Workshop Program
1. Precision measurement of microwaves and metrology
Summary: Precision Measurement of Microwaves and Metrology Workshop is an interesting topic in the fields of communication, remote sensing, national defence and healtycare. It contains new physical principle and techniques, with goal of high sensitivity for weak signals, broadband response and wide tunability. Quantum technique has been used in precision metrology. It has excellence performance in stability and sensitivity, and will bring new revolution in the field of metrology. New trends in microwave measurement and metrology involve new materials,new novel design in antenna, and new physical principle and techniques. The workshop will serves as a platform for researchers to exchange research works, new ideas and push forwards development of metrology technology.
Keywords: Precision measurement of microwave, Rydberg atoms
Chair 1: Prof. Yandong Peng, Shandong Unviersity of Science and Technology, China
Yandong Peng is a Professor at Shandong University, director of Optical Sensing and Precision Measurement Research Center, vice president of College of Electronic and Information Engineering at Shandong University of Science and Technology, senior member of Chinese Optical Society, executive director of Shandong Society of Optical Engineering, reviewer of international academic journals such as Optics Letters/Express, Physics Review A, IEEE Photonics Technology Letters. He presided 3 National Natural Science Foundation and 2 Natural Science Foundation of Shandong Province, China. He has published more than 70 academic papers in the journals of APL, OL, PRA, Result Phys., EPJ QT, JOSA B and AO.
Chair 2: Prof. Xiaoxian Song, Jiangsu University, China
Xiaoxian Song was a lecture with school of mechanical engineering, Jiangsu University, China, in 2017. His current research interest includes Micro-nano optoelectronic devices, and the area of terahertz science and applications. In the study of photodetectors based on two-dimensional materials, it is found that different two-dimensional materials have different photoelectric response properties. For example, Bi2Te3 has positive conductance phenomenon in three wavebands, PtTe2 has negative conductance phenomenon, and PtSe2 and Bi2Se3 have the coexistence phenomenon of positive and negative conductance. Based on the positive and negative conductance co-existing of the two-dimensional materials photodetector, the photoelectric logic gate and its applications, such as imaging and synapse, are studied. E-Mail: songxiaoxian@ujs.edu.cn
2. Intelligent signal processing and precision measuring instrument
Summary: Areas relevant to the progress of intelligent signal processing and precision instrument include, but are not limited to, advances in intelligent method for signal processing, machine learning, advanced information fusion method, intelligent perception and advanced sensors or precision measuring instrument, machine vision and imaging technique, machine learning, deep learning neural network, multimode and large scale models, signal processing applications in industrial, agricultural, medical and aircraft settings are within the workshop range. The newest signal, image and video processing, recognition and mathematical modeling methods are also the topics of interest.
Keywords: Intelligent signal processing, precision instrument, information fusion, large scale model, application
Chair 1: Assoc. Prof. Shiwei Liu, Huazhong Agricultural University, China
Shiwei Liu is an Associate Professor, Doctor Supervisor and Deputy director of Intelligent Nondestructive Testing and Digital Equipment research group in College of Engineering, Huazhong Agricultural University (HZAU). His research interests include Non-destructive testing and evaluation, structural health monitoring, advanced sensor technique, signal processing, NDT/SHM equipment and application. He has presided over more than 10 national and provincial projects, such as the National Natural Science Foundation of China, sub-projects of the National Key Research and Development Program of China, Natural Science Foundation of Hubei Province, China Postdoctoral Science Foundation. He won the Second Prize for Scientific and Technological Progress of China shipbuilding industry corporation (CSIC), “Best Paper Award” in ICMSD2023, “Young Scientist Award” in 2024 International Research Data Analysis Excellence Awards, “Best Researcher Award” in 2024 International Research Awards on High Energy Physics and Computational Science. He has published more than 40 research papers and book chapters in such as Advanced Science, IEEE TIE, SHM and MSSP. He is a member of Singapore Viser Mechanical Engineering Expert Database, editorial board member of《Mechanical Science and Engineering》and《Journal of Intelligent Agricultural Mechanization》, Guest editor of 《Applied Sciences》.
3. Advanced Performance-Oriented Evaluation, Diagnosis and Fault-Tolerant Control Techniques for Industrial Processes
Summary: With the rapid development of computer techniques, electronics and information technology, modern industrial processes are generally becoming more and more complex. For such processes, safety and reliability issues are of significant importance since fault or failure may result in disastrous consequences and hazards for personnel, plant and environment, in particular when the systems are embedded in systems that must be safe, such as networks, robots, or power plants. The potential for disaster underscores the necessity for robust performance evaluation, diagnostic tools, and fault-tolerant control (FTC) systems. Over the past several decades, there are many complex and challenging issues in the performance monitoring and FTC techniques, such as data-driven performance monitoring and recovery methodologies, advanced model-based fault diagnosis and FTC techniques, as well as machine-learning-aided FTC techniques and their application in complex industrial processes and safety-relevant processes.
Keywords: performance monitoring, condition evaluation, fault diagnosis, fault-tolerant control
Chair 1: Assist. Prof. Dan Yang, Hunan University of Science and Technology,, China
Dan Yang received the B.S. degree in control science and engineering from the East China University of Science and Technology, Shanghai, China, in 2019, and the Ph. D degree from the Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, in 2017, in control science and engineering. From 2022-2023, she was a visiting Ph.D student with the University of Duisburg-Essen, Duisburg, Germany.
She is currently an Assistant Professor with Hunan University of Science and Technology, Xiangtan, China. Her current research interests include machine learning and transfer learning.
She is currently an Assistant Professor with Hunan University of Science and Technology, Xiangtan, China. Her current research interests include machine learning and transfer learning.
Chair 2: Assist. Prof. Xin Peng, East China University of Science and Technology, China
Xin Peng received the B.S. and M.S. degrees from the East China University of Science and Technology, Shanghai, China, in 2009 and 2012, respectively, and the Ph.D. degree from the Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, in 2017, all in control science and engineering.
He is an Assistant Professor with the East China University of Science and Technology. From 2017 to 2019, he was a Post-Doctoral Researcher with the University of Duisburg-Essen, Duisburg, Germany. His current research interests include process monitoring and system modeling of chemical and biological processes, data mining, and feature extraction of process data.
He is an Assistant Professor with the East China University of Science and Technology. From 2017 to 2019, he was a Post-Doctoral Researcher with the University of Duisburg-Essen, Duisburg, Germany. His current research interests include process monitoring and system modeling of chemical and biological processes, data mining, and feature extraction of process data.
Chair 3: Assist. Prof. Haojie Huang, Fuzhou University, China
Haojie Huang received the B.S. and Ph.D. degrees from the East China University of Science and Technology, Shanghai, China, in 2017 and 2023, respectively. From 2021-2022, he was a visiting Ph.D student with the University of Duisburg-Essen, Duisburg, Germany.
He is currently an Assistant Professor with Key Laboratory of Industrial Automation Control Technology and Information Processing, Fuzhou University, Fuzhou 350116, China. His current research interests include machine learning, Gaussian process regression and Industrial Internet of Things.
He is currently an Assistant Professor with Key Laboratory of Industrial Automation Control Technology and Information Processing, Fuzhou University, Fuzhou 350116, China. His current research interests include machine learning, Gaussian process regression and Industrial Internet of Things.
4. Target Search and Rescue with Intelligent Systems
Summary: In critical scenarios such as natural disasters, terrorist attacks, or large-scale accidents, the timely and efficient location of victims or valuable assets is paramount. Traditional search and rescue (SAR) operations often face limitations due to unpredictable environments, restricted accessibility, and human resource constraints. Recent advancements in intelligent systems and autonomous technologies offer the potential to revolutionize SAR operations by enabling faster, safer, and more accurate target search efforts. This workshop aims to bring together researchers, industry professionals, and government stakeholders to explore cutting-edge solutions in SAR missions. It will focus on the integration of autonomous robots, drones, machine learning, and advanced perception systems to develop innovative approaches for identifying and rescuing targets in complex environments. Key topics include multi-robot collaboration, deep reinforcement learning for decision-making, vision-based perception for obstacle detection and navigation, and swarm intelligence for large-area coverage.
