Speakers

Prof. Youmin Zhang

IEEE Fellow, CSME Fellow, AIAA Senior Member, ASME Member, CASI Member, AUVSI/USC Member, Concordia University, Canada

Dr. Youmin Zhang is a tenured full professor in the Department of Mechanical, Industrial, and Aeronautical Engineering and the Concordia Institute of Aeronautical Design and Innovation, Concordia University, Canada. He is a Fellow of the Canadian Institution of Mechanical Engineers (CSME), a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), and a Senior Member of AIAA. He has been engaged in the research and development of control theory and engineering applications for a long time. Specializing in fault detection and diagnosis, fault tolerance control, perception and obstacle avoidance, aircraft navigation, guidance and control, multi-agent/multi-moving body (air, ground, water, and wind) fault tolerance collaborative control and combined with remote sensing measurement technology for forest fire prevention and forest resource management, power line patrol and monitoring, environmental monitoring, search and rescue, and other fields of research and application development. Since 1992, he has published more than 500 journal and conference papers and 4 books, and he has been cited 12,791 times on Google Scholar with an impact factor of 52 (h-index) and 205 (i10-index). He was the founding editor of the international journal "Instrumentation, Automation, and Systems" and currently serves as honorary editor and senior editor of the international journal "Intelligent Robot Systems" (editor-at-large). The editor and editorial board of the three international magazines related to unmanned systems recently published, including "Unmanned Systems,"  "International Intelligent Unmanned Systems Journal,"  "Unmanned Systems Technology," and other international magazines.

Prof. Baoming Bai

Xidian University

Baoming Bai (Senior Member, IEEE) received the B.S. degree from the Northwest Telecommunications Engineering Institute, China, in 1987, and the M.S. and Ph.D. degrees in communication engineering from Xidian University, China, in 1990 and 2000, respectively. From 2000 to 2003, he was a Senior Research Assistant at the Department of Electronic Engineering, City University of Hong Kong. Since April 2003, he has been with the State Key Laboratory of Integrated Services Networks (ISN), School of Telecommunication Engineering, Xidian University, where he is currently a Professor. In 2005, he was with the University of California, Davis, CA, USA, as a Visiting Scholar. In 2018, he has spent one month as a Senior Visiting Fellow at McMaster University, ON, Canada. He has coauthored the book Channel Coding for 5G (in Chinese, 2020). His research interests include information theory and channel coding, wireless communication, and quantum communication.

Prof. Pingyi Fan

IET Fellow, Tsinghua University, China

Dr. Pingyi Fan is a professor and the director of open source data recognition innovation center,  Department of Electronic Engineering, Tsinghua University. He received Ph.D. degree at the Department of Electronic Engineering of Tsinghua University in 1994. From 1997 to 1999, he visited the Hong Kong University of Science and Technology and the University of Delaware in the United States. He also visited many universities and research institutes in the United States, Europe, Japan, Hong Kong and Singapore. He has obtained many research grants, including national 973 Project, 863 Project, mobile special project and the key R&D program, national natural funds and international cooperation projects. He has published more than 500 papers including 156 IEEE journals and more than 10 ESI highly cited papers as well as 4 academic books. He also applied for more than 40 national invention patents, 7 international patents. He won 10 best paper awards of IEEE international conferences, including IEEE ICCCS2023 and 2024, ICC2020 and Globecom 2014, and received the best paper award of IEEE TAOS Technical Committee in 2020, the excellent editor award of IEEE TWC (2009), the most popular scholar award 2023 of AEIC, the second natural Prize of CIC (2023) and several international innovation exhibition medals, i.e. Gold Medal at the Russian Invention Exhibition-2024, Silver Medal at Geneva Invention Exhibition-2023, and Silver Medal at Paris Invention Exhibition-2023 etc. and served as the editorial board member of several Journals, including IEEE and MDPI. He is a Fellow of IET and currently the editorial board member of Open Journal of Mathematical Sciences and IAES international journal of artificial intelligence, the deputy director of China Information Theory society, the Co-chair of China's 6G-ANA TG4, and the chairman of Network and Communication Technology Committee of IEEE ChinaSIP. His current research interests are in 6G wireless communication network and machine learning, semantic information theory and generalized information theory, big data processing theory, intelligent network and system detection, etc.

