Andrew Y. C. Nee

Fellow of Academy of Engineering Singapore, Fellow of CIRP, National University of Singapore, Singapore

Professor Andrew Y. C. Nee received his PhD from the Institute of Science and Technology, Victoria University of Manchester (UMIST) in 1973. He joined the then University of Singapore in 1974. In 1982, he became the first recipient outside USA to receive the Outstanding Young Manufacturing Engineer’s Award from the Society of Manufacturing Engineers. In 1986, he was appointed Director of CAE/CAD/CAM Centre when it was first established, a position which he held until 1995. In 1989, he was promoted to full professorship. In the same year, he was appointed Vice-Dean (Academic Affairs). In 1990, he was elected an Active Member of CIRP and a Fellow of the Society of Manufacturing Engineers, he was the first person in the ASEAN region to be elected in each case. From 1993 to 1996, he was appointed Head of the Department of Mechanical Engineering. He was appointed Dean of the Faculty of Engineering from August 1995 to August 1998.

After completing the 3-year term as Dean of Engineering in August 1998, he was appointed directors of two offices at the University level, Director (Special Projects) of the Office of University Relations, and Director of the Office of Quality Management. From 1 August 1999 to December 2000, he was appointed Dean of Admissions to head the then newly established Office of Admissions. From January 2001 to February 2002, he was appointed the Deputy Executive Director, Science and Engineering Research Council (SERC) of the National Science and Technology Board. From 1 March 2002 to 28 February 2005, he was appointed the Co-Director of Singapore-MIT Alliance (SMA), which is an innovative engineering education and research collaboration among the National University of Singapore (NUS), Nanyang Technological University (NTU), and the Massachusetts Institute of Technology (MIT). In 2002, he was awarded the Doctor of Engineering (DEng) degree from UMIST for his research achievements in manufacturing engineering. From September 2003 to August 2004, he was appointed as the CEO of the Design Technology Institute. From 1st March 2005, he was appointed the Director of the Office of Research of NUS. In August 2006, he was elected the Chairman of the CIRP STC Design (Dn), and a Council Member of CIRP. He is currently Chairman of an NUS-spin off company, Manusoft Technologies Pte Ltd which specializes in plastic injection mold design, and markets the commercial product IMOLD. In 2012, he was elected President of CIRP, and was the first ethnic Chinese to hold this office since 1953. In July 2018, he was awarded the prestigious title of Emeritus Professor, the first recipient in the Department of Mechanical Engineering. He has a google citation of more than 28,976, and a H-index of 82.

Title: Metaverse and digital twin evolution 

Abstract: Metaverse is a digital copy of the real world, and it presents an entry point for fully connected, immersive and engaging 3D experience. It is a digital space where users can enter and interact with other users virtually. Metaverse can bring significant changes to manufacturing as it can account for how humans interact with factory equipment. Digital twins, already well established in the sector, are digital replicas of a system, but humans are not a dynamic part. On the other hand, the Metaverse allows humans to interact dynamically in environments made up of digital images. This talk addresses the current developments of DT, and challenges of DT in the Metaverse environment, where humans interact with Avatars and DT facilities.

Zhiyun Lin

Foreign academician of the Russian Academy of Engineering, Fellow of IEEE、IET, Southern University of Science and Technology, China

Zhiyun Lin is currently a tenured full professor at Southern University of Science and Technology. He received his PhD degree in Electrical and Computer Engineering from University of Toronto, Canada, in 2005, and then he worked as a Postdoctoral Research Associate in University of Toronto from 2005 to 2007. He joined College of Electrical Engineering, Zhejiang University in 2007 as a research professor and then promoted to a tenured full professor in 2011. In 2017, he moved to Hangzhou Dianzi University and worked as the Director of Artificial Intelligence Institute. He joined Southern University of Science and Technology in 2021. He held visiting professor positions at several universities including The Australian National University (Australia), University of Cagliari (Italy), University of Newcastle (Australia), University of Technology Sydney (Australia), and Yale University (USA). His research interests focus on multi-agent systems, distributed artificial intelligence, autonomous systems and swarm robots, and cyber-physical systems. He has authored and coauthored two monograph and over 230 peer-reviewed papers in leading international journals and conferences. According to Google Scholar, his work has been cited over 8600 times. From 2014 to 2023, he has been consecutively selected in the list of Mostly Cited Chinese Researchers by Elsevier. He is a Foreign Full Member of the Russian Academy of Engineering, a Fellow of IEEE, a Fellow of IET and a Fellow of AAIA.

