6 board is positive isconsistent with this result.
For handicap stones, we display the location of thehandicap stones as Fig.5.

Figure.2: The number of situations of each point
difference on 4 × 4 board
The advantage of Black is -0.642 points on average,
which implies a first move disadvantage. Thus thehandicap of White becomes -0.642. In fact, it has beenfound that white can certainly win on 4 × 4 board.
Similarly, it has been found that White can win on 6 × 6
board [1]. Although the result of the 8 × 8 regular board
has not been clear, many people believe that White canwin. In this way, Othello game does not become clear
whether the first move is advantageous.
For handicap stones, we display the location of thehandicap stones as Fig.3.

Figure.5: 4 × 6 board
The handicaps for White due to the color and locationof a handicap stones are followings. (See Table.2.)
Table. 2: Handicap stones of 4 × 6 board
Black stone
White stone
A1+5.112- 3.742A2- 0.119+1.812
A3+0.223+1.934A4+5.080- 2.878
B1- 1.057+2.267C1+1.740- 0.381D1+2.118+0.135E1- 0.564+3.255Figure.3: 4 × 4 board
The handicaps for White due to the color and locationof a handicap stones are followings. (See Table.1.)
Table. 1: Handicap stones of 4 × 4 board
Black stone
White stone
A1+6.000- 3.796B1- 0.689+1.540C1- 2.322+1.991
D1+0.929- 6.773

4. Conclusion
In this research, we tried to measure the handicap in
Othello game quantitatively. By choosing a situation at
random from all possible choice, we can estimate thehandicaps for White (=the defensive hand) of 4 × 4 and

3.2 Board of 4 × 6
Fig.4 shows the number of situations of each point
difference (Black’s – White’s) on 4 × 6 board.
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4 × 6 boards at -0.642 and +0.778, respectively. Sign ofthe handicap is decided whether the first move isadvantageous. Unlike the game of Go that an advantageof the first move is confirmed empirically, the Othellogame does not become clear whether the first move isadvantageous. However, our results of handicap are
consistent with results with an exhaustive search. This
indicates that the evaluation method that we proposed iseffective.
Moreover, since the defensive hand was believed tobe advantageous in the game of Othello, it is a very
interesting result that advantage and disadvantage of thefirst move is determined depending on the board. We
have the following conjecture.
Conjecture: Suppose that m,n∈N={1,2,3,…}. For agiven 2m×2n board in the game of Othello, we get thefollowings.
(1) Assume that m=1 or n=1, the game drow.
(2) Assume that m,n≧2 and n+m is odd number, thefirst move is advantageous.
(3) Assume that m,n≧2 and n+m is even number, thefirst move is disadvantageous.
For handicap stones, we found that the side that isplaced in the corner is advantageous. However, we were
not able to find the law except in the corner. To find therules of general, experiments with a large board isrequired.

Reference[1] Joel Feinstein (1993), Perfect Play in 6x6 Othellofrom two alternative starting positions,
http://www.feinst.demon.co.uk/Othello/6x6sol.html[2] Michael H. Albert, Richard J. Nowakowski andDavid Wolfe (2007), Lesson in Play: An
Introduction to Combinatorial Game Theory[3] Elwyn Berlekamp and David Wolf (1994),
Mathematical Go: Chilling gets the Last Point©ICAROB 2014

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Japan, January 11-13, 2014

On Parameter Setting in Identifying the Same
Languages Involved in Different Language Data
Ren Wu1 and Hiroshi Matsuno2
1

Yamaguchi Junior College, 2 Graduate School of Science and Engineering, Yamaguchi University
11346-2, Oaza Daido, Hofu city, Yamaguchi prefecture, 747-1232, Japan
2
1677-1, Yoshida, Yamaguchi city, Yamaguchi prefecture, 753-8511, Japan
Tel: 1 +81-835-32-0138, 2 +81-83-933-5697
1
Email: wu@yamaguchi-jc.ac.jp, 2 matsuno@sci.yamaguchi-u.ac.jp