Speech Title: GAN-based Approaches For Anomaly Detection of Machines with Sounds

Abstract: Digital Twins and Industry 4.0 are becoming the most promising trends in the near future for modern industrial manufacturing and production managements.  Anomaly detection is the critical issue for them.  There are two different ways to do it. One is based on the images or videos observed by using sensors with camera;  Another is based on the sensors of audios.  In fact, the techniques with images or video can only check the abnormal statuses of the machine or equipment  appearing in the surfaces. But the sounds from the machine or equipment can be used to check their inner anomaly statuses.  Machine Sounds have been considered as one important feature in future digital twins and industry 4.0. In this talk, we first review the developments of the anomalies identification problem by machine sounding and then present a new generative adversarial network (GAN) which combines GAN with autoencoder, AEGAN, where anomalies are detected from two complementary perspectives: error reconstruction measured by the generator and embedding features extracted from the discriminator. The experimental results will show that AEGAN reaches the state-of-the-art performance over two DCASE datasets among unsupervised methods, which indicates that the AEGAN performs well on widely-used working scenarios. Later on, we also introduce the MIM-GAN theory and its applications in Anomaly detection. Finally, some conclusions and future research directions are given.

Prof. Rugui Yao

Northwestern Polytechnical University, China

Rugui Yao (Senior Member, IEEE) received the B.Sc., M.Sc., and Ph.D. degrees in telecommunications and information systems from the School of Electronics and Information (SEI), Northwestern Polytechnical University (NPU), Xi’an, China, in 2002, 2005, and 2007, respectively, where he worked as a Postdoctoral Fellow from 2007 to 2009. Since 2009, he has been with SEI and NPU, where he is currently an Associate Professor. In 2013, he joined the ITP Lab at Georgia Tech in Atlanta, USA, as a visiting scholar. He has worked in the areas of physical layer security, cognitive radio networks, channel coding, OFDM transmission, and spread-spectrum systems. He is a senior member of the Chinese Institute of Electronics.

Speech Title: Integrated Sensing and Communication technology towards Coordination Gain

Abstract: With the requirement of higher rate and wider-area sensing in wireless communication, the integrated sensing and communication (ISAC) technology has become the key technology of next generation communication network. The ISAC technology has the advantages of integration gain and coordination gain. In previous studies, the ISAC  integration gain has been fully realized through integrated design of hardware and signal, but the work pattern and system design towards higher coordination gain still need further exploring. In this speech, we would like to introduce some of our research results in this field. Towards the coordination gain enhancement in frequency domain, we jointly optimization of subcarrier selection, beamforming design and power distribution to improve the overall communication and sensing performance. Towards the coordination gain enhancement in mission domain, we proposed a sensing-assisted communication channel estimation method, which improved the time-frequency resource utilization. Finally, to solve the high peak to average power ratio (PAPR) problem of orthogonal chirp division multiplexing (OCDM)-ISAC signal, we proposed an optimized subcarrier selection scheme, which could effectively decrease the PAPR of the integrated signal. We wish these studies can provide a theoretical basis for coordination gain enhancement and PAPR suppression of the ISAC system.

Prof. Ben Niu

Dalian University of Technology, China

Ben Niu is a professor and doctoral supervisor of the School of Control Science and Engineering, Dalian University of Technology. He has been selected as a National High-level Young Talent Program, and has been selected as a Clarivate Global Highly Cited Scientist, Elsevier China Highly Cited Scholar, and the world's Top 100,000 scientists for several years. In January 2013, he received his Doctor of Engineering degree in Control Theory and Control Engineering from Northeastern University, and he is mainly engaged in the research of information physical system, robot intelligent control and application. He has published more than 100 papers in major academic journals and conferences in the fields of control science and artificial intelligence, including more than 50 papers in Automatica and IEEE Transactions. He has presided 4 National Natural Science Foundation projects and 5 provincial projects.