Title: Multi-Agent Systems: Distributed Localization and Coordinated Control 

Abstract: In a wide array of applications, the deployment of multiple autonomous agents holds the promise of delivering superior performance, capabilities, robustness, and efficiency that a single agent cannot achieve alone. However, the realization of these benefits is contingent upon the agents' ability to operate in a coordinated manner. Fundamental requirements include localizing agents within a common coordinate system and achieving desired coordinated behaviors. This talk aims to discuss recent advancements in the distributed localization and coordinated control problem for networked multi-agent systems. The focus is on providing a unified framework for distributed localization and control that accommodates various types of tasks. Several distributed algorithms will be presented, based on local information and local interaction, yet ensuring global convergence—a critical concern for many applications.

Yongsheng Ma

Southern University of Science and Technology, China

Dr. Yongsheng Ma has joined Southern University of Science and Technology (SUSTech) in Shenzhen, China since July, 2021 as a full professor. Before that, Dr. Ma had been a full professor with the University of Alberta (UA) since 2007. He was an associate professor with Nanyang Technological University, Singapore during 2000-2007. Dr. Ma started his career as a polytechnic lecturer in Singapore (1993-1996); and then a research fellow, senior research fellow and group manager (1996-2000) at Singapore Institute of Manufacturing Technology.

Dr. Ma received his B.Eng. from Tsinghua University, Beijing (1986), both M.Sc. (1990) and Ph.D. (1994) from UMIST, UK. Dr. Ma has had an established research profile with many research projects from different sources, and published more than 200 papers internationally in recognized top journals, conferences, and book chapters. Dr. Ma had served as an Editorial Board Member of Advanced Engineering Informatics (ADVEI, Elsevier) since 2012, and became an associate editor since 2020. Concurrently, he also serves as an associate editor for ASME Journal of Computer Information Science and Engineering (JCISE), and an Editorial Member of Scientific Reports (Springer Nature). Dr. Ma had also been an associate editor of IEEE Transaction of Automation Science and Engineering (2009-2013). Dr. Ma is a member of ASEE, SME, SPE, ASME, CSME and a Canada (Alberta) registered Professional Engineer (P.Eng.) since 2009. In 2012, he won the prestigious ASTech award sponsored by The Alberta Science and Technology Leadership Foundation together with Drader Manufacturing Ltd. Dr. Ma had also served as a senator of UA during 2014-2016.

Title: Feature-based Engineering Informatics Modeling, Data Integration and Cyclic Evolvement in Complex CPS Systems

Abstract: Feature modeling has been applied in product design and manufacturing with great success. With the growth of computer-aided engineering (CAE), computer-aided process planning (CAPP), computer-aided manufacturing (CAM), and other applications for product engineering, the definitions of features have been mostly application-driven. This speech reviews feature modeling historical evolution first. Subsequently, various approaches to resolving the interoperability issues during product lifecycle management are reviewed. In view of the recent progress of emerging technologies, the focus of this speech is on the state-of-the-art application of features in the emerging research fields. The interactions among these trending techniques constitute the cyber-physical system (CPS)-based manufacturing which demands for feature interoperability across heterogeneous domains. Future efforts required to extend feature capability in CPS modeling, control, and optimization are discussed.

Yanguo Jing

FBCS, MIEEE, MIET, PFHEA, CMBE

Leeds Trinity University, UK

Professor Yanguo Jing is the Dean of Faculty of Business, Computing and Digital Industries, Leeds Trinity University, UK. He is a Professor of Artificial Intelligence, a Principal Fellow of the Advance HE (PFHEA), a Fellow of the British Computer Society, and a Certified Management & Business Educator. He is a council member of the Chartered Association of Business Schools in the UK. He is interested in the use of AI and machine learning algorithms to learn behaviour patterns and develop intelligent applications. His recent work focuses on the use of AI and machine learning algorithms in sports science, HR, business and education.  

Title: The use of AI in Job Grading in Higher Education in the UK

Abstract: Job grading is a time consuming and to some extend a subjective task. The HERA (High Education Role Analysis) method is used broadly by most universities in the UK to provide the job grading consistency. This project aims to use machine learning algorithm to learn the grading process and automatically provide the job grade. A preliminary has been carried out, among four different type of job elements in the job descript, this research has produced an accuracy between 80% to 92%. Future recommendations of this work will be provide, as well as the ethical considerations of using AI in human resource domain.

