Keynote Speech 1: Perspectives on Contraction Theory and Neural Networks⇒

By Francesco Bullo ( University of California, Santa Barbara, USA)

Keynote Speech 2: Computational  Approaches for Solving Systems of Nonlinear Equations⇒

By Panos M. Pardalos ( University of Florida, USA)

Keynote Speech 3: Structure Identifiability of Networked Dynamic Systems and Beyond⇒

By Tong Zhou ( Tsinghua University, Beijing, China)

Keynote Speech 4: Computational Intelligence for Multiscopic Topological Twin⇒

By Naoyuki Kubota ( Tokyo Metropolitan University, Japan)

Keynote Speech 5: An Integrated Model Predictive Control (MPC) Framework for Autonomous Intelligent Systems⇒

By Yang Shi ( University of Victoria, Canada)

Keynote Speech 6: Semi-Supervised Classification Based on Laplacian Support Vector Machine with Quasi-Linear Kernel⇒

By Jinglu Hu ( Waseda University, Japan)

Keynote Speech 7: Forensic Biometrics: Bridging Technology with Forensic Science⇒

By Massimo Tistarelli (Università di Sassari, Italy)

Keynote Speech 8: Recent Advances on Hand Rehabilitation Robots for Post-Stroke Patients⇒ 

By Long Cheng ( Chinese Academy of Sciences, China)

Keynote Speech 9: Algorithm of Fuzzy Clustering and Application to Local Data Analysis⇒

By Katsuhiro Honda (Osaka Metropolitan University, Japan)

 

Keynote Speaker 1: Prof. Francesco Bullo

Title:  Perspectives on Contraction Theory and Neural Networks

Abstract: Basic questions in dynamical neuroscience and machine learning motivate the study of the stability, robustness, entrainment, and computational efficiency properties of neural network models. I will present some elements of a comprehensive contraction theory for neural networks. Using nonEuclidean norms I will review recent advances in analyzing and training a class of recurrent/implicit models.

Bio: Prof. Francesco Bullo is a Professor with the Mechanical Engineering Department and the Center for Control, Dynamical Systems and Computation at the University of California, Santa Barbara. He was previously associated with the University of Padova (Laurea degree in Electrical Engineering, 1994), the California Institute of Technology (Ph.D. degree in Control and Dynamical Systems, 1999), and the University of Illinois. He served on the editorial boards of IEEE, SIAM, and ESAIM journals, as the IEEE CSS President and as the SIAG CST Chair. His research interests focus on contraction theory, network systems and distributed control with application to robotic coordination, power grids and social networks. He is the coauthor of “Geometric Control of Mechanical Systems” (Springer, 2004), “Distributed Control of Robotic Networks” (Princeton, 2009), and “Lectures on Network Systems” (Kindle Direct Publishing, 2022, v1.6). He received best paper awards for his work in IEEE Control Systems, Automatica, SIAM Journal on Control and Optimization, IEEE Transactions on Circuits and Systems, and IEEE Transactions on Control of Network Systems. He is a Fellow of IEEE, IFAC, and SIAM.

Keynote Speaker 2: Prof. Panos M. Pardalos

Title:  Computational  Approaches for Solving Systems of Nonlinear Equations

Abstract: Finding one or more solutions to a system of nonlinear equations (SNE) is a computationally hard problem with many applications in sciences, engineering, machine learning and artificial intelligence. First, we will briefly discuss classical approaches for addressing (SNE). Then, we will discuss the various ways that a SNE can be transformed into an optimization problem, and we will introduce techniques that can be utilized to search for solutions to the global optimization problem that arises when the most common reformulation is performed. In addition, we will present computational results using different heuristics.

Bio:  Prof. Panos Pardalos received his  PhD (Computes and Information Sciences) from the University of Minnesota. He  is a Distinguished Professor  in the Department of Industrial and Systems Engineering at the University of Florida, and an affiliated faculty of Biomedical Engineering and Computer Science & Information & Engineering departments. He is the director of the Center for Applied Optimization. In addition, he is the academic advisor of the laboratory on networks and applications at the Higher School of Economics, Moscow (funded by a mega grant in 2011).