Prof. Yanjun Zhang

Beijing Institute of Technology, China

Yanjun Zhang received the Ph.D. degree in control science and engineering from Nanjing University of Aeronautics and Astronautics, China, in 2017. From Jun. 2019 to Jun. 2021, he was a postdoc at Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China. He subsequently joined the Beijing Institute of Technology, where he currently serves as a Full Professor. He received a grant from the National Natural Science Foundation of China Excellent Young Scientists Fund for his research project titled “Parametrized Adaptive Control of Non-canonical Form Uncertain Systems”. He now holds the position of Associate Editor at the Journal of Systems Science & Complexity. His research interests include adaptive control, quantized control, multi-agent systems control, and their applications to aircraft control systems.

Prof. Meng Zhang

Xi’an Jiaotong University, China

Meng Zhang is a professor and doctoral supervisor at Xi'an Jiaotong University, a young Changjiang Scholar from the Ministry of Education, a class A Young Top Talent at Xi'an Jiaotong University, and a young scholar named Wang Kuancheng from Xi'an Jiaotong University. Meng Zhang graduated with a Ph.D. from the School of Control Science and Engineering, Zhejiang University. Meng Zhang has received honors such as the First Prize for Excellent Achievements in Science and Technology Research at Shaanxi Higher Education Institutions, the First Prize for Natural Science at the Chinese Association of Automation, the Outstanding Youth Award for Artificial Intelligence of Wu Wenjun, the Nomination Award for Excellent Doctoral Dissertation at the Chinese Association of Automation, the IEEE ICPS Best Paper Award, the IEEE SmartGridComm Workshop Best Paper Award, the ICICIC Best Paper Award, and the ICGNC Best Paper Nomination Award. Meng Zhang has published more than 40 papers in journals such as Automatica, IEEE TAC Long Paper, IEEE TIFS, IEEE TC, IEEE TASE, IEEE TSG, IEEE CDC, etc. Among them, 5 papers were selected as highly cited ESI papers.Meng Zhang Serves as the deputy editor of IEEE Transactions on Automation Science and Engineering and the chairman of IEEE IESONCON and other conference industrial forums, guiding students to win the first prize in the 6th China Graduate Smart City Technology and Creative Design Competition as a mentor. Meng Zhang’s research directions include cyber-physical systems, intelligent grid optimization control and security, nonlinear system control, mobile robots, etc.  

Prof. Zhengrong Xiang

Nanjing University of Science and Technology, China

Zhengrong Xiang (Member, IEEE) received the B.S degree in theoretical physics  and the M.S. degree in fundamental mathematics from Xinjiang University in 1989 and 1995 respectively, and received the Ph.D. degree in control theory and control engineering from the Nanjing University of Science and Technology in 1998.

He was appointed as a Lecturer and an Associate Professor at the Nanjing University of Science and Technology in 1998 and 2001, respectively. Since 1998, he has been a Faculty Member with the Nanjing University of Science and Technology, where he is currently a Full Professor. He has coauthored around 380 journal and conference papers, many of which are highly-cited status, ESI 1%. One paper was  selected as one of the "China's 100 most influential international academic papers". His main research interests include switched systems, nonlinear control, multi-agent systems, reinforcement learning, and networked control systems.

 Prof. Xiang was the recipient of many prestigious international awards and recognitions such as the CAA Outstanding Doctoral Dissertation Nomination and Mentor Award in 2022 and the Excellent Doctoral Dissertation Supervisor in Jiangsu Province in 2021.

Speech Title: Fully Distributed Optimal Consensus for a Class of Nonlinear Multi-agent Systems

Abstract: In this talk, an optimal consensus protocol is proposed for a class of leaderless multi-agent systems. Any global information, including the eigenvalues of the Laplacian matrix, is unavailable in the control scheme development. The reference trajectory is designed for each agent, and the corresponding performance function, which reflects the off-track error evolution and control cost, is proposed. The sufficient condition for the synchronization of reference trajectories, which does not rely on the topology dwell time, is established by constructing an appropriate current topology independent Lyapunov function. Due to the fact that the nonlinear function in the system dynamical equation of each agent is unknown, an equation termed integral reinforcement learning equation is provided, and it is strictly proven that the provided IRL equation is equivalent to the given Hamilton–Jacobi–Bellman equation. The model-free optimal feedback control law is then derived based on the IRL technique. In the implementation of the developed control scheme, the neural network approximation tool is adopted, and the scheme is applied to a numerical system to show its effectiveness.