Keywords: Search and rescue, Intelligent system, Artificial intelligence
Chair 1: Dr. Jiaping Xiao, Nanyang Technological University, Singapore
Dr. Jiaping Xiao received the B.E. degree in aircraft design and engineering and the M.S. degree in flight dynamics and control from Beihang University, Beijing, China, in 2014 and 2017. He received the Ph.D. degree from Nanyang Technological University (NTU), Singapore in 2024. From 2017 to 2020, he was an Engineer with the Institute of Software, Chinese Academy of Sciences, Beijing. He is currently a Research Fellow with the School of Mechanical and Aerospace Engineering, NTU. He has published over 20 papers in top journals and conferences, such as IEEE TNNLS, IEEE TVT, IEEE TAI, IEEE TRO and IJCAI etc. His research interests include cyber-physical system, deep reinforcement learning, machine vision and aerial robotics.
Chair 2: Dr. Jiaorao Wang, Lingnan University, China
Dr. Jiaorao Wang is currently a Postdoctoral Fellow at Lingnan University, Hong Kong, supported by the Research Grants Council (RGC) Postdoctoral Fellowship Scheme, an honor awarded to only 60 recipients in Hong Kong. She received her B.E. degree from Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2018 and the Ph.D. degree with the College of Control Science and Engineering, Zhejiang University, Hangzhou, China, in 2023. She was also a Visiting Researcher at the University of Duisburg-Essen, Duisburg, Germany, from 2022 to 2023. After completing her Ph.D., she conducted postdoctoral research at School of Data Science at City University of Hong Kong and School of Data Science at Lingnan University. Her research focuses on data-driven modeling, monitoring, and control of industrial processes.
Chair 3: Dr. Yaowei Yu, Nanjing University of Aeronautics and Astronautic, China
Yaowei Yu, Ph.D. of Nanjing University of Aeronautics and Astronautics, Joint training of Ph.D. of Nanyang University of Technology. Mainly engaged in multi-UAV cooperative path planning and task allocation in complex scenes; lidar, ultrasonic radar, RTK and IMU fusion based on Beidou to build high-precision maps of efficient systems; research on highly coupled complex task allocation and target matching of multi-UAV systems under the requirements of reconnaissance, strike, surveillance and evaluation tasks, as well as resource allocation of sensor networks for different surveillance targets. In the past five years, he has presided over 1 national social science fund youth project, 3 JKW fund projects, 3 military scientific research projects, 1 national smart city construction project, 1 Jiangsu university student entrepreneurship base project, and participated in 2 projects. It has been honored by XXX District Entrepreneurship Leader (1 million yuan) and so on. Currently, he serves as an expert in the XXX military modeling expert database and a judge of the XXX UAV skills Competition. He has issued a total of 20 papers, of which 17 have been published as the first author and communication author of SCI. Currently serves as the reviewer of IEEE TITS, TIV, IEEE Systems Journal, ESWA, KBS, IS, ASC and other periodicals.
5. Intelligent Sensing and Array Signal Processing
Summary: Intelligent sensing and array signal processing are crucial in modern technology, especially in fields like communications, radar, navigation and biomedical applications. Intelligent sensing involves using sensors and algorithms to perceive and interpret environmental data, and its key components include sensor technology, data fusion, pattern recognition and real-time processing. Array signal processing focuses on utilizing multiple sensors to improve signal quality and directionality, and its important aspects include direction of arrival (DOA) estimation, beamforming, spatial filtering and adaptive processing. Combining intelligent sensing with array signal processing leads to advanced systems capable of dynamic adjustments and improved signal detection in complex environments. This synergy enhances applications like autonomous vehicles, smart cities, and advanced communication systems. In autonomous vehicles, sensor fusion combines data from multiple sensors to navigate and avoid obstacles, and environment perception uses array processing to identify and track objects around the vehicle. In smart home systems, integrated sensing monitors environmental conditions, security and energy usage, and voice control utilizes array processing for accurate voice recognition and command execution. In internet of things (lOT), networked sensors collect and process data to provide insights and automation in various applications like smart agriculture, healthcare, and industrial monitoring. These applications demonstrate the versatility and critical importance of intelligent sensing and array signal processing in advancing technology and improving the quality of life across multiple domains.
Keywords: Integrated sensing, Sensor fusion, Autonomous vehicles, Array signal processing, Beamforming, Adaptive processing, Direction of arrival estimation, Kalman filtering, Radar signal processing, Sonar signal processing, SLAM (Simultaneous Localization and Mapping), Navigation signal processing
Chair 1: Prof. Guochen Wang, Harbin Institute of Technology, China
Guochen Wang is a professor at Harbin Institute of Technology, Harbin, China. He received the B.S. degree and the Ph.D. degree from the Harbin Engineering University, Harbin, China, in 2012 and 2016, respectively. His current research interests include new inertial instruments and navigation technology, integrated optics and fiber optic sensing technology, spatiotemporal information intelligent perception and machine learning technology. In recent years, won 3 provincial and ministerial level scientific and technological progress/invention awards; Develop one national military standard; Published 3 books; Published over 40 SCI/EI papers; Applied for and obtained 17 authorized national invention patents.
Chair 2: Assoc. Prof. Feng Weike, Air Force Engineering University, China
Weike Feng is an associate professor at Air Force Engineering University, Shaanxi, China, and the leader of a youth innovation team of Shaanxi universities. He received his B.E. from Air Force Engineering University, Shaanxi, China, in 2013 and his Ph.D. from Tohoku University, Sendai, Japan, in 2019. He has published more than 70 peer-reviewed papers, 4 books, and 1 textbook, authorized 15 invention patents. Dr. Feng received the Young Researcher Award from the IEEE GRSS All Japan Joint Chapter, the Student Award from IEEE AP-S Japan, and the Excellent Paper Award from IET International Radar Conference in 2018, as well as the Professor Fujino Incentive Award from Tohoku University and the Best Student Paper Award from PIERS in 2019. His current research interests include artificial intelligence (AI)-based radar target detection, imaging, and recognition.
Chair 3: Assoc. Prof. Zhen Meng, China University of Mining and Technology, China
Zhen Meng received the B.S. degree and the Ph.D. degree from the Harbin Engineering University, Harbin, China, in 2015 and 2020, respectively. From October 2018 to April 2020, he was a Visiting Scholar with the Department of Electrical and Computer Engineering, Queen’s University, Kingston, Canada. From February 2021 to December 2023, he was a Lecturer with the School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China. Since January 2024, he has been an Associate Professor with the School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China. Since August 2024, he has been an Editorial Board Member of American Journal of Remote Sensing (AJRS). His research interests include array signal processing, anti-jamming, anti-spoofing and measurements in GNSS.
6. Mechanical Signal Processing and Fault Diagnosis in Industry Applications
Summary: Mechanical Signal Processing and Fault Diagnosis in Industrial Applications plays a pivotal role in ensuring the reliability and efficiency of machinery systems. By analyzing mechanical signals such as vibrations, temperatures, and acoustic emissions, engineers can detect anomalies that may indicate impending failures. Advanced signal processing techniques, including wavelet transforms and machine learning algorithms, enhance the accuracy and speed of fault diagnosis, which leads to protective maintenance, and reducing operational costs. As industries increasingly adopt automation and smart manufacturing, the importance of robust mechanical signal processing and fault diagnosis frameworks grows, enabling a seamless transition towards predictive maintenance and sustainable operations. Key topics include machine and structural health monitoring, control of vibrations and noise, signal processing in manufacturing/machining, data-driven based diagnosis.
Keywords: Predictive Maintenance, Machine Learning, Signal Processing
Chair 1: Assist. Prof. Tongyang Pan, Central South University, China
Tongyang Pan was born in Shaanxi, China, in 1994. He received the B.S. and Ph.D. degrees in mechanical engineering from Xi’an Jiaotong University, Xi’an, China, in 2017 and 2022, respectively.
He is currently a Lecturer with the School of Traffic and Transportation Engineering, Central South University, Changsha, China. His research interests include mechanical signal processing, intelligent fault diagnosis, and machinery condition monitoring.
He is currently a Lecturer with the School of Traffic and Transportation Engineering, Central South University, Changsha, China. His research interests include mechanical signal processing, intelligent fault diagnosis, and machinery condition monitoring.
Chair 2: Assoc. Prof. Jinsong Yang, Central South University, China
Jinsong Yang received the B.S. degree from the School of Quality and Reliability Engineering, Beihang University, Beijing, China, in 2013, and the Ph.D. degree in systems engineering from Beihang University.