Weidong Li

University of Shanghai for Science and Technology, China

Weidong Li is currently with University of Shanghai for Science and Technology (China) as a Chang Jiang Chair Professor and Dean of Mechanical Engineering School. Before that, he worked at Singapore Institute of Manufacturing Technology, University of Bath, Cranfield University, and Coventry University as a Research Engineer, Lecturer, Senior Lecturer, Reader, and Chair Professor. He is Fellow of Institution of Engineering and Technology (FIET), and Fellow of Institution of Mechanical Engineers (FIMechE). His research interests include sustainable manufacturing and human-robot collaboration. His research has been sponsored by Singapore A*Star, European Commission, Innovate UK, EPSRC (U.K.) and NSFC (China). He has published 260 research papers in international journals and conferences, and 5 books (Springer).

Title：Human-Robot Collaboration for Product Remanufacturing

Abstract: Disassembly is a critical step in the remanufacturing of end-of-life products. It has traditionally been performed by either humans or robots. However, challenges such as high labor costs of humans and the limited ability of robots to perform intricate disassembly tasks have led to the increasing use of human‒robot collaboration (HRC) for disassembly. In this keynote, some key enabling technologies of human‒robot collaboration (HRC) for disassembly will be discussed. The application of HRC for disassembling end-of-life lithium-ion batteries will be briefly summarized.

Hong Zhang

Fellow of Canadian Academy of Engineering, Fellow of IEEE, Southern University of Science and Technology, China

Zhang Hong, a member of the Canadian Academy of Engineering, IEEE Fellow, a leading talent in Guangdong Province's "Pearl River Talent Program", is currently a professor in the Department of Electronic and Electrical Engineering, Southern University of Science and Technology. He was a tenured professor in the Department of Computer Science at the University of Alberta, Canada, for many years. While working in Canada, he completed a number of major research and development projects and served as the Chief Industrial Research Professor of the Natural Sciences and Engineering Foundation of Canada (NSERC IRC). His current research interests include mobile robot navigation, autonomous driving, computer vision, and image processing. He has trained more than 80 masters, doctors and postdocs, many of whom are teaching at famous universities at home and abroad, including the University of Toronto in Canada. He has served as editorial board member and conference Chair of several international journals, and is currently the chief editor of the Editorial Board of IROS, the flagship conference of the IEEE Robotics and Automation Society (2020-2023).

Title:  Applications of Foundation Models in Robotics 

Abstract: Development in artificial intelligence (AI) has always opened doors to its robotics applications. Most recently, the emergence of foundation models such as large language models, visual language models, image segmentation models, etc. has led to solutions to existing challenges in robot navigation and manipulation. Foundation models in these cases serve as a rich source of prior information for robot sensory perception and decision making that is difficult or impossible to obtain otherwise.  This presentation first quickly sumarizes representative state-of-the-art foundation models.  It is then followed by a description of some research projects in the Shenzhen Key Laboratory on Robotics and Computer Vision at SUSTech on (a) robot grasping (b) mobile robot task planning, and (c) embodied AI.  

Zhihai He

Fellow of IEEE, Southern University of Science and Technology, China

Professor He has worked in the Department of Electronic Engineering at the University of Missouri for 18 years. Before leaving, he was the tenured full professor of the department and the Robert Lee Tatum Distinguished Chair Professor. During his work in the United States, he undertook more than ten key research projects of the National Science Foundation (NSF), Department of Defense (DoD), and National Institutes of Health (NIH). The current research directionis deep learning, machine vision & artificial intelligence Internet of Things (AIoT). He was selected into the Stanford University's World's Top 2% Scientists Lifelong Science Impact Ranking and the 2019 Science Impact Ranking.

Title: Data-Driven Modeling with Reciprocal Learning 

Abstract: One central problem in learning is how to characterize the prediction error. In this talk, we introduce our recent work on reciprocal learning. While the baseline network is learning the forward model from the training data, a dual network is designed to learn the inverse model based on the transposed training data. We found that the loop prediction error by the baseline network and the dual networks is highly correlated with the prediction error. Based on this interesting finding, we develop a set of new adaptive learning methods which advances the state-of-the-arts in different applications, including trajectory / attention prediction, pose estimation, few-shot learning, and physical system modeling.