PROCEDING CONFERENCE INTERNATIONAL MODELLING, IDENTIFICATION AND CONTROL (AsiaMIC 2012)

April, 2-4 2012. At, NUVOTEL PHUKET RESORT, T.PATONG, PHUKET, THAILAND

LAMAN : http://www.actapress.com/Abstract.aspx?paperId=453790

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PRELIMINARY CONFERENCE PROGRAM

The 31st IASTED Asian Conference on Modelling, Identification, and Control (AsiaMIC 2012)

& The 7th IASTED International Conference on

Advances in Computer Science and Engineering (ACSE 2012) April 2 – 4, 2012

Phuket, Thailand

LOCATION Novotel Phuket Resort Kalim Beach, Patong Phuket 83150 Thailand

MODELLING, IDENTIFICATION AND CONTROL (AsiaMIC 2012)

SPONSORS The International Association of Science and Technology for Development (IASTED)

Technical Committee on Control and Intelligent Systems

Technical Committee on Modelling and Simulation

International Journal of Modelling and Simulation

World Modelling and Simulation Forum (WMSF)

CONFERENCE CHAIR Assc. Prof. Wudhichai Assawinchaichote - King Mongkut's University of Technology Thonburi, Thailand

TUTORIAL CHAIR Dr. Sarawut Sujitjorn - Suranaree University of Technology, Thailand

TUTORIAL SESSION Asst. Prof. Mohamed Hamdi - Engineering School of Communication (Sup'Com), Tunisia

KEYNOTE SPEAKER Dr. Sarawut Sujitjorn - Suranaree University of Technology, Thailand

INTERNATIONAL PROGRAM COMMITTEE F. Abdul Aziz – Universiti Putra Malaysia, Malaysia J. Abonyi – The University of Veszprém, Hungary

G. K. Adam – Technological Educational Institute of Larissa, Greece J. C. Amaro Ferreira – ISEL, Portugal C. Angeli – Technological Institute of Piraeus, Greece W. Assawinchaichote – King Mongkut's University of Technology Thonburi, Thailand F. Assous – Ariel University Center, Israel H. Attia – McGill University and National Research Council Canada, Canada J. Boaventura – University of Tras-os-Montes and Alto Douro, Portugal W. Borutzky – Bonn-Rhein-Sieg University of Applied Sciences, Germany X. Chen – Shibaura Inst. of Tech., Japan J. H. Chin – National Chiao Tung University, Taiwan T. Dhaene – Ghent University, Belgium A. Dolgui – School of Mines of Saint-Étienne, France D. Dutta – Monash University, Australia R. Dutta – University of New South Wales, Australia J. Dvornik – University of Split, Croatia A. Elkamel – University of Waterloo, Canada L. Fan – Shenyang Institute of Chemical Technology, PR China P. Fishwick – University of Florida, USA E. Furutani – Kyoto University, Japan W. Ghie – Université du Québec en Abitibi-Témiscamingue, Canada D. Gorgan – Technical University of Cluj-Napoca, Romania G. A. Gravvanis – Democritus University of Thrace, Greece V. Grout – Glyndwr University, UK K. E. Häggblom – Åbo Akademi University, Finland D. He – CSSI, Inc., USA R. Henriksen – Norwegian Univ. of Science and Technology, Norway D. Honc – University of Pardubice, Czech Republic G. Horton – University of Magdeburg, Germany E. Innocenti – University of Corsica, France I. Jesus – Institute of Engineering of Porto, Portugal V. Jotsov – SULSIT-State University in Sofia, Bulgaria B. Kaewkham-ai – Chiang Mai University, Thailand T. Kawabe – University Tsukuba, Japan

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S. H. Kim – Korea Advanced Institute of Science and Technology, Korea C. Kyrtsou – University of Macedonia, Greece K. Lavangnananda – King Mongkut’s University of Technology Thonburi, Thailand S. Lazarova--Molnar – United Arab Emirates University, UAE S. Liang – Chongqing University, PR China A. Løkketangen – Molde University College, Norway M. Lotfalian – University of Evansville, USA P. Mahanti – University of New Brunswick, Canada K. L. Man – Xi'an Jiaotong-Liverpool University , PR China T. Masood – Cranfield University, UK R. V. Mayorga – University of Regina, Canada N. Melao – Catholic University of Portugal, Portugal S. Mitaim – Thammasat University, Thailand Y. Morita – JAXA, Japan P. Nahodil – Czech Technical University in Prague, Czech Republic S. Narayanan – Wright State University, USA T. Niculiu – University , Romania G. Nikolakopoulos – University of Patras, Greece H. Oya – The University of Tokushima, Japan G. Petuelli – South-Westphalia University of Applied Sciences, Germany C. Pinto – Instituto Superior de Engenharia do Porto, Portugal M. Poboroniuc – The Gheorghe Asachi Technical University of Iasi, Romania M. M. Polycarpou – University of Cyprus, Cyprus P. Pongsumpun – King Mongkut's Institute of Technology Ladkrabang, Thailand Y. B. Reddy – Grambling State University, USA M. Rodrigues – Sheffield Hallam University, UK S. Sanguanpong – Kasetsart University, Thailand Y. S. Shmaliy – Guanajuato University, Mexico B. Singh – Lakehead University, Canada R. Snow – Riddle Aeronautical University, USA W. Song – National Tsing Hua University, Taiwan R. Spolon – UNESP - State University of São Paulo, Brazil G. Sun – Beijing University of Technology, PR China J. A. Tenreiro Machado – Instituto Superior de Engenharia do Porto, Portugal A. Tornambè – University of Rome Tor Vergata, Italy M. Trabia – University of Nevada, USA H. Trinh – Deakin University, Australia K. Tsakalis – Arizona State University, USA H. Unger – Fern University in Hagen, Germany G. Varga – University of Miskolc, Hungary Q. G. Wang – National University of Singapore, Singapore K. P. White – University of Virginia, USA W. Yu – CINVESTAV-IPN (National Polytechnic Institute), Mexico S. H. Zeng – Beijing University of Technology, PR China L. Zhang – Harbin Institute of Technology, PR China

T. Zhang – Tsinghua University, PR China Z. Zhang – University of Exeter, UK ADDITIONAL PAPER REVIEWERS R. Lobato – UNESP - State University of Sao Paulo, Brazil K. Zhu – CSSI, Inc., USA

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ADVANCES IN COMPUTER SCIENCE AND ENGINEERING (ACSE 2012) SPONSORS The International Association of Science and Technology for Development (IASTED) Technical Committee on Artificial Intelligence Technical Committee on Computer Graphics Technical Committee on Databases Technical Committee on Parallel & Distributed Computing & Systems Technical Committee on Software Engineering TUTORIAL SESSION Prof. Nader F. Mir - San Jose State University, USA INTERNATIONAL PROGRAM COMMITTEE D. Alcaide – University of La Laguna, Spain N. Amano – Okayama University, Japan C. Anderson – Colorado State University, USA V. Bevilacqua – Polytechnic of Bari, Italy M. Ceci – University of Bari, Italy J. Chandy – University of Connecticut, USA D. Chen – Uniformed Services University of the Health Sciences, USA S. Chittayasothorn – King Mongkut's Institute of Technology Ladkrabang, Thailand D. Connors – University of Colorado, USA A. Cuzzocrea – University of Calabria, Italy B. Dasgupta – University of Illinois at Chicago, USA D. Dinakarpandian – University of Missouri-Kansas City, USA A. DUTTA – National Institute of Technology, Durgapur., India E. Fink – Carnegie Mellon University, USA E. Grant – University of North dakota, USA J. Guo – California State University Los Angeles, USA P. Gupta – Microsoft Corporation, U.S.A., USA M. Halgamuge – Department of EEE, University of Melbourne, Australia N. Ikram – Riphah International University, Pakistan N. Karacapilidis – University of Patras, Greece M. Li – Nanjing University, PR China K. M. Liew – City University of Hong Kong, PR China J. Lindstrom – IBM, Finland S. Lodha – University of California, Santa Cruz, USA P. Mahanti – University of New Brunswick, Canada P. Netinant – Illinois Institute of Technology, USA M. Ogiela – AGH University of Science and Technology, Poland