Panos Pardalos is a world-renowned leader in Global Optimization, Mathematical Modeling, Energy Systems, Financial applications, and Data Sciences. He is a Fellow of AAAS, AAIA, AIMBE, EUROPT, and INFORMS and was awarded the 2013 Constantin Caratheodory Prize of the International Society of Global Optimization. In addition, Panos  Pardalos has been awarded the 2013 EURO Gold Medal prize and a prestigious Humboldt Research Award (2018-2019). 

Panos Pardalos is also a Member of several  Academies of Sciences, and he holds several honorary PhD degrees and affiliations. He is the Founding Editor of Optimization Letters, Energy Systems, and Co-Founder of the International Journal of Global Optimization, Computational Management Science, and Springer Nature Operations Research Forum. He has published over 500 journal papers, and edited/authored over 200 books. He is one of the most cited authors and has graduated 70 PhD students so far. Details can be found in www.ise.ufl.edu/pardalos.

Keynote Speaker 3: Prof. Tong Zhou

Title: Structure Identifiability of Networked Dynamic Systems and Beyond

Abstract: Networked dynamic systems (NDS) have been attracting research attentions for a long time. With technology developments, especially those in communications and computers, the scale of an NDS becomes larger and larger. Moreover, some new issues also arise, such as attack prevention, random communication delay/failure, etc. In addition, recent marvelous success in artificial intelligence greatly stimulates constructions of artificial NDSs with a huge number of nodes. On the other hand, some classic problems including revealing the structure of an NDS from measurements, computationally efficient conditions for NDS controllability/ observability verifications, etc., still remain challenging.

In this talk, a model is introduced for a large scale NDS in which subsystems are connected through their internal outputs in an arbitrary way, and subsystems may have distinctive dynamics. A matrix rank based necessary and sufficient condition is given for the global identifiability of subsystem interactions, which leads to several conclusions about NDS structure identifiability when there is some a priori information. This matrix also leads to an explicit description for the set of subsystem interactions that can not be distinguished from experiment data only. Importance of “structure identifiability degree” is also revealed through numerical simulations, with a discussion on its influences on model prediction capabilities and system performances.

Bio: Prof. Zhou received the B.S. and M.S. degrees from the University of Electronic Science and Technology of China, China, in 1984 and 1989, respectively, another M.S. degree from Kanazawa University, Japan, in 1991, and the Ph.D. degree from Osaka University, Japan, in 1994. After visiting several universities in The Netherlands, Japan and China, he joined Tsinghua University, Beijing, China, in 1999, where he is currently a Professor of control theory and control engineering. His current research interests include networked dynamic systems, distributed/robust estimation and control, system identification and their applications to real-world problems in molecular cell biology and communication systems. Dr. Zhou was a recipient of the First-Class Natural Science Prize in 2020 from the Chinese Association of Automation (CAA), a recipient of the First-Class Natural Science Prize in 2003 from the Ministry of Education, China, and a recipient of the National Outstanding Youth Foundation of China in 2006. He has served as an Associate Editor of the IEEE TRANSACTIONS ON AUTOMATIC CONTROL, and is now on the editorial board of AUTOMATICA. He is an IEEE Fellow and a CAA Fellow.

Keynote Speaker 4: Prof. Naoyuki Kubota

Title: Computational Intelligence for Multiscopic Topological Twin

Abstract: Cyber-Physical System, Digital Transformation, and Digital Twin have been discussed based on the integration of information, intelligence, communication, and robot technologies in various research fields. In order to simulate a real-world phenomenon in the cyber world, we often have to extract features and structures from given or measured big data based on topology. Therefore, we proposed the concept of topological twin. The aim of topological twin is to (1) extract topological structures hidden implicitly in the real world, (2) reproduce them explicitly in the cyber world, and (3) simulate and analyze the real world in the cyber world. The topological twin plays the important role in extracting and connecting structures hidden in real world from the mutliscopic point of view. In this talk, we discuss the concept of topological twin in robotics in order to bridge the cyber-physical gap from the multiscopic point of view. First, we introduce the concept of multiscopic topological twin, and next, various types of topological mapping methods, unsupervised learning methods, and graph-based methods. One of them is Growing Neural Gas (GNG) that can dynamically change the topological structure composed of nodes and edges. One important advantage of GNG is in the incremental learning capability of nodes and edges according to a target data distribution, but the computational cost of standard GNG is very expensive. Therefore, we proposed a method of multi-scale batch-learning GNG called Fast GNG. Next, we show the comparison result of Fast GNG with other methods. Furthermore, we show several experimental results of robot partners and mobility support robots. Finally, we discuss the applicability and future direction of multiscopic topological twin in robotics.

