Speakers-AIRC 2020

Prof. Okyay Kaynak (IEEE Fellow)
UNESCO Chair on Mechatronics, Bogazici University, Turkey

Biography: Okyay Kaynak received the B.Sc. degree with first class honors and Ph.D. degrees in electronic and electrical engineering from the University of Birmingham, UK, in 1969 and 1972 respectively.
From 1972 to 1979, he held various positions within the industry. In 1979, he joined the Department of Electrical and Electronics Engineering, Bogazici University, Istanbul, Turkey, where he is currently a Professor Emeritus, holding the UNESCO Chair on Mechatronics. He is also a 1000 People Plan Professor at University of Science & Technology Beijing, China. He has hold long-term (near to or more than a year) Visiting Professor/Scholar positions at various institutions in Japan, Germany, U.S., Singapore and China. His current research interests are in the broad field of intelligent systems. He has authored three books, edited five and authored or co-authored more than 400 papers that have appeared in various journals and conference proceedings.
Dr. Kaynak has served as the Editor in Chief of IEEE Trans. on Industrial Informatics and IEEE/ASME Trans. on Mechatronics as well as Co-Editor in Chief of IEEE Trans. on Industrial Electronics. Additionally, he is on the Editorial or Advisory Boards of a number of scholarly journals. He recently received the Chinese Government’s Friendship Award and Humboldt Research Prize (both in 2016).
Dr. Kaynak is active in international organizations, has served on many committees of IEEE and was the president of IEEE Industrial Electronics Society during 2002-2003. He was elevated to IEEE fellow status in 2003.

Speech Title: Digital Transformation

Abstract: This presentation discusses the profound technological changes that have taken place around us during the last two decades, supported by the new disruptive advances both on the software and the hardware sides, as well as the cross-fertilization of concepts and the amalgamation of information, communication and control technology driven approaches. In recent years, in an attempt to change the whole format of industrial automation, these developments have been taken further, especially in Germany, under the label “Industry 4.0”. The dominant feature of Industry 4.0 is the integration of the virtual world with the physical world through the Internet of Things (IoT). Such engineered systems are named Cyber Physical Systems built from, and depends upon, the seamless integration of computational algorithms and physical components. A more comprehensive description of what is happening around us is the digital transformation. After reviewing these profound changes, the presentation is concluded with a discussion of the integration of AI in digital transformation, in the form of smart manufacturing.

Prof. Hao Luo
Harbin Institute of Technology, China

Biography: Prof. Dr.-Ing. Hao Luo received his B.E. degree in electrical engineering from Xi’An Jiaotong University, China, in 2007, M.Sc. degree in electrical engineering and information technology from University of Duisburg-Essen, Germany, in 2012, and the Ph.D. degree at the Institute for Automatic Control and Complex Systems (AKS) at the University of Duisburg-Essen, Germany, in 2016. He is currently an associate professor in School of Astronautics, Harbin Institute of Technology, China. His research interests include model-based and data-driven intelligent process monitoring and performance optimization, as well as their plug-and-play applications to industrial control systems. He currently serves as Associate Editor of IEEE Trans. on Artificial Intelligence and IEEE Access, as well as Guest Editor of IEEE Trans. on Industrial Informatics.

Speech Title: Recent Advances in Control-Performance-Oriented Process Monitoring and Control Systems

Abstract: During the past 80 years, control of systems for which a model is available, has been well studied and a number of a model-based design frameworks has been established. The practicality and the efficiency of various approaches based on these frameworks has been proven and numerous successful applications have been reported. Among the model-based techniques, the gap metric provides a measure to the “distance” from a nominal system to a set of uncertain systems. Together with the (generalized) stability margin, they are very powerful tools in control system designs to “measure” the magnitude of the system uncertainty (or fault) that can be tolerated until a feedback control loop becomes unstable, or to “guide” the design of the feedback controller to reach maximal achievable stability margin (to have maximal uncertainty/fault tolerant ability). Their computations and applications can be observed in the literature in a model-based manner. However, due to a priori requirement on the system model, the stability margin and the gap metric are mostly used for offline designs. Aiming at developing a data-driven framework for the robustness analysis and control design of the closed-loop system, this talk is dedicated to the study on the real-time monitoring and control issues regarding the closed-loop stability, in which the data-driven realizations of the v-gap metric and the stability margin using time domain closed-loop input/output (I/O) data are investigated.

Prof. Amir H. Gandomi
University of Technology Sydney, Australia

Biography: Amir H. Gandomi is a Professor of Data Science and an ARC DECRA Fellow at the Faculty of Engineering & Information Technology, University of Technology Sydney. Prior to joining UTS, Prof. Gandomi was an Assistant Professor at the School of Business, Stevens Institute of Technology, USA and a distinguished research fellow in BEACON center, Michigan State University, USA. Prof. Gandomi has published over two hundred journal papers and seven books which collectively have been cited 18,000+ times (H-index = 60). He has been named as one of the most influential scientific mind and Highly Cited Researcher (top 0.1% publications and 0.01% researchers) for four consecutive years, 2017 to 2020. He also ranked 18th in GP bibliography among more than 12,000 researchers. He has served as associate editor, editor and guest editor in several prestigious journals such as AE of SWEVO, IEEE TBD, and IEEE IoTJ. Prof Gandomi is active in delivering keynotes and invited talks. His research interests are global optimisation and (big) data analytics using machine learning and evolutionary computations in particular.

Speech Title: Evolutionary Computation for Modelling of Mechanical and Aerospace Systems

Abstract: Evolutionary Computation (EC) has been widely used during the last two decades and has remained a highly-researched topic, especially for complex engineeing problems. The EC techniques are a subset of artificial intelligence, but they are slightly different from the classical methods in the sense that the intelligence of EC comes from biological systems or nature in general. The efficiency of ECis due to their significant ability to imitate the best features of nature which have evolved by natural selection over millions of years. The central theme of this presentation is about EC techniques and their application to complex structural and mechanical problems. On this basis, first I will talk about an evolutionary approach called genetic programming for data mining. Applied evolutionary computing will be presented, and then their new advances will be mentioned such as big data mining. Here, some of my studies on big data mining and modelling using EC and genetic programming, in particular, will be presented. Case studies’ topics include seismic design, inverse design, and material design. In the second section, the evolutionary optimization algorithms and their key applications in the design optimization of complex and nonlinear systems will be discussed. It will also be explained how such algorithms have been adopted to structural and mechanical problems and how their advantages over the classical optimization problems are used in action. Optimization results of large-scale systems and many-objective problems will be presented which show the applicability of EC. Some heuristics will be explained which are adaptable with EC and they can significantly improve the optimization results.