M. Ouyang – University of Louisville, USA N. Passos – Midwestern State University, USA K. Piromsopa – Chulalongkorn University, Thailand S. Ponnabalam – Monash University, Malaysia Campus, Malaysia J. Puustjärvi – Helsinki University of Technology, Finland O. K. SAHINGOZ – Turkish Air Force Adademy, Turkey M. Sekijima – Tokyo Institute of Technology, Japan S. M. Shamsuddin – Universiti Teknologi Malaysia, Malaysia B. Stantic – Griffith University, Australia K. Sundaraj – University Malaysia Perlis, Malaysia K. Takano – Kanagawa Institute of Technology, Japan A. Takasu – National Institute of Informatics, Japan N. Taylor – Heriot-Watt University, UK I. F. Vega-López – Autonomous University of Sinaloa, Mexico D. Wang – University of Rochester Medical Center, USA K. L. Wen – Chienkuo Technology University, Taiwan H. Williams – Heriot-Watt University, UK M. E. Yahia – King Faisal University, Saudi Arabia S. Q. Zheng – The University of Texas at Dallas, USA PLEASE NOTE

� Paper presentations are 15 minutes in length with an additional 5 minutes for questions.

� Report to your Session Chair 15 minutes before the session is scheduled to begin.

� Presentations should be loaded onto the presentation laptop in the appropriate room prior to your session.

� End times of sessions vary depending on the number of papers scheduled.

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PROGRAM OVERVIEW

Monday, April 2, 2012 07:00 – Registration (Siam Foyer) 08:30 – AsiaMIC/ACSE Welcome Address 09:00 (Siam B Room) 09:00 – AsiaMIC Session 1 – Artificial Intelligence and

Soft Computing (A) (Siam B Room) 09:00 - ACSE Session 1 – Software Engineering,

Computational Intelligence and Data Mining (A) (Siam C Room) 11:00 – Coffee Break 11:30 (Siam Foyer) 11:30 – AsiaMIC Keynote Speaker – “The Roles of

Metaheuristics on Control Design Optimization and Identification Research” – Dr. Sarawut Sujitjorn

(Siam B Room) 12:30 – Lunch Break (TBA) 14:00 – AsiaPES Keynote Speaker – “Phasor

Measurements as Smart Device for Observing Power System Dynamics” – Prof. Yasunori Mitani

(Siam A Room) 15:00 – Coffee Break 15:30 (Siam Foyer) 15:30 – Asia MIC Session 2 – Artificial Intelligence and

Soft Computing (B) (Siam B Room) 15:30 – ACSE Session 2 - Software Engineering,

Computational Intelligence and Data Mining (B) (Siam C Room)

Tuesday, April 3, 2012 08:30 – AsiaMIC Session 3 – Fault Analysis and Process

Systems (Siam B Room) 08:30 – ACSE Session 3 – Computer Networks,

Communication and Web Technologies (Siam C Room) 10:30 – Coffee Break 11:00 (Siam Foyer) 11:00 – AsiaMIC Session 3 Continued (Siam B Room) 11:00 – ACSE Session 3 Continued (Siam C Room) 12:00 – Lunch Break (TBA) 14:00 – AsiaMIC Tutorial Session – “Biologically–

Inspired Communication and Networking” – Asst. Prof. Mohamed Hamdi

(Siam B Room) 14:00 – ACSE Tutorial Session – “Latest Applications in

Computer Networks: from IPTV to Mobile Multimedia Networks” – Prof. Nader F. Mir

(Siam C Room) 16:00 – Coffee Break 16:30 (Siam Foyer) 16:30 – AsiaMIC Tutorial Session Continued (Siam B Room) 16:30 – ACSE Tutorial Session Continued (Siam C Room) 19:00 – Dinner Banquet

(TBA)

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Wednesday, April 4, 2012 08:30 – AsiaMIC Session 4 – Modelling and Identification (Siam B Room) 08:30 – AsiaMIC Session 5 – Optimization and Control

Applications (Siam C Room) 10:30 – Coffee Break 11:00 (Siam Foyer) 11:00 – AsiaMIC Session 4 Continued (Siam B Room) 11:00 – AsiaMIC Session 5 Continued (Siam C Room) 12:30 – Lunch Break (TBA) 14:00 – AsiaMIC Session 6 – Recent Advances in MIC

and their Applications (Siam C Room) 15:00 – Coffee Break 15:30 (Siam Foyer) 15:30 – AsiaMIC Session 6 Continued (Siam C Room)

Monday, April 2, 2012 07:00 – REGISTRATION Location: Siam Foyer 08:30 – 09:00 AsiaMIC/ACSE WELCOME ADDRESS Location: Siam B Room 09:00 – AsiaMIC SESSION 1 – ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING (A) Chairs: TBA Location: Siam B Room

769-038 Fuzzy Control with Quadratic Performance for a Class of Nonlinear Systems Dušan Krokavec and Anna Filasová (Slovakia)

769-072 KTX Noise ANC Performance Evaluation using a Multiple-LMS-based Neural Network Hyeon Seok Jang, Kung Wan Koo, Young Min Lee, Young Jin Lee, and Kwon Soon Lee (Korea)

769-064 Dynamic Neural Network-based Fault Diagnosis of Gas Turbine Engines Sina S. Tayarani-Bathaie, Zakieh Sadough, and Khashayar Khorasani (Canada)

769-058 Mathematical Modeling and Numerical Simulation for Microbial Depolymerization Processes of Exogenous Type Masaji Watanabe and Fusako Kawai (Japan) 09:00 – ACSE SESSION 1 – SOFTWARE ENGINEERING, COMPUTATIONAL INTELLIGENCE AND DATA MINING (A) Chairs: TBA Location: Siam C Room 770-037 Application of Simulation Systems in Training Security Services Grzegorz Gudzbeler, Mariusz Nepelski, and Andrzej Urban (Poland)

770-040 Proposal of Motion Caputuring System for Authentication Keiichiro Awaji, Yutaro Watanabe, and Ryuya Uda (Japan)

770-032 Dynamic Simulation of a 3-D 4BL Engineering Problem using Augmented Reality Manjit S. Sidhu (Malaysia) and Waleed Maqableh (Saudi Arabia)

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770-043 Top-View based Human Action Recognition using Depth and Color Information Sittisuk Seawpakorn and Nikom Suvonvorn (Thailand)

770-039 Profile-based Action Recognition using Depth Information Pongsatorn Chawalitsittikul and Nikom Suvonvorn (Thailand)

11:00 – 11:30 COFFEE BREAK Location: Siam Foyer

11:30 – AsiaMIC KEYNOTE SPEAKER – “THE ROLES OF METAHEURISTICS ON CONTROL DESIGN OPTIMIZATION AND IDENTIFICATION RESEARCH” Presenter: Dr. Sarawut Sujitjorn (Thailand) Location: Siam B Room

Many challenging applications in science, engineering and technology can be formulated as optimization problems. Some of them are complex and difficult to solve by an exact method within a reasonable amount of time. These problems usually contain multiple local solutions that can easily trap inefficient algorithms. Approximate algorithms are alternatives; among those metaheuristics have been main tools for solving this large class of problems. Numerous metaheuristics are multi-discipline involving AI, soft-computing, computational intelligence, mathematical programming, operation research, and biology. In recent years, efficient metaheuristics have been developed for combinatorial, continuous, and multi-objective optimization problems; they have found many successful real-world applications. Foremost applications include multi-objective control design optimization and complex model identification.

