Welcome to CIIS2017!

The 2017 International Conference on Computing Intelligence and Information System [CIIS2017]

April 21st-23rd, 2017, Nanjing, China 

Note: All accepted papers will be submitted to EI, IEEE Xplore and CPCI for indexing.

The 2017 International Conference on Computing Intelligence and Information System [CIIS2017]

April 21st-23rd, 2017, Nanjing, China 

Note: All accepted papers will be submitted to EI, IEEE Xplore and CPCI for indexing.

KEYNOTES




Dr. Joseph Emerson Raja, Multimedia University, Malaysia
Biography:
Joseph Emerson Raja, Ph.D, is currently lecturing in the Faculty of Engineering and Technology at Multimedia University, Malaysia. His work is centered on applying soft computing techniques to monitor the health of machines. He has published the results obtained in his research on “machine tool condition monitoring” in several international journal and conference papers. Dr. J Emerson Raja received his Ph.D. in Engineering from the Multimedia University, Malaysia (2014).   His doctoral thesis developed a robust method for tool condition monitoring by emitted sound analysis using Hilbert Huang transform and competitive neural network.  He was honoured with the best executive award and group CEO merit award in the year 2015 from TM, the leading integrated telecommunications company in Malaysia. He has also co-authored a book , “C Programming for beginners”, published by Pearson, Malaysia, 2009. He has received the excellent teaching award for the two consecutive years, 2012 and 2013, from the president of Multimedia University, Malaysia. He, as a Ph.D. student, is the recipient of the best research poster award in the MMU-Infineon technical symposium 2011.  He received bachelor’s and master’s degrees in Computer Science and Engineering from University of Madras, India in 1989 and 2001. Dr. J Emerson Raja is an active SENIOR member of IEEE and has given many talks in technical symposiums arranged by IEEE Signal Processing Society of Malaysia. 

Speech Title:      
Design of Intelligent Tool Condition Monitoring System for Turning Machines using Artificial Neural Network

Abstract:
Turning operation is one of the important machining processes in manufacturing industries. Tool wear in turning, is one of the major problems which may lead to production loss and down time. An effective tool wear monitoring system is therefore required to minimize production loss and down time. The intelligent method I devised for Tool Condition Monitoring will be explained during my speech. The system includes a competitive artificial neural network to classify the condition of the tool-bit into fresh, slightly-worn or severely-worn states by sensing the emitted sound. Hilbert Huang Transform (HHT), an adaptive signal processing technique, is used for extracting the required features from emitted tool sound, which are non-linear and non-stationary in nature. The extracted features, the instantaneous frequency and the instantaneous amplitude, were used to train the neural network. I will be explaining how I achieved better accuracy of tool wear classification from the results obtained in my research, which includes experiments conducted at real industry. 







Prof. Wen-Tsai Sung, National Chin-Yi University of Technology, Taiwan
Biography:
Wen-Tsai Sung is working with the Department of Electrical Engineering, National Chin-Yi University of Technology as a professor and Vice-Dean of Academic Affairs. He received a PhD and MS degree from the Department of Electrical Engineering, National Central University, Taiwan in 2007 and 2000. He has won the 2009 JMBE Best Annual Excellent Paper Award and the dragon thesis award that sponsor is Acer Foundation. His research interests include Wireless Sensors Network, Data Fusion, System Biology, System on Chip, Computer-Aided Design for Learning, Bioinformatics, and Biomedical Engineering. He has published a number of international journal and conferences article related to these areas. Currently, he is the chief of Wireless Sensors Networks Laboratory. At present, he serves as the Editor-in-Chief in three international journals: International Journal of Communications (IJC), Communications in Information Science and Management Engineering (CISME) and Journal of Vibration Analysis, Measurement, and Control (JVAMC), he also serves as the other international journals in Associate-Editor and Guest Editor (IET Systems Biology).

Speech Title:      
Innovative IoT System view based on Wireless Sensors Networks technology 

Abstract :
IoT (Internet of Things) System is a rapidly developing area, a combination of Network, mathematics and computing technology, in order to enhance the complex sensors network and data aggregation. Traditional Wireless Sensors Networks method does not have the ability to process hung amounts sensors signals that is why the Wireless Sensors Networks design often only one cluster or one layer framework. This speech issue brings together some of the optimal fusion of innovative information technology and methods and it provides to the listeners on this issue have further improved System Integration and Applications in Wireless Sensors Networks. This will allow scientists to develop smarter process strategies for multi-sensors signals and data. 

