ICAROB 2015
PROCEEDINGS OF THE 2015
INTERNATIONAL CONFERENCE
ON ARTIFICIAL LIFE AND ROBOTICS

January 10-12, 2015
Horuto Hall, Oita, JAPANInternational Meeting Series
Editor-in-Chief
Masanori Sugisaka
Editors: Takao Ito, Ju-Jang Lee, Yingmin Jia
ISBN 978-4-9902880-9-9

The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

Proceedings of the 2015 International Conference on

ARTIFICIAL LIFE AND ROBOTICS(ICAROB2015)

January10‐12, 2015
Horuto Hall Oita, Japan
Editor‐in‐Chief: Masanori Sugisaka
Editors: Takao Ito, Ju‐Jang Lee, Yingmin Jia
ICAROB 2015 ISBN 978‐4‐9902880‐9‐9©ICAROB 2015

The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

Contents
1

Organization, etc.

1

2

Messages

6

3Time Table

104

Opening Ceremony

13

5

Technical paper index

14

6

Abstracts
PS abstracts
IS abstracts
OS abstracts
GS abstracts

26
26
27
28
43

7

Authors index

628

Conference room

68

6‐1
6‐2
6‐3
6‐4

©ICAROB 2015

The 2015 International ConferenceC
on Artificial
A
Life annd Robotics (ICCAROB 2015), Horuto Hall, O
Oita,
Japan, Jannuary 10-12, 2015

SPONSEEREDAROB (A
ALife Robotics Corporation Ltd.)NIZED BY
ORGAN
Internattional Steerring Committtee of International Co
onference ono Artificial Life and Ro
obotics(ICAROBB)
TECHNICAL Co‐SPO
ONSORED BY
B
IEEE Fukkuoka Section (Japan)
GANIZED BY
Y
CO‐ORG
Chinesee Association for Artificcial Intelligence (CAAI, P. R. China)
University of Sultann Zainal Abiidin (UniSZAA) (Malaysiaa)
ADVISOORY COMMITTEE CHAIR
J
Institu
ute of Technnology, Formmer IEEE Preesident, USAA)
Moshe Kam (New Jersy
ORY COMMITTEEADVISO
F. Harasshima (TokyoMetropoliitanUniversity, President , Japan)
B. Eisennstein (Drexel Universitty, Former IEEEE Presideent, USA)
H. Kimuura (RIKEN, Japan)J
M. Tomizuka (University of California Berkeley, USA)
M. Kam (New Jersyy Institute ofo Technologgy, Former IEEE Presideent, USA)
A. Grzecch (Wroclaw
w Universityy of Technology, Polandd)K. Aiharra (The Univversity of To
okyo, Japan))
D. J.G. James (Coveentry University, Unitedd Kingdom)
J. Johnson (The Op
pen Universiity, UK)
K. Kyumma (Mitsubisshi Electric CorporationCn, Japan)
S. Rasmmussen (Univversity of So
outhern Denmark, Den
nmark)J. M. Eppstein (The JohnsJ
Hopkins University, USA)J. Świąteek (Wroclaw
w Universityy of Technology, Polandd)P. Kalataa (Drexel Un
niversity, USSA)
P. Oh (DDrexel Univeersity, USA)
R. Fischl (Drexel Un
niversity, USSA)da ( MeijoUniversity, Jaapan)
T. Fukud
Kai‐Tai Song(NationS
nal Chiao Tu
ung Universsity, Taiwan))©ICARO
OB 2015

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The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

GENERAL CHAIRS
Masanori Sugisaka (Alife Robotics Corporation Ltd.(Japan), Open Univesity(UK),
University of Sultan Zainal Abidin (UniSZA), University of Malaysia‐Peris(Malaysia))
Co GENERAL CHAIRS
Yingmin Jia (Beihang Univesty, P. R. China)
Takao Ito (Hiroshima University, Japan)
Ju‐Jang Lee (KAIST, Korea)
VICE GENERAL CHAIR
C. Zhang (Tinghua Univesity, P. R. China)
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)
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)©ICAROB 2015

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The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

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)
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)©ICAROB 2015

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The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

Y. Fan (University of California‐Davis, USA)
Y. Liu (University of Aizu, Japan)
Y. I. Cho (The University of Suwon, Korea)
S. Mabu (Yamaguchi University, Japan)
H. Matsushita (Kagawa University, Japan)
LOCAL ARRANGEMENT COMMITTEE
M. Sakamoto (University of Miyazaki, Japan)
M. Sugisaka (ALife Robotics Co., Ltd., Japan,
Open University, UK,
University of Sultan Zainal Abidin (UniSZA), University of Malaysia‐Peris, Malaysia)
Takao Ito (Hiroshima University, Japan)
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(4)GS3 Evolutionary computation(3)GS5 Neuromorphic systems(4)GS7 Pattern recognition (3)GS9 RoboticsⅡ(5)GS2 Complexity(4)GS4 Intelligent control(4)GS6 Poster Sessions(14)GS8RoboticsⅠ(5)GS10RoboticsⅢ(5)ORGANIZED SESSION TOPICS
OS1Intelligent Control(5)OS2Software Development Support
Method (6)OS3Image Analysis, Human Interface, OS4Graph Theory and Its Application(3)and Text Mining(5)©ICAROB 2015

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The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

OS5Bio‐Inspired Algorithms and Their
Applications(3)OS6 Computer Science and Information
Processing(4)OS7 Computer Network and Security(3)OS8 Kansei Engineering(5)COPYRIGHTS
Accepted papers will be published in theproceeding of The 2015 International Conference on
Artificial Life and Robotics (ICAROB 2015). Some of high quality papers in the proceeding will be
requested to re‐submit their papers for the consideration of publication in an international
journal ROBOTICS, NETWORKING ANDARTIFICIAL LIFE under agreement of both Editor‐in‐Chiefand 3 reviewers. All correspondence related to the conference should be addressed to ICAROB
Office.

ICAROB Office
ALife Robotics Corporation Ltd.
3661‐8 Oaza Shimohanda, Oita 870‐1112, JAPANTEL/FAX：+81‐97‐597‐7760
E‐MAIL：icarob@alife‐robotics.co.jp
Home Page：http://alife‐robotics.co.jp/©ICAROB 2015

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The 2015 International ConferenceC
on Artificial
A
Life annd Robotics (ICCAROB 2015), Horuto Hall, O
Oita,
Japan, Jannuary 10-12, 2015

MESSAGGES
Masanoori Sugisaka

General Ch
hair of ICAROOB

Masanori Sugisaka
Generall Char
(Prrofessors,Openn
Univvesity(UK),Univversity of Sultan
Zain
nal Abidin, Univversity of
Mallaysia-Peris(Mallaysia) and
Pressident of Alife Robotics
R
Co.,
Ltd..(Japan))©ICARO
OB 2015

It is myy great honor to invite youu all to The Innternational ConferenceC
onn
Artificial Life and Rob
botics (ICARO
OB 2015).
hanged as thee old symposiuum from the first (1996) tooThis Coonference is ch
the Eigteeenth(2013) anuallyawhichh were organnized by Oitta Universityy,
Nippon Bunri
B
Universiity(NBU), andd ALife Robootics Corporatiion Ltd. under
the sponssorship of the Science and Technology P
Policy Bureau
u, the Ministryyof Educaation, Sciencee, Sports, annd Culture (MMonbusho), presently,ptheeMinistry of Educatiion, Culture, Sports, S
Science, and Technologyy(monkashho), Japanesee Governmentt, Japan Sociiety for the Promotion of
Science (JSPS),(
The Commemorattive Organizaation for the Japan Worldd
Expositioon (’70), Air Force Office of Scientific Research, Assian Office ofAerospacce Research anda Developm
ment (AFOSR
R/AOARD), USA.
U
I wouldd
like to exxpress my siincere thanks to not only Monkasho (aannually fundd
support from 1996 to 2013) butbalso JS
SPS, the Co
ommemorativeeE(’700) , Japanese companies forr
Organizattion for the Jaapan World Exposition
their repeeated support.
The old symposium was
w organizedd by Internatioonal Organizin
ng Committeee
B and was co--operated by thet Santa Fe IInstitute (USAA), RSJ, IEEJJ,
of AROBICASE (NNow ICROS)) (Korea), CA
AAI (P. R. Chhina), ISCIE, IEICE, IEEE
E
(Japan Coouncil), JARA
A, and SICE.T
The old AROB
B symposiumm was growingg
up by abssorbing many new knowleddge and technoologies into it..
This hisstory and chaaracter was inherited also from ICARO
OB 2014(Thee2014 Inteernational Co
onference on Artificial
A
Lifee and Robotics) now.Fromm
now on, ALifeA
Roboticcs Corporatioon Ltd. is in chharge of management. Thiss
year we haveThe 20
015 InternatioonalConferencce on Artificcial Life andd
015).The futuure of The IC
CAROB is brrilliant from a
Robotics (ICAROB 20
point of viewvof yieldinng new technoologies to hum
man society inn 21st century..
I hope thattfruitful diiscussions andd exchange off ideas betweeen researcherssduring Coonference(ICA
AROB 2015) will yield neew merged tecchnologies forrhappinesss of human beeings and, hennce, will faciliitate the establlishment of ann
internatioonal joint reseaarch institute on Artificial L
Life and Robo
otics in future..

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The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

Yingmin Jia
Co-General Chair of ICAROB

Yingmin Jia
Co-General Chair(Professor,

It is my great pleasure to invite you to the 2015 International Conference on
Artificial Life and Robotics (ICAROB 2015), in Oita City, Oita, Japan from Jan.
10th to 12th, 2015.
ICAROBdevelops from the AROB that was created in 1996 by Prof. Masanori
Sugisaka and celebrated her birthday of 19th years old in 1996. Doubtless, newmission and big challenges in the field of artificial life and robotics will promote
ICAROB to start a new stage and attract wide interests among scientist,
researchers, and engineers around the world.
For a successful meeting, many people have contributed their great efforts to
ICAROB. Here, I would like to express my special thanks to all authors andspeakers, and the meeting organizing team for their excellent works.
Looking forward to meeting you at ICAROB in Oita City and wishing you enjoy
your stay in Japan.

Beihang University,
R .P. China)©ICAROB 2015

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The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

Takao Ito
CoGeneral Chair of ICAROB

Takao Ito
Co-General Chair(Professor Hiroshima
university, Japan)©ICAROB 2015

It is my great honor to invite you all to The 2015 International Conferenceon Artificial Life and Robotics (ICAROB 2015).This Conference is changed
as the old symposium from the first (1996) to the Eighteenth I am pleased towelcome you to the 2015 International Conference on Artificial Life andRobotics 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 oflife-as-it-could–be. It will provide us a good place to present our new
research results, good ideas, and valuable information about artificial
intelligence, complex systems theories, robotics, management of technology,etc.
In order to provide an outstanding technical level for the presentations at
the conference, we have invited more than 60 distinguished experts in thefield of artificial life in the organizing committee and program committee.
We will have 22 sessions during 3 days of conference, including 4 invited
sessions.
The conference site is the Horuto Hall, one of the finest congress centers in
Oita. It is situated near the center of the city and in front of Oita railway
station. You can find many fantastic scenic spots and splendid hot-springs.
Enjoy your stay and take your time to visit the city of Oita.
I am looking forward to meeting you in Oita during ICAROB 2015 and to
sharing a most pleasant, interesting and fruitful conference

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The 2015 International ConferenceC
on Artificial
A
Life annd Robotics (ICCAROB 2015), Horuto Hall, O
Oita,
Japan, Jannuary 10-12, 2015

Ju-JangJ
Lee

Co-Generral Chair of ICAROBIJ
Ju-Jang
Lee
Co-G
General Chaiir(Proffessor, KAISTT)©ICARO
OB 2015

boticsThe First Internnational Confference on Artificial Liife and Rob
ROB) was helld in Oita City, Oita, Japann from Jan. 11th to 13th, 2014.
2(ICAR
This year’syConferrence will bee held amidsst the high eexpectation ofo theincreassingly importtant role of th
he new interddisciplinary paaradigm of sccienceand enngineering reppresented by the field of artificial life and roboticss that
continuuously attraccts wide interests amonng scientist, researchers, andengineeers around the globe.
Distinnguished reseearchers and technologistts from arounnd the world
d arelookinng forward to attending andd meeting at IC
CAROB. ICA
AROB is beco
oming
the annnual excellennt forum thaat represents a unique oppportunity for theacademmic and inddustrial commmunities to meet and assess the latest
develoopments in this fast growin
ng artificial liife and robotiics field. ICA
AROB
enablees them to adddress new challenges,cshhare solutionss, discuss research
directiions for the fuuture, exchangge views and ideas, view thee results of ap
ppliedresearcch, present annd discuss thee latest develoopment of new
w technologies andrelevannt applicationns.
In adddition, ICARROB offers thet opportuniity of hearingg the opinion
ns ofwell-kknown leadingg experts in thee field throughh the keynotee sessions, proovidesthe bases for regionnal and internaational collabborative researrch, and enablles too new scienntific paradiggms and theeories
foreseee the future evolution ofcontribbuted by the fieldfof artificcial life and roboticsrand aassociated researcharea. TheT twenty-fiirst century will
w become thhe century off artificial lifee andintelliggent machines in support of humankindd and ICARO
OB is contrib
butingthrouggh wide techniical topics of interestithat suupport this dirrection.
It is a great honorffor me as a Co-GeneralC
Chhair of the 2nddICAROB 20
015 towelcomme everyone to this imporrtant event. Also,A
I would like to extend myspecial thanks to alla authors annd speakers for contributting their researchm of the 2nd IICAROB.
works,, the participaants, and the orrganizing team
I’m lookinglforwaard to meetinng you at the 2ndICAROB
B in Oita Cityy andwishinng you all the best.
b

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The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

TIME TABLE (1/10)
1/10
9:30~

Room404

Room406

Room407

GS8(5)RoboticsⅠ

GS2(4)Complexity

Chair T. Sethaput

Chair K. Kobayashi

Registration

10:00-11:40
11:40-11:50

Coffee break

11:50-12:10

Opening Ceremony (Room405)

12:10-13:00

Lunch

13:00-14:00

GS7(3)Pattern

GS3(3)Evolutionary

Recognition

Computation

Chair J. Wang

Chair S. Mabu

14:00-14:20

Coffee break

14:20-16:00

GS9(5)RoboticsⅡ

GS5(4)Neuromorphic

ChairY. Morita

Systems
Chair T. Kondo

16:00-16:20

Coffee break

16:20-18:00

GS10(5)RoboticsⅢ

GS4(4)IntelligentControl

Chair S. Kim

ChairJ-M Lee.

GS1 Artificial intelligence(4)GS2 Complexity(4)GS3Evolutionary computation(3)GS4Intelligent control(4)GS5Neuromorphic systems (4)GS6Poster Sessions(14)GS7Pattern recognition (3)GS8RoboticsⅠ(5)GS9 RoboticsⅡ(5)GS10 RoboticsⅢ(5)©ICAROB 2015

OS1Intelligent Control(5)OS2Software Development Support Method (6)OS3 Image Analysis, Human Interface, andText Mining(5)OS4 Graph Theory and Its Application(3)OS5 Bio-Inspired Algorithms and Their
Applications(3)OS6 ComputerScience andInformation
Processing(4)OS7 Computer Network and Security(3)OS8 Kansei Engineering(5)P - 10

The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

TIME TEBLE (1/11)
1/11
9:30～

Room404

Room405

Room406

Room407

Registration

10:00-11:00

GS6(14)Poster

Room403

Chair J.J. Lee andPlenary Speech

J. Wang.

Prof. Kai-Tai Song

11:00-12:30

Invited Speech(403)
Prof. Henrik H. Lund
Prof. Luigi Pagliarini
Dr. Jovana Jovan Jovic

12:30-13:20

Lunch

13:20-15:00

GS6(14)Poster

15:00-15:20

GS1(4)Artificial

OS8(5)Kansei

Intelligence

Engineering

Chair M.Kubo

Chair T.Hattori

Coffee break

15:20-17:00

GS6(14)PosterOS1(5) Intelligent

OS3(5)Image Analysis,

Control

Human Interface, andChair Y.Jia

Text Mining
Chair: Y. Yoshitomi

18:00-20:00

Banquet: HOTEL HOKKE CLUB OITA©ICAROB 2015

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The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

TIME TABLE (1/12)
1/12
9:30～

Room404

Room406

Room407

Registration

10:00-10:30

Room403
Plenary SpeechProf. Kenji Hashimoto

10:40-12:40

OS5(3)+OS7(3)OS2(6)

Chair H. Furutani

Chair T. Katayama

Chair H. Yamaba
12:40-13:10
13:10-15:30

Lunch
OS6(4)+OS4(3)Chair M. Sakamoto
Chair: T. Ito

15:40-16:40©ICAROB 2015

Farewell Party (3rd Floor: Restaurant)

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The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

The InternationalConference on
ARTIFICIALLIFEANDROBOTICS 2015
(ICAROB2015 )January 10 (Saturday)
Room 40511:50‐12:10

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

M. Sugisaka( ALife Robotics Co., Ltd. Japan,
The Open University, UK, University of Sltan Zainal
Abidin (UniSZA), Malaysia, University of
Malaysia‐Perlis, Malaysia(UniMAP))

2. Co‐General Chairman of ICAROB Y. M. Jia (Beihang University, China)
3. Co‐General Chairman of ICAROB T. Ito (Hiroshima University, Japan)
4. Co‐General Chairman of ICAROB J. J. Lee (KAIST, Korea)

January 11 (Sunday)
HOTEL HOKKE CLUB OITA
18:00‐20:00

BanquetChair: J.M. Lee (Pusan National University, Korea)
Welcome Addresses
K.T. Song(National Chiao Tung University(NCTU), Taiwan)
H.H.Lund(Denmark Technical University of Denmark,Denmark)
J.J. Jovic (AIST, Japan),©ICAROB 2015

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The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

TECHNICAL PAPER INDEXJanuary 10 (Saturday)
9:30‐ Registration (Room404)

Room 406
10:00‐11:40 GS8(5)Robotics I
Chair: Thunyaseth Sethaput (Thammasat University, Thailand)
GS8‐1 Production effects by form changes of autonomous decentralized FMSs with mind
Kakeru Yokoi, Hidehiko Yamamoto, and Takayoshi Yamada (Gifu University, Japan)
GS8‐2 Development of an autonomous‐drive personal robot“Improve the accuracy of object area determination by boundary detection”
Mikiko Hirai, Eiji Hayashi (Kyusyu Institute of Technology, Japan)
GS8‐3 Construction of a supermicro sense of force feedback and vision for micro‐objects:
development of a haptic device
Yusei Ishii, Eiji Hayashi (Kyushu Institute of Technology, Japan)
GS8‐4 Error Recovery of Pick‐and‐Place Tasks in Consideration of Reusability of Planning
Akira Nakamura, Kazuyuki Nagata, Kensuke Harada and Natsuki Yamanobe(National Institute of Advanced Industrial Science and Technology (AIST), Japan)
GS8‐5 Design of Sliding Mode Controller for Droplet Position in EWOD Microfluidic Sysem
Thunyaseth Sethaput (Thammasat University, Thailand), Arsit Boonyaprapasorn(Chulachomkloa Royal Military Academy,Thailand)

13:00‐14:00 GS7(3) Pattern Recognition
Chair: Jiwu Wang (Beijing Jiaotong University, China)
GS7‐1 Fast motion detection based on cross correlation
Panca Mudjirahardjo, Joo Kooi Tan, Hyoungseop Kim and Seiji Ishikawa(Kyushu Institute of Technology, Japan)
GS7‐2 Detecting moving objects on a video having a dynamic background
FX Arinto Setyawan, Joo Kooi Tan, Hyoungseop Kim, Seiji Ishikawa( Kyushu Institute of Technology, Japan)
GS7‐3 Study on the Target Recognition and LocationTechnology of industrial Sorting Robotbased on Machine Vision©ICAROB 2015

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The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

Jiwu Wang, Xianwen Zhang, HuazheDou( Beijing Jiaotong University, China)
Sugisaka Masanori (Alife Robotics Corporation Ltd, Japan, Open University, United
Kingdom)

14:20‐16:00 GS9(5)RoboticsⅡ
Chair: Yoshifumi Morita (Nagoya Institute of Technology, RIKEN‐RSC, Japan)
GS9‐1 Mechanism Designs for Bio‐inspired Flapping Wing Robots
Palakorn Tantrakool, Eakkachai Pengwang(King Mongkut’s University of Technology Thonburi, Thailand)
GS9‐2 Effective rocking motion for inducing sleep in adults– Verification of effect ofmother’s embrace and rocking motion –Keishi Ashida, Yoshifumi Morita (Nagoya Institute of Technology, RIKEN‐RSC, Japan)
Ryojun Ikeura (Mie University, RIKEN‐RSC, Japan)
Kiyoko Yokoyama (Nagoya City University, RIKEN‐RSC, Japan)
Ming Ding, Yuki Mori (RIKEN‐RSC)
GS9‐3 Postural Sway Response to Local Vibratory Stimulationin Young, Middle‐aged andElderly People in Standing Position
Ayaka Yamada, Eishi Nakamura, Noritaka Sato, Yoshifumi Morita(Nagoya Institute of Technology, Japan)
Tadashi Ito, Yoshihito Sakai (National Center for Geriatrics and Gerontrogy, Japan)
Kazunori Yamazaki (Fujita Health University, Japan)
GS9‐4 Development of Unmanned Transport System for automated systems
Hyunhak Cho, Jungwon Yu, Yeongsang Jeong, Hansoo Lee, Sungshin Kim(Pusan National University, Korea)
GS9‐5 Localization method for AGV using magnetic devices and IMU
Moonho Park, EunKyeong Kim, Yeongsang Jeong, Hansoo Lee, Jungwon Yu,
Sungshin Kim (Pusan National University, Korea)

16:20‐18:00 GS10(5)Robotics Ⅲ
Chair: Sungshin Kim (Pusan National University, Korea)
GS10‐1 On the Effects of Epigenetic Programming on the Efficiency of Incremental Evolution
Ofthe Simulated Khepera Robot
Yasuto Nishiwaki , Ivan Tanev and Katsunori Shimohara(University of Doshisha, Japan)
GS10‐2 The Effect of Duration of Both Stages of Incremental Genetic Programming on its
Efficiency of Evolution of Snakebot
N. Mukosaka, I. Tanev, K. Shimohara (Doshisha University, Japan)©ICAROB 2015

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The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

GS10‐3 Design of an effective shoulder joint mechanism for an upper‐limb exoskeleton robot
Masahito Akiyama, Kazuo Kiguchi (Kyushu University, Japan)
GS10‐4

A Machine Learning Approach to a Lateral Continuous Force Estimation for a Walking
Biped Robot
Yeoun‐Jae Kim, Jun‐Yong Lee and Ju‐Jang Lee (KAIST, Korea)

GS10‐5 The Improvement of Robust Robot SLAM Algorithm Based on Sensor Fusion
Jiwu Wang,Shunkai Zheng, Fangbo Liao(Beijing Jiaotong University, China)
Sugisaka Masanori(Alife Robotics Corporation Ltd, Japan and Open University, United
Kingdom)

Room 407
10:00‐11:40 GS2(4) Complexity
Chair: Kunikazu Kobayashi(Aichi Prefectural University, Japan)
GS2‐1 Interactive musical editing system to support human errors and offer personal
preferences for an automatic piano
Kenji Tsunenari, Eiji Hayashi (Kyushu Institute of Technology, Japan)
GS2‐2 Modeling of collaboration in design process Based on Channel Theory
PatchaneePatitad, Hidetsugu Suto (Muroran Institute of Technology, Japan)
GS2‐3 Sterilizing system of ballast water using an arc discharge
Piao shengxu, Jae‐cheol Lee, Zheng Tao, Heeje Kim(Pusan National University, Korea)
GS2‐4 The design of medical ruby laser power supply system using LLC resonant converter
Jaecheol Lee, Piao shengxu, Zheng Tao, Heeje Kim(Pusan National University, Korea)

13:00‐14:00 GS3(3) EvolutionaryComputation
Chair: Shingo Mabu(Yamaguchi University, Japan)
GS3‐1 Online Rule Updating System Using Evolutionary Computation for Managing
Distributed Database
Wirarama Wedashwara, Shingo Mabu, Masanao Obayashi and Takashi Kuremoto(Yamaguchi University, Japan)
GS3‐2 Reinforcement Learning with Symbiotic Relationships for Multiagent Environments
Shingo Mabu, Masanao Obayashi and Takashi Kuremoto, (Yamaguchi University, Japan)
GS3‐3 Development of a Dividual Model Using a Modular Neural Networkfor Human‐Robot
Interaction
Toshiyuki Tanaka and Kunikazu Kobayashi (Aichi Prefectural University, Japan)©ICAROB 2015

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The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

14:20‐16:00GS5(4)Neuromorphic Systems
Chair: Tadashi Kondo (Tokushima University, Japan)
GS5‐1 Associative Memory with Class I and II Izhikevich Model
Yoshika Osawa, Takashi Kohno (University of Tokyo, Japan)
GS5‐2 Medical image recognition of heart regions by deep multi‐layered GMDH‐typeneural network using principal component‐regression analysis
Tadashi Kondo, Junji Ueno and Shoichiro Takao (Tokushima University, Japan)
GS5‐3 Deep feedback GMDH‐type neural network using principal component‐regression
Analysisand its application to medical image recognition of abdominal multi‐organs
Tadashi Kondo, Junji Ueno and Shoichiro Takao (Tokushima University, Japan)
GS5‐4 Synchronized Response to Grayscale Image Inputs in the Chaotic Cellular Neural
Network
MasayukiFujiwara, AkihiroYamaguchi(FukuokaInstituteofTechnology,Japan)
MasaoKubo(NationalDefenseAcademyofJapan, Japan)

16:20‐17:40GS4(4)Intelligent Control
Chair:Jang‐Myung Lee (Pusan National University, South Korea)
GS4‐1 Design of 1/40 scale simulator to applythe Flying Touch Method in hot rolling process
Sung‐jin Kim, Hyun‐hee Kim, Min‐cheol Lee(Pusan National University, South Korea)
GS4‐2 Improving Accuracy of Inertial Measurement unit using Piscrete Wavelet Transform
Jae‐Hoon Jung, Dong‐Hyuk Lee, Jang‐Myung Lee(Pusan National University, South Korea)
GS4‐3 Outdoor Localization for Quad‐rotorusing Kalman Filter and Path Planning
Chen‐Hu, Yo‐Seop Hwang, Jang‐Myung Lee (Pusan National University, South Korea)
GS4‐4 Distributed Terminal Backstepping Controlfor Multi‐Agent Euler‐Lagrange Systems
Seong‐Ik Han, Yun‐Ki Kim, Jang‐Myung Lee(Pusan National University, South Korea)©ICAROB 2015

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The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

January 11 (Sunday)
9:30‐ Registration (Room404)

Room 403
10:00‐11:00 Plenary Speech
Chair: Ju‐Jang Lee (KAIST, Korea)

PS‐1: Kai‐Tai Song(National Chiao Tung University, Taiwan)
PS‐1

Vision‐Based Grasp Planning and Experiments of a Mobile Manipulator
Yi‐Fu Chiu and Kai‐Tai Song(National Chiao Tung University, Taiwan)

11:00‐12:30 Invited Speech
Chair: Yingmin Jia (Beihang University, China)

IS‐1: Henrik Hautop Lund(Centre for Playware,Technical University of Denmark, Denmark)IS‐2: Luigi Pagliarini (Centre for Playware, Technical University of Denmark, Denmark,Academy of Fine Arts of Macerata, Italy)
IS‐3: Jovana Jovic (AIST, Japan)
IS‐1

Combining playware exergaming with a mobile fitness app
Emmanouil Giannisakis, Henrik Hautop Lund
(Technical University of Denmark, Denmark)IS‐2

Parallel Relational Universes – experiments in modularity
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)IS‐3

Identifying humanoid and human physical parameters
Jovana Jovic, Eiichi Yoshida (AIST, Japan), Gentiane Venture (TUAT, Japan)

Room 405
10:00‐17:00GS6 Poster Session(14)Chair: J. J. Lee (KAIST, Korea)
Jiwu Wang(Beijing Jiaotong University, China)
GS6‐1

The construction of evaluation index system for graduate course
Ai Dongmei, Wen Jiawei, Ning Xiaojun(University of Science and Technology Beijing, China)©ICAROB 2015

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The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

GS6‐2

Extracting Pattern of Arm Movements based on EMG Signal for Stroke Therapy
Khairunizam Wan, Rashidah Suhaimi,Aswad A.R(Universiti Malaysia Perlis, MALAYSIA)
D. Hazry, Zuradzman M. Razlan, Shahriman AB (Universiti Malaysia Perlis, MALAYSIA)
Mohd Asri Ariffin and Haslina M (Universiti Sains Malaysia, MALAYSIA)

GS6‐3

Cascade Controller Design for Steering Control of Nonholonomic Autonomous Mobile
Robot Vehicle
S. Faiz Ahmed, D. Hazry (Universiti Malaysia Perlis (UniMAP),Malaysia)
F. Azim (Hamdard University, Pakistan)

GS6‐4

Research on Iris Recognition Based on the BP Neural Network
Fengzhi DAI, Li FAN, Chunyu YU, Bo LIU

GS6‐5

Synchronization Control of a Four‐wing Fractional‐Order Chaotic System and Its Analog
Circuit Design
Hongyan Jia, Qian Tao, Jinfang Li, Wei Xue(Tianjin University of Science & technology, PR China)

GS6‐6

A fractional‐order hyper‐chaotic system and its circuit implementation
Wei Xue, HuiXiao, Jinkang Xu, Hongyan Jia(Tianjin University of Science and Technology,PR China)

GS6‐7Research on Early Crop Monitoring Using Photosynthetic Production Indexin China
Fengzhi DAI1, Li FAN1, Daijiro KANEKO, Nozomu HIROSE, Chunyu YU1(1Tianjin University of Science & technology, China)

GS6‐8

Design and Implementation of Motor Test System based on Virtual Instrument
Yulong Xia, Huailin Zhao (Shanghai Institute of Technology, China)
Jihong Zhu, Yang He(Tsinghua University, China)

GS6‐9Consensus Problem of Distributed Multi‐agent System
Huailin Zhao (Shanghai Institute of Technology, China) Wei Ren (UCR, USA),
Masanori Sugisaka ( Alife Robotics Corperation LTD, Japan)

GS6‐10 Dingle’s Model‐based EEG Peak Detection using a Rule‐based Classifier
Asrul Adam, Norrima Mohktar, Marizan Mubin(University of Malaya, Malaysia),
Zuwairie Ibrahim (Universiti Malaysia Pahang, Malaysia),
Mohd Ibrahim Shapiai (Malaysia‐Japan International Institute of Technology Universiti
Teknologi Malaysia, Malaysia)
GS6‐11 Different Learning Functions for Weighted Kernel Regression in Solving Small Sample
Problem with Noise
Zuwairie Ibrahim, Nurul Wahidah Arshad(Universiti Malaysia Pahang, Malaysia),
Mohd Ibrahim Shapiai(Malaysia‐Japan International Institute of Technology Universiti Teknologi Malaysia,
Malaysia),©ICAROB 2015

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The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

Norrima Mokhtar(University of Malaya,Malaysia)
GS6‐12 Simultaneous Computation of Model Order and Parameter Estimation of a Heating
System Based on Particle Swarm Optimization for Autoregressivewith Exogenous
Model : An Analysis
Teoh Shin Yee, Zuwairie Ibrahim, Kamil Zakwan Mohd Azmi
(Universiti Malaysia Pahang, Malaysia),
Norrima Mokhtar (University of Malaya,Malaysia)
GS6‐13 Maximum Probability Algorithm for Fault Diagnosis
Fengzhi DAI, Li FAN, Bo LIU (Tianjin University of Science & technology, China)
GS6‐14 The Fractional Order Hyperchaotic Generalized Augmented Lü System and its Circuit
Implementation
Wei Xue, Jinkang Xu, Hongyan Jia( Tianjin University of Science and Technology, PR China)

Room 406
13:20‐14:40 GS1(4)Artificial intelligence
Chair: Masao. Kubo (National Defense Academy of Japan, Japan)
GS1‐1 Selecting Words and Notation Using Literary Data in the Integrated Narrative
GenerationSystem
Jumpei Ono, Takashi Ogata (Iwate Prefectural University, Japan)
GS1‐2

Evaluation of a Narrative Discourse Generation System Based on the Concept of “Normand Deviation”
Taisuke Akimoto (The University of Electro‐Communications, Japan)
Takashi Ogata (Iwate Prefectural University, Japan)

GS1‐3 An aggregating approach of target enclosure of robot swarm
Masao KUBO, Hiroshi SATO, Akira Namatame(National Defense Academy of Japan, Japan)
Akihiro Yamaguchi(Fukuoka Institute of Technology, Japan)
GS1‐4 Probability of mixing up a nearest neighbor robot under target enclosure by robot
swarm
Masao KUBO, Hiroshi SATO, Akira Namatame(National Defense Academy of Japan, Japan),Akihiro Yamaguchi (Fukuoka Institute of Technology, Japan)©ICAROB 2015

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The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

15:20‐17:00 OS1(5)Intelligent Control
Chair: Yingmin Jia (Beihang University, P.R.China )Co‐Chair: Weicun Zhang (University of Science and Technology Beijin, P.R.China)
OS1‐1 Adaptive Multiple‐Model Control of a Class of Nonlinear Systems
Chao Yang and Yingmin Jia (Beihang University, P.R.China)
OS1‐2 Attitude reorientation of spacecraft with attitude forbidden zones
XuhuiLu and Yingmin Jia ( Beihang University, P.R.China)
OS1‐3 Weighted Multiple Model Adaptive Control of Slowly Time‐Varying Systems
Weicun Zhang,Qing Li (University of Science and Technology Beijing, P.R.China)
OS1‐4 A Reduced‐Complexity Interacting Multiple Model Algorithm for Location Tracking in
Heterogeneous Observation
Xiaoyan Fuand Yuanyuan Shang (Capital Normal University, P.R.China)
OS1‐5 Single Image Dehazing on Mobile Device based on GPU Rendering Technology
Yuanyuan Shang, Yue Meng, Xiuzhuang Zhou, Xiaoyan Fu, and Hui Ding
(Capital Normal University, P. R. China)

Room407
13:20‐15:00 OS8(5) Kansei Engineering
Chair: Tetsuo Hattori (Kagawa University, Japan)
Co‐Chair:Hiromichi Kawano( NTT AT, Japan）
OS8‐1 Investigation of Feature Quantity in Sound Signal and Feeling Impression Using
PCA
Yusuke Kawakami, Tetsuo Hattori (Kagawa University, Japan),
Hiromichi Kawano (NTT AT, Japan), Tetsuya Izumi (Micro‐Technica Co., ltd., Japan)
OS8‐2 Automated Color Image Arrangement Method Using Curvature Computation in
Histogram Matching
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)
OS8‐3Change Detection Experimentation for Time Series data by New Sequential Probability
Ratio
Yoshihide KOYAMA, Tetsuo HATTORI (Kagawa University, Japan)
Hiromichi KAWANO (NTT AT, Japan)Katsunori TAKEDA (Canon IT Solutions Inc., Japan)

OS8‐4 Analysis of Navier‐Stokes Equation from the Viewpoint of Advection Diffusion (I)‐‐‐ Analytical Solution of Diffusion Equation ‐‐‐Hiroki SAKAMOTO, Tetsuo HATTORI (Kagawa University, Japan)©ICAROB 2015

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The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

Akiomi TADA (Japan)Vanhoa NGUYEN (Japan)Hiromichi KAWANO (NTT AT, Japan)
OS8‐5 Analysis of Navier‐Stokes Equation from the Viewpoint of Advection Diffusion (II)‐‐‐ Approximate Solution ‐‐‐Hiroki SAKAMOTO, Tetsuo HATTORI (Kagawa University, Japan)
Akiomi TADA (Japan)Vanhoa NGUYEN (Japan)Hiromichi KAWANO (NTT AT, Japan)

15:20‐17:00 OS3(5)Image Analysis, Human Interface, and Text Mining
Chair: Yasunari Yoshitomi (Kyoto Prefectural University, Japan )Co‐Chairman: Masayoshi Tabuse (Kyoto Prefectural University, Japan)
OS3‐1 Development of Mouse Cursor Control System Based on Face Direction Using Kinect
Masayoshi Tabuse (Kyoto Prefectural Univ., Japan)
Kaori Tamura (ISI Software Corp., Japan)
OS3‐2 Quantitative Evaluation of Facial Expressions and Movements of Persons While Using Video
Phone
Taro Asada, Yasunari Yoshitomi, Ryota Kato, Masayoshi Tabuse,(Kyoto Prefectural University, Japan)
Jin Narumoto (Kyoto Prefectural University of Medicine, Japan)
OS3‐3 Facial Expression Recognition Using Facial Expression Intensity Characteristics of Thermal
Image
Yasunari Yoshitomi, Taro Asada, Ryota Kato, and Masayoshi Tabuse(Kyoto Prefectural University, Japan)
OS3‐4 Method for Character Domain Extraction from Image Using Wavelet Transform
Taiki Taniguchi (ZENSHO HOLDINGS Co., Ltd., Japan)
Yasunari Yoshitomi (Kyoto Prefectural University, Japan)
OS3‐5 Classification of Japanese Documents and Ranking of Representative Documents
Using Characteristic of Frequencies of Words
Jun Kimura(JustSystems Corp., Japan)
Yasunari Yoshitomi, Masayoshi Tabuse(Kyoto Prefectural University, Japan)

January 12 (Monday)
9:30‐ Registration (Room 404)

Room403
10:00‐10:30 Plenary Speech
Chair: Takao Ito (Hiroshima University, Japan)©ICAROB 2015

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The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

PS‐2:Kenji Hashimoto (Waseda University, Japan)
PS‐2

Biped Robot Research at Waseda University
Kenji Hashimoto, Atsuo Takanishi (Waseda University, Japan)

Room 406
10:40‐12:40 OS5(3) + OS7(3)OS5(3) Bio‐Inspired Algorithms and Their Applications
Chair Hiroshi Furutani (University of Miyazaki, Japan)
Co‐Chair Kenji Aoki (University of Miyazaki, Japan)
OS5‐1 Analysis of Genetic Disease Haemophilia A by Using Machine Learning
Kenji Aoki, Makoto Sakamoto, Hiroshi Furutani (University of Miyazaki, Japan)
OS5‐2 Analysis of Asymmetric Mutation Model in Random Local Search
Hiroshi Furutani, Yifei Du, Kenji Aoki, Makoto Sakamoto (University of Miyazaki, Japan)
OS5‐3 Hitting Time Analysis of OneMax Problem in Genetic Algorithm
Y. Du, Q. Ma, k. Aoki, M. Sakamoto, H. Furutani (University of Miyazaki, Japan)
Y. Zhang (Qinghai University, China)

OS7(3) Computer Network and Security
Chair Hisaaki Yamaba (University of Miyazaki, Japan)
Co‐Chair Kentaro Aburada (Oita National College of Technology, Japan)
OS7‐1 An Authentication Method for Mobile Devices that is Independent of Tap‐Operation ona Touchscreen
Hisaaki Yamaba, So Nagatomo,Shinichiro Kubota,Tetsuro Katayama, Naonobu Okazaki(University of Miyazaki, Japan)
Kentaro Aburada (Oita National College of Technology, Japan)
Mirang Park (Kanagawa Institute of Technology, Japan),
OS7‐2 Proposal of Security Evaluation System using User's Reviews and Permissions
for Android Application
Naonobu Okazaki (University of Miyazaki, Japan)
Yoshihiro Kita (Kanagawa Institute of Technology, Japan)
Kentaro Aburada (Oita National College of Technology, Japan)
Mirang Park (Kanagawa Institute of Technology)
OS7‐3 Evaluation of Neighbors Based Routing for ad hoc networks
Kentaro Aburada(Oita National College of Technology, Japan)
Hisaaki Yamaba, Shinichiro Kubota,Tetsuro Katayama, Naonobu Okazaki(University of Miyazaki, Japan)
Mirang Park(Kanagawa Institute of Technology, Japan)©ICAROB 2015

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The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

13:10‐15:30 OS6(4)+OS4(3)OS6(4) Computer Science and Information Processing
Chair Makoto Sakamoto (University of Miyazaki, Japan)
Co‐Chair Yasuo Uchida (Ube National College of Technology, Japan)
OS6‐1 Sufficient spaces for seven‐way four‐dimensional Turing machinesto simulate
four‐dimensional one‐marker automata
Makoto Nagatomo, Makoto Sakamoto, Hikaru Susaki, Tuo Zhang, Satoshi Ikeda, andHiroshi Furutani (University of Miyazaki, Japan)
Takao Ito (Hiroshima University, Japan)
Yasuo Uchida (Ube National College of Technology, Japan)
Tsunehiro Yoshinaga (Tokuyama College of Technolory, Japan)
OS6‐2 Some Properties of k‐Neighborhood Template A‐Type Three‐Dimensional Bounded
Cellular Acceptors
Makoto Sakamoto, Makoto Nagatomo, Hikaru Susaki, Tuo Zhang, Satoshi Ikeda, andHiroshi Furutani(University of Miyazaki, Japan)
Takao Ito(Hiroshima University, Japan)
Yasuo Uchida(Ube National College of Technology, Japan)
Tsunehiro Yoshinaga (Tokuyama College of Technology, Japan)
OS6‐3 Perfect Analysis in miniature Othello
Yuki Takeshita, Satoshi Ikeda, Makoto Sakamoto (Miyazaki University, Japan)
Takao Ito(Hiroshima University, Japan)
OS6‐4 A proposal for teaching programming through the Five‐Step Method
Y. Uchida,S. Matsuno(National Institute of Technology, Ube College, Japan)
T. Ito (Hiroshima University, Japan)
M. Sakamoto (University of Miyazaki, Japan)

OS4(3)Graph Theory and Its Application
Chair Takao Ito (Hiroshima University, Japan)
Co‐Chair K. Ogata (University of Nagasaki, Japan)
OS4‐1 The Role of National Standards Setter in the Global Convergence Era‐In the Case of the Japanese Setter during the first decade‐
Ogata, K. (University of Nagasaki, Japan)
OS4‐2 An Empirical Research on Inter‐firm Capital Relationship in Yokokai using IDE Spatial
Model
Takao Ito (Hiroshima University, Japan)
Makoto Sakamoto, S. Ikeda (University of Miyazaki, Japan)
R. Mehta (New Jersey Institute of Technology, U.S.A)
Tsutomu Ito (Hino Motors, Ltd. Japan)
OS4‐3 Design and Experimental Evaluation of a Human Skill‐Based PID Controller
Yuntao Liao, Yamamoto Toru (Hiroshima University, Japan)©ICAROB 2015

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The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

Room407
10:40‐12:40 OS2(6)Software Development Support Method
Chair: Tetsuro Katayama (University of Miyazaki, Japan)
Co‐Chair:Makoto Sakamoto (University of Miyazaki, Japan)
OS2‐1 Prototype of a Supporting Tool to Generate Testing Communication Diagram
Tetsuro Katayama, Seiya Urata, Yohei Ogata, Hisaaki Yamaba, and Naonobu Okazaki(University of Miyazaki, Japan)
Yoshihiro Kita, (Kanagawa Institute of Technology,Japan)
Kentaro Aburada (Oita National College of Technology, Japan)
OS2‐2 Code Coverage Visualization on a Web‐Based Testing Tool for Java Programs
Mochamad Chandra Saputra (Universitas Brawijaya, Indonesia)
Tetsuro Katayama (University of Miyazaki, Japan)
OS2‐3 TFVIS: a Supporting Debugging Tool for Java Programsby Visualizing Data Transitions
and Execution Flows
Hiroto Nakamura, Tetsuro Katayama, Hisaaki Yamaba, Naonobu Okazaki(University of Miyazaki, Japan)
Yoshihiro Kita (Kanagawa Institute of Technology, Japan)
Kentaro Aburada (Oita National College of Technology, Japan)
OS2‐4 Proposal of a testing method using similarity of interleaving for Java multi‐threaded
programs
Shoichiro Kitano, Tetsuro Katayama,Hisaaki Yamaba, and Naonobu Okazaki(University of Miyazaki, Miyazaki, Japan)
Yoshihiro Kita (Kanagawa Institute of Technology, Japan)
Kentaro Aburada (Oita National College of Technology, Japan)
OS2‐5 Proposal of a Modification Method of a Source Code to Correspond with a Modified
Model in MDA.
Yuuki Kikkawa,Tetsuro Katayama, Hisaaki Yamaba, and Naonobu Okazaki(University of Miyazaki, Japan)
Yoshihiro Kita (Kanagawa Institute of Technology,Japan)
Kentaro Aburada (Oita National College of Technology, Japan)
OS2‐6 Prototype of a Decision Table Generation Tool from the Formal Specification
Kenta Nishikawa, Tetsuro Katayama, Hisaaki Yamaba and Naonobu Okazaki(University of Miyazaki,Japan)
Yoshihiro Kita (Kanagawa Institute of Technology,Japan)
Kentaro Aburada (Oita National College of Technology, Japan)©ICAROB 2015

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The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

Abstract
Plenary Speech
PS-1Vision-Based Grasp Planning and Experiments of a Mobile Manipulator
Yi-Fu Chiu and Kai-Tai Song(National Chiao Tung University,Taiwan)
ivan790721.ece01g@nctu.edu.tw, ktsong@mail.nctu.edu.tw
In this paper, a motion planner is designed and implemented for of a mobile
manipulator to travel to a spot for grasping of an object. In this work, theprobability of successful grasping inside the workspace of the robot arm is used
for grasping planning. A vision SLAM system is combined with reachability
calculation to figure out the grasping position. Using a laboratory dual-arm robot,
we conducted experiments in different conditions to verify the effectiveness ofthe developed system.

PS-2 Biped Robot Research at Waseda University
Kenji Hashimoto, Atsuo Takanishi (Waseda University, Japan)
contact@takanishi.mech.waseda.ac.jp
Waseda University has researched on biped robots since 1967. In this talk I will
introduce our latest biped robots, WABIAN-2, a running robot and WL-16.
WABIAN-2 has 41-DOF, and its height is 1480 mm with 63.8 kg weight.
WABIAN-2 has realized a human-like walk with the knees stretched, heel-contact
and toe-off motion by utilizing a foot mechanism having a passive toe joint and a
2-DOF (Roll, Yaw) waist mimicking a human’s pelvis motion. Now we are goingto build a new biped humanoid robot capable of running and walking in order tostudy human running and other features. The running robot can jump by utilizing
a pelvic movement and leg elasticity. WL-16 is a human-carrying bipedvehicleconsisting of two Stewart Platform type legs and waist with a passenger
seat. WL-16 can be used as a substitute for a wheel chair.

©ICAROB 2015

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The 2015 International ConferenceC
on Artificial
A
Life annd Robotics (ICCAROB 2015), Horuto Hall, O
Oita,
Japan, Jannuary 10-12, 2015

Invitedd SpeechIS-1 Coombining playwarepexxergaming with a mobbile fitness app
Em
mmanouil Giaannisakis, Henrik Hautopp Lund
(T
Technical Uniiversity of Denmark,Den
Dnmark)
We propoose a novel pllayware as a mergembetweeen exergames and mobile fitnessfapps to engageethe useers in physicaal exercises, notn only as coompetitive plaay, butalso in thhe form of coooperative plaay. The conceept connects modularminteractive
tiles withh radio commmunication too Android tabblets and sm
mart phones, whichw
canconneect to the Internet. This allowsathe plaayers to moniitor their playyware
exergamiing performannce on the sm
mart device(s)). The test su
ubjects playinng thegames werew
school children (12--13 years oldd). As a social playwaree, weinvestigatted how thee playware mediatedmcoooperative and competitive playamongst the users. It was
w found thhat the majoritty of game pllay involved social
interactioon between players, and thaat 8 out of 10 pupils on thee top-10 were girls.
The playw
ware seemed tot motivate thhe girls to becoome physicallly active.

F
Figure 1. Mod
dular tiles
connnected to smarrt devices andinterneet.

IS-2 Paarallel Relaational Univverses – exp
periments ini modularrity
Luigi Pagliaarini1,2 Henriik Hautop Luund1( Centre for Playware,P
Tecchnical Univ
versity of Deenmark, Denm
mark)2
( Academy of Fine Arts ofo Macerata, Italy)
1

We here describe Paraallel Relationaal Universes, ana artistic meethod used forr boththe psychhological analyysis of group dynamics andd speculationss on aestethicss. The
design off the artistic syystem, which mediates grooup dynamics,, emerges fromm ourformer exxperiments onn modular plaayware and reemixing playw
ware. Inspired fromconsolidaated psycholoogical and arttistic practicee and founded
d on the rem
mixingmodular Playware logiic, where userrs remix sampples in the forrm of physicaal andainstantiiation of such a concept witth thefunctionaal modules, wee created an artistic
Parallel Relational
R
Universes, allow
wing arts alum
mni to remix artisticaexpresssions.
Here, wee report the datademerged from a first pre-test, run with gymnasium’s
alumni. We
W then repoort both the artistic and thet psycholog
gical findingss. We
finally discussdpossibble variations of such an instrument underuthe ligght ofmodern technologies.
tgical test, at a first
Between an art piece andd a psycholog
cognitivee analysis, it seeems to be a promisingpreseearch tool.

Figurre 1. An examp
ple outcome of
Paarallel Relationaal Universes

IS-3 Ideentifying humanoidha human physical parameters
andJovaana Jovic1, Eiiichi Yoshidaa1, Gentiane Venture2(1AIST, Jap
pan),(2TUAT, Jap
pan)
Dynamical and kinem
matic analysis of humanoid and human movementsmreequiremparameteers (mass, cennter of mass, andaccurate estimation of segment massmand thheir misinterppretation can lead to signiificant variation in
inertia matrix),
estimatedd joint kinemaatics. In the fieldfof robottics, several methodsmhave beendevelopedd for estimattion of mass parameters ofo humanoid robots, as weell as
human subjects, baseed on linear properties off dynamic eq
quation of biipedal
w respect too the set of massmparameteers. This talk will
w focus on thosesystems withmethods addressing thhe state-of-thee-art research in the topic. Examples off bothrmass parameterspesttimation will beb given. Idenntified
human annd humanoid robots
mass parrameters imprrove output of human dyynamic analy
ysis and hum
manoidsimulatioon and model-bbased control.

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Examples of exciting
trajectories used for themass
pparameters esttimation.

The 2015 International ConferenceC
on Artificial
A
Life annd Robotics (ICCAROB 2015), Horuto Hall, O
Oita,
Japan, Jannuary 10-12, 2015

OS
OS1:Inntelligent Control
OS1-1 Adaptive
A
M
Multiple-Mo
odel Contrrol of a Classs of Nonlin
near System
ms
Chao Yang and Yiingmin Jia(Beihanng University,, P.R.China)
In this paaper, an adapttive multiple-m
model controlller is develop
ped for nonlinnear
systems in parametricc-strict-feedbaack form. Unnlike the preevious resultss, amapproprriate
switchingg scheme is not requiredd here to switch to the mostmodel.Thhe new schem
me reduces thee number of identificationimodels and usesuinformatiion from all thet models moremefficientlly via convex
x combinationn ofestimatess of parameteers. The paraameter converrgence and globalg
asympttotic
stability of tracking errors in thhe closed-loopp system aree guaranteed. A
simulatioon example is included to demonstrate thhe effectivenesss of the obtaiined
results.

A
reeorientationn of spacecrraft with atttitude forb
bidden zon
nes
OS1-2 Attitude
XuhuuiLu and Yin
ngmin Jia
(Beihanng University,, P.R.China)
This papeer investigatess the attitude reorientation control schemme for spacecrraft,
considering attitude foorbidden zone and externaal disturbancees. Here a noovel
potential function is proposed. Besides
B
a dyynamical scaaling factor andbacksteppping techniquue are syntheesized to acccommodate th
he avoidancee of
attitude forbiddenfzonees and the dissturbance attennuation. Simu
ulation results are
given to verifyv
the effeectiveness of thet proposed method.
m

OS1-3 Weighted
W
M
MultipleM
Model
Adapttive Contro
ol of Slowly Time-Varyiing Systemss
Weiicun Zhang,Q
Qing Li(Universsity of Sciencce and Techno
ology Beijingg, P.R.China)
Weighteed multiple model ad
daptive conttrol (WMM
MAC) is acombinaation of offf-line desig
gn and onnline decisio
on, whichcombinees a finite number of simple con
ntrollers by weighting
algorithm. This pap
per presents an improved weighting
g algorithm
ying systemss and a new
w method too partitionof slowly time-varyuusing v-Gap
p metric. Th
he effectiven
ness of themodel uncertainty
proposedd method is verified thrrough Matlab
b simulationn results.

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The 2015 International ConferenceC
on Artificial
A
Life annd Robotics (ICCAROB 2015), Horuto Hall, O
Oita,
Japan, Jannuary 10-12, 2015

OS1-4 A Reduced
d-Complexity Interactting Multip
ple Model AlgorithmAffor LocationnTracking in
n Heterogeeneous Observation
Xiaoyann Fuand Yuan
nyuan Shangg(Capital Noormal University, P.R.Chinna)
This papeer is devoted to the probleem of state esstimate of discrete-time stoochastic
systems with Markovv jump param
meters. A low
w-complexity and high acccuracy
m is presenteed to reducee the compuutational loadd of the tradditionalalgorithm
interactinng multiple model algorithm
m with heteroogeneous obseervations for locationltracking. By decouplinng the x and y dimensions to simplify th
he implementaation offu
updatedlocation and formulatting the probblem in a faactor graph framework,dto obttain theinformatiion is iterativeely passed bassed on an adapptive fusion decisionstate estim
mation of maaneuvering tarrget. Extensivve Monte Carrlo simulationns showthat the proposed alggorithm is moremcomputaationally attraactive than existingemmethodds.
multiple model

OS1-5 Single
S
Image Dehazinng on Mobiile Device basedbon GP
PU Renderring Techno
ology
Yuannyuan Shangg, Yue Meng, Xiuzhuang Zhou, Xiaoyyan Fu, and H
Hui Ding(Capital Normal
N
University, China)
Images capturedcin baad weather coonditions suchh as haze, duust, fog, and rainrareoften subbstandard beccause of thee attendant poor visibility
y and low inntensity
contrast. Over the pastt few years, immage dehazinng has becomee popular in im
magingcbecause of itts potential applicationain many visionn tasks.
science communities
Image deehazing utilizees a very com
mplex algorithhm that requires intensive filteringf
and floatting-point aritthmetic operaations. Conseequently, proccessing speedd is themost signnificant bottleeneck in its appplication in some vision taskstsuch as mobile
platformss. On the otheer hand, graphhic processingg unit (GPU) renderingrtechhnology
is widelyy used in imaage processinng applicationns to speed up
u image processing
algorithm
ms. In this paper, we propose an optimizeed single imag
ge parallel processing
dehazing algorithm foor mobile plattforms and im
mplement it on
n a Windowss Phone
device baased on GPU rendering tecchnology. Expperimental ressults on hazy images
demonstrrate the efficaccy and effectiiveness of ourr proposed ap
pproach in impproving
image quuality.

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120
100
80
60
40
20
0

105.
5
33.3
11.1 18.8 82
3.2
22
220.4
46140.68 1.127
7
8

CPU

The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

OS2 Software Development Support Method
OS2-1 Prototype of a Supporting Tool to Generate Testing Communication Diagram
Tetsuro Katayama*, Seiya Urata*, Yohei Ogata*, Yoshihiro Kita†,
Hisaaki Yamaba*, Kentaro Aburada‡and Naonobu Okazaki*(*University of Miyazaki, Japan)( Kanagawa Institute of Technology, Japan)(‡Oita National College of Technology, Japan)†

This research has implemented a prototype of a supporting tool to generatetesting communication diagram. The testing communication diagram visualizes
messages, which are written in a part of test cases, between objects in softwaresystem and helps a developer to understand where the software system is tested
by a large quantity of test cases written in text. The testing communication
diagram is generated by adding the information of test cases to communication
diagram in UML (Unified Modeling Language). The implemented prototype can
support that a developer draws testing communication diagram. Moreover, it can
detect more efficiently deficiency and/or contradiction in communication
diagram and/or test cases.

OS2-2 Code Coverage Visualization on a Web-Based Testing Tool for Java Programs
Mochamad Chandra Saputra*, Tetsuro Katayama†(*Universitas Brawijaya, Indonesia)(†University of Miyazaki, Japan)
This research implements a web-based testing tool which displays code
coverage visualization result testing for java programs using statement
coverage (C0) and branch coverage (C1).The result displays for visual
information have been highlighted in bright green as information of executedlines, bright yellow for C0 and dark green for C1. The testing tool informs theuser using visualization to understand the behavior of the tested code as asequence of the executed lines. The results of testing are satisfied C0 and C1
100%, it’s meant the testing tool successfully checked each line and verified asa true condition of all based on the covering status of a statement and branchcoverage. The testing tool significantly reduces the time for testing a sample
software code, 716 ms using our testing tool and over 4 minutes using manual
testing.

©ICAROB 2015

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The 2015 International ConferenceC
on Artificial
A
Life annd Robotics (ICCAROB 2015), Horuto Hall, O
Oita,
Japan, Jannuary 10-12, 2015

OS2-3T
TFVIS: a Supporting Debuggingg Tool for JavaJ
Prograamsby Visu
ualizing Data
Transitionss and Execution Flowws*
Hiroto NakamuraN, Tetsuro
T
Kataayama*, Yoshhihiro Kita†,*
Hisaaki Yamaba
Y, Kenntaro Aburadda‡ and Naonnobu Okazakki*(*Univeersity of Miyazaki, Japan)( Kanagawa Institute of Teechnology, Jappan)(‡Oita Nationaal College of Technology,
T
Jaapan)†We have developed TFVIS in ordeer to improve efficiency off debugging forf Javaprogramss. TFVIS cann perform thhe visualizatiion of data transitions andathevisualizattion of execuution flows.Thhe visualizatiion of data trransitions shoows theflow of variablev
renew
wals in execuuting program
ms.It becomess easier to grasp the
behavior in executing the programss whose behaavior is unexp
pected by a bug.Thebe
iss usefulvisualizattion of executtion flows shoows an entire flow of the execution.It
to select the part wherre users wantt the visualizaation of the datadtransitionss.Whent visualization of the daata transitions based on th
he source coode, thewe use thevisualizattion of executtion flows suppports to graspp behavior of the program and theplace wheere users visualize.At last, thet efficiency of TFVIS is shown.
s

OS2-4 ProposalPof a testing methodmusiing similariity of interlleaving forr Java multti-threadedpprograms
Shoichiro Kitano*, TetsuroKatay
yama*, Yoshhihiro Kita†,*
Hisaaki Yamaba
Y, Kenntaro Aburadda‡and Naonobu Okazakii*(*Univeersity of Miyazaki, Japan)( Kanagawa Institute of Teechnology, Jappan)(‡Oita Nationaal College of Technology,
T
Jaapan)†In order tot improve thhe efficiency ofo testing Javva multi-thread
ded programss, thisresearch proposes a testingt
methood to detect order violation in them usinguo the distincttive bug patterrns insimilarityy of interleavinng. Order violation is one ofconcurrennt programs. ThisT pattern meansmthat somme threads cann be executed as anaccess too a certain memorymin ann unexpectedd order. Our proposed meethod
improvess the efficienccy of testing byb executing interleaving whichw
can tesst theplaces wherew
lead thee order violattion easily in source codess and by reduucing
interleaviing which is similar to exxecuted one already. The efficiency of our
method iss shown by exxperiments forr confirmationn.

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The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

OS2-5 Proposal of a Modification Method of a Source Code to Correspond with a
Modified Model in MDA.
Yuuki Kikkawa*,Tetsuro Katayama*, Yoshihiro Kita†,
Hisaaki Yamaba*, Kentaro Aburada‡and Naonobu Okazaki*(*University of Miyazaki, Japan)( Kanagawa Institute of Technology, Japan)(‡Oita National College of Technology, Japan)†

This study improves efficiency of software development using MDA.This paper
proposes a method that reduces time and effort to keep consistency between
models and a source code after requirement specification is modified.The
proposed method consists of four steps as below.(1) The method generates EAD(Extended Activity Diagram) from an activity diagram and a source code added
detail specification.EAD is a diagram that adds a part of the source code which
has the detail specification to the activity diagram.Here, the detail specification is
information omitted in drawing an activity diagram.(2) A developer modifies theactivity diagram to fit the changed requirement specification.(3) The method
modifies EAD to correspond with the modified activity diagram.(4) The method
generates a new source code from the modified EAD.

OS2-6 Prototype of a Decision Table Generation Tool from the Formal Specification
Kenta Nishikawa*, Tetsuro Katayama*, Yoshihiro Kita†,
Hisaaki Yamaba*, Kentaro Aburada‡and Naonobu Okazaki*(*University of Miyazaki, Japan)( Kanagawa Institute of Technology, Japan)(‡Oita National College of Technology, Japan)†

This research has implemented a prototype of a decision table generation toolfrom the specification (the formal specification) described in a formal
specification language. This paper uses the formal specification description
language VDM++ which is the lightweight formal methods VDM (Vienna
Development Method) to write the formal specification.This prototype
generates a decision table from the specifications of VDM++, and displays it.
We applied some general specifications to the prototype, in order to evaluate
its usefulness. This prototype could generate decision tables from each of theapplied specifications. We confirmed that this prototype works properly. As aresult, the prototype has improved the efficiency in test design with formal
methods.

©ICAROB 2015

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The 2015 International ConferenceC
on Artificial
A
Life annd Robotics (ICCAROB 2015), Horuto Hall, O
Oita,
Japan, Jannuary 10-12, 2015

OS3 Immage Analysis, Humann Interface,, and Text Mining
M
OS3-1 DevelopmeD
ent of Mousse Cursor Control
C
Sysstem Basedd on Face D
Direction Using
K
KinectMasayoshhi Tabuse1, KaoriK
Tamuraa2
1( Kyoto Prefectural Univ.,
U
Japan))
2( ISI Software Co
orp., Japan)
We propoose a methodd of operatingg mouse cursoor and contro
olling televisioon
based on face directioon using Kineect. Using ourr system anyo
one can contrrolo a user easilly and cheaplyy. It is not neccessary to fixx atelevisionn by motion ofuser’s heaad or to restricct user’s headd movement. Therefore,T
it caan ease a userr’sburden comparedcwitth conventionnal devices. Furthermore,F
a person wiith
trouble inn a hand cann control teleevision easilyy.Our system measures facce
direction and controlss mouse cursor using 4 faace directionss of rightwarrd,u
Kinect. It changes aleftward, upward andd downward directions using
televisionn’s channel annd volume byy controlling mouse cursorr based on facce
direction using Kinectt in a range withw distance lesslthan 4 meeters betweenn aK
It is also applicablee to operation of other appliication softwaare
user and Kinect.
controlledd by mouse cuursor.

OS3-2 Quantitativ
Q
ve Evaluatioon of Facial Expression
ns and Moveements of Persons Whiile Using
V
Video
Phon
ne
Taro Asada1, Yasuunari Yoshitoomi1, Ryota Kato1, Masaayoshi Tabusee1, and Jin N
Narumoto2
1( Kyoto Preefectural University, Japaan)
2( Kyooto Prefecturral University
y of Medicinne, Japan)
We have developed a methodmfor analyzing facial expressions anda movemenntsvphone totof a persson while sppeaking with another persoon using a video
improve the QOL of elderlyepeoplee living in carre facility, or ata home. In thhepresent study,s
the viideo is analyyzed using image processsing softwarre(OpenCVV) and the new
wly proposedd feature param
meters of faccial expressionns
and movvements, whicch are extractted in the mouth-partmareea. The featurre
parameter for expressiing facial exprression is deffined as the av
verage of facial
T feature parrameter for exxpressing faciaal movement isexpressioon intensity. The
defined asa the averagee of absolute valuevof vertical coordinatee for the centeer
of gravityy of mouth arrea in the relaative coordinate system. Th
he experimenttal
result shoows the usefullness of the prroposed methood.

©ICARO
OB 2015

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The 2015 International ConferenceC
on Artificial
A
Life annd Robotics (ICCAROB 2015), Horuto Hall, O
Oita,
Japan, Jannuary 10-12, 2015

OS3-3 Facial
F
Exprression Recoognition Usiing Facial Expression
E
I
Intensity
Ch
haracteristics ofT
ThermalIm
mage
Ya
Yasunari
Yoshhitomi, Taro Asada,A
Ryotaa Kato, and Masayoshi
M
T
Tabuse(Kyoto Preefectural Uniiversity,Japaan)
In the preesent study, we proposed a methodmfor recognizing facial expressionns.
The recorrded video is analyzed by thermal imagge processing and the featuure
vector off facial expresssion, which iss extracted in thet area of moouth and jaw bybf each domaain having 8 × 8
applying 2D-DCT (Disscrete Cosine Transform) for
pixels in the area of mouthmand jaw..The facial exxpression inten
nsity defined as
m of differencce vector betw
ween the featture vector off neutral faciial
the norm
expressioon and that off observed onne can be useed for analyzin
ng a chance offacial exppression.The averageaand thhe standard deviationdof faacial expressioon
intensities are used foor recognizingg facial expreession. The averageaand thhestandard deviation off time of utteerance are heelpful for recognizing faciialo the proposeed
expressioon. The experrimental results show the usefulness of
method.

OS3-4 MethodM
forr Characterr Domain Extraction
Efrom Imagge Using W
Wavelet TrannsformTaiki Taniguchi1 and Yasunari Yoshitoomi2
T(1ZENSHO HOLDINGS
H
Co., Ltd.,Jaapan)(2Kyoto Preefectural University, Japaan)
The numbber of imagess having privaate information and/or URL
L of illegal Web
W sitehas been increasing inn the cyber spaace on the Intternet. These images mightt cause
ment of humann right and/or criminal act.IIn the present study, a methhod for
infringem
extractingg the region(ss) having charracters on an image has beeen developedd using
Wavelet Transform
T(W
WT) and the em
mpirical know
wledge that a characterchas strong
vertical and/orahorizonntal element(ss).Finally, the output RGB image is gennerated
by the opperation that thhe color of thhe pixel on thee original RGB image is chhanged
into white when the vaalue of correspponding pixell on the mask image made by the
proposedd method is '0', resulting inn extracting thhe region(s) havinghcharactters on
the originnal RGB imaage.The expeerimental resuults show thee usefulness of the
proposedd method.

©ICARO
OB 2015

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The 2015 International ConferenceC
on Artificial
A
Life annd Robotics (ICCAROB 2015), Horuto Hall, O
Oita,
Japan, Jannuary 10-12, 2015

OS3-55 Classificaation of Jap
panese Doccuments an
nd Rankingg of Represeentative Do
ocuments
Using Ch
haracteristtic of Frequuencies of Words
W
Jun Kimuura1, Yasunarri Yoshitomi2, and Masayyoshi Tabusee2(1JustS
Systems Corrp.,Japan)
2( Kyoto Preefectural University, Japaan)
It is veryy difficult to reead through all
a of Web pagges in which wew are interessted.A
Web page has some kinds
kof mediia, such as doocument, imag
ge, and soundd. We
have deveeloped a methhod for classiffication of Jappanese documeents and rankiing ofrepresenttative documeents using characteristic of frequencies of words. All nounsnin a docuument are exttracted with use
u of MeCabb (http://mecaab.sourceforgee.net/)with whicch the docum
ment is resolveed into severaal morphemes.For clustering, we
use ward method. The representativee document iss defined as th
he document whosewv
is the closestcto the center of graavity of the classcin the feeaturefeature vector
vector sppace among all
a documentss belonging too the class. The
T ranking ofo therepresenttative documeents is decideed in the desccending orderr of the numbber of
documennts belonging tot the class.

OS4 Grraph Theorry and Its ApplicationAnOS4-1 TheT Role of National Standards Setter in th
he Global ConvergenC
ce Era- the Casse of the Jaapanese Settter during the first deecade-In
O
Ogata,K. (UUniversity of Nagasaki,
N
Jappan)An aim ofo this paper is to explainn that the chaange of standdard-setting acctivities of
ASBJ, Jaapanese accouunting standaard setter, in the 2000s was
w caused byb its own
standard--setting devellopment strattegy. Researcches on accoounting standdard-setting
processess have had twotdifferentt ideas aboutt characteristtics of the setters:sthepluralisticc organizationn regarded as a political foorum of somee stakeholderss related toaccountinng standards, and the autoonomous orgaanization regaarded as an acctor which
behave withw
a will too achieve desirable policyy purposes. ThroughTan analysis
aoforganizattional structurre using graphh theory, we showsthat the ASBJ strategically alter
its structuure, and that thhe structure canc trigger thee change of setting activities. It meansthat the ASBJA
has a natture not so muuch pluralisticc as autonomo
ous organizatioon.

©ICARO
OB 2015

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The 2015 International ConferenceC
on Artificial
A
Life annd Robotics (ICCAROB 2015), Horuto Hall, O
Oita,
Japan, Jannuary 10-12, 2015

OS4-2 AnA Empirical Researcch on Interr-firm Capiital Relatioonship in Yookokai usinng IDE
Spatial Moodel
Takkao Ito1, Makkoto Sakamooto3, R. Meh
hta2, Tsutomuu Ito4, and S. Ikeda3(1 Hirosshima Univerrsity, Japan)(2 New
N Jersey Institute of Technology,
TU
U.S.A)(3 Univeersity of Miyaazaki, Japan))(4 Hinno Motors, Ltd.
L Japan)This papper introducess recent funddamental moddifications to Japanese allliancesystem known
kas the keiretsu, andd analyses hoow these channges have afffected
corporatee performancce. Specially, the perfformance off Japanese auto
manufactturers, such ass Toyota, Nisssan and otherss, has significaantly improveed due
to sophisticated producction system technologies, highly produ
uctive workerss, andrecurring transaction reelationship wiith other partnners in their neetwork familyy. One
possible determinant ofo their succeess could be dued to their uniqueuorganizzationw
providdes a strong platformp
to fo
orge their straategic
forms –thhe keiretsu– whichalliance relationshiprw their partss suppliers as wellwith
w as collab
boration in ressearchand deveelopment withh other autom
mobile makers. After econo
omic bubble ofo the1990’, thhe strong tiess between auttomobile makkers and theirr supplier parrtners
experiencced significannt changes, whichw
are knnown as “keeiretsu loosenning”.
Consequeently, what is the stattus quo of automotive keiretsus? Does
cross-shaareholding, whhich is one specific form off capital relatiionship in keiiretsu,
still contrribute to imprroving corporrate performannce? To answ
wer these quesstions,this paperr reports the resultsrof a stuudy that colleccted data on cross-shareholcldingsto shed light on the relationship between inteer-firm capitaal relationshipp andcorporatee performancee. The findinggs of this emppirical investiigation reveall that:(1) Keireetsu is a flexibble, highly addaptive organiizational formm; its scale chaanges
in responnse to econom
mic situations;; (2) Capital relationshipr
iss still a significant
determinaant of increasing profits forr keiretsu parttners even aftter the bubble burst
in the 19990s..

kai from 1985
Influeence in Yokok
to 20044.

OS4-3 DesignDandd Experimeental Evaluation of a Human
H
Skiill-Based PID Controlller
Yuntao Liao,L
Yamamooto Toru(Hirroshima Univversity,Japann)Nowadayys less and lesslpeople area willing tot do hard works.Whichmw
model
articulatioon controller (CMAC)(
is a kind of neuraal networks (NNNs) that can easilyesolve prooblems of nonnlinear system
m. Compared withw other NN
Ns the advantaage of
CMAC iss that it takes shorter learninng time. Moreeover because of the widelyy usedof PID controllerca huuman skill-baased PID conntroller using CMACs has been
proposedd in this paper..

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The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

OS5 Bio-Inspired Algorithms and Their Applications
OS5-1 Analysis of Genetic Disease Haemophilia A by Using Machine Learning
Kenji Aoki,Makoto Sakamoto, Hiroshi Furutani(University of Miyazaki,Japan)
Haemophilia A is a genetic disease resulting from deficiency of factor VIII. The
database of mutations causing haemophilia A has been developed by the world
wide collaboration. In this study, we examined the relation between activity offactor VIII and the missense mutation by using machine learning. As parameters,
we used four physical-chemical parameters of amino acids. We predicted theseverity of haemophilia A by using machine learning in factor VIII. As the result,
logistic regression is not better than other methods in the prediction of
haemophilia A severity. The result of the prediction improved in order to SVM,
Bagging, AdaBoost, RandomForest. These results suggested that we can predictthe haemophilia severity by using these methods, and Random Forest was thebest method in these five methods to predict the haemophilia A severity.

OS5-2 Analysis of Asymmetric Mutation Model in Random Local Search
Hiroshi Furutani, Yifei Du, Kenji Aoki, Makoto Sakamoto(University of Miyazaki,Japan)
There are many reports that the asymmetric mutation model is a very powerfulstrategy in EAs to obtain better solutions more efficiently.In this paper, we reportstochastic behaviors of algorithms that are asymmetric mutation models ofRandom Local Search (RLS). The mathematical structure of asymmetry model
can be derived in terms of a finite Markov chain.We demonstrate some useful
results representing the effects of asymmetric mutation.

OS5-3 Hitting Time Analysis of OneMax Problem in Genetic Algorithm
Y. Du1, Q. Ma1, k. Aoki1, M. Sakamoto1, H. Furutani1, Y. Zhang2(1University of Miyazaki,Japan)(2Qinghai University, China)
Genetic algorithms (GAs) are stochastic optimization techniques, and we havestudied the effects of stochastic fluctuation in the process of GA evolution. A
mathematical study was carried out for GA on OneMax function within theframework of Markov chain model. We treated the task of estimating
convergence time of the Markov chain for OneMax problem. Next, in order tostudy hitting time, we study the state after convergence.Our results demonstratethat the hitting time distribution h(t) has an exponential form, and the logarithmicof h(t) is linearly decreasing function.

©ICAROB 2015

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The 2015 International ConferenceC
on Artificial
A
Life annd Robotics (ICCAROB 2015), Horuto Hall, O
Oita,
Japan, Jannuary 10-12, 2015

OS6 Coomputer Sccience and InformatioI
on Processing
OS6-1 SufficientSsspacesfor seven-waysffour-dimennsional Turring machinnes
t simulatee four-dimeensional ontone-marker automataa
Makoto Nagaatomo1, Makooto Sakamoto1, Hikaru Susaaki1, Tuo Zhanng1, Takao Ito2, Yasuo Uchiida3,
Tsunehiiro Yoshinaga4, Satoshi Iked
da1, and Hirosshi Furutani1(1:Universsity of Miyazaaki, Japan)(2:H
Hiroshima University, Japann)(3:Ube Naational Collegge of Technoloogy, Japan)(4:Tokuyama Coollege of Techhnolory, Japan)mapplicaation areas succh as
We thinkk that recentlyy, due to the advances in many
motion immage processing, computer animation, and so on, it is very usefuul for
analyzingg computational complexity of multi-dimensiomonal inform
mationprocessinng to explicaate the propperties of foour-dimensional automata, i.e.,
three-dimmensional autoomata with thhe time axis. As
A far as we know, there is no
investigattion about four-dimensiofonal automatta. Then, in
n 2002, we first
introduceed four-dimennsional finitee automata in the worlld. In 2003, weinvestigatted four-dimeensional alterrnating Turingg machines. In this paperr, we
continue the investigattions, and deaal with sufficiient spaces for four-dimenssionalm
to simmulate one-maarker automataa.
Turing machines

OS6-2S
Some Propeerties of k-N
Neighborhood Templlate A-Typee Three-Dim
mensional
B
BoundedC
Cellular
Acceptors
Makoto Sakamoto1, Makoto Naggatomo1, Hikkaru Susaki1, Tuo Zhang1, Takao Ito2,
3
4
Yasuoo Uchida , Tsunehiro Yooshinaga , Saatoshi Ikeda1 and Hiroshi Furutani1(1Universityy of Miyazakki, Japan, 2Hiroshima
H
Unniversity, Jappan,
3
Ube Nationnal College ofo Technologgy, Japan,4To
okuyama College of Techhnology, Jap
pan)putational model,m
In this paper, we investigate multi-dimennsional compcacceptoron
k-neighboorhoodtemplaate A-type thrree-dimensionnal bounded cellularfour-dimeensionaltapes,, and discuss somesbasic prroperties. Thiss model consissts ofa pair off a converter anda a configuuration-readerr.The former convertscthe givenghe latterdetermmines
four-dimeensional tape tothree-dimeensional conffiguration. Th
whether or not the deriveddthreee-dimensionalcconfiguration is accepted,, andgfour-dimmensional tapee. We
concludes the acceptannce or non-acceptance of given
me open probblems about k-neighborhookodtemplate A-type
mainly innvestigate som
three-dimmensional bouunded cellulaar acceptoronn four-dimenssionaltapes whosew
configuraation-readers are L(m) spaace-bounded deterministicd(nondeterminnistic)
three-dimmensional Turiing machines.

©ICARO
OB 2015

P - 38.

The 2015 International ConferenceC
on Artificial
A
Life annd Robotics (ICCAROB 2015), Horuto Hall, O
Oita,
Japan, Jannuary 10-12, 2015

OS6-3 PerfectP
Anaalysis in miiniature Otthello
Yuki Takeshhita1, Satoshhi Ikeda2, Maakoto Sakamooto3, Takao IIto4(123Miyaazaki Univerrsity, Japan)(4Hirosshima University, Japan)
In 1993, mathematiciaan Joel. Feinsttein found thaat a perfect plaay on 6×6 boaard ofga 16-20 win for the secondsplayer; the perfect playp
is a scorre andOthello gives
the sequeence in the caase where both players weere the best play.
pMore thaan 20
years havve passed sinnce then, in a recent the mainstreammbooard game stuudy iscomputerr game such asa chess and Go,
G but standaard 8×8 board of Othello haas notbeen solvved at this tim
me. Therefore,, we suppose the nature of the game fromm theperfect annalysis of the board which is smaller thaan it.Additionaally, this studyy also
includes the meaning ofo the double-check, becauuse we were notn able to finnd thehis paper, wee show the perfectarticle that checked thhe result of Feinstein. In th
play of 4×4,
4
4×6, 4×88, 4×10 and 6×6 board. FromF
this resu
ult, we discusss thenature off the game in 8×8
8 board or moremboard.

OS6-4AA proposal for teachinng program
mming throu
ugh the Fivve-Step Meethod
1
1
Y. UchidaU, S. Matsuno
M, T. Ito2, M. Sakkamoto3( Nationnal Institute of Technolog
gy, Ube Colllege, Japan)(2Hiroshhima Univerrsity, Japan)(3Univeersity of Miyazaki, Japan))1

We teachh computer prrogramming tot students agged 17 through 18 years. Inn thecourse, a few students consider themmselves to havve insufficien
nt understandinng of
programm
ming or thinkk that they aree not good at programmingg. In responsee, we
adopted and implemeented a part of the Compputer Sciencee Unplugged (CScann effective way of teacching
Unpluggeed) method, which is consideredinformatiion science. However, although CS
S Unpluggedd has geneerated
consideraable results in motivating sttudents to learrn and in initiaal learning, wee feelthat it is not sufficienttly connected to full-fledgeed programmin
ng languages suchpadvaancing from CS Unpluggeed toas C andd Java. Accoordingly, we propose
full-fledgged programm
ming through a new Five-S
Step Method. In this paperr, we
describe thet thinking anda concepts behindbthis prooposed method.

©ICARO
OB 2015

P - 39

The 2015 International ConferenceC
on Artificial
A
Life annd Robotics (ICCAROB 2015), Horuto Hall, O
Oita,
Japan, Jannuary 10-12, 2015

OS7 Coomputer Neetwork andd Security
OS7-1 AnA Authenttication Meethod for Mobile
M
Devicest
that
is Indeependent of Tap-Operration on a Touchscreeen
1

Hissaaki Yamabaa, 1So Nagatomo, 2Kentaaro Aburada, 1Shinichiro Kubota,
1
Tetsurro Katayamaa, 3Mirang Paark, 1Naonobbu Okazaki(1Univerrsity of Miyaazaki, Japan)
2
Japan)( OitaO Nationall College of Technology,T(3Kanagawa Institute of Teechnology, Japan)
J

In these days, mobile devices suchh as tablet typpe PCs or sm
mart phones widelywpenetrated. This causes a new need for authenticaation method that has tolerrant toshoulder surfing. Unnder this situuation, we are investigaating a new userd
not usingu
screen touchingt
but usingauthentication methodd for mobile deviceseam signals. In this paper,, a series of experiments were
surface electromyogra
carried ouut in order to investigate thhe prospect off the authentication method usings-EMG. Concretely,C
seeveral motionn patterns of wristw
are intro
oduced, and s--EMG
signals generated at eaach gesture werew
measuredd. And we com
mpared the s--EMGssubject and the pattterns generateed bysignal paatterns generaated by the samedifferent subjects. As a result, it was
w found thatt patterns of samessubjectss were
w different each other.
similar annd those of diffferent ones were

OS7-2 ProposalP
off Security Evaluation
E
System using User's Reviews an
nd Permisssions
f Android Applicatiionfor
Naonnobu Okazakki, (University
y of Miyazakki,Japan)
Yoshihiroo Kita, (Kanaagawa Institu
ute of Technoology, Japann)
Kentaro Aburada, (Oita National Co
ollege of Techhnology, Jappan)
Miraang Park (Kaanagawa Insttitute of Techhnology)y.kita@@ccy.kanagaw
wa-it.ac.jp
Leakage of the perssonal informaation in Anddroid OS po
owered devicce byhmatter. The Android OS users muust be
mal-appliications is becoming the heavy
careful noot to install mal-applicationmns. The review
ws and the ussing permissioons ofapplicatioons are usefull by users to detectdmal-appplication. However, the moost ofusers readd the reviewss only. All useers must be caautious about not only the using
permissioons but also the combinattion of themm.In this papeer, we proposse thesecurity evaluation syystem to prevvent the installation of mal-applicationmns onAndroid OS. This sysstem indicatess the user revviews with thee using permiission
w users. Reviews have twwo types: poositive
informatiion of appliccation to newreviews anda negative reviews. Possitive reviews include selliing points or good
features forf the application. Negativve reviews include wrong points or probblemsof the appplication. Neew user evaluuates the onee of reviews, and determinnes todownloadd the applicattion. The evalluation reportt of the review
w is transmittted to
the revieewer which wrotew
it. Theerefore, the reviewerr
can use the repoort asreferencee for the betterr.

©ICARO
OB 2015

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The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

OS7-3Evaluation of Neighbors Based Routing for ad hoc networks
Kentaro Aburada1, Hisaaki Yamaba2,Shinichiro Kubota2,
Tetsuro Katayama2,Mirang Park3,Naonobu Okazaki2(1 Oita National College of Technology, Japan)(2 University of Miyazaki, Japan)
3( Kanagawa Institute of Technology, Japan)
In ad hoc networks, due to the mobility of nodes, radio waveinterference, andlimited resources, such as batteries, communicationlinks are unstable andrestricted. As such, an efficient routingprotocol is needed in order to solve these
problems. The existing protocols cannot adapt to theroute repair when the routeis disconnected. In the present paper, wepropose a neighbors-based routing(NBR) protocol by constructing paths in an area in which a large number ofnodes exists. Simulations confirmthat the proposed protocol has higher
connectivity and a lower controloverhead than existing protocols in topologies inwhich nodes move.

OS8 Kansei Engineering
OS8-1 Investigation of Feature Quantity in Sound Signal and Feeling Impression Using
PCA
Yusuke Kawakami, Tetsuo Hattori (Kagawa University, Japan),
Hiromichi Kawano (NTT AT, Japan),
Tetsuya Izumi (Micro-Technica Co., ltd., Japan)
This paper investigates the relationship between feature quantity of sound signal
and feeling impression using PCA (Principal Component Analysis). As thefeature quantity, we use Fluctuation value and sum of squared errors (Residual)
which is calculated by regression analysis of sound signal, in the same way as ourprevious paper. As a result, we have found that the feeling response of examinees
can be classified into three groups by a clustering analysis. And also we haveobtained the results of PCA for the feeling effects depending on each group of
examinees and four kinds of frequency zone of sound signal. In this paper, we
discuss the analysis results on the Kansei (or feeling) effect.

29
5
2
24
3
21
11
31
20
33
26
13
10
16

G3

G2

22
15
12
27
28
6
34
17
4
19
8
25
14
30
23
32
9
7
1
18

G1

0

5

10

15

20

25

30

35

OS8-2 Automated Color Image Arrangement MethodUsing Curvature Computation in
Histogram Matching
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)

This paper proposes an improved method for automated color image arrangement methodby using histogram curvature computation. The previous paper that we have presented theprinciple of our method using Histogram Matching based on Gaussian Distribution(HMGD), and how to detect input color image peakedness in its histogram. In this paper,
we describe about Variance Estimated HMGD (VE-HMGD) as improvement HMGD. We
also show how to estimate the histogram variance of original image based on the curvaturecomputation. Moreover we compare processing results between VE-HMGD and HMGD
through some experimentation. As the result, we show that, in the color, VE-HMGD is
more natural than HMGD.

©ICAROB 2015

P - 41

The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

OS8-3 Change Detection Experimentationfor Time Series databy New Sequential
Probability Ratio
Yoshihide KOYAMA, Tetsuo HATTORI (Kagawa University, Japan)
Hiromichi KAWANO (NTT AT, Japan)
Katsunori TAKEDA (Canon IT Solutions Inc., Japan)
Previously, we have proposed a novel method usingNew Sequential Probability
Ratio (NSPR) for the structural change detection problem of ongoing time series
data instead of using SPRT (Sequential Probability Ratio Test). In this paper, forcomparison. wepresent the experimental results by applying the both methods,i.e., NSPR and SPRT, to time series data that are generated by a multiple
regression model in the case where one explanatory variation is a periodic
function (sine function). And also we discuss the effectiveness of the bothmethods.

y = x1 + 20x2 + 5 + εy250

y = x1 + 10x2 + 5 + εchange point tc = 70

200

150

100

50

t

0
0

10

20

30

40

50

60

70

80

90

100

OS8-4 Analysis of Navier-Stokes Equation from the Viewpointof Advection Diffusion (I)--- Analytical Solution of Diffusion Equation --Hiroki SAKAMOTO, Tetsuo HATTORI (Kagawa University, Japan)
Akiomi TADA (Japan)
Vanhoa NGUYEN (Japan)
Hiromichi KAWANO (NTT AT, Japan)
We propose an approximate analysis method for the Navier-Stokes Equation (NSE) based on
the similarity between NSE and Advection Diffusion Equation (ADE). In this paper, we
present an analytical solution and a Green function (integral kernel) which are obtained fromthe diffusion equation over uniform flow field (or velocity field) in three dimensional (3D)boundless region under arbitrary initial condition. The solution shows that the diffusion
process is a Markov one and that the Green function becomes a Gaussian-like exponentialfunction.

∂C
∂ 2C
∂ 2C
∂ 2C
= Dx
+ Dy
+ Dz
∂t
∂x 2
∂y 2
∂z 2
−u

∂C
∂C
∂C
−v
−w
− λC + Q
∂x
∂y
∂z

(ADE)

OS8-5 Analysis of Navier-Stokes Equation from the Viewpointof Advection Diffusion (II)--- Approximate Solution --Hiroki SAKAMOTO, Tetsuo HATTORI (Kagawa University, Japan)
Akiomi TADA (Japan)
Vanhoa NGUYEN (Japan)
Hiromichi KAWANO (NTT AT, Japan)
We propose an approximate analysis method for the Navier-Stokes Equation (NSE) basedon the similarity between NSE and Advection Diffusion Equation (ADE). In thepreceding
paper titled “Analysis of Navier-Stokes Equation from the Viewpoint of Advection
Diffusion (I)”, we have presented the analytical solution of the ADE. Subsequently in thispaper, we point out the explicit similarity between NSE and ADE by illustrating thecorresponding equations. Then, we show an approximate solution of NSE using the
aforementioned analytical solution of ADE.

©ICAROB 2015

P - 42∂C= D∇2C − (v・ ∇)C − λC + Q∂t(D = Dx = D y = Dz )

(ADE)
∂v
gradp f= k∇ 2 v − ( v・ ∇ ) v −+∂tρρ( div v = 0)

(NSE)

The 2015 International ConferenceC
on Artificial
A
Life annd Robotics (ICCAROB 2015), Horuto Hall, O
Oita,
Japan, Jannuary 10-12, 2015

GS
GS1 Arrtificial inteelligence
GS1-1S
Selecting Words
W
and Notation
N
Ussing Litera
ary Data in the Integraated Narra
ative
Generationn SystemJumpeei Ono, Takaashi Ogata(Iwate Preefectural University,Japann)
We have developed a narrative geeneration systeem called “In
ntegrated Narrrative
NGS”. The arrchitecture off INGS has twwo types of macrom
Generatioon System: IN
level’s paarts: “generattion mechanissm” and “knoowledge mechhanism”. The mainelements of the latter “knowledge mechanism”
ma “conceptu
areual dictionariees (fornoun conncepts and verrb concepts)”, “language nootation (or liteeration) dictionnary”,
and so on.
o Though thhe present meechanism of INGS random
mly selects a noun
concept fromfthe choicces, various tyypes of noun concepts are mixedmin a sett suchas old/new
w, ordinary/exxtraordinary, etc.
e This papeer presents a common
cidea basedon the frrequency analysis of externnal texts’ worrds for the abbove two probblems.
For the first processinng relevant tot noun conccepts, we automatically annalyze
Aozora Bunkoo”, tofrequencyy informationn of noun woords in novelss stored in “A
select nouun concepts accordingato thet frequency information.F
For example, if we
use nounn concepts acccording to higgh-frequency nounnwords, thet output texxt will
be more readable.Inrthhis presentation, we will preesent the basicc idea, text annalysis
programss with languaage parsing, severalsresultss, and the verrification inclluding
comparinng the results withw the proceessing withoutt the proposedd mechanisms.

Integrated narrative geneeration system
Geenerative
Parameters

Structuraal
Knowledgge

Story
Mechanism

Disccourse
MechhanismStory
Techniques

Conceptual
Dictionariees

Expression
Mechanism
Discourse
Techniques‐ Natural Language‐ Music‐ Visual

Story

Disccourse

GS1-2 EvaluationEof a Narraative Discou
urse Generration Systeem Based oon the Conccept ofd Deviationn”“Norm and1
Taisuke AkimotoA, Taakashi Ogataa2( The University ofo Electro-Co
ommunicatioons, Japan)
2( Iwate Preefectural Uniiversity, Japaan)
1This papper deals withh the verificaation of the narrativendiscourse systemm thatautomaticcally producees a variety of “discoursee” structures from an inpputted“story” structuresthroough an iteraative mutual action betw
ween a “narrrator”
mechanissm and a “narrratee” mechaanism. We obbjectively con
nsider the systtem’s
behavior for identifyying its achieevements andd issues to be addressedd. In
particularr, we analyze a series of 100000 generatedd discourse strructures accorrding
to their structuralsfeatture values by focusing onn the diachro
onic alternatioon of“norm”, the narratee’s expectatioon in receiviing discoursees, caused byy its“deviation” by the narrrator. Overall, the mechaniism produced a large numbber ofdifferent discourse struuctures througgh the restriction of generatiion space baseed on
the norm at the time annd the accumuulation of smaall shifts in thee norm.

©ICARO
OB 2015

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The 2015 International ConferenceC
on Artificial
A
Life annd Robotics (ICCAROB 2015), Horuto Hall, O
Oita,
Japan, Jannuary 10-12, 2015

GS1-3 An aggreggating apprroach of tarrget enclosu
ure of roboot swarm
Masao KUBO1, Hiroshi SAT
TO1, Akihiroo Yamaguchi2, Akira Nam
matame11( NationalN
Deffense Academ
my of Japan,JJapan)(2Fukuoka Innstitute of Technology, Jaapan)
This paper presents a robot swarm
m model to ennclose a target. The robotts useninformation. In th
his paper, the robot
informatiion of the targget and their neighborobserves the most robot dense direcction as the neighborninforrmation. We expecten approachh makes the roobot swarm moremsimple annd robust for noisy
that this newenvironm
ment. We confiirm this model by computerr simulations.

nder target enclosure by robot
GS1-4 Probabilityy of mixingg up a nearrest neighboor robot un
swarm
Masao KUBO1, Hiroshi SAT
TO1, Akihiroo Yamaguchi2, Akira Nam
matame11( National
N
Defense Academ
my of Japan,,Japan)
2( Fukuoka Innstitute of Teechnology, Japan)
This paper presents a robot swarm
m model to encloseea targget. The roboots use
his paper, thee robot
informatiion of the targget and their neighbor infoormation. In th
observes the most robbot dense direction as the neighborninforrmation. We expecten approachh makes the roobot swarm moremsimple anda robust forr noisy
that this newenvironm
ment. We show
w a result of probabilistic analysis of th
his target encclosure
approachh.

GS2 Coomplexity
GS2-1 InteractiveI
e musical ed
diting systeem to suppo
ort human errors andd offer perssonalp
preferences for an automatic piaano
Keenji Tsunenarri, Eiji Hayasshi (Kyushu Institute of Technology,
T
Japan)
Recently,, electronic musicalm
instrumments are achiieving progresss for developpmentof electroonics and seenn everywhere. However, thheir sound quaality and ambbience
are inferiior to real mussical instrumeents.Thereforee, we developeed automatic pianoby using grand piano. Pre-edit is needednto play music in the mannerr of anist. In the caase of piano music,mthere are often1000 oro more notes in thelive pianscore of evenea short piece of music,, requiring thaat an editor spend a huge am
mountof time tooaccurately sim
mulate the em
motionally exppressive performance of a highlyhskilled pianist.
p
Thereefore, we havve developeddan interactiv
ve musical edditing
system thhat utilizes a databasedto ediit music more efficiently.

©ICARO
OB 2015

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The 2015 International ConferenceC
on Artificial
A
Life annd Robotics (ICCAROB 2015), Horuto Hall, O
Oita,
Japan, Jannuary 10-12, 2015

GS2-2 Modeling ofo collaborration in deesign processs Based on
n Channel T
TheoryPatchanee Patitad, Hiidetsugu Sutoo(Muroran Institute of Tecchnology,Jap
pan)
Collaboraation is one of the effectivee approaches that help us to share know
wledgetogether and exchangee ideas withiin a team meember. Somettimes, new heelpful
knowledgge that is not held by the memberss emerges ass a result of thecollaboraation. Such knnowledge ofteen contributess to get prim
me solutions duringdcollaboraation process. However, thhe way to geenerate such newnknowleddge is
implicit. In this paper, a method of creating a moodel, which reepresents effects of
B using thiss scheme, wee cancollaboraation in desiggn process iss proposed. By
illustrate what new knnowledge cann be gotten frrom a collabo
oration and wew canknow thee effect of the collaboration..

GS2-3 SterilizingSsystem of ballastbwateer using an
n arc dischaarge
Piao shengxu,sJae--cheol Lee, Zheng
Z
Tao, Heeje
H
Kim(Pusan NationalN
Univversity,Koreaa)
The innadvertent trannsfer of harmmful aquatic organismsoandd pathogens ini theballast water of ships has
h been deteermined to cauuse a significaant adverse im
mpact
to all arouund the worldd coastal regioons. The recoggnition of thesse effects has madethe ballasst managemennt system thatt is extremelyy important fo
or protection ofo themarine environment.
eRecently, inn order to soolve this isssue, a numbeer oftechnologgies have beeen developed and commercialized. Mosst of the treattment
technologgies are bareely used inddependently. In
I addition, there are seeveral
combinedd methods to treat the balllast water. thee overall aim of this study is tosuggest on
o of the bestt way of sterrilization of ballastbwater usinguhigh vooltage
pulsed arc discharge.

.

GS2-4 TheT design of medicall ruby laserr power sup
pply systemm using LL
LC resonantt
converter
Jaechheol Lee, Piaoo shengxu, Zheng
Z
Tao, Heeje
H
Kim( Pusan NationalN
Univversity,Koreaa)
Becausse of its usabiility, the use ofo ruby laser forf medical th
herapy has attrracted
a lot of innterest.LLC resonant conveerter is used tot control laseer power denssity inruby laseer power supplly. Zero voltaage switching((ZVS) is implement to minnimize
switchingg loss by LLC
C resonant connverter. The powerp
supply of solid state laser
consists ofo trigger, simmmer and maiin power suppply. In this paaper, simmer mode
triggeringg circuit is ussed. It has advvantage of miiniaturizing a circuit and geetting
stability of a circuit byb disconnectting trigger powerp
supply from main powerp
W use DAB(DDual Active Bridge)
B
to chharge the capaacitor. We obttained
supply. We
maximumm laser output of 0.5J. Repeetition of laserr out is 3Hz.

©ICARO
OB 2015

P - 45.

The 2015 International ConferenceC
on Artificial
A
Life annd Robotics (ICCAROB 2015), Horuto Hall, O
Oita,
Japan, Jannuary 10-12, 2015

GS3 Evvolutionaryy computation
GS3-1 Online
O
Rulle Updatingg System UsingU
Evolu
utionary Coomputationn for Manag
ging
D
Distributedd Databasee
Wiraramma Wedashwaara, Shingo Mabu,M
Masan
nao Obayashhi and Takashhi Kuremotoo(Yamagguchi Univerrsity,Japan)
Researchh proposed deecision suppoort of databaase clusters optimizationousing
genetic networknprogrramming (GNNP) with on-lline rule baseed clustering. GNP
optimize cluster qualitty by reanalyyze weak poinnt of each clu
uster and maiintainMofo rules definnition
rules deffinition that stored on each cluster. Maintenanceincludes : 1) adds new
w relevant ruules, 2) movees rules between cluster annd 3)
sfoccused to optim
mize cluster quuality
removes irrelevant rulees. Research simulation
response against severral data unbalaanced data groowth to the daata-set that alreadyw storage rules.
r
Simulattion results off proposed meethod shows betterbmapped withresult andd much lower iteration timee.

Rment Learnning with Syymbiotic RelationshipR
ps for Multtiagent Env
vironmentss
GS3-2 Reinforcem
Shingo Mabu,
M
Masannao Obayashhi and Takashhi Kuremotoo(Yamagguchi Univerrsity, Japan)
Studies ono multiagent systems havee been widelyy studied and realized coopperative
behaviorss between aggents, where manymagents are working together to achieveatheir objeectives. In thiss paper, a new
w reinforcemennt learning fraamework conssidering
the conceept of Symbioosis in order tot represent complicatedcreelationships betweenbagents annd analyze thhe emerging behavior. In addition, disstributed statee-action
value tabbles are also useduto efficieently solve thhe multiagent problems witth large
number ofo state-actionn pairs. Fromm the simulatiion results, it is clarified thatttheproposedd method shoows better performancept the conveentional
comparing toreinforcem
ment learningg without conssidering symbiosis.

GS3-3 DevelopmeD
ent of a Divvidual Mod
del Using a Modular Neural
N
Netw
work
f Humanforn-Robot Intteraction
Toshiyuki Tannaka and Kun
Tnikazu Kobaayashi(Aichi Preefectural Univ
versity, Japaan)
Currentlyy, in the field ofo human-roboot interaction (HRI), robotss have a problem thatcan only interact the samesat all tiimes with hummans in a steereotypical waay. We,
therefore, introduce thhe concept called a dividuall, which is originally propoosed bypwith another hum
man. We
Japanese novelist Keiiichiro Hiranoo to interact properlymneurral network and an
use two machine leaarning techniqques, i.e. a modular
ment learning method,mto construct a moddel of the diviidual toactor-critic reinforcem
grow thrrough interacctions with others. Throough computter simulationns, we
confirmedd a process too form an apprropriate indiviidual-oriented
d dividual.

©ICARO
OB 2015

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The 2015 International ConferenceC
on Artificial
A
Life annd Robotics (ICCAROB 2015), Horuto Hall, O
Oita,
Japan, Jannuary 10-12, 2015

GS4 Intelligent coontrol
GS4-1 DesignDof 1/40
1
scale siimulator too apply the Flying Tou
uch Method
d in hot rolllingp
process
Suung-jin Kim, Hyun-hee Kim,
K
Min-cheeol Lee(Pusann National University)U
In the hott rolling proceess, scratches are occurred on the surface of a slab becauseof its stroong friction. One
O of the metthods to solve this scratchess problem of slab
s is
proposedd which is the flying touch hot rolling prrocess. This paperpis focused on
how the flyingftouch methodmcan bee applied to thee gap control between uppeer andunder rolll in hot rollinng process. Thhis paper also introduces th
hat the simulattor of
40:1 sizee miniature model for applyying flying toouch method isi manufacturred toevaluate a performance of the flyinng touch methhod. Furtherm
more, it is evalluatedby the sim
mulator how much the flyying touch hott rolling process can reducce thescratch.

I
Accuracy of Inertial Measurem
ment Unit using Discreete Wavelett
GS4-2 Improving
TTransformm
Jae-H
Hoon Jung, Dong-HyukD
Lee,L Jang-Myyung Lee((PusanNatioonal Universiity, South Koorea)
In this paaper, using discrete wavelet transform in thet way of nooise removal, andawavelet analysisahas beeen used to deenoise a digitaal image corru
upted by noisee intheacquissition step. this study proposed to minimiize noise caused byinternalchharacteristics and external factors
fof inerrtial measurem
ment unit. By
resolutionn decompositiion which is characteristics
cof discrete wavelet transform,
the noise can be removved actively withw applying threshold.
tPrevious studies use
ffor removving noise. Bu
ut these filterss are
Low-passs filter or movving average filter
corresponnded unsuitably for the rapiidly changing data. This corrrespondence
cause disttortion of the original signaal and cause annother error fo
or removing noise.
n
In order tot compensatee for these disaadvantages, discrete waveleet transform iss
applied.

Outdoor Loocalization for Quad-rrotorusing Kalman Fiilter and PaathPlanninngGS4-3O
Chhen-Hu, Yo-S
Seop Hwangg, Jang-Myunng Lee((PusanNatioonal Universiity, South Koorea)
This papeer proposes a new techniqque that produuces the impro
oved local information
usinglow
w-cost GPS/IN
NS system com
mbined by Kallman filter and Path Planniingwhen a
Quad-rotoor flies. Throuughout the research, the low
wcost GPS iss combined wiithINS byusing thee Kalman filteer in order to improve locaal informationn. However,thhis system
has certaain some dissadvantages. In order toddeal with theese disadvanttages, thealgorithm
m based on thhe path plannning can be adopted.Wheen the quad-rrotor flies
outdoor, it is possiblee to predict thatt
its movinng path is shhort,since all the shortpof the quuad-rotor can be assumed to be straight. Through the foregoing
moving paths
process, ana improved kindof local informationican be obtaineed when the quad-rotorq
flies. Alsso, the perforrmance ofthe proposed syystem can be verified baseed on theoutdoor experiments.
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A cceleratio n
Senso rG eo m ag netic
Senso rG yro Senso r& B aro m eter

G PS

M CU
(m yC o rtexLM 3S88962)

Electrical Sp eed
C o ntro ller (ESC )B lueto o th

4 M oto rs

PC

The 2015 International ConferenceC
on Artificial
A
Life annd Robotics (ICCAROB 2015), Horuto Hall, O
Oita,
Japan, Jannuary 10-12, 2015

GS4-4D
Distributed
d Terminal Backsteppi
B
ing Contro
olfor Multi--Agent Euleer-Lagrangge Systems
Seong-Ik Han, Yun-Ki Kimm, Jang-Myunng Lee((PusanNatioonal Universiity, South Koorea)
This papper presents a distributed terminal (finnite-time) baackstepping consensusccontrol foor multi-agentt Euler-Lagraange systems. Terminal virttual error surffaces andvirtual controls are proposedp
to guarantee thee finite-time error consennsus andformationn convergence of a groupp of one-leadder and multii-follower coooperative
tracking Euler-LagrangE
ge system. Finnite-time stabiility including
g infinite-timee stability
was provved by the finite-time Lyaapunov candidate functionn. Simulation example
shows thhe effectivenness of the proposedpfiniite-time backkstepping cooordinatedtracking controller.
c

GS5 Neeuromorph
hic Systems
GS5-1 AssociativeA
e Memory withw Class I and II Izh
hikevich Model
M
Yoshikaa Osawa, Takkashi Kohno(Univeersity of Tok
kyo, Japan)
In the nerrvous system,, information processingpis performed byy electrical siggnals,
spikes, transmitted bettween the neurrons. Spiking neural netwo
ork is a systemm thathown in prevvious
qualitativvely reproducce this compplex behaviorr. It was sh
researchees that the performancepof associativve memory task in all-tto-all
connectedd networks is higher whenn they are com
mposed of Claass II neuronss thanClass I neurons.
n
The IzhikevichImoodel in its Claass II mode, however,hdoees nothave thiss performancee boost. In thiis study, we focus
fon Phasse Resetting CurveC(PRC) as an index thatt reflects neuroonal propertiees related to neeuron classes more
C and perform
mance
in detail and examinedd the relation between the shape of PRC
of associaative memoryy task.

GS5-2 MedicalM
immage recogn
nition of heeart regionss by deep multi-layerem
ed GMDH--typenneuralnetw
work usingg principal componentt-regression
n analysis
Tadaashi Kondo, Junji Ueno anda Shoichirro Takao(Tokushhima Univerrsity, Japan)
In thiss study, the deepdmulti-laayered GMDH
H-type neurall network alggorithm
using priincipal compoonent-regressiion analysis is developed and appliedd to the
medical image recoggnition of thee heart regioons. The mullti-detector roow CT(MDCT) images of thhe heart regioons are used and the deep GMDH-typee neuralw
has manymhidden layers, is auto
omatically orgganizednetwork architecture which
DCT images ofo the heart regions so as too fit the compplexity of the MDCT
from MD
images of the heart reegions. The deeep GMDH-tyype neural neetwork algoritthm canasos as to minim
mize theautomaticcally organizee the deep neuural network architectures
predictionn error criterioon defined as AIC or PSS.

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The 2015 International ConferenceC
on Artificial
A
Life annd Robotics (ICCAROB 2015), Horuto Hall, O
Oita,
Japan, Jannuary 10-12, 2015

GS5-3 DeepD
feedbback GMDH
H-type neu
ural networrk using priincipal com
mponent-reegression
analysisandd its appliccation to medical imag
ge recognittion of abdoominal mullti-organs
Tadaashi Kondo, Junji Ueno anda Shoichirro Takao(Tokushhima Univerrsity, Japan)
The deepdGroup Method
Mof DataD
Handlingg (GMDH)-ty
ype neural networknalgorithm
ms were propoosed in our eaarly works annd can autom
matically organnize thedeep neuural network architectures with many hidden layerss by using heuristichself-organnization methhod which is a type of the evolutionary computation. In thisstudy, deeep feedback GMDH-typee neural netw
work algorithm
m is developped andapplied too medical immage recognitiion of the abddominal multti-organs suchh as theliver and spleen. The recognitionrreesults are com
mpared with thhose obtainedd by theconventioonal sigmoid function neurral network trrained using thet back propagation
method.

S
zed Respon
nse to Grayyscale Imagge Inputs inn the Chaottic Cellularr NeuralGS5-4 Synchroniz
N
Network
M
Masayuki
FU
UJIWARA1, Akihiro YAM
MAGUCHI1, Masao KU
UBO2
1( Fukuoka Innstitute of Teechnology,Jaapan)(2National Defense
D
Acaademy of Japan)
In this article, we sttudy synchronnized responsses in the chaotic cellular neuralN) for gray-sccale visual stiimulus. One fundamental goal of
network (Chaotic-CNN
mentation usinng chaotic neu
ural synchronization.
this studyy is to realizee visual segm
The Chaootic-CNN connsists of chaottic spike respoonse neurons that exhibits chaotic
inter-spikke intervals. Fig.
F 1 (a) is thhe gray-scale image patternn as visual stiimulus.
For this visualv
stimuluus, a response of the Chaottic-CNN is nu
umerically sim
mulated.
Then, thee cross-correlaation between one specific neuronnthat inndicated by thee arrow
and otherrs are calculaated (Fig. 1 (bb)). As shownn in Fig. 1(b)), high correlaation isobserved for the neuroons in the direection of y axis and relativeely lower corrrelation
nput value foorm cellis observved for the othhers. Here, thhe neurons witth the same inassemblyy for each input value. Each assembly iss distinguisheed from the otthers interms off cross-correllation. This result indicaate a possib
bility of the visualsegmentaation using synnchronized chhaotic response.

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The 2015 International ConferenceC
on Artificial
A
Life annd Robotics (ICCAROB 2015), Horuto Hall, O
Oita,
Japan, Jannuary 10-12, 2015

GS6 Pooster Sessioons
GS6-1 TheT construction of evvaluation index system
m for graduate coursee
Ai Dongmeii, Wen Jiaweei, Ning Xiaoojun(Uniiversity of Sccience and Technology
T
B
Beijing,
Chinna)Establishhing the indeex systemis thetkeyto caarry out the student assesssment,
evaluation of teachingg largely quaality dependss on scientific index systeem.The
related faactors influenncing the reliaability and feeasibility of teeachingevaluaation isanalyzed in this article, andd fair andd scientific evaluation index
Bon thee summaryoff domestic andaforeign existingesystemiseestablished. Basedevaluation index systeem,a set of newnevaluatioon index system with our school
teaching characteristiccs are establisshed. Using thhe analytical hierarchy proocess tow
for eaach indicator, whichw
makes the evaluationn index
set up corrresponding weights
system moremcomplete and accurate..

⎛ 1 a12 L a1n ⎞⎜⎟⎛ 1 a12 L a1n ⎞ ⎜ 1⎜⎟ ⎜ a 1 ... a2n ⎟⎟
a
1
...
a
12
21
2
n
⎟=
A=⎜
⎜ M M M M ⎟ ⎜⎜ M M M M ⎟⎟⎜⎟⎝an1 an2 ... 1 ⎠ ⎜ 1 1 ... 1 ⎟⎜⎟⎝ a1n a2n⎠GS6-2 Extracting
E
Pattern off Arm Moveements bassed on EMG
G Signal foor Stroke Therapy
Khairunizam
m Wan1, Rasshidah Suhaiimi1,Aswad A.R
A 1,D. Hazrry2, Zuradzm
man M. Razlaan2,2
3
3
Shahrriman AB ,M
Mohd Asri Arriffin and Haslina
HM
1( Universiti Malaysia
M
Perllis, MALAY
YSIA)(2Universiti Malaysia
M
Perllis, MALAY
YSIA)
3( Universiti SainsS
Malayssia, MALAYYSIA)This papeer presents thhe investigatioon pattern of arm
a movemen
nts for the purrposeof the rehhabilitation foor a stroke pattient in the virrtual reality (VVR) environm
ments.
The inveestigation resuults are used to design thhe virtual objeects in the virtualv
environm
ments. The muuscle activitiees are analyzzed by using electromyogrraphy(EMG). Six
S EMG channnel are attachhed to the righht arm of the subject,s
whichh is at
the locatiion of deltoid anterior fiberrs, deltoid midddle fibers, bicep, triceps, flexorfand extennsor while peerforming arm
m movements. The electricaal signals acqquired
from EMMG are analyzeed to extract thhe signal’s paattern by using
g signal proceessing
techniquee. In the studiies, several fuundamental arm
m movementss are performeed by
the subjeect and the acquiredapatteerns of EMG signals are defineddas musclemactivities. The experim
mental resultss show that deltoid,dbicep and triceps movemwith a siignificant vallue comparedd with flexor and extensorr and are useed toinvestigatte the muscle activities, whhich is suitablee to the stroke therapy.

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The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

GS6-3 Cascade Controller Design for Steering Control of Nonholonomic Autonomous
Mobile Robot Vehicle
S. Faiz Ahmed1, D. Hazry1, F. Azim2(1Universiti Malaysia Perlis(UniMAP), Malaysia.)(2Hamdard University, Pakistan)In this research article the cascade control system is presented for steering control
of control of nonholonomic autonomous mobile robot vehicle. The propose
system consist of a master controller and two slave controllers. The master
controller is based on Fuzzy Logic Controller (FLC) which computes therequired speed and angular speed needed by the two motors to drives the robot.
Fuzzy logic is used to generate target trajectory movement. The two slave
controllers are Proportional+Integral+Derivative (PID) controllers which ensured
the desired speeds that needed for the both DC motors. PID controller parameters
were tuned according to four ranges of speeds using model based tuning method.
In addition, the control law is offered to select a suitable rule base for fuzzycontroller in order to ensure the system is stable. The proposed cascaded
controller is implemented on a nonholonomic mobile robot and the results haveshown that, the proposed controller achieved the desired turning angle and themobile robot tracks the target efficiently.

GS6-4 Research on Iris Recognition Based on the BP Neural Network
Fengzhi DAI, Li FAN, Chunyu YU, Bo LIU
Iris recognition is the high confidence personal identification technology among theother biometrics recognition. This is not only because the iris’s unique feature, but also
due to its stability that the iris is immune to age and environment. In this paper, we
design a feedforward neural network and use the back propagation algorithm to
explore an elementary iris recognition system model. Ten iris samples that werepre-processed in a simple methodology are used as the recognition objects. Finally, theexperiment demonstrates that though the recognition model is simply constructed, it
has a high recognition rate and the recognition speed is reasonable. The proposed
method provides a convenient way for iris recognition.

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The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

GS6-5Synchronization Control of a Four-wing Fractional-Order Chaotic System
and Its Analog Circuit Design
Hongyan Jia, Qian Tao, Jinfang Li, Wei Xue(Tianjin University of Science & technology, PR China)

60
40
20
x -x '

The four-wing fractional-order chaotic system is firstly introduced in this paper,andthe
state trajectories aregiven by using frequency domain approximate method .Then,achaotic synchronization control of the four-wing fractional-order systems is also
discussed, which is constructed bymaster-slave configuration with linear coupling.
Simulation results are shown to verify the effectiveness of the proposedsynchronization control.and the results from numerical analysis also show the chaotic
synchronization control reported in the paper is simple and practical. An last, ananalog circuit is designed to implementthe synchronization control of the four-wing
fractional-order system,and the results of circuit simulationare in agreement with thoseof numerical analysis, which probably provide an practical technology forapplicationof fractional-order chaos , such as secure communication and image encryption.

0
-20
-40
0

2

4

t

GS6-6 A fractional-order hyper-chaotic system and its circuit implementation
Wei Xue, HuiXiao, Jinkang Xu, Hongyan Jia(Tianjin University of Science and Technology, PR China)
In this paper, the commensurate 3.6-order Qi hyper-chaotic system is investigated.
Based on the predictor-corrector method, we obtain phase portraits, bifurcation
diagrams,Lyapunov exponent spectra of the fractional-order system, and find that afour-wing hyper-chaotic attractor exists in the system when the system parameterschange within certain ranges. On this basis, an analog circuit is designed to
implement the fractional order hyper-chaotic system by using the method of
approximation conversion from time domain to frequency domain, which verifies thatthe hyper-chaotic characteristic indeed exists in the fractional-order hyper-chaotic
system on the physical level, and thus provides the technical basis forfurther application of the fractional-order hyper-chaotic system in engineering.

GS6-7 Research on Early Crop Monitoring Using Photosynthetic Production Indexin
China
Fengzhi DAI1, Li FAN1, Daijiro KANEKO, Nozomu HIROSE, Chunyu YU1(1Tianjin University of Science & technology, China)
On the condition that there is great pressure on population growth and water resources
shortage in China, aiming to monitor the early stage of crop growth, this paper
presents a photosynthesis-based monitoring model for grain production. Not only thenormalized difference vegetation index and elements such as the growing degree day
are considered, the factors of sunshine and the cost of water resource are also
considered in the model.Combined the meteorological data with the vegetation index
that is adopted from remote-sensing, the model contains the elements of solar
radiation, effective air temperature, vegetation biomass, stomatal opening, water stress,
and the influence of temperature on grain plants, such as low-temperature sterility andhigh-temperature injury.

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10

The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

GS6-8 Design and Implementationof Motor Test System based on Virtual Instrument
Yulong Xia１, Huailin Zhao1, Jihong Zhu2 and Yang He2(1Shanghai Institute of Technology, China)(2 Tsinghua University, China)
Most of the existing motor test systems based on the general computer with windows
multimedia clock have problems of inaccurate timing and poor real-time capacity. A
new motor test system is developed for improving the above problems and testing
more motors simultaneously by serial communication with high baud rate. The test
system software applys LabVIEW developping platform and is developped with theidea of the software engineeringwhich makes each module functionally independentand improves the system reliability. Simultaneously the test system takes advantage ofthe characteristics of LabVIEW to complete relativeaccurate timing tasks and achieve
excellent portability. To its communication, a professional serial port circuitis applied
to support the high baud rate and bus connection. The experiment shows that thesystem is not only reliable,easy to operate, high availability and low test cost, but alsosatisfactory to the practical test.

GS6-9 Consensus Problem of Distributed Multi-agent System
Huailin Zhao1, Wei Ren2, Masanori Sugisaka3(1Shanghai Institute of Technology, China)(2 UCR, USA)3
( . Alife Robotics Corperation LTD, Japan)
This paper introduces the basic concept about the consensus of the distributed
multi-agent systems. To a group of autonomous vehicles which are distributed, theconsensus problem is the basic one in cooperative control to the multi-agent system. It
disscusses the problem based on the different conditions including the stochastic
network toplogy, complex dynamic system, time delay effect, and the other ones. It
gives the main research topics and some research achievements.

GS6-10 Dingle’s Model-based EEG Peak Detection using a Rule-based Classifier
1
Asrul Adam,2Zuwairie Ibrahim,1Norrima Mohktar,3Mohd Ibrahim Shapiai and1Marizan Mubin(1University of Malaya, Malaysia)(2Universiti Malaysia Pahang,Malaysia)
3( Universiti Teknologi Malaysia, Malaysia),
The employment of peak detection algorithm is prominent in several clinicalapplications such as diagnosis and treatment of epilepsy patients, assisting todetermine patient syndrome, and guiding paralyzed patients to manage somedevices. In this study, the performances of four different peak models of time
domain approach which are Dumpala’s, Acir’s, Liu’s, and Dingle’s peak models
are evaluated for EEG signal peak detection algorithm. The algorithm isdeveloped into three stages: peak candidate detection, feature extraction, andclassification. Rule-based classifier with an estimation technique based onparticle swarm optimization (PSO) is employed in the classification stage. The
evaluation result shows that the best peak model is Dingle’s peak model with thehighest test performance is 88.78%.

©ICAROB 2015

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The 2015 International ConferenceC
on Artificial
A
Life annd Robotics (ICCAROB 2015), Horuto Hall, O
Oita,
Japan, Jannuary 10-12, 2015

GS6-11 Different Learning
L
F
Functionsf Weighteed Kernel RegressionforRin Solving Small
Sample Problem witth Noise
1
2
Zuwairie Ibbrahim1,Nuruul Wahidah ArshadA, Mo
ohd Ibrahim Shapiai
S,Norrrima Mokhttar3
1( Universitii Malaysia Paahang, Malaaysia)(2Universiti Teknologi
T
Malaysia,
M
Mallaysia)(3 Univerrsity of Malaaya, Malaysiaa)

Previouslyy, weighted kernelkregressioon (WKR) for solving smaall samples
problem has
h been reportted. In the origginal WKR, thee simple iteratiive learningtechnique and the formulated learning functionfin estiimating weight parameters
blem. In thisare designned only to solvve non-noisy annd small trainingg samples prob
study, an extension of WKRW
in solvinng noisy and small training samples isinvestigateed. The objectiive of the inveestigation is to extend the cap
pability andeffectiveneess of WKR whenw
solving various probleems. Thereforee, four new
learning fuunctions are prooposed for estim
mating weight parameters.
p
In general, theformulatedd learning funcctions are addeed with a reguularization termm instead oferror termm only as in the existing WKR.. However, onee free parameterr associatedto the reegularization teerm has firstlyy to be preddefined. Hence, a simple
cross-validdation techniquue is introduced to estimate thistfree param
meter value.
The improovement, in terrms of the preddiction accuracyy as comparedd to existing
WKR is prresented througgh a series of exxperiments.

GS6-122 Simultaneeous Compputation of ModelM
Ord
der and Parrameter Esstimation of a
Heating System
S
Based on Partiicle Swarm
m Optimization for Au
utoregressiv
vewith Exoggenous Mod
del : An An
nalysisTeoh Shinn Yee1, Zuw
wairie Ibrahim
m1,Kamil Zak
kwan Mohd Azmi1,Norriima Mokhtarr2(1Universiti Malaysia Paahang, Malayysia),(2Universsity of Malayya, Malaysiaa)
System iddentification is one
o of the methhod for solvingg a mathematical model of asystem by performing onn analysis only ata its input and output behavio
our. In system
identification, the proceddure of modelliing the system is separated in
nto four mainparts. Thee first part is coonstructing an experiment to collect the inpu
ut and output
data of thhe system. Thenn, with some criteria,cthe moodel order and structure are
selected. The
T next part is
i to estimate thet parameters of the model. For the final
part, the mathematical
m
m
model
is verifiied. Model ordder selection anda parameter
estimationn are two imporrtant parts of findingf
the matthematical mod
del for system
identification. Previouslyy, a technique called simuultaneous modeel order andparameter estimation (SMMOPE), whichh is based on Particle
P
Swarm Optimisation(PSO) annd ARX modeel, has been introduced to combine thesse two parts
simultaneoously. This techhnique, howeveer, exclude the erroreterm of ARX model. In
this study,, an analysis is shown to provee that the perforrmance of SMO
OPE based on
PSO and ARX
A
model deggraded as the magnitude
mof errror increases.

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OB 2015

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The 2015 International ConferenceC
on Artificial
A
Life annd Robotics (ICCAROB 2015), Horuto Hall, O
Oita,
Japan, Jannuary 10-12, 2015

GS6-133 Maximum
m Probabiliity Algorith
hm for Fault Diagnosiis
Fengzhhi DAI, Li FA
AN, Bo LIU(Tianjinn Universityy of Sciencee & technoloogy, China)
This papeer considers thet fault diagnosis by outsside fault phennomena.The methodmonly deppends on expperience and statistical datadto set up
u the fuzzyy query
relationshhip between thhe outside pheenomena (faullt characters) and the fault sourcess(fault pattterns). From this relationnship, the most probable fault
fsources can belocated, anda the standaard fuzzy relattionship matrixx is stored as database.

GS6-144 TheFractiional OrderHyperchaaoticGenera
alized Augmmented Lü System annd itsCircuit Im
mplementattion
Wei Xue, Jinkang Xu,, Hongyan Jiia
(Tianjin UniversityU
off Science annd Technologgy, PR Chinaa)
In this paaper, a commeensurate fractiional-order hyyperchaoticgenneralized augmented
Lüsystemm is investigaated.weanalyze its chaotic characteristiccs bydrawingg phase
portraits, Poincaré mapps, Lyapunov exponent spectra and poweer spectrum, anda findscan preesent a four-w
wing hyperchaaotic attractor..In addition, a circuitthat the system
is designeed for this sysstem and the circuitc
implemmentation resullt show the exxistenceof the fouur-wing hyperchaotic attractor, which veerifies the corrrect of the thheoretic
analysis anda provides thet support for its applicatioon in engineeering.

GS7 Paattern Recoognition
GS7-1 Fast
F motionn detectionn based on crossccorreelation
Pancca Mudjirahaardjo, Joo Koooi Tan, Hyooungseop Kim
m and Seiji IIshikawa(Kyushuu Institute of Technology))
Abstractt:We present a fast motion detectiondas ann abnormal motionmbased ono cross
correlatioon. Since the camera view is not in perppendicular wiith motion dirrection,
the veloccity of motion is not uniformm spatially. Innstead of objeect detection directly,dwe separaate an image into several blocks.
b
We callculate the cro
oss correlationn of the
pixel inteensity series in these bloccks between current and previouspfram
me. Themaximumm correlation is achieved att a certain delay. This delaay shows a shhift of asimilar pattern betweeen the currentt and previouus frame.To loocalize the abbnormal
w employ hierarchical
h
b
block
size. Thhe performan
nce of the prroposed
motion, we
method iss experimentaally shown.

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The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

GS7-2 Detecting moving objects on a video having a dynamic background
FX Arinto Setyawan, Joo Kooi Tan, Hyoungseop Kim, Seiji Ishikawa(Kyushu Institute of Technology, Japan)
This paper proposes a method of detecting moving objects in a video
having a dynamic background using a method which infers the
background sequentially. The proposed method performs the updateof the pixel values in the background which are influenced by thevalue of the current pixel. The aim is to cope with changes in thevalue of the pixels in the background caused by the movement of the
background objects such as the leaves swaying on trees, the water
droplets of the rain or the change in light intensity according to thetime lapse. The performance of the proposed method is shown
experimentally using the video taken on a rainy and windy day.

.

GS7-3 Study on the Target Recognition and LocationTechnology of industrial Sorting
Robot based on Machine Vision
Jiwu Wang1,Xianwen Zhang1, HuazheDou1,Sugisaka Masanori 2(1Beijing Jiaotong University)
2( Alife Robotics Corporation Ltd, Japan and Open University, United Kingdom)
In order to improve the applications for an industrial sorting robot, it isnecessary to increase its flexibility and control accuracy. The
prerequisiteis toautomatically extract the multiple target positions
accurately and robustly. The machine vision technology is an effective
solution. Here an industrial robot arm is designed and set up forexperiment simulation with machine vision. In order to reduce theinfluence of the size, deformation, and lighting etc., the target
recognition
andlocation
methodwithfusionofscale invariant feature transform (SIFT) and moment invariants isdeveloped. Theexperiments results showed that the developed image
processing algorithms are robust, and the flexibility of the industrial
robot can be improved by machine vision.

GS8Robotics I
GS8-1 Production effects by form changes of autonomous decentralized FMSs with mind
Kakeru Yokoi, Hidehiko Yamamoto, and Takayoshi Yamada(Gifu University,Japan)

An autonomous decentralizedFMS does not have a management mechanism tocontrol the entire factory and avoid the route interference of automatic guided
vehicles (AGVs). This research aims to develop an autonomous decentralized
FMS and focus on the development of the behavior control of AGVs to avoid theroute interference, inspired by human mind; To express the mind, we propose the
Minimum Unit of Mind (MUM) which has been used to simulate the production
line in order to switching between arrogant and humble mind of AGVs. The
result of simulation has shown it is possible to avoid the AGVs collisions. We
examined the production line by applying different conditions such as changing
the number of machining centers, AGVs and the position of products’ warehouse.

©ICAROB 2015

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The 2015 International ConferenceC
on Artificial
A
Life annd Robotics (ICCAROB 2015), Horuto Hall, O
Oita,
Japan, Jannuary 10-12, 2015

GS8-2 DevelopmeD
ent of an au
utonomous--drive perssonal robot“Improve the accuraacy of objecct area deteermination by boundaary detectioon”
Mikikko Hirai, Eijii Hayashi(Kyusyu Insstitute of Tecchnology, Jappan)
In the nearnfuture, autonomousas
self-driving
roobots are exp
pected to proovide
various services in human liviing environm
ments. The ability to worka required foor theautonomoously and acccurately recoggnize surroundding objects areautonomoous robot. In previous research,the robott has enabled the recognition of
the singlee object by thhe form, colorr, and the locaal characteristiic of the objeect. In
this papeer, wepresent the system ini the robot thattenable too detect the singlesobject areea from pluraal objects in thhe camera imaage by using thet function ofo thecamera.

GS8-3 ConstructiC
ion of a suppermicro sense of forrce feedbacck and visioon for micro-objects::
developmeent of a hap
ptic device
Yuseei Ishii, Eiji Hayashi
H(Kyushu Insstitute of Tecchnology, Jappan)
Recently,, the technoloogies that caan accurately perform minnute work arenow
being souught for bothh medical treeatments and in the field of manufactturing
semicondductors. Such minute work is improved byb using the micromanipulamators,
but their operation is difficult becaause the operrator has no sense of forcce; hekilled in the use
u ofrelies onlly on sight thrrough microsccope.As a resuult, a person sk
this technnology is needded for all minnute work. It is thought thaat the efficienncy ofminute workw
would beb improved if theoperatorrcould obtainn a sense of force
while usiing a manipuulator. We maade and contrrolled the haaptic device whichw
shows thee reaction forcce to the operaator.

GS8-4 Error
E
Recoovery of Picck-and-Plaace Tasks in
n Consideraation of Reusability off Planning
Akira Nakamura, Kazuyuki
K
Naagata, Kensu
uke Harada anda Natsuki Y
Yamanobe(Nationaal Institute off Advanced Industrial Sciience and Technology (AAIST), Japan))In mannipulation tassks of compplicated plannt maintenance and induustrial
productioon, error recovvery is an impportant research theme for robots that neeed toperform actualatasks. We
W have propoosed error recoovery using th
he concepts off bothtask stratification and error classificcation. Program
mming of erroor recovery will
w bep
can beb used in maany recovery paths.
p
In this paper,p
simplifiedd if the same planningreusabilitty of planningg in error recovery is verifieed by using piick-and-place tasks
which aree used frequenntly in plant maintenancemannd industrial production.
p
M
MaintenanceR
Robot©ICARO
OB 2015

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The 2015 International ConferenceC
on Artificial
A
Life annd Robotics (ICCAROB 2015), Horuto Hall, O
Oita,
Japan, Jannuary 10-12, 2015

GS8-5 DesignDof SlidingS
Modde Controlller for Droplet Positioon in EWOOD Microflu
uidic
SystemThunyaseth Sethapuut(Thammasaat Universityy,Thailand),
Arsit Boonyaprappasorn (Chulaachomkloa RoyalR
Militarry Academy,TThailand)In microfluidic lab-onn-chip devices, electrowettting on dielecctric (EWOD
D) arewidely ussed for variouus applicationss. To manipullate the micro droplet to achhieve
the desireed path and accurateatargeet position by using electro
owetting technniqueare one of the commmon applicatioons. In this paper,p
the mootion of droplet isrbody driiven by both linear and no
onlinear forcees. Inmodeled as a single rigidethe pootential of conntroller, the slliding mode co
ontroller is apppliedorder to evaluate
to this nonlinearnmicrrofluidic systtem. The effeect of boundeed disturbancces is
included in the designeed controller. Simulation reesults provideed the feasibiliity ofEmicroffluidic manip
pulation undeer thethe slidinng mode conntroller for EWOD
effect of boundedb
distuurbances.

Thhe micro droplet motionmon the
EWOD plate.

GS9RoboticsⅡ
GS9-1 Mechanism
Mm Designs forf Bio-insp
pired Flappping Wing Robots
R
Palakorn Tanttrakool, EakkPkachai Pengw
wang(King Monggkut’s University of Techhnology Thonnburi, Thailaand)This papeer presents thhe design of flappingfsysteems for small air vehicle useduinsurveillannce and surveyying robot forr an area that is hard to acccess. This papeer also
focuses ono flapping mechanism
mof micro unmannned aerial rob
bots that are similars
to birds and insects. Our design considerationcwill have a wingspan up to 15
centimeteers and a capaability to hoveer. Enable techhnologies for fabrication off these
designs are
a Computerr-aided designn (CAD) andd 3D printerss by using poolymer
materials with low deensity and weeight. These structuressaree also connectted bycarbon roods for creatinng a crank-roccker mechanissm. Characterrizations of flaappingwings aree simulated annd examined ini this paper. With an exterrnal microconntroller
board annd power suppply, this miccro flapping wing robot will be testeed andperformed in real envirronments.

GS9-2 EffectiveEroocking mottion for ind
ducing sleep
p in adults– Verrification ofo effect of mother’smemmbrace andd rocking m
motion –Keishi Ashiida, Yoshifum
mi Morita (NNagoya Institu
ute of Technnology, RIKE
EN-RSC, Jap
pan)
Ryojunn Ikeura (Miie University
y, RIKEN-RS
SC, Japan)
Kiyoko Yokkoyama (Naggoya City Un
niversity, RIK
KEN-RSC, Jaapan)
Ming Dingg, Yuki Mori (RIKEN-RSSC)
The finall goal of this research is tot develop a relaxationr
maachine for redducing
stress andd inducing sleeep by using a rocking vibrration. In our previous worrk, wemeasuredd the mother’ss embrace andd rocking motiion, constructted two types of themother’s rocking motiion and deveeloped an exccitation apparaatus simulatinng themother’s rocking mottions. In thiss paper, we investigated the most efffectivemfor innducing sleep in adults. We
W prepared teen types of roocking
rocking motion
motion inncluding twoo types of moother’s rockinng motions. The
T sleep-indducing
effect off all the roccking motionns was evaluuated using Thurstone’sTppairedcomparison method (Case V). FromF
statisticcal analysis of the subjective
w found that, of the ten typpes of rocking
g motions, the linear
experimeental results we©ICARO
OB 2015

P - 58R
RotationalmotionR
Rotarymotor

LLinearmotor

LinearmotionThe 2015 International ConferenceC
on Artificial
A
Life annd Robotics (ICCAROB 2015), Horuto Hall, O
Oita,
Japan, Jannuary 10-12, 2015

motion coomponent of a mother’s roccking motionss (Type 2) was the most efffective
for inducing sleep in addults.

GS9-3 PosturalP
Sw
way Respon
nse to Locaal Vibratorry Stimulatiionin Younng, Middle-aged andE
ElderlyPeop
ple in Stannding PositiionA
Ayaka
Yamadda, Eishi Nakkamura, Norritaka Sato, Yoshifumi
Y
M
Morita(Nagoya Insstitute of Tecchnology, Jappan)
Tadashi Ito,I Yoshihitoo Sakai (Natiional Center for Geriatriccs and Geronntrogy, Japann)
Kazunnori Yamazakki (Fujita Health University, Japan)
The final goal of this research is to developda treaatment system for proprioceeption forrecovery of low back pain and falll prevention. In this paperr, we investiggated thepostural sway responsse to local viibratory stimuulation in you
ung, middle-aaged andelderly peoplepin thhe standing position.
pForr this purpose, we deveeloped avariable-ffrequency vibbratory stimuulation devicee and measured the posturral sway
response using a graviicorder when the vibratory stimulations of 30, 60, 1500, or 240
Hz were applied to the subjects' lumbar multtifidus or gaastrocnemius muscles.
Moreoverr, we evaluateed the posturaal sway using the anterior movementmof CoP and
the relativve propriocepptive weightinng (RPW) rattio. As a result, when the vibratoryv
stimulatioon was applieed to the gastrrocnemius muuscles, the Co
oP moves backkward inall subjecct groups. It was
w seen in alll cases of the vibration freq
quencies. It was
w foundfrom the results for thee RPW ratios thatt elderly peeople with low
w back pain peerformedbalance control usingg their trunkk more than their lower legs when vibratoryv
stimulatioon with higherr vibration freequency was applied.
aGS9-4 DevelopmeD
ent of Unmanned Tran
nsport Systtem for auttomated sysstems
Hyunnhak Cho, Junngwon Yu, Yeongsang
Y
Jeong,
J
Hansooo Lee, Sunggshin Kim(Pusan Naational University, Koreaa)

is paper introducesi
appplication and composition of the unman
nned transportt systems
in industrry to the trannsport, and deescribes the seelected metho
od by the envvironment
conditionns. Unmannedd transport systems
is coomprised of an AGV (AAutomatic
Guidancee Vehicle) andd a Monitoring system. Thee applied AGV is guided byb a lasernavigatioon guidance syystem with enncoders and gyyroscope and AGV require a virtual
driving pathpmethod withw
map infformation fromm conveyor positionspandd rotation
points inn an environnment. The monitoringmsyystem is com
mposed of thetmainmonitorinng system annd sub monitooring system. The role off the main monitoring
system is
i communicaation with otheroequipment; transmisssion/receipt of ordercommandds with the AGV,
A
and Suub Monitoringg system, andd the role off the submonitorinng system is the confirmattion of the sttate. The prop
posed applicattion casewas reporrted in video.

©ICARO
OB 2015

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The 2015 International ConferenceC
on Artificial
A
Life annd Robotics (ICCAROB 2015), Horuto Hall, O
Oita,
Japan, Jannuary 10-12, 2015

GS9-5 LocalizatioLon method forf AGV ussing magneetic devicess and IMU
M
MoonhoPark,, EunKyeongg Kim, Yeonngsang Jeongg, Hansoo Leee,Jungwon Y
Yu, Sungshin
n Kim(Pusan Naational University, Koreaa)
This papeer is a researcch of the locaalization methood for AGV(AAutomonous Guided
Vehicle) with a guiddance systemm using maggnetic localizzation devicees. Formguidaance AGV dettects a magnetic tapenavigatioon of AGV, ann established magnetic
and folloows the line.. However, theret
are somme weaknesses: disturbannce anddamage. To make up for the weakk points, thiss paper propo
osed the locallizationu
two maagnetic localizzation devicess, a gyro senssor and encodders. In
method usingorder to compensatecgglobalpositionn, AGV's locaation and anglle were compeensatedfor a maagnet positionn and gradientt information using two magneticmlocallization
devices. Between
Bspot points, a relaative position was
w calculatedd by kinematiics withthe devicces. To verify the performaance of the prroposed metho
od, it was com
mparedwith the method usinng a gyro sennsor and encooders. As a result,r
the prroposed
method iss more efficiennt than the exxisting one.

GS10 Robotics
RⅢ
GS10-11 On the Efffects of Ep
pigenetic Prrogrammin
ng on the Effficiency off Incremental
Evolutionn of the Sim
mulated Khhepera Roboot
Yasuto Nishiwaki ,IIvan Tanevannd Katsunorii Shimohara(Univerrsity of Doshhisha, Japan))e objectivve of this studdy is to develop an approachh of Epigenetiic Programminng (EP)and to verify the feassibility of inccorporating histones in geenotype from both acomputattional and natuural perspectivves.In EP, wee introduce hisstones as geneetic ON
and OFF “switches” thhat modify thee gene expresssion by activatting and deacttivating“ofo the controlllers of
the nodes in the tree representations (i.e., the “genotype”)
simulatedd floor-cleaninng Khepera robot, evolvedd via Genetic Programmingg (GP).
We hypoothesize that ata the first staage of GP, whhen we evolv
ve basic moviing andcleaning abilities, histoones inactivate genotypic areasathat coulld be used durring thevel obstacle-avvoiding
second sttage of evoluution as a playyground to evvolve the nov
capabilitiies without daamaging the allready evolvedd basic abilitiees of the robott.

GS10-22 The Effect of Duratiion of Both Stages of IncrementaI
al Genetic P
Programming on its
kebot
Efficiencyy of Evolutiion of SnakN Mukosakaa, I. Tanev, K.
N.
K Shimoharaa(Doshisha University,
U
Jaapan)The objecctive of our workw
is to inveestigate the opptimal combin
nation of durattions ofboth stagges of increm
mental Geneetic Programm
ming (IncGP)) on the sim
mulatedSnake-likke robots (Snaakebots). In prroposed IncGPPwe first evollve the bot in smoothterrain, andathen evoolve a populaation, partiallly seeded wiith these bots, in achallenging terrain. We
W experimentted with varioous combinattions of durattions ofboth stagges of IncGP, while fixing the total num
mber of evolv
ved generationns. The
obtainedrresults suggesst that neitherr the numberr of generatio
ons nor the obtained
fitness vaalue at the firrst stage of InncGP can be usedu
as criterria for separatting thetwo stagees of IncGP. We
W hypothesiized that anotther, qualitativ
ve criterion, suchsasthe emerrgence of sm
mooth sidewinnding locomotion could be used insteaad, andconductedd additional experiments thhat proved its worthiness.
w©ICARO
OB 2015

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The 2015 International ConferenceC
on Artificial
A
Life annd Robotics (ICCAROB 2015), Horuto Hall, O
Oita,
Japan, Jannuary 10-12, 2015

GS10-33 Design of an effectivve shoulder joint mechhanism for an upper-llimb exoskeeleton
robot
MasahitoAkiyama, Kazuo
K
Kiguchhi(Kyusshu Universiity,Japan)
This papper proposes an effective shoulder joinnt mechanism
m for an upper-limb
exoskeletton robot. A power-assist
p
r
robot
for hum
man is regardedd as one of thhe mostpromisingg machines which can assist laborrs in a heaavy physical work,
rehabilitaation and so on. Human glenohumerall joint movess in 3-dimenntion inaccordancce with thee movement of the uppper-limb suchh as the shhoulder
flexion/exxtension and abduction/addduction mottion. It makees the exoskeeleton’sshoulder mechanism complicated.
c
In order to reeduce the diffference betweeen the
der joint positiion, we
human gllenohumeral joint position and the exoskkeleton should
propose a passive com
mpensation mechanismmwhhich consists of links and sliders.
This mecchanism can imitateithe moovement of humanhglenohuumeral joint withoutw
additionaal motors.

GS10-44 A Machin
ne Learnin
ng Approacch to a La
ateral Conttinuous Foorce Estima
ation for a
Walking Biped
B
RobootYeoun-Jae Kim,Jun-Yon
K
ng Lee and Ju-Jang
J
Lee(KAIST,Koreea)
In this paper,pa regreessor for deteermining a laateral externaal continuous force
applied uponua walkinng biped roboot is investigaated and veriffied by a num
mericalsimulatioon. A pre-defi
fined walking gait of a bipped robot is constructed byb theTchebyshhev method. And a continnuous force-aaction classifiier is generatted. It
determinees whether thhe lateral extternal force is a continuou
us force or not.
nA
regressorr which estimaates a lateral external
econtinnuous force accted upon a walking
biped robbot is construucted by SVR
R(Support Vecctor Regressorr). The regresssor isverified byb a numericaal simulation. We assumed that only lateeral force is appplied
upon the COG(Center of Gravity) off the walking biped robot.

GS10-55 The Imprrovement off Robust Robot
RSLAM
M Algorithm Based onn Sensor Fu
usion
Jiwu Wang1,Shunkai Zhheng1, Fangbo Liao1,Sugiisaka Masanoori 2(1Beijinng Jiaotong University)U2( Alife Robotics
R
Corporation Ltdd, Japan andd Open Univeersity, Unitedd Kingdom)
The Kinnematic moddel of the roobot is a verry importan
nt part in
SLAM,IIt’s error modelmwilllinfluence the positio
oning accurracy ofrobot annd map buiilding. Geneerally, the motion mod
del relies simplyson theddata from en
ncoderfeedb
back. Due too cumulativ
ve error, thee robot
pose acccuracy isrellatively pooor by mileage positioniing with en
ncoder.
Here a new methhod is put forward ba
ased on datta fusion ofo gyrosensor withw
theenccoder data, anda the rob
bot pose accu
uracy is ana
alyzedand immproved. Th
hen using thetoptimizzation Kine
ematic moddel onSLAM tot verify thee robustness.

©ICARO
OB 2015

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The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

AUTHORS INDEX
Notation of session name
PS: PlenarySessionIS: Invited Session,OS: Organized Session, GS: General Session,
Note:33/90 =(page no. in Technical Paper Index) / (page no. in Abstracts)
Cho

[A]

Hyunhak

GS9-4

15/59

Fengzhi

GS6-4

19/51
19/52

AB

Shahriman

GS6-2

19/50

AburadaKentaro

OS2-1

25/30

OS2-3

25/31

OS2-4

25/31

GS6-7

OS2-5

25/32

GS6-13 20/55

OS2-6

25/32

Ding

Hui

OS1-5

21/29

OS7-1

23/40

Ding

Ming

GS9-2

15/58

OS7-2

23/40

Dou

Huazhe

GS7-3

14/56

OS7-3

23/41

Du

Yifei

OS5-2

23/37

OS5-3

23/37

GS6-4

19/51
19/52

[D]
Dai

Adam

Asrul

GS6-10 19/53

Ahmed

S. Faiz

GS6-3

19/51

Ai

Dongmei

GS6-1

18/50

AkimotoTaisuke

GS1-2

20/43

AkiyamaMasahito

GS10-3 16/61

GS6-7

Aoki

Kenji

OS5-1

23/37

GS6-13 20/55

OS5-2

23/37

OS5-3

23/37

[F]
Fan

Li

Fu

Xiaoyan

OS1-4

21/29

OS1-5

21/29

Ariffin

Mohd Asri

GS6-2

19/50

Fujiwara

Masayuki

GS5-4

17/49

ArshadNurul

GS6-11 19/54

Furutani

Hiroshi

OS5-1

23/37

OS5-2

23/37

OS5-3

23/37

OS6-1

24/38

OS6-2

24/38

Emmanouil

IS-1-

18/27

Han

Seong-Ik

GS4-4

17/48

HaradaKensuke

GS8-4

14/57

Wahidah
Asada

Taro

OS3-2

22/33

OS3-3

22/34

AshidaKeishi

GS9-2

15/58

Azim

F.

GS6-3

19/51

[G]
Giannisakis[B]

Boonyaprapasorn

Arsit

GS8-5

14/58

[C]
Chiu

©ICAROB 2015

Yi-Fu

PS-1

18/26

P - 62[H]The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

Hashimoto

Kenji

PS-2

23/26

Ito

Tsutomu

OS4-2

24/36

Hattori

Tetsuo

OS8-1

21/41

Izumi

Tetsuya

OS8-1

21/41

OS8-2

21/41

OS8-3

21/42

OS8-4

21/42

Yeongsang

GS9-4

15/59

OS8-5

22/42

GS9-5

15/60

GS2-1

16/44

GS6-5

19/52

GS8-2

14/57

GS6-6

19/52

GS8-3

14/57

GS6-14 20/55

GS6-2

19/50

GS6-3

19/51

Hayashi

Eiji

Hazry

D.

[J]
Jeong

Jia

HongyanJia

Yingmin

OS1-1

21/28

OS1-2

21/28

He

Yang

GS6-8

19/53

Jovic

Jovana

IS-3

18/27

Hirai

Mikiko

GS8-2

14/57

Jung

Jae-Hoon

GS4-2

17/47

HiroseNozomu

GS6-7

19/52

Hu

Chen

GS4-3

17/47

Hwang

Yo-Seop

GS4-3

17/47

KanekoDaijiro

GS6-7

19/52

Katayama

Tetsuro

OS2-1

25/30

OS2-2

25/30

GS6-10 19/53

OS2-3

25/31

GS6-11 19/54

OS2-4

25/31

GS6-12 20/54

OS2-5

25/32

OS4-2

24/36

OS2-6

25/32

OS6-1

24/38

OS7-1

23/40

OS6-2

24/38

OS7-3

23/41

OS6-3

24/39

OS3-2

22/33

OS3-3

22/34

OS8-1

21/41

OS8-2

21/41

OS8-1

21/41

[K]

[I]

Ibrahim

Ikeda

Zuwairie

Satoshi

Kato

Ryota

IkeuraRyojun

GS9-2

15/58

Imai

Yoshiro

OS8-2

21/41

Ishii

Yusei

GS8-3

14/57

Ishikawa

Seiji

GS7-1

14/55

GS7-2

14/56

OS8-2

21/41

Kawakami

KawanoYusukeHiromichi

Ito

Tadashi

GS9-3

15/59

OS8-3

21/42

Ito

Takao

OS4-2

24/36

OS8-4

21/42

OS6-1

24/38

OS8-5

22/42

OS6-2

24/38

KiguchiKazuo

GS10-3 16/61

OS6-3

24/39

Kikkawa

Yuuki

OS2-5

25/32

OS6-4

24/39

Kim

EunKyeong

GS9-5

15/60

©ICAROB 2015

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The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

Kim

Heeje

Kim

Hyoungseop

GS2-3

16/45

GS2-4

16/45

GS2-4

16/45

GS4-2

17/47

GS7-1

14/55

GS4-3

17/47

GS7-2

14/56

GS4-4

17/48

Lee

Jang-Myung

Kim

Hyun-hee

GS4-1

17/47

Lee

Ju-Jang

GS10-4 16/61

Kim

Sung-jin

GS4-1

17/47

Lee

Jun-Yong

GS10-4 16/61

Kim

Sungshin

GS9-4

15/59

Lee

Min-cheol

GS4-1

17/47

GS9-5

15/60

Li

Jinfang

GS6-5

19/52
21/28

Kim

Yeoun-Jae

GS10-4 16/61

Li

Qing

OS1-3

Kim

Yun-Ki

GS4-4

17/48

Liao

FangboGS10-5 16/61

Kimura

Jun

OS3-5

22/35

Liao

Yuntao

OS4-3

24/36

Kita

Yoshihiro

OS2-1

25/30

Liu

Bo

GS6-4

19/51

OS2-3

25/31

OS2-4

25/31

Lu

Xuhui

OS1-2

21/28

OS2-5

25/32

Lund

Henrik

IS-1

18/27

OS2-6

25/32

OS7-2

23/40

IS-2

18/27

GS6-13 20/55

HautopKitanoShoichiro

OS2-4

25/31

Kobayashi

Kunikazu

GS3-3

16/46

M

Haslina

GS6-2

19/50

Kohno

Takashi

GS5-1

17/48

Ma.

Q.

OS5-3

23/37

Kondo

Tadashi

GS5-2

17/48

Mabu

Shingo

GS3-1

16/46

GS5-3

17/49

GS3-2

16/46

[M]

KoyamaYoshihide

OS8-3

21/42

Matsuno

S.

OS6-4

24/39

Kubo

Masao

GS1-3

20/44

Matsushita

Haruna

OS8-2

21/41

GS1-4

20/44

Mehta

R.

OS4-2

24/36

GS5-4

17/49

Meng

Yue

OS1-5

21/29

OS7-1

23/40

Mohd Azmi

Kamil

GS6-12 20/54

OS7-3

23/41

GS3-1

16/46

GS3-2

16/46

KubotaShinichiro

Kuremoto

Takashi

ZakwanMohktarNorrima

GS6-10 19/53
GS6-11 19/54
GS6-12 20/54

[L]

Mori

Yuki

GS9-2

15/58

MoritaYoshifumi

GS9-2

15/58

GS9-3

15/59

Lee

Dong-Hyuk

GS4-2

17/47

Lee

Hansoo

GS9-4

15/59

GS9-5

15/60

Mubin

Marizan

GS6-10 19/53

GS2-3

16/45

Mudjirahardjo

Panca

GS7-1

Lee

©ICAROB 2015

Jae-cheolP - 64

14/55

The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

Mukosaka

N.

GS10-2 15/60
[P]

[N]

Pagliarini

Luigi

IS-2

18/27

Park

Mirang

OS7-1

23/40

NagataKazuyuki

GS8-4

14/57

Nagatomo

Makoto

OS6-1

24/38

OS7-2

23/40

OS6-2

24/38

OS7-3

23/41

Nagatomo

So

OS7-1

23/40

Park

Moonho

GS9-5

15/60

Nakamura

Akira

GS8-4

14/57

PatitadPatchanee

GS2-2

16/45

Nakamura

Eishi

GS9-3

15/59

Pengwang

Eakkachai

GS9-1

15/58

Nakamura

Hiroto

OS2-3

25/31

Piao

shengxu

GS2-3

16/45

Namatame

Akira

GS1-3

20/44

GS2-4

16/45

GS1-4

20/44
[R]

Narumoto

Jin

OS3-2

22/33

NguyenVanhoa

OS8-4

21/42

R.

Aswad A.

GS6-2

19/50

OS8-5

22/42

Rajapakse

R.P.C.

OS8-2

21/41

Ning

Xiaojun

GS6-1

18/50

Nishikawa

Kenta

OS2-6

25/32

Nishiwaki

YasutoGS10-1 15/60

JanakaRazlanZuradzman M.

GS6-2

18/52

Ren

Wei

GS6-9

19/53

[O]
Obayashi[S]
Masanao

GS3-1

16/46

Sakai

Yoshihito

GS9-3

15/59

GS3-2

16/46

Sakamoto

Hiroki

OS8-4

21/42

OS8-5

22/42

OS4-2

24/36

Ogata

K.

OS4-1

24/35

Ogata

Takashi

GS1-1

20/43

GS1-2

20/43

OS5-1

23/37

Sakamoto

Makoto

Ogata

Yohei

OS2-1

25/30

OS5-2

23/37

OkazakiNaonobu

OS2-1

25/30

OS5-3

23/37

OS2-3

25/31

OS6-1

24/38

OS2-4

25/31

OS6-2

24/38

OS2-5

25/32

OS6-3

24/39

OS2-6

25/32

OS6-4

24/39

OS7-1

23/40

OS2-2

25/30

OS7-2

23/40

OS7-3

23/41

GS1-3

20/44

GS1-4

20/44

GS9-3

15/59

Ono

Jumpei

GS1-1

20/43

Osawa

Yoshika

GS5-1

17/48

©ICAROB 2015

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SaputraMochamadChandra

Sato

Sato

Hiroshi
Noritaka

The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

SethaputThunyaseth

GS8-5

14/58

Taniguchi

Taiki

OS3-4

22/34

Setyawan

FX Arinto

GS7-2

14/56

Tantrakool

Palakorn

GS9-1

15/58

Shang

Yuanyuan

OS1-4

21/29

Tao

Qian

GS6-5

19/52

OS1-5

21/29

Tsunenari

Kenji

GS2-1

16/44

Yasuo

OS6-1

24/38

GS10-1 15/60

OS6-2

24/38

GS10-2 15/60

OS6-4

24/39

GS5-2

17/48

GS5-3

17/49

Shapiai

Mohd

GS6-10 19/53[U]

Ibrahim
GS6-11 19/54
Shimohara

KatsunoriSong

Kai-Tai

PS-1

18/26

Sugisaka

Masanori

GS6-9

19/53

GS7-3

14/56

GS10-5 16/61

Uchida

Ueno

Junji

Urata

Seiya

OS2-1

25/30

VentureGentiane

IS-3

18/27

SuhaimiRashidah

GS6-2

18/52

Susaki

Hikaru

OS6-1

24/38

OS6-2

24/38

WAN

Khairunizam

GS6-2

19/50

GS2-2

16/45

Wang

Jiwu

GS7-3

14/56

Suto

Hidetsugu[W]

GS10-5 16/61[T]
TabuseTada

Masayoshi

Akiomi

OS3-1

22/33

Wedashwara

Wirarama

GS3-1

16/46

OS3-2

22/33

Wen

Jiawei

GS6-1

18/50

OS3-3

22/34

OS3-5

22/35

[X]

OS8-4

21/42

Xia

Yulong

GS6-8

19/53

OS8-5

22/42

Xiao

Hui

GS6-6

19/52

Xu

Jinkang

GS6-6

19/52

Takanishi

Atsuo

PS-2

23/26

Takao

Shoichiro

GS5-2

17/48

GS5-3

17/49

GS6-14 20/55
Xue

Wei

GS6-5

19/52
19/52

TakedaKatsunori

OS8-3

21/42

GS6-6

Takeshita

Yuki

OS6-3

24/39

GS6-14 20/55

Tamura

Kaori

OS3-1

22/33

Tan

Joo Kooi

GS7-1

14/55

GS7-2

14/56

[Y]
YamabaHisaaki

OS2-1

25/30

TanakaToshiyuki

GS3-3

16/46

OS2-3

25/31

Tanev

Ivan

GS10-1 15/60

OS2-4

25/31

OS2-5

25/32

GS10-2 15/60©ICAROB 2015

P - 66

YamabaHisaaki

The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

Zhao

Huailin

GS6-8

19/53

GS6-9

19/53

OS2-6

25/32

OS7-1

23/40

OS7-3

23/41

Zheng

Shunkai

GS10-5 16/61

Zheng

Tao

GS2-3

16/45

GS2-4

16/45

Yamada

Ayaka

GS9-3

5/59

YamadaTakayoshi

GS8-1

14/56

Yamaguchi

Akihiro

GS1-3

20/44

Zhou

Xiuzhuang

OS1-5

21/29

GS1-4

20/44

Zhu

Jihong

GS6-8

19/53

GS5-4

17/49

Yamamoto

Hidehiko

GS8-1

14/56

Yamamoto

Toru

OS4-3

24/36

Yamanobe

Natsuki

GS8-4

14/57

YamazakiKazunori

GS9-3

15/59

Yang

Chao

OS1-1

21/28

Yee

Teoh Shin

GS6-12 20/54

Yokoi

Kakeru

GS8-1

14/56

Yokoyama

Kiyoko

GS9-2

15/58

YoshidaEiichi

IS-3

18/27

Yoshinaga

Tsunehiro

OS6-1

24/38

OS6-2

24/38

OS3-2

22/33

OS3-3

22/34

OS3-4

22/34

OS3-5

22/35

GS6-4

19/51

GS6-7

19/52

GS9-4

15/59

GS9-5

15/60

Yoshitomi

Yu

Yu

Yasunari

ChunyuJungwon[Z]
Zhang

Xianwen

GS7-3

14/56

Zhang

Tuo

OS6-1

24/38

OS6-2

24/38

Zhang

Weicun

OS1-3

21/28

Zhang

Y.

