Workshops
01
Intelligent Equipment and Machine Vision
Title: Smart Agriculture and Smart Forestry


Chair:
Hongqian Zhu, Central South University of Forestry and Technology, China.
Hongqian Zhu has long been engaged in scientific research and teaching of intelligent robot, machine vision, hydraulic servo and other aspects. He is the chief editor of the National 13th Five-Year Plan Textbook: Industrial Robot Technology (China Machine Press, July 2019). Team’s project "Pick orange robot target recognition" won first prize of national forestry robot design competition in 2019, team’s project "Forest security informatization construction based on intelligent vision" won first prize of national agriculture and forestry enterprise management optimization modeling competition in 2021.


Chair:
Xiuliang Jin, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, China.
Dr. Xiuliang Jin is a professor and doctoral tutor and a head of crop phenotypic innovation research group. He mainly engages in crop phenotyping identification and precision agriculture. The main research interests are: (1) the application of quantitative remote sensing in agricultural monitoring; (2) the application and combination of optical sensors; (3) the development and application of crop phenotyping platforms; (4) crop models and multi-source remote sensing data assimilation; (5) multi-source image data processing; and (6) association analysis between phenome and genome and key functional gene mining. Since 2011, as the first author or corresponding author, he has published 39 SCI indexed papers, and the coauthored papers were more than 50. The articles are mainly published in IEEE Geoscience and Remote Sensing Magazine、Earth System Science Data、Remote Sensing of Environment, ISPRS Journal of Photogrammetry and Remote Sensing, Agricultural and Forest Meteorology, European Journal of Agronomy, Field Crops Research, and Agricultural Water Management, etc. In current, he is an associate editor of The Crop journal、The Journal of Agricultural Science and Agronomy Journal, an editorial board of Scientific Reports and Crop Magazine, and a guest editor of Remote Sensing-Special Issue "Estimation of Crop Phenotyping Traits using Unmanned Ground Vehicle and Unmanned Aerial Vehicle Imagery. In addition, he is a member of the first Agricultural Modeling and Simulation Professional Committee of the Chinese Society of Simulation and committee member of the IEEE International Conference on Application of Computational Intelligence in Remote Sensing in 2019.


Chair:
Bin Liu, Northwest A&F University, China
Bin Liu serves as an associate professor at college of Information Engineering of Northwest A&F University in China. He received a Ph.D. degree in computer science and technology at Xi’an Jiaotong University in China, in 2014. He worked as a member at Key Laboratory of Agricultural Internet of Things of Ministry of Agriculture and Rural Affairs and Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service. His research interests include computer Vision, intelligent agriculture, deep learning and agricultural pest monitoring and early warning. He participated in projects supported by Natural Science Foundation, National Key Projects, National Key Research and Development Project, Natural Science Foundation of Shaanxi Province. Based on these projects, he published many academic papers. Moreover, he serves as a reviewer for Computers and Electronics in Agriculture, Frontiers in Plant Science, IEEE Trans. On Computers, the Journal of Supercomputing, etc。


Chair:
Jiaohua Qin, Central South University of Forestry and Technology, China.
Jiaohua Qinreceived a Ph.D. degree in computing science from Hunan University. She was a Visiting Professor with the University of Alabama, USA, from 2016 to 2017. Her research interests include information security, machine vision, machine learning and image processing. She has presided over 20 national or provincial projects with National Natural Science Foundation of China, the Key Research and Development Plan of Hunan Province,the Key projects of Natural Science Foundation of Hunan Province. Based on these projects, she has published more than 100 papers.


Chair:
Baohua Zhang, Nanjing Agricultural University, China.
Baohua Zhang received the B.S. degree in mechanical and electronic engineering from Northwest A&F University and the Ph.D. degree in mechanical engineering from Shanghai Jiao Tong University. He is currently an Associate Professor with the College of Artificial Intelligence, Nanjing Agricultural University (NJAU). He is the Founder and the Managing Editor of the international journal, Artificial Intelligence in Agriculture (ISSN: 25897217). His research interests include harvesting robots, robot vision, robotic grasping, spectral analysis and modeling, robotic systems and their applications in agriculture, and food and bio-system engineering. He has published more than 40 SCI cited articles and ten EI cited articles in national and international journals.


Chair:
Hong Sun, China Agricultural University, China.
Hong Sun currently serves as a professor of “Crop Sensing in Smart Agriculture” in College of Information and Electrical Engineering, China Agricultural University. She is the committee member of Internationalfederation of automatic control（IFAC）TC8.1,Member of Chinese Society of Agricultural Engineering (CSAE), and Memberof Chinese Society of Agricultural Machinery(CSAM). Her Main fields of recent research is: Agricultural Engineering, Such as Intelligent Agriculture System Integration, Agricultural Informatization Application, Smart Agriculture, etc. Main research interests are: Electrification and Automation in Agriculture, Advanced Sensors and Process Control, Wireless Sensor Networks, ICT and Artificial Intelligence Applications in Agriculture, Remote Sensing, Application of Spectroscopy in Agriculture, Optimization Techniques and Computational Intelligence, Internet of Things, Edge Computing and Cloud Application, Intelligent Computing, Crop Sensing and Management in the Agriculture 4.0, Education 4.0 for Agriculture 4.0. She has published over 50 research papers in refereed journals, international scientific conferences and book chapters. Moreover, she serves as an associate editor for Field Crop Research and guest associate editor for Computers and Electronics in Agriculture.