Abstract: We have proposed a method based on tree structure and string alignment technique for identifying the samelanguages involved in two language classification trees provided by different linguists. In this method, several kinds ofsimilarity measure, such as language name similarity and language general similarity etc., and an algorithm are proposed.
Several unknown parameters are used there and need to be set to constant value in order to calculate the similarities.
This paper aims to determine all the values of these parameters and get the identification results in order to confirm theusefulness and effectiveness of our proposed similarity measures and the algorithm. As the result, we obtained reasonable
good values for the parameters throughout the experiments.
Keywords: world languages tree, language name similarity, language classification similarity
I. I NTRODUCTION
We have proposed a method based on tree structureand string alignment technique for identifying the samelanguages involved in two world languages trees, denotedby W LT s, which are two language classification trees
provided by different linguists. We have named these two
W LT s as TY and TS .
In our previous work, we have quantified several kindsof similarity measure, such as language name similarity,
language classification similarity and language generalsimilarity and so forth. Language name similarity isdefined as string similarity between two language names.
On the other hand, language classification similarity isdefined as a weighted average of three kinds of similarities(family name similarity, parent name similarity and brothername similarity) that are based on language name similarity. Furthermore, language general similarity is defined
as a weighted average of language name similarity andlanguage classification similarity. At the same time, wehave developed an algorithm for finding out same language
pairs involved in TY and TS by applying above definedsimilarity measures. In this algorithm, several unknown
parameters, (e, f, g), (a, b), ∆ and threshold ρ, are usedand need to be set to constant values firstly.
This paper aims to determine all the values of theseparameters and then get the identification results in order
to confirm that our proposed similarity measures and thealgorithm are useful and effective.
This paper is organized as follows. First, we give thedefinitions of our proposed similarity measures and thealgorithm for finding same language pairs. Then, we givethe process for setting the parameters (e, f, g), (a, b), ∆and threshold ρ. Finally, we give the experimental results
by applying the parameter values that are set.

©ICAROB 2014

Fig. 1.

Two W LT s (TY and TS )II. P RELIMINARY
A. Similarity Measure
Fig. 1(1) shows an image of W LT and Fig. 1(2) shows
two W LT s called TY and TS . A language y included in
TY is denoted by y∈Vleaf (TY ). Language y and s are
assumed to be the same language included in TY and TS
respectively.
Fig. 2 shows an example of the same language pair(y, s) involved in TY and TS . The language names of yand s are “ARABIC, SUDANESE CREOLE” and “Arabic,
Sudanese Creole” respectively and the same. Here, allalphabetical notations are not case-sensitive.
Finding out such same language pairs like (y, s) automatically is our purpose. In order to solve this problem,
measure of language name similarity has been introduced
and defined. In the following definition, a term WORD is
used, which is a string consisting of only alphabets, such

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The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014
[Definition 4] Let FN y and FN s be the family namesof languages y∈Vleaf (TY ) and s∈Vleaf (TS ), respectively.
Then family name similarity between y and s, denotedby sd f n(y, s), is defined by
sd f n(y, s) = sd ln(FN y , FN s )(4)2

Fig. 2.

[Definition 5] Let PN y and PN s be the parent’s namesof languages y∈Vleaf (TY ) and s∈Vleaf (TS ), respectively.
Then parent name similarity between y and s, denotedby sd pn(y, s), is defined by

The same language that needs to be identified

as “Arabic”. Language name is a list of WORD(s), justlike language names “ARABIC, SUDANESE CREOLE”
and “Arabic, Sudanese Creole”. If two WORDs are individually included in two language names and are mostsimilar, for example “ARABIC” and “Arabic”, they arecalled WORD pair.

sd pn(y, s) = sd ln(PN y , PN s )2
Languages “NUBI” and “Nubi” shown in Fig. 2 arebrothers of “ARABIC, SUDANESE CREOLE” and “Arabic, Sudanese Creole”, respectively. A brother combinationfrom two sets of brothers, such as (“NUBI”, “Nubi”), iscalled brother language pair.

[Definition 1] Let v and w be two WORDs. WORD
Similarity between v and w, denoted by sd w(v, w), is
defined bylA (v, w) − ed(v, w)
sd w(v, w) =,lA (v, w)[Definition 6] Let x1 (x2 ) be a language included
in TY or TS (TS or TY ), BLx1 ={bl1x1 , bl2x1 , · · · } andBLx2 ={bl1x2 , bl2x2 , · · · } (|BLx1 |≥|BLx2 |>0) be respectively the sets of brother languages of x1 and x2 , andfurther BP (x1 , x2 ) be the set of brother language pair.
Then brother name similarity between x1 and x2 , denoted by sd bn(x1 , x2 ), is defined by(1)

where ed(v, w) and lA (v, w) respectively represent edit
distance and length of optimal alignment between v andw, under the condition that the operations of insertions,deletions and substitutions all cost 1.
2

sd bn(x 1 , x2 )
sd lnnode (x1 , x2 )= 0∑,x2 ) sd lnnode (µ,ν)
 (µ,ν)∈BP (x1 m+n[Definition 2] Let L1 ={v1 , v2 , · · · , vm } and L2 ={w1 , w2 , · · · , wn }(m≥n) be Language Names, (vi , wi′ )be WORD pair. Note that for any vi ∈L1 , if L2 does
not contain wi′ that corresponds to vi , then let wi′ =Null.
Language Name Similarity, denoted by sd ln(L1 , L2 ),between L1 and L2 is defined by∑msd w(vi , wi′ )sd ln(L1 , L2 ) = i=1
(2)
m
2

