9/8/2019 IAENG Conference Publications:...

9/8/2019 IAENG Conference Publications: Proceedings Book of The International MultiConference of Engineers and Computer Scientists 2014 Vol. I

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Proceedings Book of TheInternationalMultiConference ofEngineers and ComputerScientists 2014 Vol. IISBN:978-988-19252-5-1

ISSN:2078-0958 (Print) / 2078-0966 (Online)

International MultiConference of Engineers andComputer Scientists 2014 (IMECS 2014).

Price: US 40 (by surface) / US 60 (by air)

This volume includes the papers of the belowconferences: The 2014 IAENG International Conference on ArtificialIntelligence and Applications The 2014 IAENG International Conference onBioinformatics The 2014 IAENG International Conference onBioinformatics Special Session: The IAENGInternational Workshop on Biomedical Engineering The 2014 IAENG International Conference onComputer Science The 2014 IAENG International Conference on Controland Automation The 2014 IAENG International Conference on DataMining and Applications The 2014 IAENG International Conference on ImagingEngineering The 2014 IAENG International Conference on InternetComputing and Web Services The 2014 IAENG International Conference onScientific Computing The 2014 IAENG International Conference on SoftwareEngineering

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9/8/2019 International MultiConference of Engineers and Computer Scientists IMECS 2014

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Hong Kong, 12-14 March, 2014

Conferences Publications Membership About IAENG FAQ Contact Us

The IMECS 2014 Program Committee

As the IMECS is organized by the International Association of Engineers and the Engineering Letters, some ofthe editorial board members of the Engineering Letters and the IAENG journals and the committee members ofIAENG Societies are taking part in the IMECS 2014 International Program Committee.

IMECS MultiConference Co-chairs

Prof. Craig Douglas (honorary co-chair)

School of Energy Resources Distinguished Professor,Departments of Mathematics & Director, Institute for Scientific Computation, University ofWyoming, USAVisiting Professor and Associate Director, Center forNumerical Porous Media MethodsKing Abdullah University of Science & Technology, SaudiArabia & Former Senior Research Scientist (corresponding tononteaching full professor), Computer Science Department, Yale University, USA

Prof. David Dagan Feng (honorary co-chair)Head, School of Information Technologies, University of Sydney, Australia

Prof. Alexander M. Korsunsky (honorary co-chair)Professor of Engineering Science Former Dean, Trinity CollegeDepartment of Engineering Science, University of Oxford,UK

Prof. Ping-kong Alexander Wai (honorary co-chair)Vice President and Chair Professor of OpticalCommunicationsHong Kong Polytechnic University, Hong Kong

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Prof. Oscar CastilloProfessor and Research Director of Computer ScienceTijuana Institute of Technology, MexicoFormer Adjunct Professor of San Diego State University,USA President of HAFSA (Hispanic American Fuzzy SystemsAssociation)Vice-Chair of the Mexican Chapter of the ComputationalIntelligence Society (IEEE)

Prof. Jeong-A LeeProfessor of Department of Computer EngineeringChosun University, South Korea

IMECS Program Committee & Conferences Co-Chairs

Yousry H. Abdelkader Alexandria University, Egypt

Daoud Ait-Kadi University Laval, Canada

Basim Al-Najjar Vaxjo University, Sweden

Ali Allahverdi Kuwait University, Kuwait

Bala P. Amavasai Sheffield Hallam University, U.K.

Carlos Henggeler Antunes University of Coimbra, Portugal

Djamel Bouchaffra Oakland University, USA

Indranil Bose The University of Hong Kong, Hong Kong

Anthony Brabazon University College Dublin, Ireland

Mietek Brdys The University of Birmingham, UK

Fidel Cacheda University of A Coruna, Spain

Alan Hoi-shou Chan City University of Hong Kong, Hong Kong

Felix T.S. Chan The Hong Kong Polytechnic University, Hong Kong

Chin-Chen Chang Feng Chia University, Taiwan

Pranay Chaudhuri University of the West Indies, West Indies

Chun-Hsien Chen Nanyang Technological University, Singapore

Chung Shue Chen INRIA, France

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Mu-Song Chen Da-Yeh University, Taiwan

