[交流] EI Compendex/2024自动化与智能技术国际会议 (ICAIT 2024)/SPIE出版

2024 International Conference on Automation and Intelligent Technology (ICAIT 2024)

2024自动化与智能技术国际学术会议

中国·武汉

1. 主要成员
   大会主席
   沈谋全教授，南京工业大学
   大会联合主席
   刘华山教授，东华大学信息科学与技术学院教授、博导、副院长，中国 (IEEE 高级会员)
   胡豁生教授，埃塞克斯大学计算机科学与电子工程系博士生导师、机器人研究学科带头人、国际知名智能机器人专家，英国(IEEE 高级会员)
   孙英贤教授，伊斯兰阿扎德大学，伊朗 (IEEE 高级会员)
   程序委员会主席
   Michael Khoo Boon Chong教授，马来西亚理科大学，马来西亚
   出版主席
   Ryspek Usubamatov教授, 吉尔吉斯斯坦国立技术大学，吉尔吉斯斯坦
   冯收副教授, 哈尔滨工程大学, 中国 (IEEE会员)
   梅骁峻副教授, 上海海事大学，中国 (RSA Fellow, IEEE Member)
   
   
2. 主讲嘉宾
   刘华山教授，东华大学信息科学与技术学院教授、博导、副院长，中国 (IEEE 高级会员)
   Biography: Huashan Liu is a Professor at Donghua University for robotics, artificial intelligence, and mechatronics, a Senior Member of IEEE and a member of the Intelligent Control and Systems Committee of the Chinese Command and Control Society. He received the B.E. degree in Mechanical Engineering from Wuhan University, Wuhan, China, and the Ph.D. degree in Mechatronics from Zhejiang University, Hangzhou, China in 2005 and 2010, respectively. Since 2010, he has been with the College of Information Science and Technology (CIST), Donghua University, Shanghai, China, where he founded and directed the Coexisting-Cooperative-Cognitive Robot Lab. During 2015-2016 he was a Visiting Professor with the Institute of Automatic Control Engineering (LSR), Technical University of Munich (TUM), Munich, Germany. Prof. Liu has published more than 70 articles in international academic journals such as Transaction series of IEEE and ASME, and has served as editorial board member or guest editor of more than 10 international academic journals in the field of robotics and artificial intelligence, invited speaker, session chair or program committee member of more than 30 international academic conferences, and reviewer of more than 50 international academic journals.
   Title:From Dedicated to Universal: Robotic Motion Planning with AI
   Abstract: Motion planning is a crucial foundation for robotic tasks. Conventional motion planning methods are highly dependent on inverse kinematics, which are extremely onerous for robots with redundant degrees of freedom. Moreover, the robots can merely execute fixed, pre-programmed, and hard-coded command sequences. Consequently, they cannot be generalized to different task scenarios. As an essential branch of artificial intelligence (AI), deep reinforcement learning (DRL) has shown great potential in realizing versatile robotic assignments that are difficult to be implemented by conventional methods. DRL equips robots the ability to optimize their behaviors by constantly interacting with the environment, which is a huge step in materializing autonomous and intelligent robotic tasks. This talk discusses the potential of AI in terms of DRL in realizing universal robotic motion planning, and also contributes to the intersection of DRL and robotics by summarizing the previous study on universal robotic motion planning by leveraging DRL, so as to provide an overview of this field and push the boundary of interdisciplinary research.
   
   沈谋全教授，南京工业大学
   Biography: Mouquan Shen, Professor, the "Six Talent Peaks" of Jiangsu Province. Postdoctoral at Southeast University, visiting scholar at overseas universities such as the University of Hong Kong, Yeungnam University, South Korea, and the University of Adelaide, Australia. He is the PI of more than 10 provincial-level projects, including the National Natural Science Foundation of China, the National Bureau of Foreign Experts Affairs, and the Jiangsu Provincial Natural Science Foundation. In recent years, more than 100 papers with an H-index of 24 have been published in journals such as IEEE · Transactionson · Automatic Control, IEEE Transactionson · Cybernetics, and IEEE Transactions on Systems, Man, and Cybernetics: Systems. He has been severed as Editor-in-Cheif, Associate Editor, or Editorial Board Member of 12 international journals. He is also an active reviewer of over 80 domestic and international journals, including IEEE · TAC and Automatica, as well as the corresponding reviewer for the National Natural Science Foundation of China and multiple provincial and municipal science and technology projects.
   Ttile: New communication mechanisms for networked intelligent control systems
   Abstract: This report is dedicated to the important basic communication issues of networked intelligent control systems. First, an overview of the research background and some existing results for networked intelligent control systems are provided. Then, some novelty works on communication of networked intelligent control systems are presented, including the integral-type event-triggered scheme, the discrete-sampling event-triggered scheme, the threshold-dependent event-triggered scheme, and the dynamic event-triggered scheme based on instantaneous and average triggering errors. Finally, some future research topics for networked intelligent control systems are discussed.
   
