Fahmida N. Chowdhurya

Dimitar Filev

Nikola Kasabov

Marios Polycarpou

Short Biography:

Dr. Fahmida Chowdhury joined the US National Science Foundation as Program Director for the Cross- Directorate Activities Program (CDA) in February 2008. During 2005-2006, she served as a Program Director for two other NSF programs, on rotation from her faculty position. Prior to joining NSF in 2008, she was Professor of Electrical and Computer Engineering at the University of Louisiana, Lafayette, LA, where she held the W. H. Hall and M. O. Hall Endowed Chair in Computer Engineering. She has also held academic positions at Michigan Technological University, Southern University, Louisiana State University, and Bangladesh University of Engineering and Technology (Dhaka, Bangladesh). She was a Fulbright Scholar in 2001. Currently she serves on the editorial boards of the IEEE Transactions on Control Systems Technology and IEEE Transactions on Neural Networks. Within the broad fields of control systems and neural networks, her research interests include complex systems modeling and analysis, applications of control and dynamic systems theory in non-traditional fields, and detection of abnormal conditions (faults) in dynamic systems. Fahmida Chowdhury was born in Bangladesh. She received a combined B.Sc./M.Sc. degree (with High Honors) in electromechanical engineering from Moscow Power Engineering Institute, Moscow, Russia, in 1980, and PhD in electrical engineering (minor: mathematics), from Louisiana State University, Baton Rouge, Louisiana, USA in 1988.

This presentation will focus on the current funding opportunities in the cross-disciplinary areas of science and engineering at the US National Science Foundation (NSF). Some of NSF's current funding programs, for example, Cyber-enabled Discovery and Innovation (CDI) and Partnerships for International Research and Education (PIRE) are pushing the frontiers of exciting new research fields that span, intersect, and combine multiple academic disciplines in ways that were unthinkable in the past. The proliferation of Virtual Organizations plays a major role in these activities while teamwork, data and software management and increasing levels of multi-institutional collaboration create new dimensions in the entire endeavor, including the role of the funding agency . In addition to programs at the NSF, this talk will provide information about inter-agency (for example, involving NSF and the US National Institute of Health - NIH) opportunities suitable for the ICONS-related research communities. Information on opportunities for international collaboration will be a significant component of this presentation.

« Top of the page

Bio-data:

Intelligent Industrial Systems for Process Control, Diagnostics, & Prognostics
Dimitar Filev
Research & Advanced Engineering, Ford Motor Company
2101 Village Road, Dearborn, MI 48121, Rm. 1343
E-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it

Dr. Dimitar P. Filev is a Senior Technical Leader, Intelligent Control & Information Systems, with Ford Research & Advanced Engineering. He has published 3 books, and over 180 articles in refereed journals and conference proceedings and holds numerous US and foreign patents. Dr. Filev is a recipient of the 2008 Norbert Wiener Award of the IEEE Society of Systems, Man, & Cybernetics, the 2006 Technical Excellence Award of IFSA, and was awarded 4 times with the Henry Ford Technology Award. He is a co-editor of the Journal of Automation, Mobile Robotics and Intelligent Systems and Associate Editor of the Int. J. of General Systems, Int. J. of Approximate Reasoning, and Int. J. of Applied Mathematics and Computer Science. Dr. Filev is a Fellow of IEEE and a Vice Chair of the IEEE CIS Technical Committee on Fuzzy Systems. He was a President of the North American Fuzzy Information Processing Society (NAFIPS) 2006-2008. Dr. Filev received his PhD. degree in Electrical Engineering from the Czech Technical University in Prague in 1979.

Practical and theoretical aspects of design of intelligent real time diagnostics & process control and systems are discussed. The presentation covers several alternative algorithms for autonomous anomaly detection and their applications to machine health monitoring, estimation and prediction of driver state and preferences. It also makes the case for introduction of agent based tools for real time asset reliability assessment, diagnostics, and prognostics within conventional factory machine monitoring systems. Next, a general view and a practical perspective of some challenging engineering problems related to design and industrial implementation of intelligent systems is presented with special attention given to real time modeling and estimation, evolving autonomous systems, system integration of heterogeneous information & knowledge sources, coordination of distributed systems, and sensor networks inspired system structures and algorithms for condition based monitoring, diagnostics, and prognostics.

« Top of the page

The paper presents theoretical foundations and practical applications of intelligent information processing systems inspired by information principles in Nature in their interaction and integration. That includes neuronal-, genetic-, and quantum information principles. First, the paper reviews the main principles of information processing at neuronal-, genetic-, and quantum information levels. Each of these levels has already inspired the creation of efficient computational AI models. The talk also extends these paradigms with novel methods and systems that integrate these principles into Integrative Connectionist-based Systems (ICoS). Examples of such models are: evolving spiking neural networks; computational neurogenetic models (where interaction between genes, either artificial or real, is part of the neuronal information processing; quantum evolutionary algorithms and quantum inspired neural networks. The new models are significantly faster in feature selection and learning and can be applied to solving efficiently complex biological and engineering problems for adaptive, incremental learning in a large dimensional space and in a new environment. Examples include: on-line multimodal audiovisual information processing; evolving robots, that evolve during their operation their functionality and even their shape. Open questions, challenges and directions for further research are presented.