Qingfeng Chen Deakin University, Australia

Wallace Choy The University of Hong Kong, Hong Kong

T.S. Chung The Hong Kong Polytechnic University, Hong Kong

Orville Leverne Clubb City University of Hong Kong, Hong Kong

Manuel Jose DamasioUniversidade Lusofona de Humanidades e Tecnologias,Portugal

Chuangyin Dang City University of Hong Kong, Hong Kong

Alexandar Djordjevich City University of Hong Kong, Hong Kong

Yu-fai Fung Hong Kong Polytechnic University, Hong Kong

G. Ganesan Adikavi Nannaya University, India

Robert Gay Kheng Leng Nanyang Technological University, Singapore

Zhao-qiang Ge Xi'an Jiaotong University, China

Goh Ong Sing National Technical University of Malaysia, Malaysia

Angel Marin Gracia de la Universidad Politecnica de Madrid, Spain

Nalan Gulpinar University of Warwick, UK

Phil Heaton-Adegbile University of Portsmouth, UK

Enrique Herrera-Viedma University of Granada, Spain

Josep R. Herrero Polytechnic University of Catalonia, Spain

Xu Huang University of Canberra, Australia

Chi-Kuang Hwang Chung-Hua University, Taiwan

Heung Suk Hwang TongMyong University, South Korea

Shigekazu Ishihara Hiroshima International University, Japan

Limin Jia Beijing Jiaotong University, China

Hsueh-Fen Juan National Taiwan University, Taiwan

Rajgopal Kannan Louisiana State University, USA

George P. Karatzas Technical University of Crete, Greece

Hideki Katagiri Hiroshima University, Japan

Graham Kendall University of Nottingham, UK

Kensaku Kikuta University of Hyogo, Japan

Haeng Kon Kim Catholic University of DaeGu, South Korea

Sung-il Kim Korea University, South Korea

Cartik Kothari The University of Memphis, USA

Mohan KrishnamoorthyCSIRO Mathematical & Information Sciences Division,Australia

Tadahiko Kumamoto Chiba Institute of Technology, Japan

Reggie Kwan The Open University of Hong Kong, Hong Kong

Ho Woo Lee Sungkyunkwan University, South Korea

Layuan Li Wuhan University of Technology, China

Xueliang Li Nankai University, China

Yiming Li National Chiao Tung University, Taiwan

Chih-Min Lin Yuan-Ze University, Taiwan

Baoding Liu Tsinghua University, China



Liping Liu University of Akron, USA

Elsa M. Macias Lopez Las Palmas de Gran Canaria University, Spain

Kazuaki Maeda Chubu University, Japan

Prabhat K.Mahanti University of New Brunswick, Canada

Qusay H. Mahmoud University of Guelph, Canada

Nikolaos Matsatsinis Technical University of Crete, Greece

George Mavrommatis Hellenic Military Academy, Greece

Michael McGuigan Brookhaven National Laboratory, USA

Patricia Melin Tijuana Institute of Technology, Mexico

Ludmil Mikhailov The University of Manchester, UK

Hiroshi Mineno Shizuoka University, Japan

Herman Monsuur Netherlands Defense Academy, The Netherlands

Chiung Moon Yonsei University, South Korea

Alejandro Murua University of Montreal, Canada

Nysret Musliu Vienna University of Technology, Austria

Didier Nakache CNAM de Paris, France

Jun Ni The University of Iowa, USA

Lars Nolle Nottingham Trent University, UK

A. Alper Ozalp Uludag University, Turkey

Wan- Kai Pang The Hong Kong Polytechnic University, Hong Kong

Apostolos N. Papadopoulos Aristotle University, Greece

Selwyn Piramuthu University of Florida, USA

I.M. Premachandra University of Otago, New Zealand

Quah Tong Seng Nanyang Technological University, Singapore

Dharmendra Sharma University of Canberra, Australia

Jiuh-Biing Sheu National Chiao Tung University, Taiwan

Shey-Huei Sheu National Taiwan University of Science and Technology, Taiwan

Cornel I. G. ResteanuNational Institute for Research and Development in Informatics,Bucharest, Romania

Carlos Romero Technical University of Madrid, Spain

Susumu Saito The Institute for Science of Labour, Japan

Tadeusz Sawik AGH University of Science & Technology, Poland

Rosemary R. Seva De La Salle University, Philippines

Elhadi Shakshuki Acadia University, Canada

Vrijendra Singh Indian Institute of Information Technology Allahabad, India

Yannis Siskos University of Piraeus, Greece

Yuri Grigoriyevich StoyanInstitute for Problems in Machinery of National UkrainianAcademy of Sciences, Ukraine

Junping Sun Nova Southeastern University, USA

Zhaohao Sun University of Wollongong, Australia

A.B. Rad Swinburne University of Technology, Australia

Marina Resta Universita' degli Studi di Genova, Italy

Maria Luque Rodriguez University of Cordoba, Spain



Raul Rojas Freie Universitat Berlin, Germany

Manoj Kumar TiwariNational Institute of Foundry and Forge Technology (NIFFT),India

Dat Tran University of Canberra, Australia

Francisco Herrera Triguero University of Granada, Spain

Lin-Yu Tseng Providence University, Taiwan

Shinichi Tsuda Tokai University, Japan

Gwo-Hshiung Tzeng Kainan University, Taiwan

Lev V. Utkin St.Petersburg Forest Technical Academy, Russia

Ismael Vaz Universidade do Minho, Portugal

Jose Luis Verdegay Universidad de Granada, Spain

Hsiao-Fan Wang National Tsing Hua University, Taiwan

Long Wang Peking University, China

Shyh-Hau Wang Chung Yuan Christian University, Taiwan

Toyohide Watanabe Nagoya University, Japan

Frank Werner Otto-von-Guericke-University of Magdeburg, Germany

Igor Wojnicki AGH - University of Science and Technology, Poland

Ed Xuekui Wu University of Hong Kong, Hong Kong

Trong Wu Southern Illinois University Edwardsville, USA

Guangming Xie Peking University, China

Li Xu Zhejiang University, China

Susan H. Xu Penn State University, USA

Gi-Chul Yang Mokpo National University, South Korea

Xin-She Yang University of Cambridge, UK

Ali Riza Yildiz University of Michigan, USA

Kun-Ming Yu Chung Hua University, Taiwan

Cecilia Zanni Marc Bloch University, France

Xiaohua (Douglas) Zhang Merck Research Laboratories, USA

Zhe George Zhang Simon Fraser University, Canada

Yao Zhao Beijing Jiaotong University, China

Yong Zhao Nanyang Technological University, Singapore

Constantin Zopounidis Technical University of Crete, Greece

IMECS Conferences Committees:

ICOR'14 Conference Committee Members

ICAIA'14 Conference Committee Members

ICSC'14 Conference Committee Members

ICCSA'14 Conference Committee Members

ICDMA'14 Conference Committee Members

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http://www.iaeng.org/IMECS2014/ICDMA2014.html



ICIE'14 Conference Committee Members

ICB'14 Conference Committee Members

ICICWS'14 Conference Committee Members

ICCS'14 Conference Committee Members

ICSE'14 Conference Committee Members

ICEE'14 Conference Committee Members

ICINDE'14 Conference Committee Members

ICCA'14 Conference Committee Members

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9/8/2019 Proceedings of IMECS 2014, 12-14 March, 2014, Hong Kong, IAENG Open Access Publication

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Sunday, September 8, -1781

Proceedings of the International MultiConference ofEngineers and Computer Scientists 2014

IMECS 2014, 12-14 March, 2014, Hong Kong

The International MultiConference of Engineers and Computer Scientists has been organized bythe International Association of Engineers (IAENG), a non-profit international association for theengineers and the computer scientists. The IMECS 2014 takes place in The Royal Garden Hotel,Kowloon, Hong Kong, 12-14 March, 2014.