Chair:
Chunshan Wang, Hebei Agricultural University, China.
Chunshan Wang serves as an associate professor at the School of Information Science and Technology of Hebei Agricultural University in China. From 2016 to 2017, he was a visiting professor at the School of Electrical and Computer Science, Washington State University. He worked as a member at the National Engineering Research Center for Information Technology in Agriculture. His main research interests include: computer vision, smart agriculture, and digital villages. In recent years, he has participated in or presided over 10 projects, including the National Key Research and Development Program of China, the National Natural Science Foundation of China, the Hebei Provincial Key Research and Development Program and the Hebei Provincial Natural Science Foundation. Based on the above research, he published more than 40 papers, of which 20 paperswere indexed in SCI/EI. He is currently invited as a reviewer for several journals such as Computers and Electronics in Agriculture, Scientific Reports, and Transactions of the Chinese Society of Agricultural Engineering.


Chair:
Haixi Zhang, Northwest A&F University, China.
Haixi Zhang serves as a lecture at the college of Information Engineering of Northwest A&F University in China. He received a Ph.D. degree in electronic science and technology at Northwestern Polytechnical University in China, in 2020. His research interests include Computer Vision, Deep Learning and Intelligent Agriculture. He participated in projects supported by NationalNatural Science Foundation, National Key Projects, National Key Research and Development Project, Natural Science Foundation of Shaanxi Province. Based on these projects, he published many academic papers. He has published multiple paper in journals such as Neurocomputing, Computer and Electronics in Agriculture, Journal of Electronic Imaging, etc.

Summary:
With the progress of big data, artificial intelligence, Internet of things, machine vision and other technologies, agricultural and forestry intelligent equipment has been rapidly developed. This workshop aim to discuss the key technologies of agricultural and forestry intelligent equipment such as intelligent perception, intelligent control, precise operation, autonomous navigation, end-terminal-cloud collaboration. Another goal is to look ahead to the development trend of agricultural and forestry intelligent equipment. 
Machine vision is an important way for intelligent agricultural and forestry equipment to perceive the environment. This workshop will discuss the application of machine vision technology in agricultural and forestry scenarios: vehicle and personnel management; prevent illegal logging and poaching; early forest fire monitoring; tree, crop growth monitoring; disease and insect pest monitoring; tree recognition and location of cutting robot; picking robot target recognition and positioning; estimation of forest wood storage and crop yield; estimation of felling area, afforestation area and crop area; population and quantity monitoring of rare animals. The topics in this workshop include but are not limited to the above.

Keywords:
Agroforestry robot; Machine vision; Intelligent perception; Intelligent control; Accurate operation
02
Human-robot Interaction
Title: Robot and AI Enhanced Caregiving Technologies for Healthy Home Living


Chair:
Dr. Chunxu Li, Associate Professor, College of Mechanical and Electrical Engineering, Hohai University, China
Dr Chunxu Li received his PhD degree from Swansea University in 2019. He was appointed as a postdoc research fellow at Swansea University in 2019 and a lecturer at the University of Plymouth in 2020, respectively. He is currently working as an associate professor at College of Mechanical and Electrical Engineering, Hohai University. He has published over 40 academic papers, 32 of which are SCI/EI indexed, and has been successful in involving over £1,000,000 in grants funding a range of AI and robot studies since 2019, from EPSRC, ASTUTE (with industry), Doctoral College, University of Plymouth. Additionally, Dr Li is an Associate Fellow of the Higher Education Academy.

Summary:
People are living longer lives than ever before, thanks to advancements in medicine and public health. This implies that an increasing number of family caregivers are tasked with meeting the various requirements of loved ones suffering from a chronic disease or frailty. How can family caregivers combine this additional duty with their own personal needs? Caregiving technologies can offer practical solutions. Various technology-based therapies, particularly concentrated on health and well-being, social exclusion, financial, and psychological assistance, are becoming accessible. Recently, innovation in technologies such as mobile and cloud, robotics, connected sensors, virtual/augmented/mixed reality, voice, and increasingly ubiquitous tools supported by advanced data analytics, combined with the integration of multiple technologies via platform solutions, has served as a catalyst of technology-enabled approaches that can motivate and assist family caregivers.
The aim of this workshop is to bring together the research accomplishments provided by researchers from academia and the industry. The other goal is to show the latest research results in the field of robot and AI technology and understand how the implementations can influent people’s home living. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews.