References:
[1] N.Kasabov (2007) Evolving Connectionist Systems: The Knowledge Engineering Approach, Springer, London (http://www.springer.de/)
[2] L.Benuskova and N.Kasabov (2007) Computational Neurogenetic Modelling, Springer, New York
[3] N.Kasabov, Integrative Connectionist Learning Systems Inspired by Nature: Current Models, Future Trends and Challenges, Natural Computing, Springer, 2008,
[4] N.Kasabov, Brain-, Gene-, and Quantum Inspired Computational Intelligence: Challenges and Opportunities, in: W. Duch and J. Manzduk (eds) Challenges in Computational Intelligence, Springer, 2007, 193-219
[5] N.Kasabov, Evolving Intelligence in Humans and Machines: Integrative Connectionist Systems Approach, Feature article, IEEE CIS Magazine, August, 2008, vol.3, Num.3, http://www.ieee.cis.org/, pp. 23-37

« Top of the page

Bio-data:

Fault Diagnostics and Security of Critical Infrastructure Systems
Marios M. Polycarpou
Director of the KIOS Research Center for Intelligent Systems and Networks
Department of Electrical and Computer Engineering
University of Cyprus
Cyprus
E-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it

Marios M. Polycarpou is a Professor of Electrical and Computer Engineering and Director of the KIOS Research Center for Intelligent Systems and Networks at the University of Cyprus. He received the B.A. degree in Computer Science and the B.Sc. degree in Electrical Engineering both from Rice University, Houston, TX, USA in 1987, and the M.S. and Ph.D. degrees in Electrical Engineering from the University of Southern California, Los Angeles, CA, in 1989 and 1992 respectively. In 1992, he joined the University of Cincinnati, Ohio, USA, where he reached the rank of Professor of Electrical and Computer Engineering and Computer Science. In 2001, he was the first faculty and the founding department Chair of the newly established of Electrical and Computer Engineering Dept at the University of Cyprus. His teaching and research interests are in intelligent systems and control, adaptive and cooperative control systems, computational intelligence, fault diagnosis and distributed agents. Dr. Polycarpou has published more than 185 articles in refereed journals, edited books and refereed conference proceedings, and co-authored the book Adaptive Approximation Based Control, published by Wiley in 2006. He is also the holder of 3 patents.

Prof. Polycarpou is currently the Editor-in-Chief of the IEEE Transactions on Neural Networks. He serves as an Associate Editor of two international journals and past Associate Editor of the IEEE Transactions on Neural Networks (1998-2003) and of the IEEE Transactions on Automatic Control (1999-2002). He served as the Chair of the Technical Committee on Intelligent Control, IEEE Control Systems Society (2003-05) and as Vice President, Conferences, of the IEEE Computational Intelligence Society (2002-03). He is currently an elected member of the Board of Governors of the IEEE Control Systems Society and an elected AdCom member of the IEEE Computational Intelligence Society. Dr. Polycarpou was the recipient of the William H. Middendorf Research Excellence Award at the University of Cincinnati (1997) and was nominated by students for the Professor of the Year award (1996). His research has been funded by several agencies in the United States, the European Commission and the Research Promotion Foundation of Cyprus. Dr. Polycarpou is a Fellow of the IEEE.

Modern societies have reached a point where everyday life relies heavily on the reliable operation and intelligent management of critical infrastructures, such as electric power systems, telecommunication networks, water distribution networks, transportation systems, etc. Designing, monitoring and controlling such systems is becoming increasingly more challenging as their size, complexity and interactions are steadily growing. Moreover, these critical infrastructures are susceptible to natural disasters, frequent failures, as well as malicious attacks. There is an urgent need to develop a common system-theoretic framework for modeling the behavior of critical infrastructure systems and for designing algorithms for intelligent monitoring, control and security of such systems. The goal of this presentation is to motivate the need for fault diagnosis and security of critical infrastructure systems and to provide a methodology for detecting, isolating and accommodating both abrupt and incipient faults in a class of complex nonlinear dynamic systems. A detection and approximation estimator based on computational intelligence techniques is used for online health monitoring. Once a fault is detected, a bank of isolation estimators is activated for the purpose of fault isolation. A key design issue is the adaptive residual threshold associated with each isolation estimator. Various adaptive approximation techniques and learning algorithms will be presented and illustrated, and directions for future research will be discussed.

« Top of the page