The focus of our conference is on the frontier topics in the theoretical and applied engineering andcomputer science subjects. The IMECS conferences have been serving as good platforms for ourmembers and the entire engineering community to meet with each other and to exchange ideas.The conferences have also stroke a balance between theoretical and application development.

Our IMECS 2014 has been organized with conference committees that have been formed withover three hundred committee members who are mainly research center heads, faculty deans,department heads, professors, and research scientists from over 30 countries. The conferences aretruly international meetings with a high level of participation from many countries.

The response that we have received for the multiconference is excellent. The IMECS 2014 hasattracted more than six hundred participants from over 50 countries. All submitted papers havegone through the peer review process. The summary of submissions and accepted papers inIMECS 2014 is as followed: total number of submissions reviewed: 683; total number of acceptedpapers: 350. And the overall acceptance rate in our multiconference is 51.24%.

Editors: S. I. Ao and Oscar Castillo and Craig Douglas and David Dagan Feng and Jeong-A Lee

ISBN of Vol I (pp1-606): 978-988-19252-5-1

ISBN of Vol II (pp607-1256): 978-988-19253-3-6

ISSN: 2078-0958 (Print) / ISSN: 2078-0966 (Online)

Publisher: Newswood Limited

Organization: International Association of Engineers

CONFERENCE PAPERS:

IMECS 2014

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The 2014 IAENG International Conference on Artificial Intelligence and Applications

Island-Model-based Distributed Modified Extremal Optimization with Tabu Lists for Reducing Crossovers inReconciliation Graph

Keiichi Tamura, and Hajime Kitakami

Proceedings of The International MultiConference of Engineers and Computer Scientists 2014 , pp1-6 [Online Full Text]

Recurrent-Fuzzy-Neural System-based Adaptive Controller for Nonlinear Uncertain Systems


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Tzu-Wei Hu, and Ching-Hung Lee


Cluster-based Particle Swarm Algorithm for Solving the Mastermind Problem

Dan Partynski


Virtual Machine Scheduling Management on Cloud Computing Using Artificial Bee Colony

B. Kruekaew, and W. Kimpan


An Application of Fuzzy Inference System Composed of Double-Input Rule Modules to Control Problems

Hirofumi Miyajima, Fumihiro Kawai, Noritaka Shigei, and Hiromi Miyajima


Software Risk Factors: A Survey and Software Risk Mitigation Intelligent Decision Network Using Rule Based Technique

Muhammad Asif, Jamil Ahmed, and Abdul Hannan


A New Method for Improving the Performance of Linkage Pattern Mining

Saerom Lee, Takahiro Miura, and Yoshifumi Okada


Applying TAM Model to Evaluate the Indoor Parking Guidance and Information System

Kuo-Wei Su, Chen-Ni Chen, Nai-Hsin Kuo, and Hsin-Hui Tseng


Multi-Scale Subspace Grids Based Approach for Recognising Patterns in Applications Involving Multidimensional Data

M. Arif Wani


An Intelligent Model Framework for Handling Effects of Uncertainty Events for Crude Oil Price Projection: ConceptualPaper

Haruna Chiroma, Sameem Abdulkareem, and Abdulsalam Ya'u Gital


Gesture Recognition System for Human-Robot Interaction and Its Application to Robotic Service Task

Tatsuya Fujii, Jae Hoon Lee, and Shingo Okamoto













Applying Eye Tracking to Evaluate Indoor Parking Navigation System

Kuo-Wei Su, Hou-Hui Cai, Yi-Ying Wu, and Ya-Ting Li


Image Classification by Transfer Learning Based on the Predictive Ability of Each Attribute

Masahiro Suzuki, Haruhiko Sato, Satoshi Oyama, and Masahito Kurihara


Taxonomy of Low-level Hybridization (LLH) for PSO-GA

S. Masrom, Siti Z.Z. Abidin, N. Omar, and K. Nasir


SBCAW-PSO: A Sine-Based Chaotic Adaptive Inertia Weight Particle Swarm Optimization

Yu-Huei Cheng, and Che-Nan Kuo


Tissue Characterization of Coronary Plaque by Subspace Method with Consideration to Neighborhood Information ofTarget Tissue

Shota Furukawa, Eiji Uchino, Shinichi Miwa, and Noriaki Suetake


Bounds for the Permutation Flowshop Scheduling Problem with Exact Time Lags while Minimizing the Total Earlinessand Tardiness

Imen Hamdi, and Taicir Loukil


The 2014 IAENG International Conference on Bioinformatics

In-silico Study of the Role of Differentially Expressed Genes and MicroRNAs in Host-Pathogen Interaction

Nilubon Kurubanjerdjit, Ching-Yi Chen, Ke-Rung Tzeng, Jin-Shuei Ciou, Jeffrey J.P Tsai, Chien-Hung Huang, and Ka-Lok Ng


Reoptimization of Motif Finding Problem

Jhoirene B. Clemente, Jeffrey A. Aborot, and Henry N. Adorna












A Study on Gaze Estimation System using Cross-Channels Electrooculogram Signals

Mingmin Yan, Hiroki Tamura, and Koichi Tanno


Bimodal Biometrics for Health Care Infrastructure Security

O. A. Esan, S.M. Ngwira, and T. Zuva


Iterative Optimal TM_Score and Z_Score Guided Sampling Significantly Improves Model Topology

Ashish Runthala, and Shibasish Chowdhury


Multi-Scale Gaussian Mixtures for Cross-species Study

ZiHua Yang, Abdullatif Alwatban, Richard Everson, and Zheng Rong Yang


Blood Vessel Contour Estimation in Eye Fundus Image Using Fuzzy Inference for Diagnosis of Arteriosclerosis