He was a Lecturer with the School of Traffic and Transportation Engineering, Central South University, Changsha, China. His research interests include system theory, reliability analysis, and fault diagnosis.
He was a Lecturer with the School of Traffic and Transportation Engineering, Central South University, Changsha, China. His research interests include system theory, reliability analysis, and fault diagnosis.
Chair 3: Prof. Liang Jiang, Jilin University, China
Liang Jiang received the B.S. degree from the School of Mechanical and Aerospace Engineering, Jilin University, Changchun, China, in 2008, and the Ph.D. degree in Mechanical Engineering from Tsinghua University in 2015.
He is a Professor with the School of Automation, Wuxi University, Wuxi, China. His research interests include fault diagnosis, industrial application of artificial intelligence.
He is a Professor with the School of Automation, Wuxi University, Wuxi, China. His research interests include fault diagnosis, industrial application of artificial intelligence.
7. Optimization, Control, and Applications of Robotic Systems
Summary: This workshop focuses on the crucial aspects of optimizing and controlling robotic systems, aimed at maximizing their efficiency, precision, and adaptability across various real-world applications. As robots become integral to fields such as industrial automation, healthcare, autonomous vehicles, and smart homes, the need for advanced control strategies and optimization methods is more vital than ever.
Participants will explore key optimization techniques like genetic algorithms, gradient descent, and particle swarm optimization, which are used to solve challenges in path planning, energy management, and task allocation. These methods enable robots to perform tasks more efficiently and adapt to dynamic environments.
The control aspect covers both classical methods like PID and adaptive control, as well as modern approaches, including neural networks, fuzzy logic, and reinforcement learning. These techniques allow robots to handle uncertainties and complex tasks with greater stability and accuracy, even in unpredictable conditions.
The workshop also highlights the latest innovations in robotic applications. Case studies will include industrial robots in automated production lines, surgical robots in precision medicine, and service robots in logistics and human interaction. Through practical examples and hands-on sessions, participants will gain a deeper understanding of how to design and implement optimized control systems that can meet the demands of diverse application scenarios.
This workshop is ideal for researchers, engineers, and industry professionals seeking to enhance their knowledge in robotics, providing a comprehensive overview of how optimization and control strategies can be applied to achieve advanced robotic solutions.
Participants will explore key optimization techniques like genetic algorithms, gradient descent, and particle swarm optimization, which are used to solve challenges in path planning, energy management, and task allocation. These methods enable robots to perform tasks more efficiently and adapt to dynamic environments.
The control aspect covers both classical methods like PID and adaptive control, as well as modern approaches, including neural networks, fuzzy logic, and reinforcement learning. These techniques allow robots to handle uncertainties and complex tasks with greater stability and accuracy, even in unpredictable conditions.
The workshop also highlights the latest innovations in robotic applications. Case studies will include industrial robots in automated production lines, surgical robots in precision medicine, and service robots in logistics and human interaction. Through practical examples and hands-on sessions, participants will gain a deeper understanding of how to design and implement optimized control systems that can meet the demands of diverse application scenarios.
This workshop is ideal for researchers, engineers, and industry professionals seeking to enhance their knowledge in robotics, providing a comprehensive overview of how optimization and control strategies can be applied to achieve advanced robotic solutions.
Keywords: Robotic Optimization, Control Strategies, Intelligent Control, Autonomous Systems, Multi-agent Systems, Real-World Applications
Chair 1: Assoc. Prof. Chuang Li, Liaoning Technical University, China
Dr. Chuang Li
Position: Associate Professor, Liaoning Technical University
Education: Ph.D. in Biomedical Engineering, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands
Profile:
Dr. Chuang Li is an Associate Professor at Liaoning Technical University, holding a Ph.D. in Biomedical Engineering from the University of Groningen and the University Medical Center Groningen in the Netherlands. In recent years, Dr. Li has published over 10 research papers as the first author or corresponding author in internationally renowned journals and conferences. He serves as a reviewer for several prestigious international journals, including IEEE Transactions, Applied Mathematics and Computation, Nonlinear Dynamics as well as various international conferences, bringing extensive experience in academic collaboration and peer review.
Research Interests:
Intelligent Control of Nonlinear Systems: Focused on the modeling, analysis, and control of nonlinear systems, exploring advanced intelligent control techniques for complex systems.
Design and Control of Small-Scale Robots: Specializes in the design of small-scale robots, including magnetic actuation, automation control, imaging, localization, and micro-manipulation. His work aims to advance the practical applications of miniature robots in biomedical fields.
Position: Associate Professor, Liaoning Technical University
Education: Ph.D. in Biomedical Engineering, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands
Profile:
Dr. Chuang Li is an Associate Professor at Liaoning Technical University, holding a Ph.D. in Biomedical Engineering from the University of Groningen and the University Medical Center Groningen in the Netherlands. In recent years, Dr. Li has published over 10 research papers as the first author or corresponding author in internationally renowned journals and conferences. He serves as a reviewer for several prestigious international journals, including IEEE Transactions, Applied Mathematics and Computation, Nonlinear Dynamics as well as various international conferences, bringing extensive experience in academic collaboration and peer review.
Research Interests:
Intelligent Control of Nonlinear Systems: Focused on the modeling, analysis, and control of nonlinear systems, exploring advanced intelligent control techniques for complex systems.
Design and Control of Small-Scale Robots: Specializes in the design of small-scale robots, including magnetic actuation, automation control, imaging, localization, and micro-manipulation. His work aims to advance the practical applications of miniature robots in biomedical fields.
Chair 2: Dr. Yuanqing Wang, Tongji University, China
Yuanqing Wang is an Editorial Board Member of Contemporary Mathematics and serves as a reviewer for various international journals, including IEEE Transactions on Automation Science and Engineering, Chaos, Solitons & Fractals, Communications in Nonlinear Science and Numerical Simulation, and Applied Mathematics and Computation.
ResearchGate: https://www.researchgate.net/profile/Yuanqing-Wang-7
Research Interests:
Event-triggered control of multi-agent systems: Focused on designing the event-triggered mechanism for multi-agent system to save communication resources.
ResearchGate: https://www.researchgate.net/profile/Yuanqing-Wang-7
Research Interests:
Event-triggered control of multi-agent systems: Focused on designing the event-triggered mechanism for multi-agent system to save communication resources.
8. Artificial Intelligence Empowered Medical Signal Processing
Summary: There are many bottlenecks in the medical system, such as low accuracy of image diagnosis, long queue for outpatient service and uneven distribution of resources. With the development of science and technology, artificial intelligence (AI) is helpful to solve the bottleneck problem of the current medical system and has begun to show great potential in the medical field. AI has brought revolutionary changes in auxiliary diagnosis, personalized treatment, disease prediction and epidemic monitoring, drug development and management, telemedicine and health management. AI provides new possibilities for medical services, which can help medical staff to better handle medical signals, improve medical level, improve patient experience, reduce medical costs and promote the sustainable development of medical and health undertakings.
Covered topics include but are not limited to: acquisition, transmission, storage, retrieval, management, processing and analysis of biomedical and health information; applications of information and communication technologies in the practice of healthcare, public health, patient monitoring, preventive care, early diagnosis of diseases, discovery of new therapies; the integration of electronic medical and health records, methods of longitudinal data analysis, data mining and discovery tools; clinical information systems, decision support systems, medical and biological imaging informatics, wearable systems, body area/sensor networks, informatics in biological and physiological systems, personalized and pervasive health technologies, telemedicine, home healthcare and wellness management.
Covered topics include but are not limited to: acquisition, transmission, storage, retrieval, management, processing and analysis of biomedical and health information; applications of information and communication technologies in the practice of healthcare, public health, patient monitoring, preventive care, early diagnosis of diseases, discovery of new therapies; the integration of electronic medical and health records, methods of longitudinal data analysis, data mining and discovery tools; clinical information systems, decision support systems, medical and biological imaging informatics, wearable systems, body area/sensor networks, informatics in biological and physiological systems, personalized and pervasive health technologies, telemedicine, home healthcare and wellness management.
Keywords: Artificial Intelligence; Medical; Signal Processing
Chair 1: Assoc. Prof. Baodi Liu, China University of Petroleum (East China), China
Liu Baodi, Associate Professor, Master's Supervisor. He graduated from the Department of Electronic Engineering at Tsinghua University in January 2013 with a PhD in Information and Communication Engineering. He has been working at China University of Petroleum (East China) since April 2013. His main research area is machine learning theory and its applications in the field of computer vision. In recent years, he has published over a hundred academic papers, authorized more than 10 national invention patents. Serving as a guest editor for SCI journal Computational Intelligence, Area chairman of PRCV2023 and PRCV2024.