OS5-3

23/37

©ICAROB 2015

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The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

©ICAROB 2015

P - 68

The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Horuto Hall, Oita,
Japan, January 10-12, 2015

©ICAROB 2015

P - 69

Production effects by form changes of autonomous decentralized FMSs with mind
Kakeru Yokoi
Human Information Systems Engineering, Gifu University, 1-1,Yanagido,Gifu Japan
Hidehiko Yamamoto
Mechanical Engineering, Gifu University, 1-1,Yanagido,Gifu Japan
Takayoshi Yamada
Mechanical Engineering, Gifu University, 1-1,Yanagido,Gifu Japan
E-mail: yokoi1205@gmail.com, yam-h@gihu-u.ac.jp, yamt@gihu-u.ac.jp

Abstract

In autonomous decentralized FMS, the number of agents and constant installation position has been studied inprevious studies. Therefore, in this study, we are considering to change the position of the warehouse and thenumber of AGVs to redesign the factory form and evaluate the changes in the production of autonomous
decentralized FMS. By being inspired by human mind, we are proposing Minimum Unit of Mind (MUM), a new
method of controlling the behavior of AGVs to avoid the unexpected collision in autonomous decentralized FMS.

1. Introduction

evaluate the changes on the production of autonomous
decentralized FMS.

We need to avoid collision among AGVs when we use
autonomous decentralized FMS. There are some rules toavoid collision, but these rules are not good enough tocontrol the process of autonomous decentralized FMS
because all situations can not be foreseen and rules are
not described. Hence, the purpose of this study is tobring forward human mind to control the behavior of
AGVs.
In fact there are some limitations to control autonomous
decentralized FMS and avoiding the collision among
AGVs, but in this study we are trying to improve the
FMS process.
To improve and control the collision, we propose
the development of behavioral control to avoid thecollision by relying on the fact that simulating thehuman mind will help machines to make better
decisions. In this study, we are considering to change
the position of the product warehouse and change thenumber of AGVs to redesign the factory form and2.1. Overview of autonomous decentralized FMS
Fig.1 illustrates an autonomous decentralized FMS
factory scheme. As Fig1 shows, the factory floor is
divided into grid patterns, and AGVs are moving along
these lines to carry parts to the warehouse and machine
centers which is representing the system of autonomous
factory to carry out the production. The autonomous
decentralized FMS does not have a management
mechanism to integrate the entire system, (machining
centers, AGV, product warehouse (PW), parts
warehouse). Each agent that configures the systemautonomously determines the act by recognizing the
purpose of the system by cooperating and negotiating toother agent.1© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan-1-

Kakeru Yokoi, Hidehiko Yamamoto, Takayoshi Yamada

A１Path interference‐X
Fig.1: Autonomous Decentralized FMS Scheme
Stay away from the destination

A２

2.2. Model of the mind and behavior of the AGV
Fig2. Model of the mind

We describe the behavior of the AGV control of
autonomous decentralized FMS factory. In autonomous
decentralized FMS, each AGV determines the behaviorautonomously. If any of the AGVs runs on the same line
at the same time, they overlap and AGV pathinterference occurs. It is necessary to avoid suchinterference between AGVs and therefore, we havedeveloped control rules for the system. If the number of
AGVs increases, more unexpected events can occur
because the rules become more complicated. Thus, it is
impossible to generate rules that can be adapted to every
situation in an autonomous decentralized FMS factory.
This study focuses on the human mind, which can deal
with unexpected events. We propose a mind model for
the AGVs and develop a method to control their
behavior by using a mind. The mind model considers
two types of mind: an arrogant mind and a modest mind.
AGV with arrogant mind takes the action approaching
the destination forcibly. AGV with modest mind takes
the action to give way to other AGV.
Fig.2 is a diagram about mind model. In this study,
we call it Minimum Unit of Mind (MUM). Further, A1
and A2 is unit, X is load, and arrow is a stimulation
vector. The threshold is determined by
the unit.
If the internal value reaches the threshold, we call
excited and if it does not reach, normal.
When a signal is sent to the unit, it sends a signal tothe direction of the arrow when excited, and it is notsent when normal.
Load has the function to change the internal value ofthe unit. When the signal is sent to the load, the value of
unit is decreased by the value of X.
Stimulation vector is a line connecting the load andthe unit. It gives a signal to the load or unit when thesignal comes, when A1 keeps exited, we call modest
mind, and when normal, we call arrogant mind.

Next, we describe the internal functions of MUM.
When the arrogant AGV has path interference, A1 is
increased by 1. Keeping the situation of the interference
and being increased by 1, the A1 value becomes thethreshold value, and the AGV is changed to a modest
one.
When AGVs with a modest mind keep giving a way,the value of A2 is increased by 1. When the situation isrepeated an optional time and A2 becomes exited, asignal is sent to a load. The received load decreases thevalues of units A1 and A2 by optional values. Becauseof this, A1 and A2 are returned to normal and AGV
with a modest mind is changed to one with an arrogant
mind.In this way, when an arrogant mind isincorporated in the AGV, it is forceful in its motion. On
the other hand, when a modest mind is incorporated in
the AGV, it gives way to other AGVs.
3. Impact on production conditions and Change
Agent
In this section we will investigate parameters such as
capacity utilization, route interference and number of
products by applying following conditions:3.1.Changing
warehouse:thepositionoftheproducts

The product and part warehouses were immobilized
in the previous studies and to prove that moveable
warehouse method is effective, we decided to design
five new factory layouts in this study.
3.2. Changing the number of machining centers andAGVs:

In the previous studies, the number of AGV wasallocated to five AGVs and the number of themachining centers was done with 24 units. We will
© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan-2-

Production effects by form
Table2. Production number of the case of 24 cars MCchange the AGV numbers from 2 to 9 AGVs, and weadopt 24 units of machining center and 16 units in thisstudy.

Fig3.

AGV

2

3

4

5

6

7

8

9

data1

84

122

151

184

214

235

257

274

data2

85

120

155

185

212

233

252

277

data3

84

118

155

185

210

232

252

280

Next, we show simulation results of autonomous
decentralized FMS when the position of the productwarehouse is changed to 5 different positions.
Table 3, 4 illustrate the utilization rate of MC when
the number of AGVs are 8 and 5 and the number of
MCs are set on 16.

Location of product warehouse (0, 50) layout

Fig.3: is a layout of the production floor when it waschanged to the location of the product warehouse (0, 50).

Table3. Utilization rate when the AGV number is 5

4. Simulation results

Product warehouse(0,10)(0,25)(0,50)(60,0)(60,25)(60,50)Figure 4, Table 1 and Table 2 are the results of thesimulation of production when the number of machining
centers and AGVs are being changed. As the result
shown, the more number of AGVs, the moreproductions will be produced. However, we have gotten
a result that shows a little difference when the numberof machining centers are 24 units and 16 units. In
addition, in the first 30 minutes of the starting thesimulation there is a high route interference number asshown in the Fig 5. It is because, right after the starting
the simulation, there is no part in the machine centers
and all AGVs should go and pick the parts from the part
warehouse simultaneously.

Table4. Utilization rate when the AGV number is 8
Product warehouse(0,10)(0,25)(0,50)(60,0)(60,25)(60,50)

2

3

4

5

6

7

8

9

data1

92

129

160

184

209

233

254

274

data2

89

129

156

184

212

233

256

273

data3

91

132

161

184

209

230

255

277

Capacity utilization
49.86450.13650.30650.29350.07549.235

When the AGVs number is 5 and the position of
product warehouse is (0, 25) then the utilization rate of
MC gives us a better result. Table 3, 4 are shown the
results of this simulation. However, when the number of
AGVs change to 8 and the position of PW change to (0,50) then the utilization rate gets a little bit better thanthe previous condition. In the other hand, when the
AGVs number is 5 or 8 and PW position is (60, 50) the
utilization rate will be the lowest rate.
Table 5, 6 are shown the production number when the
AGVs number is 5 and 8.

Table1. Production number of the case of 16 cars MC
AGVCapacity utilization
36.52237.18736.40136.15
35.88235.248

Table5. Production number when the AGV number is 5
Product warehouse(0,10)(0,25)(0,50)(60,0)(60,25)(60,50)Fig4. AGV path interference and number of AGVs in every 30

The production number
182
180
191
181
180
175

minutes© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan-3-

Kakeru Yokoi, Hidehiko Yamamoto, Takayoshi Yamada

are limited so more number of AGVs cause moredifficulties and collisions in the production floor in theirstudy. In our research we have tried to improve thelimitations and change the rules to be able to increase
the number of AGVs as more as possible. To prove that
our study is effective, we proposed MUM in this study.
In the following paragraphs we discuss the results ofdifferent layouts of the production floor, 5 and 8 AGVs,
the number of parts, route interference number and MC
utilization rate. According to mentioned Tables andFigures, the previous study with a PW position (x, y) =(0, 10) the path interference rate was high. In our study
we change the layout of autonomous distributed system
PW positions to (0, 25), (0, 50), (60, 0), (60, 25), (60,50) and AGV route interference was reduced after
applying changes. However the utilization rate of AGV
is increased and interference number of AGV is reduced,
but sometimes the number of production will be lower.
Sometimes due to long distance between AGV to PW
position the AGV takes a lot of time to move between
two points which is time consuming.
In our study, the result of simulation shows that
production floor with PW position (0, 50) with 5 and 8
AGVs will have a better performance and will get abetter result; we expect the production efficiency byfollowing this pattern. There are several ways to design
the layout and change the number of AGVs and MCs,
however we have got a good result which seems
efficient, but we suspect that there can be other ways tomake optimum. It has also been found that it isnecessary to perform the simulation, depending on the
different conditions.

Table6. Production number in the case of 8 AGVs
Product warehouse(0,10)(0,25)(0,50)(60,0)(60,25)(60,50)The production number
250
263
263
261
253
247

According to Table 5, 6 the PW with position (0, 50)
has become the maximum production number when the
AGVs number is 5 and 8. In case of having 5 AGVs the
production number increasing but to compare with 8
AGVs the ratio of increasing the products is not
significant. The lowest result of production number
which we have got in simulation is about when the PW
position is (60, 50) with both 5 and 8 AGVs.
Table 7, 8 illustrate the route interference number incase of 5 and 8 AGVs.
Table7. AGV route interference number of times in case of 5 AGVs
Product warehouse(0,10)(0,25)(0,50)(60,0)(60,25)(60,50)
Table8.

Route interference number
997
912
729
833
850
731

AGV route interference number of times in case of 8 AGVs
Product warehouse(0,10)(0,25)(0,50)(60,0)(60,25)(60,50)

Route interference number
2668
2286
2134
2111
2113
1923

6. Conclusions
In conclusion, changing the number of MC and AGVs
affected the production rate and capacity utilization.
Applying the new conditions and layout designs has
improved the production line. Finding new optimal
agent conditions by applying to autonomous
decentralized FMS in order to improve the production
efficiency should be done in a future work.

According to Table 7, 8 the route interference
number is the lowest result in case of 5 and 8 AGVs andin the PW position (60, 50). As a result, the new
production floors have a better result to compare withthe previous production floors. Hence, Autonomous
distributed system that was newly created by thissimulation, and the results we have got through thissimulation shows that the some layouts will be more
efficient to use in the production floor.

Reference
1.Hidehiko Yamamoto, Jaber Abu Qudeiri and M.A.
Jamali, Real-time Control of Decentralized Autonomous
Flexible Manufacturing Systems by using Memory and5. Discussion
Oblivion,
Lecture Notes of Computer Science ,Springer,
pp.252-259,
ISBN 978-3-540-46535-5, Nov.,
There are different rules and techniques which have
2006.
been studied by other researchers, the number of AGVs
© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan-4-

Development of an Autonomous-Drive Personal Robot“Improve the Accuracy of Object Area Determination by Boundary Detection”

Mikiko Hirai

Department of Mechanical Information Science and Technology, Kyushu Institute of Technology,
680-4, Kawazu, Iizuka-City, Fukuoka, 820-8502, Japan
Email: hirai@mmcs.mse.kyutech.ac.jp
Eiji Hayashi

Department of Mechanical Information Science and Technology, Kyushu Institute of Technology,
680-4, Kawazu, Iizuka-City, Fukuoka, 820-8502, Japan
E-mail: haya@mse.kyutech.ac.jp

Abstract
We are developing an autonomous personal robot that able to perform practical tasks in a human environment
based on information derived from 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 themany objects. This environmental recognition system is composed of an autonomous driving system and an object
recognition system. So we confirmed the effectiveness of the object recognition system by the experimental results.
Keywords: Personal robot, Monocular camera, Image processing, Object recognition.

images and the Laser Range Sensor (LRS) which isused to acquire two-dimensional distance information.
The system for this robot is composed of anautonomous run system for movement and an object
recognition system for the recognition and grasping ofan object. First, the autonomous run system decides
upon a robot driving command based on information in
the limited space map. Information such as walls andbarricades are set to the map, and the data obtained fromthe CCD camera are compared against the map data.
The route is decided, and the robot drives. The object
recognition system is composed an object-recognition
processing part and a location-information acquisition,
processing part, both of which use the monocular

1. Introduction
In the near future, some autonomous selfdriving robots are expected to provide various
services in human living environments. For this tooccur, the robots will need to gain a grasp of the humanenvironment. Therefore, the systems to provide
environmental recognition based on image information
are being widely studied. However, it is very difficult torecognize all driving environments from image
information only. So far, no prospects for such a systemhave emerged. Here, we report on the development of
an autonomous personal robot able to autonomously
perform practical tasks in human environment based on
the several information that are derived from the camera© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan-5-

M. Hirai, E.Hayaashi

camera and the LRS. An
A object is recognized aandidentified usiing range info
formation obtaained from L
LRS
in addition too the processeed image data provided by thecamera. The rrobot perform
ms a grasping operationofor theobject accordding to this sysstem.
This studyy is related too object recog
gnition duringg an
object manipuulation process of the robo
ot. Currently, theportion of thee object maniipulation, processing part iis a
problem of hoolding and juddgment bottles were placedd on
the desk agaiinst the instruuctions of the user on wheth
theror not the opperation targett object. In this study, we ppay
attention to w
work on objecct recognitionn of environm
ment
recognition pprocessing parrts, especiallyy the object aarea
determinationn processing ofo plastic botttles in the imaage
obtained fromm the camera.

Fig.1. System Strructure of Roboot.

caan acquire thee object positiion on the asssumption, forr
ex
xample, that thhe object is pplaced on a desk.
dFirst, theerob
bot has recog
gnized the sinngle object in the form, theecoolor and the local
lcharacteer of the objeect. Next, theesyystem then accquires the loocation inform
mation of theeobbject by usinng LRS witth the recog
gnized objectt.
Affterwards, thee arm is drivven based onn the locationn
infformation, an
nd the object can be grasp
ped and heldd.
Th
he flow chart for the objeect recognitionn and holdinggobbject system iss shown in Figg. 2.

2. System of Robot
Our robott as shown inn Fig.1has a drivedmechaniism
consisting off two front andd two back wheels.
w
The frront
wheels are atttached to a motormthat opeerates the wheeels
on either sidde independenntly, while th
he back wheeels
function as castor wheeels. This methodmhas theadvantage of allowing a small turnning radius. In
addition, to acquire imagge informationn, both a sinngle
CCD cameraa with approxximately 2,000
0,000 pixels aandan LRS are innstalled on thee head of the robotrand cann berotated to all sides by two motors. DC servos
motors areused in the rrobot’s drive mechanism.
mThe
T position aandspeed controll are achievedd by the contrrol system of thedrive mechannism. The roobot also hass two arms aandhands equippped with sensoors, which enaable it to respoondto the variouss demands off humans. Finally, an installled
wireless LAN
N can providee remote conttrol for humaans.
All devices arre controlled by
b a Personal Computer (PPC),
and the leadd batteries aree used to suppply the roboot’s
electric poweer.
3. Object R
Recognition Syystem

Fig.2. Flow chart of recoognition and hollding object.

3.1. Outliine of the autoonomous systtem
3.2
2. The method
d for object reecognition

We have ddeveloped an object recogn
nition system fora robot that ccan acquire thhe target objeect position w
withimage inform
mation captureed by monocu
ular CCD cam
mera
and range innformation obbtained by LR
RS. This systtem

In this section, we expllain the methhod for objecct
reccognition andd the processs of the objecct recognitionnsyystem is shownn in Fig. 3.

© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan-6-

Develolopment of autono
omous-drive roboot

Fig.4. ProcessPof detecction the borderr flow.

3.1. Convert tot HSV proces
essing part3.3
Th
his processing
g part convertss images a color from RGBB
moodel to HSV model.
m
The HSSV color mod
del means Huee,
Saaturation and Value. Thiss model is oftenoused byypeeople who aree selecting cololors from a colorcwheel orrpaalette, becausee it correspoonds better too how peoplee
ex
xperience coloor than the RG
GB color spacce does. [1] Theeeqquations for coonversion are shown in Eq. (1) ~ (4).

Fig.33. Process of obbject recognition system.

In this sttudy, we pay attention to working on theobject area determinationn processing of the plasstic
bottles in thhe image obtaained from th
he camera. T
Therobot requirees to accurateely recognize the object aarea
when the oobject recoggnition. This processing isimportant inn object recoognition, beccause the robbot
cannot compaare the form, the color andd the local objject
characteristiccs in the imagge with the tarrget object if therobot cannott recognize thetobject areea exactly. T
Theconventional object area determination processingg isable to accurrately recognizze the object area when thhere
is only one oobject or theree are some ob
bjects which are
not overlappping objects ini the imagee. However, theobject area deetermination processingphaas a problem tthat
the robot cannnot take outt a plastic boottle area in theimage if pluraal bottles overrlap each otheer on the desk.. Inorder to solvve this probleem, we attem
mpt to add nnew
processing thhat to detect thhe border betw
ween overlappiing
objects. So, w
we focused onn the functionn of the cameera,
particularly the use of the zoom function. M ore
specifically, we constructed a system that detects theboundary by displaying a border part between objeects
by using the ccamera zoom..

, , ,
, , .

(1)

Hu
ue calculation
n
0

,
,

60
2

60

,

60

4

.

(2)

Saaturation calcu
ulation
0

,
.

(3)

Vaalue calculatioon

3.3. The m
method for obbject area deteermination
The flow for the proceessing to detecct the border for
determinationn the object arrea is shown inn Fig. 4.

.

© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan

-7-

(4)

M. Hirai, E.Hayaashind deviation valuev
3.3.2. Get thee HSV data an
calculation pprocessing parrt
This processiing part gets the HSV datta and deviattion
value calculaation. After the HSV coonversion, sett a
rectangular aarea in the imaage. In additioon, HSV dataa inthe rectangullar area are obtainedoand calculated eaachdeviation valuue.
3.3.3. Extracction of the obj
bject area proccessing partFig. 5. The experimeental environmeent.

This processiing part extraccts the object area
a in the zo om
image. First, this part com
mpares the HSV
Hdata of therectangular aadjacency relaationship for pixels with thedeviation valuue. So, the recctangular adjaacency pixels are
considered too be an objecct same as th
he object in therectangle, if tthe HSV dataa of the rectanngular adjacenncy
pixels are thee value in thee deviation vaalue. The objject
area is exttracted by repeatedly executing suuchprocessing.

(a) Source(c) Proposed

Fig. 6. Experriment results.

3.3.4. Decidee object width processing pa
art

5. Conclusion

This processsing part dettects a bordeer part betweeen
objects by deetection the rigght and left en
nds of the objject
area. So, thee object widthh is decided byb the detecttion
border.
4.

(b) Convenntional

In this paper, we proposee object recog
gnition systemmthaat accurately recognizes thhe object areaa by detectionn
thee border betw
ween overlappping objectss. This objecct
reccognition is detectiondthe boundary byy displaying aboorder part betw
ween objects bby using the camera zoomm.
Ass a result, preecision of obj
bject recognitiion improvedd.
Hoowever, this systemscannott recognize same objects. Innthee future we will try the soluution to this prroblem.

Experimment of systemm evaluation
4.1. The m
method of expperiment

We perforrmed the folloowing experim
ment to evaluuate
the performaance of objecct area determ
mination. Figg. 5
shows the exxperimental ennvironment wh
hen the objecct isa plastic botttle. Its locatioon was set thrree objects onn asingle-color ddesk in the laaboratory. Thee detected objject
area is displlayed on thee image coloor classificatiion.
Getting resultt compares wiith the precedeence research .

Reeferences
1. Convert fromf
HSV to RGB Color Spacee,“http://jp.maathworks.com/hhelp/images/con
nvert-from-hsv-to-rgb-color--space.html” (aaccessed Nov 11
1th).
d E. Hayashi, D
Development off an automous2. Y. Kibe anddrive person
nal robot “An ennvironment reccognition systemmusing image processing andd an LSI” (Jourrnal of Artificiaal
botics, Springerr 2011).
Life and Rob
Development off an automous3. K. Ito and E. Hayashi, D
drive person
nal robot “An oobject recognition system usingg
Self-Organizzing Map” (Joournal of Artificial Life andd
Robotics, Sp
pringer 2014).

4.2. Result of experimeent
The Fig. 6 shows thhe experimen
ntal result. T
This
research can accurately reccognize the siingle object aarea
overlapping plural objeccts by detectting the borrder
between objeects.

© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan-8-

Construction of a micro sense of force feedback and vision for micro-objects:
development of a haptic device
Yusei. Ishii

Graduate School of Computer Scienceand System Engineering
Kyushu Institute of Technology
680-4, Iizuka City, Fukuoka, Japan, 820-8502
ishii@mmcs.mse.kyutech.ac.jp
Eiji. Hayashi

Department of Mechanical Information
Science and Technology
Kyushu Institute of Technology
680-4, Iizuka City, Fukuoka, Japan, 820-8502
haya@mmcs.mse.kyutech.ac.jp

Abstract
The purpose of this research was to develop a combined sense system that uses both force feedback and visualfeedback to determine the shape of the microscopic features of a sample. We constructed a haptic device that gives
a sense of that force to the operator when touching the sample. The experiment was tested for recreating touching
force of salmon roe as the samples in a minute of the period time.
Keywords: force feedback, haptic interface, simulation.

was created to assess the reaction force when a minutesample was touched. A cantilever was used to touch thesample, and the reaction force was obtained from thedegree to which the sample bent. In addition, we used ahaptic device and amplified the force feedback from aminute sample of a virtual object.

1. Introduction
Technologies that can accurately perform minutework are now being sought for medical treatment and in
the field of manufacturing semiconductors. Such minutework is improved by using micromanipulators, but theiroperation is difficult because the operator has no senseof force; he or she relies only on sight through a
microscope. As a result, a person skilled in the use ofthis technology is needed for all minute work. The
efficiency of minute work would be improved if theoperator were able to have a sense of force while using
a manipulator.
Here we describe the development of a more efficientsystem for minute operations. Our aim was to develop asystem using not only the sense of sight through a
microscope but also a sense of force from the
manipulator. For this fundamental research, a system

2. System Structure
2-1. System summary
The system structure is shown in Fig. 1a, and aschematic view is shown in Fig. 1b. This system
consists of a microscope with an automatic x-y stage, apiezo stage, a feedback stage controller to control the xy stage, a piezo stage controller, a haptic device fortransmitting force feedback (Fig. 2), a cantilever (Fig. 3),
and a PC via which the user can control and operatethese components. The sample was fixed on the x-y
stage by an injector (Fig. 4) and a holding pipette (Fig.
5). When the cantilever, which was fixed to the piezo© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan-9-

Yusei.Ishii Eiji H
Hayashistage, touchedd the sample, the operator couldcmaintainn
the cantileverr’s position byy obtaining thee value of thereaction forcee through the interface.
i
Thee resolution off
the piezo stagge is 1 nm.

bee measured by the laser and the PSD. Thee rotor wasab
ble to follow any input waveeform.

Fig. 6.
6 Diagram of the haptic dev
vice.
Fig. 1a. Photoograph of the system structture.

The actuator is controlled bby a servomecchanism onthee actuator. Th
he system drivving the actuattor thereforecoonsisted of fou
ur actuators: a microcomputter, an
inp
putting AD/DA port, an out
utputting micro
ocomputer,annd a PC outputtting order vallue. The systeem controls
thee actuator during each part of the processs. Figure 7
shhows the structture of the happtic device.

Fig. 1b. A schematic vieew of the systeem structure

Fig. 7. Structure off the haptic dev
vice.
Fig. 2. Haptiic device.

Fig. 4. Innjector.

Fig. 3. Cantilever.
C

Fig. 5. Tip of holding pippette.

2-2. Haptic device
Figure 6 prrovides a diaggram of the hap
ptic device. Ittconsists primmarily of a rotoor, a laser, PSD
D. We installeeda coil on the rrotor with a poolarity magneet, which
generated eleectromagnetic induction by an electric
current and a magnetic forcce. The angle of the rotor caan

The actuator, whose actionns are governeed by the PDcoontrol, is operaated through a digital differrential
caalculus device.. A transfer fuunction for thee quadraticfunnction systemm shown in Figg. 8 is provideed for theactuator servo system. The roole of each parrameter of theecoontrol system is
i to adjust thee total offset tot a master in
Gii/Gif, to regulaate the item vi
viscosity/reson
nance point inGpp/Gv, and to regulater
the tootal gain in Gm
m.
Table
T
1 provid
des a list of thee control paraameters of the
serrvomechanism
m system.

© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 10 -

Development off the haptic devicce

Left of the fig
gure 10 showss the cantileveer touchingolding pipette. Right of the figuref10
thee tip of the hoshhows the experriment that meeasures the reaction force
off the downy haair. The imagge-processing speed of thecaantilever was improved by th
the tracking prrocess. The
beend of the canttilever is assum
med to be lineear-elastic sothaat Hooke’s law
w may be appplied. The restoring force, F,F
off the bend of th
he cantilever iis given by(1)
F  kxwhhere x is the compression diistance from theteqquilibrium position, and k iss the spring co
onstant.

Fig. 8. Blocck diagram of the servomech
hanism systemm.
Taable 1 Controol parameters ofo theservomechhanism systemm

4. Deform
ming the sam
mple in sim
mulationIn this study, we attemptedd to build a wo
orking systemmussing a microscope, a haptic ddevice, and a simulation. A
funndamental eleement was sim
mulating the deeformation off
a minutemobject.. Figure 11 shhows the graphhical user
intterface (GUI) of the simulaator. A graphicc tool wascreeated using OpenGL to draw
aw the object anda to choosethee shape of the sample, for innstance, a cub
be or sphere.
Th
he dynamic model of the sam
ample consisteed of a spring-maass array of mass
m points in bboth the vertical andhoorizontal directions. An exam
ample of the arrrangement off
maass points is shown in Fig. 12. When a foorce wasap
pplied at a masss point, the siimulation calcculated the
speed of all masss points that hhad been affected. The
immage was renew
wed after everry ten calculaations.
We defined a spring as havving a size butt no weight,an
nd a mass poinnt as having a size, a weightt, and a rigidboody. An arbitraary mass objeect can be placced on aspring on a bitm
map (Fig. 13). In addition, a sample canbee seen from vaarious viewpoiints, and the deformationdoffthee sample, which is impossibble to observee by
miicroscope, cann be checked. The shape of this object
caan be either a cubecor a spheere, and any point may be
selected as a fix
xed point or ann operating po
oint.

3. M
Measuring thet reactionn force
The reactioon force was usedu
to calculaate the force
applied by thee minute object. In this exp
periment, wetouched the mminute object withw the cantilever shown iin
Fig. 3, and thhe reaction force was obtainned based on thhedegree of bennd of the cantiilever. The lay
yout of theexperiment iss shown in Figgs. 7 and 8, an
nd theenvironment of the experim
ment is shownn in Figs. 9.
Based on thiss experiment, we
w were able to determine thereaction forcee applied by thhe minute object.

Figg. 9. Environm
ment of the exp
periment.

Fig
g. 11. Simulattor.

Fig. 10. Thee cantilever movingmFig. 122. Arrangemeent of mass po
oints© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 11 -

Yusei.Ishii Eiji H
Hayashi

0.0

Displacement [mm]

5.0
0.0
‐5.0
‐10.0
‐15.0
‐20.0
‐25.0
‐30.0
‐35.0
‐40.0

Fig. 13. P
Placing an arbbitrary object ono a bitmap.
5. C
Characteristicc measurements and areapppearance experiment for ana objectDynamic ccharacteristics of the object were measureed
and then recreeated using thhe haptic devicce. The objectt
used this timee was salmon roe. We meassured responseeof salmon roee and showed the displacem
ment of its
surface a figuure. Figure14 showss
the resp
ponse of thesalmon roe.
Salmon roe is 6 millimeteers in diameterr and we cannnot
precisely feell the sense of touch.
tHence values in the
figure are muultiplied by 688 so that humaan can feel theesense of salm
mon roe’s touchh. Figure15 shhows that
response of thhe salmon roee and haptic deevice. Table 2
shows the freequency, consttant of spring,, and dampingg
coefficient inn each ectopic focus.
In this expeeriment, there was a tendenccy for a touchh
feeling close to that of reall and big salmon roe to becreated whenn characteristicc was recreateed.
0.0

0.2

Time [s]
00.4
0.6

0.8

T
Time [s]
0.4
0.6

0.8

1..0

SSalmon roe (sevetimes)(68entytimes)
H
HapticFiig.15. Time reesponse of Sallmon roe and Haptic devicee

6. Concclusion
In the present study the chararacteristics off salmon roe
weere measured and recreatedd, the evaluatio
ons were also
caarried out withh regard to respponse and sen
nse of force
ussing a haptic device.
d
The chharacteristics ofo variousobbjects will be measuredmin thhe future, andd thechharacteristics shouldsrecreatete.
Future researrch should foccus on building
g a system
thaat allows a reaaction force too be detected anda shownmoore precisely. Such a system
m would makee it possible too
tesst smaller sam
mples.

7.
7 Acknowlledgement
This
T research was
w partially ssupported by thet Ministry
off Education, Sccience, Sportss and Culture, Grant-in-Aidd
forr Scientific Reesearch, 20122.

1.0

0.1

Refereences

0.0
Displacement [mm]

0.2

1.

-0.1
-0.2

2.

-0.3
Sa
almon roe

-0.4
-0.5

3.

-0.6

Fiig.14. Time reesponse of Sallmon roe
Table2. Happtic device param
meter of dynammic characteristiic
Displacement[mm]

Frequency[Hz]

Spring constaant[N/mm]

Dampinng
ratio

0～6
6～35

6
6

0.072
0.072

2.6
1.0

4.

Miyamoto K (2007) C
Construction ofo super-microo
sense of force feedbaack and visu
ual for microo
objects. Ky
yushu Institutte of Technoloogy.
Li X, Zh
hang Z (20008) Probe-typ
pe microforcee
sensor forr micro/nanoo experimentaal mechanicss.
Trans Techh Publicationss, Stafa-Zurichh, Switzerlandd
T.DOMOTTO (2012) C
Construction ofo a sense offorce feed
dback and vision for micro-objectsms:
Recreate thet response and a sensee of force ofobjects. Ky
yushu Institutte of Technolo
ogy.
J. IMAHA
ASE (2012) C
Construction of a sense offorce feed
dback and vision for micro-objectsms:Developing AD conveerter. Kyushu
u Institute ofTechnologgy© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 12 -

Error Recovery of Pick-and-Place Tasks
in Consideration of Reusability of Planning
Akira Nakamura, Kazuyuki Nagata, Kensuke Harada and Natsuki Yamanobe

Intelligent Systems Research InstituteNational Institute of Advanced Industrial Science and Technology (AIST)
Central 2, 1-1-1 Umezono, Tsukuba, Ibaraki, 305-8568 Japan
E-mail: a-nakamura@aist.go.jp, k-nagata@aist.go.jp,
kensuke.harada@aist.go.jp, n-yamanobe@aist.go.jp
www.aist.go.jp

Abstract
Error recovery in robotic tasks is explored to enable robots to be used for complicated tasks. The authors’ error
recovery processes make use of the concepts of both task stratification and error classification. In this paper, thereusability of planning in error recovery is verified by using the typical pick-and-place tasks that are used in plantmaintenance and industrial production.
Keywords: manipulation skill, error recovery, task stratification, error classification

complicated plant maintenance and industrial
production, it is not rare for the execution of a task toterminate before completion. Therefore, error recovery
is an important research theme for robots that need toperform such real-world tasks.4, 5
We have explored error recovery in robotic tasks toenable robots to be used for complicated tasks.6–9 Ourerror recovery processes make use of the concepts ofboth task stratification and error classification, and thesetechniques are based on the concept of skills.
Considering the use of various recovery paths, thereusability of task planning may become an important
aspect of research on error recovery. In this paper, thereusability of planning in error recovery is verified byusing the typical pick-and-place tasks that are used inplant maintenance and industrial production.

1. Introduction
In recent years, studies on robotic manipulation for
performing tasks in various fields have been conducted.
We have conducted many research studies on the
robotic manipulation used to perform plant maintenancetasks and manufacture industrial products (Fig. 1).
These manipulation tasks tend to be complex,
necessitating that composition rules be devised for theentire work process.
By analyzing the assembly and disassembly
sequences performed by humans, we found that thosetasks tend to be composed of several significant motion
primitives. We call each motion primitive a “skill” andhave shown that most maintenance and production tasks
can be composed of a number of skills.1–3
Ideally, a robotic task has to be successfullycompleted as planned. However, in the actual tasks of

2. Stratification of Tasks© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 13 -

Akira Nakamura, Kazuyuki Nagata, Kensuke Harada and Natsuki Yamanobe

important part in those tasks. A specific task iscomposed of sequences of skill primitives such as thesethree skills. Moreover, many skills can be defined basedon modified versions of these three fundamental skills3.

project (MAX)…

Project

project (2)project (1)task (max)…

task (2)task (1)task (0)Task

2.2. Stratification of tasks

Skillprimitive

Figure 2 shows a hierarchy of manipulation tasks6. If we
ignore the servo layer, the skill layer, which consists ofelements such as the move-to-touch and rotate-to-levelskills, is located in the lowest layer called the task(0)layer. One tier above the task(0) layer is the task(1) layer.
Similarly, task(i+1) is composed of sequences of task(i)elements. The top layer, where the error recovery loop
is closed, is called task(max) and one tier above task(max) iscalled the project layer. The project layer might also behierarchized, but we will not discuss this here.

Fig. 2 Manipulation hierarchy

Fig. 1 Maintenance robot
Start
Sensing(All-over task (max) )Modeling(All-over task (max) )Planning(All-over task (max) )Sensing(task( (1)
i ))

※3

(0)
task (1)

※1

※1

(0)
task (2)Classification of error※1

In an ideal environment, tasks are achieved without any
errors occurring. In actual manipulation, however,errors often do occur from various causes. Our conceptof error classification and process flow with error
recovery in the task hierarchy are described in thissection. See Ref. 6 for more details.

task (0)( i0 )※1

task ((0)i0 )3. Error Recovery in Stratified Tasks
Class T (max+1)
Stopping the process andexecuting all necessary changes

Class 2 Changing theplanning parameters
Class 3 Changing themodeling parameters
Class T (1) Changing thesensing parameters※3
Re-planning(task( (1)i1 ) )Class 1

(1)

(max)

task ( imax , …………… , i1 )task ( imax )※3Modeling(task( (1)i1 ) )Class T (2) Executing thenecessary changes

1

Execution※2

task (0)( i0 )

Sensing

3.1. Classification of errors

Modeling
Confirmation

error

The causes of manipulation failures can be attributable
to several kinds of errors. We group the error states into
several classes of errors: execution, planning, modelingand sensing, according to the possible causes6.
Merely remedying the causes of these errors does
not always solve the problem. It may be necessary toreturn to a previous step when the working environmentis greatly changed by the error.

correct

End
Accurate expression:※2(0)task (0)( i0 ) ＝ task ( imax , ……… , i0 ) ,※3(1)
task (1)( i1 ) ＝ task ( imax , …… , i1 )Fig. 3 Process flow with error recovery

This section explains our concept of skills and thestratification of tasks. See Refs. 1, 2 for more details.

3.2. Error recovery based on classification
A generalized process flow of stratified tasks that takes
error recovery into account has been shown in Ref. 6.
Figure 3 is an illustration of the central portion of Fig.
10 in Ref. 6. This process is performed based on
recovery through a backward correction process. At theConfirmation step in each skill primitive task(0)(i0) , theresult is judged to be correct or a failure by an automatic
process or by a human operator. Error recovery is
performed using the following error classification.

2.1. Concept of skills
We analyzed human motions in such tasks as
disassembly and reassembly and found that the
movements consisted of several significant motion
primitives. We call such motion primitives “skills 1, 2”.
We considered three fundamental skills: move-to-touch,rotate-to-level and rotate-to-insert, all of which play an© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 14 -

Error Recovery of Pick-and-PlaceClass 1: When the error is judged to be an execution
error, task(1)(i1) is executed again without correcting
the parameter .
Class 2: When the error is judged to be a planningerror, task(1)(i1) is executed again with a change in theplanning parameters .

and the tool may be necessary for a Class 3 error. And
additional geometry modeling of the working
environment and calibration of the vision system may
be necessary for a Class 4 error.

Class 3: When the error is judged to be a modelingerror, task(1)(i1) is executed again with a change in themodeling parameters.

5.1. About reusability

5. Planning with a View Toward Reusability

Generally, a manipulation robot operates according to acomputer program that is based on an ordered plan ofoperation. The tasks performed by a manipulation robot
are varied and may involve such grasping tasks as
gripper closing and opening, and transfer-related tasks
such as lifting and approaching.
Similar tasks can performed using a similar
computer program. As such, in systems with error
recovery functions, similar tasks can be performed in
the recovery portions. Therefore, it is possible to use asimilar program for similar kinds of tasks for the total
system, not only for the main operation part but for the
additional recovery parts as well. A system with highreusability—one in which many of the same programsin the whole system could be reused including therecovery parts—would provide a very efficientapproach to system configuration.

Class T(1): When the error is judged to be a sensing
error, task(1)(i1) is executed again with a change in thesensing parameters.
Class T(2): task(2)(i2) is executed again after the
execution of the necessary changes and returns to thestart of one tier above the task(1)(i1) layer.
::
Class T(max): task(max)(imax) is executed again after the
execution of the necessary changes and returns to thestart of the (max - 1) tier above the task(1)(i1) layer.
Class T(max+1): When it is judged that too manychanges will be required, the process being executed
is interrupted and the process returns to the start of
the all-over task.
4.

Expanded Processes in Error Recovery

5.2. How to improve reusability
Let us explain a method for improving reusability byusing the tasks involved in repacking objects from alarge box into a small box in a physical distribution
scenario as shown in Fig. 5. See Ref. 9 for more details
on this task.
Failures may occur due to various causes when
performing the given tasks, and in various situations
such as grasping, lifting, carrying and packing.
The failure in which a plastic (PET) beverage bottle
is dropped during transfer is considered here. In mostcases, the dropped object ends up in some sort ofdisordered position, for instance falling over to ahorizontal posture from its original vertical orientation.
While an object with the shape of a PET bottle topples
in most cases, sometimes it remains standing upright.
Error recovery involves grasping the object andmoving it to an objective destination. A movement
about the postures in the task means keeping the object

4.1. Forward correction process
To correct the robot’s motions at each step, a manual
operation module for robot control can be inserted in theterminal processing of each primitive motion. For
example, slight errors concerning the position andorientation of the object after transition and thecondition of the grasped object can be corrected8. This
process is recovery through a forward correction
process which differs from recovery through the
backward correction process described in 3.2.
4.2. Additional tasks
Additional tasks may be necessary in some cases toperform the corrections of Class 2, 3 and 4 errors (Fig.
4).7 For example, additional geometry modeling of theworking environment may be necessary for a Class 2
error, and additional geometry modeling of the object© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 15 -

Akira Nakamura, Kazuyuki Nagata, Kensuke Harada and Natsuki Yamanobe

6. ConclusionsHome

P10(=P1)

In this paper, we have discussed the reusability ofplanning in manipulation tasks including error recovery
processes. The techniques and validity of the reusabilityof such programs have been presented and verified
using pick-and-place tasks. Programming of the total
task including error recovery will be simplified by using
the same planning in the path of the original task and in
multiple recovery paths.
In the future, we will conduct further research on
optimum adjustment methods for the various parameters
used in the error recovery paths and for selecting
common programs. We will attempt to apply ourtechniques to actual maintenance robots.

P1
Additional task

Class kChanging theparameters

Home
P5

Move-toapproachMove-betweenreference frames

P9
P6 Move-todestination

Departure

Pre-grasp
P4

Lower

Lift

P7
Leave

Gripper-open
P2
Approach
P3

P8
Grasp

Classification of error

Fig. 4 Additional task

Small box
Large box

Fig. 5 Picking and placing task using a gripper

vertical when it is already vertical and standing upright,
and righting it back to the vertical from a toppled
horizontal position. In the original scenario, most pickand-place tasks will not require restoring to verticalposture from an existing vertical orientation. In otherwords, the task is performed by a pick-and-place
program that expresses a change in three degrees of
freedom from the position without changing theorientation. The process of the error recovery when the
failure occurred proceeds by planning the flow
including a change in the orientation of grasping,carrying with that change of posture, and packing. Thatis, the task is performed by a pick-and-place programthat expresses a change in six degrees of freedom of theposition and orientation.
It is undesirable to have many programs involved
even in a transfer task. This program represents one ofpick-and-place that expresses a change in six degrees of
freedom of the position and orientation. The change
from the original scenario can be considered as change
in three degrees of freedom of the position without achange in orientation.
Furthermore, it is desirable for the program to be
one that is usable in a wide area as well as in a local
domain. That is why calibration needs to be performed
in a wide area. Global calibration means that not only
the pick-and-place task of the original scenario but also
the processes of error recovery can be performed by thesame program. Reusability increases when as many
common programs as possible can be used for the total
manipulation tasks.

References
1. T. Hasegawa, T. Suehiro and K. Takase, A model-based
manipulation system with skill-based execution, IEEE
Trans. Robot. Autom., 8(5) (1992) 535-544.
2. A. Nakamura, T. Ogasawara, T. Suehiro and H. Tsukune,
Skill-based backprojection for fine motion planning, in
Proc. IEEE/RSJ Int. Conf. on Intell. Robots Syst., (Osaka,
Japan, 1996), 2, 526-533.
3. A. Nakamura, T. Ogasawara, K. Kitagaki and T. Suehiro,
Using robust and simplified geometric models in skillbased manipulation, in Proc. IEEE/RSJ Int. Conf. onIntell. Robots Syst., (Hawaii, USA, 2001), 1, 138-145.
4. B. R. Donald, Planning multi-step error detection andrecovery strategies, Int. J. Robot. Res., 9(1) (1990) 3-60.
5. P. Pastor, M. Kalakrishnan, S. Chitta, E. Theodorou and
S. Schaal, Skill learning and task outcome prediction for
manipulation, in Proc. IEEE Int. Conf. Robot. Autom.,(Shanghai, China, 2011), 3828-3834.
6. A. Nakamura and T. Kotoku, Systematization of error
recovery in skill-based manipulation, Artificial Life andRobotics, Springer, 14(2) (2009) 203-208.
7. A. Nakamura and Y. Kawai, Recovery technique fromclassified errors in skill-based manipulation, in Proc.
15th Int. Symp. on Artificial Life and Robotics (AROB
15th ’10), (Oita, Japan, 2010), 1010-1013.
8. A. Nakamura, K. Nagata, K. Harada, N. Yamanobe, T.
Tsuji, T. Foissotte and Y. Kawai, Error recovery using
task stratification and error classification for manipulation robots in various fields, in Proc IEEE/RSJ Int. Conf.
on Intell. Robots Syst., (Tokyo, Japan, 2013), 3535-3542.
9. A. Nakamura, K. Nagata, K. Harada and N. Yamanobe,
Error recovery using task stratification and error classification for manipulation robots in physical distribution,
Trans. Control and Mechanical Syst., 3 (2014) 122-131.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 16 -

Design of Sliding Mode Controller for Droplet Position in EWOD Microfluidic System
Arsit Boonyaprapasorn

Dept. of Mechanical Engineering, Chulachomklao Royal Military Academy,
Nakhon-Nayok, Thailand
Thunyaseth Sethaput

Dept. of Mechanical Engineering, Sirindhorn International Institute of Technology, Thammasat University,
Pathum Thani, Thailand
E-mail: urarl.urarl9@gmail.com, thunyaseth@siit.tu.ac.th
www.siit.tu.ac.th

Abstract
In microfluidic lab-on-chip devices, electrowetting on dielectric (EWOD) are widely used for various applications.
To manipulate the micro droplet to achieve the desired path and accurate target position by using electrowetting
technique are one of the common applications. In this paper, the motion of droplet is modeled as a single rigid
body driven by both linear and nonlinear forces. In order to evaluate the potential of controller, the sliding mode
controller is applied to this nonlinear microfluidic system. The effect of bounded disturbances is included in thedesigned controller. Simulation results provided the feasibility of the sliding mode controller for EWOD
microfluidic manipulation under the effect of bounded disturbances.
Keywords: Sliding mode control, Electro-wetting on Dielectric device, Lab-on-chip, Microfluidics.

[10] created a micro-cooling system for IC circuit cooling,in which EWOD required less pumping power than typicalElectro-wetting on Dielectric (EWOD) is a microfluidic mechanical pumps. In dealing with complicated analysis,transport method utilizing the disturbance behavior of diagnosis and detection processes, more complex arrayelectrical field on the free surface energy and wet contact typed EWOD devices have been created to support
angle of a droplet. As shown in Fig. 1, the contact angle sophisticated lab-on-chip platforms. Multiple droplet
and droplet shape can be changed by applying voltages on manipulation becomes essential and enquires more precise
one-side of a droplet, thus inducing the droplet to move. control algorithms. Bhattacharjee and Najjaran [11]The technique is well-known for its precise manipulation mentioned about the need of feedback control of a singleof droplet movement. Currently, many lab-on-chip devices, droplet position in a digital micro fluidic system (DMS)
equipped with EWOD micro-droplet transport are widely and presented simulation results of the feedback control
used for various applications such as in DNA sequencing system. Also, the work by Oprins et al. [12] proposed the[1], protein analysis and detection [2], Disease diagnosis [3, modeling and control of droplet motion of the electro6] molecular biology processes [4], detection of wetting system. Based on the proposed models presented
triglyceride in human fluid [5] and concentration detection in [11] and [12], various nonlinear control techniques canof L-amino acid [7]. With its immense capability to be applied to control droplet position on top of a EWOD
precisely transport tiny droplets with little power lab-on-chip platform.
consumption, EWOD technique has also been used in
micro pimps [8] and micro-conveyors [9]. Pamula et al.
© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan

1. Introduction- 17 -

A. Boonyaprapaasorn and T. Sethaputϴ 5 U 
Fd   d    a 2  2H 

ϴ*

Before V
Voltage
Appliccation

Durring Voltage
A
Application(3))

wheree d and H dennote the dropllet viscosity and heightrespectively.

After Voltagee
Application

Fig. 1. Dropleet movement byy altering contaact angle with aapplied
voltageo the designn of sliding modeThis papeer focuses on
tracking conntroller to conntrol the drop
plet position in the
EWOD micrrofluidic systtem under the disturbancee. The Fig. 2. The micro drooplet motion onn the EWOD plaate
control systeem is simulateed to evaluatee the ability oof thisAss presented in [11] and [12]], frictional foorce due to theecontrol technnique.
contaact line (Fc) caan be expresseed as(4))
Fc  U n  2  a 
2. EWOD M
Microfluidic System
S
wheree  is the coefficient of conntact-line fricction based onn
kinetiic-molecular theory.
tn is in the range fromm 0 to 2.

2.1. Equationn of Motion
In this studdy, the dynnamic respon
nse of dropplet isformulated aas a simplified one-dimenssional model basedon the propoosed by [11] anda [12]. As showns
in Fig. 2, thedroplet is mmodeled as a single rigid body movingg withvelocity U. T
The motion off droplet is drriven by the ddriving
force (Fdr) acctuated by eleectrostatic forcce and its threeshold(Fthresh).The details of thee threshold off electrostaticc force
can be deterrmined from [13-15]. Therre are 3 sourrces offrictional forrce exerting against the motion of ddroplet
including visscous dissipattion due to innternal fluid fr
friction(Fd), viscous drag due to movingmthroug
gh surroundingg fluid(Ff) and fricctional force due to the contact linee (Fc).
According to [11], the equation of droplet motiion infiguration can be represented
d by
EWOD confi

2.2. StateSSpace Reepresentationn
The equationeof mootion in (2) caan be presenteed in the formmof firrst order nonllinear differenntial equationn or the statee
equattion and the ou
utput equationn.

x1  x2.

(5))

y  x1  m1

wheree g x, t 2d x
m 2  Fdrr  Fthresh  Fd  Ff  Fcdt 

x 2  f x, t  g ( x, t ) u (t )  d (t )  m1 [  52Hx    a   x (2a)] .

f x, t 

d

2

2

n

2

(1) The state variableex andx repreesent displaceement, x andd12
of the veloccity of the micro
where m andd x are the massmand the displacementdmdroplett, x . Also, x   x1 x2 T iss
micro droplet, respectiveely. Assumin
ng 5% overllap ofdenotted as state veector. The coontrol input, u (t ) is Felect . A
adjacent eleectrodes [11],, the droplett radius, a caan be
L where L is the electrode width d ( x, t ) is boundeed disturbancee and d ( x, t )  M whereedefined as a = L/2 + 0.05L
odes. As menntioned
and l is the ggap between addjacent electroin [12], viscoous drag due to moving thhrough surrouunding M is a positive con
nstant. The ouutput variable is denoted byy
fluid (Ff) cann be neglected because airr is the surrouunding
onlinear smoot
oth function.
y . f ( x, t ) is a no
fluid. Thus, the motion off droplet in EWOD
E
microffluidic
d assystem can bbe mathematically expressed
3. Controller Dessign2d xsmode trackingtcontrroller was designed to trackk(2) The slidingm 2  Fdr  Fthresh  Fd  Fc
dt
two typestof desirred signals: 1 ) step signal 2) sinusoidall
As presennted in [14], the viscous dissipation ddue to signal. The step signal repressents the casee when microo
internal fluuid friction (Fd) for thetopen E
EWOD droplet moves fromm the currentt electrode too the adjacentt
configurationn can be descrribed as
usoidal signal was used to test
t the abilityy
electrrode. The sinu
© The 22015 Internatiional Conference on Artificcial Life and Robotics(ICAR
R
ROB 2015), Jaan. 10-12, Oita, Japan- 18 -

Sliding Mode Controller EWOD
D

of the controoller to track thhe periodic motion
mof the ddroplet
between two electrode plattes.
de controller ddesign
Accordinng to (5), the sliding modwith the seleected reachinng law as in [16] which ccan beshown as folllows. First, thhe switching su
urface is definned as

4.

Simulation
S
Reesults and Diiscussions

Intereesting points for tracking control prob
blems in thiss
researrch consists ofo two types oof reference signals whichh
are: 1) step function and 22) sinusoidal signal. Theemagn
nitude of the step function aand sinusoidal function canns  e ce(6) be deefined by the distancedbetw
ween centers of the first anddthe seecond electrod
de plate.
where c  0 . e and e are denoteddas traacking error aand its
derivative. Seecond, the Lyyapunov functiion is defined as
4.1. Simulation
S
Ex
xample1 2s2

eof a EWOD
E
microofluidic system
m is controlledd(7) The example
by the designed coontroller in secction 3 as sho
own in Fig. 3..
The
simulations
s
o
f
the
controlsystemforbothbcases aree
From (6) andd (7), the derivvative of swittching surfacee s
carrieed out in MAT
TLAB prograam. The integrration methodd




..



.


s  e c e  ( x r  x)  c( x r  x)  ( x r  x2 )  c( x r  x1 )is Ru
unge-Kutta with sampling time of 0.00
0001 seconds..
The waterw
droplet is
i used in this simulation, anda the systemms  ( x r  ( f ( x, t )  g ( x, t )u (t )  d (t ) ))  c ( x r  x 2 ) (8) is opeerated at 20 C. The systemm parameters are presentedd
where xr is thhe reference signalsor desirred displacem
ment of as folllows.
V 

the droplet. The exponenntial rate reaaching law [116] orconstant pluus proportionnal rate reacching law [117] isselected1600 10 m,
70 10 m,1.01 10 Pa. s,
0.08 Ns⁄m ,1000 kg⁄m ,0.0013
3m
8.8
8
10 m,
2.5681 110 kg,
2,
and2000 10 sin(100t). Fthresh  2.654
41 10 5 Ns   ks   sgn(( s )(9)The valuesvof  ,  d , and C aree from [11] an
nd [12]. L andd
E(8) anda (9), the ccontrol
where   0 aand k  0 . Equating
l are fromf[18]. Assuming contacct angel equall to   110 ,input is deterrmined as show
wn in (11).
H is determineddfroom L based onn [14]. is seelected at the ks   sgn( s )  [ x r  ( f ( x, t )  g ( x, t )u (t )  d (t ) )]tempe
eratureequal
to
20 C. is callculated from the(10)density, , and vollume of the drroplet defined by and .
 c ( x r  x2 )According to [15], the value of Fthresh is the vaalue of
1capilllary force and is approximaated based on thet value ofu (t ) [ks   sgn( s)  ( xr  f ( x, t )  d (t )g ( x, t )(11) the coontact angle,   110 , the eelectrode widtth L, and thesurfacce tension between water annd air   72m
mN / m [14],c( x  x ))]r2

he hysteresis anglea  7  [[14].
Based on the control inputiin (11), the stability of the and th
m can be verified as follow
ws
control systemV  ss
(12)



1
V  s{x r  ( f ( x, t )  g ( x, t )[ks   sgn( s )  ( x r  f ( x, t )g ( x, t ) d (t )  c( xr  x2 ))]  d (t )  c( xr  x2 ) )}
}


(13)

Fig. 3.The diagram of the feedback control system.

(14)
V  s ( d  kks   sgn( s ))   sd  ks   s sgn( s ))If  is seleccted such that   M , thenn the derivattive of 4.2. Step
S Reference Signal
Lyapunov fuunction satisfiees the followinng condition.
The control EWO
OD microfluuidic system has a stepp
2functiion
asreference signal pressented as(15)
V   ssd  ks   s  0
1670  106 , t  0
he designed ccontrol
Accordingg to (15), It is clear that th(19))
x1rr (t )  ,t  0input is able to output siggnal to track thet reference signal
0under disturbbance.
with thet initial condition x (0)  [[0 0]T .
(20))
2©The 2015 Internatioonal Conferen
nce on Artificiial Life and Ro
obotics (ICAR
ROB 2015), Jaan. 10-12, Oita, Japan- 19 -

A. Boonyaprapasorn and T. Sethaput

The control parameters are selected as follows, c=100,
k=90and ε=3.The simulation results of droplet position andvelocity are shown in Figs. 4(a) and 4(b). Position tracking
error of the droplet is shown in Fig. 4(c). The control input
signal is shown in Fig 4(d). Therefore, it is clear that thesliding mode controller is feasible to track the stepreference signal accurately under the effect from thedisturbance.
-3

x 10
2

1.5

x1(t)

x1r
x1
1

0.5

0
0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.25

0.3

0.35

0.4

t(sec)

(a)
0.08

x2(t)

0.06
0.04
0.02
0
0

0.05

0.1

0.15

0.2

t(sec)(b)4.3. Sinusoidal Reference Signal
The sinusoidal function is the reference signal of thecontrol EWOD microfluidic system expressed as(21)
x1r (t )  1670 106 sin(t )where the frequency  = 22 rad / swith the initial condition x(0)  [0 0]T .

(22)

The control parameters, c, k and ε are selected as thesame values in the previous case. The simulation results ofdroplet position and velocity as well as position tracking
error of the droplet are shown in Fig. 5 (a), (b), and (c),respectively. The control input signal is shown in Fig. 5 (d).
The controller with selected parameters is able to track thesinusoidal reference signal, so that the droplet can movealong desire path as shown in Fig 5(a). The plot of velocity
versus time is shown in Fig 5(b). At certain range of timefor example t  0.091 s., the plot in figure 4 (b) is similar
to the first derivative of the displacement in figure 4 (a) as
considering from the amplitude and the phase difference ofthe plots from both figures. Thus, this plot is reasonable.
The plot of position tracking error versus time in Fig 5(c)shows that the sliding mode control provides low position
tracking error. Thus, the sliding mode controller is feasible
to track the sinusoidal reference signal accurately eventhough the system is affected by the bounded disturbance.
Therefore, the sliding mode controller is an appropriatetechnique for the position control of the micro droplet in
the EWOD microfluidic system.

-4

x 10
20

-3

2

x 10

x1r
x1

1

10

x1(t)

e(t)

15

5

0

0
-1
-5
0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

t(sec)

-2
0

(c)

0.05

0.1

0.15

8

0.25

0.3

0.35

0.4

0.25

0.3

0.35

0.4

(a)

x 10

7

0.15

6

0.1

5

0.05

4

x2(t)

u(t)

0.2

t(sec)

-5

3

0
-0.05

2

-0.1

1
0

-0.15

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

t(sec)

-0.2
0

(d)

0.05

0.1

0.15

0.2

t(sec)(b)

Fig. 4.Simulation results of tracking step reference signal© The 2015 International Conference on Artificial Life and Robotics(ICAROB 2015), Jan. 10-12, Oita, Japan- 20 -

Sliding Mode Controller EWOD
[5]

-4

6

x 10

5

e(t)

4
3
2
1
0
-1
0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.25

0.3

0.35

0.4

t(sec)

(c)
-5

10

x 10

8

u(t)

6
4
2
0
-2
0

0.05

0.1

0.15

0.2

t(sec)(d)Fig. 5.Simulation results of tracking sinusoidal reference signal

5. Conclusions
The sliding mode control with exponential rate reachinglaw is feasible to control the microfluidic droplet motion in
the EWOD microfluidic system under the effect ofbounded disturbance signal. For the step reference signal,
the controller provides the step response with no overshootand tracks the reference signal accurately under the
disturbance. In the case of the sinusoidal reference signal,
the designed sliding mode controller can also provide
accurate tracking.
6. References
[1]
[2]

[3]

[4]

K. Brady, “Electrowetting for DNA sequencing on chip”, 2004
NNIN REU Research Accomplishments, pp. 26-27, 2004.
V. Srinivasan, V.K. Pamula, P. Paik, and R.B. Fair, “Protein
stamping for MALDI mass spectrometry using an electrowettingbased microfluidic platform”, in Proc. of SPIE-The International
Society for Optical Engineering, vol. 5591, 2004, pp. 26-32.
V. Srinivasan, V.K. Pamula, M.G. Pollack, and R.B. Fair, “Clinicaldiagnostics on human whole blood, plasma, serum, urine, saliva,
sweat, and tears on a digital microfluidic platform”, in Proc. the 7thInternational Conference on Miniaturized Chemical andBiochemical Analysis Systems, Squaw Valley, California, U.S.A.,
2003, pp1287-1290.
M.G. Pollack, P.Y. Paik,, A.D. Shenderov, V.K. Pamula, F.S.
Dietrich, and R.B. Fair, “Investigation of electrowetting-based
microfluidics for real-time PCR applications”, in Proc. the 7thInternational Conference on Miniaturized Chemical andBiochemical Analysis Systems, Squaw Valley, California, U.S.A.,
2003, pp.619-622.

C.S. Yu, M.Y. Lin, H.T. Hu, Y.C. Hu, and H.Y. Chou, “A dropletbased device for triglycerides colorimetric measurement”, Tamkang
Journal of Science and Engineering, vol. 8, no. 3, pp. 211-216,
2005.
[6] V. Srinivasan, V. Pamula, M. Pollack, and R. Fair, “A digital
microfluidic biosensor for multianalyte detection”, in Proc. of IEEE
The Sixteenth Annual International Conference on Micro Electro
Mechanical Machine (MEM-03Kyoto), Kyoto, Japan, 2003, pp.
327-330.
[7] W. Satoh, H. Hosono, K. Morimoto and H. Suzuki, “Micro analysis
system with an integrated microfluidic system based onelectrowetting”, in Proc. of the fourth IEEE Conference on
Sensors( IEEE SENSORS 2005), Irvine, CA, U.S.A., Oct. 30-Nov. 3,
2005, pp. 37-40.
[8] K.S. Yun, I.J. Cho, J.U. Bu, G.H. Kim, Y.S. Jeon, C.J.(CJ) Kim,
and E. Yoon, “A micropump driven by continuous electrowetting
actuation for low voltage and low power operations”,in Proc. of the
14th IEEE international conference on Micro Electro Mechanical
Systems (MEMS 2001), Interlaken, Switzerland, pp. 487-490.
[9] I. Moon, and J. Kim “Using EWOD (electrowetting-on-dielectric)
actuation in a micro conveyor system”, Sensor and Actuators A:
Physical, vol. 130-131, pp. 537-544, Aug. 2006.
[10] V. K. Pamula and K. Chakrabarty, “Cooling of integrated circuits
using droplet-based microfluidics”, in Proc. of the 13 th ACM
Great Lakes Symposium on VLSI (GLSVLSI’03), Washington, DC,
U.S.A., April 28-29, 2003, pp.1-4.
[11] B. Bhattacharjee, and H. Najjaran, “Simulation of droplet position
control in digital microfluidic systems”, Journal of Dynamic
Systems, Measurement, and Control, vol. 132, iss. 1, pp. 014501-1 014501-3, Jan. 2010.
[12] H. Oprins, B. Vandevelde, and M. Baelmans, “Modeling andcontrol of electrowetting induced droplet motion”, Micromachines,(3)1, pp. 150-167, 2012.
[13] H. Ren, R.B., Fair, M.g., Pollack, and E.J. Shaughnessy, “Dynamics
of electrod-wetting droplet transport ”, Sensor and Actuators B 87
(2002), pp.201-206.
[14] J. Berthier, Microdrops and digital Microfluidics, Norwich New
York:William Andrew, 2008.
[15] J. Berthier, P. Dubois, P. Clementz, P. Claustre, C. Peponet, Y.
Fouillet, “Actuation potential and capillary forces in electrowetting
based microsystem”, Sensor and Actuators A 134 (2007) 471-479.
[16] J. Liu, and X. Wang, “Advanced sliding mode control formechanical systems” Beijing, Tsinghua University Press and Berlin
Heidelberg, Springer-Verlag, 2011[17] W. Gao and J.C. Hung,”Variable Structure Control of Nonlinear
Systems: A New Approach”, IEEE Transasction on Industrial
Electronics, Vol. 40, No.1 February, 1993, pp. 45-55.
[18] C. Phongsomboon, “Mobility control of a droplet on top of a flat
plate”, M.S. thesis, Dept. Mechanical Engineering, King Mongkut
University of Technology Thonburi,Bangkok, Thailand, 2012. (inThai)[19] R. W. Fox, P. J., Pritchard, and A. T., Macdonald, “Introduction to
fluid mechanics”, 7thedition, John Wiley and Sons, 2010.

©The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 21 -

Fast motion detection based on cross correlation
PancaMudjirahardjo, JooKooi Tan, Hyoungseop Kim, Seiji Ishikawa

Dept. of Control Engineering, Kyushu Institute of Technology,
1-1 Sensui-cho, Tobata-ku, Kitakyushu-shi, Fukuoka, 804-8550, Japan
E-mail: {panca,etheltan,ishikawa}@ss10.cntl.kyutech.ac.jp, kim@cntl.kyutech.ac.jp
tel: 093-884-3191

Abstract
We present a method of fast motion detection as an abnormal motion based on cross correlation. Since the camera
view is not in perpendicular with motion direction, the velocity of motion is not uniform spatially. Instead of object
detection directly, we separate an image into several blocks. We calculate the cross correlation of the pixel intensity
series in these blocks between a current and a previous frame. The maximum correlation is achieved at certain
delay. This delay shows a shift of a similar pattern between the current and the previous frame. To localize anabnormal motion, we employ a hierarchical block size. The performance of the proposed method is experimentallyshown.
Keywords: Abnormal motion, cross correlation, delay, hierarchical block size.

Before the detection phase, they included a training
phase for normal condition. Baranwal et al. [3] detected
an abnormal indoor motion in a static backgroundenvironment. They trained various motions using radialbasis functions networks (RFBN). Park et al. [4] usedclustering of motion based on similarity measurement ofa feature space. They detected an abnormal motion,
especially in a different direction case, from highcamera view.
In this paper, we propose a fast motion detection
with a camera view not in perpendicular with motion
direction, as an abnormal motion among walking
motion. We capture a scene from a 2 meter height andmore for outdoor scenes, as shown in Fig. 1. Due tocamera view not in perpendicular with motion direction,
motion velocityin the image is not uniform spatially.
We need to extend the method in [5,6].
It requires no foreground segmentation, no motion
recognition and no object detection. We analyze theobject’s velocity through a certain delay where themaximum correlation occurred.

1. Introduction
In recent years, abnormal motion detection has
attracted great research attention in computer vision.
Most current surveillance systems only provide reactive
security by enabling the analysis of events after the
event has already occurred — what is really needed by
the security community is proactive security to help
prevent future attacks.
Many approaches on video event analysis are basedon the object trajectories extracted from video.
Abnormal events can be detected through a priorlearning of normal events or, without a learning process,by analyzing the trajectory result directly.
Jiang et al. [1] used spatial and temporal context andperformed frequency-based analysis to detect
anomalous video events. The normal observation ismodeled by hidden Markov model (HMM). This
research detected the anomalous car trajectory on theroad from top view. Kiryati et al. [2] recognized anabnormal human behavior from high camera view.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 22 -

Panca Mudjirahardjo, Joo Kooi Tan,T Hyoungseop
p Kim, Seiji Ishikaawa

dicating maximum correlaation and 0 indicating noo
indcoorrelation. A highhnegative correlation inndicates a highhcoorrelation but ofo the inverse of one of the series.
We describee the proposeed method fo
or fast motionn
deetection based on cross corrrelation as dep
picted in Fig.22.
StaartInpuut an
inittial
Input a ssuccessive
frarame
Convert thhe original
image intoo grayscale
Separate fr
frames into
m×n bblocks
Calculatte rxy anddifferencce din thesame bllocks in
between a ccurrent anda previouus frame.

Fig. 1.Scenes for peerformed experiiment

2. Overview
w of the Proposed MethoddA fast mootion detectionn needssome velocity data.. In
this paper, we provide velocity daata from crrosscorrelation oof pixel inteensity series in the bloccks
between a cuurrent and a prrevious framee. The maximu
mumcorrelation iss achieved att a certain deelay. This deelay
shows a shifft of a similarr pattern betw
ween the currrent
and the previoous frame.
Cross corrrelation is a standardsmeth
hod of estimati
ting
the degree too which two series of dataa are correlatted.
Consider twoo series x(i) and y(i) wheere i=0,1,2...NN-1.
The cross corrrelation rxy at delay d is deffined as [7],Determiine a fast
among themotion am
bloocks
N

Ennd
Y

Ennd
Fig.
F 2 Overview
w of the proposeed
meth
thod.

3. Methodrxy  x(i)  x  y(i  d )  y i

 x(i)  x    y(i  d )  y 
2i

In this section,, we describee the proposeed method inn
deetail.

((1)

2

i

3.1
1. Preprocesssing

Firrst, we need tot convert thee original succcessive framesswhere x andd y are the means of thee correspondi
ding
int
to
grayscale
images.
i
Subbsequently, wee separate theeseries.
immages into m×nmblocks. The calculattion of crosss
ngth of the crross
The range of delay d annd thus the lencoorrelation in th
he same blocck between a current and acorrelation seeries can be leess than N, forr example, to ttest
correlation att short delays only. The den
nominator in theprevious framee gives the in
information ofo shift of ann
expression abbove serves to normalize the correlattionobbject in this blo
ock.
coefficients sso that -1 ≤ rxy ≤ 1 holds; the bounnds© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 23 -

Fast motion detection based on cross correlation

3.2. Calculation of a shift magnitude based oncross correlation.
As was mentioned in section 2, cross correlation is astandard method of estimating the degree to which twoseries are correlated [7]. This calculation is one
dimensional computation.
As a simple example, consider two rectangularpulses shown in Fig. 3, in blue and green. The
correlation series is shown in red.

(b)

(a)

(c)
(d)
Fig. 4. Arrangement of pixel data to provide one
dimensional data series.
(a) hor_1 (b) hor_2 (c) ver_1 (d) ver_2

for (i = 0 : mn)hor_1[i] = pixel[i/m][i-(i/m)m];
ver_1[i] = pixel[imodn][i/n];hor_2[i] = pixel[i/m][(i/m+1)m-1-i];
ver_2[i] = pixel[(i/n+1)n-1-i][i/m];
end for;Here m and n are weight and height of an image
block, respectively. mod is a modulo operation.
Pixel[a][b] is a pixel intensity at ath row and bth columnof the image block.
Then we calculate the cross correlation between
current and previous image frame at the same image
block. The maximum correlation is achieved at a certain
delay. For example, the maximum correlation between
hor_1(t) and hor_1(t-1) occurs at delayhor_1.: We will getother delays, i.e. delayhor_2, delayver_1, delayver_2.

Fig.3. Example of cross correlation

The maximum correlation is achieved at a delay of 3.
Considering the equations above, what is happening isthat the second series is being slid past the first, at eachshift the sum of the product of the newly lined up terms
in the series is computed. This sum will be large when
the shift (delay) is such that similar structure lines up.
Delay of the maximum correlation can be assumed
as a shift of an object. This calculation of shift based oncross correlation is simpler than 2D correlation, because
2D correlation needs a template, then slides it over a
specified range to get the maximum value. This
calculation is also simpler than a method proposed in[6]. It is more accurate than optical flow based on
Lucas-Kanade tracker which relies on feature points.
To provide one dimensional data series, we scan animage pixel as in Fig. 4.
To provide one dimensional data series as shown in
Fig. 4, we perform the below algorithm, in which all thevariables are in an integer type:delayhor _   arg maxrhor _  (t ),hor _  (t 1) (2a)delayver _   arg maxrver _  (t ),ver _  (t 1) 

(2b)

d

d

Where {1,2}. Then the shift magnitude at a current
image block is calculated as,22
shift   delay hor_   delay ver _ (3)

Let us define the identity of |shift(i,j)|, I(i,j), as below,

1
I (i , j )  
0if | shift(i, j ) | 0otherwise(4)Here, i and j are block’s position at i-th row and j-thcolumn, respectively.
© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 24 -

Panca Mudjirahardjo, Joo Kooi Tan, Hyoungseop Kim, Seiji Ishikawa

 f ( j) r ( j) aavg _ shift j  0

3.3. Determining a fast motion
To detect a fast motion in a frame, we calculate themaximum of shift magnitude among the blocks within aframe. We do this search in hierarchical block sizes, as
depicted in Fig. 5. First, block size mn pixels, we willdetect a fast motion at block_fast1. Second, block sizemh pixels, we will detect a fast motion at block_fast2.
Finally, block size 2mh pixels, we will detect a fast
motion at block_fast3, where h is image height.
block(0,0)

mnif f ( j )  0(6c)otherwise

Thenblock_fast2 is defined as,

block _ fast 2  maxavg _ shift j 
We get block_fast2 position at c2-th column.

block(0,c-1)

Operation in 2m×h block size level. For each column,
we have an average of shift magnitude, avg_shiftk,
where k{0,…,c-2}.

hk 1 r 1

(a)

(7)

f (k )   shift (i, j )(8a)j k i 0

k 1 r 1

block(r-1,c-1)

rb (k )   I (i , j )(8b)

j  k i 0

 f (k ) r (k ) b
avg _ shift k  
 0



(c)
(b)
Fig. 5.Hierarchical block size.
(a) m×n (b) m×h (c) 2m×h

(5)Finally, we will detect a fast motion at cF column, ifc1 =c2 = cF or c1 = c3 = cF or c2 = c3 = cF or c1 = c2 = c3 = cF.
4. Experimental Result

The experimental environment is as follows:
Operating system is Windows 7 ultimate; the processor
is Intel® core™ i7 CPU 870 @2.93GHz and the usedsoftware is Microsoft Visual Studio 2010.
For experiment, we use outdoor scene, as Fig. 1,
with many people do normal motion (walking) and aperson does abnormal motion (running), with video
frame rate and the size of a frame 30 fps and 320x180
pixels, respectively.

(6a)

i 0

r 1

ra ( j )   I (i , j )(9)We get block_fast3 position at c3-th column.

Operation in m×h block size level. For each column, we
have an average of shift magnitude, avg_shiftj, wherej{0,…,c-1}.
r 1otherwiseblock _ fast3  maxavg _ shiftk 

We record block_fast1 position at r1-th row and c1-th
column.

f ( j )   shift (i, j )(8c)Thenblock_fast3 is defined as,

Operation in m×n block size level. For each block, wehave a shift magnitude, |shift(i,j)| defined by Eq.
(3),wherei{0,…,r-1}andj{0,…,c-1}.
Thenablock_fast1 is defined by,block _ fast1  max shift (i, j ) 

if f ( k )  0

(6b)

i 0

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 25 -

Fast motion deetection based onn cross correlation

We set m and n are 20
2 pixels, so we separate animage frame into 16x9 bloocks. For delaay, d, we set 10.
The experimeental results arre shown in Fig.
F 6.
Table 1shhows the evaluation of perfformance, whhere
TPR is true positive rate.
TPR 

TP
T
TP

 100%TP  FN GT

or three detecteed blocks whiich contain faast motion areeov
verlapping.
As future wo
ork, we are gooing to conducct experimentssonn the recognittion of abnorm
mal motion underustrongerroccclusion.

( 10)

Reeferences[1]] F. Jiang, J. Yuan, S.A. Tsaftaris, A.K
K. Katsaggeloo,“Anomalouss video event ddetection using spatiotemporaal
context”.Com
mputer Vision aand Image Und
derstanding 1155,
pp. 323-333,, 2011.
[2]] N. Kiryati, T.R.
T Raviv, Y. IIvanchenko, S. Rochel, “Realtime abnorrmal motion detection inn surveillancee
video”.Proceeedings of ICPR
R. 2008.
[3]] M. Baranwaal, M.T. Khan,n, C.W. De Sillva, “Abnormaal
motion detection in real ttime using vid
deo surveillancee
and body sensors”. Internaational Journall of InformationnAcquisition.Vol.
V 8, No. 2, ppp. 103-116, 2011.
[4]] M. Park, J. K.
K Tan, Y. Nakkashima, H. Kim, S. Ishikawaa,“Detecting humanhflows oon a road different from mainn
flows”. Proceedings of ARO
OB. 2011.
[5]] P. Mudjirah
hardjo, J.K. T
Tan, H. Kim,, S. Ishikawaa,“Abnormal motion detectiion in an occlusive environnE Annual Confeerence 2013, ppp.
ment”. Proceeeding of SICE
1398-1402 . 2013.
[6]] P. Mudjirahardjo, J.K. Tann, H. Kim, S. Ishikawa, “Fasst
namic backgrou
und”.Proceedingg
motion detection in a dynaof Internatio
onal Symposiumum AROB 19thh2014, pp. 896900, 2014.
[7]] D. Lyon. “T
The discrete fouurier transformm, part 6: crosscorrelation”, Journal of Objject Technology, Vol. 9, No. 2.
2
Pp. 17-22, 2010.

Here :d as fast motioon.
TP : true posiitive, fast mottion is detected
FN : false neegative, fast motionmis dettected as normmalmotion.
GT : ground ttruth.
Table 1. Evaluuation of perform
mance
scene
TP
PR (%)
Execution time (ms)1
88.7
228.752
91.5

5. Conclusioon
In this paaper, we proppose a method
d of fast motition
detection in a crowd, with cameraa view not in
perpendicularr with motionn direction. In
nstead of objject
detection andd to avoid foregroundf
seegmentation, we
calculate the cross correlaation between the current aandthe previous fframe in the same image block.
To detect a fast motion, we employ hiierarchical bloock
sizes. There aare three leveels of hierarchhical block sizzes.
The location of the fast motionmis a collumn where ttwo

Fig.6. Perform
mance of fast mootion detection from various sccenes. A detected person with fast motion isindiicated by red lin
nes.

© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 26 -

Detecting moving objects on a video having a dynamic background
FX Arinto Setyawan, Joo Kooi Tan, Hyoungseop Kim, Seiji Ishikawa

Dept. of Control Engineering, Kyushu Institute of Technology,
1-1 Sensui-cho, Tobata-ku, Kitakyushu-shi, Fukuoka, 804-8550, Japan
E-mail: {arinto,etheltan,ishikawa}@ss10.cntl.kyutech.ac.jp, kim@cntl.kyutech.ac.jp
tel: 093-884-3191

Abstract
This paper proposes a method of detecting moving objects in a video having a dynamic background using a method
which infers the background sequentially. The proposed method performs the update of the pixel values in the
background which are influenced by the value of the current pixel. The aim is to cope with changes in the value ofthe pixels in the background caused by the movement of the background objects such as the leaves swaying on trees,
the water droplets of the rain or the change in light intensity according to the time lapse. The performance of the
proposed method is shown experimentally using the video taken on a rainy and windy day.
Keywords: Object detection, foreground detection, background inference, Gaussian distribution.

done, and, without human intervention, object detection
and object tracking are done using computer [2]. The
rapid development of computer, especially in terms ofprocessing speed and a large amount of memory, allows
the implementation of a fully autonomous surveillancesystem.
Detection of moving objects is one of the importanttasks in many computer vision applications including avideo surveillance system. A moving object detection
system is a system that detects moving objects in avideo taken with the use of surveillance cameras. A
general approach used in the detection of moving
objects is background subtraction [3,4,5]. The idea ofthe background subtraction is to compare the image
scene at the current time with a reference of the
background image model. Usually a reference of the
background image model is the first image frame of thevideo and is updated all the time.
The other application of the detection of moving
objects is for a traffic control system, application in the

1. Introduction

The increasing number of crimes and accidents that
occur nowadays requires a reliable video surveillancesystem. The surveillance system can be realized byinstalling cameras in places vulnerable to crime andaccidents. There are 3 types of video surveillance
activities, i.e. manual (conventional), semi-autonomousand fully autonomous [1].
The conventional video surveillance systems canrecord what they see, but cannot find what is seen. In
conventional surveillance systems, the task of video
surveillance review was performed by trained security
personnel. The increase of surveillance video data
makes security officer jobs increasingly heavy. Solution
by adding more security personnel is an option thatspends much cost. A better solution is to replace a
conventional video surveillance system by a fullyautonomous system.
On the fully-autonomous system, the input is avideo sequence taken in the spot where surveillance is© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 27 -

F.X. A. Setyawan, J. K. Tan, H. Kim, S. Ishikawa
1  f  

field of sports, hospital patient monitoring, monitoringof students in a class, and others. Aslani and Nasab [6]performed a research on the application of detecting
moving objects for a traffic monitoring system.
Application on detecting moving objects in the field ofsports was done by Manikandan and Ramakrishnan [5].
Much research on the detection of moving objects
using a static camera has been done using the methods
other than the background subtraction. Stauffer andGrimson [7] use Gausian Mixture Model to addresschanges in the background such as changes in lightintensity, slow-moving objects and the effects ofmoving elements in a scene. Aslani and Nasab [6]employ optical flow to detect and track a moving object.
Keerthana, Ravichandran and Santhi [8] use FuzzyExtreme Learning Machine for detecting a moving
object. Zhou, Yang and Yu [9] detect a moving object
by a method of Detecting Contiguous Outliers in the
Low-Rank Representation (DECOLOR).
In this paper, we propose a method of detecting amoving object in a video having a dynamic background.
Pixel values on the background at all times are updated
to get the model background. The updated value
depends on how often the pixels are recognized as
background or foreground. After moving object is
detected, morphological operations are performed on
the image frame in order to get better detection results.
This method was tested on videos taken during a rainy
and windy day in order to get a video that has a dynamic
background.

NT ( f ;  ,  ) 

2

 

1
e 2  2 Here f is the value of the pixel intensity at pixel (x,y),and µ and σ are the mean and the variance of the pixel
intensity. The value of the initial mean is the value ofthe intensity of the pixel in image T(=0) and the value ofthe initial variance is determined as 1 in the experiment.
The next step is the judgment if each pixel of thecurrent image is in the background or on the foreground.
This judgment is done by subtracting intensity of thecurrent image pixel by the mean of previous image pixel.
For the judgment, the following equation is employed;f T 1   T



 Th

(3)

Here fT+1 is the value of the pixel intensity in acurrent image frame and µT is the mean value of the
background pixel model. Th is a threshold. A pixel isregarded as the pixels in the background, if it satisfies
Eq.(3): Otherwise it is regarded as the pixel on theforeground or on a moving object.

2. Method
The flowchart on the method used in detecting
moving objects in a video having a dynamic backgroundis shown in Fig. 1. The first step of the proposed
method is to convert an image at time T (abbr., image T)
from RGB to gray level. The purpose of this conversion
is to reduce the computational load. Standard formula toconvert RGB to grayscale is given as follows;I  0.2989 R  0.5870G  0.1140 B .

(2)

(1)

The second step is to determine the normal
distribution model, NT(f; , σ)(x,y), of a pixel at (x,y) in
Fig. 1. Flowchart of the system.
the image frame. The normal distribution is formed
using the following equation;© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 28 -

Detecting moving objects on  c  N ( f T 1 ;  T , T 2 )After obtaining a set of pixels which represents amoving object, then a morphological operation is
performed to those pixels on the image. The
morphological operation used in the proposed method is
the opening operation. The opening operation is acombination of erosion and dilation operations. They
are performed in sequence, i.e., erosion is done to theoriginal image and then dilation is applied to the result.
The opening operation of an image f by a structuring
element s is defined using the following equation:g ( x, y )  ( f ( x, y )s)  s

An example of the result of the judgment followed by
the morphological operation is shown in Fig. 2.
The next step is to update the normal distribution ateach pixel in the background. The purpose of thisupdate is to overcome the disturbance that occurs in the
background caused by the change in light intensity,
swaying leaves of trees, and slow-moving objects, etc.
The mean and the variance of the normal distribution
are updated using the following equations;

3. Experimental Results
For experiment, we use 2 video scenes. The video
frame rate and the size of an image are 30 fps and320  240 pixels, respectively. The experimental
environment is as follows: The operating system is
Windows 7 Enterprise, the processor is Intel® core™ 2
Duo E7500, 4GB RAM, and the used software is MS
Visual Studio 2008. The result of moving object
detection on video 1 is shown in Fig. 3.
The effectiveness of the proposed method is
evaluated by comparing the results with the ground truth,
as shown in Fig. 4. In the resultant image of comparison
between the result of the proposed method and theground truth, the red areas are true positive (TP) that is
an overlap part between the ground truth and detection
results. Blue means the part which is included in the
ground truth but not in the detection result and this partis False Negative (FN); Green means the part which isincluded in the detection result but not in the Ground
Truth and this part is False Positive (FP).
The sensitivity of the proposed method is expressedusing the popular parameters, Recall (R) and Precision(P), whereas the accuracy of the method is calculatedusing the F measure. They are defined by the following
formula;(5)   ( f T 1( x , y )   T 1 ( x , y ) ) 2  (1   )   T ( x , y ) 2

if f T 1 ( x, y )  background2
 T 1 ( x , y )  2(
1)( f T 1 ( x , y )   T 1 ( x , y ) ) 2     T ( x , y )





if f T 1 ( x , y )  foreground


(6)

(a)

(8)

Here  is a variable learning rate. Constant c is definedso that the maximum value of  is 1. CT+1 is the numberof successive frames where the pixel p(x,y) has been
judged as a foreground pixel, and k is a constant. Eachpixel is updated with different values depending on how
often pixels are judged as foreground or background.
These updated values affect the next judgment on the
background or the foreground.

(4)   f T 1 ( x , y )  (1   )   T ( x , y )
if f T 1 ( x , y )  background T 1 ( x , y )  (1)  f T 1 ( x , y )     T ( x , y )
if f T 1 ( x , y )  foreground1
2
1  k  CT 1

(7)

(b)

Fig. 2. Example of the performance: (a) Original image,(b) the moving object detection followed by themorphological operation.

R

N TP
N TP  N FN

(9)

P

N TP
N TP  N FP

(10)© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 29 -

F.X. A. Setyawan, J. K. Tan, H. Kim, S. Ishikawa

F 2Recall  Precision
Recall  Precision

Table 1. Evaluation of the method.

(11)

Evaluation values
Video

Here NTP is the number of pixels in the true positivearea; and NFP is the number of pixels in the false
positive area; NFN is the number of pixels in the false
negative area. The result on the evaluation of the
proposed method is given in Table 1.

R(Recall)P(Precision)F(accuracy)

Video 1

70.41

97.63

81.76

Video 2

67.84

86.66

75.97

(a)
(a)

(b)
(b)

(c)

Fig. 5. Elimination of raindrops: (a) Original image
containing raindrops, (b) the result by the proposed
method, (c) the result by the background subtraction.
The raindrops are marked in red.

(c)

4. Discussion and Conclusion

Fig. 3. The result of the moving object detection invideo 1: (a) Original frames, frame 130 (the left) andframe 140 (the right), (b) the result of detection by the
proposed method, (c) the result of detection using thebackground subtraction.

The method used to detect a moving object on avideo having a dynamic background is a background
sequential inference. This method uses updating of the
pixel values of the background based on how often the
pixel is recognized as a background or foreground. The
updating value of each pixel on a background isdifferent with every pixel on the background. On theother hand, the threshold value, appearing in Eq.(3), to
determine whether a pixel is in the background or on theforeground should probably be different with every
region on the given image. But it is actually given an
identical value such as 1 (equivalent to  ) in theexperiment and it gives satisfactory results.
The effectiveness of the proposed method was
calculated using the parameters defined by Eqs.(9), (10)and (11). The method is considered effective, if thevalues of sensitivity, i.e., R and P, are greater than 50%.
The used method achieved a high level of the precision

(a)

(b)

Fig. 4. Evaluation on the result shown in Fig. 3b: (a)Display of the TP (red), FN (blue) and FP (green), (b)the ground truth images.
© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 30 -

Detecting moving objects on

International Journal of Soft Computing and Engineering,2(3), (2012), pp. 44-48.
[3] T. Bouwmans, Recent advance on statistical backgroundmodeling for foreground detection: A systematic survey,
RCPS, 4(3), (2011), pp. 147-176.
[4] C. Guyon, T. Bouwmans, E. Zahzah, Foreground detection
based on low-rank and block-sparse matrix decomposition,
Proc. IEEE International Conference on Image
Processing, (2012), pp. 1225-1228.
[5] R. Manikandan, R. Ramakrishnan, Human object detection
and tracking using background subtraction for sports
applications, International Journal of Advanced Researchin Computer and Communication Engineering, 2(10),(2013), pp. 4077-4080.
[6] S. Aslani, H. M. Nasab, Optical flow based moving object
detection and tracking for traffic surveillance,
International Journal of Electrical, Robotics, Electronics
and Communications Engineering, 7(9), (2013), pp. 774777.
[7] C. Stauffer and W. E. L. Grimson, Adaptive backgroundmixture models for real time tracking, Conference on
Computer Vision and Pattern Recognition, Vol. 2, (1995),
pp. 246-252.
[8] N. Keerthana, K. S. Ravichandran, B. Santhi, Detecting themoving object in dynamic backgrounds by using fuzzyextreme learning machine, International Journal ofEngineering and Technology, 5(2), (2013), pp. 749-754.
[9] X. Zhou, C. Yang, and W. Yu, Moving object detection by
detecting contiguous outliers in the low-rank
representation, IEEE Transactions on Pattern Analysis
and Machine Intelligence, 35(3), (2013), pp. 597-610.

(P) and hence the accuracy (F) is also high. It has
achieved the precision level greater than 80% and theaccuracy greater than 75%.
Raindrops on a rainy day should be included in the
background, since they don’t have a particular meaning
as objects. The proposed method can reduce raindrops
effectively as shown in Fig. 5. Many raindrops areobserved in the image of Fig. 5a. They are eliminatedby the proposed method as shown in Fig. 5b, but someof them remain as in Fig. 5c, if a simple backgroundsubtraction is employed. The fields marked in red in Fig.
5c are raindrops.
The color of a moving object greatly affects the
success of this technique. The color of an object that isvery different from the background will be more easily
recognized. The speed of a moving object also affects
the success of this technique. If the object moves slowly
or even it stands still in an image, it will be recognizedas part of the background. But it is acceptable, as the
topic of this paper is 'moving' object detection.
References[1] I. S. Kim, H. S. Choi, K. M. Yi, J. Y. Choi, S. G. Kong,Intelligent visual surveillance - A survey, International
Journal of Control, Automation, and System, 8(5), (2010),
pp. 926-936.
[2] K. A. Joshi, D. G. Thakore, A survey on moving object
detection and tracking in video surveillance system,© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 31 -

Study on the Target Recognition and Location Technology of industrial Sorting Robot basedon Machine Vision
Jiwu Wang1

School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University
Beijing 100044, China1
Xianwen Zhang1, Huazhe Dou1

School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University
Beijing 100044, China1
Sugisaka Masanori 2

Alife Robotics Corporation Ltd, Japan and Open University, United Kingdom2
E-mail: jwwang@bjtu.edu.cn; sugisaka3@mocha.ocn.ne.jp

Abstract
In order to improve the applications for an industrial sorting robot, it is necessary to increase its flexibility andcontrol accuracy. The prerequisite is to automatically extract the multiple target positions accurately and robustly.
The machine vision technology is an effective solution. Here an industrial robot arm is designed and set up forexperiment simulation with machine vision. In order to reduce the influence of the size, deformation, and lighting
etc., the target recognition and location method with fusion of scale invariant feature transform (SIFT) and moment
invariants is developed. The experiments results showed that the developed image processing algorithms are robust,and the flexibility of the industrial robot can be improved by machine vision.
Keywords: Arm robot, Target recognition, Flexible control, Machine vision

programming. The position of each target ispredetermined, and robots only perform repetitive
actions. But in the applications of the automatedproduction line or a flexible manufacturing system,2such simple operations are not always satisfied to thepractical request. It is necessary to obtain the position ofeach target automatically, and then adjust the angle ofeach joint correspondingly. Increasing industrial robot’s
flexibility and improving its repetitive positioning
accuracy is the focus for their further applications. 3 In

1. Introduction
Intelligent industrial robot, as the concentration andintegration of advanced manufacturing technology,
information technology and network technology etc., isgetting more and more attentation. 1 This is because a lotof robots and automated equipments will be required in
the future applications.
Now, most of the traditional industrial robots can only
work according to some fixed paths by an operator’s© The 2015 International Conference on Artificial Life and Robotics(ICAROB 2015), Jan. 10-12,Oita, Japan- 32 -

Jiwu Wang, Xianwen Zhang, Huazhe Dou, Masanori Sugisakaorder to realize above functions, the prerequisite is to
get the target position accurately and reliably.
Machine vision is a good solution. A CCD camera canprovide more information than other sensors. And somepattern recognition algorithms can be used to extract thetarget and calculate its position automatically in one
captured image. Moreover, multiple targets and theirpositions can be determined at the same time, whichmakes robots to be flexibly controlled possible.
In order to get the position of each target, the target
should be recognized first. Due to the influence oflighting, size, rotation and small scale perspective
transformation etc., some target recognition algorithms
are not robust. It will cause target positioning unreliable.
The SIFT algorithm can effectively improve target
recognition capability. In this paper, the SIFT algorithm
is used for target recognition, and the target position is
calculated with moment invariant method.

Fig.1.Illustration of SIFT algorithm steps

Image recognition is to identify the target image withthe source images in the sample library. Compared withother pattern recognition algorithms, the necessary stepis to input the sample target image in the sample library,which simplifies the complex operation for image
processing. The target recognition process is shown infigure 2.

2. Target Recognition Based on Image Matching
For pattern recognition, some scale and grayscaletransformation can not be avoided. It will make somematching algorithm unreliable. The algorithm of thescale invariant feature transform4 (SIFT) is a local
feature detection algorithm. It is based on matching
image feature points to realize target recognition. Evenin a complex environment, the target can be quickly andaccurately recognized.
The kernel of SIFT algorithm is to search key points inthe different scale space, and calculate their direction.
The feature points in SIFT are those points, such as
corners, edges, dark spot and bright spot etc., which will
not be affected by illumination, viewpoint or occlusion.
The process of SIFT algorithm is shown in figure 1.

Fig.2. The flow chart of target recognition

In order to verify above algorithms, the following
experiments are carried. We take a picture for all sample
targets as the sample library, as shown in figure 3. We
try to use the targets with different color and shape,even brightness.

Fig.3. Sample target image library© The 2015 International Conference on Artificial Life and Robotics(ICAROB 2015), Jan. 10-12,Oita, Japan- 33 -

Study on the Target

The matching results are shown in figure 4.

Fig.5 Illustration on the target positioning

3. Experiments
Based on the above target positioning with image
processing algorithms of SIFT and moment invariants,
the positioning control experiment with our arm robot iscarried on. The experiment setting is as shown in figure
6. The CCD camera is fixed in the arm robot coordinate
system. Thus the target position can be easilytransformed to the end pose of the manipulator. With
inverse kinematics equations, the angle of each joint canbe calculated.
The experiment results show that the flexible control for
multiple target operations can be performed with image
processing methods developed in my lab. Moreover,with SIFTand moment algorithms, the influence oflighting and scale is low to the target position
calculation.

Fig.4 Illustration on the matching results

In figure 4, the scale and orientation transformation of
the target are studied. Moreover, the influence ofviewpoint and brightness modification is studied too. As
shown in figure 4, with the SIFT algorithm, thematching results are robust and reliable. For each
picture in this group, the left side is the input image tobe detected, and the right side is the sample library.
Their relationship is shown by connecting lines between
the extracted feature points.
After the target is extracted by matching with SIFT
algorithm, the next step is to determine its position.
Generally, the center is necessary for each object, so themoment invariant algorithm is developed. As shown infigure 5, each target area is marked with rectangle, andits center is market with a dot. Their cocodinates arealso given in unit of pixel.

Fig.6.Control experiment with vison matching and positioning

4. Conclusions
system based on a CCD camera is developed. Multiple
target control operations are performed with our lab arm
robot. By algorithm fusion of SIFT and moment
invariants, the multiple targets can be extracted robust
and their positions can be calculated accurately.

© The 2015 International Conference on Artificial Life and Robotics(ICAROB 2015), Jan. 10-12,Oita, Japan- 34 -

Jiwu Wang, Xianwen Zhang, Huazhe Dou, Masanori Sugisaka

Moreover, it can effectively control the influence oflighting and scale etc.
References
1. Y. B. Wang, Y. Z. Hu, and M. Y. Lu, Siemens machine
vision system and its application in automobile engine
assembly line, Automation systems engineering , vol.1(2006) 20-22.
2. K. L. Zhuang, J. Z. Wang, and J. Zhou, Application ofmachine vision in angle inspection, Equipment
Manufacturing Technology, vol.4 (2011) 4-10.
3. G. L. David, Distinctive image features from scaleinvariant key points. Int. Journal of Computer Vision,
vol.2(60)(2004) 91-110.
4. A. Khotanzad, Y. Huahong, Rotation invariant image
recognition using features selected via a systematic
method, Pattern recognition, vol.23(10)(1990) 10891101.
5. M. Sugisaka, X. Wang, Intelligent control of a mobile
vehicle using on-line learning, System science,
vol.25(1999) 41-50.
6. M. Sugisaka, X. Wang, J. Lee, Intelligent control strategy
for a mobile vehicle, Applied mathematics andcomputation, vol.20(1998) 91-98.

© The 2015 International Conference on Artificial Life and Robotics(ICAROB 2015), Jan. 10-12,Oita, Japan- 35 -

Mechanism Designs for Bio-inspired Flapping Wing Robots
Palakorn Tantrakool

Institute of Field roBOtics (FIBO), King Mongkut’s University of Technology Thonburi,
126 Pracha-Utid Road, Bangmod, Thrung Kru, Bangkok, 10140 Thailand
Eakkachai Pengwang

Micro Robotics Laboratory, Institute of Field roBOtics (FIBO), King Mongkut’s University of Technology Thonburi,
126 Pracha-Utid Road, Bangmod, Thrung Kru, Bangkok, 10140 Thailand
E-mail: pingpong_ray2@hotmail.com, eakkachai@fibo.kmutt.ac.th
www.fibo.kmutt.ac.th

Abstract
This paper presents the design of flapping systems for small air vehicle used in surveying robot for an
area that is hard to access. This paper also focuses on flapping mechanism of micro unmanned aerial robots that are
similar to birds and insects. Our design consideration will have a wingspan up to 15 centimeters. Enable
technologies for fabrication of these designs are 3D printers by using polymer materials with low density andweight. Characterizations of flapping wings are simulated and examined in this paper. This micro flapping wing
robot will be tested and performed in real environments.
Keywords: Micro Robots, Flying Robots, 3D Printers, Surveying Robot

has built a flapping wing robot and studied flow of air
when the flapping robot is moving. This robot contains
a clap and fling mechanism. Moreover, D. Mueller and J.
W. Gerdes [5] designed a flapping wing robot by using
a crack rocker mechanism. This study examined several
folding mechanism for measuring a lift force in each
design. J. H. Han et. al. [6] has reviewed information
about flapping wing robots for a wing structure,
flapping wing mechanism, flight control, and powersource. H. Y. Chao et. al. [7] has comparedspecifications on microcontroller boards. For a power
supply of flapping wing robots, M. Karpelson et. al. [8]has collected information about advantages anddisadvantages in each type of power source. This study
focuses on the mechanism design for bio-inspired
flapping wing robots with a 3D-printer construction.

1. Introduction
Flapping wing robots are inspired from nature anddeveloped for several mechanisms. Main objective is toimprove flying characteristics by imitating the flight ofbirds and insects [1-2]. Recent challenges are volume ofdevices, accessibilities, strength of material, andstability control of the flight. Designs of flapping wing
robots are studied for many perspectives, for example,
geometry and materials of wings, flapping mechanism,mechanical structure, radio system control, CFD(Computational fluid dynamics) analysis and powersources. Flapping mechanisms are investigated by using
several methods. By using a commercial 3D printer, C.
Richter and H. Lipson [3] fabricated a four-wing robot
with a total weight of 3.89 grams. For analysis of
aerodynamics of flapping wing robots, H. Liu et. al. [4]© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 36 -

Palakorn Tantraakool, Eakkachai Pengwang

Different meechanism desiigns are evalu
uated and tessted
for the lift forrce measurem
ments.
2. Design
2.1. First Gen
neration Desiign
Our first dessign of flappiing wing systtem is a Scootch
yoke mechannism. The struucture of this robotr
is madee ofABS (Acryloonitrile Butadiiene Styrene) plastics. In tthis
study, the S
Scotch yoke mechanism is chosen formovement because thiis mechanism
m can moove
continuously and generatee a high tran
nsmission forrce.
This design cconsists of linnkages to a DC
D motor andd aFigg. 2. The seconnd generation off mechanism deesign for the
flap
pping-wing rob
bot. (a) CAD moodel (b) Fabricaated prototype

nt of rotationn
grams. For theese modificatiions, the poincoonsists of two
o pivots and four joints to reduce thee
forrces that act at
a wing.
When a DC motor rotaates, the pinio
on gear drivess
thee driver gear whichw
is connnected to a tran
nsmission rodd.
Tw
wo sides of trransmission rood are linked to the carbonn
fib
ber rods that area connectedd to flapping wings.
w
Carbonnfib
ber is chosen for assemblinng this structu
ure because itihaas a low weighht and a high ddurability for external forcee.
WhenW
the transsmission rod rotates, carbbon fiber rodsspu
ush and pulll a connecteed wing for upward andd
doownward motions.

Fig.
F 1. The firsst generation off mechanism design for the
flapping-wingfrrobot. (a) CAD model (b) Fabrricated prototyppe.

circular disc.. Figure 1 shhows a concep
pt design of thefirst generatioon. When thee circular discc is rotating, theT-shape linkaage and the wingw
will connsequently moove
upward and ddownward.
A testingg result for thee first generattion showed tthat
the T-shape linkage can movem
upward anda downwardd ina slot wall. H
However, the gap
g distance betweenbthe sllotsof the upper ppart is larger than the width
h of the T-shaapestructure. Thhis issue cauuses the ineffficiency of themanipulation and the two wings cannot moove
simultaneously. Moreoveer, the T-shaape linkage aalso
moves with a high frictionn from the slo
ot wall. It is aalso
observed thatt the speed off flapping is reeduced when thewidth of the sslot is smaller.

2.3
3. Third Geneeration Designgn

2.2. Second G
Generation DesignDFig
g. 3. The third generation off mechanism designdfor theeflap
pping-wing rob
bot.

As a testing rresult from thhe first generaation, the secoondgeneration deesign is modiified by usingg a crank rockker
mechanism tto reduce a friction in th
he assembly. In
addition, the size and weiight of the seecond design are
reduced. Figuure 2 shows a concept desig
gn of the secoondgeneration. T
The total weeight of this design is eiight

forr the size and weight. Mateerial for this mechanismmhassalsso been chang
ged from ABSS plastics to a carbon fiberrrod. The total weight of thiis design is reducedr
to sixx
grams as shownn in Figure 3.

Ass a result fro
om the testinng, the seconnd generationn
deesign still has a heavy weighght. This desig
gn is improvedd© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 37 -

M
Mechanism Design
ns for Bio-inspired

3. Theoreticcal and Testinng Analysis

gn and Testinng Flight
4. Wing Desig

Mechanical sstructures of the flapping-wing robots areanalyzed for flight characcteristics. Maiin parameter forthis study is to analyzee a wingspann angle. Thhesewingspan anggles are influeenced by the lengthlof carbbon
fiber rods annd the distannce from pivoot points to thecarbon fiber jjoints. This loocation is on a sliding bar tthat
is connected to the wing. Mainly,M
the seecond generattion
design is chaaracterized forr a wingspan angle that is anangle of wingg between uppstroke and do
ownstroke fligght.
The mathemaatical calculattions are form
mulated by usiing
Crank-rockerr theorem in GashofG
law andd cosine rule.
This exxperimental resultsrof wingspanwannglecorrespond too the theoretiical analysis. In figure 4, thedifferent lenngth of carrbon fiber rodsrdoes nnot
significantly affect to thee values of wingspanw
anglles.
The range of averaged valuesvof winngspan angless isFoor a flight takke-off, micro gear motor (gear ratio of
144:9) is used forf an electriccal actuator. The
T length of
caarbon fiber rod
d is 20 millim
meters and the distance frommsliider to wing root is 10 m
millimeters. Flapping
F
wingg
rob
bots are assem
mbled to wing
ngs that are mademof plasticc
shheet. The lenngth of wingg is 15 centimeters. Theestrructure of th
he wing is a plus-like shape. Twoocoonditions of wingw
structure are evaluatedd for a testingg
fliight. The firstt design is thehe wing structture with onlyy
caarbon fiber. Th
he second winng structure has rubber rodssforr a bending area when a robot is flapping.
f
Thee
ob
bjective of rub
bber rods is too enhance a wiingspan anglee.
Figure 5 showss these two cooncept design
ns of the wingg
strructure. It is assumedathat the wing cann collect moree
airr under the wing with tthe rubber rods. This airrcoollection under the wing beecomes a thru
ust force for a
flaapping wing robot. Figure 6 shows the prototype is seetup
p wing.

Wingsapn angles (Degree)

80

Experimetal results70

Calculationn

60
50
40
30
20
10
0
5

15
25
d (mm)
Length of carbon fiber rod

35

Fig. 4. Compaarison betweenn calculation and experimenntal
results for diffferent lengths of carbon fiber rod (when the
distance from sslider to wing rooot is 12 millim
meters).

Fiig. 5. Layout of two differentt wing structurre (a) with only
caarbon fibers. (b)) with rubber roods for a flexiblle wing.

between 35 and 40 degreees. In figure 5, the distannce
from slider too wing root afffects to wingsspan angles. IIt isobserved thaat increasing distancedfromm slider to w
wing
root will resuult to a decreassing wingspann angles.

Fiig. 6. Third prototype is setuup with the seecond design ofowingw
structure. Wing is madde from plastic sheet with awingspanwof 15 centimeters.
c© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 38 -

Palakorn Tantraakool, Eakkachai Pengwang

Wing
Structures

In testinng flight, the measurementt of lift forcee isevaluated forr these bio-innspired flappinng wing roboots.
The values of lift forces are approx
ximated for thechange in w
weight on a precisepscale. The robots arehanged on a suspension anda a rope is used to connnect
these robots tto the scale asa shown in Figure 7. Whenn arobot is flappping, a tensionn force from thet rope changges
the weight vvalue on the scale.
sThis chhange in weigght
represents ann approximateed lift force of the flapp ing

Using only
carbon fiber
Adding rubberr
rods for a
flexible joints

Wing
span
(cm)

Fllappinganngle ataaxis 1

Total
ght
weig
(g))

Avg.
lift
force
(g)

15

0

6.6

1.5

15

155

6.7

2.5

5. ConclusionnTh
his study shows the desiggn of bio-insp
pired flappingg
wiing robots by using a 3D pprinter. The fin
nal design hassa totaltweight of 6.6 grams. M
Moreover, thee two differennt
wiing structures are analyzed for a lift. It iss observed thaat
thee flexible join
nt with rubberr rods can gen
nerate a higherr
lifft force than th
he rigid wing structure. Hence, a flexiblee
joiint can increase a lift forcee for a bio-inspired flappingg
wiing robot. Hoowever, these structures caannot generateeann efficient lift force to overccome a weighht of the robott.
Th
he modificatio
ons of motor speed and wiingspan lengthh
wiill be implem
mented to im
mprove the peerformance oftheese robots.

Fig. 7. A test ssetup with a susspension beam and a precise sccale
for measuring a lift force for bio-inspired flaapping wing robbots.

wing robot.
As a teesting result, the first win
ng structure ccan
generate an aaveraged lift force
fo at 1.5 graams. The secoondwing structurre that containns rubber rodss can generatee anareaveraged lift force at 2.5 grams. The performancespcompared in T
Table 1.

Accknowledgem
ments

Table 1 Coomparison on performances of bio-inspireed
flapping winng robots for diffference wing structuressat 5 V
V.

80

Exp
perimetal results

70
Wingsapn agles (Degree)Th
his work is financially ssupported froom the Kingg
Mongkut’sM
Univversity of Tecchnology Thon
nburi grant forr
neew researchers. Authors allso would lik
ke to thank too
Prrofessor Ben Parslew
Pfromm University ofo Manchesterrannd Professor Sutthipong
S
Sriigrarom from University of
Gllasgow Singap
pore for intelleectual discusssions.

Callculation

60
50
40

Reeferences30

1. T. J. Muellerr, “Fixed and fllapping wing aeerodynamics for
micro air vehiclev
applicaations”, Restonn: Progress inn
astronautics and aeronauticss, vol. 195, 20001.
k, D. R. Powers,
2. B. W. Tobalske, D. R. Warrrick, C. J. Clark
T. L. Hedricck, G. A. Hyderr and A. A. Bieewener, “Threedimensional kinematics oof hummingbirrd flight”, Thee
Journal of Experimental Biology, Thee company ofBiologists, pp.
p 2368-2382, 22007.
3. C. Richter and H. Lippson, “Untethered Hoveringg
Flapping Fliight of a 3D-PPrinted Mechan
nical Insect”, inn
Proc. A life XII
X Conferencee, Odense, Denm
mark, 2010.

20
10
0
0

5

10

15

20

Distance from sliders
to wing rooot (mm)Fig.
F 5. Comparrison between calculation andaexperimenntal
resultsr
for diffeerent distance from slider to wing root (whhenthet length of caarbon fiber rod is
i 20 millimeterrs).

© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 39 -

Mechanism Designs for Bio-inspired

4. H. Liu, X. Wang and T. Nakata, “Aerodynamics andFlight Stability of A Prototype Flapping Micro Air
Vehicle”, in Proc. International Conference on Complex
Medical Engineering, Japan, 2012.
5. D. Mueller and J. W. Gerdes, “Incorporation of Passive
Wing Folding in Flapping Wing Miniature Air Vehicles”,
in Proc. ASME Mechanism and Robotics Conference,
San Diego, USA, 2009.
6. J. H. Han, J. S. Lee and D. K. Kim “Bio-inspired
Flapping UAV Design: A University Perspective”, in
Proc. SPIE Conference Proceedings Vol.72951, 2009.
7. H. Y. Chao, Y. C. Cao and Y. Q. Chen, “Autopilots for
Small Unmanned Aerial Vehicles: A Survey”,
International Journal of Control, Automation and
Systems, pp. 36-44, 2010.
8. M. Karpelson, G. Y. Wei and R. J. Wood, “A Review ofActuation and Power Electronics Options for FlappingWing Robotic Insects”, in Proc. IEEE International
Conference on Robotics and Automation 2008, May 1923, 2008.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 40 -

Effective rocking motion for inducing sleep in adults- Verification of effect of mother’s embrace and rocking motion Keishi Ashida*,**, Yoshifumi Morita*,**, Ryojun Ikeura**,***, Kiyoko Yokoyama**,****, Ming Ding **, Yuki Mori***Department of Computer Science and Engineering, Nagoya Institute of Technology
Gokiso, Syouwa, Nagoya, Aichi 466-8555, Japan
E-mail: k.ashida.353@stn.nitech.ac.jp / morita@nitech.ac.jp**RIKEN- SUMITOMORIKO Collaboration Center for Human-Interactive Robot Research (RSC RIKEN)
2271-130 Anagahora, Shimoshidami, Moriyama, Nagoya, Aichi 463-0003, Japan***Department of Mechanical Engineering, Mie University
1577 Kurimamachiyacho, Tsu, Mie 514-8507, Japan****Department of Design and Architecture, Nagoya City University
2-1-10 Kitachikusa, Chikusa, Nagoya, Aichi 463-0083, Japan

Abstract
We investigated the most effective rocking motion for inducing sleep in adults. We prepared ten types of rocking
motions, including two types of mother’s rocking motions. The sleep-inducing effect of all the rocking motions wasevaluated using Thurstone’s paired comparison method (Case V). From statistical analysis of the subjective
experimental results we found that, of the ten types of rocking motions, the linear motion component of a mother’srocking motions (Type 2) was the most effective for inducing sleep in adults.
Keywords: Rocking motion, Mother’s rocking motion, Sleep-inducing effect, Thurstone’s paired comparison
methodand falls asleep quickly. Therefore, we hypothesizedthat a rocking motion simulating a mother’s embraceand rocking motion would have the same effects on
adults. Against this background, the aim of our projectis to develop a relaxation machine for reducing stressand inducing sleep by using a rocking motion
simulating that of a mother. We expect that our research
results will be used for other relaxation systems or
robots in the future, for example the Robot forInteractive Body Assistance (RIBA), which is designed
to assist caregivers by lifting patients in and out of theirbeds and wheelchairs.3
In our previous study, we analyzed a mother’sembrace and rocking motion, and we designed andconstructed an excitation apparatus simulating amother’s rocking motion. We found that there are two

1. Introduction
Recently, the number of people suffering from highlevels of stress has increased, a phenomenon that has
been called a stressful society.1 Every year, more andmore people suffer from stress-related illnesses.
Moreover, sleep disorders are becoming morewidespread in modern society because stress is one of
the causes of insomnia. In nursing homes, it is a large
burden for caregivers to assist patients to ensure thewellbeing of people who suffer from these problems.2
Therefore, stress reduction and sleep induction arenecessary for many people in modern society. It is
generally known that when a baby is embraced androcked by his or her mother, the baby feels comfortable© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 41 -

Keishi Ashida, Yoshifumi Morita, Ryojun Ikeura, Kiyoko Yokoyama, Ming Ding, Yuki Mori

types of rocking motions given by a mother, and weconstructed two models of these rocking motions.4 From
analysis of brain signals and subjective assessment, wefound that the mother’s rocking motions were more
effective for inducing sleep in two healthy adults than in
the case without rocking.5 However, there were two
problems with that study. One is that the number ofsubjects was small, and the other is that the sleepinducing effects of different rocking motions have to beinvestigated.
In the work described in this paper, in addition to
the two types of mother’s rocking motions mentioned
above, we prepared eight types of rocking motions tofind the rocking motion that is most effective forinducing sleep in adults. We performed threeexperiments and evaluated the sleep-inducing effect byusing Thurstone’s paired comparison method.

Rotational motion

Rotary motor

Linear motor

Fig. 1. Photograph of excitation apparatus.

order to simulate a mother's rocking motions in the
excitation apparatus.
3. Experimental method

2. Excitation apparatus simulating a mother'srocking motion

We performed experiments for finding the mosteffective rocking motion for inducing sleep. The sleepinducing effect of each rocking motion was evaluatedon a numerical scale using Thurstone’s paired
comparison method (Case V)7,8. We verified the validityof adopting this method by using Mosteller’s goodnessof-fit test. Moreover, we evaluated the confidence of thescale values.
The subjects were nine healthy males (average age,
25.4 years). The subjects wore ear muffs for soundinsulation during the experiment, had their eyes closed,
and rested in a supine position on the chair which was
rocked by the excitation apparatus. The experimental
protocol is shown in Fig. 2. R1, R2, R3, and R4 denotethe types of rocking motions used in the experiment.
After every two rocking motions, the subjects answered
the question, “Which of the two rocking motions wasmore effective for inducing sleep?”. The evaluation was
performed with six different combinations of four
rocking motions for each experiment. In order to reducethe influence of an order effect, we divided the subjects
into two groups and used six combinations (Types
R1and R2, Types R1 and R3, Types R1 and R4, Types
R2 and R3, Types R2 and R4, and Types R3 and R4) in
two different orders, namely, Procedure 1 andProcedure 2, as shown in Fig. 2.

In our previous work we analyzed a mother’s embraceand rocking motion to identify the features of the
motion. From the results, we modeled a mother'srocking motion. The model of a mother’s rocking
motion is represented byxref(t)=Ax sin(2fxt),ref(t)=Asin(2ft+)+B,

Linearmotion(1)where xref(t) and ref(t) are the position and the angle ofthe center of gravity of the baby, respectively, and B
was determined from the angle of a comfortable sitting
position reported in Ref. 6. Then, we realized thisrocking motion using the excitation apparatus shown in
Fig. 1. The excitation apparatus has two degrees of
freedom, one for linear motion and one for rotationalmotion. The rocking chair is driven by a linear motor
and a rotary motor. The rotary motor is connected to thechair through a timing belt for rotation. The drive
systems of the excitation apparatus are composed of
speed-reference-type servo amplifiers for the linear androtary motors, linear and rotary encoders, and a DSP
board (dSPACE DS1104). The DSP board is used to
implement the controllers for the linear and rotary
motors. We designed controllers for the excitation
apparatus using PD control and feed-forward control in© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 42 -

Rocking motion for sleep-inducing

Time [min.] 0
Procedure 1(5 subjects)
Procedure 2(4 subjects)

3.5

7.0

10.5

14.0

17.5

21.0

Eye closed and relaxed with the rocking vibration
R1 (1 [min.])R3 (1 [min.])Eye closed and relaxed without rocking (0.5 [min.])R2 (1 [min.])R4 (1 [min.])

Subjective assessment (1 [min.])Fig. 2. Experimental protocol for finding the most-effective rocking motion in Experiments I, II, and III.

position reported in Ref. 6. The Type 3 rocking motion
is shown in Fig. 4.

4. Experiments
We performed three different experiments, Experiments
I, II, and III, which are detailed below.
4.1. Rocking motions used in Experiment I
We prepared four types of rocking motions, namely two
types of mother’s rocking motions (Type 1 and Type 2),
a rocking motion imitating the motion of a rocking chair(Type 3), and a rocking motion that has a highcorrelation with motion sickness (Type 4). In Fig. 2, R1,
R2, R3, and R4 correspond to Type 1, Type 2, Type 3,
and Type 4, respectively. Type 3 was added to thecandidates because a rocking chair is known to have arelaxation effect.9 Type 4 was added to the candidates as
an uncomfortable rocking motion in this comparative
study. The parameters of Type 1, Type 2, and Type 4
are shown in Table 1.
In order to produce the motion of a rocking chair
shown in Fig. 3, we first measured the motion of an
actual rocking chair by using a 3D motion capturesystem (Motion Analysis) when an adult male wasseated in the rocking chair. Type 3 is the rocking motion
produced after the experimenter lowered the backrest of
the rocking chair and released it. The motion of therocking chair consists only of rotary motion. We set theamplitude of the rotary motion to as large a value as
possible within a range that was comfortable for the
subject. Moreover, the rotary angle in the steady state
was set to 25 degrees, which is the same as the angle
determined from the angle of a comfortable sitting

Fig. 3. Photograph of rocking chair.
0.25
0.25

31

31

-0.05
-0.05

23

-0.15

19

Position [m]

27

23
19

-0.15

-0.25
-0.25
0
0

Angle [deg]

27

0.05
0.05

Angle [deg]

0.10

0.15

Position [m]3535Position
Angle

10

10

20

20

30
Time [sec]

30

40

40

50

50

1515
60
60

Time [s]Fig. 4. Rocking motion (Type 3).

The Type 4 rocking motion is represented by Eq. (1).
The frequency was determined to be 0.063 Hz, which
has been shown to have a high correlation with motion
sickness10. The amplitude was determined so that theacceleration was almost the same as those of otherrocking motions and within the range of accelerationthat can be produced by the excitation apparatus. The© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 43 -

Keishi Ashida, Yoshifumi Morita, Ryojun Ikeura, Kiyoko Yokoyama, Ming Ding, Yuki Mori

Table 1. Parameters of the rocking motions.
Parameter

Type 1

Type 2Type 4

Type 5

Type 6

Type 7

Type 8Type 9Type 10

Ax [m]

0.115

0.098

0.250

0.095

0.100

0.098

0.098

0.060

0

A [deg]

2.455

2.734

10.0

3.136

2.332

2.734

0

0

2.734

B [deg]

25

25

25

25

25

25

25

25

25

fx [Hz]

0.326

0.234

0.063

0.226

0.243

0.234

0.234

0.468

0

f [Hz]0.326

0.234x2

0.063

0.226x2

0.243x2

0.234x2

000.234x2

 [rad]



π/2

π

π/2

π/2

-π/2

π/2

π/2

π/2

that Type 2 was the most effective for inducing sleep in
adults and Type 4 was the least effective.

parameters of the Type 4 motion are also shown in
Table 1.
4.2. Results in Experiment I

Table 2. Frequency distribution matrix in Experiment I.

From the results of the paired comparison method, we
obtained the frequency distribution matrix shown in
Table 2. Table 2 shows the number of subjects whooffered the response “The first rocking motion was
more effective for inducing sleep than the secondrocking motion”. The first and second rocking motions
are shown in the rows and columns of Table 2,
respectively. The scale values for the sleep-inducing
effect of the rocking motions were derived by applying
Thurstone’s paired comparison method (Case V) to
Table 2. The scale values are shown in Table 3. From
Table 3, we can rank the four types of rocking motions
based on their sleep-inducing effects as follows:

Type 1

Type 2Type 3

Type 4

Type 1－6

3

2Type 23－20Type 36

7－

2Type 47

9

7－

Table 3. Scale values of different rocking motions inExperiment I.
Type 1

Type 2Type 3

Type 4

0.19

1.06

-0.11

-1.15

Scale value

Scale value and 95%
confidence interval

2Type 2 ＞ Type 1 ＞ Type 3 ＞ Type 4.
Moreover, we verified the validity of adopting
Thurstone’s paired comparison method (Case V) by
using Mosteller’s goodness-of-fit test. The goodness-offit, 2, was 3.52, and the flexibility, df, was 3 , meaningthat a 5% significance level was not rejected. Therefore,
Thurstone’s paired comparison method (Case V) can be
adopted for evaluating the sleep-inducing effect.
We examined the significant differences between
the four rocking motions by using the method proposed
in Ref. 11. The result is shown in Fig. 5. We found thatthe 95% confidence interval of Type 1 overlapped withthat of Type 3. The combination of Type 1 and Type 3
showed no significant difference, whereas the other fivecombinations showed significant differences. In otherwords, of the four types of rocking motions, we found: Scale value: 95% confidence interval

1
0
-1
-2
0

1

2

3

4

5

Type

Fig. 5. Scale values and 95% confidence intervals of the fourtypes of rocking motions in Experiment I.

4.3. Rocking motions used in experiment II
We prepared three types of rocking motions, namely
Type 5, Type 6 and Type 7, in addition to Type 2. In Fig.
2, R1, R2, R3, and R4 correspond to Type 2, Type 5,
Type 6, and Type 7, respectively. Type 5 was a rocking
motion based on one given to a 5 kg baby in anotherstudy5, and Type 6 was a rocking motion based on one© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 44 -

Rocking motion for sleep-inducing

given to a 7 kg baby in the same study. More details ofthese rocking motions are shown in Ref. 5. Type 7 was
the same as Type 2 except for the phase, which differedby 180 deg. These parameters are shown in Table 4.

4.5. Rocking motions used in Experiment III
In order to find a more effective rocking motion for
inducing sleep, in Experiment III, we focused on thecomponents of the Type 2 motion. We prepared three
types of rocking motions, namely, Type 8, Type 9 andType 10, in addition to Type 2. In Fig. 2, R1, R2, R3,
and R4 correspond to Type 2, Type 8, Type 9, and Type10, respectively. Type 8 was a rocking motion
consisting of only the linear motion component of Type
2. Type 9 was also a rocking motion consisting of only
linear motion that was reported to be effective forinducing sleep in babies in Ref. 12. Type 10 was arocking motion consisting of only the rotational motion
component of Type 2.
When the frequency of the rocking motion used inRef. 12 is used instead of the frequency of Type 8, theacceleration is too large. For this reason, the amplitudeAx was set to a smaller value, and the frequency fx was
set to a small value, namely, the frequency f of therotary motion component of Type 7. This rocking
motion is Type 9. These parameters are shown in Table
1.

4.4. Results in Experiment II
Figure 6 shows the scale values and the 95% confidence
intervals for the sleep-inducing effect of the rocking
motions obtained using Thurstone’s paired comparison
method (Case V) and the method proposed in Ref. 11.
From the scale values in Fig. 6, we can rank the fourtypes of rocking motions based on their sleep-inducing
effects as follows:
Type 2 ＞ Type 6 ＞ Type 5 ＞ Type 7.
Moreover, the validity of adopting Thurstone’s paired
comparison method (Case V) for evaluating the sleepinducing effects was confirmed in the same way as inExperiment I.
In Fig. 6, the combination of Type 2 and Type 7
showed a significant difference, whereas the other fivecombinations showed no significant differences. We
concluded that, of the four types of rocking motions,Type 2 was the most effective for inducing sleep in
adults. As for the reason why no significant differences
appeared, we considered that the evaluations were
uneven because it was difficult for subjects todiscriminate the small differences in the frequency andamplitude. In this situation, Type 7 was chosen as theleast effective rocking motion. With the Type 7 motion,
the subjects reported slight motion sickness.

Scale value and 95%
confidence interval

1

4.6. Results in Experiment III
Figure 7 shows the scale values and the 95% confidence
intervals for the sleep-inducing effect of the rocking
motions obtained by using Thurstone’s paired
comparison method (Case V) and the method proposed
in Ref. 11. From the scale values in Fig. 7, we can rank
the four types of rocking motions based on their sleepinducing effects as follows:
Type 8 ＞ Type 10 ＞ Type 2 ＞ Type 9.

: Scale value: 95% confidence interval

0.5

Moreover, the validity of adopting Thurstone’s paired
comparison method (Case V) for evaluating the sleepinducing effect was confirmed in the same way as inExperiment I.
In Fig. 7, the three combinations including Type 9(Types 2 and 9, Types 8 and 9, and Types 9 and 10)
showed significant differences, whereas the other threecombinations showed no significant differences.
However, there is a significant difference in the
combination of Type 8 and Type 10 if the confidence
interval is changed to 80% instead of 95%. Therefore,of the four types of rocking motions, we concluded that

0

-0.5
-1

0

12

25

36

47

5

Type

Fig. 6. Scale values and 95% confidence intervals of the fourtypes of rocking motions in Experiment II.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 45 -

Keishi Ashida, Yoshifumi Morita, Ryojun Ikeura, Kiyoko Yokoyama, Ming Ding, Yuki Mori

3. RIKEN-TRI Collaboration Center for Human-Interactive
Robot Research (RTC), World’s first robot that can lift
up a human in its arms, RIBA, (online), available from<http://rtc.nagoya.riken.jp/RIBA/index-e.html>,(accessed on 13 June, 2014)
4. Yamaguchi, K., Kobayashi, H., Morita, Y. Sakaida, Y.
Ikeura, R. and Yokoyama, K., Motion Analysis ofMother's Embracing and Rocking a Baby andDevelopment of Excitation Apparatus, Proc. of Int. Conf.
on Control, Automation and Systems 2011 (ICCAS2011)(2011) pp.1792-1796.
5. Morita, Y., Yamaguchi, K., Ashida, K., Ikeura, R. andYokoyama, K., Verification of Sleep-Inducing Effect by
Excitation Apparatus Simulating Mother's Embrace andRocking Motion, Proceedings of the 9th International
Workshop on Robot Motion and Control (RoMoCo '13)
(2013) , pp.80-85.
6. Sasaki, Y., Kawamoto, T. and Ymazaki, N.,
Development of an Experiment Bed for Searching
Comfort Semi-Sitting Position, Japanese Journal ofErgonomics, Vol.42, No.6 (2006), pp.373-380.
7. Research Committee of Sensory Evaluation, Sensory
Evaluation Handbook, JUSE ed., JUSE Press, Ltd., Japan(1973), pp.471-476.
8. Ichikawa, H., A Study of Representing Content for aSpecial 3D Display (2010) (in Japanese), pp.34-41.
9. Tamakoshi, S., Yamamoto, N., Morimoto, F., Nagae, S.,
Ichiba, H. and ISO, H., Engineering Psychology on the
Relaxation Effects of a Rocking chair, Behavioral
Science Research, Vol.50, No.2 (2012) (in Japanese),
pp.101-105.
10. Siroto, H., Evaluation Method of Motion Sickness inTrains Using Low-frequency Lateral Vibration, Railway
Research Review, Vol.130 (2006) (in Japanese), pp.3839.
11. Tabata, Y., Ohga, Y., Kakuta, M., Nakamae, M.,
Morioka, M., Uto, F., Okunishi, T., Oti, T. and Maeda, K.,
Confidence Coefficient of Subjective Scale Value in
Method of Paired Comparisons (Case V), JapaneseSociety of Radiological Technology, Vol.51, No.4 (1995)(in Japanese), pp.445-449.
12. Fuji, T., Furusawa, T., Takenouchi, T. and Tomotari, M.,
Development of Electric Bed for Babies for Parenting
Support, Japan Society for Design Engineering (2006) (inJapanese), pp.19-20.

Type 8 is the most effective for inducing sleep in adults.
The reason why Type 9 was identified as the least
effective rocking motion was that the acceleration ofType 9 is too large, based on the subjects’ opinions. We
found that the acceleration was related to the effective
motion for inducing sleep.

Scale value and 95%
confidence interval

2: Scale value: 95% confidence interval

1
0
-1
-2

0

12

28

93

4
10

5

Type

Fig. 7. Scale values and 95% confidence intervals of the fourtypes of rocking motions in Experiment III.

5. Conclusion
From subjective experimental results, we found that, of
the ten types of rocking motions examined, the linear
motion component of a mother’s rocking motion (Type2) was the most effective rocking motion for inducing
sleep in adults.
In future work, we plan to confirm that the Type 8
rocking motion, which is the linear motion componentof a mother’s rocking motion (Type 2), is effective forinducing sleep and reducing stress in adults by using
electroencephalogram (EGG) analysis. We will also
compare the Type 8 rocking motion and aromatherapy,
which is a popular natural way of inducing sleep andbringing about relaxation.
References
1. Ito, H. and Ishino, Y., Adverse Effects of Sleep
Deprivation, Iho Fuji, No.124 (2003) (in Japanese), p.1.
2. Mishima, K., Sleep Problems in Dementia, Proceedingsof the Annual Meeting of the Japanese Research Groupon Senile Dementia, Vol.17 (2010) (in Japanese), pp.109113.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 46 -

Postural Sway Response to Local Vibratory Stimulationin Young, Middle-aged and Elderly People in Standing Position
Ayaka Yamada, Eishi Nakamura, Noritaka Sato, Yoshifumi Morita
Nagoya Institute of Technology
Gokiso-cho, Showa-ku, Nagoya, Aichi 466-8555, Japan
Tadashi Ito, Yoshihito Sakai
National Center for Geriatrics and Gerontology
7-430, Morioka-cho, Obu City, Aichi 474-8511, Japan
Kazunori Yamazaki
Fujita Health University
1-98 Dengakugakubo, Kutsukake-cho, Toyoake, Aichi 470-1192, Japan
E-mail: a.yamada.067@stn.nitech.ac.jp, e.nakamura.335@stn.nitech.ac.jp, sato.noritaka@nitech.ac.jp
morita@nitech.ac.jp, tada-ito@ncgg.go.jp, jsakai@ncgg.go.jp, ymzkk@fujita-hu.ac.jp

Abstract
We investigated the postural sway in response to local vibratory stimulation applied to young, middle-aged andelderly people in the standing position. For this purpose we developed a variable-frequency vibratory stimulation
device and measured the postural sway using a gravicorder. As a result, when the vibratory stimulation was applied
to the gastrocnemius muscles, the center of pressure moved backward in all subject groups. We found that elderly
people with low back pain may perform balance control using their trunks more than their lower legs.
Keywords: Vibratory stimulation device, Proprioception, Postural sway, Low back pain, Fall prevention

system uses not only sensory information from visualand vestibular senses but also somatosensory receptors4
that give superficial sensation and proprioceptive
sensation. Moreover, proprioception in the leg and trunk
muscles plays an important role in maintaining postural
stability.5 Proprioceptive receptors have eigen response
frequencies. Past studies have reported that when
vibratory stimulations are applied, the postural sway inthe standing position changes.6 Therefore, from thechange of the postural sway during vibratory
stimulations with various frequencies, we may be able
to determine the cause of low back pain and falls anddevelop a treatment system for proprioception forrecovery to solve these two problems.

1. Introduction
Falls accidents and low back pain in the elderly are two
common problems in modern Japanese society. Up until
2009, over 1800 elderly people died from fall accidents
per year.1 Low back pain is also a serious issue, but thecauses are unidentifiable in 85% of patients.2 One
possible factor is the problem of postural control
involving voluntary and reflexive muscle responses, assuggested by one study in which it was reported that theelderly and people with low back pain have posturalcontrol
problems
involving
deteriorationofsomatosensory receptors.3
To detect the motion of the body and adjust
voluntary and reflexive muscle responses, the balance© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 47 -

Ayaka Yamada, E
Eishi Nakamura, Noritaka Sato, Yoshifumi
Y
Morita,, Tadashi Ito, Yosshihito Sakai, Kazzunori Yamazaki

On the otther hand, othher studies haave reported tthat
proprioceptioon and vibratioon sensation in
i the lower leegs
7decrease withh aging. Morreover, posturaal instability hhasbeen observed in elderly people.8 That studysand otheers,
however, exaamined the postural sway off each age grooup
in response too local vibrattory stimulatio
on applied us ing
different vibrratory stimulaation devices under differrent
experimental conditions.
Against ssuch a backgground, the fiinal goal of oour
research is to developp a treatment system for
proprioceptioon. To achievve this goal,, the procedu
dure
involves inveestigating posttural sway in responser
to loocal
vibratory stim
mulation appllied to humann subjects in thestanding posiition, to find thet frequency of the vibratoory
stimulation annd the body partsprelated tot low back ppain
and falls, whiich will help tot determine thhe causes of llow
back pain andd falls.
In the woork described in this paper, we developeed avariable-frequuency vibratoory stimulatiion device aandmeasured thee postural swaay response off young, midddleaged, and eldderly people tot local vibraatory stimulatitionwhen in the sstanding posittion. We focused on low baack
pain and innvestigated thhe frequenciees of vibratoory
stimulation annd the body parts related too low back paiin.

Fig.1. Photograaph and block ddiagram of variaable-frequency
vibratory stimuulation device.

Fig
g.2. Subject weearing the devicce.

2. Variablee-frequency vibratoryv
sim
mulationdevice

3. Experimennt

We developped the variable-frequev
ency vibratoory
3.1
1. Purpose
stimulation ddevice shown in Fig. 1. Thee device consiistsTh
he purpose off the experim
ment was to investigate theeof a laptop coomputer, an auudio amplifier, four vibratoors,poosturalsway
responsetollocal vibratorry stimulationn
and a switch for selectingg one of the vibrators.
v
A ssineap
ppliedtoyoung,middle-age
ed,and elderly
y people in theewave signal with an arbittrary frequenccy generated onsta
andingpositio
on
by
using
the
vibratorry stimulationn
the laptop coomputer is inpput to the aud
dio amplifier. By
deevice that we developed,dtoggether with th
he Wii Balancee
regulating thhe gain of the audio amplifier, theBo
oard.
amplitudes of the vibratorrs can be chan
nged. The ranngeof displacem
ment of the vibbrators is 0–0
0.8 mm, and the
3.2
2. Subjects
frequency rannge is 30–4000 Hz. Each vib
brator is fixedd toTh
he subjects werew
25 healthhy young people (12 malessa holder. Thee four vibratorrs are attached to the left aandan
nd 13 femaless, aged 21.6 ±1.2), 25 heealthy middleright gastrocnnemius musclees (GM) and thet left and rigght
ag
ged people (133 males and 122 females, ageed 46.0 ± 3.0)),
lumbar multiifidus (LM) withw rubber beelts. The lenggths
46
6
elderlypeopplewith lumbaar spondylosiis without low
wof the rubber belts are adjusted acccording to thebaack pain (28 malesmand 18 females, age = 73.8 ± 5.3)),
perimeters off the body parrts to which theyt
are fixedd toan
nd 28 elderly peoplepwith luumbar spondy
ylosis with lowwkeep the pushhing forces coonstant. As a gravicorder, webaack pain (10 malesmand 18 fe
females, age = 75.5 ± 5.1).
use a Wii Baalance Board (Nintendo(Co., Ltd.). The W
Wii
Balance Boarrd has been reported to be reliable enouughfor medical uuse.9© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 48 -

Postural Response to Vibration

3.4. Experimental results

Table 1. Measurement procedure(GM: Gastrocnemius muscles, LM: Lumbar
multifidus)
Frequency
Body part where vibratory
stimulation is applied
Step 130 Hz
GMStep 230 Hz
LM
Step 360 Hz
GMStep 460 Hz
LM
Step 5150 HzGM
Step 6150 HzLM
Step 7240 HzGM
Step 8240 HzLM

We paid attention to the Pre-section and the Dur-sectionin order to analyze postural sway during local vibratory
stimulation in the standing position. For this purpose weused two parameters related to the CoP, namely, theanterior movement of the CoP (MY), and the relative
proprioceptive weighting (RPW) ratio. ΔMY is definedas the difference between the mean values of the

3.3. Method
The center of pressure (CoP) in postural sway while thesubject stood still on the Wii Balance Board wasmeasured as shown in Fig. 2. The subject stood barefooton the Wii Balance Board with his/her feet together andhis/her eyes open or closed. The subject was instructed
to remain still and relaxed in the standing posture withhis/her arms hanging loosely at his/her side.
One measurement for each subject consisted of eightsteps for the different vibration frequencies and thedifferent body parts where the vibratory stimulation wasapplied. The measurement procedure is shown in Table
1. An interval of 60 s was added after every step, during
which each subject sat resting in a chair. Figure 3 showsthe experimental procedure in one step. The timeneeded for one step was 75 s. One step consisted of fivesections, namely, “EO”, “EC”, “Pre”, “Dur” and “Post”.
The time needed for one section was 15 s. In the EOsection, the subject looked at a marker on the wall. In
the EC-section and the Pre-section, the eyes were closed.
Only in the Dur-section, the vibratory stimulation wasapplied to the subject with the eyes closed. In the Postsection, the subject's eyes were closed.
The vibratory stimulation was applied alternately tothe two muscles (GM and LM). The amplitude of thevibration was set to 0.8 mm. The frequencies were set to30, 60, 150, and 240 Hz. It is well-known that the eigen
response frequencies of Meissner’s corpuscle, muscle
spindle, and the corpuscle of Vater-Pacini are 30 Hz, 60
Hz, and 240 Hz, respectively.10

Fig.3. Experimental procedure.

anterior displacement of the CoP in the Pre-section andthe Dur-section, which is given by(1)MY  MYPre  MYDur ,where

MYPre  Mean Pre YCoP ( k )MYDur  Mean Dur YCoP (k )
YCoP ( k )  yCoP ( k )  yCoP (0)Here, Meanxx{ ･ } denotes the mean of { ･ } during XX,
yCoP(k) is the y-displacement of the CoP, k represents the
sample index, and yCoP(0) is the value of yCoP(k) at thestart of measurement. MY is calculated in each step.
MYGM and ΔMYLM denote the ΔMY values for the
gastrocnemius muscles and lumbar multifidus,
respectively. The RPW ratio is defined by

RPW 

MYGM
.
MYGM  MYLM© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 49 -(2)

Ayaka Yamada, Eishi Nakamura, Noritaka Sato, Yoshifumi Morita, Tadashi Ito, Yoshihito Sakai, Kazunori Yamazaki

As the RPW ratio approaches 100%, GM is used morethan LM in motor control during quiet standing. Thismeans that a lower leg-steered strategy is used in motor
control. As the RPW ratio approaches 0%, this meansthat a trunk-steered strategy is used in motor control.
The experimental results are shown in Fig. 4, whichshows the relationship between the frequency and MY,and the relationship between the frequency and the RPW
ratio.

vibratory stimulation device and measured the postural
sway according to our experimental method.
As a result, we found that our device and theexperimental method were effective in measuring thepostural sway during local vibratory stimulation.
Moreover, from the result for the RPW ratio, we foundthat elderly people with low back pain used a trunksteered strategy in motor control.
Future work related to this study will be to analyze
the relationship between the postural sway and falls, todetermine the causes of low back pain and falls throughadditional experiments, and to develop a treatment
system for proprioception.

3.5. Discussion
Fig. 4(a), (d), (g) and (j) show that when the 60 Hz
vibratory stimulation was applied to the GM, the CoP
moved backward in all subject groups. This result issimilar to the results obtained in previous studies6,7.
Therefore, the proposed vibratory stimulation device
and the experimental method using the developed
device were confirmed to be effective in measuring thepostural sway during local vibratory stimulation.
Moreover, Fig. 4(a), (d), (g) and (j) show that when thevibratory stimulations with the frequencies of 30 Hz,
150 Hz, and 240Hz were applied to the GM, the CoP
also moved backward in all subject groups.
On the other hand, Fig. 4(b), (e), (h) and (k) showthat when the vibratory stimulations were applied to theLM, the CoP moved backward or frontward regardlessof the change of frequency. This means that thebiological response to vibration is uncertain.
Fig. 4(l) shows that as the frequency increased, theRPW ratio decreased. This response is a characteristic
seen only in elderly people with low back pain. The
same tendency was not seen in the other subject groups,
as shown in Fig. 4(c), (f) and (i). This means that the
RPW ratio does not depend on the frequency. Therefore,
the results for the RPW ratios suggest that elderly
people with low back pain performed balance control
using their trunk more than their lower legs when
vibratory stimulation with higher vibration frequency
was applied.

References
1.

2.

3.

4.
5.

6.

7.

8.
9.

4. Conclusion

10.

In this paper, we investigated the postural sway inresponse to local vibratory stimulation applied to young,middle-aged, and elderly people in the standing position.
For this purpose we developed a variable-frequency

VITAL STATISTICS, Statistics and Information
Department, Minister's Secretariat, Ministry of Health,
Labor
and
WelfareJAPAN,
http://www.mhlw.go.jp/english/database/dbhw/index.html
A. Richard, R. James and L. Daniel, What can the history
and physical examination tell us about low back pain? J.
the Am. Med. Assoc. 268 (1992) 760-765.
T. Urushihata and T. Kinugasa, Aging effects on the
structureunderlying
balanceabilities
tests,
Tairyokukagaku 56(6) (2007) 711. (In Japanese)
R. Johansson and M. Magnusson, Human posturaldynamics Crit. Rev. Biomed. Eng. 18 (1991) 413–437.
B. Bloem, J. Allum, M. Carpenter and F. Honegger, Is
lower leg proprioception essential for triggering humanautomatic postural responses?, Experimental Brain
Research 130 (2000) 375–391.
B. Simon, C. Paul and V. Sabine, Proprioceptive
weighting changes in persons with low back pain andelderly persons during upright standing Neuroscience
Letters. 366 (2004) 63-66.
B. Skinner, L. Barrack and D. Cook, Age-related decline
in proprioception Arch. Phys. Med. Rehabil. 44 (1963)
645–650.
I. Pyykkö, P. Jäntti and H. Aalto, Postural control inelderly subjects, Age Aging. 19 (1990), 215–221.
A. Clark and L. Bryant, Validity and reliability of theNintendo Wii Balance Board for assessment of standingbalance Gait & Posture. 31 (2010) 307-310.
T. Ito, Y. Sakai, A. Kubo, K. Yamazaki, Y. Ohno, E.
Nakamura, N. Sato, Y. Morita. The relationship between
physical function and postural sway during local
vibratory stimulation of middle-aged people in thestanding position J. Phys. Ther. Sci. 26(10) (2014) 16271630.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 50 -

150

240

30

60

150

240

0.8
0.4
0.0
-0.4
-0.8
-1.2
-1.6

30

60

150

240

0.8
0.4
0.0
-0.4
-0.8
-1.2
-1.6

30

(j)

60

150

240

Frequancy(Hz)MY with stimulation of GM in
elderly people with low back pain.

RPW(%)

240

60

150

30

0.8
0.4
0.0
-0.4
-0.8
-1.2
-1.6

30

60

150

0.8
0.4
0.0
-0.4
-0.8
-1.2
-1.6

30

60

150

240

100
80
60
40
20
0

30

60

150

240

30

60

150

240

30

60

150

240

(f)

100
80
60
40
20
0

(i)

Frequancy(Hz)RPW ratio in elderly people without
low back pain.

100
80
60
40
20
0

240

Frequancy(Hz)(k) MY with stimulation of LM inelderly people with low back pain.

150

Frequancy(Hz)RPW ratio in healthy middle-aged
people.

240

Frequancy(Hz)(h) MY with stimulation of LM inelderly people without low back
pain.

60

Frequancy(Hz)(c) RPW ratio in healthy young people.

240

Frequancy(Hz)(e) MY with stimulation of LM in
healthy middle-aged people.

ΔMY(cm)ΔMY(cm)Frequancy(Hz)(g) MY with stimulation of GM in
elderly people without low back
pain.

150

0.8
0.4
0.0
-0.4
-0.8
-1.2
-1.6

30

ΔMY(cm)ΔMY(cm)Frequancy(Hz)(d) MY with stimulation of GM in
healthy middle-aged people.

60

Frequancy(Hz)(b) MY with stimulation of LM in
healthy young people.

ΔMY(cm)ΔMY(cm)

0.8
0.4
0.0
-0.4
-0.8
-1.2
-1.6

30

RPW(%)

60

Frequancy(Hz)(a) MY with stimulation of GM in
healthy young people.

100
80
60
40
20
0

RPW(%)

30

0.8
0.4
0.0
-0.4
-0.8
-1.2
-1.6

RPW(%)

0.8
0.4
0.0
-0.4
-0.8
-1.2
-1.6

ΔMY(cm)ΔMY(cm)Postural Response to Vibration(l)Frequancy(Hz)RPW ratio in elderly people withlow back pain.

Fig.4. Frequency dependence of MY and RPW ratio.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 51 -

Development of Unmanned Transport System for Automated Systems
Hyunhak Cho

Department of Interdisciplinary Cooperative Course: Robot, Pusan National University,
10313, 2, Busandaehak-ro 63beon-gil, Geumjeong-gu, Busan, 609-735, Korea
Jungwon Yu, Yeongsang Jeong, Hansoo Lee, Sungshin Kim

Department of Electrical and Computer Engineering, Pusan National University,
10313, 2, Busandaehak-ro 63beon-gil, Geumjeong-gu, Busan, 609-735, Korea
E-mail: {darkruby1004, garden0312, dalpangi03, hansoo, sskim}@pusan.ac.kr
www.pusan.ac.kr(leave space here should be 4 lines between www.university_name.edu and Abstract)

Abstract
Unmanned transport systems are used in many industries owing to their high efficiency, such as transport of highloads, 24 hour a day use and low cost. Many components of industry are being changed to unmanned transport system. Therefore, this paper introduces application and composition of the unmanned transport systems in industry to
the transport, and describes the selected method by the environment conditions. Unmanned transport system iscomprised of an Automatic Guidance Vehicle and a Monitoring system. The applied AGV is guided by localization
information, which is measured by a laser navigation guidance system with encoders and gyroscope. AGV require avirtual driving path method with map information from conveyor positions and rotation points in an environment,
as well as automatic load/unload system for cargo transport, an emergency system for safety, and a communication
system with an automatic charger and monitoring system.
Keywords: Autonomous guidance system, Localization, Telescope Load/Unload System

1.

such as localization, driving control, load/unload system,
path planning, monitoring system, and so on. The technologies of the unmanned transport system are general,and detailed technologies for the LGV are selected according to the conditions of the environment. The
AGVs are a changed guidance system; floor state, plantsize, maintenance, and so on, wheel type; floor state, oil,and so on, load/unload; conveyor, palette type, and soon system according to the conditions of the environment [1]-[6].
This article introduces the application of the unmanned transport system; AGV and Monitoring systemwith the combined technology of the mentioned content
to the industry field. Among the range of wireless guid-Introduction

Many industries; manufacturing plants, warehouses,
port facilities and so on are changing to automatic systems through the development of unmanned robot technology. Automatic production systems in various industrial systems have many components, and the material
handling system of components is very important because of its potential to reduce cost. Material handlingsystem means a load /unload/ transport/storage system.
Recently, there has been increasing interest in unmanned transport system to satisfy the flexibility andefficiency of material handling system. The unmannedtransport system is an aggregate of various technologies,©The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 52 -

Hyunhak Cho, Jungwon Yu, Yeongsang Jeong, Hansoo Lee, Sungshin Kim

ance systems available, this study used encoders, a gyroscope and laser navigation. In addition, virtual path
planning was used for the wireless guidance system, thedriving wheel structure was the differential driving
structure, and the controller was an industrial PC.
Load/unload system for the cargo used the telescopic
type due to the type of palette and conveyor.
Chapter 2 discusses the hardware configuration of
the unmanned transport system; load/unload system,
body frame of AGV Chapter 3 presents the software
configuration of the unmanned transport system; localization, driving controller, virtual path planning. Chapter
4 presents the conclusion.

Fig. 2. Telescopic Load/Unload System

The LGV is guided by the laser navigation, encoders
and gyroscope of the localization sensors. The laser
navigation sensor measured the difference distance andangle between the header of the laser navigation andattached reflectors in the field through a time delay;transmit-receive. The laser navigation calculated thecurrent position of the LGV through a triangulation
method using the measured information and saved position information of the reflectors.
AGV with a wireless guidance system is controlled
by the error angle. The control system using this method
was unstable because of the large increase in the error
angle when the distance between the AGV position anddestination point was close. Therefore, in this study, theerror angle and derailment distance between the current
position and driving path was used in equation 1.

2. Hardware Configuration
In this article, unmanned transport system; AGV,
Monitoring system was designed by the present and
ATIS (Company in Korea). Fig 1 shows the appearanceof the manufactured LGV. Laser navigation, encoders
and a gyroscope were used for the localization of LGV.
The global position (x, y, t) of localization information
was measured using laser navigation. The local position(x, y, t) of localization information was calculated by thekinematics with encoders and a gyroscope.
The load/unload system is affected by cargo, conveyor and pallets. Therefore, the telescopic typeload/unload system was selected. The system was composed a fixed frame, body frame for moving; left, rightand two arms on the body frame for settling the pallet;
cargo. The detailed components are the left-right motor
for the body frame, up-down motor 2 for the arms.
3.

E  Ed  a  E  (1  a)

(1)

The a in the equation is the ratio, and Ed and Eθ are
the derailment distance and error angle. The composition of the equation is the same as shown in Fig 3.
Ed | aAx  bAy  c |a2  b2(2)

E  D  T

Software ConfigurationD  N  S(3)

T  N  ADθ in the equation is the difference between the angles (angle of LGV position Destination position, angleof Virtual Path Destination position). Tθ is the angle of
the LGV. The voltage difference of the wheels was calculated by E in Equation 6 with a PD controller, and theFig. 3. Error Calculation

Fig. 1. Autonomous Guidance Vehicle©The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 53 -

Development of Unmanned Transport(a)Fig. 4. Result Graph of Localization and Control

LGV was controlled by the difference in voltage.
Fig 4 presents a graph of the experiment results. The
driving and stop accuracy of the experiment results are
sufficient due to the greater than required accuracy (±30and ±15 mm). When the LGV stops, 0.7v (less 200 mm
distance between AGV position and destination point),
and 0.4v (less 100 mm), and 0.2v (less than 30 mm)
were applied to prevent a collision between the LGV
and fixed conveyors.
Virtual Path Planning is a very important configuration in the LGV, and it was used to guide from the startpoint to the destination point. The configuration form of
the conveyor appears like a straight line. In practice,
however, the configuration form of the conveyor in thefield is a zigzag conformation. In practice, the configuration from of the conveyor in the field is a zigzag conformation. The nodes of virtual path a zigzag path planning to prevent a collision with conveyor, planning arecomposed of the conveyor position, rotation position,and special position.
Fig 5 shows the configuration of the modified tree
structure. The search direction of the virtual path planning method is bidirectional, because the driving direction of the LGV through the Main monitoring system isforward and backward. The modified tree structure isexpressed by the adjacent nodes, the node mean conveyor, rotation node, and charger. The link line is thevirtual driving path. Whenever searching the virtual
path from a new position in map, it should create a tree
structure continually. Therefore, a recursive structure
was used to solve the previous problem.

(b)
Fig. 5. Result Graph of Localization and Control; (a) Nodestructure, (b) Tree structure

4.

Conclusion

The proposed unmanned transport system is composed of a LGV, conveyor and monitoring system, andthe LGV contains a different driving system, a telescopic type (for transporting cargo with a high load) and acommunication system. The conveyor type is classified
as the A, B and C type through the pallet type or attached equipment. The LGV is guided by the controller
using laser navigation but the stability of the system islow. To solve this problem, encoders and a gyroscope
are used. The LGV is controlled by the error angle andderailment distance, and LGV is driving through thevirtual path from the start node to the destination node.
In the present study, the unmanned transport system,
which is an aggregate system of various technologies,
such as navigation systems, a driving system, load
/unload system, path planning, monitoring system, andemergency system, was examined closely.

©The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 54 -

Hyunhak Cho, Jungwon Yu, Yeongsang Jeong, Hansoo Lee, Sungshin Kim

Acknowledgment
This work was supported by BK21PLUS, Creative
Human Resource Development Program for IT Convergence and was supported by the MOTIE (Ministry ofTrade, Industry & Energy), Korea, under the Industry
Convergence Liaison Robotics Creative Graduates Education Program supervised by the KIAT (N0001126).
References
1. J. L. Crowley, Navigation for an intelligent mobile robot,
IEEE Journal of Robotics and Automation, 1(1) (1985),
pp. 31-41.
2. S. Y. Jung, J. M. Kim and S. S. Kim, Simultaneous localization and mapping of a wheel-based autonomous vehicle with ultrasonic sensors, Artificial Life and Robotics,
14(2) (2009), pp. 186-190.
3. M. Gunes and A. F. Baba, Speed and position control of
autonomous mobile robot on variable trajectory depending on its curvature, Journal of Scientific & Industrial
Research, 68(6) (2009), pp. 513-521.
4. J. Lee, S. I. Han and J. M. Lee, Decoupled Dynamic Control for Pitch and Roll Axes of the Unicycle Robot, IEEE
Transactions on Industrial Electronics, 60(9) (2013), pp.
3814-3822.
5. E. DiGiampaolo and F. Martinelli, A Passive UHF-RFID
System for the Localization of an Indoor Autonomous
Vehicle, IEEE Transactions on Industrial Electronics,
59(10) (2012), pp. 3961-3970.
6. G. Zhao, K. Xuan and D. Taniar, Path kNN Query Processing in Mobile Systems, IEEE Transactions on Industrial Electronics, 60(3) (2013), pp. 1099-1107.

©The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 55 -

Localization method for AGV using magnetic devices and IMU
Moonho Park, Eun Kyeong Kim, Yeongsang Jeong, Hansoo Lee, Jungwon Yu, Sungshin Kim

Department of Electrical and Computer Engineering Pusan National University,
Engineering BLDG #10 10313, 2, Busandaehak-ro 63beon-gil, Geumjeong-gu, Busan, 609-735, Korea
E-mail: { 82akakak, kimeunkyeong, dalpangi03, hansoo, garden0312, sskim }@pusan.ac.kr

Abstract
This paper is a research of the localization method for AGV (Autonomous Guided Vehicle) with a guidance systemusing magnetic localization devices. For navigation of AGV, an established magnetic guidance AGV detects amagnetic tape and follows the line. However, there are some weaknesses: disturbance and damage. To make up forthe weak points, this paper proposes the localization method using two magnetic localization devices, a gyro sensor
and encoders. In order to compensate the global position, AGV's location and angle were compensated for a magnet
position and gradient information using two magnetic localization devices. Between spot points, a relative position
was calculated by kinematics with the devices. To verify the performance of the proposed method, it was comparedwith the method using a gyro sensor and encoders. As a result, the proposed method is more efficient than theexisting one.
Keywords: Autonomous Guided Vehicle, localization method, magnetic device, wireless guidance

below. Chapter 2 explains the components of AGV:IMU (Inertial Measurement Unit), magnetic localization
devices, a hardware part. Chapter 3 describes the
localization system, chapter 4 explains angle
compensation, and chapter 5 shows the experimental
environment and results. Finally chapter 6 discusses theconclusion.

1. Introduction
An automation system is a sort of automatic processsystem widely used in design, control, facility
management, quality inspection and distribution.1-2 Oneof the representative automation systems is AGV. It can
reduce personnel expenses and improve productivity.3
In industrial field, magnetic guidance AGV is often
used because of its high accuracy, stability and lower
price sensors.4 However, there are some weaknesses
vulnerable to disturbance and damage. To make up forthe weak points, the researches into a sensor fusion arecontinued about magnetic device and the other sensors.5-

2. AGV
2.1. IMUIMU is used to calculate an object’s location or angle
using an object’s inertial measurement. In this paper,
IMU composes one-axis gyro sensor and encoders
included a motor of the wheel. The angle needs tocalculate by only one-axis in AGV’s case. It is why the7This paper proposes the localization method using two
magnetic localization devices, a gyro sensor, encoders
and magnet spots. The rest of this research paper is as©The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 56 -

Moonho Park, Eun Kyeong Kim, Yeongsang Jeong, Hansoo Lee, Jungwon Yu, Sungshin Kim

position to the next position and follows that line. As
AGV follows a virtual path, it finds its position using
sensors. In case of the proposed method, the absolutecoordinate value is not obtained. Therefore, thecoordinate must be updated after an initial position isdecided arbitrarily.

one-axis gyro is used in the AGV. Also encoders aresubstituted for an acceleration sensor.
2.2. Magnetic localization devices
With 75mm gap, two magnetic localization devices areattached to front of the AGV. These devices are locatedat intervals of 20mm in the same level from the floor.
Figure 1 is shown magnetic localization devices used in
the experiment.8-10

3.1. Calculation of AGV’s position
By encoders’ values, LWD and RWD are calculated.
LWD is a moving distance of AGV’s left wheel. And
RWD is a moving distance of AGV’s right wheel. L is adistance between wheels. An angle θEncoder made by a
gap between LWD and RWD is obtained by Eq. (1).
LWD  RWD
(1)
 Encoder (radian)  tan 1 ().
L
As θEncoder is accumulated, we can calculate AGV’sbody angle. Also X-axis variation and Y-axis variation
are obtained by Eq. (2).
 Encoder  Gyro
.
2( LWD  RWD )
(2)
.
dv 
2
dx  sin   dv .