Shota Furukawa, Eiji Uchino, Miyu Kawachi, Hideaki Misawa, and Noriaki Suetake


The 2014 IAENG International Conference on Bioinformatics Special Session: The IAENG InternationalWorkshop on Biomedical Engineering

Characteristic of Spectral Exponent of Epileptic ECoG Data Corresponding to Levels in Wavelet-Based Fractal Analysis

Suparerk Janjarasjitt, and Kenneth A. Loparo


The Comparison of Pronation and Supination between Typically Developing Children and Children with ADHD

M. Kaneko, H. Okui, K. Higashi, Y. Noguchi, Y. Katayama, T. Ohya, Y. Yamashita, and K. Iramina


The 2014 IAENG International Conference on Computer Science











Using Software-Defined Networking for Real-Time Internet Applications

Tim Humernbrum, Frank Glinka, and Sergei Gorlatch


An Algorithm for Enumerating All Maximal Tree Patterns Without Duplication Using Succinct Data Structure

Yuko ITOKAWA, Tomoyuki UCHIDA, and Motoki SANO


Exertainment: Designing Active Video Games to Get Youth Moving

Ilung, Pranata, R. Altamimi, and Geoff D. Skinner


Unsupervised Adaptation of ASR Systems Using Hybrid HMM / VQ Model

Akella Amarendra Babu, Yellasiri Ramadevi, and Akepogu Ananda Rao


Pronunciation Tutoring using Kinect

Farah binti Dinbandali, and Satoshi Ichimura


RFID-based Location Tracking System Using a Peer-to-Peer Network Architecture

Felix C. P. Hui, Henry C. B. Chan, and S. H. Fung


An Enhanced Evaluation Approach: Computer Science and its Framework

M. Satoskar


Restricted Poly-Time SAT-Solvable CNF's

Stefan Porschen, and Tatjana Schmidt


A Framework for the Design of Cloud Based Collaborative Virtual Environment Architecture

Abdulsalam Ya'u Gital, Abdul Samad Ismail, Min Chen, and Haruna Chiroma


Safety Verification of Floodgate Operation Protocols Using Hybrid Automata












M. V. Panduranga Rao, and Akhilesh Chaganti


Virtual Network Construction with K-Shortest Path Algorithm and Prim's MST Algorithm for Robust Physical Networks

Yasuhiro Urayama, and Takuji Tachibana


Termination Detection for Synchronous Algorithms in P Systems

Huiling Wu


A Novel Approach towards Very High Level Programming

Darshan Patel, Devashish Thakur, and Kavi Mahesh


Adaptive PCA-SIFT Matching Approach for Face Recognition Application

Isra'a Abdul-Ameer Abdul-Jabbar, Jieqing Tan, and Zhengfeng Hou


Multiple Link Faults Restoration Mechanism in An Enhanced EPON Architecture

Andrew Tanny Liem, I-Shyan Hwang, and AliAkbar Nikoukar


Semantic Web Improved with IDF Feature of the TFIDF Algorithm

Jyoti Gautam, Ela Kumar, and Mehjabin Khatoon


Techniques for Scaling Components of Web Application

Ademola Adenubi, Olanrewaju Lewis, and Bolanle Abimbola


The 2014 IAENG International Conference on Control and Automation

Designs of Minimal-Order State Observer and Servo Controller for a Robot Arm Using Piecewise Bilinear Models

Tadanari Taniguchi, Luka Eciolaza, and Michio Sugeno












Set-Point Manipulation Approach toward Overcoming Limitation of Closed Architectural Framework

Tan Kok Kiong, and Kyaw Ko Ko Htet


Astaticism in the Motion Control Systems of Marine Vessels

Maria A. Smirnova, Mikhail N. Smirnov, and Tatiana E. Smirnova


Computer Simulations and Experiments on Vibration Control of a Flexible Link Manipulator Using a PiezoelectricActuator

Abdul Kadir Muhammad, Barlas Raheel Khan, Shingo Okamoto, and Jae Hoon Lee


Multiprogram Digital Control

Nikolay V. Smirnov, Tatiana E. Smirnova, Mikhail N. Smirnov, and Maria A. Smirnova


2DOF H infinity Control for DC Motor Using Genetic Algorithms

Natchanon Chitsanga, and Somyot Kaitwanidvilai


Fault Tolerance in Asynchronously Operated Machines Allowing Temporary Violation of Normal Specification

Jung-Min Yang, and Seong Woo Kwak


Velocity Grid Map Approach and Its Application to Collision-Free Navigation

Tomoshi Yamashita, Jae Hoon Lee, and Shingo Okamoto


An Exploration of Robot Utilization for Vehicles in Tracking Shortest Route

Mahamaya Mohanty, and Abhinav Bhardwaj


Optimum Tuning of the PID Controller for Stable and Unstable Systems Using Nonlinear Optimization Technique

Fares Alariqi, and Adel Abdulrahman














Semi-passive Biped Robot using Motion Control Combining Energy and PD Controls

Toshiya Yamada, Shingo Okamoto, and Jae Hoon Lee


The Method of Accounting of Bounded External Disturbances for the Synthesis of Feedbacks with Multi-purposeStructure

Mikhail N. Smirnov, Maria A. Smirnova, and Nikolay V. Smirnov


The Assessment and Analysis of using a Temperature Sensor to Regulate the Automatic Gain Control of the Amplifier in aFLASH-OFDM System

Wei Chien, Hsien-Wei Tseng, Yang-Han Lee, Ying-Sen Ho, Tsai-Hua Kang, and Yu-De Liao


Virtual Taxi Stand Management System

Kar Leong TEW, Jessie CAI, Elliot CHIAM, Ziheng LIN, Bernice NG, Manning ZHANG, and Steve LEE


Nonlinear Adaptive Control for Wind Energy Conversion Systems Based on Quasi-ARX Neural Networks Model