(6)

(m≥n>0)2
Then language classification similarity, and further language general similarity that integrates language namesimilarity and language classification similarity, are defined as follows.
[Definition 7] language classification similarity betweeny∈Vleaf (TY ) and s∈Vleaf (TS ), denoted by sd lc(y, s), is
defined bysd lc(y, s)=e∗sd f n(y, s)+f ∗sd pn(y, s)+g∗sd bn(y, s),(7)where e, f, g satisfies 1≥e≥0, 1≥f ≥0, 1≥g≥0 and
e+f +g=1.
2

(m=0)

(m>0)
(3)
2
Based on language name similarity, family name similarity denoted by sd f n(y, s), parent name similarity denoted by sd pn(y, s) and brother name similarity denotedby sd bn(y, s) are defined as follows. These three similarities are designed for language classification similarity,
which is intended to be a different similarity measure, fromthe aspect of language classification.

©ICAROB 2014

(m=n=0)(m>0, n=0)2

[Definition 3] Let y∈Vleaf (TY ) and s∈Vleaf (TS ) be languages and LTy Y and LTs S be the primary language namesof y and s. Let m be the number of alternate names with s,furthermore let all the alternate names be As1 , As2 , · · ·, Asm
if m > 0. Then language name similarity of y and s,
denoted by sd lnnode (y, s), is defined bysd lnnode (y, s)

TY
TS

sd ln(Ly , Ls )= max {sd ln(LTy Y , LTs S ), sd ln(LTy Y , As1 ),
sd ln(LTy Y , As2 ), · · ·, sd ln(LTy Y , Asm )}(5)- 344 -

[Definition 8] language general similarity betweeny∈Vleaf (TY ) and s∈Vleaf (TS ), denoted by sd gen(y, s),is defined bysd gen(y, s) = a ∗ sd ln(y, s) + b ∗ sd lc(y, s),(8)where a, b satisfies 1≥a≥0, 1≥b≥0 and a + b=1．

2

B. Finding same language pairsSame language pairs such as (y, s) shown in Fig. 2 canbe found out by applying language name similarity andlanguage general similarity as follows.
Two dummy W LT s with 3 languages in TY and 4 in TY
are used to describe how to find them. (1) First, generate a

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014
s1

s2

s3

s4

s1

s2

s3

s4

s1

y1

0.16 0.99 0.15 0.95

y1

0.16 0.99 0.15 0.95

y1

y2

0.13 0.22 0.99 0.36

y2

0.13 0.22 0.99 0.36

y2

y3

0.63 0.98 0.23 0.56

y3

0.63 0.98 0.23 0.56

y3

(a)

s1
y1

s2
0.78

y2
y3

s4
0.83

s1
y1
y3

0.85

(d)

s4

Fact

0.83

Are two languages
the same?

0.32

Yes

(c)

s3

0.78

y2

0.32

Fig. 3.

s2

s3

0.85

(b)

s3

s2
0.78

s4
0.83

0.32
0.85

s1

s2

s3

y1

0.16 0.99 0.15 0.95

y2

0.13 0.22 0.99 0.36

y3

0.63 0.98 0.23 0.56

(e)

①

TP
(True Positive)③

FN(False Negative)

No

②

FP
(False Positive)④

TN(True Negative)(f)Fig. 5.

Process of finding same language pairs

Algorithm: FSLV

True-false judgment for outputted same language pairs

A. Evaluation method and test data

Input：Vleaf (TY ), Vleaf (TS ), e, f, g, a, b, ∆, ρ
Output：Same language pairs SLP for all y∈Vleaf (TY )Procedure：
1◦ Set SLP ←ϕ, P ←ϕ, γ←1. For all the pairs of y∈
Vleaf (TY ) and s∈Vleaf (TS ), do ψy,s =sd lnnode (y,s) and generate language name similarity matrix
Ψ=(ψy,s ).
2◦ If γ≤0 goto 3◦ ; otherwise do P ←P ∪{(y, s) | γ≥ψy,s≥γ−∆, ψy,s : elements of Ψ} and do the following
operations (i) ∼ (v):(i) For all (y, s)∈P do ωy,s =sd gen(y, s) by using
the coefficients e, f, g, a, b.
(ii) Do ωmax = max{ωy,s | (y, s)∈P }. If ωmax < ρ,
set γ←γ−∆ and goto 2◦ ．
(iii) Select one pair (y ′ , s′ )∈P with ωy′ ,s′ = ωmax
and do SLP ←SLP ∪(y ′ , s′ ).
(iv) Do P ←P −{(y ′ , s′ )} and delete y ′ ’s row and s′ ’scolumn from Ψ.
(v) If P ̸=ϕ goto (ii); otherwise do γ←γ−∆ and goto2◦ .
◦3 Output SLP and stop.
Fig. 4.