Bio: Prof. Kubota is currently a Professor in the Department of Mechanical Systems Engineering, the Graduate School of Systems Design, and Director of Community-centric System Research Center, Tokyo Metropolitan University, Japan. He graduated from Osaka Kyoiku University in 1992, received the M.E. degree from Hokkaido University in 1994, and received the D.E. from Nagoya University, Nagoya, Japan, in 1997. He was an Assistant Professor and Lecturer at the Department of Mechanical Engineering, Osaka Institute of Technology, Japan, from 1997 to 2000. In 2000, he joined the Department of Human and Artificial Intelligence Systems, the School of Engineering, Fukui University, Japan, as an Associate Professor. He joined the Department of Mechanical Engineering, the Graduate School of Engineering, Tokyo Metropolitan University, Japan, as an Associate Professor in 2004. He was an Associate Professor from 2005 to 2012, and a Professor from 2012 at the Graduate School of Systems Design, Tokyo Metropolitan University, Japan. He was a Visiting Professor at University of Portsmouth, UK, in 2007 and 2009, and was an Invited Visiting Professor at Seoul National University from 2009 to 2012, and others. His current interests are in the fields of topological mapping, coevolutionary computation, spiking neural networks, perception-based robotics, robot partners, and informationally structured space. He has published more than 500 refereed journal and conference papers in the above research fields. He received the Best Paper Award of IEEE IECON 1996, IEEE CIRA 1997, MHS 2011, WAC 2012, HSI 2016, and so on. He was an associate editor of the IEEE Transactions on Fuzzy Systems from 1999 to 2010, the IEEE CIS Intelligent Systems Applications Technical Committee, Robotics Task Force Chair from 2007 to 2014, IEEE Systems, Man, and Cybernetics Society, Japan Chapter Chair from 2018 to 2021, Vice Director, Tokyo Biomarker Innovation Research Association, Japan since 2020, and others.

Keynote Speaker 5: Prof. Yang Shi

Title: An Integrated Model Predictive Control (MPC) Framework for Autonomous Intelligent Systems

Abstract: Autonomous intelligent systems, which lie at the intersection of unmanned systems, robotics, systems and control, multi-agent systems, networked and distributed systems, machine learning, etc. Autonomous intelligent systems are equipped with abilities such as sensing and perception, data processing and information fusion, intelligent decision making, autonomous control, learning and adaption, communications and computation, thus can achieve a high level of autonomy to perform missions without human intervention or can naturally interact and collaborate with humans and/or environment. The fundamental control theory and methods in autonomous intelligent systems are of central importance in orchestrating all related functions. Autonomous control and intelligence can be applied to various systems, e.g., aerial vehicles, marine vehicles, ground robots, space exploration, energy and power systems, transportation and smart city, intelligent agriculture, smart manufacturing, smart health care systems, Internet of Things, etc. Model predictive control (MPC) is a promising paradigm for high-performance and cost-effective control of autonomous intelligent systems. This talk will firstly summarize the major application requirements and challenges to innovate in designing, implementing, deploying and operating autonomous intelligent systems. Further, the robust MPC and distributed MPC design framework will be presented. Finally, the application of MPC algorithms to various autonomous intelligent systems will be illustrated.

Bio: Prof. Yang Shi received his B.Sc. and Ph.D. degrees in mechanical engineering and automatic control from Northwestern Polytechnical University, Xi’an, China, in 1994 and 1998, respectively, and the Ph.D. degree in electrical and computer engineering from the University of Alberta, Edmonton, AB, Canada, in 2005. From 2005 to 2009, he was an Assistant Professor and Associate Professor in the Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK, Canada. In 2009, he joined the University of Victoria, and now he is a Professor in the Department of Mechanical Engineering, University of Victoria, Victoria, BC, Canada. His current research interests include networked and distributed systems, model predictive control (MPC), cyber-physical systems (CPS), robotics and mechatronics, navigation and control of autonomous systems (AUV and UAV), and energy system applications.