Mohammad Abu Jami'in, Imam Sutrisno, and Jinglu Hu


The 2014 IAENG International Conference on Data Mining and Applications

On a Fuzzy c-means Algorithm for Mixed Incomplete Data Using Partial Distance and Imputation

Takashi Furukawa, Shin-ichi Ohnishi, and Takahiro Yamanoi


Combination Features for Semantic Similarity Measure

Dat Huynh, Dat Tran, and Wanli Ma


Comparing Performances of Markov Blanket and Tree Augmented Naive-Bayes on the IRIS Dataset

Haruna Chiroma, Abdulsalam Ya'u Gital, Adamu Abubakar, and Akram Zeki












Extracting Characteristics of Items Based on Patterns in Recommendation Graphs

Daisuke KITAYAMA, and Kazutoshi SUMIYA


Using Decision Trees to Identify Key Factors of Keyword Advertisements

Long-Sheng Chen, and Tai-Cheng Kuo


Comparison of Classification Techniques in Education Marketing

Sheila A. Abaya, Bobby D. Gerardo, and Bartolome T. Tanguilig III


A Comparative Study of Algorithms for Grouping Cancer Data

Ran Qi, and Shujia Zhou


Bag of Features Based Remote Sensing Image Classification Using RANSAC And SVM

Bharathi S, Karthik Kumar S, P Deepa Shenoy, Venugopal K R, L M Patnaik


A New Density-based Spatial Clustering Algorithm for Extracting Attractive Local Regions in Georeferenced Documents

Tatsuhiro Sakai, Keiichi Tamura, and Hajime Kitakami


IURA: An Improved User-based Collaborative Filtering Method Based on Innovators

Qianru Zheng, Chi-Kong Chan, and Horace H.S. Ip


iRecruit Tool : A Marketing Application for Higher Educational Institution using C4.5 Classification Technique

Sheila A. Abaya, Bobby D. Gerardo, and Bartolome T. Tanguilig III


A Discernibility Degree and Rough Set Based Classification Rule Generation Algorithm (RGD)

Honghai FENG, Hongjiang FENG, Yanyan CHEN, and Junhui HUANG













Route Recommendation Method Based on Driver's Intention Estimation Considering the Route Selection When Using theCar Navigation

Keisuke Hamada, Shinsuke Nakajima, Daisuke Kitayama, and Kazutoshi Sumiya


Ratio-Type Estimators in Stratified Random Sampling using Auxiliary Attribute

R.V.K. Singh, and A. Ahmed


Improved Training of Predictive ANN with Gradient Techniques

Dharminder Kumar, Sangeeta Gupta, and Parveen Sehgal


A Data Cube for Mobility Observation (D3MO)

Ting Wang


Application of Fuzzy Logic to Multimedia Streaming Dissemination System for Vehicular Networks

Chenn-Jung Huang, Yu-Wu Wang, Chuan-Hsiang Weng, Heng-Ming Chen, and Chao-Yi Chen


The 2014 IAENG International Conference on Imaging Engineering

Optoelectronic Pattern Recognition Based on YUV Color Model with Shifted Training Images and Liquid Crystal SpatialLight Modulator

Yuezhe Li, Chulung Chen, Yachu Hsieh, and Hsinyi Chen


A Navigational Aid System for Visually Impaired Using Microsoft Kinect

Mahmud Ridwan, Ehtesham Choudhury, Bruce Poon, M. Ashraful Amin, and Hong Yan


A Novel Way to Improve Facial Expression Recognition by Applying Fast Fourier Transform

Dehai Zhang, Da Ding, Jin Li, and Qing Liu












Fast and Memory Efficient 3D-DWT Based Video Encoding Techniques

V. R. Satpute, Ch. Naveen, K. D. Kulat, and A. G. Keskar


Computer Based Pattern Placement Optimization on a Frame

Wikaria Gazali, and Fredy Purnomo


Rope Skipping Motion Recognition System Using Kinect

Youji OCHI


Face Recognition Enhancement Based on Image File Formats and Wavelet De-noising

Isra'a Abdul-Ameer Abdul-Jabbar, Jieqing Tan, and Zhengfeng Hou


The 2014 IAENG International Conference on Internet Computing and Web Services

CB-Cloudle: A Centroid-based Cloud Service Search Engine

Shengjie Gong, and Kwang Mong Sim


Serendipitous Recommendation Method based on WWW Access Log

Toshiharu Nakashima, Kenji Nakamura, and Shigeru Oyanagi


Why Users Leave? Discussion about Users' Switching Behaviors in Social Network Sites

Y. L. Wu, Y. H. Tao, C. M. Chung, and C. P. Li


Measurement of Bloggers' Buzzword Prediction Ability Based on Analyzing Frequency of Early Mentions of PastBuzzwords

Seiya Tomonaga, Shinsuke Nakajima, Yoichi Inagaki, Reyn Nakamoto, Tsukasa Ogura, and Jianwei Zhang


The Less Communicated Story of Location Based Service in Retail Applications











Jessie Cai


Inferring Intentions of Twitter Users to Visit Places

Chengcheng Zhang, Haruhiko Sato, Satoshi Oyama, and Masahito Kurihara


Developing a Theory of Diagnosticity for Online Reviews

Alton Y. K. Chua, and Snehasish Banerjee


Comprehensive Web Search based on Sentiment Features

Katsutoshi Minami, Shoko Wakamiya, Noriaki Hata, Yukiko Kawai, Tadahiko Kumamoto, Jianwei Zhang, and Yuhki Shiraishi


The Study for Selecting the WWW Service Consignment Performance of Shared Sales Resource of Publishing

Tsai-Hua Kang, Wei Chien, Jan-Ou Wu, Jian-An Sue, and Kai-Jun Zhang


An E-commerce Recommender System Based on Degree of Specialties in Online Shops

Motoki Zaizen, Daisuke Kitayama, and Kazutoshi Sumiya


A Tool for Impact Analysis of Test Cases Based on Changes of a Web Application

Surasak Phetmanee, and Taratip Suwannasart


A Linguistic Framework to Distinguish between Genuine and Deceptive Online Reviews