Parameters (e, f, g) are used to calculate language classification similarity which is defined as a weighted averageof family name similarity, parent name similarity andbrother name similarity, and need to be set at first. These
three parameters are individually the coefficients of thethree similarities. Take (y, s) shown in Fig. 2 as theexample, we consider that, if there are same language pairs
such as (“NUBI”, “Nubi”) within the brothers, then it ismost probably that y and s may also be the same language.
Hence, we lay weight on g that is the coefficient of brotherlanguage similarity, and consider that e=0.25, f =0.25,
g=0.5 should be appropriate.
On the other hand, parameters (a, b), ∆ and thresholdρ will be determined throughout experiments.
We use test data of 200 languages which were selected
out of all 2,869 languages included in TY in a randommanner. In advance of setting parameters, we investigate
the corresponding languages in TS for all the languagesof this test data. However, it does not mean that thecorresponding same languages can always be found in TS
for all the languages.
For the test data, we repeat experiments according toalgorithm FSLV shown in Fig. 4 by changing the valuesof parameters (a, b), ∆ and threshold ρ, and then checkthe results with the investigated facts to get the values ofT P , F P , T N and F N as shown in Fig. 5. Here, T P +F P + T N + F N = 200 should be satisfied.
We use evaluation measures F −measure, Recall,
P recision [5] to choose the best (a, b), ∆ and threshold
ρ. These measures are defined as follows.
TP
Recall =(9)
TP + FN
TP(10)
P recision =TP + FP

Algorithm: FSLV

matrix Ψ with ψyi ,sj =sd lnnode (yi , sj ) as its element foryi ∈Vleaf (TY ) and sj ∈Vleaf (TS ) as shown in Fig. 3 (a).
(2) Then select all the combinations of yi and sj satisfying
γ≥sd lnnode (yi , sj )>γ−∆. The selected combinations
are boxed as shown in Fig. 3 (b). Here, the initial value ofγ and ∆ are set to 1 and 0.1, respectively. (3) Calculate
the values of sd gen(yi , sj ) for the selected combinations
as shown in Fig. 3 (c). Values of coefficients e, f, g anda, b will be determined by experiments. (4) Select the pair(y3 , s2 ) with the highest language general similarity value
from all the values of sd gen(yi , sj ). If we set ρ=0.5, thislanguage pair (y3 , s2 ) will be passed over and identified as
the same language. Then the language general similarity
values as shown Fig. 3 (d) are deleted. Similarly, (y1 , s4 )is identified as well as (y3 , s2 ) as shown in Fig. 3 (e). (5)Delete the values related to the identified same language
pairs as shown in Fig. 3 (f). (6) Update γ as γ = γ−∆
and repeat this process from （2）∼（5) till γ≤0. The
algorithm of this processing is shown in Fig. 4.

F −measure =2·Recall·P recision
Recall + P recision(11)

We consider that the values of parameters (a, b), ∆ andthreshold ρ, with the highest values of F −measure and
P recision, should be the best ones.

III. PARAMETER SETTING

B. Process of parameter setting

In algorithm FSLV shown in Fig. 4, parameters (e, f, g),(a, b), ∆ and threshold ρ are used as input arguments, andare unknown. What should be done first is setting theirvalues. In this section, we are going to give a solution forparameter setting.

©ICAROB 2014

R
e
s
u
l
t

s4

Are twolanguages Yes
outputted
as thesame
language No
pair?

