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Guest Editorial Special Section on Soft Computing in Industrial Informatics
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OFT COMPUTING (SC), as opposed to hard computing, was coined as one of the key future intelligent systems technologies, which has been researched and applied in solving various practical problems extensively. Key SC technologies include neural networks (NNs), fuzzy logic (FL), evolutionary computation (EC), knowledge-based systems (KBS), and other heuristic algorithms which are paradigms for mimicking human intelligence and smart optimization mechanisms observed in the nature. Industrial Informatics (II) is concerned with developments of information collection, analysis, manipulation, and distribution techniques to improve efficiency, effectiveness, reliability, and security of industrial systems and processes. Modern industrial systems and processes are becoming highly automated, and intelligent technologies are a key to build smarts into them and make them smarter. The interplay of SC and II will significantly advance the knowledge in both fields and enhance the performances of the industrial systems and processes. This Special Section presents to the II community the most recent advances in SC and its applications. Particular attention is paid to the significant industrial applications of SC technologies in modeling, control and optimization. Neural Networks, as a computational model inspired by observed processes in the brain, have been a technique for those tasks that require learning without knowing the inner structure of the system itself. In paper “Minimal Resource Allocating Networks for Discrete Time Sliding Mode Control of Robotic Manipulators,” by Ippoliti et al., the radial-based NN is used for learning uncertainties for sliding mode control. The paper “Model Predictive Control of Nonlinear Systems with Unmodeled Dynamics Based on Feedforward and Recurrent Neural Networks,” by Wang and Yan, develops a model predictive control for dynamical systems with uncertainties estimated by NN. NN is also used in paper “An Adaptive Speed Sensorless Induction Motor Drive With Artificial Neural Network for Stability Enhancement,” by Maiti et al., for improving the induction motor stability without speed sensor. Fuzzy Logic is an approach for expressing vague information and decision-making processes, which has become a popular modeling and control technique. In paper “Fuzzy Adaptive Internal Model Control Schemes for PMSM Speed-Regulation System,” Li and Gu develop a FL-based adaptive control to regulate PMSM. The paper “Flame Image-Based Burning State Recognition for Sintering Process of Rotary Kiln Using Heterogeneous Features and Fuzzy Integral,” by Li et al., adopts fuzzy integral for flame-image recognition, and the Digital Object Identifier 10.1109/TII.2012.2215335
paper “Novel Adaptive Gravitational Search Algorithm for Fuzzy Controlled Servo Systems,” by Precup et al., proposes a new adaptive tuning algorithm for fuzzy control. The paper “Identification and Learning Control of Ocean Surface Ship Using Neural Networks,” by Wang et al., uses NN to learn uncertainties for better control of ocean surface ship. The paper “Hybrid Incremental Modeling Based on Least Squares and Fuzzy -NN for Monitoring Tool Wear in Turning Processes,” by Haber et al., suggests a hybrid incremental approach for monitoring tool wear based on FL. The paper “Design a Wind Speed Prediction Model Using Probabilistic Fuzzy System,” by Zhang et al., models wind speed using probabilistic FL, and an evolutionary pinning control is used in UAV coordination in paper “Evolutionary Pinning Control and Its Application in UAV Coordination,” by Tang et al. The paper “A Fuzzy-Based Sensor Validation Strategy for AC Motor Drives,” by Li et al., develops a FL-based approach for measurement validation, and the paper “Knowledge-Based Global Operation of Mineral Processing Under Uncertainty,” by Ding et al., uses a near-FL approach to develop a dynamic operation strategy. Finally, the paper “A Multiobjective Optimization Based Fuzzy Control for Nonlinear Spatially Distributed Processes With Application to a Catalytic Rod,” by Wu and Li, designs a FL-based optimization method for spatially distributed processes. The EC paradigm attempts to mimic the nature evolution processes for solving modeling and optimization problems, which exhibits attractive derivative-free and population-based search features. The paper “Enhancement of Speech Recognitions for Control Automation Using an Intelligent Particle Swarm Optimization,” by Chan et al., makes use of Particle Swarm Optimization (PSO) to enhance speech recognition. PSO is also used in paper “Quantum-Inspired Particle Swarm Optimization for Power System Operations Considering Wind Power Uncertainty and Carbon Tax in Australia,” by Yao et al., for power systems operation optimization, paper “Optimal Dispatch of Electric Vehicles and Wind Power Using Enhanced Particle Swarm Optimization,” by Zhao et al., for optimal dispatch of electric vehicles and wind power, and paper “Optimizing RFID Network Planning by Using a Particle Swarm Optimization Algorithm With Redundant Reader Elimination,” by Zhang and Gong, for network planning. Another paper “Energy-Efficient Thrust Allocation for Semi-Submersible Oil Rig Platforms Using Improved Harmony Search Algorithm,” by Yadav et al., uses an EC algorithm for efficient thrust allocation. The paper “Optimal Switch Placement by Alliance Algorithm for Improving Microgrids Reliability,” by Siano et al., uses the alliance algorithm for optimal switch placement in microgrids. In paper “A New Dimensionality Reduction Algorithm for Hyperspectral Image
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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 8, NO. 4, NOVEMBER 2012