Snehasish Banerjee, and Alton Y. K. Chua


Extraction of Naming Concepts Based on Modifiers in Recipe Titles

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International Association of Engineers

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© Copyright International Association of Engineers


mailto:[email protected]

Proceedings of the International MultiConference of Engineers and Computer Scientists 2014 Vol I, IMECS 2014, March 12 - 14, 2014, Hong Kong

ISBN: 978-988-19252-5-1 ISSN: 2078-0958 (Print); ISSN: 2078-0966 (Online)

IMECS 2014

II. DYNAMIC MODELING OF WECS SYSTEM

A. Wind Speed Modeling

The power produced by a wind turbine generator (WTG)at a particular site is highly dependent on the wind regimeat that location. There is a number of ways that windspeed can and has been modeled in power system reliabilityevaluation. This method uses the ARMA model to predictwind speeds in the reliability evaluation process and isdesignated as the ARMA approach. An ARMA model with pautoregressive terms and q moving average terms is denotedas ARMA(p, q). The ARMA model created for the SwiftCurrent site in Saskatchewan, Canada based on 1996 to 2003data is shown in the following [15]:

s(t) = 1.1772s(t− 1) + 0.1001s(t− 2)− 0.3572s(t− 3)

+0.0379s(t− 4) + ν(t)− 0.5030ν(t− 1)

−0.2924ν(t− 2) + 0.1317ν(t− 3)

ν(t) ∈ (0, 0524762). (1)

The simulated wind speed at hour t, designated as V (t), canbe calculated as follows:

V (t) = µ(t) + σ(t)s(t). (2)

where µ(t) is the mean observed wind speed at hour andσ(t) is the standard deviation of the observed wind speed athour.

B. Dynamic Modeling of WECS

The power captured by a wind turbine is given by

Pm = 0.5ρπCp(λ, β)R2V 3 (3)

where ρ is the air density (typically 1.25 kg/m3), R is radiusof blades ( in meter), Cp(λ, β) is the wind-turbine powercoefficient, and V is the wind speed (in m/s). The coefficientCp(λ, β) depends on the pitch angle of the blades β (indegrees) and the tip-speed ratio λ, which is defined as theratio of the linear velocity of the blade tip (ωtR) to the windspeed V as follows:

λ =ωtR

V(4)

where ωt is the wind turbine shaft speed (in rad/s).The relation of Cp versus λ of a three-blade horizontal-

axis wind turbine for various blade pitch angles β is illus-trated in Fig. 1. The curves have been obtained by using thefollowing equation that is commonly used in wind turbinesimulators [5], [16]:

Cp(λ, β) = 0.5176(116

λi− 0.4β − 5)e−21/λi + 0.0068λ

(5)1

λi=

1

λ+ 0.008β− 0.035

β3 + 1. (6)

WECS can be structured into several interconnected sub-system models as shown in Fig. 2. This system consists ofwind turbine, a drive train, and a generation unit.

The objective of the proposed control is to maximize thepower that the turbine extracts. This can be achieved if Cpis maximized. To maximize Cp, λ must be kept constantat its optimum value , regardless of the wind speed. Fig. 3illustrates the steady-state power-speed characteristics (solid

0 5 10 150

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Tip−speed Ratio λ

Po

we

r C

oe

ffic

ien

t C

p

β=−0.9

β=−0.8

β=0

β=2

β=6

β=12

β=20

Fig. 1. Power coefficient versus tip-speed ratio, for various blade pitchangles β.

Fig. 2. Structural diagram of WECS systems.

curves) and the maximum power point curve (dashed curve)attained at each wind speed, for a blade pitch angle of 0o.The aerodynamic torque on the wind turbine rotor can beobtained using the following relationships:

Tm =Pm

ωt=

ρπCp(λ, β)R3V 2

2λ. (7)

0 1 2 3 4 5 60

0.5

1

1.5

V=6m/s

V=8m/s

V=9m/s

V=10m/s

V=11m/s

V=12m/s

Tur

bin

Out

put P

ower

Pm

[M

Wat

t]

Wind turbine speed [rad/s]

Fig. 3. Power-speed characteristics of wind turbine, for various wind speedsat pitch angle 0o.

The basic idea of the proposed MPPT technique is toretrieve the optimal rotor speed ωt (meaning the speedcorresponding to the maximum generable power) for anyinstantaneous value of the wind speed. In Fig. 2, the inputsignals coming from the turbine control system are thegenerator torque set point Tg,ref and the desired pitch angleβref . The measured outputs are assumed to be the turbinerotor speed ωt. The wind speed V is the disturbance signal



IMECS 2014

affecting the WECS. Its model is given as

θ = ωt − ωg (8)Jgωg(t) = Ksθ +Bsωt −Bsωg + Tg(ωg, Tg,ref )(9)Jtωt(t) = −Ksθ −Bsωt +Bsωg + Tm(β, V ) (10)

The generator torque Tg is a nonlinear function of ωg andthe control variable Tg,ref . The generator usually operatesin the linear region of its torque characteristic which can,therefore, be approximated by a linear form

Tg = Bgωg − Tg,ref . (11)

The pitch actuator is modelled as a first-order dynamicsystem with saturation in the amplitude and derivative ofthe pitch angle β as [4], [5].

β =−1

τβ +

1

τβref . (12)

It can be seen that the overall WECS model describedin (3)-(12) is nonlinear. Fig. 4 shows the block diagramof a control scheme to track the optimal rotor speed tomaximize the power that the turbine extracts. The control

Fig. 4. Block diagram of nonlinear dynamic of WECS.

system acts on the generator in order to apply the referenceelectromagnetic torque Tg,ref and on the pitch actuator inorder to control the pitch angle of the blades β. The systemparameters are given as follows [17]:Turbine and drive train parametersR=30.30m,Ks=15.66x105N/m,Bs=30.29x102N.ms/rad,Jt=83.00x104kg.m2

Generator parametersBg=15.99 N.ms/rad, Jg=5.9 kg.m2

Pitch actuatorτ=100 ms.