Here, we determine the values of parameters (a, b), ∆and threshold ρ. First of all, we deal with parameters(a, b). Repeat executing algorithm FSLV with parameters∆=0.01 and ρ=0.5 by changing the values of parameters- 345 -

0.950

0.955

0.949

0.950

0.948

0.945
Precision

F-measure

The International Conference on Artificial Life and Robotics (ICAROB 2014), Compal Hall, Oita,
Japan, January 11-13, 2014

0.947
0.946

TABLE 1. Experimental resultsTN
number of(total number
languagesof languages)
found out

0.940
0.935

0.945

0.930

0.944

0.925

2,869

2,687

TP

FP

2,648

39

e=0.25, f=0.25, g=0.5, ∆=0.13, a=0.5, b=0.5,

0.943

0.920
0.00

0.05

0.10

∆

0.15

0.20

0.25

0.00

0.05

0.10 ∆ 0.15

0.20

TP /TNTP /(TP+FP)92%98%ρ =0.540.25(e=0.25, f=0.25, g=0.5, a=0.5, b=0.5, ρ=0.5)(1) F −measure

Fig. 6.

(2) P recision

Ups and downs of the evaluation values

IV. E XPERIMENTAL R ESULTS
We have done experiment by applying the obtained
parameters to original data TY and TS to confirm theaccuracy of parameter setting and the usefulness andeffectiveness of our proposed method. By setting theparameters to (i) ∆=0.10, a=0.5, b=0.5, ρ=0.54 and(ii) ∆=0.13, a=0.5, b=0.5, ρ=0.54 (both under e=0.25,
f =0.25, g=0.5), we have executed algorithm FSLV for
TY and TS and gotten the results shown in TABLE 1:
in all the 2, 869 languages of TY , we have gotten 2, 687
same language pairs as the output of algorithm FSLV, inwhich 2, 648 languages (92%) are the true cases.

(a, b) by steps of 0.1 as follows: (1.0, 0.0), (0.9, 0.1), · · · ,(0.1, 0.9), (0.0, 1.0). Then do the same thing as well by
increasing the values of ∆ from 0.01 to 0.25 by steps of
0.01 such as ∆=0.02, 0.03, · · ·, 0.25. Here, threshold ρ isset as ρ=0.5.
After tallying the values of T P , F P , T N and F N for
every execution of algorithm FSLV, compute the valuesof Recall，P recision and F −measure and use them toestimate the trend of parameters (a, b). We get to noticethat the values of F −measure and P recision have thebest values when a = 0.5 and b = 0.5. So we set the valuesof parameters (a, b) to (0.5, 0.5).
What need to be set continuously is the value ofparameter ∆. As described above, we repeat executing
algorithm FSLV by increasing the values of ∆ from 0.01
to 0.25 by steps of 0.01. The values of F −measureand P recision have ups and downs due to the changeof ∆. We show a part of values of F −measure and
P recision in Fig. 6(1) and Fig. 6(2) respectively by
setting ∆ as 0.01, 0.05, 0.10, 0.15, 0.20, 0.25, 0.13 under
the condition of a=0.5, b=0.5, ρ=0.5. Fig. 6(1) indicatesthat F −measure has a peak at ∆=0.10 and 0.13. At
the two points, Fig. 6(2) has the same highest values.
We cannot differentiate the effect between the cases when∆=0.10 and ∆=0.13, So we set ∆=0.10 or ∆=0.13.
Finally, we set the value of threshold ρ. Under the condition of ∆=0.10, a=0.5, b=0.5 or ∆=0.13, a=0.5, b=0.5,
we repeat execution of algorithm FSLV by increasing thevalue of ρ from 0.50 to 0.80 by steps of 0.05, then we
found that F −measure has a peak at ρ=0.55. So werepeat the execution additionally by setting ρ as 0.51, 0.52,
0.53 or 0.54 individually. F −measure shows the samevalue at ρ=0.54 or ρ=0.55 as well as P recision. Under
the condition of the same F −measure and P recision,
the threshold with lower value is better. Therefore, we set
ρ=0.54.
Up to here, we have gotten the best values of parameters (a, b), ∆ and threshold ρ under the conditionof e=0.25, f =0.25, g=0.5 by using the test data. As
the result, ∆=0.10, a=0.5, b=0.5, ρ=0.54 or ∆=0.13,
a=0.5, b=0.5, ρ=0.54 are obtained.

©ICAROB 2014

V. C ONCLUDING R EMARKS
We have given a way of parameter setting for our
previously proposed method. Using the parameter values,
about 92% languages of TY have been identified, andthe precision of identification is up to 98%. These results
imply that our parameter setting described above is reasonably good. And at the same time, we have confirmed thatour proposed method previously is useful and effective.
It should be noticed that the values of parameters (a, b),∆ and threshold ρ are set under the condition of e=0.25,
f =0.25, g=0.5. As the future work, we need to do further
tests to confirm the parameter setting.
VI. ACKNOWLEDGEMENT
This work was supported by JSPS KAKENHI Grantin-Aid for Challenging Explorotory Research Numbers
23650129.
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