2.3. Hardware of AGV
AGV is made of frames, two driving wheels and two
caster wheels. DSP (Digital Signal Processor) carries
out communication with sensors and laptop. The total
system configuration of designed AGV is same as
Figure 2.
3. Localization Systemdy  cos   dv .
θGyro is an angle calculated by a gyro output value. dv isconstant needed a coordinate calculation, dx is X-axis
variation, dy is Y-axis variation. AGV’s position is
calculated by accumulating X-axis variation and Y-axis
variation.

3.2. Control with driving error angle

Fig. 1. Magnetic localization devices

A driving error angle control AGV’s direction using themeasured values of a gyro sensor, a body angle and apath angle. AGV’s path angle is calculated by thecurrent position and the next position. AGV’s bodyangle is calculated by Eq. (3).
 Encoder  Gyro

.
2
(3)
1  X 2  X 1   tan .
 Y2  Y1 
α is a body angle and β is a path angle. Finally, we
obtain an error angle θError by Eq. (4).
 Error     .
(4)Fig. 2. System configuration

After AGV with a wireless guidance type decides a goal
position, it makes a virtual path from the current©The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 57 -

Localization method for AGV

4. Angle Compensation

When AGV changes the path, an angle is compensatedwith a point angle and AGV’s angle.

Hall sensor measures Gauss of magnet and outputs
proportional voltages to measured Gauss.

5. Experiments & Results

4.1. Central value calculation
5.1. Experiment environment

To calculate the central value of magnetic localization
devices, an initial reference value is established. A
reference value is measured by the experiment.
Threshold is used for determining the ON/OFF of eachof the hall sensors. To determine the threshold, we makeEq. (5) using heuristic method.
2
Th  M NM  ( M M  M NM )  .
(5)3
Th is Threshold. MNM is an average value of each of hallsensors' measured values about 100 times when there isno magnet. MM is an average value of each of hallsensors' measured values about 100times when there isa magnet. If a measured value exceeds threshold, itmeans there is a magnet and hall sensor turns ON. We
calculate the central value of magnet by Eq. (6).
 H ONPOSC.
(6) H ONCNT

AGV’s size is 760 x 490 x 480mm. A driving path isstraight-line section. There are 2 spot points on a driving
path. To compensate a driving error angle, there ismagnet spot in each point and its size is 80 x 80mm.
5.2. Localization using IMU

In the localization experiment using IMU, AGV drives
by an angle calculation of encoders and a gyro sensor. If
AGV arrives a spot point using a position calculation, apoint angle is updated towards next point and AGV
starts to drive.
5.3. Localization using magnetic devices and IMU

To reduce accumulative errors, a body angle iscalculated by magnetic localization devices on eachpoint and angles of encoders and a gyro sensor are
updated. For comparison of errors in two experiments,
mean error, root mean square error and maximum error
are calculated. Table 1 is shown for a comparison of the
proposed method with an established IMU.

HONPOS is the position value of hall sensor turned ON,
and HONCNT is the number of hall sensor turned ON. And
C is the central value of magnet.

Table 1. Result of the position measurement
Unit: mm
IMU
Magnetic device & IMU
Mean
Max
Mean
Max
RMSE
RMSE
Error
Error
Error
Error
226.96329.74 1089.40
70.24
91.41
186.484.2. Angle calculation

Because of the sensor’s accumulative errors, a bodyangle has always errors. To make up for this weak point,
AGV’s body angle needs to be compensated andupdated. First, we calculate central values of twomagnetic localization devices. Second, we calculate abody angle by Eq. (7) when AGV passes throughmagnet.
1  C  C1   tan  2
(7)

 L 
C1 is a central value of first magnet device and C2 issecond one.

In case of only IMU, error values are immense. On theother hand, error values decrease in case of magnet
devices and IMU. Maximum error is six times less. Asan angle is compensated using magnetic localization
devices, we can measure more accurate position andreduce error value.
6. Conclusion

4.3. Angle compensation

In this paper, we proposed the localization method for
AGV with a guidance system using magnetic
localization devices and IMU. AGV is designed by us
and magnetic localization devices made for advanced

A point angle is a required angle to change from AGV’scurrent direction to the direction toward next point.

©The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 58 -

Moonho Park, Eun Kyeong Kim, Yeongsang Jeong, Hansoo Lee, Jungwon Yu, Sungshin Kim

research are attached. Using encoders and a gyro sensor,
AGV’s position is calculated and a body angle iscontrolled. When AGV arrives at each point, a bodyangle is calculated by values of two magnetic
localization devices and compensated. As a result, theproposed method is more efficient than an existing one.
Acknowledgements

This work was supported by BK21PLUS, Creative
Human Resource Development Program for IT
Convergence and was supported by the MOTIE(Ministry of Trade, Industry & Energy), Korea, underthe Industry Convergence Liaison Robotics Creative
Graduates Education Program supervised by the KIAT
(N0001126).
References
1. K. Jung, et al, Line Tracking Method of AGV using
Sensor Fusion, Journal of Korean Institute of Intelligent
Systems 20(1), pp. 54-59, 2010.
2. J. Kim, et al, Fuzzy and Proportional Controls for
Driving Control of Forklift AGV, Journal of Korean
Institute of Intelligent Systems 19(5), pp. 699-705, 2009.
3. Wu Xing, et al, Design and Control of Material Transport
System for Automated Guided Vehicle, UKACC
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4. Pedro Santos, et al, Magnetic vehicle guidance, Sensor
Review 28(2), pp. 132–135, 2008.
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6. H. G. Xu, et al, Extended Kalman Filter Based Magnetic
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7. D. Y. Im, et al, Development of Autonomous Vehicle
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8. M. Park, et al, Improvement of Bipolar Magnetic
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9. M. Park, S. Kim, Magnetic Position Sensor of Analog
Type using Optimized Fuzzy Rules, in Proc. 29th Conf.
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10. H. Song, et al, Pattern Recognition of Landmark Using
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©The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 59 -

On the Effects of Epigenetic Programming on the Efficiency of Incremental Evolution of the
Simulated Khepera Robot

Yasuto Nishiwaki

Graduate School of Science and Engineering, Doshisha University, 1-3 Tatara-Miyakotani,
Kyotanabe, 610-0321 Kyoto, Japan
Ivan Tanev

Graduate School of Science and Engineering, Doshisha University, 1-3 Tatara-Miyakotani,
Kyotanabe, 610-0321 Kyoto, Japan
Katsunori Shimohara

Graduate School of Science and Engineering, Doshisha University, 1-3 Tatara-Miyakotani,
Kyotanabe, 610-0321 Kyoto, Japan
E-mail: {nishiwaki2012, itanev, kshimoha} @sil.doshisha.ac.jp

Abstract
We present the results of our work on epigenetic programming (EP) and its application to navigation of mobile
cleaning robot. We verify the effect of genetic switch and the feasibility of incorporating histones. The results show
individuals applying LEP are better performance and less the number of generations than applying GP but takes
costs of calculation. As a result, we believe the histone accelerates efficient evolution of nature.
Keywords: Epigenetic Programming, Genetic Programming, Epigenesis, Histone.

that the modifications to histones are implemented after
the completion of GP, and these modifications wereconsidered as a local search (hill-climbing) around the
genotype of the best-evolved individuals. No
modifications to histones in due course of evolution
were assumed in our previous work.

1. Introduction
The objective of our study is to develop an approachof Epigenetic Programming1-2 (EP) and to verify thefeasibility of incorporating histones (the family ofproteins which DNA is wrapped around) in genotype
from both the computationally and natural perspectives.
Epigenesis refers to a changeable gene expression (andconsequently – phenotype) as a result of the interactionof genotype and surrounding environment (food, stress,
UV, etc…). We intend to mimic the recent discoveries
in molecular biology that suggest that epigenesis plays
an important role in evolutionary adaptation of species.
In our previous study1, we verified the beneficial
effects of histones on the quality of solutions obtained
via Genetic Programming (GP). Out approach implied

2. Mechanism of EP
2.1. The mechanism of modifying the histoneswitches
We introduce histones as genetic on and off“switches” that modify the gene expression by
activating and deactivating the nodes (andcorresponding sub-trees) in the tree representations (i.e.,the “genotype”) of the evolved individuals in Genetic© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 60 -

Yasuto Nishiwakki, Ivan Tanev, Kaatsunori Shimoha
ara

Fig. 1. System
m architecture

Programmingg (GP). In caanonical GP the abilities ofindividuals eevolve as a result of mo
odifications ((viacrossover annd mutation) to the genes alone. In the
proposed EP this genetic modificationss are superpossedwith the moodifications of histones too facilitate evven
better evoluutionary adapptation to th
he environmeent.
Moreover, inn some cases the modification to histoone
alone – i.e., bby enabling a disabled code (or vice verrsa)– would achiieve a better performingpindividual withhout
the need to sppend extra tim
me for compu
utationally cosstly
evolution of a new genootype. In thiss study, we uuse
Lamarckian E
EP (LEP) as elaborated beloow.

The evaluattion of the fitn
tness of evolv
ving controllerr
is accomplishedd as follows:(i))

XGP send
ds the genotyype of the individuali(ass
XML-text)) for evaluattion to Web
bots via UDP
Pchannel,(iii) Using XM
ML parser, W
Webots parsess the receivedd
individual into a DOM-ttree,(iiii) For each tiime step of sim
mulation, Weebots evaluatessthe DOM--tree, and bassed on curren
nt perceptionss(sensory innformation) off the simulateed bot, decidess
its currentt action (e.g.,, moving forw
ward, movingg
backward, turning left or turning right).
r
Webotss
applies thee currently isssued commandd on the robot
and updates its speed and orientation, which, innturn, defin
nes the posiition, orientaation and theevelocity off the bot for thhe next time sttep.

2.2. The mecchanism of LE
EP
LEP is an epigenettic approach in which themodified gennetic switches (histones) arre stored backk in
the genotype (i.e., inheriteed). The Lam
marckism impllies
that the modiifications to thhe histones arre inherited baack
into the gennotype of thee evolving inndividuals. L
LEP
evaluates thhe fitness off individual after applyying
changes to hiistones, whichh, in turn, moddifies the statee ofthe genetic oon and off switches. In suchs
a way, bychanging thee expression of the gennes (rather thhanchanging thee genes) by these switch
hes, it could bepossible to obtain a differentdpheenotype, whhich
eventually features a betteer fitness value of the evolvved
individuals. F
Furthermore, we believe th
hat is possiblee to
evolve betterr individuals as a result ofo crossover aandmutation opeerations that operate oveer the space of
genotype superposed by hiistones.

(ivv) Upon thee completionn of simulation, Webotss
returns (viia UDP channnnel) the fitneess value (i.e.,
the area onn the floor thaat was cleaned
d by the robot)
to the XGPP.
The controlllers of the rrobot are ev
valuated for anuumber of time steps, given tto the robot to
o clean a roomm,staarting from a given sstarting position in twoocoonsecutive sam
mple 2D scennes – (i) without- and (ii)
wiith obstacles, respectively,r
aas shown in FigureF2.

3. System A
Architecture
In this sstudy, we useu
the XML
L-based geneetic
programmingg frameworkk3 (XGP) too maintain the
population oof evolving individuals andato perfoorm
genetic operaations (selectiion, crossoveer and mutatioon)on them. Thee evolved inddividuals are controllers oof asimulated Khhepera vacuum
m cleaning rob
bot. The latterr issimulated inn a Webotss4 mobile roobot simulati
tion
environment.
The systtem architectuure is shown inn Figure 1.

Fig
g. 2. Experimental setup cons
nsisting in two sample sceness:wiithout- (left) an
nd with obstaccles (right), respectively. Theerob
bot evolves the ability to clean thesse two rooms
consecutively, at two consecuttive stages of an incrementaal
evolution.

4.

Experimennts

To verify th
he feasibility of incorporaating histoness,
wee performed two experimennts. These two experimentss© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 61 -

On the Effects ofoFigg. 3. The resullts of applyingg LEP and GP on individuals
evolved: transition of fitness LE
EP (blue line) an
nd GP (red line)
Nuumber of popu
ulation of one generation is 200.
2
Maximummgenerations are 10
00. Number of evaluations perr one generationn
plying LEP is tw
wo times as maany as GP.
of individuals app

were intendedd to allow us to compare thet efficiencyy of
evolution via LEP and canoonical GP, resspectively.
4.1. Explorinof LEPng evolution characteristicc
In this experiment, we explore how histonnes
influence the efficiency off evolution. The
T controllerr of
the robot is eevolved in the first stage (i.e., room withhout
obstacles) of LEP under thhe same condiitions as GP. W
We
believed thatt introducingg histone wo
ould facilitatee afaster evoluution in thaat it would require low
wercomputationaal effort (i.e., would feaature a reducced
number of evvolved generattions).

As Figure 3 indicates, thhe controllerss, evolved viaa
LE
EP have betteer fitness andd evolve fasteer (with lowerr
nuumber of geneerations) than canonical GP
P. On the otherr
haand, the averag
ge complexityy of genotype (in number ofnoodes in their DOM-trees)Diss higher in LEP,
L
which, inn
turrn result in an increased evaluation timetof thesee
geenotypes. Thiss finding impliies that the im
mprovement ofcoomputational efforte
due to thhe incorporatiion of histonessin LEP comess at the costt of some degradationdofcoomputational performance.
pThis degradattion, howeverr,is rather neg
gligible, becaause, in the considereddappplication of LEP,L
the runtiime, needed too simulate theephhysics of the Khepera roboot is much lo
onger than theetimme spent for evaluating the DOM-tree off the genotype.

4.2. Two-stagged incremental evolution
During tthe first stage of the increm
mental evolutiion,
the robot evoolves via GP its ability to efficiently cleean
the room witthout obstaclees. Then, the best controlllers
are used to seeed the secondd stage of the evolution whhich
involves an eevolution (via GP) of the abbility to clean theroom with obbstacles. The same two-staaged incremenntal
evolution iss used appplying LEP with histonnemodification at both stagess.
After thatt, the best coontrollers of secondsstage areevaluated agaain in the rooom without ob
bstacles (i.e., thesame environnment that is useduduring th
he first stage)) toinvestigate ttheir abilities to adapt to the “neew”
environment. Because off changing en
nvironment, thecontrollers w
would need to evolve new featuresfin orrder
to perform w
well in it. Wee believe that individual ussed
applying LEP
P can adapt too the new environment by jjust
switching some of their genes on andd off instead ofevolving new
w genotype froom scratch.

5.2
2. Result of tw
wo-staged inccremental evolution
In second sttage, even if tthere is no diffference in theeefffficiency of th
he first stage, performance of controllerssap
pplying LEP was better thhan GP. LEP
P’s individuaal
tennded to live bu
ut GP’s indiviidual easy to beb weeded ouut
diffficult stage. However,H
som
me individualss tended to not
addapt in first stages
when ththe best indiv
viduals in theesecond stage aree returned.
6. Discussionss
The experim
mental resultss suggest thaat introducingg
hisstones as geneetic switches ffacilitates a faaster evolutionn
off a simulated Khepera roboot. The simulaated evolutionn
is relatively fasst anyway, annd, features abbout the samee
runntime overheead as the addaptation viaa changing ofhisstones. In th
he real biolog
ogical world however, theeev
volution of one generationn requires years
yof timee,whhile the effect of modificaations to histo
ones could bee
obbserved muchh sooner. Thherefore, we believe thaat
hisstones incorporated in thee genotypes area capable offaccilitating the biologicalbevoolution.
In addition, in latter expeeriment, we discovereddthaatsome of the con
ntrollers couldd not adapt well
w to the oldd“nnew” environm
ment (room w
without obstacles), becausee
thee parts of their respecttive genotypeesthat areeressponsible for dealing with that environm
ment, was losst
duuring the seco
ond stage of evolution (inn a room withhobbstacles). Therefore, it could be interestingitoo

5. Experimeental Results
5.1. Result off one-staged evolutione
The resullts of applyinng LEP or GP
P on individuuals
evolved are shown in Figurre 3.

© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 62 -

Yasuto Nishiwaki, Ivan Tanev, Katsunori Shimohara

investigate such a genetic mechanism during the second
stage of LEP that could protect the useful features (e.g.,ability to move and clean a room without obstacles)
evolved at the first stage of evolution, from thedestructive effect of crossover and mutation operations.
One possibility is to allow the inactivated histones torepel the crossover and mutation operations, in order topreserve the inactivated code from destruction. This
code could be deactivated later, when an old “new”
environment arises.
7. Conclusion
We proposed an approach of LEP and verified thefeasibility of incorporating histones. In order toimplement histone, we introduced genetic “switches”
that modify the gene expression by activating anddeactivating the nodes in the tree representations (i.e.,the “genotype”) of the evolved individuals in GP. In our
approach we propose LEP in that the modified genetic
switches are inherited back in the evolving genotypes.
Also, we verified the effect of epigenesis on theefficiency of evolution of controllers of simulated
Khepera cleaning robot in two experiments. From theresults of the former experiment we concluded that
applying LEP facilitates an earlier adaptation comparedto canonical GP. The results of the latter experiment with two-staged incremental evolution – indicate thatusing LEP contributes to the efficiency of evolution to
old “new” environment. We think these results suggestthat histones could be seen as a mechanism to “store”
dormant abilities once needed to deal with challenging
environments. These abilities could be activated again
when the organisms face “new” environments that havebeen already explored in their evolutionary past.
References
1. Y.Nishiwaki, I.Tanev, and K.Shimohara: Epigenetic
Programming and its Application to Mobile Robot, Int.
Conf. on Humanized Systems 2013, pp.66-68, September
2013
2. I.Tanev and K.Yuta: Epigenetic programming Genetic
programming incorporating epigenetic learning throughmodification of histones, Information Sciences, p13(2008)
3. I.Tanev:
DOM/XML-based
portable
genetic
representation of the morphology, behavior andcommunication abilities of evolvable agents, Artificial
Life and Robotics, 8:52-56,2004.
4. O.Michel, Webots: Professional mobile robot simulation,
Int. J. Adv. Robot. Syst, 1, 39–42 (2004).

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 63 -

The Effect of Duration of Both Stages of Incremental Genetic Programming on its Efficiency
of Evolution of Snakebot
Naoki Mukosaka

Graduate School of Science and Engineering, Doshisha University,
1-3 Tatara-Miyakotani, Kyotanabe, 610-0321 Kyoto, Japan
Ivan Tanev

Graduate School of Science and Engineering, Doshisha University,,
1-3 Tatara-Miyakotani, Kyotanabe, 610-0321 Kyoto, Japan
Katsunori Shimohara

Graduate School of Science and Engineering, Doshisha University,
1-3 Tatara-Miyakotani, Kyotanabe, 610-0321 Kyoto, Japan
E-mail: {mukosaka2012, itanev, kshimoha}@sil.doshisha.ac.jp

Abstract
The objective of our work is to investigate the optimal combination of durations of both stages of incremental
Genetic Programming (IncGP) on the simulated Snake-like robots (Snakebots). The obtained results suggest thatthe number of generations cannot be used as criteria for separating the two stages of IncGP. We hypothesized that
qualitative criterion, such as the emergence of smooth sidewinding locomotion could be used instead, andconducted additional experiments that proved its worthiness.
Keywords: Genetic Programming, Snake-like Robot, Incremental Genetic Programming, Evolution.

challenging environment. The main reason for such
inefficiency is that the evolution needs tosimultaneously discover both (i) the generic locomotion
traits (that are common for all well-moving bots,
regardless of the environment) and (ii) the specific traits
that are needed to overcome the obstacles in thesechallenging terrains.
In order to improve the efficiency of such an
evolution, in our previous work2, we divided the task ofevolving the locomotion of simulated Snakebot inchallenging terrain into two consecutive, simpler subtasks, implemented as two consecutive stages of anincremental Genetic Programming (IncGP): at the firststage we applied GP to evolve a pool of bots that areable to move fast in a smooth, open terrain. Then,
during the second stage, we used this pool to seed the

1. Introduction
Compared to the wheeled and legged vehicles, thewheel-less, limbless Snake-like robots (Snakebots) are
more robust and adaptable to both the environmental
changes and partial damage. Some of the useful featuresof Snakebots include smaller size of the cross-sectionalarea, stability, ability to operate in rugged terrain, good
traction, high redundancy, and complete sealing of theinternal mechanisms1. Due to the morphologicalcomplexity of Snakebots, an automated approach ofdeveloping their controller via Genetic Programming(GP) is often used1.
However, the evolution of Snakebot from scratch
via GP is rather inefficient when the bot is situated in a

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 64 -Naoki Mukosaka, Ivan Tanev, Katsunori Shimohara First, we implemented 30 independent runs ofcanonical GP to evolve the Snakebot in a smoothterrain for N generations (N=10, 20, 30, …, 70), andcreated a pool of 6 best-of-run, fast moving
Snakebots for each corresponding value of N.
 In the second stage of IncGP, we conducted
additional 30 independent runs, (for 80-N
generations) to evolve the bot, situated in achallenging terrain. The initial population of each ofthese runs is seeded with the 6 fast moving bots,obtained in the first stage of GP, plus additional 194
randomly created bots. For the second stage of
IncGP, we considered two cases of challenging
environment, and one case of partial damage, as
elaborated below.

initial population of the bots that are further subjected toevolution in the challenging environment. We
demonstrated that the two-staged incremental evolution
via IncGP, consisting of two stages of 40 generations
each, features a much improved efficiency over thecanonical GP2.
The objective of our current work is to investigate
the optimal combination of durations of both stages ofincGP. We assume a fixed runtime budget of evolution,corresponding to 80 generations overall. Therefore, ourexperiments involved evolution of Snakebot via incGP
with 7 combinations of durations of both stages of
evolution: from 10 generations of the first stage and 70
generations of the second one, to 70 generations of thefirst and 10 generations of the second one (i.e., 10 and
70, 20 and 60, 30 and 50,…, and, finally, 70 and 10,
respectively).

The considered two cases of challenging terrain
include (i) a “pen” of walls (which surrounds the botfrom all four sides), and (ii) multiple small immobile(i.e., fixed to the ground) obstacles. In the first case, theheight of the wall is automatically increased each time
the bot overcomes it. Therefore, the maximum height ofthe cleared wall (at the end of evolutionary run) could
be considered as a measure of the ability of the bot to
overcome walls. In the second case, the average speedof the bot (calculated from the travelled center of thegravity of the latter and the time of the trial) is
considered as a measure of the ability of bot to
overcome obstacles.
In addition, we conducted experiments with IncGPon evolutionary adaptation of the bot to its partial
damage to six segments. In this case, we assumed thatthe damage renders the joint at the tail-side of theaffected segment completely immobile.
The main parameters of IncGP are summarized inTable 1. We used our in-house DOM/XML-based
implementation of GP6.

2. Simulated Snakebot
In our work we used the Open Dynamics Engine3(ODE) physics library to simulate the articulated rigid
body dynamics of the Snakebot2. ODE allows toaccount for all forces, resulting from friction, gravity,
actuators, and collisions between the segments of thesimulated bot, and the environment. The Snakebot is
identical to the one as used in our prior work1, 2, 4, and 5. It
is simulated as 15 identical spherical morphologicalsegments (“vertebrae”). Pairs of nearby segment arelinked by universal (Cardano) joints and all joints are
powered by two (horizontal and vertical) identicalactuators (“muscles”).
In this study we use IncGP to evolve such motion
patterns of the segments of the bot that result in fastest
speed of locomotion of the bot. Considering therepresentation of Snakebot, this task could be rephrased
as developing the temporal patterns of desired turning
angles of horizontal and vertical actuators of eachsegment, that result in fastest overall locomotion of
Snakebot. The proposed representation of Snakebot as ahomogeneous
systemcomprisingidentical
morphological segments is intended to significantly
reduce the size of the search space of the IncGP (as weneed to evolve the functionality of one pairs of actuators
only) and to facilitate favorable scalability
characteristics of the latter.

Table. 1. Main parameters of IncGP
Category
Genotype
Population size
Selection
Elitism
Mutation

3. Simulation Setup
We employed IncGP in
evolutionary stages as follows:

twoconsecutive

Value
Patterns of the turning angles of:(i) horizontal actuator, and(ii)Vertical actuator
200 individuals
Binary Selection, ratio: 0.1
Reproduction ratio: 0.9
Best 4 individuals
Random, single point, ratio: 0.01

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 65 -

The Effect of Duration4.

of the Snakebots (in tree nodes) evolved in the firststage, and (ii) the performance of the best-evolved bot at
the second stage of IncGP.

Experimental Results

The experimental results of each of cases are shownin Fig.1, Fig.2, and Fig.3, respectively.

Table. 2. The complexity of genotypes and fitness values in
IncGP. FNavr and FFavr denote the average number of tree
nodes and the average fitness in the first stage of IncGP,
respectively. HofWavr is the height of wall that the bot
overcame in the second stage of IncGP. FMOavr and D6Favr
are the average fitness values in the second stage for the casesof environment with multiple small obstacles and damage tosix segments, respectively.
Duration of First(F) and second (S)stages of IncGP
F:10 S:70
F:20 S:60
F:30 S:50
F:40 S:40
F:50 S:30
F:60 S:20
F:70 S:10

Fig. 1. Dynamics of the height of walls overcame by Snakebotin the second stage of IncGP. Unit on the ordinate corresponds
to 1/4th of the diameter of the spherical segments of the bot.

FNavr FFavr HofWavr FMOavr D6Favr
54.67 87.3
74
89.5
82.67 103
95.83 10393.17 10768
112
77.67 100

8.067
6.7
7.933
7.7
8.233
9.133
6.667

109
79.8
105
89.3
106
110
91.4

95.17
72.6
99.2
91.53
88.63
100.1
79.07

Fig. 2. Fitness convergence of evolving Snakebot inchallenging environment with many small obstacles

Therefore, we hypothesize that the criteria forterminating the first stage of IncGP and initializing thesecond one might not be necessarily a quantitative one.
Rather, an aesthetic, intuitive, subjective, and informal
evaluation from the human about the presence ofgeneric locomotion would suffice for the switch
between the both stages of IncGP.
In order to verify this hypothesis, conducted
additional experiments intended to estimate theimportance of human-judged evaluation of the
Snakebots as follows:

Fig. 3. Fitness convergence of evolving Snakebot with afailure of 6 (out of 15) segments

5. Discussion

 First, we implemented 6 independent runs ofcanonical GP to evolve the Snakebot in a smoothterrain for N generations (N=10, 20, 30, …, 70), andcreated a pool of 6 fast moving Snakebots.
 In the second stage of IncGP, we conducted
additional 6 independent runs (for 80-N generation)of evolution of the bot, situated in the challenging
terrain. For the second stage of IncGP, we only
considered the case of “pen” of walls.

The experimental results suggest that neither thenumber of generations nor the obtained fitness value atthe first stage of IncGP can be used as criteria for
optimization of the duration of the two stages of IncGP.
Moreover, as the results in Table 2 suggest, there is nocorrelation between (i) the complexity of the genotype

We conducted the above mentioned experiments
four times for each combination of durations of bothstages. We looked at the visual phenotypic traits of thebest-of-run, fast moving generic bots obtained at thefirst stage of IncGP. We noticed that the number of bots

The statistical results including the complexity ofgenotype (in number of tree nodes) and fitness value aresummarized in Table 2.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 66 -Naoki Mukosaka, Ivan Tanev, Katsunori Shimoharathat move smoothly via sidewinding, and featuresinusoidal shape, correlates well with the success at thesecond stage of IncGP. These results are summarized in
Fig.4. As figure illustrates, the quality of the bots (i.e.,the height of the wall it overcomes), evolved in thesecond stage of IncGP, increases with the increase of
the number of smooth sidewinders from one to four inthe pool of the six bots used for seeding of the second
stage of IncGP. Additional increase of the number ofsmooth sidewinders to five and six is associated with adecrease of the quality in bots evolved at the second
stage of IncGP. The reason for such an anomaly is that
achieving five or six smooth sidewinding at the firststage usually requires an evolution for too many
generations N, and the remaining budget of the numberof generations (80-N) for the second stage does notallow enough time for the bot to learn how to overcometall walls. From another perspective, the nonmonotonous nature of the dependency illustrated in
Fig.4 suggests that there is an optimal point in
configuring the IncGP, and that our consideration of thevisual, qualitative phenotypic traits of the evolved
Snakebots facilitated the discovery of this point.

We experimentally proved our hypothesis that thesmooth sidewinding with an aesthetically wellperceived sinusoidal shape of the bot can be consideredas phenotypic traits of the generic locomotion gaits thatare very important for the success of the second stage ofevolution. Once the evolution discovers these genericgaits, it could terminate the first stage of evolution andswitch its efforts to the second stage. During the latter,these generic gaits could be enhanced further byincorporating additional features that are specificallyneeded to deal with the surrounding challenging terrain.
This discovery opens up the opportunity for an original
way of incorporating interactive evolution into IncGP ina way that allows a human to subjectively influence theoptimal breakdown between the both stages of IncGP.
We are planning to investigate this opportunity in our
future work.
Acknowledgement
The first author would like to thank the members of
the Laboratory of Socioinformatics at Doshisha
University for their support of this study.
References

Fig. 4. Height of walls that are overcame by Snakebot insecond stage as a function of the number of smoothsidewinding bots evolved in first stage of IncGP.

6. Conclusion
In this paper, we investigated the effect of durationof both stages of incremental genetic programming on
its efficiency of evolution of Snakebot. We discoveredthat there the success of the second stage does notdepend on the quantitative metrics (e.g., number ofgenerations, fitness value, complexity of genotype, etc.)
pertinent to the first stage of evolution of the bot.
Consequently, we could conclude that these quantitative
metrics could not be used as reliable criteria to decide
the switch from the first stage of IncGP to the secondone.

1. I. Tanev, T. Ray and A. Buller, Automated Evolutionary
Design, Robustness and Adaptation of Sidewinding
Locomotion of Simulated Snake-like Robot, IEEE
Transactions on Robotics, Vol.21, Number 4, August 2005,
pp.632-645
2. N. Mukosaka, I. Tanev, and K. Shimhara, Performance ofIncremental Genetic Programming on Adaptability of
Snake-like Robot, 17th Asia Pacific Symposium onIntelligent and Evolutionary Systems, IES2013, Volume 24,
2013, pp.152-157
3. R. Smith, Open Dynamics Engine, URL: http://ode.org/
4. T. Kuyucu, I. Tanev and K. Shimohara, Incremental Genetic
Programming Via Genetic Transpositions For Efficient
Coevolution Of Locomotion And Sensing Of Simulated
Snake-Like Robot Proceedings of the 20th he European
Conference on Artificial Life (ECAL-2011), 8th - 12th
August 2011, Paris, France, pp. 439-446
5. I. Tanev, T. Kuyucu, and K. Shimohara, On the Cumulative
Effect of Bloat and Genetic Transposition on the Efficiency
of Incremental Evolution of Snake-like Robot Genetic andEvolutionary Computation Conference, GECCO-2012, July
7-11, 2012, Philadelphia, USA, pp.161-168
6. I. Tanev. Dom/xml-based Portable Genetic Representationof the Morphology, Behavior and Communication Abilities
of Evolvable Agents, Artificial Life and Robotics, 2004,
8:52–56© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 67 -

Design of a new shoulder joint mechanism
for an upper-limb exoskeleton robot.
Masahito Akiyama

Mechanical Engineering, Kyushu University, Fukuoka-shi nishi-ku motooka 744
Fukuoka, 8190395, Japan
Kazuo Kiguchi

Kyushu University, System Engineering Laboratory, Fukuoka-shi nishi-ku motooka 744
Fukuoka, 8190395, Japan
E-mail: 2TE13301T@s.kyushu-u.ac.jp, kiguchi@mech.kyushu-u.ac.jp
http://zeus.mech.kyushu-u.ac.jp

Abstract
This paper proposes an effective shoulder joint mechanism for an upper-limb exoskeleton robot. Humanglenohumeral joint moves in 3-dimention in accordance with the movement of the upper-limb such as the shoulder
flexion/extension and abduction/adduction motion. In order to reduce the difference between the humanglenohumeral joint position and the exoskeleton shoulder joint position, we propose a passive compensation
mechanism which consists of links and sliders. This mechanism can imitate the movement of human glenohumeral
joint without additional motors.
Keywords: exoskeleton robot, shoulder joint, glenohumeral joint

and MGA3 have some additional motors to follow the
movement of the human upper limb. Its flexibility andprecision are good, but its weight and costs are increased.
For simplify the mechanisms, SUEFUL-74, 5 use links andsliders to follow the movement without adding motors.
Its weigh is light, but the following of motion is not
perfect. This paper proposes a shoulder joint mechanism
for an upper-limb exoskeleton robot with links andsliders.

1. Introduction
Recently, the progress in robotics and mechatronics
technology brings much benefit not only in industries, butalso in medicine and welfare. In the medical field,
applications of robot supporting operation are spreading
such as less invasive surgery, telemedicine services andhigh precision medical treatment. In the meantime, an
exoskeleton power assist robot is expected in the field of
welfare. In Japan, especially, researches on exoskeleton
robots focus on caregivers support, rehabilitation due tothe declining birthrate and aging society.
However, it is not easy to design an exoskeleton power
assist robot because the movements of human joints arecomplex. In an upper limb, a shoulder joint movement is
especially complicated. Therefore developed shoulder
joints for exoskeleton robots are intricate. IntelliArm1, 2

2. The human glenohumeral joint
The human shoulder joint, called a glenohumeral joint,
moves in 3-dimention by the movement of the upper-limb
astheshoulderflexion/extension
andsuch
abduction/adduction motion. Several measurement data
about shoulder joints’ angles from a medical point of© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan.10-12, Oita, Japan- 68 -

Masahito Akiyama, Kazuo Kiguchi

100

100

80

80

60

60

40

40

20

20

0

0
-100

-80

-60

-40

-20

-20

0

0

20

40

60

80

Displacement (z axis) [mm]Displacement (x axis) [mm]Fig. 1. Measurement results (shoulder abduction motion)

100

100

80

80

60

60

40

40

20

20

0

Fig. 3. Proposed shoulder joint

0
-80

-60

-40

-20

0

Displacement (x axis) [mm]

20

-60

-40

-20

0

20

40

Displacement (z axis) [mm]Fig. 2. Measurement results (shoulder flexion motion)

view are reported in Ref. 6. Kiguch et.al7 measured thecoordinate data of glenohumeral joint.
Their measurement data is shown in Figs 1 and 2. In theshoulder abduction motion, the glenohumeral joint
moves to inside and high position in x-y plane. In theshoulder flexion motion, the glenohumeral joint moves tohigh position in z-y plane.
In this paper, a new shoulder joint mechanism for an
exoskeleton robot is designed based on the coordinates
measured by Kiguchi et.al.
3. Mechanisms of the robot’s shoulder joint
This shoulder joint mechanism use links and sliders tofollow the movement and to save its weight. This robot
shoulder joint has three DOF (Figure 3) for three DOF ofthe human shoulder joint. The 1st axis is used forshoulder horizontal abduction/adduction motion. The 2nd
axis is used for shoulder flexion/extension motion. The 3rd
axis is used for shoulder internal/external rotation motion.
Pulleys are rotated by wires around theFig. 4. Top mechanism at shoulder horizontal flexion 0 deg.
and 90 deg. (Top view)

1st and 2nd axis. A motor rotates the forearm holder
around the 3rd axis.
The angles of the 1st and 2nd axis are limited in 0 deg. to
90 deg. for safety. Two mechanisms linked the 1st and 2nd
axis reduce the error between the human© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan.10-12, Oita, Japan- 69 -

Design of a new

Table 1. The coordinates of the robot

name
Human glenohumeral
joint (the origin)
1st axis
Link 1initial point
Link 1end point

axis

value [mm]

x

0

y

0

z

0

x

0

z

0

x

0

z

28

x

112.5

z

38.6

Length of Link 1

113
y

2nd axis
Link 2 initial point
Link 2 end point

10

z

48

y

38

z

38

y

-140

z

10

Length of Link 2
Link 2 end point

184
ideal point

40

Fig. 5. Side mechanism at shoulder flexion 0 deg. and 90 deg.
(Side view)

0-40

glenohumeral joint position and the exoskeleton shoulder
joint position. Figure 4 shows the top mechanism.
The link 1 and slider 1 change the position of the link 2’s
initial point. This difference of the positions of link 2’s
initial point enable this exoskeleton shoulder joint tofollow both the human shoulder flexion motion andshoulder abduction motion. The side mechanism isshown in Fig 5. The link 2 changes the length of the slider
2. The slider 2 connect its forearm holder. Shrinking thelength of slider 2 means that the center of the exoskeleton
robot shoulder joint moves to up or inside.
4.

-80

-120

-160

-120

-80

-40

0

Displacement(z axis)[mm]Fig. 6. Results of the simulations(Right: shoulder flexion motion,
Left: shoulder abduction motion)

2 initial point is limited. The z value of the point must be
bigger than 38mm because of its mechanical interference.
The results of the simulation shows in Figure 6. The blue
data means the path traced by a moving Link 2 end point.
The black data means ideal point calculated from thehuman measured value. The difference between thepositon of human glenohumeral joint and the position of
the exoskeleton shoulder joint is less than 25 mm.

Simulation in 3D CAD

This proposed mechanism is evaluated by a simulation in
3D CAD. The coordinates of its points when the angle ofhorizontal flexion is 0 deg. and the angle of shoulder
flexion is 0 deg. show in Table 1. The position of the link

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan.10-12, Oita, Japan- 70 -

Masahito Akiyama, Kazuo Kiguchi

5. Conclusion
In this paper, the simple following exoskeleton shoulder
joint for an upper-limb power assist robot is proposed. The
proposed shoulder joint mechanism enable theexoskeleton robot to follow the movement of humanglenohumeral joint and reduce the difference of thesejoints. The effectiveness of the proposed mechanism isdemonstrated by 3D CAD simulations. We are presently
assembling a real robot with this mechanism.

6. References
1. Y. Ren, H. -S. Park, L. –Q. Zhang, Developing awhole-arm exoskeleton robot with hand opening andclosing mechanism for upper limb stroke rehabilitation.
In: IEEE international conference on rehabilitation
robotics. 2009.
p. 761–765.
2. Y. Ren, H. -S. Park, L. –Q. Zhang, IntelliArm: an
exoskeleton for diagnosis and treatment of patients withneurological impairments. In: Biennial IEEE/RAS-EMBS
international conference on biomedical robotics andbiomechatronics. 2008. p.109–114.
3. C. Carignan, J. Tang, S. Roderick, Development of an
exoskeleton haptic interface for virtual task training. In:
IEEE/RSJ international conference on intelligent robots
and systems. 2009. p. 3697–3702.
4. Gopura RARC, K. Kazuo, Y. Yi, SUEFUL-7: a 7DOF
upper-limb exoskeleton robot with muscle-model-oriented
EMG-based control. In: IEEE/RSJ international
conference on intelligent robots and systems. 2009. p.
1126–1131.
5. K. Kazuo, M. H. Rahman, M. Makoto, T. Kenbu,
Development of a 3DOF mobile exoskeleton robot forhuman upper-limb motion assist. Robotics andAutonomous Systems 2008; 56:678–691.
6. N. K. Poppen, P. S. Walker, Normal and abnormal motionof the shoulder, J Bone Joint Surg Am.
1976;58:195-201.12
7. K. Kazuo, K. Kenryu, H. Yoshiaki, Design of a 7DOF
upper-limb power-assist exoskeleton robot with moving
shoulder joint mechanism. In: Robotics and Biomimetics(ROBIO), 2011 IEEE International Conference on. IEEE,
2011. p. 2937-2942.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan.10-12, Oita, Japan- 71 -

A Machine Learning Approach to a Lateral Continuous Force Estimation for a Walking Biped
Robot
Yeoun-Jae Kim

Robotics Program, KAIST, Eu-Seong-Gu Dae-hak-Ro 291 KAIST EE 4231
Daejeon, 305-701, South Korea
Ju-Jang Lee

Electrical Engineering, KAIST, Eu-Seong-Gu Dae-hak-Ro 291 KAIST EE 4231
Daejeon, 305-701, South Korea
E-mail: lethkim@kaist.ac.kr, jjlee@ee.kaist.ac.kr
iliad.kaist.ac.kr

Abstract
In this paper, a regressor for determining a lateral external continuous force applied upon a walking biped robot is investigated andverified by a numerical simulation. A pre-defined walking gait of a biped robot is constructed by the Tchebyshev method. And acontinuous force-action classifier is generated. It determines whether the lateral external force is a continuous force or not. A regressor
which estimates a lateral external continuous force acted upon a walking biped robot is constructed by SVR (Support Vector
Regressor). The regressor is verified by a numerical simulation. We assumed that only lateral force is applied upon the COG (Centerof Gravity) of the walking biped robot.
.
Keywords: walking, biped robot, SVR, regressor, force estimation..

human being, the robot can accidently collide with anobject or a human beings. The worse consequence isfalling down and gets damage. Many researches are
performed to alleviate and avoid this kind of humanoid
damages. Fujiwara et.al.[5] made a falling motion
control to minimize the falling damage. Morisawa
et.al.[6] prevented the collision of a humanoid robot
with other objects by incorporating an emergency stop
motion planning. Renner and Behnke[7] make use of
attitude sensors and reflexes to detect instability andavoid falling down for a humanoid robot. O. Hohnet.al.[8] made a classifier for biped instability. Kim
et.al.[9] made a falling detection and avoidanceclassifier for a biped robot by using SVM(Support
Vector Machine). All the above works are about
alleviating and avoiding the humanoid damages
irrespective of the amount of the external force. If theexternal force is small, the biped robot can recover fromthe instability by its inertial. And if the external force is

1. Introduction
The biped robot (humanoid robot) is the robot which
resembles human beings. Not only resembles theappearance, but also resembles its functionality.
Compared with other function-specific robots, thehumanoid robot is versatile in its functionality. In
theorem, it can do almost all the works which humans
do. That's why many robot researchers are enthusiastic
for making and researching a perfect humanoid robot.
The most prominent ones of these humanoid robots are
Asimo[1], Petman[2] and Hubo[3]. Other state-of-theart humanoid robots are found in DARPA (Defense
Advanced Research Projects Agency) robotics challenge
2013[4]. The humanoid robot may interact with ahuman being. For example, it may serve a human beingby going and taking a cup of coffee to a human being.
And it can sweep the house in which the human andhumanoid live together. During interacting with a© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 72 -

Yeoun-Jae Kim, JJu-Jang LeeK
E ABOUT TH
HE
intermediate and large, KNOWLEDGE
OF THE EXTTERNAL FORCE will maake
AMOUNT O
the biped robbot control moore specific anda efficient. So,
the authors mmade a SVR baased regressorr for determini
ning
a continuous lateral externnal force in a walking bipped
robot. This rregressor willl be used too determine thelateral continnuous external force and control the bipped
robot not to ffall down by thhe external forrce. The papeer isorganized as follows. Sectiion 2 explainss the pre-definned
walking gait of a biped robot.
rSectionn 3 set forth thecontinuous fforce-action classifier whhich determinnes
whether the aapplied externnal force is coontinuous or nnot.
The SVR annd SVR bassed continuou
us lateral foorce
estimator is explained inn Section 4 and 5 and thenumerical sim
mulation resuult is in Secttion 6. And theconclusion is drawn in Secction 7.
Contributionss are to bee in English
h. Authors areencouraged to have theiir contributioon checked forgrammar. Am
merican or Brritish spelling should be ussed.
Abbreviations are allowed but should bee spelt out in ffull
when first ussed. Integers tent and below
w are to be sppelt
out. Italicizee foreign lannguage phraases (e.g., Lattin,
French).

Fig. 1 Walk
king gait sequuence (in sagitttal view)
3. Continuouss Force-Actioon Classifier
WhileW
the bipeed robot is walking in a pre-definedd
waalking gait, a sudden lateeral external force can bee
ap
pplied to the walking
w
biped robot. The biiped controllerrmu
ust determinee whether som
me force is appliedaor nootan
nd furthermoree if it is, it mmust also deterrmine whetherr
thee applied foorce is contin
inuous or im
mpulsive. Theecoontinuous force-action classsifier determin
nes these. Figg.
2 representsrthee ZMPy graphh during walkiing in the predeefined gait. Th
he width of tthe sole is 0.2
2m, so ZMPyy
spans -0.1 ~ 0.1
1m. The ZMPPy graph is a smooth curvee
wiithin this spann, which reprresents the walking
w
gait iss
weell constructed for the bipped robot nott to fall downn
duuring walking. The samplin
ing rate is 1m
msec which iss
previously stateed and all thhe ZMPy poiints in a SSP
P
waalking gait are
a 1300. Thhe continuouss force-actionn
claassifier takes 4 points ZMPy(t-3),, ZMPy(t-2)),
ZM
MPy(t-1) andd ZMPy(t) from ZMP sensors anddcoompares this 4 ZMP points with the pre-ddefined ZMPyy
at the specified
d walking tim
me and if all the 4 ZMPyypooints differ, it returns 1 orr returns 0. The
T returned 1
vaalue means th
hat the continnuous lateral force is actedd
up
pon a biped robot.
r
Algoritthm 1 in Fig. 3 representss
theese working ofo the continuoous force-actio
on classifier.

2. Pre-defin
ned Walking Gait
As pointed oout in the abbstract, we asssumed that thelateral continnuous force is applied uponn the COG of thewalking biped robot. The walking gait of a biped robbot
is important in making thhe force-estim
mating regresssor
for it changees the lateral dynamics of the biped robbot.
So, in this seection, we expplain the pre-defined walkking
gait of a bipped robot. In our previouss work [10], anenergy-efficieent walking gait of a biped robot isconstructed bby using Tchebbyshev metho
od. We adopt themethodologyy in this paper and made an energy-efficiient
walking gait. The walkinng gait is com
mprised of S
SSP(Single Suppport Phase) and DSP (D
Double Suppport
Phase). Howeever, we onlyy use SSP wallking gait for the
regressor estiimates the latteral continuo
ous force in S
SSP
walking gait. Fig. 1 illusstrates the SS
SP walking ggait
made by Tchebyshev methhod. The SSP walking stridee is
0.6m and SSP
P walking tim
me is 1.3sec. The sampling rrateof the simulaation is 1msecc. The walkinng gaits in Figg. 1
are viewed saagitally.

© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 73 -

A Machine Leearning Approachh

Fig. 2 ZMPy graph (lateral direction ZMP)

ntinuous Forc
rce Estimatorr
5. Lateral Con
Fig. 3 Conntinuous forcee-action classiffier algorithmm4.

us force-action
on classifier determines thaat
If the continuou
some continuou
us force is appplied, the laterral continuouss
forrce estimator is activated to estimate thet amount ofthee continuous force.
f
The whhole process ofo constructingg
thee lateral contiinuous force eestimator is deepicted in Figg.
5. In Fig. 5, the SSP gait off a biped robo
ot is depictedd.
Th
here are 5 connsecutive pictuures that represent one SSP
P
gaait. The walkking times arare 0, 400, 600,
6
800 andd
13
300msec. The biped picturees are viewed
d sagitally andd
thee inertial coordinate system
m is represen
nted as X-Y-ZZ
cooordinates in right side of thhe picture. Wee assumed thaat
thee external con
ntinuous forcee is activated laterally in -YYdirrection to the COG (Centeer Of Gravity) of the bipedd
rob
bot at any SSP
P gait sequencce.
We
W divided the SSP walking gait as 5 timees (0, 400, 6000,
80
00 and 1300m
msec) to make it easy to con
nstruct a forcee
esttimator. At eacheof this 5 gait timees, the lateraalcoontinuous forcce estimator iis constructed
d by utilizinggSV
VR with lineaar kernel. Andd if the force is activated atathee 4 time intervals (0~4000, 400~600, 600~800 andd
8000~1300msec)) the linear interpolation technique iss

SVR (Su
upport Vectorr Regression))

Because the regressor whiich determinees the amountt of
the lateral conntinuous forcee acting upon a walking bipped
robot makes uuse of the SV
VR, we briefly
y explains whaat a
SVR in tthis section.. SVM (SSupport Vecctor
Machine)[11]] is basicallly used as a classificati
tion
problem, howwever, if it is usedu
as a regrressor, it is SV
VR.
The SVR inccorporates thee same princip
ples as the SV
VM
for classificattion with onlyy minor differrences. In SV
VM,
the constraintt is that the distance betweeen the real poiints
and the hypeerplane must beb larger thann ε(the margiin).
However in SVR, the coonstraint is th
hat the distannce
between the real points andathe hypeerplane must be
smaller than ε(the margin)). In fact, the SVR solutionn issolving the opptimization prroblem in Fig.. 4.

Fig. 4 Suppport vector reegression basiic framework© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 74 -

Yeoun-Jae Kim, JJu-Jang Lee

plied continuoous
adopted to deecide the amoount of the app
force. All thiss procedure iss expressed in Fig. 5.

gular points represents thetestimatedd
thee red triang
exxternal force. As the tw
wo graphs reepresents, theeesttimated valuee is fairly welll matches the exact valuee.
Th
he RMS (Rooot Mean Squaare) errors at 500msec andd
7000msec are 0.1838N
0
and 8.3336N. The
T
reason ofrellatively large RMS error at 700msec com
mpared withFig.
F 6 ZMP-applied force ggraph at half walking
wtimethaat at 500msecc is the largee estimation erroreat ZMPyy
vaalue 0 in 700m
msec graph. Thhis is the limittation of linearrSV
VR and otherr kernel-basedd SVR will copecwith thiss
esttimation errorr.

Fig. 5 Laterral continuouss force estimaator frameworkk
6. Simulatioon Results
In this sectioon, the simulaation results area presented.. In
Fig. 6 the Z
ZMP vs. the applied force graph at hhalf
walking timee after trainingg is presented
d. The points are
acquired by thhe numerical simulation off the biped robbot(a) 5500msec

Fig. 6 Z
ZMPy vs. Forrce at half wallking time
and the linearr line represennts a trained linear SVR. T
The
results show that the ZMP
P-force graph is almost linnear
and the resuulting trainedd SVR curve is also alm
mostlinear. In Figg. 7 there arre two graphs represents theperformance of the lateraal continuous force estimattor.
The upper graaph is when thhe force is app
plied at 500m
msec
in the walkinng time and the latter graaph is when theforce is appliied at 700mseec. The ZMPyy points are 2. 75,
20.22, 37.69, 55.16, 72.13 and 90.1mm at 500msec aand
3.91, 13.46, 30.84, 48.222, 65.60 an
nd 82.98mm at
700msec. thee blue rectanngular pointss represents theexact appliedd force from thet numericall simulation aand(b) 7700msec
Fig. 7 Lateral continuouscforrce estimator performancesp
7. Conclusionn
In this paper, thet lateral conntinuous force estimator iss
coonstructed andd verified by nnumerical sim
mulations. Theelatteral continu
uous force estimator estimates
e
thee
ex
xternally applied lateral forcce when a bip
ped is walkingg.

© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 75 -

A Machine Learning Approach

9. J. J. Kim, Y. J. Kim and J. J. Lee, A Machine Learning
Approach to Falling Detection and Avoidance for Biped
Robot in Society of Instrument and Control Engineers
Annual conference 2011, pp.562-567.
10. Y. J. Kim, J. Y. Lee, J. J. Lee, Bipedal Walking
Trajectory Generation Using Tchebychev Method inInternational Conf. on Mechatronics and Informatics,
pp.265-271.
11. V. Vapnik, S. Golowith and A. Smola, Support Vector
Method for Multivariate Density Estimation in Advancesin Neural Information Processing Systems, vol.12,
pp.659-665.

We assumed that the external continuous force isapplied to the COG of the biped laterally. It makes useof linear SVR and linear interpolation technique. The
numerical simulation results show that the estimatorwell predict the external force. Future works are to train
the estimator with different kernel to reduce the RMS
error and to perform other regression model except SVR.
8. Acknowledgement
This research was supported by the MOTIE (TheMinistry of Trade, Industry and Energy), Korea, under
the Technology Innovation Program supervised by
KEIT (Korea Evaluation Institute of Industrial
Technology), 10045252, Development of robot task
intelligence technology.
References
References are to be listed in the order cited in the text.
Use the style shown in the following examples. For
journal names, use the standard abbreviations. Typeset
references in 9 pt Times Roman.
1. K. Hirai, M. Hirose, Y. Haikawa and T. Takenaka, The
development of Honda humanoid robot in Proc. of 1998
IEEE Int. Conf. on Robotics and Automation, pp.421-439.
2. http://www.bostondynamics.com/.
3. J. H. Oh, D. Hanson, W. S. Kim, Y. Han, J. Y. Kim and I.
W. Park, Design of Android type Humanoid Robot Albert
HUBO in Intelligent Robots and Systems, 2006 IEEE/RSJ
Int. Conf. on, pp.1428-1433.
4. E. Ackerman , DARPA Robotics Challenge Trials: Final
Results
availableat
http://spectrum.ieee.org/automaton/robotics/humanoids/d
arpa-robotics-challenge-trials-results.
5. K. Fujiwara, F. Kanehiro, F. Kajita, K. Kaneko, K. Yokoi,
and H. Hirukawa, UKEMI: Falling Motion Control to
Minimize Damage to Biped Humanoid Robot in Proc. ofthe 2002 IEEE/RSJ Int. Conf. on Intelligent Robots andSystems, pp.2521-2526.
6. M. Morisawa, K. Kaneko, F. Kanehiro, S. Kajita, K.
Fujiwara, K. Harada, H. Hirukawa, Motion Planning of
Emergency Stop for Humanoid Robot by State Space
Approach in Proc. of the 2006 IEEE/RSJ Int. Conf. onIntelligent Robots and Systems, pp.2986-2992.
7. R. Renner and S. Behnke, Instability Detection and Fall
Avoidance for a Humanoid using Attitude Sensors andReflexes in Proc. of 2006 IEEE/RSJ Int. Conf. onIntelligent Robots and Systems, pp.2967-2973.
8. O. Hohn, J. Gacnik and W. Gerth, Detection andClassification of Posture Instabilities of Bipedal Robots
in Climbing and Walking Robots, pp.409-416.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 76 -

The Improvement of Robust Robot SLAM Algorithm Based on Sensor Fusion
Jiwu Wang1

School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University
Beijing 100044, China1
Shunkai Zheng1, Fangbo Liao1

School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University
Beijing 100044, China1
Sugisaka Masanori 2

Alife Robotics Corporation Ltd, Japan and Open University, United Kingdom2
E-mail: jwwang@bjtu.edu.cn; sugisaka3@mocha.ocn.ne.jp

Abstract
The Kinematic model of the robot is a very important part in SLAM, It’s error model will influence
the positioning accuracy of robot and map building. Generally, the motion model relies simply on the data fromencoder feedback. Due to cumulative error, the robot pose accuracy is relatively poor by mileage positioning withencoder. Here a new method is put forward based on data fusion of gyro sensor with the encoder data, and the robot
pose accuracy is analyzed and improved. Then we use the optimization Kinematic model on SLAM to verify therobustness.
Keywords: sensor fusion; DCM; EKF filtering; SLAM

microprocessor reads and writes data correction of1
sensors.
Gyroscope, accelerometer, compass data from thesethree sensors output are implemented on an I ² C bus
interface, the sensor run as a slave device, a2
microprocessor done as the master device. The system
structure is shown in Figure 1.
The MEMS sensor with 3 axes digital output angularvelocity, each axis is provided with a 16bit A/Dconverter, built-in low-pass filter with adjustable3
bandwidth, shown in figure 2. The measuring range is

1. Introduction (Basic Measurement Unit)
The idea of pose measurement system based on thefusion is as following: firstly, build motion analysis
model; then with the inertial sensor data, give theaccurate pose estimation.
Hardware IMU measurement system used in this paper
to achieve mini atomization of inertial measurement
through the MEMS technology, the IMU module
contains ITG3205 gyroscope, ADXL345 accelerometer,
HMC5883L electronic compass. The sensor isconnected to the microprocessor via a bus, and the© The 2015 International Conference on Artificial Life and Robotics(ICAROB 2015), Jan. 10-12,Oita, Japan- 77 -

Jiwu Wang, Shunkai Zheng, Fangbo Liao,Sugisaka Masanori

2. Analysis of DCM Algorithm± 2000 ° /s, and ADC output frequency can be adjust in
the range of 8000HZ to 3.9HZ.

The DCM algorithm is as followings: a nonlinear
correlation gyro measurement data and the direction
4
cosine change rate of difference equations. The flow
algorithm in digital system is shown in figure 4.

Fig.4. ITG-3205 structure diagram
Fig.1. IMU pose measurement system architecture diagram

2.1. The Kinematics analysis algorithm
The kinematics equation is:

d r (t )  (t )  r (t )dt(1)

 ( t ) is the angular velocity vector.
With the initial state and rotating vector, it can be:t r ( t )  r (0 )   d  ( )  r ( )
（2）

0

d  ( )   ( ) d Fig.2. ITG-3205 structure diagram

ADXL345, the three axis acceleration of an ultra-low
power design, measurement range of up to ± 16g.
Moreover, the device also has the knock detection, free
fall detection, etc., shown in figure 3.

Where r ( 0 ) is the initial vector
Further, in the global coordinate system, using themethod of approximate matrix, it will be rg lo b a l ( t  d t )  rg lo b a l ( t )  rg lo b a l ( t )  d  ( t ) （3）d  (t )   (t ) d t
For error compensation of angular velocity, a modified
angular velocity is introduced, which is determined by
the angular velocity and its correction.

 ( t )   g y ro ( t )   co r ( t )（4 ）Formula (5) is the DCM update equation matrix about
Fig.3. ADXL345 structure diagram

gyroscope.

Electronic compass HMC5885, as a low cost electronic
compass with a high precision, has a 12 bit ADC
conversion accuracy, compass navigation precision canbe 1° or 2 °.

 1R (t  dt )  R (t )  d z  d y d z1d xd y  d x 
1 （5）Here, d  x   x dt , d  y   y dt , d  z   z dt
© The 2015 International Conference on Artificial Life and Robotics(ICAROB 2015), Jan. 10-12,Oita, Japan- 78 -

The Improvement of Robust

2.2. Drift compensation

In figure 6, the movement state of three axises are given.
Z axis acceleration is caused by the acceleration ofgravity. In figure 7, they are three axis angular velocity.

In order to calculate the Roll, Pitch, Yaw forcompensation, a feedback PI controller is introduced.
The controller error calculation of Roll and Pitch uses
the acceleration and the electronic compass Yaw error.
In our experiments, the following figure is drawn basedon the sensor data by IMU sensor.

3. The Extended Kalman Filter Algorithm

Now the encoder measuring odometry data (Odom) andthe inertial measurement data (IMU) can be determined.
The full state vector in the space is expressed asT（6）x k   X k , Y k , Z k ,  k ,  k , k , k ,  k With  t time, we can get
NL vR   t v   N RRtvR  vL L v    L

Fig.5. Orientation sampling curve

Where （7）

is parameter of the wheel diameter andencoder. The pose after  t will be X k  v k c o s ( k )  t  Y k  v k s i n ( k )  t 
0



0

x k  1  0 k   k  t



k




 k


Fig.6. Three axis speed curves（8）

The EKF data integration system model is given as
follows:xk

Fig.8. EKF data integration system model

The extended Kalman filter with fusion of encoder andgyroscope data, the system state equation is derived:© The 2015 International Conference on Artificial Life and Robotics(ICAROB 2015), Jan. 10-12,Oita, Japan

Fig.7. Three axis angle velocity curves- 79 -

Jiwu Wang, Shunkai Zheng, Fangbo Liao,Sugisaka Masanorix k  1   k  1| k x k   k W k v  0.3m / s ,  G  3radians（9）Then we carries on the simulation test of SLAM using
the different control noises, the results as below:

The measurement model for encoder is
Xk 
Y 
 k 
y odom , k  C odom x k   k   eodom , k ( d ) k 
 
 k 

（ 10）

The simplified IMU mathematical model is k 
y gyro ,k  C gyro x k    e gyro ,k k 

（11）

Error covariance of the matrix is: R odom ( d )R (d )  0

0 
R g y r o 

（12）With above the measurement model and state transition
model, using the extended Kalman filtering, the robot
pose estimation can be determined.
In order to validate the Kalman filter, with the above
data, Matlab simulation is performed.

Fig.10.The simulation test of SLAM
( v 0.4m/ s , G  6radians )Fig.9. The introduction of Kalman filter wave curve

Figure 9 is the introduction of Kalman filter wave curve.
The output curve gets smoothing, and angle estimation
accuracy is greatly improved.

Fig.11.The simulation test of SLAM
( v 0.3m/ s , G  3radians )We can see from the Fig10 and Fig11, after using the
new method on SLAM, Robot positioning is moreaccurate, the robustness is improved obviously.

4. The Simulation Experiment of SLAM

When we use only the encoder as the sensor, We
measured the control noises of mobile robot
Kinematics model is: v  0.4m / s ,  G  6radians
And when we use the improvement of our new method,
the control noises is:

5. Conclusions

In this paper, the Kalman filtering accelerometer, gyroand the electronic compass fusion method is studied.

© The 2015 International Conference on Artificial Life and Robotics(ICAROB 2015), Jan. 10-12,Oita, Japan- 80 -

The Improvement of Robust

Based on dynamic test data, the Kalman filter
compensates the sensor drift, noise and other factors
caused by the accelerometer, gyroscope and theelectronic compass. It reduces the angle measurement
error, and improves the operation accuracy. The
experiment results show the effectiveness of the
proposed method.
References
1. R. A. Mohammad, Sensor fusion by pseudo information
measure: a mobile robot application, ISA Transactions,
41 ( 2002)283-301.
2. A. J. Terrya, M. Z. John, Sensor fusion by a novel
algorithm for time delay estimation, Digital Signal
Processing, 22(2012) 439-452.
3. R. S. Blum, Multisensor image fusion performance limits
from an estimation theory perspective, Information
Fusion, 7( 2006) 250-263.
4. A. Weckenmann, X. Jiang, Multisensor data fusion indimensional metrology, CIRP Annals-Manufacturing
Technology, 58(2009)701-721.
5. H. Youngjoon, H. Hernsoo, Localization andclassification of target surfaces using two pairs ofultrasonic sensors, Robotics and Autonomous Systems,
33(2000) 31-41.
6. J. B. Gao，C. J. Harris, Some remarks on Kalman filters
for the multisensor fusion, Information Fusion,3(2002)191-201
7. G. Gerasimos, G. Rigatos. Extended kalman and particle
filtering for sensor fusion in motion control of mobile
robots, Mathematics and Computers in Simulation,
81(2010)590-607© The 2015 International Conference on Artificial Life and Robotics(ICAROB 2015), Jan. 10-12,Oita, Japan- 81 -

Interactive Musical Editing System to Support Human Errors
and Offer Personal Preference for an Automatic Piano
Kenji Tsunenari

Department of Mechanical Information Science and Technology, Kyushu Institute of Technology
680-4, Kawazu, Iizuka-City, Fukuoka, 820-8502, Japan
Email: ktsune@mmcs.mse.kyutech.ac.jp

Eiji Hayashi

Department of Mechanical Information Science and Technology, Kyushu Institute of Technology
680-4, Kawazu, Iizuka-City, Fukuoka, 820-8502, Japan
E-mail: haya@ mmcs.mse.kyutech.ac.jp

Abstract
We have developed a system that allows a piano to perform automatically. In order to play music in the manner of alive pianist, we must add expression to the piano’s performance. In the case of music, there are often 1,000 or morenotes in the score, requiring that an editor spend a huge amount of time to edit. Therefore, we have developed aninteractive musical editing system that utilizes a database to edit music more efficiently.
Keywords: Automatic Piano, Knowledge Database, Computer Music, Music Interface

music, and the editor must spend a huge amount of timeto accurately simulate an actual emotionally expressive
performance that a highly skilled pianist could give.
Moreover, a highly skilled pianist is able to play anunfamiliar piece of music by sight, even if theperformance is not completely in accord with anintended specific musical interpretation. Current
computing systems cannot perform a new piece ofmusic by sight, and thus they cannot simulate a humanpianist’s musical expression. Therefore, we havedeveloped an interactive musical editing system thatutilizes a database in order to edit music more
efficiently 1.

1. Introduction
We have developed a system that allows a piano toperform automatically. In this system, 90 actuators havebeen installed on the keys and pedals of a grand piano.
These actuators execute key strokes and pedal
movements to govern the piano’s performance. (SeeFig.1)In order to develop an automatic piano that plays
music in the manner of a live pianist, we have to add
expression to the piano’s performance. Essentially,variations in tempo, dynamics, and so on are needed inorder to arrange the respective tones in a desired way.
Moreover, in the case of piano music, there will often be1000 or more notes in the score of even a short piece of© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 82 -

K. Tsunenari, E. Hayashi

In a current research, MIDI data regarding theperformances of highly skilled pianists has been
analyzed in order to observe the stylistic tendencies of
their performances. The results showed that phrases
having similar patterns in the same composition were
performed in the similar style.
We developed a system that searches for similarphrases throughout a musical score and infers the styleof the performance. Here, we proposed a method using
Dynamic Programming (DP) matching as a way tosearch for similar phrases. In our interactive musicalediting system, we have created the Score Databasewhich contains information regarding a musical score.
This database contains a field called “Note Value,” inwhich data indicates the type of note--e.g., a quarter
note, a triplet, and so on. This system converts notes
into character strings using the “Note Value” data.
In addition, the system computes DP matching using
the character strings and calculates the degree of
disagreement between these strings. It uses an index tojudge the resemblance between the strings. For its
method of inferring performance expression, it uses thebest alignment for DP matching, which enables it toexpress the best correspondence between notes. In orderto edit music more efficiently, we must consider
dynamic marking, beat and so on and we createddatabase contains them. We developed an inferring
process with regards to similar phrases using the bestalignment and database.
In this paper, we describe the results of searching forsimilar phrases using DP matching and inferring forthem using DP matching and database.

Fig.1. The automatic piano.

Fig.2. DP Matching

defined as the distance between patterns. In this system,
this distance is handled as a threshold to judge whether
the phrases are similar to each other.
For example, if the cost moves up or to the right,
then it is increased by 1. If it moves to the upper right,
then it does not increase. Also, if the characters do notcorrespond in each point, then the cost is increased by 5.
3. Musical Editing Support System

2. Searching for Similar Phrases

3.1. System Architecture
As a result of the analysis, it was found that phrasesof the same pattern existing in the same tune are
The structure of the system is shown in Fig.3, The
performed in a similar expression 2. This time, we used
DP matching to search for similar phrases.
DP matching is a technique used widely in the fieldof speech recognition, bioinformatics and so on. It has afeature that can calculate the similarity between two
words that are different in a number of characters fromeach other.
In Fig.2, the route of minimum cost in each point istaken, and the route with the lowest cost is assumed
Fig.3. Structure of the editing system
finally to be the optimal path. The cost at that time is© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 83 -

Automatic Piano

user edits music via the user’s interface on a computer
display. The user can also access a database that hasmusical grammar, the user’s preferences, and so on. As
a result, edition work is reduced and efficient editing
becomes possible.

notation. It is composed of five tables containing“Dynamics marks”, “Articulation marks”, “Symbol of
Changing Dynamics or Changing Tempo” (symbol that
affects the speed of a note or the increase or decrease of
the volume), “Time signature”, and “Tempo marks”.
Analyzing a music symbol according to its usage allows
efficient information processing by the system.

3.2. Format of Performance Information
The parameters of performance are shown in Tables
1 and 2. The automatic piano that we have developed
uses a music data structure that is similar to MIDI. We
defined performance information, dividing it into two
categories: the notes and the pedals. The note
information is comprised of the six parameters involvedin producing a tone: “Key” (note), “Velo” (velocity),“Gate”, “Step”, “Bar” and “Time”. “Velo” is the
dynamics, given by the value of 1-127. “Gate” is theduration of the note in milliseconds. “Step” is theinterval of time between notes, and it also exhibits
tempo. “Bar” is the vertical line placed on the staff todivide the music into measures.
The pedal information is comprised of four parameters:“Key” (indicating the kind of pedal: “Damper” or

3.3.2 Score Database
This database has symbols including time signatures,notes, rests and so on in standard musical notation.
Symbols were pulled together in order of bars, and bar
symbols were arranged in a time series. Performance
expression in itself is only information such as pitch,strength, and length and concerns only the enumerationof a sound. Because the identification of each sound isdifficult, editing of the performance expression isdifficult. By adding the Score Database’s information to
performance expression, we can connect each note to its
enumeration. In doing so, it becomes easy to edit eachphrase.
This database consists of three tables, the “Element

Fig.4. Automatic translation with database“Shifting”), “Velo” (the pedaling quantity), “Time” (theduration for which the pedal is applied)”, and “Bar”.
3.3. Editing Support Process with Database
Our system can automatically apply a roughperformance expression using Musical Rules Databaseand Score Database. (See Fig.4)
In addition, the system has Preference Database,which sores the editing characteristic of the user.
3.3.1 Musical Rules Database
Fig.5. Note Value

This database contains the architecture of musicalgrammar necessary to interpret symbols in musical© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 84 -

K. Tsunenari, E. Hayashi

table” (showing the position of the note and thecomposition of the chord), the “Symbol table” (showing
the position of the music symbol) and the “Same table”(showing the position of the repetition of the phrase).
The Element table contains the field “Note Value”. Datain this field indicates the type of note, e.g., a quarter
note, a triplet, and so on. “Note Value” is expressed bythree hexadecimal numbers, which are shown in Fig.5.

No.

Existing result

New result

Oppitz data

1
2
3
4
5

1.00001.2115
1.16481.04401.15000.8962
1.21150.91840.9144
0.97120.7596
1.29810.82400.6992
0.69233.4. Beat DatabaseWe created Beat Database to edit music more
efficiently. Beat Database is stored data which isGerhard Oppitz’s performance of beat expression.
Using it, only step is inferred to change tempo.
4. Inferring Result
In Fig.6a is an input phrase used to search similarphrase for comparative experiments. Fig.6b is searchedsimilar phrase. It is inferred two methods and results are
outputted. First method, similar phrase is inferred, not touse Beat Database. The second method, Beat Databaseis used. The two results and data that performed bypianist in the similar phrase are compared to confirm
improvement of the result.
The results of the experiment are shown Table1 andFig.7. The results provide three values: the “Existing
result” which don’t use Beat Database, the “New result”
which use Beat Database and the ”Oppiz data” which isthe actual emotional expression in a performance by a
Oppitz in the similar phrase. “No.” means note number.
“No.1” means the first note in the phrases.
We have consistently found that the emotional
expression represented by the “New result” resembles
the “Oppitz Data” more than it resembles the “Existing
result”

Fig.7. Results of inference

5. Conclusion
We developed an interactive musical editing system
to edit music more efficiently. This system is composedof a "Searching system of similar-patterned phrases"
and an "Inferring system of emotional expression in a
performance. The Inferring system infers the emotional
expression in a performance of similar phrases byreferring to the databases automatically, greatly
reducing the time needed for editing. Moreover, theinteractive musical editing system provides edited
phrases that resemble the "Pianist Data," which are theactual emotional expressions in a performance by askilled human pianist.
References(a) Input phrase

1. E. Hayashi et al, Behavior of piano-action in a grandpiano.I, Journal of acoustical Society of America,
Vol.105, pp.3534-3544, 1999.
2. Y. Hikisaka and E. Hayashi, Interactive musical editing
system for supporting human error and offering personal
preferences for an automatic piano –Method of searchingfor similar phrases with DP matching and inferring
performance expression-, Proc. Of the 12th International
Symposium on Artificial Life and Robotics, GS4-3, 2007.

(b) Similar phrase

Fig.6. Phrases for inferring

Table 1. Results of inference
StepRate© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 85 -

Modeling of collaboration in design process Based on Channel Theory
Patchanee Patitad and Hidetsugu Suto

Muroran Institute of Technology, 27-1 Mizumoto-cho,
Muroran-shi, Hokkaido, 050-8585, Japan
Email: info@sdlabo.net
www.sdlabo.net

Abstract
Collaboration is one of the effective approaches that help us to share knowledge together and exchange ideas withina team member. Sometimes, new helpful knowledge that is not held by the members emerges as a result of thecollaboration. Such knowledge often contributes to get prime solutions during collaboration process. However, theway to generate such new knowledge is implicit. In this paper, a method of creating a model, which represents
effects of collaboration in design process is proposed. By using this scheme, we can illustrate what new knowledge
can be gotten from a collaboration and we can know the effect of the collaboration.
Keywords: Collaboration, Channel Theory, design process, synergetic effects

1. Introduction
viewpoints [3]. Designers can share their viewpoints
Collaboration is one of the effective approaches thatthrough collaboration process. Thus, collaboration is a
help us to share knowledge together and exchange ideas
promising method, which assists the designer to create
within a team member. It is a communication process inmore outstanding design and increase ability to fulfill
which two or more people from different disciplines
client’s requirement more perfectly. However, the way
participate in knowledge transfer to achieving a goal ofto generate new knowledge is implicit. Thus, this study
team [1]. During collaboration process, synergetic
aims to represent the process of generating such
effects among a team member may contribute toknowledge by using mathematical model.
generatenovel
knowledge.
Thus,effective
As a result of synergetic effects, new knowledge iscollaborations are expected in a team.
produced within in collaboration process. To investigate
In design area, viewpoints of designers play aneffective collaboration, a representation model ofimportant role to bring about a novel design idea [2].
collaboration mechanism is proposed. Channel Theory
Different experiences and knowledge cause different© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 86 -

Patchanee Patitaad, Hidetsugu Suto[4] is introduuced to create the model. Moreover, C
Chu
space [5], thhat is a mathhematical construction, whhich
represents sccheme of innfomorphism, is adopted toaccount for thhe new knowledge in collab
boration systemm.
2. Literaturre reviews
2.1. Related w
works
There are m
many researcches have appliedaChannnel
Theory to sstudy about communicatioon system. FForexample, Sutto et al. havve proposed a representattion
model for com
mmunication medium with Channel Theoory[6]. This moddel could desccribe the semaantic informatitionflow, which is corresponnding to a kiind of mediuum.
Kawakami eet al. have proposedpa modelingmsysttemframework that involves diversity
y and conttext
dependenciess base on Chhannel Theory
y [7]. It has thepotential to mmodel diversiity using the arbitrariness of
information flows, Schorlemmer [88] proposed aformalizationn of knowledgge sharing sceenarios by us ing
diagram in the Chu cattegory, etc. In
I this sectiion,
Channel Theoory and Chu spacess
are introduced brieflyy.

Fig.
F
1. A frramework of collaboration mechanism
representedr
by usinguChannel T
Theoryclaassification , a set ⊢ off sequents off
called theecoonstraints of , and a seet⊆of tokenss
called the norm
mal token off , which satisfysall theecoonstraints of .
An infomorp
phism is impoortant relationnship betweenn
twwo classificatiions and proovides a way
y of movingg
infformation backband forth betw
ween themm.
Inffomorphism 〈 ⋀ , ⋁ 〉 is a pair of functiions, in whichh⋀
is a function from thee types of one of thesee
claassifications tot the types of the other, and ⋁ is afunnction from th
he tokens of oone of these classificationss
to the tokens off the other. G
Given two classsifications, Aan
nd B, an inffomorphism ffrom
towritten ass⇌ satisfiess the followinng Fundamenttal Property off
Inffomorphisms:⋁
iff ⊨ ∧
(1)⊨forr each tokenn∈and eacch type∈.
An channel
is an indeexed family: ⇄∈off infomorphismms with a com
mmom codom
main , calledd
thee “core of .”” is an indeex set.

2.2. Channell Theory

Channel Theoory provides a mathematical frameworkk ofqualitative thheory of inform
mation. The basicbconceptss ofChannel Theeory consist ofo classificatiion, local loggic,
infomorphismm, and informaation channel..
〈A
claassification,,⊨ 〉consists of(i) a set,ccallled, of obbjects to be classified,the “tokkens of A,”(ii) a set,
, of objectsoused to classify thetokens, ccalled the “typpes of A,” andd(iii) a binarry relation, ⊨ , betweeen
aand.
A classifiication is reppresented withh indicating the
3. Proposed modelmand exaamples
types to whicch tokens to bee classified.
Given a cclassification , a pair 〈Γ, ∆〉 of subsetss of
1. A model of collaboratioon mechanismm
3.1
is caalled a “sequennt of A.” A tok
ken ∈satisfies 〈Γ, ∆∆〉 ifforr ∀ ∈ Γ thennis off typeTh
he framework
k of proposed model is sho
own in Fig. 1.
is of typefor ∃ ∈ ∆. If every token ∈ satisffies
Eaach circle ind
dicates a set of knowledg
ge held by a〈Γ, ∆〉, say Γ ⊢ ∆, and 〈ΓΓ, ∆〉 is calledd a “constraiint”
meember. Due too synergetic efffects in collab
boration, teammsupported by .
peerformance cannnot be calcullated as simplle union of thee〈 , ⊢ . 〉 consists of a
A local logic© Thhe 2015 Internnational Confeerence on Artifificial Life andd Robotics (ICCAROB 2015),, Jan. 10-12, Oita,
OJapan- 87 -

Modelingg of collaboratio
on in design prorocess Based on Channel Theoryy⊨

abilities of each membber. Possibility domain ofcollaboration can be descrribed as
This
∪ . T
situation can be representted by using classificationn of
Channel Theoory as shown below the circcles in the figuure.
Here, we caan deduce thee knowledge, which can beobtained fr
from synerggetic effectts by us ing
infomorphismm.
By using this scheme, we can demo
onstrate effecttive
collaboration by representiing how new reachabler
des ignsolution broaadens. To verrify potential of the propossed
framework, eexample of colllaboration in webpage des ign
is used to be a case study.

,

⊨
,

⊨
⊨

.

,

⊨
⊨

,

⊨
,
,

,
,

⊨

,
,

⊨
⊨

,

⊨

Infomorphhisms from to(I1): Each line in thee(I1) means a combinationn between a token inn
claassification L and a tokenn in classificcation C. Forr
insstance, Cool in (L) is com
mbined with thet Natural inn(CC) because the first line of ((I1) has the samme element off
thee fourth line of (C). Whille, each colum
mn in the (I1)
meeans a combin
nation betweeen a type in cllassification L
annd a type in claassification C.. For instance, Red in (C) iss
coombined with W1.75 in (L) bbecause the lefftmost columnn

Fig. 2. A mmodel of colllaboration in webpage desiign
between layouut designer and colorcdesignlayout designn and designerr B, who is proficient on coolor
design is connsidered as ann example. Th
his situation ccan
be representeed as matrices shown in Fig
g. 2 by using the
proposed meethod. In this case, the model consists ofthree classificcations, i.e. L,, C, and I1.
Classificaation of layoout design knowledgek((L):
The classificaation of layouut design know
wledge is shoown
in Fig. 3 (L). Here, each tooken stands forr impression aandeach type staands for layoout item, fontt weight or ffontstyle. For exxample, whenn font weight is thin and thestyle is Minncho (Japanesse Serif stylee), it gives c ool
impression too the observeers. Such kno
owledge can be
described as a classificationn as following
g:
,

,

,

⊨

.

,

,
,

,

,

.

⊨

⊨

A collaboratioon between deesigner A, wh
ho is practicedd on

,

⊨

,

Types
and
mean weights of letterss,.
whhich define asa normal andd bold respecctively. TypessGoothic and Min
ncho mean foont styles of letters in titlee
paart, which definedas Goothic and MinchoMstyless
resspectively.
Classificatioon of color ddesign knowleedge (C): Theeclaassification off color design knowledge iss shown in Figg.
3 (C). Here, eaach token stannds for an immage and eachhtyppe stands forr color decorrating. For ex
xample, whenn
weebpage is decoorated with Reed and Blue, it
i gives classicc
immage to the observers. SSuch knowleedge can bee
deescribed as a classification aas following:

3.2. Examplee: Layout desiigner and Collor designer

,

,

⊨

Fiig. 3. Infomorphisms from layout design knowledge toocoolor design know
wledge,,off (I1) has the same element of the secondd column frommthee left in (L).
© The 22015 Internatiional Conferen
nce on Artificiial Life and Robotics (ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 88 -

Patchanee Patitad, Hidetsugu Suto

We can say that the proposed model can explicitly
represent the situation, which possible to occur incollaboration in a webpage design process. However,
we cannot say that all situations are correctunderstanding because all possible situations are derived
with the proposed method. Nevertheless, with using the
proposed model, we can illustrate potential ofcollaboration by representing how new reachable design
solution broadens.

In this case, infomorphisms are established from“classification of layout design knowledge” to“classification of color design knowledge.” Eightinfomorphisms have been obtained as shown in Fig. 3.
It means there are eight situations that may occur whenlayout designer and color designer collaborate in adesign process. Each situation explains new knowledge,
which layout designer and color designer obtained fromcooperation in the design process. However, all possible
situations are derived with the proposed method. Thus,
we cannot say that all situations are proper
understanding in the context. For example, in Fig. 3.,
infomorphism 1 shows that
∧

,

∧

∧

Acknowledgement
This work was supported by Grants-in-Aid for
Scientific Research from Japan Society for the
Promotionof
Science(NO.23611025)
andGrants-in-Aid for research advancement from MuroranInstitute of Technology (2014).

,

∧

,

From this infomorphism, a designer can understand thatthe design, which has cool imoression, is corresponding
with classic image, while warm impression iscorresponding with casual image. Moreover, soft
impression is corresponding with clear image and hard
impression is corresponding with natural image. It cansay that this new knowledge is possible to be true
because this knowledge is consistent in semantics.
Meanwhile, infomorphism 2 shows that
∧

,

∧

∧

,

∧

References
1. G.W. Dickson and G. DeSanctis, Information
Technology and the Future Enterprise: New Models
for Managers, 1st edn. (Prentice Hall PTR Upper
Saddle River, NJ, USA, 2000).
2. H. Suto, P. Patitad and N. Kang, A collaboration
Support Tool for Multi-cultural Design Team Basedon Extended ADT Model, in Proc. HCI Conf. (Crete,
Greece, 2014), pp. 548-557.
3. F. DeALtienne, Viewpoints in co-design: a fieldstudy in concurrent engineering, DES STUD. 26(3)(2005) 215-241.
4. J. Barwise and J. Seligman, Information Flow: The
Logic of Distributed Systems (Cambridge Univ. Press,
1997).
5. M. Barr, -Autonomous Categories, LECT NOTESMATH 752. (Springer-Verlag, Berlin, 1979).
6. H. Suto, T. Taniguchi and H. Kawakami, A Study ofCommunication Scheme for Media Biotope, in Proc.
SICE Annu. Conf. (Tokyo, Japan, 2011), pp. 206-209.
7. H. Kawakami, T. Hattori, T. Shiose and O. Katai,
Modeling of System Diversity Based on Qualitative
Information Theory, WSEAS Trans. Syst. 10(5)(2006) 2411-2417.
8. M. Schorlemmer, Duality in Knowledge Sharing(The university of Edinburgh, UK, 2002).
9. S. Kobayashi, A book of colors: matching colors,
combining colors, color designing, color decoration,(Kodansha, USA, 1987).

,

From this infomorphism, cool impression and hard
impression can be implied as same as infomorphism 1.
But soft impression is corresponding to casual image
and warm impression is corresponding to clear image. It
must be misunderstanding because warm impression is
conflict with clear image according to the theory ofscience of color [9].
4. Conclusion
A modeling method of collaboration mechanism is
proposed based on Channel Theory. An example ofwebpage design case was shown as case studies. The
example is illustrated the situation that two designers
who have different knowledge, collaborated in a
webpage design process. In the example, designer A ispracticed on layout design whereas designer B is
proficient on layout design. They could have several
possible knowledge as a result of the collaboration.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 89 -Sterilizing system of ballast water using an arc discharge
Jae-cheol Lee,Seung-hwa Baek,Sheng-Xu Piao
Department of Electrical Enginneering, Pusan National University, Address
Jangjeon-dong ,Geumjung-gu, Korea
Hee-Je Kim
Department of Electrical Enginneering, Pusan National University, Address
Jangjeon-dong ,Geumjung-gu, Korea
E-mail: piaoshengxu88@hotmail.com, heeje@pusan.ac.kr
www.pusan.ac.kr

Abstract
The inadvertent transfer of harmful aquatic organisms and pathogens in the ballast water of ships has been
determined to cause a significant adverse impact to all around the world coastal regions. Recently, in order to solve
this issue, a number of technologies have been developed and commercialized. Most of the treatment technologies
are barely used independently. In addition, there are several combined methods to treat the ballast water. The
overall aim of this study is to suggest on of the best way of sterilization of ballast water using DAB Charging andhigh voltage pulsed arc discharge.
Keywords: Sterilization System,Ballast Water,DAB.

which could bring catastrophic effect on local fisheries.
It is therefore imperative that introduction of harmful
aquatic organisms
is prevented rather than cured
afterwards.
In Process of loading and discharging theballast water, we apply our arc discharge technologies
to the object species that is not effectively disinfectedchemically or filtered physically.

1. Introduction

Ballast water from ships has been established as apotential vector for transference of various species
around the world. The shifts will have negative impacts
on the environment through factors such as competition
for food, altered substrate or ambient temperature andlight availability [1].
The International Maritime
Organization(IMO) has estimated that every year theworld’s fleet moves ten billion tons of ballast wateraround the world and that on average more than 3000
species of plants and animals are being transported daily
around the world. Once these are introduced to local
environment,it is virtually impossible to get rid of them.
This could have a permanent effect on the environment,©The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 90 -

Jae-cheol Lee, Seung-hwa Baek, Sheng-Xu Piao, Hee-Je Kim

2. The sterilization of ballast water

However the oxidation and combustion of needle
electrode is inevitable during arc discharge. Therefore,
the distance between the electrode edge and theseawater surface increased as the electrode edge wasworn out, the discharge was not lasted more over.In thisprocess, we want to keep the certain distance between
the electrode edge and the seawater surface using a
motor control.

Fig.1 The schematics of ballast water.

As shown in Fig. 1, an introduction of harmful aquaticorganisms is prevented rather than cured afterwards.
Especially the processes for the balance of the large ship,
many kinds of the harmful aquatic organisms are moved
to the different ocean by this ballast water. Because ofthis problem,IMO declared the importance ofsterilization. In the processes of loading and unloading
the ballast water, it is very important to eliminate many

Fig. 3. The boosting circuit for 20KVDV arc discharge.

For the first active experiments of sterilizing organisms
with high voltage arc discharge treatment system, we
have made the high voltage circuit that can cause
reasonable arc discharge. The first circuit in this picture
can make 2kV with 20kHz from 12VDC Input. A
MC34063 for boost topology, IR2153 and 2:340 turnratio transformer were used for fly-back topology. After
that, this output connected to the Cockcroft Walton
voltage multiplier circuit that can boost from 2kVAC tomore than 20kVDC. To make the active arc discharge,
the voltage more than 5kV is needed. But in this case,asthe applied arcing voltage between the seawater and theelectrode edge is needed more than between metalelectrodes.

3. Experimental processes
Arc discharge generates under the certain condition likesome distance between electrodes or voltages applied.
In this study, the distance between the seawater surface
and electrode edge was an important factor. To makethe constant discharge during the sterilization
experiments, we have to keep that distance within 1cm
between the seawater surface and the needle electrodeedge.
Fig. 2. The electrode edge and the seawater surface.

©The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 91 -Sterilizing system of ballast

Fig. 4. DAB converter
Fig. 6. Artemia cysts and their eggs before andexperiments.

Shown Fig.4, we use DAB to charge a capacitor. The
DAB is a bidirectional converter having characteristicof Phase shift, Buck+Boost biderection, not needing
additional snuber and high efficiency and ZVS.

after theAfter arc discharge about 10minutes, most of themwere sterilized. They were sterilized and melted by theozone and OH radical produced from arc discharge.
However the fixed needle electrode was shortened by
the oxidation and combustion for discharge process. As
increasing the distance between needle electrode andsaltwater, the discharge processes were stopped.
Originally sharpened needle electrode was shortened
and flattened after many trials of discharging
experiments.

If use this circuit we can Quicker Charging.

4. Conclusion
Our studies show that arc discharge treatment system ispotentially effective to adopt the technology for ballast
water management. Species which cannot easily disinfected chemically or filtered physically are subject tosterilized by pulsed power and electrical energy duringarc discharge experiment. As the arc phenomenon by
the pulsed power, it is more cost effective than the othertreatment methods. This study also showed thatoxidized and flattened electrodes by the arc discharge
need to be controlled by motor control to increase theefficiency of stabilizing processes, the distance between
electrode and seawater has to keep the certain distance.
In this part, the control system can move automaticallyto sustain a constant rate by the speed of shortening
needle. Further research has to be needed from thesebasic ideas and active experimental results.

Fig. 5. Arc discharge experiment to Artemia cysts.

To confirm the sterilizing effect of this arc discharge
treatment system, we bred
artemia cysts that is thekind of brine shrimp causing harmful effects to nonnative coastal region. The cysts hatch after 48hours insalt water illuminated light source.
In the arc discharge experiment, the 20kVDC output
from the circuit generating high voltage was applied to
between the seawater surface and the needle electrode.
Saltwater same as salinity of seawater of 500ml is usedin this experiment to spread the hatched cysts in this salt
water.

©The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 92 -

Jae-cheol Lee, Seung-hwa Baek, Sheng-Xu Piao, Hee-Je Kim

Reference
1. Sutherland T. F, Levings C. D, Petersen S, Hesse W. W.,
Mortality of Zooplankton and Invertebrate Larvae
Exposed to Cyclonic Pre-Treatment and Ultraviolet
Radiation., MTS Journal. 37(2) (2001) .
2. Hee-Je Kim et. al, terilization system for ballast water,(patent No. 10-1178382 , korean ,2012).
3. “App Note 500 – Calculating Capacitor Charge
Time”, TDK-Lambda, 2009

©The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 93 -

The design of medical ruby laser power supply system using LLC resonant converter
Jaecheol Lee

Electrical Engineering Department, Pusan National University, Jangjeon 2-dong, Geumjeong-gu
Busan, Korea
Piao Shengxu

Electrical Engineering Department, Pusan National University, Jangjeon 2-dong, Geumjeong-gu
Busan, Korea
Zheng Tao

Electrical Engineering Department, Pusan National University, Jangjeon 2-dong, Geumjeong-gu
Busan, Korea
Heeje Kim

Electrical Engineering Department, Pusan National University, Jangjeon 2-dong, Geumjeong-gu
Busan, Korea
E-mail: dl7553@naver.com, heeje@pusan.ac.kr
www.pusan.ac.kr

Abstract
Because of its usability, the use of ruby laser for medical therapy has attracted a lot of interest. LLC resonant
converter is used to control laser power density in ruby laser power supply. Zero voltage switching(ZVS) is
implemented to minimize switching loss by LLC resonant converter. We use DAB(Dual Active Bridge) to charge
the capacitor. We obtained maximum laser output of 0.5J. Repetition of laser out is 3Hz.
Keywords: Ruby laser, LLC resonant converter, ZVS, DAB

diagnosis and treatment of disease, and frequency of use

1. Introduction

is increasing.

According to development of laser over almost all

Medical ruby laser was used for vascular lesion atindustrial fields, the application using laser in medical

the beginning of development but not used now.

field is also expanding rapidly. Nowadays, as a one of

Recently, laser is used widely in dyspigmentation, forthe most convenient and strong clinical devices, theexample freckle or blemish, and used in tattoo removal

range of laser use is becoming wider more and more inclinical parts such as not only curable disease but also

©The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 94 -

Jaecheol Lee, Piiao Shengxu, Zhenng Tao, Heeje Kiim

using Q-switcched pulse inccreased streng
gth and

When triggerring acts like FFig.2 steamerr discharge is
geenerated in Xeenon lamp. Thhen lamp impeedance islowwered, so Arcc discharge cann be made of relatively lowwvooltage.

shortened irraadiation time.
This paperr aims the devvelopment of powerp
supplyfor laser outpput of 3Hz*0.335J. The Poweer supply was

3. LLC resona
ant converterr design.

designed usinng LLC resonaance converter in 2kW clas s.

The main power supply w
was designed applied LLC
ressonant converrter. The aims of main poweer supply arechharging voltage into high caapacity conden
nser
co
ontinuously, geenerating Arcc discharge thrrough highcuurrent conducted into Xenonn lamp when thet capacitor
disscharges.
The converteer enables ‫ܮ‬௠ to be concern
ned withressonant tank thhrough reducinng difference between ‫ܮ‬௥annd ‫ܮ‬௠ comparred with LC reesonant conveerter. LLC
ressonant converrter can make regulated pow
wer over wideeran
nge of input voltagev
and in condition of relativelyrlitttle frequency fluctuation. A
Also the efficieency ofcoonverter is incrreased by ZVSS(Zero Voltag
ge Switching)thaat reduces swiitching losses..

2. Laser pow
wer supply

Figure 1 Lasser power suppply(left) andd Simmer moode
triggering circuit(right)
Solid laserr power suppply consists ofo trigger pow
wer
supply, simm
mer power suppply, and main power suppply,
as shown Figg.1. Laser is geenerated by inntense visible lay
made from Xenon lampp through conductingchhighcurrent instanntaneously to Xenon lamp.. For conducttinghigh current llamp, impedaance should bee low. Lowerring
lamp impedaance is triggering. Throug
gh this we ccan
make visiblee ray in Xeenon lamp in
i condition ofrelatively low
w voltage.
In this papper, we use sim
mmer mode triggeringt
circcuit
like Fig 1. Thhis method ennables miniatu
urization and hhas
large advantaage of enablinng to secure sttability of circcuitby separationn main power from triggerinng power suppply.
Lamp is ionnized stably byb keeping lowlcurrent D
DC
voltage in Xeenon lamp.

Figure 3 LLC resonnant converteer
4. Soft start ap
pplied UCC225600
In laser poweer supply, exccessive inrush
h current occurr
beecause when laserloscillatiion, the voltage charged inn
caapacitor is disscharged so tto recharge thhis. Thereforee
soft start shouldd be applied tto block the inrush currennt
so to assure sttability of poower supply ini addition too
beeginning partss, whenever laaser oscillatioon occur. Thiss
is made by coombining phooto coupler anda
SS pin ,coontrolling soft start opeeration of thetchip, ofUC
CC25600
or is dischargeed when photoo
As shown Fig. 4, capacitorcooupler act, and then the capaacitor recharged
d operated sofft

Figure 2 Triggering ofo Xenon lam
mpstaart.
©The 2015 Internatioonal Conferen
nce on Artificiial Life and Ro
obotics (ICAR
ROB 2015), Jaan. 10-12, Oita, Japan- 95 -

The design of medicaal

We use this circuitcjust resstrictively to test
t this circuiit
beefore design roobot battery chharge system.

Table 1. Specificatioon of conveerter
Maximuum input

6. Result

310V
volttageIn
n 3Hz experim
ment, 0.5J of llight output iss generated
wiith 600V of ch
harging voltagge. Depending
g on
inccreasing charg
ging voltage, llaser output inncreases.

Minimuum input
270V
2
volttage
Average innput voltagee

290V
2

Output voltage

600V

Outputt current

3.5A

Maximuum power

2.1kW
2Figure 7 Capacitor chaarging, dischargiing voltage in 3H
Hz operating

Figuree 4 Soft start circuit of UC
CC25600
Figure 7 laser outpu
ut result

5. Dual Actiive Bridge
7. Conclusion

Today, a med
dical laser is tthe most activ
vely studied
fieeld of many laaser fields andd the optimizattion of powersu
upply is urgentt. In this studyy we design ru
uby laserpoower supply th
hat has 3Hz*00.35J output an
nd researchthee change of laaser output witth regard to chhargingvooltage. When charging
cvoltaage increases, light output
alsso increases an
nd 0.5J of lighht output is geenerated at
60
00V, the maxim
mum chargingg voltage. If wew increase
reppetition to 4H
Hz, it is expecte
ted to 0.25J off light output
geenerated.

Figure 5 DAB conveerter
Shown Figg.5, we use DA
AB to charge a capacitor. T
The
DAB is a bidirectional coonverter havinng characterissticof Phase shiift, Buck+Boost biderectio
on, not needding
additional snuuber and high efficiency an
nd ZVS.
It is approppriate low volltage battery chargecsystemm sogenerally useed for robot baattery charge system.
s

8. References[1]] Jin Yun-Sik, Kim Hee-Je, “F“Fabrication of Compact Rubyy©The 2015 Internatioonal Conferen
nce on Artificiial Life and Ro
obotics (ICAR
ROB 2015), Jaan. 10-12, Oita, Japan- 96 -

Jaecheol Lee, Piao Shengxu, Zheng Tao, Heeje Kim

Laser and its Operation Characteristics”, KIEE, 1995.7.
[2] Alex D. McLeod, “Design Consideration for Triggering ofFlashlamps”, PerkinElmer, 1998.
[3] “App Note 500 – Calculating Capacitor Charge Time”,
TDK-Lambda, 2009.
[4] Sam Abdel-Rahman, “Resonant LLC Converter :Operation and Design”, Infineon, 2012.
[5] UCC25600 Datasheet, Texas Instruments, 2011.

©The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 97 -

Online Rule Updating System Using Evolutionary Computation for Managing Distributed
Database
Wirarama Wedashwara, Shingo Mabu, Masanao Obayashi and Takashi Kuremoto

Graduate School of Science and Engineering, Yamaguchi University
Tokiwadai 2-16-1, Ube, Yamaguchi 755-8611, Japan
E-mail:{ t001we, mabu,m.obayas, wu}@yamaguchi-u.ac.jp

Abstract
This research proposes a decision support system of database cluster optimization using genetic network
programming (GNP) with on-line rule based clustering. GNP optimizes cluster quality by reanalyzing weak pointsof each cluster and maintaining rules stored in each cluster. The maintenance of rules includes : 1) adding new
relevant rules, 2) moving rules between clusters and 3) removing irrelevant rules. The simulation focuses on
optimizing cluster quality response against several unbalanced data growth to the data-set that is working withstorage rules. The simulation results of the proposed method shows better results compared to GNP rule based
clustering without on-line optimization.

Keywords: Genetic Network Programming, Rule Based Clustering, Cluster Optimization

algorithm to maintain the cluster adaptability against
several unbalanced data growth. For example, theunbalanced data growth occurs when different kinds ofitems (data) comparing to the items stored in the current
database begin to be stored as the time goes on (thetrend of data is changed).

1. Introduction
Nowadays many large scale database systems with very
high data growth are being utilized to improve theglobal human activity, such as communication, social
networking, transaction, banking, etc. A distributed
database management system becomes a solution to
improve data access speed by organizing data in
multiple storages for multiple types of user accesses.
Problems of distributed database management system
are not only how to manage the number of data, but also
how to organize data patterns in distributed storages.
Clever data organization is one of the best ways toimprove the retrieval speed and reduce the number ofdisk I/Os and thereby reduce the query response time.
In this paper, we propose a decision support systemfor database cluster optimization using Genetic Network
Programming (GNP) with on-line rule based clustering.
Main purpose of this research is to provide an on-line

2. Review of the Proposed Framework
2.1. Rule Based Clustering
Rule based clustering is one of the solutions to provide
automatic database clustering and interpretation of data
storage patterns. Rule based clustering represents data
patterns as rules by analyzing database structures onboth of attributes and records3,4.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 98 -

Wirarama Wedaashwara, Shingo Mabu,
M
Masanao Obayashi
Oand Taakashi Kuremotosba
maxa, b(1)1  a / b if a  b  0if a  bb / a  1 if a  bngle sample. The
T Silhouettees: Silhouette vaalue for a singvaalue for a set of samples iss given as thee mean of thee
Silhouette valu
ues of each sample, a: meanmdistancee
beetween a samp
ple and all thhe other pointts in the samee
clu
uster, b: meann distance bettween a samp
ple and all theeothher points inn the secondd nearest clu
uster. A goodd
clu
ustering resultt will make thhe silhouette valuev
be closee
to 1.0 and a bad
b clusteringg result will close to -1.00.
ue is used ass a thresholdd in the ruleeSilhouette valu
pdating processs of GNP.
up

Fig. 1. GNP Implementationn on Cluster Op
ptimization.
Table 1. Genne Structure of GNP
Corresponding too the Program ini Fig. 2.
i

NTi

Ai

Ri

Ci

1

1

0

0

4

2

1

0

0

7

3

1

0

0

9

4

2

A

1

5

5

2

A

2

6

6

2

B

1

7

7

2

D

2

8,12

8

2

C

2

5

9

2

C

1

10,13

10

2

D

1

11,15

11

2

B

3

4,14

12

3

1

1

0

13

3

2

3

0

14

3

3

2

0

15

3

4

4

0

3. Detail of the Proposed M
MethodGN
NP optimizes the cluster ququality by re-aanalyzing eachhcluuster and upd
dating rules sstored in eachh cluster. Thee
proposed metho
od provides a rule updatin
ng mechanismmas an additionall function to m
maintain rulees as shown inn
Fig 1. The rule updating mecchanism includes: 1) addingg
neew relevant ru
ules; 2) movving rules bettween clusterssannd 3) removinng irrelevant rules. The rule extractionnannd rule updating task is exxecuted by thee evolutionaryyopptimization technique of G
GNP to get th
he best clusterraddaptability agaainst several ununbalanced datta growth.
GNP is useed to extract rules from a data-set byyannalyzing all the records. Phenotype andagenotypee
strructures of GN
NP are descriibed in Fig. 1 and Table 1,
resspectively. In
n Fig. 1, eacch node has its own nodee
nuumber (1–15), and in Tablee 1, the node informationiofeaach node num
mber is desccribed. The program sizee
deepends on thee number off nodes, whicch affects theeammount of rules created by the program
m. There are 3kin
nds of nodess used in GN
NP rule extraaction for theecluuster rule updating.
u1)) Start node (rectangle)
reppresents the startspoint off the sequencee of judgmennt
noodes which are executeed sequentially by theirrcoonnections. Multiple
M
placeements of staart nodes willl
alllow one indiv
vidual to extraact a variety ofo rules, whichh
is shown in TableT
1 2) JJudgment noode (hexagon)
reppresents an attribute off the data-set, which iss
reppresented by Ai showing aan index of attribute i suchh
as price, stock, etc., and Ri showing a raange index of
atttribute i. For example,e
Ai=A
A represents pricepattributee,

2.2. Genetic Network Proogramming
Genetic netw
work prograamming (GNNP) that is anevolutionary optimization technique witth directed graaph
structures is used to provvide data classsification. GN
GNP
has a distinguuished representation abilitty with comppact
programs, w
which is derivved from thee re-usability ofnodes that is inherently eqquipped function of the graaph
structures. For the purpoose of rule basedbclusteriing,
GNP is usefuul to handle rulerextractionn from data-ssets
by analyzing the records.

2.3. Silhouettte
Silhouette value is used to evaluate the clusteringcresuults.
Silhouette prrovides a succcinct graphicaal representattionof how welll each objecct lies withiin its clusterr5,8.
Silhouette vallue is calculatted by Eq. 1.

© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 99 -

Online Rulee Updating System
m

Table 2. Example
Eof Rulule Extraction foor Cluster Optim
mization.
Start Nod
de
1

2

3

Extracted Rules
A1^B
^ 1

Support3

Optimization
Add rule to cluster 1

A1^B^ 1^D2

1

Add rule to cluster 1

A1^B^ 1^D2^C2

1

Add rule to cluster 1

D2^C
^ 2

2

Add rule to cluster 2

D2^C^ 2^A2

1

Add rule to cluster 2

D2^C^ 2^A2^B1

0

Add rule to cluster 2

C1^D
^ 1

2

Remove rule from cluster 3

C1^D^ 1^B3

1

Remove rule from cluster 4

C1^D^ 1^B3^A1

1

Add rule to cluster 1

and Ri=1 reepresents vallue range [00,50] and Rii=2
represents vaalue range [51,80].
[
3) ProcessingP
noode(round) show
ws the end point of th
he sequence of
judgment nodes and proccesses the rulle updating inn acluster whosse cluster nuumber is deescribed in theprocessing noode . For exam
mple P1 proceesses an additi
tion
of extracted rrules to clusteer no 3. Multip
ple placementt ofprocessing nnodes will makem
one individual extrract
variety of rulles, which is shown in Tab
ble 2. Sequenncesof nodes staarting from eaach start nodde (S1,S2,S3) arerepresented bby dotted line a, b and c. A node sequennce
flows until support for thhe next combiination does nnot
satisfy the thhreshold. The nodes with th
he attributes tthat
have already appeared in thet sequence will be skippped.
Candidate ruules extracted by the progrram of Fig. 1 tothe data-set oof Table 1 are shown in Tab
ble 2. In Tablee 2,
three rules aare extracted by the node sequence frrom
each start nodde.

Fig. 2.
2 Example of Silhouette Valuues

Fig. 2 show
ws and exampple of the Silhhouette valuess ofrules with only one attribuute. In Fig. 3 thhreshold is sett at
8.5, so only tthe attributes with Silhoueette values unnder
8.5 will be ussed for the juddgment nodes in GNP for rrule
extraction. Inn Fig. 2, attribbute of price iss not includedd in
the rule extraaction of GNP
P because its silhouettesvaluuesare never undder 8.5 (rule updatingu
is not necessary), bbut
the values off stock and weeight for som
me data are unnder
8.5, thus thoose attributess are includeed in GNP for
updating rulees. Each clusster has dom
minant values of
attributes whhich are anchors of clusterr quality, whhichthen influencce the silhouettte values. Forr example, whhen
10kg is a dom
minant value forf the attribute of weight iin acluster, farthher values froom 10kg willl have a low
wersilhouette annd should be moved to an
nother clusterr toimprove the ccluster qualityy.

Figg. 3. Graph off Simulation Results of RuleR
Updatingg
Freequency and Nu
umber of Data C
Comparison.

4. SimulationTh
he simulationn focuses oon verifying
g the clusterraddaptability agaainst several uunbalanced datadgrowth ofthee data-set, whhere cluster aadaptability iss evaluated byy
sillhouette valuees. Data-set uused in the simulations hass
10000 data wiith 8 attribuutes. The ad
daptability iss© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 100 -

Wirarama Wedashwara, Shingo Mabu, Masanao Obayashi and Takashi Kuremoto

Table 3. Simulation Result of Rule updating Frequency and Number of Data Comparison.
Rule updating frequency

Step

Number of Data

Increment/Decrements

No rule updating

1000

2000

4000

1

1000 (Default)

-

0.967

0.967

0.967

0.967

2
3
4
5
6
7
8
9
10

2000
3000
4000
5000
6000
5000
4000
3000
2000

+1000
+1000
+1000
+1000
+1000
-1000
-1000
-1000
-1000

0.945
0.902
0.892
0.882
0.812
0.787
0.765
0.723
0.698

0.965*
0.923*
0.935*
0.912*
0.902*
0.892*
0.901*
0.912*
0.909*

0.944
0.915*
0.902
0.909*
0.897
0.901*
0.888
0.892*
0.879

0.947
0.899
0.895
0.903*
0.887
0.821
0.797
0.815*
0.802

0.832

0.938

0.923

0.8845

Averageevaluated by the following two points. 1) The number ofdata in the database is changing as the time goes on. The
initial number of data is 1000, then every time step,
1000 new data are added to the database. After thenumber of data reaches 6000, 1000 data are decreased
every time step. 2) The rule updating frequency, where
the rule updating is executed when a predefined numberof new data are given to the data-set (this predefined
number is defined as "rule updating frequency"). For
example, if the rule updating frequency is 1000, the rule
updating will be processed every increments ordecrements of 1000 data. The comparisons of thesimulation results are carried out between four methods,i.e., the proposed method with rule updating frequencyof 1000, 2000 and 4000, and the clustering method ofstandard GNP without online rule updating.
The silhouette values obtained by the four methods
are shown in Table 3, and Fig. 3. Star marks (*) on theside of silhouette values show the times when the rule
updating is carried out. The rule updating frequency of4000 shows only a slight difference from no rule
updating but shows increment of silhouette values instep 5 and 9, which means that the rule updating iseffectively carried out.. The best results are obtained by
the rule updating frequency of 1000, where silhouette
values are stable with relatively high level compared toother frequency parameters. Rule updating frequency of2000 also shows decrements on step 3, which previous
silhouette value are high enough.

will apply fuzzy membership functions to attributejudgment to make rules with better clustering ability.
References
1.

2.

3.

4.

5.

6.

7.

5. Conclusions
This paper proposed a new rule updating mechanism for
distributed database with unbalanced data growth. The
simulation results of the proposed method showed thebetter clustering results comparing to GNP rule-based
clustering without on-line adaptation. In the future, we

8.

Kaoru Shimada, Kotaro Hirasawa, and Jinglu Hu.
Genetic network programming with acquisition
mechanisms of association rules, JACIII. 10(1) (2006)
102–111.
Shingo Mabu, Ci Chen, Nannan Lu, Kaoru Shimada,and Kotaro Hirasawa. An intrusion-detection model
based on fuzzy class-association-rule mining using
genetic network programming Systems, Man, andCybernetics, Part C: Applications and Reviews, IEEE
Transactions on. 41(1) (2011) 130–139.
Mansoori, E.G., FRBC: A Fuzzy Rule-Based Clustering
Algorithm, Fuzzy Systems, IEEE Transactions. 19(5)(2011) 960-971.
Sinaee, M.; Mansoori, E.G., Fuzzy Rule Based
Clustering for Gene Expression Data, Intelligent Systems
Modelling & Simulation (ISMS), 4th International
Conference (2013) 7-11.
Zhao, JiangFei and Huang, TingLei and Pang, Fei andLiu, YuanJie, Genetic algorithm based on greedy
strategy in the 0-1 knapsack problem, Genetic andEvolutionary Computing, 3rd International Conferenceon (2009) 105-107.
Sylvain Guinepain and Le Gruenwald. Using clustercomputing to support automatic and dynamic databaseclustering, Cluster Computing IEEE International
Conference, (2008) 394–401.
Sylvain Guinepain and Le Gruenwald. Automatic
database clustering using data mining. Database andExpert Systems Applications, DEXA IEEE 06(17)(2006) 124–128.
Yanchi Liu, Zhongmou Li, Hui Xiong, Xuedong Gao,and Junjie Wu. Understanding of internal clustering
validation measures, Data Mining (ICDM) IEEE 10th
International Conference, 10 (2010) 911–916.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 101 -

Reinforcement Learning with Symbiotic Relationships for Multiagent Environments
Shingo Mabu

Graduate School of Science and Engineering, Yamaguchi University, Tokiwadai 2-16-1
Ube, Yamaguchi , 755-8611, Japan
Masanao Obayashi

Graduate School of Science and Engineering, Yamaguchi University, Tokiwadai 2-16-1
Ube, Yamaguchi , 755-8611, Japan
Takashi Kuremoto

Graduate School of Science and Engineering, Yamaguchi University, Tokiwadai 2-16-1
Ube, Yamaguchi , 755-8611, Japan
E-mail: mabu@yamaguchi-u.ac.jp, m.obayas@yamaguchi-u.ac.jp, wu@yamaguchi-u.ac.jp

Abstract
Studies on multiagent systems have been widely studied and realized cooperative behaviors between agents, where
many agents work together to achieve their objectives. In this paper, a new reinforcement learning framework
considering the concept of “Symbiosis” in order to represent complicated relationships between agents and analyze
the emerging behavior. In addition, distributed state-action value tables are also used to efficiently solve themultiagent problems with large number of state-action pairs. From the simulation results, it is clarified that the
proposed method shows better performance comparing to the conventional reinforcement learning without
considering symbiosis.
Keywords: reinforcement learning, symbiosis, multiagent system, cooperative behavior,working cooperatively with each other, for example,
cloud networks, car navigation systems and automation
by robots. Research on complex networks began around19982 and have attracted attentions recently as animportant research for analyzing phenomena in social
systems. Therefore, a model that can predict problems
caused by the complex networks and propose theoptimal solutions for the problems will be useful for
realizing safe and secure social systems. In addition, if

1. Introduction
There are many situations where interests of someparties are coincided or conflicted1, for example, humanrelationships, cooperation or competition between
companies, and even international relationships.
Recently, the globalization is rapidly progressing, thus,
the relationships between persons and organizations
have become very complicated networks. On the other
hand, information systems have been intelligent and© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 102 -

Shingo Mabu, Masanao Obayashi, Takashi Kuremoto

the best relationships between parties can be found, it
will contribute to the development of the whole society.
In this paper, a novel reinforcement learningalgorithm that introduces a concept of "Symbiosis" inorder to build Win-Win relationships between parties
even if each party is pursuing the maximization of its
own profits. Symbiosis can be defined as a relationshipwhere two or more organisms live in close association
with each other3, and several computational models
based on the symbiosis in the ecosystem have beenstudied4-7.
In the proposed reinforcement learning method,
multiagent environments are considered where there areseveral agents (persons and organizations) that havecooperative, competitive or self-satisfied relationships,
and such relationships are defined as "symbiotic
vectors". The symbiotic vectors consist of six basic
symbiotic relationships, i.e., mutualism, harm, predation,
altruism, self-improvement and self-deterioration. The
symbiotic vectors are used to calculate rewards given toeach agent when updating Q values in reinforcement
learning. The symbiotic vectors contain not only thetarget direction of self-profit, but that of the other agentsworking in the same environment. Therefore, theproposed method can predict the results under thecurrent symbiotic relationships by implementing
simulations.
This paper is organized as follows. In section 2, Q
learning algorithm with distributed state-action value
table is introduced for efficiently solving the multiagent
problems with large number of state-action pairs.
Section 3 explains the proposed learning algorithm
using symbiotic vectors. Section 4 describes thesimulation environments and results. Finally, section 5
is devoted to conclusions.

Fig. 1. Q-table division

2. Q learning with distributed state-action value
tables2.1. Representation of distributed Q tables
Suppose that a set of inputs (sensors) of agents is I ,and a set of possible actions is A . Then I is manuallydivided into several subsets, i.e., I 1 , I 2 ,, I n( I  I1 , I 2 ,  I n ), depending on the problems. For
example, in the self-sufficient garbage collecting
problem used in the simulations of this paper, there aremainly three tasks which have to be achieved by agents,
thus, I is divided into three subsets, i.e., I1 , I 2 , I 3 .
Therefore, three sub-Q-tables are created based on
I1 , I 2 , I 3 , respectively (Fig. 1).



2.2. State transition and learning
In this subsection, the procedure of deciding an action is
explained based on an example shown in Fig. 1 (three
sub-Q-tables are used).
The procedure of deciding an action is as follows.
1) When inputs are given from an environment, eachsub-Q-table independently determines the current states n (n is the sub-Q-table number. n  1,2,3 ).
n2) Three actions a are independently selected by eachsub-Q-table using greedy policy9.
n3) Compare the three Q-values of a , and the sub-Qtable selecting the action with the maximum Q-value isdefined as nwinner(winner-Q-table), and its current state

winner.
is defined as s4) The action selected by winner-Q-table is executedwith the probability of , or random action is executedwith the probability of 1-This executed action is

In the standard reinforcement learning, the number ofstate-action pairs increases exponentially as the numbersof inputs, objects to be perceived, and possible actions
increase, that is called “The curse of dimensionality” 8.
Therefore, Q learning algorithm with distributed stateaction value table (Q table) is introduced in this paper.

winner.
defined as a
The procedure of Q-learning is as follows.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 103 -

RL with Symbiotic Relationships

Fig. 3. Symbiotic relationships between two agentssymbiotic strategy of agent 1 for agent 1, and E12 shows
the symbiotic strategy of agent 1 for agent 2. Therefore,
if agent 1 aims to maximize rewards for both agents, itwill take "Mutualism" strategy where symbiotic vector
v1=(E11, E12)=(1.0, 1.0) (  1.0  E11, E12  1.0 ). Fig. 3
shows a symbiotic relationship between two agents,
where one agent takes mutualism strategy and the otheragent takes predation strategy. In this case, it can beconsidered that the symbiotic vector of agent 1 is
v1=(E11, E12)=(1.0, 1.0), and that of agent 2 is v2=(E21,
E22)= (-1.0, 1.0). Intermediate values between -1.0 and
1.0 can be also used to define a symbiotic relationship.
For example, a symbiotic vector v=(1.0, 0.1) shows aweak mutualism that consider the other agent's profit alittle. Therefore, the symbiotic vector flexibly represents
any degree of symbiotic relationships, and moreover, it
can be extended to the relationships between many
agents.

Fig. 2. Symbiotic vector and six symbiotic relationships foragent 1 (An example of two dimensions)







Q ntwinner , stwinner , atwinner  Q ntwinner , stwinner , atwinner



 




  r   max
Q n, stn1 , a n  Q ntwinner , stwinner , atwinner ,n ,a n(1)where, ntwinner, stwinner and atwinner show the number, state
and action of the winner sub-Q-table at time t,
respectively. n is a sub-Q-table number, snt+1 is the stateof sub-Q-table n at time t+1, and an is a possible action
in sub-Q-table n. r is a reward,  is a learning rate, and 
is a discount rate
3. Reinforcement Learning with Symbiosis

3.2. Reinforcement learning with Symbiotic
vectors

This section introduces a symbiotic vector and how toapply the symbiotic vector to reinforcement learning.
3.1. Symbiotic vectors
Standard reinforcement learning aims to maximize
rewards that the self-agent obtains, however, in the
proposed method, not only the rewards for the selfagent, but also the rewards for other agents are consider
to execute reinforcement learning. In addition, six
symbiotic relationships are considered to build theaction strategies, that is, Predation, Mutualism, Altruism,
Harm, Self-improvement and Self-deterioration. Fig. 2
shows symbiotic strategy for "agent 1", where one axis
shows the weight (E11) on the profit of agent 1 (selfagent), the other axis shows the weight (E12) on the
profits of agent 2. In other words, E11 shows theThis subsection explains how to update Q values
considering a symbiotic vector. Here, suppose there are
p agents (agent #1 – #p), where the symbiotic vector ofagent k ( 1  k  p ) is vk=(Ek1, Ek2,..., Ekp). After agent ktakes an action and finds rewards for all the agents (r1,
r2, ..., rP), the modified reward used for updating Q
value of agent k in (1) is calculated as follows.
prk   Ekl r lEq. (2) means that the modified reward rk for agent k isbased on all the rewards given to agents #1 – #p and thesum of the weighted rewards is calculated.
4.

Simulations© The 2015 International Conference on Artificial Life and Robotics (ICAOB 2015), Jan. 10-12, Oita, Japan- 104 -(2)l 1

Shingo Mabu, Masanao Obayashi, Takashi Kuremoto

Table 1. Inputs and actions#

Input contents

1

forward cell

2
3
4
5
6

Fig. 4. A simulation environment

784.1. A simulation environment
Self-sufficient garbage collecting problem10 is used forthe performance evaluation of the proposed method. Fig.
4 shows the simulation environment used in this paper,
where there are two agents, 11 trashes, one charging
station, and two trash collecting places. The aim of thisproblem is to collect many trashes in the environment
and take them to the collecting place assigned to eachagent, i.e., agent k has to take trashes to the collecting
place for agent k to obtain reward. In addition, eachagent has a limited energy to move, thus the agentsshould check the remaining energy and go to thecharging station before running out of the energy. Table
1 shows the inputs and possible actions that the agents
can use. The initial energy is 100 (full charge), andwhen an agent goes forward, energy is used by three,
and when it turns right or left, energy is used by one.
The energy can be recharged gradually if the agent stays
at the charging station. The total time for one episode is
100 steps. Reward 100 is given when an agent takes one
trash to the colleting place for agent k, 10 is given whenit collect a trash, and 0 .1  ( charged energy) is given
when it stays at the charging station.

9
10

1
2
3
4

4.2. Conditions of Q-learning with Distributed Qtables
Table 2 shows the setting of distributed Q-tables, where
three sub-Q-tables are prepared for dealing with tasks
for agent 1's benefit, for agent 2's benefit, and charging
energy, respectively. Each agent has its own Q-table(three sub-Q-tables), i.e., the learning of the two agentsare carried out independently. The learning parameters
are set as learning rate =0.1, discount rate =0.9, and=0.05.

Input value

nothing, obstacle, collecting place foragent 1, collecting place for agent 2,
trash, charging station
backward cellthe same as "forward cell"right cellthe same as "forward cell"left cellthe same as "forward cell"
direction of nearest trash forward, backward, right, left
direction of charging
forward, backward, right, left
stationdirection of collecting
forward, backward, right, left
place for agent 1
direction of collecting
forward, backward, right, left
place for agent 2
the number of holding
0, 1, 2 (max 2)
trashescurrent energy level
low (less than 30), high (more than 70),
middle (other values)
Actions
go forward
turn rightturn left
no action

Table 2. Sub-Q-table setting
table #

Main task

1
2
3

for agent 1's benefit
for agent 2's benefit
Charging energy

Input # used in eachsub-Q-table
1,2,3,4,5,7,9
1,2,3,4,5,8,9
1,2,3,4,6,10

4.3. Simulation results
In this subsection, in order to confirm the basic effectsof the symbiotic relationship, the proposed method withmutualism strategy is compared with the conventional
Q-learning, i.e., both agents take Self-improvement
strategy.
Fig. 5 shows the number of trashes taken to thecollecting places for agent 1 and that for agent 2,
respectively, obtained by the conventional method. Eachline is the average over 20 independent simulations. As
the episode goes on, the number of trashes increases,
and in 5000th episode, 1.5 trashes are taken to thecollecting place for agent 1 and 2.25 trashes are foragent 2. Fig 6 shows the results of the proposed method,where 2.1 trashes are taken for agent 1 and 3.9 trashes
are for agent 2. It should be noted that the number of© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 105 -

RL with Symbiotic Relationships

multilateral relationships are also considered to build
simulation models dealing with real situations ofcooperation or conflict between agents.

References
1. Ken Binmore, Game Teory: A Very Short Introduction(Oxford Uniersity Press, 2007)
2. D. J. Watts, S. H. Strogatz, Collective dynamics of smallworld networks, Nature, 393 (1998) 440-442.
3. L. Margulis, The Symbiotic Planet, Contact, (1999).
4. T. Eguchi, K. Hirasawa, J. Hu, and N. Ota, A Study ofEvolutionary Multiagent Models Based on Symbiosis,
IEEE Trans. on Systems, Man, and Cybernetics, part B,(36) 1, (2006).
5. J. Y. Goulermas and P. Liatsis, Hybrid symbiotic genetic
optimization for robust edge-based stereo correspondence,
Pattern Recognition, (34), (2001) 2477–2496.
6. J. Y. Goulermas and P. Liatsis, A collective-based
adaptive symbiotic model for surface reconstruction inarea-based stereo, IEEE Trans. on Evolutionary
Computation, (7) 5, (2003), 482–502.
7. C. P. Pieters, Symbiotic networks, in Proc. of the IEEE
Congress on Evolutionary Computation, (2003) 921–927.
8. R. E. Bellman, Dynamic Programming, (Princeton
University Press, 1957).
9. R. S. Sutton, A. G. Batro, Reinforcement Learning - An
Introduction -, (1998).
10. R. Pfeifer, and C. Scheier. Understanding intelligence,(MIT press, 1999).

Fig. 5. The number of collected trashes at collecting places
for Agent 1 and Agent 2 (Conventional method)

Fig. 6. The number of collected trashes at collecting places
for Agent 1 and Agent 2 (Proposed method with Mutualism)

collected trashes for agent 1 increases without
decreasing that for agent 2 (even increasing).Therefore,
it is clarified that the proposed method with mutualism
strategy can obtain cooperation behavior and show
better performance than the conventional method.

5. Conclusions
In this paper, a novel reinforcement learning algorithm
based on symbiotic relationships is proposed, where
symbiotic vector is introduced to represent various
kinds of relationships. In the simulations, theeffectiveness of the proposed method with mutualism
strategy has been shown. By considering not only the
profits of self-agent, but also those of the other agents,
cooperative behaviors emerged. In the future, othercombinations of symbiotic relationships are considered
to analyze the emerging behaviors, and moreover,© The 2015 International Conference on Artificial Life and Robotics (ICAOB 2015), Jan. 10-12, Oita, Japan- 106 -

Development of a Dividual Model Using a Modular Neural Network
for Human-Robot Interaction
Toshiyuki Tanaka 1 and Kunikazu Kobayashi 1*
1 Aichi Prefectural University,

1522-3 Ibaragabasama,
Nagakute, Aichi 480-1198, Japan* E-mail: kobayashi@ist.aichi-pu.ac.jp

Abstract
Currently, in the field of human-robot interaction (HRI), robots have a problem that can only interact
the same at all times with humans. In this paper, therefore, we introduce the concept called a dividualand build a model of the dividual to grow through interactions with others. In addition, using a modular
neural network and reinforcement learning (actor-critic), we confirmed process to choose an appropriate
dividual out of plural dividuals.
Keywords: Model of dividual, Human-robot interaction, Robot, Modular neural network, Reinforcement
learning

1. Introduction

ment to obtain communication skills through social
interactions6 . Shibata made robots play a role inhuman society. Then he developed a seal-shaped
robot and reported that elderly people get pleasureand spiritual comfort through physical interactions
such as touching and petting7 . Naya et al. also presented a system dealing with haptic interactions between humans and robots8 .

In recent years, many types of robots have beendeveloped and successfully applied to a variety offields, such as medical care and disaster relief. Currently, in the field of human-robot interaction (HRI),
robots have a problem that they can only interact
with humans in a stereotypical way. Humans canhowever change correspondence depending on a human to be interacted, and realize a variety of interactions (communications). On conventional interaction between humans and robots, robots receive
unilateral orders by performing prearranged movement and utterance from humans, and perform giventasks1,2,3,4,5 . This cannot however realize robots
which live together and support humans.
As one of researches on HRI, Kojima tried
to have a mind like human for building a social relationship and proposed a model of develop-

In the present paper, in the field of HRI, we develop a dividual model to grow through interactions
with others. Then, we introduce a concept of dividual that it is formed into a self with respect toanother human through repetitive communications
with others9,10 . Individual cannot divide anymore
whereas dividual can divide into plural ones. We use
two machine learning techniques to construct the dividual model. Then, we confirm a process to choose
an appropriate dividual out of plural dividuals when
we appropriately prepare an input set for a dividualc⃝The2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan 10–12, Oita, Japan- 107 -

Toshiyuki Tanaka and Kunikazu Kobayashi

model defined by category elements and action ones.

teraction between humans and robots. Secondly, themodel chooses an appropriate dividual out of plural ones according to input information by an output
value from each modules. This corresponds robots
could change interaction according to humans.

2. Dividual
A concept of dividual is proposed by Japanese novelist Keiichiro Hirano to interact properly with another human9 . Individual cannot divide anymore
whereas dividual can divide into plural ones. Basedon the concept of dividual, a human can change toan another self according to environments and human relations. Dividual is formed into a self withrespect to another human through repetitive communications with others. In addition, dividual is strictly
divided into three types as shown in Fig.1.

Figure 2: Configuration of a dividual model.

3.2. Learning of Dividual Model
We introduce an actor-critic reinforcement learning method to train each modular neural network in
Fig.313 . Reinforcement learning is a learning framework that acquires the desirable output and time sequence with a trial and error by being maximized
value for result of interaction with environment, thatis reword of expectation. One of the representativetechnique is actor-critic. The actor-critic method
consist of an actor to select an action and a critic
to evaluate its action.

Figure 1: Conceptual diagram of dividual.
The first one is a social dividual. This is a standard dividual to interact with a stranger or an unfamiliar person. The second one is a group-oriented
dividual. This corresponds to a dividual for a specific group such as a school class or a tennis club.
The third one is an individual-oriented dividual.
This is a dividual for a specific person such as family
members or a close friend. In the present paper, thelast one, i.e. individual-oriented dividual is treated.
3. Dividual Model
3.1. Design of Dividual Model

Figure 3: Structure of a neural network based on
actor-critic.

We introduce a modular neural network as a generalframework of the dividual model11,12 . One module
corresponds to one dividual as show in Fig.2. Firstly,
a dividual model creates a dividual as a module bygiving inputs and learning. This corresponds to in-

The parameters are repeatedly modified as follows.

c⃝The2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan 10–12, Oita, Japan- 108 -

Developing a Dividual ModelTD error: Critic observes the rewords r and the next
state s ′ = (x1 , x2 , · · · , xN )T , and calculate temporal difference error (TD error) as learning index ofcritic and actor. TD error δ at time t is expressedby Eq.(1).

δ = r + γ V (ss′ ) −V (ss),4. Computer Simulation
Through computer simulations, we confirm a process to choose an appropriate dividual out of plural
dividuals.

(1)

4.1. Design of Input Information

where γ is a discount rate, 0 ⩽ γ ⩽ 1.
Learning of middle unit: The output function of middle unit is assumed as Gaussian function with anaverage c j and a variance σ 2j in Eq.(2).
()∥ss − c j ∥2
y j = exp −
,
(2)
2σ 2j

We prepare an input set for the dividual model in advance. It basically consists of two kinds of information. The first one is category information to identify
a person, e.g. name, sex, age, nationality, hobby andetc. The second one is action information to express
interactions, e.g. question, answer and etc. For convenience of identifying a person, we should keep the
input dimensions of action information as small as
possible. Further, we design four patterns for each
person whose category elements are identical but action elements are not.

The average c j becomes close to input s as Eq.(3).
c j ← c j +ζ δ νj

s −cj
y j,
σ 2j

(3)

where ζ is a learning coefficient, 0 ⩽ ζ ⩽ 1. In thepresent paper, the variance σ 2j of Gaussian function is assumed to be fixed.
Learning of critic: A weight ν j is updated by Eq.(4)so that TD error becomes zero.

ν j ← ν j + η δ y j,

4.2. Simulation Results
When the proposed model newly gets an input for an
unfamiliar person, it creates a novel dividual module
for him/her. We designed that each dividual module
gets a positive reward for an input containing goodaction and a negative reward for the other inputs
so as to form an appropriate dividual in a module.
The accumulated rewards for three dividual modules is illustrated in Fig.4. This figure shows thateach module learns correctly because the accumulated rewards is monotonically increasing.

(4)

where η is a learning coefficient, 0 ⩽ η ⩽ 1.
Learning of actor: A weight ω j is updated so as totake a higher state value V (s) using Eq.(5).

ωj ← ωj +ρ δ,(5)where ρ is a learning coefficient, 0 ⩽ ρ ⩽ 1.
Selection probability of modules: We introduce a
soft-max technique based on Gibbs distribution
in Eq.(6) so as to adjust the selection probabilityof modules.

π (ss) =exp (p(ss)),∑s ′ ∈X exp (p(ss′ ))(6)

where X refers to an input set and the parameterp(ss) is updated by Eq.(7).
p(ss) ← p(ss) + β δ ,(7)where β is a learning coefficient, 0 ⩽ β ⩽ 1.

Figure 4: Accumulated reward for three persons.

c⃝The2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan 10–12, Oita, Japan- 109 -

Toshiyuki Tanaka and Kunikazu Kobayashi

Figure 5 illustrates the transition of output V (ss).
As shown in this figure, the output of each module takes higher value than other two modules after
3,000 learning steps. We can therefore identify the
person by observing V (ss). Thus, we can confirm theprocess to choose an appropriate dividual out of plural dividuals for input information.

Acknowledgments
This work was partly supported by President’s Special Research Fund from Aichi Prefectural University, Japan.

References

Figure 5: Output value V (x) from each module.

5. Conclusion
In the present paper, in the field of HRI, we developed a dividual model to grow through interactions
with others. Through computer simulations, we confirmed a process to form an appropriate individualoriented dividual.
As a future problem, we should implement social dividual and group-oriented dividual. Then weshould verify the validity of a dividual model.
In the near future, when a home robot is introduced to our house, we hope that it should be developed to learn and grow through interactions withfamily members, and can separately correspond toeach family member. In order to realize such a homerobot, we must develop a system to enable smoothcommunication between humans and robots. Oncea dividual based human-robot interaction system isdeveloped, robots can take a role in a family member
and a doctor and then relationship between humans
and robots will be dramatically changed.

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Systems, 42, 143–166 (2003).
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6. H. Kozima, “Epigenesis of social intelligence and”theory ofmind,” IPSJ SIG Notes. ICS, ICS122-3, 13–18 (2000) (inJapanese).
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10. M. Kawamura and K. Kobayashi, “An Action Selection
Method Using Degree of Cooperation in a Multi-agent Reinforcement Learning System,” Journal of Robotics, Networking and Artificial Life, 1(3), 231–236 (2014).
11. T. Tanaka and K. Kobayashi, “Construction of a Dividual
Model Using a Modular Neural Network for Human-Robot
Interaction,” Proceedings of Tokai-Section Joint Conference on Electrical and Related Engineering, N1-6 (2014)(in Japanese).
12. R. Anand, K. Mehrotra, C. K. Mohan, and S. Ranka, “Efficient Classification for Multiclass Problems Using Modular
Neural Network,” IEEE Trans. Neural Networks, 6(1), 117–
124 (1995).
13. R. S. Sutton and A. G. Barto, “Reinforcement Learning: An
introduction,” MIT press (1998).

c⃝The
2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan 10–12, Oita, Japan- 110 -

Associative Memory with Class I and II Izhikevich Model
Yoshika Osawa

Department of Electrical Engineering and Information Systems, The University of Tokyo
7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
Takashi Kohno

Institute of Industrial Science, University of Tokyo
4-6-1, Komaba, Meguro-ku, Tokyo, 153-8505, Japan
E-mail: osawa@sat.t.u-tokyo.ac.jp, kohno@sat.t.u-tokyo.ac.jp
www.sat.t.u-tokyo.ac.jp/~kohno/

Abstract
Spiking neural network is a system that qualitatively reproduce the nervous system. It was shown in previous
researches that the performance of associative memory task in all-to-all connected networks is higher when they arecomposed of Class II neurons than Class I neurons. The Izhikevich model in its Class II mode, however, does nothave this performance boost. In this study, we focus on phase resetting curve as an index that reflects neuronalproperties related to neuron classes in detail.
Keywords: Spiking neural network, associative memory, Izhikevich model, Phase resetting curve, Hodgkin's
classification

equations. It is very easy to simulate using this model
but it reproduces limited aspects of neuronal behaviors.
In the meanwhile simplified expression while
maintaining the mechanism of the conductance-basedmodels develops the qualitative modeling. One of them,
Digital Spiking Silicon Neuron (DSSN) model3 iscomposed of two differential equations that expresses
the spike generation process without reset of systemvariables. The Izhikevich (IZH) model4 captures only
the spike decision process qualitatively, and represents
the spike by reset of its system variables. It is possible
to reproduce a variety of spike patterns.
Neurons are classified by characteristics of the periodic
firing in the Hodgkin’s classification5. Associative
memory simulation using the DSSN model built by Li
et al.6 showed that an all-to-all connected network ofClass II neurons has higher recall ability than that of

1. Introduction
Nervous system has robust, autonomous, and powerefficient information processing capacity. Neurons
generate overshoot of their membrane potential called
spike, when sufficiently large stimulus is applied, which
travel through their axon and transmit signals to postsynaptic neurons via synapses by transmitter.
There are several approaches in modeling neuronal
activities. Conductance-based models that focus on thecurrent through the cell membrane such as the HodgkinHuxley model1 can reproduce those activities precisely.
Since this type of models are composed of complex
differential equations, large-scale simulation is
impractical. The Leaky Integrate-and-Fire (LIF) model2
represents the spike by resetting of membrane potential.
This method yields simple and low-dimensional system© International Conference on Artificial Life and Robotics 2015, 01/10-12 (ICAROB 2015)- 111 -

Yoshika Osawa and Takashi Kohno

Fig. 1. The phase plane of Izhikevich model in (a) Class I and(b) Class II modes.
Fig. 3. Change of the shape of the phase resetting curve
depending on the resetting parameter d.

Fig. 2. Phase resetting curve of Izhikevich model in (a) Class I
and (b) Class II modes.

Class I neurons. Higher recall ability here means that an
original stored pattern is recalled from an associatedinput pattern with higher error. We configured similar
associative memory using the IZH model and evaluatedthe difference of its performance by focusing on the
shape of the phase resetting curve (PRC)7.

Fig. 4. Time course example of membranetransmitter concentration, and synaptic output.

potential,

2. Neuron Model and Synapse Model
neuron arise periodic firing before he intersection of thetwo lines disappear although no occur in Class I. The
PRC is a plot of spiking phase shift in a neuron atperiodically spiking state induced by sufficiently small
pulse stimulus. Its horizontal axis is the phase at which
the stimulus is applied. Fig. 2 shows the PRC of the IZH
model. There are two types of the shape. The Type 1 isalways positive, and Type 2 is biphasic. The PRC of the
IZH model is Type 1 and 2 in Class I and II models,
respectively. The PRC of Class II has a jump point
induced by reset. Its jump is reduced by reset parameterd (Fig. 3).

2.1. Izhikevich Model
The IZH model captures the mechanism of spike
decision process based on its bifurcation structure.
Saddle-node and Hopf bifurcations produce Class I and
II properties, respectively. In the IZH model, thismechanism is described by two differential equation (1),and membrane potential v is reset when it exceeds 30.

(1)2.2. Synapse Model
Synapses transmit signals to post-synaptic neurons.
Chemical synapses release transmitter when its presynaptic neuron fires. In our model, the neurotransmitteris in the same manner as Li et al., approximated to vary
in a pulse form while the spike exceeds a threshold. The
expression of synaptic output is shown in Eq. (2)8.

Here, u is a variable that represents membrane recovery
the parameters a, b, c, and d are constants that specify
the model’s firing property, and I represents a stimulus
input. The phase portraits of Class I and II neuron
modes of this model are described in Fig. 1. Class II© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 112 -

Associative Memory with IZH-modelFig. 5. Stored patterns in our associative memory simulation.

Fig. 6. Examples of input patterns, generated by applying
errors to the stored pattern 1.

(2)

Fig. 7. (a) Waveform example of membrane potential and (b)its raster plot. Input pattern is the upper left in Fig. 6. Blue andred waveforms and squares correspond to the black and white
dots of the stored pattern 1 in Fig. 5.

Here, and are constants that represent the time
constant of rising and phase of synaptic outputs,
respectively. The amount of transmitter [T] is set to 1
when v > 0, and 0 when v < 0. Figure 4 represents
example of time variation of the membrane potential,
neurotransmitter concentration, and the synaptic current.
3. Simulation
We examined the performance of the associative
memory composed of Class II Izhikevich model varying
resseting parameter d. The associative memory iscomposed of all-to-all connected networks and outputs a
stored pattern similar to the input data. In this work, ppatterns of N dots which are orthogonal to each otherare stored (p = 4, N = 256). The patterns are listed in Fig.
5. The weightof the synapses from i-th to j-thneuron is calculated by Eq. (3).

Fig. 8. Time course of the overlap
Fig. 7(a).

(3)
Input patterns are generated by applying random errors
to a stored pattern (Fig. 6). We made 100 input patterns
for every error rate. The error level vary from 5 to 50%
with basically 5% steps and 1% in a region where therecall rate starts to decrease rapidly.
Firstly, we apply a pulse stimulus only to the neurons
corresponding black dot. Then, stimulus current to allneurons was set to the value that induce periodic firing.

that corresponds toFigure 7(a) is an example of time series data of 256
neuron’s membrane potential when the upper left
pattern was input. Figure 7(b) shows the raster plot of
this example. In this figure, we can see that at earlyphase of the recall process the neurons fire relatively inan asynchronous manner. The synchronicity increases
as the neurons continue firing. And finally the neurons
that correspond to black (white) dots fire synchronously.
These two synchronously firing neuron groups are
antiphasic. To assess this quantitatively, we used
overlapthat calculates the matching degree of uth stored pattern from the phase in the period of eachneuron at a time t (Eq. (4)).

(4)© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 113 -

Yoshika Osawa and Takashi Kohno

PRC shown in Fig. 3, the balance between the regionsof positive and negative value of the phase shift may be
important for the performance of associative memory.
4. Conclusion
We have configured the associative memory consistingof 256 neurons of the IZH model. The performance ofClass II network was worse than Class I, but theperformance of the associative memory is improved byincreasing the reset parameter d, by which the shape of
the PRC changes. In the future, the validation by aphase model representing the shape of the quantitative
PRC will be performed to quantitatively evaluate how
the shape of the PRC contributes to the performance of
the associative memory.
Acknowledgements
Fig. 9. Overlap averageof each error level. (a) From 5%
to 50% by 5%. (b) From 30% to 45% by 1%.

This work was supported by JSPS Grant-in-Aid forscientific Exploratory Research Number 25240045
References

Fig. 10. Maximum input error level with which the recall is> 0.95 for all trial).
correct (=1Here, is the time that k-th spike is generated.
indicates that the u-th pattern is recalled successfully.
The time course of
calculated by applying Eq. (4)to the data in Fig.7 is shown in Fig. 8. This result
indicates that the stored pattern 1 is recalled correctly.
We performed simulation for 100 input patterns for eacherror level. The reset parameter d of each neuron wasvaried from 0 to 200 by 20 in this simulation. Figure 9shows the overlap averagefor each error level.
Since that the change ofis relatively large when
input error level is 30-45%, we varied the error level by
1% step. We define that the recall is correct when>
0.95 for all trials. Figure 10 plots the maximum input
error rate from which our network recalled the correct
pattern. As shown in Fig. 10, it got almost saturated in
the vicinity of d = 120. Compared with the shape of

1. Hodgkin, A. L, and Huxley, A. F, A quantitativedescription of membrane current and its application to
conduction and excitation in nerve. (J. Physiol. 117, 500–
544, 1952).
2. Stein R.B, Some models of neuronal variability(Biophysical Journal, 7: 37–68, 1967).
3. Kohno, T. and Aihara, K, Digital spiking silicon neuron:concept and behaviors in GJ-coupled network(inProceedings of International Symposium on Artificial
Life and Robotics, Beppu, OS3–OS6, 2007).
4. Izhikevich, E. M, Dynamical Systems in Neuroscience:
The Geometry of Excitability and Bursting (Cambridge:
MIT Press, 2006).
5. Hodgkin, A. L, The local electric changes associated withrepetitive action in a nonmedullated axon (J. Physiol. 107,
165-181, 1948).
6. J. Li, Katori, Y, and Kohno, T, An FPGA-Based Silicon
Neuronal Network with Selectable Excitability Silicon
Neurons (Frontiers in neuroscience 6 183, 2012).
7. Ermentrout B, Type I membranes, phase resetting curves
and synchrony (Neural Computation 8:979-1001, 1996).
8. Destexhe, A., Mainen, Z. F., and Sejnowski, T. J, Kinetic
models of synaptic transmissionm, (in Methods in
Neuronal Modeling, eds C. Koch and I. Segev Cambridge,
FL: MIT Press, 1–25, 1998).

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 114 -

Medical Image Recognition of Heart Regions by Deep Multi-layered GMDH-type Neural
Network Using Principal Component-regression Analysis
Tadashi Kondo†

Graduate School of Health Sciences
3-18-15 Kuramoto-cho Tokushima 770-8509 Japan
Email: †kondo@medsci.tokushima-u.ac.jp
Juniji Ueno

Graduate School of Health Sciences
3-18-15 Kuramoto-cho Tokushima 770-8509 Japan
Shoichiro Takao

Graduate School of Health Sciences
3-18-15 Kuramoto-cho Tokushima 770-8509 Japan

Abstract
In this study, a deep Group Method of Data Handling (GMDH)-type neural network using principal componentregression is applied to the medical image recognition of the heart regions. The deep GMDH-type neural network
algorithm can organize the neural network architecture with many hidden layers fitting the complexity of thenonlinear systems so as to minimize the prediction error criterion defined as AIC (Akaike’s Information Criterion)
or PSS (Prediction Sum of Squares). This algorithm is applied to the medical image recognition of the heart regions
and it is shown that this algorithm is useful for the medical image recognition of the heart regions because deepneural network architecture with many hidden layers is automatically organized using the principal componentregression analysis, so as to minimize AIC or PSS criterion.
Keywords: Deep neural networks, GMDH, Medical image recognition, Evolutional computation

1.

network, radial basis function (RBF) neural network andpolynomial neural network so as to minimize theprediction error criterion defined as AIC4 or PSS5. Thisalgorithm is applied to the medical image recognition of
the heart regions and results show that the deep GMDHtype neural network algorithm is useful for the medical
image recognition of the heart regions and is easy toapply practical complex problem because the deepneural network architecture with many hidden layers is
automatically organized so as to minimize AIC or PSS
criterion.

Introduction

The deep Group Method of Data Handling(GMDH)-type neural networks and their applications
have been proposed in our early works1,2. Deep GMDHtype neural networks can automatically organize neuralnetwork architecture by heuristic self-organization
method3, which is a type of evolutional computation. In
this study, a deep GMDH-type neural network algorithm
using the principal component-regression analysis isapplied to the medical image recognition of the heartregions. In this algorithm, optimum neural networkarchitecture is automatically selected from three neuralnetwork architectures such as sigmoid function neural2.

Deep multi-layered GMDH-type neural network© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 115 -

T.Kondo, J.Ueno, S.Takao

Fig.1 shows the architecture of the deep multilayered GMDH-type neural network.
X1
X2
X3
.
.

f

f

f

f

f

f

f

f

f

.
.

.
.

.
.

f

f

f

.
.

XP

f



f



.
.

.
.

f



f : (Nonlinear function)y k  e (  zk )2

Fig.1 Architecture of the deep GMDH-type neural network

2.1 The first layer
uj=xj (j=1,2,…,p)(1)
where xj (j=1,2,…,p) are input variables of the nonlinear
system, and p is the number of input variables.
2.2 The second layer
All combinations of r input variables are generated.
For each combination, optimum neuron architectures
are automatically selected so as to minimize AIC or PSS.
Deep GMDH-type neural network algorithm can selectoptimum neural network architecture from three neuralnetwork architectures such as sigmoid function neuralnetwork, RBF neural network and polynomial neuralnetwork.
(1) Sigmoid function neural network
1) The first type neuron
: (Nonlinear function)zk=w1ui+w2uj+w3uiuj+w4ui2+w5uj2- w01
(2)
f : (Nonlinear function)
1
(3)
yk 
1  e (  zk )2) The second type neuron
: (Linear function)zk= w1u1+w2u2+w3u3+ ··· +wrur - w01 ( r<p ) (4)f : (Nonlinear function)
(5)
1

yk 

1  e (  zk )(2) Radial basis function neural network
1) The first type neuron
: (Nonlinear function)zk=w1ui+w2uj+w3uiuj+w4ui2+w5uj2 - w01
f : (Nonlinear function)(6)y k  e (  zk )2

(7)2) The second type neuron
: (Linear function)zk= w1u1+w2u2+w3u3+ ··· +wrur - w01 ( r<p ) (8)(9)(3) Polynomial neural network
1) The first type neuron
: (Nonlinear function)zk=w1ui+w2uj+w3uiuj+w4ui2+w5uj2 - w01
(10)
f : (Linear function)yk= zk(11)2) The second type neuron
: (Linear function)zk= w1u1+w2u2+w3u3+ ··· +wrur - w01 ( r<p ) (12)f : (Linear function)yk= zk(13)
Here, 1 =1 and wi (i=0,1,2,,9) . Value of r is thenumber of input variables u in each neuron. p is thenumber of input variables xi (i=1,2,,p).
2.2.1 Estimation procedure of weight wiFirst, values of zk** are calculated for each neuralnetwork architecture as follows.
a)Sigmoid function neural network
'
**
(14)
zk  log e ()
1 'b)RBF neural network**(15)
zk   loge  'c)Polynomial neural network
zk**=
(16)
where ’ is the normalized output variable. Then theweights wi (i=0,1,2,,5) are estimated by using theprincipal component-regression analysis.
2.2.2 Principal component-regression analysis
In the GMDH-type neural network, the multicolinearity is generated in the function  of the neurons.
In this study, the function  is calculated using theprincipal component-regression analysis.
In the case of Eq.(2), orthogonal vector v is calculated .
v=Cu
(17)
Here, v=(v1,v2,…,v5) , u=(ui uj, uiuj, ui2, uj2)
v is orthonormal vectors and C is orthonormal matrix. C
is calculated using the following eigenvalue equation.
R・C＝C・
(18)
Here, R is a correlation matrix. Then, variable zk iscalculated using orthogonal regression analysis.
zk = wT v=w1v1+w2v2+…+w5v5(19)© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 116 -

Deep multi-layered GMDH-type NN

network architecture had the smallest recognition error.
Five useful neurons were selected in each hidden layer.
Fig.3 shows the PSS values of the three types ofneurons in the second layer. The sigmoid function
neural network architecture was selected by the deep
GMDH-type neural network algorithm. Fig.4 shows thevariation of PSS values. PSS values decreased graduallyand very small PSS values were obtained at the tenthlayer. The heart region was recognized by using theorganized neural network and was extracted
automatically. Fig.5 shows the output image of the deep
GMDH-type neural network. This output image wasprocessed by the post-processing analysis. In the postprocessing, the small isolated regions were eliminatedand the outlines of the heart regions were expanded
outside by N/2 pixels. Fig.6 shows the output image
after this processing. In order to check the matching
between the original image and the output image of theneural network, the output image was overlapped on theoriginal image of Fig.2. The overlapped image is shownin Fig.7. From Fig.7, we can see that the output image
was very accurate. Fig.8 shows the extracted heart
image.

Using the principal component-regression analysis,
variable zk in the function  is calculated without multicolinearity. In (19), useful orthogonal variables i(i=1,2,…,5) are selected using AIC4 or PSS5.
From these generated neurons, L neurons which
minimize AIC or PSS values are selected. The output
values (yk) of L selected neurons are set to the input
values of the neurons in the third layer.
2.3 The third and successive layers
In the second layer, optimum neural networkarchitecture is selected from three neural networkarchitectures which is sigmoid function neural networkor RBF neural network or polynomial network. The
same calculation of the second layer is iterated until
AIC or PSS values of L neurons with selected neuron
architecture, stop decreasing. When iterative calculation
is terminated, neural network architecture is produced
by L selected neurons in each layer.
3. Application to the medical image recognitionof heart regions
In this study, the heart regions were automaticallyrecognized using the deep GMDH-type neural networkand these regions were extracted. Multi-detector row CT(MDCT) images of the heart were used in this study.





0.7
0.6





Error

3.1 Results of the medical image recognition by the
deep GMDH-type neural network
The MDCT image shown in Fig.2 was used fororganizing the neural network. x and y coordinates andthe statistics of the image densities in the neighboring
regions of the NN pixels at the positions of the learning
points are used as the input variables of the neuralnetwork. Only five input variables which are the mean,
the standard deviation, the variance and x and ycoordinates were automatically selected as useful input
variables. The output value of the neural network is zeroor one. When NN pixel region is contained in the heartregions, the neural network set the pixel value at thecenter of the NN pixel region to one and this pixel isshown as the white point. The neural networks wereorganized when the values of N were from 3 to 10. It
was determined that when N was equal to 4, the neural

0.5





0.4

0

Polynomial RBF Sigmoid

Fig. 2 Original image

Fig. 3 PSS values of three
types of neurons

o

10
o
oo

PSS

o

5
o
o
ooo
o
oooo

o
oooo
ooooo

2

3

4

5

6

ooooo ooooo

78ooooo ooooo

9

10

Layer

Fig. 4 Variation of PSS in each layer© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 117 -

T.Kondo, J.Ueno, S.Takao

4.

Fig. 5 Output image

In this paper, the deep multi-layered GMDH-type
neural network algorithm was applied to the medical
image recognition of heart regions and the results werecompared with those of the conventional sigmoid
function neural network trained using the backpropagation algorithm. It was shown that the deep
multi-layered GMDH-type neural network algorithm
was a useful method for the medical image recognitionof heart regions because the deep neural networkarchitecture with many hidden layers is automaticallyorganized so as to minimize the prediction error
criterion defined as AIC or PSS.

Fig.6 Output image
after the post processing

Fig. 7 Overlapped image

Conclusions

Acknowledgment
This work was supported by (JSPS) KAKENHI
26420421.

Fig. 8 Extracted image

3.2 Extraction by the conventional neural network
using sigmoid function.
A conventional neural network trained using theback propagation algorithm was applied to the same
recognition problem. The learning calculations of theweights were iterated changing structural parameterssuch as the number of neurons in the hidden layer andthe initial values of the weights. The output images,
when the numbers of neurons in the hidden layer (m) are
5, 7 and 9, are shown in Fig.9. These images containmore regions which are not part of the heart and theoutlines of the heart are not extracted with required
clarity compared with the output images obtained using
the deep GMDH-type neural network algorithm, which
are shown in Fig.5.

References
1.

T. Kondo, J. Ueno and S. Takao, Hybrid multi-layered
GMDH-type neural network using principal component
regression analysis and its application to medical image
diagnosis of liver cancer, Procedia Computer Science, 22,(2013) 172-181.

2.

T. Kondo, J. Ueno and S. Takao, Hybrid feedback
GMDH-type neural network using principal componentregression analysis and its application to medical image
diagnosis of lung cancer, ICIC Express Letters (ICIC-EX)
8 (4) (2014) 1053-1060.

3.

S. J. Farlow ed., Self-organizing methods in modeling,
GMDH-type algorithm, New York: Marcel Dekker Inc.,
1984.

4.

H. Akaike, A new look at the statistical model
identification, IEEE Trans. Automatic Control AC-19 (6)(1974) 716-723.

5.

H. Tamura, T. Kondo, Heuristics free group method ofdata handling algorithm of generating optimum partial
polynomials with application to air pollution prediction,(a) m=5(b) m=7

Int. J. System Sci. 11 (9) (1980) 1095-1111.

(c) m=9

Fig. 9 Output images of the conventional sigmoid
function neural network© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 118 -

Deep Feedback GMDH-Type Neural Network Using Principal Component-Regression
Analysis and Its Application to Medical Image Recognition of Abdominal Multi-Organs

Tadashi Kondo†
Graduate School of Health Sciences
3-18-15 Kuramoto-cho Tokushima 770-8509 Japan
Email: †kondo@medsci.tokushima-u.ac.jp
Juniji Ueno
Graduate School of Health Sciences
3-18-15 Kuramoto-cho Tokushima 770-8509 Japan
Shoichiro Takao
Graduate School of Health Sciences
3-18-15 Kuramoto-cho Tokushima 770-8509 Japan

Abstract
The deep feedback Group Method of Data Handling (GMDH)-type neural network is proposed and applied to the
medical image recognition of abdominal organs such as the liver and spleen. In this algorithm, the principal
component-regression analysis is used for the learning calculation of the neural network, and the accurate andstable predicted values are obtained. The neural network architecture is automatically organized so as to fit thecomplexity of the medical images using the prediction error criterion defined as Akaike’s Information Criterion(AIC) or Prediction Sum of Squares (PSS). The recognition results show that the deep feedback GMDH-type neuralnetwork algorithm is useful for the medical image recognition of abdominal organs.
Keywords: Deep neural networks, GMDH, Medical image recognition, Evolutional computation

medical image recognition of the abdominal organs andthe recognition results are compared with those obtainedusing the conventional neural networks trained using theback propagation algorithm.