III. CONTROL STRATEGY

To control a given system, the controller design includestwo steps: the first step for identification and prediction ofWECS by quasi-ARX neural network model; and the secondstep for deriving and implementing control law. In Fig. 5, weshows the adaptive controller scheme based on quasi-ARXmodel. To regulate turbine speed at MPPT operating pointis performed by using blade pitch control, with generatortorque assumed to be constant.

Fig. 5. Block diagram of the MPPT controller of WECS.

A. System Identification

Through using Taylor expansion series [8], [10], nonlinearcontinuous function can be presented as

y(t) = y0 + ϕT (t)ℵ(ϕ(t)) + e(t) (13)

whereℵ(ϕ(t)) = [a(1,t) · · · a(ny,t) b(1,t) · · · b(nu,t)]

T and ϕ(t) =[−y(t − 1) · · · − y(t − ny) u(t − 1) · · ·u(t − nu)]

T areTaylor coefficients (nonlinear parameter estimation) and theinformation or input regression vector, respectively. ϕ(t) ∈Rn=nu+ny , n is the dimension of information vector, equalsto the sum of nu and ny that represent orders of time delayin input and output data. ℵ(ϕ(t)) ∈ Rn=nu+ny is a functioncalled as the core-part sub-model to parameterize the inputregression vector. e(t) and y0 are gaussian white noise addedto the system and initial condition of output, respectively.Assumption 1. The pairs of the input and output of trainingdata are bounded.Assumption 2. The input and output of nonlinear functionℵ(ϕ(t)) are bounded.

By performing Taylor expansion series, nonlinear systemis decomposed into linear correlation between the informa-tion vector and its coefficients. It is the same in form likeARX model with nonlinear coefficients. If the system islinear, then the coefficients are constant; and if the system isnonlinear, then the coefficients are not constant or nonlinear.By putting nonlinear function into its coefficients, quasi-linear ARX model is defined as follows,

y(t, ϕ(t)) = b(1,t)u(t− 1) + · · ·+ b(nu,t)u(t− nu)

− a(1,t)y(t− 1)− · · · − a(ny,t)y(t− ny). (14)

The system identification are performed by quasi-ARX neu-ral network model is shown in Fig. 6. The embedded of MLPnetwork of quasi-ARX model has input dimension of ϕ(t) isequal to n, the number of hidden layer is m and the numberof output layer is n. The quasi-ARX incorporating neuralnetwork can be expressed as,

y(t, ϕ(t)) = ϕT (t)ℵ(ϕ(t)) (15)ℵ(ϕ(t)) = W2ΓW1(ϕ(t) +B) + θ. (16)

where Ω = W1,W2, B, θ,W1 ∈ Rmxn,W2 ∈ Rnxm, B ∈Rmx1 are the weights matrix the first and the second layer.θ ∈ Rnx1 is the bias vector of output nodes, and Γ isthe diagonal nonlinear operator with identical sigmoidalelements on hidden nodes.



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Fig. 6. Quasi-ARX neural network with MLP network as embeddedsystems.

If model in (15) satisfies to mapping the input-output of thesystem, and Assumption 1. and Assumption 2. are fulfilled,then we can estimate the output of the system at time (t+d).The equation (15) is regressed at time (t + d) to calculatethe output at d step ahead prediction, described as,

y(t+ d, ϕ(t+ d)) = b(1,t+d)u(t+ d− 1) + · · ·+b(nu,t+d)u(t− nu + d)− a(1,t+d)y(t− 1 + d)

− · · · − a(ny,t+d)y(t− ny + d) (17)

where, ϕ(t + d) = [y(t + d − 1) y(t + d − 2) · · · y(t + d −ny) u(t+ d− 1) u(t+ d− 2) · · ·u(t+ d− nu)]

T , for onlinestep ahead prediction d is equal to one.

The learning algorithm for quasi-ARX model is per-formed by the back propagation error algorithm for em-bedded MLP network and LSE algorithm for the toupdate θ. Let we introduce two sub-models zl(k) =y(t, ϕ(t))−ϕ(t)[W2(k)ΓW1(k)(ϕ(t)+B(k))]T , and zn(k) =y(t, ϕ(t)) − ϕ(t)θ(k)T , and k is the learning number. Thestep of learning algorithm of quasi-ARX neural network isdescribed by,

1) set k = 0 for initial conditions, θ(k) = 0; and smallinitial values to W1(k), W2(k), and B(k), then setk = 1, where k is the learning number.

2) calculate zl(k), then estimate θ(k) for by using a least-squares error algorithm.

3) calculate zn(k), then estimate W1(k), W2(k), andB(k). It is realized by using the well-known back-propagation (BP) algorithm.

4) use the (16) to update ℵ(k, ϕ(t))5) stop if pre-specified conditions are met and update

ℵ(ϕ(t)) by using ℵ(k, ϕ(t)), otherwise go to Step 2,and repeat the estimation of θ(k), and W1(k), W2(k),and B(k), set k = k + 1.

B. Controller Design

The quasi-ARX prediction model is improved to guaranteesystem stability expressed by

y(t, ϕ(t)) = ϕT (t)ℵ(ϕ(t), χ(t)) (18)ℵ(ϕ(t), χ(t)) = χ(t)W2ΓW1(ϕ(t) +B) + θ. (19)

where W2ΓW1(ϕ(t) +B) is nonlinear part, θ is linear part.Obviously, through introducing the switching function χ(t),the improved quasi-ARX neural network model is differentfrom the conventional quasi-ARX model. When χ(t) = 1, it is

a nonlinear prediction model which can insure the predictionaccuracy. And when χ(t) = 0, it is a linear prediction modelwhich can insure the control stability [18].