Prof. Shi received the University of Saskatchewan Student Union Teaching Excellence Award in 2007, and the Faculty of Engineering Teaching Excellence Award in 2012 at the University of Victoria (UVic). He is the recipient of the JSPS Invitation Fellowship (short-term) in 2013, the UVic Craigdarroch Silver Medal for Excellence in Research in 2015, the 2017 IEEE Transactions on Fuzzy Systems Outstanding Paper Award, the Humboldt Research Fellowship for Experienced Researchers in 2018. He is a member of the IEEE IES Administrative Committee and the IES Fellow Evaluation Committee during 2017-2019; he is the Chair of IEEE IES Technical Committee on Industrial Cyber-Physical Systems. Currently, he is Co-Editor-in-Chief for IEEE Transactions on Industrial Electronics; he also serves as Associate Editor for Automatica, IEEE Transactions on Automatic Control, IEEE Transactions on Cybernetics, etc. He is General Chair of the 2019 International Symposium on Industrial Electronics (ISIE) and the 2021 International Conference on Industrial Cyber-Physical Systems (ICPS).

He is a Fellow of IEEE, ASME, CSME, and Engineering Institute of Canada (EIC), and a registered Professional Engineer in British Columbia, Canada.

Keynote Speaker 6: Prof. Jinglu HU

Title: Semi-Supervised Classification Based on Laplacian Support Vector Machine with Quasi-Linear Kernel

Abstract: In this talk, we first introduce a semi-supervised classification algorithm based on Laplacian support vector machine (SVM) with a quasi-linear kernel. The algorithm consists of two steps. In the first step, it composes a quasi-linear kernel through implementing a piecewise linear model based on neural networks. In the second step, the piecewise linear model is further optimized by a Laplacian SVM algorithm using the quasi-linear kernel function. In both steps, it effectively leverages a small amount of labeled and a large amount of unlabeled data so as to achieve good classification performance. Then we introduce an application of the algorithm to construct a semi-supervised classifier for parasite image classification, by including a semi-supervised feature extractor based on deep CNN using contrastive learning.

Bio: Prof. Jinglu Hu received a M.Sci degree in Electronic Engineering from Sun Yat-Sen University, Guangzhou, China in 1986, and a Ph.D degree in Computer Science and System Engineering from Kyushu Institute of Technology, Iizuka, Japan in 1997. From 1986 to 1988, he worked as a Research Associate and from 1988 to 1993 a Lecturer at Sun Yat-Sen University. From 1997 to 2003, he worked as a Research Associate at Kyushu University. From 2003 to 2008, he worked as an Associate Professor and since April 2008, he has been a Professor at Graduate School of Information, Production and Systems of Waseda University. His research interests include Computational Intelligence and its applications to system modeling and identification, bioinformatics, time series prediction, financial analysis, data mining and so on. Dr. Hu has published more than 180 journal papers indexed by SCI or EI and 140 prominent conference papers. Dr. Hu is a member of IEEE, IEEJ, SICE and IEICE.

Keynote Speaker 7: Prof. Massimo Tistarelli

Title: Forensic Biometrics: Bridging Technology with Forensic Science

Abstract: In the last decades, digital technologies have been applied in forensic investigations only to a limited extent of their possibilities. A number of factors have hindered the wider adoption of these technologies to operational scenarios. However, there has been a number of successful applications where digital biometric technologies were crucial to support investigation and to provide evidence in court. Given the great potential of biometric technologies for objective and quantitative evidence evaluation, it would be desirable to see a wider deployment of these technologies, in a standardized manner, among police forces and forensic institutes.

In this talk, the actual state of the art in forensic biometric systems will be briefly reviewed, trying to identify the outbreaks and pitfalls of current technologies. Despite of their impressive performance, some recent biometric technologies when applied to forensic evaluation demonstrated sometimes to be lacking under some respect. Other technologies will need adaptations to be ready for the forensic field. We postulate that there is a challenge to be faced with more advanced tools and testing on operational data. This will require a joint effort involving stakeholders and scientists from multiple disciplines as well as a greater involvement of forensic institutes and police forensic science departments.

Bio: Massimo Tistarelli received the Phd in Computer Science and Robotics in 1991 from the University of Genoa. He is Full Professor in Computer Science (with tenure) and director of the Computer Vision Laboratory at the University of Sassari, Italy. Since 1986 he has been involved as project coordinator and task manager in several projects on computer vision and biometrics funded by the European Community. Since 1994 he has been the director of the Computer Vision Laboratory at the Department of Communication, Computer and Systems Science of the University of Genoa, and now at the University of Sassari, leading several National and European projects on computer vision applications and image-based biometrics. 