9. Stability Control for the Power-Electronic-Based Power System
Summary: The pursuit of "carbon neutrality" and sustainable development with green and low-carbon advantages promotes the integration of renewable energy sources, such as wind power and photovoltaic, into power grids through power electronics. Consequently, power grids are developing towards high penetration renewable energy power systems. However, the inherent randomness and intermittency of renewable energy sources can lead to power imbalances and challenge the stability of high penetration renewable energy power systems. Moreover, the high penetration of power electronics may result in low inertia and weak damping. These factors underscore the importance of studying stability control techniques for Power-Electronic-Based Power System to improve the power quality and stability. The topic of interest includes but not limited to:
Stability control for the power convertor;
Advanced control strategy for the Power-Electronic-Based Power System;
Control technology for the grid-forming renewable energy;
Stability control for the AC/DC microgrid;
Control technology of the energy storage;
Application of AI Technology for the stability of the Power-Electronic-Based Power System;
Wide-frequency oscillation suppression strategy for the Power-Electronic-Based Power System
Stability control for the power convertor;
Advanced control strategy for the Power-Electronic-Based Power System;
Control technology for the grid-forming renewable energy;
Stability control for the AC/DC microgrid;
Control technology of the energy storage;
Application of AI Technology for the stability of the Power-Electronic-Based Power System;
Wide-frequency oscillation suppression strategy for the Power-Electronic-Based Power System
Keywords: Renewable Energy; Power Systems; Stability Control
Chair 1: Lecturer Yang Zhou, Changsha University of Science and Technology, China
Yang Zhou is a Lecturer and supervisor with the National Key Laboratory of Power Grid Disaster Prevention and Reduction, Changsha University of Science and Technology. She received the Ph.D. degree in Electrical Engineering from TU Dortmund University in Germany in 2021. Afterwards, she was a Research Associate at the Institute of Energy Systems, Energy Efficiency and Energy Economics, Germany. She has published more than 40 academic papers, published 1 academic monograph, and authorized/applied for 8 patents. Her research interests include stability operation and control of hybrid AC and DC power systems.
Chair 2: Researcher Sunhua Huang, The Hong Kong Polytechnic University, China
Sunhua Huang received the Ph.D. degree in electrical engineering from Shanghai Jiao Tong University, Shanghai, China, in 2022. From 2018 to 2019, he worked as a Research Engineer with Huawei Technologies Company Ltd., Shenzhen, China. From 2022 to 2023, he worked as an Assistant Professor in the School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China. Currently, he is working as a Research fellow in the Hong Kong Polytechnic University. His research interests are power system stability control, renewable energy, grid-forming control and AC/DC microgrids. In the past 5 years, he has published more than 50 academic papers.
Chair 3: Assoc. Prof. Fanrong Wei, Huazhong University of Science and Technology, China
Fanrong Wei received the B.S., and Ph. D degrees in electrical engineering from Huazhong University of Science and Technology, China, in 2013 and 2018, respectively. From 2018 to 2020, he worked as a Research Associate at University of Rhode Island, US. From 2020 to 2023, he worked as an Assistant Professor at the School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, where he is now working as an Associate Professor. His researches mainly focus on cyber-physical security, microgrid scheduling and protective relay.
10. Intelligent wireless sensing and edge and cloud computing
Summary: This workshop explores the integration of intelligent wireless sensing with cloud-edge-end collaboration, focusing on how these technologies work together to enable real-time data processing, efficient resource management, and scalable solutions for diverse industrial applications. With advancements in wireless sensor networks (WSNs), edge computing, and cloud infrastructure, there is a growing need to understand how data collected from distributed sensors can be processed and analyzed collaboratively across different computing layers—cloud, edge, and end devices.
Key topics covered in the workshop include:
(1)Wireless Sensor Networks (WSNs): The latest developments in low-power, high-efficiency wireless sensors that collect data in dynamic environments such as manufacturing, healthcare, and smart cities.
(2)Edge Computing: The role of edge computing in performing localized, low-latency data processing near the data source, reducing cloud dependency for time-critical applications and enabling quicker decision-making at the network's edge.
(3)Cloud Computing: Leveraging the scalability and computational power of the cloud for large-scale data analysis, storage, and machine learning, where data is aggregated from various edge nodes for more comprehensive insights.
(4)End Computing (Devices): How devices at the end nodes (e.g., mobile devices, embedded systems) collaborate with the edge and cloud layers for real-time processing and feedback, making intelligent systems more responsive and adaptive.
(5)Cloud-Edge-End Collaboration: A deep dive into the collaborative approach, where edge computing offloads heavy tasks to the cloud while end devices manage real-time data collection and interaction. This hierarchical approach enables a balance between performance and resource optimization, ensuring efficient, low-latency data flow and decision-making.
(6)Intelligent Data Processing: The application of AI and machine learning models across cloud-edge-end infrastructure to enhance data processing capabilities, enabling predictive analytics, anomaly detection, and system optimization.
(7)Use Cases: Real-world applications demonstrating cloud-edge-end collaboration in industries such as smart manufacturing, precision agriculture, smart grids, and connected healthcare systems. These examples highlight how this integrated framework improves system performance, reduces latency, and increases energy efficiency.
The workshop emphasizes the challenges and opportunities in deploying intelligent wireless sensing with cloud-edge-end collaboration, discussing critical topics such as security, privacy, energy efficiency, system scalability, and the role of AI in enabling seamless data integration across the entire computing hierarchy.
(1)Wireless Sensor Networks (WSNs): The latest developments in low-power, high-efficiency wireless sensors that collect data in dynamic environments such as manufacturing, healthcare, and smart cities.
(2)Edge Computing: The role of edge computing in performing localized, low-latency data processing near the data source, reducing cloud dependency for time-critical applications and enabling quicker decision-making at the network's edge.
(3)Cloud Computing: Leveraging the scalability and computational power of the cloud for large-scale data analysis, storage, and machine learning, where data is aggregated from various edge nodes for more comprehensive insights.
(4)End Computing (Devices): How devices at the end nodes (e.g., mobile devices, embedded systems) collaborate with the edge and cloud layers for real-time processing and feedback, making intelligent systems more responsive and adaptive.
(5)Cloud-Edge-End Collaboration: A deep dive into the collaborative approach, where edge computing offloads heavy tasks to the cloud while end devices manage real-time data collection and interaction. This hierarchical approach enables a balance between performance and resource optimization, ensuring efficient, low-latency data flow and decision-making.
(6)Intelligent Data Processing: The application of AI and machine learning models across cloud-edge-end infrastructure to enhance data processing capabilities, enabling predictive analytics, anomaly detection, and system optimization.
(7)Use Cases: Real-world applications demonstrating cloud-edge-end collaboration in industries such as smart manufacturing, precision agriculture, smart grids, and connected healthcare systems. These examples highlight how this integrated framework improves system performance, reduces latency, and increases energy efficiency.
The workshop emphasizes the challenges and opportunities in deploying intelligent wireless sensing with cloud-edge-end collaboration, discussing critical topics such as security, privacy, energy efficiency, system scalability, and the role of AI in enabling seamless data integration across the entire computing hierarchy.
Keywords: Wireless Sensor Networks (WSNs); Edge Computing; Cloud Computing; End Devices; Cloud-Edge-End Collaboration; Real-time Data Processing; Intelligent Sensing; Predictive Analytics; AI-driven Systems; Scalable Solutions; Low-latency Communication; Industrial IoT (IIoT); Smart Manufacturing; Data Privacy and Security; Energy Efficiency.
Chair 1: Assoc. Prof. Chi Ma, Chongqing University, China
He has independently led six research projects, including a National Key Research and Development Program project (with a national funding allocation of 3.38 million RMB), National Natural Science Foundation of China (NSFC) general/young projects, Chongqing Natural Science Foundation general projects, China Postdoctoral Science Foundation projects, and Chongqing Returned Overseas Scholars Support Program projects. He has also participated in six other projects, including the Two Machines Major Special Projects, National Defense *73 Projects, NSFC key projects, 04 Major Science and Technology Special Projects, and National Key Research and Development Program projects.