1. Introduction

The deep GMDH-type neural network algorithms
were proposed in our early works1 and can
automatically organize the neural network architectures
2. Deep feedback GMDH-type neural networkby using heuristic self-organization method2 which is a
type of the evolutional computation. In this study, deep
The architecture of the deep feedback GMDH-type
feedback GMDH-type neural network algorithm isneural network developed in this paper has a feedback
applied to the medical image recognition of theloop as shown in Fig.1.
abdominal organs such as the liver and spleen. The
2.1 First loop calculation
learning calculations of the weights is the principal
First, all data are set to the training data. Then thecomponent-regression analysis and the accurate andarchitecture of the input layer is organized.
stable predicted values are obtained. The deep feedback
2.1.1 Input layer
GMDH-type neural network algorithm is applied to the
uj=xj (j=1,2,…,p)(1)© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 119 -

T.Kondo, J.Ueno, S.Takao

: (Nonlinear function)(10)
zk=w1ui+w2uj+w3uiuj+w4ui2+w5uj2 - w01
f : (Linear function)yk= zk(11)2) The second type neuron
: (Linear function)zk= w1u1+w2u2+w3u3+ ··· +wrur - w01 ( r<p) (12)f : (Linear function)yk= zk(13)
Here, 1 =1 and wi (i=0,1,2,,5) and wi (i=0,1,2,,r)
are weights between the input and hidden layer. Weights
wi (i=0,1,2,) in each neural network architecture are
estimated by the principal component-regression
analysis.
[Estimation procedure of weight wi ]First, values of zk** are calculated for each neuralnetwork architecture as follows.
a) Sigmoid function neural network
'
(14)
**
zk  log e ()
1 'b) RBF neural network**(15)
z k   log e  'c) Polynomial neural network
zk**=
(16)
where  is an output variable and ’ is the normalized
output variable whose values are between 0 and 1.
[Principal component-regression analysis]
Multi-colinearity is generated in the function  ofthe neurons. In this study, the function  is calculatedusing the principal component-regression analysis.
In the case of Eq.(2), orthogonal vector v is calculated .
v=Cu
(17)
2
2
Here, v=(v1,v2,…,v5) , u=(ui uj, uiuj, ui , uj )v is orthonormal vectors and C is orthonormal matrix. C
is calculated using the following eigenvalue equation.
R・C＝C・
(18)
Here, R is a correlation matrix. Then, variable zk iscalculated using orthogonal regression analysis.
zk = wT v=w1v1+w2v2+…+w5v5(19)
Using the principal component-regression analysis,
variable zk in the function  is calculated without multi-

where xj (j=1,2,…,p) are the input variables of thesystem, and p is the number of input variables.
 f
X1

f

X2

 f

X3
.
.
.
XP

.
.
.

 f







.
.

f
 f
f

Fig.1 Architecture of the deep feedback GMDH-type neuralnetwork

2.1.2 Hidden layer
All combinations of the r input variables are
generated. For each combination, three types of neuron
architectures which are the sigmoid function neuron, theredial basis function (RBF) neuron and the polynomial
neuron, are generated and L neurons which minimize
AIC3 and PSS4 value are selected for each type ofneuron architectures.
(1) Sigmoid function neural network
1) The first type neuron
: (Nonlinear function)zk=w1ui+w2uj+w3uiuj+w4ui2+w5uj2 - w01
(2)
f : (Nonlinear function)
1
yk 
(3)
1  e(  z )2) The second type neuron
: (Linear function)zk= w1u1+w2u2+w3u3+ ··· +wrur - w01 ( r<p ) (4)f : (Nonlinear function)
1
yk 
(5)
1  e(  z )(2) Radial basis function neural network
1) The first type neuron
: (Nonlinear function)zk=w1ui+w2uj+w3uiuj+w4ui2+w5uj2- w01
(6)
f : (Nonlinear function)2(7)
y k  e (  zk )2) The second type neuron
: (Linear function)zk= w1u1+w2u2+w3u3+ ··· +wrur - w01 ( r<p ) (8)f : (Nonlinear function)2(9)y k  e (  zk )(3) Polynomial neural network
1) The first type neuronkk© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 120 -

Deep feedback GMDH-type NN

neural networks were organized when the values of N
were from 3 to 10. When N was equal to 6, the neuralnetwork architecture had the smallest recognition error.
Fig.3 shows errors in the first feedback loop. Error of
the sigmoid neuron was smallest in the three types ofneurons. Fig.4 shows the variation of PSS value in eachlayer. The PSS values were decreased gradually throughthe feedback loops and small PSS value was obtained in
the fifth feedback loop. The deep feedback GMDH-type
neural network output the liver image (Fig.5) and thefirst post-processing analysis of the output liver image
was carried out. Fig.6 shows the output image after the
first post-processing. The output image after the firstpost-processing was overlapped to the original image(Fig.2) as shown in Fig.7. The recognized liver region
are very accurate. The liver region was extracted fromthe original image as shown in Fig.8. A conventional
neural network trained using the back propagation
algorithm was applied to the same recognition problem.
The output images, when the numbers of neurons in thehidden layer (m) are 5, 7 and 9, are shown in Fig.9.
These images contain more regions which are not partof the liver.

colinearity. In (19), useful orthogonal variables
i(i=1,2,…,5) are selected using AIC3 or PSS4 criterion.
L neurons having the smallest AIC or PSS values
are selected for three types of neuron architectures. The
output variables yk of L selected neurons for three typesof neuron architectures are set to the input variables of
the neurons in the output layer.
2.1.3 Output layer
For three types of neural network, the outputs yk of
the neurons in the hidden layer are combined by thefollowing linear function.
(20)
Here, L is the number of combinations of the input
variables and yk is the intermediate variables. Eq. (20) is
calculated for three types of neural networkarchitectures. The neural network architecture which
has smallest AIC or PSS value is selected. Then, theestimated output values * which is selected in the
output layer is used as the feedback value.
2.2 Second and subsequent loop calculationsFirst, the estimated output value * is combinedwith the input variables x and all combinations between
the estimated output value * and the input variables x
are generated. The same calculation as the first feedback
loop is iterated. When AIC or PSS value of the linear
function in (20) is not decreased, the loop calculation is
terminated and the complete neural network architectureis organized by the L selected neurons in each feedback
loop.

8

7



PSS


6



5

0

Polynomial RBF Sigmoid

3. Application to the medical image recognitionof abdominal multi-organs

Fig. 2 Original image

Fig. 3 Errors of three types ofneurons (1)In this study, the regions of the liver and spleen in
the abdominal regions were recognized and extracted
automatically. Multi-detector row CT (MDCT) imagesof the abdominal regions are used in this study.
3.1 Extraction of the liver regions
An abdominal MDCT image shown in Fig.2 wasused for organizing the deep feedback GMDH-type
neural network. The statistics of the image densities andx and y coordinates in the neighboring regions, the NNpixel regions, were used as the image features. The

PSS

Number of feedback loops

Fig. 4Variation of PSS (1)Fig. 5 Output image(1)© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 121 -

T.Kondo, J.Ueno, S.Takao

Fig. 6 Output image Fig.7 Overlapped
after the firstimage (1)post processing

Fig.8 Extracted
image (1)Fig.12 Output image Fig.13 Overlapped Fig.14 Extracted(2)
image (2)image (2)(a)m=5(a)m=5(b) m=7(c)m=9Fig.9 Output images of the conventional neural network (1)4. Conclusions
In this paper, the deep feedback GMDH-type neuralnetwork algorithm using principal componentregression analysis was applied to the medical image
recognition of the abdominal multi-organs, and theseresults were compared with those of the conventionalsigmoid function neural network.
Acknowledgment
This work was supported by (JSPS) KAKENHI
24560497.
References
1. T. Kondo, J. Ueno and S. Takao, Medical image diagnosisof liver cancer by hybrid feedback GMDH-type neuralnetwork using principal component-regression analysis,
Proceedings of the nineteenth international symposium on
artificial life and robotics (2014) pp.339-342.
2.

S. J. Farlow ed., Self-organizing methods in modeling,
GMDH-type algorithm, New York: Marcel Dekker Inc.,5

PSS(c) m=9

Fig.15 Output images of the conventional neural network (2)3.2Extraction of the spleen regions
The deep feedback GMDH-type neural network wasapplied to the recognition of the spleen region using thesame MDCT image shown in Fig.2. Fig.10 shows theerrors of three type neurons. The RBF neural networkarchitecture was selected. Fig.11 shows the variation of
PSS values. Small PSS value was obtained in the ninth
feedback loop. The deep feedback GMDH-type neuralnetwork output the spleen image as shown in Fig.12.
The output image after the second post-processing was
overlapped as shown in Fig.13. The recognized spleen
region was very accurate. The spleen region was
extracted from the original image as shown in Fig.14. A
conventional neural network trained using the backpropagation algorithm was applied and the output
images, when the numbers of neurons in the hidden
layer (m) are 5, 7 and 9, are shown in Fig.15.

4(b) m=7



1984.




3.
PSS

H. Akaike, A new look at the statistical model
identification, IEEE Trans. Automatic Control, AC-19,

3(6) (1974) 716-723.
2

4.

0

H. Tamura, T. Kondo, Heuristics free group method ofdata handling algorithm of generating optimum partial

Polynomial RBF Sigmoid

Number of feedback loops

Fig. 10 Errors of three typesof neurons (2)Fig. 11 Variation of PSS (2)polynomials with application to air pollution prediction,
Int. J. System Sci. 11 (9) (1980) 1095-1111.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 122 -

Synchronized Response to Grayscale Image Inputs in the Chaotic Cellular Neural Network
Masayuki FUJIWARA, Akihiro YAMAGUCHI
Department of Information and Systems Engineering, Fukuoka Institute of Technology,
3-30-1 Wajiro-higashi, Higashi-ku, Fukuoka, 811-0116, JAPANMasao KUBO
Department of Computer Science, National Defense Academy of Japan,
1-10-20 Hashirimizu, Yokosuka, Kanagawa, 239-8686, JAPANE-mail: s11c1040@bene.fit.ac.jp, aki@ fit.ac.jp, masaok@nda.ac.jp

Abstract
In this article, synchronized response in the chaotic cellular neural network for grayscale visual stimulus was studied
in the viewpoint of neural coding. Simple gradation patterns were used as visual stimuli and the synchronizedresponse was analyzed by the correlation of spike firing times. As results, synchronized responses were observed forthe neurons which have similar input value and they formed chaotic cell assemblies. Each assembly was distinguishedfrom the others in terms of cross-correlation.
Keywords: chaotic synchronization, neural coding, spike response model, visual segmentation

goal of this study is to realize visual segmentation using
chaotic synchronization.
In our previous study, chaotic cell assemblies isformed in Chaotic-CNN for each localized stimulus
when the stimulus is a two-dimensional binary pattern 6.
For the segmentation of real image, it is, however,necessary to analyze the case that the stimulus has analog
continuous values. In this study, synchronized chaotic
responses of Chaotic-CNN to the grayscale visual
stimulus are numerically analyzed.

1. Introduction
Recently, several information coding schemes usingsynchronized firing of neurons have been proposed1, 2. As
one of such schemes, the correlated firing is also regarded
having an important role for information processing in
the brain as a binding mechanism of neural information1.
On the other hand, chaotic system is well-known to
produce various complex behaviors by simple equations.
When we consider to represent binding information by
the correlated firing, the chaotic firing pattern has anadvance in its variety compared to the periodic firing
pattern. There is some possibility to represent morevarious information by the chaotic spike sequence thanthe periodic sequence3, 4.
Authors have been studied formation of chaotic cell
assembly in the chaotic cellular neural network (ChaoticCNN) that is a two dimensional coupled network ofchaotic spike response model (Chaotic-SRM)5,6. The
Chaotic-SRM is an extended spike response model 7 thatexhibits chaotic inter-spike interval by adding thebackground sinusoidal oscillation 4, 8. One fundamental

2. Chaotic-SRM
The spike response model (SRM) was introduced by
Gerstner and Kistler7. In SRM, the dynamics of neuron is
directory described by kernel functions and it is notnecessary to solve differential equations for its
simulation. We proposed the Chaotic-SRM that is
extended SRM to exhibit the chaotic inter-spikeintervals5. The definitions of Chaotic-SRM is as follows.
The membrane potential 𝑢(𝑡) of neuron at time 𝑡 isdefined as- 123 -

𝑢(𝑡) = 𝑢𝑟𝑒𝑠𝑡 + 𝜂(𝑡 − 𝑡 (𝑓) ) + 𝛽,

(1)

(𝑓)

where 𝑢𝑟𝑒𝑠𝑡 , 𝑡 and 𝛽 denote the resting potential ofneurons, the last firing time of this neuron and theexternal input, respectively. The kernel function 𝜂
describes the response of membrane potential after firing.
The definition of the kernel function 𝜂 is𝑡 − 𝑡 (𝑓)𝜂(𝑡 − 𝑡 (𝑓) ) = −𝜂𝑖𝑛𝑖𝑡 𝑒𝑥𝑝 () 𝜃(𝑡 − 𝑡 (𝑓) ),
𝜏𝜂0

(a)

(2)

where 𝜏𝜂0 is the time constant of spike response and 𝛩 isthe step function such that 𝛩(𝑠) is 1 for 𝑠 ≥ 0 and 0 for
the others. In this model, when the membrane potential
exceeds the threshold value 𝜗, this neuron is firing andthe membrane potential is reset by the update of the last
firing time 𝑡 (𝑓) . The term −𝜂𝑖𝑛𝑖𝑡 is an initial value of thekernel function 𝜂 after firing. In the original SRM, thisterm is constant. Therefore, the original SRM isperiodically firing and its period is determined by theexternal input value 𝛽.
We extended the original SRM to exhibits chaotic
response by adding background sinusoidal oscillation in
the same way of the bifurcating neuron8 and the chaotic
pulse coupled neural network4. In the extended SRM, thebackground oscillation is added to the term 𝜂𝑖𝑛𝑖𝑡 and its
definition is𝜂𝑖𝑛𝑖𝑡 = 𝜂0 − 𝐴𝜂0 𝑠𝑖𝑛(2𝜋𝜔𝜂0 𝑡 (𝑓) ),(b)
Fig.1. (a) The bifurcating diagram and (b) Lyapunov Exponent 6.

Fig. 2. Chaotic-CNN 6.

(3)

the nearest neighbors in four directions (up, down, left
and right) as shown in Fig. 2. Let 𝑛𝑥,𝑦 be a neuron that
allocated at the location (𝑥, 𝑦). The membrane potentialof 𝑛𝑥,𝑦 is denoted by 𝑢𝑥,𝑦 and it is defined aswhere 𝜂0 denotes the constant 𝜂𝑖𝑛𝑖𝑡 in the original model
and, 𝐴𝜂0 and 𝜔𝜂0 denote the amplitude and the frequency
of background oscillation. In this article, we call thisextended model the Chaotic-SRM. The Chaotic-SRM
exhibits various chaotic behavior depending on theparameter values of 𝐴𝜂0 and the external input 𝛽.
The bifurcation diagram and the Lyapunov
exponent for the external input 𝛽 are obtained bynumerical simulation6 (Fig.1), where the parametervalues of Chaotic-SRM are set as follows: 𝑢𝑟𝑒𝑠𝑡 =−70mv , 𝜃 = −35mv , 𝜂0 = 55 , 𝜏𝜂 = 10msec , 𝜔𝜂0 =0.75/2𝜋 and 𝛢𝜂0 = 10.9 . In the bifurcation diagram(Fig.1(a)), the period doubling bifurcation that is typicalbehavior of chaotic system were observed. In Fig. 1(b),several regions where the Lyapunov exponents is
positive, are existing and these regions correspond to thechaotic region in the bifurcation diagram.

(𝑓)𝑢𝑥,𝑦 (𝑡) = 𝑢𝑟𝑒𝑠𝑡 + 𝜂 (𝑡 − 𝑡𝑥,𝑦 ) + 𝛽𝑥,𝑦+𝜉 × [𝑜𝑥−1,𝑦 (𝑡) + 𝑜𝑥+1,𝑦 (𝑡) + 𝑜𝑥,𝑦−1 (𝑡)+ 𝑜𝑥,𝑦+1 (𝑡)],

(4)

(𝑓)

where 𝑡𝑥,𝑦 , 𝛽𝑥,𝑦 and 𝜉 denote the last firing time, theexternal input of 𝑛𝑥,𝑦 and the coupling weight, respectively.
The boundary condition is fixed as follows: 𝑜𝑥 ∗ ,𝑦 (𝑡) =𝑜𝑥,𝑦∗ (𝑡) = 0 (𝑥 ∗ ∈ {0, 𝑁 + 1}, 𝑦 ∗ ∈ {0, 𝑀 + 1}).
The function 𝑜𝑥 ′ ,𝑦′ is the output from the connected
neuron 𝑛𝑥 ′ ,𝑦′ and it is also defined as𝑜𝑥 ′ ,𝑦′ (𝑡) = ∑ (𝑓)

(𝑘)

(𝑘)

𝑡𝑥,𝑦 <𝑡 ′ ′ <𝑡𝑥 ,𝑦𝜀(𝑡 − 𝑡𝑥 ′ ,𝑦′ ),

(5)

(𝑘)

where 𝑡𝑥 ′ ,𝑦′ denotes the 𝑘-th firing time of the neuron𝑛𝑥 ′ ,𝑦′ . The kernel function 𝜀 describes the response ofsynaptic connection. The definition of the kernel function
𝜀 is

3. Chaotic-CNN
The Chaotic-CNN is defined as a two dimensional
coupled system of Chaotic-SRM6. In the Chaotic-CNN,
each neuron is put on the 𝑁 × 𝑀 lattice and connected to𝜀(𝑠) =𝑠𝜏𝜀𝑠

𝑒𝑥𝑝 (− ) Θ(𝑠),𝜏𝜀(6)where 𝜏𝜀 is the time constant of the synaptic connection.

- 124 -

4. Correlation Analysis
In this article, a synchronized response is analyzed by thecorrelation. As an index of the synchronized response,the cross-correlation between two neurons are analyzed.
Let 𝑆𝑥,𝑦 be a set of the firing times of the neuron 𝑛𝑥,𝑦 .
The cross-correlation between 𝑆𝑥,𝑦 and 𝑆𝑥 ′ ,𝑦′ with time
shift 𝜙 is defined as
𝐶𝐶(𝑆𝑥,𝑦 , 𝑆𝑥′,𝑦′ ; 𝜙)
(𝑘)

=

(𝑙)

(𝑘)

(𝑙)

#({𝑡𝑥,𝑦 |∃𝑡𝑥′,𝑦′ ∈ 𝑆𝑥′,𝑦′ , |𝑡𝑥,𝑦 − 𝑡𝑥′,𝑦′ − 𝜙| ≤ 𝛥𝑠})(a) The external input 𝛽𝑥,𝑦 as a visual stimulus.

(7)#(𝑆𝑥,𝑦 )where #(𝑋) denotes the number of elements of 𝑋 and 𝛥𝑠
is a time resolution of coincident firing. In this article, 𝛥𝑠
is set to 0.5 msec. The auto-correlation is also defined as
𝐴𝐶(𝑆𝑥,𝑦 ; 𝜙) = 𝐶𝐶(𝑆𝑥,𝑦 , 𝑆𝑥,𝑦 ; 𝜙). The maximal value ofthe cross-correlation is defined as
𝐶𝐶 ∗ (𝑆𝑥,𝑦 , 𝑆𝑥 ′ ,𝑦′ ) = max 𝐶𝐶(𝑆𝑥,𝑦 , 𝑆𝑥 ′ ,𝑦′ ; 𝜙).
𝜙(8)(b) The raster plot of synchronized response.

In the case that 𝐶𝐶 ∗ = 𝐶𝐶(𝑆𝑥,𝑦 , 𝑆𝑥 ′ ,𝑦′ ; 𝜙) ≃ 1, 𝑆𝑥,𝑦and 𝑆𝑥 ′ ,𝑦′ are synchronized with the time shift 𝜙. In thecase that 𝐴𝐶 ∗ = 𝐴𝐶(𝑆𝑥,𝑦 ; 𝜙) ≃ 1, 𝑆𝑥,𝑦 is periodic withthe period 𝜙. When the spike sequence 𝑆𝑥,𝑦 is chaotic,
𝐴𝐶(𝑆𝑥,𝑦 ; 𝜙) exponentially decays for the time shift 𝜙.
5. Numerical Experiments
As numerical experiments, we simulated the ChaoticCNN for 20 × 20 grayscale image patterns shown in Fig.
4(a) and Fig. 5(a), where the parameters of the single
neuron are set as the same values mentioned in thesection 2 and the parameters of coupling are set as
follows: 𝜏𝜀 = 0.5msec and 𝜉 = 4. The grayscale pixel
value 𝑔𝑥,𝑦 ∈ {0,1, ⋯ ,255} is mapped to the external
input 𝛽𝑥,𝑦 ∈ [𝛽0 , 𝛽1 ] such that𝑔𝛽𝑥,𝑦 = (𝛽1 − 𝛽0 ) × 𝑥,𝑦 + 𝛽0 .
255(c) The maximal cross-correlation between the neuron
indicated by arrow and the others.

(9)

In this simulation, we chose the interval [48,54] as[𝛽0 , 𝛽1 ] that includes the chaotic region in the bifurcation
diagram (Fig.1).
The input pattern shown in Fig. 4(a) is a simple
gradation pattern from black to white in the direction of
x-axis. The neurons aligned in the direction of y-axis
have the same input value. A response of the ChaoticCNN is shown in Fig. 4(b) as a raster plot of firing times,
where the abscissa is time and the ordinate is an id ofneuron such that 𝑖𝑑 = 20𝑥 + 𝑦 for the neuron 𝑛𝑥,𝑦 .
Spike responses are roughly synchronized for each input
values. The maximal cross-correlations between theneuron indicated by the arrow and the others were(d) The auto-correlation profile of the neuron indicated in (c).
Fig.4. Response to the simple gradation pattern.

calculated (Fig.4(c)). As shown in Fig.4(c), highcorrelation is observed for the neurons in the direction of
y-axis and relatively lower correlation is observed for theothers. The auto-correlation profile was also calculated
for the neuron indicated in Fig. 4(c). As a result,exponential decay of auto-correlation is observed asshown in Fig. 4(d). These results support the formationof the chaotic cell assemblies.

- 125 -

6. Conclusions
For grayscale visual stimulus, synchronized response of
the Chaotic-CNN was analyzed by the correlation of
spike firing times. As results, synchronized responses tothe similar input value and a formation of chaotic cell
assembly were observed. These results indicate apossibility of visual segmentation using synchronizedchaotic response. Analysis for the real image input is oneof our future works.

(a) The external input 𝛽𝑥,𝑦 as a visual stimulus.

Acknowledgements
This work was partially supported by JSPS KAKENHI
Grant Numbers 24650120.
References(b) The maximal cross-correlation for the chaotic response.

(c) The maximal cross-correlation for the periodic response.
Fig.5. Response to the repeated gradation pattern.

In order to demonstrate a difference between the
chaotic cell assembly and the periodic one, responses toa twice repeated gradation pattern shown in Fig. 5(a) was
analyzed. The maximal cross-correlation for the case of
the chaotic spike response is shown in Fig. 5(b) and forthe case of the periodic one is shown in Fig. 5(c). For thelatters, the amplitude of background oscillation 𝐴𝜂 was0
set to 0 to generate periodic spike responses. For this case,the neuron indicated by arrow has high correlation withneurons in the two regions where neurons have similar
input value (Fig. 5(c)). In terms of correlation, these tworegions are not distinguished from each other. On theother hand, for the case of the chaotic spike response, theneuron indicated by arrow has high correlation withneurons in one region included itself and has relatively
low correlation with the other neurons. In this case,chaotic cell assemblies invoked by the grayscale input isdistinguished from the others even if they have similar
input value.

- 126 -

1. Gray, C.M., Koenig, P., Engel, A.K., & Singer, W.,
Oscillatory responses in cat visual cortex exhibit intercolumnar synchronization which reflects global stimulus
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Mitsuo Wada, Chaotic synchronized cluster in the networkof spike response neurons, IEICE Tech. Rep., Vol.99,
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Chaotic synchronized assembly in Pulse Coupled Neural
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localized synchronization in the two dimensional coupledsystem of chaotic spike response neurons, Fukuoka
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1, Neural Networks, Vol. 14, pp.115-131, 2001.

Design of Down Scaled Simulator to Apply
the Flying Touch Method in Hot Rolling Process
Sung Jin Kim

School of Interdisciplinary Program in Robotics, Pusan National University,
San 30 Jangjeon-dong,Geumjeong-gu,
Busan, 609-735, South Korea
Hyun Hee Kim

School of Interdisciplinary Program in Robotics, Pusan National University,
San 30 Jangjeon-dong,Geumjeong-gu,
Busan, 609-735, South Korea
Min Cheol Lee

School of Mechanical Engineering, Pusan National University,
san 30 Jangjeon-dong,Geumjeong-gu,
Busan, 609-735, South Korea
E-mail:jins2410@naver.com, sleepingjongmo@nate.com, mclee@pusan.ac.kr
www.pusan.ac.kr

Abstract
In the hot rolling process, scratches are occurred on the surface of a slab because of its strong friction.
One of the methods to solve this scratches problem of slab is proposed which is the Flying Touch hotrolling process. This paper introduces that the simulator for applying Flying Touch Method is
manufactured to evaluate a performance of the Flying Touch Method. Furthermore, it is evaluated by
the simulator how much Flying Touch hot rolling process can reduce the scratch.
Keywords :Flying touch , hot rolling, simulator

process, the shaping of molten metal into a plate. The
result of this process is slab (Slabs), which can be
modified into coils through a process called Rolling
process. These coils are then sold after going throughprocesses such as Cold rolling.

1. Introduction
Steel plates, or slabs, used in industries are produced
through the following process: Iron making process, thesmelting of iron ores into pig iron, Steel manufacture
process, the elimination of impurities, and Casting© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 127 -

Sung Jin Kim, Hy
Hyun Hee Kim, Miin Cheol Lee(b), friction andd impact betw
ween the rolleer and the slabb
is minimized byb synchronizzing the movvement of theeslaab with the rotation of thee roller and usingu
the Soffttou
uch type, in whichw
the rolleers are not fixeed, but mobilee.
Th
he speeds of the slab befo
fore being rollled and afterr
beeing rolled aree different beecause as the slab becomess
thiinner its amo
ount increasess. At this moment,ma slippocccurs betweenn the slab andd the roller, an
nd to decreasee
thiis slip, the speeed value calcculated by forrward slip mustbee applied to th
he roller. forw
ward slip, the averageaof theeslaab’s speed before and affter being rollled, must bee
ap
pplied in order to synchronnize the speed
d of the rollerrannd the speed of the slab.

One of the mmany defeats createdcby hott rolling is callled
scratch. Scraatch is createed when the iron plate is inmotion. It shhows in a lonng line on thee surface of the
product and drastically reduces the quality of the
product. The flying touch method is one solution to thescratch.
However, thiis new processs is only theooretical and mmaycause huge fiinancial loss ini its application. Thereforee itis preferably applied in miinimized simu
ulators insteadd ofin actual systtems. This paaper explains the flying touuch
method and introduces a simulator of 40:1 ratio, towhich this theeory can be appplied.

Fig. 1. Hot rollingr
processg. 3. Flying T
Touch MethoddFig
d2. Flying Toouch Method

Fig. 3 depicted the idea of flyying touch sim
mply. v1 is theespeed of the slaab, w1 is the angular speed
d of the upperrroller, and w2 is
i the angularr speed of thee lower rollerr.
Beecause w1 andd w2 are conn
nnected by joints, they haveeeqqual speeds. Thus the sccratch and im
mpact can bee
miinimized if th
he speed of thhe slab and thee speed of theerollers are synchhronized.

(aa)(b)
Fig. 2. Prevvious method
d and Flying Touch
T
Methood

3. Design of siimulator
he simulator in Fig. 4 waas constructed in order too
Th
ap
pply the flying
g touch methood and it’s ratiio to an actuaal
rolling mill is 40:1. Fig. 5 is a floorr plan of theesimmulator’s moddeling picturee. Because pro
oducing actuaaliro
on plates was too difficult for a 40:1 sim
mulator due toothee huge torque of the motor,, a rubber belt was used.

The previouss method of rollingr
process rolled the sslab
through two stationary rollers as depicteed in Fig. 2. ((a).
As each rolleer processes onnly 2-4% of th
he slab at a timme,multiple instaalled rollers rooll slabs. In th
his process, eaach
installed rolleer interacts wiith the slab, an
nd the impactt of
the last roller creates a sccratch at the end of the cooil.
However, in the flying touuch method, asa seen in Fig . 2.

© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 128 -

Flying Touch Method
M
SimulatoorFoor getting the rubber belt's elasticity, preessing test hassdoone by deform
mation processsing compresssor. The resullt
is represented in Fig. 6. Beccause the rubb
ber belt’s areaa
ompressed by the deformaation machinee is 300
co,thee elastic mod
dulus is 0.7 NN/by calculating theeslo
ope of deform
mation-force ggraph. This ellastic moduluss
is used to selectt the motors.
he motors’ speec is shown inn table.1.
Th
Tab
ble 1. Motorss’ specificatioon
Stall toorque
Velocity
M1
6.5N
N/m
3000rpm
M2
6.5N
N/m
3000rpm
M3
3.25N
N/m
3000rpm
M4
3.25N
N/m
3000rpm

Fig. 4. Simu
ulator picture

Fig. 7 shows thet results off the flying touch controolbaased on the 1/4
40 downscaledd simulator.
Th
he results showed that a sppeed between simulator rolllannd assumed steelsslab cann be synchro
onized by theedeesigned simulaator.

Fig. 5. Simulatorr of modeling picture
M M3 and M4 are posittion
M1 is a feedd motor, and M2,
control motorr. M3 and M4 are the drivve motors of theupper and low
wer motor. Thhe upper and lowerlrollers ccan
be controlledd by a ball screw.
s
Becausse the simulaatorcannot processs the 2-4% ofo the rubber beltb as an acttual
rolling mill w
would a slab, it is construccted to comprress
10% of it, or approximatelyy 1mm.

Fig.
F 7. Simulaation graph

4.

Conclusionn

his research designed thee 1/40 downsscaled pseudooTh
simmulator to app
ply the flyingg touch metho
od. The flyingg
tou
uch experiment results shoowed that the simulator cannubber belt’s deformationd-force graphFig. 6. Ru© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 129 -

Sung Jin Kim, Hyun Hee Kim, Min Cheol Lee

synchronize a speed between simulator roll andassumed steel slab.

Acknowledgments
This research was supported by the MOTIE (Ministry ofTrade, Industry & Energy), Korea, under the Industry
Convergence Liaison Robotics Creative Graduates
Education Program supervised by the KIAT
(N0001126)."
5. References
1. Choi Y. J and Lee M. C. “PID Sliding Mode Control for
Steering of Lateral Moving strip in got strip Rolling,”
International Journal of Control, Automaion, andSystems, pp. 399-407, 2009 7(3):2. Sansal K. Yildiz, J. Fraser Forbes, Biao Huang, Yale
Zhang, Vit Vaculik, Mike Dudzic, “Dynamic modelingand simulation of a hot strip finishing mill, Applied
Mathmatical Modeling 33, pp. 3208-3225, 2009
3. Choi Y.J , Yoo K.S, and Lee M. C, “Development of
the strip Off-center Meter Using Line Scan Camera in
FM Line“, Journal of Control, Automation, and Systems,
Engineering Col.11, No.6, 2005
4. Rohde, W. and Rosenthal, D, “High-tech rolling in hot
strip mills – theory and practice“, Metallurgical Plant
and Technology, Vol.1,p p.48-55, 1990

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 130 -

Improving Accuracy of Inertial Measurement Unit using Discrete Wavelet Transform
Jae-Hoon Jung

Dep. of Electronic Eng., Pusan National University, Jangjeon-dong, Geumjeong-gu
Busan, 609-735, Korea Republic
Dong-Hyuk Lee
Dep. of Electronic Eng., Pusan National University, Jangjeon-dong, Geumjeong-gu
Busan, 609-735, Korea Republic
Jang-Myung Lee
Dep. of Electronic Eng., Pusan National University, Jangjeon-dong, Geumjeong-gu
Busan, 609-735, Korea Republic
E-mail: jaehoon1696@pusan.ac.kr, ldh0917@pusan.ac.kr, jmlee@pusan.ac.kr
www.pusan.ac.kr

Abstract
In this paper, using discrete wavelet transform in the way of noise removal. wavelet analysis has been used todenoise a digital image corrupted by noise in the acquisition step. Previous studies use Low-pass filter or moving
average filter for removing noise. But these filters are corresponded unsuitably for the rapidly changing data. This
correspondence cause distortion of the original signal and cause another error for removing noise. In order tocompensate for these disadvantages, discrete wavelet transform is applied.
Keywords: Discrete Wavelet Transform, Inertial Measurement Unit, Thresholding, Noise Removal .

1. Introduction

Fig. 1 The structure of Inertial Measurement Unit
Inertial measurement unit is a single of Integrated
unit that consist of the accelerometer which canmeasure the movement of inertia and the gyroscopes
which can measure the rotation of inertia, the earthmagnetic which can measure the azimuthal.
In order to measure the free movement in threedimensional space, this unit has each sensor(acceleration, gyroscopes, earth magnetic) that consistsof a 3-axis. Here, ARS(Attitude Reference System)measures the attitude with accelerometer andgyroscopes. And AHRS(Attitude Heading Reference
System) measures the attitude with accelerometer,gyroscopes and geomagnetic.
An acceleration sensor is not to measure the moving
distance but to use a combined data of acceleration andgyro by calculating the angle with acceleration due togravity. AHRS is the combined form of fusion of thetwo sensors and a geomagnetic sensor.
Currently, these inertial measurement units arewidely used in localization, attitude control andnavigation at water, ground and air. In particular, thisunit is applied to an inertial navigation. And this systemcomes into the spotlight in the field of localization.
But such inertial measurement unit includes
instantaneously changing noises and internallygenerated noises. If these slight noises of data arecontinued, as time goes fatal cumulative error will be
generated in the case of localization
Besides, biasing and misalignment errors generated
by the sensor will also affect the antibody, including
noise and quantization error, non-aligned mounting
error, and the error caused by inaccuracy of thesensor[1]For this reason, the study for improving theperformance with applying to a variety of signal
processing algorithms in the inertial navigation system
is becoming an issue.

- 131- Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan© The 2015 International Conference on Artificial Lifeand

Jae-Hoon Jung, Dong-Hyuk Lee, Jang-Myung Lee

In order to reduce the error when operating
localization, attitude control and navigation system, by
the wavelet transform which is relatively new signal
processing technique, a data of the inertial
measurement unit is processed to a more accurate value[2],[3].
Using a characteristic of the wavelet transform,
original studies mainly perform a similar role like thelow-pass filter or the high-pass filter which remove thedata for a specific frequency range [2].
But in this paper, generated errors in the inertial
measurement unit more efficiently is reduced withthresholding which is one of the techniques ofremoving noise.
2. Wavelet transform
2.1. Discrete wavelet transform
Wavelet transform is a signal conversion techniquewhich is utilized most with FFT (Fast Fourier
Transform) and STFT (Short Time Fourier Transform)in the recent signal processing field.
Among them, FFT is often used as a signal
processing technique and a solution of the differential
equation. But because signal analysis is possible only infrequency domain, this technique has disadvantage that
can’t know the time information and the frequency
information of signal at the same time. To overcome
this limitation, STFT which is called the windowfunction and is added the time-dependent weighting
function at Fourier building blocks is introduced.
However, because analysis region is always constantfor a time-frequency, STFT has disadvantage that can
not efficiently analyze non-stationary signal which has
the change of stochastic characteristics according to thechange of time.
In contrast, the wavelet transform compose ameasure using a expansion or contraction of the motherwavelet without the window function. And because
functions that occur by a result of moving are used asbuilding blocks, this technique can accomplish timefrequency analysis more efficiently than STFT.
Wavelet transform which is the way to extract partial
scale component of the signal with wavelet which canchange a size can be defined as Eqs. (1) ~ (2) [4],[5].

W f (b, a )   a ,b (t ) f (t ) b*, a (t )dt

1  t b a  a (1)
(2)

The function  as the mother wavelet, can be
expanded, contracted by the compression coefficientand shifted by the transition coefficient.

Fig. 2 Time-frequency analysis area by(a) Fourier transform, (b) Local Fourier transform,(c) Wavelet transformDiscrete wavelet transform provide Multi-resolution
analysis for the signal. And from a signal processing
point of view, this is closely related to reconstruction ofeach band signal based on the filter bank. Multiresolution analysis can divide into approximation whichis the low-frequency component and detail which is thehigh-frequency component. This process can be
expanded by the concept of two kinds of filter using the
low-pass filter and the high pass filter at same time[4],[2],[5].

Fig. 3 Multi-resolution analysis ofDiscrete wavelet transform

Fig. 4 Multi-resolution decomposition ofDiscrete wavelet transform

2.2. Wavelet Thresholding
The technique to reduce the noise with the wavelet
transform is developed in order to remove noisecontained in the image. The most common way of noise
removal techniques is wavelet shrinkage techniquewhich is represented by thresholding technique [6],[3].
If the wavelet coefficients calculated by performing
wavelet transform do not reach the threshold value, thewavelet thresholding technique makes the wavelet
coefficients to zero. This way assumes that the size ofthe actual signal is greater than the noise level and thenoise is mixed by the frequency area. In this case, if thevalue of the specific frequency component has a- 132- Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan© The 2015 International Conference on Artificial Life
and

Improving Accuracy of Inertial

smaller data than the known noise level of the actual
signal, its value is made of zero by considering thenoise.
Because of the characteristics to remove all
frequencies components above a specific frequency,
typical low-pass filter bring the actual signal loss. In
particular, the accelerometer signal for the signals of
the inertial sensor is instantaneously changed when
there is the acceleration of a vehicle. And if theaccelerometer signal goes through the low-pass filter,
this signal is made to be slow by distorting amomentary change of the signal.

the acceleration values that most largely include theinternal noise of the Inertial measurement unit and theexternal noise by the external cause are used as aexperimental data. And the level 3 is applied to thelevel of the discrete wavelet.
After receiving the acceleration value, the configured
system performs resolution decomposition using
discrete wavelet transform. And using the inverse
discrete wavelet, this system restores removed noise
acceleration data.

Fig. 6 Filter system block diagram ofDiscrete wavelet transform
Micro control unit used in the experiment is
TMS320F28335. And inertial measurement unit used in
the experiment is EBIMU-9DOF.

Fig. 5 Hard thresholding function andSoft thresholding function
The thresholding technique is divided into the soft
thresholding and the hard thresholding. Ep. (3) is hardthresholding function and Ep. (4) is soft thresholding
function.
u if | u |  Thard  0 otherwise(3)

(u  sign(u) ) if | u |  Tsoft  otherwise0

(4)

Fig. 7 Test board and inertial measurement unit
The underwater straight running experiment is tested
in the 262 (w) × 175 (h) × 50 (d) cm size water tankwith AUV.

The value u which is wavelet coefficients makes thecoefficients below the reference value to zero.
For applying the thresholding technique, general way
to set the reference value depends on Ep. (5)  2 log n

3.

(5)

Noise removal algorithm in the wavelet

Wavelet analysis is a good tool for denoising, owing toachieving good localization in both space and scaledomains, and superior separation of noise and signal
contents. Because of the absence of space invariance in
the Wavelet, thresholding rather than convolution is the
typical technique for denoising with wavelets.[7]Fig. 8 The underwater straight line driving
experiment
To know degree of internal noise removal, filtering
the acceleration data in a stopped state is performed inreal-time. Fig. 9 represents that noises of data with thediscrete wavelet transform are largely removed thanoriginal data.

4. Experiment
Experiments are performed by the test board is madefor processing the wavelet transform in real time.
© The 2015 International Conference on Artificial Life
and
- 133
- Robotics (ICAROB 2015), Jan. 10-12, Oita, JapanJae-Hoon Jung, Dong-Hyuk Lee, Jang-Myung Lee

References

Fig. 9 Comparing original acceleration data andfiltered acceleration data in a stopped state
Next experimental data is acceleration data which ismeasured by straight line driving for 3.5 second. Fig.
10 represents that noises of data with the discrete
wavelet transform are largely removed than originaldata.

1. G. H. Jang, S. B. Chun, E. S. Lee, J. Y. Lee, T. S. Kangand J. R. Kim, “Low Grade IMU Calibration Using
Reference Sensor”, KSAS April.6, pp.772-776, 2014
2. S. P. Kim, E. T. Kim and K. J. Seong, “Sensorz Fusionof GPS/INS/Baroaltimeter Using Wavelet Analysis,” theJournal of Institute of Control, Robotics and Systems,
Vol. 14, No. 12, pp. 1232-1237, 2008
3. Andrew. K. Chan, Cheng Peng, Wavelets for Sensing
Technologies, Artech House Publishers, 2003
4. B. D. Yoon, H. N. Yoon, S. H Choi and J. M. Lee,
“Estimated Position of Sea-surface Beacon Using
DWT/UKF,” the Journal of Institute of Control,

Robotics and Systems, Vol. 19, No. 4, pp. 341-348,
2013
5. S. H. Lee, Introduction to the Wavelet Transform, Jinhan
M&B,2003
6. C. W. Kang, C. G. Park and N. I. Cho, “Improvement of
INS-GPS Integrated Navigation System using Wavelet
Thresholding,” the Journal of The Korean Society for
Aeronautical and Space Sciences, Vol. 37, No. 8, pp.
767-773, 2009
7. B. S. Kang, G. S. Shin and M. G. Kang, “Noise Removal
Algorithm Using the Wavelet Dependences in the Scaleand along the Scale”,Proceedings of ICEIC2006, June
27-28,2006, Ulaanbaatar,Mongolia

Fig. 10 Comparing original acceleration data andfiltered acceleration data inunderwater straight line driving
5. Conclusion
In this paper, We studied to revise the internal noise
and the external noise of the IMU(Inertial Measurement
Unit) using the discrete wavelet transform.
By dividing into high frequency component and low
frequency component of the noise, removing noises isdetermined by the threshold value. And the more active
filtering effect can be expected unlike the moving
average filter or the low pass filter by removing ormaintaining the noises.
Finally, we will apply to localization of the actual
submarine or mobile robot using the wavelet transform.
In the case of localization, the cumulative error canbe reduced when applying effective noise removal. And
the robust localization system design will be possible
for the disturbance.

Acknowledgements
This research was supported by the MOTIE (Ministryof Trade, Industry & Energy), Korea, under the
Industry Convergence Liaison Robotics Creative
Graduates Education Program supervised by the KIAT
(N0001126).

- 134- Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan© The 2015 International Conference on Artificial Life
and

Outdoor Localization for Quad-rotor
using Kalman filter and Path planning *Chen-Hu†
Department of Electronic Engineering, Pusan National University,
Jangjeon-2-dong, Geumjeong-gu, Busan 609-735, South Korea‡
Yo-Seop Hwang
Department of Electronic Engineering, Pusan National University,
Jangjeon-2-dong, Geumjeong-gu, Busan 609-735, South Korea
Wang Zhitao
Department of Electronic Engineering, Pusan National University,
Jangjeon-2-dong, Geumjeong-gu, Busan 609-735, South Korea
Jang-Myung Lee
Department of Electronic Engineering, Pusan National University,
Jangjeon-2-dong, Geumjeong-gu, Busan 609-735, South Korea
E-mail: chenhu@pusan.ac.kr, mmx001@pusan.ac.kr, jmlee@pusan.ac.kr,zhitao7379@pusan.ac.kr
www.pusan.ac.kr

Abstract
This paper proposes a new technique that produces the improved local information using low-cost GPS/INS system
combined by Kalman filter and Path Planning when a Quad-rotor flies. Throughout the research, the low cost GPS
is combined with INS by using the Kalman filter in order to improve local information. However, this system has
certain disadvantages as follows. The level of estimation accuracy could get worse when the quad-rotor flies
through the air by forming a curve. Also, the level of precision for the position information is influenced by theperformance of GPS. In order to deal with such disadvantages, the algorithm based on the path planning can be
adopted. When the quad-rotor flies outdoor, it is possible to predict that its moving path is short, since all the shortmoving paths of the quad-rotor can be assumed to be straight. The path planning is used to make such a shortmoving path and determine the closest local information of the GPS/INS system. Through the foregoing process, animproved kind of local information can be obtained when the quad-rotor flies. Also, the performance of the
proposed system can be verified based on the outdoor experiments.
Keywords: Path Planning, Cell Divided Algorithm, Kalman filter, Quad-rotor, GPS, INSvarious monitoring tasks for disasters, life-saving
situations, environmental conditions, traffic congestion
and military reconnaissance, has been increased [1]. The
low-cost and small-sized quad-rotor can be regarded as

1. Introduction
Recently, the level of interest regarding the unmanned
aerial vehicle (UAV), which can effectively carry out© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 135 -

Chen-Hu, Yo-Seop Hwang, Wang Zhitao, Jang-Myung Lee

a rotary-blade-type UAV which flies by using four rotors. Since it has various advantages including suchfunctions as vertical takeoff and landing, hovering andomnidirectional movements, the quad-rotor has been
widely used in real life for such tasks as serving meals,delivering various objects and supporting various
outdoor activities including jogging. Also, researches
have been actively carried out in regard to the field. Inorder to carry out various tasks, the quad-rotor needs tohave an autonomous navigation function which ispossible only when it can precisely recognize its
position. As the quad-rotor carries out its tasks mainly
outside, it is necessary to use a precise kind ofnavigation technology for the estimation of outdoor
positions. For such a technology, the recently –executed researches [2][3] have focused on the processof precisely estimating positions based on the
combination of the inertial navigation system (INS) andGPS. The INS system is composed of such sensors as
the acceleration sensor, the gyro-sensor and thegeomagnetic sensor which can be used to estimate bothabsolute and relative positions simultaneously [4]. It can
provide precise information regarding positions within ashort time. However, due to the disturbance caused by
the property errors of the sensors and the external
environment, it is possible to see an accumulation oferrors when it is used for a long time. GPS has such ashortcoming as the possibility for having a great
position error within a short time. Also, it would be hard
to use GPS in the radio-shadow area where the GPS
signals can be disconnected [5]. However, it provides astable kind of information for absolute positions in along time, since it keeps correcting the position
information by continuously receiving signals from thesatellite in real time without having any accumulation oferrors. In order to establish the GPS/INS fusion position
estimation system with the advantages mentioned above,
GPS and INS are combined by using the Kalman filter,the extended Kalman filter and the Particle filter, while
the properties of each other are compensated throughsuch a process.
This paper is concerned with the process of designing,
implementing and experimentally validating the
combination of the GPS/INS-fusion position estimation
system where the Kalman filter and the path planningalgorithm are properly adopted. The Kalman filter issuitable for estimating the states of linear systems with asmall amount of computations. However, this Kalmanfilter is not enough by itself to overcome the linear
errors occurring during the flight of the quad-rotor since
it has a low level of accuracy for the state estimation
regarding the nonlinear path. Even though the precise
GPS/INS fusion system can be implemented by using
the Kalman filter, its accuracy is also limited by theaccuracy of each sensor. To overcome the sensor
capabilities as well as the filtering limits, the path
planning information has been utilized for the
localization with the direction information captured by
the geomagnetic sensor in the INS system. In otherwords, the estimated path has been mapped onto thenearest path provided by the path planning to improve
the localization accuracy overall.
The paper consists of the following sections. In
Section II, the configuration of the system is introduced.
In Section III, the GPS/INS fusion algorithm is
described. After that, in Section IV, the Path planningalgorithm is discussed. Then, in Section V theperformance of the position estimation process, which issuggested in this paper, is verified through anexperiment. Lastly, a conclusion is given.
2. System Configuration
In this paper, the configuration of the proposed system
is given as shown in Figure 1. The micro-controller unit
(MCU), which was used to control the system, is
ATMEGA 2560 made by Atmel. Also, MPU-6050,
which is a combined form of a tri-axis gyro-sensor and a
tri-axis acceleration sensor, HMC-5883, which is a triaxis geomagnetic sensor, and BMP-085, which is apressure sensor, were used, while MTK-3329, which isa GPS sensor, was applied for the recognition of
positions. In order to generate the thrust of the quad-

Fig. 1. System Block Diagram

rotor, the electrical speed controller (ESC) and thebrushless motor from RoHS were used.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 136 -

Outdoor Localization for Quad-rotor)

The propellers of the quad-rotor were designed tooffset the rotating force of one another and generate athrust in order to prevent the body from rotating, while
RN-42 Bluetooth from RoHS was used to command therobot to move and return. Figure 2 shows the hardware
configuration of the system. The GPS receiver was
designed to be located on the upper part of the quadrotor in order to receive the data as accurately as

f b

fˆ n

â
 p̂ 

Rˆnb

ibb

p̂
̂

Rˆnb

m b

 p̂ mˆ n mˆ np

m nFig. 3. Fusion algorithm of GPS and INS

1qˆ  q  ( ibb  bˆgyro )2(1)

As shown in Equation (1), the position expressed inqˆ12  qˆ22  qˆ32  qˆ42 2(qˆ22qˆ32  qˆ12qˆ42 ) 2(qˆ12qˆ32  qˆ22qˆ 42 ) Rˆnb   2(qˆ22qˆ32  qˆ12qˆ42 ) qˆ12  qˆ12  qˆ12  qˆ12 2(qˆ32qˆ42  qˆ12qˆ22 )  2(qˆ22qˆ42  qˆ12qˆ32 ) 2(qˆ12qˆ22  qˆ32qˆ42 ) qˆ12  qˆ22  qˆ32  qˆ42 Fig. 2. Total Quad-rotor Configuration

possible, while MCU and IMU sensors were arranged
below.

(2)quaternions can be used to calculate the transformation
matrix which transforms the structural coordinates tothe navigation coordinates.

3. GPS/INS Fusion
Since INS consecutively provides information
regarding the velocity of the body and the change of
positions by integrating the acceleration data twice, its
dynamic feature could be good, but it is possible to havean accumulation of errors as time goes by. Also, while
GPS provides the position information of the bodyoutside, it could create severe errors based on thegeographical features. In order to compensate thedisadvantages of GPS and INS by combining the data
given by each system, the Kalman filter (KF) and theExtended Kalman filter (EKF) can be used. The Kalmanfilter can be applied to the linear system, while theExtended Kalman filter can be applied to the nonlinear
system. The position and state of the quad-rotor in astraight path contains some linear factors. Therefore, in
the research, GPS and INS can be combined by using
the Kalman filter.
The GPS/INS-fusion algorithm used in this paper isshown in Figure 3.
By using the angular velocity (  ibb ) obtained from thegyro-sensor, the position expressed in quaternions ( q̂ )can be updated

The difference between the position ( p̂ ) which is
obtained by integrating the acceleration ( â ) of thenavigation coordinates twice and the position ( p ) which
is obtained through the reception of GPS can becalculated as shown in the following equation.

 pˆ   p  pˆ(3)

By using the transformation matrix, the measured
value ( mˆ b ) of the geomagnetic sensor can be transformed
to the value of ( mˆ n  Rˆ nb m b ) in order to measure the valueof the magnetic value of the earth. The difference
between the actual magnetic value of the earth ( m n ) andthe calculated magnetic value of the earth can be
defined as  mˆ n  m n  mˆ n . By using  p̂  and  mˆ n as themeasured values of KF, the following state spaceequation can be designed to estimate the position error( ̂ ) and the location error ( p̂ ). Based on the calculated
error, the position and the location can be updated inorder to obtain corrected values.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 137 -

Chen-Hu, Yo-Seop Hwang, Wang Zhitao, Jang-Myung Lee xk  fk ( xk 1 )  k ,  y k  hk ( xk )  vk

(4)

The estimated value of k can be given as k̂ , while themeasured value can be defined as k . f k is the state
propagation function and hk is the measurement
equation, while k is the system error and vk is themeasurement error.  yk is the measured value. KF canbe considered to have 15 states with such randomvariables as the 3-dimensional location errors ( p ), thevelocity errors ( v ), the position errors (  ) and the bias
errors given by the acceleration sensor (  bacc ) and the
gyro-sensor (  bgyro ).
qx   pT

 vT

T
T

 T  bacc
 bgyro(5)

Fig. 4. Block diagram of cell divide algorithmˆHere, the equation of  k  k  k can be given, while

while the planned route is transformed into approximate
longitude and latitude values through an equation before
being divided into cells [8]. When the quad-rotor flies
along a curve, the route is subject to the rotationaltransformation process based on the directional values
given by the geomagnetic sensor. As a result, it ispossible to carry out the path planning process evenwhen the quad-rotor moves along a curve. The center
coordinates of the divided cells can be compared withthe factors of each random variable can be defined asfollows.
p  xyzv   vx

vy

vz 

   N

rl  D 

T
T

(6)

T

Here,  N is a tilt error, while  D is a heading error.
4. Path Planning Algorithm
The path planning process is carried out by using thecell divide algorithm and the geomagnetic information.
The cell divide algorithm is one of the methods used toplan the path of a moving object, which could also be
used to divide free space into various cells where such apath can be easily planned in order to plan the entire
moving path. By making a graph which connects theneighboring cells based on the divided ones, an optimal
path can be traced. In such a case, it is possible to divide
the process into the approximate-cell division and thecomplete-cell division based on the way of dividing
cells. In case of the complete-cell division, the divided
cells need to become the original free space when they
are united [6][7]. Unlike any robot moving on the
ground, the flying quad-rotor is not subject to any
limitation regarding an obstacle. Therefore, in this paper,
the complete-cell division was used.
The order for the determination of positions throughthe cell divide algorithm proposed in this paper isshown in Figure 4.
A 10m straight route from the current position
obtained from the GPS sensor is planned in advance,

Fig. 5. Position determined by cell divide algorithm

the GPS/INS-fusion position information in order tochoose the position information which is the closest to
the actual position. Figure 5 shows how the closest
position is selected.
5. Experiment
In this paper, the low-cost GPS position information
having a relatively great error and the INS position
information having an accumulation of errors werecombined by using the Kalman filter in order toestimate the outdoor positions of the quad-rotor.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 138 -

Outdoor Localization for Quad-rotor)

Fig. 7 compares the performance of the position
estimation process carried out by the proposed systemwith the performance of the process carried out by the
GPS-INS-fusion system using the Kalman filter.

However, since the performance of the Kalman filter
tends to be poor in terms of the estimation on a curve
which shows a high level of nonlinearity and the
GPS/INS-fusion system is greatly influenced by theperformance of GPS, the system, which can be used to
improve the performance of the position estimation
process by applying the path planning algorithm, is
proposed to compensate such a problem. In order toevaluate the performance of the proposed system, we
carried out an experiment for the position estimation

Table 1. The caption should be placed before the table.

process by flying the quad-rotor over 150m in theplayground of our school. The position information of
the moving quad-rotor was received in real time byusing the Bluetooth communication technology.

2.89551.8953
1.612

6. Conclusion

10

GPS

Start

0

In conclusion, the experimental results reveal that the
proposed system, using the Kalman filter and Pathplanning, is more accurate than a single low cost GPS or
GPS/INS system fused by Kalman filter. The results
also suggest that when tracking location outdoors, theproposed system is a good alternative to DGPS and
RTK-GPS. This is because the proposed system is morecost efficient and similarly as accurate as the otherlocalization systems. After this research, it will be
configured that position estimation system which iscapable of estimating the mobile robot’s position as it
travels an outdoor curved path.

Devided Cell
Reference-10

GPS/INS fused by Kalman-20

Y-meters(m)Average Local Error(m)GPS
GPS/INSPath planning+GPS/INS
systemAs shown in Fig. 7 and 8, it is possible to see that thelevel of precision for the estimation of positions is
improved more as the information which is close to theactual positions is chosen out of the position
information given by the GPS/INS-fusion system whichis combined by using the Kalman filter based on thepath planning algorithm. Table 1 show the resulting
average position errors.

Fig. 6. The experimental environment

GPS/INS fused by Kalman
with Cell Division Algorithm

-30
-40

Finish
-50
-60
-70
-80

Sort

0

20

40

60
80
X-meters(m)

100

120

Fig. 7. The experimental result
10

GPS

Start

0

Devided Cell
Reference-10

Acknowledgements

GPS/INS fused by Kalman

Y-meters(m)-20This research was supported by the MOTIE (Ministryof Trade, Industry & Energy), Korea, under the Industry
Convergence Liaison Robotics Creative Graduates
Education Program supervised by the KIAT
(N0001126).

GPS/INS fused by Kalman
with Cell Division Algorithm

-30
-40

Finish
-50
-60
-70
-80

0

20

40

60
80
X-meters(m)100References

120

Fig. 8. Partial magnification image of the experimental result© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 139 -

Chen-Hu, Yo-Seop Hwang, Wang Zhitao, Jang-Myung Lee

1. M.G.Kim, Y.D.Kim, “Multiple UAVs Nonlinear
Guidance Laws for Stationary Target Observation with
Waypoint Incidence Angle Constraint”, Int’l J. of
Aeronautical & Space Sci, Vol.14, No.1, pp 67-74, 2013
2. S. Kim, C. Roh, S. Kang, and M. Park, “Outdoor
navigation of a mobile robot using differential GPS andcurb detection”, Proceeding of IEEE International
Conference on Robotics and Automation, pp 3414-3419,
Roma, April 10-14, 2007
3. G. T. Schmidt, “INS/GPS Technology Trends”, NATO
Research and Technology Organization, May 2009
4. J.H. Seung, D.J, Lee, J.Y.Ryu, “Precise Positioning
Algorithm Development for Quadrotor Flying Robots
Using Dual Extended Kalman Filter”, Journal of Instituteof Control, Robotics and System, Vol.19, No.2, pp 183163, 2013
5. K.W.Chiang, Y.W.Huang, “An intelligent navigator for
seamless INS/GPS integrated land vehicle navigation
applications”, Appied Coft Computing, Vol 8, No 1, pp
722-733, 2008
6. J.T.Kim, D.J.Kim, “New Path Planning Com bining
Visibility Graph and Adaptive Cell Decomposition”,
Journal of KIISE : Computer Systems and Theory, Vol.
36, No. 1, pp 357-361, 2009
7. J.W.Kang, S.J.Kim, “Path Planning for Complete andEfficient Coverage Operation of Mobile Robots”,
International Conference on Mechatronics andAutomation, pp 2126-2131, Harbin, China, August 5-8,
2007
8. M.Rengarajan, G. Anitha "Algorithm Development And
Testing Of Low Cost Way Point Navigation System",Engineering Science and Technology: An International
Journal, Vol 3, No. 2, pp 411-414, April 2013

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 140 -

Distributed Terminal Backstepping control for Multi-Agent Euler-Lagrange Systems
Seong-Ik Han
Dep. of Electronic Eng., Pusan National University, Jangjeon-dong, Geumjeong-gu
Busan, 609-735, Korea Republic
Yun-Ki Kim
Dep. of Electronic Eng., Pusan National University, Jangjeon-dong, Geumjeong-gu
Busan, 609-735, Korea Republic
Hoang Nhat Minh

Dep. of Electronic Eng., Pusan National University, Jangjeon-dong, Geumjeong-gu
Busan, 609-735, Korea Republic
Jang-Myung Lee
Dep. of Electronic Eng., Pusan National University, Jangjeon-dong, Geumjeong-gu
Busan, 609-735, Korea Republic
E-mail: skhan@pusan.ac.kr, mecha8404@pusan.ac.kr, nhatminh1696@pusan.ac.kr, jmlee@pusan.ac.kr
www.pusan.ac.kr

Abstract
This paper presents a distributed terminal (finite-time) backstepping consensus control for multi-agent EulerLagrange systems. Terminal virtual error surfaces and virtual controls are proposed to guarantee the finite-timeerror consensus and formation convergence of a group of one-leader and multi-follower cooperative tracking
Euler-Lagrange system. Finite-time stability including infinite-time stability was proved by the finite-time
Lyapunov candidate function. Simulation example shows the effectiveness of the proposed finite-timebackstepping coordinated tracking controller.
Keywords: Euler-Lagrange multi-agent system, backstepping control, Terminal virtual error surface.

1. Introduction
In recent years, there has been a great interest for
researches of multi-agent systems, whose applications
include spacecraft, mobile robots, sensor networks, etc.
Interesting research directions are containment control,
consensus, formation, and flocking control [1]. These
problems focus on two cases, namely, the case that
there does not exist a leader and the case where there
exists a leader. The coordinate tracking problems to
track a single leader have been investigate for followers
with single-integrator, double-integrator,high-order
dynamics, nonlinear or Euler-Lagrange dynamics [2-5].
Linear control theory and variable structure control
methods in most researches are used. On the other hand,
there are few examples that use the backstepping
control technique [6] for nonlinear or Euler-Lagrange
multi-agent system. In this method, the problem of
unmatched uncertainty and neglecting the efficientnonlinearities is overcome via adopting step-by-steprecursive process.

However, although a controller designed using thistheorem guarantees the infinite-time stability of aclosed-loop system, it has drawbacks such as a slow
convergence rate and reduced robustness to uncertainty.
On the other hand, systems with finite-time settlingtime design possess attractive features such as
improved robustness and disturbance rejection
properties [7], In this paper, terminal backstepping
control based multi-agent consensus control for EulerLagrange system with one-leader and multi-followers isdeveloped.
2. Background and Preliminaries
2.1. Concept of Graph Theory
In this paper, multi-agent robot Euler-Lagrange systems
consisting of one leader and n followers are considered.
Graph theory is introduced to solve the coordination
problem and model information exchange between
agents. The communication topology is a directed
graph, G ={V ,E } , whereV ={0,1, 2, ...,n} is the set ofnodes, node i represents the ith agent, E is the set of- 141- Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan© The 2015 International Conference on Artificial Life
and

Seong-Ik Han, Yun-Ki kim, Hoang Nhat Minh, Jang-Myung Leeedges, and an edge in G is denoted by an ordered pair(i, j ) . (i, j )  E if and only if the ith agent can send
information to jth agent directly, but not necessarily
vice versa. A directed tree is a directed graph, where
every node has exactly one parent except for the root,
and the root has directed paths to every other node. A
directed graph , G ={V ,E } , has a directed spanning
tree if and only if {V ,E } has at least one node with adirected path to all other nodes. A  [ai , j ]  R ( n 1)( n 1) iscalled the weighted adjacency matrix of G ,where aii  0 and aij  0 with aij  0 if there is an edge
between the ith agent and jth . The Laplacian of theweighted
graph
can
be
definedasL  D  A  R ( n 1)( n 1) , where D  diag (d 0 , d1 ,..., d n )n R( n 1)( n 1) is the degree matrix and di   j 0 aij fori  0,1,..., n . For simplicity, it is assumed thataij  1 if (i, j )  E and 0 otherwise. The connection
weight between agent i and the leader is denoted by bi
such that bi  1 if agent i connected to the leader and 0
otherwise.

3.

Distributed
Terminal
backstepping
Controller Design and Stability Analysis

3.1. Controller Design
The tracking errors and virtual error surfaces aredefined as follows:n(3)
zi ,1   j 1 aij ( yi  y j )  bi ( yi  x0 ) ,zi ,2  xi ,2  ci ,1 sig ( zi ,1 ) i ,1   i ,1 , i  1,..., n ,(4)where x0 is the position of the leader,  i ,1 are thevirtual controls, sig ( zi ,1 )  zi ,1

 i ,1sgn( zi ,1 ) , ci ,1  0 are

constants, and  i ,1  i ,1 /  i ,1 , i ,1 and  i ,1 are positive
odd numbers, i ,1   i ,1  2i ,1 , sgn( zi ,1 ) is a signfunction,. (3) can bechanged for the formation control case as follows:

zi ,1   j 1 aij ( yi  i  y j   j )  bi ( yi  i  x0  0 )n

(5)
The time derivative of the first error surfaces zi ,1 along(2) iszi ,1  (di  bi )( zi ,2  ci ,1 sig ( zi ,1 ) i ,1   i ,1 ) j 1 aij xi ,2  bi x0 .
n2.2. Multi-Agent Euler-Lagrange Systems

The nonlinear dynamics of a group of n  1 fully
actuated Euler-Lagrange systems are described asfollows:
M i ( qi ) qi  Ci (qi , qi )qi  Gi ( qi )   di   i , i  1,..., n  1 ,(1)
where M i (qi ) is a symmetric and positive definite
inertiamatrix; Ci (qi , qi ) isavelocity-dependent

centripetal and Coriolis forces matrix; Gi (qi ) is a
gravitational vector;  di is a bounded unknown
disturbance including unmodelled dynamics andexogenous disturbance; and  i is an input torque. The
simple dynamic equation can be expressed as thefollowing state space model:xi ,1  xi ,2 ,xi ,2  f i ( x2 )  gi ( x2 )ui ,yi  xi ,1 ,The Lyapunov function candidate Vi ,1  ziT,1 zi ,1 / 2 todesign the distributed virtual controller. Differentiating
Vi ,1 yields
Vi ,1  ziT,1[(di  bi )( zi ,2  ci ,1 sig ( zi ,1 ) i ,1   i ,1 ) j 1 aij xi ,2  bi x0 ] .
nwhere ki ,1  0 arealong (2) and (4),V  k zT z  (d  b )ci ,1 i ,1 i ,1(2)

ui   i .
  di , K  M C (xi,2 )  ci ,KiG xi,1  Mi1Gi (xi,1 )  gi

,

and

the

i

i

i ,1

zi ,1

 i ,1 1 (di  bi ) ziT,1 zi ,2 i ,1 1zi ,1  i ,1 ]hiˆhi / i .

(10)

Choosing the control inputs and adaptive laws as
ui  gi1[ki,2 zi,2  (di  bi ) ziT,1  KiC xi,2  KiG xi ,1

1ii

 ci   gi   di   hi

Differentiating

 ziT,2 [ fi ( x2 )  gi ( x2 )ui  ci ,1 i ,1 zi ,1M i1Ci ( xi ,2 ) xi ,2 M i1Gi ( xi ,1 )  M i1 di , gi  Mi1 , andAssumption 1. M constants.

Lyapunov function, Vi ,2  Vi ,1  ziT,2 zi ,2 / 2  hi2 / 2i ,where xi ,1  qi , xi ,2  qi , x2  [ xi ,1 , xi ,2 ] , f i ( x2 ) 

C
i





T

1
i
di

(7)

Choosing the distributed virtual control as1n i ,1 ki ,1 zi ,1   i 1 aij xi ,2  bi x0 ,(8)di  bi(7) becomes i ,1 1Vi ,1  ki ,1 ziT,1 zi,1  (di  bi )ci ,1 zi,1 (di  bi ) ziT,1 zi,2 ,(9)i ,2i  1,..., n  1 ,(6)

 i ,1 1ci ,1 i ,1 zi ,1,where K iC and K iG are positive definite diagonal matrices
and vectors, respectively, and  hi  0 are upper bounds.

ˆhi zi,2
zi ,2  i ,2ˆhi  i zi ,22zi ,1  ci ,2 sig ( zi ,2 ) i ,2 i,1 ] ,

(11)



/ ( zi ,2   i ,2 )  iˆhi ,- 142- Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan© The 2015 International Conference on Artificial Life
and

(12)

Distributed Terminal Backstepping Control

where

ki ,2  0

ci ,2  0,andi  0,i  0,where f i ,2  [Gi (qi )   di ] / J i , gi  1 / J i , ui   i

0.5   i ,2  1

areconstants,

J i  mi L2i / 3 , Gi ( qi )  mi Li cosqi , the mass of the linkmi  1kg , and the length of link Li  0.25m . Let theinitial condition of four followers be x1,1  1, x1,2  0 ,,hi   hi  ˆhi , ˆhi are estimates of  hi , we obtain thefollowing expression:2

x2,1  1.2, x2,2  0 , x3,1  2, x3,2  0 , x4,1  1.2, x4,2  0 . Thek 1

Laplacian can be written as i ,1 1 i ,2 1Vi ,2   ki ,1 ziT, k zi , k  (di  bi )ci ,1 zi ,1
 ci ,2 zi ,2hi ( ziT,2  ˆhi / i )2
 1
  2 2
  ki ,1 ziT, k zi , k  i i    i , k zi , ki i2 / 2
2
k 1
k 1
 1
2
 2
  2 i ,k  2 
    ki , k ziT, k zi , k  i i  (  i, k ziT, k zi , k  i i ) 2
2  k 12
 k 1i ,k i
 i ,k 1

 i ,k 1

  aVi ,2 b 2 Vi ,2 2 i , 0 0

L   0
1  1

0

0

0

3
0

1
1

1
1

0
0

1
0

1
0

0 
 1 
, b3  1, b4  1 .
0 

0 
0 

Simulation results are obtained with thetime-varying control input to the leader being designed
as u0   sin( x0,1 ) / (1  e  t ) , x0,1   / 2 , and x0,2  0 .

(13)

where i,k  min[(di  bi )ci,1 , ci,2 ], a  min[2ki ,1 , 2ki ,2 ,i] ,22 2   2b  min[2  i ,1 , 2  i ,2 ,i] , i  i zi,ki   i i   i i2
 2 Fig. 1. Directed graph of the manipulator group

3.2. Finite-Time Stability Analysis((13) can be rewritten the following two forms:  1  1
 i ,k2i  i ,k2
Vi ,2   aVi ,2   b
  i ,k 1  Vi ,2

Vi ,2 2 
  a Vi ,2 b Vi ,2 , i ,k 1

where a   a  i / Vi ,2 , b  b 2  i / Vi ,2 2

 i,k  12(14)

 i ,k 1, and. From (14), if a and b is selected such2

that a  i / Vi ,2 and b  

 i ,k 1/ Vi ,2 , respectively. Then,from the definition of finite-time stability [7], theequilibrium point x  0 is globally finite-time stable
and the settling time ts can be given byaV 1 ( x0 )  b
1.
lna(1   )
b


ts 

The error functions for the illustration of the formation)
control are changed into (5), where 1  1 ,
2  2 , 3  3 , and 4  4 . Simulation results arepresented in Fig. 2 (consensus control) and Fig. 3(formation control), where the settling time of the
proposed TBSC system is 31% faster than that of the
BSC system. In addition, the steady state errors of the
TBSC system are smaller compared to the BSC system.

(15)

5. Conclusion

A terminal backstepping control scheme to guarantee
the fast error convergence and small tracking error
performance for a multi-agent Euler-Lagrange system
is developed in this paper. A virtual finite-time error
surface is defined to design a virtual control. The finitetime convergence is proved by the finite-time stability
analysis of Lyapunov function. Simulation for one-link
manipulator agents confirms the theoretical proposal.

4. Simulation Example

To validate the proposed control scheme, the following
group of one leader indexed by 0 and four followers
indexed by 1, 2, 3, and 4, respectively as shown in Fig.
1. The strict feedback state equations of each agent are
expressed as
xi ,1  xi ,2 ,xi ,2  fi ,2 ( xi ,1 )  gi ,2 ui ,(16)

© The 2015 International Conference on Artificial Life
and
- 143
- Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan

Seong-Ik Han, Yun-Ki kim, Hoang Nhat Minh, Jang-Myung Lee

Graduates Education Program supervised by the KIAT
(N0001126).
References

Fig. 2. Consensus control simulation results. (a) Tracking
outputs of BSC system. (b) Tracking outputs of TBSC system.
(c) z1,1 of BSC. (d) z1,1 of TBSC.

1. W. Ren, Distributed consensus in multi-vehicle
cooperative control (Springer-Verlag, London, 2008).
2. H. Arcak, and M. Wen, Adaptive motion coordination:using relative velocity feedback to track a reference
velocity, Automatica, 45(2) (2009) 1020-1025.
3. H. Su, G. Chen, and Z. Lin, Adaptive second-orderconsensus of networked mobile agents with nonlinear
dynamics, Automatica, 47(2) (2011) 368-375
4. H. Zhang, and F. L. Lewis, Adaptive cooperative
tracking control of high-order nonlinear systems withunknown dynamics, Automatica, 48 (2012) 1432-1439.
5. S. Chung, and J. J. Slotine, Cooperative robot control
and concurrent synchronization of Lagrangian systems,
IEEE Tran. Robotics, 25(3) (2012) 686-700.
6. K. Kristic, I. Kanellakopoulos, and P. V. Kokotovic,
Nonlinear and adaptive control design (Wiley, New
York, 1995).
7. S. Yu, X. Yu, B. Shirinzadeh, and Z. Man, Continuous
finite-time control for robotic manipulators with terminal
sliding mode, Automatica, 41 (2005) 1975–1964.

Table 1. Settling time (sec) of BSC and TBSC systems
Consensus
Formation
BSC
TBSC
BSC
TBSC
z1,1  0.01
2.03 s0.77 s2.23 s0.83 sz2,1  0.05

2.20 s

0.67 s

2.63 s

0.91 sz3,1  0.01

2.07 s

0.76 s2.83 s0.86 s2.15 s100%0.42 s32%

1.46 s100%0.29 s31%

z4,1  0.05
Mean (%)

Acknowledgements

This research was supported by the MOTIE (Ministryof Trade, Industry & Energy), Korea, under the
Industry Convergence Liaison Robotics Creative- 144- Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan© The 2015 International Conference on Artificial Life
and

Vision-Based Grasp Planning and Experiments of a Mobile Manipulator
Yi-Fu Chiu

Institute of Electrical Control Engineering, National Chiao Tung University
1001 Ta Hsueh Road, Hsinchu 30010, Taiwan
Kai-Tai SongInstitute of Electrical Control Engineering, National Chiao Tung University
1001 Ta Hsueh Road, Hsinchu 30010, Taiwan
E-mail: ivan790721.ece01g@nctu.edu.tw, ktsong@mail.nctu.edu.tw

Abstract
In this paper, a motion planner is designed and implemented for of a mobile manipulator to travel to a spot for
grasping of an object. In this work, the probability of successful grasping inside the workspace of the robot arm isused for grasping planning. A vision SLAM system is combined with reachability calculation to figure out thegrasping position. Using a laboratory dual-arm robot, we conducted experiments in different conditions to verify
the effectiveness of the developed system.
Keywords: Mobile manipulation, visual servoing, visual navigation, robot grasping.

1.

representation of the robot body are used to reduce thepossibility of collision4. In the work of Stulp et al.5,
through experience-based learning, the robot first learns
a so-called generalized success model which
distinguishes among positions from which manipulation
may succeed or fail. The model is used to compute a
probability distribution that maps positions to a
predicted probability of successful manipulation, taking
into account the uncertainty of the robot and object’s
positions.
A key factor of mobile manipulation design is that asuitable pose of the mobile robot needs to be obtained
through navigation in order to conduct object
manipulation. One possibility is simply to travel fromthe current position to a position such that the target
object is reachable, as depicted in Fig.1. As shown in
Fig 1(a), the object is placed in front of the table, wherean obstacle exists to influence the grasping. In this case,the object might not be successfully grasped due to thelimited workspace of the robot arm. However, if theIntroduction

Mobile manipulation technologies have been developed
rapidly in recent years. It has been expected that various
robots will come into our everyday life for living aids.
In order to help people with housework, a robot needs tobe equipped with many abilities such as communication,navigation, grasping and object recognition. Therefore,
developing a mobile manipulator which is able to moveand handle objects such that it can support people in ahome setting deserves urgent attention.
Cosero1hasachieved
notablesuccessin
RoboCup@Home contests for its mobile manipulation
performance. It localizes objects and plans path basedon a 2D occupancy grid map constructed from multiple
3D scans of the environment2. Collision with obstacles
can be avoid using the map, even though it cannot
perceive an obstacle with current sensor’s view. PR23
processes sensory data from 3D visual sensors in point
clouds. Multilayered 2D costmaps and a layered© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 145 -

Yi-Fu Chiu, Kai--Tai Song

mobile robot travels to thee side of the taable as shownn in
Fig. 1(b), the object can bee easily graspeed. It is thereffore
desirable to develop a meethod to plann a pose for the
robot such thaat the target objectois easy-tto-reach.
Attamimi et al.6 proposedd a method to
o use percepttion
maps that exppress the robot arm reachaability and robbot
platform naviigation reachaability to a taarget object. T
Theauthors also sshow that by integrating thhese two mapss, aproper posittion that has maximum probability ofsuccessful grrasping is deetermined. In this study, wedeveloped a mmotion planning system forr determining theproper pose ffor grasping objects.
o
The robotrpositionn isdetermined bbased on thee arm’s reacch and ease of
movement. W
We adopted thee design of Atttamimi et al.6 todescribe thee limitations of robot workspace, thesituation of eenvironment anda target objject position. By
constructing the reachability map that represents theease of robbot mobile platform motionmand thegraspability map that deescribes the success rate ofgrasping of rrobot arms inn workspace, robot is ablee todetermine thhe proper poosition to moove toward aandincrease the success rate of mobile maanipulation taask.
Visual EFK-SLAM and visual serv
vo technics areintegrated intto the design for vision-baased graspingg ofthe object.
2..

ran
nge finders area equipped on the mobille platform inn
forrward and bacckward directtions for obstaacles detectionn.
Ann on-board industrialiPC
C (IPC) is usedufor dataa
processing and motion contrrol of the robbot. The steerannd-drive mechhanism allow
ws the robot to move innommnidirection7. The implem
mented EKF-S
SLAM8 allowss
thee robot to navigate annd localize itself in theeennvironment. The visual servvo system9 alllows the roboot
to move and fetch an object w
without collisiions.
A. Visual EKF--SLAM
A visual SLAM
M system haas been developed for theeommnidirectionall mobile roboot8. There arre three mainn
steeps of the EKF-SLAM: a mmeasurement steps to acquiree
thee location vecctor of robot rrelative to the feature in theeennvironment, a prediction steep to predict the
t location ofrob
bot using motionmmodell and an up
pdate step too
caalculate Kalmaan gain and up
update robot sttare accordingg
to above two steps.
sThen, the updated robot state iss
deelivered to predictionpsteep to calcullate the nexxtmooment state off robot. Thesee steps repeat to achieve theereaal time localizzation and map
apping.
B. Visual servo graspingByy using the deepth data fromm Kinect, the inputi
image isssegmented intoo several plaanes. In this method9, theeplaane that the target object llocates is first labeled. Theelab
beled plane isi featured byy SURF algoorithm and iss
maatched with daata base to finnd where the targettobject iss.
Ex
xcept for the target objectt, the other objectsoin theeennvironment areaobstacle for robot manipulationn.
Pootential field is then combbined gravity and repulsionn
wiith these indicces for safe paath planning ofo robot armss.
Acccording to thhis, the robot is able to mo
ove its arms innthee workspace without colllisions and to grasp theetarrgeted object.

The Duaal Arm Robott

A dual-arm roobot is designned and constrructed for mobbile
manipulation experiments.. The 6-DOF dual-arm desiign
allows the robot to graasp various objects in theenvironment. A Kinect RGBD
R
cameraa is installed on
top of the roobot for visuaal servo contrrol. The forwward
and inverse kkinematics off two robot armsaare derivved
and implemeented on the robot contro
oller. Two laaser

3.

Grasp PlannningTh
he proposed systemsarchitetecture is shown in Fig. 2.
2
Foor representatiion of the envvironment, wee utilize EKFSL
LAM algorith
hm and Kineect sensor too construct a
feaature map of the environm
ment. For grasp
ping planningg,
wee adopted the method of Atttamimi et al.6 in this designn.
Th
he feature map
p is used as innput to the moobile platformmto calculate thee easiness of traveling to the reachablee

(a)
(bb)
Fig.1 An exaample of mobilee manipulation is influenced bby
an obstacle inn front of the taarget.

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nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 146 -

Vision-Baseed Grasp Planningg

ove to the maximummproobability of thet integrateddmo
6
rep
presentation ofo the navigatiion and arm motionm.
4.

Experimennts and discusssion

Thhree interestinng experimentts have been carriedcout forrgrasp planningg to verify the effectiveness of thee
proposed system
m. We first veerify that diffeerent locationss
off target objectt in the same environment will result inn
diffferent robot navigation bbehavior. Theen we add ann
obbstacle aroundd the target arrea to verify thet adaptationn
off the robot.

A. Target objecct in different locations

Fig.2 System
m architecture

region of thhe robot. On the other hand,hthe tarrget
location is innputted to arm
m graspabilitty calculationn torepresent the extent of graaspable regionn of robot arm
ms.
We fuse thee two represeentations to decide the ggoal
location for rrobot to graspp the target ob
bject. This woork
uses EKF-SL
LAM algorithm
m for robot naavigation conttrol.
First, we exxtract SURF features thaat represent theconfigurationn of the enviroonment. Then, EKF is utilizzed
to improve tthe localizatiion error andd to correct thecoordinates oof features. Foor grasping, we
w use Kinect forenvironment detection so thatt
the robot is able to dettect
not only the target objectt but also thee obstacle in theenvironment and to guide its
i arms to fetcch the target.
The inverse kinematics soolution map suggestedsin thework of Atttamimi et al.
a 6 is adopteed to calcullate
reachable/unrreachable pooints. Then a probabillity
distribution iis given to alla reachable points for laaterdecision makking of the graasping positionn.
To include tthe descriptionn of robot navigation in the
motion plannning of graspiing, we have to consider thenavigation ccontrol of the mobile robot in anenvironment. Obstacles inn the environ
nment influennce
the motion pllanning of thee robot. In thiss work, a featu
ture
map is built tto express thee environmentt by using EK
KFSLAM algoriithm. We adoopted the methhod proposed by
Attamimi et al.6 to consttruct a reach
hability map for
motion plannning. The clloser to obstacles, the lless
probability iss given for thhe robot to reaach. The motitionplanning of thhe robot is theen determinedd for the robott to

Fig. 3 shows the environm
nment of thiss experimentt,Co
ondition A an
nd Condition B are termed to distinguishhtwwo locations of the object oon the table. We
W first utilizee
EK
KF-SLAM too construct the feature map of theeennvironment. Th
hen we replacce the featuress with the sizee
off robot. Fig. 4(a) showss the planniing result ofcoondition A. It
I is noted tthat the plannned graspingg
poosition is in front
fof the ttable, which is depicted inn
dootted line. Forr condition B, the planned grasp positionn
is at the side off the table, ass shown in Fig
g. 4(b). Fig. 5
shhows the experiment by reaal robot in Co
ondition A andd
Fig. 6 shows thee experiment iin Condition B.
B

B. Grasping pla
anning affecteed by obstacleesTh
he purpose of this experimeent is to verify
y the proposedd
meethod is able to judge the pproper grasp positionp
underrthee obstacle situ
uation. Fig. 7 (a) shows thee setup of thissex
xperiment, term
med Conditioon C. The targ
get object is atathee same position as Condi
dition A but is behind theeobbstacle. The pllanning resultt is shown in Fig. 7(b). Thee
graspable regionn moves to thhe side of the table becausee

(a))
(b))
Fig.
F 3 Environm
ment setup for eexperiments (a) Condition A,
targettin the front of the front oof the table, (b)) Condition B,
targetton the sid
de of the table.

© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 147 -

Yi-Fu Chiu, Kai--Tai Song(a)(b)
n A, (b) the
Fig. 4 (a) Thhe planning resuult of Condition
planning resuult of Conditionn B.

(a)
(b)
Fig.
F 7 (a) Enviro
onment setup off Condition C (b)( theplanningpresult ofo Condition C..

Reeferences

(a)

(
(b)

(c)

Fig. 5 Taskk execution of ConditionC
A on realr robot

(a)

(
(b)

(c)

C
B on realr robot
Fig. 6 Taskk execution of Conditionof the graspability affected by the obbstacle changges.
Compare wiith Conditionn A, the difference is the
obstacle effecct that causess the differen
nt planning frrom
proposed metthod.
5..

Conclusiions and Futu
ure Work

A dual-arm robotic systtem has beenn designed aand
implemented for mobile manipulatio
on tasks in an
unstructured environment.. A grasp plaanning system
m isdeveloped baased on visual SLAM and visual servoingg of
the robot. Thhe proposed system has beenbverifiedd inthree differeent conditionss. The experimental resuults
show that thhe robot cann adapt to thetvariation ofenvironment and find a suiitable place to grasp the objject.
In the futuure, object fetchingfin more extenssive
environmentss will be studied.
Acknowledgements
This work waas partly suppported by Min
nistry of Sciennce
and Technoloogy of Taiwann, under grant NSC 102-22221E-009-140.

1. Jorg Stuckleer and Sven Beehnke, “Benchm
marking Mobilee
Manipulationn in Everydayy Environmentss,” in Proc. of2012 IEEE Workshop onn Advanced Ro
obotics and itss
Social Impaccts (ARSO), (M
Munich, Germanny, 2012), pp. 16.
2. J. Klaess, J. Stuckler and S. Behnke, “E
Efficient Mobilee
Robot Navig
gation Using 3D
D Surfel Grid Maps,”M
in Procc.
of 7th German Conf. on Roobotics (ROBO
OTIK), (Munichh,
Germany, 20012), pp. 1-4.
3. Sachin Chittta, E. Gil Jonees, Matei Ciocaarlie and Kaijenn
Hsiao, “M
Mobile Maniipulation in Unstructuredd
Environments: Perception, Planning, andd Execution,” innIEEE Robotiics & Automatiion Magazine, 19(2012),
1
58-711.
4. Hornung, A., Phillips, M.,, Jones, E.G., Bennewitz, M..,
Likhachev, M. and Chittaa, S., “Navigaation in ThreeDimensionall Cluttered Environments for Mobilee
Manipulationn,” in Proc. of 2012 IEEE
E Internationaal
Conference on Robotics aand Automatioon, (Minnesotaa,
USA, 2012),, pp. 423–429.
5.
5 Freek Stulp
p, Andreas Feedrizzi and MichaelM
Beetzz,“Action-Related Place-Bassed Mobile Maanipulation,” inn
Proc. International Confereence on Intellig
gent Robots andd
Systems, (St.. Louis, MO, U
USA, 2009), pp. 3115-3120.
6.
6 Muhammad Attamimi, Keiisuke Ito, Tommoaki Nakamuraa
and Takayuk
ki Nagai, “A PPlanning Metho
od for Efficiennt
Mobile Man
nipulation Conssidering Ambig
guity,” in Procc.
International Conference on Intelligen
nt Robots andd
Systems, (Viilamoura-Algarrve, Portugal, 2012),
2
pp. 965972.
7. Kai-Tai Son
ng, Sin-Yi Jiangg and Chia-Ch
hang Wu, “Posee
Control of a Four-Wheel D
Drive Omni-Directional Mobilee
Robot,” in Proc.
Pof 2012 C
CACS Internatiional Automaticc
Control Conf
nference, ( Yunllin, Taiwan, 2012).
Tai Song, “A Neew Approach too
8. Chien-Hung Liu and Kai-T
Map Joining
g for Depth-Au
Augmented Visu
ual SLAM,” inn
Proc. of ASC
CC 2013, (Istanb
nbul, Turkey, 20013).
9. Kai-Tai Song and Shih--Cheng Tsai, “Vision-Basedd
Adaptive Grrasping of a Huumanoid Robot Arm,” in Procc.
of IEEE ICA
AL 2012, (Zhenngzhou, China, 2012), pp.160165.

© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 148 -

Combining playware exergaming with a mobile fitness app
Emmanouil Giannisakis

Henrik Hautop Lund

Centre for Playware, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
hhl@playware.dtu.dk
www.playware.dk
Abstract: We propose a novel playware as a merge between exergames and mobile fitness apps to engage the users inphysical exercises, not only as competitive play, but also in the form of cooperative play. The concept connects modular
interactive tiles with radio communication to Android tablets and smart phones, which can connect to the Internet. This
allows the players to monitor their playware exergaming performance on the smart device(s). A test was set up over 8 days
allowing two school classes to compete with each other on which class was going to collect the most points playing on themodular tiles during the test. The test subjects were from 6th and 7th grade (12-13 years old children). As a social playware,
we investigated how the playware mediated cooperative and competitive play amongst the users. It was found that the
majority of game play involved social interaction between players, and that 8 out of 10 pupils on the top-10 were girls. The
playware seemed to motivate the girls to become physically active.

Keywords: Playware, Exergaming, Mobile App, Modular Technology, Cooperative Play, Gender, Social Playware.

of the effect of the competitive and cooperative social
play that the mobile app adds to the playware tool.

I. INTRODUCTION
Exergaming has been a popular area of research anddevelopment in recent year, for instance resulting ingames for health. Exergaming ("exercise" and "gaming")
is fitness gaming where video games provide exercise,and it may often rely on technology that tracks body
movement or reaction. Some exergames results from theresearch field of playware [1, 2], which has a coretechnology research activity focusing on research intomodular playware technology and its supporting
fundamental research areas of modern AI, adaptivity,
modular robotics, and tangible interfaces. This iscombined with a deep knowledge of play and play
culture to create exergames that are fun, playful andmotivating for the users to engage with out of their own
free will and desire to play.

Figure 1: The concept connects modular interactive tiles withradio via a board with XBee and Bluetooth to Android tabletsand smart phones, which can connect to the Internet (e.g. forstoring database). Players can choose games and player IDs on
the smart device(s) and monitor their performance on the smart
device(s).

With the work presented here, we propose a novel
playware as a merge between the exergames and mobile
fitness apps to engage the users not only in competitiveplay, but also in cooperative play. We try to incorporate
both competitive and cooperative play, since wehypothesize that this may attract both genders, especiallyalso girls, to become physically active. Hence, we build a
simple mobile app for smart devices on top of a playware
tool (modular interactive tiles) – see Fig. 1 – with wellknown health benefits [3], and make a qualitative study

A system of modular robotic tiles was developed [4, 5]
and used extensively in physiotherapy and rehabilitation
using this platform as a form of exergaming, e.g. [6]. The
way the system works is that in the tiles different games
can be programmed and the patients are performing
physiotherapy exercises by playing these physicalinteractive games. A challenge for a system like themodular interactive tiles and other exergaming systems is© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 149 -

Emmanouil Giannisakis, Henrik Hautop Lund

how they can motivate the users to keep playing.
Therefore we conducted a user testing in a school where
pupils from two classes acted as testers in order to see ifcompetition between the users, cooperation between theusers, seeing the score from a game and seeing the scorefrom the other players affected the motivation of thepupils to play.

II. MOBILE APP FOR SMART DEVICES
Figure 2. Arduino Uno with beeshielded Bluetooth and Xbee
bee.

We developed a simple app to run on top of the modular
interactive tiles to provide feedback to the users on atablet (in a shared form) or a smart phone (privately).
When a user is playing a game on the modular interactive
tiles, the system can monitor different variables such as
points, time, level, etc. These variables are provided tothe mobile app, which can provide feedback to the user
after the end of a game. The feedback provided to theuser consists of the following attributes:
1.
2.
3.
4.
5.
6.

The mobile app was developed to run on Android devices.
It was developed to be able to run on both Android
tablets and Android smart phones. This application
supports user creation, group creation and competition
creation. In addition, it is connected with the modular
interactive tiles so that the user can choose the game thatthe user wants to play and to receive and store the scorefrom the game. With the tablet, more than one user canplay a game, and in that case the score is divided by the
number of users. The received score is being saved in a
local database. Finally, the users have access to theircurrent total score, the high score they have achieved foreach game as well as the current total score and the highscores of the other players.

The points that the user got
The duration of the gameThe number of misses
The level of difficulty
The winner of the gameThe number of tiles that were used

Not all the attributes are relevant to every game. For
example the winner attribute is not being used in one
player games.

III. USER TESTING SETUP
A test was set up over 8 days in the Oesterbrointernational school in Copenhagen, Denmark. The
premise of the test was that two classes were going tocompete with each other on which class was going tocollect the most points during the test. For this purpose
6th and 7th grade (12-13 years old children) was chosen
and they would compete with each other.

To simplify the setup of the experiment presented here,
we used an Arduino Uno board to facilitate thecommunication between the tiles, which communicate
with XBee, and the mobile device(s), which
communicate with Bluetooth. To the Arduino Uno board,
we attached a Bees shield with an Xbee radio module anda Bluetooth Bee module (see Fig. 2). The way we achievecommunication is that the master tile communicates with
the Xbee radio module of the Arduino board and themobile device is connected with Bluetoothcommunication with the Bluetooth Bee (see Fig. 1). The
Arduino Uno is responsible of receiving the messages
from one radio module and properly forward and modify
the received message to the other radio module. The
tablet application sends messages that contain request for
the tiles to change the game to another one, while thetiles send messages that contain the feedback of the game.

We provided the school with a set of 10 modular tiles (1master and 9 normal), a tablet with the tablet applicationand the Arduino board with the Bees shield.
The pupils had the chance to play with the tiles during
the breaks. The school had two break sessions: one from
09:35-09:55, and one from 11:45-12:05, i.e. two times 20
minutes per day. During those periods whoever of theparticipants who wanted to play could come, choose thedesired game and the other participants that they wanted
to play with (if they wanted to play with other people),and they would start playing. Hence, in total the pupils
could play for 8 days * 2 * 20 minutes/day.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 150 -

Combining playware exergaming with

IV. RESULTS
Of the 33 active participants, 14 were male and 19 werefemale (i.e. 42.5% were male and 57.5% were female).
On average, each of the 33 participants played 29.2 times
and for 2.9 days. On average, each player played for1171 seconds during the testing with the most active
player to have played 4057 seconds. Out of the total 401
games that were played, 222 were played by groups ofpeople. The groups that were formed were consisting ofstudents of the same class. Table 1 shows the top 10
players of the high score list at the end of the test. It isnoteworthy that from the ten players with the highest
total score 8 were female and 2 were males (the number
one player being male).

V. DISCUSSION
Even if the collection and display of scores may seem topromote competitive play with focus on getting a higherscore, the results showed that more than 55% of the use(222 out of 401 games played) was group play withsocial interaction between the players, and girls
participated in 90% of these group plays. Even if theplayers tried to get a higher score, they did so in acooperative manner in the majority of cases. This may be
a contributing factor in explaining the relative highsuccess amongst the girls (the girls were twice as active
as the boys, and 8 out of 10 pupils on the top-10 weregirls), who may possibly enjoy the social, cooperativeplay. It is interesting that this playware motivated thegirls to become so physically active.

Table 1. The top 10 players at the end of the experiment. 80%of the top 10 players were female.

Player id

Total
sore

42

3934

2630

49

Female

53

4645

3679

117

Female

19

5016

2423

38

Male

8

5112

2519

49

Female

39

5403

2680

85

Female

37

5419

3414

91

Female

9

5589

2178

38

Female

48

5989

2120

64

Female

27

6087

4057

91

Female

55

7520

3201

94

Male

Social, competitive play: groups would
cooperate to achieve a higher group score thananother group
Social play to support individual competitiveplay: a group would form to help one individual
to gain a higher score
Individual competitive play: an individual would
compete against herself or another individual on
getting a higher score or reaching a specific goal

Total
Number of
durationgames
Gender

We may speculate that the relative high success of thisplayware with the girls may be attributed partly to thefacilitation of social, cooperative play in the competitive
game scenario. The modularity of the modular interactive
tiles facilitates social interaction by the physical layout of
the modules, and the games are designed so that players
can cooperate in the physical play. For instance, as moretiles are added to a platform of tiles, the more players canparticipate at the same time and cooperate to succeed in
the game. One example is the Final Countdown game inwhich the user(s) has to keep the platform ‘alive’. Eachtile is counting down its LEDs, but all LEDs on the tile
shine up again if the tile is being stepped on. If one tile in
the whole platform goes to zero LEDs on, then the gameis lost. The game can be played by one player, who willrush around on the tiles platform to keep all tiles alive,
but a group of players soon discovers that they can help
each other by each being in control of keeping a few of
the tiles alive. So they distribute amongst each other thetask of keeping the whole platform alive. Other games
are designed in a similar fashion to allow social
interaction and cooperation amongst the players.

When looking more detailed at the play of the boys andthe girls, there are some significant differences. Most
notably, on average each participating girl played for a
total of 1464 seconds, while each participating boy, on
average, played for a total of 774 seconds. Hence, eachgirl would play almost double as much as each boy.
Since the participants were free to come to the room toplay whenever they wanted during the breaks during the
8 experiment days, it indicates that the girls wereattracted more to the playware game than the boys.
As a qualitative evaluation of the test, a research assistantmade observations during the intervention and madenotes of these observations. Three cases wererepresentative of the observations that were made during
the experiment: the pupils would engage in© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 151 -

Emmanouil Giannisakis, Henrik Hautop Lund

[5] US Patent No: 8,241,183. “Modular robotic tiles forphysical interaction”, 2012.
[6] Nielsen, C. B. and Lund, H. H. “Adapting Playware
to Rehabilitation Practices”. International Journal ofComputer Science in Sport, 11:1, 2012.
[7] Leaper, C. “Gender, affiliation, assertion, and theinteractive context of parent-child play”. Developmental
Psychology, 36, 381-393, 2000.
[8] Dawson, C., Cragg, A., Taylor, C., and Toombs, B.
“Video games: research to improve understanding ofwhat players enjoy about video games, and to explain
their preferences for particular games”. British Board ofFilm Classification, 2007.
[9] Lund, H. H. and L. Pagliarini, L. “LEGO Mindstorms
Robot Soccer”. In Proceedings of RoboCup’98, LNAI
1604, Springer-Verlag, Heidelberg, 1999.
[10] Lund, H. H. “Adaptive Robotics in Entertainment”.
Applied Soft Computing, 1:1, 2001.
[11] Lund, H. H. and T. Thorsteinsson, T. “Social
playware for mediating teleplay interaction overdistance”. International Journal Artificial Life andRobotics, 16:4, 435-440, 2012.
[12] Suzuki, K. “Social Playware: Device-mediatedsocial interaction for therapeutic activities.” Robot andHuman Interactive Communication, 2014 RO-MAN: The
23rd IEEE International Symposium on. IEEE, 2014.

Research in play, toys, computer games, and robotic
games show that girls often tend to prefer cooperative
play and boys tend to prefer competitive play, e.g. [7, 8,
9, 10]. It is therefore also interesting, that with thephysical interactive games, we can observe here that thefacilitation of social, cooperative play within acompetitive game scenario seem to attract the girls toengage in the play and to become physically active.
It suggests that the playware in this experiment becomesa mediator for playful social interaction. Indeed, social
playware has been defined as playware which aims at
creating playful social interaction between several users[11], see also [12]. With social playware, the starting
point for the research and development becomes thesocial interaction mediated by the technology rather than
the individual interaction mediated by the technology. In
the present case, the results suggest that users – boys andgirls – become physically active in a social way withsocial playware like the one which was carefully
designed for the present experiment in the form ofmodular interactive tiles with a mobile app for bothcooperative and competitive play.

ACKNOWLEDGEMENT
The author would like to thank colleagues from theCenter for Playware, DTU for collaboration, help anddiscussions related to the experiment presented here.
Also, the authors would like to thank the participating
children and teachers at Oesterbro International School,
Denmark.

REFERENCES[1] 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.
[2] 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.
[3] Lund, H. H., and Jessen, J. Effects of Short-Term
Training of Community-Dwelling Elderly with ModularInteractive Tiles, Games for Health Journal, 3:5, 2014.
[4] Lund, H. H. “Modular Robotics for Playful
Physiotherapy”. In Proceedings of IEEE International
Conference on Rehabilitation Robotics, IEEE Press, 571575, 2009.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 152 -

Parallel Relational Universes – experiments in modularity
Luigi Pagliarini 1,2Henrik Hautop Lund 1

Centre for Playware, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark2
Academy of Fine Arts of Macerata, Via Berardi, 6, 405111 Macerata, Italy
luigipagliarini@gmail.com
www.playware.dk
Abstract: We here describe Parallel Relational Universes, an artistic method used for the psychological analysis of groupdynamics. The design of the artistic system, which mediates group dynamics, emerges from our studies of modular
playware and remixing playware. Inspired from remixing modular playware, where users remix samples in the form ofphysical and functional modules, we created an artistic instantiation of such a concept with the Parallel Relational Universes,
allowing arts alumni to remix artistic expressions. Here, we report the data emerged from a first pre-test, run withgymnasium’s alumni. We then report both the artistic and the psychological findings. We discuss possible variations of suchan instrument. Between an art piece and a psychological test, at a first cognitive analysis, it seems to be a promisingresearch tool.

Keywords: Art, Playware, Modular Technology, Remixing Playware, Social Art.

[10], and sport [11]. In all such cases, users interact withthe playware as a free and voluntary activity that they
engage in for the pleasure of play, even if the activity
may be shown to have collateral effects e.g. in terms ofhealth and skills. It has been proposed that modular
playware is of particular interest to develop solutions forsuch varied areas of application, since modularity may
facilitate easy assembly and adaptation of the playware todifferent interaction modalities [12]. We can view themodular playware as enabling the user to remix withmodules to combine and construct new playware
solutions.

I. INTRODUCTION
Parallel Relational Universes (URP) is an attempt, bothartistic and scientific, to conceive a method to investigate
the small groups psycho-dynamics expressed throughout
a Relational Modularity logic into a graphical modality.
The project is located in between an artistic conception
and a psychological need of a new method for small
groups’ analysis, since it aspires at catching the“essence” of a group by materializing and framing thesocial interaction into a unique graphical expression
resulting from a collective action. All of that, to look fortangible artefacts that might be “witnesses” of small
groups’ dynamics readable throughout a Psychology ofShape analysis. In other words, the basic assumption isthat the emerging shape might reveal the psychologicalstructure of the related group.

Indeed, the proposed design of the artistic system, whichmediates group dynamics, emerges from our studies ofmodular playware [12] and remixing playware [5].
Inspired from remixing modular playware, where users
remix samples in the form of physical and functional
modules, we created an artistic instantiation of such a
concept with the Parallel Relational Universes, allowing
arts alumni to remix artistic expressions.

URP was inspired by different artistic, psychological andinformatics sources as the Cadavre exquis [1], the Pittura
Stocastica [2], the Tree Testing Tool [3], Modular Tiles[4], Remixing Playware [5].

Remixing playware has been defined [6, 7] to allow
sampling and remixing of both physical and functional
aspects of a system. Such remixing playware has anumber of distinguished features which include: userconfigurable modularity, which allows the users tointeract and manipulate with samples; user-guided
behavior-based system, which allows compositions to

Such playware is defined as intelligent hardware andsoftware that creates play and playful experiences forusers of all ages [6, 7], and R&D in playware has led tonumerous applications in various areas such as
rehabilitation [4, 8], playgrounds [7], education [9], art© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 153 -

Luigi Pagliarini, Henrik Hautop Lundon a continuum of interpersonal relational, emotional andaffective forces.

emerge from the way performers interact with the“instruments” that provide the primitive behaviours;
intelligent sampling as the ability of creating samples thatallow anybody to remix with the samples ensuring an
engaging outcome.

After that, we asked participants to paint the canvases in
two specific colours, black (or white) as background andwhite (or black) as foreground. We strongly, but notstrictly, suggested (C. almost-a-rule) to cover 50% of thespace with the foreground colour leaving the resting 50%
for the background.

When created in the right manner, playware can mediatesocial creativity, as exemplified with the MusicTiles
MagicCubes, which allow anybody in an easy andintuitive way to remix music samples according to theirindividual taste [5, 13]. Designing and developing thesamples in large, soft forms as 1m3 cubes pushes theusers into social interaction, since a user will only be able
to control one instrument (sample set) himself, and will
have to coordinate actions with other people tocreate/perform a remix. As a social playware, such asystem explores the cooperative creativity: theMagicCubes seamless push the users into social play
dynamics resulting in the users interacting andcooperating in their play to create and perform theircollective new hit song versions.

Finally, we introduced the unique must (A. rule) which isthe continuity of the sign (or gesture/geometry) on eachand every side of the module-borders where a module
collide with other modules (see Figure 1). In other words,
each canvas border has to shape a continuous drawing
with the neighbourhood and each and every executor has
to negotiate such drawing with all of its neighbours.
There must be a perfect continuity in each direction,
north, east, west, south, south-west, etc.

The first pilot study of Parallel Relational Universes [14]was run with an analogical method and conducted withthe alumni of an Italian art school, and its output has
been exhibited to the public as an art piece while, below,
we describe an automated ALife version of the same
concept that allows the widening of the method to larger
and more heterogeneous audiences.

Figure 1.
Tiles’ borders must are continuous.
The A rule has been introduced both for aesthetical
purposes and, mainly, to push people interactivity which
is necessary to highlight the pre-existing and emerging
conflicts. (Those, indeed, are very descriptive of thespecific group dynamic).

II. DESCRIPTION AND PROCEDURE
The first, analogical, version of URP was conceived to be
an “art game” with as simple as possible procedure sothat it might consent to any people of any age to be
involved in the building of the art piece. Indeed, we onlyused a dichromatic scale (black and white) and we gave
users very easy instructions/suggestions/rules. In
particular 3 of them: A. a strict rule; B. an elastic
demand; C. a suggestion (that we then defined as almosta-rule).

Besides
those
described
above,
twomorerules/possibilities emerged from the interaction with thealumni: D. the Good Neighborhood; E. the Teleport.
Shortly, the first one (i.e. D) is a negotiation, whichallows one painter to penetrate (i.e. in a more or lessreciprocal way) into others’ painters canvases. The
second one (i.e. E) is a negotiation, which allows theexecutor to engrave a far canvas with his/her sign. Boththe emerging behaviours where kept because useful forboth the aesthetic aspects and the psychological analysisof the behaviours.

The procedure itself was the following one. Once
individualized and chosen a specific group, it was
summoned in a room and given to each component (i.e.
in our first experiment art gymnasium alumni) an
expressive module (i.e. in this case a canvas). Later we
imparted the group of participants with a general
disposition (B. elastic demand) to freely locate
themselves in the physical space so to get close to eachother. We moved from the in part prejudicial idea that thegroup elements would convey to spatial subgroups based

III. HYPOTHESIS AND RESULTS
Paintings realized with the URP method will tend to© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 154 -

Parallel Relational Universes – experiments

solicited both in terms of virtual and physical boundaries.
More than that, the playful and pitoric spirit of the gamemakes it so that even the weakest personalities can easily
deal with the concept of borders and improve theircapacity in mediating social actions without getting
scared. Therefore, it has to be noticed that, URP for thevery same reasons kind of stimulate the selfconsciousness of both aggressive and remissive
personalities. This makes of it a good candidate has
method to be used in art-therapy.

structure around an unpredictable shape. On the contrary,
they will be largely descriptive (i.e. in terms of shape) of
most of the group dynamics, while poorly suffering forthe interfering chaotic and occasional intervening factors.
The supervisor can, as well, decide if the painting is to bea unity or can be spontaneously subdivided in islands (i.e.
emerging subgroups).
The theoretical hypothesis serving the entire model isthat the group dynamics will force the execution towards
a specific result, which can be interpreted both under themorphological and the graphical point of view. The twoelements, besides all the aesthetical aspects, will be able
to depict many of the group, such as cohesion,
collaboration, competition, harmony, conflicts, etc. , aswell as to describe most of the group members’
personality as, for example, dominant or gregarious,
weak or strong, aggressive or myths, timid or extrovert,
etc.
The URP method discloses and reports, through the signs,
the innumerable groups’ dynamics. It camouflages withthe pictorial context those behavioural rules andregulations that could result heavy, bulky or frustrating inother circumstances. Such an influence will bring to asemiotic drift the whole system, transforming theexperience in a highly descriptive expression of aparticular group in a particular historical moment of its
existence.

Figure 2.
Aesthetical results.
Under the artistic point of view, URP instantiates a new
approach to modularity that allows users to play with theshape (i.e. intended as painting and painting content) in a
deep and articulated way. In other words, it proposes anescape from canonical painting shape (see Figure 2),
opening up to a number of geometrical solutions, which
can be perceived as unlimited.

The administration of our relational painting game has
produced appreciable results in, at least, three domains ofknowledge: Education-Pedagogy; Psychology-Sociology;
Art. Although, for this very first trial we have made no
measurements, all the teachers and professors involved in
the experiment agreed on the extreme efficiency of themethod under the motivational point of view. In
particular, it has been underlined that, educationally, itpushes in a quite gentle way the young students at
dealing with “abstract representations”, a pretty difficult
theme to communicate in normal classes. At the verysame time, pedagogically speaking, it impresses andforces the classes into a cooperative and collaborative
modality that is both practical (i.e. alumni have to do)
and theoretical (i.e. alumni have to understand).
This last consideration drives us directly to the mostimportant aspect of the game, the extremely strong
interactive modality the game carries along with itself.
Indeed, under a psychological and sociological light, the
URP method plays with the concept of individuality as
counterweighted to the idea of sociality. The students ego– defined as peripersonal and interpersonal space – isIV. DISCUSSION
As a matter of facts, the URP method idea is to build aprojective test that can measure groups’ dynamics (i.e.
cooperation, collaboration, etc.) – psychodynamics ofgroups is reported in [15]. Such a method, indeed, could
catch particular structures of groups’ cohesion (i.e.
opening, esteem, trust, etc.) and desegregation (i.e.
closing, mistrust, depreciation, etc.) returning them bothin the form of geometry and in the form of interrelational aesthetical contents. That is why, besides any
possible aesthetical consideration, the authors’ interest
was focused on building an instrument able to measureand predict psychological aspects of groups, and under
this point of view this first attempt can be considered agreat success. The hope is that in future we will be able
to reiterate such an experiment with different groups ofdifferent ages so to properly measure, analyse and define© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 155 -

Luigi Pagliarini, Henrik Hautop Lund

Interactive Communication, 2014 RO-MAN: The 23rdIEEE International Symposium on (pp. 49-55). IEEE..
[6] H. H. Lund, and C. Jessen, “Playware - Intelligent
technology for children’s play.” Technical Report TR2005-1, June, Maersk Institute, University of Southern
Denmark, 2005.
[7] H. H. Lund, T. Klitbo, and C. Jessen, “Playware
Technology for Physically Activating Play,” Artificial
Life and Robotics Journal, 9:4, 2005, pp. 165-174.
[8] H. H. Lund, “Modular Playware as a Playful
Diagnosis Tool for Autistic Children.” In Proceedings ofIEEE International Conference on Rehabilitation
Robotics, IEEE Press, 2009, pp. 899-904.
[9] H. H. Lund, P. Marti, “Physical and Conceptual
Constructions in Advanced Learning Environments,”
Interaction Studies 5:2, 2004, pp. 269-299.
[10] H. H. Lund, M. Ottesen, “RoboMusic – A BehaviorBased Approach,” Artificial Life and Robotics Journal,
12: 1-2, 2008, pp. 18-23.
[11] H. H. Lund, T. Thorsteinsson, “Social playware formediating teleplay interaction overdistance.”
International Journal Artificial Life and Robotics, 16:4,
2012, pp. 435-440.
[12] H. H. Lund, P. Marti, “Designing Modular Robotic
Playware.” In 18th IEEE International Symposium on
Robot and Human Interactive Communication (Ro-Man
2009), IEEE Press, 2009, pp. 115-121.
[13] Pagliarini, L., & Lund, H. H. (2012). MagicTiles.
ALife for Real and Virtual RoboMusic. In Proc. of 17th
International Symposium on Artificial Life and Robotics,
ISAROB, Japan.
[14] Pagliarini L. Universi Relazionali Paralleli, Rivista
di Psicologia dell’Arte, N.25 in stampa (2014).
[15] Di Maria F., Lo Verso G.. La psicodinamica dei
gruppi. Teorie e tecniche. Cortina Raffaello Ed. (1995)

unkown clusters of behaviour that can be found in small
groups.
Indeed, the encouraging results obtained with theanalogical instantiation of the URP method has driven us
to move on to an automation of it, which could facilitateand speed up the testing.

3a3bFigure 3a and 3b.
Two e-URP screenshots

At the moment a first instantion of such algorithm, called
e-URP has taken its first steps, as shown in Figure 3aand 3b.

ACKNOWLEDGEMENT
The author would like to thank Antonello Tolve,
Parallelo 42°, the teachers Pierpaolo Serini, Amalia
Ricci, Maria Valori, Silvia Volpe, and students of the
Istituto Statale d’Arte “Bellisario-Misticoni” of Pescara,
Italy.

REFERENCES[1] Alastair B.; Gooding M. Surrealist Games. London:
Redstone Press. pp. 143–144. (1991).
[2] Lombardo S., Interpretazioni e preferenze di strutture
casuali. Rivista di Psicologia dell’Arte anno XV, nn. 3-45, 1994, (1994)[3] Crotti E., E tu che albero sei? Come interpretare lapersonalità attraverso il disegno dell'albero, Mondadori
Ed. (2006).
[4] H. H. Lund. Modular Robotics for Playful
Physiotherapy, in Proceedings of IEEE Int. Conference
on Rehabilitation Robotics, IEEE Press, 571-575, 2009.
[5] Lund, H. H., Marti, P., & Tittarelli, M. (2014,
August). Remixing playware. In Robot and Human© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 156 -

Identifying humanoid and human physical parameters
Jovic Jovana*

CNRS-AIST JRL (Joint Robotics Laboratory), UMI3218/CRT
Tsukuba, 305-8568, Japan
Adrien Escande*
CNRS-AIST JRL (Joint Robotics Laboratory), UMI3218/CRT
Tsukuba, 305-8568, Japan
Gentiane Venture†

Tokyo University of Agriculture and Technology
Tokyo, 183-8538, JapanKo Ayusawa*

CNRS-AIST JRL (Joint Robotics Laboratory), UMI3218/CRT
Tsukuba, 305-8568, Japan
Eiichi Yoshida*

CNRS-AIST JRL (Joint Robotics Laboratory), UMI3218/CRT
Tsukuba, 305-8568, Japan
E-mail: jovic.jovana@aist.gp.jp.com, adrien.escande@gmail.com, venture@cc.tuat.ac.jp, k.ayusawa@aist.go.jp,
e.yoshida@aist.go.jp
www.aist.go.jp
www.tuat.ac.jp

Abstract
Dynamical and kinematic analysis of humanoid and human movements require accurate estimation of segment
mass parameters (mass, center of mass, and inertia matrix), and their misinterpretation can lead to significant
variation in estimated joint kinematics. In the field of robotics, several methods have been developed for estimationof mass parameters of humanoid robots, as well as human subjects, based on linear properties of dynamic equationof bipedal systems with respect to the set of mass parameters. This talk will focus on those methods addressing thestate-of-the-art research in the topic. Examples of both human and humanoid robots mass parameters estimation*

1-1-1 Umezono, Tsukuba, Ibaraki 305-8568 Japan.
†
3-8-1 Harumi-cho, Fuchu-shi, Tokyo 183-8538© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 157 -

Jovana Jovic, Adrien Escande, Gentiane Venture, Ko Ayusawa, Eiichi Yoshida

will be given. Identified mass parameters improve output of human dynamic analysis and humanoid simulation andmodel-based control.
Keywords: Inertia parameters, Identification, Optimization, Dynamics.

• fk is the vector of the external forces at contact k. Nc
is the number of contact points with theenvironment;• J1 and J2 are Jacobian matrices at contact k that map
external forces to the joint space of the baselink and body segments, respectively.

1. Introduction
Body segment inertia parameters (BSIPs) are importantvariables in biomechanical analyzes. In case of humaninertia parameters are usually estimated using scaling
equations provide in anthropometric tables based ondata collected from young subjects [1]. Extrapolating
those data to the different population or different age
subjects is restrictive due to different bodymorphologies.
In case of humanoid robots, BSIPs given by robot
manufacturers are estimated using CAD software.
Those parameters are rough estimation of true inertia
parameters and they often do not take into accountwiring materials.
Hence, in this study we propose a method for estimationof subject specific whole body segment mass
parameters. The method applies is based on use of
Hierarchical Quadratic Programming (HQP) [2]
optimization approach and linear properties of rigid
bipedal body dynamics with respect to the BSIPs.

From equation (1) the following equation of motions ofbase link can be written in its linear form with respect tothe set of BSIPs [4]:2where:• Y is the regressor matrix which is a function of q1,
q2 and their derivatives;• F is the vector of external forces applied to the link;• φ is a vector of BSIPs to estimate.
For each body segment i, vector φi is composed of 10
parameters: the mass of the segment mi ; the Center of Mass (CoM) of the segment i; the components of the inertia matrix Ii
expressed in the segment frame;

2. Method
The dynamic equations of dynamics of bipedal multibody systems is expressed by the following equation
[3]:
11
21

12
22

1
2

1
2

0

1
2

The equation (2) can be solved by formulating a fourlevel optimization problem that we solve with an HQP
solver [2]:11Where:

1

1 ≺
≺

2

2

2 ≺
3

where vector φref is a vector of BSIPs parameters foundin Dumas Anthropometric Tables (AT) [5] in case ofhuman BSIPs identification, or extracted from CAD
date in case of humanoid robot BSIPs identification.
The notation is taken from [2]. The first level inequality
constraints is chosen to enforce the solution to bephysically plausible. The second level inequality
constraints is chosen to enforce symmetry between the
BSIPs of the left side of the body.

• q1 represents position and orientation of base link,and q2 is vector of joint angles of the bodysegments;• H1j and H2j (j = 1,2) are the inertia matrix of thebase link and body segments, respectively;• vectors b1 and b2 are the bias force vectors including
centrifugal, Coriolis, and gravity forces of thebase link and body segments, respectively;• τ is the vector of joint torques of the body segments;© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 158 -

Jovana Jovic, Addrien Escande, GentianeG
Venture, Ko Ayusawa, Eiiichi Yoshida

3. Experimeental validatiion
3.1. Experim
ments with hum
man subject
Motions of oone healthy adultavolunteeer (male, 700kg,
1.82m) were measured ussing a stereop
photogrammettric
system and force plates.. The particiipant perform
med
optimal motioon defined in [4] that excittes the dynammics
of the considdered system. In order to teest the abilityy of
the algorithm
m to detect segment masss changes, theparticipant w
was asked tot perform validation trrial
executing thee same motionn and wearing
g a weighted bbelt
located at thee lower trunk level.
lJoint angles w
were estimatedd from marker positions us ing
Cortex Motioon Analysis sooftware.

Fig
g. 2. GRF momments in verticaal (V), anterior- posterior (AP)),andd medio-lateral (ML) direections reconstructed usingg
ideentified BSIPs (dark gray linee), BSIPs from AT (light grayylinne), obtained usinguforceplattes measuremeents during theeexp
periment with thet human subjeect (black line).

3.2. Experim
ments with hum
manoid robot
The excitingg trajectoriess [6] were executed innto
humanoid robbot. Examplees of excitingg trajectories areshown in Figg. 1. The robbot is 1.54m height, with the
CAD mass off 56kg. It is coomposed of 31 segment linnks,
and has 30 ddeegres of freeedom. The roobot is equippped
with sensors measuring thhe joint anglees, force senssors
under the feeet and hands, accelerometer and gyroscoopesensors. Due to the mainteenance of the robotrsome paarts
were replacedd.
The robot m
modifications resulted in changes of its
segment inerttial properties.

4.

Results

Ex
xample of Grround Reactioon Force (GRRF) momentss
recconstructed using proposedd method, method based onn
usse of AT, andd those obtainined using the force platessmeeasurement duuring the expeeriment with humanhsubjecct
is shown in thee Fig. 2. Figuure 3 illustrates increase ofideentified segments masses oof human subject model anddthoose computedd using AT based metho
od during theevaalidation triall when the volunteer wasw
carryingg
weeighted belt loocated at loweer trunk levell. The mass ofeaach segment link of humanooid robot obtained using ourr
meethod and th
he ones giveen by CAD software aree
presented in Fig
g. 4.
5. Conclusion

Fig. 1. Exampples of exitinng trajectoriess performed
humanoid roboot.

We
W presented a method, based on useuof HQPP
op
ptimization teechnique, for identificationn of segmennt
ineertia parameteers. We comppered results obtainedousinggou
ur method withw
the resuults obtained using BSIPsscoomputed from Dumas AT [[5] in case of human BSIPss
ideentification annd extracted form CAD datadin case ofideentification off BSIPs of huumanoid robott. The methodd
proposed in thiis study show
wed better performances inn
esttimation of th
he atypical assymmetric seg
gment weightss
off human subjject, and waas able to estimateemasss
properties of mo
odified segmeents of humanoid robot.

by© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 159 -

Ide
entifying humanooid and human phhysical parameterrs

Moreover, thhe proposed methodm
was abble to reconstrruct
the ground reaction foorces and respective foorce
moments moore accuratelly compared with methoods
based on use of anthropom
metric tables orr CAD data.

References
1. ”Human Bod
dy Properties D
Database”, Av
vailable:
htttps://www.dh.aist.go.jp/datatabase/propertties/indexe.hhtml.
2. A. Escande et al., ”Hierarcchical Quadrattic Programming: Fastt Online Humaanoid-Robot Motion
M
Geeneration”, Th
he Int. J. of Roobotics Research, vol. 33,
noo. 7, pp 1006-1
1028, 2014.
3. W. Khalil, E. Dombre, ”MModeling, Iden
ntification anddCo
ontrol of Roboots”, Taylor & Francis, Incc., Bristol, PA
A,
20
002.
4. G. Venture ete al., ”A Num
merical Methodd For Choosing
g Motions Wiith Optimal Exxcitation Prop
perties For
Identification Of
O Biped Dynaamics - An Ap
pplication to
Huuman”, IEEE//RAS-Proc., 22009.
5. R. Dumas et al., ”Adjustmments to McCo
onville et al.
an
nd Young et all. body segmeent inertial parrameters”, J
Biiomech., vol. 40,
4 no. 3, pp. 543-53, 2006.
6. Jovic. F. Philipp,P
A. E
Escande, K. Ayusawa. E.
EYooshida, A. Kheddar.
K
G. V
Venture, ”Ideentification ofdyynamics of humanoids:hsyystematic exciting motionn
geeneration ”, submitteds
to Proc. IEEE Int. Conf. onn
Roobotics and Au
utomation, W
Washington, US
SA, 2015.

Fig. 3. Segmennt mass increasse (gray) of iden
ntified bodysegment massees (white) and bodybsegment massesmfrom AT
T(black) for the human subject for validation trial.
t
Right anda abbreviated with R and L,
left sides of thee human body are
respectively.

hrobot identified usingg
Fig. 4. Segmennts masses of humanoid
proposed methhod (red) and exxtracted from CAD data (blue)).
Dotted lines shhow expected seegment mass baased on theknowledge of rreplaced compoonents.

Acknowledgements
This study hhas been parttially supportted by Japannese
Society for Prromotion of Science.
S© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 160 -

Construction of Evaluation Index System for Graduate Course
Ai Dongmei *Mathematics Laboratory ,University of Science and Technology Beijing, China
Wen Jiawei

Department of Mathematics and Physics , University of Science and Technology Beijing, China
Ning Xiaojun

Graduate School, University of Science and Technology Beijing, China
E-mail: first_ aidongmei@ustb.edu.cn

Abstract
Abstract: Establishing the index system is the key to carry out the student assessment, evaluation of teachingquality largely depends on scientific index system. The related factors influencing the reliability and feasibility ofteaching evaluation is analyzed in this article, and fair and scientific evaluation index system is established. Basedon the summary of domestic and foreign existing evaluation index system, a set of new evaluation index systemwith a school teaching characteristics is established. The analytical hierarchy process is used to set up
corresponding weights for each indicator, which makes the evaluation index system more complete and accurate.
Keywords: Curriculum evaluation; Indicator system; Analytic hierarchy process

1. Factors Affecting the Students’ Evaluation ofTeaching

2. Principles in Establishing Scientific Index
SystemThe key which achieves the desired purpose of theassessment work lies in if the specific indexes canobjectively and scientifically reflect the actual situation
about the teaching work and teaching effect. Therefore,
it is necessary to carefully study all the elements that
may impact the fairness and scientificalness ofevaluations before establishing high quality index
system, such as the differences between students andteachers, individual and group, as well as the nature ofcourses, etc.

The following principles should be abided by when webuild an index system: people-oriented, comprehensive,
coordinated[1], scientific and feasible[2]. The following
details combining with the specific teaching situation
should also be noted:i) Choose and establish indicators from the standpointof students.
ii) Besides the quantitative questions, open questions
should also be included in the evaluation index
system.
iii) The indicators should be measurable so that we canget quantitative results.

This paper is supported by the postgraduate education and development grant of University of Science & Technology
Beijing© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 161 -

Ai Dongmei, Wen Jiawei, Ning Xiaojun

used by most universities. Specific indicators are shownin table 2.

iv) Weight distribution should be reasonable andstrong basis is needed[3].
3. Establishment of Reasonable and ScientificEvaluation Index System

Table2. Index system in teaching evaluation of
our school

The establishment of evaluation index system is torefine the teaching activity process and form different
levels to reflect the real teaching process and teachingeffect. Some evaluation indicators used by universities
both at home and abroad are selected and all these
indicators can reflect actual teaching situation and effectfrom some certain aspects, as shown in table 1.
Table1.
Evaluation indicators
universities both at home and abroadused

The first levelindicators

The secondary level indicators
Serious and fully prepared

Teaching attitude
Express clearly and fluently
Content of courses

Well organized, Highlight keys anddifficulties
Enrich content properly
Use multimedia correctlybyTeaching method
Clean and tidy blackboard writing

The first levelindicators
Curriculum
designThe secondary level indicators
Explicit teaching goal
Teaching efficiency

Detailed teaching plan

Teaching efficiency
Moderate difficulty
Curriculum

Inspection andtest
Teaching
materials andsupplementarymaterials

Reasonable schedule
Fair and justice

The analytic hierarchy process[4] (AHP) is a systemanalysis method put forward by Saaty (T.L.S Saaty), a
professor at university of Pittsburgh, in the mid-1970s.
In this way, the qualitative thinking process can beturned into a standard quantitative output which can bemeasured and also to keep the consistency of thethinking process and the decision making process.

Teaching materials are easy to understandSupplementary materials are useful

Teachers can timely find problems and solve
difficulties for students
Explain new terms, concepts and principles
clearly

Student
feedback

Cultivate the ability of independent
thinking
Combine scientific research withproduction practice

4. Analytic Hierarchy Process (APH)

Reasonable content

Express clearly and fluently, Full of passion
Teaching

Pay attention to the interaction withstudents, Inspire students' learning
enthusiasm effectively

4.1. Concrete implementation steps of analytic
hierarchy process

Understand the main content of this course
Develop an interest in this course

4.1.1. Establishment of the hierarchy relationships
The top layer of hierarchical structure is the target, thatis, the evaluation activities we want to carry out. The
middle layer or criterion layer is the first level indicators.
The bottom layer is the evaluation objects corresponded
to the secondary level indicators.

Based on the summary of domestic and foreign existingteaching evaluation index system, combined with theactual situation of the school for many years to carry out
the assessment activities, we designed a new evaluationindex system involved four primary indicators: teaching
attitude, teaching contents, teaching methods andteaching effect, which are consistent with the method© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 162 -

Construction of Evaluation Index

The first step: Normalize the column elements of A,

4.1.2. Construction of judgment matrix_

Indicators Ti and Tj are equally important3

Indicator Ti is a little important than Tj

5

Indicator Ti is obviously important than Tj

7

Indicator Ti is more important than Tj

9Indicator Ti is much more important than Tj

else

Between the above judgment value

 a

ij

_

The second step：Add up the row elements of A ,___we get matrix W  [ wij , wij ,..., wij ]Tn__w i   a ij

Illustration

1

n

i1

_

t

a ij

_

a ij 

Table3. Assignment standard for the elements ofjudgment matrix
Assignmen_

we get the matrix A  ( a ij )Given the influence degree to the target layer isdifferent, we thus compare different indicators within
the same level of N and the results of the comparison
constitute the judgment matrix. Elements of judgment
matrix represent the relatively importance degree related
to the upper layer.

i 1

_

The third step：Normalize the matrix W ，we canget matrix

W  [ wij , wij ,..., wij ]T
_

wi 

wi
n

_

w
i 1

i

4.2. The application of analytic hierarchy
process— — Take a certain university as an
exampleTi and Tj represent any two different evaluation indexes
within the same level, aij and a ji respectively represent

4.2.1. Construction of judgment matrix A

the judgment value acquired from comparing
Ti and T j as well as T j and Ti , we make theAccording to the relative importance among nine
indicators provided by the graduate school, we get the
judgment matrix A

definition: aij  1/ a ji .
Get the n-order judgment matrix as follows:
a12 
 1

1
a
a


1
12
1n 
1
...

  a
a
1
...
a
12
21
2n 

A

 


   



 
1
1
 an1 an 2 ... 1 ...
 a1n a2 n

a1n 
a2 n 


 

1 


 1

1 / 5 1

1 / 3A  1 / 7
1 / 7
1 / 31 / 3
1 / 5


5
1
5

1
1/5
1

3
1/3
3

7
3
7

7
3
7

3
1/3
3

3
1/3
3

3
1/3
1/3

1/3
1/7
1/7

1
1/5
1/5

5
1
1

5
1
1

1
1/5
1/5

1
1/5
1/5

3
3
1

1/3
1/3
1/5

1
1
1/3

5
5
3

5
5
3

1
1
1/3

1
1
1/3






3 
1/3

1/3
3 

3 
1 
5
1
5

4.1.3. Method of weight calculation
There are many methods of weight calculation, such as
eigenvalue method, least squares method，sum method
etc. We use the way of sum one.
© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 163 -

Ai Dongmei, Wen Jiawei, Ning Xiaojun_

4.2.2. The sum method is used to get the matrix A 0.2713 0.2308 0.2713 0.2908 0.1892 0.1892 0.2908 0.2908 0.2308
 0.0543 0.0462 0.0543 0.0331 0.0811 0.0811 0.0331 0.0331 0.0462

 0.2713 0.2308 0.2713 0.2908 0.1892 0.1892 0.2908 0.2908 0.2308

 0.0904 0.1385 0.0904 0.0993 0.1351 0.1351 0.0993 0.0993 0.1385
A   0.0388 0.0154 0.0388 0.0199 0.0270 0.0270 0.0199 0.0199 0.0154

 0.0388 0.0154 0.0388 0.0199 0.0270 0.0270 0.0199 0.0199 0.0154
 0.0904 0.1385 0.0904 0.0993 0.1351 0.1351 0.0993 0.0993 0.1385

 0.0904 0.1385 0.0904 0.0993 0.1351 0.1351 0.0993 0.0993 0.1385
 0.0543 0.0462 0.0543 0.0331 0.0811 0.0811 0.0331 0.0331 0.0462

_

4.2.3. Add up the row elements of A_W   2.2550 0.4625 2.2550 1.0259 0.2221 0.2221 1.0259 1.0259 0.4625

T_

4.2.4. Normalize the matrix W
W   0.2518 0.0516 0.2518 0.1145 0.0248 0.0248 0.1145 0.1145 0.0516

T

others, instead, a number of additional factors such as
the actual teaching situation, students’ learning style andthe school culture were all taken into consideration. A
set of new evaluation index system was established sothat teachers can be more freely to play their own
unique teaching style, at the same time students canfocus fully on the evaluation to reduce factors affecting
the authenticity and credibility of assessment process.
However, the evaluation index system is not able toadapt to different course categories and we only
consider for students, the corresponding teacher mutual
evaluations and expert assessments were ignored.
As for evaluation questionnaire itself, there are also
some inevitable defects. It is difficult to getcomprehensive and detailed information because of thelimitation of indicator quantity. It’s not enough topersuade us purely by evaluation scores, other effective
ways must be combined with, for instance,
communications and symposiums[5].

Table4. Evaluation index system with the weight
Evaluation indicators

Acknowledgements

WeightThis paper is supported by the postgraduate educationand development grant of University of Science &Technology Beijing and National and Natural Science
Foundation of China (61370131).

Serious and fully prepared

0.2518

Express clearly and fluently

0.0516

Well organized, Highlight keys anddifficulties

0.2518

Enrich content properly

0.1145

References

Use multimedia correctly

0.0248

Clean and tidy blackboard writing

0.0248

Pay attention to the interaction withstudents, Inspire students' learning
enthusiasm effectively

0.1145

1. Gu Yao, Establishment of the Evaluation Index System
for the Construction of Disipline in Colleges andUniversities[J]. Academic Degrees & Graduate
Education, 2007(5), 56-57.
2. Xu Ci-ning, and GuoBao-xing, Establishment of
Reasonable Evaluation System of Teaching Quality inHigher Education[J]. Theoretical Front in Higher
Education, 2002(11), 44-45.
3. Chang Xue-qin, and Xing Xi-zhe, Establishment &Practice of Evaluation System of Classroom Teaching
Quality[J]. Theory and Practice of Education, 2004, 24(7), 51-52.
4. Wang Jian-dong, A Survey on the Integrative
Quantitative Evaluation Methodology of the Classroom
Teaching Quality in the Chinese Higher Education
Institutions[J]. Journal of Hubei University (Philosophy
and Social Science), 2007, 34(7), 113-115.
5. Liu En-yun and Yang Cheng-de, Reflection &Construction of Evaluation System of Teaching Quality

Cultivate the ability of independent
thinking
Combine scientific research withproduction practice0.11450.0516

5. Conclusions
With the development of Chinese higher education,differences exist in colleges and universities in aspects
like the level of academic, quality of faculties andstudents, etc. In the process of designing index system,
we did not solely comply with the index system of© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 164 -

Construction of Evaluation Index

in Higher Education[J]. Jiangsu Higher Education,
2004(1), 85-87.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 165 -

Extracting Pattern of Arm Movements based on EMG Signal for Stroke Therapy
Khairunizam Wan, Rashidah Suhaimi and Aswad A.R

Advanced Intelligent Computing and Sustainability Research
School of Mechatronic, Universiti Malaysia Perlis, MALAYSIA
khairunizam@unimap.edu.my
D. Hazry, Zuradzman M. Razlan and Shahriman AB

Centre of Excellence for Unmanned Aerial Systems (COEUAS)
Universiti Malaysia Perlis, MALAYSIA
Mohd Asri Ariffin and Haslina M

School of Health Science,
Kampus Kesihatan Universiti Sains Malaysia, MALAYSIA

khairunizam@unimap.edu.my
Abstract: This paper presents the investigation pattern of arm movements for the purpose of the rehabilitation for a stroke patient in thevirtual reality (VR) environments. The investigation results are used to design the virtual objects in the virtual environments. The muscle
activities are analyzed by using electromyography (EMG). Six EMG channel are attached to the right arm of the subject, which is at thelocation of deltoid anterior fibres, deltoid middle fibres, bicep, triceps, flexor and extensor while performing arm movements. The electricalsignals acquired from EMG are analyzed to extract the signal’s pattern by using signal processing technique. In the studies, several
fundamental arm movements are performed by the subject and the acquired patterns of EMG signals are defined as muscle activities. The
experimental results show that deltoid, bicep and triceps move with a significant value compared with flexor and extensor and are used toinvestigate the muscle activities, which is suitable to the stroke therapy.
Keywords: Stroke Therapy, Arm movement, Signal pattern

1 INTRODUCTION
Stroke rehabilitation is based on the continuous
movement of the stroke patient affected limb to stimulate
the brain function that has been deteriorated. The brain
damage effects not only cognition impairment but also a
motor impairment [1]. The motor impairment can be treatedby intense use of active movement in repetitive tasks andtask-orientated activities which will result in improving
motor skills and muscular strength. The treatment done by
first introducing the external movement to the patient todevelop their muscle activity but the amount of movement
done is crucial whereas to not stress the patient to much orcause movement to little that will slow the treatment [2].
Repetitive rehabilitation exercises is one of the method
used in the treatment by slowly developed the muscle andmotor function, but without knowing which fundamentalarm movement that will affect the muscle first can halted
the course of treatment. The amount of muscle activity is tobe slowly increased for steady muscle development andtreat the motor impairment [3].
Virtual Reality (VR) is a digital environment which canbe manipulated by the respective party. VR has its various
function in today’s development in various applications
which most have been used by the medical doctor forsurgery simulation and psychological therapy for the mind
[4-8].
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.
2 LITERATURE REVIEW
Stroke is the second leading cause of death worldwide,with fatality of 4.4 million of the total of more than 50
million deaths each year and only 10 percent recover almost
completely while some other survivor have to live with few
disability or impairment [1]. Thus this stroke patient needs
special care and rehabilitation to gain back their bodyfunction. Obesity, high blood pressure and many otherfactors could be taken to considerations of early stroke [9].
Stroke is when there is a blood clot block the flow in a
vessel or artery interrupting the blood flow to an area of thebrain which causes the brain cells to die [10].
Kate Laver et. al [11] mentioned that virtual reality andinteractive video gaming have emerged as new treatment- 166 © The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, JapanK. Wan, R. Suhaimi, Aswad A.R, D. Hazry, Z. M. Razlan, S. AB, M. A. Ariffin, Haslina M.

approaches in stroke rehabilitation. These approaches may
be advantageous because they provide the opportunity topractice activities that are not or cannot be practiced within
the clinical environment. Furthermore, virtual reality
programs are often designed to be more interesting andenjoyable than traditional therapy tasks, thereby
encouraging higher numbers of repetitions. Various
applications of the VR which have make rise to thedevelopment of interfacing devices such as dataglove,head-mounted display (HMD) and mechanical suit [12].
The application has various usages in the rehabilitation
therapy for the stroke patient. Recently many types ofequipment have been developed based on VR applications.

Fundamental
movements

EMG signal

Signal pattern: Muscle
activities

STD, Mean & Amount of movement(AOM)

Movement analysis

3 METHODOLOGIES
Fig. 1 shows the flow chart of the proposed works. In
the experiments, a subject performs 22 fundamental
movements. Six EMG channels are attached at to the upperand lower arms. The acquired EMG signals are analyzed byusing signal processing techniques. The signal patterns are
extracted and the investigation of the motion sequences are
conducted based on the extracted signal patters.
3.1 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.

Features selection AOM

Fig. 1. Flow chart of the proposed works
3.2 Fundamental Movements in Stroke Rehab

Fig. 2. Kinematics of human arm
Human arm consists of seven degree of freedom, whichis shoulder, elbow and wrist as shown in Fig. 2. In
rehabilitation for disability and hemiparesis stroke patient it
is important to first assess their movement and limitation.
The human arm can be model by the shoulder with 3 degreeof freedom, elbow 2 degree of freedom and wrist with twodegree of freedom. In the shoulder deltoid muscle involves
in elevation, depression, shoulder flexion, shoulder
extension, abduction, adduction, horizontal abduction,
horizontal adduction, external rotation and internal rotation.
Biceps and triceps muscles involve in the movement
associate with elbow flexion, elbow extension, elbow
supination and elbow pronation. The wrist uses movement
as extension, flexion, ulnar deviation and radial deviation.
All these movements are being used as reference offundamental arm movement.

- 167 © The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan

Extracting Pattern of Arm

detect the deltoid muscle as for every movement thatinvolves the shoulder for example elevation, depression,
shoulder flexion, shoulder extension, abduction, adduction,
horizontal abduction, horizontal adduction, external rotationand internal rotation. The second part of the arm was theelbow, the movement associate with the elbows were elbow
flexion, elbow extension, elbow supination and elbow
pronation. All this movements were used as references offundamental arm movements

3.3 Processing EMG signal

Fig. 3. (a) Placement of
EMG channel, (b) EMG
signal(a)(b)

4.2 Experimental results

EMG is used to record the electrical activity producesby the skeletal muscle. Fig. 3 shows EMG signal of thedeltoid muscle recorded when the subject performed thearm movements. The sampling rate is 1000 Hz/s.
Butterworth low pass digital filter is used to remove noises
in the signal which the cut off frequency of 10 Hz and0.001 normalization value.

(a)

3.4 Signal patterns(b)Fig. 5. (a) Elbow Flexion Signal, (b) Shoulder Flexion
SignalFig. 4. EMG signal after preprocessing(a)Fig. 4 shows the EMG signal after processing by using
signal processing technique. Then, means, standard
deviation and amount of movement (AOM) of the filteredsignal are extracted and are defined as features or muscle
activity [13].
4 EXPERIMENTS(b)
Fig. 6. AOM (a) Biceps (b) Triceps

4.1 Experimental setup

Fig. 4. Placement of EMG channel
Fig. 4 shows the locations of the electrode placement
where the main muscle can be detected which were deltoid,
biceps, triceps, extensor and flexor. The shoulder was toFig. 5 (a) and (b) show EMG signals of elbow andshoulder flexion after preprocessing step. The results showthat the noise was removed from the signal. The motion
features, which were mean, standard deviation and amountof movement (AOM) were measure from this signal. Fig. 6(a) and (b) show the AOM from the selected fundamental
movements and it could be used to represent movement
patterns of the arm. From the experiments, it was decided touse locations for EMG measurement, which were deltoid,
biceps and triceps. Fig. 7 shows the AOM for deltoid,
triceps, biceps, extensor and flexor as denoted by (a), (b),(c), (d) and (e), respectively. The graphs show that the© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 168 -

K. Wan, R. Suhaimi, Aswad A.R, D. Hazry, Z. M. Razlan, S. AB, M. A. Ariffin, Haslina M.

deltoid, triceps and biceps had a significant value of AOM.
In the future research for the investigation of movement
sequence deltoid, triceps and biceps will be used for theelectrode placements. Fig. 8 shows the placement of theelectrode at the human arm.

(a)

(c)

(b)

(d)

(e)

Fig. 7. Amount of movement (a) Deltoid (b) Biceps(c) Triceps (d) Extensor (e) Flexor

Fig. 8. (a) Elbow Flexion Signal, (b) Shoulder Flexion
Signal
5 CONCLUSIONSThe paper discusses the analysis of arm movements byusing EMG device. The signal processing technique is
proposed to remove noises and to smoothen the acquired
EMG signal. Then, the signal pattern is extracted, which ismean, standard deviation and amount of movement (AOM).
The experimental results show that AOM is the best pattern
to represent arm movements. In the future three electrodeplacement is chosen which is deltoid, triceps and biceps.

Muscles Reflects Improved Two Joint Arm Movement
Performaces In Stroke Patients," IEEE International
Conference on Systems, Man and Cybernetics (SMC 2008),
pp. 1350-1354.
[2] A. Phinyomark (),"Evaluation of EMG Feature
Extraction for Hand Movement Recognition Based on
Euclidean Distance and Standard Deviation," Department
of Electrical Engineering, Faculty of Engineering, Prince ofSongkla University, Thailand.
[3] Y. M. Aung and A. Al-Jumaily (2011), "Rehabilitation
Exercise with Real-Time Muscle Simulation based EMG
and AR," 11th International Conference on HybridIntelligent Systems (HIS), pp. 641-646.
[4] Holden M (),"Virtual Environments for Motor
Rehabilitation: Review," Cyber Psychology and Behavior
Vol. 8, No. 3, pp. 187-211.
[5] Forducey P. et al. (),"Tele-Rehabilitation using the
Rutgers Master II glove following Carpal Tunnel Release
surgery," IEEE Trans Neural Syst Rehabil Eng., Vol. 15, No.
1, 43-49.
[6] 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.
[7] Sveistrup H., Mccomas J. and Thornton M.
(),"Experimental Studies of Virtual Reality-Delivered
Compared to Conventional Exercise Programs for
Rehabilitation," CyberPsychology & Behavior, Vol. 6, No.
3, pp. 245-249.
[9] Aswad A.R, Khairunizam Wan, Nazrul H. ADNAN,
Shahriman A.B, D. Hazry, Zuradzman M. Razlan, Nabilah
H.E and M. Hazwan ALI (2014), " Glove Based Virtual
Reality (VR) Interaction for the Purpose of Rehabilitation,"
Australian Journal of Basic and Applied Sciences, Vol. 8,
No.4, pp. 170-175.
[9] H. Kim (2013), "Kinematic Data Analysis for PostStroke Patients," Transaction on Neural Systems andRehabilitation Engineering, Vol. 21, No. 2, pp. 153-164.
[10] R.Sanchez (2004), "Monitoring Functional Arm
Movement for Home-Based Therapy after Stroke,"
Proceedings of the 26th Annual International Conference of
the IEEE EMBS, USA.
[11] Kate Laver, Stacey George, Susie Thomas, Judith E.
Deutsch and Maria Crotty (2014), "Virtual Reality for
Stroke Rehabilitation," Journal of the American Heart
Association, Print ISSN: 0039-2499. Online ISSN: 15244628.
[12] Nazrul H. ADNAN et al., "Principal Component
Analysis For The Classification Of Fingers Movement Data
Using DataGlove “GloveMAP”, " International Journal ofComputer Engineering & Technology (IJCET), 4(2): pp.
79-93.
[13] Rashidah Suhaimi and Khairunizam WAN (2014),"Analysis of EMG-based Muscles Activity for Stroke
Rehabilitation, ". ICED 2014.

REFERENCES[1] Tasuku Miyoshi and T. Komeda (2008),"Antiphasic
Electromyographic Activities between BiarticularArm- 169 © The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan

Cascade Controller Design For Steering Control Of Nonholonomic Autonomous Mobile
Robot Vehicle
1

S. Faiz Ahmed, D. Hazry, 2F. Azim
1
Centre of Excellence For Unmanned Aerial Systems (COEUAS) Universiti Malaysia Perlis (UniMAP),
Seriab, 01000, Kangar, Perlis, Malaysia.
2
Hamdard University, Karachi Pakistan
hazry@unimap.edu.my

Abstract- In this research article a cascade control system is presented for steering control of control of nonholonomic autonomous
mobile robot vehicle. The propose system consist of a master controller and two slave controllers. The master controller is based on
Fuzzy Logic Controller (FLC) which computes the required speed and angular speed needed by the two motors to drives the robot.
Fuzzy logic is used to generate target trajectory movement. The two slave controllers are Proportional+Integral+Derivative (PID)
controllers which ensured the desired speeds that needed for the both DC motors. PID controller parameters were tuned according tofour ranges of speeds using model based tuning method. In addition, the control law is offered to select a suitable rule base for fuzzycontroller in order to ensure the system is stable. The proposed cascaded controller is implemented on a nonholonomic mobile robot
and the results have shown that, the proposed controller achieved the desired turning angle and the mobile robot tracks the target
efficiently.
Keywords:nonholonomic autonomous mobile robot vehicl, Fuzzy Logic Controller, PID controller

different trajectories [6]. Figure 1 shows the differential
steering system of mobile robot.

I. INTRODUCTION
Autonomous navigation is the most important topic ofartificial intelligence, various approaches have been tried
to solve navigation problems [1]. The researchers havedone studies to both holonomic and nonholonomic mobile
robots [2-3]. Autonomous navigation is the ability of amobile robot to move in an environment that is available
to achieve a goal, without interacting with humans. In
recent years, various new approaches to steering mobile
robots have been developed. In 2004 Marichal , designed
and present a fuzzy logic control system to decide how amobile robot steer the wheel to achieve the desiredorientation [4]. So, the desired target can be achieved
simply by tuning the steering wheel towards the target.
Besides that PID feedback controller is employed in thesystem to improve the performance of the system [5]. The
purposed cascade fuzzy -PID controller is implemented on
the mobile robot and the results showed the proposed
method can achieve the desired turn angle to ensure thatthe autonomous mobile robot can reach the target set.
II. KINEMATICMODELOFNONHOLONOMIC MOBILE ROBOTX
Target (xt , yt)
ν
Y u
θd
L
{R}
θ
p
y
ω uR
2d
2r
{W}
x


 L



 R

Fig 1: Differential steering system of the mobile robot
In this figure (x, y) is reference frame,(X, Y) is robot
coordinates fame, R is the position of robot in (x, y), θ
represents orientation angle of robot, r is wheel radius, 2d:
distance between two wheels, uR/L are the speed of rightand left wheels respectively, u, ω are the speed andangular speed of robot and R andright and left wheels respectivelyTHEL : angular speed of

The speed of the center of mass of the robot is
orthogonal to the axis of the wheel. It is also assumed thatthe mass of wheels and wheel inertias is neglected.
Meanwhile, center of mass of the mobile robot is locatedin the middle of the axis connecting the drive wheels.

A. Differential Steering System
In the differential steering system to prevent slippage andachieve pure rolling motion, the mobile robot rotatesaround a point on the same axis in the two-wheel drive.
This point is called the instantaneous center of rotation(ICR). By changing the drive speeds of two wheels move,
the ICR will also be moved and will be followed byp defines the origin of robot coordinate systemwith coordinate (x, y). θd is the angle between X-axis of© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 170 -

S. Faiz
F Ahmed, D.. Hazry, F. Azimmwoorld coordinatte system dennoted by {W} and the X-axxisof robot coordinnate system deenoted by {R}.

ass the distan
nce of targeet and the orientation of
taarget. We taake the speeed of mobilee robot, for theroobot speed could be fast, medium or sloow.
Using
U
fuzzy
y logic term
ms, speed is
i a linguisstic
vaariable thatt takes the fuzzy sets: fast, mediuum
orr
slow
w.
Thhe
tw
wo
slaaveProportional++Integral+D
Derivative(PIID)
ontrollers are used to ccontrol the speed of eaach
co
wheel
w
as per desire valuue.

Th
he speed relativve to the grouund of the righht wheel and tthe
lefft wheel can bee expressed ass follows, resp
pectively.

 R
t

(1))


u L  rL  r  L
t

(2))

u R  rR  r Wiith, R , L caan be calcullated b y usingufollowiing
equuation:R , L 

xR , LN R,L  60

(3))

Fig
gure 2: A cascaade control sy
ystem

wh
here, NR,L arre numbers ofo encoder peer revolution ofIV
V. The Fuzzy Controllerxrig
ght and left w
wheel respectivvely, R , L aree the number of
enccoder pulses obtained fromm the encodeers for the rigght
whheel and the leeft wheel, resppectively.

A block diaggram of the fuzzy contrroller is showninn Figure 3. The desireed commannd signalθdanda
Dt are transm
mitted fromm the visionn system to thefuuzzy contrroller insidde the PC
C. The errror
beetween thee commandd signal annd the acttualpoosition, as well
w as the change in error
eof signalarre calculateed and fed into the fu
uzzy controllleremmbedded inn the DAQ. From the EquationE(5)), itcaan be seen that the diffferent betw
ween the speeed
off the right and left whheels determ
mines the tuurnsp
peed. The fu
uzzy controoller is desig
gned to outpput
PWM signall correspondnding to uR and uL to thea left mottor respectiv
vely to conttrol
riight motor andth
he mobile roobot turn anngle θto thee desired anngle
θd and target distance D t.

Therefoore speed annd the angullar speed off amoobile robot rellated to the wheelw
speeds can be expresssed
as follows:u(u R  u L )r( R   L )
2
2t

(4))

r
1
(u R  u L ) ( R  L ).
2dt
2d
(5))

u u

here in Equattion (5), R L and 2d is thet width of ttheWhmoobile robot frrom the centeer of the righht wheel to tthecennter of the leftt wheel.
IIII.Proposed Coontrol techniique for Mobile Robot

A cascade control systemm has been implementeed
to control thee movementt of the robo
ot that consiist
a Fuzzy logicc based maaster contro
oller and tw
woh DC motoors
PID based slave controlller for each
Figure 2. Fuzzy
F
logic is applied toas shown in F
geenerate targget trajectory movement with thheinfformation eextracted frrom vision system succh

Figure 4: Block diagram oof the fuzzy loogic controllerr© The 2015 Internnational Conference on Artificcial Life and Ro
obotics (ICARO
OB 2015), Jan. 110-12, Oita, Jap
pan- 171 -

Casccade Controller Design ForFTh
he fuzzificattion proceddure maps th
he crisp inpput
vaalues to the membershiip values beetween 0 annd
1. Here we uuse three membershipmfunctions ffor
tarrget distance Dt and fivve membersship functioons
deesired angle θd. Figure 4 and Figure 5 illustraate
thee input meembership functionsffor
fo Dt and θdresspectively.
too closse

close

Angle oof target (θθ)

Distanceofofar

target(DD)

membersmship (µ))

Neglarge

N
NegssmallcenterPossmallPosslargge

more
left,
fast

leeft,
ffast

zero,
fast

right,
fastmoore
righht,
fastt

more
left,
medi
clo
ose umleeft,
m
medi
uum

zero,
mediu
m

right,
medi
um

moore
righht,
mediu
m

more
too
left,
clo
ose slow

leeft,
sslow

zero,
slow

right,
slow

moore
righht,
slow
w

farr

Distance (mm)
Figure 4: Inputt membership functions for target distancceDDtneglargemembershipmnegsmal
l

(µ
µ)

cente
r

po
ossmallposslargeThe
T de-fuzziification prrocedure mapsmthe fuzzzyouutput from the inferennce mechan
nism to a crrisp
siignal. We useu the “cenenter of area” (COA) ded
fu
uzzification method for com
mbining thereecommendaations repreesented byy the impliedfuuzzy sets fromfr
all thee rules. Th
he fuzzy logiccoontroller theen uses the Equation (6) to calcullateth
he geometriic center off this area. x donate thevaalue for ν or ω. Lett  f (x) deenote the area
a

an
ngle
(θθ)

un
nder the membershipmfunction f (x ) .The CO
OA
methodmcompputes the crrisp value too begure 5: Input mmembership functions
fu
for desireddangle θ
Fig

x max

Th
he control rrules are deesigned bassed on expeertknnowledge annd testing. FurthermorFe, the contrrol
rulles also mmeet the trajectory requiremennts
deerived fromm PI controll. For exam
mple, if θd is“pposlarge” annd is Dt “ffar”, then th
he left mottor
should be m
much than the right motor, i.ee.,u L  u R shoulld be “more left” and sp
peed u L  u R

 f ( x)  xdx
COAC x minx maax
 f ( x)dx

(66)

x min
n

where
w
COA is the centter of area, x is the vaalue
off the lingu
uistic variaable, and xmin and xmax
reepresent thee range off the lingu
uistic variabble.
Figure 6 andaFiguree 7 show
w the outpput
membershipmfunctions.. Note thaat the outpput
PWM signals should bbe selected
d to meet thecoonvergence requiremennts.

should be “faast”. Basedd on knowleedge, we caanobbtain 15 rulles. Table 1 representts conclusioon
unnderstandingg that the experte
has abouta
how tocoontrol the turn anglee given thee angle annd
disstance as inputs. The
T
input and outpput
lin
nguistic variiables are shhown in thee table.
Taable 1: Rulees table for thet steering system© The 2015 Internnational Conference on Artificcial Life and Ro
obotics (ICARO
OB 2015), Jan. 110-12, Oita, Jap
pan- 172 -

S. Faiz
F Ahmed, D.. Hazry, F. Azimmorrder transfeer functionss are obtained for varioous
seegmentation
ns of speedd and PID parametersparecaalculated ussing classicaal controlleer design. ThisT
deesign method is know
wn as pole assignment orpoole shifting method.
The
T transfer function off a DC mottor in discreetetime model isi describedd as second
d order systtembyy [7] as:Figure 6: Outtput membershhip functions for the speed

b1 z 1y (t )
u (t ) 1  a1 z 1  a 2 z  2
a. Desig
gn with Claassical Metthod and PID
PConttrolDuringD
designing par
arameters forf
the PIDP
coontroller, coonsider the feedback controlcsysttemass shown in Figure
F8.
Figgure 7: Outpuut membershhip functions for the anguularspeeedV. The PID Coontroller

In the nonlinnear controol of mobile robots, thheprooblems to aachieve thee desired sp
peed for rigght
whheel u R andd left wheell u L are ofteen caused bbysu
uch factors as actuatorr constraintt, time delaayannd other ddisturbancess. One sim
mple way tocoontrol the speed is tot use a Pulse Widdth
Modulation ((PWM) pullse generattor with PIID
feeedback conntroller whhich can be used toimmprove the pperformancee of the systtem.

Fiigure 8: Feedb
back control sysystemThe
T Close lo
oop transferr function forf this systtem
caan be write as :b1z1(g0  g1z1  g2z2)y(t)
r(t) (1z1)(1a1z1 a2z22) b1z1(g0 g1z1  g2z2)(88)

Th
he PID coontroller trransfer fun
nction in Z
Do
omain as shhown below
w:

PIID g  g1 z 1  g 2 z 2
1  z 1

(77)

Using
U
pole assignmentt method defineddin [9],[
wew compare characteristtic equation
ns with desirred
seecond orderr polynomiaal as:Tuuning of thee PID contrrollers can beb done usinng
Model Based Method(1z1)(1a1z1 a2z2) b1z1(g0 g1z1 g2z2) 1t1z1 t2z2(99)VII. Model Basedd Method

In the Model based methhod the trannsfer functioon
is derived froom calculatiion of modeel parameteers
froom a step experimentt response. The Seconnd

withw
t1  2e  nt cos Ts  n 1   2© The 2015 Internnational Conference on Artificcial Life and Ro
obotics (ICARO
OB 2015), Jan. 110-12, Oita, Jap
pan- 173 -

(110)

Cascade Controller Design For

and t 2  e2ntoscillation is more drastic. The fast tuning PID
controller shows the best result for free wheel
time response experiments.

(11)

4

Speed [RPS]

Thus, the Parameters of PID controller can becalculated by comparing the coefficients form
(9).
The PID parameters results for four
different speeds of right wheel and left wheel are
shown in Table 2.

2

Kp=483.48766, Ti=0.007,
Td=0.00168

0

-4

1

Table 2: The result of the PID model basedtuning for right wheel and left wheel

2.5-3.0RPS

3.0-3.5
RPS

Kp

2.276925.24841.079176.7889

Ki

4.659216.643117.1421152.4726

Kd

16.7932

210.1562

39.5797431.8131

VII.

Left Wheel PID Tuning0-1.0
1.0-2.5
PID RPS
RPS
Kp 1.9571 21.4094
Ki
4.286
24.088
Kd 18.8677 318.7978
Speed [RPS]

1.0-2.5
RPS

11

16

Figure 9: Time response of left wheel speed with 3.25
[RPS] step input, Fast Tuning blue, Normal Tuning red,
Model Based Tuning green

Right Wheel PID Tuning
0-1.0
PID RPS

Kp=290.469191, Ti=0.007,
Td=0.00168

6
Times [s]Results & Simulationa) Discussions

3.0-3.5
RPS
72.0978
34.523366.2725

11

16

4
2
0

‐4

The speed and angular speed of mobile robot
was determined by the number of encoder pulses
for the right wheel and the left wheel. The PID
controller is used to produce the same speed of
the right wheel and left wheel in order to makethe mobile robot move in straight line.