The linear part error and nonlinear part error, respectivelyis defined as follows :

e1(t) = y(t+ d)− ϕ(t+ d)T θ. (20)e2(t) = y(t+ d)− ϕ(t+ d)ℵ(ϕ(t+ d)). (21)

The switching criterion function are described as follows:

Ji(t) =t∑

l=k

ai(l)(∥ei(l)∥2 − 42)

2(1 + ai(l)ϕ(l − k)Tϕ(l − k)

+ ct∑

l=t−N+1

(1− ai(l)∥ei(l)∥2), i = 1, 2 (22)

χ(t) =

1, if J1(t) > J2(t)0, otherwise (23)

The value of is determined by designer where ≤ϕ(t)ℵ(ϕ(t)). The detail of switching technique and its sta-bility analysis refer to [18].

A minimum variance controller is used for WECS, defineas follows,

M(t+ 1) =

(1

2(y(t+ d)− y∗(t+ d))2 +

λ

2u(t)2

)(24)

where λ is a weight of control input, the controller can beobtained by solving,

∂M(t+ 1)

∂u= 0 (25)

In the case where a conventional neural network is used asa prediction model, a controller can not be derived directlyfrom an identified model because of the nonlinearities. How-ever, the quasi-ARX neural network model is linear in theinput variable u(t). Therefore, a controller is derived fromthe proposed model [8], [18]:

u(t) =b1(t)

b21(t) + λ((b1(t)− b(q−1, ϕ(t))q)u(t− 1)

+ y∗(t+ 1)− a(q−1, ϕ(t))y(t)) (26)

IV. SIMULATION AND RESULTS

To further demonstrate the effectiveness of the proposedMPPT control strategy, the control action is to arrange bladepitch ratio β to track angular velocities of turbine operating inMPPT point. The pitch angle command signal is determinedby the wind speed and pitch angle. Wind speed is generatedby ARMA model with the mean observed wind speed ofµ(t) = 12 m/s and the standard deviation of the observedwind speed of σ(t) = 1.5. The results of simulation in detailare shown in Fig. 7 - Fig. 13. In order to obtain maximumoutput power from a wind turbine generator system, it isnecessary to drive the wind turbine at an optimal rotor speedfor a particular wind speed.

The kernel of MIMO multi layer parceptron neural net-works has one hidden layer, nu=3, ny=4, and m=nu+ny=7.The parameter of switching criterion c=1.2 and N=3. Fig. 7and Fig. 8 illustrate the WECS response in the MPPToperating point. Before t = 0s,V = 12.48m/s, MPPTpower tracking 1.45MW , β = 0deg, angular velocity



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0 5 10 15 20 25 308

9

10

11

12

13

14

15

16Wind velocity (m/s)

Time (sec)

Fig. 7. Wind speed.

0 5 10 15 20 25 300.5

1

1.5

2

2.5

3

MPPT Power (MWatt)

Time (sec)

Fig. 8. MPPT aerodynamic power.

0 5 10 15 20 25 30−0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

Co

ntr

ol s

ign

al

β (r

ad

)

Time (sec)

Fig. 9. Control signal.

0 5 10 15 20 25 301

2

3

4

5

6

7

Rotor speed

tracking (rad/s)

Time (sec)

Fig. 10. Trajectory of ωt of minimum variance controller with switchingbased quasi-ARX model.

0 5 10 15 20 25 30−1

−0.5

0

0.5

1

1.5

Ro

tor

spe

ed

tra

ckin

g e

rro

r (r

ad

/s)

Time (sec)

Fig. 11. Tracking error of turbine angular velocity.

0 5 10 15 20 25 30−0.2

0

0.2

0.4

0.6

0.8

1

1.2

Sw

itch

ing

Time (sec)

Fig. 12. Switching sequance.

0 5 10 15 20 25 300.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

RMS

Time (sec)

Fig. 13. RMS error versus time.

ωt = 4.12rad/s. When the wind speed change to decreaseor increase the MPPT power also should be change in orderto keep maximum operating point of WECS with arrangeturbine rotor speed ωt by controlling blade pitch ratio β.Fig. 9 and Fig. 10 shows the control signal and wind turbinerotor speed tracking. The dot dash line denotes the output ofsystem using proposed method and solid line denotes rotorspeed reference ωt in MPPT operating point, respectively.

The tracking error of turbine rotor speed is shown inFig. 11. Switching function between nonlinear and linear partto keep system stability and control accuracy is shown inFig. 12. The performance of the proposed controller is alsomeasured by the rooted mean squared (RMS) error indexversus time shown in Fig. 13 defined as,

RMS =

√∑Nt=1(y

∗(t)− y(t))2

t(27)

where y∗(t) is the reference signal and y(t) is the controlledsystem output.

V. CONCLUSION

In this paper, quasi-ARX neural network model is used toidentification and prediction nonlinear system. The controllerdesign is derived from the proposed model with switchingfunction to keep system stability. Switching law a made bylogical signal 0 for linear part and 1 for nonlinear part,as we know quasi-ARX neural network model is dividedinto two part; nonlinear and linear. The quasi-ARX modelalso has good properties, it is used to modeling a systeminto linear correlation between regression vector and itscoefficients, so it is easy to derive the controller law by usinglocal linear properties in nonlinear system such as minimumvariance controller. By using minimum variance controllerwith switching law, the proposed model successfully is usedto track maximum power point tracking (MPPT) of WECS.



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ACKNOWLEDGMENT

This research has been supported by Indonesian Gov-ernment Scholarship with Directorate General of HigherEducation, Ministry of National Education, (Beasiswa LuarNegeri DIKTI Kementrian Pendidikan dan Kebudayaan Re-publik Indonesia) and Shipbuilding Institute of PolytechnicSurabaya (Politeknik Perkapalan Negeri Surabaya).

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