Biography: Huashan Liu is a Professor at Donghua University for robotics, artificial intelligence, and mechatronics, a Senior Member of IEEE and a Member of the Intelligent Control and Systems Committee of the Chinese Command and Control Society. He received the B.E. degree in Mechanical Engineering from Wuhan University, Wuhan, China, and the Ph.D. degree in Mechatronics from Zhejiang University, Hangzhou, China in 2005 and 2010, respectively. Since 2010, he has been with the College of Information Science and Technology (CIST), Donghua University, Shanghai, China, where he founded and directed the Coexisting-Cooperative-Cognitive Robot Lab. During 2015-2016 he was a Visiting Professor with the Institute of Automatic Control Engineering (LSR), Technical University of Munich (TUM), Munich, Germany. Prof. Liu has published more than 70 articles in international academic journals such as Transaction series of IEEE and ASME, and has served as editorial board member or guest editor of more than 10 international academic journals in the field of robotics and artificial intelligence, invited speaker, session chair or program committee member of more than 30 international academic conferences, and reviewer of more than 50 international academic journals.

Title: From Dedicated to Universal: Robotic Motion Planning with AI
Abstract: Motion planning is a crucial foundation for robotic tasks. Conventional motion planning methods are highly dependent on inverse kinematics, which are extremely onerous for robots with redundant degrees of freedom. Moreover, the robots can merely execute fixed, pre-programmed, and hard-coded command sequences. Consequently, they cannot be generalized to different task scenarios. As an essential branch of artificial intelligence (AI), deep reinforcement learning (DRL) has shown great potential in realizing versatile robotic assignments that are difficult to be implemented by conventional methods. DRL equips robots the ability to optimize their behaviors by constantly interacting with the environment, which is a huge step in materializing autonomous and intelligent robotic tasks. This talk discusses the potential of AI in terms of DRL in realizing universal robotic motion planning, and also contributes to the intersection of DRL and robotics by summarizing the previous study on universal robotic motion planning by leveraging DRL, so as to provide an overview of this field and push the boundary of interdisciplinary research.

Biography: Prof. Shunli Wang is a Professor, Doctoral Supervisor, Executive Vice President of Smart Energy Storage Institute, Academic Dean of Electric Power College at Inner Mongolia University of Technology, Academician of the European Academy of Natural Sciences, Academician of Russian Academy of Natural Sciences, IET Fellow, Provincial Senior Overseas Talent, Academic Leader of the National Electrical Safety and Quality Testing Center, Tianfu Qingcheng Provincial Scientific and Technological Talent, Academic and Technical Leader of China Science and Technology City, Top 2% Worldwide Scientist. His research interests include modeling, state estimation, and safety management for energy storage systems. 56 projects have been undertaken, supported by National Natural Science Foundation of China and the Provincial Science and Technology Department et al. 258 research papers have been published with RIS value of 11617 and h-index value of 29. 52 intellectual property rights have been approved. 9 monographs have been published by famous publishers of Elsevier and IET and so on. The total print number of New Energy Technology and Power Management reaches 6300 copies that are reprinted 4 times. He has guided students on 29 science and technology innovation projects with 6 excellent completion and 34 awards in science and technology competitions. He has won 13 scientific and technological awards, including the Gold Award at the 48th Geneva Invention Exhibition. He served as chair of 17 international conferences, and 4 editorial boards of international journals, hosting the international new energy and energy storage system conference 3 times. The appraisal result has reached the internationally advanced level, as reported by People's Daily.

Ttile: Core State Factor Monitoring of Smart Energy Storage Systems
Abstract: As an important component of the smart grid energy storage system, high-precision state of health estimation of lithium-ion batteries is crucial for ensuring the power quality and supply capacity of the smart grid. To achieve this goal, an improved integrated algorithm based on multiple layer kernel extreme learning machine and genetic particle swarm optimization algorithm is proposed to estimate the SOH of Lithium-ion batteries. Kernel function parameters are used to simulate the update of particle position and speed, and genetic algorithm is introduced to select, cross and mutate particles. The improved particle swarm optimization is used to optimize the extreme value to improve prediction accuracy and model stability. The cycle data of different specifications of LIB units are processed to construct the traditional high-dimensional health feature dataset and the low-dimensional fusion feature dataset, and each version of ML-ELM network is trained and tested separately. The numerical analysis of the prediction results shows that the root mean square error of the comprehensive algorithm for SOH estimation is controlled within 0.66%. The results of the multi-indicator comparison show that the proposed algorithm can track the true value stably and accurately with satisfactory high accuracy and strong robustness, providing guarantees for the efficient and stable operation of the smart grid.