   Michael Khoo Boon Chong教授，马来西亚理科大学，马来西亚
   Biography: Michael B.C. Khoo is a full professor at the School of Mathematical Sciences, Universiti Sains Malaysia (USM) in Malaysia. He holds a BAppSc and PhD in Applied Statistics from USM. He specializes in Statistical Quality Control. He has over 300 articles which were published or accepted for publications in reputable international journals, such as International Journal of Production Research, Computers & Industrial Engineering, Quality Technology & Quantitative Management, Quality and Reliability Engineering International, Quality Engineering, Communications in Statistics – Simulation and Computation, and Communications in Statistics – Theory and Methods. Most of his publications were indexed in the Web of Science (WoS) database. He has also reviewed numerous papers for journals indexed in the WoS database. He was a former member of the American Society for Quality and a life member of the Malaysian Mathematical Society.
   Title: Machine Learning and Control Charts
   Abstract: In today's manufacturing industry, there is a shift towards digitalization and machine learning is used to solve complex datasets by tackling the challenges of high dimensionality and large sample sizes in such datasets. This technique improves the efficiency of a production process and results in cost savings for the industry and ultimately enhances the quality of the products produced. An important application of machine learning in manufacturing is in control charts. A control chart is a vital tool in Statistical Process Control which is used in the monitoring of a manufacturing process for distinguishing between special and common causes of variations. The implementation of a control chart involves plotting control charting statistics on the chart to determine whether a process shift occurs in the quality characteristic being monitored. Machine learning algorithms have the potential to enhance the effectiveness of control charts by providing accurate predictions and real-time monitoring of shifts in the process parameters. Numerous studies are available, where machine learning methods have been adopted to enhance the efficiency of control charts. In this presentation, we will look at some existing studies that involve a fusion of machine learning and control charting techniques. This presentation will also highlight the challenges faced in the said fusion and identify future directions in using machine learning to make control charts an effective process monitoring tool.
   
   冯收副教授, 哈尔滨工程大学, 中国 (IEEE会员)
   Biography: Feng Shou, Associate Professor, Master Supervisor of Harbin Engineering University, Deputy Director of the Key Laboratory of Advanced Ship Communication and Information Technology of the Ministry of Industry and Information Technology, IEEE member, Senior member of the Chinese Society of Communications, Member of the Imaging Detection and Perception Committee of the Chinese Society of Image and Graphics, peer review expert of the National Natural Science Foundation of China, academic dissertation review expert of the Ministry of Education, Visiting Scholar of Indiana University Bloomington, Guest Editor of Remote Sensing, an international authoritative journal in the field of remote sensing. Member of the editorial board of international Journal Frontiers in Imaging, American Journal of Remote Sensing, He also serves as a reviewer for many authoritative academic journals such as IEEE TIP, IEEE TGRS, IEEE GRSL, and Remote Sensing. In the past three years, he has published 20 academic papers as the first/corresponding author in top journals in the field of Remote Sensing such as IEEE TIP, IEEE TGRS, and Remote Sensing, and 2 papers have been selected as ESI highly cited papers. Published 6 conference papers in IGARSS and other international conferences, applied for 8 national invention patents as the first inventor, and has authorized 4. Served as the Branch Chair of IGARSS2020 and IGARSS2021, as a member of the Organizing Committee of IGARSS2023, organized the Community-Contributed Session "CCS.9: Recent Advances in Hyperspectral Image Processing: Methodology and Application ". As a guest editor, he organized 3 special issues in Remote Sensing.
   Title: Remote Sensing Image Change Detection
   Abstract: Remote sensing technology is an important technical means for human beings to perceive the world, and change detection technology has become the mainstream of current research. Change detection is a pixel-level task, which is mainly used for fine extraction and recognition of changed ground object information from bitemporal images. Change detection is the basis for subsequent practical application tasks of remote sensing images and has very important research significance, which is widely used in digital precision agriculture, environmental monitoring, national defense and military strategy and other fields. With the rapid development of artificial intelligence technology, many new change detection methods and algorithms have been proposed. Moreover, rapid advances in these methods have also promoted the application of associated algorithms and techniques to problems in many related fields. This keynote aims to report and cover the latest advances and trends about the Recent Advances for Remote Sensing Image Change Detection.