Keywords:
Machine Vision, Machine Learning, Robotics, Human-robot Interaction, Human-computer Interaction, Caregiving Technologies, Smart Homes, Signal Processing, Pattern Recognition, Intelligent Wearable Devices.
03
WearableRobotic Technology
Title: Human-exoskeleton Coupling and Intelligent Control


Chair:
Dr. Yi Long, Northeastern University, Shenyang, China
Yi Long received the Bachelor’s Degree in Mechanical Engineering from Sichuan University, Chengdu, China, in 2010, the Master’s Degree in Mechanical Engineering and the Ph.D. degree in Mechanical Engineering from Harbin Institute of Technology, Harbin, China, in 2012 and 2017 separately. He is currently a associate professor in  Northeastern University, China., where he leads a lab focusing on Robotics Technology and Machine Vision. His current research interests include wearable exoskeletons, mobile robots, machine learning and computer vision. He has published more than 20 papers and 70 patents.


Chair:
Dr. Zhong Li, Nanjing University of Science and Technology, China
Zhong Li received a Ph.D. degree in Mechanical Engineering from Nanjing University of Science and Technology. He is currently a post-doctoral researcher at Nanjing University of Science and Technology. His research interests include exoskeleton robot design and their kinetic analysis, control strategy for exoskeleton, and also sEMG signal processing and analysis. He has participated 7 national or provincial projects with Rapid response project of CMC Science and Technology Commission, National Defense Basic Research Program, and Army Pre-research Program in China. Based on these projects, he has published more than 15 papers.

Summary:
With the development of robotics technology, exoskeletons have aroused a global research upsurge. On the one hand, humans will decrease or lose limb movement ability due to motor nervous system injury, aging and other reasons, exoskeletons can help the human body recover or assist the motor function of limbs, so as to ensure daily life. On the other hand, People are unable to do manual labor such as carrying heavy objects because of their limited capacity, exoskeleton can help the human body when carrying loads, reducing fatigue and enhancing human ability. However, there are still many challenges in developing functional, human-machine wearable, comfortable and coordinated exoskeletons, such as the methods for rapid and accurate perception of human motion intention by the exoskeleton, and the intelligent control of human-exoskeleton coordination and synchronization for different wearers, the lightweight design of the system under the premise of ensuring the comfort and safety of human wearing exoskeleton, and the reasonable evaluation for such a complex human-exoskeleton coupling system and so on.
This workshop aims to bring together the research accomplishments provided by researchers from academia and the industry. The other goal is to show the latest research results in the field of the exoskeleton and understand how intelligent control can influence it. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews.


Keywords:
Exoskeleton Robot, Human-machine Interaction, Intelligentcontrol, Motion Intention Recognition
04
Artificial Intelligence Driven Cyber-Physical System: Algorithms and Applications
Title: Artificial Intelligence Driven Cyber-Physical System: Algorithms and Applications


Chair:

Dr. Heng Li, School of Computer Science and Engineering, Central South University, China
Heng Li works as an associate professor at Central South University. He received his bachelor’s and Ph. D. degrees from Central South University in 2011 and 2017 respectively. He worked as a Research Assistant at University of Victoria from November 2015 to November 2017. He joined Central South University in November 2017. His research areas include cyber-physical systems, smart energy systems and smart factories. Dr. Li was a recipient of the Excellent Ph.D. Thesis Award of Central South University and Hunan Province, Hunan Provincial Natural Science Award and China Railway Academy Science and Technology Award. 

Co-chair:

Prof. Yanhui Song, Hunan Post and Telecommunication College, China
Yanhui Song works as a Professor at Hunan Post and Telecommunication College. She graduated from Chongqing University of Posts and Telecommunications in 2001. She joined Hunan Post and Telecommunication College in 2005. Now, she is the vice President of Hunan Post and Telecommunication College. She has finished over 15 research projects and published over 30 academic papers. Prof. Song is a leading expert in post and telecommunication areas in Hunan Province. 

Co-chair:

Dr. Pingping Dong, Hunan Normal University, China
Pingping Dong received her B.S., M.S. and Ph.D degree from the School of Information Science and Engineering at Central South University. Currently she is an associate professor at Hunan Normal University. Her research interests include protocol optimization and protocol design in wide area networks (WANs) and wireless local area networks (WLANs). 

Co-chair:

Dr. Guanghui Wang, Henan University, China
Guanghui Wang works as an associate professor at the School of Software of Henan University (HENU). He is a member of Key Technology of Henan International Joint Laboratory of Intelligent Network Theory. He received his Ph. D. degree in Information Networks at Nanjing University of Posts and Telecommunications (NUPT) in 2019. He was a visiting research student at the Department of Computer Science of University of Victoria (UVic) and his tutor is Prof. Jianping Pan. His research areas include Blockchain, Federated Learning, Privacy Preservation, and Internet of Things. 

Co-chair:

Dr. Yun Zhou, Hunan University of Finance and Economics, China
Yun Zhou works as an assistant professor in Hunan University of Finance and Economics. She received her PhD degree in Computer Application Technology from Central South University. As a major member of the "Institute of Networking Communication and Control of Intelligent Transportation" of Central South University, Dr. Zhou is engaged in research on information visualization and scientific visualization.

