js33333金沙线路检测“六朝松∙智控论坛”—名家讲坛系列报告
日程安排
时间：2024 年 12 月 25 日（周三下午）
地点：东南大学四牌楼校区中心楼二楼教育部重点会议室
组织单位：js33333金沙线路检测
| 时间 | 报告题目 | 报告人 |
14:00-15:00 | Implicit Communication in Markov Decision Processes | 陈功谱 博士后 英国帝国理工学院 |
15:00-16:00 | Real-time Internet of Things: Architecture, Algorithms and Applications | 金炯 教授 澳大利亚斯威本科技大学 |
16:00-17:00 | Privacy-Preserving Average Consensus and Its Application in Smart Grids | 叶枫 博士后 加拿大维多利亚大学 |
报告人：金炯 教授 澳大利亚斯威本科技大学
报告主题：Real-time Internet of Things: Architecture,Algorithms and Applications
邀请人：曹向辉教授，js33333金沙线路检测
报告人简介：
Jiong Jin is currently a full Professor and Associate Dean Research (acting) in the School of Science, Computing and Engineering Technologies, Swinburne University of Technology. He received the B.E. degree with First Class Honours in Computer Engineering from Nanyang Technological University, Singapore, in 2006, and the Ph.D. degree in Electrical and Electronic Engineering from the University of Melbourne, Australia, in 2011. His research interests include network design and optimization, edge computing and intelligence, robotics and automation, and cyber-physical systems and Internet of Things as well as their applications in smart manufacturing, smart transportation and smart cities. He is recognized as an Honourable Mention in the AI 2000 Most Influential Scholars List in IoT (2021 and 2022) and included in Stanford University’s list of the world Top 2% scientists for citation impact since 2019. He is currently an Associate Editor of IEEE Transactions on Industrial Informatics.
报告摘要：
The Internet of Things (IoT) is an emerging revolution, which targets anytime connectivity for anything to create smart environments in which there is fast-paced interaction between systems (networked sensors, heterogeneous devices, actuators, robots) and between such systems and people. To further enable real-time services in IoT, a new multi-tier computing paradigm is recently introduced and explored in both academic and industrial fields. Its basic concept is to construct local computing nodes (aka edge/fog nodes), which moves computation, control, networking, storage and security functionalities from traditional remote cloud right to a place closer to the end-users in order to optimally support time-critical applications. Meanwhile, it also empowers a new set of industrial applications, such as networked robotics and cloud-fog automation, to achieve real-time operations. In this talk, a complete overview and recent developments of real-time IoT will be presented with its applications in smart manufacturing, smart transportation and smart cities.
报告人：陈功谱 博士 英国帝国理工学院
报告主题：Implicit Communication in Markov Decision Processes
邀请人：曹向辉教授，js33333金沙线路检测
报告人简介：
Gongpu Chen is a postdoctoral research associate at Imperial College London. He received his B.S. degree in Automation Engineering from the University of Electronic Science and Technology of China (UESTC), Chengdu, China, in 2016. He then obtained his M.S. degree in Control Science and Engineering from Southeast University, Nanjing, China, in 2019, and his Ph.D. degree in Information Engineering from The Chinese University of Hong Kong (CUHK) in 2023. His research interests encompass the intersection of communication and control, including networked control, information theory, channel coding, Markov decision process, and reinforcement learning.
报告摘要：
The impact of communication on decision-making systems has been widely studied under the assumption of dedicated communication channels. Instead, we explore communication through actions, embedding messages into an agent’s actions within a Markov decision process (MDP) framework. Here, the MDP environment is conceptualized as a finite-state channel (FSC), where the agent’s actions serve as the channel input and the MDP states observed by another agent (receiver) act as the channel output. The environment is treated as a communication channel, with the agent simultaneously aiming to maximize its reward. We first characterize the optimal trade-off between average reward and reliable communication rate in the infinite-horizon regime. Next, we introduce a novel joint control/coding policy framework, Act2Comm, which integrates messages into actions. From a communication perspective, Act2Comm operates as a learning-based channel coding scheme for non-differentiable FSCs under input-output constraints. From a control perspective, it learns an MDP policy with communication capabilities, albeit at some cost to control performance. Experiments demonstrate Act2Comm’s ability to achieve reliable communication while maintaining acceptable control performance.
报告人：叶枫 博士 加拿大维多利亚大学
报告主题：Privacy-Preserving Average Consensus and Its Application in Smart Grids
邀请人：曹向辉教授，js33333金沙线路检测
报告人简介：
Feng Ye received the B.S. degree in electrical engineering and automation from the College of Information Science and Engineering, Northeastern University, Shenyang, China, in 2019, and the Ph.D. degree in control science and engineering from the School of Automation, Southeast University, Nanjing, China, in 2024. He is currently a Postdoctoral Fellow at the Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada. His research interests includ distributed control and optimization, smart grids, privacy preservation, and cybersecurity.
报告摘要：
Average consensus is a fundamental principle in distributed coordination, with widespread applications in distributed systems such as microgrids, UAVs, etc. However, during the average consensus process, internal privacy information of distributed devices, such as their initial states, may be exposed. Although several privacy-preserving algorithms for average consensus have been proposed, the theoretical conditions for privacy preservation and the development of a general framework for privacy preservation across various average consensus dynamics remain unresolved. This presentation will introduce an analysis of privacy leakage risks in the average consensus process, a general privacy-preserving framework based on multiplicative noise, and its application in smart grids. The presentation will first systematically analyze the privacy leakage risks in the average consensus process, including four types of privacy leakage scenarios, as well as the theoretical upper bounds of privacy-preserving algorithms. Next,a general privacy-preserving framework based on multiplicative noise will be introduced, which addresses the three main average consensus dynamics. Compared with traditional algorithms, the proposed approach ensures privacy preservation while achieving finite or infinite-time convergence of average consensus, maintaining convergence accuracy. Finally, the report will demonstrate the application of this framework in smart grids, particularly in preserving the privacy of power information from different devices.
