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2024 7th International Conference on Computing and Big Data(ICCBD 2024)

Plenary Speakers 全体报告

ICCBD 2022 Plenary Speakers


Prof. Lihui Wang

Academician of Canadian Academy of Engineering, Canada


Lihui Wang is a Professor and Chair of Sustainable Manufacturing at KTH, Sweden. He is also the Director of Centre of Excellence in Production Research (XPRES) - one of the five national strategic research centres at KTH. He has served as the President (2020-2021) of North American Manufacturing Research Institution (NAMRI) of Society of Manufacturing Engineers (SME), and the Chairman (2018-2020) of Swedish Production Academy. In 2020, he was elected one of the 20 Most Influential Professors in Smart Manufacturing by Society of Manufacturing Engineers.
He received his PhD and MSc from Kobe University (Japan) in 1993 and 1990, respectively, and BSc in China in 1982. He was an Assistant Professor of Kobe University and Toyohashi University of Technology (Japan) prior to joining National Research Council of Canada (NRC) in 1998, where he was a Senior Research Scientist before moving to Sweden in 2008. His research interests are presently focused on real-time monitoring and control, human-robot collaboration, brain robotics, digital twin, cyber-physical and sustainable production systems. His research work has won one Best Poster Award in Switzerland (2003), four Best Paper Awards in Germany (2002), USA (2016), Serbia (2020) and Sweden (2020), and two Outstanding Paper Awards in Mexico (2008) and USA (2016). In 2021, he received the Best Paper Award of Journal of Manufacturing Systems. He is also an eight-time winner of NRC Institute Awards on Excellence and Leadership in R&D, Multidisciplinary Collaborative Research, Global Reach, and Outstanding People.
Professor Wang has published 10 books, 15 conference proceedings and 32 journal special issues. He has authored or co-authored in excess of 600 scientific articles in archival journals, books, and peer-reviewed conference proceedings in the above research areas. In addition to the research work, he is actively engaged in various committee and community activities. He was the Conference Chair of FAIM 2004 and CIRP CMS 2018, a member of Grant Selection Committee (GSC-20 for Industrial Engineering) of Natural Sciences and Engineering Research Council of Canada (2004-2007), the Chair of the Scientific Committee of NAMRI/SME during 2016-2018, and a member of Technical Committee 282 (Machinery Safety) of Swedish Standards Institute (2014-2017). He is the Editor-in-Chief of International Journal of Manufacturing Research, Editor-in-Chief of Journal of Manufacturing Systems, Editor-in-Chief of Robotics and Computer-Integrated Manufacturing, Editor of Journal of Intelligent Manufacturing (2007-2019), Associate Editor of International Journal of Production Research, and an Editorial Board Member of other 17 international journals. He is also a Fellow of The Canadian Academy of Engineering (CAE), a Fellow of The International Academy for Production Engineering (CIRP), a Fellow of SME, a Fellow of ASME, and a registered Professional Engineer in Canada.

Title: A Deep Dive into Human-Robot Collaboration: Status and Trends

Abstract: Human-robot collaboration has attracted increasing attentions, both in academia and in industry. For example, in human-robot collaborative assembly, robots are often required to dynamically change their pre-planned tasks to collaborate with human operators in a shared workspace. However, the robots used today are controlled by pre-generated rigid codes that cannot support effective human-robot collaboration. In response to this need, multi-modal yet symbiotic communication and control methods have been developed. These methods include voice processing, gesture recognition, haptic interaction, and brainwave perception. Deep learning is used for classification, recognition and context awareness identification. Within this context, this seminar provides an overview of the current status of human-robot collaboration including its classification, definition and characteristics. At the end of the seminar, remaining challenges and future research directions will be highlighted.


Prof. Xun William Xu

Auckland University, New Zealand

Dr. Xu joined the Department of Mechanical Engineering, The Univeristy of Auckland in 1996 after obtaining a PhD from the University of Manchester (then, UMIST), UK. He has been working at the University of Auckland as a Lecturer, Senior Lecturer, Associate Professor and now Professor. His teaching at the Department of Mechanical Engineering cuts across a number of fields, e.g, mechanical engineering design, manufacturing systems, advanced manufacturing technology, manufacturing information systems and advanced CAD/CAM/CNC.
Professor Xu's main research focuses are on computer-aided design, process planning and manufacturing, in particular STEP-compliant CNC (STEP-NC), cloud manufacturing, smart manufacturing and Industry 4.0.
Professor Xu set up the first Industry 4.0 laboratory in New Zealand, Laboratory for Industry 4.0 Smart Manufacturing Systems (LISMS), and leads one of top research teams (IIMS research group ) in the world working in the field of STEP-compliant design and manufacturing. In 2020, Professor Xu was listed as one of the 20 most influential professors in smart manufacturing in the world.

Title: Digital Twin – the State of the Research

Abstract: The technological fundamentals of Industry 4.0 are unpinned by that of the Internet of Things and exist in various forms of Cyber-Physical Systems, where “systems” can be products and machines. A key element in a Cyber-physical System is Cyber Twin or otherwise commonly known as Digital Twin. This talk gives an overview of the background, concept and major technologies, in particular digital models, digital shadows and digital twins for smart manufacturing solutions. The focus is on digital twin and one of the key elements in a digital twin is the information model. The talk also touches upon the latest development at the International Organization for Standardization (ISO) concerning digital twin framework for manufacturing. Some case studies will be presented with an aim to demonstrate how digital twin solutions can help industry stay competitive.