Summary:
A cyber-physical system (CPS) is a computer system in which a mechanism is controlled or monitored by computer-based algorithms. In cyber-physical systems, physical and software components are deeply intertwined, able to operate on different spatial and temporal scales, exhibit multiple and distinct behavioral modalities, and interact with each other in ways that change with context. CPS involves transdisciplinary approaches, merging theory of cybernetics, mechatronics, design and process science. Examples of CPS include smart grid, autonomous automobile systems, industrial control systems, and robotics systems. Edge intelligence aims to facilitate the deployment of artificial intelligence (AI) on cyber-physical systems. However, there are many challenges existing for a novel design of edge intelligence on cyber-physical applications, and their co-optimization. For instance, conventional AI techniques usually entail powerful computing facilities (e.g., cloud computing platforms), while cyber-physical systems may have only limited resources for computations and communications. This suggests that AI algorithms should be revisited for cyber-physical systems to provide the efficient processing. In this workshop, we are pleased to invite manuscripts on edge intelligence in cyber-physical systems. Research areas may include (but are not limited to) the following:

CPS system architecture
Control, Communication and Computation of CPSs
Machine learning for CPS
Design and verification of CPS
Safety, security and privacy for CPS
Mobile and cloud computing for CPS
Practical application-oriented system design for CPS

Keywords:
Cyber-Physical System; Artificial Intelligence
05
Intelligent Perception and Decision-Making of Robots
Title: Intelligent Robot Perception, Control Technology and Application


Chair:
Keqiang Bai, Southwest University of Science and Technology, China

Keqiang Bai, male, associate professor, master's supervisor, member of China artificial intelligence society and China simulation society. he received a Ph.D. degree in control science and engineering from University of science and technology of China, and received a doctor of engineering. At present, in the advanced control and Modeling Laboratory of Southwest University of science and technology, he has been engaged in the research of bionic robots and robot intelligent control algorithms for a long time, especially in the anthropomorphic control and dual arm coordinated control algorithms of humanoid dual arm robots, and has made a lot of research results. More than 40 academic papers have been published, including 11 papers in SCI journals, 5 papers in EI, and 2 papers in the top conference in the robot field. In recent years, he has participated in one major special "intelligent robot" project of the Ministry of science and technology, two projects of the National Natural Science Foundation of China, one project of the science and technology support fund of Jiangsu Province, one project of the equipment development department of the Central Military Commission, one project of China Aviation Development Group, one application basic research project of the Department of science and technology of Sichuan Province, one science and technology project of the Department of education of Sichuan Province, one Doctoral Fund of Southwest University of science and technology, and many horizontal projects. In terms of engineering application, we have carried out in-depth theoretical exploration and engineering semi physical simulation around intelligent control algorithm.


Chair: 
Jing Zhang, Southwest University of Science and Technology, China

Jing Zhang, lecturer, master's supervisor, PhD candidate in control science and engineering, University of science and technology of China. He has been engaged in the research of robot intelligent perception and hand eye coordinated control technology for a long time, including robot multi-source perception and image understanding, robot visual guidance and dexterous operation, etc. In the past five years, he has presided over or mainly completed 19 scientific research projects, including 3 national scientific research projects, 1 National Nature Fund Project and 1 key project of Sichuan Provincial Department of education. More than 20 academic papers have been published, including 2 SCI searches and 4 EI searches. In the past three years, more than 10 invention patents have been declared and authorized, and more than 20 utility model patents have been declared and authorized. In 2018, it won the second prize of the 8th Sichuan Provincial Teaching Achievement Award for higher education. In 2022, it won the first prize of the third science and Technology Award (Science and technology industrialization Award) of the China Association for the promotion of science and technology industrialization.

Summary:
With the rapid development of sensing and detection technology and artificial intelligence technology, robots are gradually moving from traditional industrial fields to more extensive application fields such as agricultural harvesting, household services, public medical care, warehousing and logistics, and space exploration. In recent years, robots have played an important role in various fields and become the core tooling of high-end equipment and intelligent manufacturing. However, due to the insufficient perception ability of robots in unstructured application environments, the task adaptability and application scope of robots are seriously restricted. 
In terms of robot perception, researching robot multi-sensor information fusion algorithms and effectively using visual sensors, lidar, ultrasonic sensors, tactile sensors, force sensors, etc. to obtain rich perception information is the basis for realizing robot multi-source perception collaboration. This is also the key to improving the robot's ability to perceive unstructured environments and novel targets, and it is also important to improve the dexterity of the robot. 
In terms of robot decision control, it includes key technologies such as robot indoor and outdoor real-time autonomous positioning, robot high-precision mapping and navigation technology, robot hand-eye coordination and dexterous operation technology, and multi-robot collaborative control. Robot control technology. Breakthroughs in these key technologies are essential to improve the adaptability of robots for flexible tasks in unstructured environments. 
The aim of this workshop is to bring together the research accomplishments provided by researchers from academia and the industry. The other goal is to show the latest research results in the field of robot perception and control technology. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews.