Biography: Feng Shou, Associate Professor, Master Supervisor of Harbin Engineering University, Deputy Director of the Key Laboratory of Advanced Ship Communication and Information Technology of the Ministry of Industry and Information Technology, IEEE member, Senior member of the Chinese Society of Communications, Member of the Imaging Detection and Perception Committee of the Chinese Society of Image and Graphics, peer review expert of the National Natural Science Foundation of China, academic dissertation review expert of the Ministry of Education, Visiting Scholar of Indiana University Bloomington, Guest Editor of Remote Sensing, an international authoritative journal in the field of remote sensing. Member of the editorial board of international Journal Frontiers in Imaging, American Journal of Remote Sensing, He also serves as a reviewer for many authoritative academic journals such as IEEE TIP, IEEE TGRS, IEEE GRSL, and Remote Sensing. In the past three years, he has published 20 academic papers as the first/corresponding author in top journals in the field of Remote Sensing such as IEEE TIP, IEEE TGRS, and Remote Sensing, and 2 papers have been selected as ESI highly cited papers. Published 6 conference papers in IGARSS and other international conferences, applied for 8 national invention patents as the first inventor, and has authorized 4. Served as the Branch Chair of IGARSS2020 and IGARSS2021, as a member of the Organizing Committee of IGARSS2023, organized the Community-Contributed Session "CCS.9: Recent Advances in Hyperspectral Image Processing: Methodology and Application ". As a guest editor, he organized 3 special issues in Remote Sensing.

Ttile: Remote Sensing Image Change Detection
Abstract: Remote sensing technology is an important technical means for human beings to perceive the world, and change detection technology has become the mainstream of current research. Change detection is a pixel-level task, which is mainly used for fine extraction and recognition of changed ground object information from bitemporal images. Change detection is the basis for subsequent practical application tasks of remote sensing images and has very important research significance, which is widely used in digital precision agriculture, environmental monitoring, national defense and military strategy and other fields. With the rapid development of artificial intelligence technology, many new change detection methods and algorithms have been proposed. Moreover, rapid advances in these methods have also promoted the application of associated algorithms and techniques to problems in many related fields. This keynote aims to report and cover the latest advances and trends about the Recent Advances for Remote Sensing Image Change Detection.

Biography: Mouquan Shen, Professor, the "Six Talent Peaks" of Jiangsu Province. Postdoctoral at Southeast University, visiting scholar at overseas universities such as the University of Hong Kong, Yeungnam University, South Korea, and the University of Adelaide, Australia. He is the PI of more than 10 provincial-level projects, including the National Natural Science Foundation of China, the National Bureau of Foreign Experts Affairs, and the Jiangsu Provincial Natural Science Foundation. In recent years, more than 100 papers with an H-index of 24 have been published in journals such as IEEE · Transactionson · Automatic Control, IEEE Transactionson · Cybernetics, and IEEE Transactions on Systems, Man, and Cybernetics: Systems. He has been severed as Editor-in-Cheif, Associate Editor, or Editorial Board Member of 12 international journals. He is also an active reviewer of over 80 domestic and international journals, including IEEE · TAC and Automatica, as well as the corresponding reviewer for the National Natural Science Foundation of China and multiple provincial and municipal science and technology projects.

Ttile: New communication mechanisms for networked intelligent control systems
Abstract: This report is dedicated to the important basic communication issues of networked intelligent control systems. First, an overview of the research background and some existing results for networked intelligent control systems are provided. Then, some novelty works on communication of networked intelligent control systems are presented, including the integral-type event-triggered scheme, the discrete-sampling event-triggered scheme, the threshold-dependent event-triggered scheme, and the dynamic event-triggered scheme based on instantaneous and average triggering errors. Finally, some future research topics for networked intelligent control systems are discussed.

Biography: Michael B.C. Khoo is a full professor at the School of Mathematical Sciences, Universiti Sains Malaysia (USM) in Malaysia. He holds a BAppSc and PhD in Applied Statistics from USM. He specializes in Statistical Quality Control. He has over 300 articles which were published or accepted for publications in reputable international journals, such as International Journal of Production Research, Computers & Industrial Engineering, Quality Technology & Quantitative Management, Quality and Reliability Engineering International, Quality Engineering, Communications in Statistics – Simulation and Computation, and Communications in Statistics – Theory and Methods. Most of his publications were indexed in the Web of Science (WoS) database. He has also reviewed numerous papers for journals indexed in the WoS database. He was a former member of the American Society for Quality and a life member of the Malaysian Mathematical Society.

Ttile: Machine Learning and Control Charts
Abstract: In today's manufacturing industry, there is a shift towards digitalization and machine learning is used to solve complex datasets by tackling the challenges of high dimensionality and large sample sizes in such datasets. This technique improves the efficiency of a production process and results in cost savings for the industry and ultimately enhances the quality of the products produced. An important application of machine learning in manufacturing is in control charts. A control chart is a vital tool in Statistical Process Control which is used in the monitoring of a manufacturing process for distinguishing between special and common causes of variations. The implementation of a control chart involves plotting control charting statistics on the chart to determine whether a process shift occurs in the quality characteristic being monitored. Machine learning algorithms have the potential to enhance the effectiveness of control charts by providing accurate predictions and real-time monitoring of shifts in the process parameters. Numerous studies are available, where machine learning methods have been adopted to enhance the efficiency of control charts. In this presentation, we will look at some existing studies that involve a fusion of machine learning and control charting techniques. This presentation will also highlight the challenges faced in the said fusion and identify future directions in using machine learning to make control charts an effective process monitoring tool.