As the first or corresponding author, he has published/accepted 50 SCI papers in renowned journals in the CNC equipment field, with an average impact factor (IF) of 6.7355 (Table 1, Appendix 6). Among these, 41 papers have an IF≥3.0, 29 papers have an IF≥6.0, and 21 papers have an IF≥8.0. He is World top 2% scientists reported by Stanford University. His papers have been cited more than 2200 times in SCI, with the highest citation for a single paper exceeding 150 times, and his H-index is 25. Non-repetitive citing authors include over 20 Fellows of CIRP/ASME/IEEE/IET/IMechE/AIAA/SAE/ASCE and academicians from the United States, Germany, Switzerland, Canada, Japan, and China.
His research has been praised as “capable of accurately simulating the temperature field and thermal deformation of spindle systems,” “yielding useful conclusions,” “reducing machine tool processing errors by over 30%,” “exhibiting excellent accuracy, convergence, and robustness,” “improving machining accuracy from 67% to 78% and 89%,” and “reducing dimensional errors while improving surface quality.”.
As the first or corresponding author, he has published/accepted 50 SCI papers in renowned journals in the CNC equipment field, with an average impact factor (IF) of 6.7355 (Table 1, Appendix 6). Among these, 41 papers have an IF≥3.0, 29 papers have an IF≥6.0, and 21 papers have an IF≥8.0. He is World top 2% scientists reported by Stanford University. His papers have been cited more than 2200 times in SCI, with the highest citation for a single paper exceeding 150 times, and his H-index is 25. Non-repetitive citing authors include over 20 Fellows of CIRP/ASME/IEEE/IET/IMechE/AIAA/SAE/ASCE and academicians from the United States, Germany, Switzerland, Canada, Japan, and China.
His research has been praised as “capable of accurately simulating the temperature field and thermal deformation of spindle systems,” “yielding useful conclusions,” “reducing machine tool processing errors by over 30%,” “exhibiting excellent accuracy, convergence, and robustness,” “improving machining accuracy from 67% to 78% and 89%,” and “reducing dimensional errors while improving surface quality.”.
Chair 2: Prof. Jialong He, Jilin University, China
Jialong He, born in February 1989 in Qishan, Shaanxi, is a Ph.D., professor, and doctoral supervisor. He is a member of the Communist Party of China and currently serves as the Deputy Director of the Key Laboratory of CNC Equipment Reliability Technology, Ministry of Industry and Information Technology, as well as the Assistant Director of the Key Laboratory of CNC Equipment Reliability, Ministry of Education. He also holds leadership roles as a member of the Academic Committee of the School of Mechanical and Aerospace Engineering and the Party Branch Secretary of the Department of Intelligent Manufacturing Engineering. He earned his Ph.D. from Jilin University in June 2017, where he remained as a faculty member, and was promoted to doctoral supervisor in 2019 and professor in 2022. His research focuses on reliability technologies for CNC manufacturing equipment, including fault diagnosis, predictive maintenance, structural reliability analysis, and generalized reliability design. He has led over 20 national and provincial-level projects, authored more than 30 SCI papers, and holds 50 authorized national patents. His team is recognized as one of China’s top research groups in CNC equipment reliability. Dr. He has received several prestigious awards, including the First Prize in the China Mechanical Industry Science and Technology Progress Award.
11. Machine Learning Empowered Fluid Machinery Design and Optimization
Summary: Fluid machinery is widely used in aerospace, petrochemical, industrial equipment and other fields, occupies a high proportion in the national economy. With the development of energy saving and emission reduction, fluid machinery represented by compressors, pumps, etc. have higher requirements for parameters such as performance, energy consumption ratio and noise. Recently, machine learning empowered fluid machinery design and optimization has received extensive attention from researchers. The addition of machine learning has made the intelligent integration of fluid machinery and the knowledge discovery of fluid mechanics more rapid and efficient.
The topics of this symposium mainly include the following directions:
Intelligence of fluid mechanics theory and approach.
Advanced fluid mechanical design methods.
Machine learning-assisted fluid machinery optimization technology.
The topics of this symposium mainly include the following directions:
Intelligence of fluid mechanics theory and approach.
Advanced fluid mechanical design methods.
Machine learning-assisted fluid machinery optimization technology.
Keywords: Machine learning, Fluid Mechanics Theory and Approach, Fluid Mechanical Design Methods, Fluid Machinery Optimization Technology.
Chair 1: Dr. Kunhang Li, Jiangsu University of Science and Technology, China
Li Kunhang, Ph.D., graduated from the School of Energy and Power Engineering, Xi'an Jiao Tong University, and is currently a teacher at the School of Energy and Power Engineering, Jiangsu University of Science and Technology. He is mainly engaged in the research of gas turbine blade experiment and ternary blade design/optimization methods. He has published 9 SCI papers, 7 international/domestic conference papers, 6 authorized invention patents, and 5 authorized software Copyrights.
Chair 2: Dr. Junwei Zhong, Jiangxi University of Science and Technology, China
Zhong Junwei, Ph.D., graduated from the School of Energy and Power Engineering, Xi'an Jiao Tong University. He works at the School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology and specializes in flow control and optimization of wind turbine blade. He has published 21 academic papers (including 8 SCI papers), 3 authorized invention patents, and 2 authorized software Copyrights.
12. Optimal sensor placement for fault diagnosis and health monitoring
Summary: In system status monitoring and fault diagnosis, sensors are used for signal extraction, and the effective placement of sensors is essential to improve diagnosis efficiency and accuracy. Thus, the arrangement of a limited number of sensors in the most reasonable position of the structure, that is, the optimal sensor placement (OSP) technology is a key issue in fault diagnosis. Over the last several years, various OSP methods have been developed for structural health monitoring systems, wireless sensor networks, building monitoring systems, environmental monitoring system and IoT systems. Establishing a model and communication mechanism model, correctly evaluating the layout of the sensor layout optimization plan, and the high-speed convergence of the optimization process is the core problem that the sensor layout is optimized. Related research can effectively improve the perceive performance of observation and monitoring systems, and can provide guidance for high-diagnosable design for equipment and structure. The topic of interest includes but not limited to:
Failure mechanism research;
Wireless sensor network design and optimization;
Structural health monitoring;
Pipe leak positioning;
Sensor optimization design;
Sensor placement optimization algorithm;
Observator design
System perception ability evaluation
High diagnostic design of the system and structure
Failure mechanism research;
Wireless sensor network design and optimization;
Structural health monitoring;
Pipe leak positioning;
Sensor optimization design;
Sensor placement optimization algorithm;
Observator design
System perception ability evaluation
High diagnostic design of the system and structure
Keywords: Optimal sensor placement; Fault diagnosis; Structural health monitoring
Chair 1: Dr. Xiangdi Kong, Hohai University, China
Xiangdi Kong received the B.S. degree in mechanical design, manufacturing, and automation and the Ph.D. degree in mechanical engineering from the China University of Petroleum, Qingdao, China, in 2017 and 2024, respectively. He is currently a lecturer with of College of Harbour, Coastal and Offshore Engineering from the Hohai University, Nanjing, China. His current research interests include optimal sensor placement and fault diagnosis of subsea blowout preventer system. He has published more than 20 research papers in such as IEEE SMCA, MSSP, OE, and ESWA, and 4 of them were selected as ESI highly cited papers.
13. Measurement Technology in Smart Manufacturing
Summary: Intelligent manufacturing puts forward higher requirements on measurement technology, such as higher accuracy, faster speed and larger range, which challenges the development of measurement technology.
The amount of measurement data in smart manufacturing is large, how to effectively handle and utilize these data is an important issue
Measurement technology in smart manufacturing needs to be deeply integrated with other technologies and systems, how to realize this is also a challenge.
The amount of measurement data in smart manufacturing is large, how to effectively handle and utilize these data is an important issue
Measurement technology in smart manufacturing needs to be deeply integrated with other technologies and systems, how to realize this is also a challenge.
Keywords: Measurement Technology, Smart Manufacturing
Chair 1: Assoc. Prof. Cuiping Zhang, Nanjing University of Science and Technology Zijin College, China
Research direction intelligent sensors and system predictive maintenance. Selected by Jurong City, Jiangsu Province, 2019 Fudi Talent Program, presided over and participated in four provincial and municipal projects, a number of horizontal topics, published more than 10 papers in important domestic and international journals, authorized one national invention patent and seven utility model patents.
