Speakers
Prof. Yang Yue
School of Information and Communication Engineering/Xi’an Jiaotong University, China
Experience:
Prof. Yang Yue received the B.S. and M.S. degrees in electrical engineering and optics from Nankai University, China, in 2004 and 2007, respectively. He received the Ph.D. degree in electrical engineering from the University of Southern California, USA, in 2012. He is a Professor with the School of Information and Communications Engineering, Xi'an Jiaotong University, China. Dr. Yue’s current research interest is intelligent photonics, including optical communications, optical perception, and optical chip. He has published over 200 journal papers (including Science) and conference proceedings with >10,000 citations, five edited books, two book chapters, >50 issued or pending patents, >200 invited presentations (including 2 tutorial, >40 plenary and >50 keynote talks). Dr. Yue is a Senior Member of IEEE, Optica and SPIE. He is an Associate Editor for IEEE Access and Frontiers in Physics, Editor Board Member for four other scientific journals, Guest Editor for >10 journal special issues. He also served as Chair or Committee Member for >100 international conferences, Reviewer for >70 prestigious journals.
Prof. Shengrong Bu
Department of Engineering, Brock University, Ontario, Canada
Title: AI-Driven Decarbonization for Energy Systems
Abstract: The ongoing decarbonization of energy systems is currently altering the fundamental structure of system operation and planning by increasing the penetration of renewable energy resources (RESs), while forecasting an increase in the asset utilization of high electrification of transportation and efficient heating facilities. To meet the carbon budgets, the pace of decarbonization in energy systems with deregulating markets needs to be significantly accelerated. These transitions, however, present crucial techno-economic challenges for energy systems. More flexibilities are required to balance the less predictable and controllable outputs of RESs. A large number of small-scale distributed energy resources (DERs) are located and operated in a distributed manner, which may increase the challenges of managing them. Finally, privacy concerns also need to be addressed due to the massive amount of data communicated by the decentralized energy systems. In this context, this talk will focus on several kinds of approaches for energy systems decarbonization, and how state-of-the-art reinforcement learning and AI tools could be applied to address challenges.
Experience:
Professor Shengrong Bu received her Ph.D. degree in Electrical and Computer Engineering from Carleton University, Canada in 2012. From 2012 to 2014, she held a research position at Huawei Technologies Canada Inc., Ottawa, as an NSERC Industrial R&D Fellow. From 2014 to 2021, she was a Lecturer with the James Watt School of Engineering, University of Glasgow, UK. Currently, she is an Associate Professor at Brock University, Canada. Her research interests include multi-energy microgrids, smart grids, future wireless networks, cyber security, deep reinforcement learning, and big data analytics. Dr. Bu was a recipient of three best paper awards at IEEE International conferences. Her work has been supported by EPSRC(UK) and NSERC (Canada). Highlights of her professional activities include duties as a peer reviewer for EPSRC and Carnegie Trust, an Associate Editor for Wireless Networks (Springer), a Topic Editor for Energies and the TPC Co-Chair for seven international conferences/workshops or conference symposiums.
Professor Shengrong Bu was featured in the Women in Energy Transformation Series 2022-2024, as one of Canada’s most inspiring climate leaders. Promoting engineering to younger girls and supporting junior female researchers are a passion of hers, and she has been involved with Ontario Go Eng Girl in Canada, Monster Confidence in UK, and N2Women as a Mentoring Co-Chair.
Prof. Shijie Jia
School of Computer and Communication Engineering, Dalian Jiaotong University, China
Research Area: Image processing and pattern recognition, machine learning theory and technology.
Experience:
Prof. Shijie Jia, male, PhD in Engineering, professor and vice president of School of Computer and Communication Engineering, Dalian Jiaotong University, member of Expert Committee of China Simulation Technology Industry Alliance, member of China Communication Society; mainly engaged in research and teaching of image processing and pattern recognition, machine learning theory and technology. He has published more than 50 academic papers in important academic journals and international conferences, among which more than 30 have been included in the three major search engines, published one academic monograph, authorized 7 invention patents, and won the Dalian excellent IT teacher award, the second prize of natural science and technology achievements of Liaoning Province.
Prof. Ir. Dr. Sevia Mahdaliza Idrus Sutan Nameh（IEEE Senior Member）
School Of Electrical Engineering, Faculty Of Engineering,Universiti Teknologi Malaysia (UTM),81310 Skudai, Johor, Malaysia.
Research Area:Optical communication system and network, optoelectronic design, and engineering management
Experience:
Professor Ir. Dr. Sevia Mahdaliza Idrus is a Professor in communication engineering at Faculty of Electrical Engineering, Universiti Teknologi Malaysia (UTM). She received her Bachelor in Electrical Engineering (1998) and Master in Engineering Management (1999) from UTM. She obtained her Ph.D in Engineering (2004) from the University of Warwick, United Kingdom. She has served UTM since 1998 as academic and administrative staff including Deputy Dean Faculty of Engineering, Deputy Director of Innovation Commercialisation Centre and many more. Her main research interests are communication system and engineering management. Her research output has been translated into numerous publications (WoS H-index: 25) including 7 high-end reference books, 49 book chapters, 152 Scopus indexed journals, 8patents granted, 36 patent filings and holds 31 UTM copyrights. To date, Professor Sevia has secured and been involved in 84 research and consultation projects with a total value of USD25M. She is therecipient of ‘The Top Research Scientists Malaysia 2021’ by Academy Science Malaysia, Ministry of Science, Technology and Innovation, andNational Intellectual Property Award 2013 by MyIPO, Ministry of Domestic Trade and Consumerism.She has been appointed as Guest Professor at Osaka Prefecture University and Tokai University, Japan in 2011 and 2014 respectively.
Prof. Wei Yue
Dalian Maritime University, College of Marine Electrical Engineering
Research Area: Unmanned cluster collaborative planning and control, including vehicle collaborative control, multi-UAV cooperative search, and path planning
Experience:
Professor Yue Wei is an Associate Professor and doctoral supervisor at the School of Marine Electrical Engineering, Dalian Maritime University. His main research areas include vehicle cooperative control in intelligent transportation systems, path planning and task allocation in multi-UAV cooperative search, etc. He has published a total of 50 peer-reviewed publications, including 30 SCI papers, and has authored 2 academic monographs. In addition, he has been granted 10 invention patents. Yue Wei is an Associate Professor and doctoral supervisor at the School of Marine Electrical Engineering, Dalian Maritime University. His main research areas include vehicle cooperative control in intelligent transportation systems, path planning and task allocation in multi-UAV cooperative search, etc. He has published a total of 50 peer-reviewed publications, including 30 SCI papers, and has authored 2 academic monographs. In addition, he has been granted 10 invention patents.
Title: Connected and automated vehicles control with communaciton constraints
Abstract:
Connected and automated vehicle control with communication constraints is a critical research area in the field of intelligent transportation systems. The efficient and safe operation of connected and automated vehicle systems is highly dependent on the ability of the vehicles to communicate with each other and with the infrastructure. However, in real-world scenarios, communication constraints such as limited bandwidth, latency, and data loss can significantly impact the effectiveness of vehicle control systems. Therefore, researchers are exploring various techniques to overcome these communication constraints, including optimizing communication protocols, developing new algorithms for control and coordination, and using predictive models to anticipate communication delays. Ultimately, the goal of this research is to ensure that connected and automated vehicle systems can operate effectively and safely in the face of communication constraints, enabling the widespread adoption of this technology to enhance the efficiency and safety of transportation systems.