Speech Issue Topics:
1. Automation Aquaculture and Environmental Monitoring System
2. Remote Medical Care System
3. Wisdom LED Lighting Control System
4. Intelligent Life Environmental Monitoring
5. Ongoing projects


Prof. Rong-Jong Wai, National Taiwan University of Science and Technology, Taiwan
Biography:
Rong-Jong Wai was born in Tainan, Taiwan, in 1974. He received the B.S. degree in electrical engineering and the Ph.D. degree in electronic engineering from Chung Yuan Christian University,Chung Li, Taiwan, in 1996 and 1999, respectively.
From August 1998 to July 2015, he was with Yuan Ze University, Chung Li, Taiwan, where he was the Dean of the General Affairs Office from August 2008 to July 2013, and the Chairman of the Department of Electrical Engineering from August 2014 to July 2015. Since August 2015, he has been with National Taiwan University of Science and Technology, Taipei, Taiwan, where he is currently a full Professor, and the Director of the Energy Technology and Mechatronics Laboratory. He is a chapter-author of Intelligent Adaptive Control: Industrial Applications in the Applied Computational Intelligence Set (Boca Raton, FL: CRC Press, 1998) and the co-author of Drive and Intelligent Control of Ultrasonic Motor (Tai-chung, Taiwan, R.O.C.: Tsang-Hai, 1999), Electric Control (Tai-chung, Taiwan, R.O.C.: Tsang-Hai, 2002) and Fuel Cell: New Generation Energy (Tai-chung, Taiwan, R.O.C.: Tsang-Hai, 2004). He has authored more than 150 conference papers, over 170 international journal papers, and 52 inventive patents. His biography was listed in Who's Who in Science and Engineering (Marquis Who’s Who) in 2004-2016, Who's Who (Marquis Who’s Who) in 2004-2016, and Leading Scientists of the World (International Biographical Centre) in 2005, Who's Who in Asia (Marquis Who’s Who), Who's Who of Emerging Leaders (Marquis Who’s Who) in 2006-2016, and Asia/Pacific Who’s Who (Rifacimento International) in Vols. VII-X. His research interests include power electronics, motor servo drives, mechatronics, energy technology, and control theory applications. The outstanding achievement of his research is for contributions to real-time intelligent control in practical applications and high-efficiency power converters in energy technology. He is a fellow of the Institution of Engineering and Technology (U.K.) and a senior member of the Institute of Electrical and Electronics Engineers (U.S.A.).

Speech Title:      
Intelligent Control Design for Single-Stage Boost Inverter

Abstract :
In general, a dc-dc boost converter plus a dc-ac inverter is always required for the power conversion of a lower dc-voltage energy source (e.g., renewable energy or energy storage system) to produce a utility ac power. By this way, the manufacturing cost of this power conversion mechanism will be broadly increased. Thus, a single-stage boost inverter composed of two conventional boost converters has become one of interesting research topics in recent years. This speech mainly focuses on the recent development of four newly-designed control strategies including an adaptive control scheme, a fuzzy-neural-network (FNN) control framework, a total sliding-mode control (TSMC) strategy, and an adaptive FNN control (AFNNC) system for a single-stage boost inverter. First, the dynamic model of a single-stage boost inverter is analyzed and built for the later control manipulation. Then, a model-based adaptive control scheme and a model-free FNN control framework with varied learning rates are designed sequentially. For enhancing the robustness of the adaptive control scheme during the transient response of the voltage tracking control, a TSMC strategy without the reaching phase in conventional SMC is developed. In order to alleviate the control chattering phenomena caused by the sign function in the TSMC design and relax the requirement of detailed system dynamics, an AFNNC scheme is further investigated to imitate the TSMC law for the boost inverter. In the AFNNC scheme, on-line learning algorithms are derived in the sense of Lyapunov stability theorem and projection algorithm to ensure the stability of the controlled system without the requirement of auxiliary compensated controllers despite the existence of uncertainties. The output of the AFNNC scheme can be easily supplied to the duty cycle of the power switch in the boost inverter without strict constraints on control parameters selection in conventional control strategies. In addition, the effectiveness of the proposed four control frameworks is verified by realistic experiments, and the advantages of the proposed AFNNC system are indicated in comparison with the other strategies.





Dr. Sead Spuzic, University of South Australia, Australia
Biography:
Dr. Sead Spuzic. His industrial experience in design, management and technology developed in several states both in Europe and Australia. In addition, he has been appointed at several universities (Australia, Europe, Middle East, New Zealand) to teach a number of courses related to science, management and statistics. His multi-disciplinary background evolved into an interest in transparent and cross-disciplinary knowledge management and inter-academic collaboration.
First three years of his undergraduate study were undertaken in Czech Republic at Vysoka Skola Banska, one of the oldest universities in Europe. Their curriculum covered variety of courses in natural sciences, engineering and statistics. The same curriculum was covered at the University of Sarajevo, where he continued study to obtain MSc degree. His PhD research at the University of South Australia was sponsored by the Australian Government Department of Education. His present research focus is on applications of Big Data in (re)design of steel rolling technology and Knowledge Engineering.