This lecture will deliver an orientation on metaheuristics, the quests on performance and convergence of algorithms, multi-objective control design optimization problems, model identification problems, generic, and specific algorithms. Illustrative algorithms are multi-path adaptive tabu search and co-operative adaptive bacterial-foraging-tabu-search algorithms. The following case studies serve to demonstrate the practicality and effectiveness of the algorithms:

identification of Stribeck friction model on a linear slide bed,

computational stability analysis based-on Lyapunov's direct method, and

extreme control design optimization of a road-way simulator.

The works to be presented have come from a team of researchers who fond of control and computing through our hard-working years. The followings are my students, colleagues and friends who deserve for acknowledgements: T. Kulworawanichpong, K-N. Areerak, K-L. Areerak, D. Puangdownreong, J. Kluabwang, N. Sarasiri, S. Phanikhom and K. Suthamno.

Sarawut Sujitjorn was awarded the BSc (Hons) degree in Electrical Engineering from the Royal Thai Air Force Academy, in 1984, and in 1987 the PhD degree in Electronic & Electrical Engineering from the University of Birmingham, UK, where he worked on automated coast-control of rapid transit trains. He is currently a Professor of Electrical Engineering at Suranaree University of Technology (SUT), Thailand, where he founded the School of Electrical Engineering, the Control & Automation Research Group, now the Power Electronics, Machines and Control Research Group, and co-founded the Scientific and Technological Equipment Centre. He is past Head of the EE School, Vice Rector for Academic Affairs and Director of the Research & Development Institute at SUT. He teaches postgraduate and undergraduate courses in Electric Circuits and Automatic Control. Before coming to SUT, Korat, he was a lecturer at the Royal Thai Air Force Academy from 1988-1993.

Prof Sujitjorn has worked in academia for over 20 years and has published over 150 research and technical papers, 17 patents, three books and one monograph. His research interests span the areas of control, electrical machine, power converter and computing, particularly the application of metaheuristics to modelling, identification and control. He also serves the National Research Council of Thailand as sub-committee member and reader.

12:30 – LUNCH BREAK TBA 14:00 – AsiaPES KEYNOTE SPEAKER – “PHASOR MEASUREMENTS AS SMART DEVICE FOR OBSERVING POWER SYSTEM DYNAMICS” Presenter: Prof. Yasunori Mitani (Japan) Location: Siam A Room

Phasor Measurement Unit (PMU) is an apparatus which detects the absolute value of phase angle in sinusoidal signal. Here, suppose that more than two measurement units for instantaneous voltage with short sampling time are located distantly apart from each other. Then, we can get multiple measured voltage data but they cannot be compared exactly along with time since we do not know the reference of time. However, once they are used with GPS signal which tells us the information on exact time, it becomes ready to get phase differences among them. Thus,

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PMU with GPS receiver is applied to the monitoring of AC power system dynamics and usually installed at substations of transmission lines.

On the other hand, our group is developing the system with PMUs installed at user power outlet; 100V in Japan and 240 V in Thailand. In Japan we are developing a power system monitoring system with PMUs installed at University’s campuses, which is called Campus WAMS (wide area measurement system). As a result, we can monitor the wide area power system stability and dynamics viewed from user side. In addition, we have developed a signal processing method with an FFT filtering or a wavelet transformation to eliminate the noises of voltage around user power outlets and a method to identify an equivalent vibration model for the evaluation of power system stability with the processed signals. The PMU system provides us useful information on phasor voltage distributions on the power system map. From these data we can get the dynamic behaviors of power flows. In this context the PMU system is expected as a new device to support the smart grid.

This keynote speech presents some results on the power system observation in Japan and in South-East Asia (Singapore-Malaysia and Thailand).

Yasunori Mitani received the B.S., M.S., and D.Eng. Degrees in electrical engineering from Osaka University, Osaka, Japan, in 1981, 1983, and 1986, respectively. Currently he is a Professor in the Department of Electrical Engineering, Kyushu Institute of Technology, Fukuoka, Japan. He was a Visiting Research Associate at the University of California, Berkeley from 1994 to 1995. His research interests are in the areas of analysis and control of power systems.

15:00 – 15:30 COFFEE BREAK Location: Siam Foyer 15:30 – AsiaMIC SESSION 2 – ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING (B) Chairs: TBA Location: Siam B Room

769-018 Complete Stability Ranges of a Class of Interval Matrices - The Stability Feeler Approach Tadasuke Matsuda, Michihiro Kawanishi, and Tatsuo Narikiyo (Japan)

769-013 Wavelet Feature Selection using Genetic Algorithms for Text Independent Speaker Recognition Shung-Yung Lung (Taiwan)

769-039 PID Control for Micro-Hydro Power Plants based on Neural Network Lie Jasa, Ardyono Priyadi, and Mauridhi H. Purnomo (Indonesia)

769-053 Evapotranspiration Prediction using System Identification and Genetic Algorithm Robiah Ahmad, Saiful Farhan Mohd Samsuri, and Mohd Zakimi Zakaria (Malaysia) 15:30 – ACSE SESSION 2 – SOFTWARE ENGINEERING, COMPUTATIONAL INTELLIGENCE AND DATA MINING (B) Chairs: TBA Location: Siam C Room 770-014 Hybrid Algorithm using Genetic Algorithm and EDA Introducing Partial Search Kenji Tamura (Japan)

770-034 A Hybrid CS/DE Algorithm for Global Optimization Mansooreh Soleimani, Shahriar Lotfi, and Amirhossein Ghodrati (Iran) 770-044 Implementation of Hybrid Naive Bayesian-Decision Tree for Childhood Obesity Predictions Muhamad Hariz B. Muhamad Adnan, Wahidah Husain, and Nur`Aini Abdul Rashid (Malaysia)

770-019 An Interdisciplinary Approach to Automatically Capture Knowledge in Dialogues on the Spot Keedong Yoo (Korea) 770-015 Influence Nets based Decision Support System Sajjad Haider, Samad Hassan, and Mohammad Nishat (Pakistan)

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Tuesday, April 3, 2012 08:30 – AsiaMIC SESSION 3 – FAULT ANALYSIS AND PROCESS SYSTEMS Chairs: TBA Location: Siam B Room 769-026 Modelling Researches of the Limitations for Fault-Tolerance Measurements of Ultra Small Displacements and Vibrations Alexander V. Liapidevskiy, Vadim A. Zhmud, Denis O. Tereshkin, and Vladimir I. Gololobov (Russia)