Keywords:
Multi-Sensor Information Fusion; Autonomous Navigation; Intelligent Perception; Self-Localization; Multi-Robot Collaborative Control
06
Information Security
Title: Security Big Data and Governance for Cyberspace


Chair:
Assoc. Prof. Xizhi Wu, Hunan University, China
Xizhi Wu, Ph.D., Mechanical Engineering, Hunan University, associate professor and postgraduate tutor, Central South University of Forestry and Technology. His research interest covers intelligent equipment and machine vision. He has undertaken 7 national and provincial projects, such as National Natural Science Foundation of China, Hunan Provincial Science and Technology Major Special Project, China Postdoctoral Science Foundation, and 12 national invention patents. He has published 1 monographs and more than 20 papers.

Summary:
Current Cyberspace are increasingly becoming pervasive, complex, and ever-evolving due to factors like enormous growth in the number of network users, continuous appearance of network applications, increasing amount of data transferred, and diversity of user behaviors. Understanding traffic and behaviors in such networks is a difficult yet vital task for network management but recently also for cybersecurity purposes. Security big data analysis can, for example, enable the analysis of the spreading of malicious software and its capabilities or can help to understand the nature of various network threats including those that exploit users’ behavior and other user’s sensitive information. On the other hand cyberspace governance can help to assess the effectiveness of the existing countermeasures or contribute to building new, better ones. Recently, cyber security big data analysis have been utilized in the area of economics of cybersecurity e.g. to assess ISP “badness” or to estimate the revenue of cybercriminals. 
The aim of this workshop is to bring together the research accomplishments provided by researchers from academia and the industry. The other goal is to show the latest research results in the field of cybersecurity technology and understand how governance strategy can influence it. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews.

Keywords:
Cybersecurity, Cyberspace Governance, Security Big Data, Artificial intelligence, International Strategy for Cyberspace
07
Advanced Mobile Robot Technology
Title: Progress of Mobile Robot Technology in Automation and Intelligent Control


Chair:
Dr. Dongqing He, China University of Petroleum, East China, China
Dongqing He received a Ph.D. degree in mechatronics from Shanghai Jiao Tong University. His research interests include robot mechanism and dynamics, automation of oil field equipments. He has participated in over 4 national projects with 863 Plan of the Ministry of science and technology, etc. He has published more than 20 papers.

Summary:
With the development of artificial intelligence and other related technologies, advanced mobile robot technology has become the most dramatic technology in robotics. It has made greater progress in recent years. It is an important cornerstone for robots to move from factory to society, and even into family. The development of mobile robot, such as walking robot, from walking to running, even to running faster than people is inseparable from the development and support of mechanics, machinery, automation, intelligent control and other related technologies. The aim of this workshop is to bring together the research accomplishments provided by researchers from academia and the industry. The other goal is to show the latest research results in the field of advanced mobile robot technology. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews.

Keywords:
Mobile Robot, Automation, Intelligent Control
08
Intelligent Robot
Title: Intelligent Robot Design and Control Algorithm


Chair:
Dr. Xiaoqing Zhu, Beijing University of Technology, China
Xiaoqing Zhu received a Ph.D. degree in Control Science and Technology from Beijing University of Technology. He is a master tutor at Beijing University of Technology, he also served as Deputy Secretary General of the Beijing Association of Artificial Intelligence, Committee member of the integrated robot Committee of the Chinese Society of Automation, and Committee member of the Science Popularization Committee of the Chinese Society of Artificial Intelligence. His research interests include intelligent robot design and machine learning. He has presided over 5 national or provincial projects with National Natural Science Foundation of China, and Beijing Natural Science Foundation. Based on these projects, he has published more than 30 papers and over 15 patents, he won the second prize of Wenjun WU AI Science and Technology Award on Technological Invention Award.

Summary:
Intelligent Robots are becoming more and more important in national life. The design and control theory of robots is a research hotspot at home and abroad. As a typical representative in the field of artificial intelligence, intelligent robot can symbol a country's artificial intelligence and even the level of science and technology to a certain extent。 Especially for the service robot, it can perform certain tasks and help people complete dangerous or repetitive work, it is very valuable to study the design theory and control method of service robot. Researchers at home and abroad get inspiration from nature and make creative improvements to complete the structural design and control algorithm of the robot. The skill learning of the service robot mainly focuses on two aspects, one is its basic motion skills, and the other is its operation skills. 
The aim of this workshop is to bring together the research accomplishments provided by researchers from academia and the industry. The other goal is to show the latest research results in the field of intelligent robot design and understand how control strategy can influence it. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews.

Keywords:
Intelligent Robot, Service Robot, Control algorithm, Machine Learning, Skill Learning
09
Medical Robots
Title: Medical Robotics Technology and Applications


Chair:
Dr. Haiyan Tu, Sichuan University, China
HaiyanTureceived a Ph.D. degree in biomedical engineering from Sichuan University, and a M.S. degree in automation from Sichuan University. Sheis an Associate Professor with the College of Electrical Engineering at Sichuan University. Herresearch interests include nonlinear intelligent control theory and biomedical engineering.She has presided over and participate in 5national or provincial projects with National Natural Science Foundation of China, and the Natural Science Foundation of SichuanProvince. Based on these projects, shehas published more than 31papers and the monographs"Principle and interface technology of single chip microcomputer".