Prof. Tistarelli is a founding member of the Biosecure Foundation, which includes all major European research centers working in biometrics. His main research interests cover biological and artificial vision (particularly in the area of recognition, three-dimensional reconstruction and dynamic scene analysis), pattern recognition, biometrics, visual sensors, robotic navigation and visuo-motor coordination. He is one of the world-recognized leading researchers in the area of biometrics, especially in the field of face recognition and multimodal fusion. He is coauthor of more than 150 scientific papers in peer reviewed books, conferences and international journals. He is the principal editor for the Springer books “Handbook of Remote Biometrics” and “Handbook of Biometrics for Forensic Science”. Prof. Tistarelli organized and chaired several world-recognized several scientific events and conferences in the area of Computer Vision and Biometrics, and he has been associate editor for several scientific journals including IEEE Transactions on PAMI, IET Biometrics, Image and Vision Computing and Pattern Recognition Letters. Since 2003 he is the founding director for the Int.l Summer School on Biometrics (now at the 19th edition – http://biometrics.uniss.it). He served as vice president of the IEEE Biometrics Council, first vice president of the IAPR and chair of the IAPR Fellow committee. He is a Fellow member of the IAPR and Senior member of the IEEE.

Keynote Speaker 8: Prof. Long Cheng

Bio: Prof. Long Cheng received the B.S. (Hons.) degree in control engineering from Nankai University, Tianjin, China, in 2004, and the Ph.D. (Hons.) degree in control theory and control engineering from the Institute of Automation, Chinese Academy of Sciences, Beijing, China, in 2009. He is currently a Full Professor with the Institute of Automation, Chinese Academy of Sciences. He is also an adjunct Professor with University of Chinese Academy of Sciences. He has published over 100 technical papers in peer-refereed journals and prestigious conference proceedings. He was a recipient of the IEEE Transactions on Neural Networks Outstanding Paper Award from IEEE Computational Intelligence Society, the Aharon Katzir Young Investigator Award from International Neural Networks Society and the Young Researcher Award from Asian Pacific Neural Networks Society. He is taking the role of Chair of IEEE Computational Intelligence Society Beijing Chapter (2022); the Associate Vice President of IEEE Systems, Man and Cybernetics Society (2022). He is currently serving as an Associate Editor/Editorial Board Member of IEEE Transactions on Cybernetics, IEEE Transactions on Automation Science and Engineering, Science China Technological Sciences, and Acta Automatica Sinica. His current research interests include the rehabilitation robot, intelligent control and neural networks.

 

 Keynote Speaker 9: Prof. Katsuhiro Honda

Title: Algorithm of Fuzzy Clustering and Application to Local Data Analysis

Abstract: Fuzzy clustering is a basic technique for unsupervised classification and is utilized in summarization of intrinsic data structures. In this talk, the theoretical characteristics of several fuzzy clustering algorithms induced from Fuzzy c-Means (FCM) are reviewed. Then, some of their applications to local data analysis are introduced. By replacing the prototypical centroids of FCM with least-square-type modeling, the basic data modeling is extended to local data analysis, which is the hybrid of data space partition and local area modeling. When we hybridize non-negative matrix factorization (NMF) with fuzzy clustering, we can achieve simultaneous estimation of multiple decomposition models, which is available in such applications as seasonal effect analysis in Environmental Data Analysis.

Bio: Prof. Katsuhiro Honda received the B.E., M.E. and D.Eng. Degrees in industrial engineering from Osaka Prefecture University, Osaka, Japan, in 1997, 1999 and 2004, respectively. From 1999 to 2021, he was a Research Associate, Assistant Professor, Associate Professor and Professor at Osaka Prefecture University. Since 2022, he has been a Professor at Graduate School of Informatics, Osaka Metropolitan University. His research interests include hybrid techniques of fuzzy clustering and multivariate analysis, data mining with fuzzy data analysis and neural networks. He has published over 90 papers in refereed journals and has presented over 200 papers in refereed international conferences. He received the best paper awards at FUZZ-IEEE 2008, SCIS&ISIS 2016 and SICE2021, publication award and paper awards from Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT) in 2010 and 2002, 2011 and 2012, respectively. He has been an executive board member of SOFT since 2017.