769-015 The Improvement of Modulating Function Method for Fast Identification of Parameter Faults in Linear Continuous Systems Witold Byrski and Jędrzej Byrski (Poland)

769-016 Tolerating Permanent Changes of State Transitions in Asynchronous Machines Seong Woo Kwak and Jung-Min Yang (Korea)

769-041 Effects of Foot Shape on Fault Tolerant Gaits of a Quadruped Robot Seong Woo Kwak and Jung-Min Yang (Korea)

769-011 Determining Water Patterns in Chakmak Canal and Pradoo Bay, Rayong Province, Thailand Nuanchan Singkran (Thailand)

769-021 Tracking of Choke Pressure during Managed Pressure Drilling Espen Hauge, Ole Morten Aamo, and John-Morten Godhavn (Norway)

769-045 Dynamic Modelling of Gas Rising in a Wellbore Espen Hauge, John-Morten Godhavn, Øyvind N. Stamnes, and Ole Morten Aamo (Norway)

769-032 Studies on the Measurement of Achievement in Simple Arithmetic Drills from the Inflections of Event-Related Potentials Miki Shibukawa, Mariko Funada, and Yoshihide Igarashi (Japan)

769-024 Petri Net Representation of Switched Stochastic Systems Jiaying Ma, Jueliang Hu, Zuohua Ding, and Jing Liu (PR China)

769-036 Impact-Echo Non-Destructive Testing and Evaluation with Time-Frequency Process and Analysis Mark Emde and Ruichong Zhang (USA) 08:30 – ACSE SESSION 3 – COMPUTER NETWORKS, COMMUNICATION AND WEB TECHNOLOGIES Chairs: TBA Location: Siam C Room

770-002 Analysis of IPTV Traffic over Computer Communication Networks Nader F. Mir, Mohit Vashisht, and Sagar Agarwal (USA)

770-025 A Framework for Evaluation of 3G Communication Systems Freeha Azmat and Junaid Imtiaz (Pakistan)

770-030 Improvement on Enhanced Secure Anonymous Authentication Scheme for Roaming Service in Global Mobility Networks Iuon-Chang Lin and Chen-Hsiang Chen (Taiwan)

770-020 Application of Wireless Sensor Networks to the In-Line River Monitoring of Nitrate Alberto Bonastre, Juan Vicente Capella, Rafael Ors, and Miguel Peris (Spain)

770-013 Personalizing the User's Physical Environment in a Pervasive System Elizabeth Papadopoulou, Sarah Gallacher, Nick K. Taylor, and Howard Williams (UK)

770-051 Lifelog Ontology Schema Definition for Personal Identification Yuuki Hotta, Haruki Ogata, and Ryuya Uda (Japan)

770-036 GuideME: An Effective RFID-based Traffic Monitoring System Fadi Aloul, Assim Sagahyroon, Ali Nahle, Makram Abou Dehn, and Raneem Al Anani (UAE)

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10:30 – 11:00 COFFEE BREAK Location: Siam Foyer 11:00 – AsiaMIC SESSION 3 CONTINUED Location: Siam B Room 11:00 – ACSE SESSION 3 CONTINUED Location: Siam C Room 12:00 – LUNCH BREAK TBA 14:00 – AsiaMIC TUTORIAL SESSION – “BIOLOGICALLY-INSPIRED COMMUNICATION AND NETWORKING” Presenter: Asst. Prof. Mohamed Hamdi (Tunisia) Location: Siam B Room

Bio-inspired networking and communication protocols and algorithms are devised by considering biology as source of inspiration, and by adapting behaviors, laws, and dynamics governing biological systems. While the literature related to biologically-inspired computing is abundant, the focusing on the application of biological concepts in networking is still in infancy. In this tutorial, we address the applicability of biological mechanisms and techniques in various communication fields. Particularly, we explore the mechanisms and the challenges in embedded communication systems with primary focus on recent applications of bio-inspired techniques in communication networks. Nonetheless, the results that have been reached so far show that this area is very promising.

Objectives The tutorial is at the graduate school level and is accessible to postgraduate level. It is intended to introduce the tutees to the biologically-inspired concepts that are being intensively used by the scientific community in the computer science and communication networks fields. The content of the tutorial is structured as follows: 1. Social insects and insect colony: The use of insect mobility models to solve optimization algorithms will be illustrated. 2. Epidemic worm spreading: Stochastic models for the propagation of digital worms will be investigated. 3. Artificial immune systems: Security and protection systems that mimic natural immunity systems will be described. 4. Cognitive networks: This new concept, which is becoming very popular in the context of wireless networks and radio communications, will be studied. 5. Homeostatic communication systems: The use of homeostasis in self-organizing ad hoc and sensor networks will be addressed.

6. Firefly synchronization: This nonlinear model for microcontroller design will be illustrated and discussed. Timeline Time allocations for the major course topics 1. Overview on biologically-inspired schemes (30 minutes) 2. Social insects and insect colony (30 minutes) 3. Epidemic worm spreading (30 minutes) 4. Artificial immune systems (30 minutes) 5. Cognitive networks (30 minutes) 6. Homeostatic communication systems (30 minutes) 7. Firefly synchronization (30 minutes)

The level of presentation assumes that the attendees have a background knowledge in computer science and communication network architectures. More precisely, the key pre-requisites relate to algorithms and data structures, network protocols, and communication architectures.

Dr. Mohamed Hamdi (PhD, habilitation) co-authored more than 80 scientific publications published in international journals and conferences. He has also chaired and co-chaired international conferences and special issues in international conferences including the ‘Trust, Security, and Privacy’ symposium in the IEEE IWCMC 2012 conference and for the special issue on ‘Web Services in Multimedia Communication’ for the journal on Advances in Multimedia. He presented multiple tutorials and invited speeches in international conferences such as the GEOSS Forum (Globecom 2011). In addition, Dr. Hamdi has been invited at the ITU World Telecom conference to serve as a panellist in a forum on the security of social networks. He also passed prestigious professional certifications including the CISSP and the CISCO Security certifications. He is conducting research activities in the areas of wireless sensor networks, risk management, algebraic modeling, relational specifications, intrusion detection, and network forensics.

14:00 – ACSE TUTORIAL SESSION – “LATEST APPLICATIONS IN COMPUTER NETWORKS: FROM IPTV TO MOBILE MULTIMEDIA NETWORKS” Presenter: Prof. Nader F. Mir (USA) Location: Siam Room

We are witnessing the Internet applications such as mobile multimedia over IP network technology being destined to play an increasingly important role in communication systems. With the demand for multimedia applications, there will be a growing interest in identifying suitable network architectures and transmitting facilities for this technology. Communication industry has spent considerable effort in designing an IP-based media transport mechanism, voice over IP (VoIP), and multimedia

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networks that can deliver voice-band telephony with the quality of the telephone networks. The Internet offers phone services less expensive and with numerous additional features such as video conferencing, online directory services, and the Web incorporation.

In this tutorial, we present the fundamentals the latest applications in computer networks. Applications such as Video on Demand (VoD) IPTV, VoIP, and also Multimedia over IP networks schemes. We explain the transportation of real-time signals along with the signaling protocols used in voice over IP (VoIP) telephony and multimedia networking. The tutorial covers the signaling protocols as H.323 series of protocols, and Session Initiation Protocol (SIP) which are responsible for session signaling. The H.323 protocols interact to provide ideal telephone communication, providing phone numbers to IP address mapping, handling digitized audio streaming in IP telephony, and providing signaling functions for call setup and call management. The H.323 series support simultaneous voice and data transmission and can transmit binary messages that are encoded using basic encoding rules. We also review the Session Initiation Protocol (SIP) as one of the most important VoIP signaling protocols operating in the application layer of TCP/IP model. SIP can perform both unicast and multicast sessions and supports user mobility and handles signals and identifies user location, call setup, call termination, and busy signals. SIP can use multicast to support conference calls and uses the Session Description Protocol (SDP) to negotiate parameters.