Summary:
Medical robots are mainly used for the rescue, transfer, surgery and rehabilitation of the wounded and patients, and have a wide application prospect in both the military and civil fields. It is a research hotspot in the field of robots, and also one of the important development directions of informatization and intelligence of medical and health equipment. The research, development and application of medical robots have made a great contribution to improving medical efficiency and reducing patient pain. Medical robots are developing towards the trend of intelligence, miniaturization, accurate control and telemedicine. The application scenarios of robots in the medical field are also becoming more and more abundant, including rehabilitation robots, drug distribution robots, disinfection robots, guidance robots, etc. Thanks to exquisite institutions, precise control and precise navigation, the doctor in the surgical robot assistance, can complete more minimally invasive, more accurate, safer surgery for patients with small trauma, less bleeding, quick recovery benefits. Through multi-modal information real-time feedback and intelligent decision-making, rehabilitation robot realize the online evaluation and adjustment of rehabilitation process, which improve the rehabilitation process in hommization, intelligent degree. In the limb movement function rehabilitation and daily life auxiliary applications, the robot play an important role.
The aim of this workshop is to bring together the research accomplishments provided by researchers from academia and the industry. The other goal is to show the latest research results in the field of medical robots. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews.


Keywords:
Medical robotics, intelligence, accurate control, hommization
10
Optimizing Artificial Intelligence
Title: Optimizing Artificial Intelligence using Nature-Inspired Computing

Chair:
Mr. Sukanta Ghosh Sukanta
Ghosh is pursuing his Ph.D. (Computer Application) from Lovely Professional University, Phagwara, India. He has completed his MCA from Punjab Technical University, Kapurthala, India. He has more than 7 years of teaching experience. He has published various articles and book chapters in various journals and conferences. His current research interests are Image Processing, Machine Learning, Deep Learning and Natured Inspired Computing. Currently he is working as Asst. Professor in School of Computer Applications, Lovely Professional University, Punjab, India. 

Dr. Amar Singh
Dr. Amar Singh has done his Ph.D. (Computer Science & Engineering) from IKG Punjab Technical University, Jalandhar, Punjab, India. He has completed his M. Tech. (Information Technology) M.M. University, Mullana, Ambala, Haryana, India. He is the member of ISCA (Indian Science Congress Association). He has more than 12 years of experience in Teaching and Research. He has published around 70 research articles in various Journals and Conferences. His current research interests are Soft Computing, Machine Learning and Computer Networks. Currently he is working as Associate Professor in School of Computer Applications, Lovely Professional University, Punjab, India.

Summary:
In nature, things happen in a unique way. There are innumerable invisible causes behind the visible phenomena. Philosophers and scientists have been observing these phenomena in nature for centuries and trying to understand, explain, adapt, and replicate them in artificial systems. In nature-inspired computing (NIC), computing science is complemented by knowledge from various fields of science, e.g. physics, chemistry, biology, mathematics and engineering, which allows development of new computational tools such as algorithms, hardware, and wetware for problem-solving, synthesis of patterns, behaviors, and organisms. Innovation in robotics has made great strides, but now it needs to be optimized. A better algorithm is needed to improve the robotic system's performance in the current scene. There is no other teacher than nature itself. Nature inspired algorithms are used to optimize the robotic process to perform better and efficient.


Keywords:
Optimization, nature-inspired algorithm, meta-heuristic algorithm, swarm intelligence algorithm, bio-inspired algorithm
11
Intelligent Control & Power Electronics & Artificial Intelligence
Title: Intelligent Control and Fault Diagnosis based on Artificial Intelligence

Chair:

Pro. Xianbo Sun, Hubei Minzu University, China
Xianbo Sun received a Ph. D. degree in traffic information engineering and control at Wuhan University of technology. He has been engaged in the teaching and research of control theory and control engineering, industrial automation, Internet of things and other disciplines for a long time. He published more than 20 papers in academic journals, including 4 papers indexed by SCI, 2 papers indexed by Ei and 1 monograph. He presided over and completed 1 project supported by National Natural Science Foundation of China, 2 provincial projects, 4 Enterprise scientific research projects. He owns more than 10 national invention patents, utility model patents and software copyrights.

Summary:
Intelligent control and fault diagnosis of industrial equipment have always been the focus of scholars. Because of the rapid development of industrial technology and traditional equipment with low robustness and accuracy, it has been unable to meet the current industrial requirements for intelligence. According to the characteristics of industrial equipment, it is very important to design an excellent intelligent control and fault diagnosis method. The research of intelligent control and fault diagnosis methods based on artificial intelligence algorithm has attracted extensive attention in recent years such as artificial neural network, support vector machine, fuzzy theory and deep learning. These methods can be applied to the vast majority of industrial equipment and achieved excellent results, but there are still many areas worthy of improvement and innovation, e. g.  the use of optimization algorithm to improve the artificial intelligence algorithm or combine with the mechanism of equipment targeted research.