2.5-3.0
RPS
42.5378
15.91682.95861

Kp=273.161446, Ti=0.007,
Td=0.00168
Kp=51.07937, Ti=0.01,
Td=0.0024

6

Times [s]Figure 10: Time response of left wheel speed with 2.75
[RPS] step input, Fast Tuning blue, Normal Tuning red,
Model Based Tuning green

Speed [RPS]

The robot system provides a stable speed
property after implementing an onboard PID
controller on free wheels time response
experiments. Figure 9, 10, 11, 12, 13, 14, 15 and16 show eight examples of the speeds time
response with different speeds for left wheel andright wheel. The rising time increase when thedesired speed is higher. The range of oscillations
gets smaller for higher speeds. Through theexperiences, the PID controller is also available
for low speed such as 0.5 RPS except model
based tuning method controller, but the

4
2
0

‐4

1

Kp=29.744456,
Ti=0.009667, Td=0.00232

6

11

16

Times [s]Figure 11: Time response of left wheel speed with 1.75
[RPS] step input, Fast Tuning blue, Normal Tuning red,
Model Based Tuning green© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 174 -

S. Faiz Ahmed, D. Hazry, F. Azim

Speed [RPS]

Speed [RPS]

2
1
0

‐4

1

6

11

Kp=26.495513,
Ti=0.009, Td=0.00216

Times [s]16

Speed [RPS]

Figure 12: Time response of left wheel speed with 0.5[RPS] step input, Fast Tuning blue, Normal Tuning red,
Model Based Tuning green

0
1

6

11

16

Times [s]Speed [RPS]

4
2
0
1

Kp=192.273673,
Ti=0.009,
Td=0.00216

6

11

16

Times [s]321

Speed [RPS]

Speed [RPS]

Figure 14: Time response of right wheel speed with 2.75
[RPS] step input, Fast Tuning blue, Normal Tuning red,
Model Based Tuning green005
Kp=24.903797, Ti=0.009,

10

Td=0.00216
Kp=16.673681, Ti=0.008,
Td=0.00192

Times [s]

15

0

5

10

Kp=25.72248,
Ti=0.009,
Td=0.00216

15

20

Times [s]In the Figures, the horizontal axis is the traveling
distance of mobile robot in centimeters and thevertical axis is the speeds of the wheel rotations
defined by Equation (5.7) and Equation (5.8).
The data plotted in the Figure 19 and 20 provide
evidence that the speeds in wheel rotations of theFast Tuning PID controller for the right wheel
and the left wheel are in unstable condition atlower desired speed. The mobile robot showshigh speeds from beginning of moving distanceand does not move ahead to the target point instraight line tracking. In Figure 18 and Figure 19,
we deduce that the small differences between theright wheel of speeds and the left wheel ofspeeds. Therefore, the mobile robot will movestably in tracking straight line.

Figure 13: Time response of right wheel speed with 3.25
[RPS] step input, Fast Tuning blue, Normal Tuning red,
Model Based Tuning green‐40

Experiments were conducted to make acomparison of the test results between four
ranges of speeds using Fast Tuning PID
controller parameters in Table 2. The results arepresented in Figures 17, 18, 19 and 20.

2

Kp=372.818681,
Ti=0.0075, Td=0.0018

1

Figure 16: Time response of right wheel speed with 0.5[RPS] step input, Fast Tuning blue, Normal Tuning red,
Model Based Tuning green

4

-4

2

20

Figure 15: Time response of right wheel speed with 1.75
[RPS] step input, Fast Tuning blue, Normal Tuning red,
Model Based Tuning green

4
3
2
1
0
0

Right Wheel
Left Wheel

200

400

600

Distance [cm]Figure 17: Comparison of the speeds in wheel rotations instraight line tracking at 3.25 [RPS] desired speed© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 175 -

Speed [RPS]

Cascade Controller Design For

were determined by performing fine tuning
experiment explained. The system of two wheelsof the mobile robot works concurrently withdifferent PID controller parameter to perform astable movement in straight line tracking forhigh desired speed.

4
2
0
0

200

Right Wheel
Left Wheel

400

Distance [cm]

600

VIII.

Speed [RPS]

Figure 18: Comparison of the speeds in wheel rotations instraight line tracking at 2.75 [RPS] desired speed

In this article, an analysis and design of fuzzycontrol law for steering control of the developed
nonholonomic mobile robot are presented. PID
controller method is exploited to guarantee thestability of the straight line and turning trajectory.
The proposed fuzzy controller with PID
controller is implemented on the developed
mobile robot. The system has performed welland satisfactory results are obtained which showthat the proposed fuzzy controller and PID
controller achieved the desired turn angle thus it
can make the autonomous mobile robot moving
to the target successfully.A system for two
wheels of a mobile robot moves simultaneouslywith different parameters of PID controller toensure the stability of movement in a straightline.

3
Right Wheel

2
1
0
0

200

400

600

Distance [cm]Speed [RPS]

Figure 19: Comparison of the speeds in wheel rotations instraight line tracking at 1.75 RPS desired speed
1.5
Righ…

1
0.5
0
0

200

400

600

Distance [cm]Speed [RPS]

Figure 20: Comparison of the speeds in wheel rotations instraight line tracking at 0.5 RPS desired speed
1.5
Righ…

1
0.5
0
0

200

400

Distance [cm]600

Conclusion

References[1]. Rimon, E. ; Dept. of Electr. Eng., Yale Univ.,
New Haven, CT, Exact robot navigation
using artificial potential functions, IEEE
Transactionson
Robotics
andAutomation, Vol.8, Issue: 5, Oct 1992, pp:
501 - 518
[2]. Qing Yu and I-Ming Chen, A General
Approach to the Dynamics of Nonholonomic
Mobile Manipulator Systems, J. Dyn. Sys.,
Meas., Control 124(4), Dec, 2002, pp: 512521

From the experimental result studies, the mobile
robot shows a stable movement in straight line
tracking after using Fast Tuning PID controller
for the right wheel and the left wheel for highdesired speed as shown in Figure 17 and Figure
18 respectively. These PID control parameter© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 176 -

S. Faiz Ahmed, D. Hazry, F. Azim

[3]. Dongjun
Lee,Semi-Autonomous
Teleoperation of Multiple Wheeled
Mobile Robots Over the Internet,
Conference Sponsors: Dynamic Systems
and Control Division, October 20–22,
2008, pp. 147-154[4]. Yang, Simon X , An embedded fuzzycontroller for a behavior-based mobile
robot with guaranteed performance,
IEEE
Transactionson
Fuzzy
Systems, Vol:12 (4) August 2004, pp:436 – 446[5]. Hongfu
Zhou,
DC
Servo
Motor PID Control in Mobile Robots wit
hEmbedded
DSP,
International
Conference on Intelligent Computation
Technology and Automation (ICICTA),
2008 Vol: 1[6]. MohdSaifizi Saidonr#1, HazryDesa,
RudzuanMd Noor, A Differential
Steering Control with Proportional
Controller for An Autonomous Mobile
Robot,
IEEE
7th
International
Colloquium on Signal Processing and its
Applications, 2011 pp: 90-94[7]. Sudjana, O. ,Trajectory control of analog
servo motor with limited state
information using estimated discrete timemodel, IEEE International Conference on
Robotics, Biomimetics, and Intelligent
Computational
Systems(ROBIONETICS), 2013 pp: 73 - 76

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 177 -

Research on Iris Recognition Based on the BP Neural Network
Fengzhi Dai, Li Fan, Chunyu Yu, Bo Liu
College of Electronic Information and Automation, Tianjin University of Science and Technology,
80 MailBox, 1038 Dagunanlu Road, Hexi District, Tianjin 300222, China
E-mail: daifz@tust.edu.cn, fanli0476@126.com
www.tust.edu.cn

Abstract
Iris recognition is the high confidence personal identification technology among the other biometrics recognition.
This is not only because the iris’s unique feature, but also due to its stability that the iris is immune to age andenvironment. In this paper, we design a feed forward neural network and use the back propagation algorithm to
explore an elementary iris recognition system model. 10 iris samples are used as the recognition objects. The
experiment demonstrates that though the recognition model is simply constructed, it has a high recognition rate andthe recognition speed is reasonable. The proposed methodology provides a convenient way for iris recognition.
Keywords: iris recognition, artificial neural network, back propagation algorithm, image processing

1. Introduction
At present, iris recognition is used in various fields due toits significant advantages: (1) Iris is human’s internalorganization which is covered with a transparent
membrane (cornea). For this reason, it is hard to bechanged. However, fingerprint is apt to be blurred after a
few years manual labor and can be forged by fingerprint
paster. (2) Iris is quite smooth as its geometry shape is
only controlled by two cooperative muscles (sphincter
pupillae and dilator pupillae). Therefore, the shape of irisis easier to be predicted. (3) Like the texture offingerprint, the texture of iris is random determined in the
period of gestation. Many factors can influence theformation of iris so the error probability of matching isquite low even though it is not testified technically thatthe iris’s texture is absolutely unique1,2. (4) Iris scanning
is as simply as taking photograph. It can be processedfrom 10cm to several meters. Thus, the measured object
needs not to touch the scanner.
Many applications of iris recognition are used. For
instance, Canada Border Services Agency introduces
CANPass program3. Netherlands, Arab, Britain andGermany also apply the iris recognition technology into

immigration clearance. Meanwhile, many scientists havedevoted to relevant study4. Boles and Boashash built a 1D
representation of the gray level signature of the iris andapplied it to zero-crossing of the dyadic wavelet5. Ma,
Wang, and Tan used a bank of Gabor filters to capture theiris profile6. Poursaberi generated a binary code
representation of the iris and used a minimum Euclidian
distance for matching7.
In this paper, an algorithm that simplifies the procedureof recognition is proposed. Our approach is characterizedby: (1) Image pre-processing is used so as to avoid using
complex mathematical algorithms. (2) The BP neuralnetwork is adopted. (3) Use grayscale value of each pixel
for learning and recognition.
The paper is organized as follows: Section 2 discusses
the reason of using iris as object and the status quo of irisrecognition technology. In section 3, the related andbackground of artificial neural network is reviewed.
Section 4 presents the pre-processing of 10 iris samples,the design of the BP neural network, the application of
the entire recognition system model and the experimentresults. Finally, section 5 draws some conclusions.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 178 -

Fengzhi Dai, Li Fan, Chunyu Yu, Bo Liu

2.

Iris Recognition

3.

Neural Network

2.1. Biological characteristics of iris3.1. Biological neuron

The iris is the colored, thin, circular structured muscle
within the eye, which regulates the size of the pupil andthus controls the amount of light that enter the eye. The
iris begins to form as soon as the third month of gestation.
By the eighth month, the structures that creating the iris
patterns are largely completed. However, pigment
accretion can continue during the first postnatal years.
Because the iris patterns are formed randomly, evengenetically identical twins will not have the same iris
patterns8. Fig.1 shows the iris image and its position in
the eye.

Neuron is the basic unit of structure and function of thenervous system, Fig. 2 shows a typical neuron structure.

Fig. 3.

Fig. 1.

Biological neuron

When a neuron is stimulated and excited, exciting will
be converted into a signal and be conveyed to synapse
through axon. After the rapid diffusion of chemicalmolecules defused by the synapse in the synaptic space,
the neighboring neurons generate new impulses and passthem to the next neurons in the same way.
This simplified mechanism of signal transfer
constituted the fundamental step of early neurocomputing
development16.

Iris and its position in the eye

2.2. The history of the iris identification
The French ophthalmologist Alphonse Bertilon (1885) is
considered to be the first for iris identification based on
its color9. In 1985, ophthalmologists Leonard Flom andAran Safir proposed that no two irises are exactly thesame, and they got the patent application in 198710. Thenthey cooperated with Dr. John Daugman11, a computerscientist of the University of Cambridge, UK, anddeveloped an algorithm to automatically identify thehuman iris. In 1993, the Defense Nuclear Agency began
to test and deliver a prototype unit, which was
successfully completed by 1995 due to the combined
efforts of Flom, Safi and Daugman.
After that, many scientists have devoted themselves tothe relevant study, such as R.Wildes12, W.Boles13 andR.Sanchez-Reillo14 proposed different algorithms. In
1995, the first commercial products became available15.
Many companies began to develop their own irisrecognition algorithm. For example, L-1 Identity Solution
which was established in 2006 provides all service andsolution concerning human identification. Oki also
provides a variety of security system based on iris
identification.

3.2. Artificial neuron
Artificial neuron simulates the nonlinear characteristics
of biological neuron (multiple input and single output). In
1943, McCullon and Pitts proposed the McCulloh-Pitts
neuron model (shown in Fig. 3). The mathematicalexpression is:

Fig. 2.

Artificial neuron model© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 179 -

Research on Iris Recognition(1)(2)
xi (i=1,2,...,n) is the input signal from other neurons; wi(i=1,2,...,n) presents the weight between two neuron; b is
the threshold of the neuron17, y is the output of theneuron;is the activate function. The output y is 0 or

1, which presents suppression or exciting respectively.

and extracted the grayscale pixel features as the inputs tothe neural network. All the iris images are processed by
OpenCV, and converted the size to 100×100 pixels.
4.2. Neural network designFirst, get the input and target samples of the neuralnetwork. We treat the processed image as a 100×100
matrix, each element stores the grayscale pixel value asshown in Eq. (4). After that, the matrix is converted into a
10000×1 column vector that is treated as the input vectorof the neural network (Fig. 6).

(3)

(4)

3.3. Multilayer feedforward neural network
In 1985, Rumelhart and McClelland research groupproposed the multilayer feedforward theory and the error
back propagation algorithm base on parallel distributed
information processing. The BP neural network may
contain one or several hidden layers (Fig.4).

Fig. 4. The input of network

Fig. 5.

4.

Thus, the input layer of neural network has 10000
neurons. Learning assumes that each input vector ispaired with a target vector representing the desired
output; together they are called a learning pair21. In thisstudy, 10 learning pairs would be required, so the output
layer neurons is 10. Output results are restored in a 10×1
column vector.
For the number of hidden layer neurons, we refer to theempirical formula Eq.(5). After that, we delete the hidden
neurons whose influence is little and then set the final
hidden layer neurons number as 40.

Multilayer neural network

Application on Iris Recognition

4.1. Pre-processing of raw images
Ten different colored iris images are selected to bepre-processed18-20 to grayscale images (shown in Fig.5)(5)Here, n1, n2 and n3 are the numbers for the input, hidden
and output layer neurons.
4.3. Neural network learning
The learning rate η and learning times are 0.4 and 5000
respectively. All the weights (Fig. 6.

Image processing

values (,,) and threshold) must be initialized to small random© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 180 -

Fengzhi Dai, Li Fan, Chunyu Yu, Bo Liu

decimals before staring the learning process.
Training the BP neural network follows the five steps:(1) Select the learning pair (
=(

,

,… ,

) from the 10 pairs.

,
)T

(6)

grayscale iris images. The 10 images are displayed. (2)Select the region of iris model (as the 100×100 region)
from the 10 iris images then generates the model. After
this step, 10 iris models will be converted to 10 matrixes
and displayed below the original images (circled in Fig.
8).

= ( , ,… ,)(7)(2) Calculate the hidden and output vector of the neuralnetwork by Eq.(8) and Eq.(9) respectively.

(8)(9)(3) Calculate the general errors of each layer of neuralnetwork,and.
(4) Adjust the weights of the network.
(10)
(11)
(12)
(13)
(5) Repeat steps 1 through 4 for the 10 pairs until thesum squared error in Eq.(14) for the entire set is
acceptable low.
(14)
The result of the neural network learning is illustrated
in Fig. 7. After 3000 times training, the sum squared error
is acceptable low.

Fig.8. Neural network learning(3) Train the neural network by clicking the button"Start Learning", and the training procedure is also shownin Fig.8. (4) Click the button "Start Recognition" torecognize whether the selected iris is one of those 10
irises or not.
The recognition procedure is shown in Fig.9 and therecognized object is marked by the square. The
experiment result shows the match rate is 99.946% for
the selected object.

Fig. 7. Sum squared error for 5000 times learning

4.4. Recognition and results
Recognition requires the steps22: in Fig. 8, (1) Click themenu ‘File’ on the left top corner, to load the originalFig.9. Recognition results© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 181 -

Research on Iris Recognition

5. Conclusion
In this paper, we design a neural network and used theback propagation algorithm to develop an elementary irisrecognition system. The experiment demonstrates that thesystem has an ideal identification rate. With thedevelopment of the demand of identity recognition, thissystem can be applied in many fields such as security
check and entrance guard.
In this study, the extraction of the basic features of theiris mainly utilize image processing, future research will
use more shape and structural characteristics of the iris
itself as the feature values.
Acknowledgment
The research is partly supported by the Scientific
Research Foundation of Tianjin University of Scienceand Technology (20130123), and the Open Foundation(YF11700102) of Key Laboratory for Water Environment
and Resources, Tianjin Normal University.
References
1. P. C. Kronfeld, The gross anatomy and embryology of theeye, The Eye, Vol. 1,(1968) pp. 1–66.
2. I. Mann, The Development of the Human Eye, (1950)
3. http://www.cbsa-asfc.gc.ca/media/facts-faits/009-eng.html,
04/07/2012
4. F.N. Sibai, Iris recognition using artificial neural networks,
Expert Systems with Applications Vol.38, (2011) 5940–
5946.
5. Boles. W. and Boashash. B., A human identification
technique using images of the Iris and wavelet transform,
IEEE Trans. Signal Processing, Vol. 46, No. 4 (1998)
1185–1188.
6. Ma. L., Wang. Y. and Tan. T., Iris recognition using
circular symmetric filters, In Proc. 16th Int. Conf. Pattern
Recognition, Vol. 2, (2002) 414–417.
7. Poursaberi. A. and Araabi. B., A novel iris recognition
system using morphological edge detector and wavelet
phase features, ICGST-GVIP Journal, Vol. 5, No.6 (2005).
8. F. H. Adler, Physiology of the Eye, St. Louis, MO: Mosby(1965)
9. A.Bertillon, La couleur de l’iris, Revue scientifique,France, (1885)
10. L. Flom and A. Safir, Iris recognition system, U.S. Patent 4
641 349, 1987.
11. J. Daugman, High confidence personal identification by
rapid video analysis of iris texture, the IEEE Int. Carnahanconf. on security technology, (1992)
12. R.P.Wildes, J.C. Asmuth, G.L. Green and S.C. Hsu, A
system for automated iris recognition, IEEE paper, (1994)
13. W.W.Boles, A security system based on human iris
identification using wavelet transform, in 1st Int. Conf.
Knowledge-based intelligent electronic systems, Adelaide,
Australia. Ed, 21-23 May 1997.

14. R.Sanchez-Reillo, C.Sanchez-Avila and J-A.Martin-Pereda,
Minimal template size for iris recognition, in Proc.
BMES/EMBS Conf., IEEE Publication, Atlanta, October1999.
15. Irisian Technologies, Historical Timeline, (2003)
16. I.A. Basheera and M. Hajmeerb, Artificial neuralnetworks: fundamentals, computing, design, andApplication, J. Microbiological Methods, Vol.43 (2000)
3–31.
17. Jain. A.K., Mao. J. and Mohiuddin. K.M., Artificial neuralnetworks: a tutorial, Comput. IEEE March, (1996) 31–44.
18. Daugman. J., How iris recognition works. In Proc. Int.
Conf. Image Processing, Vol. 1, (2002).
19. Wildes. R. et al., A system for automated iris recognition,
In Proc. 2nd IEEE workshop on applications of computer
vision, pp.121–128
20. Lim, S., Lee, K., Byeon, O. and Kim, T., Efficient Iris
recognition through improvement of feature vector andclassifier, ETRI Journal, Vol.23, No.2, (2001) pp.1–2.
21. P. D. Wasserman, Neural computing: theory and practice,
Van Nostrand Reinhold Co. New York, NY, USA (1989)
22. Sakai Kouichi, Foundation and Application of digital
image processing by Visual C#.NET and Visual Basic.NET(in Japanese), CQ Press (2006)© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 182 -

Synchronization Control of a Four-wing Fractional-Order Chaotic System
and Its Analog Circuit Design
Hongyan Jia

Department of Automation , Tianjin University of Science and Technology,
1038 Dagunanlu Road, Hexi District, Tianjin 300222, PR China
Qian Tao

Department of Automation , Tianjin University of Science and Technology,
1038 Dagunanlu Road, Hexi District, Tianjin300222, PR China
Jinfang Li

Department of Automation , Tianjin University of Science and Technology,
1038 Dagunanlu Road, Hexi District, Tianjin300222, PR China
Wei Xue

Department of Automation , Tianjin University of Science and Technology,
1038 Dagunanlu Road, Hexi District, Tianjin 300222, PR China
E-mail: jiahy@tust.edu.cn, susantaoqian@sina.com
www.tust.edu.cn

Abstract
The four-wing fractional-order chaotic system is firstly introduced in this paper, Then, its synchronization control
is also discussed, and the results from numerical analysis show the chaotic synchronization control is simple andpractical. An last, an analog circuit is designed to implement it, and the results are in agreement with numericalanalysis, which probably provide an practical technology for application of fractional-order chaos , such as securecommunication and image encryption.
Keywords: Fractional-order, chaotic system, synchronization control, analog circuit

conditions can be synchronized .18 Subsequently, someother methods of synchronization have been
represented and researched, such as the PC method, thePAD method, the one-way method and the bidirectional
coupled method.19-25
In 2008, Chen et al. reported a integer-order fourwing chaotic system.26 Some interesting phenomenon
are found, for example, the system shows four-wingchaotic attractors, three-wing chaotic attractors, andperiodic attractors in different periods when selecting
different system parameters. In 2011, Jia et al. further
analyzed its dynamic behaviors by computer-aid
topological horseshoe analysis and analog circuit
experiments.27 In 2014, based on the theory of
fractional calculus and the method of frequency domain

1. Introduction
Although Fractional calculus has a history of morethan 300 hundred years,1-2 it hasn’t attracted more andmore attention until many systems are found to show
fractional-order dynamics, such as the fractional-order
Chua’s circuit,3 the fractional-order Lorenz system,4 thefractional-order Chen system,5-6 the fractional-order
Rössler system,7 and so on. 8-10 Recently, some study on
synchronization and control of fractional-order chaotic
systems are attracting more and more attention from anapplication point of view,such as secure communication,
image encryption and control processing.11-17 It was
firstly reported in Pecora and Carrolfor’s paper in 1990
that two chaotic trajectories with different initial© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 183 -

Hongyan Jia, Qian Tao, Jinfang Li, Wei Xue

approximation, Jia et al. study the fractional form of theinteger-order four-wing system, and found that chaotic
characteristic exist in it.28All these work show the
dynamics of the four-wing chaotic system are very
complex, it may be more interesting to make some
application study by utilizing the system.
In this paper, the fractional-order four-wing chaotic
system is firstly reported. Then, a synchronization
method is discussed, and the simulation results haveconfirmed the effectiveness of the synchronization
method. At last, an analog circuit is designed to realize
the chaotic synchronization, and all of results show thesynchronization method is rather simple and convenient.
2. The Four-Wing Fractional-Order
System

40

20

z

0

-20

-40
40
20

40
20

0

0

-20

y

-20
-40

-40

x

Fig. 1. Phase plots of the fractional-order system

3. SynchronizationoftheFour-Wing
Fractional-Order Chaotic System

Chaotic

In this paper, based on master-slave configuration
method with linear coupling, a chaotic synchronization
control is briefly discussed between the two four-wing
fractional-order systems whose structure are same. Here,the master four-wing fractional-order chaotic system
can be written as system(1), and the coupled slave fourwing fractional-order system has the following form.
 d  x' ax' ky ' y ' z ' k1( x  x' ) dt 
 d  y '(3)   by ' z ' x' z ' k 2 ( y  y ' ) dt
 d  z'  x'cz ' x' y ' k3 ( z  z ' ) dt The four-wing fractional-order system in Ref.28 is
described by d x
 ax  ky  yz

 dt 
 d  y(1)   by  z  xz
 dt
d z
  x  cz  xy dt Where a, b, c, k  R ; 0    1 .
Based on frequency domain approximate method, thefractional operator of order “  ” can be finished by the

The constant parameters k1 , k 2 , k3 are couplingstrength. Now let the coupling strength k1  0 , k 2  0 ,k 3  0 , by continuously increasing the couplingstrength k1 from zero, in step 1,a value of k1 is obtained
to make the two four-wing fractional-order systems
synchronized. That is, When k1  4 , the two coupledchaotic systems can be synchronized. Simulation results
are shown in Fig.2 and Fig.3, which are the time
response and the phase synchronization, respectively.

transfer function 1  in the frequency domain. Thens
the transfer function 1  can be represented by ansapproximated integer-order transfer function with errors
of approximately 2 dB according to Ref.29, and thus
the approximation adopted in this paper is2
1
1.766s  38.27 s  4.914

(2)
0.9
3
2
s
s  36.15s  7.789s  0.01
When   0.9 , a, b, c, k   (5,12,5,1) , the four-wing
fractional -order system is chaotic as shown in Fig. 1.
Its corresponding chaotic characteristics analysis has
been given in the Ref..28 one can see it for the details.

60
40

x-x'

20
0
-20
-40
0

2

4

t

6

8

10

Fig. 2. time response of x (-) and x ' (--);© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 184 -

Synchronization Control of a
6.665
6.664

x’

6.663
6.662
6.661
6.66
6.402

6.40256.403

6.4035 6.404

6.4045 6.405 6.4055

xFig. 3. Phase synchronization of x and x’

4. Analog Circuit for Synchronization of theFour-Wing Fractional-Order Chaotic System
Finally, we designed an analog circuit to realize thesynchronization control for the four-wing fractionalorder system by using the basic devices such as Ad633,
LF347, resistors, and capacitors,as shown in Fig. 4 andFig. 5. Fig. 4 is an analog approximate circuit for
fractional-order 1  , and Fig.5 is an analog circuit fors
the synchronization control. Resistors and capacitors in
the circuit are R3 = R3 ' = R4 = R4 ' = R6 = R6 ' = R7
= R7 ' = R10 = R10 ' = R11 = R11 ' = R13 = R13 ' = R14 =R14 ' = R17 = R17 ' = R18 = R18 ' = R20 = R20 ' = R21= R21 ' = 10k , R2 = R2 ' = R9 = R9 ' = R15 =R15 ' = 100k , R8 = R8 ' = 8.3k , R16 = R16 ' = 20 k ,R5 = R5 ' = R12 = R12 ' = R19 = R19 ' = 1k , Ra = 1.55M ,Rb = 61.54M , Rc = 2.5k , C1 = 730nF , C2 = 520nF ,C3 = 1.1uF ,respectively. In addition, The resistors
R1 , R1 ' , and R22 are adjustable according to thesystem parameters a and k1 . When selecting
R1 = 20 k , R1 ' = 100k , R22 = 25k , the two coupledchaotic systems are synchronized, and the simulation
results are shown in Fig. 6.

Fig. 5. Analog circuit of synchronization between system (1)and system (3) .

5. Conclusion

In this paper, by linear coupling and frequency domain
approximation, a synchronization control method for a
four-wing fractional-order chaotic system is studied,and the simulation results have confirmed the feasibility
and effectiveness of this synchronization method. In
addition, An analog circuit is also designed to
implement the synchronization control. Numericalsimulations and circuit experiments are given to verify
the effectiveness of the proposed synchronization
scheme, and it is rather simple and convenient for thesynchronization scheme to be realized. Some work inthis paper probably provide an practical technology for
application of fractional-order chaos , such as securecommunication and image encryption.

Fig. 4. Approximate Circuit for 1 s© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 185 -

Hongyan Jia, Qian Tao, Jinfang Li, Wei Xue

8. W. Deng, C.P. Li, Physica A 353 (2005) 61.
9. Qi G Y, Chen G R, Du S Z, Chen Z Q, Yuan Z Z 2005
Physica A 352 295.
10. Z.M. Ge, A.R. Zhang, Chaos Solitons Fractals 32 (2007)
1791.
11. C.G. Li, X.X. Liao, J.B. Yu, Phys. Rev. E 68 (2003)
067203.
12. T.S. Zhou, C.P. Li, Phys. D 212 (2005) 111.
13. X. Gao, J.B. Yu, Chaos Solitons Fractals 26 (2005) 141.
14. J.G. Lu, Chaos Solitons Fractals 26 (2005) 1125.
15. J.P. Yan, C.P. Li, Chaos Solitons Fractals 32 (2007) 725.
16. G. Peng, Y. Jiang, F. Chen, Phys. A 387 (2008) 3738.
17. G. Peng, Y. Jiang, Phys. Lett. A 372 (2008) 3963.
18. Pecora, L.M., Carroll, T.L.: Synchronization in chaotic
systems. Phys. Rev. Lett. 64, 821–824 (1990).
19. Pecora LM, Carroll TL. Synchronization in chaotic
systems. Phys Rev Lett 1990;64:821–4.
20. Sira-Ramirez H, Cruz-Hernandez C. Synchronization of
chaotic systems: a generalized Hamiltonian systems
approach. Int J Bifurcat Chaos 2001;11(5):1381–95.
21. Petras I. Control of fractional-order Chua’s system. J
Electr Eng 2002;53(7–8):219–22.
22. Lu JG. Chaotic dynamics and synchronization of
fractional-order Chua’s circuits with a piecewise-linear
nonlinearity. Int J Modern Phys B 2005;19(20):3249–59.
23. Li CP, Deng WH, Xu D. Chaos synchronization of theChua system with a fractional order. Physica A
2006;360:171–85.
24. Junwei Wang, Xiaohua Xiong, Yanbin Zhang. Extending
synchronization scheme to chaotic fractional-order Chensystems.Physica A 2006;370:279–85.
25. Changpin Li, Jianping Yan. The synchronization of
fractional differential systems. Chaos, Solitons &Fractals 2007, 32:751–7.
26. Z.Q. Chen, Y.Yang, Z.Z. Yuan, A single three-wing or
four-wing chaotic attractor generated from a threedimensional smooth quadratic automous system, Chaos,
Solitons & Fractals, 38 (2008) 1187-1196
27. H.Y.Jia, Z.Q. Chen, Q. Y. Qi, Topological horseshoe
analysis and the circuit implementation for a four-wingchaotic attractor, Nonlinear Dynamics, 65 (2011) 131140.
28. H.Y.Jia, Z.Q. Chen, Q. Y. Qi, Chaotic characteristics
analysis and circuit implementation for a fractional-ordersystem, IEEE TRANS. CIUCUITS & SYSTEMS -I:
REGULAR PAPERS, 61(3) (2014) 845-853.
29. Tom T. Hartley, Carl F. Lorenzo, and Helen Killory
Qammer, Chaos in a Fractional Order Chua’s System,
IEEE TRANS. CIRCUITS & SYSTEMS , 42 (8) (1995)
485-449.

(a) Time response of x  x '(b) Phase synchronization of x and x’
Fig. 6. Circuit simulation observations of synchronization

Acknowledgements

This work was supported in part by the Young
Scientists Fund of the National Natural Science
Foundation of China (Grant No. 11202148), the Natural
Science Foundation of China(Grant No. 61174094).
References
1. I. Podlubny, Fractional Differential Equations,(Academic Press, New York, 1999).
2. R. Hilfer (Ed.), Applications of Fractional Calculus in
Physics, (World Scientific, New Jersey, 2001).
3. T.T. Hartley, C.F. Lorenzo, Nonlinear Dyn. 29 (2002)
201.
4. I Grigorenko, E Grigorenko. Chaotic dynamics of thefractional Lorenz system. Phys. Rev. Lett, 2003, 91:
034101.
5. C.P. Li, G.J. Peng, Chaos in Chen’s system with afractional order , Chaos Solitons Fractals 22 (2004) 443450.
6. J.G. Lu, G.R. Chen, A note on the fractional-order Chensystem. Chaos Solitons Fractals 27 (2006) 685-688.
7. C.G. Li, G. Chen, Phys. A 341 (2004) 55.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 186 -

A fractional-order hyper-chaotic system and its circuit implementation
Wei Xue

Department of Automation , Tianjin University of Science and Technology,
1038 Dagunanlu Road, Hexi Distric, Tianjin 300222, PR China
Hui Xiao

Department of Automation , Tianjin University of Science and Technology,
1038 Dagunanlu Road, Hexi Distric, Tianjin 300222, PR China
Jinkang Xu

Department of Automation , Tianjin University of Science and Technology,
1038 Dagunanlu Road, Hexi Distric, Tianjin 300222, PR China
Hongyan Jia

Department of Automation , Tianjin University of Science and Technology,
1038 Dagunanlu Road, Hexi Distric, Tianjin 300222, PR China
E-mail:xuewei@tust.edu.cn, xiaohuiJC@126.com
www.tust.edu.cn

Abstract
In this paper, the commensurate3.6-order Qi hyper-chaotic system is investigated. Based on the predictor-corrector
method, we obtain phase portraits, bifurcation diagrams, Lyapunov exponent spectra of this system, and find thatthe system can present a four-wing hyperchaotic attractor. In addition, a circuit is designed for the fractional-order
hyperchaotic system and the circuit implementation result show the existence of the four-wing hyperchaotic
attractor, which verifies the correct of the theoretic analysis and provides the technical support for its application inengineering.
Keywords: Fractional-order; Qi hyper-chaotic system; Lyapunov exponent; Circuit design;Implementation.

1. Introduction
Since Lorenz discovered the famous Lorenz chaotic
attractor in 1963,1a great deal of new chaotic systems
have been proposed, such as Chen system,2 Lü system,3and so on.4-5which have made very significant influencesin the research on nonlinear science. Recently, theresearch on the chaotic dynamics are gradually expanding
to the engineering applications on the basis ofmathematical theory and physical theory, such as securecommunication,6 image encryption,7 and other fields.8-9
There also are some specific characteristics in thefractional-order system, that is, the fractional-orderchaotic system not only can enhance the nonlinear andcomplexity of the systems, but also possess a very strong
historical memory to reflect historical information of thesystem. Therefore, The fractional-order equation could
describe the physical phenomenon more accurately,10 inrecent years, the fractional differential operator was
introduced into the integer-order chaotic system toconstruct the fractional-order chaotic system.11-13 It is also
been verified that the fractional-order chaotic systems
have more complex dynamics characteristics thaninteger-order chaotic systems. And thus the research on
the fractional-order chaotic system has attracted more
and more focus from application point of view. In
addition, as the fractional-order hyper-chaotic system
has two positive Lyapunov exponent , it could be more

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 187 -

Wei Xue, Hui Xiao, Jinkang Xu, Hongyan Jia

suitable for the application in secure communication andimage encryption.
Therefore, it is necessary to investigate the analog
circuit design and implementation of the fractional-order
hyper-chaotic systems, which provides the technical basis
for its further applications in engineering. In this paper,
the commensurate fractional-order Qi hyper-chaotic
system is investigated and an analog circuit is designed to
implement the hyper-chaotic system.

2.2. Lyapunov exponent & bifurcation diagrams

2. Numerical analysis of fractional-order Qi
hyper-chaotic system

shown in Fig.2 and Fig.3, respectively.

The characteristics of some system motion can becharacterize by its Lyapunov exponent, and for a hyper
chaotic system, there must have at least two positive
Lyapunov exponents. Here, through numericalsimulations, the effect on the Lyapunov exponent andthe bifurcation of system(1) are analyzed when systemparameter a changes.The Lyapunov exponent spectra andbifurcation diagrams of system(1) with a   0 , 60  are
10
L1
0

L2
L3

-10

Qi hyper-chaotic system has been proposed in Ref. [14],and it is constructed by adding a linear feedback control
to the chaotic system in Ref.[5]. On this basis , we will
analyze the commensurate fractional-order Qi hyperchaotic system which is described as follow:
d qx
 a  y  x   eyz ,

q
 dt
d qy

 cx  dy  xz  w
 dt q q
d z
 dt q   bz  xy

d qw
 fy

 dt q

-20

LE

-30
-40
L4

-50
-60
-70
-80

0

10

20

30
q

40

50

60

40

50

60

(a)
10
8
6

(1)

L1

4
2
LE

L2
0
-2
-4
L3

-6
-8
-10

Where 0  q  1, a, b, c, d , e, f  R , andwhen q  0.9 , a  15 , b  40 , c  5 , d  20 , e  5 ,f  50 , system(1) is 3.6-order system, and it is chaotic .

0

10

20

30
q

(b) intercept [10,10] on the y axis
Fig.2 (color online) Lyapunov exponent diagrams of system (1)
300

200

100

x

2.1. Phase portraits
When q  0.9 , a  15 , b  40 , c  5 , d  20 , e  5 ,f  50 ,the phase portraits of 3.6-order Qi hyperchaotic system is shown in Fig.1, It can be seen that
this system presents four-wing chaotic attractor.
80

60

60

40

40

20

20
z

y

0

0

-20

-20
-40

-40

-60

-60
-80
-200

x-y

-150

-100

-50

0
x

50

100

150

x-z
-80
-200

200

-150

-100

-50

0
x

50

100

150

-60

-40

-20

0
y

20

40

60

200

400
60
300
40
200
20
100
0

z

w

0

-20

-100

-40

-200
-300

-60

y-w
y-z

-80
-80

-60

-40

-20

0
y

20

40

60

-400
-80
80

Fig.1 (color online) Phase portraits of system (1)

0

-100

-200

-300

0

10

20

30
a

40

50

60

Fig.3 (color online) Bifurcation diagram of system (1) on x axis

In Fig. 2 and Fig. 3, it can be seen that
the Lyapunov exponent on the parameter a is inagreement with the bifurcation diagram. As L 4 always
less than -10 in Fig.2, in order to observe
the other three exponent expediently, so we intercept[ 10,10] on the y axis as shown in Fig.2(b). Whena  15 ,four Lyapunov exponents of system (1)are L1  2.199, L 2  0.4077, L 3  0, L 4   44.64 respectively.It
can be seen that this system is hyper-chaotic.

80

3. Circuit implementation of fractional-order Qi
hyper-chaotic system
Through carrying out a series of numerical simulation
and analysis of system (1), further for circuit simulation© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 188 -

A fractional-order hyper-chaotic system andand realization of the system. Based on the method of
approximation conversion from time domain to frequency

R1
3.34kΩ
IO1

0.9

of

1/ s

IO2

f0.9

Vee -15v

domain, we utilize the approximation of 1/ s withdiscrepancy 2dBto design the analog circuit, the circuit
unit
Where

SC1

R2
6.67kΩ

A1

R3
800Ω
-

0.1v/v 0v+
Vcc 15v
R4
5kΩ

0.9

is
shown
in
Fig.4.
R1  62.84M, R2  250 K, R3  2.5 K

IO1

SC2

R5
500Ω

A2

R6
100Ω

0.1v/v 0v

R11
10kΩ

IO2

R12
10kΩ

f0.9

Vee -15v
R10
10kΩ

-

C1  1.232F , C 2  1.84F , C3  1.1F .

Vee -15v-++
Vcc 15v

Vcc 15v

R7
2kΩ
IO1

SC3

IO2

f0.9

Vee -15v

R8
100Ω

A3

+

0.1v/v 0v

Vcc 15v

Fig. 4 Unit circuit of 1/ s

0.9
IO1

SC3

IO2

f0.9

Vee -15v

The fractional-order hyper-chaotic circuit isdesigned. In this circuit, LF353P is selected to be theamplifier and AD633JN (the output gain is 0.1) is
selected to be the multiplier. In order to make the change
range of state variables can work in the voltage range ofthe operational amplifier, The parameters are scaled
according to the phase portraits of the system, let x=50x′，y=25y′ ， z=25z′ ， w=100w′, the system(1) is modified
for circuit implementation, which is described as follow:
 d q x  a x   0 . 5 a y   12.5 e y z ,q
 dt
 d q y
 2 c x   d y   50 x z   4 w  dt q
 q
 d z    b z   50 x y  dt q d q w 0 . 25 f y  dt qR9
10kΩ
+Vcc 15v

Fig. 5 Circuit diagram of system(1)（ a） x-y plan(b)x-z plan(c) y-z plan(d) y-w plan

(2)

Then
the
time scaleisamplifiedbyletting t   0 ,  0  200 . Thus the circuit design isshown in Fig.5.Where the module of f 0 .9 is the circuit
unit of 1 / s 0 .9 as shown in Fig.4.
According to the circuit diagram in Fig.5, the actual
circuit is built to implement the fractional-order hyperchaotic system(1), and the circuit experimental results areshown in Fig.6 through the TDS2014B digital
oscilloscope ， which is consistent with the numericalsimulation result as shown in Fig.1, which verifies theexistence of hyper-chaotic attractor of the fractional-order
hyper-chaotic system on the physical level.

Fig.6 Phase portraits of hyper-chaotic system(1) through theoscilloscope TDS2014B

4. Conclusion
In this paper, the fractional-order Qi hyper-chaotic
system is investigated. By analyzing phase portraits,bifurcation diagrams, Lyapunov exponent spectra offractional-order Qi hyper-chaotic system, it is found thatthe system has four-wing hyper-chaotic attractors incertain range of parameters. Based on the numericalsimulation of the fractional-order hyper-chaotic system.
An analog circuit is designed to implement the fractionalorder chaotic system, and circuit experiment results arecoincide with the numerical simulation results, which
provides technical basis and support for the further
application of the fractional-order hyper-chaotic system
in engineering.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 189 -

Wei Xue, Hui Xiao, Jinkang Xu, Hongyan Jia

Acknowledgements
This work is supported by the Young Scientists Fund of
the National Natural Science Foundation of China (Grant
No.11202148).
References
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2.
3.
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5.

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10.
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13.

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Chen G R, Ueta T., Yet another chaotic attractor, Int. J.
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Lü J H, Chen G R. A new chaotic attractor coined. Int. J.
Bifurcation and Chaos, 2002, 12(3): 659-661.
Liu C X, Liu L, Liu T, et al., A new butterfly-shaped
attractor of Lorenz - like system. Chaos ， Solitons andFractals．2006, 28(5): 1196-1203.
Qi G Y, Chen G R, van Wyk M A, van Wyk B J and
Zhang Y, A four-wing chaotic attractor generated froma new 3-D quadratic autonomous system. Chaos ，Solitons & Fractals. 2008, 38(3): 705-721.
Li W, Hao J H, Qi B, An encryption approach tochaos synchronization communications by using
CPRNG. Acta．Phys．Sin．2008, 57(3): 1398-1403.
Peng Z P, Wang Ch H, Lin Y, Luo X W, A novel fourdimensional multi-wing hype-chaotic attractor and its
application in image encryption. Acta. Phys. Sin. 2014,
63(24): 240506.
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encryption. Electric Machines and Control. 2012, (03):96-100.
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Jia H Y, Chen Z Q, Xue W, Analysis and circuit
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Huang L L, Xin F, Wang L Y, Circuit implementationand control of a new fractional-order hyper-chaotic
system. Acta. Phys, Sin. 2011, 60(1): 010505.
Min F H, Yu Y, Ge C J, Circuit implementation andtracking control of the fractional-order hyper-chaotic Lüsystem. Acta. Phys. Sin. 2009, 58(3): 1456-1461.
Xue W, Qi G Y, Mu J J, Jia H Y, Guo Y L, Hopf
bifurcation analysis and circuit implementation for anovel four-wing hyper-chaotic system. Chin. Phys. B.
2013, 22(8): 080504.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 190 -

Research on Early Crop Monitoring Using Photosynthetic Production Index in China

Fengzhi Dai1, Li Fan1, Daijiro kaneko2, Nozomu Hirose3, Chunyu Yu1
1

College of Electronic Information and Automation, Tianjin University of Science and Technology,
80 MailBox, 1038 Dagunanlu Road, Hexi District, Tianjin 300222, China
2
Remote Sensing Environmental Monitor, Inc., Japan3
Department of Civil and Environmental Engineering, Matsue College of Technology, Japan
E-mail: daifz@tust.edu.cn, fanli0476@126.com
www.tust.edu.cn

Abstract
For monitoring the early stage of crop growth in China, this paper presents a photosynthesis-based monitoringmodel for grain production. Not only the normalized difference vegetation index and elements such as the growing
degree day are considered, the factors of sunshine and the cost of water resource are also considered in the model.
Keywords: Crop Monitoring, Photosynthesis, Remote sensing, Water resource

GDD 

1. Introduction
In China, huge population and the shortage of water
resource becomes the main contradiction between supply
and demand of grain. But Chinese grain production isvery important because of its huge consumption.
The World Bank, the Worldwatch Institute1 and theWorld Water Council2 have warned about the present
unsustainable use of water resources for irrigation in
China, India, and the U.S., which have significant
influence on the total quantity of grain production.
On the condition that both the crop yield and water
resource are needed, it is required to monitor the grain
production and irrigation water synchronously3,4.
Early crop monitoring method is mainly based on themeteorological data such as temperature and precipitation.
By the application of satellite, remote sensing data areconsidered gradually5. Thus both the remote sensing andthe meteorological data are used in the paper6.
2. Monitoring Crop Production
Conventional crop studies have correlated grain quantitywith the growth index of growing degree day (GDD).

Tmax  Tmin
 Tb
2

(1)

where，Tmax and Tmin are the maximum and minimum
daily air temperature. Tb is the threshold of temperature
for the crop, below which the plant physical activity is
inhibited and Tb is always set equal to 10°C.
Rasmussen presented the net primary production (NPP)
according to Eq. (2)7:t

NPP    ((aNDVI  b )  PAR ) dt
0(2)

where ε is the efficiency coefficient, t is the time, a and b
are regression coefficients, and PAR is thephotosynthetically active radiation (MJ m-2). NDVI is thenormalized difference vegetation index.
NPP is calculated by the accumulation of NDVI. And
the value NDVI can be measured and calculated bysatellite remote sensing. Although NPP in Eq.(2) is thephotosynthesis-based model, it does not consider the
influence of the temperature.
The former photosynthesis-type crop production index(CPI) is defined in Eq.(3) that is concerning the period© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 191 -

Fengzhi Dai, Li Fan, Daijiro kaneko, Nozomu Hirose, Chunyu Yufrom the seeding time ts to the harvest time th. PSN is thephotosynthesis velocity (g m-2 day-1).
CPI  tt h PSN dts(3)
But during the grain growth, low-temperature sterility
and high-temperature injury to the grain should beconsidered8, 9. The present research also develops thephotosynthesis-type monitoring method by measuring thewater stress so as to improve Eq.(2). The final form of thephotosynthesis rate is defined in Eq.(4)10, which
considered the solar radiation, air temperature, stomatal
opening, and vegetation biomass.

PSN a  APAR f Syn (Tc )   s  eLAIb  APAR(4)a and b are Michaelis -Menten constants, APAR is theabsorbed photosynthetically active radiation, Tc is the
canopy temperature, βs is the stomatal opening, eLAI isthe effective leaf area index, and fSyn is the temperature
response function of photosynthesis.
Define KLster and KHster as the coefficients of lowtemperature and high-temperature that affect the crop
growth. TLster and THster are the minimum and maximum
temperatures for the crop sterility. Fster in Eq.(5) is thetemperature response function for the crop sterility due toboth the low and high temperatures:

Fster (Tc )  ttr f Ster (Tc ) dt FLster (Tc )  FHster (Tc ) (5)fwhere FLster (Tc )  1  exp[k Lster (TLster  Tc )] ,FHster (Tc )  1  exp[ k Hster (Tc  THster )] , tf and tr are thetimes of flowering and ripening.
Eq.(5) is obtained from Fig.1(a) and Fig.1(b), and theresult is shown in Fig.2(a)9. Here KLster=0.8, KHster=0.4.
Thus CPI is revised from Eq.(3) to Eq.(6):CPI  tt h PSN  FSter (Tc )  dts

(6)

3. Data Used in The Model
Many researchers have presented crop simulation models
that involve growth of crops and incorporate remotesensing data11. By measuring growth of crop vegetation
using remote sensing instead of simulation, the present
paper estimates the photosynthesis rate by treating the
growth of crop as a known variable.
Estimation of the photosynthesis rate needs daily
weather data of solar radiation and air temperature, so thatthe daily meteorological data must be taken. The world
weather data is most suitable for the index CPI because
daily regular data are currently observed as weather
reports in real time. The CPI requires the NDVI at thepositions of the world weather sites using a database
derived from NOAA AVHRR12.
4. Result of iNDVI
Fig.3 shows the distribution of NDVI data in Southeast
Asia13. And Fig.4 is the integrated NDVI (iNDVI) of themonitoring points in China. If there is neither water stressnor low temperature sterility around the time of flowering,the crop quantity of production should be high in cropareas where the iNDVI is large.
Therefore the large values of the integrated vegetationindex at Jinan and Yichan in Fig.4 suggest a good harvestin those areas. The iNDVI is a vegetation growing index,
which measures the crop plant density. However, theiNDVI cannot express the effect of a lack of sunshine, or
the influence of low-temperature sterility during
flowering and filling.

FHsterFLsterMean air temperature(a) low-temperature sterility

Mean air temperature(b) high-temperature injury

Fig.1 Relationship between FLster and FHster to air temperature© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 192 -

Research on Early Crop(a) Temperature response function(b) Michaelis –Menten type photosynthetic rate

Fig.2 Function and coefficients in temperature response function for the crop sterility

Fig.3 Distribution of NDVI in Southeast Asia
Fig.4. Integrated NDVI (iNDVI) of paddy rice fields in China
as a growth index for crop production

5. Experiment on Water Stress
For containing the element of water resource into CPI, theeffect of water stress to crop growth is experimented. As
we know, the reflectance spectrum of plant leaves
changes with the different water stress situation. The
paper tests the influence of water stress on rice plant by
MODIS and Aster14. Rice plants are firstly given theappropriate amount of irrigation, and then the watering is
stopped to strengthen water stress to the rice plants. The
results of NDVI and NDWI (normalized difference water
index) are shown in Fig.5 and Fig.6 respectively3.
The relationship between NDVI and NDWI is shown in
Fig.7 (Four regions are selected: Matsue, Jinan, Hikawa
and Thailand). As we know, if the value NDVI is small, itmeans that the vegetation amount is small. And for thesame value of NDVI, the smaller the value NDWI is, thehigher the water stress strength is. The distribution of

Fig.5 NDVI data in water stress experiment© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 193 -

Fengzhi Dai, Li Fan, Daijiro kaneko, Nozomu Hirose, Chunyu Yu

temperature, vegetation biomass, and stomatal opening
using satellite data and world weather data.
Acknowledgements
The research is partly supported by the Open Foundation(YF11700102) of Key Laboratory for Water Environment
and Resources, Tianjin Normal University; and theScientific Research Foundation of Tianjin University of
Science and Technology (20130123).
References
Fig.6 NDWI data in water stress experimentFig.7 Relationship between NDVI and NDWINDVI and NDWI in Fig.7 shows a linear relationship,from which the parameter βs (the stomatal opening) in CPI
can be defined.
6. Conclusion
The paper aims to develop a method of early monitoring
the crop quantity in production that would be useful in thepresent era.
Organization of crop monitoring using the modelthat is based on daily meteorological and satellite data
should be established for China to solve the problem withcrop growth15. Strategies for crisis management should beavailable in advance. Technical collaboration andinformation supply for early warning by the monitoring
method proposed in this paper would be useful in guiding
agricultural policies of Asian countries.
The proposed photosynthesis-based crop production
index CPI considers the factors such as solar radiation, air

1. Postel S. Pillar of sand – Can the irrigation miracle last? ,W. W. Norton & Company, Inc., New York, U.S.A. with theWorldwatch Institute, U.S.A. 1999.
2. World Water Council. The 3rd World water forum,
Thematic/Regional statements Ministerial declaration,
2003, 105.
3. Daijiro Kaneko. Application of crop production index(CPI) on the Japanese paddy rice and Chinese wheat.
Annual Research Report of Chiba University, from NIIElectronic Library Service, 2005,pp. 28-33.
4. Kaneko D, Onishi M, and Ishiyama T. Proposal of earlymonitoring method for crop production in China and India
in the recent era of water resources restriction.
Environmental Systems Research, Japan Society of Civil
Engineering, 31(2003): 235-244.
5. Food and Agriculture Organization of the United Nations.
(FAO FAOSTAT Database, 2003).
6. Kaneko D. Estimation of global solar radiation using world
weather data for early monitoring of crop production bysatellites, in Proc. 6th symp. water resources, 2002, 677682.
7. Rasmussen MS. Developing simple, operational, consistent
NDVI-vegetation models by applying environmental andclimatic information: Part II. Crop yield assessment. Int. J.
Remote Sensing, 19(1)(1998): 119-137.
8. Matsui T, Namuco OS, Ziska LH and Horie T. Effects ofhigh temperature and CO2 concentration on spikelet
sterility in indica rice. Field Crops Research, 51(1997):
213-219.
9. D. Kaneko, M. Ohnishi. Modeling on indices formonitoring crop production using meteorological data andVegetation Index NDVI. Japanese J. Crop Science. 72(2)(2003): 92-93.
10. D. Kaneko, T. Itohara, M. Ohnishi, T. Ishiyama. Early
warning method for rice production using remote sensing
and meteorological data: Photosynthetic crop production
index. WIT Trans. Ecology and the Environment, Vol.84(2005): 815-824.
11. Monteith J L. The quest for balance in crop modeling.
Agronomy J., 88(1996): 695-697.
12. Tateishi R. Twenty-year global 4-minute AVHRR NDVI
dataset, (CEReS. Chiba University, 2001).

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 194 -

Research on Early Crop

13. Daijiro Kaneko, Masao Ohnishi, Takashi Ishiyama, Ryutaro
Tateishi. Proposal for Early Crop Monitoring Using a
Photosynthetic Production Index and Remotely Sensed
Data, in Proc. 4th Int. Crop Science Congress, 2004.
Available from http://www.cropscience.org.au/icsc2004/poster/2/8/252_ daijir.htm#TopOfPage
14. Li Jun, Dai Fengzhi, Li Yuan, Yu Chunyu, Hirose Nozomu,
Analysis the vegetation and soil moisture in Beijing-Tianjin
area to prevent desertification by long-term satellite remotesensing. Applied Mechanics and Materials, Vol. 385-386(2013): 388-391.
15. Elenita Dano, Elpidio Peria. Emergency or expediency? A
study of emergency rice reserve schemes in Asia. A joint
publication of AFA and AsiaDHRRA, 2006. Available fromhttp://asiadhrra.org/wordpress/wp-content/uploads/2008/05/rice%20reserve%20scheme.pdf or http://asiadhrra.org/wordpress/2007/12/16/emergency-or-expediency-a-studyof-emergency-rice-reserve-schemes-in-asia/© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 195 -

Design and Implementation of Motor Test System based on Virtual Instrument*

Yulong Xia , Huailin Zhao
School of Electrical & Electronic Engineering, Shanghai Institute of Technology, Shanghai, 201418, China
Jihong Zhu, Yang He
Department of Computer Science & Technology, Tsinghua University, Beijing, 100084, China
zhao_huailin@yahoo.com
Abstract
Because of using Windows Multimedia Clock, most of existing test system based on General Computer has
problems of inaccurate timing and poor real-time capacity. A motor test system is developed for improving above
problems and testing more motors simultaneously by serial communication with high baud rate. The test system
software uses LabVIEW development platform and is developed with the idea of software engineering, which isused to make each module functionally independent and to improve reliability of the system. Simultaneously thetest system uses the characteristic of LabVIEW to complete relatively accurate timing tasks and has excellent
portability. In communications, it is used that professional serial port board supporting high baud rate and bus
connections. After using the system, it shows that the system is not only reliable, easy to operate, high availability
and reduces test costs, but also it is able to meet the needs of the actual test.
Key words: Virtual Instrument; Motor Test System; Timing Tasks; High baud rate; Bus connections

National Instruments (referred to as NI). LabVIEW is agraphical programming language, known as the "G"language. G refers Graphical Programming Language.
Compared with traditional software development
language, graphical programming approach can savemore than 85 percent of program development time andthe speed of developed program is not affected. This
doesn’t only reflect a high level of efficiency, but also
brings convenience from the LabVIEW controls.
Coupled with LabVIEW’s outstanding performance atdata acquisition, analysis, chart displays and otheraspects, LabVIEW program language has became a firstalternative to this system2.
In the aspect of communication, because of higher baudrates in the system, common serial port devices do notmeet the requirement and devices’ reliability is not well,so professional MOXA serial port board is selected. The
bus is selected as the connection of communication link.
Through broadcasts the command is sent to the next

1. Introduction
Virtual instrument, which integrates computer hardware
resources with instrument hardware resources bysoftware, is a computer-based automated test equipment
system. Compared with traditional instruments, Virtual
Instrument stresses that software is the core of
instruments.
In the development of software, this system needs tocollect and transfer data, storage and do other work.
Software design is not only the most important andcomplex part of the entire test system, but the core of
the test system. Therefore, choosing the right softwaredevelopment tools can play a multiplier effect. Becauseof easy to use and convenient, LabVIEW is more fit tothis system1.
LabVIEW (Laboratory Virtual Instrument Engineering
Workbench) is an innovative software product of*201418, No.100, Haiquan Rd., Shanghai, China© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 196 -

Yulong Xia, Huailin Zhao, Jihong Zhu, Yang He

crew. At the same time there is only one answer
audience-bit machine, so data interference on the link
can be reduced greatly.

3. Software Design of Motor Test System

2. The Structure of Motor Test System

Fig.2 is a diagram of Each module of Motor Test
System.

3.1. Requirement Analysis

In Fig.1, it shows that the entire test system is made of a
PC and four next crews, each of which is made ofcontroller, Drive and sensor. Based on internal
communication protocol, the PC communicates witheach controller via RS-422 at bus link. Through graphic
user interface, PC encapsulates the command that comesfrom the test staff into 9 bytes data frame with header,device code, command data, the checksum. Because PC
connects with the next crew by bus connections, after
PC sends the data, all the controller can receive this data.
After receiving the data frame from PC, firstly,
controller determines whether the device code field of
data frame matches device equipment receiving thisframe data. If not match, this frame data is discarded.
Otherwise controller will calculate the checksum toverify the accuracy of the frame data. If controller
confirms the correct frame, decapsulates the frame dateto get command data, then send command data to thedrive. After getting command from the controller, thedrive makes motor work as testers’ intention. Voltage,current, speed and other parameters of the motor
running captured by the sensor and forming an analog
signal sent to the A / D converter. A / D converter
analog signal coming from the sensor is converted into a
digital signal, and then sent to the controller. According
to internal communication protocol, controller
encapsulates data into 21-byte frames, sent to the hostcomputer by serial devices through RS422 bus. After
receiving the data frame, the same as controller, PC also
verify the checksum to ensure the correct frame data,
analyze the frame to get voltage, current, speed andother information from tested motor, and displayed in
the interface3.

Fig. 1 The diagram of Motor Test System’s structure

Fig. 2 Each module of Motor Test System
Serial port settings module provides many functions,which are setting number, baud rate and other serialinterface attributes. Because the requirements of thissystem is that the baud rate 614.4Kbps, while common
serial port device does not yet support such a high baudrate, so this system selected MOXA serial port board.
This system is divided into three test modules: self test,
special tests and flight tests.
In the self test, the main task is to test the state ofcommunication link. Defined test state is divided intochecking, normal and abnormality. Firstly, after turning
low-voltage power on, the PC sends self test commands
to the four devices in round robin way and maintains acounter for each device. Secondly, if only the PC
receives the next crew feedback information thatindicates that this equipment is normal, the PC will
make the counter of the equipment plus one. Finally, if
only there are three continuous data frame that indicatesthe state is normal, self test will be passed.
In the special test, the main task is to test the state of thesingle specified motor at low speed. Firstly, after
turning high voltage power on, the next crew receives
from the PC’s special test execution command and the
motor runs 5 seconds at low speed. And the next crew
timely feedback to the PC with information thatindicates that the motor has begun to run and could send
status query command. Then the PC send "status querycommand" every 10ms, the PC displays feedback thatcome from the controller. Finally, testers will findwhether the next crew and the motor are normal or not.
In the flight test, the control can be divided into single
control and program control. Single control can becontrolled by modifying the interface control value; inprogram control, after configuring the timing commandin the timing configuration module for single or© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 197 -

Design and Implementation ofmultiple motor, then start program control. The PC
sends these preconfigured programmed commands at
one command in 2ms intervals. For each motor, it will
receive one command at each 8ms. After receiving
command data, the next crew will follow the commands
to complete control and feedback status information to
the PC.
After the test, the PC can save data and do further
analysis offline. The PC supports to analysis for frame
count in order to count the number of loss frames, heavy
frames frame, frame loss rate, and heavy frame rate4.

Fig. 3 The producer loop based event
As shown in Fig.3, the outermost layer is a while loop,
whose inside is the event structure. Event structure in awhile loop can handle a mouse click, keyboard pressing
and many other events until the end of the loop. After asingle control event and the program control event
occurs, the system will both place commands in thequeue. And each consumer loop fetches command fromthe queue and sends it to controller. The flag variable
should be set in the respective event branch, whichmake that consumer loops will not affect each other.

3.2. Design of Main Modules
3.2.1 Flight Test Module
Round robin mode has a clear structure, clear program
flow, fast response. But if a single control does notoccur, the round robin mode will still check the flag
variable, which will be a great waste of CPU. On thecontrary, the event structure is not only to reduce theCPU load, but has a timely response. More importantly,
it uses queue to store time of the trigger mode, thus
which avoids missing any events. Different from asingle control, once program control is launched, it will
be necessary to send onel command in 2ms interval.
There are two schemes could be used. One is togenerate a command and to send it to controller
immediately, the benefits of which is to need lessmemory space but requires CPU to read and write
memory operations. The other is to generate all thecommands before sending the data and to store thecommands into disk file. Once the program control isstart, the commands will be read into memory one timefrom disk file and be sent one by one, the benefits ofwhich can greatly reduce CPU’s load duringtransmission greatly, which is because the commands
has been read in contiguous memory space. It
guarantees the timing requirements of 2ms. At the sametime, because the data stored in the hard disk file, you
can also reuse the same program control. In comparison,
the second solution is clearly better.
By using one or more queues, multiple threads issue issolved in elegant and safe way. Producer thread insert
elements to the queue, the consumer thread and remove
them. Using the queue can safely pass data from one
thread to another thread. Blocking queue can not only
accomplish this task, but if elements are tried to add tothe full queue, or to remove from the empty queue,
thread will both be blocked. This will further improvethe utilization of the CPU. Fortunately, LabVIEW has
integrated producer consumer model based on blocking
queue into its own development environment. This is
producer-consumer recycling based on data or event5.

3.2.2

Analysis frame count moduleThis system uses serial communication at 614.4Kbps
baud rate and bus connections. High-voltage power
affecting for baud rate and seizing bus are likely to be
the weak part that affects for the stability of the system.
Therefore, it is necessary to detect and analyze theframe count. In internal communication protocol of thesystem, frame count field is an 8-bit unsigned integer
type, ranging from 0 to 255. Under normal
circumstances, due to data overflow will become 0 andthen continue to accumulate the feedback information in
the next crew; the frame count field will accumulate
from 0 to 255, and then continue to accumulate.
Therefore, the quantity of loss frames can be got by thefollowing formula: n  n + 1 , n2  n1
m 2 1
 255  n2  n1 , n2  n1

( 1.1 )

n2 represents current frame count, n1 represents previous
frame count.
As shown in Fig.4 and Fig.5, it is single motor/ multiple
motors frame count analysis under the high-power
supply. In anyone of these charts, the vertical axis is thedifference between the current frame count and frame
count of the previous frame. Normally, if the overflow
does not occur, the difference will be 1; if the overflow
occurs, the difference will be -255.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 198 -

Yulong Xia, Huailin Zhao, Jihong Zhu, Yang He

Fig. 6 Timing loop

Fig. 4 Frame count analysis for single motor

Fig. 5 Frame count analysis for two motors
As shown in Table 1, when there is only one runningmotor, even if in the case of high baud rate,
communication would not have dropped any frame.
When multiple motors are running simultaneously,
because of bus connection, the data will seize the bus toresult in dropped frames. However, frame loss rate isless than 1%, so communication is very reliable.

Fig. 7 Sending interval measured by oscilloscope
As shown in Fig.6, the main task in the timing loop is tosend data through the serial port every 2ms. As shownin Fig.7, in the actual testing, the time measured byoscilloscope to send data through the serial port is 2ms,
and no frame loss.

Table. 1 The analysis of frame countType

Motor_
id

The total
number offramesSingle

09
0C
09

2524
2498
2479

Multiple

3.2.3

The
number ofloss
frames024
24

4. Conclusion

Frame
loss rate

This test system uses LabVIEW as a development
platform. The code is clear, easy to be maintained,reused and expanded. This system has achieved theserial communication, motor control, data acquisition,
preservation and analysis for frame count. In addition,
the motor control module of the system is able toexecute more precise time control on top of theoperating system. Testers can not only enjoy theconvenience of the operating system, but can use thesystem to complete high precision timing test tasks. Test
system described here provides a solution for copingwith high real-time and high baud rate serial
communications, and can provide certain references tothe college labs or enterprises in building up testing
systems based on virtual instrument.

0.00%
0.960%
0.968%

Timing Transfer Modul

Programmers rack their brains to pursue the goal that is
to control precisely for timing. Regrettably, because of
the absence of a hardware timer cases, The highest
timing accuracy under Windows operating system is
1ms. And the 1ms talking about here refers to thecomputer system time maintained by a battery. After
LabVIEW 7.x version, there is emerging a new timing
structure - timing loop, which is available as a Windows
operating system to improve the accuracy of the
program effective means of time control. However, thetiming loop will take up more system resources6.

5. References
1.

T.H. Lu, J. Wang, Design of Real-time Monitoring-survey
System for Motor Power Consumption Based on
LabVIEW, Computer Measurement & Control 2012,20（3）: 580-582，609.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 199 -

Design and Implementation of

2.

3.

4.

5.

6.

R. Bitter, T. Mohiuddin, M. Nawrocki, LabVIEW:
Advanced Programming Techniques, 2nd ed., CRC
Press,Boca Raton, 2007.
J.P. Chen, S.Z. Wang, W.Y. Wu, Implementing methodof serial communication data checksum
based onLabVIEW,
Process Automation Instrumentation 29(2008) 32-34.
J. Huang, J.H. Zhu and X.M. Yuan, Software Engineeringin the Application of Virtual Instrument-Based Dynamics
Testing System for UAV, Journal of Communication andComputer 10 (2013) 926-932.
Y. Yang, M.G. Wang, Y.L. Zhao, Design of test system
for flight control computer based on LabVIEW, The
Electronic Measurement Technology 32 (2009) 96-99
LabVIEW User Manual, National Instrument Corporation.
April,2007.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 200 -

Consensus Problem of Distributed Multi-agent Systems*†

Huailin Zhao
School of Electrical & Electronic Engineering, Shanghai Institute of Technology, Shanghai, 201418, China
Wei Ren
Department of Electrical Engineering, UCR, CA92521, USA
Jian Jiang
School of Mechanical Engineering, Shanghai Institute of Technology,Shanghai,201418, China
Masanori Sugisaka
Alife Robotics Corperation LTD, Oita, Japanzhao_huailin@yahoo.com
Abstract
The paper focuses on the consensus problem of distributed control systems. It’s the basic research subject related with the
distributed coordination of multiple robots such as the unmanned robots which has been a very active research one studied
widely by the control researchers. The paper mainly discusses the most typical topics which have made large progress in
distributed multi-agent coordination in recent years including the system dynamics, the time delay effect, the network
topology, the convergence, and so on.
Keywords: multi-agent system, consensus, distributed coordination, network topology, convergence

relationship among all robots in the group where
information sharing plays a key role. The distributed
method has many advantages in achieving cooperative
group performances, especially with low operationalcosts, less system requirements, high robustness, strong
adaptivity, and flexible scalability, therefore has been
widely interested and recognized [1-2]Let’s express the typical consensus problem based on
which we discuss the consensus problem. Consider agroup of n agents, each with single-integrator
kinematics described by

1. The general description
Consensus refers to the typical group behavior of themulti-agent system that all the agents reach
asymptotically a certain common agreement based on a
local distributed protocol, with or without predefinedcommon speed and orientation.
To the distributed control of a group of autonomous
robots, the main objective is typically to have the wholegroup of robots working in a cooperative way base on a
distributed protocol. The cooperative refers to a close*

201418, No.100, Haiquan Rd., Shanghai, China© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 201 -

Huailin Zhao, Wei Ren, Jian Jiang, Masanori Sugisakaxi  ui (t ); i=1, 2, …, n(1)
where xi and ui (t ) are, respectively, the state and theConsensus is concerned with the behavior of a group ofrobots, so it is natural to consider the system dynamics
for practical robots in the study of the consensus
problem. Although the study of consensus under various
system dynamics is due to the existence of complex
dynamics in practical systems, it is also interesting toobserve that system dynamics play an important role indetermining the final consensus state. For example, thewell studied consensus of multi-agent systems withsingle-integrator kinematics often converges to a
constant final value. However, consensus for doubleintegrator dynamics might admit a dynamic final value.
These important problems make the study of consensus
under various system dynamics become hot.
As a direct extension of the study of the consensus
problem for systems with simple dynamics, for example,
with single-integrator kinematics or double-integrator
dynamics, consensus with general linear dynamics was
also studied [10-12], where research is mainly devoted tofinding feedback control laws such that consensus (withthe output states) can be achieved for general linear
systems.

control input of the ith agent. A typical consensus
control algorithm is designed asnui (t )   aij (t )[ x j (t )  xi (t )]

(2)

j 1

where aij (t ) is the (i, j)th entry of the corresponding
adjacency matrix at time t. The main idea with formula(2) is that each agent moves towards the weighted
average of the states of its neighbors. Given theswitching network pattern due to the continuous
motions of the dynamic agents, coupling coefficientsaij (t ) in formula (2), so the graph topologies, aregenerally time-varying. There is the conclusion thatconsensus is achieved if the underlying directed graphhas a directed spanning tree in some jointly fashion interms of a union of its time-varying graph topologies [14].
Consensus does as a fundamental principle for thedesign of distributed multi-agent coordination
algorithms. Therefore, investigating consensus has beena main research direction in the study of distributed
multi-agent coordination. To establish the relationshipbetween the study of consensus algorithms and many
physical properties inherited in practical systems, it isnecessary and meaningful to study consensus byconsidering many practical factors such as actuation,
control, communication, computation, robot dynamics,
and so on, which characterize some important featuresof practical systems [1-6].
Please make sure that shared information is a necessary
condition for coordination. Information necessary forcooperation may be shared in various ways. For
example, relative position sensors may enable robots toconstruct state information for other robots [7],knowledge may be communicated among robots using awireless network [8], or joint knowledge might bepreprogrammed into the robots before a mission begins[9]. Based on this concept, information exchange
becomes a key problem in coordination control.

xi  Axi  Bui ,yi  Cxi(3)

where A, B, and C are constant matrices withcompatible sizes. Apparently, the well studied singleintegrator kinematics and double-integrator dynamics
are special cases of system (3) for properly choosing A,
B, and C.
Consensus for complex systems has been extensively
studied too. Here, the term consensus for complex
systems is used for the study of consensus problem
when the system dynamics are nonlinear or withnonlinear consensus algorithms [13-17].
Although the complex system dynamics are different
from the well studied single-integrator kinematics anddouble-integrator dynamics, the main research problem
is same, namely, to drive all agents to some common
states through local interactions among agents.
Similarly to the consensus algorithms proposed forsystems with simple dynamics, the consensus
algorithms used for these complex models are also

2. The system dynamics© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 202 -

Consensus Problem of Distributednxi (t )   aij (t )[ x j (t  Ti p )  xi (t  Ti p )] (5)based on a weighted average of the state differences,with some additional terms if necessary. Main research
work has been conducted to design proper control
algorithms and derive necessary or sufficient conditions
such that consensus can be achieved ultimately.

j 1

Clearly, (4) refers to the case when only communication
delay is considered while (5) refers to the case when
only input delay is considered. It should be emphasizedthat both communication delay and input delay might betime-varying and they might co-exist at the same time.
The main problem involved in consensus with timedelay is to study the effects of time delay on theconvergence and performance of consensus [18]. The
existing study of consensus with time delay mainly
focuses on analyzing the stability of consensus
algorithms with time delay for various types of system
dynamics, including linear and nonlinear dynamics.
Generally, consensus with time delay for systems withnonlinear dynamics is more challenging. For mostconsensus algorithms with time delays, the main
research topics is to determine an upper bound of thetime delay under which time delay does not affect theconsensusability. For communication delay, it ispossible to achieve consensus under a relatively large
time delay threshold. A notable phenomenon in thiscase is that the final consensus state is constant.
Considering both linear and nonlinear system dynamics
in consensus, the main tools for stability analysis of theclosed-loop systems include matrix theory [19],Lyapunov functions [20], frequency-domain method [21],passivity [22], and the contraction principle [23].

3. The time delay
Time delay exits in almost all practical systems due toseveral reasons such as limited communication speed,
extra time required by the sensor to get themeasurement information, computation time requiredfor generating the control inputs and execution timerequired for the inputs being acted. Generally, timedelay reflects an important property inherited inpractical systems due to actuation, control,
communication, and computation.
Knowing that time delay might degrade the system
performance or even destroy the system stability,
studies have been conducted to investigate its effect on
system performance and stability. A well studied
consensus algorithm for system (1) is given in formula(2), where it is now assumed that time delay exists. Twotypes of time delays, communication delay and input
delay, have been considered in some research.
Communication delay accounts for the time fortransmitting information from source to destination.
More precisely, if it takes time Tij for agent i to receive
information from agent j, the closed-loop system of (1)with (2) under a fixed network topology becomes

4. The Network Topology

In multi-agent systems, the network topology among all
robots plays a very important role in determiningnconsensus. The objective here is to explicitly identifyxi (t )  aij (t )[ x j (t  Tij )  xi (t )](4)j 1necessary or sufficient conditions on the network
An explaination of (4) is that at time t, agent i receives
topology such that consensus can be achieved underinformation from agent j and uses data x j (t  Tij )properly designed algorithms.
instead of x j (t ) due to the time delay. Please note that
It is often reasonable to consider the case when theagent i can get its own information instantly, therefore,
network topology is deterministic under ideal
input delay can be considered as the sum ofcommunication channels. Accordingly, main research
computation time and execution time. More precisely, if
on the consensus problem was conducted under ap
the input delay for agent i is given by T i ,then thedeterministic fixed or switching network topology. Thatclosed-loop system of (1) with (2) becomesis, the adjacency matrix A(t) is deterministic. In someother times, when considering random communication© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 203 -

Huailin Zhao, Wei Ren, Jian Jiang, Masanori Sugisaka

given network topologies. Considering the fact thatconsensus under different network topologies may
behave different convergence speeds, a natural topic is
how to design an optimal network topology with proper
adjacency matrix such that optimal convergence speed
can be achieved [28-29].
The study of convergence speed for the consensus
problem, finite-time consensus, reaching consensus in a
finite time, has also been studied. Compared with mostexisting research on the consensus problem, finite timeconsensus behaves a disturbance rejection property androbustness against uncertainties. In addition, due to thefinite-time convergence, it is often possible to decouple
the consensus problem from other control objectives
when they are considered simultaneously [30-31].
Please note that the existing research on finite-timeconsensus mainly focuses on systems with simple
dynamics, such as single-integrator kinematics anddouble-integrator dynamics, in the continuous-time field.
Because many practical systems are better and more
proper to be described by general linear or nonlinear
dynamics, it is natural to study finite-time consensus for
systems with general linear or nonlinear dynamics
further. Besides, it is meanful to study finite-timeconsensus in the discrete-time field. Some research on
this topic can be found in [32-33], where the objective is tocompute the final consensus value for all agents in a
finite number of steps.

failures, random packet drops, and communication
channelinstabilities
inheritedinphysicalcommunication channels, it is necessary and important
to study consensus problem in the stochastic setting
where a network topology evolves according to somerandom distributions. That is, the adjacency matrix A(t)is stochastical. By now the current study on consensus
over stochastic network topologies has shown someinteresting results regarding that how to determine theprobability of reaching consensus almost surely andwhen the network topology itself is stochastic as well as
what are the advantages and disadvantages of thestochastic network topology regarding such as
robustness and convergence rate when compared withthe deterministic network topology.
As is well known, disturbances and uncertainties often
exist in networked systems such as channel noise,communication noise, uncertainties in network
parameters, etc. In addition to the stochastic network
topologies mentioned above, the effect of stochastic
disturbances and uncertainties on the consensus problem
also needs investigation [24-25]. Study has been mainly
devoted to analyzing the performance of consensus
algorithms subject to disturbances and to presenting
conditions on the uncertainties such that consensus canbe achieved. Besides, another interesting direction in
dealing with disturbances and uncertainties is to design
distributed local filtering algorithms so as to save energy
and improve computational efficiency. Distributed local
filtering algorithms play an important role and seems
more effective than traditional centralized filtering
algorithms for multi-agent systems [26-27].

6. The conclusion
The consensus problem is the basic and very important
topic in coordination control of the multi-agent system.
It mainly refers to some basic concepts including thesystem dynamics, the time delay and the system
network topolgy. The comvergence is also an very
important concept. But the paper doesn’t introduce theconsenseu and the convergence related with the subjects
such as asynchronous effects, sampled-data framework,
quantization, and so on. The existing research on theconsensus problem has covered a number of physical

5. The convergence analysis
To the control system, the convergence problem isalways an important topic. Of course it is one of the key
performance measure for consensus algorithms too.
The existing study mainly focuses on the analysis of theconvergence speed under various network topologies
and optimization of the convergence speed for certain© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 204 -

Consensus Problem of Distributed

properties for practical systems and control performance
analysis. However, the study of the consensus problem
covering multiple physical properties or control
performance analysis has been largely ignored.
Therefore more problems discussed in the above need tobe taken into consideration simultaneously whenstudying the consensus problem in the future.

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© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 206 -

Dingle’s Model-based EEG Peak Detection using a Rule-based Classifier
Asrul Adam1, Norrima Mokhtar, Marizan Mubin

Applied Control and Robotics (ACR) Laboratory, Department of Electrical Engineering, Faculty of Engineering, Universityof Malaya, 50603 Kuala Lumpur, Malaysia
Zuwairie Ibrahim

Faculty of Electrical and Electronic Engineering, Universiti Malaysia Pahang, 26600 Pekan, Pahang, Malaysia
Mohd Ibrahim Shapiai

Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia
E-mail: asrul.adam@siswa.um.edu.my, zuwairie@ump.edu.my, norrimamokhtar@um.edu.my, md_ibrahim83@.utm.my, andmarizan@um.edu.my
www.um.edu.my,www.ump.edu.my,www.mjit.utm.my
Abstract
The employment of peak detection algorithm is prominent in several clinical applications such as diagnosis andtreatment of epilepsy patients, assisting to determine patient syndrome, and guiding paralyzed patients to manage somedevices. In this study, the performances of four different peak models of time domain approach which are Dumpala’s,
Acir’s, Liu’s, and Dingle’s peak models are evaluated for electroencephalogram (EEG) signal peak detection algorithm.
The algorithm is developed into three stages: peak candidate detection, feature extraction, and classification. Rule-basedclassifier with an estimation technique based on particle swarm optimization (PSO) is employed in the classification
stage. The evaluation result shows that the best peak model is Dingle’s peak model with the highest test performance is
88.78%.
Keywords: Electroencephalogram (EEG) signal, Peak detection, Rule-based classifier, Particle swarm optimization(PSO), Biomedical applications.

domain approach, the peaks are analyzed in both time andfrequency domain. In nonlinear approach, some statisticalparameters of the peaks are analyzed.
Several algorithms [1, 3, 5, 6] are designed byconsidering different peak models in the time domain
approach which are Dumpala’s [5], Acir’s [6], Liu’s [3],and Dingle’s [1] peak models. Therefore, this study
focuses on the employment on the different peak models
into the proposed peak detection algorithm. The
performances on the different peak models are evaluatedand the best peak model is presented.

1. Introduction
Electroencephalogram (EEG) signal is a microvolt
electrical brain signal that is used for recording the brain
activity of human behaviors. The most traceable signal
pattern exists in the EEG signal is a peak point whichsignifies the brain activity on particular events or stimulus.
The known peak point through the response of the brain
can be translated into commands, for example, wheelchair
movement.
Peak detection algorithm can be categorized based onfour approaches which are time [1], frequency [2], timefrequency [3], and nonlinear [4] domains. In the time
domain approach, the peaks are analyzed with respect totime. In frequency domain approach, the peaks areanalyzed with respect to frequency. In time-frequency
1

2. Methodology
Figure 1 shows the algorithm of the EEG signal peakdetection using rule-based classifier. In the first stage, thedetection of peak candidates is performed to differentiate

Applied Control and Robotics Laboratory (ACR), Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Malaysia.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 207 -

Asrul Adam, Zuwairie Ibrahim, Norrima Mokhtar, Mohd Ibrahim Shapiai, Marizan Mubin

Fig. 1. The EEG signal peak detection algorithm

Fig. 2. Peak model parameters

point at first half wave (TP1i), a turning point at second
half wave (TP2i), and a moving average curve(MAC(PPi)).

Table 1. List of Different Peak Models and Sets of Feature
Peak Model
Dumpala et al.
Acir et al.
Liu et al.
Dingle et al.

Set of Feature
f1, f6, f11, f12
f1, f2, f7, f8, f13, f14
f1, f2, f3, f4, f6, f9, f10, f11, f12,
f13, f14
f5, f6, f11, f12

Number of Features
46
11

2.2. Feature Extraction
The peak features are calculated based on the eightparameters as shown in Fig. 2. The total peak features
obtained from the existing peak models are 14 [7]. Thosefeatures are peak-to peak amplitude at the first half wave,
f1, peak-to peak amplitude at the second half wave, f2,turning point amplitude at the first half wave, f3, turning
point amplitude at the second half wave, f4, moving
average amplitude, f5, peak width, f6, first half wave width,
f7, second half wave width, f8, turning point width, f9,half point width, f10, peak slope at the first half wave, f11,
peak slope at the second half wave, f12, 7urning point
slope at the first half wave, f13, and turning point slope atthe second half wave, f14. The list of peak models with
their feature set is tabulated in Table 1.

4

between a peak candidate and a non-peak point. The
second stage is the extraction of peak candidate features.
The selected features of all peak candidates based on
different peak models are extracted in this stage. Then, theselected peak features of peak candidates act as input tothe rule-based classifier. During the training, the decisionthreshold values are estimated to find the optimal value.
The estimation process is done by using PSO algorithm.
The optimal decision threshold values are then usedduring the testing phase. The final output of the training
and testing phases is the predicted peak points and nonpeak points of the identified peak candidates.

2.3. Rule-based Classifier
2.1. Peak Candidate Detection

A rule-based classifier is employed to distinguish either
the candidate peak is a true peak or true non-peak fromthe extracted features. Each feature has a corresponding
threshold value in the classification process. Given a setof features, a true peak only can be identified if all thefeature values are greater or equal than the decisionthreshold values. The form of the rule is,
IF f1  th1 AND f2  th2 AND … AND
fM  thM THEN Candidate Peak is a True Peak
where fi is denoted as selected peak features, thi is thecorresponding decision threshold value of fi, M is total
number of selected peak features, and true peak ispredicted peak point.

A discrete-time signal, x ( I ) , of L points is considered inthis stage. Next, the ith candidate peak point, PPi, areidentified using three-points sliding window method.
Those three-points are denoted as, x ( I  1) , x ( I ) , andx ( I  1) for I  1, 2 ,3,  , L . A candidate peak point is
identified when x ( PP i  1)  x ( PP i )  x ( PP i  1) and two
associated valley points, VP 1 i and VP 2 i , are in between
as shown in Fig. 2. Both valley points exist whenandx (VP 1 i  1)  x (VP 1 i )  x (VP 1 i  1)
x (VP 2 i  1)  x (VP 2 i )  x (VP 2 i  1) . Another parametersof the ith peak location are a half point at first half wave
(HP1i), a half point at second half wave (HP2i), a turning© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 208 -

Dingle’s Model-based EEG Peak

Algorithm 1. PSO Algorithm
1:
Initialization2:
while not stopping criteria do
3:for each ith particle in a population do
4:
calculate fitness function
5:
update pbest and gbest
6:
end for7:
for each particle in a population do
8:
update the ith particle’s velocity and9:update the ith particle’s position10:
end for11:
end while

Table 3. Parameters Setting of PSO Algorithm

Table 2. Representation of Particle Position
Particlesik

Decision Thresholds
2
…
xik,2
…

1

xik,1

F

xik,d







2

Social component, c22

Random value, r1 and r2

Random [0,1]Velocity vector for each particle

0

Initial pbest score for each particle

0

Initial gbest score
Range of search space for F=1 to F=5
Range of search space for F=6 to F=10
Range of search space for F=11 to F=140[0 30][0 781.25][0 24.16]

3.1. Experimental Protocols

The EEG signal recording was conducted using theg.MOBIlab portable signal acquisition system. The EEG
signal was recorded from C4 channel. The EEG signal ofchannel CZ was used as a reference. The ground electrodewas located on the forehead. The electrode was placedusing the 10-20 international electrode placement system.
The sampling frequency was set to 256-Hz.
The filtered EEG signal is shown in Figure 3. The
total length of EEG recording is 40-second. 40 locationsof true peak points are highlighted in the red circle. The
next process is to prepare the training and testing data.
From the data collection, 40 true peak points havebeen identified. In 40-second signal there are 10280
sampling points, x(I). There are only 40 peak points andthe remaining of 10240 sampling points are the non-peakpoints. For preparing the training and testing signal, thetraining signal is selected from 1 to 5140 sampling points
while the remaining EEG signal is used for testing signal.

 max  min 
k
(2)

k max


where  max and  min denote the maximum andminimum values of inertia weight, respectively, andk max is the maximum iteration. Then, the particle’s
position is updated based on Eq. (3).
sik 1  sik  vik 1(3)
Table 2 illustrates the representation of particle
position. The ith particle at iteration k, xi (k ) , inrepresents
continuous
typeof
dimension,s ik  xik,1 , xik, 2 , x ik,3 ,  , xik,d . The d  1,2,3,  , F is a dth
dimension. F is the total number of decision thresholds.
The total number of decision thresholds is equal of thetotal number of peak features.

  max  



Cognitive component, c1

The experiment for each peak model is conducted in 10
independent runs. In PSO, 30 particles are used. For eachparticle, the total number of dimensions is depending on
the number of features in a feature set. The maximum
iteration was set to 1000. The parameters setting of PSO
algorithm are tabulated in Table 3. The fitness functionused in this study is geometric mean (Gmean).

Fundamentally, the PSO algorithm follows several steps
as described in Algorithm 1.
In PSO, particles search for the best solution andupdate the position information among each other
iteration to iteration. Each particle in the populationconsists of a vector position and vector velocity in ddimension. The position of particle i at iteration k isdenoted as s ik  xik,1 , x ik, 2 , x ik,3 ,  , xik,d . To obtain the
updated position of a particle, sik 1 , each particle changes
its velocity as the following:(1)vik 1  v ik  c1 r1 p ik  x ik  c 2 r2 p gk  x ik
where c1 is a cognitive coefficient, c2 is a social
coefficient, r1 and r2 are random values [0,1], and ω is adecrease inertial weight calculated as follows:



Value
0.9 ~ 0.4

3. Experimental Setup

2.4. Parameters Estimation using Particle Swarm
OptimizationParameters
Decrease inertia weight, ω

4. Results and Discussions

Four peak models are employed for evaluating those peakmodel performances in the proposed algorithm. The
training and testing performance based on those four
different measures for each model is shown in Table 4.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 209 -

Asrul Adam, Zuwairie Ibrahim, Norrima Mokhtar, Mohd Ibrahim Shapiai, Marizan Mubin

micro−volts

20

0

−20

−40

0

2000

4000

6000
Sampling Points

8000

10000

12000

Fig. 3. Filtered EEG Signal

The testing performance for average, maximum,
minimum, and STD is 81.22%, 91.83%, 74.15%, 9.13 for
Dumpala et al.’s peak model; 68.59%, 77.43%, 54.77%,
6.97 for Acir et al.’s peak model; and 88.78%, 94.75%,
77.44%, 7.98 for Dingle et al.’s peak model, respectively.
Compared to the test average performance of the peakmodels, the highest performance is obtained by Dingle etal.’s peak model, which is 88.78%.
For the Liu et al.’s peak model, will give 0%
performance for training and testing phase. This result
indicates that the limitation of rule-based classifier when
dealing with this feature sets. During the training process
on this feature sets, the particles in the PSO algorithm
does not meet the optimum decision threshold values andthe particles might also be trapped at local optima. Basedon the preceding rule, a true peak only can be identified if
all the feature values are greater or equal than the decisionthreshold values. So, if one of the feature values do notsatisfy the decision threshold value, the classifier will
decide the peak candidate as a non-peak point. When thishappens to all peak candidates, Gmean will give 0%
performance.

Table 4. Training and Testing Performance of Peak Detection
for each Peak Model
Peak Model

Training (%)

Testing (%)

Avg Max Min STD Avg Max Min STD
Dumpala et al. 84.01 89.15 80.58 4.43 81.22 91.83 74.15 9.13
Acir et al.

74.4 80.59 67.08 3.71 68.59 77.43 54.77 6.97

Liu et al.

0

Dingle et al.

0

0

0

0

0

0

0

90.98 94.76 83.66 5.1 88.78 94.75 77.44 7.98

References
1. A. A. Dingle, R. D. Jones, G. J.Carroll, and W. R. Fright, A
Multistage System to Detect Epileptiform Activity in the
EEG, IEEE Transactions on Biomedical Engineering 40(1993) 1260-1268.
2. A. Juozapavi, G. Bacevi, D. Bugelskis, and R. Samaitien,
EEG analysis – automatic spike detection, Journal of
Nonlinear Analysis: Modelling and Control 16 (2011) 375386.
3. H. S. Liu, T. Zhang, and F. S. Yang, A Multistage,
Multimethod Approach for Automatic Detection andClassification of Epileptiform EEG, IEEE Transaction on
Biomedical Engineering 49 (2002) 1557-1566.
4. M. Putignano, A. Intermite, and C. P. Welsch, A non-linear
algorithm for current signal filtering and peak detection in
SiPM, Journal of Instrumenatation 7 (2012) 1-19.
5. S. R. Dumpala, S. N. Reddy, and S. K. Sarna, An algorithm
for the detection of peaks in biological signals, Computer
Programs in Biomedicine 14 (1982) 249-256.
6. N. Acir, I. O. Kuntalp, B. Baklan, and C. Güzelis,Automatic Detection of Epileptiform Events in EEG by aThree-Stage Procedure Based on Artificial Neural
Networks, IEEE Transactions on Biomedical Engineering
52 (2005) 30-40.
7. A. Adam, M. I. Shapiai, M. Z. Mohd Tumari, M. S.
Mohamad, and M. Mubin, Feature Selection and Classifier
Parameters Estimation for EEG Signals Peak Detection
Using Particle Swarm Optimization, The Scientific World
Journal (2014).

5. Conclusions

In this study, a rule-based classifier with PSO-based
estimation technique was employed in the proposed
algorithm of EEG signal peak detection. The four
different peak models that consist of different feature setsare used in the feature extraction stage. The best peakmodel is Dingle et al.’s peak model with highest
performance obtained is 88.78%.
Acknowledgement

This project is funded by the Ministry of Education
Malaysia (UM.C/HIR/MOHE/ENG/16 Account code:
D000016-16001). The first author would like to thank the
Ministry of Education Malaysia for awarded him MyPhD
scholarship.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 210 -

Different Learning Functions for Weighted Kernel Regression in Solving
Small Sample Problem with Noise
Zuwairie Ibrahim, Nurul Wahidah Arshad

Faculty of Electrical and Electronic Engineering, Universiti Malaysia Pahang, 26600 Pekan, Pahang, Malaysia
Mohd Ibrahim Shapiai

Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia
Norrima Mokhtar

Applied Control and Robotics (ACR) Laboratory, Department of Electrical Engineering, Faculty of Engineering,
University of Malaya, 50603 Kuala Lumpur, Malaysia
E-mail: zuwairie@ump.edu.my
Abstract
Previously, weighted kernel regression (WKR) for solving small samples problem has been reported. In the original
WKR, the simple iterative learning technique and the formulated learning function in estimating weight parameters aredesigned only to solve non-noisy and small training samples problem. In this study, an extension of WKR in solving
noisy and small training samples is investigated. The objective of the investigation is to extend the capability andeffectiveness of WKR when solving various problems. Therefore, four new learning functions are proposed for
estimating weight parameters. In general, the formulated learning functions are added with a regularization term insteadof error term only as in the existing WKR. However, one free parameter associated to the regularization term has firstly
to be predefined. Hence, a simple cross-validation technique is introduced to estimate this free parameter value. The
improvement, in terms of the prediction accuracy as compared to existing WKR is presented through a series ofexperiments.
Keywords: Learning functions, Small sample problem, Regression.

samples. The estimation of the weight parameters depends
on the learning functions and learning techniques.
In general, the ability of WKR is only restricted tosolve non-noisy training samples. Hence, the formulated
learning function and simple iterative learning technique
in WKR may fail in estimating weight parameters if theobserved training samples are corrupted by noise. An
example of regression using WKR in the presence ofnoise is shown in Fig. 2. Therefore, in this study, fourlearning functions are considered to particularly solve
small and noisy samples problems.

1. Introduction
In general, the kernel based regression aims at regressing
the unknown function based on the available training
samples. In real world applications, to obtain sufficienttraining samples is too expensive as when dangerous realmeasurements have to be performed [1]. There are
numerous techniques in machine learning for regression.
However, all the available techniques mainly focus insolving sufficient training samples problem. As mostexisting techniques perform well under sufficiently large
training samples, the performance of those techniquesdegrades as the size of samples decreases.
Weighted Kernel Regression (WKR) [2] has proved tosolve small sample problems with high accuracy byassuming all the available training samples are free fromerror. An example of regression using WKR is shown in
Fig. 1. To design a WKR, one must estimate the weightparameters, W, before it can be used to predict unseen

2. Weighted Kernel Regression
Given training samples, ,, where n is the numberof training samples, input is denoted as   d , and  is the target output. WKR is the technique todregress the output space by mapping the input space to  . In general, WKR is a modified Nadaraya-Watson
kernel regression (NWKR) [3] by expressing the weightbased on the observed samples through a kernel function.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 211 -

Z
Zuwairie Ibrahimm, Nurul Wahidahh Arshad, Mohd Ibbrahim Mohd Shaapiai, Norrima Mokhtar
Mo

TThe existing W
WKR relies onn the Gaussiaan kernel functtion
aas given in Eqq. (1).

K  X,XX i X  Xi
1
exp
h
2

2



(1)
w
where h is thhe smoothing parameter. A
As in NWKR, thesselection of smoothing pparameter, h, is importantt to
ccompromise between smooothness andd fitness. As ineexisting WKR
R, Eq. (2) is em
mployed to dettermine the vaalue
oof h.

h  max Xk1  X k
2

2

X

 X

2



(2)

2

kk1
k .
w
where 1 < k < n-1 andT
The kernel matrix K = [Kij]], where i = j = 1,..., n, witth aggeneralised keernel matrix based on the G
Gaussian kerneel, isggiven in Eq. (3). The maatrix K transforms the linnear
oobserved sam
mples to non-linnear problemss by mapping theddata into a higgher dimensionnal feature spaace.
dpp  K X i ,X jp 1 n  dpp
   K X i  j ,X j 
K ijj   l 1  p 1


1
 n d
  K X p ,X p 

i j
j 


 l 1  p 1



Fig 1. Regresssion using WKR











i j
(3)

i j

In WKR, the most poopular functioon for regresssion
pproblems is uused which to minimize thee RSS to estimmatetthe weight parrameters, W.

mmin f W   min Kw  y2

Fig 2. Reegression using W
WKR in the presennce of noise(4)
O
Once the optim
mum weight is estimated, thhe model is reeady
tto predict anyy unseen sam
mples (test saamples). The test
ssamples can bbe predicted byy using Eq. (5)).

 d

p
p


ˆ
w

i K X , Xi 
i 1
p 1


ŷ X , Wˆ n  d  K X p , X ip 
i 1  p 1




n







regularization term to the fformulated leearning functioon.
T
The regularization term is ussually of the formfof a penaalty
teerm for compllexity, such ass restrictions fo
for smoothnesss.
Therefore, all the investtigated learninng functions willw
ccomprise nott only the error term but also tthe
regularization tterm as given in Eq. (6).
(6)
wwhere fe referss to error termm and fr refers to regularizatiion
teerm.
The additioon of regularrization term is to avoid tthemmagnitude of estimated weight parameeters to be veery
laarge which m
may lead to over-fitting probblem. Hence, tthe
aaddition of regularization tterm gives addvantages to tthe
regression quuality. In geeneral, the eerror term aandregularization term can be fo
formulated eithher with L1 noorm
oor L2 norm function. Thherefore, thee four learniingfufunctions are formulatedfwitth combinationn of L1 and L2 as
eerror term andd regularization term baseed on the WK
KR
cconcept are prooposed in this study.





(5)

33. Extension
n of Weighted
d Kernel Regrression
IIn general, mminimizing thee error term oonly may leadd tonnumericalinstabilities
andbadgeneralizattion
pperformance. The instabilityy yields a highh variance moodelwwhich potentiially produces large differrences of weiight
pparameter vallues given diffferent traininng samples, eevenmminor perturbbation of thee same trainning samples. In
ggeneral, this innstability can be addressed by restricting thecclass of peermissible soolution by introducing the© Thee 2015 Internaational Confereence on Artificcial Life and R
Robotics (ICAR
ROB 2015), JJan. 10-12, Oitta, Japan- 212 -

D
Different Learningg Functions for

Table 1. Parammeter setting of GA
A
Geneeration
300
Populaation size
100
Probability of cross-over
0.7
Probabilityy of mutation
0.001

Prior to esstimating the weight param
meters based onnnew formulateed learning funnctions, an asssociated value
hhas firstly to be estimated. Cross-validatioC
on is a techniqque
too evaluate modelmin orderr to generalizze the predictive
pperformance w
when predictinng unseen sam
mples. The neeed
oof cross-validaation is impoortant in moddel selection assome models parameters, such as value has to beeestimated. In general, crross-validationn separates theaavailable trainning sampless into two sets,scalled tthe
trraining set andd validation seet. Training seet is used to buuild
thhe model and validation sett is used to evaluate the modelbbased on the selected modeels parameterr with respectt tothhe cross-validdation error. Typically,Tthe cross-validatiion
eerror is measuured based onn MSE perforrmance criterioon.
T
The model witth the lowest cross-validatiion error is thhen
uused as a finnal model whhich possibly offers a bettter
ggeneralization performance.
There aree various cross-validatiion techniquues
aavailable in litteratures suchh as hold-out method, K-fo
foldccross-validatioon, and leaave-one-out cross-validatiion(L
LOOCV). In general, LO
OOCV is verry expensive to
ccompute but it is able to retrrieve a lot of information froom
thhe available trraining samplees. As the focuus of the studyy is
too solve smalll and limitedd training saamples probleem,L
LOOCV is fouund to offer several advanttages in terms ofinnformation rettrieval and com
mputational tiime.
In general, LOOCV separates the avaailable n trainiing
samples into a training set oof size n-1 andd a validationn ofsize 1. For eveery selected mmodels param
meter, there aree n
ddifferent combbinations of traaining and vaalidation set. The
Tloowest cross-validation on thhe validation set is used as aninndicator to sellect the final mmodel.

The error term with L2 norm is a poopular choice andmmost widely uused for lineaar model but it is less robbust.
M
Meanwhile, eerror term in L1 norm form
m is not sensitive
aand produces robust estimaation as comppared to L2 noorm
bbut L1 norm
m function is not diffferentiable. For
rregularizationn term, the L2 norm offers lower varianncesmmodel by shrinnking the estim
mated weight parameter vallues
aas compared to learning fu
function withoout regularizattion
tterm. The L2 norm regularrization term iis also knownn asTTikhonov reggularization [44] or Ridge rregularization [5]ffor solving m
matrix inverse problem to leearning probleems
wwith good genneralization. L1 norm regulaarization not oonly
ooffers lower vvariances modeel but also prooduces sparsenness
ssolution whiich offers a better genneralization. T
The
ssparseness solutionsforces some esstimated weiight
pparameter vaalue equals to zero. Also,A
L1 noorm
rregularizationn term creates aaccurate prediictive models thataalso have inteerpretable or parsimoniouss representatioons.
T
The proposedd formulated learning funcctions, which arebbased on learnning function iin the existingg WKR, are giiven
iin Eq. (7) to E
Eq. (10).
(7)
(8)
(9)

44. Experimen
nt, Result, and Discussion((10)
w
where K is kkernel matrix, W is weight parameter too beeestimated, Y iis observed ouutput domain values, ∥. ∥1 iss L1
nnorm functionn, ∥. ∥2 is L2 nnorm functionn, and is a ffree
pparameter thaat control the ggeneralizationn of the regresssed
ffunction.
In generall, the formulaated learning functions cann be
ccategorized innto two types; closed form solution functtion
aand non-clossed form solution functioon. Closed fo
form
ssolution functtion can be deerived analyticcally as compaared
tto non-closed form solutionn function whhen estimating thewweight parameters. For nonn-closed form function, therre is
nno analytical solution can be obtained aas the functionn is
nnondifferentiaable. As evolutionary com
mputing offerss aneeffective wayy to optimizze i.e. estim
mate the weiight
pparameters forr non-differenntiable functionn, hence, GenneticA
Algorithm (GGA) is introduuced as a leaarning techniqque.
T
The formulatted learning function withh L1 norm teerm
eeither as errorr term or regullarization termm is consideredd asnnon-closed forrm solution fuunction.

S
Since the learnning function whichw
possessses L1 norm teerm
iss considered as non-closedd form solutiion function, anaanalytic formm solution cannot be obtained whhenmminimizing thhe corresponnding learninng function ineestimating the weight param
meter. This draw
wback has ledd tothhe use of G
GA. The parrameter settinngs of GA aresummarised inn Table 1. A specific functtion is employyedwwith three different Gaaussian noisee distributioons,NN~(0,0.1), N~((0,0.3), and N~~(0,0.5).
The qualityy of predictioon for every leearning functiion
fo
for three differrent problems is tabulated inn Table 2, Tabble
33, and Table 44. In general, the learning ffunction with L2
nnorm of error term offerss a better ggeneralization as
ccompared to thhe learning fuunction with L1 norm of errror
teerm.

© Thee 2015 Internaational Confereence on Artificcial Life and R
Robotics (ICAR
ROB 2015), JJan. 10-12, Oitta, Japan- 213 -

Z
Zuwairie Ibrahimm, Nurul Wahidahh Arshad, Mohd Ibbrahim Mohd Shaapiai, Norrima Mokhtar
Mo

Table 2. Resultss of 100 experimeents to predict 1- eexp(-2u)4 with n = 5
and contam
minated by Gaussiian noise, N~(0,0..1) with various
learninng functions

immprovement, in terms oof quality of prediction iseexperimented and presentedd. In general, the selection ofleearning technnique must bbe based on the formulatted
leearning functtion, which implies the dependency of
pproblem beingg solved. Alsoo, the quality of predictionn ismmainly determ
mined by the selectionsof L2 norm as errror
teerm regardlesss of norm type of reguularization terrm.
H
Hence, the incclusion of reggularization teerm is a mustt in
foformulating thee learning funnction.

Table 3. Resultss of 100 experimeents to predict 1- eexp(-2u)4 with n = 5
and contam
minated by Gaussiian noise, N~(0,0..3) with various
learninng functions

A
Acknowledgem
mentTThis work is financially suupported by thhe RAGS Grrant
S
Scheme (RDU
U131416) awarrded by the Ministry of Highher
E
Education (MOOHE) to Univeersiti Malaysiaa Pahang (UMMP).
R
References

Table 4. Resultss of 100 experimeents to predict 1- eexp(-2u)4 with n = 5
and contam
minated by Gaussiian noise, N~(0,0..5) with various
learninng functions

1. C. Huang aand C. Moragaa, A diffusion-nneural-network forlearning frrom small sam
mples, Internattional Journal of
Approximate Reasoning 355(2) (2004) 1377-161.
2. M. I. Shappiai, Z. Ibrahimm, M. Khalid, L
L. W. Jau, and V.
Pavlovic, E
Enhanced Nadaaraya Watson K
Kernel Regressiion:
Surface Appproximation foor Extremely Sm
mall Samples, The
T
5th Asia Mo
Modeling Sympossium (2011) 7-12.
3. E. A. Naddaraya, On Estimating Regreession, Theory ofProbabilityy and its Applicaations 9(1) (19664) 141-142.
4. A. Tikhonnov and V. A
Arsenin, Solutiions of Ill-possed
Problems (1977).
5. A. E. Hoerrl and R. W. Keennard, Ridge Regression:
R
BiaasedEstimation for Nonorthoogonal Problem
ms, Technometrrics
12(1) (19700) 55-67.

55. Conclusionns
A
An extensionn of WKR is investigaated to addrress
rregression prroblem with noisy traininng samples. T
Theiinvestigation emphasized on formulattion of learnningffunctions. Prior to these two investiggations, the ffree
pparameter, value has ffirstly to be estimated. T
The© Thee 2015 Internaational Confereence on Artificcial Life and R
Robotics (ICAR
ROB 2015), JJan. 10-12, Oitta, Japan- 214 -

Simultaneous Computation of Model Order and Parameter Estimation of a Heating System
Based on Particle Swarm Optimization for Autoregressive with Exogenous Model : An
Analysis
Teoh Shin Yee, Zuwairie Ibrahim, Kamil Zakwan Mohd Azmi

Faculty of Electrical and Electronic Engineering, Universiti Malaysia Pahang
26600 Pekan, Pahang, Malaysia
E-mail: zuwairie@ump.edu.my
www.ump.edu.my
Norrima Mokhtar

Applied Control and Robotics (ACR) Laboratory, Department of Electrical Engineering, Faculty of Engineering
University of Malaya, 50603 Kuala Lumpur, Malaysia

Abstract
System identification is one of the method for solving a mathematical model of a system by performing on analysis
only at its input and output behaviour. In system identification, the procedure of modelling the system is separated
into four main parts. The first part is constructing an experiment to collect the input and output data of the system.
Then, with some criteria, the model order and structure are selected. The next part is to estimate the parameters ofthe model. For the final part, the mathematical model is verified. Model order selection and parameter estimation
are two important parts of finding the mathematical model for system identification. Previously, a technique called
simultaneous model order and parameter estimation (SMOPE), which is based on Particle Swarm Optimisation(PSO) and ARX model, has been introduced to combine these two parts simultaneously. This technique, however,exclude the error term of ARX model. In this study, an analysis is shown to prove that the performance of SMOPE
based on PSO and ARX model degraded as the magnitude of error increases.
Keywords: ARX, Particle swarm optimization (PSO), System identification.

area of black-box identification system. The mostcommon models are autoregressive model withexogenous inputs (ARX) [1], autoregressive moving
average with exogenous inputs (ARMAX) [2], and BoxJenkins (BJ) model [3].

1. Introduction
System identification is a method to find themathematical model of a dynamic system which uses
statistical methods to build mathematical models frommeasured data. Two types of models are common in thefield of system identification: grey-box model andblack-box model. Grey-box model refers to the model
where some information is partially known from firstprinciple while the rest of the information is obtained
from experiment. On the other hand, a black-box model
uses no priori physical knowledge of the system. A
number of model structures have been proposed in the

Autoregressive with exogenous (ARX) model is thesimplest model in linear black box identification. In
solving the linear black box identification problem of
the ARX model, the model order selection andparameter estimation part is solved as separate process.
Previously, a technique called simultaneous model orderand parameter estimation (SMOPE), which is based on
Particle Swarm Optimisation (PSO) and ARX model,© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 215 -

Teoh Shin Yee, Zuwairie Ibrahim, Kamil Zakwan Mohd Azmi, Norrima Mokhtar

has been introduced to combine these two parts
simultaneously [4]. This technique, however, exclude
the error term of ARX model. In this study, an analysis
is conducted to investigate the performance of SMOPE
based on PSO and ARX model with error term included.
2. Simultaneous Model Order and Parameter
Estimation
In ARX model, AR refers to the autoregressive part andX refers to the exogenous input. The single-input singleoutput (SISO) ARX mathematical model can be definedas:(1)
where u(t) and y(t) are input and output variables,
is a Gaussian white noise process,respectively,
A(q) and B(q) are polynomials in the backward shiftoperator. In ARX, a mathematical model of a system
can be represented as:(2)
is ignored andIn SMOPE, however, the error term
the maximum order of 9th is considered. To decide theparameter ‘a’ and ‘b’, the constraint n ≥ m is taken into
account. This is due to the transfer function form which
the order value of poles (n value) must be the same orless than the order of zeroes (m value).

Fig. 1. The flowchart of PSO algorithm.

In the earlier stage of PSO algorithm, some parameters
are initialized. The PSO parameter values used in thisstudy is shown in Table 2. The PSO parameter includes
the number of particles, the inertia weight, cognitive
component, c1, social component, c2, and the maximum
number of iterations, k. The initial position and velocityof particle is randomly located in a search space. Afterthe initialization stage is done, the fitness function is
calculated as follows:

The purpose of PSO algorithm [5], as shown in Fig. 1, is
to search for the best mathematical model. The
combination of model order and parameter for ARXequation are considered in particle representation, whichis shown in Table 1. For example, if the model order is
2, then ‘n’ value is 2 and all possible mathematicalmodels are subjected to fitness calculation. Specifically,
the calculations involve two mathematical models,
which are

and

(3)

.

The personal best or pbest is the best solution found byeach particle and gbest is defined as the best pbest. Bothpbest and gbest are updated at every iteration. The
velocity of a particle is updated using Eq. (4):(4)© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 216 -

Simultaneous Computation of Model

Dimension
Variable in
ARX

1
Order, n

2
a1

3
a2

4
a3

5
a4

Table 1. Particle representation.
6
7
8
9
10
11
a5
a6
a7
a8
a9
b1

12
b2

13
b3

14
b4

15
b5

16
b6

17
b7

18
b8

19
b9

order is discrete. Hence, for the first dimension, thefloating value is converted to discrete data by rounding
its value. If the value is not in range, the previous value
is used instead. Then, the particle refers to otherparameters for the calculation of fitness.
3. The Heating System
In heating system, the input drives a 300 Watt Halogen
lamp, suspended several inches above a thin steel plate.
The output is a thermocouple measurement taken fromthe back of the plate. The input and output of theexperiment is shown in Fig. 2, which is taken fromhttp://www.esat.kuleuven.ac.be/sista/daisy. The datacollect from the system is halfly separated for
estimation and validation. This estimation data is used
to calculate the model order and parameter of the ARX
while the validation data is used to validate the model.

Fig. 2. Input and output behavior of heating system.

4.

Table 2. PSO parameters.
Parameters
Maximum number of particles, iDecrease inertia weight,
Cognitive Component, c1
Social Component, c2
Random value, rand
Maximum iterations, k
Number of run

Value
10
0.9~0.4
2
2
[0,1]
150
100

Experiment, Result, and Discussion

Using MATLAB for simulation and based on the PSO
parameter shown in Table 2, an example of aconvergence curve is shown in Fig. 3. The minimum
and maximum value of best fit obtained in estimation is
99.0018% and 99.3211%, respectively. Average valueof best fit is 99.2242%. In validation, the minimum,
maximum, and average best fit are 92.6557%,
98.7011%, and 98.0763%, respectively. This
experiment gives model order value as 3 andmathematical model as follows:

whereis the velocity particle j at iteration k, rand is
random numbers [0,1], is inertia weight, and anddenote the cognitive and social coefficients,
respectively. The particle’s new velocity is then used to
update the particle’s position using Eq. (5).
(5)
where is the position of particle i at iteration k . In
this study, the linear dynamic inertia weight is used andcalculated according to Eq. (6) as follows:(6)(7)
where the model order, n = 4, parameter value b1 =0.0024, b2 = 0.0020, b3 = 0.0720, a1 = −1.6169, a2 =0.7306, a3 = 0.0833, and a4 = −0.0321.
An analysis has been conducted to investigate theperformance of SMOPE based on PSO and ARX modelwith error term,, included. The complete ARX
model shown in Eq. (1) and the same PSO parameters
have been used. The error term is a white noise, which
can be generated in MATLAB using rand command.

anddenote the maximum andwhereminimum values of inertia weight, respectively, and kmax
is the maximum iteration.

Note that the PSO will assign floating values to every
dimension of a particle even though the value of model© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 217 -

Teoh Shin Yee, Zuwairie Ibrahim, Kamil Zakwan Mohd Azmi, Norrima Mokhtar
Fig. 4. Best fit decreases as the magnitude of error increases.

References
1. L. Ljung, System Identification Theory for the User, 2nded., Prentice Hall, CA: Linkoping University Sweden,
1999.
2. S. M. Moore , J. C. S. Lai, K. Shankar, ARMAX modalparameter identification in the presence of unmeasured
excitation—I: Theoretical background, Mechanical
Systems and Signal Processing 21 (2007) 1601-1615.
3. R. Pintelon, Y. Rolain, J. Schoukens, Box-Jenkins
identification
revisited—PartII:
Applications,
Automatica 42 (2006) 77-84.
4. J. Kennedy, Particle Swarm Optimization, Encyclopediaof Machine Learning (2010) 760-766.
5. Z. Ibrahim, S. W. Nawawi, S. Razali, B. Muhammad, K.
Z. Mohd Azmi, Z. Aspar, N. A. Ab. Aziz, Simultaneous
Computation of Model Order and Parameter Estimationof a Heating System Based on Particle Swarm
Optimization for Autoregressive with Exogeneous Model,
ICIC Express Letters, 9 (2015) 1159-1165.

Fig. 3. An example of convergence curve.

The average best fit obtained is 98%, which is verymuch similar to the result of the previous experiment.
Additional experiments with different magnitude ofnoise have been done and the result is shown in Fig. 4.
Note that only 10 runs are considered. This analysis
shows that the best fit decreases as the magnitude oferror increases.
5. Conclusion
In the previous study, a system identification resultsfrom a heating system case study has been presented toinvestigate the performance of simultaneous modelorder and parameter estimation technique based on PSO
and ARX model. However, the error term is excluded
from the previous experiment. In this paper, withdifferent magnitude of white noise are considered andincluded in the experiment. Based on the 10 runs anddifferent magnitude of white noise up to 30, this study
proved that the performance of SMOPE based on PSO
and ARX model degraded as the magnitude of error
increased.
Acknowledgements
This work is financially supported by the Exploratory
Research Grant Scheme (RDU130605) awarded by the
Ministry of Higher Education (MOHE) to Universiti
Malaysia Pahang (UMP).

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 218 -

Maximum Probability Algorithm for Fault Diagnosis
Fengzhi Dai, Li Fa, Bo Liu
College of Electronic Information and Automation, Tianjin University of Science and Technology,
80 MailBox, 1038 Dagunanlu Road, Hexi District, Tianjin 300222, China
E-mail: daifz@tust.edu.cn, fanli0476@126.com
www.tust.edu.cn

Abstract
This paper considers the fault diagnosis by outside fault phenomena.The method only depends on experience andstatistical data to set up the fuzzy query relationship between the outside phenomena (fault characters) and the fault
sources (fault patterns). From this relationship, the most probable fault sources can be located, and the standard
fuzzy relationship matrix is stored as database.
Keywords: maximum probability algorithm; fault diagnosis; fault phenomena-sources tree; servo system

1.

fault sources are listed, and they can be examined basedon their fault probabilities that are from large to small.

Introduction

It is difficult to detect and diagnose the faults for nonlinear systems, because it is difficult to obtain its accurate
state description1. And if there is no particular techniqueis adopted2, it is always difficult to set up a complicateddetection device based on the state description for thiskind of systems.
In an actual control system, it is often difficult to findout where the faults are if only based on the outside fault
phenomena (fault characters), but what we often obtain
are these phenomena. This paper will discuss how to use
these fault characters and some points’ measurement tolocate the fault sources.
The maximum probability algorithm is based on thetheory of fuzzy recognition, has the effect of the expert
system. It is to find the position of the most probablefault source that has the maximum fault probability.
When the fault occurs, according to the outside
phenomena and some points’ measurement, all possible

2. Fault phenomena-sources TreeIn an actual control system, there are strong logic bonds
between the fault phenomena and the fault sources. The
fault tree that describes the system can be built up by
verifying the truth and the integrity of this relationship.
When the system’s fault tree is completed, from which
the system’s fault phenomena- sources tree (Fig.1) can beobtained.
In Fig.1, A is the top event (the whole system); Ai, Bj
are the second and third level events; and xk are thebottom events (components or the sub-systems).
From the system’s fault phenomena-sources tree, therelationship between the system’s fault sources and all
kinds of fault characters (Table 1.) can be obtained andanalyzed. In the paper, there are 14 sensors are mounted
on the servo system at the experiment stage3.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 219 -

Fengzhi Dai, Li Fa, Bo Liumax Ai ( X j )AiAi  1,2, , n

(1)n is the number of fault states.
A1

A2

A4

A3

Thus the maximum value of  Ai ( X j) (i=1,2…n) is
the maximum probability of the probable fault state.

A1

3.2.

x1

B3

B2

B1

…

xm

…

First, set up a typical fault state Ai, based on the system’s
fault tree, the corresponding standard fault phenomena(fault characters) X0i ={xi1, xi2,…xip}, give each fault

B4

x2

…

xn

character xir a weight  ir , then the characteristic
parameter that symbols X0i results from Ai is

A : system abnormality
A1: controller cannot rotate0where xir =1 (i=1,2…p), p is the number of fault

A4 : unsatisfied dynamics
System’s fault phenomena-sources tree

phenomena that can cause the fault state of Ai (Ai can beobtained from the system’s fault tree),  ir is the weight

3. Fault Diagnosis Approach
When faults occurs, there will be all kinds of fault
phenomena, such as x1, x2,… The purpose is to diagnose
the faults from these outside fault phenomena and somepoints’ measurement results.

of xir in the fault state Ai.
Second, according to a series of fault characters
Xj={x1, x2,…xq}, define the parameter that symbols Xj
results from Ai is

Fault diagnosis theoryq

PAi    r x rWhen one fault state Ai (a sub-system or a component)
occurs, it may result in many fault phenomena, such as

(3)

r 1

If x r (r =1,2…q) is in the X0i ={xi1, xi2,…xip}, thenx j , designated by x j =1, otherwise x j =0.
On the other hand, if a series of fault phenomena Xj={x1, x2,…}, then an array composed of 0,1 for the faulty
state of Ai can be achieved.
We want to know, when the fault phenomena Xj ={x1,x2,…} occur, how much is the probability that it is thefault state Ai causing these phenomena.
The maximum of this probability

(2)

r 1

A3 : abnormal noise

3.1.

P

PAi    ir xir

A2 : system oscillation

Fig.1

μAi(Xj)x r =1,  r =  ir , otherwise  r =0.
Then for the obtained fault characters Xj={x1,x2,…xq}, the probability of all these fault characters
result from fault Ai is: Ai ( X j) – theμAi(Xj) =

PAi
PAi

0

probability that Ai causes these phenomena Xj ={x1,x2,…} should be obtained© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 220 -

(4)

Maximum Probability Algorithm for

3.3.

Steps
start

From the analysis above, the steps of fault diagnosis canbe obtained by the following three steps:(i) Finding: The first step is to analyze and obtain thesystem’s fault sources and all kinds of fault characters.
(ii) Inspection: Obtain the system’s fault tree and set up
the standard fuzzy relationship matrix of X0i-Ai (Table 1.)
off-line.
Table 1.

ωir

read infuzzy relationshipmatrix

faultsStandard fuzzy relationship matrix (X0i – Ai).

X0i
xi1

xi2

N

Y

…

xip

input symptoms

A1
ω11
ω12
…
ω1p
A2
ω21
ω22
…
ω2p
:
:
:
:
:
Ai
ωi1
ωi2
…
ωip
:
:
:
:
:
An
ωn1
ωn2
…
ωnp
When faults occur, they are identified by using thesampled data. After removing noise signals, the faults canbe found according to the maximum probability
algorithm by analyzing the fault tree and the fault
information on-line.
(iii) Detection, diagnosis and precaution: Give thealarm and the results of the fault diagnosis.
3.4.

amend
fuzzy relationshipmatrixN

calculateμAi(Xj)modifymatrixfind
faultsY

N

Y

print μAi(Xj)from large to small

The algorithm diagram

Fig.2 gives the flow chart of the algorithm. And the fault
searching steps are as follows:
For the certain relationship between fault phenomena
and fault sources, once the phenomena occur, the fault
components can be found easily.
For the relationship with fuzzy characters, they are
queried based on the method presented in the paper.
All the probable fault sources, which have non-zerofault probabilities, are listed from large to small.
For the unsolved faults, a method for isolating faults ispresented.

N

exit
Y

end
Fig.2

The algorithm diagram

is imported to the computer as a system database.
Some faults have been set intentionally, and they all
can be detected. The detailed analysis is given by Ref. 4.
For the purpose of detailed explanation, an example isgiven in Table 2.

4. Experiment
Based on the above approach, the software is developed
using C language. The standard fuzzy relationship matrix

Table 2. An example.
ωir

X1

X2

X3

X4

X5

X6

X7

X8

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 221 -

Fengzhi Dai, Li Fa, Bo Liu

A1
1
0
15
5
100
0
2
10
A2
5
10 1000
0
2
4
7… … … …
… …
… … …
If there are symptoms of x2, x3, x4, that is Xj ={x2, x3,x4}, then0  15  5= 0.15,1  0  15  5  100  0  2  10
10  100  0
μA2(Xj)== 0.86.
5  10  100  0  0  2  4  7
μA1(Xj)=

Then from symptoms of x2, x3, x4, the probability thatthe fault state is A2 is larger than A1. Because the weightof x3 in A2 is very large, if symptom x3 happens, the mostprobable fault state is A2. Of course this is a simple
example, but the principle is the same.
5.

Workshop on Advanced Control Systems in System
Modeling, Fault Diagnosis and Fuzzy Logic, (Sydney,
1996), pp. 69-79.
3. F. Dai, The research of fault diagnosis on the servo system.
(Beijing Institute of Technology ,1998).
4. D. MA, Y. HU, F. DAI, Application of Maximum
Probability Approach to The Fault Diagnosis of a Serve
System. Journal of Beijing Institute of Technology, 11(1),(2002):29-32.
5. LP. Khoo, SB. Tor, JR. Li, A Rough Set Approach to theOrdering of Basic Events in a Fault Tree for Fault
Diagnosis, Int. J. Advanced Manufacturing Technology,
17(10) (2001):769-774.

Conclusion

Based on experience and statistical data, accurate fuzzy
relationship matrix between the outside fault phenomena
and fault sources can be identified after a series oftraining. This relationship can be stored in the computer
as a database, and the important parameters can be online sampled and analyzed. When faults occur, the faults
can be found, alarm is given5.
One thing worthy of mention is that this method does
not provide the exact fault positions, but the maximum
probability of fault sources. To detect and diagnose thefaults quickly and accurately, the key work is to optimize
the X0i-Ai standard fuzzy relationship matrix and obtain
the perfect weight ir .

Acknowledgements
The research is partly supported by the Open Foundation(YF11700102) of Key Laboratory for Water Environment
and Resources, Tianjin Normal University; and theScientific Research Foundation of Tianjin University of
Science and Technology (20130123).
References
1. Frank PM, Fault diagnosis in dynamic systems using
analytical and knowledge-based redundancy – a survey,
Automation, 26 (1990) : 459 - 474.
2. Zhou DH, Frank PM., Sensor fault tolerant control of
non-linear system: A unified approach. In Proc. TEMPUS© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 222 -

The Fractional Order Hyperchaotic Generalized Augmented Lü System and its Circuit
Implementation
Xue Wei

Department of Automation , Tianjin University of Science and Technology,
1038 Dagunanlu Road, Hexi Distric, Tianjin 300222, PR China
Jinkang Xu

Department of Automation , Tianjin University of Science and Technology,
1038 Dagunanlu Road, Hexi Distric, Tianjin 300222, PR China
Hongyan Jia

Department of Automation , Tianjin University of Science and Technology,
1038 Dagunanlu Road, Hexi Distric, Tianjin 300222, PR China
E-mail:xuewei@tust.edu.cn, xiaohuiJC@126.com
www.tust.edu.cn

Abstract
In this paper, a commensurate fractional-order hyperchaotic generalized augmented Lü system is investigated. weanalyze its chaotic characteristics by drawing phase portraits, Poincaré maps, Lyapunov exponent spectra andpower spectrum, and find that the system can present a four-wing hyperchaotic attractor. In addition, a circuit isdesigned for this system and the circuit implementation result show the existence of the four-wing hyperchaotic
attractor, which verifies the correct of the theoretic analysis and provides the support for its application inengineering.
Keywords: Fractional-order; Generalized augmented Lü system; Hyperchaos; Numerical simulation; Circuit design.

physical phenomenon more accurately and fractionalorder chaotic systems have more complex dynamics
characteristics than integer-order chaotic systems9.
In this paper, a new fractional order hyperchaotic
system is proposed and we analyze its chaotic
characteristics through Matlab numerical simulations.
Furthermore, an analog circuit is designed to realized
the proposed fractional order hyperchaotic system ,andthe circuit experiment results agree with the theoretic
analysis.

1. Introduction
Since Lorenz found the first chaotic attractor in 1963,1
the study of chaotic phenomenon have received great
attention during the past forty years and many chaotic
systems have been proposed to research2-5. Due to its
complex characteristics such as extremely sensitive toinitial conditions, inherent randomness, continuous
broadband spectrum and so forth, chaotic system can be
widely applied in engineering like securecommunication, image encryption and so on6-8. While
hyperchaos is characterized as at least two positive
Lyapunov exponents, which is more complex thanchaos, so it is more suitable for the engineering
application.
Fractional order calculus is a kind of calculus with anarbitrary order, which has become a hot spot during thepast decades for its application in engineering. It isfound that the fractional-order system could reflect the

2. System Analysis
According to the generalized augmented Lü system10,
by introducing linear state feedback controllers into its
second and third equation, Xue et al11 adopt chaos anticontrol to construct the hyperchaotic generalized
augmented Lü system. On this basis, we will
investigate
thecommensurate fractional© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 223 -

Xue Wei, Jinkang Xu, Hongyan Jia

order hyperchaotic generalized augmented Lü system
which is described as follow:
 d q x1ab
x1  x2 x3 ,
 q 
bdta d q x2
 dt q  ax2  x1 x3  x4 , q
 d x3  bx  x x  cx  x x ,
3
1 2
1
1 4
 dt q q
 d x4  dx ,2
 dt qexponents, system(1) presents hyperchaos and we get
the dimension of system(1) asDL  j 

(1)

2

1

LE1  LE2j

LE( j 1)

 LE  2  LE  LE
i

i 1

3

1.6987  0.6500

4 2.118

0.2817  19.5887

indicating that hyperchaotic system(1) has a fractional
dimension with a fractal feather.

Where0<q<1, a,b,d<0 and c  R .Here when q=0.9,
system(1) is characterized as a 3.6 order system(1).

4. Lyapunov Exponent
3. Phase Portraits and Poincaré Maps

In order to analyze the chaotic characteristics ofsystem(1) as system parameters change, we draw the
Lyapunov exponent spectra of system(1) as shown in
Fig. 3(As L4 is always less than -10, so we only give
curves of L1,L2,L3) when parameter a,b change in acertain range.From Fig. 3, we can observe that as a,bvary in a larger range, system(1) has two positive
Lyapunov exponents, which shows that system(1)presents hyperchatic in a large range as parameterschange.

When q=0.9,a=-9,b=-14,c=1,d=-1,the phase portraits ofsystem(1) is shown in Fig.1, It can be seen that thefractional-ordersystempresents
four-wingchaotic attractor.
40

50

30

40
30

20

20
10
10
y

z

0
0

-10
-10
-20

-20

-30

-30

-40

-40

-50
-60

-40

-20

0
x

20

40

-50
-60

60

-40

(a)x1-x2plan

-20

0
x

20

40

60

6

(b) x1-x3 plan

50

L1
4

8

40

L2

2

6

4

20
10

0

2

0

w

z

LE

30

-2

L3

0

-10
-20

-4

-2

-30
-4
-40
-50
-50

-6
-40

-30

-20

-10

0

10

20

30

-6
-50

40

-40

-30

-20

-10

y

0

10

20

30

(c) x2-x3plan

-70

-60

-50

-40

-30

-20

-10

0

a

40

y

(a)q=0.9,b=-14,c=1,d=-1, a  [75,0](d) x2-x4 plan5

Fig.1 Phase portraits of system (1)

4
3

L1

2
6

40

5

30

4

1
LE

50

L2

0
-1

20

3

10

2

-2

1

-3

x3

x3

L3
0
-10

0

-20

-1

-30

-2

-40

-3

-50
-50

-4
-40

-40

-30

-20

-10

0

10

20

30

40

x2

(a) x1=1.5 plan;

-4
-5
-80

-30

-20

-10

0

10

20

30

40

-70

-60

-50

-40
b

-30

-20

-10

(b) q=0.9,b=-14,c=1,d=-1, b  [80,0]50x1(b) x2=0 plan

Fig.3 Lyapunov exponent spectra of system (1)

Fig.2 Poincaré maps of system (1)5. Power Spectrum

Poincaré map is known as an intuitive way todetermine whether a system is chaotic. From Fig. 2, we
can see that Poincaré maps of system(1) are formed bypoints in confusion,which indicates system(1) is inchaos state. Furthermore, by calculating the Lyapunov
exponents of system(1) when q=0.9,a=-9,b=14,c=1,d=-1, we get L1=1.6987,L2=0.6500,L3=0.2817,L4=-19.5887.For it has two positive Lyapunov

In addition, the power spectrum of system(1) is shownin Fig. 4 when q=0.9,a=-9,b=-14,c=1,d=-1. it can beseen that the power spectrum of system(1) is acontinuous decreasing curve with noise background andwide band.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 224 -

The Fractional Order Hyperchaotic
1
0.9
0.8

spectrum

0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

0

1

2

3

4

5
f/Hz

6

7

8

9
4

x 10

Fig.4 Power spectrum of system(1)6. Circuit Implementation
we usually adopt the method of the approximation
conversion from time domain to frequency domain todesign a chaotic circuit. Here we utilize theapproximation of 1/s0.9with discrepancy 2dB to design
the analog circuit of 3.6 order hyperchaotic system(1).
According to Ref. [12], we can get
1
2.2675( s  1.292)( s  215.4)
.
s 0.9 ( s  0.01292)( s  215.4)( s  359.4)

Fig. 6 Circuit diagram of 3.6 order system(1)Based on the circuit diagram in Fig.6, we conduct thecircuit experiment and the result is observed throughthe YB4365 analog oscilloscope as shown in theFig.7，which agrees with the numerical simulation
result, verifying the existence of hyperchaotic attractor
in 3.6 order system(1).

In Ref.[13], a circuit unit is proposed to realize the
1/s0.9as shown in Fig.5.Where R1=62.84MΩ,R2=250KΩ;
R3=2.5KΩ;C1=1.232μF,C2=1.84μF,C3=1.1μF.

Fig. 5 Circuit unit of 1/s0.9

Here LF347N is selected as the amplifier and
AD633JN (the output gain is 0.1) as the multiplier todesign the hyperchaotic circuit. In order to restrict thechange of state variables to the operating voltage of theanalog circuit, the state variables are decreased by 10times, namely ( x1 , x2 , x3 , x4 )  (20 x1 , 20 x2 , 20 x3 , 20 x4 ) .
The analog circuit design of 3.6 order hyperchaotic
system(1) when a=-9,b=-14,c=1,d=-1 is shown in Fig.
6 ,where F09 represents the circuit unit of 1/s0.9 andR1=1.825KΩ, R3=1.111KΩ, R4=10KΩ,R6=0.714KΩ,
R7=R10=10KΩ, R2=R5=R8=R9=50Ω. If we choose R4 as
a reference resistance, we can change the value of
a,b,c,d, by changing the ratio of R3,R6,R7,R10 to R4
respectively.

(a)x1-x2plan(b)x1-x3 plan(c) x2-x3plan(d) x2-x4 planFig.7 Phase portraits of 3.6 order hyperchaotic system(1)through the oscilloscope YB4365

7. Conclusion
In this paper, the fractional order hyperchaotic
generalized augmented Lü system is investigated . By
analyzing phase portraits, Poincaré maps, Lyapunov
exponent spectra and power spectrum of the fractional
order system, it is found that there exists four-wing© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 225 -

Xue Wei, Jinkang Xu, Hongyan Jia

hyperchaotic attractors in the system and the system ishyperchaos with a fractional dimension in a large rangeof parameters. Moreover, take 3.6 order system forexample, an analog circuit is designed to implement thefractionalorder
hyperchaotic
systemandcircuit experiment results agree with the numericalanalysis, providing technical basis for its further
application in engineering.
Acknowledgements
This work is supported by the Young Scientists Fund of
the National Natural Science Foundation of China(Grant No.11202148).
References
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3.
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Lorenz E N. Deterministic Non-periodic Follow.
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Liu C X, Liu L, Liu T, et al. A new butterfly-shaped
attractor of Lorenz-like system.Chaos,Solitons andFractals.2006,28(5) 1196-1203.
Lü J H，Chen G R.A new chaotic attractor coined. Int.
J. Bifurcation and Chaos. 2002，12(3) 659-661.
Chen G R, Ueta T. Yet another chaotic attractor. Int. J.
Bifurcation and Chaos,1999, 9(7):1465-1466.
Qi GY, Chen GR, van Wyk MA, van Wyk BJ and
Zhang Y. A four-wing chaotic attractor generated froma new 3-D quadratic autonomous system.Chaos,
Solitons &Fractals. 2008,38(3): 705－721.
Han Q, Liu C X, Sun L Zhu D R. A fractional order
hyperchaotic system derived from a Liu system and its
circuit realization. Chin. Phys. B 2013,22(2) 020502.
Peng Z P, Wang C H, Lin Y, Luo X W. A novel fourdimensional multi-wing hyper-chaotic attractor and its
application in image encryption. Acta.Phys.Sin. 2014,
63(24):240506.
Sheng Q W, Jiang B. Chaotic encryption algorithm inthe network security technology research andapplication. Network Security Tech. & Appl. 2014,(8):51-51.
Hifer R. Applications of Fractional Calculus in
Physics. (New Jersey: World Scientific, 2001).
Qiao X H, Bao B C. Three-dimensional four-wing
generalized augmented Lü system, Acta. Phys. Sin.
2009,58(12) 8152-8159.
Xue W, Fang Y F, Li Q. A novel four-wing hyperchaotic system and its circuit implementation,
Procedia Engineering, 2012, 29:1264-1269.
Ahlnad W M, Sprott J C. Chaos in fractional-orderautonomous nonlinear systems. Chaos, Solitons andFractals, 2003,16(2) 339-351．
Wang F Q, Liu C X. Study on the critical chaotic
system with fractional order and circuit experiment,
Acta Phys. Sin.2006,55: 3922-392© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 226 -

Selecting Words and Notation Using Literary Datain the Integrated Narrative Generation System
Jumpei Ono†

Department of Software Information Science, Iwate Prefectural University152-52 Sugo, Takizawa, Iwate Science, 020-0693, Japan
Takashi Ogata‡

Department of Software Information, Iwate Prefectural University152-52 Sugo, Takizawa, Iwate, 020-0693, Japan
E-mail: †g236m001@s.iwate-pu.ac.jp, ‡t-ogata@s.iwate-pu.ac.jp

Abstract
This paper presents a way for selecting noun concepts based on the analysis of appearance frequency of noun words
in the data of modern Japanese novels (“Aozora Bunko”). The proposed method is incorporated into somemechanisms in our integrated narrative generation system (INGS). We also show an overview of INGS, inparticular the mechanism relating the proposed method in this paper. Additionally, we introduce a preliminary
experiment to confirm and discuss the effectiveness.
Keywords: Integrated Narrative Generation System, Word Frequency, Conceptual Dictionary, Noun Concepts,
Word Notation, Aozora Bunko.

elements including conceptual dictionaries [3] andlanguage notation dictionary [4]. Though we will givethe mechanism in the next section, overview, INGS
selects the noun concepts (terminal nodes in thehierarchical structure) in an event to be generated in thelimited range (the lower range of one or moreintermediate noun concepts) in the noun conceptual
dictionary. But, the selection is randomly processed. As
the lower range of an intermediate noun concept
includes various types of noun concepts, for example
difficult/easy, new/old, etc., various types noun
concepts are mixed in the generated events in manycases. In the mechanism of INGS, noun concepts arebasically defined by the level of words and noun
concepts are transformed to words with each actual
letter representation (notation) using a language notation
dictionary. So selecting the concepts directly has an
influence on the quality of generated sentences. Our

1. Introduction
Language generation controlled by a story or as anarrative is an important communication ability inrobotics and artificial life. We have been developing anarrative generation system called “Integrated Narrative
Generation System: INGS” [1,2]. In intelligent
informatics such as natural language processing andartificial intelligence, narrative generation or story
generation is an important and challenging topic in thefollowing scientific and applicable points: aninterdisciplinary approach of informatics and literary
theories including narratology, generating consistent
discourse structures, creating advanced contents
technologies, etc. INGS is a synthesis of our various
previous researches of narrative generation.
INGS generates narrative events and the sentencesusing the generation techniques and knowledge© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 227 -

Jumpei Ono, Takashi Ogata

goal to solve it is implementing more strategic
mechanisms for concepts (and notations) selection andthis paper will present a simple method based on thefrequency analysis of words’ appearance as the first step.
In particular, we automatically analyze frequency
information of noun words from novels stored in“Aozora Bunko”, which is a digital library that includes
a variety of texts of modern Japanese novels mainly, toselect noun concepts in events to be generated according
to the acquired frequency information. For example, if
we use noun concepts according to high-frequency nounwords, the output text will be more readable.
There are studies of word familiarity relating to thetheme of this paper. [5] considered the readability of asentence based on the relationship between wordfamiliarity and word frequency and sorted sentences
based on readability according to the idea that highfrequency words are high-familiarity words.
In the large framework, our goal is not necessarily
only readable story generation. Readable story
generation based on high appearance frequency, namely
concepts and words selection easy to read, is one of thestrategic choices. In contrast, we will be able to makemore difficult or strange sentences intentionally using
low-frequency words and concepts. Additionally, for theprocessing relating to the final words notation in INGS,
for instance, we will be able to concentrate on analyzing
specific authors to simulate or imitate words
representation in each style of authors. Though it is not
the theme of this paper, we will mention the attempt.
2. An Overview of INGS and Noun Concept
Selection Mechanism

Generative
Parameters

Structural
Knowledge

Story
Mechanism

Discourse
Mechanism

Story
Techniques

Conceptual
Dictionaries

Expression
Mechanism
Discourse
Techniques‐ Natural Language‐ Music‐ Visual

Story

Discourse

Fig. 1. The architecture of INGS

2.1. Generation Mechanisms
In story generation, “story” means the content of anarrative or a temporal sequence of events. In narrative
generation process, a story is described as a tree
structure in which the basic elements are an event that is
described by conceptual representation and a relation
for combining among events or sub-structures.
Additionally, an event is principally combined with apreceding state and a subsequent state. The major
functions are holding the knowledge about a story world
and managing the coherency of the flow of events. A
story structure including events and relations isgenerated using one or more story techniques and states
associating with the events are made according toanother mechanism.
In the next discourse mechanism, “discourse” means
how to form a narrative, which is a sequence or astructure of events to be narrated actually. A discourse
is corresponded to the structure as the transformation ofa story’s structure and various discourse techniques
process the structural transformation. We use thenarrative discourse theory by Genette [6] for the
categorization of discourse techniques. In the expression
mechanism, “expression” means the part for
representing the above conceptual representations withsurface media including mainly natural language, such
image or visual media as animated movie, and music.
The first two phases are conceptual generation partsin a process. Both of story and discourse structures arecommonly represented by each structure to be operated
respectively using various types of techniques. As statedabove, we call the structural operation techniques “story
techniques” and “discourse techniques”.

This section will give an overview of INGS architecture
to show the noun concepts selection mechanism. The
architecture of INGS has two types of macro level parts(Fig. 1.): “generation mechanism” and “knowledge
mechanism”. The former part has three main elementsof “story generation mechanis m”, “discourse
mechanism”, and “surface generation mechanism(including language, music, and image)”. The mainelements of the latter “knowledge mechanism” are“conceptual dictionaries (for noun concepts and verb
concepts chiefly)”, “language notation (letter notation)
dictionary”, “state-event transformation knowledgebase”, and so on.
© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 228 -

Selecting Words and Notation

2.2. Conceptual Dictionaries

2.3. Concept Selection in Story Generation

An event is constructed with the instantiation ofconceptual materials stored in conceptual dictionaries
for noun concepts and verb ones. Each dictionary has ahierarchical structure from higher concepts to lower
ones. The noun dictionary currently contains 115765
terminal concepts and 5809 intermediate ones. Eachintermediate concept has (1) a list of hyponymy
concepts, (2) the number of depth in the hierarchy, (3)the serial number of the super-ordinate concepts, and (4)the range of serial numbers of the hyponymy concepts(Fig. 2.). Fig. 3 shows the description of a noun concept.

First, we show a story structure to be generated in thestory generation mechanism in INGS to explain a story
generation process (Fig. 5.). As indicated in the above
figure, the hierarchical structure of a story is constructedwith three elements: (1) an “event” consists of a verb
concept and some noun concepts (as stated above), (2) a“state” is the attribute information to position before andbehind of the events (an event changes a state to thenext state), and (3) a “relation” connects the lower
structure semantically.
Relation1(<intermediate concept>(hierarchy(depth <number>)(hype <intermediate concept>)(hypo <intermediate concept>)(terminal <terminal concept>)))Relation2

Event1

Fig. 2. Description form of an intermediate concept

State
1

(男[Man](hierarchy(depth 8)(hype 人間〈男女〉[Human<Gender>])(hypo 男[接辞] [Man[Affix]])(terminal 男[Man] 父[Father] 少年[Boy]…)))

Relation3

Event2State
2

Event3

State
3

State
4

Event4

State
5

Fig. 5. The form of a story structure

A story generation process is equal to expanding ortransforming a story structure. In particular, under someparameters (“macro-structure”, “length”, etc.) given in
the first step, a story technique makes a new event or a
sub-structure including one or more new events using avariety of story contents knowledge to integrate theminto the original structure using various relations.
According to a generated new event, new states are also
generated. When an event is generated, the above story
contents knowledge gives the basic form based on thedescription of a case-frame in Fig. 4. Each constraint
indicates the range in the noun conceptual dictionary to
decide a noun concept. Actually, each noun concept inan event is transformed into an instance using attributeinformation if necessary. Finally, each generated event
is transformed into a natural language sentence using
specific word representation. Fig. 6 shows the process.

Fig. 3. The description of the noun concepts, “Man”

On the other hand, the verb concepts hierarchy has
11951 terminal concepts and 36 intermediate categories.
Each terminal verb concept describes the following
three elements: (1) a “sentence-pattern” to show apattern for a sentence including the verb, (2) one ormore “case-frame(s)” to show the types of required
noun cases, and (3) one or more “constraint(s)” todefine the range in the noun conceptual dictionary inwhich each noun concept in the above case-frame(s)requires. For example, Fig. 4. is the description of theverb concept, “eat”. When INGS materializes theframework of a case-frame, it selects concepts in thenoun conceptual dictionary. The objective of the paper
is revising the selecting method.
((name 食べる2 [eat])(sentence-pattern “N1 が N2 を食べる”[N1 eat N2])
(case-cons-set((case-frame ((agent N1) (object N2) …))-死人[corpse]
N1 (constraint ((人[human]-人間〈人称[person]〉-準人間[semi-human])(食料[food] -調味料[flavoring]N2-飲物[drink]・たばこ[cigarette]))) …) …)

Selecting
Referringverb concepts noun concepts(eat(agent Human)(object Food))

Verb conceptual
dictionary

Human ⇒ “Man”
Food ⇒ “Toast”

Noun conceptual
dictionary

Transforming noun
concepts into
instances(eat(agent Man#1)(object Toast#1))((ID Man#1)(age 20)…)((ID Toast#1)(size small)…)

Generating
sentences
The Man eats aslice of Toast.

Language notation
dictionary

Fig. 4. The description of a verb concept
Fig. 6. The process of generating an event© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 229 -

Jumpei Ono, Takashi Ogata

mechanism adds frequency 1 to all the concepts. We
count the overlapped noun concepts as one concept, so
the above 28421 are equal to the number of sets ofoverlapped concepts and 36187 concepts that the 28421
are eliminated from all the overlapped concepts are notincluded in the range of the noun conceptual dictionary
to add frequency. Considering the above conditions, thenumber of terminal noun concepts to which appearance
frequency should be added is 76029 (for 115765
terminal concepts in the noun conceptual dictionary).
According to the conditions, the mechanism
analyzes the word appearance frequency in the target
texts by morphological analysis. As stated above, themechanism accounts the frequency of “general nouns”,“nominalized adjectives”, and “nouns as stems ofadjectival verbs”. Next, the proposed mechanism relates
the above nouns with frequency to terminal noun
concepts in the noun conceptual dictionary by simple
matching. Though each description of noun concepts istheoretically a kind of label and the linguistic notation is
proposed by the language notation dictionary, a generalnotation is actually used for each of the description.
As a result, we have acquired word frequency for
57131 nouns in the target texts and linked the frequency
to 44332 terminal noun concepts. 44332 concepts areequal to 58% for all terminal noun concepts in the noun
conceptual dictionary in INGS.

As mentioned above, in applying constraints to choose
noun concepts, terminal concepts under an intermediate
node in the noun conceptual dictionary are not fullyorganized. In the following sections, we will present a
method using words’ appearance frequency instead ofrevising the organization or structure.
3. Concept Selection Based on Word Frequency
We will describe the method to analyze word frequency
and show some generation examples using the result.
3.1. The Method of Word Frequency Analysis
We have analyzed word frequency in 4980 texts (mainly
novels) in Aozora Bunko from 1872 through 1963 touse it for noun concept selection in story generation.
The image of the processing is shown in Fig. 7. KH
Coder [7] is used in the analysis in the first step toaccount the word frequency. And the mechanism links
acquired frequency information to the corresponded
noun concepts in the noun conceptual dictionary.
2.Linking word and noun
1. Measuring word
concept
frequency
Word frequency Noun conceptual dictionary
Boy 170

Text

Father98

GenderParentMan
Boy

FatherFather

3.2. Concept Selection Based on Frequency
Fig. 7. An image to use frequency

Fig. 8. is an example of story generation using noun
concepts with the highest frequency. Actually, it is the
We describe the method in detail. Firstly, in the texts
result transformed by “sentence generation mechanism”(corpus) to be used, we have accounted the wordin INGS. An event in a story is simply transformed intofrequency for “general nouns”, “nominalized
a sentence based on the sentence pattern. Additionally,
adjectives”, and “nouns as stems of adjectival verbs”.
the future version of INGS is necessary to change someOn the other hand, in the noun conceptual dictionary
noun representations in each sentence to another nouns
to give frequency, for such 3549 noun concepts thatusing attributes for each noun concept. For instance,
become a concept by the combination with another“child” will be changed to an actual name such asconcepts as “ ～ 学 校 [ ～ school]”, we do not give
“Taro”. The mechanism will enable to represent a noun
frequency. Additionally, terminal noun concepts thatconcept by a noun phrase, such as “Taro in a forest”.
have a same name sometimes exist under different
intermediate concepts. For example, two intermediate
4. Preliminary Evaluation and Discussion
concepts, “ 君 主 [ 王 ](monarch)” and “ 貴 人 [ 君主](nobleman)”, respectively include a terminal concept,
We conducted a survey to find out the effectiveness of“ 王 (king)”. The number of this type of terminal
selecting noun concepts in stories generated based on
concepts was 28421. When such overlapped terminal
the acquired frequency. The subjects were 4 students.
noun concepts appear in analyzing a text, the© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 230 -

Selecting Words and Notation
子が「子が煙草を摘む」ために出かける。蛇が毒素を薬に混入する。蛇が薬を女に与える。女が眠る。蛇が名人に対して「名人が女を樽に詰める」と命令する。名人が女を樽に詰める。蛇が名人に「名人が樽を海へ投げる」と命令する。名人が樽を海へ投げる。勇士が土から国まで来る。殿が勇士に助けを求める。殿が勇士を送り出す。
勇士が冒険に備える。勇士は国から帝国へ出国する。婆が「勇士が屑を食べる」ことを勇士に命じる。勇士が命に従う。勇士が屑を食べる。婆が百姓を勇士に紹介する。勇士が百姓に会う。百姓が杖を勇士に譲渡する。杖が勇士を庭に連れる。勇士が蛇と戦う。蛇が時計を奥より下に落とす。勇士が時計を拾う。蛇が勇士に敗れる。勇士が女を発見する。勇士が森を飛び出す。勇士が女を捕らえる。勇士が女を方向に連れる。勇士が帝国から脱出する。勇士が帝国より国へ到着する。男が言い張る。男が礼を殿に要求する。殿が勇士と会う。勇士が時計を持参する。勇士が時計を女に返す。女が現実を知る。女が現実を殿に伝える。殿が現実を知る。殿が「男が殿に「男が女を助ける」と嘘を言う」ことに気付く。男の夢が露見する。勇士が公に昇格する。殿が蛇を容赦する。殿が男を容赦する。勇士が女と結婚する。 <<<A child goes out to cull tobacco. A snake mixes a toxin
in medicine. The snake gives a woman the medicine. The woman
sleeps. The snake orders a master to pack the woman in a barrel.
The master packs the woman in a barrel. The snake orders themaster to throw the barrel into the sea. The master throws thebarrel into the sea. A warrior comes from a ground to a nation. A
lord asks for helping the warrior. The lord sends away the warrior.
The warrior provides against an adventure. The warrior departs
from the nation to an empire. An old maid orders eating rubbish to
the warrior. The warrior obeys the old maid. The warrior eats the
rubbish. The old maid introduces a farmer to the warrior. The
warrior meets the farmer. The farmer transfers a staff to thewarrior. The staff takes the warrior to a garden. The warrior fights
the snake. The snake drops a clock from the inside to the bottom.
The warrior picks up the clock. The warrior is defeated by thesnake. The warrior discovers the woman. The warrior rushes out offorest. The warrior catches the woman. The warrior takes thewoman to the direction. The warrior escapes from the empire. The
warrior arrives at the nation from the empire. A man insists to help
the woman. The man demands a reward from the lord. The warrior
meets the lord. The warrior brings the clock with the warrior. The
warrior returns the clock to the woman. The woman knows theactuality. The woman conveys the actuality to the lord. The lordknows the actuality. The lord realized “the man lies “the man helps
the woman”. The man’s dream discovers. The warrior is promoted
to a duke. The lord pardons the snake. The lord pardons the man.
The warrior marries the woman. >>>time limit (5 minutes) for a story. And we prescribed to
not change the already described values and notnecessary to confirm previous values.
Table 1. shows the result. The values indicates thatthe nearer 1, the smaller the sense of strange. This result
was same with previous survey [8] which had been
conducted for 8 subjects in the halfway stage of theanalysis of Aozora Bunko. An interesting point was asfollows. As we did the experiments using generated
sentences directly, the subjects were sometimes had aneffect on contextual information within a sentence or
between sentences. It indicates that contextual
conditions were also considered in selecting concepts.
Considering the problem will be a future issue. On theother hand, in the “average” in this experiment,
differences between [Max] and the others wererelatively small. This indicates that controlling
extremely unreadable word representation is difficult inthis way. It will be also an important future topic.
Table 1. The evaluation result
Subject
A
B
C
D
AverageRandom
1.79
1.40
2.39
1.06
1.66

Max
1.04
1.00
1.69
1.06
1.20

Middle
1.48
1.46
2.25
1.11
1.57

Min
1.63
1.45
2.77
1.26
1.78

Zero
1.85
1.23
2.79
1.04
1.73

The result of the frequency analysis, there were 12799
words which did not exist in the noun conceptual
dictionary. Table 2. shows the two types. “No existingwords” (7928) will be able to add to the noun
conceptual dictionary. This category includes many
complex words. And, in many cases, as the parts are
stored in the current version of the noun conceptual
dictionary, we would like to address the way for using
the knowledge to add the concepts.
In the type of “the problem of notation”, a similar
noun concept with a word exists in many cases. The
number of such words was 4871. In Table 3., we show
concepts to be corresponded to such words. We will beable to connect such words to elements in the language
notation dictionary as mentioned above. The language
notation dictionary is a writing system which stores
letter representations for all concepts using Kanji,
Hiragana, Katakana, etc. The extracted words from
Aozora Bunko which does not match with concepts
directly sometimes are found out in the language

Fig. 8. The result of a story generation based on the frequency(Japanese: the original sentences, English: the translation)

For the survey, we prepared five stories including theabove example based on the following frequency:[Random] when the mechanism selects a noun concept
in an event, it randomly selects a concept from the range
of intermediate concepts, [Max] the mechanism selectsa concept with the highest frequency, [Middle] it selectsa concept with the middle frequency, [Min] it selects a
concept with the lowest frequency, [Zero] it selects a
concept with no description of frequency. In all cases, if
there are two or more candidates, the mechanism
randomly selects one concept.
The procedure of the experiment is as follows. The
subjects read each story and select one of the evaluation
values for pointed nouns: (1) no strange, (2) a few
strange, and (3) very strange. Additionally, we set a© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 231 -

Jumpei Ono, Takashi Ogata

noun concepts based on the frequency analysis of thedata of Japanese modern novels. We have incorporated
the proposed method into our integrated narrative
generation system (INGS), in particular the story
generation sub-system and the noun conceptual
dictionary. And, we have confirmed the effectivenessthrough the preliminary experiments. Our future plan
includes the following immediate and technological
topics: the processing of complex words, expanding thenoun conceptual dictionary using the result of theanalysis, increasing the matching using the language
notation dictionary. Additionally, the strategic control of
the readability (including un-readability) of texts to begenerated is more theoretical issues to be addressed.

notation dictionary. We will be able to use themechanism for such words and concepts.
Table 2. Types of words which did not exist
Types

Exampleネジ[Screw]
The problem of notation
ボランティア[Volunteer]食慾[Appetite]
No existing Complex words 駅前通り[Street in front of station]
words
Othersカフェ[Cafe]

Table 3. Extracted words and the similar concepts
Words
Corresponding concepts
ネジ[Screw]
螺子[Screw]
ボランティア[Volunteer] ボランテア[Volunteer]食慾[Appetite]
食欲[Appetite]

5. Connecting to Word Representation

References

As described above, we have been developing alanguage notation dictionary to give some notation orword (letter) representation for each noun concept (andverb concept). In the part of sentence generation in
INGS in which transforms concepts to actual word
representation, we have been trying to simulate thecharacteristics in various authors’ novels [9]. In
particular, for a novel or a part of a novel, we
statistically analyzed the percentage of Kanji, Hiragana,
Katakana, etc. for main word classes. In Fig. 9. and Fig.
10., we apply the above method to sentences generated
by the proposed method in this paper (Fig. 8.).

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Narratology: The Use of Three Literary Theories in a
Narrative Generation System, The International Journalof Visual Design 7(3) (2014) 31-61.
[3] K. Oishi, Y. Kurisawa, M. Kamada, I. Fukuda, T.
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Narrative Generation System, Proc. of 34th Annual
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[4] S. Kumagai, S. Funakoshi, T. Akimoto and T. Ogata,
Development of a Language Dictionary and a Simple
Narrative Sentence Generation Mechanism, Proc. of the
26th Annual Conference of The Japanese Society for
Artificial Intelligence (2010) 1J1-OS2-8.
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by Readability, Journal Computational Linguistics 33(2)(2010) 203-227.
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Differentiation and Coordination of Two Approaches,
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[8] J. Ono and T. Ogata, Selecting Noun Concept Based on
Quantitative Data: As a Mechanism in the Integrated
Narative Genration System, IEICE Technical Report.
(2015. in printing)[9] M. Kamata and T. Ogata, Analysis and Imitation of theJapanese Letter Notation in Narrative Texts, Proc. of the27th Annual Conference of the Japanese Society for
Artificial Intelligence (2013) 2I4-5in.