The tutorial further covers Compression of multimedia components such as Digital Voice and Video, focusing on data-compression techniques for voice and video to prepare digital voice and video for multimedia networking will be [resented. The topic starts with the analysis of information-source fundamentals, source coding, and limits of data compression and explains all the steps of the conversion from raw voice to compressed binary form, such as sampling, quantization, and encoding. The discussion also summarizes the limits of compression and explains typical processes of still-image and video-compression techniques, such as JPEG, MPEG, and MP3.

The tutorial will then present real-time transport protocols, such as Real-Time Transport protocol (RTP) and the Real-Time Control Protocol (RTCP). The next topic is streaming video in a single server, using content distribution networks (CDNs). Also discussed is the Stream Control Transmission Protocol (SCTP), which provides a general-purpose transport protocol for transporting stream traffic. The tutorial describes detailed streaming source modeling and analysis. In real-time applications, a stream of data is sent at a constant rate. This data must be delivered to the

appropriate application on the destination system, using real-time protocols. The most widely applied protocol for real-time transmission is the Real-Time Transport Protocol (RTP), including its companion version: Real-Time Control Protocol (RTCP). UDP cannot provide any timing information. RTP is built on top of the existing UDP stack. Real-time applications may use multicasting for data delivery.

We also cover video streaming that presents a significant challenge to network designers. A video in a single server can be streamed from a video server to a client at the client request. The high bit-rate video streaming must sometimes pass through many Internet service providers, leading to the likelihood of significant delay and loss on video. One practical solution to this challenge is to use content distribution networks (CDNs) for distributing stored multimedia content. Video streaming, e-mail, and image packets in the best-effort Internet are mixed in the output queue of the main exit router of a domain. Under such circumstances, a burst of packets, primarily from the image file source, could cause IP video streaming packets to be excessively delayed or lost at the router. One solution in this case is to mark each packet as to which class of traffic it belongs to. This can be done by using the type of-service (ToS) field in IPv4 packets. As seen in the figure, transmitted packets are first classified in terms of their priorities and are queued in a first in, first out (FIFO) order. The priority of an image file can be equal to or less than the one for video streaming, owing to the arrangement of purchased services.

The Tutorial will then focus on the Stream Control Transmission Protocol (SCTP) providing a general-purpose transport protocol for message-oriented applications. SCTP is a reliable transport protocol for transporting stream traffic, can operate on top of unreliable connectionless networks, and offers acknowledged and non-duplicated transmission data on connectionless networks. SCTP has the following features. The protocol is error free. A retransmission scheme is applied to compensate for loss or corruption of the datagram, using checksums and sequence numbers. It has ordered and unordered delivery modes. SCTP has effective methods to avoid flooding congestion and masquerade attacks. This protocol is multipoint and allows several streams within a connection. In TCP, a stream is a sequence of bytes; in SCTP, a sequence of variable-sized messages. SCTP services are placed at the same layer as TCP or UDP services. Streaming data is first encapsulated into packets, and each packet carries several correlated chunks of streaming details. The Tutorial will ultimately present multimedia over general Wireless and WiMAX networks.

11

Timeline Overview of IP Telephony (0.5 hour) •VoIP Signaling Protocols •H.323 Protocols •Session Initiation Protocol (SIP) •Softswitch and MGCP Overview of Digital Voice and Compression (1 hour) •Signal Sampling •Quantization and Distortion •Still Images and JPEG Compression •Raw-Image Sampling and DCT •Quantization and Encoding •Moving Images and MPEG Compression •MP3 and Streaming Audio •Limits of Compression with Loss •Basics of Information Theory •Entropy of Information •Shannon’s Coding Theorem •Compression Methods Without Loss •Run-Length Encoding •Huffman Encoding •Lempel-Ziv Encoding •FAX Compression for Transmission Video Streaming Applications and Real-Time Media Transport Protocols (1 hour) •Real-Time Transport Protocol (RTP) •Real-Time Control Protocol (RTCP) •Estimation of Jitter in Real-Time Traffic •Distributed Multimedia Networking •Stream Control Transmission Protocol (SCTP) •SCTP Packet Structure •Self-Similarity and Non-Markovian Streaming Analysis •Self-Similarity with Batch Arrival Models •Content Distribution Networks (CDNs) •CDN Interactions with DNS •Multimedia Security •Providing QoS to Streaming •IPTV •Video on Demand (VOD) Technology Mobile multimedia and Voice and Video Streaming over Wireless Networks (1 hour) •Introduction to WiMAX technology •Mobile Transport Protocols •Mobile Computing and Mobile IP •TCP and UDP for Mobility •Protocols for Voice over Mobile IP •Protocols for Video Streaming over Mobile IP

Audience can be from academia or industry. Any individual with basic knowledge of computer science and engineering can benefit from this tutorial.

Nader F. Mir received a B.Sc. degree with honors in electrical and computer engineering in 1985 and MSc and PhD degrees, both in electrical engineering, from Washington University in St. Louis, in 1990 and 1994, respectively.

He is currently a Professor and Department Associate Chairman of Electrical Engineering at San Jose State University, California. He is also the Director of the MSE Program in Optical Sensors Networks for Lockheed Martin Space Systems.

His research interests are analysis of computer communication networks, design and analysis of switching systems, network design for wireless ad hoc, internet and sensor systems, information systems and applications of digital integrated circuits in computer communications.

16:00 – 16:30 COFFEE BREAK Location: Siam Foyer 16:30 – AsiaMIC TUTORIAL SESSION CONTINUED Location: Siam B Room 16:30 – ACSE TUTORIAL SESSION CONTINUED Location: Siam C Room 19:00 – Dinner Banquet Location: TBD

12

Wednesday, April 4, 2012 08:30 – AsiaMIC SESSION 4 – MODELLING AND IDENTIFICATION Chairs: TBA Location: Siam B Room 769-044 Modelling Dependencies and Couplings in the Design Space of Meshing Gear Sets Mohammad Rajabalinejad (The Netherlands)

769-030 Internal Model Control of Piezoelectric Actuator based on Sandwich Model with Hysteresis Yangqiu Xie, Yonghong Tan, Ruili Dong, and Hong He (PR China)

769-086 A Model Reflecting the Changes of ERPs during Repeated Learning of Calculations Mariko Funada, Yoshihide Igarashi, Tadashi Funada, and Miki Shibukawa (Japan)

769-065 A Bayes Shrinkage Estimation Method for Vector Autoregressive Models Sung-Ho Kim and Namgil Lee (Korea)

769-027 Linear System Analysis and State Observer Design of Grid Connected Inverter Model based on System Identification Nopporn Patcharaprakiti, Jatturit Thongprong, Krissanapong Kirtikara, and Jeerawan Saelao (Thailand)

769-004 A Short Contribution on Efficient Modelling of Parallel Queues Nader F. Mir (USA)

769-069 3D Modeling of a Class of Objects in Different Engineering Analysis Field Mei Chen, Fei Zheng, and Na Li (PR China)