Speech Title:      
Big Data Model-An Application to Design of Rolling Process

Abstract :
Motivation  for  this  keynote  is  triggered  by urging  evidences  of  global climate disturbances,  natural  resource decline,  and  resulting  socioeconomic  disruptions.  The  large-scale  man-made  systems  - such as steel manufacturing plants - are  amongst the major resource consumers and environment polluters.  These industries governed by  multinational corporations driven by narrow-minded perceptions of profit do sufficient not take advantage of potential improvements (i.e. possible reduction in resource consumption while improving product quality and quantity). Steel is among the most used and most recycled engineering materials worldwide, and the foreseeable long-term forecasts predict growing requirements for products of  increasing  quality. The key systems in steel industry – hot rolling mills – are employed in processing nearly   90%  of  total volume of  steel  products. In this environment, there is pressing need for upgrading the unsustainable operations. The promising avenue for decisive progress in (re)designing rolling systems is by implementing Big Data strategy for mitigating the problems in untenable production and in designing new processes. This strategy stems from recognising importance of knowledge extracted from data accumulated in industrial repositories. Big Data strategy will be demonstrated by way of example of application to design of rolling mill production systems.  An internationally maintained collaboration between industry and academe is the prerequisite for realisation of proposed strategy.





Prof. Piotr Kulczycki, AGH University of Science and Technology, Poland
Biography:
Piotr Kulczycki graduated with a Master’s degree in Electrical Engineering from the AGH University of Science and Technology, and a Master’s degree in Applied Mathematics from the Jagiellonian University.  He received the title of Professor in Technical Sciences in 2007.  Prof. Kulczycki currently holds the professor positions at the Systems Research Institute of the Polish Academy of Sciences, where he is the head of the Centre of Information Technology for Data Analysis Methods, as well as the AGH University of Science and Technology, Faculty of Physics and Applied Computer Science, where he is the head of the Division for Information Technology and Biometrics.  He has also held the position of visiting professor at the Aalborg University, and closely cooperated with the Technical University of Budapest, the Helsinki University of Technology, the Universite Catholique de Louvain, and the Tampere University of Technology. 
Professor Piotr Kulczycki has published 4 books and more than 200 scientific works in reputable journals, edited volumes, and international conference proceedings.  The field of his scientific activity to date is the applicational aspects of information technology and data analysis and mining, mostly connected with the use of modern statistical methods and fuzzy logic in diverse issues of contemporary systems research and control engineering. 
E-mail: kulczycki@ibspan.waw.pl , kulczycki@agh.edu.pl .

Speech title:
Nonparametric Estimation for Data Analysis and Systems Research 

Abstract: 
The current dynamic growth in the potential of computer systems enables the equally intensive development of one of the main areas of modern information technology: highly specialized data analysis and exploration procedures.  The subject of this paper is the idea of kernel estimators – currently a leading concept of nonparametric estimation methodology.  It allows to establish application-suitable characteristics for a probability distribution independent of its form. 
The presented text summarizes results obtained by the author's research team in the field of data analysis using kernel estimators for systems research, especially identification, control engineering in the presence of uncertainties, fault detection in dynamic systems, as well as for telecommunications marketing and medicine. 