子が「子が煙草を摘む」ために出かける。蛇が毒素を薬に混入する。蛇が薬を女に与える。女が眠る。蛇が名人に「名人が女を樽へ詰める」と命令する。名人が女を樽に詰める。蛇が名人に対して「名人が樽を海に投げる」と命令する。名人が樽を海へ投げる。勇士が土から邦に来る。<The rest is omitted>

Fig. 9. Sentences represented according to the notation
analysis of 「蜘蛛の糸 (The Spider’s Thread)」
子が「子が煙草を摘む」ことにでかける。蛇が毒素を薬にこんにゅうする。蛇から薬を女へあたえる。女がねぶる。蛇が名人に対して「名人が女を樽へ詰める」こととめいれいする。名人が女を樽へつめる。蛇が名人に「名人が樽を海へ投げる」こととめいれいする。
名人が樽を海へなげる。<omission> 勇士ガトケイをヒラウ。ヘビガ
勇士にヤブレル。ユウシが女ヲハッケンスル。勇士ガモリをトビダス。ユウシが女ヲトラエル。勇士が女ヲホウコウヘツラネル。ユウシ
ガ帝国よりダッシュツスル。<The rest is omitted>

Fig. 10. Sentences represented according to the notation
analysis of 「みずから我が涙をぬぐいたまう日 (The Day
He Himself Shall Wipe My Tears Away)」

6. Conclusion
In this paper, we have proposed a way for selecting© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 232 -

Evaluation of a Narrative Discourse Generation System
Based on the Concept of “Norm and Deviation”
Taisuke Akimoto

Graduate School of Informatics and Engineering, the University of Electro-Communications, 1-5-1 Chofugaoka
Chofu, Tokyo 182-8585, Japan
Takashi Ogata

Faculty of Software and Information Science, Iwate Prefectural University, 152-52 Sugo
Takizawa, Iwate 020-0693, Japan
E-mail: t8akimo@yahoo.co.jp, t-ogata@iwate-pu.ac.jp

Abstract
This paper deals with the verification of the narrative discourse system that automatically produces a variety of“discourse” structures from an inputted “story” structure through an iterative mutual action between a “narrator”
mechanism and a “narratee” mechanism. In particular, we analyze a series of 10000 generated discourse structures
according to their structural feature values by focusing on the diachronic alternation of “norm”, the narratee’s
expectation in receiving discourses. Based on the results, we discuss the achievement and issues to be addressed.
Keywords: narrative generation system, automatic analyses, narrative discourse, norm and deviation, Jauss.

system that we have developed as a practice of the
expanded literary theory.7, 8
The system automatically produces a series of“discourse” structures from an inputted “story” structure
through an iterative mutual action between a “narrator”
mechanism, which generates discourse structures, and a“narratee” mechanism, which receives the generated
discourses. This cyclic generation model continuously
produces discourse structures through the diachronic
alternations of “norm”, the narratee’s “expectation” inreceiving discourse structures, via its “deviation” by thenarrator. The term “norm” means a fixed frame at thetime of generating discourse structures and “deviation”
means an action to try to produce a new type of
discourse structure by breaking the norm.
This paper objectively consider the behavior of theimplemented system for identifying its achievements,
limitations, and issues to be addressed. We use

1. Introduction
Automatic narrative generation is a challenging topic in
the field of artificial intelligence. In this topic,
methodology for evaluating systems is a difficult issue
and has addressed by several researchers. Rowe et al.1
and Zhu2 argued for the need to combine the multiple
aspects including the authorial process, generated texts,
and reading process in the evaluation of narrative
generation systems. Pérez y Pérez3 and Peinado et al.4
tried to formalize such evaluation criteria as coherence,
interestingness, and novelty in generated narratives.
We have been addressing the development of the“Integrated Narrative Generation System” (INGS) basedon an “expanded literary theory”, an interdisciplinary
approach to narrative generation mechanism in INGS
across informatics and literary theories.5, 6 This paper
deals with the verification of the narrative discourse© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 233 -

Taisuke Akimoto, Takashi Ogata

to the 13 discourse techniques are commonly used in thegenerative goal and the expectation: supplement,complexity, suspense, length, hiding, descriptiveness,
repetition, diffuseness, implication, and temporalindependency. Each parameter takes a value of 1 (small),2 (medium), or 3 (large).
The diachronic change of output discourses is arisen
from the change of parameters’ values in both thegenerative goal and expectation. This mechanism ismodeled based on our original reinterpretation of a partof the literary history model by Jauss10. The narrator
basically sets the generative parameters to fit thenarratee’s expectation and generates discourse
structures iteratively. The narratee increases his
satisfaction by receiving the fitted discourses to the
expectation. The process, however, eventually reaches apoint where the narratee gets tired or his satisfaction
begins to fall. The turning point of the satisfaction isarbitrary set by the variable np. When this happens,“deviation” occurs and the narrator abandons a portionof the old generative parameters and moves to a newcycle of discourse grounded on the newly found strategy.
The narratee’s expectations change according to thereconstruction. In this generation model, the role of thenarratee’s expectation is to hold a “norm” for discourse
generation.

quantitative analyzing methods based on a conceptual
framework of “norm and deviation”.
2. An Outline of the Narrative Discourse System
The narrative discourse system was implemented with
Common Lisp. It automatically produces a series of
discourse structures from an input story structure. The
details of the mechanism and the generation examples
were shown in our previous papers.7, 8
2.1. The discourse structure generation
The generation of a discourse structure in the systemmeans a transformation from a story structure into a
discourse structure. A story is a content of a narrative ora temporal sequence of events. In contrast, a discourse
means a structure of how the story is narrated. Each of astory and a discourse is represented with a tree structure.
The main element of the tree structure is “event”, acharacter’s action, at each terminal node. Each internal
node is a “relation” among the child nodes.
The structural transformation is done by using“discourse techniques” which define transformational
operations of a part of the structure. The techniques aredefined by referring a part of the discourse categories by
Genette.9 13 types of discourse techniques including
temporal ordering, repetition, and so on are
implemented in the system. Different discourse
structures are generated according to what techniques
are used and where the techniques are applied in the tree
structure. The techniques to be used are determinedbased on the narrator’s “generative goal” which we will
describe latter. On the other hand, since the target ofeach technique is decided at random with several
conditions, the output structures by a same generative
goal have relatively small differences.

3. The Framework of the Analyses
This section describes the framework of the analyses fora series of discourses produced by the system.

3.1. The structure of a series of discourses
A series of discourses can be structured as Fig. 1. Eachallow beside “generative goal” and “expectation” in the
figure means the duration of same parameters’ values. A“shift in norm” means a change of a parameter’s value
2.2. The generation cyclein the expectation. We use a subscript number to
The output discourse structures gradually change
indicate a specific norm like “normi”. We call athrough the mutual action between the narrator and thediscourse generated by the generative goal equal to thenarratee, called “generation cycle”. In each step, theexpectation “normative discourse” and a discourse by anarrator generates a discourse structure from a story
generative goal not equal to the expectation “deviatedstructure according to a set of parameters as “generative
discourse”. The processing of “deviation” is to change agoal” or targeting structural features. On the other hand,parameter’s value in the generative goal at random andthe narratee feeds back an evaluation of the generated
it causes a “shift in norm” in the next step. By
discourse according to a set of parameters as
segmenting the series with each “shift in norm”, the tale“expectation” or desiring structural features. The ten
discourse in each segment is the “deviated discourse”
parameters corresponding to structural features relevant
and the others are “normative discourses”.
© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 234 -

Evaluating Narrative Discourse System
Set of normative
discourses
D

Generative
goal
Expectation

Norm1

D

D

(iii) Degree of shift in norm: For clarifying the mannerof actual changes in local generation spaces, itcalculates the magnitude of the difference of eachnorm from the last norm.
(iv) Novelty of norm: If local generation spaces arealways different with all the past local spaces, thesystem can produce novel norms and discourses
continuously. This analysis calculates themagnitude of the difference of each norm from themost similar norm in all the past norms.

Deviated
discourse…

D

Deviation

D

D

…

D

…

Norm2

Shift in norm

D =a discourse structure

Fig. 1. The structure of a series of discourses.

3.2. Four aspects of the analyses
The basic idea of the analyses is to treat each discourse
structure as a set of numerical values which represent
structural features corresponding to the ten parameters.
We call the values “discourse feature values” (DFVs).
Each DFV is automatically calculated from a generated
discourse. For example, “length” and “complexity” arerespectively calculated based on the number of terminal
nodes in a discourse structure and the number ofrelations needed for defining temporal ordertransformation. A set of discourse structures can bespacially treated based on their DFVs. The degree of
deviation is calculated as the distance between a spaceof normative discourses and the deviated discourse. The
difference between two norms is also calculated as thedifference of their spaces.
Based on the above method, we programed theanalyses which consists of following four aspects.
(i) Local generation space: The role of the norm is torestrict the generation space into a certain range.
For confirming the behavior of norm-based
generation, it analyzes the characteristics of the setof normative discourses in each norm.
(ii) Degree of deviation: The deviation is a process totranscend the generation from the local generation
space at the time. For clarify the actual action of thedeviation, it calculates the distance of the deviateddiscourse from the local generation space.

4. Results and Discussions
We executed the system 10000 steps. The input story
was same with the story in Ref. 8: A warrior rescues aprincess who was abducted by a snake, the plot
consisting of 16 total events. The value of np, theturning point of the narratee’s satisfaction, was 200.
The DFVs of each discourse were automaticallycalculated. The program preliminary analyzed the range(minimum and maximum), average, and standard
deviation of each DFV in all the discourses (Table 1). In
addition, 8982 patterns of discourse structures based on
DFVs were counted from all the outputs.
Next, the series of discourses was divided into 271
segments, namely norms. The average of segment
length (number of discourses) was 36.90 and theminimum and the maximum were 17 and 113.
4.1. Local generation space
The analysis (i) analyzed the normative discourses ineach norm by the same manner with the above analysis.
Table 2 shows two examples of the results. The ranges
of DFVs were restricted from the entire set and eachnorm has different characteristics.
As an issue to be considered, although each local

Table 1. Ranges, averages, and standard deviations of DFVs in all the discourses.

supplement complexity suspense length hiding descriptivenessminimum
0
0
0
11
0
0
maximum
4
50
8
52
10
8
average
2.74
8.07
0.69
28.97 3.86
3.44
SD
1.32
4.48
0.99
6.16
2.34
2.17

repetition diffuseness implication
0
-9
0
24
20
6
6.78
2.92
1.90
3.67
4.21
1.29

temporal-independency
0
2
1.22
0.71

Table 2. The local generation spaces in norm1 and norm181.

Norm1 (cycles 1-113)
supplement complexity suspense length hiding descriptiveness repetition diffuseness implication
minimum
0
0
0
11
3
0
0
-5
0
maximum
0
0
0
13
5
0
0
-3
0
average
0.00
0.00
0.00
12.30 3.70
0.00
0.00
-3.70
0.00
SD
0.00
0.00
0.00
0.65
0.65
0.00
0.00
0.65
0.00
Norm181 (cycles 6674-6710)
supplement complexity suspense length hiding descriptiveness repetition diffuseness implication
minimum
4
8
0
19
5
1
3
-5
1
maximum
4
40
5
32
8
3
14
6
3
average
4.00
14.94
1.97
25.67 6.72
2.42
6.50
-0.22
2.61
SD
0.00
7.20
1.26
3.24
0.90
0.72
2.75
2.69
0.54

temporal-independency
0
0
0.00
0.00
temporal-independency
0
0
0.00
0.00

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 235 -

Taisuke Akimoto, Takashi Ogata

quantitatively showed that the diverse discourse
structures were arisen through the restriction ofgeneration space based on the norm and theaccumulation of small shifts in the norm.
In addition, mainly two issues in the mechanism
were clarified: the saturation of the narratee’s
expectation is arbitrary defined regardless the actual
reception of generated discourses, and the narrator fails
the deviation often in a practical sense. A solution is toembed the analyzing program into the narratee
mechanism for controlling the generation cycle basedon the actually generated discourses.
The diversity of generable narratives will an
important ability for narrative generation systems. The
idea of analyzing method proposed in this paper will beapplied to other narrative generation systems forclarifying the holistic generation ability.

generation space had different characteristics, the timing
in which the narratee’s expectation is saturated isarbitrary defined by the value of np. On one hand a lotof overlapped discourses with others were appeared ingeneration spaces which have small ranges like norm1.
On the other hand, large generation spaces like norm181
were moved to the next norm before the space was not
filled sufficiently. A solution is to redefine thesaturation as the filling of the local generation space.
This calculation will be embedded into the narratee side.
4.2. Degree of deviation
The analysis (ii) calculated the degree of deviation ineach norm based on the distance between the local
generation space and the deviated discourse. As theresult, on one hand about 66% of deviated discourses
were positioned outside of the space at the time. On theother hand, about 34% of deviated discourses were
included in the space (i.e., the deviation was failed in apractical sense). Such failures occurred due to thepartial overlapping between the current local generation
space and the deviated (subsequent) space. A solution is
to use this analyzing method in the narratee mechanism
for judging the success or failure of the deviation.

References
1. J. P. Rowe, S. W. McQuiggan, J. L. Robison, D. R. Marcey,
and J. C. Lester, STORYEVAL: An empirical evaluation
framework for narrative generation, in Papers from the 2009
AAAI Spring Symposium, TR SS-09-06 (2009), pp. 103–110.
2. J. Zhu, Towards a mixed evaluation approach forcomputational narrative systems, in Proc. 3rd Int. Conf.
Computational Creativity (2012), pp. 150–154.
3. R. Pérez y Pérez, The three layers evaluation model forcomputer-generated plots, in Proc. 5th Int. Conf.
Computational Creativity (2014).
4. F. Peinado, V. Francisco, R. Hervás, and P. Gervás,
Assessing the novelty of computer-generated narratives using
empirical metrics, Minds & Machines, 20(4) (2010) 565–588.
5. T. Ogata, Expanded literary theory for automatic
narrative generation, in Proc. Joint 7th Int. Conf. Soft
Computing and Intelligent Systems and 15th Int. Symp.
Advanced Intelligent Systems (2014), pp. 1558–1563.
6. T. Akimoto and T. Ogata, An information design ofnarratology: The use of three literary theories in anarrative generation system, The International Journal of
Visual Design, 7(3) (2014) 31–61.
7. T. Akimoto and T. Ogata, A narratological approach fornarrative discourse: Implementation and evaluation of thesystem based on Genette and Jauss, in Proc. 34th Annual
Conf. the Cognitive Science Society (2012), pp. 1272–1277.
8. T. Akimoto and T. Ogata, Towards a discourse mechanism
in narrative generation system: Proposal of a system
introducing narrative discourse theory and reception theory,
Cognitive Studies, 20(4) (2013) 396–420.
9. G. Genette, Narrative discourse: An essay in method,transl. J. E. Lewin, (Cornell University Press, NY, 1980).
10. H. R. Jauss, Literary history as a challenge to literary theory,in Toward an Aesthetic of Reception, transl. T. Bahti,(University of Minnesota Press, MN, 1982), pp. 3–45.

4.3. Degree of shift in norm
The analysis (iii) calculated the degree of shift in eachnorm based on the difference of each local generation
space from the last space. We confirmed that the local
generation spaces were gradually shifted. It means thatthe holistic diversity of generated discourses was arisen
through the restriction of generation space based on thenorm and the accumulation of small shifts in the norm.
4.4. Novelty of norm
The analysis (iv) calculated the novelty of each norm
based on the difference of each local generation spacefrom the most similar norm in the past. We clarified thatthe novelty was gradually decreased through thegeneration cycle. The reason is that there is a limitationof the possible combinations of the parameters’ values
in the generative goal and expectation.
5. Conclusion

In this paper, we analyzed a series of discourse
structures produced by the narrative discourse system.
We used an analyzing program based on a conceptual
framework of “norm and deviation”. The results© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 236 -

An aggregating approach of target enclosure of robot
swarm
Masao KUBO
Dep. of Computer Science National Defense Academy of Japan
Yokosuka,Kanagawa,239-8686,Japan Email: masaok@nda.ac.jp
Hiroshi Sato
Dep. of Computer Science National Defense Academy of Japan
Yokosuka,Kanagawa,239-8686,Japan Email: hsato@nda.ac.jp
Akihiro Yamaguchi
Department of Information and Systems Engineering
Fukuoka Institute of Technology Fukuoka, 811-0295, Japan Email: aki@fit.ac.jp

Abstract—This paper presents a robot swarm model to enclosea target. The robots use information of the target and theireach neighbors. Every robots have a own but same torch likesignal emitter and they can observe the sum of the intensityof their torches. In this paper, the robot uses a direction withstrongest intensity of the emitter as direction of its nearest
neighbor. We expect that this new approach makes the robot
swarm more simple and scalable. We confirm this model bycomputer simulations.

I. I NTRODUCTION
In this paper, a new implementation of a robot swarm forenclosing a target is examined. The swarm employs a same
limited transmission range signal emitter. We suppose that thiscommunication system makes a large swarm be built moreeasier.
Target enclosure task, which is useful for monitoring disastersites and unknown vehicles, has recently become an important
goal for multiple robots. Robots can operate in dangerous
circumstances, replacing human presence.
These sites are usually far from where its operator is. It
happens that the group of robots notices the fact of the exact
number of sites to be observed and their location. Therefore, it
is desirable that the larger number of robots than the necessary
size is employed. At least, it will accept the large number oftargets than their expectation.
We focused on the study of Takayama et al.[?]. In thismodel, each robot needs information of directions to one
neighbor and to its target. As in other studies, this model also
requires the Hamiltonian cycle constraint[?][?].
Recently, research[?] uses hybrid system theory[?][?] andshows robots controlled by Takayama’s work referencing nearest neighbors can also enclose a target. The robot swarm withthis new reference model can enclose targets while the size ofgroup is changing[?].
Scalability is one of important properties of a robotic swarm.
Although this target enclosing algorithm has this ability, theaccurate observation of the neighborhood usually becomesdifficult as the number of robot increases.

For example, ways to know who is a nearest neighboring
robot can be enumerated as follows. (1) every robot has its
unique signal emitter. and a receiver measures the power ofthe incoming signals. (2) each robot observes its neighborhood
visually and measures the size of robots. These 2 methods
are based on relative distance among robots. (3) each robot
calculates own location on a common reference frame. The
information of the robot’s location are exchanged by broadcasting. The first method is stable but the number of robot islimited. The second method is reasonable but the visibility
is strongly influenced by environment condition. The third
method is the best way when we can use GPS and a wirelesscommunication network.
We focus on a system with limited range homogeneous
signal emitter. We call it torch system. For example, Kilobot[?]
has a same small LED emitter as a communication device anda flock of 1000 kilobots can form a large shape. However, thetransmission range of their LED light is about 6 robot length.
Swarmbot[?] proposes that the robot having a homogeneouslight can collect the sufficient number of robots to solve a task.
Each robot has same color lights around its body. The intensityof light becomes strong as robots increase. If the intensity oflight is too strong a robot will not join the group.
Advantage of this torch system is its high procurability.
The very limited transmission range provides less interferenceof communication. Therefore, emitters which use same signal
band can be installed into many robots. Also, its small energy
consumption is also an advantage. The more smaller range
communication only needs smaller energy consumption.
In this paper, we examine an robotic swarm with torch
system for the target enclosing task. We adopt Takayama’s
work for the control scheme of robot but the referencing robot
is different to this work. We propose that a most powerful
signal direction is used as the direction of the referencing
robot. We call this direction “MOPS” direction. In this paper,
we show the proposed robotic swarm can enclose a target
successfully by computer simulation.
This paper is composed as follows. Firstly, Takayama’s
work is introduced. Next, the proposed method based on

c The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan.10-12, Oita, Japan- 237 -

Masao Kubo, Hiroshi Sato, Akihiro Yamaguchi

Fig. 1.

Fig. 2.

Diffusion of light of torch

Fig. 3.

The most powerful signal(MOPS) direction

Model of Takyama’s enclosing a target algorithm: α, β.

MOPS is shown. In the third section, communication protocols
are proposed. In this section, we show one of the protocols
has reasonable scalability for this robotic swarm. Then, by
the computer simulations, we show the ability of the target
enclosing task of the proposed system.
II. TAKAYAMA’ S TARGET ENCLOSING MODELFirstly, Takayama’s target enclosing model is explained．
We assume that all robots choose the same target. We
assume that on a two-dimensional (2D) plane, there is only
one target O at the origin and n robots. Robots are numberedcounterclockwise as P1 , . . . , Pn , and ri is the position vectorof the robot Pi .
To achieve this task, Takayama et al.[?] proposed thefollowing model. Each robot determines its control input,
speed vi , and angular velocity ωi using two aspects of angular
information: relative angles with respect to the target and an
anterior neighboring robot,denoted as αi and βi , respectively.
As a result, rotational movement occurs with a central focus
on the target.
vi = f βi(1)

ωi = vi /r̄ − k cos αi(2)where the parameters r̄, k, and f > 0 specified beforehand.
Pi+1 is the robot to which Pi refers, and r̄ is the expecteddistance to the target. In Takayama et al.’s model, the i-th
robot refers to the i + 1-th robot, and the n-th robot refers to
the first robot P1 . That is, if the relationship between a robot
and its reference robot is considered as a link in graph theory,
the graph of the group of robots must be a Hamiltonian cycle.
The authors proved the convergence to the goal state of thetarget enclosing task under this constraint.
A. Nearest neighboring robot as the reference[?] examined a new reference robot scheme in which eachrobot considers its nearest neighboring robot as its reference
robot. Each robot controls itself as described in equations ??
and ??, but it chooses its nearest neighbor as its reference
robot. This system has higher scalability because individual
robots need not be identified to observe the nearest robot.

III. T HE PROPOSED REFERENCE MODEL : THE MOST
POWERFUL SIGNAL DIRECTION REFERENCE MODEL ,MOPS
MODEL
We propose a new robotic swarm for the taget enclosuretask. Each robot has the own but same specification torch.
Additionally we propose a most powerful direction of theirsignal is used as the direction of the referencing robot. First,
we explain the torch which the robot has.
A. Torch
Every agent has a same torch and it can turn on/off its torch.
The maximum intensity of all the torch is same and known
beforehand. We suppose that the speed of propagation of thesignal of the torch is so fast that an agent can observe thesignal immediately.As shown in Fig.??, during the flight ofthe signal, the power of the signal is attenuated. The signal
propagation function p(d) is described as follows.
{ (d/L)γd>Lp(d) = (d/L)2(3)
1(otherwise)
where d is the distance from the emitter, L is the length of anagent, γ is the attenuation coefficient per L. The equation ??
represents the spherical diffusion of a light. An agent closer
to an emitter receives more stronger signal.
B. target enclosing model with MOPS direction referencemodel
We explain procedure of receiving signal of the proposed
method. Agent Pi uses the most powerful signal direction

c The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan.10-12, Oita, Japan- 238 -

Aggregating approach target enclosure

average of the number of observed robot

2.0
10 robots/sketch_141116a
random_start+fixed_output_span
reactive+random_stop
reactive+minimum_span
greedy_fixed_output_span
greedy_random_stop

1.5

1.0

0.5

0.0
0.0

0.2

0.4

0.6

0.8

1.0

parameter

Fig. 4.

The number of signal which a robot can receive
Fig. 5.

instead of the angle from Pi to Pj (=βi ). Also, we call theangle βi0 . Fig.?? shows this angle. Now, we suppose agent Pi
has several signal receivers. We call it sensor primitive. The
direction of k-th sensor primitive spk is θk . If |θk −θij | ≤ θs /2,
the sensor primitive spk can sense the signal of Pj . We callθs a visible range of sensor primitive.
The power of sensor primitive spk , sk is∑
sk =p(di,m )(4)

The performance of the 5 proposed strategies (10 agents)
5.0

N/eval(B)

4.5

4.0

3.5

Pm ∈visible

3.0
50

100

150

200

250

300

N

where di,m is the distance between Pm and Pi and p is thesignal propagation function in eq.??. βi0 is the substitution ofβi .
βi0 = 6 vi Oθarg−max sk(5)kIV. T ORCH FLASHING SIGNAL STRATEGY
In this section, we propose several torch flashing strategies
and evaluate them by computer simulation. We show thehigh performance of protocol B which can collects sufficient
information about neighbor agents.
Generally, an agent cannot observe its neighborhood when
its torch is turned on because the power of own torch is toostrong. This is a kind of dilemma. An agent should turn its
torch on to notify other agents its existence. However, an agent
should turn its torch off to know existence of other agents.
Therefore, some reasonable torch flashing signal strategies are
required.
We examine the following 5 strategies.(A) turn on with probability r and stop after the lapse of a fixed predetermined seconds(we call this strategy random start+fixed output span).
(B) turn on when someone starts and stop with probabilityr(reactive+random stop)[?]. (C) turn on when someone starts
and stop after the lapse of a fixed predetermined seconds(reactive+minimum span).(D)turn its torch on always andtake a break with fixed timing(greedy fixed output span).
(E)turn its torch on always and take a break with probabilityr(greedy random stop).
The criteria of the performance of protocol is the averageof the number of signals recognized per interval on whichan agent emits. We call this interval slot.Fig.?? illustrates
an example of emission and signal receiving process of anagent.All agents turn their own torches on or off according
to the slot. The numbers above the line represent the number

Fig. 6. The performance of strategy B according with the number of agents.

of agents which turn their torch on at each slot. If an agent
turns its torch off, the agent can collect correct information ofthese agents. The number of information of agents which theagent can collect is shown below the line. The performance of
protocol x, evalx is the average of these, namely,∑
evalx =Ps∈S receive(s,i)

|S|

i∈N

|N |

{
0
i ∈ Emit(s)receive(s, i) =|Emit(s)| (otherwise)

(6)
(7)

where Emit(s) is a set of agents who their torches are on
during slot s.
Fig.?? shows the performance of the 5 strategies in the caseof a group of 10 agents by computer simulation. The x axis
indicates the parameter r and the y axis shows evalx . Strategy
B is better than other. This strategy can provide average 2.24
agents’ information. This suggests that an agent with strategy
B can collect information of all agents during 5 slots >10/2.24.
Fig.?? shows |N |/eval(B) of strategy B. |N |/eval(B) isthe average number of slots which a robot required to collectinformation of all agents. As this graph indicates, |N |/eval(B)is almost flat and it shows the agent can collect all members’
information during the fixed time span despite of swarm size|N |.
The above experiment, the agent with strategy B can collectsufficient information to do the target enclosing task in aconstant time span. In the next section, we show the resultof target enclosing by computer simulation.

c The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015),Jan. 10-12, Oita, Japan- 239 -

Masao Kubo, Hiroshi Sato, Akihiro Yamaguchi
its nearest neighbor agent is large, namely (|βi0 −βi | > θs ), thetriangle is filled. As you see In Fig.??, the agents can enclose
the target and according with the increase of the number of theagents on this orbit, the number of the filled triangle becomes
small.
We repeated 2000 times this attempt to enclose the target.
The group size |N | is 100. At every attempt the agents started
from different positions. All the attempts succeeded. Fig.??
shows the histogram of time to enclose the target.

Fig. 7.

VI. C ONCLUSION
In this paper, a new implementation of a robot swarm forenclosing a target is examined. The swarm employs a same
limited transmission range signal emitter. Every robots havea own but same specification torch and they can observe thesum of the intensity of light of their torches. The robot uses the
direction with strongest intensity of the light which is an alternative to direction to its nearest neighbor agent. We proposed
the scalable torch flashing strategy and the performance ofthem was evaluated. By the computer simulation, we confirmedthat the proposed system can enclose a target.

An example of initial state of taget enclosure with 60 agents

R EFERENCES

Fig. 8.

An example of near completion of taget enclosure with 60 agentsV.

C OMPUTER SIMULATIONIn this section, the result of computer simulation of enclosing a target by a robot swarm with the proposed MOPS
direction reference model (eq.??) is shown.
The setting of experiment is explained. There is 1 target atthe origin. There are |N | agent which are deployed at random
far from the origin. We suppose that the agents use strategy B
and every agent can receive signal of torch of all agents.
Examples of process of enclosing the target are shownby Fig.??,??. In this case, we set that |N | = 60 and theattenuation coefficient γ = 0.9999. The circle indicates theorbit on which agents enclose the target. A triangle indicatesan agent. The 2 lines from each triangle represent its target andthe direction βi0 −. If the gap between βi0 − and the direction to

Fig. 9. Histogram of time to enclose a target by 100 agents.2000 trials are
examined.

[1] E.Atlee Jackson, Perspectives of Nonlinear Dynamics 1,Cambridge University Press(1995)[2] Tae-Hyoung Kim, Toshiharu Sugie:Cooperative control for target capturing task based on a cyclic pursuit strategy, Automatica 43,14261431(2007)[3] Kobayashi Y., Otsubo K. , Hosoe S., Autonomous Decentralized Control
of Capturing Behavior by Multiple Mobile Robots,Transactions of theSociety of Instrument and Control Engineers, 43(8), 663-671(2007)[4] Masao Kubo, Tatsurou Yoshimura, Akihiro Yamaguchi, Hiroshi Sato,
Takashi Matsubara, Analysis of Angular Information based Target Enclosure Model with Nearest Neighbor Topology,Journal of Japan Society
for Fuzzy Theory and Intelligent Informatics 24(3), 778-790, 2012[5] Masao Kubo, Hiroshi Sato, Tatsuro Yoshimura, Akihiro Yamaguchi,
Takahiro Tanaka, Multiple targets enclosure by robotic swarm, Robotics
and Autonomous Systems 62,pp1294-1304,2014[6] Joshua A Marshall,Mireille E.Brouvke and Bruce A Francis:Formationsof Vehicles in Cyclic Pursuit;IEEE Transactions on Automatic Contorol,
vol49,no11 (2004)[7] Masubuchi I., Zhai G.,Control of Hybrid Systems-V : Analysis andControl of Switched Systems,Systems, Control and Information,52(1),25-31(2008)[8] Michael Rubenstein Christian Ahler, Nick Hoff, Adrian Cabrera, Radhika
Nagpal, Kilobot: A low cost robot with scalable operations designed forcollective behaviors,AAMAS 2013.
[9] Takayama Y., Yamamoto S., Takimoto T., Distributed Formation Control
of a Nonholonomic Multi-agent System for Target-enclosing Operations,
Proceedings of 9th SICE System Integration Division Annual Conference(2008)[10] Xuping Xu; Guisheng Zhai,Some Results on Practical Asymptotic
Stabilizability of Switched Systems,44th IEEE Conference on Decision
and Control, 2005 and 2005 European Control Conference. CDC-ECC’05,3998 - 4003(2005)[11] Yamaguchi H., A Cooperative Hunting Behavior by Nonholonomic
Mobile Robot Troops,Transactions of the Japan Society of MechanicalEngineers. C, 69(688), 3285-3292(2003)[12] F. Ducatelle, A. Förster, G. D. Caro, and L. Gambardella, New task
allocation methods for robotic swarms, in 9th IEEE/RAS Conference on
Autonomous Robot Systems and Competitions, May 2009.

c The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015),Jan. 10-12, Oita, Japan- 240 -

Probability of mixing up a nearest neighbor robot
under target enclosure by robot swarm
Masao KUBO
Dep. of Computer Science National Defense Academy of Japan
Yokosuka,Kanagawa,239-8686,Japan Email: masaok@nda.ac.jp
Hiroshi Sato
Dep. of Computer Science National Defense Academy of Japan
Yokosuka,Kanagawa,239-8686,Japan Email: hsato@nda.ac.jp
Akihiro Yamaguchi
Department of Information and Systems Engineering
Fukuoka Institute of Technology Fukuoka, 811-0295, Japan Email: aki@fit.ac.jp

Abstract—This paper presents a robot swarm model to enclosea target. The robots use information of the target and theireach neighbors. Every robots have a own but same torch likesignal emitter and they can observe the sum of the intensityof their torches. In this paper, the robot uses a direction withstrongest intensity of the emitter as direction of its nearest
neighbor. We expect that this new approach makes the robot
swarm more simple and scalable. We confirm this model bycomputer simulations.

I. I NTRODUCTION
In this paper, a new implementation of a robot swarm forenclosing a target is examined. The swarm employs a same
limited transmission range signal emitter. We suppose that thiscommunication system makes a large swarm be built moreeasier.
Target enclosure task, which is useful for monitoring disastersites and unknown vehicles, has recently become an important
goal for multiple robots. Robots can operate in dangerous
circumstances, replacing human presence. These sites areusually far from where its operator is. It happens that the groupof robots notices the fact of the exact number of sites to beobserved and their location. Therefore, it is desirable that thelarger number of robots than the necessary size is employed.
At least, it will accept the large number of targets than their
expectation.
We focused on the study of Takayama et al.[6]. In thismodel, each robot needs information of directions to one
neighbor and to its target. As in other studies, this model
also requires the Hamiltonian cycle constraint[6][3]. Recently,
research[1] uses hybrid system theory[7][4] and shows robots
controlled by Takayama’s work referencing nearest neighbors
can also enclose a target. The robot swarm with this newreference model can enclose targets while the size of groupis changing[2].
We focus on a system with limited range homogeneous
signal emitter. We call it torch system. For example, Kilobot[5]has a same small LED emitter as a communication device anda flock of 1000 kilobots can form a large shape. However, theFig. 1.

Model of Takyama’s enclosing a target algorithm: α, β.

transmission range of their LED light is about 6 robot length.
Swarmbot[8] proposes that the robot having a homogeneouslight can collect the sufficient number of robots to solve a task.
Each robot has same color lights around its body. The intensityof light becomes strong as robots increase. If the intensity oflight is too strong a robot will not join the group.
Advantage of this torch system is its high procurability.
The very limited transmission range provides less interferenceof communication. Therefore, emitters which use same signal
band can be installed into many robots. Also, its small energy
consumption is also an advantage. The more smaller range
communication only needs smaller energy consumption.
In this paper, we examine an robotic swarm with torch system for the target enclosing task. We adopt Takayama’s workfor the control scheme of robot but the referencing robot isdifferent to this work. We propose that a most powerful signal
direction is used as the direction of the referencing robot. We
call this direction “MOPS” direction. In this paper, we show
the proposed robotic swarm can enclose a target successfully
by computer simulation. We discuss the probability of mixing
up a nearest neighbor robot under the target enclosure task by
robot swarm.
II.

TAKAYAMA’ S TARGET ENCLOSING MODELFirstly, Takayama’s target enclosing model is explained．

c The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan.10-12, Oita, Japan- 241 -

Masao Kubo, Hiroshi Sato, Akihiro Yamaguchi
III. T HE PROPOSED REFERENCE MODEL : THE MOST
POWERFUL SIGNAL DIRECTION REFERENCE MODEL ,MOPS
MODEL
We propose a new robotic swarm for the taget enclosuretask. Each robot has the own but same specification torch.
Additionally we propose a most powerful direction of theirsignal is used as the direction of the referencing robot. First,
we explain the torch which the robot has.
Fig. 2.

Diffusion of light of torch

Fig. 3.

The most powerful signal(MOPS) direction

A. Torch
Every agent has a same torch and it can turn on/off its torch.
The maximum intensity of all the torch is same and known
beforehand. We suppose that the speed of propagation of thesignal of the torch is so fast that an agent can observe the signal
immediately.As shown in Fig.2, during the flight of the signal,
the power of the signal is attenuated. The signal propagation
function p(d) is described as follows.
{ (d/L)γd>Lp(d) = (d/L)2(3)
1(otherwise)
where d is the distance from the emitter, L is the length ofan agent, γ is the attenuation coefficient per L. The equation
3 represents the spherical diffusion of a light. An agent closer
to an emitter receives more stronger signal.

We assume that all robots choose the same target. We
assume that on a two-dimensional (2D) plane, there is only
one target O at the origin and n robots. Robots are numberedcounterclockwise as P1 , . . . , Pn , and ri is the position vectorof the robot Pi .
To achieve this task, Takayama et al.[6] proposed thefollowing model. Each robot determines its control input,
speed vi , and angular velocity ωi using two aspects of angular
information: relative angles with respect to the target and an
anterior neighboring robot,denoted as αi and βi , respectively.
As a result, rotational movement occurs with a central focus
on the target.
vi = f βi(1)

ωi = vi /r̄ − k cos αi(2)

B. target enclosing model with MOPS direction referencemodel
We explain procedure of receiving signal of the proposed
method.
Agent Pi uses the most powerful signal direction instead of
the angle from Pi to Pj (=βi ). Also, we call the angle βi0 .
Fig.3 shows this angle. Now, we suppose agent Pi has
several signal receivers. We call it sensor primitive. The
direction of k-th sensor primitive spk is θk . If |θk −θij | ≤ θs /2,
the sensor primitive spk can sense the signal of Pj . We callθs a visible range of sensor primitive.
The power of sensor primitive spk , sk is∑p(di,m )(4)
sk =Pm ∈visible

where the parameters r̄, k, and f > 0 specified beforehand.
Pi+1 is the robot to which Pi refers, and r̄ is the expecteddistance to the target. In Takayama et al.’s model, the i-th
robot refers to the i + 1-th robot, and the n-th robot refers to
the first robot P1 .
A. Nearest neighboring robot as the reference[2] examined a new reference robot scheme in which eachrobot considers its nearest neighboring robot as its reference
robot. Each robot controls itself as described in equations 1 and2, but it chooses its nearest neighbor as its reference robot. Thissystem has higher scalability because individual robots neednot be identified to observe the nearest robot.

where di,m is the distance between Pm and Pi and p is thesignal propagation function in eq.3. βi0 is the substitution ofβi .
βi0 = 6 vi Oθarg−max sk(5)kIV. C OMPUTER SIMULATIONThe setting of experiment is explained. There is 1 target atthe origin. There are |N | agent which are deployed at random
far from the origin. We suppose that the agents use strategy B
and every agent can receive signal of torch of all agents.
Example of process of enclosing the target are shown by
Fig.4. In this case, we set that |N | = 60 and the attenuation
coefficient γ = 0.9999. The circle indicates the orbit on which
agents enclose the target. A triangle indicates an agent. As you
see In Fig.4, the agents can enclose the target.
[dvipdfmx]graphicx amsmath fancyhdr

c The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015),Jan. 10-12, Oita, Japan- 242 -

Probability of mixing up
successfully, the relative position to the other agents is same.
The distance to a neighbor agent i, i = 1, . . . , |N | − 1 isdi =√2r̄2 (1 − cos(iCθ )).

(6)

Therefore,
di=1 = di=|N |−1 < di6=0,1,|N |−1(7)

There are 2 nearest neighbor agents for the reference agent
when they enclose the target.

Fig. 4.

An example of near completion of taget enclosure with 60 agentsyçThe power of signal of neighbor agent i at the reference
agent can be approximated by Taylor expansion. It is describedas follows.

10ç

B. Fluctuation of power of signal caused by displacement ofneighbor agents

ç
5

di

ç
-10

ó
10

5

-5

γ L L2Si = Ai + Ki ∆i , Ai =, Ki = [ki,r ki,θ ]d2i

x

ki,r =

-5

ç

)
(
di
Lγ L r̄(1 − cos(iCθ )) 2r̄2 log(γ) cos(iCθ ) − 2r̄2 log(γ) + 2Ldi
−
d5i
(9)

ç

ç

-10

Fig. 5. The location of agents when they enclose the target completely(|N | = 8)．The target is at the origin O and 4 indicates the reference agent
andmeans its neighbor agents.

V.

(8)

A NALYSIS OF THE PROPOSED DIRECTION REFERENCE
MODEL

In this section, we describe the result of analysis of the
proposed MOPS reference model. [6] proved that an agent for
any reference model converges to the circle having a radiusof r̄. [2] proved that a group of 3 and 4 agents with thenearest neighbor agent reference model can achieve a uniform
deployment on the circle. In this section, we clarify that it ispossible for a set of adequate sensor primitives to detect thenearest neighbor agent direction if the variance of location ofrobot is small.
A. Definition of this target enclosing task
The following 2 conditions for successful enclosing a target
are required. For any agent Pi , the first condition is that the
distance between Pi and the target at O is equal to r̄. The
second condition is that for any agent Pi angle 6 Pi OPj =2π/|N | where Pj is the nearest neighbor agent of Pi .
Now, we suppose that an agent, i = 0 is at (ri , θi ) = (r̄, 0).
We call the agent with i = 0 the reference agent. Also otheragents, i = i = 1, . . . , |N | − 1 are called neighbor agent.
When they enclose the target successfully, the location of theneighbor agent are (ri , θi ) = (r̄, iCθ ), Cθ = 2π/|N |, i =i = 1, . . . , |N | − 1. We call this location regular position. Even
if the reference agent moves while they keep enclosing a targetki,θ =()
di
Lγ L r̄2 sin(iCθ ) 2r̄2 log(γ) cos(iCθ ) − 2r̄2 log(γ) + 2Ldi
−
d5i
(10)
where ∆i = [∆ri ∆θi ]T . ∆ri is a displacement along thetarget direction from its regular position, ∆θi is a circumferential displacement from its regular position. We assume thatthe displacement ∆ri , ∆θi follows the normal distributions
having average 0 and variance σr2i , σθ2i respectively. Also, we
assume that the fluctuation of power of signal at the reference
agent follows a normal distribution having σi . In this case, theaverage of power of the signal at the reference agent is Ai ,and its distribution σi2 is22
σr2i + ki,θσθ2i
σi2 = Ki Σi KiT = ki,r(11)

where Σi = diag(σr2i , σθ2i ).
The deviation of the signal at the reference agent can becalculated if |N |andσri ,σθi are known. We suppose that Is
means the set of agents which are in visible range of sensor
primitive s. The averagep¯s and the standard deviation Ms ofsignal at the reference agent are described as follows.
√∑
∑
p¯s =
Aj , Ms =
σj2
(12)
j∈Is

j∈Is

c The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015),Jan. 10-12, Oita, Japan- 243 -

Masao Kubo, Hiroshi Sato, Akihiro Yamaguchi

Fig. 6. The probability with which an farther neighbor is recognized as acloser neighbor.
σM:σs=0.5-3

1.2x10M:σs=1
M:σs=21.0

MLimit:∆=1m
MLimit:∆=2m

0.8

The variance of power of signal at reference agent becomes
smaller as the σθi becomes small.
The solid and dashed line represent MLimit with ∆=1m and2m respectively.
In order to detect the difference in distance of ∆m between
the nearest agents almost certainly, the variation of the received
signal M must be smaller than MLimit lines. For example,
this figure shows that even if the nearest neighbor agent’s
location is distributed σθi = 0.5 degree, the reference agent
can detect 1m difference between the 2 nearest for any γ.
On the other hand, the reference agent cannot detect it if thenearest neighbor agent’s location is distributed σθi = 1 and 2
degree.
By this experiment, we expect that if the σθi is sufficient
small, the reference agent can select a correct sensor primitive
which observes the nearest neighbor agent that is ∆ closer
than 2nd nearest neighbor agent.

0.6
0.4
0.2
0.0
0.0

0.2

0.4

γ

0.6

0.8

1.0

Fig. 7. Limit of variance of signal under which an agent can detect thenearest neighbor agents

C. Limit of variance of signal under which an agent can detect
the nearest neighbor agents
We assume that there are 2 sensor primitives s1,s2 of thereference agent. Each sensor primitive observes one of thenearest neighbor agents in eq.7. Also visible ranges of s1 ands2 are so narrow that it receives signal from only one of thenearest neighbor agents. Namely, |Is1 | = |Is2 | = 1.
Now, we suppose that a small displacement at the nearest
neighbor agents occurs while all of the agents move. We
assume that the difference of distance between the 2 nearest
neighbor agents is ∆. In this case, it can be deduced by using
the 3-σ rule the condition for the reference agent to choose asensor primitive correctly which observes the closer agent.
p̄s1 − kM >p̄s1 + p̄s22(13)where p̄s1 ≥ p̄s2 and k is a positive constant. Here k=3 in therest of this paper.
Therefore, if the standard deviation of the sensor primitive
1 +∆)M is smaller than MLimit = p(d1 )−p(d, the reference agent
2k
can almost certainly select the correct sensor primitive. MLimit
is described as follows.
M≤p(d1 ) − p(d1 + ∆)2k=MLimit(14)

MLimit is changed by signal’s attenuation coefficient γ.
Fig.7 illustrates M and MLimit while γ changes. M withσθi = 0.5, 1, 2 degree are illustrated by line with marker +,◦,
and respectively . |N | = 8, r̄ = 10m. σri = 0.1m.

VI. C ONCLUSION
In this paper, a new implementation of a robot swarm forenclosing a target is examined. Every robots have a own but
same specification torch and they can observe the sum of theintensity of light of their torches. The robot uses the direction
with strongest intensity of the light which is an alternative todirection to its nearest neighbor agent.
We analyzed the relation between the most powerful signal
direction and the direction of the nearest neighbor. We can
expect that if the fluctuation of agent is sufficient small and agap of distance ∆ between the nearest neighbor agent and thesecond nearest neighbor agent is given, the agent can select acorrect sensor primitive which observes the nearest neighbor
agent.
R EFERENCES[1] Masao Kubo, Tatsurou Yoshimura, Akihiro Yamaguchi, Hiroshi Sato,
Takashi Matsubara, Analysis of Angular Information based Target Enclosure Model with Nearest Neighbor Topology,Journal of Japan Society
for Fuzzy Theory and Intelligent Informatics 24(3), 778-790, 2012[2] Masao Kubo, Hiroshi Sato, Tatsuro Yoshimura, Akihiro Yamaguchi,
Takahiro Tanaka, Multiple targets enclosure by robotic swarm, Robotics
and Autonomous Systems 62,pp1294-1304,2014[3] Joshua A Marshall,Mireille E.Brouvke and Bruce A Francis:Formationsof Vehicles in Cyclic Pursuit;IEEE Transactions on Automatic Contorol,
vol49,no11 (2004)[4] Masubuchi I., Zhai G.,Control of Hybrid Systems-V : Analysis andControl of Switched Systems,Systems, Control and Information,52(1),25-31(2008)[5] Michael Rubenstein Christian Ahler, Nick Hoff, Adrian Cabrera, Radhika
Nagpal, Kilobot: A low cost robot with scalable operations designed forcollective behaviors,AAMAS 2013.
[6] Takayama Y., Yamamoto S., Takimoto T., Distributed Formation Control
of a Nonholonomic Multi-agent System for Target-enclosing Operations,
Proceedings of 9th SICE System Integration Division Annual Conference(2008)[7] Xuping Xu; Guisheng Zhai,Some Results on Practical Asymptotic Stabilizability of Switched Systems,44th IEEE Conference on Decision
and Control, 2005 and 2005 European Control Conference. CDC-ECC’05,3998 - 4003(2005)[8] F. Ducatelle, A. Förster, G. D. Caro, and L. Gambardella, New task
allocation methods for robotic swarms, in 9th IEEE/RAS Conference on
Autonomous Robot Systems and Competitions, May 2009.

c The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015),Jan. 10-12, Oita, Japan- 244 -

Adaptive Multiple-Model Control of A Class of Nonlinear Systems
Chao Yang
The Seventh Research Division and the Department of Systems and Control, Beihang University (BUAA),
Beijing 100191, China
Yingmin Jia
The Seventh Research Division and the Department of Systems and Control, Beihang University (BUAA),
Beijing 100191, China
E-mails: yangchao_buaa@163.com; ymjia@buaa.edu.cn

Abstract
In this paper, an adaptive multiple-model controller is developed for nonlinear systems in parametric-strict-feedback
form. Unlike the previous results, a switching scheme is not required here to switch the most appropriate model into
the controller design. The new scheme reduces the number of identification models and uses information provided by
all the models more efficiently than previous results by using the convex combination of estimates of parameters. The
method guarantees parameter convergence and global asymptotic stability of the closed-loop system. The global
boundness of closed-loop signals and asymptotic convergence to zero of tracking error are proved. A simulation
example is included to demonstrate the effectiveness of the obtained results.
Keywords: adaptive control, multiple-model design, nonlinear systems, asymptotic tracking

1. INTRODUCTION
In recent years, adaptive control has been wellinvestigated1-5. It is proposed to cope with the problemof parametric uncertainties in systems lager than
traditional method can handle. The typical idea ofadaptive control is estimating unknown parameters online and designing controller with the estimates as thereal ones2. Plenty of different stable adaptive controllers
have been designed for linear or nonlinear uncertain
systems7-9. A distinguished problem for adaptive control
is that transient performance of the system may include
unacceptable large peaks, especially when there exist
large initial estimation errors. To overcome thisdrawback and enhance the transient performance,
adaptive control using multiple models for linear
systems was proposed in Refs.7, 8, 10and 11. The basic
idea of Ref.10 was running in parallel multiple models,and design an index of performance based on
identification errors, so that model closest to the realplant can be selected by switching quickly and transient
response can be improved. Latter, the method was
extended to switching and tuning in Refs.12 and 13.
Numerous of simulations and applications havedemonstrated adaptive multiple-model control canenhance transient performance significantly.
However, the methods are mainly concentrated onlinear systems. Adaptive multiple-model control for
nonlinear systems is initially considered in Ref.14. Then,
adaptive multiple-model control method for nonlinear
requiring persistence of excitation is proposed in Ref.15,
where the unknown parameters can be calculated in thefirst place.
In Ref.16, a sufficient condition concerning theparameter convergence is given by constructing a global,
explicit strong Lyapunov function. Inspired by this, wedevelop a novel multiple model adaptive controller for aclass of nonlinear system in parametric-strict-feedback
form. Unlike the usual hypothesis, the restrictive
matching conditions are not required, but thepersistency of excitation condition is assumed. The
control scheme employs adaptive identification models
based on adaptive parameters from a known compact
parameter set and a virtual identification model, of
which the estimation parameter is both adaptive andresettable. The algorithm reduces the number ofidentification models by using convex combination
method to estimate parameter even as the system is in© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 245 -

Chao Yang, Yingmin Jia

operation. Also, global asymptotic stability of theclosed-loop system is proved.
This paper is organized as follows. The problem
formulation and assumptions are given in Section 2. In
Section 3, backstepping adaptive method and the newadaptive multiple-model method are introduced. In
Section 4, the closed-loop system dynamics is analyzed
including stability and parameter convergence. Section
5 presents a simulation which compares the proposed
method with single model adaptive control.

3. Design of adaptive multiple model controller
3.1 Introduction of backstepping adaptive control [2]The standard backstepping adaptive control andadaptation law for parametric-strict-feedback system aregiven by(4)u   n ( x,ˆ, yr( n ) )ˆ(5)   n  WZ
where

2. Problem formulationθ  [1 , 2 , ,  p ]T , θ  θ  θConsider the following nonlinear systems inparametric-strict-feedback form [2]:Z  [ z1 , z2 , , zn ]T

z1  y  yr , zi  xi   i 1 , 2  i  n,x1  x2  f1T ( x1 )θ

xi  xi 1  fi T ( x1 , x2 ,..., xi )θ, i 1 ，fk
k 1 xki 1

1  f1 , i  fi  

xn  u  f nT ( xn )θ

Where xi  R, i  1, 2...n i s t h e state o f sys t e m,

1  c1 z1  ˆT f1 ( x1 )  y r (t ),u  R is the control input , θ  R is a n
unknown parameter vector belonging to a known compact set S  R p and y  R is the output. The functions

 i   z i  ci z i  ˆ  i Tp

the reference signal to be tracked. We suppose th
at the full system states are available. The object
is to improve transient performance in the presence oflarge parametric uncertainties, and meanwhile, ensure the stability of the closed-loop system.
It should be noted that in the classical backstepping
adaptive control design, transient performance can be
improved by choosing sufficiently large high-gain
parameters2. However, a possible shortcoming of suchmethod is that the resulting control efforts may be verylarge. Another way to improve transient performance isusing multiple identifications models.
Before the design of adaptive multiple-model
controller, we need necessary assumptions which areuseful in the following analysis.
Assumption 1(A1) There is a known positive realvalued constant B satisfying

max{| yr (t ) | ,| y r (t ) | }  B

t T

T

f1 ( yr (l )) f1 ( yr (l )) dl(3)

 i 1
 i
ˆi 1 
  ( k 1 ) i z k  y r ( t ), 2  i  n
ˆ
k 2

(10)
where     0 is the adaptation gain matrix, yr isthereference
signaltobetrackedandci  0,1  i  n are constants to be designed. The
T

above control design is based on the Lyapunov function
1 n
1Vn ( z1 , z2 , , zn ,  )   zi2  (ˆ   )T  1 (ˆ   )2 i 1
2
The time derivative of Vn , computed with (5)-(10) isn.

Vn ( z1 , z2 , , zn ,  )   ci zi2 .This

impliesthei 1boundness of the state zi ,1  i  n and ˆ , which in turn
indicates

theboundnessofsystemstate
xi ,1  i  n and control u . Under the standard(2)

Assumption 2(A2) f1 ( yr (t )) satisfies the classicalpersistent excitation condition，that is，for all tR,
there exist positive real-valued constants μ and T such
that

(9)

i 1 
 i 1 ( k )yr )  ( i 1 xk 1  i 1 xk 1 
x
x


k 1
yr( k 1)kkfi , i  1, 2, , i are known smooth functions. yr  R is

I  

(8)

 1  1 z1 , i   i 1  i zi ,y  x1t(7)

W  [1 , 2 , , n ](1)

i  1, 2...n  1(6)arguments of adaptive control theory, it follows thatzi (t ),1  i  n tend to zero asymptotically, and thus

lim z1 (t )  lim( y (t )  yr (t ))  0. Then asymptotic
t 

t 

tracking can be achieved.
Remark: In addition to the boundness of theparameter estimation, there are seldom results aboutparameter convergence. Ref.6 provides sufficientcondition for parameter estimates convergence, but thecondition is difficult to verify. With assumptions (2) and(3), Ref.16 constructs an explicit, global, strong- 246 -

Adaptive Multiple-Model Control

Lyapunov function, and proves parameter estimation
convergence. See Ref.16 for more details.

[·] means the floor function. Thus finite number ofresetting is proved. Further, after every resetting time tr ,3.2 Design of adaptive multiple-model controller
We will run in parallel (p+2) models. N (N=p+1)
models are in the same structure but different initial
parameter values  j (0), j  1, 2..., N . The initial valuesof  j (0), j  1, 2..., N are known and the region of
uncertainty S of the plant parameter vector θ lies in theirconvex hull K (i.e. S  K).The other one model called‘ virtual model ’ is designed with adaptive andresetting mechanism. The N models will run paralleland parameter estimates are evaluated online by acriterion function J(t).Define

all adaptive models and the resetting model operate
under respective control input and adaptation law, thenall closed-loop state is bounded. Once t  tmax , thetJ j (t )   e 2j (t )    et  e 2j ( )d

(11)

0

as the criterion function for the j-th adaptive model,
where   0,   0 are constants to be designed. Jc is thecriterion function for virtual model. At each resetting
time tr, if J c    min{J1 , , J N } , where   (0,1) is aconstant to be designed, reset the estimate parameter
vector e (t ) of virtual model byN(12) (t )   



e

i 1

i i

where
i 

1
J i (t )

N

1

j 1

j

 J (t ) , i  1, 2, , N

.

(13)

Otherwise, the estimate parameters remainunchanged and the process repeats. An obvious problem
is that whether the resetting is finite or infinite. In thefollowing section, we will prove that only a finite
number of resetting can occur.

4. Stability analysis
Theorem Suppose assumptions (2),(3) hold, and theadaptive multiple-model controller (4) and adaption law(5) in the paper are applied to system (1). Then, for all
initial conditions only a finite number of resetting canoccur, all closed-loop states and  , j  1, 2,, n are bounded,furthermore asymptotic tracking is achieved ,i.e.,lim( y(t )  yr (t ))  0 as t→∞.
t system will operate under the classical backstepping
scheme,sowecanfurther
point
out
lim(
y
(
t
)

y
(
t
))

0
r
.This completes the whole proof.
that t 

5. Simulation result
The nonlinear system in parametric-strict-feedback
form is as follows:x1  x2   x1
x2  u   cos( x1 x2 )y  x1
where   [0.1,5.5] is an unknown parameter. The
output y  x1 is to asymptotically track the reference
signal yr  sin t .
In simulation, the controller and adaptation law is
B  1 , (2) holds.
developed as (4), (5). For all constantt
2
For any 0  (0,  ) , t  2 yr (l ) dl   0 follows for
all t  R , then let   0 , T  2 , and (3) holds. The
unknown parameter is   1 , c1  1.2, c2  1 , the adaptive
gain   1 , initial state x1 (0)  x2 (0)  0 ,and the initial
estimates of parameter for classical adaptive control andadaptive
multiple-mode
controlare1(0)  2.8,2 (0)  0.1,3 (0)  5.5, (0)  2.8

As seen from Figure 1, the state x1 of the system isbounded, and asymptotic tracking is achieved. Adaptive
multiple-model control (d) performs better than single
model (a-c) especially when initial parameter estimation
error is large (b and c). In Figure 2, all parameter
estimations of θ asymptotically converge to its true
value while the estimation in adaptive multiple-model
control (d) is the fastest. Figure 3 illustrates
the control input u is bounded in the two different
control schemes. Thus the scheme proposed in thispaper is feasible and effective.

j

Proof.
First, we will prove that the number ofresetting is finite. After the above analysis, wehave  j (t )  L [0, ), j  1, 2,, n , for i  1, 2,, n, ・  0, ti [0, ) ,such that | i (t ) | ・, t  ti . Let tmax=max{t1,t2,…,tn}.
For t  tmax ,| i (t ) | ・, i  1, 2, , N . Based on the resettingnmechanism,
Then theresetting adaptive model will estimate parameters withzero error. So, in the worst situation, the amount of e (t ) j 1

 j  j (t )  max{ j (t ) , j  1, 2,..., N}  ・.

Fig.1. Output and reference signal: (a), (b), (c) single modelwith  (0)  0.1,5.5, 2.8 respectively, and (d) retting modelwith  (0)  2.8t] , where T is the resetting interval, andresetting N r  [ maxT© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 247 -

Chao Yang, Yingmin Jia

Fig.2. Parameter estimation: (a), (b), (c) single modelwith  (0)  0.1, 5.5, 2.8 , (d) retting model with  (0)  2.8

Fig.3. control input u: dashed line, dash-dot line and dottedline for single model with  (0)  0.1,5.5, 2.8 , solid line forvirtual model with  (0)  2.8

6. Conclusion
In this paper, an adaptive multiple-model controller
is developed for a class of nonlinear systems inparametric-strict-feedback form. Unlike previous
results ， a switching scheme is not necessary toguarantee the model closest to the real plant to be
switched into the controller design. Global asymptotic
stability of the closed-loop system, globally uniformly
bounded of all the closed-loop signals and convergenceof parameter estimates are proved. The simulation
results illustrate the feasibility and effectiveness of the
proposed method.

[4] Ioannou PA, Sun J (1996), Robust Adaptive Control.
Prentice Hall: Englewood Cliffs, NJ[5] Middleton RH, Goodwin G C, Hill D J, Mayne D Q(1988), Design issues in adaptive control. IEEE Trans.
Automat. Control 33:50-58.
[6] Lin JS, Kanellakopoulos I (1999) Nonlinear enhanceparameter convergence in strict feedback systems. IEEE
Trans. Automat. Control 44:89-94[7] Morse AS (1996), Supervisory control of families oflinear set-point controller, part 1: Exact matching, IEEE
Trans. Automat. Control 41:1413-1431.
[8] Anderson BDO, Brinsmead T S, Bruyne FD, et al.
(2000), Multiple model adaptive control, part 1: Finite
controller coverings. Int. J. Robust Nonlinear Control
10:909-929[9] Morse AS (1997), Supervisory control of families oflinear set-point controller. 2. Robustness. IEEE Trans.
Automat. Control 42:1500-1515[10]Narendra KS, Balakrishnan J (1994), Improving
transient response of adaptive control systems using
multiple models and switchings. IEEE Trans. Automat.
Control 39:1861-1866[11]Narendra KS, Balakrishnan J (1997), Adaptive
control using multiple models. IEEE Trans. Automat.
Control 42:71-187[12]Han Z, Narendra K S (2010)， Multiple adaptivemodels for control. Proceedings of the 49th IEEE
conference on decision and control, Atlanta, Georgia,
60-65[13]Narendra KS, Han Z (2011).The changing face ofadaptive control: the use of multiple models. Annual
reviews in control 35:1-12[14]Narendra KS, George K (2002), Adaptive control ofsimple nonlinear systems using multiple models.
Proceedings of American Control Conference: 17791784[15]Ciliz MK, Cezayirli A (2006)， Increased transient
performance for the adaptive control of feedback
linearizable systems using multiple models, Int. J.
Control 79:205-1215[16]He B L, Li J M (2011), Systematic method ofparameter convergence in strict-feedback nonlinear
systems (in Chinese).J.Sys.Sci.Math.Scis.31(5):501-511

Acknowledgements
This work was supported by the National Basic Research Program of China (973 Program: 2012CB821200,
2012CB821201) and the NSFC (61134005, 60921001,
61327807).

References[1] Narendra KS, Annaswamy AM (1989), Stable
Adaptive Systems. Prentice Hall Inc.: Englewood Cliffs,
NJ[2] Krstic M, Kanellakopoulos I, Kokotovic PV (1995),
Nonlinear and adaptive control design. Wiley, New York
[3]Tao G (2003), Adaptive Control Design and Analysis.
John Wiley & Sons

- 248 -

Attitude Reorientation of Spacecraft with Attitude Forbidden Zones
Xuhui LuThe Seventh Research Division and the Department of Systems and Control, Beihang University (BUAA), 37 XueYuan
Road, Beijing, 100191, P.R. China
Yingmin Jia

The Seventh Research Division and the Department of Systems and Control, Beihang University (BUAA), 37 XueYuan
Road, Beijing, 100191, P.R. China
E-mail: luxuhuiluxuhui@163.com, ymjia@buaa.edu.cn
www.buaa.edu.cn

Abstract
This paper investigates an attitude reorientation control scheme of spacecraft, considering attitude forbidden zone
and external disturbances. A novel potential barrier function is proposed with an attitude constraint term and a
dynamical scaling term. Here the dynamical scaling term is synthesized with a backstepping-based control scheme
to accommodate both the avoidance of attitude forbidden zones and disturbance attenuation. Simulation results are
given to verify the effectiveness of the proposed method.
Keywords: attitude stabilization, attitude constraints, backstepping, dynamic scaling

convex parameterization can be even used in theconstruction of logarithmic barrier function to derive
controller.2 Other methods, such as invariant set,3
randomized algorithm (RA),4 etc., have also been
applied to solve the problem.
However the spacecraft is not free from disturbances,which are not considered in the above methods. Those
disturbances are from, e.g., the external environment
like atmosphere drag, and the attitude reorientation
property would be reduced and even the avoidance of
attitude constraint zone is not ensured if disturbances
are neglected. Thus a fault-tolerant sliding-mode control
scheme is derived in Ref. 5 to accommodate
disturbances and actuator fault, where exact upperbound of uncertain inertia matrix can be online
estimated. Besides, a finite-time output feedback control

1. Introduction
The attitude reorientation of spacecraft has been
extensively studied in the past decades. However,during large-angle attitude maneuvering of spacecraft,some sensitive on-board instruments such as infrared
telescopes and interferometers should avoid direct
exposure of bright objects like the sun. Therefore, thespacecraft reorientation with the attitude forbidden
zones, or the constrained attitude control problem(CAC), is a topic of great interest in applications. In theearly work,1 via implicit magnitude constraint ofquaternion, the non-convex attitude constraints can betransformed into linear matrix inequalities (LMIs) andthis constrained optimization problem can be analyzed
in the semi-definite programming framework. Then, this

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 249 -

X. Lu and Y. Jia

Furthermore the above constraint can be transformed
into2
q T M ( x, y ,  ) q  0(3)
T
where M ( x, y, )  [ A, b; b , d ] with A  xyT  yxT scheme is derived in Ref. 6 for spacecraft stabilization
with bounded disturbances.
The aim of the paper is to provide a controller todeal with spacecraft repointing problem in the presenceof attitude forbidden zone and disturbances. The main
contributions of this paper, compared with the previous
results, is the construction of a novel attitude barrier
potential function, where the unwanted term in thebackstepping controller design is compensated by ascaling factor. Therefore both the avoidance of attitude
forbidden zones and disturbance attenuation areachieved.
The remainder of the paper is organized as follows.
Section 2 lists the preliminaries including the modelingof spacecraft, attitude forbidden zones and control
objective. Controller design with the corresponding
proof is given in Section 3. Simulation results andconclusions are offered in Section 4&5.

( xT y  cos ) I 3 , b  x  y, d  xT y  cos  . Moreover, theinequality (3) can be written in more accurate
form 1  cos   qT M ( x, y, )q  0 .
It is noted that qT Mq  0 if    . The property
facilitates the construction of barrier potential function.
For simplicity, only one bore-sight vector and one
attitude forbidden zone are considered here.
The control objective is to render the spacecraft
rotate towards the prescribed attitude qd or qd , both ofwhich is equivalent in the physical meaning, from any
permissible initial value of attitude q(0) and angularvelocity w(0) . During the process the spacecraft
circumvent the attitude forbidden zones  q  {q  S 3

2. Preliminaries
The dynamics of spacecraft is listed below [6]q  L(q) w
Jw  S ( w) Jw  u  d| qT Mq  0} in the presence of external disturbances. It
is obvious that qd , q(0) are outside attitude forbidden

(1)
(2)

zone, i.e. qd , q (0) ・  q . Moreover in the design, there
exist J min  0, J max  0 such that J min I 3  J  J max I 3 , anddisturbances d (t ) are bounded.

where, for vectors in (1)(2), q  [q0 ; qv ]  R 4 isquaternion with qT q  1 , w  R 3 angular velocity ofspacecraft in the body-attached frame, u  R 3 control
torque from actuators of spacecraft, and d  R 3 external
disturbances. Moreover, for the matrices in (1) and (2)L(q )  [qv ; q0 I 3  S (qv )] where S (a)  [0,  a3 , a2 ; a3 ,0,  a1 ;  a2 , a1 , 0] with a  R 3 , and J  R 33 is inertia
matrix with J  0 .
In practice, when spacecraft is rotated towards
specific orientation, some sensitive on-board
instruments like interferometer should circumvent direct
exposure of bright celestial objects such as the sun.
These unwanted orientations are considered as attitude
forbidden zones, and it can be described as2
xT Ry  cos

3. Controller Design

Compared with the barrier potential function in Ref. 1
and 2, the barrier function of quaternion here ismodified to accommodate external disturbances.
First consider quaternion dynamics (1). The
corresponding Lyapunov-like barrier function is derived
to be Vq  (k1 / rq  k2 ln(2 / qT Mq ))‖q  qd‖2 where thepositive variable rq is a scaling factor that would be
defined later, and k1  0, k2  0 . Here the virtual
control wc for w is set as wc  k3 LT (q)qd withk3  k3 / rq and k3  0 , and the dynamics of rq iskk
2)(1  qdT q )rq   2 3 (rq  rqc ) ln( T
8k1
q Mq

where R  (q02  qvT qT ) I 3  2qv qvT  2q0 S (qv ) is rotation
matrix of spacecraft from body-attached frame to inertia
frame, x is the direction of bright and celestial object in
the inertia frame, y is the instrument's bore-sight vector
in the body-attached frame, Ry is the counterpart in theinertia frame,  is the angle between x and Ry , and  (0,  ) is the minimum angle allowed for  . Notethat x and y are constant.

 Projrq (2k2 k3 rq
k1qT Mq

qT ML(q) LT (q )qd )© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 250 -

(4)

Attitude Reorientation of Spacecraft

where Projrq () here is defined in Ref 7, with rqc  0 .

function Vt and the boundedness of rq should be

Thus after a lengthy calculation, the derivative of Vq isconsidered, both of which lead to Theorem 1.
Theorem 1. Consider system (1)(2) and controller (4)(9). For q (0) with qT (0)q(0)  1, q (0) ・  q , w(0),simplified as
T

k
q Mq
Vq  ( 1  k2 ln())2qdT L(q ) wr
rq
2
and rq (0)  rqc , the following properties hold:(i). The variables q, w are bounded for t  0 , andqT (t) Mq(t)  0 , meaning that spacecraft would not
rotate into attitude forbidden zone all the time.
(ii). The variables q, w would be within a smallneighbor of desired Equivalent points ( qd , 03 ) .
(iii). The scaling factor rq (t ) is bounded all the time with

4k2 (1  qdT q) T
q ML(q) wr
qT Mq

k k k
qT Mq
))(1  (qdT q ) 2 )
 3 ( 1  2 ln(2 rq 22
1(1 
4(qdT q) 2 ) used. Here it should be also noted that rq  rqc

with q  qd2

 2(1  qdT q ), qdT L(q ) LT (q )qd 

the appropriate parameters in the controller and aswitching strategy of desired quaternion fromqd to qd when qdT q  1   where   (0,1] .

given rq (0)  rqc .
Besides the dynamics of w is considered. Take thederivative of wr and we get
Jw r =  S ( w) Jw  u  d  Jw c(5)

Proof. First from (8), it is concluded that Vt is bounded

all the time, and besides qT Mq  0 all the time.
For the second property,  2 can be set as sufficiently
small as possible via k5 , k6 ,  . Thus like[6], Vt would
converge into a neighbor of origin, as sufficient close as
possible, in finite time and property 2 is ensured.
Moreover, based on properties 1 and 2, there
exist   0 such that qT Mq   , leading to forwardwith w c   k3 LT (qd ) L(q) w  k3 (rq / rq ) LT (qd )q . Let Vwr

 wrT Jwr / 2 and accordingly u  S ( wc ) Jw  J 0 w c  u1 ,thus the derivative of Vwr isVwr  wrT {u1  d }Then Vq and Vwr is

combined(6)
togetherandcompleteness of rq .

Vc  k4Vq  Vwr with k4  0 . If u1 is set as

As for the third property, via appropriate parameters,
there exists M  0 for rq such that ‖(2k 2 k3 rq ) / (k1qT Mq )qT ML(q‖)  Mrq . Besides, (k 2 k3 ) / (8k1 ) ln(2 / qT Mq )T
(1  qd q)  kq (k2 k3 ) / (8k1 ) ln(2 / (1  cos  ))
basedon 1  cos   qT Mq and switching strategy of desired
quaternion qd Thus rq  kq (rq  rqc )  M‖LT (q )qd‖rq .
Moreover based on property 2, there exist ・1  0 ,where ・1  kq / (2 M ) , and the corresponding T1  0such that ‖LT (q)qd‖ ・1 , t  T1 . Therefore it isconcluded that rq  0 when rq  2rqc , t  T1 , whichmeans rq is bounded when t  T1 . Besides rq is also
bounded when 0  t  T due to boundedness of q and
qT Mq   . In all, rq is bounded all the time and theproof is complete.

k
qT Mq Tu1  2k4 ( 1  k2 ln()) L (q )qdrq2
4k 2 k 4(1  qdT q ) LT (q ) Mq  (k5  k6 ) wr ,
qT Mq

(7)
with k5  0, k6  0, the derivative of Vc can be scaled asV    
(8)
c

2

1

where
2 

k3 k 4
qT Mq
1
(k2 
ln(
))(1  (qdT q ) 2 )  k6 wrT wr
2
2rq2

and 1  d T d / 4k5 , and young inequality is used here.
Here the total control torque u is summarized askqT Mq Tu  S ( wc ) Jw  2k4 ( 1  k2 ln()) L (q )qdrq2

4. Simulation Results
4k 2 k 4(1  qdT q ) LT (q ) Mq  (k5  k6 ) wr
Tq Mq
The controller scheme is simulated to verify its(9)
Before the main result is preceded, it should be noted
effectiveness. In the simulation, J  [350,3, 4;that both the stability of the variables in Lyapunov
3, 270,10; 4,10,190] with J max  600, J min  100 . As for© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan J 0 w c - 251 -

X. Lu and Y. Jia

attitude forbidden zone, x  [0.35;0.24; 0.9055],y  [1;0;0],  60. Moreover initial values are set as
q(0)  [0.5, 0.5, 0.5, 0.5]T , w(0)  [0.1, 0.2, 0.12]T ,rq (0)  1 , and control parameters are k1  2, k2  4,
k3  0.6, k4  4, k5  0.4, k6  25, rqc  0.5 .Moreover inthe simulation, the control torque u is bounded,i.e., | ui | 10 N  m, i  1, 2,3 , and external disturbances
are set as d  [0.5sin(0.2t  45 );0.6sin(0.2t  30 );0.3cos(0.2t )] .
It can be seen from Fig. 1 that the spacecraft isinitially located where the bore-sight vector is close to
the attitude forbidden zone. Here the controller renders
spacecraft circumvent the attitude forbidden zone, andeventually stop at the desired attitude, where the boresight vector is also in proximity to attitude forbidden

constrained reorientation of spacecraft with disturbances
is derived. A novel attitude potential function with ascaling factor is derived, and a corresponding controller
is designed to accomplish the avoidance of attitude
forbidden zone and disturbance attenuation. Future
work would focus on the constrained attitude control
law without measurements of angular velocity.

Acknowledgements

This work is supported by the National Basic Re-search
Program of China (973 Program: 2012CB821200,
2012CB821201) and the NSFC (61134005, 60921001,
61327807).
References

Quaternion
1
q

q

q

q

q

0

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search and optimization", in Preceding of the 2007 IEEE
International Conference on Mechatronics andAutomation, Harbin, China, 2115-2119.
5. Q. L. Hu (2010), Robust adaptive sliding-mode faulttolerant control with L2 -gain performance for flexible
spacecraft using redundant reaction wheels, IET Control
Theory and Applications, 4(6): 1055-1070.
6. A. M. Zou (2009), Finite-time output feedback attitude
tracking control for rigid spacecraft, American Control
Conference, IEEE Transactions on Control System
Technology, 22(1), 338-345.
7. . N. Stamnes, O. M. Aamo, G. O. Kaasa (2011), A
constructive speed observer design for general EulerLagrange systems, Automatica, 47(10), pp. 2233-2238.

1

0
2

−1

0

10

20

30

3

40

50

time(s)Angular velocityw(rad/s)

0.5
w

1

0
w2
−0.5

0

10

20

30
time(s)the attitude margin
towards the forbidden zone

w3
40

50

qTMq/20T

q Mq/2
−0.5
−1

0

10

20

30

40

50

time(s)Fig.1, simulation of result of the proposed controller.

zone. Besides disturbances are attenuated during theprocess.

5. Conclusion

A Lyapunov-based control scheme for attitude© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 252 -

Weighted Multiple Model Adaptive Control of Slowly Time-Varying Systems
Weicun Zhang, Qing Li
School of Automation and Electrical Engineering, University of Science and Technology Beijing
Beijing 100083, China
E-mails: weicunzhang@ustb.edu.cn; qingli@ies.ustb.edu.cn

Abstract
Weighted multiple model adaptive control is a combination of off-line design and on-line decision, which combines afinite number of simple controllers by weighting algorithm. This paper presents an improved weighting algorithm ofslowly time-varying systems and a new method to partition model uncertainty using v-Gap metric. The effectiveness ofthe proposed methods is verified through Matlab simulations.
Keywords: MMAC; v-Gap; Weighting Algorithm; Time-Varying System

1. INTRODUCTION
Weighted multiple model adaptive control(WMMAC) and weighted multiple model adaptive
estimation (WMMAE) appeared around 1960’s to1970’s, which consists mainly of three components, i.e.,the model-set, local controller/estimator design, andweighting algorithm. Multiple Kalman filter is the basisof classical MMAC or MMAE. The purpose of using
multiple models is to improve the accuracy of state
estimation or control problems with parameter
uncertainties1, 2. This was followed in later years byseveral practical applications. In recent years, a new
type of WMMAC, i.e., robust multiple model adaptive
control (RMMAC) was put forward with convincing
experiment results3,4, which arouses once again theenthusiasm of researchers in the field of adaptive
control in recent years.
Some results are made on MMAC of time-invariant
systems. MMAC is proved to be a useful control
method to deal with time-invariant system whoseparameter is not quite certain.
As previous weighting algorithm could not easilychange weight once sub-model’s weight converged to 0,
it is not suitable for time-varying system. This paper
comes up with an improved weighting algorithm tosolve this problem. With the help of improved algorithm,
the changes of system parameters can be detected andthen sub-models’ weights can be recalculated. In thisway, WMMAC can be used to slow-varying systems.
For WMMAC, weighting algorithm is one of the corecontents, it will directly affect whether the weighting
algorithm could choose the sub-model from model-set

which is the nearest to the actual control systemaccurately and rapidly.
The corresponding WMMAC system is shown in
Fig. 1 and Fig. 2.

Fig.1 Block diagram of a general control system

Fig. 2 Block diagram of WMMAC
Consider the following time-varying plant:x(t )  At( )x(t )  B(t )u(t )y(t )  C(t )x(t )2. Weighting algorithm
The traditional weighted multi-model adaptive
control use maximum a posteriori probability method to
calculate the weights of each controller. In the processof calculation, traditional method is quite complex andits calculation is huge.
When one sub-model of model-set is very close to
the actual system, its weight should be close to 1. That’s© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 253 -

Weicun Zhang, Qing Li

the core concept of weighted multi-model adaptive
control method5. As the change of weight is smoothrather than mutations, so we could achieve the purposeof model soft switching by using weighted multiple
model adaptive control method.
As the actual controlled system and sub-model ofmodel-set is not necessarily the same, the output error

ei ( k ) is exist between actual system output y( k )Me mi n( k ) ｹ Me mi n( k - 1) andMe mi n( k - 1) = Me mi n( k - 2) = Me mi n( k - 3)
Initialize the weight of each sub-model. In this way,
we can recalculate each sub-model’s weight and reselectthe best sub-model’s controller to control the actual
system when system parameter is changed.

and each sub-model’s output y i ( k ) .

3. Partition the Model-set Using v-

Gap Metric

Model’s similar degree is evaluated by the magnitude
of output error ei ( k ) . If one of the sub-model’s output
error is the smallest when system became stable, thenwe can say this sub-model is the nearest to the actual
system and its weight should finally converge to 1
Based on the above concept, designing the following
weighting algorithm:
1)
Initialize weights.
2)
Calculation of error performance metrics.
3)
Compare the error performance metrics, findthe minimum error performance metrics.
4)
Calculation of weight metrics.
5)
Calculation of sub-models’ weights based onweight metrics.
6)
Calculation of control law.
The advantage of this method is smooth switchingprocess and stabilized system output. This could
decrease the damage to actual system when systemparameter changed, as a result this method is quite
suitable for industrial.
The weight metrics and sub-models’ weights ofweighting algorithm are designed as follows:

to 0, its weight metric li

・・
P1(ej w ) - P2(ej w )・・2
2
dv( P1, P2) = ・jw
jw
・ 1 + P1(e ) 1 + P2(e ) ･・・
1・subject todet (1 + P1* P2 ) ( ej w ) ｹ 0, " w And
wno(1 + P1* P2 ) + h( P2 ) - h( P1 ) = 0l｢ kli ( k ) = min ( ) li ( k - 1)
li｢( k )Once sub-model’s weight

3.1 v-Gap metric basic concepts and definitions
In order to measure the distance between two
nominal plants from a closed-loop perspective, we needa measure to quantify the closeness of closed-loop
behavior of two open-loop plants. For this reason, the vGap metric introduced by Vinnicombe in is usedthroughout this paper [6].
The v-Gap metric is defined as:pi ( k )has converged( k ) is also converged to 0.

It is not difficult to find that no matter how error
performance metrics changes, this sub-model’s weights
could not become 1. In other word, weighting algorithm
could not adapt to the change of actual systemparameters.
In order to solve this problem, this paper comes up
with an improved weighting algorithm that could makeweights recalculate to adapt to system parameterchanges.
Compare the output error of each sub-model at
every sample instance. Mark the sub-model’s number i

Where, wno denotes the winding number evaluatedon the standard Nyquist contour indented around any
imaginary axis poles of P1 and P2.
When v( P1, P2 )consider that the controller stabilizing system P1 could
also make system P2 stable.
3.2 Partitioning the parameter set
As v-Gap can be used to measure the distance of two
systems, we use v-Gap to partition the model-set.
For the system with only one uncertain parameter, we
can choose a certain value r as v-Gap metrics (0<r<1),
in order to find sub-models’ parameter and partition themodel-set into N sub-models.
If actual system’s parameter as follows:which output error ei ( k ) is the smallest. Whensystem parameter has changed, Remark the sub-model
which output error is the smallest as

is small enough, then we can

Me mi n( k ) . If the

鴿
鴿
鴿
0 1
1 1
1 -2
A = ・
,B = ・,C = ・,
・・l -4・・0 1
・・2 1l ﾎ ( -103, -50]

System transfer function is calculated
sub-model marked at current sample instance isdifferent from the one marked at previous sample
G = C( sI - A)-1 B , Assume two sub-models:instance:© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 254 -

as

Multiple Model Adaptive Control鴿
Ga 11( s ) Ga 12( s )Ga( s ) = ・・G ( s ) G ( s )a 22
・ a 21
鴿
Gb 11( s ) Gb 12( s )Gb( s ) = ・・G ( s ) G ( s )・ b 21b 22

me of 20s, as Table 3.
Table 3 Sub-models output error at the time of 2
0s

Calculate the v-Gap metric between those transfer
functions in the same position. In this way, we can get a
v-Gap matrix of two sub-models. Find the minimum
parameter of this v-Gap matrix.

Sampling Pointe1( k )e2( k )e3( k )

19.9s
20s

0
5.20

1.19
5.27

2.18
5.52









21.2s
21.3s

1.28
1.45

0.37
0.13

1.02
1.7

d v( Ga , Gb ) =From table 3, when system parameter changed at thetime of 20s, weighting algorithm determined the change
max dv ( Ga 11 , Gb 11 ) dv ( Ga 12 , Gb 12 ) dv ( Ga 21 , Gb 21 ) dv ( Ga 22 , Gb 22of) system parameter and initialized each sub-model’s
weight, and then recalculate each sub-model’s weight.
Choose a certain v-Gap value r=0.2, partition the
In this way, weighting algorithm can reselect the submodel-set model parameter based on r.
model which is nearest to the actual system.
Through matlab simulation, we can clearly see that
Table 1 Sub-model parameterstheeffectiveness of weighted multiple model adaptive
Parameter 1
Parameter 2
v-GAP
controlof slowly time-varying systems. Simulation-50-60
0.2083
results are shown in Fig. 5 and Fig. 6, respectively-60-720.2096-72-860.2054-86-103
0.2094{}

From table 1, we can easily found that though vGaps between each two sub-models are the same, submodel parameter selection are not uniform.

4. Simulation result
Assume the actual system’s parameter mentioned
in chapter 3 varied from -50 to -72. Partitioning theparameter set into three sub-model by a certain v-Gap
r=0.2. The three sub-models’ parameters are as
follows:

踐0
踐0
1
1
銷
=
，
A1 = 銷
A2
銷-50 -4
銷-60 -4顏顏踐01
A3 = 銷
銷-72 -4
顏Fig. 5Controller weight curves，

If system is a slowly time-varying system, and its
parameter varies according to Table 2.
Fig. 6 Slowly time-varying system

Table 2 System parameters
Sample Time
System Parameter
0-20s
-50
20-40s-60
40-60s-72
60-80s-50
80-100s-71
Record each sub-model output error data when system parameter changes from -50 to -60 at the ti

From Fig. 5 and Fig. 6, we can easily see that at thetime of 20s、40s、60s、80s when system parameterchanged, sub-models
weights are initialized andrecalculated, and finally the nearest sub-model weight isconverged to 1. Actual system output finally become
stable.
From 80s to 100s, as actual system’s parameter is
not equal to one sub-model of model-set, steady-state
error is existed between system stable output and© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 255 -

Weicun Zhang, Qing Li

reference input. What’s more, the weight convergedmuch more slowly than usual. However, weighting
algorithm still finally identified the nearest sub-modelfrom model-set, and uses its controller to control actual
system.

changes, weighting algorithm can quickly realized thischange and initialized each weight. If the real model ofsystem to be contrlled is included in the model set,
WMMAC can quickly find the right model and thecorresponding controller.

Acknowledgements
This work is partially supported by National Basic
Research Program of China (973 Program) (No.
2012CB821200), National High Technology Researchand Development Program of China (863 Program) (No.
2011AA060408), Beijing Higher Education Young Elite
Teacher Project (No. YETP0362), National Key
Technology Research and Development Program ofChina (No. 2013BAB02B07).

Fig. 7 Slowly time-varying system
From Fig. 7, we can see that even system is disturbed
by white-noise signal, WMMAC using weighting algorithm presented in this paper can still control systemwell. No matter how system parameter changed,
weighting algorithm can accurately choose the submodel which is the nearest to the actual system frommodel-set (parameter set), and finally stabilized system
output.

References

6. Conclusion
Weighted multiple model adaptive control is a usefulcontrol method to deal with large uncertainty of systemparameters.
V-Gap metric is an important metric of two systems
distance, through simulation we can draw a conclusion,
though v-Gaps between each two sub-model are thesame, sub-model parameter selection are not uniform. vGap can be used as the basis of the division of modelset.
To the slowly time-varying system, an improved
weighting algorithm mentioned in this paper could
make weights recalculate to adapt to system parameterchanges. Through simulation, when system parameter

1. Magill D.T. Optimal adaptive estimation of sampled
stochastic processes. IEEE Transactions on AutomaticControl. 1965, 10:434-439
2. Lainiotis, D.G. Optimal adaptive estimation structure
and parameter adaption, IEEE Transactions on
Automatic Control,1972,16:160-170
3. M. Athans, S. Fekri, and A. Pascoal, Issues on robust
adaptive feedback control, in Preprints 16th IFAC
World Congress, pp. 9-39, Prague, Czech Republic,
July 2005
4. Sajjad Fekri, Michael Athans, and Antonio Pascoal,Issues, progress and new results in robust adaptive
control, International Journal of Adaptive Control and
Signal Processing, Vol. 20(10):519-579, 2006
5. Zhang Weicun. Stable Weighted Multiple Model
Adaptive Control [J]. International Journal of Adaptive
Control and Signal Processing, Vol. 27(7):562-581,
2013
6. Hessam Mahdianfar, Sadjaad Ozgoli, Hamid Reza
Momeni. Robust multiple model adaptive control:
Modified using v-GAP metric. International Journal of
Robust and Nonlinear Control, 2011, 21:2027–2063© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 256 -

A Reduced-Complexity Interacting Multiple Model Algorithm for Location Tracking
in Heterogeneous Observation
Xiaoyan Fu

The Beijing Engineering Research Center of High Reliable Embedded System, Capital Normal University
Beijing, 100191, P.R. China
Yuanyuan Shang

The Beijing Key Laboratory of Electronic System Reliability Technology, Capital Normal University
Beijing, 100191, P.R. China
E-mail: fuxiaosg@163.com, syy@bao.ac.cn
www.cnu.edu

Abstract
This paper is devoted to the problem of state estimate of discrete-time stochastic systems. A low-complexity andhigh accuracy algorithm is presented to reduce the computational load of the traditional interacting multiple model
algorithm with heterogeneous observations for location tracking. By decoupling the x and y dimensions to simplify
the implementation of location, updated information is iteratively passed based on an adaptive fusion decision.
Simulations show that the algorithm is more computationally attractive than existing multiple model methods.
Keywords: data fusion, interacting multiple model algorithm, location tracking, wireless sensor networks.

energy consumption is worth developing lowcomplexity schemes for WSNs2.
Date fusion deals with the problem of how to extract
State estimate problem of discrete-time Markovian jumpand utilize useful information contained in multiple setslinear system (MJLS) is always the focus of interest inof data in order to estimate unknown parameters or
the community of maneuvering target tracking.
processes1, which has been widely applied in militaryMultiple-model (MM) algorithms (such as generalized
and civilian fields, e.g., target tracking and localization,
pseudo-Bayesian (GPB), interacting multiple model
air traffic control, guidance and navigation, fault(IMM), variable-structure MM (SVMM)) are generally
diagnosis, surveillance and monitoring. Estimationconsidered as mainstream approach to address thisfusion is one of the important applications involving
problem. Among them, IMM algorithm proposed bywireless sensor networks (WSNs), but the WSNs haveBlom3 is most prevalent.
very limited sensing range and communication
Nevertheless, the high accurate location estimation forbandwidth which restrict the application of the
maneuvering target based on the traditional IMM
centralized signal processing method. In other words,
algorithm requires models interacting and inverse
not only are accurate positioning algorithms essential tooperations. The location data fusion algorithm with an
useful location-estimation systems, but also to reduce
IMM technique has high computational complexity, anddirect implementation of the IMM algorithm may be too© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan
1. Introduction- 257 -

Xiaoyan Fu, Yuaanyuan Shangcomplex for ppractical systeems. Moreoveer, local statioons
in WSNs mayy not be able to afford IMM
M estimation ddue4
to limited com
mputational poower . Conseq
quently, it wouuld
be useful to rreduce the com
mputational coomplexity of the
IMM algorithhm.
Our idea tto deal withh reduced-co
omplexity sttate
estimation iss to reconstrruct the IMM
M algorithm. In
contrast to tthe existing reduced-comprplexity approaach
including: allpha-beta filteering, Kalmaan filtering, oour
proposed methods is baseed on a factorr graph (FG) by
decoupling tthe x and y dimensions to simplify the
implementation of location and adaptiv
ve data decis ion
for state estim
mation of a MJJLS.

paassed among the predictioon phases and correctionnphhases with the error propagaation law5.

Fig. 1. Message passsing from nod
de to node
1) Message froom Variable Nodes to Factor
F
Nodess:Asssume that eachemessagee in the FG
G flows is a
Gaaussian PDF. A message from a variab
ble node to a
facctor node is the product oof incoming messages.
m
Forr
ex
xample, a variiable node wiith two incomming messagess
is illustrated ini Fig. 1, w
where a variiable node iss
reppresented by a circle; a facttor node is rep
presented by asolid square. Inn fact, the messsage from a variable nodee
to a factor node can be takeen as the corrrection step inn
Baayesian filteriing, where thhe mean is the estimatedd
ressult based onn data reliabillity for locatiion estimationnannd tracking.
2) Message fromm Factor Noddes to Variab
ble Nodes: Forrtwwo continuous variables x aand y, the maarginal densityyfunnction of y is obtained by integrating the joinnt
disstribution oveer variable x. T
This operationn can be takenn
as the predictiion step usinng the Bayeesian filteringg
appproach for loccation estimattion and trackiing.

2. Backgrou
und2.1. Model
Consider thhe dynamic system of tracked tarrget
described witth the state sppace form. Th
he mathematiicalmodels on thhe target cann be taken by the follow
wingMarkov jumpp linear systemm:(1)
x ( k  1)  F j x ( k )   j  j ( k )z (k )  H j x(k )   j (k )(2)where the sttate x(k ) is ana n-dimensio
onal vector, thevector, and theobservation z (k ) is an m-dimensionalmsubscript j  S  {1, 2,  , s } denotes the moddel.
The matrix ffunctions F j () ,  j () and H j () are knowwn.
The model-ddependent proocess noise annd measurem
mentnoise are assuumed to be a GaussianG
rand
dom process.
kMLetotes the flight model j at tim
me k. The moodel
j deno3. Proposed LocationL
Traccking Algorithm

dynamics aree modeled ass a finite Marrkov chain w
withknown modeel-transitions probabilitiespfromfmodel i attime k-1 to mmodel j at time k.
 ij  P
Pr ob[ M kj | M ik 1 }  P{M kj | M ik 1 }((3)

Th
his article focuses on locattion tracking approaches inn
terrms of the X anda Y groups individually, and the X andd
Y results of thee state and thhe covariance equations aree
deecoupled as individual input. Moreeover, otherss
heeterogeneous observationsoccan be viewed
d as individuaal
inpput, and coorresponding state estimaation can bee
obbtained individually. All eestimations coombined withhdaata fusion algoorithm, whichh can improvee the accuracyy
off location estim
mation and traacking.
Giiven the com
mputationally aattractive feaature of FGs6,
thiis article prop
poses a locatiion tracking scheme baseddonn the FG approoach and the eerror for locattion and speedd
esttimations to implement the recursiive Bayesiann
esttimation and to reduce thee computation
nal load of thee

2.2. Operation
ns of Factor Graphs
G

In terms off the operatioons of FG operations, oone
advantage off multiplicationns is the distrributive law. F
Forexample, a  b  a  c  a  (b  c) , wheere the left-haandside involvess three arithmeetic operationns, and the rigghthand side invvolves only twotoperation
ns. Furthermoore,
according to tthe inherent distributeddfeature of the bellief
propagation algorithm to make the decomposition inaccordance w
with predictiion-correctionn recursion, themessages of the reliable innformation arre processed aand
© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 258 -

Reducuced-Complexity Location
L
Trackingg

traditional IM
MM algorithm
m. Furthermoree, decoupling the
X and Y dim
mensions for differentdtrackking groups aalso
can reduce thhe computationnal complexity
y. Therefore, the2-D problem is reduced too two 1-D pro
oblems. The 22-D
problem can be represented by two inndependent m
maingroups, the X
X-coordinate annd the Y-coord
dinate groups .
Accordingg to the conceept of undirectted graph moddel7,
the representaation related tot estimate speed and locattionof the X-cooordinate grouup is illustrateed as the blaackcolor diagramm in Fig. 2, where a corrrection nodee isillustrated byy a circle; a prrediction nodee is illustratedd bya solid squaree.

Step3: Filtering（j  S ）Prrediction statex j (k | k  1)  N oper 3 ( x j (k | k  1); Sx0 j ( k |k ) , Vx0 j ( k |k ) )(7))

x j (k | k  1)  N oper 2 ( x j (k | k  1); Sx0 j ( k |k ) , Vx0 j ( k |k ) )

(8))

Co
orrection statee
N oper1 ( x kj ;Sx j (k|k 1) , Vx j ( k |k 1) )N oper1 ( x kj ;Sx j (k|k 1) , Vx j ( k |k 1) )

x kj
x kj

(9)
(10)

Step4: Combine the statee estimates annd correspondingg covariance accordingato th
the updated weightss 
xI (k)   kj N (x kj ;S; xk , Vxk )
(11))
j 1
s

x I (k); ,V )
  N (x ;S
j 1

k
j

k
j

xk

xk

(12))

Up
pdated weightt of model M kjkj is kj
P{M kj Z k }Fig. 2. Graph mmodel of the daata-fusion locattion estimation for
X-coordinate group from th
he kth to (k+11)th.

=decision[P{M kj z1, k , s1, k }, P{M kj z2, k , s2, k },P{M kj s*, k }]]=decision[  xk, j ,  yk, j , *,k j ]The measurem
ment model based on IMM
M algorithm is thesituation that the error proppagation law canc be appliedd tothe linear dyynamical systtem, and theen the propossed
IMM algorithhm based on factor graphss (IMM-FG) for
location estim
mation and trracking are illustrated in thefollowing parrt.
Step 1: Calcuulate the mixedd initial probaability for thekfilter m-atcheed to model M（
j j  S）

i | j ( k | k )

wh
here



=

P{M kj z0:k 1}

 ij 

In this section, we evaluate the performaance of IMMFG
G algorithm, and the classsical IMM alg
gorithm usingg
thee target track
king examplee. The resultss are obtainedd
fro
om 100 Montee Carlo runs. T
The X position and velocityy
RM
MSE are show
wn in Fig.3 annd Fig.4, respeectively. The Y
poosition and velocity RMSE
E are omitted due to limitedd
space. It can be found thaat accuracy esstimate of theeIM
MM algorithmm as nearly aas the propo
osed IMM-FG
G
alg
gorithm in alm
most time. Altlthough IMM--FG algorithmm((3)

where c j is thhe normalizatiion constant.
Step 2: Calcuulate the mixed initial state and corresponnding covariannce for the filter matcched to moodel
k
s


M（
k 1
j j  S）

x0 j (k | k)

  (k | k )N (x ;S ,VV )x0 j (k | k)

  (k | k )N (x ;S ,VV )

i 1

i| j

s

i 1

i| j

i

k 1
i

c


N [ z x , k ;Sx0 j (k|k) , Vx0 j (k|k) ]  c j

4. Simulation Resultsi, j  S

cj

P{z x , k M kj , z0:k 1}  P{M kj z0:k 1}(14))
cIt'ss worth menntioning that decision [.]] refers to adeecision-making
g or data fussion process, for examplee,meean[.], max[.]], min[.] or more intellig
gent decisionn,diffferent locatiion and trackking problem
ms can adoppt
diffferent data fu
usion for betteer tracking acccuracy.

P{M kj M ik 1 , z00:k 1}  P{M ik 1 z0:k 1}
k 1
i




P
P{M ik 1 M kj , z0:k 1}
=

k
x, j

(13)

xk 1

xk 1





xk 1xk 1(55)(66)© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 259 -

Xiaoyan Fu, Yuaanyuan Shang

It is principallyy similar to thhe popular IM
MM algorithmm.
Th
he difference lies in the usse of filtering.. To avoid theeinv
verse of co
ovariance mmatrix and reducing theecoomplexity of trracking algoriithm, the FG algorithm andd
thee idea of deecoupling thee X and Y dimensions
d
too
sim
mplify the im
mplementation of location estimationeanddtraacking is indu
uced to constrruction of IM
MM algorithmm.
Co
omputer sim
mulations inddicate that thet
IMM-FGG
alggorithm has almostathe samame tracking accuracy withhthee IMM algoritthm.

has a higher pposition estim
mate error thann IMM algorith
thm
during the seecond maneuvvering (time samples 61-666),
theAccknowledgem
ments
Th
his work wass supported bby the NSFC
C (111780177,
611373090, 613
303104, 612003238) and the BNSFC
C(4132014).

Fig. 3. X position RMSE
R
versus tim
me step.

Reeferences
1. D.Jeon, E. Yeonju and K
K. Hyounkyoun
ng, ”Estimationnrand ADS--B for air traffiic surveillance””,
fusion with radar
Information Fusion, 16th International ConferenceC
onn,
2013, pp. 1328-1335
uced-complexityy
2. C.L.Wang, Y.S.Chiou andd F.Tsai,”Redutracking scheeme based on aadaptive weightting for locationn
estimation ”,, IET communiications, 2012, pp.
p 673-684.
P., Bar-Shalomm, Y.,”The interracting multiplee
3. Blom, H.A.P
model algorithm for system
ms with Markoovian switchingg
coefficients””, IEEE Transaactions on Autoomatic Controll,
1988, 33, (8)), pp. 780–783.
X Li and Z.S.. Duan, ”Estimaation Fusion for4. Y.X. Gao, X.R.
Markovian
Jump
Linnear
Systemm
via
Dataa
Transformation”, IEEE Trransactions on Aerospace andd
Electronic Syystems, vol. 500, no. 1, pp. 240
0-253, Jan.20144
A Reduced-Co
omplexity Dataa5. Y.S. Chiou and F. Tsai, ”A
Fusion Algoorithm Using Beelief Propagation for Locationn
Tracking iniHeterogene
neous Observaations”, IEEE
E
Transactionss on Cyberneticcs, vol. 44, no.6, pp. 922-9355,
Jun. 2014g, Y.S. Chiouu, and F. Tssai, ”Reducedd6. C.L. Wang
complexity trackingtschem
me based on adaaptive weightingg
for location estimation”, IE
ET Communicaation,vol. 7, noo.
684, May. 20133
7, pp. 673C6
L. Wang, and C.-T.
C
Huang, ’A
A
7. Y.-S. Chiou, F. Tsai, C.-L
reduced-com
mplexity schem
me using messaage passing forlocation tracking,’EURASIIP J. Adv. Signaal Process ., vool.
2012, Jun. 2012.
mparing an interracting multiplee
8. Ho, T.J., Farrooq, M.: ’Commodel algorrithm and a mmultiple-process soft switchingg
algorithm’.In
nformation Fussion, 2000. pp. 17-24.
1

MSE versus tim
me step
Figg. 4. X speed RM

estimate errorr converges too zero within a short time. T
Theoccurrence off this phenomenon maybe dued to that IM
MMFG algorithm
m pays more atttention to thee rapid changee ofthe velocity dduring the highh maneuvering. On the whoole,
the estimatioon accuracy ofo the IMM-F
FG algorithm
m isalmost the saame with the classical IMM
M algorithm, bbut
the computinng time is shoort. It can be verified throuughthe comparisson computinng time betw
ween IMM aandIMM-FG given in Table 1..
Table1. Coomputing time betweenb
IMM anda IMM-FG

50 Monte C
Carlo runs
100 Monte C
Carlo runs

IMM
1.37672.4825

IM
MM-FG
1..1228
2..1691

5. Conclusioon
FG
The conclusiion comes here.
hIn the paper,p
IMM-Falgorithm is ppresented for maneuveringmtarget trackingg.

© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 260 -

Single Image Dehazing on Mobile Device Based on GPU Rendering Technology
Yuanyuan Shang

College of Information Engineering, Capital Normal University, 56 Xisanhuanbei Road
Beijing, 100048, P.R. China
Yue Meng

College of Information Engineering, Capital Normal University, 56 Xisanhuanbei Road
Beijing, 100048, P.R. China
Email: syy@bao.ac.cn, mengyue19890119@outlook.com
www.cnu.edu.cn

Abstract
Image dehazing utilizes a very complex algorithm that requires intensive filtering and floating-point arithmetic operations.
Consequently, processing speed is the most significant bottleneck in its application in some vision tasks such as mobile
platforms. In this paper, we propose an optimized single image parallel processing dehazing algorithm for mobile
platforms and implement it on a Windows Phone device based on GPU rendering technology.
Keywords: Fog degradation model, GPU rendering, Image Dehazing, Parallel processing, Windows Phone.

1. Introduction
A variety of approaches to image dehazing has been
proposed in the literature. They include titles such as Fastvisibility restoration from a single color or gray leverimage, by Tarel and Nautiere1; Visibility restoration in
bad weather from a single image, by Tan2; Single image
dehazing, by Fattal; and Single image haze removal using
dark channel prior, by He3.
With the continuous improvements in image dehazing,
the result of haze removal is getting increasingly better.
However, processing speed is the most significant
bottleneck to the application of dehazing in some visiontasks, especially on mobile platforms.

Graphic processing unit (GPU) rendering technology
is widely used in image processing applications to speed
up image processing algorithms. In this paper, we propose
an optimized single image parallel processing dehazing
algorithm for mobile platforms (based on J.P. Tarel’s
method), and implement it on a Windows Phone device
based on GPU rendering technology. The DirectX
programming interface, a powerful tool for accessing
GPU resources, can be used on the Windows Phone
platform. Thus, we chose it to implement and test thealgorithm on the Windows Phone device.
2. Algorithm Overview
The fundamental concept underlying this fast visibility© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 261 -

Yuanyuan Shang, Yue Meng

restoration algorithm, which was proposed by Tarel, is thefog degradation model. Koshimider proposed the fog
degradation model, and Tarel summarized it as inEquation (1):
,

,

1

,

,

,
,

CPU

(1)

The restored image can thus be obtained by calculating
R(x).
V(x, y) in Equation (1) can then be estimated byfollowing the four steps below:(1)
Calculate the minimum value of RGB
components W(x, y) in the original image:W x, ymin ∈ , ,,where I (x, y) is the original input image.
(2)
Find the median of W(x, y):,A x, ywhere Sv is the template size of the median filter.
(3)
Calculate the difference between W and A,
then calculate the absolute value of the difference:abs W A,
B x, yA
where Sv is the template size of the median filter.
(4)
Find the maximum value of the minimum:V x, ymax min, ,,,0
On obtaining a value for V(x, y), the restored imageR(x, y) can then be calculated using Equation (2), which isderived from Equation (1):5,

White BalanceGPU

Get minimum
value of 3 channels
Compute quantile
Get mean value of
selected

Get WB parameters
WB correction
Other restoration
processes

White balancecorrection

Phase

I x, y1 and 2, in order to conveniently implement these parts
separately in CPU and GPU.

Fig. 1. Division of white balance algorithm between CPU
and GPU
Tone Mapping
CPU

GPU

CalculatedI and dR(2)

Get U(x ,y)where factor p is used to control the intensity of hazeremoval, and can be adjusted for different images.

Get MG

Some parts of the algorithm cannot be applied to the GPU,in which case the CPU is used. A reasonable division ofwork between CPU and GPU is the key factor that
determines the efficiency of the process.
In this paper, we divide the algorithm into three mainparts: white balance, restoration core processing, and tone
mapping. Some actions cannot be accomplished in GPU
because of its structure. Therefore, we have to transfer
these parts of the process to CPU. Accordingly, the whitebalance algorithm described by Tarel8 and tone mapping
have to be divided into several parts, as depicted in Fig.s

Get T(x ,y)Phase

3. Algorithm Parallelization

Fig. 2. Division of tone mapping algorithm between CPU
and GPUAll processes in the core processing section, whichobtains V(x, y) in four steps and R(x, y) in the final step,
can be carried out in GPU; thus, none of this work needs
to be transferred to CPU. This is very beneficial for rapid
processing. However, because we need to get the process
results for neighboring pixels when performing thesecond median filter to obtain B(x, y), the process has tobe split into the two parts: calculating A and calculating R,© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 262 -

Image Dehazing Mobile Devicee

as previouslyy discussed. Inn this processs, the final steep of
the white ballance process is combined with the firstt stepof the restooration core processing process, theereby
reducing the ccost of reassem
mbly.
4. Implemenntation on Windows
W
Phon
ne
As mentionedd before, the GPU
Gprocesso
or contains a sset ofshaders, onee of whichh, pixel shaader, has too be
programmed in order to implementi
the image dehaazing
method. In the DirectX
X programm
ming interfacee, alanguage callled high-leveel shader lan
nguage (HLSL
L) isused to proggram the shadders.9 A piecce of HLSL code
corresponds tto only a singgle pixel, not the whole immage.
All the pixelss being processsed by the samme piece of H
HLSL
program arre processedd in paralllel, rather thansequentially.
Fig. 3. is the workflow
w chart of thee whole algorrithm
optimized to implement ono the GPU, wherew
all textturesand parameteers have the same name asa in the Equaation
were
given beforee. The details of each trransfer step w
discussed in tthe previous section.

Whole Defog AlgoorithmGPU (Display M
Memory)PU implem
mentation
Note that in the flows acrross CPU andd GPU, curvedd
arro
ows represen
nt data copie
ies while strraight arrowws
reprresent parameeter transmisssions. These are two keyytechhniques in DirrectX program
mming. Data copy refers too
the movement of data from display mem
mory to systemmmemmory, in orderr to get accesss to these dataa via the usuaal
metthods. Parameter transmisssion is moree complex. A
classs containing the parameterrs to be transsmitted and ann
ID3
3D11Buffer too store the parameters in the displayymemmory have to be creeated. Subseequently, theConntext->PSSetC
ConstantBuffeer() function has
h to be usedd
to trransmit the paarameters to thhe shader, witth the start sloot
set by this functiion the same as the registeer index set inn
the pixel shader. This is the oonly interactio
on between the
HLS
SL and C++ codes.
c5. Comparisonss and Analysees

ults. Fig.s 4(a)) and 4(d) aree
Fig. 4 shows the process resul
the original imag
ges, 4(b) andd 4(e) are the defog resultss
usinng GPU, andd 4(c) and 44(f) are the defog resultss
usin
ng the originaal algorithm.

CPU (System Memorry)

Source Imagee

Flow 1

WB parameter
W
A

(a))

(d)

(b)

(e)

(c)

(f)

Flow 3

R

U

Flow 4

Flow 2B

T((Destination Ima
age)

Gamma
Correction
parameters

Tone Mapping
Parameter

Fig. 4. Exp
perimental ressults on hazy images

Fig. 3. Workfflow of the ovverall algorithm
m optimized ffor G

As can be seen in Fig. 4, the process resultrof GPUU© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 263 -

Yuanyuan Shang, Yue Meng

processing is very close to that of the original algorithm.
In Fig. 2(b), which has more intricate details, the detailsof the processed image are not as clear as the originalalgorithm because of changes in the filtering method;
however, they are still within the acceptable range.
During the implementation, process, we adjusted theintensity of the tone mapping, such that the restored
image is more vivid than the image restored by theoriginal algorithm; the color of the image is more
realistic.
The focus of our research is improvement of theprocessing speed. Table 1. shows the processing time ofthe original algorithm running in CPU and our optimized
algorithm running in CPU and GPU. All the tests wereconducted on a Windows Phone device with Snapdragon
MSM8260A CPU and 1 GB RAM.
Resolution
CPU
CPU+GPU
640 × 480
11.122 s
0.463 s800 × 600
18.814 s
0.680 s1024 × 768
33.382 s
1.127 s1920 × 1080
105.50 s
3.227 sTable 1. Time cost of CPU only versus CPU+GPU

image dehazing process. Following parallelization andoptimization, we implemented our proposed optimized
single image parallel processing dehazing algorithm on a
Windows Phone device. Comparisons and analyses showthat the efficiency of the algorithm is significantly
improved and the defog effect excellent. The algorithm,
implemented in GPU, is written as a Windows Phone
application and will be published in the Windows Phone
app store. In summary, in terms of effectiveness andefficiency, this design has achieved the expected goals.
Acknowledgement
This work is supported by National Natural Science
Foundation of China (11178017, 61373090, 61303104,
61203238 ） , Beijing Natural Science Foundation（4132014）, The Project of Construction of Innovative
Teams and Teacher Career Development for Universities
and Colleges Under Beijing Municipality, and Beijing
Center for Mathematics and Information Interdisciplinary
Sciences.
References

algorithms
In the above chart, it can clearly be seen that there is
an improvement in the processing speed when using GPU
technology; in the case of the large image, the speed
increased as much as 3,000%. This is because we havetaken advantage of parallel computing and thefloating-point arithmetic of GPU. The mosttime-consuming part of the original algorithm is bilateralfiltering, which becomes a virtual real-time process whenrunning in GPU. Furthermore, other simple operations
have also been accelerated in GPU to various degrees.
Accelerations of these parts significantly reduced theoverall process time of the whole algorithm.
6.

Conclusion

With increasing use of image dehazing methods,
defogging results are improving significantly. However,
the efficiency of the algorithms used in these methods isstill very low. GPU technology has developed rapidly inrecent years, to the point where general-purpose
computing for GPU can be exploited to speed up the

1. J. Tarel and N. Hautière, Fast Visibility Restoration from aSingle Color or Gray Level Image. ICCV, 2009, pp: 2201–
2208
2. R. Tan, Visibility in bad weather from a single image.
CVPR, 2008, pp: 1–8.
3. K. He, J. Sun, and X. Tang, Single image haze removalusing dark channel prior. CVPR, 2009, pp: 1956–1963.
4. J. Tarel and N. Hautière, Fast Visibility Restoration from aSingle Color or Gray Level Image. ICCV, 2009, pp: 2201–
2208.
5. Yue Meng and Yuanyuan Shang, Design of Real-timeHaze Image Restoration System Based on FPGATechnology. Information Technology Journal, 2013, pp:7481–7488.
6. J. Tarel and N. Hautière, Fast Visibility Restoration from aSingle Color or Gray Level Image. ICCV, 2009, pp: 2201–
2208.
7. P. Shirley, J. Ferwerda, E. Reinhard, and M. Stark.
Photographic tone reproduction for digital images. In
ACM SIGGRAPH’ 02, 2002, pp:267–276.
8. J. Tarel and N. Hautière, Fast Visibility Restoration from aSingle Color or Gray Level Image. ICCV, 2009, pp: 2201–
2208.
9. HLSL and Pixel Shader for XAML Developers, Walt
Ritscher. O’reilly Media, 1st edition July 2012

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 264 -

Investigation of Feature Quantity in Sound Signal and Feeling Impression Using PCA
Yusuke Kawakami*, Tetsuo Hattori

Graduate School of Engineering, Kagawa University, 2217-20, Hayashi-Cho
Takamatsu, Kagawa 761-0396, Japan
Hiromichi Kawano

NTT Advanced Technology Corporation, 19-18, Nakamachi
Musashino, Tokyo 180-0006, Japan
Tetsuya Izumi

Micro-Technica Co., ltd., 3-12-2, Higashi-Ikebukuro
Toshima, Tokyo 170-0013, Japan
E-mail: s11d621@stmail.eng.kagawa-u.ac.jp, hattori@eng.kagawa-u.ac.jp,
hiromichi.kawano@ntt-at.co.jp, t-izumi@microtechnica.co.jp
www.kagawa-u.ac.jp
Abstract
This paper investigates the relationship between feature quantity of sound signal and feeling impression using PCA.
As the feature quantity, we use Fluctuation value and sum of squared errors (Residual) which is calculated byregression analysis of sound signal, in the same way as our previous paper. In order to investigate the feeling
impression and effect from sound signal, we have used a questionnaire survey method. As a result, we have foundthat the feeling response of examinees can be classified into three groups by a clustering analysis. And also we haveobtained the results of PCA for the feeling effects depending on each group of examinees and four kinds offrequency zone of sound signal. we also discuss the analysis results on the Kansei (or feeling) effect.
Keywords: Signal processing, Fluctuation, Intercept, Sum of squared errors, Feeling impression, PCA

signal obtained from the calculation of the signals’
fluctuation degree. And we have investigated therelation between feeling impression and thoseparameters, by using multiple regression analysis8. And
we eliminate “Intercept” from the analysis, because thisquantity (or parameter) is substantially equal to thevolume of sound8.
Moreover we had considered possibility of the effect offeeling impression from frequency domains. So we
divided into three frequency domains (Low Frequency(LF); 0~300Hz, Middle Frequency (MF); 300~1000Hz,
High Frequency (HF); 1000~22050Hz) and analyzed
each domain10, 11, 16.
As the results, we have understood that feeling
impression have an impact on Residual more than

1. Introduction
Recently, 1/f fluctuation in various fields of signal has
been actively researched, and it brings about an effect ofsuch healing as a human being psychologically feels atease, if there is a 1/f relation between the power
spectrum of the signal and the frequency f 1-7. However,
we focused that the power spectrum have same
fluctuation but the distribution are different. And we
doubted the strong influence of the emotional
impression factors other than fluctuation value.8
Therefore, in the previous research, we have defined
three kinds of parameters such as fluctuation value (orFluctuation), intercept (or Intercept), and sum ofsquared errors (or Residual) as feature quantity in sound*Graduate School of Engineering, Kagawa University, 2217-20, Hayashi-Cho, Takamatsu, Kagawa, 761-0396, Japan hattori@eng.kagawa-u.ac.jp© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 265 -

Yusuke Kawakami, Tetsuo Hattori, Hiromichi Kawano Tetsuya Izumiy f   a log f  log k  ˆ f 

Fluctuation, especially high frequency. In the regression
analysis, we can be seen the impact of the soundfeatures for the evaluation value of individual sensibility
adjective items (for example, bright, fast, etc.).
Although, it is difficult to capture the main factors on
the relation between feature quantity of sound which
presented to examinees, and feeling impression of
examinees.
In this paper we analyze the affect of the feeling
impression, from the music's Fluctuation and Residual,
by use of principle component analysis. At first, weinvestigate the feeling impression of the music by using
questionnaire survey. Then, from result we separate theexaminees into the groups using clustering analysis.
After that, we perform the PCA by the feeling
impression or feature quantity of sound signal eachfrequency domains. Furthermore we analyze
relationship between feeling impression, andFluctuation or Residual through the correspondence
relationship of principal component axis.

  a log f  b  ˆ  f (3)Fig. 1 (b) shows the regression line of Eq. 3. The
vertical axis and the horizontal axis are logarithm of PS
and logarithm of frequency f, respectively. In this paper,
we define the absolute-degree of the regression line a“Fluctuation”. Then, we define the intercept of theregression line (“Intercept”) as b, and we also define theerror from the line as e8. Furthermore, we define thesum of squared errors (“Residual”) as Eq. (4). In Eq. (4),y and Y mean the actual measurement value and thetheoretical value, respectively8.
s   ei    yi  Yi 
2

2

i

Y

y

Y f  (4)ik  f fay f 
alog f b ̂ f flog f(b) Regression line14,15(a) PS curve14, 15
Fig. 1. Conceptual image of PS curve and regression line.

2. Investigation between 3 Parameters
Accompanying on the Calculation Fluctuation
value (3PACF) and Feeling Impression

2.2. Evaluation of feeling impression
2.1. Fluctuation and 3PACF

Next, we have used questionnaire survey in order toinvestigate the relation between 3PACF and feeling
impression of music. The examinees are 34 students inthe age of early twenties. The list of music used in thissurvey is shown in Table 1.
These sampling frequency and file format are 44.1 kHz
and 16 bit wav, respectively. For every piece of themusic, we have taken 20 seconds to play it. The
examinees evaluated the 4 items as shown in Table 2,
by scoring from one to four. Also they have judged thepreference for each of music by scoring from one to ten.

Among fluctuations, the well-known 1/f fluctuationmeans that the power spectrum (PS) of a signal is
proportional to the 1/f of frequency. Moreover, it ispointed out that there is an effect that a human being
feels pleasantness1-6. Fig. 1 (a) is shown the conceptual
image of PS. Let Y(f) and (f) be the power of PS and its
error, respectively. And we define the PS which isshown in Fig. 1(a) as Eq. (1).
Y f  k f fa

(1)

Taking the logarithm of both sides,

2.3. Clustering analysis

Subsequently, we have conducted clustering analysis todivide into examinees groups who was similar feeling
impression, by using the results of questionnaire survey.
As preprocessing, we convert the evaluation (Item1~Item4) and Preference as follows;(2) {Item1 ~ Item4} ≥ 3 → 1 {Item1 ~ Item4} ≤ 2 → 0ˆ  f  and y(f) define ˆ  f   log1  ~  f  and y(f)≡logY(f) Preference≥6→1
respectively, we derive Eq. (3).
© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan

 k

k
logY  f   log a    f   log a 1 ~ f 

f
f

 k 
 log a   log1 ~ f 
f 
logY  f   a log f  logk  log1 ~ f 

- 266 -

Investigation of Feature Quantity

 Preference≤5→0
Therefore, the data of whole examinees are consist from34 set of 50 dimensional ((Item1 ~ Item4, andPreference) × 10songs) data. That is, this clustering
analysis is performed in the 50-dimensional space. And
we adopt Ward method9 as the analysis.
And then, we perform Wilk’s Lambda test as thestatistical test9. It is a test of the difference between themean values of the vector comprising a plurality ofvariables. The null hypothesis H0 and alternative
hypothesis H1 are as follows;
H0: Difference of all mean vectors are equal.
H1: Difference of all mean vectors are not equal.
Whenever each time of clustering has finished, weapply Wilk’s Lambda Test repeatedly. In this test, we
set significance to 1%. And then, if it will not come into
effect significance of 1% on next-time test, theclustering is quit10, 15, 16.

2.4. Principal Component Analysis (PCA)
In this section, we describe the PCA12, 13 performing
space and the set of vectors. However, we decide toeliminate Intercept because it substantially equal to thevolume of sound.
First, we define Physical Quantity Space (PQS) that is
two-dimensional space which axis is the fluctuation
value and Residual. The subject of PCA is a set of 10
pieces of music vectors (Table 3), and also we perform
PCA in each frequency domain (AF, LF, MF, and HF).
The other hand, we define Feeling Adjective Space(FAS) that is five-dimensional space which axis is the
feeling impression of the examinees. The subject of
PCA is a set of 10 pieces of music’s Feeling Impression
Vectors (FIVs).
2.4.1. Feeling Impression Vector (FIV)This subsection, we describe the FIVs.
First, we define the evaluation S(i, k) of examinee k as Eq.
(5). i and e1, …, e5 are the music number and theevaluation of Item1 ~ Preference, respectively.

Table 1. Music list of wave files.14
No

Title (.wav)
1 Another_Sky
2 Londonderry_Air
3 Blieve_you
4 Drafting
5 Down_by_the_Riverside
6 Space_Odessey3_Revelation
7 TOMORROW
8 Old_French_Song
9 Freedom
10 Red_River_Valley (brass)

Genre
Easy Listening
Classic
Easy Listening
Easy Listening
Jazz
Easy Listening
Pops
Classic
Pops
JazzSi ,k   e1 , ..., e5 Then, we convert the evaluation (Item1 ~ Item4) andPreference as same as Section 2.2. And we define theconverting processed vector, as Eq. (6). a1, …, a5 arezero or one in Eq. (6). That is;~Si ,k   a1 ,, a5 

Table 2. Evaluation items of the questionnaire survey.
Item1
Slow
Quick
1⇔4
Item2
Heavy
1⇔4
Light
Item3
Natural1 ⇔ 4 Artificial
Item4
Negative 1 ⇔ 4
Positive
Preference Dislike
1 ⇔10
Like

Title (*.wav)

1 Another_Sky
2 Londonderry_Air
3 Blieve_you
4 Drafting
5 Down_by_the_Riverside
6 Space_Odessey3_Revelation
7 Tomorrow
8 Old_French_Song
9 Freedom
10 Red_River_Valley (brass)

AF
Fl.
Re.
1.715
0.414
1.627
0.399
2.111
0.466
1.593
0.431
1.724
0.448
0.990
0.323
2.268
0.555
1.398
0.433
2.173
0.589
1.520
0.526

LF
Fl.
Re.
0.454
0.487
1.023
0.489
0.457
0.503
0.917
0.474
0.553
0.502
1.614
0.195
0.129
0.619
1.509
0.275
-0.259
0.535
0.499
0.653

MF
Fl.
Re.
1.713
0.674
1.873
0.906
0.527
0.844
1.296
1.006
0.470
0.679
1.522
0.470
0.721
0.545
1.633
1.255
-0.047
0.575
0.723
0.733

(6)

Hence, the vector Si* of the sum of evaluation r people
is FIV which is defined by Eq. (7). Z1, …, Z5 are thesum of evaluation Item1 ~ Preference of r people,
respectively.
r~*
S i  Z1 , ..., Z 5    S i ,k 

Table 3. Fluctuation and Residual
No.

(5)

(7)

k 1

HF
Fl.
Re.
1.777
0.394
1.706
0.369
2.472
0.409
1.706
0.404
2.115
0.406
1.004
0.316
2.903
0.463
1.515
0.399
2.705
0.517
1.658
0.507

That is, we apply Eq. (5) ~ Eq. (7) to each group, which
is divided by using clustering analysis, and we perform
PCA of the each group. Subsequent sections, we define
un (n=1, 2) and vm (m=1, …, 5) in the principal
component axis of set of vectors on PQS and FAS,
respectively. Additionally, we define fi and xi in thevectors of music number i (i=1, …, 10) on PQS and
FAS, respectively. So we can express fi and xi on thecoordinate axes un and vm respectively as Eq. (8).

Fl.: Fluctuation, Re.: Residual© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 267 -

Yusuke Kawakami, Tetsuo Hattori, Hiromichi Kawano Tetsuya Izumi

 f u , f u ,  x v ,  , x v 
i

1

i

2

i

1

i

5

(8)

consider that the music that is used in the investigationof this paper, the weight of Fluctuation and the Residual
are same degree. Because the components of theFluctuation and Residual are contained in u1 and u2
approximately equal proportions. The other hand, Fig. 4
shows the results of PCA on FAS (Feeling Adjective
Space). In the space, we do not consider principal
component v3, because the cumulative contribution ratio
of principal component v1 and v2 are about 94.7% inminimum of each groups.
From Fig. 4, in group G1, v2 is Preference, and the2.5. Correlation of the principal component axis
Subsequently, we investigate the correspondence ofprincipal component axes of PQS and principal
component axes of FAS. Let Cu and Cv be thecoefficient matrix which is calculated by PCA of PQS
and the coefficient matrix which is calculated by PCAof FAS, respectively. And we can describe the basis
vectors un and vm by using pn (the basis vectors on PQS)
and im (the basis vectors on FAS), as shown in Eq. (9)and (10).
Cu12   n1 C n  k  1
un  C u pn   u11
  ,  nk  n  k  0CC
u 22   n 2  u 21
Cv11  Cv15   m1  m  k  1
v m  C v im        ,  mk  m  k  0
Cv 51  Cv 55   m 5 

29
5
2
24
3
21
11
31
20
33
26
13
10
16
22
15
12
27
28
6
34
17
4
19
8
25
14
30
23
32
9
7
1
18

(9)

(10)

From Eq. (8) ~ (10), correspondence between un and vm
can be judged by the correlation coefficient R between
f i u n and x i v m . R is defined by Eq. (11).
Rfu xvfu xv
i

n

i

n

i

G2

G1

0

5

10

15

20

25

30

35

Fig. 2. Dendrogram of clustering result by using questionnaire
results of examinees.

(11)

m

2

i

G3

2

Table 4. Results of feeling impression questionnairem(a) Group G1
Music# *.wav
1 Another_Sky
2 Londonderry_Air
3 Blieve_you
4 Drafting
5 Down_by_the_Riverside
6 Space_Odessey3_Revelation
7 Tomorrow
8 Old_French_Song
9 Freedom
10 Red_River_Valley (brass)

3. Results of Feeling Impression and Discussion
3.1. Clustering analysis
Fig. 2 shows the result of clustering analysis and its
statistical test. As shown in Fig. 2, 34-person examinees
are divided 3 groups. The names of groups (G1, G2, and
G3) are given in descending order of head-count.
Examinees belonging to A, B, and C are 19 person(55.9% of total), 8 person (23.5% of total), and 7 person(20.6% of total), respectively. Table 4 also shows theresults of the aggregate of evaluation items that ispreprocessed describing in Section 2.3.

Item1
(Quick)
3
1
3
0
18
1
12
1
19
9

Item2
Item3
Item4
Preference(Llight) (Artificial (Positive)
14
17
15
7
6
3
8
9
5
2
18
10
3
3
0
7
18
15
19
5
11
6
1
5
12
18
19
13
7
7
1
3
15
15
18
8
17
15
18
6

(b) Group G2
Music# *.wav

Item2(Llight)

Item1(Quick)1 Another_Sky
2 Londonderry_Air
3 Blieve_you
4 Drafting
5 Down_by_the_Riverside
6 Space_Odessey3_Revelation
7 Tomorrow
8 Old_French_Song
9 Freedom
10 Red_River_Valley (brass)

1
0
0
0
6
0
7
0
8
4

4
0
2
0
7
2
6
0
7
8

Item3
Item4
Preference(Artificial (Positive)
5
8
8
0
4
7
0
8
8
2
0
7
6
8
7
1
0
8
8
8
8
2
0
8
5
8
7
7
8
8

5
1
3
2
4
4
1
4
3
5

Item4
Item3
Preference(Artificial (Positive)
4
7
7
4
3
6
4
4
5
4
2
6
5
6
6
4
2
4
4
4
7
5
3
4
5
5
3
6
6
5

(c) Group G3
Music# *.wav
1 Another_Sky
2 Londonderry_Air
3 Blieve_you
4 Drafting
5 Down_by_the_Riverside
6 Space_Odessey3_Revelation
7 Tomorrow
8 Old_French_Song
9 Freedom
10 Red_River_Valley (brass)3.2. PCA
Fig. 3 shows the results of PCA on PQS (PhysicalQuantity Space). The plotted marks in Fig. 3 indicate
endpoints of the vectors from origin. From Fig. 3, we

Item2(Llight)

Item1(Quick)
0
0
0
2
1
1
2
5
4
5

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 268 -

Investigation of Feature Quantity

0.8

0.05

Residual

RRV

0.04

0.6

0.03

0.4
OFS

0.02

0.2

SO3R

0.01

u2

Drafting

0.00

0.0

FreedomDR-0.2

Londonderry _Air

-0.01

Tomorrow

Another_Sky

-0.02
-0.03

Believe_y ou-0.4

u2 (Fluctuation, Residua

correspondence between v1 and Item2 (Light) or Item3(Artificial) are also strong. In group G2 and G3, thecorrespondence between v1 and Item4, v2 and Item1 arestrong respectively.
That is, 56% of examinees (G1) prefer brightly andartificial music in which have used this paper. However
tendency of music to suit the taste of the remaining of
examinees (G2 and G3) are not clear, because there areindividual differences. From Fig. 4(a), we consider thatexaminees of group G1 prefer “Believe_you”.

-0.6

Fluctuation

-0.04

-0.8
0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

u1

(a) AF (All Frequency domain)

3.3. Correlation of the principal component axis

0.25

We have calculated the correlation coefficient between
principal component axes of PQS (un) and FAS (vm)based on the results of PCA on each space, in eachfrequency domains (AF, LF, MF, and HF).
Table 5 shows the results of correlation coefficient ineach group (G1, G2, and G3). The colored portion of
Table 5 indicates that the absolute value of thecorrelation coefficient is 0.700 or higher.

0.8

Residual

Freedom

0.20

Tomorrow

0.6

RRV

0.4

Believe_y ou

0.10

Another_Sky

DR

0.05

0.2

Drafting

u 2 0.00
-0.05

0.0

Londonderry _Air

-0.2

-0.10
-0.4

u2 (Fluctuation, Residua

0.15

-0.15
OFS

-0.20

-0.6

Fluctuation

SO3R

-0.25

-0.8
0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

u1

(b) LF (Low Frequency domain)
0.8

0.15

From Table 5 (a), the correlation coefficient on AF and
HF between u1 and v1 are 0.926 and 0.931 respectively,
so they have strong positive correlation.
We refer Fig. 3(a) and (d), increase in Fluctuation and
Residual are tendency in response to increase of u1.
Also from Fig. 4(a), increase in Item2 (Light) and Item3(Artificial) are tendency in response to increase of v1.
Similarly, the correlation coefficient on LF and MF
between u1 and v1 are 0.774 and 0.704 respectively, sothey have positive correlation.
We refer Fig. 3(a) ~ (c), Fluctuation and Residual on LF
of all music are lower than AF. And the music which
Fluctuation and Residual are small on AF, there aretendency they increase on MF.
Therefore, we consider the music become brightly andartificial impression by increasing Fluctuation and
Residual, on AF, MF, and HF.

Residual

Freedom

0.10

0.6

Believe_y ou

0.4

DR
RRV

0.05

0.2

OFS

Drafting
Tomorrow

u 2 0.00

0.0
-0.2

-0.05
Londonderry _Air

-0.4

u2 (Fluctuation, Reidua

3.3.1. Group G1

Another_Sky

-0.10

SO3R

-0.6

Fluctuation
-0.15

-0.8
0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

u1

(c) MF (Middle Frequency domain)
0.08

0.8

Residual

0.4

SO3R

0.04

OFS

0.02

0.2
Draftingu2

Londonderry _Air

0.00

0.0

Another_Sky

-0.2

DR

Freedom

-0.02

-0.4
Believe_y ou

-0.04

-0.6

Fluctuation

Tomorrow

-0.06

-0.8
0.0

3.3.2. Group G2

u2 (Fluctuation, Residua

0.6

RRV

0.06

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

u1

(d) HF (High Frequency domain)

From Table 5 (b), we understand that G2 have tendency
same as G1. And, the correlation coefficient of eachdomain between u1 and v1 are higher than 0.761, so they
have positive correlation. Besides, we refer Fig. 4(b),Fig. 3. PCA results on Physical Quantity Space.
DR: Down_by_the_Riverside, SO3R: Space_Odessey3_Reveration,
OFS: Old_French_Song, RRV: Red_River_Valley (brass)© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 269 -

Yusuke Kawakami, Tetsuo Hattori, Hiromichi Kawano Tetsuya Izumi

Table 5. Correlation coefficient between principal axes.
(a) Group G1

3.0

AF
Principal
u1
u2
axis
v1
0.926 0.028
v2
0.216 -0.579

Believe_y ou

2.5
2.0
1.5

v 2 0.5

Drafting

I4

Another_Sky

0.0

I2 OFS
I1

I3

-0.5

AF
Principal
u1
u2
axis
v1
0.956 0.049
v2
0.251 0.000

Tomorrow

Pref.

MF
u1
u2
0.704 0.351
0.288 -0.080

HF
u1
u2
0.931 -0.059
0.203 -0.294(b) Group G2

Londonderry _Air

1.0

LF
u1
u2
0.774 0.547
0.230 0.068

AF
Principal
u1
u2
axis
v1
0.976 0.115
v2
0.024 -0.784

Freedom

-1.0

DR

-1.5

MF
u1
u2
0.761 0.341
0.611 -0.351

HF
u1
u2
0.960 -0.015
0.233 0.371(c) Group G3

RRV

SO3R

LF
u1
u2
0.827 0.506
0.528 -0.660
LF
u1
u2
0.945 0.280
0.031 0.083

MF
u1
u2
0.911 0.226
0.075 -0.425

HF
u1
u2
0.975 0.166
0.016 -0.482

-2.0
0.0

1.0

2.0

3.0

4.0

v1

5.0

6.0

7.0

8.0

9.0

increase in Item4 (Positive) is tendency in response toincrease of v1.
Therefore, we consider the music become positive
impression by increasing Fluctuation and Residual.

(a) Group G1
1.50
OFS

1.25

SO3R
Londonderry _Air

Drafting

1.00

Believe_y ou

3.3.3. Group G3

Pref.

0.75
0.50

From Table 5 (c), we understand that the correlation
coefficient of each domain of G3 between u1 and v1 arehigher than 0.911, so they have strong positive
correlation. Especially the correlation coefficient of AF
between u2 and v2 is -0.784, so they have negative
correlation. We refer Fig. 4(c), increase in Item4(Positive) is tendency in response to increase of v1. And
from Fig 3 (a) ~ (d), increase in Fluctuation and
Residual are tendency in response to increase of u1.
Furthermore, decreasing Fluctuation and increasing
Residual are tendency in response to decrease of v2.
Therefore, we consider the music become positive
impression by increasing Fluctuation and Residual.
Especially, by increasing Fluctuation and Residual on
AF, the music becomes fast impression.

Another_Sky

v 2 0.25
0.00
-0.25

I3

-0.50

I1

I4
I2

RRV
Tomorrow
DR-0.75

Freedom

-1.00
0.0

0.5

1.0

1.5

2.0

2.5

v1

3.0

3.5

4.0

(b) Group G2
0.8
0.6

Londonderry _Air

Pref.

0.4

Another_Sky

Believe_y ou

0.2

Drafting

I4

0.0

v2

SO3R

I3

I2

-0.2

TomorrowDR-0.4

4. Conclusion-0.6

Freedom

In this paper, we have investigated the effects between
feature quantity of sound signal and feeling impression
using PCA. As feature quantity, we have used
Fluctuation and Residual. As for the feeling impression
questionnaire, we have presented 10 piece of music to
examinees and they evaluated 5 items, i.e. quickness,brightness, artificial, positiveness, and preference (likeor dislike). Then, we performed clustering analysis
based on the evaluation results, the examinees feeling
impression could be divided into 3 groups.

RRV

OFS

I1

-0.8
-1.0
0.0

0.5

1.0

1.5

v1

2.0

2.5

3.0

(c) Group G3

Fig. 4. PCA results on Feeling Adjective Space.
DR: Down_by_the_Riverside, SO3R: Space_Odessey3_Reveration,
OFS: Old_French_Song, RRV: Red_River_Valley (brass),
I1: Item1 (Quick), I2: Item2 (Light), I3: Item3 (Artificial),
I4: Item4 (Positive), Pref.: Preference (Like)© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 270 -

Investigation of Feature Quantity

Next, we have performed PCA in the Physical Quantity
Spaces of the each frequency domain (AF, LF, MF, andHF) and performed PCA in the Feeling Adjective
Spaces of each group. Furthermore, we also investigatedcorrelation between the principal component axes.
As the results, we have understood that 56% of
examinees feel brightly and artificial impression fromthe music which both of Fluctuation and Residual were
high. And we have also found that they preferred such
music. Then, 44% examinees were not seen suchcorrelativity, but they felt positive impression from themusic which both of Fluctuation and Residual were high.
Moreover, we have found that high frequency feature
quantity of sound have the strongest impact to feeling
impression.
References

11. T. Kado, Basic knowledge of descriptive audio (Ohmsha,
2001) (in Japanese).
12. S. Muto, Statistical Analysis Handbook (Asakura Shoten,1995) (in Japanese).
13. H. Yanai, Multivariate data analysis - Theory andApplications (Asakura Shoten, 1994) (in Japanese).
14. Y. Kawakami, T. Hattori, T. Yamamatsu, T. Izumi, and
H. Kawano, Investigation of Feeling Effect by Feature
Quantity Including Fluctuation in Sound Signal, in Proc.
The Institute of Electrical Engineers of Japan C (CD)(Kitami, Hokkaido, 2013), pp. 1490-1495 (in Japanese).
15. Y. Kawakami, T. Hatori, H. Kawano, and T. Izumi,
Statistical Investigation on Relation between Feeling
Impression and Feature Parameters of Sound Signal,
International Journal of Affective Engineering 13(1),ISSN: 2187-5413 (2014) 71-80.
16. Y. Kawakami, Research on Correlation between Feature
Quantity of Sound Signal and Kansei Impression,
Doctoral dissertation, Kagawa University, Graduate
School of Engineering (2014) (in printing, in Japanese).

1. T. Musha, Reason why person feels pleasantness andunpleasantness --sound, image, and sense of touch-- thatapproaches to the mystery of “pleasantness” (Kawade
Shobo Shinsya (KAWADE dream new book), 1999) (inJapanese).
2. T. Musha, The world of fluctuation -Mystery of 1/f
fluctuations of the world- (Kodansha Bluebacks.
Kodansha, 1980) (in Japanese).
3. H. Akaike and T. Wada, Fluctuation and rhythm of living
body (Kodansha Scientific, 1997) (in Japanese).
4. S. Horiuchi, Mystery of fluctuation (Shinano Mainichi
Newspapers Co. 1997) (in Japanese).
5. E. Teramoto, R. Hirota, T. Musha, and M. Yamaguchi,
Infinity, chaos, and fluctuation (Bifukan, 1985) (inJapanese).
6. T. Musha, Conception of fluctuation that approaches tothe mystery of 1/f fluctuation (NHK Books, 1998) (inJapanese).
7. T. Musha, Science of fluctuation, Vol.1~10 (Morikita
Publication, 1991-1999) (in Japanese).
8. Y. Kawakami, T. Hattori, T. Yamamatsu, T. Izumi, and
H. Kawano, Experimental Investigation of the Relation
betweenFeeling
Impression
andQuantities
Accompanying Fluctuation Calculation in Sound Signal,
Transactions of Japan Society of Kansei Engineering
10(3), ISSN: 1884-5258 (2011) 365-374 (in Japanese).
9. B. S. Everitt, The Cambridge Dictionary of Statistics, 2ndedn. (Cambridge University Press, 2002).
10. Y. Kawakami, T. Hattori, H. Kawano, T. Izumi,
Statistical Investigation on Correlation between Feeling
Impression and Feature Quantity of Sound Signal, in
Proc. First International Symposium on Affective
Engineering 2013, ISSN: 2187-669X (Kita-Kyushu,
Fukuoka, 2013), pp.297-302.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 271 -

Automated Color Image Arrangement Method
Using Curvature Computation in Histogram Matching
Yusuke Kawakami*, Tetsuo Hattori, Yoshiro Imai, Haruna Matsushita

Graduate School of Engineering, Kagawa University, 2217-20, Hayashi-cho
Takamatsu, Kagawa 761-0396, Japan
Hiromichi Kawano

NTT Advanced Technology Corporation, 19-18, Nakamachi
Musashino, Tokyo 180-0006, Japan
R. P. C. Janaka Rajapakse

Tainan National City of the Arts, 66, Daci
Guantian, Tainan 72045, Taiwan
E-mail: s11d621@stmail.eng.kagawa-u.ac.jp, {hattori, imai, haruna}@eng.kagawa-u.ac.jp
hiromichi.kawano@ntt-at.co.jp, janakaraja@gmail.com
www.kagawa-u.ac.jp

Abstract
This paper proposes improvement color image arrangement method by using curvature computation. The previous
paper, we have presented the principle of our method using Histogram Matching based on Gaussian Distribution(HMGD), and how to detect input color image peakedness in its histogram. In this paper, we describe about
Variance Estimated HMGD (VE-HMGD) as improvement HMGD. We also show how to estimate the histogram
variance of original image based on the curvature computation. Moreover we compare processing results between
VE-HMGD and HMGD through some experimentation. As the result, we show that VE-HMGD is natural color
than HMGD.
Keywords: Automated color arrangement, histogram matching, HMGD, curvature variance estimation

In the previous papers, we have proposed a novel color
image automated arrangement method using an elastictransform (ET) 4, 5. And we have proposed a principle ofHistogram Matching based on Gaussian Distribution(HMGD) on brightness axis as one of the ET method
based on histogram and/or histogram matching. Then
we also described how to detect input color image
peakedness in its histogram by using curvaturecomputation for automated HMGD processing.

1. Introduction
Automated image processing for enhancement and/or
arrangement of color images has been more familiar tous according to the spreading of Digital Camera, Smart
Phone, DVD, etc.1-3. However, we consider that the
research on the automated arrangement method thatbrings about good sensibility effect (or Kansei effect) is
still on the way to practical use.
*

Graduate School of Engineering, Kagawa University, 2217-20, Hayashi-Cho, Takamatsu, Kagawa, 761-0396, Japan hattori@eng.kagawa-u.ac.jp© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 272 -

Yusuke Kawakami, Tetsuo Hattori, Yoshiro Imai, Haruna Matsushita, Hiromichi Kawano, R. P. C. Janaka Rajapakse

Furthermore, through experiments, we found that
HMGD processing is improved the image of Kansei
impression if applied to single-peakedness image.
In this paper, first, we explain the principle of HMGD.
Second, we explain the method of histogram varianceestimation to optimize the shape of the HMGDreference histogram by using curvature computation.
And then, we illustrate the results of improved HMGD
(Variance Estimated HMGD; VE-HMGD) processing
which is working better than previous HMGD
processing, through some experiments.

yy   (x)

 (L)g ( y)

(x0 dx)y ( x0 )

0

x

L

x

x0 dxx0

2. Principle of HMGDf(x)In this chapter, we describe the principle of HMGD
based elastic transform. Let f(x) and g(y) be two
probabilistic density functions on real variables x and y,
respectively. The probabilistic density function (PDF) iscorresponding to histogram of image brightness level
which is discretely defined. In addition, let y=(x) be acontinuous and monotonous increase function between
variables x and y as shown in Fig.17-9.
In addition let value of x be the range from 0 to L,
therefore, variable y is ranges from 0 to (L). And let P
mean the probability. From above definition and Fig. 1,
we can derive (1) ~ (3).
xL

P (0  x  L)   f ( x)dx  1

Fig.1. Continuous and monotonous increase function y=(x)and probabilistic density functions f(x) and g(y)7-9.

HMGD processing converts the original image
histogram into the Gaussian image histogram as shownin Fig. 2. First, because we aim to match Gaussian
distribution image histogram, we have to expand
brightness level of original image, due to convert into
uniform distribution histogram. Let (x) be defined by
(6).

(1)

x

y    x   L  f  x dx

x0

(6)

0

P(0  y   ( L)) y  ( L ) g ( y)dy  1

Since ’(x) = Lf(x), according to (5) and Fig. 2., we
derive (7).

(2)

y 0

f ( x )  h ( y ) ' ( x )  h ( y ) Lf ( x )f ( x)dx  P( x0  x  x0  dx)

Therefore, we can derive (8) P( ( x0 )  y   ( x0  dx))
 P ( y0  y  y0  dy )  g ( y ) dy

(7)h y L  1,

(3)

From (3), we obtain (4) and (5) because y0 = (x0) andy0 + dx = (x0 + dx).
f x dx  g  y dy  g  y  '  x dx(4)f  x  g  y  '  x(5)h y   1 L(8)

From (8), we understand that original image histogram
f(x) becomes uniform distribution histogram h(y) andexpands brightness level.
Similarly, let Gauss(z) and (z) be defined by (9) and(10), respectively.

Gaussz  Thus, if we know y='(x) and g(y), then we have f(x).
Using the above equations, we derive the principle of
HMGD.

  z   2 
exp 2 2 
2 2

1© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 273 -

(9)

Automated Color Image Arrangement0z  z   2  (10)
L


exp

2 2 
 2 0yThen, we obtain (11) because (x) = (z) from Fig. 2.
x

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

z

L
L
x

 z    ddLL  f  x dx exp dx 0
2 2 
dz  2 0
L ' x   L ' z 

(x0+dx)

(x)f  x   Gauss z 

x0

z

2

z

y=(x)

(L)

y

(11)

And, we can derive (12) from (6) and (10) bydifferentiating both sides of (11).
xyy=(z)h(y)x  z   L  Gauss z dz 

z0

x0+dx

x

z z0+dz

1
L

(12)

f(x)

Gauss(z)Therefore, we understand that if we take the transformfunction as (6) and (10), f(x) becomes Gaussian
distribution. It corresponds to the HMGD processing,which means that function defined by cumulative
histogram transforms the original histogram into theGaussian histogram.

Fig. 2. Conceptual image of HMGD.

 ( x  a) 2 dyKexp  g ( x) 2 2 
dx
 2

3. Variance Estimated HMGD (VE-HMGD)(17)

By the same way,
3.1. Approximate of Curvature Computation( xa )2


d 2 y dg ( x)
K
2
 1 


・e 2 ・  2 2( x  a)
2
dx
dx
 2
 2 

In this section, we describe how to calculate thecurvature approximation using for variance estimation.
Let y be a function with respect to x, the definition of
the curvature R is given by (13).

  1   d2y

R  dx2
g ( x) 3
dy 2 2dx ( x  a) 2 K
exp 2 2 
 2
( x a )  K2 a  x   x  a ・e 2 ・  2   g( x・)  2  (18)  2    2Hence, we obtain the following (19) and we canapproximate to (13).

(13)

a  x g ( x)(14)2a  x g x

R 
3
2
1  g ( x) 2 2

In (14), K means a coefficient that satisfies (15).
L (u  a) 2 K du  1exp
2 2 
 2 0(15)Then, let y=f(x) be a function representing the
cumulative histogram. So f(x) can be represented (16).
x
Since y  f  x   g u du , g(x) can be described as (17).



0

x

f ( x)   g(u)du 
0



(19)

The curvature R varies the sign according to the value ofx;
 x<a → R>0 x=a → R=0
 x > a → R < 0.
3.2. Variance Estimation of image histogram
In this section, we describe how to estimate thehistogram variance 2 of the original image by using

K x  (u  a)2  (16)duexp 
 2 0  2 2 

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 274 -

Yusuke Kawakami, Tetsuo Hattori, Yoshiro Imai, Haruna Matsushita, Hiromichi Kawano, R. P. C. Janaka Rajapakse

curvature computation optimize the shape of thereference histogram, which is used in the VE-HMGD
processing. Fig. 3 and Fig. 4 shows a conceptual imageof the original histogram and cumulative histogram ofvariance 2 and average a, respectively.
Let g(a) be a Gauss density function with variance 2 ataverage a. From (14),g (a)  (a  a) 2 KK exp 
2

2
 2

2





y
g(a)

(20)
0



2 as follows.

Thus, we can describe g a y

Let R a  2 be curvatures at a  2 . From (19),
we describe these curvatures in (22).



R a  2 a  a  2  g ( x)21  g (a  2 ) 

3
2 2

a

a

2

x
L

Fig. 3. Conceptual image of the original image histogram ofvariance 2 and average a.

 (a  2  a)2 K  gxe1 (21)exp g(a  2 ) 22 2 




2

a



R a 

2



R(a)
(22)



R a 

2



From (21), g (a  2 )Ra  2  

0

2

a 



1  g (a  2 ) 

3
2 2



g ae122H    
3   1  ( g (a)e1 )2 2   





(23)

2

L

x

[Step 1] Proceed to Step 3 if input image has single
peakedness histogram after performing
curvature calculation. Otherwise go to Step 2.
[Step 2] Abort VE-HMGD, and outputting inputtedimage in Step 1.
[Step 3] Detect the peakedness brightness value of thehistogram a. Proceed to Step 5 if the curvature
R(a) is obtained. Otherwise go to Step 4.
[Step 4] Set =50 (default value), and perform HPAHMGD7, 8. Then proceed to Step 11.
[Step 5] Calculate curvature R a  2 according to(23) and (24). Then, calculate following (26).

(24)

Hence, we can obtain reference histogram variance 2
by using (23). In the below equation v  2 , and v
means distance from average a.
Ra  v  Ra  v a of HMGD from the histogram of the image, by using
curvature computation. In this section, we describe theprocess of Variance Estimated HMGD (VE-HMGD).

3  g a   2  2 
1    e  aFig. 4. Conceptual image of the original image cumulative
histogram of variance 2 and average a.

In (23), H is a constant that means following (24).

g a 
H
e2

v 2 (25)
 4
H   H  2 

2
v

2 2





3.3. Process of Variance Estimated HMGD

 

 



R a  2  R a  2  2 2 H 

In the previous section, we have described the method
to estimate the optimum variance in reference histogram  0 (26)© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 275 -

Automated Color Image Arrangement[Step 6] Proceed to Step 7 if 2 satisfies (26).
Otherwise modified 2 and return to Step 6.
[Step 7] Calculate variance 2 according to (25).
[Step 8] Calculate the reference histogram by using
variance 2 and average a. Then perform
HMGD.
[Step 9] Output the processed image.

5. Conclusion
Aiming at improvement Histogram Matching based on
Gaussian Distribution (HMGD), we have describedprincipal of HMGD, and we have proposed a method
for estimate variance of original image histogram. As
for concrete method, we have applied the curvaturecomputation to estimation variance.
Furthermore, we suggested the VE-HMGD as
improvement of HMGD and described its processing
sequence. Through experimentation, we consider that
VE-HMGD processing method will be useful andpromising than previous HMGD. For further study, we
have to verify the effect of VE-HMGD throughquestionnaire survey.

4. Experimentation
Fig. 5 (a) shows example of results of HMGD and VEHMGD processing which the original image have single
peakedness histogram. The set of histogram andcumulative histogram (original image, HMGD image,and VE-HMGD image) are shown in Fig. 5 (b) and (c),respectively. In this case, VE-HMGD have a goodfeeling impression (or Kansei effect) than HMGD. And
we found that shading of VE-HMGD is enhancing than
HMGD.

References
1. C. R. Gonzalez and E. R. Woods, Digital Image
Processing (Addison-Wesley Publishing Company,
1993).
2. B. Jahne, Digital Image Processing --Concepts,
Algorithms, and Scientific Applications-- 4th edition(Springer, 1995).
3. E. S. Umbaugh, Computer Vision and Image Processing:
A Practical Approach Using CVIPtools (Prentice Hall
PTR, 1998).
4. W. Burger and J. M. Burge, Principles of Digital Image
Processing: Fundamental Techniques (Springer 2009).
5. T. Izumi, T. Hattori, S. Sugimono, and T. Takashima,
Color Image Arrangement Using Elastic Transform on
Principal Component Axis (in Japanese), Journal ofJapan Society of Kansei Engineering 8(3) (2009) 667674.
6. Y. Kawakami, T. Hattori, D. Kutsuna, H. Matsushita, Y.
Imai, H. Kawano, and R. P. C. Janaka Rajapakse, An
automated color image arrangement method based on
histogram matching, in Proceedings of 2013
International Conference on Biometrics and Kansei
Engineering (Japan, Tokyo, 2013), ISBN 978-0-76955019-0, pp. 31-34.
7. Y. Kawakami, T. Hattori, Y. Imai, H. Matsushita, H.
Kawano, and R. P. C. Janaka Rajapakse, AutomatedColor Image Arrangement Method and Kansei
Impression, in Proceedings of International Conferenceon Artificial Life and Robotics 2014 (Japan, Oita, 2014),
ISBN 978-4-9902880-8-2, pp. 260-263.
8. Y. Kawakami, T. Hattori, Y. Imai, H. Matsushita, H.
Kawano, and R. P. C. Janaka Rajapakse, Kansei
Impression and Automated Color Image Arrangement
Method and Kansei Impression, Journal of Robotics,(a) Original image6, 9 (left), HMGD image6, 9 (center), and VE-HMGD
image (right).

(b) Histogram of original image (left), HMGD image (center), and VEHMGD image (right).

(c) Cumulative histogram of original image (left), HMGD image(center), and VE-HMGD image (right).

Fig. 5.
Example of results by HMGD, VE-HMGD, and thecorresponding histograms.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 276 -

Yusuke Kawakami, Tetsuo Hattori, Yoshiro Imai, Haruna Matsushita, Hiromichi Kawano, R. P. C. Janaka Rajapakse

Networking and Artificial Life, 1(1) (2014), ISSN 23526386, 60-67.
9. Y. Kawakami, Research on Correlation between Feature
Quantity of Sound Signal and Kansei Impression,
Doctoral dissertation, Kagawa University, Graduate
School of Engineering (2014) (in printing, in Japanese).

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 277 -

Change Detection Experimentation for Time Series data by New Sequential Probability Ratio
Yoshihide Koyama *, Tetsuo Hattori,

Graduate School of Engineering, Kagawa University,
2217-20, Hayashi-cho Takamatsu, Kagawa 761-0396, Japan
Hiromichi Kawano

NTT Advanced Technology Corporation, 19-18, Nakamachi
Musashino, Tokyo 180-0006, Japan
Katsunori Takeda

Canon IT Solutions Inc., Tosabori Dai Bldg. 2-2-4, Tosabori,
Nishi-ku,Osaka-shi, Osaka 550-0001, Japan

Abstract
Previously, we have proposed a novel method using New Sequential Probability Ratio (NSPR) for the structuralchange detection problem of ongoing time series data instead of using SPRT (Sequential Probability Ratio Test). In
this paper, for comparison, we present the experimental results by applying the both methods, i.e., NSPR and SPRT,
to time series data that are generated by a multiple regression model in the case where one explanatory variation isa periodic function (sine function). And also we discuss the effectiveness of the both methods.

Keywords: Time series data, structural change detection, SPRT, New Sequential Probability Ratio (NSPR)(SPRT) and New Sequential Probability Ratio (NSPR)(Refs. 6, 11-12).

1. Introduction
Change point detection (CPD) problem in time series(see Refs.1-7) is to identify whether the generation
structure of monitoring data has changed at some time
point by some reason, or not. We consider that the
problem is very important and that it can be applied toa wide range of application fields.
For the structural change detection problem of ongoingtime series data, we have already proposed some kindsof methods (see Refs. 6, 8-12). In this paper, we
present the results of comparison experiment by two
methods that are Sequential Probability Ratio Test

2. SPRT and Chow Test
2.1. SPRTThe Sequential Probability Ratio Test (SPRT) is used
for testing a null hypothesis H0 (e.g. the quality isunder pre-specified limit 1%) against hypothesis H1(e.g. the quality is over pre-specified limit 1%). And it
is defined as follows:
Let Z1, Z2, …Zi be respectively observed time series
data at each stage of successive events, the probability
ratio i is computed as follows.

*2217-20 Hayashi Cho Takamatsu City, Kagawa, 761-0396 Japan,
Kagawa University,
Email: hattori@eng.kagawa-u.ac.jp© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 278 -

Yoshihide Koyama, Tetsuo Hattori, Hiromichi Kawano, Katsunori Takeda

P ( Z1 | H 1 )  P ( Z 2 | H 1 )    P ( Z i | H 1 )λi P ( Z1 | H 0 )  P ( Z 2 | H 0 )    P ( Z i | H 0 )i  i 1

(1)


1

, C2 

1 



(3)

where, if the data yi goes “OUT” from thetolerance band, (P(i |H0), P(i |H1))= (θ0, θ1),otherwise (i.e., the data yi goes “IN”),(P(i |H0), P(i |H1))= ((1-θ0), (1-θ1)).
[Step 6]
Execution of testing.
(i) If the ratio i is greater than C2 (= (1-)/ ),dismiss the null hypothesis H0, and adopt thealternative hypothesis H1, and then End.
(ii) Otherwise, if the ratio i is less than C1 (=/(1-) ), adopt the null hypothesis H0, anddismiss the alternative hypothesis H1, andthen set i = 1 and return to Step3.
(iii) Otherwise (in the case where C1  i  C2 ),advance to Step7.
[Step 7]
Observe the following data yi
incrementing i. Evaluate the error | i | and judge
whether the data yi goes in the tolerance band, ornot. Then, return to Step5 (i calculation).

where P(Z | H0) denotes the distribution of Z if H0 istrue, and similarly, P(Z | H1) denotes the distribution of
Z if H1 is true.
Two positive constants C1 and C2 (C1 < C2) are chosen.
If C1 < i < C2, the experiment is continued by taking
an additional observation. If C2 < i, the process is
terminated with the rejection of H0 (acceptance of H1).
If i < C1, then terminate this process with theacceptance of H0.
C1 

P ( i | H1 )P ( i | H 0 )(2)

where  means type I error (reject a true null
hypothesis), and  means type II error (accept a null
hypothesis as true one when it is actually false).
2.2 Procedure of SPRT

2.3 Chow Test

The concrete procedure of applying the SPRT method
to the structural change detection problem is described.