769-071 Modeling of a Large Deployable Space Antenna Structure Fei Zheng, Mei Chen, and Peng Li (PR China)

769-006 The Novel Analytical Probabilistic Model of Random Variation in the MOSFET’s High Frequency Performance Rawid Banchuin (Thailand)

769-059 Study on Bird Flu Infection Process within a Poultry Farm with Modeling and Simulation Masaji Watanabe (Japan) and Tertia Delia Nova (Indonesia) 08:30 – AsiaMIC SESSION 5 – OPTIMIZATION AND CONTROL APPLICATIONS Chair: TBA Location: Siam C Room

769-012 Memoryless Solution to the Infinite Horizon Optimal Control of LTI Systems with Delayed Input Francesco Carravetta, Pasquale Palumbo, and Pierdomenico Pepe (Italy) 769-029 An Optimizing Parameter-Tuning of Multi-Loop Controllers for Boiler Combustion Process Hong He and Yonghong Tan (PR China)

769-087 Optimal Frequency Regulation of a Single-Area Power System Sayed Z. Sayed Hassen and Mohamed I. Jahmeerbacus (Mauritius)

769-082 Optimal Vibration Control of a Rectangular Piezothermoelastic Plate Marwan Abukhaled and Ibrahim Sadek (UAE)

769-028 Local Controllability of Fractional Discrete-Time Semilinear Systems with Multiple Delays in Control Jerzy Klamka (Poland)

769-083 Enhanced Simplified Decoupling for Multivariable Processes with Multiple Time Delays Truong Nguyen Luan Vu and Moonyong Lee (Korea)

769-096 Active Noise Control in Large Industrial Halls Marek Pawelczyk (Poland)

769-005 Applying Posture Identifier and Backstepping Method in the Design of an Adaptive Nonlinear Predictive Controller for Nonholonomic Mobile Robot Ahmed S. Al-Araji (Iraq), Maysam F. Abbod, and Hamed S. Al-Raweshidy (UK)

13

769-089 Fractional Order Controller and its Applications: A Review Swati Sondhi and Yogesh V. Hote (India)

769-061 A Cost-Effective, Robust and an Efficient Design of a Motor Controller for UGVs Soyiba Jawed, Freeha Azmat, and Muhammad Z. Khan (Pakistan) 10:30 – 11:00 COFFEE BREAK Location: Siam Foyer 11:00 – AsiaMIC SESSION 4 CONTINUED Location: Siam B Room 11:00 – AsiaMIC SESSION 5 CONTINUED Location: Siam C Room 12:30 – LUNCH BREAK TBA 14:00 – AsiaMIC SESSION 6 – RECENT ADVANCES IN MIC AND THEIR APPLICATIONS Chairs: TBA Location: Siam C Room

769-031 Firmware for the Receiving and Processing of Meteorological Information from the Space Satellites “AKTOMIKA” Alexander V. Liapidevskiy, Vladimir I. Gololobov, Vadim A. Zhmud, Anton V. Zakharov, and Aleksey S. Drozdov (Russia) 769-054 Modelling of a Hobbing Tool Series for Generating Spur Gears with Circular Fillet Andromachi N. Zouridaki (Greece)

769-010 A Web System that Allows for Decision-Making through Citizen Participation Mahito Hosoi and Yukio Uchida (Japan)

769-078 Analysis, Evaluation, and Design of an Overlapped Ultrasonic Sensor Ring for Minimal Positional Uncertainty in Obstacle Detection Sungbok Kim and Hyunbin Kim (Korea)

769-056 Simulation and Comparative Studies of Dead Space Loading for Human Respiratory Control under Exercise and CO2 Inhalation Shyan-Lung Lin and Nai-Ren Guo (Taiwan)

769-095 The Application of GPU-based K-Means in Analysis of RFID Data Huifang Deng, Zhen Liang, and Chunhui Deng (PR China)

769-050 Mold Filling Simulation in the Injection Molding Process with OpenFOAM Software for Non-Isothermal Newtonian Fluid Farivar Fazelpour, Majid Vafaeipour, Habib Etemadi, Amir Dabbaghian, Raoof Bardestani, and Mohammadreza Dehghan (Iran)

769-035 Anomaly Detection based on GA&FART Approach of Computer Network Security Preecha Somwang, Woraphon Lilakiatsakun, and Surat Srinoy (Thailand)

769-051 Causal Impact Price Transmission of the Rice Markets in Thailand Wanvilai Chulaphan (Taiwan), Chalermpon Jatuporn (Thailand), Shwu-En Chen (Taiwan), and Pattana Jierwiriyapant (Thailand)

769-033 An Intelligent Flow Measurement Scheme using Ultrasonic Flow Meter Santhosh K. Venkata and Binoy K. Roy (India) 15:00 – 15:30 COFFEE BREAK Location: Siam Foyer 15:30 – AsiaMIC SESSION 6 CONTINUED Location: Siam C Room ************************************************

IASTED would like to thank you for attending AsiaMIC and ACSE 2012. Your participation helped make this international event a success, and we look forward to seeing you at upcoming IASTED events.

************************************************

PID CONTROL FOR MICRO-HYDRO POWER PLANTS BASED

ON NEURAL NETWORK

Lie Jasa , Ardyono Priyadi , Mauridhi Hery Purnomo

A) B) C)

A) Electrical Engineering Udayana University, Bali, Indonesia. Email: liejasa@unud.ac.id

B) Electrical Engineering of Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia. Email: priyadi@ee.its.ac.id C)

Electrical Engineering of Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia. Email: hery@ee.its.ac.id

ABSTRACT

Micro-hydro power plants are power plants with

small capacity, which is built in specific locations. The

main problem of micro-hydro is the voltage generated is

not stable at 220 VA and frequency of 50 Hz. A micro-

hydro that was constructed by Lie Jasa et al. in Gambuk

village at Pupuan sub-district, Tabanan district of Bali

province, Indonesia in 2010 is still an open loop system in

which spin turbine is stable when it is set from the high

water level in reservoirs. This will be problematic when

the generator load changes. This study will overcome the

problem by proposing to build a closed loop system from

the change in output frequency for the control circuit. The

control circuit is a circuit constructed neural network-

based PID control by using the Brandt-Lin algorithm to

control the governor. The governor function is to regulate

the amount volume of water running into turbine. By

applying Matlab simulation, the result shows that the best

output is obtained when the the change in frequency will

stabilize at about 40 seconds and using the value of Kp =

0.0637533, Ki=0.00021801 and Kd=0.00301846.

KEY WORDS

PID, Turbine, Neural network, Micro-hydro, Frequency

1. Introduction

There is a growing research related to micro-hydro,

such as the advanced control of micro-hydro [1], the

simulation of ANN Controller of Automatic Generation

Control micro-hydro [2], Artificial Neural Networks to

Predict River Flow Rate into Dam of Micro-hydro [3].

Scholars also have studied neural network focused on

control sensor base linearization Neural Network [4], and

Experimental Study of Neural Network Control System

for Micro Turbines [5]. Research on a series of neural-

based PID control with a variety of algorithms has also

been conducted, such as PID-Neural Controller based on

the AVR Atmega 128 [6], the PID-Controller based on

BP neural network in the application of wind power

generation [7], Application of Neural Network to Load-

Frequency Control in Power Systems [8], Automatic

tuning of PID controller using Particle Swarm

Optimization (PSO) algorithm [9], and Design for Auto-

tuning PID controller based on Genetic Algorithms [10].