Chair 2: Assoc. Prof. Li Wang, Nanjing University of Science and Technology Zijin College, China
Research direction: Digital design and manufacturing, intelligent equipment and environmental perception in coal mine tunnels, participated in one collaborative education project of the Ministry of Education, one natural science foundation project of Jiangsu Province, led two teaching and research projects of the Education Department, and more than 10 school level projects; Published over 20 papers, including 1 indexed by EI and 3 core papers by Peking University.
Chair 3: Prof. Jianxin Deng, Guangxi University, China
Jianxin Deng is currently a professor and doctoral supervisor in mechanical engineering at Guangxi University, and vice director at Guangxi Key Lab of Manufacturing System and Advanced Manufacturing Technology, China. He earned his master’s degree in Industrial Engineering from Chongqing University, China, and his Ph.D. degree in mechanical engineering from South China University of Technology, China. His research interests include intelligent manufacturing, manufacturing systems and informatics, squeeze-casting technology and sharing manufacturing. And recently, he mainly focused on the basic theory, key technology and industrial software of data-driven manufacturing process design, intelligent processing based on industrial robots, digital twins, and sharing manufacturing. He was granted Natural Science Foundation of China, Guangxi Key Research and Development Program of China, etc. He has published over 60 related academic papers and holds 15 national invention patents and a U.S. patent.
14. Context-Aware Internet of Things (IoT) Intelligent Interaction Model
Summary: The context-aware IoT intelligent interaction model is a model that utilizes contextual information from the environment and users to improve interactions between IoT devices. In traditional IoT systems, interactions between devices are typically static and rule-based, lacking the ability to perceive and respond to environmental and user states. However, the context-aware intelligent interaction model achieves more intelligent and personalized services by comprehensively considering factors such as environmental information, user behavior, and preferences. With the rapid development of Internet of Things (IoT) technology, the context-aware intelligent interaction model has attracted widespread attention in the IoT field. This model emphasizes the use of contextual information from the environment and users to improve interactions between IoT devices, thereby achieving smarter and more personalized services. However, achieving this goal faces many challenges, including technical issues such as acquiring, analyzing, and applying context data. This workshop aims to explore the principles, applications, and challenges of context-aware IoT intelligent interaction models. We will share the latest research findings and practical experiences, as well as discuss future development directions and solutions.
Keywords: Intelligent Interaction Model, Internet of Things (IoT), Context-Aware
Chair 1: Assoc. Prof. Ang Li, Nanjing University of Science and Technology Zijin College, China
Li Ang is an Associate Professor at Nanjing University of Science and Technology Zijin College, China. In 2008, he obtained his Bachelor's degree in Engineering from Nanjing Forestry University, China, followed by a Master's degree in Engineering from Guilin University of Electronic Technology, China, in 2011. Later, he earned his Ph.D. from Nanjing University of Posts and Telecommunications, China. In 2020, Dr. Li Ang was selected as an outstanding young backbone teacher candidate of the "Qinglan Project" in Jiangsu Province, China. Dr. Li has published relevant papers in journals such as IEEE SENSORS JOURNAL and the Journal of Function Spaces. His research primarily focuses on image processing, deep learning, Internet of Things (IoT), saliency regions, and video processing.
Chair 2: Assoc. Prof. Kejing Wei, Nanjing University of Science and Technology Zijin College, China
Wei Kejing is an Associate Professor at Nanjing University of Science and Technology Zijin College.Her Mainly engaged in atmospheric detection, intelligent transportation and other fields of research. She Presided over and participated in 16 scientific research projects , published more than 20 academic papers, applied for 5 invention patents and utility model patents, and won the second and third prizes of provincial and ministerial science and technology progress.
15. Machining Planning and Control of Intelligent Robots
Summary: The structure of key parts for high-end equipment is becoming gradually complicated and large-scale, which poses challenges to high-precision machining and detection technology. Industrial robots represented by multi-axis serial robots are characterized by multiple degrees of freedom, strong flexibility and wide spatial accessibility. With corresponding machining and detection devices, industrial robots provide an effective solution for the in-situ integrated high-precision machining and detection of local complex features of key parts of high-end equipment. Due to the complex structure of key parts and the limited operating space of robots, some problems need to be solved in the process of robot machining and detection, including 1) difficult feature recognition of key parts, 2) robot end vibration caused by rapid changes of robot joint acceleration, 3) serious cumulative effect of robot end trajectory error, 4) increasing flexible effect of robot joints and links etc. The above technical problems have promoted the research of industrial robots in the fields of visual tracking and guidance, complex motion trajectory planning, end error compensation and accuracy improvement, and stiffness performance improvement etc. Nowadays, the machining planning and control of industrial robots has become one of the key technologies to realize high-precision machining and detection of key parts of high-end equipment.
Keywords: Intelligent robot, integration of machining and detection, robot machining planning, robot control, machine vision
Chair 1: Assist. Prof. Ze Wang, Tsinghua University, China
Dr. Wang received the B.S. degree in automation from the School of Electricity Engineering and Automation, Tianjin University, Tianjin, China, in 2015, and the Ph.D. degree in mechanical engineering from the Department of Mechanical Engineering, Tsinghua University, Beijing, China, in 2020. He is currently an assistant professor with the Department of Mechanical Engineering, Tsinghua University. His research interests include precision motion control, neural network adaptive control, and high-performance multi-axis contouring control. Dr. Wang was the recipient of the Best Student Paper Finalist at the 2017 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, the 2017 Best Student Paper from the IEEE Internal Conference on Information and Automation, and 2019 Top Grade Scholarship for Graduate Students of Tsinghua University.
Chair 2: Assist. Prof. Heng Shi, Tsinghua University, China
Heng Shi is currently an Assistant Research Fellow in the Department of Precision Instrument at Tsinghua University. He earned his Ph.D. in Computer Science and Technology from Tsinghua University in 2020, following M.S. and B.S. degrees in Astronautics from Beihang University in 2015 and 2012, respectively. Dr. Shi's research primarily focuses on the guidance and control of vehicles, particularly in the context of unmanned aerial vehicles (UAVs). His current projects involve advancing cooperative guidance techniques and enhancing intelligent control systems through innovative applications of sensor fusion and reinforcement learning. Throughout his career, Dr. Shi has led several national scientific projects, published over 30 papers in esteemed journals and conferences, and holds 14 authorized national invention patents. He also contributes to the academic community as a Guest Editor and Topical Advisory Panel member for the journal Drones, and he has served as a reviewer for several top-tier journals. His work significantly impacts the field of autonomous systems, driving advancements in UAV technology and applications.
16. New Intelligence Optimization Algorithms and its Application in Signal Processing
Summary: With the continuous advancement of scientific research and the integration of various disciplines, traditional methods from a single field are no longer sufficient to effectively address the technical challenges encountered in practical engineering applications. The emergence of intelligent optimization algorithms, such as evolutionary computation, swarm intelligence, and heuristic algorithms, has profoundly changed the way we tackle complex problems in the field of signal processing, opening up new opportunities for innovation and improvement. The application of intelligent optimization algorithms in signal processing has led to significant progress, particularly in areas such as array signal processing and beamforming, communication signal processing, biomedical signal processing, graph signal processing, and acoustic signal processing, driving substantial technological innovation and development. These algorithms have not only improved the efficiency and accuracy of signal processing but also provided more flexible and efficient solutions for systems. As intelligent computing technologies continue to evolve, we anticipate the application of more intelligent and efficient solutions in signal processing and related fields, laying a solid foundation for the development of digital society and the intelligent future. However, as the application of intelligent optimization algorithms expands in practical engineering, the challenges it faces are becoming increasingly complex. To address these challenges, there is a pressing need to design intelligent optimization algorithms with high precision, superior performance, and fast computation speed. By integrating intelligent computing theory with the practical engineering applications in signal processing, developing new methods that can overcome the limitations of existing algorithms will provide new perspectives for both theoretical research and practical applications in signal processing, holding significant theoretical and practical value.
This symposium aims to provide a platform for academic researchers and industry professionals to share and discuss the latest advancements in intelligent optimization algorithms and their innovative applications. We invite authors to submit original research and review articles related to intelligent optimization algorithms, covering topics including, but not limited to, array signal processing and beamforming, communication signal processing, biomedical signal processing, graph signal processing, acoustic signal processing, and other fields related to the integration and application of intelligent optimization algorithms.