Keywords:
Intelligent Control, Fault Diagnosis, Power Electronics, Optimization Algorithm
12
Computer Vision for Intelligent Scene Perception
Title:Deep Learning for Intelligent Scene Perception

Chair:

Prof. Zhigang Liu, Department Director of Computer Science and Engineering, Northeast Petroleum University
Zhigang Liu, received a Ph.D. degree in Computer Resources and Information Engineering from Northeast Petroleum University, and was a visiting scholar with the Department of Electrical & Computer Engineering at National University of Singapore from 2018 to 2019. As a senior CCF member, he is currently the Department Director of Computer Science and Engineering, Northeast Petroleum University. His research interests include machine learning, computer vision, especially, data/label- and computation-efficient deep learning for visual recognition.
He participated in the National Natural Science Foundation, Natural Science Foundation of Heilongjiang Province, Scientific and Technological Projects of Petro-China, and Youth Science Foundation of Northeast Petroleum University. Based on these projects, he published many academic papers.

Summary:
Visual understanding and multi-modality representation fusion are essential to intelligent scene perception. With the rapid progress in machine learning technologies, there are tons of remarkable advances in intelligent scene understanding, whose performance and application fields are extended greatly. However, the complexity of scene could be a challenge for efficient perception. For some application as automatic drive, pedestrian re-identification and robot tracking, the performance and efficiency are typically affected by disturbances in the natural scene. How to efficiently combine information from visual and other modalities to enhance the robustness of perception systems under accidental perturbation and complexity issues is crucial and meaningful.
This workshop aims to bring together the research accomplishments provided by researchers from academia and the industry. The other goal is to show the latest research results in the field of intelligent scene perception. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews.
Keywords:
Computer Vision, Multi-modality Representation Learning, Intelligent Scene Perception and Application, Person re-identification, Deep Learning.
13
Fuzzy Analysis, Modeling and Control for the Decision-Making System with Complex Data
Title: Fuzzy Analysis, Modeling and Control for Complex Systems

Chair:
Assco. Prof. Shi-Yuan Han, Shandong Provincial Key Laboratory of Network Based Intelligent Computing, China
Shi-Yuan Han received the M.S. and Ph.D. degrees in computer science and technology from the Ocean University of China, Qingdao, China, in 2009 and 2012, respectively. From 2011 to 2012, he had been a Visiting Scholar with the School of EECS, Queensland University of Technology, Brisbane, QLD, Australia. In 2012, he joined the University of Jinan, Jinan, China, where he is currently an Associate Professor with the Shandong Provincial Key Laboratory of Network Based Intelligent Computing. From 2021 to 2022, he had been a Visiting Professor with David R. Cheriton school of Computer Sciences, University of Waterloo, Canada. His current research interests include the areas of fuzzy control, vibration control, intelligent transportation systems, time-delay systems, evolutionary computation, networked control systems, and their applications in intelligent transportation systems.

Summary:
With the development of information science and technology, practical processes such as those relevant to the industry, transportation, electronics, metallurgy, and logistics, have undergone significant changes. Many processes generate and store huge amounts of process data at every time instant of every day, containing all valuable state information of process operations and equipment. Using those data, both on-line and off-line, to directly predict the trend, evaluate performance and make decisions for complex system, would be very significant, especially under the lack of accurate supervision. However, the complex characteristics of those data, such as high dimension, nonlinearity, heterogeneity, and uncertainty, lead to the ineffectiveness of the existing analysis, modeling and control algorithms to deal with. Computerized methods, particularly combining with the fuzzy algorithms, perform a fundamental role in the field of analysis, modeling and control for the decision-making systems, such as fuzzy clustering algorithm, fuzzy evolutionary algorithm and fuzzy classification algorithm. For this reason, one of the main challenges is that how to combine computerized methods and fuzzy algorithms to deal with the complex data, and thus provide the effective analysis, modeling and control methods for the decision-making both in theory and application. 

Keywords:
Fuzzy analysis, Fuzzy Modeling, Fuzzy control, Decision making, Complex data
14
Intelligent Modeling and Control

Title: Intelligent Modeling and Control of Spatiotemporal Dynamic Systems

Chair:
Dr. Jun-Wei Wang, University of Science and Technology of Beijing, China
Jun-Wei Wangreceived thePh.D. degree in in control theory and control engineering from Beihang University, Beijing, China, in 2013. From September 2013 to December 2015, he was a Postdoctoral Researcher with the School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing.From February 2016 to February 2018, he was a Postdoctoral Fellow under the Hong Kong Scholars Scheme with the City University of Hong Kong. Currently, he is an Associate Professor at the School of Intelligent Science and Technology, University of Science and Technology Beijing. His research interests include intelligent system modeling and control theory in infinite-dimensional space, analysis and control of linear/nonlinear distributed parameter systems, and multiple-robot coordination theory and its application in environmental monitoring.He has published more than 60 papers. Dr. Wang serves as a Reviewer for the Mathematical Reviews and an Associate Editor for the Journal of Fuzzy Logic and Modeling in Engineering and the Frontiers in Artificial Intelligence (Fuzzy Systems Section).