Using Evolutionary Strategy,” by Yin et al., EC is used to reduce the dimensionality problem for image process. Another trend is to integrate NN, FL, and EC for modeling, control, and optimization. The paper “Real Time Operation of Smart Grids via FCN Networks and Optimal Power Flow,” by Siano et al., develops an energy management system to optimize grid operations. Other methods such as KBS have also attracted contributions. The paper “Effective Noise Estimation-Based Online Prediction for Byproduct Gas System in Steel Industry,” by Zhao et al., develops an effective on line prediction model for byproduct gas systems using a support vector machine. The paper “A Physically Segmented Hidden Markov Model Approach For Continuous Tool Condition Monitoring: Diagnostics and Prognostics,” by Geramifard et al., studies a continuous condition monitoring problem using a hidden Markov model. The paper “Multifollower Trilevel Decision Making Models and System,” by Lu et al., develops a new decision-support framework for multilevel decision-making. The paper “Stationary Consensus of Asynchronous Discrete-Time Second-Order Multi-Agent Systems Under Switching Topology,” by Qin et al., studies consensus of multi-agent systems in dynamical environments. Finally, the paper “An Intelligent Dynamic Security Assessment Framework for Power Systems With Wind Power,” by Xu et al., applies SC in a security assessment framework. We hope that this Special Section will increase the interest of the scientific community in the highly dynamic area and motivate the generation of new ideas for future research. The Guest Editors would like to express their gratitude to the authors for sending their contributions, to the reviewers for their expertise and dedication to the review process. Our special acknowledgement goes to the Editor-in-Chief of the IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, Prof. Bogdan Wilamowski, for his enthusiastic support and guidance. XINGHUO YU, Guest Editor RMIT University Platform Technologies Research Institute Melbourne, VIC 3001 Australia
[email protected] OKYAY KAYNAK, Guest Editor Bogazici University Department of Electrical and Electronic Engineering 34342 Bebek, Istanbul, Turkey
[email protected] MILOS MANIC, Guest Editor University of Idaho Department of Computer Science Idaho Falls, ID 83402 USA
[email protected]
Xinghuo Yu (M’91–SM’98–F’08) received the B.Sc. and M.Sc. degrees from the University of Science and Technology of China, Hefei, China, in 1982 and 1984, and the Ph.D. degree from South-East University, Nanjing, China, in 1988, respectively. He is now with RMIT University, Melbourne, Australia, where he is the Founding Director of RMIT’s Platform Technologies Research Institute. His research interests include variable structure and nonlinear control, complex and intelligent systems, and industrial applications. Prof. Yu is a Fellow of the Institution of Engineers, Australia, a Fellow of the Australian Computer Society, and a Graduate of the Australian Institute of Company Directors. He is serving as an Associate Editor of the IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS PART I, the IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, the IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, and several other scholarly journals. He is an IEEE Distinguished Lecturer of the IEEE Industrial Electronics Society, and serves as Vice President (Publications) of IEEE Industrial Electronics Society.
Okyay Kaynak (M’80–SM’90–F’06) received the B.Sc. degree (first Class Honors) and Ph.D. degrees in electronic and electrical engineering from the University of Birmingham, Birmingham, U.K., in 1969 and 1972, respectively. In 1979, he joined the Department of Electrical and Electronics Engineering, Bogazici University, Istanbul, Turkey, where he is presently a Full Professor. He has served as the Chairman of the Computer Engineering and the Electrical and Electronic Engineering Departments and as the Director of the Biomedical Engineering Institute at Bogazici University. Currently, he is the UNESCO Chair on Mechatronics and the Director of Mechatronics Research and Application Centre. He has held long-term (near to or more than a year) Visiting Professor/Scholar positions at various institutions in Japan, Germany, U.S., and Singapore. He has authored three books and edited five and authored or coauthored more than 250 papers published in various journals, books and conference proceedings. His current research interests are in the fields of intelligent control and mechatronics. Dr. Kaynak has served on the Editorial or Advisory Boards of a number of scholarly journals. Currently, he is a Co-Editor-in-Chief of the IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS and an Associate Editor of the IEEE SENSORS JOURNAL and the IEEE/ASME TRANSACTIONS ON MECHATRONICS.
Milos Manic (S’95–M’05–SM’06) received the M.S. degree in electrical engineering and computer science from the University of Niˇs, Niˇs, Serbia, in 1997, and the Ph.D. degree in computer science from the University of Idaho, Idaho Falls, in 2003. He has lead the Computer Science Program at the University of Idaho and is a Director of the Modern Heuristics Group. He has over 20 years of academic and industrial experience and has lead a number of research grants with the Department of Energy, Idaho National Laboratory, National Science Foundation, EPSCoR, the Department of Air Force, and Hewlett-Packard, in the area of data mining and computational intelligence applications. He is currently serving as Officer for the IEEE Industrial Electronics Society, chairing technical committee on resilience and security for industrial applications, and is involved in various capacities in technical committees on education, industrial informatics, factory automation, smart grids, and standards. He has published over 100 refereed articles in international journals, books, and conferences. Dr. Manic is an Associate Editor of the IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, the IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, and several other scholarly journals.