This symposium aims to provide a platform for academic researchers and industry professionals to share and discuss the latest advancements in intelligent optimization algorithms and their innovative applications. We invite authors to submit original research and review articles related to intelligent optimization algorithms, covering topics including, but not limited to, array signal processing and beamforming, communication signal processing, biomedical signal processing, graph signal processing, acoustic signal processing, and other fields related to the integration and application of intelligent optimization algorithms.
Keywords: Intelligent optimization algorithm, array signal processing and beamforming, communication signal processing, biomedical signal processing, graph signal processing, acoustic signal processing
Chair 1: Assoc. Prof. Hongyuan Gao, Harbin Engineering University, China
Hongyuan Gao, received a Ph.D. degree in Communication and Information System from Harbin Engineering University. He works as an associate professor at the College of Information and Communication Engineering, Harbin Engineering University. His research interests include wireless communications, intelligent computing, artificial intelligence, signal processing, image processing, Internet of Things, massive MIMO, 6G communication, signal recognition and classification.
He has undertaken more than 20 projects such as the National Natural Science Foundation, Special Grant from the China Postdoctoral Science Foundation, China Postdoctoral Science Foundation, etc. He has won three first prizes for scientific and technological progress at provincial and ministerial levels. He has participated in more than 20 projects including the National Natural Science Foundation of China and various horizontal and vertical projects. Based on these projects, he published more than 100 papers, more than 180 patents and 2 monographs.
He has undertaken more than 20 projects such as the National Natural Science Foundation, Special Grant from the China Postdoctoral Science Foundation, China Postdoctoral Science Foundation, etc. He has won three first prizes for scientific and technological progress at provincial and ministerial levels. He has participated in more than 20 projects including the National Natural Science Foundation of China and various horizontal and vertical projects. Based on these projects, he published more than 100 papers, more than 180 patents and 2 monographs.
Chair 2: Prof. Longzhe Han, Nanchang Institute of Technology, China
Longzhe Han, received a PhD degree in Computer Science and Engineering from Korea University, Korea, under the supervision of Prof. Hoh Peter In and Prof. Minho Jo. He works as a Professor at the college of Information Engineering at Nanchang Institute of Technology. His research interests include Intelligent Endogenous Networks, Semantic Communications, Information Centric Networking (ICN), Mobile Edge Computing (MEC), and Reinforcement Learning.
He has led and participated in multiple research projects, including serving as PI for two National Natural Science Foundation of China projects and one research initiation project for returned overseas scholars funded by the Ministry of Education. He has also participated in five National Natural Science Foundation projects and more than ten provincial-level projects. He has published over 60 research papers and currently serves as an area editor for the SCI journal KSII Transactions on Internet and Information Systems.
He has led and participated in multiple research projects, including serving as PI for two National Natural Science Foundation of China projects and one research initiation project for returned overseas scholars funded by the Ministry of Education. He has also participated in five National Natural Science Foundation projects and more than ten provincial-level projects. He has published over 60 research papers and currently serves as an area editor for the SCI journal KSII Transactions on Internet and Information Systems.
Chair 3: Researcher Jin Yue, Dalian Institute of Measurement and Control Technology, China
Yue Jin, received his Master degree in Marine Engineering and Automation department in Dalian Maritime University, currently employed at the Dalian Institute of Measurement and Control Technology. He mainly engages in research on machine learning, data mining, virtual testing and verification, and related software development.
In recent years, he has led 2 national-level key basic scientific research projects and participated in 10 national-level basic scientific research projects. And he has published 10 papers, holds 6 patents, contributed to 1 national standard, and owns 8 software copyrights.
In recent years, he has led 2 national-level key basic scientific research projects and participated in 10 national-level basic scientific research projects. And he has published 10 papers, holds 6 patents, contributed to 1 national standard, and owns 8 software copyrights.
17. Power Electronic Converter Control
Summary: Power electronic converters play a crucial role in the field of electrical engineering, widely used in the drive of motors, energy conversion in power systems, and more. Efficient control methods ensure the stable and reliable operation of power electronic converters. Therefore, control methods have always been a research hotspot and difficulty in the field of power electronic converters. . This workshop focuses on the control of power electronic converters and can involve the following topics: (1) Research on control methods for implementing soft switching technology in power electronic converters; (2) Research on control methods for power electronic converter to drive motors; (3) Research on control methods for power electronic converters to achieve energy conversion in power systems, etc. The research content is not limited to the above three aspects, as long as it is related to the control of power electronic converters.
Keywords: Power Electronic Converter; Control Method; High Efficiency
Chair 1: Assoc. Prof. Binbin Wang, Nanjing University of Science and Technology ZiJin College, China
Binbin Wang, associate professor, master degree candidate, graduated from Zhejiang University and majored in Electrical Engineering. Serve as the department head of Electrical Engineering at Nanjing University of Science and Technology ZiJin College. Dedicated to research on the application of power electronics in power systems, focusing on cutting-edge technologies such as new energy technology, distributed generation, microgrids, flexible direct current transmission, and active distribution networks. Published 6 scientific research papers (including 2 indexed in EI and 2 in Chinese Core Journals) and 5 research papers. Participated in 3 provincial-level scientific research and teaching projects, led 5 school level scientific research and teaching projects, and obtained 1 invention patent. Edited a textbook and obtained the provincial-level 14th Five Year Plan textbook.
Chair 2: Assist. Lecturer Weijian Wang, Nanjing University of Science and Technology ZiJin College, China
Weijian Wang, assistant lecturer, received B.E. degree in electrical engineering and automation, and first M.S. degree in systems science from Nanjing University of Information Science and Technology, Nanjing China, in 2014 and 2017. Received second M.S degree in power systems engineering from University College London (UCL), London UK, in 2021. Now working as a assistant lecturer in School of Electronic and Optical Engineering, Nanjing University of Science and Technology ZiJin College. Dedicated to research on power electronic converters and new energy generation. Published 3 scientific research papers (including 1 indexed in CPCI-S and 1 in Chinese Core Journals) and accepted 2 scientific research papers (1 in Chinese Core Journals and 1 in Chinese Science and Technology Core Journals).
18. GNSS Technologies For Disaster Monitoring
Summary: The Global Navigation Satellite System (GNSS), with its high-precision positioning and all-weather, global coverage capabilities, has gradually become one of the key technologies in disaster monitoring, assessment, and early-warning systems. To promote the development and application of this technology, the workshop aims to explore the innovative applications of GNSS technology in disaster monitoring and discuss how information sensing and fusion technologies can improve the accuracy and efficiency of disaster monitoring. The topics include GNSS-based natural disaster monitoring, information sensing and fusion, disaster monitoring (such as typhoons, extreme precipitation, storm surges, etc.), disaster warning, and emergency response. The workshop will discuss how the fusion of GNSS with meteorological and other multi-source information can enhance disaster monitoring and emergency response capabilities. It will also share successful application cases and future technological development directions. This event aims to provide a platform for experts and scholars engaged in GNSS technologies for disaster monitoring research to share scientific achievements and cutting-edge technologies, understand academic trends, expand research perspectives, strengthen academic discussions, and promote collaboration between academic achievements and industry. We sincerely invite experts, scholars, industry professionals, and other relevant individuals from universities, research institutions, and enterprises at home and abroad to attend and exchange ideas!
Keywords: GNSS technologies; information fusion; disaster monitoring;
Chair 1: Prof. Rui Sun, Nanjing University of Aeronautics and Astronautics, China
Rui Sun is currently a professor in the College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu Province, China. She has a Ph.D. in Intelligent Transport Systems (ITS) from the Imperial College Engineering Geomatics Group (ICEGG) at Imperial College London and holds a M.Sc. in Satellite Positioning Technology from the University of Nottingham. She is also the recipient of the National Natural Science Foundation of China’s Excellent Young Scientists Fund and the Chief Scientist of the National Key R&D Program and the Deputy Director of the Ministry of Education Key Laboratory of Maritime Intelligent Cyberspace Technology. Her research interests include navigation and positioning theories, methods, and applications. She has published 84 academic papers in prestigious international journals and conferences, including 41 papers indexed by SCI, such as in GPS Solutions, IEEE Internet of Things Journal, and Satellite Navigation. She holds over 10 academic positions, including being a Fellow of the Royal Institute of Navigation and an editorial board member of the Journal of Navigation.