Dr. Zi-Peng Wang, Beijing University of Technology, China, China
Zi-Peng Wang received the Ph.D. degree in Control Theory and Control Engineering from Beihang University, Beijing, China, in 2017. From July 2017 to August 2022, he was an Associate Professor in School of Electrical Engineering, University of Jinan, Shandong. In September 2022, he joined the Faculty of Information Technology, Beijing University of Technology, Beijing, where he is currently an Associate Professor (Distinguished Professor). In 2016, he was a Research Assistant with City University of Hong Kong, for six months. From December 2019 to January 2020, he was a Postdoctoral Research Associate with Texas A&M University at Qatar. His current research interests include robust control and filtering, fuzzy modeling and control, secure control, networked control systems, distributed parameter systems, and neural networks.


Dr. Yun Feng, Hunan University, China
Yun Fengreceived his B.E. degree in automationand M.S. degree in control theory and controlengineering both from the Department ofAutomation, Wuhan University, Wuhan, China, in2014 and 2017, respectively. He received the Ph.D.degree with the Department of Systems Engineeringand Engineering Management, City Universityof Hong Kong, Hong Kong in 2020. From Julyto November 2019, he was a visiting student atthe Institute for Automatic Control and ComplexSystems (AKS), University of Duisburg-Essen,Germany.
He is currently an Associate Professor with the College of Electrical and InformationEngineering, Hunan University, Changsha, China, and also with the NationalEngineering Research Center for Robot Visual Perception and ControlTechnology, Hunan University, Changsha, China. His researchinterests include modeling and fault diagnosis of distributed parameter systems.

Summary:
Most systems arising in practical applications arespatiotemporal distributed processes in nature so that their behavior must depend on time as well as spatial position, for example, some systems related with heat flows, fluid flows, elastic wave, flexiblestructure, chemical engineering, biodynamics, ecosystems, and social systems, etc. This type system is named as spatiotemporal dynamic systemsand also named as distributed parameter systems (as opposed to lumped parameter systems). These spatiotemporal dynamic systems arein general modeled by partial differential equations(PDEs) or partial differential equation coupled with ordinary differential equations or integral-differential equation or differentialequations in abstractspace, or delay differential equations (the time-delay phenomenonis caused by the transmission in the space fields).Such systems are also known as infinite-dimensional systems as their infinite-dimensional function space. However, only a finite number of actuators and sensors are available for modeling and control design due to the limitation of implementation cost and existing actuation and sensing technology.On the other hand, almostall systems in practices are nonlinear ones in nature. It is difficult, even impossible to develop an accurate mathematical model for practical applications. Therefore, intelligent modeling and control of spatiotemporal dynamic systems have been paid a lot attention in the literature. 
The proposed session will bring expertise to audiences in thearea of spatiotemporal dynamic systems (distributed parameter systems, or infinite-dimensional systems). This session will focus on spatiotemporal dynamic systems modeled by PDEs, and coupled ODE-PDE and covers applications including time-delay systems, flexible mechanical systems, reaction-advection-diffusion systems, and their specific applications, etc. This session discusses most recent results in theory and applications of intelligent modelingand control for spatiotemporal dynamic systems.

Keywords:
Artificial intelligence, Industrial Intelligence, Intelligent system modeling and control, Performance enhancement, Spatiotemporal dynamic systems
15
Medical Robots

Title: Medical Robots

Chair:
Assoc. Prof. Qingjuan Duan, Xidian University, China
Qingjuan Duan, Associate Professor, School of Mechanical and Electrical Engineering, Xidian University. As a visiting scholar, she has studied at the University of Delaware, the Robotics Laboratory of Columbia University (New York) and the State Key Laboratory of Robotics of Harbin Institute of Technology. She has participated in the National Natural Science Foundation of China (NSFC) project on multi-functional coupling mechanism of gear transmission system and its diagnosis method, the theory and method of high surface accuracy design of large space expandable cable-net frame structure, and the study of wind-driven polymorphic spherical robot for environmental detection. She presided over the Shaanxi Provincial Natural Science Fundamental Research Project on the key issues of modular and reconfigurable cable-driven robots, and the Ministry of Science and Technology Project on the motion synthesis and optimization design of minimally invasive spinal surgery robots based on the theory of spin volume. Her main research interests are in robotics, including: configuration optimization design, kinematic calibration, and dynamics and control of cable-driven systems. She has published nearly 20 relevant papers.

Summary:
Medical robots are robotic machines utilized in health sciences. They can be categorized into three main classes: (1) medical devices including surgery robotic devices, diagnosis and drug delivery devices, (2) assistive robotics including wearable robots and rehabilitation devices, and (3) robots mimicking the human body including prostheses, artificial organs, and body-part simulators. A variety of other classification approaches for medical robotics are proposed in the vast literature. During the last three decades, medical robots have been increasingly used to perform a growing number of health tasks. As such, they show promising future potential for use in a wide range of health issues. 
This workshop aims to bring together the research accomplishments provided by researchers from academia and the industry. The other goal is to show the latest research results in the field of medical robots. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews.

Keywords:
Manipulator; Haptics; Grippers;  Artificial  Intelligence; Actuators ; Robotic System; Industrial Robot