The study specifically control systems based on PID

neural network with Brandt-Lin algorithm to control the

micro-hydro that does not exist. Researchers have also

studied micro-hydro using PI controller based on NN-

perceptron [1], however, there is no research on PID

control system is based Neural Network with Brandt-Lin

algorithm. This paper discusses this system to determine

the values of Ki, Kp and Kd simulated with a micro-hydro

plants, which has already existed [13].

The main purpose of this paper is to build a model-

based neural network PID control that is used to control

the Micro-Hydro Power (MHP). This control is set to be

able to control the turbine rotation to become stable at a

certain round when the load changes. The system is a

closed loop control using feedback from the output of the

generator. By adjusting the volume of water from the spill

away through the governor, the turbine rotation can be

maintained automatically. With the stability of turbine

rotation, the generator will generate a voltage stabilized at

220 VA with a frequency of 50 Hz.

2. Plant Models and Control for MHP Plants

Automatic control system of micro-hydro is built in a

closed loop. First some water are flow in the valve, it

continue to the spill way and rotate the turbine.

Water

Turbine

Generator

Sensor

Frequency

Frequency

Output

Frequency

set point

60 Hz

Integral

controller

AmplifierRegulator

Valve

-+

Delta

Frequency

Speed

Change

Figure 1. System Control of Micro-Hydro

Proceedings of the IASTED Conference

April 2 - 4, 2012 Phuket, ThailandModelling, Identification, and Control (AsiaMIC 2012)

Asian

DOI: 10.2316/P.2012.769-039

The generator will produces electricity in the next

step and output it will through on the sensor frequency.

The frequency measurement will be compared with the

reference frequency. Difference frequency (∆f) will be

entered into the control integrator. It will used to set and

behind the valve. The Illustration it is of control system

shown in Fig 1.

Plants models for MHP plant was controlled using a

servo motor as governor in the study by M. Hanmandlu

[1]. Consists of five blocks: (1). PI control, (2). Governor,

(3). Servo motor, (4). Turbine and (5). Generator, Detail

of model MHP as shown in Fig 2.

PI

1 / R

1

(1+sT2)

(1-sTw1)

(1+0.5sTw1)

Kp

1+sTp

PL

1

(1+sT3)

Figure 2. Model of MHP using servomotor as a

Governor [1]

The transfer function for the servo motor based governor

was written like equation 1 as :

)1(

1

)1(

1)(

21 sTsTsG

++= ......................................1)

Where T1 = mechanical time constant and T2 =

Electronic time constant. In addition, unity gain is applied

as a feedback. A PI Controller with the following transfer

function is superimposed on the servomotor based

governor as :

s

KiKpsG +=)( ...................................................2)

Where Kpl = Proportional constant, Ki = Integral sonstant

2.1 Existing Plant Model

Plant models of a micro-hydro that was constructed

by Lie Jasa et al. in Gambuk village at Pupuan sub-

district, Tabanan district of Bali province, Indonesia in

2010. Existing Plant Model MHP shown in Fig. 3. The

part components of the plant were: 1). 2 meters diameter

of water turbine; 2). 25 meters of spill away; 3). Tansfer

pulley; and 4). generator. The video of this plant can be

watched on

http://www.youtube.com/watch?v=IdyVX_1RQGs. The

plant is now capable to generate electrical energy of

approximately 1000 VA 5000 VA installed capacity. The

amount can be supplied to 10 houses for power at night.

The plant, however, has not been yet equipped with the

control circuit that can control the governor to produce the

output of generator of the frequency of 50Hz and voltage

at 220V. This problem becomes a central focus on this

research.

Figure 3. Existing Plant Model MHP at Gambuk,

Pupuan, Tabanan, Bali, Indonesia[13]

Figure 4. Existing Turbine Plant Model MHP [13]

a. Spill away

Spill away is used to channel water from top to

bottom and direct the water flow onto the turbine. The

length of pipe diameter will affect the volume of water

that runs. The larger the volume of water passes the

bigger water impetus to the turbine. The spill away allows

placing micro-hydro in the secure area from flooding

during the wet season.

b. Governor

To set the influx of water from spill away to the

turbine, governor is used. Governor model can be

classified in several forms, such as hydraulic mechanical,

electro-hydraulic and mechanical governor. Which

governor used is based on the size of spill away that has

been set. To set governor, so far it is done manually by an

operator. Arrangements are made by turning the faucet on

the end of spill away.

c. Water turbine

Turbines are used to change water energy into

mechanical energy. Turbine that is connected with some

pulleys is used to turn a generator. Past studies used

turbine [13]

sizing diameter of 2 meters, width of 30 cm,

weight of 300 kg and material of iron. The larger the

volume of water turning turbine, the greater mechanical

energy produced. Besides the volume of water, water

pressure falls on the turbine help to speed the turbine

rotation. Overshot water turbines works with the water

that falls into the blades of upper side, because of the

gravity of water, turbine wheel can spin. Existing of

turbine plant model MHP is show Fig. 4.

d. Generator

Generator is used to transform energy mechanic into

electric energy. By rotating magnetic field on the rotor, it

will cause the magnetic field in the stator. The magnetic

field that occurs at the stator with certain patterns will

produce electric. The larger the generator is used, the

greater the electrical energy generated.

2.2 Neural Network Control for MHP Plants

a. PID Control

A PID-Controller with the following transfer function

is superimposed on the servomotor based governor as :

Kdss

KiKpsG ++=)( ........................................3)

Where it’s Kp = proportional constant, Ki = integral

constant and Kd = derivative constant.

System control close loop with feedback control system is

illustrated in Fig. 5; where r, e, u, y are respectively the

reference, error controller output and controlled variables.

PID-Controller block receives input e (t) and produces

output u, where u is the combined output of all

components Ki, Kp and Kd such as shown in equation 3.

MHP

Planty(t)

e(t)PID

Controller

+

-

r(t)u

Figure 5. Micro-hydro power with feedback control [9]

Where is PID-Controller in time described in equation

(3) as:

dt

tde )( K dt e(t) K e(t)K u(t) dip ++= ∫ .………4)

Where u(t) is the controller output, et is the error, and t is

the sampling instance.

b. Brandt-Lin Algorithm Neural Network

The Brandt-Lin algorithm which is originated from

gradient descent considers a complex system consisting of

subsystems, called nodes which interact with each other

through connection weights. Fig. 6 shows a typical

system, which is decomposed for Brandt-Lin algorithm.

Node

11

Node

21

Node

12

Node

22

Node

31

InputLayer

HidenLayer

OutputLayer

W11

W22

W12

W21

W13

W23

y1

y2

x11

x22

x21

x12

y1

y2

y1

x11

x21

1

1

2

2

2

2

22

2

2

2

2

3

3

3

3

3

Figure 6. A typical decomposition of a systems for

Brandt-Lin algorithm [6]

Brandt-Lin algorithm is given in the following theorem.

Theorem : For the systems with dynamics given by

= ∑

=

−p

j

i

iij

i

j

i

j ywFy1

11

If connections weights are adapted according to

wyxFy

E

ywww

I

iij

vq

k jj

i

jk

i

jkij

11

1

111

111 )(1 −

=

++

∂

∂−= ∑ γ

Then the performance index it will decrease

monotonically with time.

c. PID-Controller with Neural Network

The controller based on neural network has ability to

make the unstable system because of its nonlinearity and

input-output mapping. In addition, training procedure

enables the controller to adapt changes of plant or noise.