Prof. Patrick S.P. Wang, Northeastern University, USA

Biography:
Prof. Patrick S.P. Wang, PhD. Fellow, IAPR, ISIBM, WASE, and IEEE & ISIBM Outstanding Achievement Awardee, is Tenured Full Professor, Northeastern University, USA, iCORE (Informatics Circle of Research Excellence) Visiting Professor, University of Calgary, Canada, Otto-VonGuericke Distinguished Guest Professor, Magdeburg University, Germany, Zijiang Visiting Chair, ECNU, Shanghai, China, as well as honorary advisory professor of several key universities in China, including Sichuan University, Xiamen University, East China Normal University, Shanghai, and Guangxi Normal University, Guilin.
Prof. Wang received his BSEE from National Chiao Tung University (Jiaotong University), MSEE from National Taiwan University, MSICS from Georgia Institute of Technology, and PhD, Computer Science from Oregon State University. Dr. Wang has published over 26 books, 200 technical papers, 3 USA/European Patents, in PR/AI/TV/Cybernetics/Imaging, and is currently founding Editor-in-Chief of IJPRAI (International Journal of Pattern Recognition and Artificial Intelligence) , and Book Series of MPAI, WSP. In addition to his technical interests, Dr. Wang also published a prose book, “Harvard Meditation Melody” 《哈佛冥想曲》, 《劍橋狂想曲》 and many articles and poems regarding Du Fu and Li Bai’s poems, Beethoven, Brahms, Mozart and Tchaikovsky’s symphonies, and Bizet, Verdi, Puccini and Rossini’s operas. For further details, please contact:
Prof. Patrick S. Wang, Ph.D., Zijiang Visiting Chair, ECNU, Shanghai, China
IEEE Outstanding Achievement Awardee IAPR Fellow and Co-Chief Editor, IJPRAI and MPAI Book Series, WSP
Northeastern University, Boston, MA ,USA (617)281-5345, (617)373-5121(F)
pwang@ccs.neu.eduUH, patwang@ieee.org,
IEEE Outstanding Achievement Awardee
http://ejournals.wspc.com.sg/ijprai/mkt/editori al.shtml, Founding Editor-in-Chief
HUhttp://www.worldscibooks.com/series/smpai_series.shtml
HUhttp://www.isibm.org/leadership .php
HUhttp://www.dcs.warwick.ac.uk/~ctli/IJDCF.htmlUH Advisory Board

Bibliography (selected from over 2 dozens of technical papers and book
[1]P.S.P.Wang, New Development of IPR and e-Forensics, Theories and Applications, Shanghai, China, November, 2016
[2]P.S.P.Wang, Intelligent Pattern Recognition and Big Data, ICGIP 2016, Tokyo, Japan, October, 2016
[3]P.S.P.Wang, IPR, Big Data, and Applications, ICCIS2015, Shenzhen University, December, 2015
[4]P.S.P.Wang, Similarity-Base AI and PR, Theory and Applications, WCSC2014, UC Berkeley, Keynote
[5]P.S.P.Wang, Situational Awareness through Biometrics, with A. Poursaberi et al, IEEE-Computer May 2013
[6] P.S.P.Wang, A Review of Wave-based Edge Detection Methods for Image Understanding and Interpretation,
with J. Yang ,Int. J. Pattern Recognition & Artificial Intelligence (IJPRAI), v26, n 8, (2012)
[7]P.S.P.Wang, Intelligent Pattern Recognition and Biometrics, Springer/HEP, 2011
[8]P.S.P.Wang, Pattern Recognition and Machine Vision, River Pub, Denmark, 2010
[9] P.S.P.Wang, “Concept of Ambiguity and Application to Security and Transportation Safety”, IEEEICSSE2010,179-183 (2010)
[10] P.S.P.Wang,Object Recognition, http://sites.google.com/site/mozart200/ (2009)
[11]P.S.P.Wang, Pattern Recognition and Artificial Intelligence in Biometrics - EDITORIAL, S.N. Yanushkevich,
D. Hurley, and P.S.P. Wang, IJPRAI, Vol. 22, No. 3, 367-369 (2008)
[12] Anil K. Jain, Arun A. Ross, Patrick Flynn, Handbook of Biometrics, Springer Verlag, 2012
[13]P.S.P.Wang and S. Yanushkevich, "Biometrics Technologies and Applications", Proc. IASTED AIA2007
(Artificial Intelligence Applications), Innsbruck, Austria, 2007, p226-231 (2007)
[14]P.S.P.Wang, "Some Concerns on the Measurement for Biometrics Analysis and Applications", in
“Image Pattern Recognition - Synthesis and Analysis in Biometrics” WSP, 2007 (ed)
S.N. Yanuskevich, P.S.P.Wang, S.N. Srihari, and Marina Gavrilova). P321-337 (2007)

Speech title:
Intelligent Foresnsics, Big Data and Applications--- Security, Safer Transportation and Greener World in Interacrtive Learning Environment

Abstract:
This talk is concerned with fundamental aspects of Intelligent Pattern Recognition (IPR) and applications. It basically includes the following: Basic Concept of Automata, Grammars, Trees, Graphs and Languages. Ambiguity and its Importance, Brief Overview of Artificial Intelligence (AI), Brief Overview of Pattern Recognition (PR), What is Intelligent Pattern Recognition (IPR)? Interactive Pattern Recognition Concept, Importance of Measurement and Ambiguity, How it works, Modeling and Simulation, Basic Principles and Applications to Computer Vision, Security, Road Sign Design, Safer Traffic and Robot Driving with Vision, Ambiguous (Dangerous and Bad) design of Road Signs vs Unambiguous (Good) Road Signs, How to Disambiguate an Ambiguous Road Sign? What is Big Data? And more Examples and Applications of Learning and Greener World using Computer Vision. Finally, some future research directions are discussed.


Scroll to Top