The well-known Chow Test checks if there are
significant differences or not, among residuals forthree Regression Lines, where regression Line 1
obtained from the data before a change point tc, Line 2
from the data after tc, and Line 3 from the whole data
so far, by setting up the change point hypothesis ateach point in the whole data (Fig.1).

[Step 1]Make a prediction expression and set
the tolerance band (a) (e.g. a=2s) that means
permissible error margin between the predicteddata and the observed one. (s denotes a standard
deviation in learning sample data at early stage.)[Step 2]
Set up the null hypothesis H0 andalternative hypothesis H1.
H0：Change has not occurred yet.
H1：Change has occurred.
Set the values ,  and compute C1 and C2,
according to Eq. (2). Initialize i = 0, 0 = 1.
[Step 3]
Incrementing i (i = i+1), observe thefollowing data yi. Evaluate the error | i | between
the data yi and the predicted value from theaforementioned prediction expression.
[Step 4]
Judge as to whether the data yi goesin the tolerance band or not, i.e., the i is less than(or equal to) the permissible error margin or not. If
it is Yes, then set i = 1 and return to Step3.
Otherwise, advance to Step5.
[Step 5]
Calculate the probability ratioi,using the below Eq.(3) that is equivalent to Eq.(1).

yLine 3 for the whole data

Line 1 before tc
Line 2 after tc

Assumed change point tc
Fig.1. Conceptual image of Chow Test

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 279 -t

Change Detection Experimentation for3.2. Recursive Form

3. New Sequential Probability Ratio (NSPR)

When the NSPR is greater than 1.0, we can regard thatthe structural change has been occurred before thepresent time.
In the similar way as SPRT (see Eq.(3)), the definitionof NSPR can also be described in a recursion formula.
Let Λn be the value of NSPR defined by Eq.(8), we
have the following recursive equation.

3.1. Definition of NSPR11-12
Let C n  a1a2 ,, ai ,an ai {IN, OUT} be a string (orsymbol sequence) obtained from the observed data.
~Let θi and θi be the conditional probability thatreceives the observed data ai in the state S0 and S1,
respectively, where S0 and S1 mean the state ofunchanged state and changed state, respectively.
~That is, it means that θi {R, 1  R} and θi {Rc , 1  Rc } ,respectively, where R  P (OUT | S0 ) , Rc  P (OUT | S1 ) .
And let P(Cn, H0) and P(Cn, H1) be the joint
probability of the symbol sequence Cn that happen
with the event H0 (the structural change is notoccurred) and H1 (the change is occurred), respectively.
Then, the following equations hold.
P ( a1  a n , H 0 )  (1 γ) nθ1 θn  (1 γ) n

Λn 

n

4. Experimentation for Comparison
We have done the comparison experimentation by
SPRT and NSPR for a time series data that is generated
based on the following equations.

nΠθ (4)
i



n

θ )

~
θi  ((1  γ)θ1 )(γ

i 1

~

i

i2

n

θ )   ((1  γ) 2θ1θ2 )(γ
n



i

k 1



( (1 γ) k 1･

k 1

n

θ )

θ j )(γ

j0

i

ik

(5)

P(Cn , H0 )P(Cn )(6)

P(H1 | a1 an )  P(H1 | Cn ) P(Cn , H1)
P(Cn )(7)

Using these above equations (Eq.(4)-(7)), the New
Sequential Probability Ratio (NSPR) that we propose
can be represented as follows.
P (H 1 | a1  a n ) P (H 1 | C n ) P ( C n , H 1 )NSPR 
P (H 0 | a1  a n ) P (H 0 | C n ) P ( C n , H 0 )




k 1

k 1

n



( (1 γ) k 1･(1 γ)

j 0
n
n

i 1

~

i

ik

Πθ

i

y  x1  10 x 2  5  (t c  t)

(10)

1 
x 1  t , x 2  sin   t 
8 

(11)

1 
x1  random, x2  sin t 
8 

(12)

For the two kinds of time series data (400 time series
data in total), we have examined the change detection
ability and the accuracy of each method (SPRT and
NSPR) by setting some various evaluation criteria.
In the experimentation, the prediction model isconstructed using the first 40 time points and the restθ )θj )(γ(t  t c )where ε~N(0,σ2), i.e., the error ε is subject to theNormal Distribution with the average 0 and thevariation σ2, and tc means the change point. In addition,
we have set tc=70 and σ=5. The number of the time
points is 100 (i.e. .
Setting the two kinds (or cases) of coefficients inEq.(10) as shown in Eq.(11) for Case 1 and Eq.(12) for
Case 2, respectively. And we have generated 200 setsof time series data for the two kinds (or cases) of timeseries, respectively.

~P(H0 | a1an )  P(H0 | Cn ) ny  x1  20 x 2  5  ε

~

i3



(9)

~
where Λ0  0,  0  1,  0  1 .

i 1

P ( a1  a n , H 1 )  γ~1(Λn 1 γ)(  n )
n
1 γ

(8)

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 280 -

Yoshihide Koyama, Tetsuo Hattori, Hiromichi Kawano, Katsunori Takeda

of time points from t=41 to t=100 are used for change
detection.
y  x1  20x2  5  

y250

References
1. C. G. E. P. Box and G. M. Jenkins, Time Series
Analysis: Forecasting and Control (Prentice Hall, 1976).
2. Peter J. Brockwell and Richard A. Davis, Introduction
to Time Series and Forecasting, 2nd edn. (Springer;
2003)
3. C. Han, P. K. Willet and D. A. Abraham, Some methods
to evaluate the performance of Page's test as used todetect transient signals, IEEE Trans. Signal processing.
47(8) (1999) 2112-2127.
4. S. D. Blostein, Quickest detection of a time-varying
change in distribution, IEEE Trans. Information Theory.
37(4) (1991) 1116-1122.
5. A. Wald, Sequential Analysis (John Wiley & Sons,
1947).
6. H. Kawano, T. Hattori, and K. Nishimatsu, Structural
Change Point Detection Method of Time Series Using
Sequential Probability Ratio Test -Comparison with
Chow Test in the ability of early detection- (inJapanese), IEEJ Trans. EIS. 128(4) (2008) 583-592.
7. G. C. Chow, Tests of Equality Between Sets of
Coefficients in Two Linear Regressions, Econometrica.
28(3) (1960) 591-605.
8. K. Takeda, T. Hattori, T. Izumi and H. Kawano,
Extended SPRT for Structural Change Detection ofTime Series Based on Multiple Regression Mode, inProceedings of the 15th International Symposium on
Artificial Life and Robotics, (Feb. 2010), ISBN: 978-49902880-4-4, pp.755-758.
9. K. Takeda, T. Hattori, Tetsuya Izumi and H. Kawano,
Extended SPRT for Structural Change Detection ofTime Series Based on Multiple Regression Model,International Journal of Artificial Life and Robotics
Springer, ISSN: 1433-5298, 15(4), (2010) 417-420.
10. T. Hattori and H. Kawano, Change Detection Method ofTime Series as an Optimal Stopping Problem -Constructive Proof of Optimal Solution Theorem --, in
Proc. of 2011 International Conference on Biometrics
and Kansei Engineering, (2011), IEEE Computer
Society, ISBN: 978-0-7695-4512-7, pp.100-105.
11. Yoshihide Koyama, Tetsuo Hattori, Hiromichi Kawano,
Model Introduced SPRT for Structural Change
Detection of Time Series (I), in Proc. of The
International Conference on Artificial Life and Robotics(Jan. 2014), ISBN: 978-4-9902880-8-2, pp.252-255.
12. Yoshihide Koyama, Tetsuo Hattori, Hiromichi Kawano,
Model Introduced SPRT for Structural Change
Detection of Time Series (II) --- Kansei Channel andBayes’ Estimation ---, in Proc. of The International
Conference on Artificial Life and Robotics (Jan. 2014),
ISBN: 978-4-9902880-8-2, pp.256-259.

y  x1  10x2  5  change point tc = 70

200

150

100

50

t

0
0

10

20

30

40

50

60

70

80

90

100

(a) (Case 1)(b) (Case 2)
Fig.2. Example of two kinds of time series data
generated based on Eq.(10).
(a)Case1 (x1=t, and x 2  sin(t / 8) ).
(b)Case2 (x1=random, and x 2  sin(t / 8) ).
We define a two dimensional vector I=(I1, I2) as
follows.

t 100 t  70I  ( I 1, I 2 )  
fr ( t )  t  tc ,fr (t )  t  tc (13)
t  71
 t  41
where tc=70, and fr (t ) means the detection frequency

at time point t in 200 time series data.
We can consider that the detection ability and theaccuracy is high as the absolute value of I and I1
approach to 0.
As a result, in the sense of the abovementioned criteria,
the NSPR method has shown that it is superior to
SPRT for the both kinds of time series data used in theexperimentation.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 281 -

Analysis of Navier-Stokes Equation from the Viewpoint of Advection Diffusion (I)-- Analytical Solution of Diffusion Equation -Hiroki Sakamoto *, Tetsuo Hattori, Akiomi Tada, Vanhoa Nguyen

Graduate School of Engineering, Kagawa University, 2217-20, Hayashi-cho Takamatsu, Kagawa 761-0396, Japan
Hiromichi Kawano

NTT Advanced Technology Corporation, 19-18, Nakamachi
Musashino, Tokyo 180-0006, Japan

Abstract
We propose an approximate analysis method for Navier-Stokes Equation (NSE) based on the similarity between
NSE and Advection Diffusion Equation (ADE). In this paper, we present an analytical solution and a Greenfunction (integral kernel) which are obtained from the diffusion equation over uniform flow field (or velocity field)in three dimensional (3D) boundless region under arbitrary initial condition. The solution shows that the diffusion
process is a Markov one and that the Green function becomes a Gaussian-like exponential function.

Keywords: approximate anlysis method, Navier-Stokes Equation (NSE), Advection Diffusion Equation (ADE)

problem in the computation precision for the analysis of
viscous flow of high Reynolds number.
Even if we obtain the result by using such numericalcomputations, those methods take much time and theresults are involved by not a little computation error.
So, in order to reach the more accurate solution, it
would be desirable to have an analytical approximate
solution that is as much as close to the exact one.
In order to obtain such an analytical approximate
solution, we focus on the similarity between the NSE
and Advection Diffusion Equation (ADE). And in thispaper, we derive the exact analytical solution of the
ADE over uniform flow field (or velocity field) in three
dimensional (3D) boundless region under arbitrary
initial condition2-4.

1. Introduction
Navier-Stokes Equation (NSE) is well known as
fundamental one in Fluid Mechanics1. Because the exact
analytical solution of NSE is not yet obtained, we haveto use numerical computation for the solution under thearbitrary initial and boundary conditions.
For the numerical computation method, Difference
Method, Finite Element Method and Boundary Element
Method are well known. However, as for the Difference
Method, it tends to be difficult to deal with complicatedboundary conditions. In addition, the method has tosatisfy the Courant condition to obtain the stablecomputation solution.
On the other hand, the Finite Element Method takes
much time to solve simultaneous equations appeared in
the method, and the Boundary Element Method has a*2217-20 Hayashi Cho Takamatsu City, Kagawa, 761-0396 Japan, correspondence Email: hattori@eng.kagawa-u.ac.jp© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 282 -

Hiroki Sakamoto, Tetsuo Hattori, Akiomi Tada, Vanhoa Nguyen, Hiromichi Kawanotransformed C, and we have the following Eq.(5) andEq.(6).
2. Advection Diffusion Equation
Let C be a fluid of density (or density of material), andlet D x , D y , D z denote the diffusion coefficient in     C ( x, y, z, t )e~C

 j ( px  qy  rz )dxdyxz  x , y , z axis direction, respectively. Similarly, let u, v, w…(5)denote the flow velocity in x , y , z axis direction,

  

1

~

C ( p, q, r , t )e2    

respectively. Moreover, let  and Q be the attenuation
coefficient that is spatially uniform and the load
generation rate function, respectively.

CThe partial differential equation of the ADE is shown asEq.(1).

From the Fourier transform (FT) of the left hand side(LHS) and right hand side (RHS) in Eq.(1), we have thefollowing Eq.(7) and Eq.(8), respectively.

…(6)

FT of LHS in Eq.(1)
  

C
C
C
u
v
w
 C  Q
x
y
z





C ( x, y , z , t )  j ( px  qy  rz )edxdyxzt  
  

(1)



Here we introduce the Dirac’s  function as in thefollowing (2).

t   C ( x, y, z, t )e j ( px  qy  rz )dxdyxz  

~
C

t

(x  0)  ( x)  , ( x  0)  ( x)  0,



dpdqxr   

C
 2C
 2C
 2C
 Dx

D

D
y
z
t
x 2
y 2
z 2



 j ( px  qy  rz )

3

…(7)

(2)

 ( x)dx  1

FT of RHS in Eq.(1)~  p 2 Dx  q 2 D y  r 2 Dz  j( pu  qv  rw)   C



  

dxdyxz   C( x, y, z,0) e~ p D  q D  r D  j( pu  qv  rw)   C

The initial condition and the load generation ratefunction are shown in Eq.(3) and Eq.(4), respectively.

  
2
x

  

C ( x , y , z ,0)  j ( px qy  rz )   C ( , ,  )
0

2

y

2

z

  

   ( x   ) ( y   ) ( z   )ddd   Q( ,, , t)e j ( px  qy  rz )ddd  

…(3)

…(8)

  

Q( x, y, z, t )     Q( , ,  , t )As a result from the above Eq.(7) and Eq.(8), theFourier transform of Eq.(1) becomes as follows.

 ( x   ) ( y   ) ( z   )ddd
…(4)

~
~
C
  ( p , q , r , t )  C   ( p , q , r , t )tNext, we think of the Fourier transform for C in 3~
dimensional space. Then, let C be the Fourier

…(9)

where

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 283 -

Analysis of Navier-Stokes Equation

( p, q, r, t)  p2 Dx  q2 Dy  r 2 Dz  j( pu  qv  rw)  ( x    u) C0 ( ,,  ) exp{ p Dx   j
 3


8

2 Dx             ( p, q, r, t ) Q( , ,  , t )e  j ( p  q  r )ddd

2

Since Eq.(9) is a linear differential equation of firstorder with respect to time t, using an infinitesimal timeparameter  (0    t) , the solution is represented asshown in Eq.(10).
t~~C ( p, q, r, t )  C0  exp    ( p,q,r,t )ds 0( x    u)
( y   v) ( z    w) * exp  ddddpdqdr
4Dx
4 Dy
4Dz


2

First, we perform the integration with respect to p , q , r ,then we have the following Eq.(12)

0

C1 

where C~0 is represented as the following equation in the  

As for the C2 , we have the following equations.



Next, we apply the inverse Fourier transform to Eq.(10).
Let the first and second term of the inverse Fouriertransform of right hand side be C1( x, y, z, t) andC2 ( x, y, z, t ) , respectively.

   ~  t  ( p,q,r,s)ds  C e 0e  j  pxqyrzdpdqdr
032          

C0 ( , ,  )e  j{ p ( x  )  q( y  )  r ( z  )}



8 3

t

   ds  
Qe j ( p  q  r )ddd  e 0 d 
 3



8


    0   t      

18 3

       Q expjp( x   )  jp( y  )  jr( z   )0       p D  q D  r D  j pu  qv  rw   ds
t





2

2

x

2

y

z

dpdqdrdddd

C0 ( , ,  ) exp  p Dx  jp( x    u)       e j ( px  qy  rz)dpdqdr

      t 2
 p D x  q 2 D y  r 2 Dz  j ( pu  qv  rw )   

 0
e
ddddpdqdr




1

 

   t    

10     


 t
 

  ( p, q, r , t )  exp     ( p , q , r ,t ) ds d ( 2 )      0
3e j ( px  qy  rz ) dpdqdr

 Xds .

   t

1

C2 

t

8 3



C0 ( , ,  )8  3 D x D y Dz      

…(12)

~
C0 ( p, q, r ,0)     C0 ( ,  ,  )e j ( p  q  r ) dddAnd, let X denote

  

1


( x    u ) 2 ( y    v ) 2 ( z    w) 2 exp   ddd
4 Dx
4Dy
4 Dz



same way as  ,

1

2

t





2

…(11)

 t

  ( p, q, r, t )  exp     ( p,q,r,t )ds d …(10)
 


1



2 ( y    v )  
( z    w)  q Dy   j }  r Dz   j
2 Dy  
2 Dz   C1 

2     

1

2

1
8 3

t         Q    exp  p D  q D  r D 
20   x2   jp ( x    u )  jq ( y    v )  jr ( z    w )   q2 D y  jp( y    v )  r 2 Dz  jp( z    w)   ddddpdqdr

2

y



dpdqdrd d  d  d © The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 284 -z

Hiroki Sakamoto, Tetsuo Hattori, Akiomi Tada, Vanhoa Nguyen, Hiromichi Kawanot     ( x    u) 

exp
Q
p
D
j






x
8 3
 
2 D x 0      C x , y , z , t 

2     

1







   {C  , ,  G x , y , z , t ,  , ,  ,0 
0  
t

2

2




 

( y    v)  ( z    w)   q D y  j   r Dz  j 
2 D y  
2 Dz   Q  , ,  , G x , y , z , t ,  , ,  , d  }d d  d 0
( x    u ) 2 ( y    v ) 2 ( z    w ) 2 
* exp    



4 Dx
4Dy
4 Dz


dpdqdrd ddd

where
G( x, y, z, t ,  , ,  , )
1

t



Therefore, we have
t

C2 

  

8  D D D   
1

3

x

0



8 

t

x

y

０

   C ( ,, )  

z









References

1. T. Tanahashi, Introduction to Computational Fluid
Dynamics --Advection Diffusion Equation--, (inJapanese), (Corona Publishing Co., Ltd., 1996).
2. A. Tada,. An Iteration Method Applying Analytical
Solution of the Diffusion Equation, (in Japanese), Japan
Society of Civil Engineering, vol. 2-26, No. 485, (1994)
pp.1-10
3. T.Hattori and A.Tada: A Novel Computational Method
for Convection Diffusion Equation under Arbitrary Initial
and Boundary Condition, 15th IMACS World Congress
on Scientific Computation, Modelling and Applied
Mathematics, Edited by A.Sydow, (Wissenschaft &Technik Verlag, vol.3, Berlin, 1997), pp.645-650.
4. T.Hattori, K.Shinagawa, and A.Tada, Averaged Green
Function Method for Diffusion Problem, Proc. of 1998
International Symposium on Nolinear Theory and Its
Applications (NOLTA’98) 1998, pp.979-982.

dddd

(14)
t

0

  

1
t

8 

3

t

t

 D ds  D ds  D dsxy

   Q  , ,  ,   

z

t
t
t


 ( x    uds ) 2 ( y    vds ) 2 ( z    wds ) 2

t







exp  
  ds 
t
t
t



4 D x ds
4 D y ds
4 D z ds








d d  d  d 







In this paper, we have presented the exact analyticalsolution of the ADE over uniform flow field (or velocity
field) in three dimensional (3D) boundless region underarbitrary initial condition.

０







3. Conclusion

t
t
t


 ( x    uds) 2 ( y    vds) 2 ( z    wds) 2

t




0
0
0
exp




ds

t
t
t


0
4 Dx ds
4 D y ds
4 Dz ds




0
0
0

C2 





0

t

 D ds D ds D ds

０



(16)

  

1
t





z   y

By returning the notation “ ” to the original
integration representation in Eq.(12) and Eq.(13), we
have

3



t

2
2
2

 
t
t
t






 





x

uds
y

vds
z

wds

   
  

  


t










  ds 
 
 
exp 

t
t
t



4
4
4
D
ds
D
ds
D
ds


x
y
z








Q  , ,  , exp ( x    u ) 2 ( y    v ) 2 ( z    w ) 2



 

4Dx
4D y
4Dz


d d  d  d 
…(13)

C1 

t

8  3 Dx ds D y ds Dz ds











(15)
Because C  C1  C2 , we have the solution as follows.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 285 -

Analysis of Navier-Stokes Equation from the Viewpoint of Advection Diffusion (II)--- Approximate Solution --Hiroki Sakamoto *, Tetsuo Hattori, Akiomi Tada, Vanhoa Nguyen

Graduate School of Engineering, Kagawa University
2217-20, Hayashi-cho Takamatsu, Kagawa 761-0396, Japan
Hiromichi Kawano

NTT Advanced Technology Corporation, 19-18, Nakamachi
Musashino, Tokyo 180-0006, Japan

Abstract
We propose an approximate analysis method for the Navier-Stokes Equation (NSE) based on the similarity between
NSE and Advection Diffusion Equation (ADE). In the preceding paper titled “Analysis of Navier-Stokes Equationfrom the Viewpoint of Advection Diffusion (I)”, we have presented the analytical solution of the ADE.
Subsequently in this paper, we point out the explicit similarity between NSE and ADE by illustrating thecorresponding equations. Then, we show an approximate solution of NSE using the aforementioned analyticalsolution of ADE.

Keywords: approximate anlysis method, Navier-Stokes Equation (NSE), Advection Diffusion Equation (ADE)the ADE over uniform flow field (or velocity field) inthree dimensional (3D) boundless region under arbitrary
initial condition2-4, that we have presented in theprevious paper of ICAROB2015, to the NSE.

1. Introduction
It is well known that Navier-Stokes Equation (NSE) isthe most fundamental one in Fluid Mechanics1, and also
known that the exact analytical solution of NSE is not
yet obtained.
Although we have to use numerical computation forsolving the NSE under the arbitrary initial and boundary
conditions, if possible, it is still desirable to have ananalytical approximate solution that is as much as close
to the exact one.
In this paper, in order to obtain such an analytical
approximate solution, we focus on the similarity
between the NSE and Advection Diffusion Equation(ADE). And we apply the exact analytical solution of

2. Advection Diffusion Equation (ADE)Let C be a fluid of density (or density of material), andlet D x , D y , D z denote the diffusion coefficient in
x , y , z axis direction, respectively. Similarly, let u, v, w
denote the flow velocity in x , y , z axis direction,
respectively. Moreover, let  and Q be theattenuation coefficient that is spatially uniform and theload generation rate function, respectively.

*2217-20 Hayashi Cho Takamatsu City, Kagawa, 761-0396 Japan,
Kagawa University,
Email: hattori@eng.kagawa-u.ac.jp© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 286 -

Hiroki Sakamoto, Tetsuo Hattori, Akiomi Tada, Vanhoa Nguyen, Hiromichi Kawano

The partial differential equation of the ADE is shown asEq.(1).

C
 2C
 2C
 2C

D

D
 Dx
y
z
t
x 2
y 2
z 2
C
C
C
v
w
 C  Q
x
y
z

u

G(x, y, z,t,,, , )
1

t

3

t

t

 D ds D ds D ds
x

y

z

2
2
2

 
t
t
t

 
 

 

 
 
  x   uds  y   vds  z   wds t 

 

 




  ds  exp  t

t
t



4 Dxds
4 Dyds
4 Dzds
















(x  0)  ( x)  , ( x  0)  ( x)  0,



8

(1)

Here we introduce the Dirac’s  function as in thefollowing (2).



where G is a Green function and Gaussian-likeexponential one as shown in Eq.(6).









(2)

 ( x)dx  1

(6)



3. Navier-Stokes Equation (NSE)
The initial condition and the load generation ratefunction are shown in Eq.(3) and Eq.(4), respectively.

We consider that the NSE shows a Law of conservationof momentum with respect to vi (i  x, y, z) and that NSE
is a kind of Advection Diffusion Equation (ADE)
regarding momentum (Eq.(5))

     C ( ,, )C ( x, y , z,0) 0vi p
 div vi vi v   
 fi
t
xi

   ( x   ) ( y   ) ( z   )ddd
(3)
  

Q ( x, y , z, t ) (7)where  , v ,  , p , f are density, velocity, coefficientof viscosity, pressure, and external force, respectively.

   Q ( , , , t )   ( x   ) ( y   ) ( z   ) ddd
(4)

3.1. Similarity between ADE and NSE
In Eq.(1), let D x  D y  D z  D . Using therelational equation in Eq(8), Eq.(1) can be represented
as Eq.(9).

Then, we can obtain the exact solution as follows.

C x, y , z, t 
  







    
( v ・  )  v x , v y , v z・, ,  x y y 



 vx
 vy
 vz
x
y
y

   {C  ,, Gx, y, z, t, ,, ,0
0  t

 Q  , ,  , G x, y, z, t ,  , ,  , d }ddd
0

(5)

…(8)

C
 D 2C  ( v・)C  C  Q
t© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 287 -

Analysis of Navier-Stokes Equation

3.2. Application of the Solution of ADE to NSE…(9)From NSE Eq.(7), putting   k where k iscoefficient of kinematic viscosity, we have

v
gradp
f
 k 2 v  ( v   )v 

t



Since the diffusion coefficient D appeared in theGreen function in Eq.(4), is invariant with respect totime, we can compute the integration term including
the D. Then for the Green function, we have(10)

G ( x , y , z , t ,  , ,  , )
2
 
t

 

  x    uds 

 
1



exp   
3
3

4
D
(
t

)

8  D ( t   )At t  t0 , we have the following relation.

v t0 
gradp f k 2 v t0   v t0   v t0  

(11)


t
Here, we consider that v (t0 )  v (t0  t ) where t is
infinitesimal time parameter. So we have

2

t
t












y

vds
z

wds
















4 D (t   )
4 D (t   )v t0 
gradp f k 2 vt0   vt0  t   v t0  

t


…(12)



Then putting v  v t0  t  , we have another form of
the Eq.(9) and Eq.(10) as follows.

the present time point and the general position at thepast time point, respectively. Since we can regard NSE
as a kind of ADE, we can obtain the Green function of
the analytical approximate solution of NSE by making
an analogy from the solution of ADE, as follows..

…(13)

v
gradp
f
 k 2 v  (v   )v 



t

G( x, y, z, t,,, , )
2


t


1




exp rp   r  v ds
2 2 
8  3D(t  )3

 



…(14)



Since the term  C in Eq.(9) is attenuation one, letting  0 , we have the following correspondent relation.

gradp













…(15)

Let r p  ( x, y , z ) , r  ( , ,  ) , that means the position atC D2C  (v  )C  C  QtC v ，k D, Q2

where v  (u, v, w) ,   2 D (t   ) ,

f



K

If we regard the v as a kind of constant comparing withv , we can obtain the solution of Eq.(14) in the same
way as the solution of ADE, using the aforementioned
correspondent relation.

1
8  3 D t   3



 



3

(16)
Then we have the approximate solution of NSE using
the aforementioned Green function (Eq.(16)) as follows.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 288 -

Hiroki Sakamoto, Tetsuo Hattori, Akiomi Tada, Vanhoa Nguyen, Hiromichi Kawano

4. Conclusion
In this paper, focusing on the similarity between the
Navier-Stokes Equation (NSE) and Advection
Diffusion Equation (ADE), we have described thesimilarity and pointed out the correspondence relation.
Moreover, based on the viewpoint that NSE is a kind of
advection (or convection) diffusion equation of
momentum, we have derived a solution of NSE by
applying the exact analytical solution of the ADE to the
NSE.
Although this solution can be regarded as an analytical
approximate solution of the NSE, we consider that it
infinitely approaches to the exact analytical solution if
t approaches to 0.

v ( x, y , z, t )
  



   v  ,, Gx, y, z, t, ,, ,0ddd0     t
 Q  , ,  , G x, y , z, t ,  , ,  , d     0ddd

 .…(17)

where

Q

gradp





f



This means the velocity (or momentum) at the pointr p  ( x , y , z ) is decided by taking the diffusion of the
momentum from the surrounding points that can becomputed by the convolution of the Green function(Eq.(16)).

References
1. T. Tanahashi, Introduction to Computational Fluid
Dynamics --Advection Diffusion Equation--, (inJapanese), (Corona Publishing Co., Ltd., 1996).
2. A.Tada,. An Iteration Method Applying Analytical
Solution of the Diffusion Equation, (in Japanese), Japan
Society of Civil Engineering, vol. 2-26, No. 485, (1994)
pp.1-10
3. T.Hattori and A.Tada: A Novel Computational Method
for Convection Diffusion Equation under Arbitrary
Initial and Boundary Condition, 15th IMACS World
Congress on Scientific Computation, Modelling andApplied Mathematics, Edited by A.Sydow, (Wissenschaft& Technik Verlag, vol.3, Berlin, 1997), pp.645-650.
4. T.Hattori, K.Shinagawa, and A.Tada, Averaged Green
Function Method for Diffusion Problem, Proc. of 1998
International Symposium on Nolinear Theory and Its
Applications (NOLTA’98) 1998, pp.979-982.

点P

rp  ( x , y , z )Timet t=tt00+Δt

y
t

~

rr

x

r  ( ,,  ) v ds

ηt  t0  t=tt0
ξt

 v ds
Fig.1. The situation that the present point rp receives themomentum (or velocity) diffusion from the surrounding~
points r where a point r at the past time is shifted by
the velocity (or momentum).

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 289 -

Development of Mouse Cursor Control System Based on Face Direction Using Kinect
Masayoshi Tabuse

Graduate School of Life and Environmental Sciences, Kyoto Prefectural University
1-5 Hangi-cho, Shimogamo, Sakyo-ku, Kyoto 606-8522, Japan
E-mail:tabuse@kpu.ac.jp
Kaori Tamura

ISI Software Corporation
4-6-17 Honmachi, Chuo-ku, Osaka 541-0053, Japan
Abstract
We propose a method of operating mouse cursor and controlling television based on face direction using Kinect.
Using our system anyone can control television by motion of a user easily and cheaply. Our system measures face
direction and controls mouse cursor using 4 face direction using Kinect. It changes a television’s channel andvolume by controlling mouse cursor based on face direction using Kinect.
Keywords: Mouse Cursor Control System, Kinect, Face detection, Face Direction, Television Tuner Software

face direction and skeleton of a user. Kinect was madefor the purpose of a game and entertainment, but isapplied to the support of medical care and a person witha disability. By using our system, anyone can control
television by motion of a user easily and cheaply. It is
not necessary to fix a user’s head or to restrict user’s
head movement. Therefore, it can ease a user’s burden
compared with conventional devices. Furthermore, aperson with trouble in a hand can control television
easily.
In our system, Kinect is connected to a personal
computer. A television tuner is installed in the personal
computer. Television tuner software is controlled by amouse cursor and a mouse wheel. Our system measuresface direction and controls mouse cursor using 4 face
directions of rightward, leftward, upward and downward
directions using Kinect. In the case of a rightward face
the system turns a television channel to the forward
direction. In the case of a leftward face it turns atelevision channel to the opposite direction. In the caseof an upward face it raises a television’s volume. In thecase of a downward face it lowers a television’s volume
in the downward face direction. Our system changes atelevision’s channel and volume by controlling the

1. Introduction
In recent years, development of many new kinds of
input devices is performed, for example, video andaudio input devices. Gaze measurements are used as
operation of a mouse cursor. For gaze measurement,
input devices are classified into a contacted type and a
non-contact type. For a contacted type, detection of amotion is easy and high accuracy. However, equipment
and removal of it and adjustment according for a user
are required whenever it is used. For a non-contact type,
removal of it is not required. However, in order toacquire high accuracy, it is necessary to fix a user’s
head or to restrict user’s head movement, andadjustment according for a user is required. Therefore,
the burden on a user is heavy when a user uses a gazemeasurement device.
In the present study, we propose a method of
operating mouse cursor and controlling television basedon face direction using Kinect. Kinect consists of a
RGB camera, a depth sensor, a multi-array microphoneand software (Kinect for Windows SDK), released byMicrosoft. It can recognize a position, a voice, a face,© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 290 -

Masayoshi Tabuse and Kaori Tamura

3.2. Face Detection and Face Tracking

mouse cursor and the mouse wheel based on face
direction using Kinect in a range with distance less than
4 meters between a user and Kinect. It is also applicable
to operation of other application software controlled bymouse cursor, such as a web browser.

Face Tracking SDK extracts human face region in RGB
image using Shoulder-Center and Head positions in 3D
joint data, depth data and RGB data2. It fits adeformable face model in face region and obtains
2D/3D position of 100 landmark points on human face.
It tracks these points based on a feature of landmark
points in the previous frame.

2. Outline of Our System
Fig.1 shows the flowchart of our system.

3.3. Face Direction
Face Tracking SDK tracks the 100 points on humanface and outputs tracking status, 2D position of points,
3D head pose. The head pose is expressed by three
angles: pitch, roll and yaw, shown in Fig.3. A pitch
angle  ranges from -90 degrees (looking down
towards the floor) to 90 degrees (looking up towards theceiling). A roll angle  ranges from -90 degrees(horizontal parallel with right shoulder) to 90 degrees(horizontal parallel with left shoulder). A yaw angle ranges from -90 degrees (turned towards the rightshoulder) to 90 degrees (turned towards the leftshoulder). Therefore the face direction is determined bythese tree angles. In our system we divide face
directions into 5 directions: facing up, facing down,
facing right, facing left, facing the front. For   15
and  15    15 human faces up. For   15and  15    15 human faces down. For 15    15 and   15 human faces right.
For  15    15 and   15 human faces left.
Otherwise human faces the front.

Fig. 1. The flowchart of our system.

3. Face Tracking and Face Direction
Microsoft Face Tracking SDK together with Kinect for
Windows SDK enables us to track human face in realtime.
3.1. Human Body Detection and Skeleton
Tracking
Kinect for Windows SDK detects human bodies using
depth data acquired from the depth camera in Kinect,
estimates 3D joint positions and tracks skeletons1. Fig.2
shows joint data.

Fig. 3. Head pose2.

Fig. 2. Joint data

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 291 -

Mouse Cursor Control System

3.4. Mouse Cursor Control by Face Direction
In our system we install a television tuner and television
tuner software in a personal computer. The television
tuner software is controlled by a mouse cursor and amouse wheel. In the right region of the software
window we roll the mouse wheel, so that the softwareturns the television channel. In the left region of thesoftware window we roll the mouse wheel, so that thesoftware changes the television’s volume. Therefore,
our system moves a mouse cursor in response to a face
direction and it controls the television tuner software.
4.

Fig. 5. Compuer operation screen.

Experiments

4.1. Experimental Environment
In our experiments we have used a PC (Dell Optiplex
790; CPU : Core i7-2600, Memory : 4GB) , Kinect for
Windows and a television tuner and software (Buffalo
DT-F120/U2). For programming, we have used
Microsoft Visual C++ 2010, Kinect for Windows SDK
v1.7, Kinect for Windows Developer Toolkit v1.7. Fig.
4 shows mouse cursor control system.

Fig. 6. A region of face direction turns into white.

Fig. 7. A region of face direction turns into yellow.

4.3. Experiments of television control
Fig. 4. Mouse cursor control system.

4.2. Operation Screen
We show a computer operation screen in Fig.5. When
our system detects and tracks user’s face, it displays
image of user’s face in the upper right on the window. If
our system detects one of 4 face directions of a user
(facing up, facing down, facing right, facing left), a
region of face direction turns into white, shown in Fig. 6.
Then if the user keeps the same direction more than two
seconds, the region of face direction turns into yellow,
shown Fig 7.

We instruct a subject to operate the television and let the
subject perform the operation according to the
instructions. Subjects are 10 (4 males and 6 females).
The evaluation (5 points are perfect) of subjects is asfollows.
(i)Arrangement of the region and the design
The evaluation for arrangement of the region and thedesign is shown in Table 1.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 292 -

Masayoshi Tabuse and Kaori Tamura

Table 1. The evaluation for arrangement of theregion and the design .

Point
Frequency

5
8

4
4

3
0

2
1

1
0

We improved our system as a result of the
television operation experiments. The calculation ofpitch and roll angles was changed as follows.

Mean
4.6

We calculate average values of pitch and roll angles, and  , in 30 frames after detecting a face,respectively. We redefine pitch and roll angles, ' and  ' , as follows.

(ii)Operability
The evaluation for operability of our system is shown in
Table 2.

'   '  

Table 2. The evaluation for operability.

Point
Frequency

5
3

4
3

3
2

2
1

1
1

(1)

where  and  are pitch and roll angles of a facemeasuredbyKinect.
For '  15 and 15   '  15 human faces up. For  '  15 and 15   '  15 human faces down. For 15   '  15 and  '  15 human faces right.
For  15   '  15 and  '  15 human faces left.
Otherwise human faces the front. Two subjects (1 male,
1 female) operate the television using the improvement
system. Subjects’ comments are as follows.
 It is easy to operate the television.
 Reactions for faces become good.
As a result of the improvement of our system, a subject
operates a television whenever the location of the face
to a RGB camera of Kinect was different.

Mean
3.6

Comments of subjects are as follows.
(i)Arrangement of the region and the design(a)Good points
 Reaction of the display when changing the directionof the face is easy to understand.
 It's easy to see, and it's easy to understand.
 It's colored, so it's easy to understand.
 When seeing television screen, a button of the left,right, up or down isn’t annoying.
(ii)Operability(a)Good points
 When a face is recognized once, it reacts accurately.
 It's simple, so it's easy to understand.
 It reacted by a small movement more than I thought.
 A face was turned to the side, but it was possible tolook at a screen, so it was good.
(b)Improvement
 The system doesn’t detect a face of looking up easily.
 A reaction of looking up wasn't a little good.
 When facing down, it's difficult to look at a screen.

6. Conclusion

We have developed a mouse cursor control system
based on face direction using Kinect and applied it
to a television operating system. Furthermore, byusing the average angle of a subject facing thefront, a subject operates a television whenever the

5. Discussions
In our experiments every subject could do operation of atelevision stably. But after beginning to experiment, asubject wearing glasses couldn't sometimes detect a face.
For a male subject, even if the subject is turning to thefront Kinect has often detected it with declining. We
setup Kinect which indicates a pitch angle   0 and aroll angle   0 when a female of the height of 160cm
faces to the front of Kinect. Therefore a taller male has ahigh location of the head to a RGB camera of Kinect.

location of the face to a RGB camera of Kinect wasdifferent.
At present, our system operates only channel andvolume changes of a television. In the future, we will
add various operations, such as on-off of a power supply
and recording operation, to our system. It is also
applicable to operation of other application software
controlled by mouse cursor, such as a web browser.
References
1. Skeletal Tracking, Kinect for Windows SDK v1.7,
http://msdn.microsoft.com/en-us/library/hh973074.aspx.
2. Face Tracking, Kinect for Windows SDK v1.7, http://
msdn.microsoft.com/en-us/library/jj130970.aspx.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 293 -

Quantitative Evaluation of Facial Expressions and Movements of Persons While Using Video Phone
Taro Asada, Yasunari Yoshitomi, Ryota Kato, and Masayoshi Tabuse

Graduate School of Life and Environmental Sciences, Kyoto Prefectural University,
1-5 Nakaragi-cho, Shimogamo, Sakyo-ku, Kyoto 606-8522, Japan
E-mail:{t_asada, r_kato}@mei.kpu.ac.jp, {yoshitomi, tabuse}@kpu.ac.jp
Jin Narumoto

Graduate School of Medical Science, Kyoto Prefectural University of Medicine,
Kajii-cho, Kawaramachi-Hirokoji, Kamigyo-ku, Kyoto 602-8566, Japanjnaru@koto.kpu-m.ac.jp

Abstract
The video is analyzed using image processing and the feature parameters of facial expressions and movements,
which are extracted in the mouth area. The feature parameter for expressing facial expressions is defined as theaverage of facial expression intensity. That for expressing movements of a person is defined as the average ofabsolute value of vertical coordinate for the center of gravity of mouth area in the relative coordinate system. The
experimental result shows the usefulness of the proposed method.
Keywords: Facial expression analysis, Movement analysis, Mouth area, OpenCV, and Skype.

while speaking with another person, using our reported
method2, the standardization of size of face to beanalyzed, and the newly proposed feature parameters on
facial expressions and movements of a person.

1. Introduction
In Japan, the average age of the population has been
increasing, and this trend is expected to continue.
Because of the growing trend of aging, the number ofolder people with dementia and/or depression living inrural area is increasing very rapidly. Due to mismatch
between the number of patients and health care
professionals there, it is difficult to provide psychologicalassessment and support for the patients living there.
We have been developing a method for analyzing
facial expressions of a person while speaking withanother person using a video phone to improve the QOLof elderly people living in care facility, or at home.1-3 In
the present study, we have developed a method foranalyzing facial expressions and movements of a person

2. Proposed Method
2.1. System overview and outline of the method
The platform is composed of Skype4 for video phone. In
addition to record the audio and video dialogue, Netralia
Pty Ltd’s VodBurner5 and Tapur6 are introduced. The
talks are recorded for the analysis of facial expressions
and movements of a person. The recorded data are
analyzed using image processing software, Open Source
Computer Vision Library for real-time computer visiondeveloped by Intel (Open CV)7, the standardization of© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 294 -Taro Asada, Yasunari Yoshitomi, Ryota Kato, Masayoshi Tabuse, and Jin Narumoto

size of face to be analyzed, and the newly proposedfeature parameter on facial expressions and movementsof a person described in the following subsections. The
Y component obtained from each frame in the dynamic
image is used for analyzing facial expressions andmovements of a person. The proposed method consistsof (1) Standardization of lower part of face-area in size,(2) extraction of mouth area, (3) measurement of facial
expression intensity, (4) judgment of utterance, (5)calculation of feature parameter on facial expression
strength, and (6) calculation of feature parameter onmovements of a person. In the following subsections,these six are explained in detail.

of face image, a lower part of face image before and after
standardization of size, and mouth-area image extracted.

Fig. 1. Face image (upper), lower part of face image before(lower & left) and after (lower & center) standardization ofsize, and mouth-area image extracted (lower & right).

2.2. Standardization of lower part of face-area insizeFirst, a face-area obtained from each frame in the
dynamic image is extracted using the classifier for afront-view face included in OpenCV. In the classifier,Haar-like feature parameter and Adaboost algorithm forlearning are used.8 It can be assumed that the distance
between a subject and the camera is almost always kept
during conversation using Skype. The face of the frame
where the face-area extracted using OpenCV has theminimum size among those for a period in the dynamic
image is assumed to be the most likely front-view among
those in the period,1 and the frame is selected and used. In
the present study, we set 1/3 seconds as the period. Then,a low part of face-area extracted by the above method isstandardized in length and width for extracting a mouth
area. This standardization in size is performed with theaim of not only improving the performance of extracting
a mouth area using OpenCV but also normalizing thefeature parameter generation using 2D-DCT (Discrete
Cosine Transform) there. Under the circumstance that thesize of face in a dynamic image cannot be kept constantevery time, this standardization in size is indispensable
for reliably measuring the feature parameters described in
the sections 2.6 and 2.7.

2.4. Measurement of facial expression intensity
For the Y component of the frame selected by theprocessing described above, the feature vector of facial
expression is extracted in the mouth area with use of
2D-DCT performed for each domain having 8×8 pixels.
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.2 Then, we obtain the mean of theabsolute value for each 2D-DCT coefficient component
in the area of mouth.2 Therefore, we obtain 15 values as
the elements of the feature vector. 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 beused for analyzing a change of facial expression.2
2.5. Judgment of utterance
The sound data are smoothed and sampled to erase noise.
Then,
all
sampled
data
that
fall
withinxs  14 s , xs  14 s  , where xs and  s respectively
express the average and the standard deviation of thesound data value for one second under the condition of noutterance, are considered to be the range of no utterance.
When at least one sampled datum has a value outsidexs  14 s , xs  14 s  , our system judges that the

2.3. Extraction of mouth area
Next, using OpenCV, the mouth area is extracted as arectangle shape. The mouth area is selected because thedifference between the facial expressions of neutral andhappy remarkably appears there. Fig. 1 shows an example

sound data contain an utterance after erasing the noise.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 295 -

Facial Expressions and Movements

3.2. Results and discussion

2.6. Feature parameter on facial expression
strength

Fig. 2 shows mouth-area images at starting point. Facial
expression intensity changes of subjects A and B during
their conversation (Fig. 3) and changes of coordinates ofcenter of gravity on mouth area (Fig. 4) are shown
respectively. In Fig. 5, face-images are shown at thecharacteristic timing positions. Feature parameters on
facial expressions and movements of subjects are shownin Table.1. The definitions of these parameters aredescribed in the sections 2.6 and 2.7.
As shown in Figs. 3 and 5, facial expression
intensity was very sensitive for facial expression change.
Both of subjects A and B did not move vertically so muchduring conversation (Fig. 5 and Table 1). The number ofmouth-area images extracted using OpenCV was increased
for subject A from 196 to 197 by performing the size

In diagnosing a patient having dementia and/or
depression, it might be useful for health care
professionals to evaluate the strength of facial
expression using a simple measure. Moreover, it might
be more advantageous for a diagnosis of dementia
and/or depression to separately evaluate the strength of
facial expression as a speaker and a listener. Therefore,
we measure as the feature parameter of facial expression
strength the average of facial expression intensity in thefour cases of (1) both subjects A and B speak, (2)subject A speaks and subject B does not speak, (3)subject A does not speak and subject B speaks, and (4)both subjects A and B do not speak, using the method
for judging an utterance described in the section 2.5.
2.7. Feature parameter on movements of a person
Head movements such as a nodding during conversation
might suggest a mental state and/or recognition abilityof a patient having dementia and/or depression.
Therefore, we measure as the feature parameter ofmovements of a person the average of absolute value ofvertical coordinate for the center of gravity of mouth
area in the relative coordinate system in the four cases
described in the section 2.6. The relative coordinate
system is defined using the mouth area extracted at thestarting point for measuring the feature parameter onmovements of a person. At the starting point, the heightof the mouth area is set to be one and the verticalcoordinate for the center of gravity of the mouth area is
set to be 0 in the relative coordinate system.

Fig. 2. Mouth-area images of subjects A (left two) and B (righttwo) at starting point with (left side) and without (right side)
size standardization before extracting mouth area.

3. Experiment3.1. Condition
Two males (subject A in his 50s and subject B in his20s) participated in the experiment where they took
conversation for about 70 seconds using Skype. The
videos saved using VodBurner were transformed into
AVI files, and the audios saved using Tapur weretransformed into WAV files. The AVI files were used
for measuring feature parameters on facial expressions
and movements of a subject. The WAVE files wereused for judgment of utterance. The size of image frame
was 720×480 pixels, and the size of standardized lower
part of face image was set to be 240×96 pixels.

Fig. 3. Facial expression intensity change of subjects A and B
during the conversation between these two subjects with size
standardization before extracting mouth area.

Fig. 4. Changes of coordinates of center of gravity on mouth
area with size standardization before extracting mouth area.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 296 -Taro Asada, Yasunari Yoshitomi, Ryota Kato, Masayoshi Tabuse, and Jin Narumoto

facial expressions and movements of subjects were notinfluenced so much by size standardization before
extracting mouth area (Table 1). The value of featureparameter of facial expression was relatively high for
subject A in the all three cases, while it was relatively
high for subject B in the only case that subject B spoke
and subject A did not speak (Table 1). Because theperiod in which both subjects A and B spoke was very
short, the data were not described in Table 1.
4. Conclusion
The video is analyzed using image processing and the
newly proposed feature parameters of facial expressions
and movements. The experimental result shows theusefulness of the proposed method. We will develop the
method for estimating a mental state and/or recognition
ability of a patient using the proposed method.

Fig. 5. Face-images at characteristic timing positions on facial
expression intensity value of subject A; upper: 0 (starting
point), lower & left : maximum, lower & right : minimum
except that at starting point.
Table 1. Feature parameters on facial expressions andmovements.
(1) With size standardization before extracting mouth area
Utterance
Feature parameter
Subject
FacialMovement
A
B
expression of person
without
without
11.41
0.05
A

B

with

without

9.47

0.08

without

with

10.50

0.05

B

A

without

without

4.53

0.05

with

without

12.51

0.07

Acknowledgment
This research is partially supported by COI STREAM of
the Ministry of Education, Culture, Sports, Science andTechnology of Japan.
References
1. T. Asada, Y. Yoshitomi, A. Tsuji, R. Kato, M. Tabuse, N.
Kuwahara, and J. Narumoto, Method of facial expression
analysis of person while using video phone, in Proc. of Human
Interface Symposium 2013 (Japan, Tokyo, 2013), pp.493-496.
2. T. Asada, Y. Yoshitomi, A. Tsuji, R. Kato, M. Tabuse, N.
Kuwahara, and J. Narumoto, Facial expression analysis
while using video phone, in Proc. of Int. Conf. on Artif.
Life and Robotics (Japan, Oita, 2014), pp.230-234.
3. J. Narumoto, N. Kuwahara, Y. Yoshitomi, T. Asada, Y.
Kato, H. Kamimura, K. Fukui, Development of support
system for patients with dementia through teleconference
system, in Proc. of 4th World Conf. of Asian Psychiatry(Thailand, Bangkok, 2014), pp.1-4.
4. Skype Web page, http://www.skype.com/ Accessed 5
November 2013.
5. VodBurner Web page, http://www.vodburner.com/
Accessed 11 July 2014.
6. Tapur Web page, http://www.tapur.com/jp/ Accessed 18
December 2014.
7. OpenCV Web page, http://opencv.willowgarage.com/
Accessed 11 July 2014.
8. P. Viola and M. Jones, Rapid object detection using aboosted cascade of simple features, in Proc. of the 2001
IEEE Computer Society Conf. on Computer Vision and
Pattern Recognition (USA, Kauai, 2001), Vol.1, pp.511-518.

without
with
3.14
0.06
(2) Without size standardization before extracting mouth area
Utterance
Feature parameter
Subject
FacialMovement
A
B
expression of person
without
without
8.55
0.03
A

B

with

without

8.50

0.04

without

with

10.06

0.05

B

A

without

without

5.48

0.04

with

without

10.88

0.05

without

with

5.18

0.03

standardization before extracting mouth area, while it wasincreased for subject B from 27 to 138 by thestandardization. Though mouth-area images were
influenced by size standardization before extracting
mouth area (Fig. 2), the feature-parameter values of both
© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 297 -

Facial Expression Recognition Using Facial Expression Intensity Characteristics of Thermal Image
Yasunari Yoshitomi, Taro Asada, Ryota Kato, and Masayoshi Tabuse

Graduate School of Life and Environmental Sciences, Kyoto Prefectural University,
1-5 Nakaragi-cho, Shimogamo, Sakyo-ku, Kyoto 606-8522, Japan
E-mail:{yoshitomi, tabuse}@kpu.ac.jp, {t_asada, r_kato}@mei.kpu.ac.jp
http://www2.kpu.ac.jp/ningen/infsys/English_index.html

Abstract
A video was analyzed using thermal image processing and the feature parameter of facial expression, which was
extracted in the area of mouth and jaw using a two-dimensional discrete cosine transform. The facial expression
intensity defined as the norm of difference vector between the feature vector of neutral facial expression and that ofobserved one was used for analyzing facial expression. The feature vector made by facial expression intensity andtime at utterance was used for recognizing facial expression.
Keywords: Facial expression recognition, Area of mouth and jaw, Thermal image, and Utterance judgment.

1. Introduction
The goal of our research is to develop a robot that canperceive human feelings and mental states. Although themechanism for recognizing facial expressions of humanfeelings has received considerable attention in computer
vision research, it currently falls far short of humancapability. This is due to the decreased accuracy offacial expression recognition, which is influenced by theinevitable change of gray levels due to nuances of shade,reflection, and local darkness. To avoid this problem
and to develop a robust method for facial expression
recognition applicable under widely varied lighting
conditions, we use an image registered by infrared rays
to describe the thermal distribution of the face.1-3 The
timing of recognizing facial expressions is also
important for a robot because the processing can betime-consuming. We adopted an utterance as the key ofexpressing human feelings because humans tend to saysomething when expressing their feelings.2, 3
In the present study, we proposed a method forrecognizing facial expressions using the facial
expression intensity4 and the time at utterance.
2. Proposed Method
The proposed method consists of (1) extraction of the areaof the mouth and jaw, (2) measurement of facial expression
intensity, (3) judgment of utterance, (4) calculation offeature parameters for facial expression and voice.
2.1. Extraction of area of mouth and jaw
The frame extracted every 0.1 second in the dynamic
image is used for thermal image processing. Six faceareas (Fig. 1) are extracted by the thermal image
processing reported in our study.3 The area of mouthand jaw is selected because the difference between the© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 298 -

Yasunari Yoshitomi, Taro Asada, Ryota Kato, and Masayoshi Tabusesound data value for one second under the condition of
no utterance, are considered to be the range of noutterance.4 When at least one sampled datum has a value
outside xs  14 s , xs  14 s  , our system judges that thesound data contain an utterance.4

facial expressions of neutral and happy distinctly
appears in this area.4 Fig. 2 shows an example ofthermal face image and image of area of mouth and jaw.

2.4. Feature parameters
We use two feature parameters as the elements of feature
vector. One is a mean of standardized facial expression
intensity at each utterance and for 0.3 seconds before andafter the utterance. The other is the standardized time ateach utterance. The standardization used for making
feature parameters is expressed by Eq. (1).
x i, j  x i(1)x i*, j 
iFig. 1. Blocks for extracting face-part areas (left), thermal image
after face part extraction (right).2, 6,where xi , j , xi , j , xi and  i respectively express the*standardized feature parameter, the measured featureparameter, the average and the standard deviation of themeasured feature parameters of training data, and i, j
respectively denote no. (1 or 2) of feature parameter andno. ( 1, 2,  , m ) of utterance. Then, clustering using
Ward method is performed for each of training and test
data to decide major and minor clusters for each class of
facial expressions in the feature vector space. Then, forrecognizing facial expressions of test data, we use thecoordinates of center of gravity of the major cluster foreach class of facial expressions for training and test data.

Fig. 2. Thermal face image (left) and image of area of mouthand jaw (right).4

2.2. Measurement of facial expression intensity
For the extracted frame, the feature vector of facial
expression is extracted in the area of the mouth and jaw by
applying a two-dimensional discrete cosine transform (2DDCT) for each domain of 8  8 pixels.4 We select 15 lowfrequency components of the 2D-DCT coefficients,
except for a direct current component, as the featureparameters for expressing facial expression.4 Then, we
obtain the mean of the absolute value for each 2D-DCT
coefficient component in the area of mouth and jaw.4
Therefore, we obtain 15 values as the elements of thefeature vector. The facial expression intensity, definedas the norm of the difference vector between the feature
vector of the neutral facial expression and that of theobserved expression, can be used for analyzing a change
of facial expression.4

3. Experiments
3.1. Conditions
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) and
Visual C++ 2008 Express Edition (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.

2.3. Judgment of utterance
The sound data are smoothed and sampled to erase
noise. Then, all sampled data that fall withinxs  14 s , xs  14 s  , where xs and  s respectively
express the average and the standard deviation of the© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 299 -

Facial Expression Recognition

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
expressions of “angry,” “happy,” “neutral,” “sad,” and“surprised,” while speaking the semantically neutral
utterance of each of the Japanese first names of “taro”(the first and last vowels of which are /a/ and /o/), and“tsubasa” (the first and last vowels of which are /u/ and/a/). In the experiment, Subject A intentionally
maintained a front-view in the AVI files, which were
saved as both training and test data. We assembled 15
samples as training data and 15 samples as test data.
The AVI files were used for measuring the facial
expression intensity. The WAV files obtained from the
AVI files were used for measuring time at utterance.

Table 1 shows the number of utterance belonging toeach cluster. Fig. 4 shows the facial expression intensity
Table 1. Number of utterance belonging to each cluster.
(1)tarotraining
test

major
minor
major
minor

angry

happy

neutralsadsurprised

13
2
13
2

11
4
13
2

10
5
13
2

14
1
8
7

11
4
8
7

angry

happy

neutralsadsurprised

12
3
14
1

10
5
12
3

9
6
14
1

11
4
13
2

14
1
10
5

(2)tsubasa
training
test

major
minor
major
minor

(1)taro
major
angry

minor

40

40

20

20

0

0
0

happy

0.5

1

1.5

40
20
0.5

1

1.5

40

happy

neutral

sad

0.5

1

1.5

40

1

1.5

0

0.5

1

1.5

0

0.5

1

1.5

0

0.5

1

1.5

40

20

20

0

0
0

0.5

1

1.5

40

40

20

20

0

0
0

0.5

1

1.5

(2)tsubasa
major
angry

angry

0.5

0
0

surprised

0

20

0

sad

1.5

40

20

The thermal face image depended on the emotion of subject
even at 0.3 seconds before starting to speak (Fig. 3). The
deference of the time series of facial expression intensity
among those for five kinds of emotion became more distinct
in picking up those data in the time range from 0.3 seconds
before starting to speak to 0.3 seconds after finishingspeaking than that in picking up those data at utterance.

1003.2. Results and discussion

0.5

20

0

neutral040surprised

minor

40

40

20

20

0

0
0

happy

0.5

1

1.5

20
0.5

1

1.5

0

0.5

1

1.5

0

0.5

1

1.5

0

0.5

1

1.5

0

0.5

1

1.5

20

0

0

0

0.5

1

1.5
40

40
20

20

0

0

0

0.5

1

1.5
40

40
20

Fig. 3. Thermal face images belonging to major clusters of
training data at 0.3 seconds before starting to speak “taro”(upper) and “tsubasa” (lower).

1.5

40

40
20

surprised

1

0

0

sad

0.5

20

0

neutral

0
40

40

20

0

0

0

0.5

1

1.5

Fig. 4. Example of time serious of facial expression intensity
corresponding to each cluster of training data; vertical axis:
facial expression intensity, horizontal axis: time in second.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 300 -

Yasunari Yoshitomi, Taro Asada, Ryota Kato, and Masayoshi Tabuse(1)taro
major

minor

major

minor

corresponding to each cluster in the time range from 0.3
seconds before starting to speak to 0.3 seconds after
finishing speaking. Fig. 5 shows the wave form atutterance corresponding to each cluster. Fig. 6 shows
the two-dimensional distribution of center of gravity ofthe major cluster for each class of facial expressions fortraining and test data. The recognition accuracy of facial
expressions was 100% and 60% for utterance of “taro”
and “tsubasa” using the nearest neighbor rule,
respectively. In the case of “taro” in Fig. 6, the angry’s
mark of training data was almost covered with that oftest data.

angry
happy
neutralsadsurprised(2)tsubasa

4. Conclusion

angry

We proposed a method for recognizing facial
expressions using the thermal image processing and thefacial expression intensity. The standardized mean valueof facial expression intensity for a major cluster, and thestandardized mean value of time at utterance for a majorcluster are used for recognizing facial expression. The
experimental results show the usefulness of the
proposed method.

happy
neutralsadsurprisedAcknowledgements
The present study was
KAKENHI (22300077).

Standardized utterance time

Fig. 5. Example of wave form at utterance corresponding toeach cluster of training data.
angry
happy
neutralsadsurprisedtraining
◇
○
△
□
×

test
◆
●
▲
■
＊

Standardized utterance timeStandardized facial expression intensity

Standardized facial expression intensity

Fig. 6. Two-dimensional distribution of center of gravity ofmajor cluster of training and test data; upper: “taro”, lower:“tsubasa”.

partially

supported

References
1. Y. Yoshitomi, N. Miyawaki, S. Tomita, and S. Kimura,
Facial expression recognition using thermal image
processing and neural network, in Proc. 6th IEEE Int.
Workshop on Robot and Human Communication, (Japan,
Sendai, 1997), pp. 380–385.
2. Y. Yoshitomi, T. Asada, K. Shimada, and M. Tabuse,
Facial expression recognition of a speaker using vowel
judgment and thermal image processing, J. Artif. Life andRobotics 16(3) (2011) 318–323.
3. Y. Yoshitomi, M. Tabuse, and T. Asada, Facial
expression recognition using thermal image processing,in Image processing: methods, applications andchallenges ed. V. H. Carvalho (Nova Science Publisher,
New York, 2012), pp. 57–85.
4. Y. Yoshitomi, T. Asada, R. Kato, M. Tabuse, Method of
facial expression analysis using video phone and thermal
image, J. Robotics, Networking and Artif. Life 1(1)(2014) 7-11.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 301 -

by

Method for Character Domain Extraction from Image Using Wavelet Transform
Taiki Taniguchi

ZENSHO HOLDINGS Co., Ltd., 2-18-1 Konan Minato-ku Tokyo 108-0075, JAPANYasunari Yoshitomi

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
http://www2.kpu.ac.jp/ningen/infsys/English_index.html

Abstract
The number of images having private information and/or URL of illegal Web site has been increasing in the cyber
space on the Internet. These images might cause infringement of human right and/or crime. In the present study, amethod for extracting the region(s) having characters on an image has been developed using the discrete wavelet
transform and the empirical knowledge that a character has strong vertical and/or horizontal element(s). The
experimental results show the usefulness of the proposed method.
Keywords: Internet, Private information, URL of illegal Web, Character domain extraction, Wavelet Transform,
and Image processing.

In the present study, a method for extracting theregion(s) having characters on an image has beendeveloped using the discrete wavelet transform (DWT).
For the course of developing the method, we use theempirical knowledge that a character has strong vertical
and/or horizontal element(s).

1. Introduction

The number of images having private information
and/or URL of illegal Web site has been increasing in
the cyber space on the Internet. These images mightcause infringement of human right and/or crime. It isvery time-consuming and inefficient to detect them
2. Wavelet Transform of Image Signals
through watching each Web page one by one by nakedeyes. The method for checking images to judge whether
Hierarchical decomposition of DWT has been widely
they have private information and/or URL of illegal
used in image compression and other image processing
Web site has been missing. For achieving the task,
techniques. The image is first decomposed into four
firstly, we must extract the region(s) having characters
subbands, 1LL, 1LH, 1HL, and 1HH. The subbandson the image. The algorithm for extracting the region(s)labeled 1LH, 1HL, and 1HH represent the finest scale
having characters on an image has received
wavelet coefficients. Fig.1 describes level 1
considerable attention in computer vision research.1-3
decomposition, in which the image is decomposed into
four subbands for one scale.
However, it falls far short of practical capability.
© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 302 -

Taiki Taniguchi and Yasunari Yoshitomi

Step 1：RGB elements of image (Fig.3) are transformed
into YCrCb elements (Fig.4) , followed by DWT for eachelement to obtain 1HL and 1LH elements corresponding
to vertical and horizontal elements respectively (Fig.4).

Fig. 1. Mallat division.

In general, the wavelet coefficients on the threedomains described as HH, HL and LH are called
elements of multi-resolution representation, whereas the
wavelet coefficients of LL are called elements of multiresolution analysis. For further information on the DWT,
see Ref.4.

Fig. 3. Sample of input image.

3. Proposed Method
Fig.2 illustrates the flowchart of the proposed method.
The algorithm of the proposed method is as follows:

Fig. 4. 1HL (upper & center) and 1LH (upper & right) elements
for Y element (upper & left), 1HL (middle & center) and 1LH(middle & right) elements for Cr element (middle & left), and
1HL (lower & center) and 1LH (lower & right) elements for Cb
element (lower & left) obtained from input image (Fig. 3).

Step 2 ： A segmentation for both the 1HL and 1LHelements using each threshold for the elements is performed(Fig.5). Then, the scattered regions having '255' in the binary
image for each of YCrCb elements are merged withchanging the values of pixels existing among pixels of '255'
from '0' to '255' when the pixels of '0' have the same value ofhorizontal coordinate as that of '255' (Fig.6).
Step 3：The operations of erasing isolated regions andexpanding the regions of the value of ‘255’ areperformed (Fig. 7).

Fig. 2. Flow chart of the proposed method.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 303 -

Character Domain Extraction

Fig. 8. Input image (left) and output image (right).

Fig. 5. Binary images obtained for both 1HL and 1LH elements
for Y, Cr , and Cb elements, obtained used related images in
Fig.4; left: Y, center: Cr, right: Cb.

4. Experiments
4.1. Conditions
Three standard images (Earth, Couple, and Mandrill) in
24-bit BMP form having 256×256 pixels were preparedfor evaluation of the proposed method. The images usedin this experiment were selected from Standard Image
Data-BAse (SIDBA). We used the Daubechies wavelet
for DWT. Several black characters of abc09056 were
written on the original images with one of white, nonwhite color, and transparent backgrounds (Fig.9). The
experiment was performed in the following environment
forcomputation:
personalcomputer;
DELL
OPTIPLEX780(CPU ： Intel(R) Core(TM)2 Duo CPU
E8400 3.00GHz, main memory ： 4.00GB), OS;
Microsoft Windows 7 Professional, Development
language; Microsoft Visual C++6.0.
In the process of segmentation in Step 2 described in
the section 3, the binary image was obtained by the waythat the value of pixel in binary image was set to 255
when the value of 1HL or 1LH element was not higher
than each threshold for the elements, and otherwise it
was set to be 0. The threshold was set to be half ofminimum value for each element except that it was
higher than -40. In this exceptional case, the threshold
was set to be -40. In the process of segmentation in Step
3 described in the section 3, the regions having 50
pixels or less were erased and the expansion was
performed five times.

Fig. 6. Binary images after horizontally merging for binary
images for Y, Cr, and Cb elements, obtained used related
images in Fig.5; left: Y, center: Cr, right: Cb.

Fig. 7. Binary images just after finishing processing of Step 3
for Y, Cr, and Cb elements, obtained used related images in
Fig.6; left: Y, center: Cr, right: Cb.

Step 4：Then a mask image for extracting the region(s)having characters on the input RGB image is generated
by merging the regions of '255' with logical sum
operation for all of three binary images obtained for Y,
Cr, Cb elements. Finally, the output RGB image is
generated by the operation that the color of the pixel on
the input RGB image is changed into white when thevalue of corresponding pixel on the mask image is '0',resulting in extracting the region(s) having character(s)on the input RGB image (Fig.8).

Fig. 9. Examples of original image (left end) and input images
with characters (others).

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 304 -

Taiki Taniguchi and Yasunari Yoshitomi

4.2. Results and discussion
Figs. 10, 11 show the input images and the output
images obtained using the proposed method. When the
background of characters was not transparent, allregions with characters were successfully extracted by
the proposed method (Fig.10). However, when the
background of characters was transparent, some regionswith characters were not extracted and other regions
having no characters were extracted by the proposed
method (Fig.11).

Fig. 11. Input images (left) with characters on transparent
backgrounds, and output images (right).

5. Conclusion
A method for extracting the region(s) having characterson an image has been developed using the discrete
wavelet transform and the empirical knowledge that acharacter has strong vertical and/or horizontal
element(s). The experimental results show theusefulness of the proposed method.
Acknowledgements
The authors would like to express their sincere thanks to
Associate Professors M. Tabuse and T. Asada of Kyoto
Prefectural University for their useful advices andsupports of the present research.
References
1. H. Hirano and M. Okabe, Detection of text area inserted
by image editing (in Japanese), in IPSJ SIG Notes, vol.
2012-CVIM-183, no. 9, (Japan, Koganei, 2012), pp. 1-8.
2. K. Ashida, H. Nagai, M. Okamoto, H. Miyao, and H.
Yamamoto, Extraction of characters from scene images(in Japanese), Trans IEICE J88-D-II(9) (2005) 18171824.
3. S. Masuko, T. Hoshi, and H. Hirano, A system forsearching texts in images by using reliability factor andsaliency (in Japanese), J. IPSJ 51(9) (2010) 1520-1529.
4. D. Inoue and Y. Yoshitomi, Watermarking using wavelet
transform and genetic algorithm for realizing hightolerance to image compression, J. IEEJ 38(2) (2009)
136-144.

Fig. 10. Input images (left) with characters on color
backgrounds, and output images (right).

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 305 -

Classification of Japanese Documents and Ranking of Representative Documents
Using Characteristic of Frequencies of Words
Jun Kimura

JustSystems Corp., 6-8-1 Nishinjuku, Shinjuku-ku, Tokyo 163-6017
Yasunari Yoshitomi, and Masayoshi Tabuse

Graduate School of Life and Environmental Sciences, Kyoto Prefectural University,
1-5 Nakaragi-cho, Shimogamo, Sakyo-ku, Kyoto 606-8522, Japan
E-mail:{yoshitomi, tabuse}@kpu.ac.jp
http://www2.kpu.ac.jp/ningen/infsys/English_index.html

Abstract
We have developed a method for classification of Japanese documents and ranking of representative documents
using characteristic of frequencies of nouns. The representative document is defined as the document whose feature
vector is the closest to the center of gravity of the class in the feature vector space among all documents belonging
to the class. The ranking of the representative documents is decided in the descending order of the number of
documents belonging to the class.
Keywords: Document classification, Extraction of representative document, Clustering, and Frequency of nouns.

Although the classification of documents has received
considerable attention in document analysis research,1-8
there is no research for selecting a representative
document in a class of documents, followed by ranking
several representative documents in order of importanceor in any meaning useful for us, to our best knowledge.
In the present study, we have developed a method
for classification of Japanese documents and ranking ofrepresentative documents using characteristic offrequencies of nouns.

1. Introduction
Recently, Web pages on the Internet have been
increasing, resulting in that it is very difficult to readthrough all of Web pages in which we are interested.
However, as a fact, there are too many similar Web
pages among them. For efficiently acquiring useful Web
pages, it is necessary to select only Web pages having
important and independent contents with which we canunderstand essential parts on an event adequately. A
Web page has some kinds of media, such as document,
image, and sound.
We focus on selecting Web page on the Internet
according to characteristics of document on the page.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 306 -

Jun Kimura, Yasunari Yoshitomi, and Masayoshi Tabuse

2. Proposed Methodin the feature vector space among all documents
belonging to the class.

2.1. Extraction of nouns

2.6. Ranking of representative documents

Firstly, all nouns in a document are extracted using
MeCab9 with which the document is resolved into
several morphemes (Fig. 1).

The first-rank document is defined as the document
whose feature vector is the closest to the center ofgravity of all documents in the feature vector space. In
this case, the number of class is one. Afterward, thenumber of class is increased one by one, and then the
ranking from the second-rank for the representative
documents is decided in the descending order of thenumber of documents belonging to the class for eachnumber of classes. The maximum number J of classes
in the stepwise clustering is given beforehand. Though adocument can be selected more than once in the ranking
process, the only first selection for the document is
accepted.

Fig. 1. Output of MeCab.

2.2. Connection of nouns having a meaning as a set
Some nouns directly connecting each other are treatedas one noun in the case that they have a meaning inassuming one noun. For example, 2014 and 年 inJapanese has a meaning as a set of 2014 年. 年 inJapanese means year in English.

3. Calculation Environment
The development of system and the experiment forevaluation of the proposed method were performed in
the following environment for computation: personal
computer; DELL OPTIPLEX 780(CPU: Intel Core2
Duo CPU E8400 3.00GHz ， RAM: 4.00GB), OS;
Microsoft Windows 7 Professional, Development
language; Python 2.7.3.

2.3. Addition of negative attribution
When a sentence expresses a negative meaning with useof 'not', the extracted nouns in the sentence are treatedas having a negative attribution. In other words, a noun
can have either positive or negative attribution. The
noun having a negative attribution is treated as being
different from the noun with a positive attribution in
making a feature vector for the document where thenoun exists.

4. Experiments and Discussion
4.1. Document classification

2.4. Feature vector generation

Firstly, we evaluated the performance of document
calcification by the proposed method. We gathered 20
documents on politics (document nos. 1-10) and horse
racing (document nos. 11-20) from Yahoo! Japan
News10 in January 2013, and then the number of clusters
was set to be two, resulting in that our system gave theoutputs shown in Table. 1. The clusters of C1 and C2
were composed of the documents on politics and horse
racing, respectively (Table 1). Accordingly, thedocument calcification by the proposed method was
perfect.

After every noun composing of only one of a hiragana,
which is the rounded Japanese phonetic syllabary, or akatakana, which is the angular Japanese syllabary, or asymbol is erased, a feature vector having a relative
frequency of each noun as each element is generated foreach document. The relative frequency is defined as theration of frequency of the noun to that of all nouns in the
document except nouns erased using the above criterion.
2.5. Document classification and extraction ofrepresentative one in each class

Table 1. Nos. of documents belonging to each cluster.
Cluster C2
Cluster C1
1, 2, 3, 4, 5,
11, 12, 13, 14, 15,
6, 7, 8, 9, 10
16, 17, 18, 19, 20

For clustering, we use Ward method. The representative
document is defined as the document whose feature
vector is the closest to the center of gravity of the class© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 307 -

Classification of Japanese Documents

4.2. Extraction of representative documents

The
four
representative
documents
weresuccessfully extracted one by one from all categories
except the category of 'Rugby' in the order of 'Board ofeducation', 'Center exam', 'Distress accident', and'Others' (Table 5).

4.2.1. Experiment I
Next, we evaluated the performance of extraction ofrepresentative document by the proposed method. We
gathered top 20 documents obtained by a retrieval from
Google News11 and those from Yahoo! Japan News
using the retrieval keyword of ' 大 阪 府 高 校 ' inJapanese, which is Osaka Prefecture high school inEnglish, on 22 January 2013. The name of document
obtained was set to be the same as the rank by each
retrieval, and then all documents were categorized.
The name of category was decided to be the content
name when more than two documents having the similar
content each other existed, and otherwise the document
was assigned to be a category of 'Others'. The
categorization was manually performed through ourunderstanding for each document, while the clustering
was performed by the proposed method. Therefore, it
was not guaranteed for the clustering result tocorrespond with the document group structure given by
the manual categorization.
(a) Google News
Table 2 shows the document group structure when weused Google News in our experiment. There were fivekinds of categories (Table. 2). Table 3 shows the
ranking of representative documents given by the
proposed method when using J  4 .

Table 4. Document group structure II.
Category
Rugby
Board of education
Document No. 15, 19
2, 3, 4, 11, 14, 16, 17, 18, 20
Category
Distress accident
Center exam.
OthersDocument No. 8, 12, 13
9, 10
1, 5, 6, 7
Table 5. Result II.
Ranking of representative documents expressed by Nos.
18, 10, 8, 5

4.2.2. Experiment II
We gathered top 20 documents obtained by a retrieval
from Google News and those from Yahoo! Japan News
using the retrieval keyword of 'Microsoft' on 22 January
2013. The name of document obtained was set to be thesame as the rank by each retrieval, and then all
documents were categorized in the same manner as
those in the section 4.2.1.
(a) Google News
Table 6 shows the document group structure when
we used Google News in our experiment. There werefour kinds of categories (Table. 6). Table 7 shows the
ranking of representative documents given by the
proposed method when using J  4 . The six
representative documents were extracted from all
categories in the order of 'Others', 'Others', 'Windows 8','MS Essentials', 'Surface' and 'Others' (Table 7).

Table 2. Document group structure I.
Category
Rugby
Board of education
Document No. 1, 9, 12, 18, 20 2, 4, 5, 11, 16, 17, 19
Category
Skating
Distress accident Others
Document No. 3, 14
6, 7, 8, 15
10, 13

Table 6. Document group structure III.
Category
Windows 8
MS Essentials
Document No. 3, 5, 14
2, 6, 12Category
Surface
OthersDocument No. 9, 11, 15
1, 4, 7, 8, 10, 13,
16, 17, 18, 19, 20

Table 3. Result I.
Ranking of representative documents expressed by Nos.
6, 1, 4, 3

The four representative documents weresuccessfully extracted one by one from all categories
except the category of 'Others' in the order of 'Distressaccident', 'Rugby', 'Board of education', and 'Skating'(Table 3).

Table 7. Result III.
Ranking of representative documents expressed by Nos.
18, 4, 3, 6, 11, 20(b) Yahoo! Japan News
There were two kinds of categories (Table. 8). Table 9shows the ranking for the document group shown in
Table 8 by the proposed method.

(b) Yahoo! Japan News
Table 4 shows the document group structure when weused Yahoo! Japan News in our experiment. There were
five kinds of categories (Table. 4). Table 5 shows the
ranking of representative documents given by the
proposed method when using J  4 .
© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 308 -

Jun Kimura, Yasunari Yoshitomi, and Masayoshi Tabuse

Table 8. Document group structure IV.
Category
Cannon ITS Others
Document No. 6, 9, 14
1, 2, 3, 4, 5, 7, 8, 10, 11, 12,
13, 15, 16, 17, 18, 19, 20

5. Conclusion
We have developed a method for classification ofJapanese documents and ranking of representative
documents using characteristic of frequencies of nouns.
The experiments where Web pages were collected andused for evaluating the efficiency of the proposed
method proved the usefulness of the proposed method.

Table 9. Result IV.
Ranking of representative documents expressed by Nos.
2, 9, 15, 13, 20, 11

The six documents were extracted in the order ofcategories of 'Others', 'Cannon ITS', and four sets of'Others' (Table. 9). In this document group, almost all
documents belonged to the category of 'Others'.
However, one document was extracted from thecategory of 'Cannon ITS' in the second order.

References
1. F. Can and E. A. Ozkarahan, Computation ofterm/document discrimination values by use of the covercoefficient concept, J. Amer. Soc. for Inf. Sci., 38(3)(1987) 171-183.
2. A. Kawai, An automatic document classification method
based on a semantic category frequency analysis (inJapanese), J. IPSJ 33(9) (1992) 1114-1122.
3. N. Yuasa, T. Ueda, and F. Togawa, Classifying articles
using lexical co-occurrence in large document databases(in Japanese), J. IPSJ 36(8) (1995) 1819-1827.
4. K. Hatano, R. Sano, Y. Duan, and K. Tanaka, A
classification view mechanism for web documents basedon self-organizing maps and search engines (in Japanese),
J. IPSJ 40(SIG 3(TOD1)) (1999) 47-59.
5. H. Takamura and Y. Matsumoto, Co-clustering for text
categorization (in Japanese), J. IPSJ 44(2) (2003) 443450.
6. H. Takamura and Y. Matsumoto, Constructive induction
and text categorization with SVMs (in Japanese), J. IPSJ
44(SIG 3(TOD 17)) (2003) 1-10.
7. R. Kusaya, T. Yamamura, H. Kudo, T. Matsumoto, Y.
Takeuchi, and N. Ohnishi, Measuring similarity between
documents using term frequency (in Japanese), Trans
IEICE J87-D-Ⅱ(2) (2004) 661-672.
8. Y. Bamba, K. Shinzato, T. Shibata, and S. Kurohashi,
Web information observation using keyword distillation
based clustering (in Japanese), J. IPSJ 50(4) (2009)1399-1409.
9. MeCab, http://mecab.sourceforge.net/
10. Yahoo! JAPAN News, http://headlines.yahoo.co.jp/hl
11. Google News, https://news.google.co.jp/

4.3. Discussion
4.3.1. Document group structure dependency
When the document group had a distinct structure suchas that in the sections 4.2.1 (a) & (b) and 4.2.2 (a), theperformance of the proposed method was almost soundin the meaning that the documents can be extracted one
by one from all categories except 'Others'. On the otherhands, when the document group had a scatteredstructure such as that in the section 4.2.2 (b), it might
not be meaningful to try to cover almost all contents byextracting the representative documents using the
proposed method.
4.3.2. Performance improvement
It might be necessary to apply the proposed method tomany document-groups for finding out assignments ofthe proposed method. It might be effective to use athesaurus for reducing the dimension of feature vector
space, potentially resulting in extracting more
appropriately representative documents and/or reducing
the calculation cost.
4.3.3. Definition of representative document
In the present study, the representative document wasgeometrically defined in the feature vector space. It isnecessary to investigate the validity of the definition
through questionnaires. In the investigation, otherdefinitions on the representative document might be on
the discussion.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 309 -

Biped Robot Research at Waseda University
Kenji Hashimoto

Research Institute for Science and Engineering, Waseda University,#41-304, 17 Kikui-cho, Shinjuku-ku, Tokyo 162-0044, Japan
Atsuo Takanishi

Department of Modern Mechanical Engineering, Waseda University,
TWIns Room# 3C-202, 2-2, Wakamatsu-cho, Shinjuku-ku, Tokyo 162-8480, Japan
E-mail: contact@takanishi.mech.waseda.ac.jp
www.takanishi.mech.waseda.ac.jp/top/index.htm

Abstract
Waseda University has researched on biped robots since 1967. This paper describes our latest biped robots: (i)WABIAN-2, (ii) a biped running robot, and (iii) WL-16. WABIAN-2 is a biped humanoid robot and has realized ahuman-like walk with the knees stretched by utilizing a 2-DOF waist mimicking a human’s pelvis motion. We aredeveloping a new biped humanoid robot which can jump by utilizing a pelvic movement and leg elasticity. WL-16
is a human-carrying biped vehicle.
Keywords: Biped walking, Running, Humanoid robot, Legged robot, Walking chair

We have developed a biped humanoid robot named
WABIAN-2 as a human motion simulator to mimic
human’s motions and mechanisms (see Fig. 1).
WABIAN-2 has 41 degrees of freedom (DoF) (two
6-DoF legs, a 2-DoF waist, a 2-DoF trunk, two 7-DoF
arms, two 3-DoF hands, a 3-DoF neck and two 1-DoF
feet having passive toe joint). Its height is 1480 mm
with 63.8 kg weight. So far, WABIAN-2 has realized ahuman-like walk with the knees stretched, heel-contact
and toe-off motion by utilizing a foot mechanism having
a passive toe joint and a 2-DOF (Roll, Yaw) waist
mimicking a human’s pelvis motion. A notable featureof human walking is that the vertical ground reaction
force (GRF) has two peaks. Two peaks do not appear
when a robot walks with bending the knees as shown in
Fig. 2(a). On the other hand, during the knee-stretchedwalking, we can find two peaks of the vertical groundforce which are similar to the human ones (Fig. 2(b)).

1. Introduction
Waseda University has researched on biped robots since
1967 and has been one of the leading research sites forhumanoid robots since the late Prof. Ichiro Kato and hiscolleagues started the WABOT Project in 1970. We
have developed a variety of humanoid robots including
WABOT-1 which is the first full-scale human-like robot
made in 1973 and the biped humanoid robot WABIAN
in 1997.
This paper describes our latest biped robots,
WABIAN-21 (WAseda BIpedal humANoid - No. 2
Refined), a running robot2 and WL-163 (Waseda Leg No. 16).
2. Biped Humanoid Robot, WABIAN-2© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 310 -

Kenji Hashimotoo, Atsuo Takanishhi

WABIAN
N-2 can coontribute to quantitativvely
evaluate welffare and rehabbilitation instru
uments such aas awalk-assist m
machine, an arrtificial leg an
nd so on insteeadof human suubjects. As onne of the app
plications as the(a) Ovverview

huuman motion
n simulator, we carried out walkingg
ex
xperiments wiith a walk-asssist machine4 (see Fig. 3)).
Geenerally, an arrmrest of a w
walk-assist maachine is set tooeqqual height of a user’s elbow
w. It is also knownkthat theesev
verer his/her disability is, tthe lower we should set theearmmrest. Measu
uring the currrent value of the motors atathee knee joints and the forcces and torqu
ues which aree
ap
pplied to the arms and leegs, we can calculate theeennergy consump
ption at knee jjoints and thee load to armss.
Th
hen we foundd the fact withh quantitativee data that theelow
wer we set the height of thhe armrest, thee less the loadd
off knees is. Thiis is a good eexample that humanhmotionnsimmulator can evaluate emppirical facts quantitativelyqy.
Frrom the results of these eexperiments, wew confirmedd
thaat we can evaluate welfarre equipment quantitativelyy
wiith a human motionmsimulattor.
We also usee WABIAN-22 to clarify th
he function of
5thee foot arch structure.
s
We developed a human-likee
foot mechanism
m that mim
mics the chaanges of theeelaasticity of th
he medial loongitudinal archawith theechhange of the arch heigght during walking. Assex
xperimental reesults using a biped hum
manoid robott,
WABIAN-2
W
mountedm
onn the hum
man-like fooot
meechanism, we confirmed thhat the arch elasticity couldd
abbsorb a foot-laanding force aat the plantar contact phaseeannd the windlass mechanism
m which caussed change ofthee arch height contributedctoo a strong thru
ust at the pushofff phase.

(b) DOF coonfiguration

Fig. 11. Biped humannoid robot, WA
ABIAN-2.

3. Biped Runnning RobotWe
W are now deeveloping a nnew biped hu
umanoid roboot
thaat can mimic various charaacteristics of humanhrunningg
to investigate humanhrunninng mechanism
ms and humannmootion control. If a biped ruunning robot couldcperformmmootions that wouldw
improvve sports peerformance, iti(a) Knee--bent walking(b) Knee-strretched walkinggFig. 2. Grounnd reaction forcce.

Fig. 3. Quantitative evaluatioon of walk-assiist machine.

© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 311 -

Bipeed Robot Researchwould be poossible to ideentify more effective
e
hum
man
running motioons.
Human ruunning can bee modeled by a Spring Loadded
Inverted Penddulum (SLIP) model, which
h is composedd ofa body mass and springg leg inspiredd by the linnear
relationship bbetween the groundg
reactio
on force and thevertical bodyy displacementt during runniing.6 This moodel
describes hum
man running in a simple, straightforw
ward
way and is ussed on the stuudies of bipedd running roboots.
Moreover, thhe knee and annkle joints of the standing leg
act like torsioon springs whhich emerge th
he leg stiffnesss.7

urthermore, ann important chharacteristic th
hat has not yeet
Fu
beeen reported is that a humaan pelvic movement in theefroontal plane cann help to increease takeoff forces.
fo
On the bassis of the annalysis of hu
uman runningg
mootion, we propose a new mmodel named SLIP2 (SpringgLo
oaded Inverted
d Pendulum uusing Pelvis). This model iss
coomposed of a body mass, a pelvis, and leg
l springs. Inn
thiis model, the knee and ankkle joints of th
he standing legg
act like torsion
n springs and the pelvic siine movemennt
caauses the reson
nance for hoppping.
We have developed a bbiped running robot with apeelvis and a joint stiffness aadjustment mechanismmthaatusses two leaf springss
to mimmic the joint stiffness of ahuuman leg duriing running ((see Fig. 4). The
T robot hassreaalized hopping motions by utilizing
g the pelvicc
moovement and leg
l elasticity.
4.

Biped Vehicle, WL-16

We
W consider thaat a biped wallking wheelch
hair is a viablee
solution in barrrier-free enginneering that is
i much moree
effffective and low-cost than innfrastructure improvementss.
Th
herefore, we havehdevelopeed a human-ccarrying bipedd
waalking vehiclee named WL-116 as shown inn Fig. 5.
The weightt of WL-16 iis 74 kg inccluding 11 kgg
baattery weight. We adopt a liithium-ion baattery of Microo
Veehicle Lab., Ltd.,
L
which haas no memoryy effect and iss
recchargeable withoutw
refreshhing. Each leeg mechanismmhaas six linear actuatorsaandd passive joints at the bothhsiddes of each liinear actuatorr. The upper passive jointss
aree commerciall universal jooints using neeedle bearingss
whhich are smalll, lightweighht and have liittle backlashh.
Th
he lower paassive joints are lightw
weight 3-DOF
Fcoombination passivepjoinnts, which were newlyy
deeveloped in coooperation witth HEPHIST Seiko
S
Co., Ltdd.
Each linear actuator connsists of a 150
0W DC servoomootor and a ball screw. The control computer iss
arrranged at the rear of the peelvis, and the batteries, DCC
serrvo drives andd an IMU sennsor are arranged inside theepeelvis. A passenger seat is oon the pelvis, and a control
stiick is mounted
d on the passeenger seat. A passenger cannchhange the direection of the robot freely by using theecoontrol stick.

(a) Overview
O(b) DOF configuration
Fig. 4. Bipeed running robot.

© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 312 -

Kenji Hashimotoo, Atsuo Takanishhi

caarrying bipedd vehicle coonsisting of two Stewarrt
Platform type leegs and waist with a passen
nger seat. WL16
6 can be used asa a substitutee for a wheel chair.
cAccknowledgemmentsTh
his study waas conducted as part of the Researchh
Institute for Science annd Engineeriing, Wasedaa
Un
niversity, andd as part of thhe humanoid project at theeHuumanoid Robo
otics Institute,, Waseda Uniiversity. It wass
alsso supported in part byy MEXT/JSPSS KAKENHI(G
Grant Numberr: 24360099, 25220005 an
nd 25709019)),
So
olidWorks Jap
pan K.K., HEPPHIST Seiko Co., Ltd., andd
DY
YDEN Corporation whom we thank for their financiaalan
nd technical su
upport.
Reeferences(a) Overview
O

1. Y. Ogura, H.
H Aikawa, K
K. Shimomura, H. Kondo, A.
A
Morishima, H.
H O. Lim and A. Takanishi, Development
Dofa Humanoidd Robot WABIIAN-2, in Pro
oc. of the 20066
IEEE Interrnational Connference on Robotics andd
Automation (2006) 76-81.
2. T. Otani, M. Yahara, K. U
Uryu, A. Iizuka, K. Hashimotoo,
H
T. Kishi, N.. Endo, M. Sakkaguchi, Y. Kaawakami, S. H.
Hyon, H. O.. Lim and A. T
Takanishi, Runnning Model andd
Hopping Robot
R
Using PPelvic Movemment and Legg
Elasticity, in Proc. of thhe 2014 IEEE
E Internationaal
Conference on Robotics aand Automationn (2014) 23132318.
3. K. Hashimotto, H. O. Lim aand A. Takanisshi, Disturbancee
Compensatioon Control foor Biped Vehicle, Advancedd
Robotics 25 (3) (2011) 407--426.
4. Y. Ogura, H.
H Aikawa, K
K. Shimomura, H. Kondo, A.
A
Morishima, H.
H O. Lim and A. Takanishi, Development
Dofa humanoid robot capablee of leaning on
n a walk-assisst
machine, in Proc. of the IE
EEE RAS/EMB
BS Internationaal
Conference ono Biomedical R
Robotics and Biomechatronics
Bs(2006) 835–840.
5. K. Hashimo
oto, Y. Takezak
aki, K. Hattori,, H. Kondo, T.
TTakashima, H. O. Lim annd A. Takanish
hi, A Study ofS
Usingg
Function off the Human’ss Foot Arch Structure
Biped Humaanoid Robot, inn Proc. of the 2010 IEEE/RSJJInternational Conference on Intelligen
nt Robots andd
Systems (20110) pp. 2206-22211.
6. T. McMahonn and G. Chengg, The Mechan
nics of Runningg:
How does Sttiffness Couplee with Speed?, J.
J Biomechanicss
23 (1990) 65
5-78.
7. M. Gunther, R. Blickhan, JJoint stiffness ofo the ankle andd
the knee in running, J. Biiomechanics 35
5 (2002) 1459––1474.

(b) Mechannism and sensors
Fig. 5. Bipedd vehicle, WL-1
16.

5. Conclusioons
We introduced our latestt biped robots developed at
Waseda Univversity. WAB
BIAN-2 is a bipedbhumannoid
robot and hass realized a huuman-like wallk with the knnees
stretched, heeel-contact andd toe-off motio
on by utilizinng aOFfoot mechanism having a passivep
toe joint and a 2-DO
(Roll, Yaw) w
waist mimickking a human’s pelvis motiion.
We are developing a new biped running robbot
mimicking bbased on the analysis of human runnningmotion. The running robbot can jump
p by utilizingg apelvic movem
ment and leg elasticity.
e
WL
L-16 is a humman-© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 313 -

Analysis of Genetic Disease Haemophilia A by Using Machine Learning
Kenji Aoki

Information Technology Center, University of Miyazaki
Gakuen-kibanadai-nishi 1-1, Miyazaki City, Miyazaki, 889-2192, Japan
Makoto Sakamoto and Hiroshi Furutani

Faculty of Engineering, University of Miyazaki
Gakuen-kibanadai-nishi 1-1, Miyazaki City, Miyazaki, 889-2192, Japan
E-mail: aoki@cc.miyazaki-u.ac.jp, sakamoto@cs.miyazaki-u.ac.jp, furutani@cs.miyazaki-u.ac.jp
www.miyazaki-u.ac.jp

Abstract
Haemophilia A is a genetic disease resulting from deficiency of factor VIII. The database of mutations causing
haemophilia A has been developed by the world wide collaboration. In this study, we examined the relation
between activity of factor VIII and the missense mutation by using machine learning. As parameters, we used fourphysical-chemical parameters of amino acids. We predicted the severity of haemophilia A by using machinelearning in factor VIII. As the result, logistic regression is not better than other methods in the prediction of
haemophilia A severity. The result of the prediction improved in order to SVM, bagging, boosting and random
forest. These results suggested that we can predict the haemophilia A severity by using these methods, and random
forest was the best method in these five methods to predict the haemophilia A severity.
Keywords: Haemophilia A, Machine Learning, Factor VIII, Amino-acid, Mutation

1.

applying various approaches. We used a multiple
regression model to predict the effect of a missense
mutation in factor IX gene of haemophilia B patients [3].
In the past, we have demonstrated the calculations using
Support Vector Machin (SVM) for the analysis ofmutant factor VIII genes [4].
There have been reported a variety of defects infactor VIII gene from haemophilia A patients [5], andthese are summarized in the haemophilia A database [6].
This database has data of clotting activity, nucleotideNo., position, changed amino-acid and mutation type. In
this study, we analyzed amino acid changing mutations,or missense mutations in the database described withfactor VIII activity values. We adopted 439 cases fromthe database. We use the distances between 20 aminoacids by using the four physical-chemical properties:

Introduction

The haemophilia is a group of hereditary genetic
disorders, in which one of the coagulation factors is
deficient [1]. Haemophilia A is the most common formof disorder caused by low concentration of thecoagulation factor VIII. Haemophilia B is another formof disorder caused by deficient factor IX. Haemophilia
A accounts for about 85% of this disorder, while
haemophilia B for 10−12% [2].
Haemophilia
A
and
B
are
clinicallyindistinguishable from each other. Diagnosis must be
confirmed by specific factor assay. It becomes very
important to study mutations in genes responsible fordiseases by biological experiment. However, it is atime-consuming, laborious and expensive task. Thus, itis necessary to develop computational method by©The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 314 -

Kenji Aoki, Makoto Sakamoto, Hiroshi Furutani

1
1⋮

Molecular volume, Hydropathy, Polar requirement andIsoelectric point. These distances are the differences
between physical-chemical values before mutation andafter mutation. In this study, we used some machinelearnings to analyze of haemophilia A severity, and wecompared these methods.

The gene coding for human factor VIII consists of 26
exons and 25 introns, and is located on the X
chromosome [5]. Factor VIII is an essential bloodclotting protein, and synthesized as a precursor proteinof 2351 amino acids. This includes a signal peptide anda mature protein of 2332 amino acids with domain
structure A1-A2-B-A3-C1-C2. Classification of
haemophilia A is presented in Table 1.Three A domains
display approximately 30% homology to each other.
The C domains are structurally related to the C domainsof factor V. The B domain exhibits no significant
homology with any other known protein. We used
Haemophilia A Mutation Database [6]. The part of thedatabase is shown in Table 2. This database includes
exon number, amino-acid number, amino-acid change
and activity of factor VIII (FVIII:C). Activity of factor
VIII in a patient's blood depends on a position of thesubstitution and combination of original and substituting
amino acids.

Logistic regression is a probabilistic statisticalclassification (regression) model. It is used forpredicting the outcome of a categorical dependent
variable based on predictor variables. It is a kind ofgeneralization linear model using a logistic function.
2.2.2. Support Vector Machine
SVM (Support Vector Machine) is supervised learningmodels with associated learning algorithms [7]. It is
used for classification and regression analysis. Given a
set of training data, SVM builds a model. It assigns new
data into one category or the other. It is a nonprobabilistic binary linear classifier.
2.2.3. Bagging
Bagging is a method for generating multiple versions ofa predictor and using these to get an aggregatedpredictor [8]. The aggregation does a plurality votewhen predicting a class. The multiple versions areformed by making bootstrap replicates of the learning
set. Tests on data sets using classification and regression
trees show that bagging can give substantial gains inaccuracy.

Number of data
111
131
18
107
39
33
439

2.2.4. Boosting
Boosting is a machine learning based on the idea ofcreating a highly accurate predictor by combining many
weak rules of thumb [9]. A remarkably rich theory has
evolved around boosting with connections to a range ofseveral topics. Boosting algorithms have also madepractical success in such fields as biology, vision, andspeech processing.

Table 2. Mutation database of haemophilia A.
Amino-acid
Number3
6
10

Amino-acid
ChangeArg Thr
Tyr Cys
Val Gly⋮

2.2.1. Logistic Regression

Table 1. Domain structure and number of data in
Factor VIII.

Exon
Number
1
1
1

1.5
5

We used five machine learnings for analysis of
haemophilia A database. These are logistic regression,
support vector machine, bagging, boosting and random
forest. We used statistical application software “R” andpackages for calculations. The packages are ‘kernlab’,‘ipred’, ‘ada’ and ‘randomForest’.

2.1. Haemophilia A Database

Location
1 ~ 329330 ~ 711
712 ~ 1648
1649 ~ 20192020 ~ 2172
2173 ~ 2332
total

⋮

Glu LysTrp Gly⋮

2.2. Machine Learning

2. Methods

Domain
A1
A2
B
A3
C1
C2

11
14

FVIII:C(%)
1
6<1©The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 315 -

Analysis of
o Genetic Diseasse

2.2.5. Randomm Forest

peerformance, whichw
in the upper left inndicates moreesuuperior perform
mance.
The result ofo prediction using all data is shown inn
Fig. 1. In Fig. 1,
1 there are fivve curve thesee are the resullt
off prediction byb each machhine learning method. Thee
ressult of using logistic regrression lies inn more downnrig
ght than otther methodss in the prediction
pofhaaemophilia A severity. Thhe result of usingurandommforrest situated ini the most uppper left thann other curvess.
Th
herefore, this result suggessted that the predictions
p
byy
ran
ndom forest is
i the most suuperior perforrmance in fivee
maachine learninngs.
The result ofo prediction using one domain data iss
shhown in Fig. 2,
2 3 and 4. Figg. 2, Fig. 3, Fiig. 4 show theeRO
OC curve of predictionpusinng A1, A2, an
nd A3 domainn
daata, respectiveely. In all figuures, the ROC
C curve usingg
ran
ndom forest method lies in upper leeft than otherrcuurves. This ressult is more reemarkable in Fig.
F 2, 3 and 4
thaan Fig. 1. This result suggeested that the predictions
p
byy
ran
ndom forest isi also the moost superior performance inn
fivve machine learnings usinng each dom
main data. Wee
weere not able to get a cleaar result in other domainss,
beecause numberr of data is tooo small in thesse domains.

Random foreests are an ennsemble learnning method forclassification and regresssion [10]. It operate by
constructing a multitude of decision treestat trainning
time and outtputting the classcthat is thet mode of theclasses outpuut by individuaal trees. The methodmcombinnes
bagging ideaa and the raandom selectiion of featurres,
introduced inn order to construct a collecction of decisiion
trees with conntrolled variannce.
2.3. Dataset
We used dataa of haemophiilia A Mutatio
on Database. T
The
number of daata in A1, A2, B, A3, C1 and C2 domain are
111, 131, 18,, 107, 39 andd 33 respectiveely. We used alldata in each domain for trainingtdata and test dataa inmachine learrning. We considered serio
ous illness w
withless than 1%% of factor VIIII activity, anda slight illnness
with more than 1% of onee. We predicteed the seriouss or
slight illnesss of haemophhilia A by machinemlearnningbased on thesse data.
We used a distance between aminoo acids for eaachfour aminoo acid phhysical-chemiccal parametters(Molecular voolume, Hydroopathy, Polar requirementr
annd
Isoelectric pooint). The k-thh distance betw
ween amino aacid
Ai and Aj is ddefined as
1
In this studyy, Ai is a norrmal amino acid, and Aj iis a
substituting aamino acid.
3.

Results

At first, we ppredicted the severity of haaemophilia A bylogistic regreession, SVM, bagging, booosting (adabooost)and random fforest using alla domain daata. Furthermoore,
we predictedd severity off haemophiliaa A using eaach
domain data.
In all figuures, the horizzontal axis is thet false posittive
ratio, and the vertical axis is
i the true possitive ratio. Faalse
positive meaans that the predicted reesult is posittive(serious), butt observed ressult is negativve (slight). Trrue
positive meaans that bothh the predicction result aandobserved ressult are positiive. False neegative and tr
true
negative are similar to theese. We plot the relationshhipFig
g. 1. ROC curv
ve of predictioon of haemophhilia A severityy
usiing all domain data.
between the false positive ratio and thet true posittive
ratio in the fi
figures. Such a figure is callled ROC currve.
ROC curve iis used for a comparison ofo the inspecttion©The 2015 Internatioonal Conferen
nce on Artificiial Life and Ro
obotics (ICAR
ROB 2015), Jaan. 10-12, Oita, Japan- 316 -

Kenji Aoki, Makooto Sakamoto, Hiiroshi Furutani

Fig
g. 4. ROC curv
ve of predictioon of haemophhilia A severityy
usiing A3 domain data.

Fig. 2. ROC ccurve of predicction of haemo
ophilia A seveerity
using A1 domaain data.

Reeferences
1. P.H.B. Bolto
on-Maggs, K.J.. Pasi, Haemop
philias A and B,
B
The Lancet 361
3 (2003) 180 1-1809
B
Furie, The
he Molecular BasisB
of Bloodd
2. B. Furie, B.C.
Coagulation, Cell 53 (1988)) 505-518
miya, M. Sakam
moto, H. Furutani, Regressionn
3. M. Utsunom
Analysis off Amino Acid Substitutions and Factor IX
X
Activity in Hemophilia
H
B, A
Artificial Life anda Robotics 133(2008) 531-5
534
4. K. Aoki, K. Yamamori, M.. Sakamoto, H. Furutani, SVM
M
Analysis of Hemophilia A by Using Protein Structuree,Lecture Notes in Compuuter Science, Springer 82277
688(2013) 681-6
oralka, et all.
5. J. Gitschier, W.I. Woood, T.M. Go
Characterizaation of the Humman Factor VIIII Gene. Naturee
312 (1984) 326-330
3
6. G. Kemballl-Cook, The Haemophilia A Mutationn,
Structure, Test
T
and Reesource Site (HAMSTeRS)),
http://hadb.oorg.uk/ (11 Febrruary 2013 upd
dated)
pf, C.J.C. Burgges, A.J. Smola, Advances inn
7. B. Schölkop
Kernel Methhods, The MIT PPress, London (1999)
8. L. Breiman, Bagging Predicctors, Machine Learning 24(2)(1996) 123-1
140
dditive Logisticc
9. J. Friedman,, T. Hastie, R. Tibshirani, Ad
Regression: A statistical vview of boostting, Annals of
Statistics 28((2) (2000) 337--374
rest, Machine LearningL45(1)10. L. Breiman,, Random Fore
(2001) 5-32

Fig. 3. ROC ccurve of predicction of haemo
ophilia A seveerity
using A2 domaain data.

©The 2015 Internatioonal Conferen
nce on Artificiial Life and Ro
obotics (ICAR
ROB 2015), Jaan. 10-12, Oita, Japan- 317 -

Analysis of Asymmetric Mutation Model in Random Local Search
Hiroshi Furutani* and Makoto Sakamoto

Faculty of Engineering, University of Miyazaki, Gakuen Kibanadai-nishi 1-1
Miyazaki City, Miyazaki Prefecture, 889-2192, Japan
Yifei Du

Graduate School of Engineering, University of Miyazaki, Gakuen Kibanadai-nishi 1-1
Miyazaki City, Miyazaki Prefecture, 889-2192, Japan
Kenji Aoki

Information Technology Center, University of Miyazaki, Gakuen Kibanadai-nishi 1-1
Miyazaki City, Miyazaki Prefecture, 889-2192, Japan
E-mail: furutani@cs.miyazaki-u.ac.jp
Abstract
In a standard Evolutionary Algorithms (EAs), one uses the same rate for mutations from bit 1 to bit 0 and its
reverse direction. There are many reports that the asymmetric mutation model is a very powerful strategy in EAs toobtain better solutions more efficiently. In this paper, we report stochastic behaviors of algorithms that areasymmetric mutation models of Random Local Search (RLS). The mathematical structure of asymmetry model canbe derived in terms of a finite Markov chain. We demonstrate some useful results representing the effects ofasymmetric mutation.
Keywords: Random Local Search, Asymmetric mutation, Hitting time, Markov chain

The asymmetric mutation model is a very powerfulstrategy in EAs to obtain better solutions more
Theoretical studies of EAs have been performed fromefficiently [4]. In biology, spontaneous misreading ofvarious viewpoints. One of the most attractive objects ofbases during DNA synthesis, mutation, is considered asthem is the convergence properties of EAs [1]. In thea major factor contributing to evolution [5]. Nei statedprevious conference, we have reported a randomized
that the driving force behind evolution is mutation, withheuristics, which mainly treated the computational
natural selection being of only secondary importance.
complexity of Random Local Search (RLS) [2]. Our
Wada et al. showed that double-stranded DNA typestudy used the results obtained in researches on Coupon
strings can solve the knapsack problem effectively by
Collector Problem (CCP), and made a mathematicalusing the asymmetric machinery of DNA replication [6].
analysis of hitting time in RLS by extending the original
They used different mutation rates for the leading andmodel of CCP.
lagging DNA strands.
In this conference, we report another extension of RLS,
To analyze the behavior of the evolution processes, it
an asymmetric mutation model. We apply theis necessary to take into account of effects due toasymmetric mutation in evolution of RLS; that is,stochastic fluctuations. During the study of asymmetricfor mutation 0 → 1 andfor 1 → 0, respectively.
mutation model of RLS, we noted mathematical papers
We carry out a theoretical analysis for the evolution of
in learning model with reinforcement, which gave thestrings in the framework of a finite Markov chain [3].
Markov chain model of learning processes [3]. We
found that results in these papers can be interpreted as a
© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan
1. Introduction- 318 -

Hiroshi Furutani, Makoto Sakamoto Yifei Du Kenji Aoki

3: Does not changewith probability 1.
If.
0 then1 with probability
4: If
is flipped then ≔ .
5: Go to 2 until a termination condition is fulfilled.

model of RLS with asymmetric mutation rates, andapply these results in the present study.
In this model, we obtained an explicit form of Markov
chain transition matrix, and furthermore the eigenvaluesof this matrix were calculated by using rather technicalmethods. The largest eigenvalue is naturally
1,
and the second largest one is1/.
Since the most important factor to decide the speed ofconvergence is the second largest eigenvalue, we know
the averaged value of two mutation rates mainly
controls the speed of evolution. We will show in ourreport the behavior of solutions from the aspects ofmathematical analysis and numerical simulations.

This model is also defined as the variation of ARLS byputting
0.
3. Markov Chain Model
This section presents the Markov chain approaches tothe EAs. The search space of OneMax function isΩ
0,1 , and we divide Ω into
1 subsetsΩ∪ ∪ ⋯ ∪ , where.
3.1. Asymmetric mutation model of RLS

2. Evolutionary Algorithms

The transition matrix
evolution of ARLS.
1. For1, 0

As a test function, we adopt OneMax function,∈ 0,1 ,

1

,

where
is a binary string of length . We consider the
maximization of OneMax function. The optimum
solution is
1 , and.
1. The first choice of Evolutionary Algorithm is theRandom Local Search (RLS). We define RLS as
Algorithm 1
Random Local Search

represents the

.

1, 0

2. For.

,, 0

3. For1,

1: Initialize ∈ 0,1 uniformly at random.
2: Createby flipping one bit inwhich isselected at random.
3: Select ifthen ≔ .
4: Go to 2 until a termination condition is fulfilled.
2. The next one is RLS with asymmetric mutation(ARLS). The ARLSis defined as
Algorithm 2
RLS of Asymmetric Mutation

|

,

1

.
3 is given

For example, the transition matrix for
by
1
1
3

1

2
3

0
0

1: Initialize ∈ 0,1 uniformly at random.
2: Select one bitinat random.
3: With probability 1,.
If.
0 then1 with probability
If.
1 then0 with probability
4: If
is flipped then ≔ .
5: Go to 2 until a termination condition is fulfilled.
3. The third one is a lazy version of RLS, which isdefined as
Algorithm 3
Lazy RLS

1
3

2
3
0

1
3

1

,

The left eigenvectors
the case of
3,

2
3

1

2

1

3

3

1
3

1
3

0
0
2
3

,⋯,

1
3
1

satisfy, in

1
3

1
1

0
2
3

,

2

,

3

2

,

3
2

1

3.

3

We define the th order polynomial function

1: Initialize ∈ 0,1 uniformly at random.
2: Select one bitinat random.

⋯.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 319 -

Analysis of Asymmetric Mutation

Multiplying eigenvalue equations with 1, ,respectively, and summing them up, we have

,…,

,

1
0

1

1

0.

Considering this as a differential equation of
have

, we

1

,

,

0,1, … , .

⑴

From this, we can obtain
ones,

1

.

.

The expected step
to enter into the absorbing
state from the initial state is given by the vector
of
1 [3]
,

⑵

where
is the expected step from $i$th state, andis a column vector whose all entries are 1. Thus, we
have

the average number of bit

1.
Similarly, we have the variance
∙

0

⑶

1

,

thus the components of eigenvector with normalization
are given by0⋮

After some calculations, we have
0,
,

,

,

⋱

The
submatrix
shows transition probabilities
among transient states , , ⋯ ,. Since there is
only one absorbing state, the unit matrix is a scalar 1.
For the calculation of the hitting time of the optimum
solution, we use the fundamental matrix.
⑸

1 , and the second
The largest eigenvalue islargest oneis1/ , whichdetermines the convergence speed of the chain.
The eigenvector corresponding to the largest
eigenvalue
1 presents the distribution of thestationary state. In this case of0, we have

/

⋱
⋯
⋯

0
⋮

⑷

where
is an arbitrary constant. Since
is apolynomial of th order,
must be an integer of
0,1, … , . Then the eigenvalues are given by
1

⋮
0
0

⋯
0

This equation shows that the Markov chain isabsorbing one, and there aretransient and one
absorbing states, respectively. For absorbing Markov
chains, the transition matrix is represented as

,

1

1

⋮
0
0

0
1

1

0

.
where

is theBoth quantities depend on the ratio of two mutation
rates
/ .

⑹

-th harmonic number
1
1
⋯
.
1
2

The variance of
3.2. Lazy RLS
The transition matrix for the lazy RLS is given by1 ,is given by
2

,

where
is a column vector whose elements are
The explicit form of the variance is given by,
where

is the⑺

generalized harmonic number© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 320 -.

Hiroshi Furutani, Makoto Sakamoto Yifei Du Kenjji Aoki

1

1
2

We have useed the identityy
1
2

⋯

1

.

.

4. Numericaal ExperimenntFig. 2. Distribu
ution of the first hitting time in Lazyyand. . The initial state iss
RL
LS witha bit string of all zeros. D
Dots are RLS calculationss,nd the solid lin
ne is the theooretical predicction.
an
FigureF
2 show
ws the distributtion of the firrst hitting timee
off the optimum solution in L
Lazy RLS. Thee string lengthh
and muutation rate
. . The initial state iss
a bitb string of alla zeros. Dots
ts are the num
merical resultss.
Th
he solid line isi the theoretitical prediction obtained byy
ussing Markov chain transitionn matrix.

In this sectiion, we demonnstrate theoreetical predictioons
for ARLS aand Lazy RLS,R
and preesent results ofnumerical caalculations forr comparison. We perform
med
10000 runss for each calculation, and obtainned
statistical quaantities from thhem.

Fig. 1. Distribution of the statio
onary state inasymmetric mutation moodel of RLS
S with.
Mutation raates are
.
and
.
,
respectively. Crosses aree RLS calcullations, and tthe
solid line is tthe theoreticaal prediction.
Fig. 3. Mutation rate deppendence of the averagee
hittting time an
nd its standarrd deviation in Lazy RLS
S
wiith. The initial sstate is a bit string of alllzeeros. The soliid lines are tthe theoreticaal predictionss
ussing Markov chain modell. Circles and crosses areeresults of numeerical calculattions.

Figure 1 sshows the sttationary disttribution of theasymmetric m
mutation moddel. The stringg length,.
aand. . The crossses are the ressultof numerical calculation. The
T solid line is the theoretiical
prediction obtained by eq.⑵⑵.

FigureF
3 show
ws the dependdence of the averageahittinggtim
me and its staandard deviattion (SD) on mutation rateein Lazy RLS. The string length is 20.
2 The initiaal
staate is a bit striing of all zeroos. The thin soolid line is theetheeoretical preddiction of thee average hittting time, andd
thee thick solid line representss the theoreticcal SD. Circlessannd crosses are numerical ressults.
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan© The 22015 Internatiional Conferen- 321 -

Analysis of Asymmetric Mutation

4. Summary
In this paper, we considered the behavior of theasymmetric mutation in the solving the optimization of
OneMax function. This problem can be solved
analytically in terms of a finite Markov chain [3]. We
derived the explicit form of eigenvalues andeigenvectors, and obtained various quantities
theoretically.
In genetic biology, it has been suggested by manystatistical studies that asymmetric directional mutationpressures are commonly observed in weakly selected
positions [7]. It is interesting to study the phenomenausing the theoretical approach presented in this paper.
References
1. P. S. Oliveto and X. Yao (2011), Runtime analysis ofevolutionary algorithms for discrete optimization, in
Theory of Randomized Search Heuristics, A. Auger and
B. Doerr (eds), World Scientific, Singapore, pp.21--52.
2. H. Furutani, H. Tagami, Y. Du, and M. Sakamoto (2014),
Markov chain analyses of Random Local Search andEvolutionary Algorithm, in Proceedings of theInternational Conference on Artificial Alife and Robotics
2014.
3. M. Iosifescu (1980), Finite Markov chain processes andtheir applications, John Wiley & Sons, New York.
4. B. Doerr, N. Hebbinghaus and F. Neumann (2007),
Speeding up Evolutionary Algorithms throughasymmetricmutation
operator,
Evolutionary
Computation 15, pp.401--410.
5. M. Nei (2013), Mutation-Driven Evolution, Oxford
University Press, Oxford.
6. K. Wada, H. Doi, S. Tanaka, Y. Wada and M. Furusawa(1993), A neo-Darwinian algorithm: Asymmetricalmutation due to semiconservative DNA-type replication
promote evolution, Proceedings of National Academy of
Science, USA 90, pp.11934--11938.
7. J. R. Lobry and N. Sueoka (2002), Asymmetricdirectional mutation pressures in bacteria, Genome
Biology 3, pp.58.1--58.14.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 322 -

Hitting Time Analysis of OneMax Problem in Genetic Algorithm
Yifei Du

Graduate School of Engineering, University of Miyazaki, Gakuen Kibanadai-nishi 1-1
Miyazaki City, Miyazaki Prefecture, 889-2192, Japan
QinLian Ma

Interdisciplinary Graduate School of Agriculture and Engineering, University of Miyazaki
Kenji Aoki

Information Technology Center, University of Miyazaki
Makoto Sakamoto and Hiroshi Furutani

Faculty of Engineering, University of Miyazaki
Yu-an Zhang

Department of Computer Science and Technology, Qinghai University, No. 251, Ning Road
Xining City, Qinghai Province, 810016, China
E-mail: furutani@cs.miyazaki-u.ac.jp
Abstract
Genetic algorithms (GAs) are stochastic optimization techniques, and we have studied the effects of stochastic
fluctuation in the process of GA evolution. A mathematical study was carried out for GA on OneMax function
within the framework of Markov chain model. We treated the task of estimating convergence time of the Markov
chain for OneMax problem. Then, in order to study hitting time, we study the state after convergence.
Keywords: genetic algorithms, OneMax problem, Markov model, convergence time, hitting time

for predicting the convergence time to the stationary
state in terms of one parameter.
In this paper, we report the hitting time analysis of GA
Since GAs are stochastic optimization methods, weby the use of Markov chain theory. The hitting time ishave to take into account a stochastic fluctuation to
the step at which the optimum solution appears in a
explain the behaviors of evolution. To do this, we madepopulation for the first time during the process of GA
use of the Wright-Fisher model1, a type of Markov
evolution. To simplify the analysis, we have separated
chain method. For example, we considered the effects ofand hitting time
the process into convergence timecrossover on the evolution speed of OneMax problem2,
after convergence .
and carried out the microscopic investigation in terms oflinkage analysis3. We also studied the convergence time
can be estimated by using
The convergence timeof Markov chain in OneMax problem4. This analysis
the Markov chain theory. On the other hand, we
was performed by using the eigenvalues of transition
by experiments of OneMax function, andestimate
matrix representing the behavior of population in thesurvey the impact of parameters on the hitting time after
GA. From this analysis, we found that the convergence
convergence. Our results demonstrate that the hittingof GA to the stationary state can be represented
time distribution
has an exponential form, and theapproximately by mutation rate and string length. It is
logarithmic ofis linearly decreasing function.
well known from the theory of finite Markov chain that
This means the distribution is almost determined by one
ergodic Markov chain converges to the stationary
parameter . We estimate the value of b from the
distribution5. We obtained the approximate expression
experiments, and report the dependence ofon the© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan
1. Introduction- 323 -

Yifei Du, Qinlian Ma, Kenji Aoki, Makoto Sakamoto, Hiroshi Furutani, Yu-an Zhang

population size and mutation rate.

is defined as
12

2. Mathematical Model
We treat the evolution process of a population withindividuals. The individuals are represented by binary
strings of length , and there are
2 genotypes,,…, 1 ,
∈ 0,1 .
⑴
The OneMax fitness function

Thus the string of all ones
optimum solution of this function.

is the3.2. Analysis of hitting time after convergence

3. Analysis of Hitting TimeIn this study, we obtained the hitting time distribution
after convergenceby using the experiments of
OneMax problem. The distribution
is the result ofresetting generation
0 when the populationachieves the convergence state.
Our results demonstrate that the hitting time
distribution
is an exponential form,⑻,
and the logarithmic ofis linearly decreasing
function
⑼
log
log
.
Then, we transform this equation to
log
log
.
⑽
We calculated the average of log
,
∑ log
log
,
⑾
and defined a constant,In our model, we will investigate the distribution ofhitting time
approximately. Though it is difficult
to obtain the theoretical expression of. Therefore,
directly, we havein order to study the hitting time
into converge timeand hitting time
separated. By analyzing
and, we canafter converge
estimate the upper bound of hitting time1. So we assume⑶
We have used the concept of Markov chain to analyze. In this study, we usedconvergence time
experimental method to estimate the hitting time afterconvergence . In this study, we obtained the hitting
time distribution after convergenceby theexperiments.
.

3.1. Analysis of convergenceIn our study, we used the Wright-Fisher model totheoretically analyze the process of population
convergence. The Wright-Fisher model is one of theMarkov model in population genetics6,7.
We consider the GA under positive mutation rate
0. In this case, all elements of the transition
matrixare 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
→

⑸

⑹
,
where C is a constant. It should be noted that theconvergence behavior is determined by only one
parameter a. The parameteris the second largest
eigenvalue of the transition matrix8 .
1
1
1 2
.
⑺

⑵
1,1, … ,1|.

By analyzing Markov chain, we have at large t

is defined as
.

|

,

⑿
exp log.
We can defined that the distribution is almost
determined by one parameter⒀.
We report the dependence ofon the parameters ofGA, population size, mutation rate, and so on.
4. Experiments⑷

We compared results of the theoretical prediction withGA experiments. Crossover is uniform crossover withcrossover rate
1. Mutation rate isfor each bit.
Selection is roulette wheel selection. We averaged theresults obtained by repeating 10000 calculations. The
1/2.
initial state was randomly generated withand all elements
are positive5.
In order to analyze the evolution of the first orderschema, we referenced the total variation distance9. The
total variation distance between the stationary
distribution and the first order schema at generation© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 324 -

Hitting
g Time Analysis ofoneration 20 too
ussed the total variation disstance at genshhow convergence rate. The
he line of “T
Theory” is theeressult of theorettical calculatioons. The line of “OneMax””
is the result off experiments.. We can kno
ow that at theeme time the smallers
the tottal variation distanced
is, theesamfasster the conveergence rate iis. Therefore we can knoww
thee result of experimentse
cconsists with the result of
theeoretical calcculations. Thee consistent result is thaatmuutation rate is larger while cconvergence rate
r is faster.

.

.

.

Fig.
F 3. Hittingg distributionn and hitting distributiondafter conveergence
FigureF
3 show
ws the resultlts with N=100, l=20 andd
0.02. The left figure is hitting tim
me distributionn. The rightt figure is hittting time distribution afterrcoonvergence.

Fig. 1. C
Convergence of the first orrder schema
Figure 1 shhows the ressults with N==100, l=20 aand0.02. W
We used the matching tim
me tm=10. T
The
dotted line iss the approxim
mate curve obbtained by eq. ⑹.
We found thhat the conveergence behav
vior of the GGAcalculation iss well reproduced by eq.⑹.

.

Fig
g. 4.
at generatio
on
FigureF
4 show
ws the resultlts with N=100, l=20 andd
0.02. Wee calculated thhe results by eq.⑽ and eqq.
⑾.
⑾

Fig. 2. Coonvergence of the first ord
der schema att
generration 20
00 and l=20.W
We
Figure 2 shhows the resuults with N=10
© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 325 -

Yifei Du, Qinliann Ma, Kenji Aoki,, Makoto Sakamo
oto, Hiroshi Furu tani, Yu-an Zhang

FigureF
6 shows the results w
with N=100 an
nd l=20. Frommthee results we canc know thatt the larger muutation rate iss,thee slower hittin
ng time is.
5. Summary.

We
W studied thhe hitting tim
me of GA calcculation usingg
thee fitness function of OneeMax. We have separatedd
hittting time into
o convergencee time and hitting time afterrcoonvergence. We
W analyzed tthe effects of mutation rateeonn hitting time. In our previoous results, wee found that if
muutation rate goes to large, convergence time becomess
fasst. In the prresent experim
ments, if mu
utation rate iss
larrger, the hittting time affter convergeence becomesssloower. Thus, wew consider thhat there is a optimal valuee
off mutation ratee which can mmake the hitting time fastest..
Reeferences
1. H. Furutani, S. Katayama, M. Sakamoto, M. Ito (2007)),
Stochastic Analysis
Aof Schema Distrribution in a
Multiplicativee Landscape, Artificial Lifee and Robotics
11:101–104.
M Y. Zhang, M
M. Sakamoto anda H. Furutanni
2. Y. Du, Q. Ma,(2014), Runtiime Analysis off OneMax Prob
blem in Geneticc
Algorithm, Jo
ournal of Robootics, Networks and Artificiaal
Life, Vol. 1, No.
N 3.
3. H. Furutani, Y.
Y Zhang and M
M. Sakamoto (2009), Study of
the Distribu
ution of Optiimum Solutio
on in Geneticc
Algorithm byy Markov Chhains, IPSJ, Transactions
T
onn
Mathematicall Modeling andd its Applicationns, 2:54-63.
4. H. Furutani (2003), Schhema Analysiss of OneMaxx
Problem–Evo
olution Equationn for First Order Schemata. inn
Foundations of
o Genetic Algoorithms 7, Morrgan Kaufmannn,
San Franciscoo, 9–26.
hang, K. Koga,, K. Yamamorii, M. Sakamotoo
5. Q. Ma, Y. Zh
and H. Furuttani (2013), Sttochastic analy
ysis of OneMaxx
problem by using Markovv chain, Artifficial Life andd
Robotics 17: 395-399.
ns (2004), Mathhematical Population Geneticss.
6. J. W. J. Ewen
I.
Theorettical
Introduuction,
Seco
ond
Editionn.
Springer-Verllag, New York..
a M. Kimurra (1970), An Introduction too
7. J. F. Crow and
Population Genetics
G
Theory,, Harper and Ro
ow, New York.
2002), Stochastiic Models with Applications too
8. W. Y. Tan (2Genetics, Can
ncers, AIDS annd Other Biom
medical Systemss,
World Scientiific, Singapore..
E. L. Wilmer (2008), Markovv
9. D. A. Levin, Y. Peres and E
mes, American Mathematicaal
Chains and Mixing Tim
Society, Providence.

Fig. 5. H
Hitting distribbution after coonvergence
Figure 5 shhows the ressults with N==100, l=20 aand0.02. T
The line of “hiitting” is the distributiondis the result oof resetting geeneration wheen the populatitionachieves the convergence state. The lin
ne of “b^t” is theresult of eq.⒀⒀. The two resultsr
are rouughly consisteent.
So it means thhat the distribbution is almost determinedd by
one parameteer .

Fig. 6. Thee changes ofcaused byy mutation ratte© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 326 -An Authentication Method for Mobile Devices
that is Independent of Tap-Operation on a Touchscreen
1Hisaaki Yamaba, 1So Nagatomo, 2Kentaro Aburada, 1Shinichiro Kubota,
1Tetsuro Katayama, 3Mirang Park, 1Naonobu Okazaki

1University of Miyazaki, 1-1, Gakuen-kibanadai nishi, Miyazaki, 889-2192, Japan
2Oita National College of Technology, 1666, Maki, Oita, 870-015, Japan
3Kanagawa Institute of Technology, 1030, Shimo-ogino, Atsugi, Kanagawa, 243-0292, Japan

yamaba@cs.miyazaki-u.ac.jp, hf11036@student.cs.miyazaki-u.ac.jp, y.kita@ccy.kanagawa-it.ac.jp,
aburada@oita-ct.ac.jp, kubota@cs.miyazaki-u.ac.jp, kat@cs.miyazaki-u.ac.jp,
mirang@nw.kanagawa-it.ac.jp, oka@cs.miyazaki-u.ac.jp

Abstract
At the present time, mobile devices such as tablet-type PCs and smart phones have widely penetrated into our daily
lives. Therefore, an authentication method that prevents shoulder surfing is needed. We are investigating a new user
authentication method for mobile devices that uses surface electromyogram (s-EMG) signals, not screen touching.
The s-EMG signals, which are generated by the electrical activity of muscle fibers during contraction, are detected
over the skin surface. Muscle movement can be differentiated by analyzing the s-EMG. In this paper, a series ofexperiments was carried out to investigate the prospect of an authentication method using s-EMGs. Specifically,
several gestures of the wrist were introduced, and the s-EMGs generated for each motion pattern were measured.
We compared the s-EMG patterns generated by each subject with the patterns generated by other subjects. As aresult, it was found that each subject has similar patterns that are different from those of other subjects. Thus, sEMGs can be used to confirm one’s identification for authenticating passwords on touchscreen devices..
Keywords: mobile device, user authentication, shoulder surfing, electromyogram.

such as passwords. Authentication operations on mobile
devices are performed in many public places, so wehave to ensure that no one can view our passwords.
However, many mobile devices have no keyboards, so
the authentication method must use a touchscreen.
When using a touchscreen, the owner of the mobile
device inputs his or her authentication information
through simple or multi-touch gestures. These gestures
include, for example, designating his/her passcode fromdisplayed numbers, selecting registered pictures or icons

1. Introduction
This paper proposes a new user authentication method
for mobile devices by using surface electromyogram (sEMG) signals, not screen touching.
At the present time, mobile devices such as tablet
type PCs and smart phones have widely penetrated into
our daily lives. Therefore, an authentication method thatprevents shoulder surfing is needed. Shoulder surfing isthe direct observation of a user’s personal information,© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 327 -

Hisaaki Yamaba, So Nagatomo, Kentaro Aburada, Shinichiro Kubota,Tetsuro Katayama, Mirang Park, Naonobu Okazaki

from a set, or tracing a registered one-stroke sketch on
the screen. People positioned close to the mobile device
owner can easily grasp these actions and obtain theuser’s authentication information.
The s-EMG signals, which are generated by theelectrical activity of muscle fibers during contraction,
are detected over the skin surface. These s-EMGs havebeen used to control various devices, including artificial
limbs and electrical wheelchairs. Muscle movement canbe differentiated by analyzing the s-EMG1. For example,
fast Fourier transform (FFT) can be adopted for the
analysis. Feature extraction is carried out through theanalysis of the s-EMGs. The extracted features are used
to differentiate the muscle movement, including hand
gestures.
In this paper, a series of experiments was carriedout to investigate the prospect of realizing anauthentication method using s-EMGs. Specifically,
several gestures of the wrist were introduced, and the sEMG signals generated for each motion pattern were
measured. We compared the s-EMG signal patterns
generated by each subject with the patterns generated byother subjects. As a result, it was found that the patternsof each individual subject are similar but they differ
from those of other subjects. Thus, s-EMGs can confirm
one’s identification for authenticating passwords ontouchscreen devices.
2. Characteristics of authentication method formobile devices
User authentication of mobile devices has two
characteristics.
One is that an authentication operation is
performed when a user wants to start using their mobile
devices. The authentication often takes place aroundstrangers. Therefore, the strangers around the user canpossibly see the user’s unlock actions. Some of thesestrangers may scheme to steal information such as
passwords for authentication.
The other characteristic is that much user
authentication of mobile devices is now performed on atouchscreen. Many current mobile devices do not have
hardware keyboards, and so it is not easy to input long
strings into such mobile devices. When users unlock
mobile touchscreen devices, they input passwords or

personal identification numbers (PINs) by tapping
numbers or characters displayed on the touchscreen. In
many cases, the user moves only one finger. Since users
have to look at their touchscreens while unlocking theirdevices, strangers around them can easily see the unlock
actions, and so it becomes very easy for thieves to steal
passwords or PINs.
To prevent shoulder-surfing attacks, many studies
have been conducted. The secret tap method introducesa shift value to avoid revealing pass-icons2. The user
may tap other icons in the shift position on thetouchscreen, as indicated by a shift value, to unlock thedevice. By keeping the shift value secret, people aroundthe user cannot know the pass-icons, although they canstill watch the tapping operation. The rhythm
authentication method relieves the user from looking at
the touchscreen when unlocking the device3. In thismethod, the user taps the rhythm of his or her favorite
music on the touchscreen. The pattern of tapping is usedas the password. In this situation, the users can unlock
their devices while keeping them in their pockets orbags, and the people around them cannot see the tap
operations that contain the authentication information.
3. Surface electromyogram signals
The s-EMG signals are detected over the skin surface
and are generated by the electrical activity of muscle
fibers during contraction. Muscle movement can bedifferentiated by analyzing the s-EMG. Usually, FFT isadopted for the analysis, and feature extraction iscarried out through analysis of the s-EMG.
However, since measured s-EMG signals vary by
subject, the extracted features do not show enoughperformance to correctly differentiate the muscle
movement in multiple subjects. Therefore, researchers
have explored other methods to improve theperformance of feature extraction. Since some methods
demonstrate good performance for some subjects but
other methods show better performance for othersubjects, a feature that can be used to distinguishgestures for everyone is desired. For example, a methodthat uses the maximum value and the minimum value ofraw s-EMG signals was proposed4.
4.

User authentication using s-EMG© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 328 -An Authentication Method foor

Figure 2 Gestures
G
used in the experiiments
Figgure 1 Experiimental Systeem
In this sectioon, the methood of user auuthentication byusing s-EMG
Gs, which do not requirre looking att a
touchscreen, is presented.
The s-E
EMG signals area measured, and the featuuresof the measurred raw signaals are extractted. We estim
mategestures of a user of a mobbile device froom the extraccted
features. Neext, combinaations of the gestures areconverted innto a code for authen
ntication. Thhese
combinationss are inputtedd into the moobile device aandused as a passsword for userr authenticatioon.
Adoptinng s-EMGs for authentication of mobbile
devices has two advantagges. First, thee user does nnot
have to look aat his/her deviice. Since the user can makke a
gesture that iis used as a passwordp
on a device insidde a
pocket or in a bag, it is exxpected that th
he authenticati
tion
information ccan be conceealed. No on
ne can see w
whatgesture is m
made. In adddition, it is expectedethatt ifanother persoon reproduces a sequence ofo gestures thaat auser has madde, the authenttication will not be successfful,because the eextracted feattures from thee s-EMG signnals
are usually noot the same beetween two peeople.
5. Experimeents
A series of eexperiments was
w carried ou
ut to investiggate
the prospect oof the authenttication metho
od using s-EM
MGs.

Sp
pecifically, wee investigatedd whether thee measured sEM
MG signals of one experim
mental subjectt were similarr
annd whether the signals oof different subjectss
weree
diffferent from eacheother.
Figure 1 shows the exxperimental syystem used inn
theese experimen
nts. An electroomyograph measuredmthe sEM
MG of each movement ppattern with two electrodeesennsors. The meeasured data w
were stored an
nd analyzed onn
a PC. Ten heaalthy personss whose agess were in theetwwenties (studdents of U
University ofo Miyazaki))paarticipated as experimental
al subjects (SSubjects A–J)).
Th
he six hand gesturesg(1–6)) shown in FigureF
2 wereeinttroduced in th
he experimentts. First, eachh subject madee
eaach gesture ten times in suuccession andd their s-EMG
G
sig
gnals were reecorded (Expp. #1). Approoximately oneeweeek later, the same experim
ment was carrried out againn(EExp. #2).
Some of the results arre shown in FigureF
3. Theeraw
w s-EMG sig
gnals of gesturure 1 made byy experimentaalsuubjects A and B are displayyed. The signaals of Exp. #11
an
nd #2 are simiilar for each ssubject. On th
he other handd,
thee signals of su
ubjects A andd B are not siimilar to thosee
off the other sub
bject. The samme results werre obtained forrev
very pair of subjects and all gestures.
These ressults show thaat the s-EMG
G is promisingg
as identificationn input for a new user authenticationn
meethod.

© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 329 -

Hisaaki Yamabaa, So Nagatomo, Kentaro
K
Aburada, Shinichiro Kuboota,Tetsuro Katayyama, Mirang Pa
ark, Naonobu Oka
kazakiFigure 3 O
Obtained s-EM
MG signals
6. Conclusioon

Accknowledgemments

We investigaated a new user authenticattion method tthat
can prevent sshoulder-surfiing attacks in mobile devicces.
We know thhat the unloocking operattions of mobbile
devices suchh as tablet-typpe PCs or sm
mart phones ccan
often be view
wed by other people. Userrs can hide suuch
operations froom others by notn looking att the touchscreeenof a mobile ddevice while unlocking it. To realize suuch
an authenticaation method, we assigned a set of gestuures
to obtain thee s-EMG signnals. A seriess of experimeentswas carried oout to confirm
m that the s-E
EMG signalss ofeach person aare similar forr that person anda the signalss ofdifferent peopple are not sim
milar to other people’spsignaals.
These results showed that s-EMG signalls can be usedd as
input for a nnew user authhentication method.
m
We w
will
conduct a quuantitative evvaluation of thistapproachh infuture work.

Usseful advice anda the experriment systemm were offereddbyy Prof. K. Tanno,T
Prof. H. Tamura and Mr. K.
K
Naagatomo of Un
niversity of M
Miyazaki.
Reeferences
1. H. Tamura, et al., A study oon motion reco
ognition withouut
FFT from su
urface-EMG, IE
EICE-part D, J9
90-D(9), (2007)
2652-2655.
mentation and evaluation of
2. Y. Kita, et al., Implem
shoulder-surrfing attack reesistant users (In Japanese)),IEICE-part D,
D J97-D(12), (22014) 1770-178
84.
Rhythm Authen
ntication and its
3. Y. Kita, et al., A Study of R
Accuracy ussing the Self-Orrganizing Maps (In Japanese)),Proceedings of DICOMO 22014, (2014) 10
011-1018.
4. H. Tamura, et al., A sttudy on the s-EMG patternn
recognition using neural nnetwork, IJICIC
C, 5(12) (2009)4877-4884.

© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 330 -

Proposal of Security Evaluation System using User's Reviews and Permissions
for Android Applications
Naonobu Okazaki

University of Miyazaki,
1-1 Gakuen-Kibanadai-Nishi, Miyazaki, 889-2192, Japan
Yoshihiro Kita

Kanagawa Institute of Technology,
1030 Shimo-Ogino, Atsugi, 243-0292, Japan
Kentaro Aburada

Oita National College of Technology,
1666 Ooaza-Maki, Oita, 870-0152, Japan
Mirang Park

Kanagawa Institute of Technology,
1030 Shimo-Ogino, Atsugi, 243-0292, Japan
E-mail: oka@cs.miyazaki-u.ac.jp, y.kita@ccy.kanagawa-it.ac.jp,
aburada@oita-ct.ac.jp, mirang@nw.kanagawa-it.ac.jp

Abstract
Leakage of the personal information in Android OS powered device by mal-applications is becoming the heavy
matter. The Android OS users must be careful not to install mal-applications. The reviews and the using
permissions of applications are useful by users to detect mal-application. However, the most of users read thereviews only. All users must be cautious about not only the using permissions but also the combination of them. In
this paper, we propose the security evaluation system to prevent the installation of mal-applications on Android OS.
This system indicates the user reviews with the using permission information of application to new users.
Keywords: Security for applications, Mal-applications, User’s reviews, Permissions, Android OS.

not read the permissions, because it is need to consider
combination of permissions, and have specialized
Recently, leakage of the personal information in
knowledge for permissions.
Android OS powered device by mal-applications is
The most of the users get the information of
becoming heavy matter. Google implements malapplication from user reviews. A part of exist reviews isapplication detection system “Bouncer” for Google
useless review that is the malicious review or theplay[1]. However, mal-applications are not eliminatedunrelated review to contents of the application. There iscompletely, even how to defeat Bouncer legally has
the problem that it is difficult to determine the malbeen found also.
application by users using the existing user reviews.
Google Play provides the using permissions ofIn this paper, we propose the security evaluation
application to users when downloads there. The users
system using user reviews for Android OS. This system© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan

1. Introduction- 331 -

Naonobu Okazakki, Yoshihiro Kitaa, Kentaro Aburada, Mirang Parkk

shows the uuser reviews with the ussing permissiion
information of applicatioon to new users.
u
New uuser
evaluates thhe one of reviews,rand determines todownload oor not downnload the ap
pplication. T
Theevaluation off the review iss transmitted tot reviewer. T
Thereviewer refeers to the evvaluation of reviewrfor nnext
review.
2. Precedingg Studies
Reviews aare useful as guidelines fo
or new users to
download thhe applicatioon. Howeverr, if the mmalapplication developer writesw
the review
rof theapplication inn order to beb installed itt by users, tthis
review is exaaggerated reviiew or fake reeview. The oththerusers trust thhese review, and are suffeered damage by
mal-applications.
It is impossible to removeofr
the vulnerabilityvapplications completely, but guideliines of secuure
applications for Androoid developeers (i.e. iSE
ECPartner[2]) aare issued by various orrganizations forsecurity inforrmation.
Existing reeview system
m (i.e. Googlee play[1]) shhow
reviews of usser and using permissionsptoo new users. IIt is
possible to prredict the riskk of applicatioon from revieews
and permissioons, because theset
are indiccate the behavvior
of applicationn. But, users canc not underrstand the riskk ofapplication aadequately, beecause these are not indiccate
the risk of application in combinattion with oththerpermissions, and it is difficult to understand the
behavior of aapplication byy users who doo not understaand
the expertisee of securitty and functtion of mobbile
terminals.
Matsudo[3] proposed seecurity adviso
or system, whhichindicates the risk of application by thee combinationn ofpermissions. This systemm shows the number ofdownload andd the risk leveel of applicatio
on to new userrs.
However, thhese values are not objective and ffair
measure, beccause other users can manipulatemthhesevalues intenntionally, andd new userss do not haave
information enough to determine the applicati
tion
downloading by these vaalues only. It needs for nnew
users to show
w the user's revviews of appllications in orrder
to get the infoormation of appplications.

Fig. 1. Structure of securrity evaluation system.

3. Security Ev
valuation Systtem
We proposee security eevaluation syystem, whichh
dicate the risk
k of applicatioon by the reviews and usingg
indpeermissions. Th
his system inddicates the rev
views and theeevvaluation of thhem. Fig.1 shoows the structu
ure of securityy
evvaluation systeem. This systeem consists off three parts ass
follows:
 Reviews Database
D
This is sto
orage of reviiews and the evaluation ofreviews, whichw
are sent from all userss.
 Mal-Appliication Databaase
This is thetstorage of permissions and thee
information of mal-appplications (i.e.. application’ss
name, verssion, developeer).
 Applicatio
on Manager
This is thee application w
which indicatees the reviewss
and the rissk of applicatitions by the combination of
permission
ns to terminaal user, and transmits theeuser's revieew to review ddatabase.

© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 332 -

Proposal of Security Evaluation

Table 1. Risk allowances of applications.
SafetyDoes the application have the NO
one or more dangerous
permission?
Does the application have the NO
dangerous combination of
permissions?
Is the application reported as NO
mal-application?

Table 2. The permissions concern the personal
information or the information leak.

Caution Danger
YES

YES

NO

YES

NO

YES/NO

The procedure of this system is described as follows:
1. The user which is using the application sends a
review to review database through the user's
application manager.
2. New user requests the download of the application
to the application market.
3. The application market sends the information of
the application (i.e. name, developer, request user'sname) to reviews database and mal-application
database.
4. Reviews database shows the all review and theevaluation of them for the application to new user
through the new user's application manager.
5. Mal-application database shows the information ofusing permissions and the information of malapplication to new user.
6. New user gives the evaluation “Good” or “Bad”
for the one of the reviews, and reports theevaluation of the review to reviews databasethrough the application manager.
7. Reviews database stores the evaluation of review,
and permit the download of the application toapplication market.
8. New user downloads the application to application
market through the application manager.
9. Reviews database reports the evaluation of thereview to existing user as reviewer.
Reviews have two types: positive reviews and negative
reviews. Positive reviews include selling points or good
features for the application. Negative reviews include
wrong points or problems of the application. The

Permissions concern personal
information

Permissions concern information
leak

READ_CONTACTSWRITE_CONTACTS
READ_CALENDAR
WRITE_CALENDAR
READ_LOGSBIND_APPWIDGET
READ_PROFILEWRITE_PROFILEACCESS_FINE_LOCATIONACCESS_COARSE_LOCATIONACCESS_MOCK_LOCATIONGET_ACCOUNTREAD_EXTRNAL_STORAGE
WRITE_EXTRNAL_STORAGE

INTERNETSEND_SMS
BLUETOOTH
NFC
USE_SIPCHANGE_NETWORK_STATEBLUETOOTH_ADMIN

reviews are evaluated not only new users but also
existing user instead of writing the review.
The permissions have the four protection levels[4]:“normal,”“dangerous,”“signature,”
and“signatureOfSystem.” Table.1 shows the risk
allowances of applications that we defined. We divide
the risk of applications into three levels as follows
according to the protection levels and the combinationof permissions.
 Safety
All permissions use the protection level “normal”
only.
 Caution
Permissions use the protection level “dangerous,”“signature,” or “signatureOfSystem.” Danger
The application is permitted the functions which
include both connecting internets and accessing thepersonal information, plus the condition of“Caution.”
Table.2 shows the permissions concern the personal
information or the information leak. The applications
include these permissions are allocated the risk ofapplications “Danger.”Fig.2 shows the example of indication for risk
allowances of applications. The application manager
indicates the risk of the application using the kinds of© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 333 -

Naonobu Okazakki, Yoshihiro Kitaa, Kentaro Aburada, Mirang Parkk

Tablee 3. Indicationn by each metthod.

Risk of permissionsn ofRisk of combination
g permissions
using
Revieews

Google pplay[1] Security
advisoorysystemm[3]Indicate
Indicatte
No Indiccate
Indicatte
Indicate

Securityy
evaluatiion
systemIndicatee
Indicatee

No Inddicate Indicatee

usseful method forf new user to prevent insttallation of theemaal-application.
However,H
all ofo the dangeroous applicatio
ons are not theemaal-applications. It is difficuult for users too recognize theemaal-applications from all of the dangerou
us applicationssev
ven if they usee the security evaluation syystem. It needss
to improve secu
urity evaluatioon system to recognize theemaal-application more exactlyy.
5. ConclusionWe
W proposed security evaaluation system using userr
rev
views for And
droid OS in oorder to preveent installationn
off the mal-ap
pplication. Thhis system indicates theerevviews, the evaluation off review, andd the risk ofcoombination off using permisssions. The reeviewer referssthee evaluation of the review
wer’s review, and will bee
wrritten the useeful review nnext time. Th
herefore, neww
ussers can reco
ognize the mmal-applications by usefuul
revviews and th
he indication of risk for installing theeappplications.

mple of indicattion for risk alllowances ofFig. 2. Examapplications.
three colors ffor each risk and descriptio
ons of the us ing
permissions. The new userrs can recognizze the dangeroous
applications eeasily by thesee indications.
4. Considerration
New users ccan understandd the behavio
or of applicatioons
in detail by tthe reviews. It
I is importantt for all userss tounderstand thhe risk of appplication correectly. It needss toshow the revviews and the risk of comb
bination of usiing
permissions ffor new userss to determinee the applicati
tion
downloading. Table.3 shows the indication of eaach
method. Gooogle play dooes not indiccate the risk of
combination of using permissions. Seecurity advisoory
system does not show thetreviews ofo users. Thhese
methods are insufficient tot prevent the installation of
mal-applications. Securityy evaluation systemsindicaates
the reviews and the riskk on combin
nation of us ing
permission. Therefore, seecurity evaluation systemm is

Reeferences[11] Google play, Google. http:///play.google.com/store[22] J. Burns, DevelopingD
seecure mobile application for
android, iSEC Partners (20008).
[33] T. Matsudo, E. Kodama, J. Wang, andd T. Takata, A
proposal of security advisoory system at the time of theeo applications on Android OS
S, in Proc. 15thh
installation ofInt. Conf. Network-BasedN
d Information Systems
S(NBiS)
S)(2012), pp.261-267,[44] B. M. A. Eg
gner and U. M
Meyer, Messing with android’s
model, in Proc.
P
the 2012 IEEE 11th In
nt. Conf. Trustt,
Security andd Privacy in Coomputing and CommunicationsCs(2012), pp.504-514.

© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 334 -

Evaluation of Neighbors Based Routing for ad hoc networks
Kentaro Aburada

Oita National College of Technology, Japan
Hisaaki Yamaba, Shinichiro Kubota, Tetsuro Katayama

University of Miyazaki, Japan
Mirang Park

Kanagawa Institute of Technology, Japan
Naonobu Okazaki

University of Miyazaki, Japan

Abstract
In ad hoc networks, due to the mobility of nodes, communication links are unstable and restricted. As such, anefficient routing protocol is needed in order to solve these problems. In the present paper, we propose a neighbors
based routing (NBR) protocol by constructing paths in an area in which a large number of nodes exists.
Keywords: Ad hoc networks, Routing Protocol, Route Repair, AODV-BR

problems such as route disconnection and a decrease in
the packet reception ratio (PRR).
In MANET, various routing protocols are actively
being investigated [2]–[5]. It is important to reduce therisk of route disconnections, for example due toincreases in power consumption and waiting time, in
wireless routing.
Accordingly, methods for repairing disconnected linksin[2] and avoiding an interference region in [4] [5] havebeen proposed. As an expansion of the ad hoc ondemand distance vector (AODV) [3]), which is the
typical routing protocol used in MANET, the AODVBR (AODV with backup routes) was proposed to repair
disconnected links

1. Introduction
In recent years, mobile ad hoc networks (MANET)
have attracted attention because of the development andpopularization of wireless communication technology[1].
Mobile ad hoc networks can be constructed by mobile
nodes without a fixed infrastructure. These nodes cansend packets directly to each other by wirelesscommunication. Moreover, a node can communicate
with other distant nodes, which are outside radio range,
by relaying packets. Therefore, MANET is expected to
be used as communication tool in emergency situations,for example, at disaster sites, and for data-gathering
using sensors. However, due to the mobility of nodes
and the limitation of battery capacity, MANET has© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 335 -

Kentaro Aburada, Hisaaki Yamaba, Shinichiro Kubota, Tetsuro Katayama, Mirang Park, Naonobu Okazaki[2]. In the AODV-BR, nodes around the constructedroute have alternate paths to other nodes on the route,which are used in order to repair disconnected links.
When the AODV-BR detects a disconnected link, thenode at the link broadcasts data packets. Thus, nodes
that receive these packets can restart communication
using an alternate path. A method of using a node as
position information by GPS as a means of routing
instead of flooding was proposed in previous studies [4]and [5]. In [4], PRR x Distance was proposed in order toavoid the interference region based on the packet
reception rate (PRR) and the distance to the destination.
In addition, in [5], interference-aware energy efficient
geographical (IEG) routing was proposed based on the
power efficiency of each link. PRR x Distance measures
the PRR by packet exchange with neighbor nodes.
However, the effectiveness of the PRR decays due tofrequent node mobility. Thus, each node must measure
PRR repeatedly. Consequently, PRR x Distance cannot
immediately adapt to changes in a network. In the IEG,
a route that has high power efficiency is constructed bymeans of measuring the radio field strength instead of
the PRR. Therefore, links on the route are shorter thanPRR x Distance and, as a result of node mobility, aredifficult to disconnect.

Fig. 1. Problems of existing protocols.

switches over to the AODV and resumes
communication. However, the AODV has problems
with packet loss and power consumption because
packets are sent to nodes to which the IEG has alreadysent an RREQ.
3. Proposed Method
In the present paper, we propose an NBR protocol thatcan easily repair disconnected links by constructing aroute based on a number of neighbor nodes andalternative paths.
First, the source node floods RREQs, and each nodeconfirms the state of links with neighbor nodes. The
destination node then sends back an RREP uponreceiving an RREQ. The RREP is then transmitted tothe source node while constructing routes andalternative paths. The source node can receive routeinformation but communicates using the route that has
the largest number of neighbor nodes on these routes. In
addition, each node can use its route information as analternative path to repair a disconnected link.
Consequently, the NBR can easily repair the
disconnected link. Moreover, the NBR introduces theconcept of the extension variable in order to increase thenumber of alternative paths and broaden the route
construction area.

2. Problems with Existing Protocols

In the AODV-BR, a route that does not consider
neighbor nodes is constructed. Hence, the number ofalternate paths is reduced and it is difficult to repair
disconnected links. The AODV-BR cannot repair theroute flexibly because the disconnected of the alternate
path is not considered. In addition, although a route is
not constructed in the interference region the route canbe constructed around the interference region (Fig. 1).
In this case, some of the nodes around the route are in
the interference region and may be not available forrepairing the route.
In the IEG, each node requires a device that canmeasure the radio field strength. In addition, since
3.1. Route Evaluation Formula
position information is used to choose the next node, theIEG ceases routing in topologies such as that shown in
In the NBR, alternative paths are recorded in the routeFig. 1. In Fig.1, as a next hop, node N must choose atable of each node around the route as the AODV-BR.
neighbor node that is closer to the destination. These
In addition, in the NBR, each node on the route records
topologies frequently appear upon expansion of thethe paths in the same manner. These paths are theinterference region. Once the routing ceases, the IEG
reutilization of received route information. Hence, a© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 336 -

Evaluation of Neighbors Basedsignificant amount of route information may lead toeasier repair. In the NBR, a node that has several
neighbor nodes can receive a great deal of routeinformation. Therefore, the evaluation value E of theroute can be derived as follows:ܰ‫݉ݑݏ‬
ࡱൌｈwhere Nsum denotes the sum of the neighbor nodes ofeach node on a route, and h denotes the number ofintermediate nodes between the destination and the nodethat has received the route information. The source nodedecides the route that has the highest E using Eq anduses this route for communication. In addition, eachnode
can reduce traffic by relaying only routeinformation that has a higher E.
Packet Delivery Ratio [%]Fig. 2. Example of NBR (k = 2).

3.2. Extension Value
The NBR obtains the hop count from the source node
by receiving an RREQ. Each node constructs a route bysending an RREP to a node that has a lower hop count.
Thus, an RREP does not reply to a node that has a highhop count. As a result, the NBR cannot construct a routethat has several alternative paths, and cannot sufficiently
respond to disconnected link. Therefore, the NBR
introduces the concept of Extension Variable fortransmitting an RREP to a node hat has a high hopcount.
In the present paper, k denotes the value of Extension
Variable. Here, k, which is configured in advance, refers
to the number of times that a node can rule out the limitof hop count in the transfer condition of an RREP. The
NBR can increase the number of alternative paths andbroaden the route construction area.
Fig.2 shows an example of a route with k = 2. The routehas 20 neighbor nodes and contains five intermediate
nodes. Therefore, the evaluation value of the route is 4.
When k = 0, the route is constructed by under route(Fig.2).
4.

Packet Delivery Ratio
100
98
96
94

NBR(0 m/sec)
AODV-BR(0 m/sec)
NBR(5 m/sec)
AODV-BR(5 m/sec)

92
90
0

10

20
30
40
Time [sec]50

60

Fig. 3. Packet delivery ratio.

In the simulation, we compare the results for a topology
with a moving node to the results for a topology without
a moving node. We evaluate the wait time until route
construction, the packet delivery ratio, and the control
overhead.
4.1. Simulation Environment
The proposed simulation modeled a network of 100
mobile nodes placed randomly within a 1,000 meter x
1,000 meter area. Two of the nodes were placed at (100,100) and (900, 900) as a source node and a destinationnode, respectively. We used IEEE 802.11 as themedium access control protocol. The radio propagation
range for each node was 250 meters, and the channel
capacity was 2Mbps. During the simulations, each node

Evaluation

In order to evaluate the performance of the proposed
method NBR, we compare the simulation results of
NBR with the traditional AODV-BR method. The
simulation measured the packet delivery ratio and thecontrol overhead using Network Simulator-2 (NS-2) [6].

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 337 -

Kentaro Aburada, Hisaaki Yamaba, Shinichiro Kubota, Tetsuro Katayama, Mirang Park, Naonobu Okazaki

moves according to the random waypoint model at a
speed of up to 5 m/s.
The source node transmits data packets to thedestination at a rate of five packets per second. The sizeof the data payload is 1,024 bytes. Each run is executedfor a simulation time of 60 seconds. These parameteruses [2] as a reference.

Traffic
Traffic [kbytes]

150

4.2. Packet delivery ratio
The packet delivery ratios with NBR and the AODVBR are shown in Fig. 3. Note that both NBR and the
AODV-BR have high packet delivery ratios in
topologies without node mobility. However, in
topologies with moving nodes, the packet delivery ratioof the AODV-BR decreases more than that of NBR.
Since NBR constructs several alternate paths, NBR can
repair broken links due to moving nodes.
On the other hand, the AODV-BR cannot repair suchbroken links because the AODV-BR has relatively few
alternate paths. Therefore, the individual nodes cannot
forward data packets to the destination, and the packet
delivery ratio of the AODV-BR decreases in topologies
with moving nodes.

120
90

NBR(0 m/sec)
AODV-BR(0 m/sec)
NBR(5 m/sec)
AODV-BR(5 m/sec)

60
30
0
0

10

20
30
40
Time [sec]50

60

Fig. 4. The control overhead.

variable k to increase the number of alternative paths
and broaden the route construction area, NBR can easily
repair disconnected links using alternative paths. In
order to evaluate NBR, we compare the packet delivery
ratio with the control overhead of NBR and the
traditional AODV-BR method using NS-2. The results
indicate that
NBR maintains a high packet delivery ratio in
topologies with moving nodes, and the present
simulations confirmed that the proposed protocol has ahigher connectivity and a lower control overhead thanexisting protocols in topologies with moving nodes.

4.3. Control overhead
Fig.4 shows the control overhead with NBR and the
AODV-BR. Both NBR and the AODV-BR have slightly
increased control overheads in topologies without
moving nodes. However, in topologies with moving
nodes, the control overhead of the AODV-BR increases
significantly to approximately 120 kbytes. The control
overhead of NBR increased but eventually settled at
approximately 30 kbytes. The frequency of routereconstruction by the AODV-BR is higher than that for
NBR for all simulations. On average, the routereconstruction frequency is 1.2 times per simulation for
NBR and 10.2 times per simulation for the AODV-BR.
This indicates that the control overhead of the AODVBR was increased due to the numerous routereconstructions performed by the AODV-BR.