PID-Neural control system is shown in Fig.5. The PID-

Neural controller has 3 inputs and 1 output. The inputs are

created by proportion, integration and derivation of error

between reference input and output.

The structure of neural network used in PID-Neural

controller has shown in Fig.6. The neural network it has 2

layers, input layer it has 2 neurons, output layer has 1

neuron. The neurons is activation function of input layer

are tansig-

function x

x

e

ex

+

−=

1

1)(1σ

, gauss-function

2

2 1)(

+

−−=

xx

xx

ee

eexσ

, and that of output layer is

linier function xx =)(3σ .

s

1s

MHPPLANTS

Ti

Td

Kp

y(t)

Neural Network

e (t) u(t)r(t)

+-

eI

eD

eP

Figure 7. Training blocks PID Neural Network [6]

W111

W132

W121

tansig

Gauss

W211

W221

Purelin

ep

eD

ei

U

S11

S12

Z11

Z12

S21

Figure 8. Structure of the neural network [6]

During the period of settling time, ep and eD decrease. At

first, ep and eD are large, raising the need of large control

step for quick going into settling time state. Then when

nearly coming to settling state, ep and eD are smaller and

smaller, requiring small control step for accurate control.

During the period of settling time ei increases, at first, ei

is small, raising the need of large control step for quick

going into settling state. Then when nearly coming to

settling state, ei is larger and larger, requiring small

control step for accurate control.

Calculating output of the neural network is following[6] :

1

1S = 1

11W . ep + 1

32W .eD and 1

1Z = σ1(1

1S )

1

2S = 1

21W . ei and 1

2Z = σ1(1

2S )

u = 1

11W . 1

1Z + 1

21W . 1

2Z

Trained the neural network using Brandt-Lin algorithm is

following[6] :

2

11W = γ1

1Z e

2

21W = γ1

2Z e

1

11W = σ1 (1

1S ) 11

1

1

1

1

2

11

2

111

1

)(Z

EeSWW

z

ep

p

σ

σγσ−

2

111

1

WeZ

Eβ

δ

δ−=

σ1 (1

1S ) = )1)(1(

211

11

1

1

1

ssees

−− −+=

δ

δσ

1

11W = σ1 (1

1S )

+ eZ

WWep γ1

1

2

112

11

= σ1 (1

1S )

+ eZ

eZWe p γ

γ1

1

1

12

11

= 2γ2

11

1

11 )( eWeS pγσ

2

11

1

11

1

32 )(2 eWeSW iγσ=

2

21

1

22

1

21 )(2 eWeSW Dγσ=

σ2 1

2S )

=

+

−−

+

−−=

−

−

−

−2

1

2

2

)1

)2

12

112

1212

12

112

1212

ss

ss

ss

ss

ee

ee

ee

ee

sδ

δσ

d. Data Simulation of MHP Plants

Data simulation in this paper uses combination data

research [1][13], total rate capacity change from 50 KW

to 5 KW, the normal operating load of 25 KW was

changed to 1 KW. This is done to adjust with the existing

MHP plant, detail as shown in Table 1.

Table 1. Data plant MHP simulation

No Data Value

1. Total rated capacity 5 Kw

2. Normal Operating Load 1 Kw

3. Inertia Constant H 7.75 seconds (2

Assumption: Load-frequency dependency is linier.

Nominal Load = 48%=0.48; ∆Pd =3%=0.03. The

dumping parameter [4,7],

D = ∂p/∂

HzpukWx

xfD /0016.0

560

148.0p/ =

=∂∂=

Generator parameter are :

Kp = 1/D = 625 Hz/pu kW

Tp = ondsxDf

xHsec 161,458

20

=

3. Formulation of Plant Models for MHP

Plant

The block diagram of the MHP Plant with PID-

Controller is shown in Fig. 9. This plant can be reduced to

a simpler transfer function representation as in Fig.10.

Kpi+Ki/s+Kd s

1 / R

1

(1+sT2)

(1-sTw1)

(1+0.5sTw1)

Kp

1+sTp

PL

1

(1+sT3)

f

XE1 XE2 XE3Pg

f

f

PID Controoler Governor Servo motor Turbin Generator

Figure 9. Models of MHP plant using servomotor as

governor with PID-Controller

Each block of Fig. 9 as following equation :

Kdss

KiKpG ++=1 ,

)1(

1

2

2sT

G+

= ,

)1(

1

3

3sT

G+

= ,

)15.01(

)1(

2

14

sT

sTwG

+

−= ,

)1(5

PsT

KpG

+= ,

RK

11 = ,

PlK ∆=2

G1 G2 G4 G5

K2

G3

K1

Figure 10. Model of micro-hydro power plant with

transfer function

With the simplification process of the transfer function,

Fig.9 above can be changed to be seen in Fig. 10.

K1.G2.G3.G4.G5

1+G2G3

K2.G5

G1

K1

yuer

Figure 11. Simplifying the model MHP Plant

The transfer function for plant analysis MHP is:

52

32

54321

1)( GK

GG

GGGGKsGc +

+= …………………(5)

Equation 5 shows the transfer function of the MHP Plant,

while the block G1/K1 is part of the PID-Controller

consisting of components of Ki, Kp and Kd as in equation

3. Firstly, the value of Ki, Kp and Kd is counted using

trial and error method. Secondly, training process is

applied offline employing Brandt-Lin algorithm, in order

to calculate the weight of each neuron as in Fig. 8. With

Matlab simulation results obtained, each value of Kp =

0.0637533, Ki and Kd = 0.00021801 = 0.00301846. By

entering KI, Kp and Kd values into formula (3) is

obtained an equation (6) as:

ss

G 0.00301846 0.00021801

0.06375331 ++=

……………….6)

4. Simulation PID-Controller for MHP

Plants

The transfer function is of PID-Controller equation 6

must be transform in simulink. Detail of Simulink Matlab

model of the PID-Controller based on neural network,

shown in Fig. 12.

Figure 12. Simulink model of PID-Controller

The Simulink MHP plant (Fig. 13) was the reference of

MHP plant Fig. 9, Table 1 and it was of Simulink PID-

Controller Fig.12.

Figure 13. Model of MHP Plant using the Servomotor

with PID-Controller

Figure 14. ∆ f for one gate schema using servomotor with

PID-Controller

To run the simulation model MHP Fig. 13 above, we

chose the Following values: Kn = 1, KaKg / Kc = 1; Tf =

0001 second; Kp = 0.0637533, Ki and Kd = 0.00021801 =

0.00301846. The simulation results showed that to make

the MHP plant to be stable, it takes a ∆f of 35 Hz as

shown in Fig. 14 and ∆ P1 by 40 second as in the Fig. 15

Figure 15. ∆ Pl for one gate schema using servomotor

with PID-Controller.

In this paper showed that MHP Plant-based PID-

Controller using neural network obtained better results.

By using the value of Kp = 0.0637533, Ki and Kd =

0.00021801 = 0.00301846. MHP plant will stabilize at

about 40 seconds. PID-Controller is able to maintain

stability in 35 seconds starting from the beginning of the

load changes.

Acknowledgement

The Author to convey gratitude to the Ministry of

Education and Culture that has provided scholarships

through the BPPS program and the national strategic

research fund in 2010.

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