Chair 2: Prof. Yuanjin Pan, Nanjing University of Information Science and Technology, China
Dr. Yuanjin Pan is a distinguished Longshan Scholar Professor at Nanjing University of Information Science and Technology, with a doctoral degree in Geodesy and Geomatics from Wuhan University, obtained in 2017. His academic career is marked by a robust research portfolio focusing on GNSS data processing, hydrogeodesy, multi-source satellite data fusion, geodynamics, and satellite gravimetry. Dr. Pan has made significant contributions to his field, evidenced by over 60 academic papers published in reputable journals, with 26 of these as the first or corresponding author in SCI-indexed publications. His work has been recognized with more than 1300 citations, reflecting the impact of his research. In addition to his scholarly publications, Dr. Pan has been granted 4 invention patents and has active involvement in 20 academic journals as a reviewer, contributing to the peer-review process and upholding academic standards. He has successfully secured and managed multiple national and provincial research projects, including 2 projects funded by the National Natural Science Foundation of China, demonstrating his capability in research leadership and project management.
19. Electromagnetic Sensing and Detection
Summary: Electromagnetic sensing and detection technology is a field that focuses on the use of electromagnetic fields to identify, locate, and characterize objects or phenomena. This technology plays a crucial role in various applications, including radar systems, medical imaging, and environmental monitoring. Electromagnetic sensing relies on the interaction between electromagnetic waves and materials, allowing for the detection of changes in properties such as conductivity, permittivity, and permeability. The detection process can be influenced by factors such as frequency, wavelength, and the environment's composition. Advances in materials science, signal processing, and computational methods have significantly enhanced the capabilities of electromagnetic sensing and detection systems. This workshop aims to explore the latest developments, applications, and challenges in electromagnetic sensing and detection. We will discuss innovative techniques, share experimental results, and address the technical challenges faced in this field, including signal-to-noise ratio improvement, resolution enhancement, and real-time data processing.
Keywords: Electromagnetic Sensing, Detection Technology, Environmental Monitoring
Chair 1: Assoc. Prof. Luyao He, Shenyang University of Technology, China
Luyao He is an associate professor at Shenyang University of Technology and a senior member of the China Instrument and Control Society, as well as a member of the China Mechanical Engineering Society. She is currently engaged in research on advanced online detection and vision detection technology. Over the past five years, she has presided over 1 Youth Fund project from the National Natural Science Foundation of China and has led or participated in 10 provincial and ministerial-level scientific research projects and horizontal topics. She has published more than 30 academic papers, with 9 indexed by SCI/EI, and has been granted more than 30 invention patents. She has won the first prize of the "Teaching Achievement Award" in Liaoning Province; she has also won one first prize, one second prize, and one third prize for natural academic achievements in Shenyang City; she has published 20 high-level academic papers, among which 15 are indexed by SCI/EI; she has been granted and applied for 31 national and international invention patents; she has participated in one university-level teaching reform project.
Chair 2: Prof. Bin Liu, Shenyang University of Technology, China
Bin Liu is the Dean of the School of Information Science and Engineering at Shenyang University of Technology and the Director of the Liaoning Province Graduate Innovation and Academic Exchange Center for Instrument Science and Technology. He is a Professor and doctoral supervisor, recognized as an Expert with Special Government Allowance from the State Council. Liu has undertaken more than 60 projects, including national Ministry of Industry and Information Technology research projects, key projects of the Equipment Development Department, the National Natural Science Foundation of China, international cooperation projects, as well as key provincial and ministerial projects and enterprise horizontal projects. He has received one First Prize for Scientific and Technological Progress in Liaoning Province and two First Prizes for Teaching Achievements in Liaoning Province. In recent years, his achievements have been applied in major pipeline projects such as the West-East Gas Pipeline and the South-to-North Gas Pipeline, creating direct economic and social benefits exceeding 10 billion yuan. He has published over 200 high-level academic papers, authored 4 books, and holds more than 70 national and international invention patents.
20. Signal Processing for New Generation Wireless Communications
Summary: The new generation wireless communications exhibit a great development potential in near future and in particular, 6G and satellite communications have presented many opportunities as well as challenges in front of us. Facing such challenges, a number of issues need to be addressed and new techniques need to be developed.
In this workshop, we will discuss the related issues of signal processing in new generation wireless communication systems and will explore some topics including but not limited to the new system implementations, optimization algorithms, MIMO and array beamforming, measurement techniques, adaptive antennas, system architectures, modulation/demodulation methods, channel estimations, equalization techniques, etc. Authors with other relevant topics are also welcome to participate.
In this workshop, we will discuss the related issues of signal processing in new generation wireless communication systems and will explore some topics including but not limited to the new system implementations, optimization algorithms, MIMO and array beamforming, measurement techniques, adaptive antennas, system architectures, modulation/demodulation methods, channel estimations, equalization techniques, etc. Authors with other relevant topics are also welcome to participate.
Keywords: Array signal processing, MIMO, beamforming, adaptive algorithm
Chair 1: Prof. Yuanping Zhou, Sichuan University, China
Yuanping Zhou received the B.S. degree from the Chongqing University, China, in 1982, the M.S. degree from the University of Illinois at Chicago, U.S.A. in 1986, and the Ph.D. degree from the Georgia Institute of Technology, Atlanta, U.S.A. in 1999, all in electrical engineering. From 2000 to 2002, he worked as a Lead Systems Engineer with the Motorola, Inc., Illinois, U.S.A., where he received the Motorola Invention Award, 2001. From 2002 to 2006, he was a Professor and Doctoral Advisor with the Department of Electronics and Communications Engineering, Zhongshan University, Guangzhou, China. In Dec. 2006, he joined the faculty of the School of Electronics and Information Engineering, Sichuan University, Chengdu, China and has been a Professor and Doctoral Advisor since then. During the period of 2015 and 2021, he served as an Associate Editor of the Chinese Journal of Electronics.
His research interests include array signal processing, adaptive antennas, MIMO wireless communication systems, phased array antennas, antenna beamforming techniques, space-time signal processing.
His research interests include array signal processing, adaptive antennas, MIMO wireless communication systems, phased array antennas, antenna beamforming techniques, space-time signal processing.
21. New Signal Processing Theory, Methods and Applications
Summary: Various non-stationary signals have emerged in fields such as radar, communication, navigation, and machinery, posing challenges for the control, measurement, and processing of non-stationary signals. High precision, low complexity, and real-time intelligent signal processing are the unified yet contradictory goals of signal processing theory development, and it is precisely for this reason that new signal processing theories emerge one after another. The practical problems in the engineering field have raised new demands for signal processing theory, while researchers in the field of signal processing have provided new methods for engineering application theory. This forum aims to provide a communication platform for researchers in the field of engineering applications and signal processing theory to exchange signal processing theories, methods, and their applications. We look forward to the emergence of new signal processing methods and applications through the collision of ideas.
Keywords: Signal processing, non-stationary signals, time-frequency analysis, intelligent signal processing
Chair 1: Prof. Jun Shi, Harbin Institute of Technology, China
Shi Jun, professor and doctoral supervisor at Harbin Institute of Technology. Visiting scholar at the University of California, Los Angeles and the University of Delaware in the United States. Mainly engaged in research on new signal processing theories, methods, and applications. Hosted more than 10 projects, including the National Natural Science Foundation of China General Program and the National Basic Strengthening Program of various ministries and commissions. Published 4 academic monographs, authorized more than 20 national invention patents, and published over 50 academic papers in high-level academic journals such as IEEE Transactions on Signal Processing and Science China Information Sciences in the field of signal processing at home and abroad. Received the Second Prize of Natural Science in Heilongjiang Province, the Ten Year Continuous Influence Award of Science China Information Sciences, and the Outstanding Author of China Science and Science Bulletin.
Chair 2: Assoc. Prof. Hongxia Miao, Beijing University Of Posts and Telecommunications, China
Miao Hongxia, Associate Researcher and Doctoral Supervisor at Beijing University of Posts and Telecommunications. Mainly engaged in research on non-stationary signal processing theory and its application in communication. Hosted projects such as the National Natural Science Foundation of China Youth Program and the Beijing Natural Science Foundation Joint Fund. Published one academic monograph and over 20 papers in international high-level academic journals such as IEEE Transactions on Signal Processing and Signal Processing in the field of signal processing. Received an excellent doctoral thesis from Beijing Institute of Technology and was selected for the Youth Talent Support Project of the Beijing Interdisciplinary Science Society.