References[1] C-K.Toh.: “Ad Hoc Mobile Wireless Networks:
Protocols ans Systems”,Prentice Hall (2001).
[2] S-J, Lee. and M, Gerla.: “AODV-BR: Backup
Routing in Ad hoc networks”, Proc. of IEEE WirelessCommunications and Networking Conference, Vol. 3,
pp. 1311-1511 (2000).
[3] C, Perkins., E, Belding-Royer. and S, Das.: “Ad hoc
on-demand distance vector (AODV) routing”,
http://www.ietf.org/rfc/rfc3561.txt (2003).
[4] K, Seada., M, Zuniga., A, Helmy. and B,
Krishnamacharim.:”Energy-efficientforwarding
strategies for geographic routing in lossy wirelesssensor networks”, Proc. ACM SenSys, pp. 108-121
5. Conclusion(2004).
We proposed neighbors-based routing (NBR), which[5] J, Kim. and Y, Kwon.: “Interference-Aware Energyconstructs alternative paths and routes based on the
Efficient Geographical Routing for IEEE 802.15.4a
number of neighbor nodes. Since NBR uses extension
Networks”,
IEICE
TRANSACTIONSon© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 338 -

Evaluation of Neighbors Based

Communications, Vol. E93-B, No. 4, pp. 1024-1028(2010).
[6] http://www.isi.edu/nsnam/ns.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 339 -

Sufficient spaces for seven-way four-dimensional Turing machines
to simulate four-dimensional one-marker automata
Makoto Nagatomo, Makoto Sakamoto, Hikaru Susaki, Tuo Zhang, Satoshi Ikeda and Hiroshi Furutani

Faculty of Engineering, University of Miyazaki, 1-1 Gakuen Kibanadai Nishi
Miyazaki, Miyazaki 889-2192, Japan
E-mail: sakamoto@cs.miyazaki-u.ac.jp
Takao Ito

Institute of Engineering, Hiroshima University, 4-1, Kagamiyama1-chome
Higashi-Hiroshima, Hiroshima 739-8527, Japan
E-mail: itotakao@horoshima-u.ac.jp
Yasuo Uchida

Department of Business Administration, Ube National College of Technology, Tokiwadai
Ube, Yamaguchi 755-8555, Japan
E-mail:uchida@ube-k.ac.jp
Tsunehiro Yoshinaga

Department of Computer Science & Electronic Engineering,
National Institute of Technology, Tokuyama College, Gakuendai
Shunan, Yamaguchi 745-8585, Japan
E-mail:yosinaga@tokuyama.ac.jp

Abstract
A multi-marker automaton is a finite automaton which keeps marks as ‘pebbles’ in the finite control, and cannot rewrite any input
symbols but can make marks on its input with the restriction that only a bounded number of these marks can exist at any given time.
An improvement of picture recognizability of the finite automaton is the reason why the marker automaton was introduced. That is, aone-marker automaton can recognize connected picture. This automaton has been investigated in the two- or three-dimensional case.
As is well known among the researchers of automata theory, one-marker automata are equivalent to ordinary finite state automata. In
other words, there is no working space usage such as Turing machines to calculate the space complexities. In this paper, we deal with
four-dimensional one-marker automata in terms of the space complexities that seven-way four-dimensional Turing machines, which
can move east, west, south, north, up, down, or in the future, but not in the past on four-dimensional rectangular input tapes, suffice tosimulate one-marker automata.
Keywords: computational complexity, finite automaton, marker, simulation, Turing machine, upper bounds.

introduced. That is, a two-dimensional one-marker
automaton can recognize connected pictures. This
automaton has been widely investigated in the two- or
three-dimensional case [2].
As is well known among the researchers of automata
theory, one-dimensional one-marker automata areequivalent to ordinary finite state automata. In otherwords, there is no need of working space usage for1. Introduction
A multi-marker automaton is a finite automaton
which keeps marks as ‘pebbles’ in the finite control, andcannot rewrite any input symbols but can make marks
on its input with the restriction that only a bounded
number of these marks can exist at any given time[1].
An improvement of picture recognizability of the finite
automaton is the reason why the marker automaton was© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 340 -

M. Nagatomo, M. Sakamoto, H. Susaki, T. Zhang, S. Ikeda, H. Furutani, T. Ito, Y. Uchida and T. Yoshinaga

position. If M has already put down the mark, and wants
to put it down elsewhere, M must first go to the positionof the mark and pick it up. Formally, we define it asfollows.
Definition 2.1. A four-dimensional nondeterministic
one-marker automaton (4-NMA1) is defined by the
6-tuple M = (Q, q0, F, Σ, { +, - }, δ), where(1) Q is a finite set of states ;(2) q0 ∈ Q is the initial state ;(3) F ⊆ Q is the set of accepting states ;(4) Σ is a finite input alphabet (# ∉ Σ is the boundary
symbol);(5) {+,－} is the pair of signs of presence and absenceof the marker ; and(6) δ : (, )((Σ ∪ # ), ) ↦,2(( Σ ∪ # ),)east, west,
south, north, up, down, future, past, no moveisthe next-move function, satisfying the following : For
any q, q’ ∈ Q, any a, a’ ∈ Σ, any u, u’, v, v’ ∈ , ,and any d ∈ east, west, south, north, up, down,
future, past, no move , if ((q’, u’), (a’, v’),d) ∈ ((q,v),(a,v)) then
and
, , ,
∈
, , , ,
, , , ,
, , , , , , , , , , ,.
We call a pair,in,an extended
state, representing the situation that M holds or does nothold the marker in the finite control according to thesignor, respectively. A pair ,inΣ,represents an input tape cell on which themarker exists or does not exist according to the signor
, respectively.
Therefore, the restrictions on
imply the following
conditions. (i) When holding the marker, M can put it
down or keep on holding. (ii) When not holding themarker, and ① if the marker exists on the current cell,
M can pick it up or leave it there, or ② if the marker
does not exist on the current cell, M cannot create a newmarker any more.
Definition 2.2. Let Σ be the input alphabet of 4-NMA1
M. An extended input tapeof M is any
four-dimensional tape over Σ,
such that forsome ∈ Σ ,(i) for each 1
4 ,,(ii) for each
1
1
,
,
1
1
, ,
, and
,
, =
, , , , for some ∈ , .

one-way Turing machines to simulate one-marker
automata, as well as finite state automata.
In the two-dimensional case, the following facts are
known : the necessary and sufficient space forthree-way two-dimensional deterministic Turingmachines TR2-DTM’s to simulate two-dimensional
deterministic (nondeterministic) finite automata
2-DFA’s (2-NFA’s) is mlogm (m2) and thecorresponding space for three-way two-dimensional
nondeterministic Turing machines TR2-NTM’s is m (m),whereas the necessary and sufficient space forthree-way two-dimensional deterministic Turingmachines TR2-DTM’s to simulate two-dimensional
deterministic (nondeterministic) one-marker automata
2-DMA1’s (2-NMA1’s) is 2mlogm ( 2) and thecorresponding space for TR2-NTM’s is mlogm (m2),where m is the number of columns of two-dimensional
rectangular input tapes.
In the three-dimensional case, the following facts are
known : the necessary and sufficient space for five-way
three-dimensional deterministic Turing machines
FV3-DTM’s to simulate three-dimensional deterministic(nondeterministic) finite automata 3-DFA’s (3-NFA’s)
is m2logm (m3) and the corresponding space forfive-way three-dimensional nondeterministic Turingmachines FV3-NTM’s is m2 (m2), whereas the necessary
and sufficient space for five-way three-dimensional
deterministic Turing machines FV3-DTM’s to simulate
three-dimensional deterministic (nondeterministic)
one-marker automata 3-DMA1’s (3-NMA1’s) is 2lmloglm(2) and the corresponding space for FV3-NTM’s islmloglm (l2m2), where l(m) is the number of rows
(columns) on each plane of three-dimensional
rectangular input tapes.
In this paper, we deal with four-dimensional
one-marker automata in terms of the space complexities
that seven-way four-dimensional Turing machines
suffice to simulate four-dimensional one-marker
automata.
2. Preliminaries

An ordinary finite automaton cannot rewrite any
symbols on input tape, but a marker automaton canmake a mark on the input tape. We can think of themark as a ‘pebble’ that M puts down in a specified© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 341 -

Sufficient spaces for seven-way

Definition 2.3. A configuration of 4-NMA1 M = (Q, q0,
F, Σ, { +, - }, δ) is a pair of an element of Σ ∪ #and an element of∪ 0,,. The first component of a configuration, , , , ,represents the extended,
input tape of M. The second component, , ,ofrepresents the input head position. The third
component ,ofrepresents the extended state.
An element ofis called a semi-configuration of M.
If q is the state associated with configuration c, then c issaid to be an accepting configuration if q is an accepting
state. The initial configuration of M on input x is
, 1,1,1,1 ,,, whereis thespecial extended input tape of M such that, , ,=, , , ,
for each
, , ,
1
,1
,1
, 1
.
If
M
moves
deterministically, we call M a four-dimensional
deterministic one-marker automaton (4-DMA1).
Definition 2.4. Given a 4-NMA1 M = (Q, q0, F, Σ, {+, -},δ), we write ├and say ′ is a successor of c if
configuration ′ follows from configurationin onestep of M, according to the transition rules . ├ ∗denotes the reflexive transitive closure of ├ . The
relation ├ is not necessarily single-valued, because
is not. A computation path of M on
is a sequence├├ …├
0 , where. An
accepting computation path of M on x is a computation
path of M on x which ends in an accepting configuration.
We say that M accepts x if there is an accepting
computation path of M on input x.
Let, , , :↦ be a function. A seven-way
four-dimensional Turing machine M is said to be, , ,space-bounded if for each , , ,1
and for each
with
,
,
,
and
, if
is accepted by M, then there is anaccepting computation path of M on
in which M uses
no more than, , ,cells of the storage tape. We
denote an, , ,space-bounded SV4-DTM(SV4-NTM) by SV4-DTM( , , , ) (SV4-NTM( , , , )).
Let L(l, m, n) : N3 ↦R be a function. A
seven-way four-dimensional Turing machine M is said
to be L(l, m, n) space-bounded if for each l, m, n 1
and for each x with l1(x) = l, l2(x) = m, and l3(x) = n, if x

is accepted by M, then there is an accepting computation
path of M on x in which M uses no more than L(l, m, n)cells of the storage tape. We denote an L(l, m, n)space-bounded SV4-DTM (SV4-NTM) by SV4-DTM(L(l,m, n)) (SV4-NTM(L(l, m, n))).
Definition 2.5. For any four-dimensional automaton M
with input alphabet Σ, define T(M) = {x ∈ Σ | M
accepts x}. Furthermore, for each X ∈ {D,N}, define
L[4-XMA1] = {T | T = T(M) for some 4-XMA1},
L[SV4-XTM(S(l,m,n))] = {T | T = T(M) for some
SV4-XTM(S(l,m,n)) M}, andL[SV4-XTM(L(l,m))] = {T | T = T(M) for some
SV4-XTM((l,m)) M}.
By using the same technique as in the proof of
Lemma 5.4 in [2], we can easily prove the following
theorem.
Lemma 2.1. For any function L(l, m, n)loglmn,
L[SV4-XTM(L(l,m,n))] ⊆, ,∪L[SV4-DTM(2)].

3. Sufficient spaces
In this section, we investigate the sufficient spaces (i.e.,
upper bounds) for seven-way Turing machines tosimulate one-marker automata. We first show that lmn
loglmn space is sufficient for SV4-NTM’s to simulate
4-DMA1’s.
Theorem 3.1. L[4-DMA1] ⊆ L[SV4-NTM(lmnloglmn)].
Proof : Suppose that a 4-DMA1 M = (Q, q0, F, Σ, ) is
given. We partition the extended states Q,
into
disjoint subsets Q+ = Q
and Q － = Q
which correspond to the extended states when M isholding and not holding the marker in the finite control,
respectively. We assume that M has a unique accepting
state qa, i.e., | F | = 1. In order to make our proof clear,
we also assume that M begins to move with its input
head on the position (l + 1, m + 1, n + 1, t + 1) and,
when M accepts an input, it enters the accepting state at
the same position (l + 1, m + l, n + 1, t + 1) with themarker held in the finite control.
Suppose that an input tape x with
,
,
, and
is given to M.
For M and x, we define three types of mappings↑∶
0,1, … ,1
0,1, … ,1
0,1, … ,1 ↦0,1, … ,1© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 342 -

M. Nagatomo, M. Sakamoto, H. Susaki, T. Zhang, S. Ikeda, H. Furutani, T. Ito, Y. Uchida and T. Yoshinaga

0,1, … ,1
0,1, … ,1 ∪ ,∶0,1, … ,1
0,1, … ,1
0,1, … ,1 ↦0,1, … ,1
0,1, … ,1
0,1, … ,1 ∪ , and↓∶
0,1, … ,1
0,1, … ,1
0,1, … ,1 ↦0,1, … ,1
0,1, … ,1
0,1, … ,1 ∪ ,(r = 0,1, ... , t+1) as follows. (Below, we attach thesuperscripts ‘ ’, ‘ ’ to any extended states in (Q+, Q－,
respectively.)↑, , ,, , , ′ : Suppose that we makeM start from the configuration, , , ,
1 ,, i.e., no marker existing either on the input x orin the finite control of M. After that, if M reaches ther-th three-dimensional rectangular array of x in sometime, the configuration corresponding to the first arrivalis,, , , , ′ ;↑, , ,: Starting from the configuration, , , ,
1 ,with no marker on the input
tape, M never reaches the r-th three-dimensional
rectangular array of x.
↑
, , ,
, , ,
: Suppose that we
make M start from the configuration, , , ,
1 ,, i.e., holding the marker in the finite control of
M. After that, if M reaches the r-th three-dimensional
rectangular array of x with its marker held in the finite
control in some time ( so, when M puts down the marker
on the way, it must return to this position again and pickup the marker), the configuration corresponding to thefirst arrival is,, , , , ′;↑, , ,: Starting from the configuration, , , ,1 ,, M never reaches the r-th
three-dimensional rectangular array of x with its marker
held in the finite control.
↓, , ,’ , ’, , ′ : Suppose that we
make M start from the configuration, , , ,
1 ,, i.e., no marker existing either on the input tapeor in the finite control of M. After that, if M reaches ther-th three-dimensional rectangular array of x in sometime, the configuration corresponding to the first arrivalis,, , , , ′ ;↓, , ,: Starting from the configuration, , , ,1 ,, M never reaches the r-th
three-dimensional rectangular array of x.

Then, we can show that there exists an
SV4-NTM(lmloglm) M' such that T(M’)= T(M). Roughly
speaking,
while
scanning
from
the
top
three-dimensional rectangular array down to the bottom
three-dimensional rectangular array of the input, M’guesses ↓ , constructs ↑and ↑ , checks ↓ ,and finally at the bottom three-dimensional rectangular↑array of the input, M’ decides by using
and↑whether or not M accepts x.
In order to record these mappings for each r, O(lmn)blocks of O(loglmn) size suffice, so in total,O(lmnloglmn) cell of the working tape suffice. Moreprecisely, the working tape must be used as a‘multi-track’ tape, but we omit the detailed constructionof the working tape of M’. It will be obvious that T(M)
= T(M').
□
From Lemma 2.1 and Theorem 3.1, we get thefollowing.
Corollary 3.1. L[4-DMA1] ⊆L [SV4-DTM(2)].
Next, we can show that l2m2n2 space is sufficient for
SV4-NTM’s to simulate 4-NMA1’s. The basic idea andoutline of the proof are the same as those of Theorem
2.1.
Theorem 3.2. L[4-NMA1] ⊆ L[SV4-NTM(l2m2n2)].
From Lemma 2.1 and Theorem 3.2, we get thefollowing.
Corollary 3.2. L[4-NMA1] ⊆L [SV4-DTM(2)].

↑

4. Conclusion
In this paper, we showed the sufficient space for
SV4-DTM’s to simulate 4-DMA1’s (4-NMA1’s) is2(2) and the sufficient space for
SV4-NTM’s to simulate 4-DMA1’s (4-NMA1’s) is
lmnloglmn (l2m2n2). It will be interesting to investigate
how much space is necessary for SV4-DTM’s ( orSV4-NTM’s) to simulate 4-DMA1’s ( or 4-NMA1’s).
References
1. M. Blum and C. Hewitt, “Automata on a
two-dimensional tape”, IEEE Symposium on Switchingand Automata Theory (1967), pp.155-160.
2. M. Sakamoto, “Three-dimensional alternating Turingmachines”, Ph.D. Thesis, Yamaguchi University (1999).

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 343 -

Some properties of
k-neighborhood Template A-type two-dimensional bounded cellular acceptors
Makoto Sakamoto, Makoto Nagatomo, Hikaru Susaki, Tuo Zhang, Satoshi Ikeda and Hiroshi Furutani

Faculty of Engineering, University of Miyazaki, 1-1 Gakuen Kibanadai Nishi
Miyazaki, Miyazaki 889-2192, Japan
E-mail: sakamoto@cs.miyazaki-u.ac.jp
Takao Ito

Institute of Engineering, Hiroshima University, 4-1, Kagamiyama1-chome
Higashi-Hiroshima, Hiroshima 739-8527, Japan
E-mail: itotakao@horoshima-u.ac.jp
Yasuo Uchida

Department of Business Administration, Ube National College of Technology, Tokiwadai
Ube, Yamaguchi 755-8555, Japan
E-mail:uchida@ube-k.ac.jp
Tsunehiro Yoshinaga

Department of Computer Science & Electronic Engineering,
National Institute of Technology, Tokuyama College, Gakuendai
Shunan, Yamaguchi 745-8585, Japan
E-mail:yosinaga@tokuyama.ac.jp

Abstract
In this paper, we investigate multi-dimensional computational model, k-neighborhood template A-type three-dimensionalbounded cellular acceptor on four-dimensional tapes, and discuss some basic properties. This model consists of a pair of aconverter 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 some open problems about kneighborhood template A-type three-dimensional bounded cellular acceptor on four-dimensional tapes whose configurationreaders are L(m) space-bounded deterministic (nondeterministic) three-dimensional Turing machines.
Keywords: configuration-reader, converter, four-dimension, neighbor, space-bounded, Turing machine.

dimensional automata as the computational models offour-dimensional pattern processing has been
meaningful. From this point of view, we first proposedfour-dimensional automata as computational models offour-dimensional pattern processing in 2002, andinvestigated their several accepting powers[2]. By the

1. 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 four-© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 344 -

M. Sakamoto, M. Nagatomo, H. Susaki, T. Zhang, S. Ikeda, H. Furutani, T. Ito, Y. Uchida and T. Yoshinaga

way, in the multi-dimensional pattern processing,designers often use a strategy whereby features are
extracted by projecting high-dimensional space on lowdimensional space. So, 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 fourdimensional tapes, and discuss some basic properties[3].
An A-3BCA(k) consists of a pair of a converter and aconfiguration-reader. The former converts the given
four-dimensionaltapetothree-dimensional
configuration. The latter determines whether or not thederived three-dimensional configuration is accepted,
and concludes the acceptance or non-acceptance ofgiven four-dimensional tape. When an input fourdimensional tape is presented to the A-3BCA(k), a threedimensional cellular automaton as the converter firstreads it to the future direction at unit speed (i.e., one
three-dimensional rectangular array per unit time). From
this process, the four-dimensional tape is converted to aconfiguration of the converter which is a state matrix ofa three-dimensional cellular automaton. Second, threedimensional automaton as the configuration-reader
reads the configuration and determines its acceptance.
We say that an input four-dimensional tape is acceptedby the A-3BCA(k) if and only if the configuration is
accepted by the configuration-reader. Therefore, theaccepting power of the A-3BCA(k) depends on how to
combine the converter and the configuration-reader. An
A-3DBCA(k) (A-3NBCA(k)) is called a k-neighborhood
template A-type three-dimensional deterministic
bounded cellular acceptor (k-neighborhood template Atype three-dimensional nondeterministic boundedcellular acceptor). This paper mainly investigates someopen problems about accepting powers of A3DBCA(k)’s whose configuration-readers are L(m)space-bounded deterministic (nondeterministic) threedimensional Turing machines[1].

m j (x ) be the length of x along the j-th axis. The set of
all x   with l1(x)=m1, l2(x)=m2, l3(x)=m3, and l4(x)=m4
denoted by ∑ (m1,m2,m3,m4). If 1 ij lj(x) for eachj (1  j  4) , let x(i1,i2,i3,i4) denote the symbol in x withcoordinates (i1,i2,i3,i4). Furthermore, we definex[(i1,i2,i3,i4),(i1’,i2’,i3’,i4’)], when 1  i j  i' j  l j ( x) for
each integer j (1  j  4) , as the four-dimensional tape ysatisfying the following (i) and (ii):(i)
for each j(1 j 4), lj(y)=i’j-ij+1;(ii)
for each r1, r2, r3, r4(1 r1 l1(y), 1 r2 l2(y),1 r3 l3(y), 1 r4 l4(y)), y(r1, r2, r3, r4) =x(r1+i1-1, r2+i2-1, r3+i3-1, r4+i4-1). (We callx[(i1, i2, i3, i4), (i’1, i’2, i’3, i’4)] x[(i1, i2, i3, i4),
(i’1, i’2, i’3, i’4)]-segment of x.);We now introduce a k-neighborhood template A-type
three-dimensional bounded cellular acceptor (A3BCA(k)), which is a main object of discussion in thispaper.
Definition 2.2. Let A be the class of an automaton
moving on a three-dimensional configuration. Then, an
A-3BCA(k) M is defined by the 2-tuple M=(R,B). R and
B are said to be a converter and a configuration-reader
in view of its property, respectively.
(1) R is a three-dimensional infinite array consists ofthe same finite state machines and is defined by the
6-tulpe M = (Z2, N2, K, Σ, σ, q0), where① Z is the set of all integer, and the finite statemachines are assigned to each point of
Z3(=ZxZxZ). The finite state machine situatedat coordinates (i,j,k)  Z3 is called the (i,j,k)-thcell and denoted by A(i,j,k),② N3(  Z3) represents the neighborhood
template of each cell and N3 = {(i,j,k) |－1 i,j,k 1},③ K is a finite set of states of each cell andcontains q#(the boundary state) and q0 (theinitial state),④ Σ is a finite set of input symbols (#  Σ is theboundary symbol),⑤ σ :K27 x ( Σ ∪ {#}) → 2K is the cell statetransition function. Let q1.j,k(t) be the state of
the A(i,j,k) at time t. Thenqi,j,k(t+1)  σ(qi-1,j-1,k-1(t), qi-1,j,k-1(t), qi-1,j+1,k-1(t),qi,j-1,k-1(t), qi,j,k-1(t), qi,j+1,k-1(t), qi+1,j-1,k-1(t),2. Preliminaries

Definition 2.1. Let  be a finite set of symbols. A
three-dimensional tape over  is a four-dimensional
rectangular array of elements of  . The set of all fourdimensional tapes over  is denoted by  （４）. Givena tape x∈  （４）, for each integer j( 1  j  4 ), we let© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 345 -

Some properties of k-neighborhood

We now consider the class of three-dimensional
automata described by the following abbreviations as
the class of the configuration-reader of A-3BCA(k) A. In
this paper, we assume that the reader is familiar with thedefinition of these automata. If necessary, see[2].
3-DTM(L(m)) … The class of L(m) space-bounded
deterministic
three-dimensional
Turing machine
3-NTM(L(m)) … The class of L(m) space-bounded
nondeterministic
threedimensional Turing machine
DO … The class of deterministic threedimensional on-line tessellation
acceptor
DB … The class of deterministic twodimensional bounded cellular
acceptor
For example 3-DTM(L(m))-3DBCA(27) represents suchthe class as its converter is deterministic and 27neighborhood, and its configuration-reader is an L(m)space-bounded deterministic three-dimensional Turingmachine. Moreover, for any A  {3-DTM(L(m)), 3NTM(L(m))}, for any X  {D, N} and for any k  {1,7,27}, the class of set of all four-dimensional tapes
accepted by A-3XBCA(k) is denoted by L[A-3XBCA(k)].
We let each side-length of each input tape of these
automata be equivalent in order to increase thetheoretical interest.

qi+1,j,k-1(t), qi+1,j+1,k-1(t), qi-1,j-1,k(t), qi-1,j,k(t), qi1,j+1,k(t), qi,j-1,k(t), qi,j,k(t), qi,j+1,k(t), qi+1,j-1,k(t),qi+1,j,k(t), qi+1,j+1,k(t), qi-1,j-1,k+1(t), qi-1,j,k+1(t), qi1,j+1,k+1(t), qi,j-1,k+1(t), qi,j,k+1(t), qi,j+1,k+1(t), qi+1,j1,k+1(t), qi+1,j,k+1(t), qi+1,j+1,k+1(t), a),wher a is the symbol on the A(i,j,k) at time t. If
qi,j,k(t) = q#, however, qi,j,k(t+1) = {q#} for each(i,j,k)  Z3 and each t 0.
(2) A set of input symbols of B is K－{q#} (where B
 A). Intuitively, M=(R,B) moves as follows, given
a four-dimensional input tape x  Σ(m1,m2,m3,m4) (m1,m2,m3,m4 1) (x is surrounded by the boundary
symbol #). First, each cell A(i,j,k) of R(1 i m1,
1 j m2, 1 k m3) reads each symbol on the first
three-dimensional rectangular array x(i,j,k,1) in theinitial state q0, and all of the other cells read theboundary symbols #’s in the boundary state q#’s attime t=0. Starting from this condition, R keeps
reading x according to the cell state transition
function, and moving down the cell array by one
three-dimensional rectangular array, every time R
reads one three-dimensional rectangular array all.
Next, B starts to move regarding a threedimensional configuration of R just after R finishedreading x as a three-dimensional input tape anddetermines whether or not can accept theconfiguration. If B accepts it, x is said to be
accepted by M. Let T(M) be the set of all accepted
three-dimensional tape by M.

3. Main results
Definition 2.3. An A-3BCA in Dfinition 2.2 is called a27-neighborhood template A-3BCA. If we deal with east,
west, south, north, up, down neighboring cells andremarkable cell, we call it 7-neighborhood template A3BCA. If we deal with only remarkable cell, we call it 1neighborhood template A-3BCA. From now on, we
denote k-neighborhood template A-3BCA by A-3BCA(k)(k  {1,7,27}) .

In this section, we discuss some properties of A3BCA(k)’s whose configuration-readers are L(m) spacebounded deterministic (nondeterministic) threedimensional Turing machines.
First, we show that a relationship between determinism
and nondeterminism, when we use L(m) space-bounded
three-dimensional Turing machines for any L(m) > m3
as the configuration-readers.

Definition 2.4. If the image generated by σ in
Definitions 2.2 and 2.3 is a singleton, the converter issaid to be deterministic, and if not, it is said to be
nondeterministic. An A-3BCA(k) (k  {1,7,27}), whichconverter is deterministic (nondeterministic), is said tobe a deterministic (nondeterministic). A-3BCA(k) anddenoted by A-3DBCA(k) (A-3NBCA(k)).

Theorem 3.1. For any X  {D,N}, L[3-XTM(m3)3DBCA(1)]= L [3-XTM(m3)-3NBCA(1)].
Proof: From above definition, it is obvious that L[3XTM(m3)-3DBCA(1)]  L[3-XTM(m3)-3DBCA(1)]. We
below show that L[3-XTMs(m3)-3NBCA(1)]  L[3-© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 346 -

M. Sakamoto, M. Nagatomo, H. Susaki, T. Zhang, S. Ikeda, H. Furutani, T. Ito, Y. Uchida and T. Yoshinaga

DTM(m3)-3DBCA(1)] (we can prove another case (i. e. ,X=N) in the same way).
Now, let M=(R,B) ((R=Z2, N2, K, Σ, σ, q0), B=(K, Q,
Σ,  ,  , p0, F)) be some 3-DTM(m3)-3NBCA(1). Then,
we consider an M’=(R’,B’) (R’=(Z3, N3, K’, Σ,σ’, q0),
B’=(K’, Q, Σ,  ,  ’, p0’, F’)) constructed as follows.
(1) Construction of R’① K’=2(K-{q#}) ⋃ {q#}, q0’={q0}.
② For any a  Σ and any K’’  K’-{q#},σ’(K’’, a) = qK '' σ(p, a).
(2) Construction of B’
For any p  Q and any K’’  K’-{q#}, ’(p, K’’) =rK ''  (p, r).
Intuitively, we explain the movement of M’=(R’, B’)constructed in this way. Let us suppose that a fourdimensional tape x  Σ (4)+ is given to M’. R’ is oneneighborhood, so we can consider each cell of R’ as
usual one-dimensional finite automata. Then, each cellof R’ moves to store all states that each corresponding
cell of R can enter in each state at each time by using
the well-known subset construction method (see (1)).
B’ nondeterministically chooses only one state fromeach state of each cell of R’, and simulates the
movement of B regarding the selected states as the input
symbol. If B’ can not accept the input, B’ selects thenext input and simulates the movement of B. From theway such as the above manner, B’ checks the all input
patterns, and if B’ can accept one input, B’ can accept
the configuration of R’ (see (2)).
It is clear that T(M’)=T(M) for M’=(R’,B’) constructedin this manner.
□
Next, we show that there exists a language accepted by
a 3-DTM(0)-3NBCA(1), but not accepted by any 3NTM(L(m))-3DBCA(27) for any L(m)) = o(ℓog m).
Theorem 3.2. For any function L(m)= o(ℓog m), L[3DTM(0)-3NBCA(1)]-L[3-NTM(L(m))-3DBCA(27)] φ.
Proof: Let C={w02w12…2wk | k 1 &  i(0 i k)
[w1 {0,1}+] &  j(0 j k ) [w0=wj r]}(where, for any
one-dimensional tape w, wr denotes the reversal of w),
and T1={x {0,1,2} (4)+ |  m>3[ℓ1(x)=ℓ2(x)=ℓ3(x)=ℓ4(x)=m & x[(1,1,m,m), (1,m,m,m)]  C]}. Then, by using atechnique similar to that in the proof of Lemma 2(1) in[4], we can show that T1 is accepted by 3-DTM(0)-3NBCA(1), but not accepted by any 3-NTM(L(m))3DBCA(27) for any L(m))= o(ℓog m).
□
Corollary 3.1. For any L(m) = o(log m) and any X {D,N}, L[3-XTM(L(m))-3DBCA(1)] ⊊ L[3-XTM(L(m))3NBCA(1)].
Remark 3.1. By using a technique to that in the proofof Theorem 3 in [4], we can show that for any function
L(m) and any k  {7,27}, L[3-XTM(L(m))-3DBCA(k)]⊊L[3-XTM(L(m))-3NBCA(k)].
Finally, by using the well-known technique, we canshow that there exists a language accepted by a DO3NBCA(1) and a DB-3NBCA(1), but not accepted byany 3-DTM(L(m)) – 3DBCA(27) for any function L (m)= o(log m).
Theorem 3.3. For any function L(m) = o(logm), (L[DO-3NBCA(1)] ⋂ L[DB-3NBCA(1)])- L[3-DTM(L(m))3DBCA(27)]  φ.
4. Conclusion
We conclude this paper by giving the following
problem. For any X  {D,N} and any L(m) (log m L(m)and L(m) = o(m2)), L[XTM(L(m)) – 3DBCA(1)] ⊊L [XTM(L(m)) –3NBCA(1)] ?
References
1. M. Sakamoto, H. Okabe, and K. Inoue, “Some propertiesof four-dimensional finite automata”, 2002 ChugokuSection Joint Convention Record of Institutes ofElectrical and Information Engineering, Shimane, Japan(2002), p.351.
2. M. Sakamoto, “Three-dimensional alternating Turingmachines”, Ph.D. Thesis, Yamaguchi University (1999).
3. M. Sakamoto, M. Nagatomo, X. Feng, T. Kurogi, T.
Zhang, T. Ito, Y. Uchida, T. Yoshinaga, S. Ikeda, M.
Yokomichi, and H. Furutani, “Hierarchy based on kneighborhood template about k-neighborhood templateA-type three-dimensional bounded cellular acceptor”,
Proceedings of the International Conference on Artificial
Life and Robotics (ICAROB2014), Compal Hall, Oita,
Japan, OS9-1 (2014)
4. H. Taniguchi, K. Inoue, and I. Takanami, “kneighborhood template A-type 2-dimensional boundedcellular acceptor”, IECE of Japan Transactions (D),Vol.69, No.3 (1986), pp.291-301.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 347 -

Perfect Analysis in miniature Othello
Yuki Takeshita*

Department of Computer Science and System Engineering, Miyazaki University,
Japan, hf11031@student.miyazaki-u.ac.jp
Satoshi Ikeda*

Department of Computer Science and System Engineering, Miyazaki University,
Japan, bisu@cs.miyazaki-u.ac.jp
Makoto Sakamoto*Department of Computer Science and System Engineering, Miyazaki University,
Japan, sakamoto@cs.miyazaki-u.ac.jp
Takao Ito†

Graduate School of Engineering, Hiroshima University,
Japan, itotakao@hiroshima-u.ac.jp

Abstract
More than 20 years has passed after J. Feinstein (1993) found that a perfect play on 6×6 board of Othello gives a
16-20 win for the second player, but standard 8×8 board has not yet been. In this paper, we analyzed for 4×4, 4×6,
4×8, 4×10 and 6×6 boards of Othello. From these results, we discuss which it is, win/loss/draw in 8×8 board or
more board.
Keywords: perfect analysis, perfect play, Alpha-Beta Pruning, rectangular Othello

was 2weeks. Then computer Othello surpasses a much
more human since more than 20 years has been passed.
However, standard 8×8 board of Othello has not beensolved (we couldn’t find the articles solved it). The
cause is that number of the positions is too large: in 8×8
board, perfect analysis in a realistic time is impossible
even if we use the latest supercomputer.
In this paper, we show the perfect play of 4×4, 4×6,
4×8, 4×10 and 6×6 boards of Othello. From theseresults, we discuss the feature of the Othello game in

1. Introduction
Othello is categorized into two-player zero-sum finite
deterministic games of perfect information [1]. Gamesin this class are possible to look ahead in theory, thus if
both players play the best move, these are classified
into a win, loss or draw game [2].
In 1993, Joel Feinstein found that a perfect play on
6×6 board of Othello gives a 16-20 win for the second
player [3]; he looked at 40 billion positions, run time*†

University of Miyazaki, 1-1, Gakuen Kibanadai Nishi, Miyazaki, 889-2192
University of Hiroshima, 1-4-1, Kagamiyama, Higashi-Hiroshima, 739-8527© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 348 -

Yuki Takeshita, Satoshi Ikeda, Makoto Sakamoto, Takao Ito

8×8 board or more. In Section 2, we introduce the rulesof Othello. In Section 3, we introduce computer Othello.
In Section 4, we explains perfect play. In Section 5, ourexperimental results are presented. We ran the perfectanalysis in 4×4, 4×6, 4×8, 4×10 and 6×6 board of
Othello. In Section 6, we discusses conclusion.
2. Othello‡First of all, we will introduce the rules of Othello. See
Figure 1. The game always begins with this setup. In
the case of Reversi, we put randomly by two in central
squares, thus there may be a parallel. One player uses
the black side of the pieces (circular chips), the otherthe white sides. Black always moves first.
Both players put the pieces of own color to an empty
board in turn. A player’s move consists of outflanking
his opponent’s the pieces. Then, He flip outflanked thepieces to his color. To outflank means to place the pieceon the board so that his opponent’s rows of the piece
are bordered at each end by the piece of his color. If a
player cannot make a move that flips at least one of his
opponent the pieces, then he has to pass. If he is able to
make a valid move however, then passing is notallowed. The game ends when neither player can makea valid move. The winner is the player who has more
the pieces than his opponent.

In addition, end-game is classified into solver for
WLD (win/loss/draw) score and solver for exact score.
Both the perfect analyses, but there is a difference inthe evaluation of the end. In solver for exact score, theend is evaluated with the piece difference. However, itis necessary to consider if one was wiped out in the
middle§ . This routine can find best one move. In solver
for WLD score, the end is evaluated in three ways win,loss, and draw. In this way, if the range of theevaluation value is small, pruning (Alpha-Beta Pruning[4]) occurs relatively large. Therefore, the solver forexact score can estimate that it is several times of theexecution time by run the first WLD.
3.1. Speed up of the program
Currently, our program read approximately 1.5-2
million moves per second. There are some ideas for this.
At first, we implemented doubly-linked list storing theempty the pieces. This function reduce search cost so as
to go to the end-game. From this, Search speed isapproximately two times faster than previous version.
Next, our program had a function to count number ofthe pieces in specified the color, but we have removed
it. Instead, it was adjusted from the time of reversal andrestoration by adding a variable that stores number ofthe pieces into structure. This effect was approximately
1.5 times. In addition, we planned the shift to the Bid
Board (way to represent the board in only logicaloperations and bit shifting) which helped speedup of
the processing. Unfortunately, we have not yet
implemented it.
4.

Fig. 1. Board and Starting position.

The perfect play is a sequence when both sides continue
to choosing the best move. In computer Othello, thesequence to an end is not saved because we should justfind even the best move of next; a return value issufficient. Because our program is based on thethinking routines for such a game, it has become
inefficient program to repeat the perfect analysis again
after finding the next move. There is little harmful
effect until perfect analysis of the 6×6 board. For
example, it will takes 11 months to select the after next

3. Computer Othello
The making of the thinking routines is indispensable instudying perfect analysis of the board game. This isbecause the end-game routine is the perfect analysis,
the evaluation function in the middle-game routine isavailable for the ordering of the search in perfectanalysis.
‡

Perfect Play§

Othello is a registered trademark.

For example, in 4×8 board, the evaluation value at 26-0 must be +32.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 349 -

Perfect Analysis in miniature

We ran a similar experiment in Reversi version(starting position is parallel). However, we obtained
similar experimental results.

move if we search the board to take one year to perfectanalysis.
5. Experiments

6. Conclusion

We derived the perfect play of 4×4, 4×6, 4×8, 4×10 and
6×6 boards. CPU that we used for the experiments isIntel Core i7-4770 processor. First, see Table 1.

See Figure 2 and Figure 3. The horizontal axis showseach board and the vertical one shows the ration
number of the pieces acquired by the first move relative
to the total number of the pieces. The central dashedline shows the boundary of the win/loss. Figure 2
shows the ration is increased in the transition from the
4×4 board to the 6×6 board. Figure 3 shows the featurethat the first move becomes advantageous along withthe expansion of the board in rectangular Othello.
From these, we guess that the first move will become
advantageous along with the expansion of the board insquare Othello.

Table 1. Execution results of perfect analysis ineach Othello board. P means number of thepositions, T means execution time and R means
results.

P
T
R

4×4
218
0.001sLOSS(-8)
B:3,W:11

6×6
884,392,099,420
5d12h16m
LOSS(-4)
B:16,W:20

P
T
R

4×6
139,8030.1s
WIN(+16)
B:20,W:4

4×8
294,430,331
2m15s
WIN(+32)
B: 28, W: 0

4×10
1,195,804,922,641
6d6h22m
WIN(+40)
B: 39, W: 0

It turned out that the result of 6×6 board is consistent
with the result of Feinstein. However, we have took
about one week to perfect analysis because number ofthe positions was about 890 billion. Second, see Table 2.
Fig. 2. Ration number of the pieces
by first move (square).

Table 2. Execution results of perfect analysis ineach Reversi board.

P
T
R

4×4
524
0.001sLOSS(-3)
B:6,W:9

6×6
1,628,664,185,199
8d12h42m
LOSS(-2)
B:17,W:19

P
T
R

4×6
274,549
0.15s
WIN(+18)
B:21,W:3

4×8
299,987,758
2m12sec
WIN(+32)
B: 28, W: 0

4×10
842,204,125,277
4d12h22m
WIN(+40)
B: 32, W: 0

Fig. 3. Ration number of the pieces
by first move (rectangle).

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 350 -

Yuki Takeshita, Satoshi Ikeda, Makoto Sakamoto, Takao Ito

In addition, we improve the Bid Board mentioned in
Section 3.1 and the drawbacks mentioned in Section 4.

Also, considering the increased range in Figure 2, weconsider the standard 8×8 board is likely to be a win for
the first move or draw. Moreover, there is a highpossibility that first move wins in 10×10 or more board.

Acknowledgements
This work was supported by JSPS KAKENHI Grant
Number 24510217.
References[1] J. v. Neumann, O. Morgenstern, Theory of Games andEconomic Behavior (Princeton Univ Pr, 1944)[2] J. Schaeffer, N. Burch, Y. Björnsson, A. Kishimoto, M. M
üller, R. Lake, P. Lu and S. Sutphen. Checkers Is Solved,
Science, Vol. 317 (2007), pp. 1518-1522.
[3] British Othello Federation. Forty Billion Nodes Under The
Tree: The Newsletter of the British Othello Federation(1993), pp. 6–8.
[4] D. E. Knuth, R. W. Moore. An Analysis of Alpha-Beta
Pruning, Artificial Intelligence, Vol. 6 (1975), pp. 293–
326.

Fig. 4. Number of the positions in rectangular Othello.

Besides, Figure 4 shows the transition in number ofthe positions in rectangular board. The horizontal axis
shows each board size and the vertical one represents
number of the positions by the exponent. From thisgraph, we can see that number of the positions
increases curvedly. Additionally, it can be guessed that
number of the positions of 4×12 board is about 104
times of 4×10 board.
7. Future work
In this paper, we were able to obtain the perfect play of
6×6 board and 4×10. We confirm that thisconsideration is correct by continue to challenge theperfect analysis which extended this (4×12, 4×14 and6×8 boards). In addition, in 8×8 board, we continue tofurther consideration by the perfect analysis of someadvanced boards from starting position to occur well inbetween the high-level players.
7.1. Improvement
Alpha-Beta Pruning becomes more effective by themove ordering. Because our program is low this quality,
it is necessary to improve.
We have to implement a hash table in order to cut theboards with duplicate and symmetry. However, it willslow down the search to take the access time to it.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 351 -

A proposal for teaching programming
through the Five-Step Method
Yasuo Uchida

National Institute of Technology, Ube College
Ube, 755-8555, Japan
E-mail: uchida@ube-k.ac.jp
Seigo Matsuno

National Institute of Technology, Ube College
Ube, 755-8555, Japan
Takao Ito

Hiroshima University
Higashi-Hiroshima, 739-8527, Japan
Makoto Sakamoto

University of Miyazaki
Miyazaki, 889-2192, Japan

Abstract
We have proposed a new method of advancing from CS Unplugged through the new process of CS Plugged to fullfledged computer programming languages, as a means of deepening understanding in computer programming
education. We also have proposed a new Five-Step Method consisting of the following steps: Step 1, A CS
Unplugged activity; Step 2, A CS Plugged activity; Step 3, A trace table, Step 4, Preparing pseudocode; and Step 5,
Writing Java source code.
Keywords: CS Unplugged, CS Plugged, Programming, Five-Step Method.

understanding of programming or think that they are notgood at programming. We are examining ways toimprove this situation. CS Unplugged [2] is a method of
The course we teach has about 40 students per class,teaching information science without using computers,
aged 17 through 18 years. Since the students’ major
first proposed by Tim Bell of the University offield of study is Management Information, they need toCanterbury in New Zealand. While CS Unplugged islearn programming [1] techniques. However, the results
said to be effective in teaching information science [3],of the survey described below show that not a few
there have been concerns that its success or failure may
students consider themselves to have insufficient
be an effect of the skill and experience of instructors. To© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan

1. Introduction- 352 -

Y. Uchida, S. Matsuno, T. Ito, M. Sakamoto

address this topic, we implemented CS Unplugged for agroup of students of a different age group, from fifth
through ninth grades, and looked at their responses. The
results showed that after first making sufficient
preparations the method could generate results without
necessarily depending on the skill or experience of
instructors. Other research underway in Japan includes a
study on use of teaching aids in learning aboutalgorithms through CS Unplugged [4] and a study onlearning the fundamentals of computer programming
through a programming learning environment for
beginners [5]. A study by Y. Feaster et al [6] concerns
practice in teaching high-school students, and it has
been reported to have had some, albeit limited, success.
However, at present almost no research has been
conducted on advancement from CS Unplugged to fullfledged programming languages. Accordingly, we
propose a new method of advancing from CS
Unplugged to full-fledged programming. The proposed
method begins with conducting a CS Unplugged
activity, and then continues on to writing a program on
the same theme and further to its abstraction in Java.
Accordingly, we propose advancing from CS
Unplugged to full-fledged programming through a new
Five-Step Method. The proposed method consists of thefollowing steps: Step 1, A CS Unplugged activity; Step
2, A CS Plugged activity; Step 3, A trace table, Step 4,
Preparing pseudocode; and Step 5, Writing Java source
code.

Fig. 1. Survey results on understanding of Java.
fifth through ninth grades as part of a summer-vacation
junior science course. The activity we implemented was
CS Unplugged’s Image Representation activity. Fig. 2
shows examples of students’ work.

Fig. 2. Examples of students’ work
2. Background of this study
4. The Five-Step Method

Every year we conduct a survey following our first-termcourse of two 90-minute sessions per week held over 30
weeks. Here we will look at numbers of students who
answered “yes” or “no” to this survey’s question, “Do
you think you largely understand Java?”
As seen in Fig. 1, the survey’s results show that nota few students consider themselves to have insufficientunderstanding of programming or think that they are notgood at programming. As such, there is a need to
improve this situation.

In this paper, we use the name CS Plugged to refer toimplementing a CS Unplugged activity through a
computer program. The goal is to advance to computer
programming through using a computer program to
conduct the work done by human beings in a CS
Unplugged activity.
Accordingly, we propose advancing from CS
Unplugged to full-fledged programming through a FiveStep Method. The proposed method consists of thefollowing steps: Step 1, A CS Unplugged activity; Step
2, A CS Plugged activity; Step 3, A trace table, Step 4,
Preparing pseudocode; and Step 5, Writing Java sourcecode.

3. CS Unplugged in practice
For about 50 minutes on August 2, 2014 we used the CS
Unplugged method for approximately 30 students from- 353 -

A proposal for teaching

4.1. Step 1: CS Unplugged

4.3. Step 3: Trace table

First, students conduct the CS Unplugged activity as
described in Section 3 above.

The task conducted in the CS Plugged activity is
analyzed and diagrammed as a trace table [7]. This isdesigned for entry of not just the execution process
but also the data areas of arrays (including initialvalues) and output areas. Fig. 5 shows an example ofa trace table prepared using a spreadsheet software,although the actual trace table may be drawn by hand.

4.2. Step 2: CS Plugged
Here we will look at the example of using a computer
program to implement the CS Unplugged activity Image
Representation. This is conducted through two activities.
The first, converting the image to code, is represented inFig. 3. When students click on squares in the grid at left
to draw a picture, the image is converted instantly tocode as displayed at right.

Array
0
□
□
□
■
■
□

0
1
2
3
4
5

1
■
□
■
□
□
■

2
■
□
■
□
□
■

3
■
□
■
□
□
■

4
□
■
■
■
■
■
Subscript i Subscript j Array[i][j]
0
0
0
0
0
1
1
1
1
1
…
5
5
5
5
5

Output
□
□

■
□

■
□

■
□

□
■

0
1
2
3
4
0
1
2
3
4
…
0
1
2
3
4

□
■
■
■
□
□

PreviousCount
character
□
1
1
□
2
■
3
■
■
1
□
1

1,3,1

Fig. 5. Sample Trace Table (in process)4.4. Step 4: Preparing pseudocode
In this step, the three elements of sequence, decision,and repetition through preparation of the trace table in
the preceding step are extracted and represented inpseudo-language. In Japan, the national Information
Technology Engineer Examinations employ pseudolanguage [8]. A pseudo-language simulator is a type ofsoftware that makes such pseudo-language executable.
In this paper, we used the freeware pseudo-language
simulator SARA [9]. Fig. 6 shows the code written inthis step and its execution.

Fig. 3. Applet for converting image to code.
The other activity is the reverse of this process, withstudents reproducing a picture from code (Fig. 4). They
enter code to the text boxes at right in a format such as“7,1,8” and press the Enter key to display the resulting
image instantly in the grid at left.

Fig. 4. Applet for reproducing an image
Fig. 6. Sample use of pseudo-language simulator- 354 -

Y. Uchida, S. Matsuno, T. Ito, M. Sakamotomeans of deepening understanding in computer
programming education. We also have proposed a new
Five-Step Method consisting of the following steps:
Step 1, A CS Unplugged activity; Step 2, A CS Plugged
activity; Step 3, A trace table, Step 4, Preparing
pseudocode; and Step 5, Writing Java source code.
Topics for the future are those of putting together
detailed procedures for abstraction of pseudocode fromtrace table as well as continually implementing the
proposed method and measuring its results.

4.5. Step 5: Writing Java source code
In this step students create a program by converting thepseudo-language from the previous step to Java sourcecode. The subsequent debugging process is handled bygoing back and examining each previous step dependingon the content of the error messages.
●
Java source code
public class Image2Code {public static void main(String args[]) {char[][] image = {{'□', '■', '■', '■', '□'},{'□', '□', '□', '□', '■'},{'□', '■', '■', '■', '■'},{'■', '□', '□', '□', '■'},{'■', '□', '□', '□', '■'},{'□', '■', '■', '■', '■'}};// Iterate only number of lines in an arrayfor(int i = 0; i < image.length; i++) {char previous = '□';
int count = 0;// Display the image in pixels
for(int j = 0; j < image[0].length; j++) {System.out.print(image[i][j]);}System.out.print(" ");// Count number of adjoining pixels of same color anddisplay in numerical form (code)for(int j = 0; j < image[0].length; j++) {if (image[i][j] == previous) {count++;} else {previous = image[i][j];System.out.print(count + ", ");count = 1;}}
System.out.println(count);}}}

References
1. D. Everitt, Introducing programming skills in education,
https://www.academia.edu/1478617/Introducing_progra
mming_skills_in_education.
2. The Unplugged Community: Computer Science
Unplugged, http://csunplugged.org/.
3. Y. Idosaka, Y. Kuno, and S. Kanemune, Attempt and thePractice of Class Method Improvement Based on"Computer Science Unplugged", Journal of the Japan
Society of Technology Education, vol.53, no.2 (2011), pp.
115-123 (in Japanese).
4. H. Manabe, S. Kanemune, and Namiki M., Effects ofTeaching Tools in CSU Algorithm Education, IPSJ
Journal, vol.54, no.1 (2013), pp. 14-23 (in Japanese).
5. T. Nishida, A. Harada, R. Nakamura, Y. Miyamoto, andT. Matsuura, Implementation and Evaluation of PEN:
The Programming Environment for Novices, IPSJ
Journal, vol.48, no.8 (2007), pp. 2736-2747 (in Japanese).
6. 6. FeasteryY., Segarsz L., Wahbay S.K., Hallstrom
J.O.: Teaching CS Unplugged in the High School (with
Limited Success). Proceedings of the 16th annual joint
conference on Innovation and technology in computerscience education, pp. 248--252 (2011)
7. Cambridge International Examinations, SYLLABUS
Cambridge O Level Computer Studies 7010 For
examination in June and November 2014, University ofCambridge
International
Examinations,
http://www.cie.org.uk/images/90258-2014-syllabus.pdf.
8. Information-technology Promotion Agency Japan,
Kyotsu ni shiyo sareru gijigengo no kijutsukeishiki(“Symbolic conventions of commonly used pseudolanguage”),
https://www.jitec.ipa.go.jp/1_13download/gijigengo_keisiki.pdf (in Japanese).
9. S. Mimura: Gijigengo shimyureta SARA (“SARA
pseudo-language
simulator”),
http://mimumimu.net/software/#sara (in Japanese).

Results of running the above program□■■■□ 1, 3, 1□□□□■ 4, 1□■■■■ 1, 4■□□□■ 0, 1, 3, 1■□□□■ 0, 1, 3, 1□■■■■ 1, 4

5.

Conclusion

We have proposed a new method of advancing from CS
Unplugged through the new process of CS Plugged tofull-fledged computer programming languages, as a

- 355 -

The role of national standards setter in the global convergence era: the case of the Japanesesetter from 2001 to 2008
Kensuke Ogata

Economic Department, University of Nagasaki,
123 Kawashimo, Sasebo, Nagasaki,858-8580, Japan
E-mail: ogata@sun.ac.jp

Abstract
An aim of this paper is to explain that the change of standard-setting activities of ASBJ in the 2000s was caused byits own standard-setting development strategy. An analysis of organizational structure using graph theory showedthat the ASBJ strategically alter its structure, and that the structure can trigger the change of setting activities.
Keywords: ASBJ, social network analysis, standards-setting process, accounting regulations

tional structure the IASB respectively constructed before or after 2005. This examination process is the firststep to approach standard-setting motivations of ASBJ.

1. Introduction
Since International Accounting Standards Board (IASB)
was founded in 2001, far more one hundred countries
adopted International Financial Reporting Standards(IFRSs) which the IASB produced; currently both theBoard and its standards should not be ignored. As accompanying with these increasing attentions, it is expected to draw a lot of interests in roles of national
standard-setters as mediators between the IASB andwide ranges of stakeholders in the country (Büthe andMattli [1]).
In Japan, although giving the listed companies permission to preparing their consolidated statements with
IFRSs, the national setter, Accounting Standards Boardof Japan (ASBJ), has kept producing its own standards;
the great majority of companies furthermore use Japanese standards. When turning to the activities of ASBJ
over times (shown in Table 1), we can see major difference in terms of volume and content of the standards
according to time. In particular, its activities varied
greatly before or after 2005. Based on traditional organizations theory (See Rumelt [2], Miles and Snow [3]), it
is presumed that organizations change their structure toachieve their desired results. Following this presumption, the ASBJ might change its structure around then.
In this paper, we focus on the activities of ASBJ in thefirst eight years, and determine what kinds of organiza-

2. Overview of ASBJ

For getting over the long-term recession, Japanese government put efforts into huge economic structural reforms. Also, in order to respond to the appearance of
IASB, standard-setting regime in Japan was transformed
from a public body to a private in order to make themost of the private sector vitality. Subsequently, ASBJ
was created as the first private setter for establishingaccounting standards in Japanese history in September
2001. The creation was initiated by Japanese government, Financial Service Agency, with some stakeholders related to securities markets in private sector(ASBJ/FASF [4]).
The ASBJ has completed a total of fifty-seven standards
from September 2001 to September 2014. Graph 1
shows the activities of ASBJ during this period with one
polygonal line and two bars. The line indicates thenumber of standards the Board developed every year,
while the two bars respectively denote the number of“Liberal” standards and of “Non-Liberal” ones. Herein,“Liberal” standards are synonymous with the term “investor-oriented,” including standards to include accounting techniques which have never been used in© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 356 -

Kensuke Ogata

practices, to reduce alternnative accounting procedurres,and to introdduce the fairr value measurement; “NoonLiberal” givee the preparerss their favorab
ble consequennces,for example, the incrementt of alternativee procedures aandthe clarificattion of accouunting proced
dures simply inresponse to chhanges of other standards oro related lawss.
Graph 1: T
The Activities ofo ASBJ from 20012
to 2014

According too this graph, we
w can separaate the standaardsetting activiities of ASBJJ into three paarts. The firstt isperiod from 22001 to 20044; the second is from 20055 to
2008; and thee third is sincee 2009. In thee first period, the
ASBJ set veery little accoounting standaards. The Bo ard
made thirty sstandards publlic, which acccounts for 52.66%of the total. Inn addition, the Board issued fifteen “Libberal” standards. It accounts forf 68.2% of thet total “Libeeral”
standards oveer the whole period.
p
In the next four yeaars,
the Board acttively developped standards from both quuantitative and qqualitative asppects. In the last period, theactivities of A
ASBJ slowed down.
d
Due to spacee limitation, wew focus on a big differennce
between the aactivities in thhe first four years
yand the second four in this paper. Therefore, we mention charracteristics of sttandard-settingg of ASBJ inn two periodss asfollows: the ASBJ develooped quite passive standaardsetting attituude during thhe period of 2001-2004; theBoard contraastingly exhibited the most positive behhavior during thhe period of 2005-2008.
2
In
n taking accouunt
for a reason oof that differennce, it seems effective to shhedlight on diffeerence of orgaanizational stru
ucture, based on
the traditionaal organizationns theory.
3. Organizattional Structu
ure of ASBJ
3.1 Social neetwork analysiis and using datad
We extract structural featuure of ASBJ with social nnetwork analysiss1. We particuularly use conttinuous corenness
analysis to iddentify a set of actors who haveha high ddensity of ties ammong themsellves (the core)) and another set

off actors who haveha low deensity of ties among themselves (the perip
phery) by havving few even
nts in commonn(B
Borgatti and EverettE[8]). H
Herein, the terrm “coreness””
reffers to who has
h a high dennsity of ties inn the networkkbyy many common events.
In analyzing thee structure off ASBJ, we usse data associateed with existiing organizatitions to whichh members ofsome bodies inside Financcial Accountiing Standardss
Fooundation (FAASF), which iis a parent orrganization ofAS
SBJ, belong. An underlyinng idea to usee such data iss
thaat individual preferencespannd opinions on
n the accounting
g standards oro techniques should be deepended uponn
hiss/her background or careerr. In sum, shedding light onn
thee relations baased on the caareer of mem
mbers who engaaged in the accountingastaandard-setting
g process canndeemonstrate wh
hat kinds of actors dominnate and whaat
vaalues are prevaailing in the seetter.
Too do so, first ofo all, we preppare a matrix data-set, compo
osed of both the existing organizationn of memberssshhown in FASF
F’s annual repports and the bodies insideeFA
ASF 2 by perio
od. This dataa-set tells us that the moreeannd the longer the organizatiions send their members toothee bodies insid
de FASF, the higher the cooreness scoress
off the organizaations get. Seecond, we trransform eachhdaata-set to thee organizatioon-to-organizaation data-seetthrrough the affiiliation processs, included inn the softwaree
off social networrk analysis, U
UCINET 6 (Boorgatti, Everetttannd Freeman [99]). Third, wee conduct coreness analysiss
ussing the organ
nization-to-org
rganization daata-set and, ataonnce, draw a graphgon the basis of deg
gree centralityyannalysis perform
med over thee same data-seet, using NetDrraw (Borgatti [10]).
We
W prepare tw
wo original ma
matrix data: on
ne is the dataa
peertain to the fiirst half, a perriod of 2001--2004; anotherris the data relatted to the secoond half, a peeriod of 200520
008. We confiirm that 69 orrganizations engagedein theestaandard-setting
g process of A
ASBJ in the firrst period; andd
77
7 organizations in the seconnd period.
3.2
2 Analytical resultsr
3.2
2.1 Analyticall results in Per
eriod 1
A result of the coreness anaalysis on the first period iss
shhown in Tablee 1. The top 330 organizatio
ons are shownn
in this table. This result tells uus some intereesting facts.

1
Some recent sstudies on accounnting standard seetters applied soocial
network analysiss. See Perry and Nöelke
N[5], Richaardson [6], Ogataa [7].

2Inncluded are Board of Directors, B
Board of Councilo
ors, Board, ThemeeAddvisory Council, Advisors,Aand Staandards Advisory
y Council.

© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 357 -

Th
he role of nationaal

First of all, thhe top four acctors, that is, Tokyo
T
Stock E
Exchange, Univversity of Tokkyo, Chuo-Aoyama, and Jap
apanesen
Bankkers
Table 1: Topp 30 coreness inn period 1
Association,A
ggot
strikinglyshhighcorenesscscooresamongaa totall of69
6 organizatioons.
It
I is an unpreecedented
d
featu
ture
that
t
organiizations
tfrom stoock
marketsmaand
academicacoommunitymplayyedcentralcroles inthetstandaardsetter.
s
Althouughveryv
few uuser
actorsaexistedd inthist
netwoork,somesacademmic
actors appearred, for exam
mple, Waseda University, H
HiSetotsubashi Unniversity, andd University ofo Shizuoka. S
cond, preparre of financiaal statements, including b othfinancial com
mmunity andd non-financcial communiity,
belonged to half of the top
t 30 organiizations. Amoong
these organizzations, non-ffinancial com
mmunity, for example, Hitachhi, Panasonic, Sumitomo Electric,
E
and T
Tokyo Electric P
Power, obtainned relatively highhmarks.
Graph 22: Network Grapph of the ASBJJ in Period 1

oyama, and Jaapanese Bankeers Associatioon) were domAo
inaant actors. In addition to thaat, more organ
nizations fromm
preparers appeaared on this graph, comppare to otherrkin
nds of groups.
3.2
2.2 Analyticall results in Per
eriod 2We
W confirm thee coreness ressult of ASBJ in the following
g four yearss:
Table 2: Top 30 coreness in period 2
20005-2008 (seeeTaable 2). Accco
ording to thiis
Taable, we caansee that therre
waas a considerrab
ble differencce
incorenesss
scores between
thee top four orrgaanizations anndthee others. Witthreggard to the top
fouur, included
weere
Azusaa,Toohmatsu,Sh
hinNihon, an
nd
FA
ASF.
Thessethrree accountinng
firrms are mem
mber firms off the Big Fou
ur 3 . Also, theeFA
ASF has gotteen staffs on tthe payroll; as a result, theecooreness score of FASF shaarply increaseed. Secondaryy,
prepares of finaancial statemeents occupied
d a half of theetop
p 30 organizaations alike thhe first period. However, itsscoomposition chhanged as fi
financial sectors includingg
Graph 3: Network
N
Graph oof the ASBJ in Period 2

Also, we see the organizatiional structuree of ASBJ at tthat
time (see Graaph 2). As is the case with the above cooreness result, w
we can tell thaat the four maain organizatioons(Tokyo Stockk Exchange, University of Tokyo, Chuuo-

3

Azusa
A
belongs to KPMG group; T
Tohmatsu to Delo
oitte; and ShinNiihonn to Ernst & You
ung.

© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 358 -

Kensuke Ogata

finnancial commmunity (18.1%%) and financiial communityy(16.2%), follow
wed this groupp. To focus atttention on theediffference betw
ween the botth periods, thhe professionn
group significan
ntly increasedd the proportiion (+23.3%)),annd the regulattors came nexxt (+11.0%); but all otherrgroups, for example, the acaademic group (-15.9%), theenoon-financial grroup (-7.5%),, and the finaancial group (5.66%), lowered their importannce.

Nomura Securities, Mitsuui Sumitomo Insurance, aand
Meiji Yasudaa Life, had relaatively high sccores.
Then, we makke sure of a networkngraph
h of ASBJ duri
ring
the same periiod (see Graphh 3). Accordin
ng to this Grapaph,
we can pointt out that the actorsaas desccribed below existed at the heart of this network: To
ohmatsu, Azuusa,
ShinNihon, F
FASF, Tokyo Stock Exchan
nge, and seveeralfinancial andd non-financiaal companies. Among otheers,
accounting prrofession grouup occupied a central positi
tionin this netwoork, in terms of the strengtth of connecti
tionshown by linee thickness.

5. ConclusionsTh
hus, it seems that the ASB
BJ, during the first periodd,coonstructed thee organizationn, in which the preparerr
group was the mostm central pplayer, althou
ugh orchestratingg the various powers of Jaapanese actorss, for the purpo
ose of setting very few stanndards. On th
he other handd,
thee Board form
med an accounnting professiion-centric orgaanization to developdthe sstandards, whhich containedd
neew accounting
g techniques tto have neverr been used inn
Jappanese practicces, in an activve attitude.

4. DiscussionnTo emphasizze the changee of the ASB
BJ’s organizati
tion
structure betw
ween the firsst period and the second, weshow the trannsition of centtral actors by attributes in T
Table 3. This taable presents thet proportion
ns of each attrribute group to all groups bettween the botth periods on thebasis of corenness scores anda the differeence. Herein, attribute group are divided intoiseven grooups as follow
ws:
academic com
mmunity (Acaa); financial community (Fiin);
non-financiall community (Non-Fin);(accounting proffessions (Pro); regulators (RReg); the users of financcialstatements (UUser); and the others (Other).
In theTable 3: Prooportions of Eacch Attribute Gro
oup
fi
first
between B
Both Periods and the Differencce
periiod,
the pprepaarer
grooup(47.33%)
obtainnedthehighest rank. Among this group, non-fiinancial commmunity group (225.5%) had suuperiority overr financial grooup(21.8%) to a small extent. Academic coommunity grooup(21.1%) and accounting professionpgrooup (20.2%) ffollowed these groups. The ASBJ thus orchestrated thevarious poweers of Japaneese actors, inccluding prepaarer
group, acadeemic commuunity, accoun
nting profess ion
community, aat that time. AmongA
them, preparer grooup
was a pillar too the ASBJ.
On the otherr hand, the accounting professionpgrooup(43.5%) becaame the most powerfulpplay
yer in the secoondperiod. Preparer group (34.3%),(com
mposed of noon-

Reeferences[1] Büthe T andd Mattli W (20011), The New Global Rulerss:
The Privatizzation of Regullation in the WorldW
Economyy.
Princeton Un
niversity Press2] Rumelt RP (1974), Strateegy, Structure, and Economicc[2
Performancee. Harvard Univversity Press[33] Miles RE an
nd Snow CC (1 978), Organizaational Strategyy,
Structure, an
nd Process. McG
Graw-Hill[44] ASBJ/FASF (2012), The 110-year history of ASBJ/FASF
F(in Japanese). FASF
[55] Perry J and Nöelke A (20005), International Accountingg
work Approachh. Business andd
Standards Setting: A Netw5): 1-32
Politics 7(3.5[66] Richardson AJ (2009), R
Regulatory Nettworks for Accounting an
nd Auditing St
Standards: A Social
S
Networkk
Analysis of Canadian and IInternational Sttandard-Settingg.
Accounting, Organizations and Society 34
4: 571-588[77] Ogata K (2010), A Study oof Accounting Standard-SettingSgh Theory. Artitificial Life and Robotics 155:
Using Graph279-283[88] Borgatti SP
S and Evereett MG (19999), Models of
Core/Peripheery Structures. Social Network
ks 21: 375-395[99] Borgatti SP, Everett MG, FFreeman LC (20002), Ucinet for
Windows: SoftwareS
for Soocial Network Analysis. Harrvard, MA: Analytic
A
Technoologies[100] Borgatti SP
P (2002), Netddraw: Network
k Visualizationn.
Harvard, MA
A: Analytic Tecchnologies© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 359 -

An Empirical Research on Inter-firm Capital Relationship in Yokokai
Using IDE Spatial Model
Takao Ito
Graduate School of Engineering, Hiroshima University, Higashi-Hiroshima, 739-8527 Japan
itotakao@hiroshima-u.ac.jp
Makoto Sakamoto and Satoshi Ikeda
Dept. of Computer Science and Systems Engineering, University of Miyazaki, Miyazaki, 889-2192 Japan
sakamoto@cs.miyazaki-u.ac.jp bisu@cs.miyazaki-u.ac.jp
Rajiv Mehta
School of Management, New Jersey Institute of Technology, New Jersey, NJ 07102-1982 U.S.A
mehta@njit.edu
Tsutomu Ito
Hamura Factory, Hino Motors, Ltd, Tokyo, 205-8660 Japanfw.eldorado.500cuin@gmail.com

Abstract
This paper introduces recent fundamental modifications to Japanese alliance system known as the keiretsu, andanalyses how these changes have affected corporate performance. Specially, the performance of Japanese auto
manufacturers, such as Toyota, Nissan and others, has significantly improved due to sophisticated production
system technologies, highly productive workers, and recurring transaction relationship with other partners in their
network family. One possible determinant of their success could be due to their unique organization forms –thekeiretsu– which provides a strong platform to forge their strategic alliance relationship with their parts suppliers aswell as collaboration in research and development with other automobile makers. After economic bubble of the
1990’, the strong ties between automobile makers and their supplier partners experienced significant changes,
which are known as “keiretsu loosening”. Consequently, what is the status quo of automotive keiretsus? Does
cross-shareholding, which is one specific form of capital relationship in keiretsu, still contribute to improving
corporate performance? To answer these questions, this paper reports the results of a study that collected data on
cross-shareholdings to shed light on the relationship between inter-firm capital relationship and corporate
performance. The findings of this empirical investigation reveal that: (1) Keiretsu is a flexible, highly adaptive
organizational form; its scale changes in response to economic situations; (2) Capital relationship is still asignificant determinant of increasing profits for keiretsu partners even after the bubble burst in the 1990s.
Keywords: Influence, Degree, Effective Size, the IDE model, keiretsu loosening.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 360 -

Takao Ito, Makoto Sakamoto, Rajiv Mehta, Satoshi Ikeda, and Tsutomu Ito

1. Introduction
Japanese automobile manufacturers still show signs of
performing at a significantly higher level than theirglobal counterparts. This could possibly be due to thesophisticated technologies deployed for their production
systems, highly productive employees, and continuoustransaction relationships with other member-partners in
the keiretsu network. Possibly, one explanatory factor
contributing to their success could be due to theirunique organization forms –the keiretsu– which
provides a strong platform to forge strategic allianceswith their parts suppliers, as well as collaboration in
research and development with other automobile makers.
This manuscript is organized as follows: Section 2
reviews the relevant literature associated with keiretsu
networks. Section 3 describes the data collection
process and the new network model. Based upon thefindings, the managerial implications are discussed in
section 4. In section 5, the study limitations are
identified and avenues of future research are proffered.
2.

Variables Selection

To shed light on these issues and to examine the
network relationship between cross shareholdings andcorporate performance, data were collected from
Mazda’ keiretsu, Yokokai,. The Mazda’s keiretsu iscomposed of three sub-organizations: Nishi-Nihon
Yokokai, Kanto Yokokai and Kansai Yokokai.
As previously noted, many structural indices of network
analysis have been developed. This study selected
degree, influence and effective size of the firms
included in Yokokai to analyze the relationship between
those indices and corporate performance.
Degree is an index of a firm’s potential communication
activity. In a network, cross shareholding degree
includes two categories: in-degree and out-degree. Thisis because cross shareholding networks are considered
to be asymmetric organizations. In-degree refers to a
firm accept investment from other member firms,
whereas out-degree reflects a firm that only buy stocks
from other firms within the network. Degree iscalculated as below [7].
nC D ( pk )   a( pi, pk )

1985

1993

1997
2004
Fig.1. Capital Networks in Yokokai.

a ( p i , p k )  1 ; if and only if pi and pk are connectedby a line= 0; otherwise
Percentage data of inter-firm’s transactions werecollected from Yokokai. In a transactional network,high value of degree is positively associated with its
corporate performance such as sales and profit [9-10].
Based upon this prior work, the following hypothesis ispostulated:
H1: Out-degree will be positively associated with its
profit, and in-degree will be negatively associated with
its profit.
Influence reflects the power to influence or have an
impact on other member firms directly and indirectly ina network. Consequently, influence is being divided into
two parts: direct influence and indirect influence.
Suppose that A is the matrix of the direct network, andAn means the indirect influence from one firm toanother firm by n steps. Then influence is calculated asfollows.

T  A  R  A  A2  A3 ・・・ An
 A( I  A) 1
where
T: Total influence;
A: direct influence;
R: indirect influence;
I: Identity matrix.

(1)

i 1

where

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 361 -

(2)

An Empirical Research on

and -0.319 respectively. In-degree is significant only in1985 and 1993. The value of in-degree is -0.2191 and 0.3493, which is illustrated in Fig. 2.
All of the out-degree is significant, but the relationshipis the inverse. This means that higher investments areassociated with less profit. According to the findings
reported by Sakamoto et al., hypothesis 1 holds intransactional network, but in capital network, out-degree
is negatively associated with profit. However, in
McGuire and Dow’s study, one of the conclusions isinconsistent because out-degree is negative even before
the bubble economy collapsed. Much more quantitative
research should be done to find support for theirarguments. As interesting findings is that value in 1997
is very high. And the value returned to normal levels in
4 years after its adjustment. The values of in-degree in1985 and 1993 are significant, but in 1997 and 2004
they are not significant. The bubble economy occurredat the beginning of 1990. Evidently, keiretsu began toadjust their structure after bubble economy collapsed.
Therefore, it is apparent that the dramatic loosening ofkeiretsu occurred in Yokokai. Out-degree is a reflex ofthis change. Upon close investigation, the member of
Yokokai is found to have been significantly changed.
Most firms with weak competition disappeared andsome strong firms such as Toyota’s suppliers become
new members in Yokokai. Thus, based upon theanalysis described above, H1 partially holds.

In an asymmetric network, cross shareholding influence
includes two categories: influence and affectedness.
Influence refers to a firm have strong impact on othermember firms, whereas affectedness reflects a firm that
only is influenced by other firms within the network. In
same network, influence has strong impact on its
corporate performance [4, 10]. Consequently, thefollowing hypothesis proffered:
H2: Influence will be positively associated with its
performance and affectedness## will be negatively
associated with its profit.
Effective size of the network refer to the number ofalters that ego has, minus the average number of ties
that each alter has to other alters. It can be calculated asfollows [8].

ES ( pk )  ( n  1) 1
 x pk
n 1

(3)

where
n: number of ego network (pk is not included);
xpk: node k’s connection line in k’s ego network.
A recent study investigated the relationship between
firm network position and corporate venture capital
investment [11]. In another study, Sakamoto et al.
reported effective size is one of the key determinantsassociated with corporate performance in transaction
network [12]. Accordingly, the following hypothesis isposited:H3: Effective-size will be positively associated with its
profit.

3. Analysis and Discussion
The implications of our results can be considered asfollows.

Fig.2. Out-degree and in-degree in Yokokai (1985-2004).

3.1. Out-degree and In-degree

3.2. Influence

In capital network, out-degree means the amount ofinvestment in other partner companies, and in-degreerefers to the amount of investment accepted from othercompanies. From 1985 to 2004, the partial correlation
coefficient of out-degree is -0.2756, -0.863, -0.3822,

Influence means one kind of power to affect persons or
events without any direct or discernible effort. Influence
reflects the power to influence or have an impact onother member firms directly and indirectly in a network.
Influence depends on network depth. The depth of© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 362 -

Takao Ito, Makoto Sakamoto, Rajiv Mehta, Satoshi Ikeda, and Tsutomu Itoand significant. Therefore, there is support for H3 only
before bubble economy collapsed. The evidence reveals
that keiretsu has already made a major policy shift bybreaking with the so-called convoy system of allianceorganization.

Yokokai is 3, which means that depth still has impact on
its influence. From 1985 to 2004, the partial correlation
coefficient of influence is 0.3023, 0.864, 0.3874, and
0.3146 respectively. It can be shown as Fig. 3.
Fig. 3 shows that all partial correlation coefficients are
positive and significant. The value in 1997 is extra high.
And the value returned to normal by 7 years after its
adjustment. This result coincides with the result of outdegree. Invest or buy stock from other companies is aneffective way to maintain its influence in network. Thismeans that higher influence is associated with higher
profits. Therefore, H2 holds completely.

4.

Conclusion and Future Research Avenues

This paper proposed three hypotheses between profit
and network indexes including degree, influence andeffective size. The relationship between profit andinfluence is supported, but the association between
profit and degree is partially supported. The linkage
between profit and effective size holds only before
bubble economy collapsed. On analyzing thebackground of the membership change in Yokokai, theresults show that rational inter-firm relationships inkeiretsu are still associated with corporate performance.
This suggests that keiretsu loosening resulted inperformance improvement as cross share-holdings still
maintained.
Some factors, such as affectedness and effective size are
not statistically significant. Thus, additional factors
should be identified as potential determinants of profit.
Data were drawn from Mazda’s Yokokai to test the
hypotheses. Thus, additional studies should replicate
these findings by drawing data from other keiretsus,
such as Toyota’s Kyohokai and Nissan’s Nishokai, thus
testing the validity of these research findings.

Fig.3. Influence in Yokokai (1985-2004).

ACKNOWLEDGMENT
This work was supported by JSPS KAKENHI Grant
Number 24510217.

Fig.4. Effective Size in Yokokai (1985-2004).

References
1. McGulre J, Dow S., (2009) Japanese keiretsu: Past,
present, future, http://download.springer.com/static/
pdf/983/art%253A10.1007%252Fs10490-008-9104-5. pd
f?auth66=1399794791_ce53db11bd93d8098b0304e16da
ae06f&ext=.pdf, retrieved May 9, 2014
2. Ito T. (2004) Quantitative analysis of the firm’s
relationship in the Keiretsu of Toyota group, Proceedingsof the 2004 Information Resources Management
Association, International Conference, Innovations
Through Information Technology, pp.1078-1079, May
23-26, 2004, New Orleans, USA.

3.3. Effective Size
All partial correlation coefficients of effective size canbe drawn as Fig. 4.
Effective size refers to the situation of ego’s network.
The firm has strong connected neighbors if the value ofeffective size is high. Basically, strong connectedneighbors can be considered as good condition to invest
because all of the firms invest with each other inkeiretsu. The value of effective size only in 1985 is high

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 363 -

Design and Experimental Evaluation of a Human Skill-Based PID Controller
Yuntao Liao*

Graduate School of Engineering, Hiroshima University,
Hiroshima Japan
Toru Yamamoto

Faculty of Engineering, Hiroshima University
Hiroshima Japan
E-mail: liao-yuntao@hiroshima-u.ac.jp, yama@hiroshima-u.ac.jp
www.hirosima-u.ac.jp

Abstract
Nowadays less and less people are willing to do hard works. Which makes that the controller based on human skill
is needed. Cerebellar model articulation controller (CMAC) is a kind of neural networks (NNs) that can easily
solve problems of nonlinear system. Compared with other NNs the advantage of CMAC is that it takes shorter
learning time. Moreover because of the widely used of PID controller a human skill-based PID controller using
CMACs has been proposed in this paper.
Keywords: Cerebellar model articulation controller, Neural network, Human skill, PID controller.

time to adjust weights of neurons, which makes it
impossible for NNs to be applied widely. However theappearance of cerebellar model articulation controller(CAMC) that was proposed at 1975 by Albus partly
solves this problem. CMAC has simple structure andlike other NNs weights of CMAC also need to be
adjusted, but this kind of adjustment has generalization
ability. This kind of ability brings CMAC good realtime performance and some CMAC controllers havealready been proposed5. Due to the above reasons using
CMAC to design a controller based on human skill is
feasible.

1. Introduction
In the last decades, the automatic controller has been
widely used in many fields. But still there are a lot offields that human skill cannot be taken place byautomatic controllers. However, the skilled workers
become less and less due to the change of modern
thoughts. As the result these specific skills will not betaken to the next generations. Therefore, to develop
human skill based controller is necessary.
Human skill is a kind of nonlinear system. In order todeal with such nonlinear system, it is effective that someneural networks (NNs) are utilized. So far many NNs
have been proposed and parts of them have beensuccessfully used to design controllers1-4. But there is aproblem that the conventional NNs need large learning

Furthermore PID controller has been widely used invarious control systems. And the experienced humanexperts have been inevitably required in tuning PID
gains. For the above proposal a human-skill based PID
controller using CAMCs have been proposed.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 364 -Yuntao Liao, Toru Yamamoto

2. Design of a Human Skill-Based Controller
2.1. CMAC
The following figure is a sample of simplified 2dimensional input CMAC model to explain the structureof CMAC.
Fig.2. Block diagram of proposed method

In figure human skill data has already been learned by
the CMAC and PID gains that tuned by these CMACs
are used to control the system. The CMAC here sets e(t)and ᇞe(t) as the input space to calculate weights. And
ᇞe(t) is the signal defined as:(2)
e(t ) : e(t )  e(t  1)
PID parameters are tuned by CMACs according to thefollowing equations:K

K p  W p ,h (t )Fig.1. 2-dimensional input CMAC model

h 1

K

K i  Wi ,h (t )In Fig.1, it is a CMAC with 3 weight tables and 4 labels.
The set of input signals S (3,6) is mapped to B, F, J andc, g, k as set of labels M. Then, from each weight table
8,9 and 3 are selected. The sum of the selected weights
is 20. Assume that the wished output value is 14, there
will be a -6/3 added to all referred weights in the tables
because of the difference between obtained value andwished value is -6 and the number of selected weighttables is 3. As explained in the example CMAC usuallyworks as follows: First the set of input signals S ismapped to the set of labels M. Then, according to theset of tables weights will be selected from each table. At
last, output signals will be performed as the sum of theweights. From the explanation, when CMAC updating
weights for one input signal the weights for the nearest
input signals are also being updated, this kind of ability
makes CMAC can obtain approximately wished value
in shorter learning time than conventional NNs.

(3)

h 1
K

K d  Wd ,h (t )h 1Where h=1,2…,K and K denotes the total number of theselected weights in CMAC. From above the human skill
based controller is designed through PID gains. And theperformance of the controller is strongly depended onthese PID gains.
Then the process of how CMACs learn human-skill datais shown in Fig.3.

2.2. Controller design

Fig.3. Block diagram of learning process

In this paper, the following PID structure is considered:u (t )  u (t  1)
 K p ( y (t  1)  y (t ))(1)

From Fig.3 there are two signals that attached asterisk(*) which means they are human-skill data. And thehuman-skilled signals are set as the teacher signals. The K i e(t )learned result u(t) approaches teacher signal u*(t) by K d (2 y (t  1)  y (t )  y (t  2))
updating weights Wh(t). The CMAC weight tables arewhere Kp, Ki and Kd denote the proportional gain, themodified by steepest descent method and shown in Eq.4.
integral gain and the differential gain. Moreover r(t)In the equation g(t) means the gradient that used todenotes the target value and e(t) means the control error
update weights, J means the error criterion. And they
signal. The block diagram that shows the process how
are expressed as Eq.5 and Eq.6. In Eq.5 a, b and c areproposed method works is Fig.2.
the appropriate positive constants set by designer.
© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 365 -

Design and Experimental Evaluation

W p ,hnew(t )  W p ,hold(t )  g (t )

J 1
K p K

J 1
new
old
Wi ,h (t )  Wi ,h (t )  g (t )K i K
Wd , hnewg (t ) (t )  Wd ,hold(t )  g (t )3.2. Simulation and Experiment Result
The aim of the experiment is to control the ball staying
at the middle of the beam, where r=0. Position of ballwill be considered as output y and in this experiment theinitial y is 20, it means at the beginning the ball stays at
the right side of the beam. And angle of the beam isconsidered as the input of the system and the initial
input is 0. The limit of u is set as: 45  u (t )  45
And the output y is limited as:
 20[cm]  y(t )  20[cm]
Human skill data u* and y* are obtained by skilled
operator and they are shown in Fig.5. And based on thehuman skill data the learning process of CMACs isperformed. In this design the CMACs with 4 weighttables and 3 labels are chosen. And the coefficients oflearning ratio are set to be a=102 ，b=102 , c=102. In
addition, the initial values of the weights were fixed PID
gains obtained by pole placement method based on themodel of the system. And these PID gains are Kp =0.0056, Ki = 0.0019, Kd = 0.0045.

(4)

J 1
K d K1c  a  exp(b | u * (t )  u (t ) |)1
J :  (t ) 2
2

(5)
(6)

  (u * (t )  u (t ))(7)Moreover, the partial differential of Eq. (4) is developed
as follows:J J  (t )  u (t )K p (t )  u (t )  K p

  ( t )( y ( t )  y ( t  1))
J J   (t )  u (t )K i
  (t )  u (t )  K i
  (t )e (t )J
 J   (t )  u (t )K d (t )  u (t )  K d
  ( t )( y ( t )  2 y ( t  1)  y ( t  2 ))(8)

3. Experimental Evaluation
3.1. Experimental Equipment
In order to illustrate the effectiveness of the proposed
method, an experiment has been performed by using abeam and ball control model. The experimentequipment is shown in Fig.4. As the figure shows, there
is an input manipulator for human to operate. It is set torecord the data of human skill. Also there is a distancesensor on beam to read output data of the system.
Moreover there is an Arduino board to transfer data
between computer and system. And the ball on the beamis the target to be controlled.

Fig.5. Control result by skilled operator

And the performance of learning process is evaluated by
the following integrated squared error (ISE) equation:30(9)ISE  (u * (t )  u (t ))
t 1

As Fig.6 shows, the learning process stops when the
integrated squared error is less than 0.00056.

Fig.6. Performance of learning process

Fig. 4. Beam and Ball control Equipment© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 366 -Yuntao Liao, Toru Yamamoto

Fig.7 shows the comparison between learning result uand teacher signal u*, where 30 iterations were
performed. Fig.8 shows the PID gains trajectories
corresponding to Fig.7. By using the PID gains thatshown in Fig.8 an experiment has been done and theresult is shown in Fig.9.From figure y is close to y*shows that by using proposed method human skill canbe learned.

and he is trying to stop moving the ball. Next kc and Ti
become smaller again and Td becomes bigger because
his prediction was wrong the ball is not reach the target.

Fig.13. Trajectories of PID parameters

Then kc and Ti becomes bigger and Td becomes smaller
because the manipulator predicts the ball is going toreach the target again and after that the change of kc, Ti
and Td is not obvious shows human is maintain his
input to move the ball to target. At last, kc and Ti
become smaller and Td becomes bigger to stop the ballat the target after that there is no change of kc, Ti andTd shows human thinks the ball stops at the target.
From analysis, the manipulator wants to control the ballstably approach to the target without overshoot.

Fig.7. Comparison between u and u*

4. Conclusions
In this paper, a human skill based PID controller using
CMAC has been proposed. And the effectiveness of
method has been experimentally examined on a beamand ball model. According to the control results the
proposed method can replace human skill and based on
the PID parameters human skill can be analyzed.

Fig.8.PID gains corresponding to Fig.7

Fig.9. Control result by proposed method

5.

References

1.

S.Liu and H.Asada ”Adaptive Control of Deburring
Robots Based on Human Skill Models,” Proc.of IEEE
Conf. on Decision and Con- trol,Brighton, pp.348
353,1991.
K.S.Narendra and K.Parthasarathy ”Identification andControl of Dy- namical Systems Using Neural
Networks,” IEEE Trans.on Neural Networks,
Vol.1,No.1,pp.4-27,1990.
S.Omatu, K.Marzuki and Y.Rubiyah ”Neuro-Control
and Its Applica- tions,” Springer-Verlag, 1995.

2.

3.3. Human Skill Analysis

Fig.13 shows the trajectories of kc, Ti and Td they are3.
got by following equations:
Kd
kc
(10)
4.
M.Kato, T.Yamamoto and S.Fujisawa ”A Skill-Basedkc  Kp
Td 
Ts
Ti 
Ts
kc
Ki
PID Controller Using Artificial Neural Networks,”
Proc.of Int. Conf. on Computational Intelligence forwhere Ts is sampling time and Ts=0.02. From the figure
Modeling, Control and Automation, Vienna, pp.702human skill is analyzed as follows: At first kc and Ti
705,2005.
become smaller and Td becomes bigger because
5.
K.Koiwai, K.Kawada and T.Yamamoto, ”Design of amanipulator tries to move the ball to target stably. And
Human-Skill Based PID Controller using CMACs,” Int.
then kc and Ti becomes bigger and Td becomes smaller
Conf. on Control, Automation and Systems, 2008shows human thinks the ball is going to reach the target© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 367 -

Prototype of a Supporting Tool to Generate Testing Communication Diagram
Tetsuro Katayama*, Seiya Urata*, Yohei Ogata*, Yoshihiro Kita†,
Hisaaki Yamaba*, Kentaro Aburada‡ 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 Japan‡Oita National College of Technology, 1666 Maki, Oita, 870-0152 Japan
E-mail: kat@cs.miyazaki-u.ac.jp, urata@earth.cs.miyazaki-u.ac.jp, ogata@earth.cs.miyazaki-u.ac.jp,
y.kita@ccy.kanagawa-it.ac.jp, yamaba@cs.miyazaki-u.ac.jp, aburada@oita-ct.ac.jp, oka@cs.miyazaki-u.ac.jp

Abstract
This research has implemented a prototype of a supporting tool to generate testing communication diagram. The
testing communication diagram helps a developer to understand where the software system is tested by a large
quantity of test cases written in text, and it is generated by adding the information of test cases to communication
diagram in UML (Unified Modeling Language). The implemented prototype can detect more efficiently deficiency
and/or contradiction in communication diagram and/or test cases.
Keywords: Software development, Software testing, Test cases, Visualization, UML(Unified Modeling Language)

1. Introduction
In recent years, test cases used in software testing havebecome a larger scale as a software system becomes alarger. It is difficult to understand where the softwaresystem is tested by a large quantity of test cases written
in text. Moreover, test cases or models to describe asoftware system with UML (Unified Modeling
Language)1 may have deficiency and/or contradiction
because the work to design the test cases and to model
the system manually. It causes a situation that defects
included in the system are not detected, leads to system
failure after its operation, and gives users a great trouble.
The testing communication diagram has been
proposed already.2 It visualizes messages, which are
written in a part of test cases, between objects insoftware system. It helps a developer to understandwhere the software system is tested by test cases written
in text.

The testing communication diagram is generated bycomparing test cases with communication diagram in
UML and then adding the information of the test cases
to the communication diagram. After the generation, adeveloper confirms whether or not deficiency and/or
contradiction exist in communication diagram and/or
test cases. Here, this process was gone manually.
In drawing testing communication diagram
manually, one of problems is troublesome points todraw it and to confirm that the drown diagram does nothave any mistake. In confirming existence of deficiency
and/or contradiction manually, one of problems istroublesome points to check it and to the checking
process become more difficult as testing communication
diagram becomes larger and more complex.
This research has implemented the prototype which
can support to reduce the burden to generate testing
communication diagram and to detect deficiency and/or
contradiction in communication diagram and/or test
cases. Because the prototype generates testing© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 368 -

Tetsuro Katayama, Seiya Urata, Yohei Ogata, Yoshihiro Kita, Hisaaki Yamaba, Kentaro Aburada, Naonobu Okazaki

communication diagram automatically, it can reduce theburden to draw the diagram and it can rid the work to
confirm that the drown diagram does not have any
mistake. And also, because highlight display in a tableof test cases and testing communication diagram are
implemented as a function of the prototype, it can
reduce the burden to detect deficiency and/or
contradiction in communication diagram and/or test
cases. Here, test cases are need to write in a template, afile format for test cases is CSV (Comma Separated
Values), and a test suite means a collection of test cases.
2. Testing Communication Diagram
We explain steps to generate testing communication
diagram.
(i) Select one of the test cases that are not yet incomparison with communication diagram.
(ii) Compare precondition with “participant name ofsending side.”(iii) Compare input with “message name.”(iv) Compare precondition with “participant name ofreceiving side.”(v) When these agree, enclose the part represented anarrow of message in the diagram with a solid line.
And, add a test case ID to inside the solid line.
(vi) If you have a test case that not yet compared, returnto (i). Otherwise, testing communication diagram iscompleted.

Fig. 1. Structure of the implemented prototype

Fig. 1 shows structure of the prototype. The prototype
consists of five parts: diagram drawing part,
communication diagram generating part, test cases
analyzing part, test case table displaying part, andtesting communication diagram generating part.
The diagram drawing part has two panels: mode
panel and paint panel, and supports a user to draw
communication diagram. In the mode panel, the user
can choose any objects the user want to draw. The paintpanel send data to the communication diagram
generating part. The data is both a type of the object
generated by the user and the coordinates of the placeclicked by the user.
The communication diagram generating part
generates communication diagram by receiving theuser’s drawing requests through the diagram drawing
part. It is regarded as communication diagram is

The completed testing communication diagram supports
to detect deficiency and/or contradiction incommunication diagram and/or test cases by confirming
whether arrows of messages is encircled, or whether
each test case ID exists.
3. Prototype of a Supporting Tool to Generate
Testing Communication Diagram
The implemented prototype has two major
characteristics to reduce the burden to find deficiency
and/or contradiction in communication diagram and/or
test cases.
result between communication diagram and a CSV
file in which test cases are written.
Highlight display in testing communication
diagram.
Based on the comparison result, test case IDs areadded each of message name in the communication
diagram as “message name : test case ID”, and theircolor is highlighted red from black.

Highlight display in a table of test cases.
Background color of a cell in a table of test cases ishighlighted red from white based on a comparison© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 369 -

Prototype of a Supporting

completed when a request to generate objects from thediagram drawing part disappears.
The test cases analyzing part generates test caseanalysis data. This part reads information by using
java.io.FileReader class3 and java.io.BufferedReader
class3 each line from the CSV file in which test cases
are written along a form “test case ID, pre-condition,
operation, post-condition”. And then, it gets test caseanalysis data from the information by using split methodof java.lang.String class3. The test case analysis data are
generated by storing each string to arrays in order of test
case ID, pre-condition, operation, and post-condition
The test case table displaying part generates a tableof test cases. This part adds the array outputted from thetest case analyzing part as one line of a table on thewindow. After finishing to add all elements of the array,this part compares test case IDs which is one of datastored to cells in the table with all test case IDs of eachmessage object on the testing communication diagram.
Here, a message object means one to express a message.
The background color of cells is managed per a line of
the table. The background color of the line where acomparison result is accorded is highlighted red fromwhite. When all test case IDs are compared, this part
displays a table of test cases.
The testing communication diagram generating part
generates testing communication diagram. This partcompares pre-conditions, operations, and postconditions in the array stored test case analysis datawith each attribute data of message objects incommunication diagram. If all of them match after
comparison, the font color of message name of its
message object is highlighted red from black. In
addition, its test case ID is added to the test case ID list
which is an attribute of the message object. And then,this part generates testing communication diagram byredrawing each object on the window and highlighting
message names and test case IDs. Here, the message
name of the message object which has a test case ID isdrawn as a form “message name : test case ID”.

Fig. 2. An overview of the implemented prototype“Mode panel”, “Paint panel”, and “Table of test cases”.
Each part is described as follows.







File menu
It is placed in the leaning to the left of the window.
If “open” is selected, a file dialog is displayed.
Mode panel
It is placed in the left of the window. In drawing
communication diagram, each mode can bechanged by user’s click. At present, we have
implemented seven modes: “Put lifeline”, “Drawlink”, “Draw message”, “Put name”, “Resize”,“Move”, and “Delete”.
Paint panel
It is placed in the middle of the window. It displays
elements in communication diagram and testing
communication diagram.
Table of test cases
It is placed in the right of the window. It is a table
which displays test cases list.

We applied the prototype to some examples andconfirmed that it worked properly. Elements which acomparison result is accorded in a table of test cases andtesting communication diagram are highlighted redcorrectly. Moreover, elements which do not accorded do
not highlighted.
In Fig. 2, test case ID A1 and A3 are highlighted
and. ID A2 is not highlighted.

4. Overview of the Prototype
Fig. 2 shows an overview of the implemented prototype.
The window consists has four parts: “File menu”,© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 370 -

Tetsuro Katayama, Seiya Urata, Yohei Ogata, Yoshihiro Kita, Hisaaki Yamaba, Kentaro Aburada, Naonobu Okazaki

5.

Discussion

In this chapter, we discuss the usefulness of theimplemented prototype.
We confirm the usefulness of our tool byexperiments using examinees. The examinees draw
testing communication diagram in hand by giving acommunication diagram and test suits. For this work, it
took an average of 232 seconds. In contrast, the time
when the tool uses the same diagram and test suits as an
input was an average of 5.2 seconds. Hence, theprototype can reduce 92% of the burden to generatetesting communication diagram. Moreover, in chapter 4,
we showed that our prototype generated testing
communication diagram properly. Works to confirm
that the generated testing communication diagram is notincluded human errors are not necessary because using
our prototype prevents the human errors in generating
testing communication diagram.
In addition, by the function of highlight display in atable of test cases and testing communication diagram,users can find deficiency and/or contradiction in them atglance. Hence, our tool can reduce the burden to detect
deficiency and/or contradiction in communication
diagram and/or test cases.
Some studies have been reported visualization oftest results (e.g. Refs. 4) and some tools have functionsof visualization of test results (e.g. Refs. 5). Also,
research to automatically generate test cases from
communication diagrams has been reported.6 In contrast,
our research attempts to visualize test cases which areusually described in text.
6. Conclusion
This research has implemented the prototype which cansupport to reduce the burden to generate testing
communication diagram and to detect deficiency and/or
contradiction in communication diagram and/or test
cases. Because the prototype generates testing
communication diagram automatically, it can reduce theburden to draw the diagram and it can rid the work to
confirm that the drown diagram does not have any
mistake. And also, because highlight display in a tableof test cases and testing communication diagram are
implemented as a function of the prototype, it can
reduce the burden to detect deficiency and/or

contradiction in communication diagram and/or test
cases. It contributes to improve the software reliability.
Future issues as follows.








Improvement in inputting communication diagram.
To generate testing communication diagram with
our prototype, users need to draw communication
diagram on the prototype. To improvement of
convenience of the users, the prototype will be
accept diagram drawn on other tools.
Improvement in displaying message names.
In implementation of our tool, only one arrow inmessage is drawn. Because of this, plural message
names are displayed parallel to the up of the arrow.
We consider improvement to be easy to see themessage name.
Adaptation extension of comparable elements ofcommunication diagram.
At present, our prototype does not support
communication diagram which has messages
including loop condition. To support them, a part ofthe method to generate testing communication
diagram will be reconstructed.
Extension of a table of test cases.
Test cases in this paper does not have expected
outputs. We need to add a field to a table todescribe the expected outputs.

References
1. UML Resource Page, http://uml.org/
2. S. Urata and T. Katayama, Proposal of testing diagramsfor visualizing test cases, Proc. 6th Int'l Conf. on
Software Testing, Verification and Validation(ICST2013) (2013).
3. Java™ Platform, Standard Edition 8 API Specification,
http://docs.oracle.com/javase/8/docs/api/
4. J. A. Jones, Fault localization using visualization of test
information, Proc. 26th Int’l Conf. on Software Eng.
(ICSE 2004) (2004) 54-56.
5. Hertland.Data Inc., Dynamic Test tool DT10,
http://hldc.co.jp/english/products/dt10/
6. P. Samuel, R. Mall, and P. Kanth, Automatic test casegeneration from UML communication diagrams,
Information and Software Technology, ScienceDirect,
49(2) (2007) 158-171.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 371 -

Code Coverage Visualization on Web-Based Testing Tool for Java Programs
Mochamad Chandra Saputra*, Tetsuro Katayama†*†

Universitas Brawijaya, Jl. Veteran, Malang 65145, Indonesia
University of Miyazaki, 1-1 Gakuen-kibanadai nishi, Miyazaki, 889-2192 Japan
E-mail: andra@ub.ac.id, kat@ cs.miyazaki-u.ac.jp

Abstract
The visualization of statement coverage (C0) and branch coverage (C1) measurement output can be used in several
ways to improve the verification and validation process. The result displays are percentage of a successful tested
code and visual information with highlighted in bright green as information of executed lines, bright yellow forstatement coverage and dark green The web-based testing tool significantly reduces the time for testing the codeand help user to understand the behavior of the tested code.
Keywords: Visualization on software testing, Code coverage, Web-based testing tool, Java

access from any machine connected to the Internet, and(4) independence from the operating system.4
To display the testing process and to understandthe behavior of a code, this research has implemented acode coverage visualization on web-based testing toolfor java programs. The testing process is shown theexecuted each line of tested code and calculation of thelines that executed several times using statement
coverage.

1. Introduction
There are many ways to measure software development
incorrectly. Testing can be the process of validating andverifying the software product to ensure the businessand technical requirements to work as expected.1
A common way to evaluate tests is to measure code
coverage. Code coverage helps software engineers tounderstanding which portion of code has been executed,
measure the percentage of source code executed during
the run and also the software engineering using a given
test suite throughout the software testing process. 2
Since software testing is a long and complex
process with probably huge result data collection, visual
information will provide testers with a quick andgeneral perspective, which leads to a better
understanding of a system’s software behavior.3
Implementing software testing as a web application forvisualizing the result of testing is one of the solution toeasily understand the behavior of a software code.
The main advantages of adopting the web
applications are (1) no installation costs, (2) automatic
upgrade with new features for all users, (3) universal

2. Specifications and Implementation Policies For
The Tool
2.1 Specifications
This research uses statement coverage (C0) and branchcoverage (C1).5
The testing tool has three parts: uploader code, java
service testing, and insertion of temporary database. The
java service testing has four sub-parts: analyzer, C0 and
C1 instrument code generator, testing part, and random
data generator as shown the design system in Fig.1.
To implement this model, several steps are followed.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 372 -

Mochamad Chandra Saputra, Tetsuro Katayama

Fig. 2. Example of the C0 and C1 instrumented Code
CheckNumber tested code.

corresponds to the executed statement. When allelements of the array C0 are assigned 1, the java service
testing judges C0 satisfies 100% and also for C1.
d) Random data generator generates random test
data. Users of the testing tool do not need to describe
the test data.The random data generator starts aftergenerating the C0 and C1 instrumented code. The
testing part executes the C0 and C1 instrumented code.
The random data generator inputs random data into theC0 and C1 instrumented code on behalf of the users
inputting data per standard input instructions.
After each execution of the C0 and C1 instrumented
code by the testing part, the testing tool obtains thecovering status of statements and measures C0 and C1.
The testing tool visualizes the covering status of thestatements by highlighting the original code that isdisplayed and animated as the sequence process
executes the tested code.

Fig. 1. Design of the testing tool.

1. Uploader code is an input of testing tool.
The input is tested code from a user into the serverthat used for the java service testing. Tested code is ajava program.
2. Java service testinga) Analyzer loads the original tested code and thenthe original tested code used by the C0 and C1
instrument code generator, testing part, and random data
test generator. The testing tool will execute the java
service testing to analyze and read the original code
based on the information on the specified file, thentesting the code and inserting it into a temporary
database for javascript visualization.
b) The C0 and C1 instrumented code generator
generates a C0 and C1 instrumented code. It is insertedor rewritten instrument code at each line of the originalcode, and it is used for calculating the number of
executions of C0 and C1.
c) Testing part views the covering status ofstatements and branches by inputting random data
during the background process. Java service testing
finds the class name of the original code by patternmatching. The class name is used when the C0 and C1
instrumented code generator generates the instrumented
code.
The testing method process will insert the data
execution line by line into the database. Data stored in
the database are the line number, number of executionsof each line, and tested code.
When the java service testing executes a testing, theservice assigns 1 to an element of the array that

2.2 Implementation
This research implements the web-based software
testing tool of an automatic unit testing tool using
random testing for java programs. This testing tool can
automatically test a program based on statement
coverage (C0) and branch coverage (C1), without
preparing test data by user. As an example of the tested
code is Class CheckNumber. Fig.2 shows the tested
code with C0 and C1 instrumented Code. To test thecode, the following steps are used to generate the code:Insert a package before the first line of Fig.2, togenerate the C0 and C1 instrumented code.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 373 -

Code Coverage Visualization on

Fig. 4. Dynamic display for investigation of the
CheckNumber tested code

Fig. 3. Static display for investigation of the
CheckNumber tested code






Rewrite an original class name as a class name
“MyCheckNumber” specified in advance by thetesting part.
Insert an assignment statement after all statements
to gain the covering status of the statements.
Insert an assignment statement to store outputs after
standard input instruction “System.out.println”.
Random data are used for data test the code andthen automatically tested for the CheckNumber2
using C0 and C1 instrumented code to verify thebranch condition. Each data testing process areinsert into the database using for visual information.

line execution, measurement percentage of success fromthe statement and branch coverage, and time execution
for testing as how many times the line was executed by
the java service testing.
The second result display is dynamic with Ajax. The
result display visualizes the behavior of the tested code
as shown in Fig.4. Certain coverage analysis tools also
depict coverage visually, often by highlighting portionsof code that are unexecuted by a test suite.7 In thisresearch, the visual information resets every time atester select a new code and then tests the code. The
testing tool performs new visualizations to know the
behavior of the code, and that it does not accumulate
with each successive test run before the testing.

Code instrumentation in this research consists of
3. Discussion
inserting some additional codes to measure coverage
results. Instrumentation can be done at the source level
The testing tool can show the correlation between visualin a separate pre-processing phase with pattern
information and software testing. This correlation means
matching or at runtime by measure of coverage result.
results collection and a better perspective of software
Data gathering consists of storing coverage data
testing. The testing tool shows the correlation as visualcollected during test runtime.
information, and it allows a better understanding of the
Random data tests provide the application underbehavior of the tested code.
testing with input data generated at random. Typically,
Visual information describes the behavior of thetesters pay no attention to expected data types.6 The
tested code as a sequence of the line executed by thetype of random data used in the testing tool is integer
testing tools. Visual information helps to understand the
only.
behavior of the tested code. The result displays for
The testing tool has two result displays. The first is a
visual information have been highlighted in bright green
static display as shown in Fig.3. The testing tool
as information on executed lines, bright yellow for
displays the static result of testing as the number of each
© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 374 -

Mochamad Chandra Saputra, Tetsuro Katayama

Table 1. Time comparison between using the testing
tools and manual testing (by human)obtained result is output as a static html page anddynamic display for visual information with Ajax.
The testing tool can show the correlation between
visual information and software testing as a resultcollection and perspective of software testing as asequence of the line executed by the testing tools. The
testing tool can reduce the time needed to describe atested code and execute unit testing. The time execution
needed to test CheckNumber was 716 ms.
Future issues are as follows:statement coverage and dark green for branch coverage.
Visual information describes the behavior of the tested
code as a sequence of the line being executed.
Code coverage visualizations are supposed to
improve developer efficiency and knowledge andpromote more productive testing strategies. The
research for the visualization leads developers toward abetter standard of test effectiveness.
The testing tool uses java file CheckNumber that
inputs 19 lines and then to measure statement coverage,
branch coverage, number of runs and the input of eachprogram to the end of testing by C0 and C1 to reach
100%. The testing measured at the web server with
CentOS release 5.9 (Final), Apache/2.2.3, Intel(R)Xeon(R) CPU 3050 @2.13GHz, PHP Version 5.3.3.
The times execution for testing the class
CheckNumber is 716 ms and if we test manually (byhumans), the average time is 4 minutes 15 second as
shown in Table 1. The testing tool can reduce time todescribe a tested code and execute unit testing in ashorter time.
Visualization concerns the graphical representationof information to assist human comprehension of andreasoning about that information.8 The testing tool result
makes possible distribution of the software testing
scalability problem, making certain key choices insteada technical distribution of responsibilities.

 Expand the type of a data test can input data tests
other than type int.
 Measurement with key performance indicators forsoftware development like resources and cost,product size and stability, product quality, process
performance, technology effectiveness.
References
1. Sivakumar.N.,Vivekanandan.K. “Comparing the Testing
Approaches of Traditional, Object-Oriented and AgentOriented Software System”, International Journal ofComputer Science & Engineering Technology (IJCSET),
3(10) (2012) 498-504.
2. Heed. Per., Westrup. Alexander., Runeson.Per.
“Automated Platform Testing Using Input Generation and
Code Coverage”. Master Thesis Document. Lund
University, Faculty of Engineering (2009).
3. Wang.Huansong., Zhang.Xiang., Zhou.Mingqi . “MaVis:
Feature-based Defects Visualization in Software Testing”,
Engineering and Technology (S-CET) 2012 Spring
Congress on (2012) 1-4.
4. Dogana.Serdar., Betin-Cana.Aysu., Garousia.Vahid. “Webapplication testing: A systematic literature review”, The
Journal of Systems and Software (ELSEVIER), (2014) 174201.
5. ISTBQ. “How to Calculate Statement, Branch/Decision
and Path Coverage for ISTQB Exam Purpose”.
http://www.ajoysingha.info
6. Agarwal,B. B., Tayal, S. P., Gupta, M. “Software
Engineering & Testing An Introduction”, Jones andBartlett Publishers, (2010) 161-179.
7. Lawrance, Joseph., Clarke, Steven., Burnett, Margaret.,
Rothermel, Gregg. “How Well Do Professional
Developers Test with Code Coverage Visualizations? An
Empirical Study”, IEEE Computer Society, (2005) 53-60.
8. Petre, Marian.,Quincey, Ed de. “A gentle Overview ofSoftware Visualisation”, Computer Society of India
Communications, (2006) 1- 10.

4. Conclusion

To improve the efficiency of testing in softwaredevelopment, this research has implemented a webbased software testing tool with java service testing of
an automatic unit testing tool for java programs withrandom testing. The implemented testing tool generates
the C0 and C1 instrumented code from the original code.
The testing tool uses java service testing toautomatically test a program by inputting random data
into the C0 and C1 instrumented code. After testing, the© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 375 -

TFVIS: a Supporting Debugging Tool for Java Programs
by Visualizing Data Transitions and Execution Flows
Hiroto Nakamura*, Tetsuro Katayama* ,Yoshihiro Kita✝
Hisaaki Yamaba*, Kentaro Aburada‡, Naonobu Okazaki**

University of Miyazaki, 1-1 Gakuen-kibanadai nishi, Miyazaki, 889-2192 Japan
Kanagawa Institute of Technology, 1030 Shimo-ogino, Kanagawa, 243-0292 Japan‡
Oita National College of Technology, 1666 Maki, Oita, 870-0152 Japan
E-mail: 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, aburada@oita-ct.ac.jp, oka@cs.miyazaki-u.ac.jp✝

Abstract
TFVIS(transitions and flow visualization) can perform the visualization of data transitions and the visualization of
execution flows. The visualization of data transitions shows the flow of variable renewals in executing programs. It
becomes easier to grasp the behavior in executing the programs whose behavior is unexpected by a bug. The
visualization of execution flows shows an entire flow of the execution. It is useful to select the part where users
want the visualization of the data transitions.
Keywords: Debugging, Java, Dynamic analysis, Visualization, Slicing

TVIS can’t visualize the data transitions between
different methods.
Therefore, this paper implements the visualization
tool TFVIS(transitions and flow visualization) which
visualizes the data transitions and the execution flows,
and shows the effectiveness of TFVIS. That is, we
combine the visualization of the execution flows withthe visualization of the data transitions in order tovisualize a larger program. Moreover, we improve thevisualization of the data transitions in order to realize
the more effective one.

1. Introduction
We need much time in order to find the cause of a bugin programs.1 We need to find the behavior of programs
which is different from ideal behavior in order to findthe cause of a bug. However, grasping the behavior isdifficult because the behavior of the program which
includes a bug becomes unexpected behavior.
We had implemented the visualization tool called
TVIS2 in our previous research. TVIS visualizes thedata transitions. The data transitions in our researchshow the flow of variable renewals in executing a
program.
TVIS expresses when and what value of eachvariable is renewed. Programmers can grasp behavior ofa program because they can prefigure the behavior ofeach variable at arbitrary timing in the program
execution by grasping the data transitions.
However, the efficiency of TVIS is lost, if the
program which TVIS uses becomes large. Moreover,

2. TFVIS
We have developed the visualization tool called TFVIS.
TFVIS visualizes the data transitions and the execution
flows of Java programs. Fig.1 shows an example of thewindow of TFVIS. Two small windows on the right of
the window are called the data transitions diagrams. The
diagram on the left-hand side of them is called theexecution flows diagram.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 376 -

Hiroto Nakamurra, Tetsuro Katayyama ,Yoshihiro Kita,
K
Hisaaki Yammaba, Kentaro Ab
burada, Naonobu Okazaki

Fig. 1. An exam
ample of the win
ndow of TFVISS.

o TFVIS shoows
The list of thhe left side of the window oflist of methoods of a progrram. The funcctions of TFVVIS
are as followss.
2.1. Programm analysis
Firstly, we eexplain the programp
analy
ysis used to thevisualization.. TFVIS perfoorms two types of analysis: thestructure anallysis and the executionseanaalysis.
The structure analysis is
i static analy
ysis for obtainning
the informatiion which is used to inserrt probes for the
execution anaalysis and visuualize. The information whhich
is obtained bby the structurre analysis is the informati
tion
about a locattion where staatements occu
ur: generatioons
and renewalss of variablees, loops, andd method caalls.
Moreover, it includes the informationiabbout the relati
tionof a class andd a method.
The execcution analyssis is dynammic analysis for
obtaining thee information about behavior in executi
ting
the programs. The informaation which iss obtained by the
execution anaalysis is the innformation abbout renewalss ofvariables, behhavior of loopps and method
d calls, and so on.
TFVIS obtaiins the inform
mation by ussing the probbes,
inserted basedd on the resultt of the structu
ure informatioon.
2.2. Data tran
nsitions diagrram
The data trransitions diaagram is gen
nerated by thevisualization of the data transitions. TFVISTin Fiig.1m.
hows the two windows
wof thhe data transittions diagramsh
TF
FVIS can sh
how the plurral windows of the dataa
traansitions diagrram in this waay.
of aThe data transitions diaggram shows renewalsr
he number ofvaariable. It is a table andd indicates th
andd
iteerations of executionein a lateral directiondde in a verticaal
inddicates the lin
ne number off a source coddirrection. Norm
mally, its tablle shows reneewal value ofvaariables to whhere the renew
wal of the vaariable occurss.
n Fig.1 showssTh
he area surrou
unded by thee red frame in6thaat the values ofo the array “aarray” becomee “0, 1, 2, 4, 6”
aftfter the meth
hod “sort” iis executed. The methodd“pprintArray” haas “array” as an argumentt the same ass
printArray” iss"soort". Howeveer, the renewaal value of “pnoot showed, beecause “printtArray” only refers to theevaalue of "array"" without updaating it.
m
The green patternpon thhe data transitions diagramshhows a loop, annd the number
er on it shows the number ofows extendingg
thee iteration off the loop. Thhe green arro
he process offroom the right of each num
mber show th
ment “for” iss
iteeration of th
he loop. In the statemsuurrounded by thet blue framme in Fig.1, the arrow at theen the arrow atafouurth loop in thist example iis shorter thanthee third loop; it expresses ““jump” by ussing statemennt“continue”. The
T
mark ““x” on the seventh loopp
ng the “break””
exxpresses the ennd of a loop ccaused by usinp.
staatement or failing the condiition of a loop

© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 377 -

TFVIS: a Supp
porting Debuggingg

Fig. 2. An
A example of tthe window of the data transitions search.

2.2.1. Data trransitions arroow

2.2
2.3. Selection of the target ffor the visualiization

The data traansitions arrow
w is a functtion of the ddata
transitions ddiagram, and visualizes thetdependennce
relations bettween each renewal of variables. T
Thedependence relations meaan ones betw
ween state off avariable and the state of otherovariablees which is ussed
for the renew
wal of its state.
The data transitions arrrow is based ono the techniqqueof the progrram slicing.3 It becomes easy to graasp
behavior of thhe data transittions in the loo
op and select thestandard valuue of the proggram slicing by combining thetechnique of tthe program slicings
and thee data transitioons
diagram.
TFVIS shhows the data transitions arrrow, when ussers
click a valuue of a variaable on the data transitioons
diagram. Thee data transitioons arrow is th
he red arrow aandconnects thee two values which the data transitioons
diagram show
ws with red font color. Fig.1 shows anexample of itt. It can visuaalize the relatiion of the valuues
between methhods. Therefoore, it is not neecessary that thestart point andd the end poinnt of the data transitionstarrrow
exists in the ssame method.
Fig.1 shoows the datta transitionss arrow in thewindow whenn users selectt the fourth vaalue of the arr
rray“array” whichh the method “sort” updates. The tip of thedata transitioons arrow of Fig.1 shows the state whhich
finally updateed the value whichw
the userr selected. Whhenusers find thhe suspicious state of a vaariable, the ddata
transitions arrrow supports to find the cau
use of it.

Th
he data transitions diagram can't visualize the whole ofthee program at a time, becaause visualizaation typicallyy
haas the issue4 th
hat the outputt of it becomes huge even ifthee visualized program is modderate size.
Therefore, users must sselect the parrt where theyy
waant the visuaalization of the data traansitions. Theeexxecution flow diagram, whhich we explaain in the nexxt
section, can sup
pport this selecction.
2.3
3. Execution flowfdiagrammTh
he visualizatio
on of the exeecution flow visualizes ann
enntire flow of execution of a program andd generates theeexxecution flow diagram. Thhe execution flow diagrammprovides the usseful informaation in orderr to select theepaart where useers want the visualizationn of the dataa
traansitions. It isi based on tthe sequence diagram anddshhows status off use of each m
methods and thet relation ofmeethod calls.
We explain the usage of tthe execution flow by usinggann example of Fig.1. The ccolumn in toopmost of theeexxecution flow diagram show
ws each class name, and theefiggures at under of it expresss each metho
od. The blackklin
nes extending from methodds are their lifeelines, and theethiick parts onn their lifellines are theeir executionn
specification whichw
means execution off the methodd.
WhenW
users cllick a execuution specification, TFVISS
shhows the dataa transitions diagram of correspondingg
wiith it. Normallly, the execuution specificaation uses theegray color. How
wever, the exxecution speccification usessthee light blue coolor, if the winndow of the datad transitionss
diaagram of corrresponding w
with it is beiing displayedd.
Th
he blue arrows on the execuution flow diaagram expresss
meethod calls, annd the red arrrows express completion ofmeethods.
The execution specificattion is surrou
unded by theeorange frame inn Fig.1 expressses the execu
ution which iss

2.2.2. Data trransitions searrch

The data trannsitions searcch shows the data transitioons
arrow by sellecting the vaariable and itss state from thesource code on the left side of the data transitioons
diagram. Fig..2 shows an exampleeof thee data transitioons
search, it is thhe window whhen users seleected the statee ofthe seventh lloop of the variablev“cheeck[i]” whichh isused as the coonditions of thhe “if” statemeent.
© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 378 -

Hiroto Nakamura, Tetsuro Katayama ,Yoshihiro Kita, Hisaaki Yamaba, Kentaro Aburada, Naonobu Okazaki

4. Conclusion

called by the only execution of the method “main” andis the second execution of the method “printArray”.

We have implemented TFVIS in order to improve theefficiency of debugging of Java program.
TFVIS can support to grasp behavior in executing a
program by visualizing the data transitions. The datatransitions diagram visualizes renewals of variables andbehavior of loops. Even if the data transition is therelation between methods, the data transitions arrow canvisualize it.
Moreover, we have implemented the visualization of
execution flow in order to improve the narrowness of
the range of the visualization of the data transitions. It
improves convenience when TFVIS visualizes a large
program. Therefore, we judge that TFVIS can support tofind the cause of a bug, and is effective of debugging forJava programs.
The future issues are as follows.
 The realization of the visualization of the multithread process.
 The improvement of the execution flow diagram byusing an idea of the fisheye view.

3. Discussion
We discuss the usefulness of TFVIS.
Programmers normally use the dynamic slicing5 ortrace tools6,7 in order to obtain data transitions inexecuting a program. However, it is difficult to grasp
positional relations of each renewal, if data transitions
include the process of a loop, and so on. The
visualization of data transitions of TFVIS uses therepresentation which exploits the source code and theprocesses of a loop as shown in Fig.1, and can show thedata transitions in a way that is easy to understand.
Moreover, the above-mentioned technique or tools needthat users search a reference point when they analyze aprogram. In the case of TFVIS, it becomes easy to
decide a reference point because the data transitions
diagram shows renewals of variables and behavior ofloops so that users understand them at a glance.
On the other hand, the range of the program which
TFVIS can visualize is still narrower than other tools
because the abstraction degree of the visualization of
TFVIS is lower. TFVIS is inferior compared with thetool8 which can visualize the multi-threading because
TFVIS can't visualize multi-threading.
We will be enable to adjust the abstraction degree ofthe visualization of the execution flow in order toenlarge the range of the program which TFVIS canvisualize. The abstraction degree of the execution flow
diagram is too low to prevent excessive expansion of its
diagram, if users want to visualize the program which
has the process of multi-thread or a larger program.
However, if it merely becomes higher, the users become
unable to grasp behavior of a program in detail.
Therefore, the information which the users obtain by the
execution flow diagram becomes not enough to selectthe part where they want the visualization of the datatransitions. We combine the visualization which theabstraction degree is low and the visualization which theabstraction degree is high by using an idea of thefisheye view9. Therefore, we will enable TFVIS toprovide the enough information which users select thepart where they want the visualization of the datatransitions and visualize the larger programs.

References
1.
2.

3.

4.
5.
6.

7.

8.
9.

Roger S. Pressman, Software Engineering A Practitioner’s
Approach 5thEdition, McGraw-Hill Science (2001).
Tetsuro Katayama et al, Proposal of a Visualizing Methodof Data Transitions to Support Debugging for Java
Programs, Journal of Robotics Networks and Artificial
Life, 1(2) (2011) 111-115.
Mark Weiser, Programmers Use Slices When
Debugging,Communications of the ACM, 25 (1982) 446452.
W. De Pauw et al, Execution patterns in object-oriented
visualization, In Proc. 4th COOTS (1998) 219-234.
H Agrawal, JR Horgan, Dynamic Program Slicing,
SIGPLAN Notices 25(6) (1990) 246-256 .
Kouhei Sakurai et al, Traceglasses: A Trace-based
Debugger for Realizing Efficient Navigation, Information
Processing Society of Japan, 3(3) (2010) 1-17.
Salman Mirghasemi et al, Querypoint : Moving
Backwards on Wrong Values in the Buggy Execution,
ESEC/FSE (2011) 436-439.
Jan Lönnberg et al, Java replay for dependence-based
debugging, PADTAD '11 (2011) 15-25.
W Furnas, Generalised sheye views, In Proc ACM
SIGCHI 86 Conference on Human Factors in Computing
Systems (1986) 16-23.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 379 -

Proposal of a Testing Method Using Similarity of Interleaving
for Java Multi-threaded Programs
Shoichiro Kitano*, Tetsuro Katayama*, Yoshihiro Kita†,
Hisaaki Yamaba*, Kentaro Aburada‡ 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 Japan‡Oita National College of Technology, 1666 Maki, Oita, 870-0152 Japan
E-mail: kitano@earth.cs.miyazaki-u.ac.jp, kat@cs.miyazaki-u.ac.jp, y.kita@ccy.kanagawa-it.ac.jp,
yamaba@cs.miyazaki-u.ac.jp, aburada@oita-ct.ac.jp, oka@cs.miyazaki-u.ac.jp

Abstract
In order to improve the efficiency of testing Java multi-threaded programs, this research proposes a testing method
to detect order violation in them using similarity of interleaving. The proposed method improves the efficiency of
testing by executing interleaving which can test the places where lead the order violation easily in source codes andby reducing interleaving which is similar to executed one already. The efficiency of the method is shown by
experiments for confirmation.
Keywords: multi-threaded program, testing, similarity, Java

integrated. One of the testing methods for multithreaded programs in unit testing is to execute programsin the plural interleaving by staggering the timing ofexecution between each thread. That method can detect
the existing bugs in multi-threaded programs. However,there is too large number of interleaving that we must
execute and many interleaving cannot detect thedistinctive bugs in a concurrent program. It takes thesame result to test by the interleaving that is similar totested one already and it is excessive testing. Moreover,each distinctive bug in a concurrent program has theown causes each other. It is not effective to detect all
such bugs by an only testing method.
In order to improve the efficiency of testing for Java
multi-threaded programs, this research proposes atesting method to detect order violation in them using
similarity of interleaving. Our proposed method
improves the efficiency of testing by executinginterleaving which can test all places which lead the

1. Introduction
In recent years, many computers are adapted multi-coreCPUs. In order to use such resources effectively, thedemand of multi-threaded programs increases.
It is difficult for even expert programmer toimplement multi-threaded programs. And it is easier toembed bugs than single-threaded programs[1]. There
are some distinctive bugs in a concurrent program
which are different from the bugs in a single-threaded
program. Those bugs often appear in the latter ofdevelopment process or when the program is used by
the users. In this case, it is difficult to fix the detectedbugs. In order to prevent this problem, we need to detect
the bugs and to fix them in unit testing. However,normal unit testing cannot test multi-threaded programs
enough. Unit testing tests often only single interleaving
because executed tasks are too small in unit testing.
Therefore, the bugs appear when the modules are© The 2015 International Conference on Artificial Life and Robotics (ICAROB2015), Jan.10-12, Oita, Japan- 380 -

Shoichiro Kitano, Tetsuro Katayama, Yoshihiro Kita, Hisaaki Yamaba, Kentaro Aburada, Naonobu Okazaki

Fig. 1. An example of a source code which includes orderviolation.
Fig. 2. An example of a source code which is inserted theprobes.

Table 1. The number of threads
which involved to detect concurrency bugs.
Application
MySQL
ApacheMozillaOpenOffice
Overall

Total
22
17
56
8
103

> 2 threads
1
0
1
0
2

2 threads
17
17
54
6
94

1 threads
4
0
1
2
7

2.2. Number of thread used in testing
In multi-threaded programs, the number of thread usedin testing is important. Table 1 is a result of theresearch[2] which shows how many threads arenecessary to detect the distinctive bugs in a concurrent
program.
Table 1 shows that two threads can detect the mostnumber of bugs. Hence, our proposed method uses two
threads that can detect bugs the most effectively.

order violation easily in source codes and by reducing
interleaving which is similar to executed one already.
2. The Kinds of Bugs That Our Proposal
Method Can Detect

3. Proposal Method

In this chapter, we explain the kinds of bugs that our
proposed method targets.
2.1. Target bug in multi-threaded programs

3.1. The four places which can lead the orderviolation easily

The kinds of bug patterns in concurrency programs areclassified[2]. The patterns are classified into dead lock,atomicity violation, order violation, and so on.
Order violation means that some threads can be
executed as an access to a certain memory in an
unexpected order. It occurs when a synchronization
protocol for several threads is deficient. Fig.1 shows anexample of a source code which includes order violation.
Consider the case which methodA and methodB are
executed in different threads each other. Statement B
expects that ham has been initialized by statement A
before statement B accesses to ham. However, thisprogram can execute statement B before statement A.
That leads the incident. The cause of this problem seems
like atomicity of flag. However, Accessing to ham is
unacceptable before ham has been initialized. Even if
accessing to ham is serialized, the incident occurs
because the initial value of ham is null.
In this research, we propose a testing method which
can detect order violation as above.

In order to detect the distinctive bugs in a concurrent
program, we have to enforce the testing which relates ontiming of execution between threads. However, eachbug pattern has own causes each other. Therefore, weneed to enforce effective testing the each bug pattern.
In this research, we propose the four places which
can lead order violation easily in source codes. We
detect the order violation by executing the interleaving
which can test the all places.
The places and reasons that we choose them arefollowing.
(i) Substitution for a shared flag variable used forsynchronization of threads
This place can detect the bugs caused by incorrect
place of statements that rewrite the value of a flag
variable.
(ii) The entry point of synchronized blocks
This place can detect the bugs caused by
defectiveness of logic for a synchronization
protocol implemented in a synchronized block.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB2015), Jan. 10-12, Oita, Japan- 381 -

Proposal of a Testing

Table 2. Result of experiments.
Number ofPlaces44
7
7All orReducedAll
ReducedAll
ReducedNumber of
Testing
16
9
128
59

Number of Testing in
Buggy Interleaving
7.4
4.8
66.4
27.9

interleaving and enforce testing each generated vector.
The method “preemptionPoint(int number)” leads
context switch to execute any interleaving by using
generated vector. The integer number of arguments
expresses index of elements that a vector has.
“preemptionPoint(int number)” chooses to lead context
switch or not to lead by using the elements at number ofarguments. Here, the first index of elements is 0.
Then, we calculate the similarity between vectors. In
this research, we use cosine similarity to calculate
similarity between vectors. When the similarity between
any vector and the vector which has used already ismore than a threshold, “preemptionPoint(int number)”does not use that vector. This reduces the interleaving
which leads the same result as the result of executed one
already. Therefore, we can improve the efficiency of
testing in multi-threaded programs.

Fig. 3. An example of a vector generation.

(iii) The start of execution of a threadThis place can detect the bugs caused whensynchronization protocol is not implemented
because programmers suppose that a program is
executed in only one interleaving.
(iv) The end of execution of a threadThis place can detect the bugs caused by a logic
which depends on the termination of other threads.
3.2. Test codes
In this research, we execute the interleaving which cantest all places explained in section 3.1. Therefore, we
describe the test code inserted some probes at the places
in a source code of a test target program.
Fig.2 shows an example of the test code.
“preemptionPoint(int number)” method which ismember of “PreemptionPointer” class is probes. We
explain integer values of arguments in the next section.
This method can lead a context switch, as needed. This
provides various interleaving which can test each place.
The numbers in comments correspond to the numbers
which are labeled each place in section 3.1.

4. Discussion
4.1. Experiment for Confirmation
We have conducted experiments to confirm efficiencyof our proposed method.
The method in experiment is that we prepare two
programs which include an order violation. And we
execute the test codes that have some probes at places
which can lead order violation easily in the source codesof those programs. They include four places and sevenplaces in their source codes. We enforce two tests thatuse all generated vectors and reduce the vectors which
are similar to vector which has used already. We
confirm the efficiency of proposed method bycomparing the number of testing that are result of eachtesting and by verifying that a bug can be detected whentests are reduced. Here, threshold used by deciding to
reduce vectors is 0.8.
Table 2 shows the results of the experiments. Each
number is the mean value of the result of executing the
programs 10 times for each condition. “Number ofTesting Buggy Interleaving” on the line of “All” showsthat the proposed method can detect the bug definitely.
Therefore, we confirmed that the proposed method can

3.3. Reduction of number of test execution byusing similarity of interleaving
Our proposed method improves efficiency of testing by
reducing interleaving which is similar to executed one
already. It is necessary to change interleaving to thevalue which can be calculated similarity. Therefore, we
generate vectors from the order and the number ofprobes in a test code. The values of the generated
vectors are 1 or 0. 1 shows that the probe leads a context
switch and 0 shows that the probe does not.
Fig.3 shows an example of vector generation. Whenthe number of probes is n, the number of generated
vectors is the n-th power of two. We treat this vector as© The 2015 International Conference on Artificial Life and Robotics (ICAROB2015), Jan. 10-12, Oita, Japan- 382 -

Shoichiro Kitano, Tetsuro Katayama, Yoshihiro Kita, Hisaaki Yamaba, Kentaro Aburada, Naonobu Okazaki

detect the order violation, which is one of the distinctive
bugs in a concurrent program definitely.
“Number of Testing” shows that proposed method
can reduce the number of each test execution by 56%
and 46%. And “Number of Testing Buggy Interleaving”
on the line of “Reduced” shows that the proposed
method can execute the interleaving which can lead the
bug definitely even if tests are reduced. These results
show that the proposed method reduces the number of
execution of testing by about 51% and can test to detectorder violation enough. Therefore, we confirmed that
proposed method can improve the efficiency of testingin multi-threaded programs.

conducted experiments that use the proposed method.
The results show that the proposed method can detect
the order violation definitely. And it can also detect thebugs even if it reduces the number of test execution by
about 51%. That shows it is effective for improving theefficiency of testing in multi-threaded programs to use
our proposed method.
Future issues are as following.
 Considering the testing methods which can detect
other bug patterns
Our proposed method is focused on only orderviolation. Therefore, it is not enough to detect otherbug patterns like dead lock or atomicity violation.
We need to consider the testing methods which candetect these bugs efficiently.
 Investigating the threshold of similarity for
reducing the vectors
Depending on the length of generated vector, there
is a case that the number of generated vector which
has similarity less than threshold is too large. In
such a case, the number of vectors which is reduced
is much the same as the number of vectors which
are not reduced. We need to investigate theappropriate threshold which is based on length ofthe vectors.
 Consider the testing method which use more than
two threads
Our proposed method uses two threads for testing.
Therefore, it cannot detect the bugs which occur intesting using more than two threads. We need toconsider it.

4.2. Related works
Concuerror[3] is a testing tool for Erlang.
Concuerror can test programs by executing pluralinterleaving and can reduce the interleaving bypreemption bounding[4]. Therefore, Concuerror canenforce testing efficiency. However, executedinterleaving is not focused on detecting the distinctive
bugs in a concurrent program. Therefore, it has
possibility not to detect such bugs.
Our proposed method executes the interleaving
which can test all places which can lead order violation
easily. Therefore, our proposed method can test more
efficiently for the distinctive bugs in concurrent
programs.
ConTest[5] is a testing tool for Java multi-threaded
programs. ConTest can enforce testing that relates ontiming by executing plural interleaving. However, it
does not ensure to detect bugs definitely even if
increasing the number of tests execution because it
executes plural interleaving in an ad hoc basis.
Therefore, the efficiency of its testing is poor.
Our proposed method executes the interleaving
which can test the all places which can lead orderviolation easily. It reduces testing in the interleaving
which is similar to executed one already and can detectorder violation definitely. Therefore, our proposed
method tests more efficiently.

References
1. J. K. Ousterhout, Why Threads Are A Bad Idea(Usenix
Annual Technical Conference, San Diego, 1996)
http://www.softpanorama.org/People/Ousterhout/Threads
2. S. Lu, S. Park, E. Seo and Y. Zhou, Learning from
Mistakes - A Comprehensive Study on Real World
Concurrency Bug Characteristics, (ASPLOS '08,
Washington, 2008), pp. 329-339.
3. A. Gotovos, M. Christakis, and K. Sagonas, TestDrivenDevelopment of Concurrent Programs using
Concuerror, (Erlang'11, New York, 2011), pp.51-61.
4. M. Madan, Q. Shaz, Iterative Context Bounding for
Systematic Testing of Multithreaded Programs,(PLDI ’07, Sam Diego, 2007), pp.11-13.
5. ConTest - A Tool for Testing Multi-threaded Java
Applications,
https://www.research.ibm.com/haifa/projects/verification/
contest/5. Conclusion
In this research, we have proposed a testing method fororder violation using similarity of interleaving. In orderto realize it, we have defined four places which can leadorder violation easily in source codes. The proposed
method reduces the interleaving by changing
interleaving to vectors which can be calculated andcalculating the similarity between a vector and usedvectors in testing already by cosine similarity. We© The 2015 International Conference on Artificial Life and Robotics (ICAROB2015), Jan. 10-12, Oita, Japan- 383 -

Proposal of a Modification Method of a Source Code to Correspond witha Modified Model in MDA
Yuuki Kikkawa*, Tetsuro Katayama*, Yoshihiro Kita†,
Hisaaki Yamaba*, Kentaro Aburada‡ 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 Japan‡
Oita National College of Technology, 1666 Maki, Oita, 870-0152 Japan
E-mail: kikkawa@earth.cs.miyazaki-u.ac.jp, kat@cs.miyazaki-u.ac.jp, y.kita@ccy.kanagawa-it.ac.jp,
yamaba@cs.miyazaki-u.ac.jp, aburada@oita-ct.ac.jp, oka@cs.miyazaki-u.ac.jp†

Abstract
This paper proposes a modification method of a source code to correspond with a modified model in MDA. The
proposed method generates, translates, and modifies EAD (Extended Activity Diagram). Also, it generates a sourcecode from the activity diagram. We use a simple ATM example to confirm availability of the method. The method
can reduce time and effort to keep consistency between models and a source code after requirement specification ismodified.
Keywords: MDA (Model Driven Architecture), Extended Activity Diagram, Activity diagram, Detail specification.

models from abstract models because these models do
not have detail specification of a system. The developer
must modify generated models to fit the modified
original models or generate a new model from themodified models with MDA Tool and then add thedetail specification to the new model by hand again.
The purpose of this study is to improve the efficiency
of software development using MDA. This paper
proposes a modification method of a source code tocorrespond with a modified model in MDA.

1. Introduction
MDA (Model Driven Architecture) is a concept ofsoftware development.1 MDA defines five models:business model, requirement model, platform
independent model (PIM), platform specific model(PSM), physics model.2 Each Model has different
abstraction level. A developer defines models andgenerates a less abstract model by softwaredevelopment in MDA. Here, MDA Tool is used togenerate a less abstract model. A developer uses UML
for modeling PIM and PSM.
One of the MDA's problems is how to keep
consistency between the original model and an edited
model which is generated from the original. A
modification method of PIM to keep consistency with
PSM is researched.3
Also, there is no consistency way if a developer edits
the original model. A developer can keep consistency if
MDA Tool generates models from the edited models
again. However, MDA Tool cannot generate complete

2. Proposal Method
As shown in Fig. 1, the proposed method has fourfunctions: generate a source code from the activity
diagram, generate EAD, modify EAD to correspondwith the modified activity diagram, and generate asource code from the modified EAD. The proposed
method consists of six steps as below.
(i) generate a source code from the activity diagram(ii) add detail specification to the generated source
code© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 384 -

Yuuki Kikkawa, Tetsuro Katayam
ma, Yoshihiro Kita
a, Hisaaki Yamabba, Kentaro Aburrada, Naonobu OkazakiO.

(iii) generate EAD(iv) modify thhe activity diaagram(v) modify E
EAD(vi) generate a source codee from the modified EAD
2.1. Generatee a source codde from the acctivity
diagram
The proposedd method generates a sourcce code from theactivity diagrram. The stepps to generate source code areshown as beloow.
(i) Acquire the function namen
The metthod generatees a skeleton of source coode.
The funnction name is the activitty name thatt is
describeed in the activvity diagram.. Here, the tyype
and the pparameter of thet function iss void.
(ii) Select thhe initial node(iii) Implemeent the functioon
The meethod executtes the process as bellow
dependinng on the typee of the selecteed node.
 Call activity nodee
Write the name ofo the call activity node to thesouurce code.
 Deciision node
Write an if-stattement to th
he source coode.
Conndition of if-statement is gu
uard conditionn ofthiss node.
 Activity final noddeFinnish the generaation of the soource code.
 Otheer than the aboove
Do nothing.
(iv) Select annother node
The metthod reselectss the node coonnected by theoutgoingg edge of the selectedsnode and go to (iii))

Fig. 1. Functions of tthe proposed method.
m(i) Generate a source codee from activitty diagram byy
the steps shhown in sectioon 2.1.
n line of LOOS(ii) Select the next(iii) If (i) and (ii) are not same charactter string, theemethod exeecutes the proccess shown ass below(a) Write LOS
L to the act
ctivity diagramm(b) Encirclle lines written
en in (a) as a noden(c) Select the original nnode of the gen
nerated sourceen (i)code in(d) Connecct the outgoiing edge forr the selectedd
node too the node gennerated in (b)(e) Make ana edge conneected with the selected nodee
and thee node generat
ated in (a)(f) Go to (ii)(v) If encircled
d nodes are coonnected eachh of them, thee(iv
method collects them as one node(vv) Go to (i)2.4
4. Modify the activity diagrram
Att the second step, a develloper modifiees the activityy
diaagram to fit th
he changed reqquirement speecification.

2.2. Add detaail specificatioon to the geneerated
source coode2.5
5. Modify EAD

A developer adds detail specificationstot the generaatedsource code.

Th
he method modifiesm
EAD
D to correspond with theemoodified activiity diagram. The method selects initiaal
noodes of the EAD and the modified actiivity diagramm.
We
W call the node extendedd in EAD “EEAD selectedd
noode”. We calll the node exxtended in acttivity diagramm“A
AD selected node”.
nThe mmethod executees the processs
shhown as below
w.
(i) Check whether AD seleccted node and EAD selectedd
node have thet same namme or not2.3. Generatee EAD

The method generates EA
AD from an activityadiagrramand a source code added detaildspecificaation. At the ffirststep, the methhod selects thhe first line off the source coodeadded detaill specificatioon. Thereafter, we call theselected sourrce code “LOOS (line of selected)”. T
The
method execuutes the process shown as below.
b© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 385 -

Proposall of a Modification(ii) Execute the process shown as belo
ow depending on
the resultt of (i)They havve the same name.
n(a) Channge EAD seleected node to the next nodee ofthe ccurrent EAD selectedsnode(b) Channge node to thet next nodee of current A
AD
seleccted node
They do not have the same name.
snodee to the activvity(a) Writte the EAD selected
diaggram(b) Makke an edge too connect thee EAD seleccted
nodee and the nodee written in (a)(c) Channge AD seleccted node to thet next nodee ofthe ccurrent AD selected nodeFig.
F 2. The acttivity diagram.

2.6. Generatee a source codde from the EAD
E
The method generates a new source code from themodified EA
AD. It hass informationn about dettail
specification and is appliied the chang
ged requirem
ment
specification.. Therefore, a new source code generaatedfrom the moddified EAD correspondscwithw the changged
requirement specification and has infformation ab out
detail specificcation.
Here, the mmethod can treeat only if-stattement. The w
way
to treat other statements is a future issuee. In addition, themethod can treat with additionaonly to a generaatedsource code aand an activitty diagram, bu
ut it cannot trreatwith deletionn or revision. Corresponden
nce to them iis afuture issue.

Fiig. 3. The sourcce code generatted from activitty diagram.

3. Applicatiion Example
Fig. 4. Thee source code addded detail speccification.

We use a ssimple ATM as an exam
mple to confi
firm
availability of the method. This ATM syystem executees awithdrawal pprocess or a depositingdproocess dependding
on a user inpput. Fig. 2 shoows the activ
vity diagram tthat
expresses proocessing flow of the ATM system.
s
The methood generates a source code fromf
the activvity
diagram. Fig.. 3 shows a geenerated sourcce code.
The developer adds thhe detail speccification to thegenerated souurce code in orderoto execu
ute it. The souurce
code added thhe detail speciification show
wn in Fig. 4.
Suppose a case that thee requirement of specificati
tion
is changed too add a proceess of balancee checking affter
adding the deetail specificattion.

The method generates thhe EAD fromm source codeeaddded the detaiil specification
on and the acttivity diagrammshhown in Fig. 2. Fig. 5 showss the generatedd EAD.
A developer adds the proccess of balancce checking too
thee activity diaggram as shownn in Fig. 2. Fig. 6 shows thee
activity diagramm added the prrocess of balan
nce checking.
The method modifies EA
AD to corresp
pond with theemoodified activity diagram. FFig. 7 shows the modifiedd
EA
AD.
The method generates thhe source coode from theemoodified EAD. Fig. 8 showss the generatedd source codee.
Th
his source codde has the detaail specificatioon.

© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 386 -

Yuuki Kikkawa, Tetsuro Katayam
ma, Yoshihiro Kita
a, Hisaaki Yamabba, Kentaro Aburrada, Naonobu OkazakiO.

Figg. 5. The Extennded Activity Diagram.
D
Fig. 8. The
T source codee generated fromm EAD.

it can reduce time and efffort to keepp consistencyy
beetween modells and a sourrce code afteer requiremennt
specification iss modified. T
Therefore, th
he method iss
usseful for efficiency of softw
ware developm
ment.
5. Conclusion
Fiig. 6. The modiified activity diagram.

Fig. 7. Thee modified EAD
D.

4. Discussioon
MDA Tool ssuch as EA4 (Enterprise Architecture)A
ccan
generate a sskeleton of a source codde from a cllass
diagram. In addition, EA can generatee a source coodefrom an activvity diagram or a state machinem
diagraam.
However, Thhe source codde generated byb EA does nnot
have detail sppecification. Itt takes time annd effort that thedeveloper addds detail specification to the source coodegenerated from the modifieed activity diaagram.
The methood can generrate a source code includding
detail specifiication. The methodm
can reducertime aandeffort to addd detail specification to thet source coodegenerated froom the modifieed activity diaagram. Moreovver,Th
his paper has proposed a modification method of awith a modiffied model inn
source code to correspond w
MDA.
M
The method can gennerate the souurce code thaatcoorresponds witth the modifieed activity diaagram and hassinfformation abbout detail specificationn. We haveecoonfirmed that the method ccan generate a source codeeinccluding the detail speciffication fromm the originaal
activity diagramm, the modifieed activity diaagram, and theeoriginal source code. Therefoore, the metho
od is useful forreff
fficiency of software develoopment.
Future issues are as follow
ws.
 Developmeent of the tool implemented
d the method
w deletion orr
 Improvemeent of the methhod to treat withrevision to source code aand activity diagram.
method to treat with otherr Improvemeent of the mstatements except if-stateement.
Reeferences
1. Yamashiro A, Sugimoto N
N, Hosoya R, From Programm
S
Modelling (in Japaneese), TOSHIBAA
Coding to Software
review, 58 (22003) 65-69.
A (Model Driveen Architecture)
2. Wada H, Yaasutake Y, MDA
and Actual Development
D
Pr
Process (in Japaanese), UNISYSSTECHNOLO
OGY REVIEW
W, No.61 (2004).
Approach to KeepK
Coherencee
3. Ueno M, Omori M, An A
between PIM
M and PSM of MDA (in Japannese), IPSJ SIG
GTechnical Reeport, SE-156 ((7), (2007).
http://www.sparxsystems.jpp
Architect,
4. Enterprise(accessed Deecember 10, 20014).

© The 22015 Internatiional Conferen
nce on Artificiial Life and RoboticsR(ICAR
ROB 2015), Jaan. 10-12, Oitta, Japan- 387 -

Prototype of a Decision Table Generation Tool from the Formal Specification
Kenta Nishikawa*, Tetsuro Katayama*, Yoshihiro Kita†
Hisaaki Yamaba*, Kentaro Aburada‡ 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 Japan‡
Oita National College of Technology, 1666 Maki, Oita, 870-0152 Japan
E-mail: nishikawa@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, aburada@oita-ct.ac.jp, oka@cs.miyazaki-u.ac.jp†

Abstract
This research has implemented a prototype of a decision table generation tool from the specification (the formal
specification) described in a formal specification language. This paper uses the formal specification description
language VDM++ which is the lightweight formal methods VDM (Vienna Development Method) to write theformal specification. We applied some general specifications to the prototype, in order to evaluate its usefulness. As
a result, the prototype has improved the efficiency in test design with formal methods.
Keywords: automatic generation, decision table, formal method, formal specification, VDM++, test design

with a specification description language based onmathematical 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 techniques, thedecision table3 is proposed in the testing process of thesoftware cycle. The decision table uses a matrix divided
the logical relationships in specifications into items ofconditions and actions. However, it takes much time andeffort to design the decision table. It is needed to extract
test items and understand contents written on
specifications. It is no exception even if you write strict
specifications with formal methods.
This research has implemented a prototype of adecision table generation tool from the specification (theformal specification) described in a formal specification
language, in order to improve efficiency of the test
design with formal methods.4 This prototype generates adecision table from the formal specifications, anddisplays it. This paper uses the formal specification
description language VDM++ which is the lightweight

1. Introduction
In recent years, the software quality cannot be
maintained with the conventional software development
methods because software system becomes large scaleand high performance. At the same time, effect ofdefects in the system becomes one of the major social
problems with the economy and life.
Hence, the software quality becomes more
important. A demand for reliability and safety of thesystem is growing.
In general, many defects are embedded in the
upstream process of the software development.1 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
methods2 are proposed. The formal methods are ameans for using strict specifications in each step in thesoftware development process. They express the system© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 388 -

Kenta Nishikawa, Tetsuro Katayama, Yoshihiro Kita, Hisaaki Yamaba, Kentaro Aburada, Naonobu Okazaki

Fig. 2. An example of CA-Table.
Fig. 1. A process to generate a decision table of the prototype.
Table 1. Extraction rules.
Pattern of condition extraction
Pattern of action extraction
if “condition” then
then “action” elseif
elseif “condition” then
then “action” else
else “action” ifelse “action” elseif
else “action” else
else “action” EOF
cases “condition” ->-> “action” cases
others “action” EOF
pre “condition” post
pre “condition” EOF
post “condition” EOF
EOF(End Of File)

Then, DT-Generator extracts conditions and actions
from an internal expression data for analysis. After, DTGenerator stores conditions and actions in an array ofString type. Table 1 shows extraction rules of conditions
and actions. DT-Generator makes CA-Table, when DTGenerator extracts conditions and actions. Fig. 2 shows
an example of CA-Table. CA-Table is a table which iscorrespondence of conditions and actions. CA-Table isthree columns of condition index, token, and action
index. DT-Generator generates truth-values based on
this CA-Table.
We show truth-values generating process as follows.
(i) Make an array to store truth-values(ii) Select the first column of this array(iii) Select a row of CA-Table(iv) Compare a token of the selected row of CA-Table(a) If this token matches “if”, “elseif”, or “cases”(I) Store “Y” into the condition index row of
this column, then store “N” from the next
column to the last column(II) Store “X” into the action index row of thiscolumn(b) If this token matches “else”, or “others”(I) Store “N” into the condition index row of
this column, then store “-” from the next
column to the last column(II) Store “X” into the action index row of thiscolumn(v) If there is a row that we have not yet selected, weselect the next column of this array and return tothird step. Otherwise truth-values is filledformal methods VDM (Vienna Development Method)
to write the formal specification.
2. Process to Generate a Decision Table of thePrototypeFig. 1 shows a process to generate a decision table ofthe prototype. The prototype consists of three parts:
Parser, Converter, and DT-Generator. We use a parser ofOverture Toolset as a Parser.5
First, Parser reads a VDM++ specification inputtedby a user, parses VDM++ specification, and outputs aparsing data. The parsing data has an abstract syntax
tree and tokens.
Next, Converter converts the parsing data into aninternal expression data for analysis by a module unit.
The internal expression data for analysis is a data
converted an abstract syntax tree of a parsing data into
information suitable for analysis such as the division ofa module or the correspondence of “if” and “else”.

Finally, DT-Generator generates a decision tablefrom conditions, actions, and truth-values.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 389 -

Prototype of a Decision

Fig. 3. Overview of the prototype.

3. Overview of the Prototype and Application
ExampleFig. 4. The formal specification of converting decimal tobinary.

Fig. 3 shows overview of the prototype. The prototype
consists of three displays: D-Tree, FS-Screen, and DTPanel. FS-Screen displays a VDM++ specification
inputted by a user. DT-Panel consists of three tabs: PTab, A-Tab, and Q-Tab. P-Tab displays a decision tableof preconditions. A-Tab displays a decision table of amodule. Q-Tab displays a decision table of postconditions. D-Tree displays a list of the definition
names of the VDM++ specification. D-Tree redraws adecision table, when a user selects any the definition
names of a VDM++ specification.
We confirm that this prototype works properly byadapting it to an example. Fig. 4 shows an example
formal specification. It stores a positive integer X in abinary expression array b. Here, the highest-order digit
must become one.
Fig. 5 shows the application example results. These
results shows that this prototype extracts conditions andactions from a specification. Also, these results showsthat this prototype generates truth-values. Therefore, wehave confirmed that this prototype works properly.
4.

Fig. 5. The application example results.

We discuss our prototype in this chapter.
4.1. Evaluation of the usefulness
We confirm the usefulness of this prototype by using
the examinees.
Specifically, we apply three specifications, which isways of combination of truth-values of the conditions
are different. Then, we measure the time of examinees
and the prototype, which is required until completion
for the decision table, and compares it.

Discussion

This research has implemented a prototype of a decisiontable generation tool from the formal specification, inorder to improve efficiency of the test design withformal methods. This prototype generates a decisiontable from the formal specifications, and displays it.
© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 390 -

Kenta Nishikawa, Tetsuro Katayama, Yoshihiro Kita, Hisaaki Yamaba, Kentaro Aburada, Naonobu Okazaki

Table 2. The measured results.
Formal Specifications (ways
Examinee A
of combination of truth-values(sec)of the conditions)
Specification A (4)252
Specification B (16)405Specification C (256)1131

Examinee B Examinee C Examinee D Examinee E Average
(sec)
(sec)
(sec)
(sec)
(sec)

Prototype
(sec)

213
559
1292

0.012
0.016
0.02

169
499
1194

134
557
1397

216
498
1629

By using the tool, we could automatically generate adecision table which has 256 ways of combination oftruth-values of the conditions in about 20 milliseconds.
Future issues are as follows.

Table 2 shows the measured results. By using thetool, we could automatically generate a decision table
which has 256 ways of combination of truth-values of
the conditions in about 20 milliseconds.
That is, we have confirmed the usefulness of thisprototype.






4.2. Related work
Few researches of test design from the formal
specification are reported6, and the method is not wellestablished.
Also, CEGTest7 is a tool supporting the generationof the decision table. CEGTest automatically generates
from a cause effect graph created by a user. However, auser must make a cause effect graph created, manually.
Therefore, it takes much time and effort. It is needed to
extract test items and understand contents written on theformal specification.
In addition, some test tools that inputs the formal
specification are proposed8, 9, but those tools do notsupport the generation of the decision table from aformal specification such as our prototype. In contrast,
our prototype can automatically get a decision tablefrom the formal specification inputted by a user.
5.

240
435
1859

The usefulness improvement of the prototype
Application to large-scale system specifications
Automatic generation of test data
Expansion to other test design techniques

References
1. G. Tassey, The Economic Impacts of Inadequate Infrastructure for Software Testing, National Institute ofStandards and Technology, Planning Report 02-3 (2002).
2. John Fitzgerald, Peter Gorm Larsen, Paul Mukherjee,
Nico Plat, Marcel Verhoef, Validated Designs for ObjectOriented Systems, Springer (2005).
3. ISO 5806, Specification of single-hit decision tables.
4. Kenta Nishikawa, Tetsuro Katayama, Yoshihiro Kita,
Hisaaki Yamaba and Naonobu Okazaki, Proposal of a
Supporting Method to Generate a Decision Table fromthe Formal Specification, International Conference on
Artificial Life and Robotics, (2014) 222-225.
5. A Scanner/Parser for the Overture Toolset,
http://overturetool.hosting.west.nl/twiki/bin/view/Main/O
vertureParser/ (accessed October 30, 2014).
6. Jeremy Dick, Alain Faiver, Automating the Generationand Sequencing of Test Cases from Model-BasedSpecifications, FME'93: Industrial-Strength Formal
Methods, Lecture Notes in Computer Science, 670 (1993)
268-284.
7. CEGTest, http://softest.jp/tools/CEGTest/ (accessed
October 30, 2014).
8. Y. Ledru, L. du Bousquet, O. Maury, and P. Bontron,
Filtering TOBIAS combinatorial test suites, Fundamental
Approaches to Software Engineering, (2004) 281-294.
9. Adriana Sucena Santos, Combinatorial Test Automation
Support for VDM++, VDM/Overture Workshop, (2008)45-53.

Conclusions

This research has implemented a prototype of a decisiontable generation tool from the formal specification, inorder to improve efficiency of the test design withformal methods. This prototype generates a decisiontable from the formal specifications, and displays it.
We have confirmed that our prototype extracts
conditions and actions from the formal specification.
Also, we confirmed that the prototype generates truthvalues.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 391 -

Kenta Nishikawa, Tetsuro Katayama, Yoshihiro Kita, Hisaaki Yamaba, Kentaro Aburada, Naonobu Okazaki

Table 2. The measured results.
Formal Specifications (ways
Examinee A
of combination of truth-values(sec)of the conditions)
Specification A (4)252
Specification B (16)405Specification C (256)1131

Examinee B Examinee C Examinee D Examinee E Average
(sec)
(sec)
(sec)
(sec)
(sec)

Prototype
(sec)

213
559
1292

0.012
0.016
0.02

169
499
1194

134
557
1397

216
498
1629

By using the tool, we could automatically generate adecision table which has 256 ways of combination oftruth-values of the conditions in about 20 milliseconds.
Future issues are as follows.

Table 2 shows the measured results. By using thetool, we could automatically generate a decision table
which has 256 ways of combination of truth-values of
the conditions in about 20 milliseconds.
That is, we have confirmed the usefulness of thisprototype.






4.2. Related work
Few researches of test design from the formal
specification are reported6, and the method is not wellestablished.
Also, CEGTest7 is a tool supporting the generationof the decision table. CEGTest automatically generates
from a cause effect graph created by a user. However, auser must make a cause effect graph created, manually.
Therefore, it takes much time and effort. It is needed to
extract test items and understand contents written on theformal specification.
In addition, some test tools that inputs the formal
specification are proposed8, 9, but those tools do notsupport the generation of the decision table from aformal specification such as our prototype. In contrast,
our prototype can automatically get a decision tablefrom the formal specification inputted by a user.
5.

240
435
1859

The usefulness improvement of the prototype
Application to large-scale system specifications
Automatic generation of test data
Expansion to other test design techniques

References
1. G. Tassey, The Economic Impacts of Inadequate Infrastructure for Software Testing, National Institute ofStandards and Technology, Planning Report 02-3 (2002).
2. John Fitzgerald, Peter Gorm Larsen, Paul Mukherjee,
Nico Plat, Marcel Verhoef, Validated Designs for ObjectOriented Systems, Springer (2005).
3. ISO 5806, Specification of single-hit decision tables.
4. Kenta Nishikawa, Tetsuro Katayama, Yoshihiro Kita,
Hisaaki Yamaba and Naonobu Okazaki, Proposal of a
Supporting Method to Generate a Decision Table fromthe Formal Specification, International Conference on
Artificial Life and Robotics, (2014) 222-225.
5. A Scanner/Parser for the Overture Toolset,
http://overturetool.hosting.west.nl/twiki/bin/view/Main/O
vertureParser/ (accessed October 30, 2014).
6. Jeremy Dick, Alain Faiver, Automating the Generationand Sequencing of Test Cases from Model-BasedSpecifications, FME'93: Industrial-Strength Formal
Methods, Lecture Notes in Computer Science, 670 (1993)
268-284.
7. CEGTest, http://softest.jp/tools/CEGTest/ (accessed
October 30, 2014).
8. Y. Ledru, L. du Bousquet, O. Maury, and P. Bontron,
Filtering TOBIAS combinatorial test suites, Fundamental
Approaches to Software Engineering, (2004) 281-294.
9. Adriana Sucena Santos, Combinatorial Test Automation
Support for VDM++, VDM/Overture Workshop, (2008)45-53.

Conclusions

This research has implemented a prototype of a decisiontable generation tool from the formal specification, inorder to improve efficiency of the test design withformal methods. This prototype generates a decisiontable from the formal specifications, and displays it.
We have confirmed that our prototype extracts
conditions and actions from the formal specification.
Also, we confirmed that the prototype generates truthvalues.

© The 2015 International Conference on Artificial Life and Robotics (ICAROB 2015), Jan. 10-12, Oita, Japan- 392 -