Special Session #01 AI for Failure Analysis and Reliability of Integrated Circuit(IC)
Special Session #03 Advanced Sensing, Monitoring and Diagnosis of Renewable Energy batteries
Special Session #05 Signal Decomposition-Integrated Intelligent Mechanical Fault Diagnosis and Remaining Useful Life Prediction
Special Session #08 Advanced Signal Processing Technologies Based on AI and Its Applications
AI for Failure Analysis and Reliability of Integrated Circuit(IC)
Session Organizers:
Prof. Ke Li, Jiangnan University,
Email: like@jiangnan.edu.cn
Prof. Guanglan Liao, Huazhong University of Science and Technology,
Email: guanglan.liao@hust.edu.cn
Assoc. Prof. Lei Su, Jiangnan University
Email: lei_su2015@jiangnan.edu.cn
Download: Special Session #1.pdf
Failure analysis and reliability of integrated circuits(IC) are critical aspects in ensuring the performance and longevity of electronic devices. With the increasing complexity of IC and the demand for higher reliability, AI becomes more prominent to aid in failure analysis and reliability assessment. AI can significantly improve the accuracy of defect inspection, realize proactive maintenance of IC equipment, and enhance overall electronic product reliability. By leveraging AI in IC industry, manufacturers can reduce downtime, improve product quality, and ultimately enhance customer satisfaction. This special session aims to showcase the current innovations and latest advancements of AI-enabled methodologies for failure analysis and reliability of ICs.
Research interests include but are not limited to:
• Modeling and simulation for defect inspection
• Defect exposure and characterization
• Visual/image analytics
• Signal processing
• Ultrasonic inspection
• Vibration inspection
• X-ray inspection
• Lock-in thermography inspection
• Reliability modeling
• Methodologies for reliability data collection and analysis
• Machine learning for prognosis and reliability
• Reliability evaluation method
• Reliability assessment for new applications of IC
• Life prediction of IC products
• Other related topics
Artificial Intelligence in Condition Monitoring, Fault Diagnosis and Maintenance of Industrial Equipment
Session Organizers:
Prof. Dr. Ling Xiang, North China Electric Power University, China
Email: xiangling@ncepu.edu.cn
Prof. Dr. Dong Zhen, Hebei University of Technology.
Email: d.zhen@hebut.edu.cn
Download: Special Session #2.pdf
Significance of the topic
The condition monitoring, fault diagnosis and maintenance of industrial equipment can improve the reliability of machine avoiding catastrophic accidents and promote the development of industry. Intelligent and data-driven condition monitoring and fault diagnosis of industry machinery has attracted more attention, which does not require strict mathematical and physical models. In recent years, many advanced artificial intelligence techniques have been developed rapidly, which also promote the progress of machinery fault diagnosis. This special session aims to showcase the latest development and achievements of advanced artificial intelligence method and the applications in the condition monitoring, fault diagnosis and maintenance of various industrial equipment.
The topics of interest include, but are not limited to:
• Deep learning tools
• Novel machine learning algorithms
• Intelligent forecasting
• Intelligent anomaly detection and early warning for various industrial equipment.
• Neural networks
• Data-driven techniques
• Novel diagnosis techniques and measurement systems.
• Health condition assessment and intelligent maintenance of equipment.
Advanced Sensing, Monitoring and Diagnosis of Renewable Energy batteries
Session Organizers:
Chair：
Prof. Lei Mao, University of Science and Technology of China
E-mail: leimao82@ustc.edu.cn
Co-Chair:
Prof. Yan Lv, Beijing University of Technology
E-mail：lvyan@bjut.edu.cn
Dr. Hang Wang, Anhui University
E-mail: hangwang@ahu.edu.cn
Dr. Dongzhen Lyu, Wenzhou University
E-mail: lvdongzhen@hrbeu.edu.cn
Download: Special Session #3.pdf
With the deepening of clean and low-carbon transformation of energy, large-scale development and utilization of renewable energy require the safe operation of renewable energy batteries, which prompts advanced sensing technology to detect and monitor the security of renewable energy batteries. Based on advanced sensing technology, the operation condition of batteries can be in-situ detected and monitored. Once abnormal states and faults are occurred, state identification and diagnosis will be further implemented. More importantly, advanced sensing technology provides multi-dimensional data reflecting the detailed status of battery, from which data processing methods (including artificial intelligence algorithms) can be applied to explore more internal information of batteries for investigating state evolution mechanism and fault early warning.
Research interests include but are not limited to:
• Advanced sensing technology and its application for renewable energy batteries, such as proton exchange membrane fuel cell (PEMFC), lithium ion battery, sodium-ion batteries.
• Condition monitoring of renewable energy batteries.
• State identification and diagnosis of renewable energy batteries.
• Early warning of renewable energy battery fault.
Diagnostic knowledge interpretation, transfer and incremental learning for critical components of equipment under variable operating conditions
Session Organizers:
Prof. Changqing Shen, Soochow University, China
Email: cqshen@suda.edu.cn
Assoc. Prof. Dong Wang, Shanghai Jiao Tong University
Email: dongwang4-c@sjtu.edu.cn
Download: Special Session #4.pdf
Significance of the topic:
With the advancement of industrial automation and intelligence, it is becoming increasingly important to accurately diagnose the health of critical components of equipment. In particular, the diagnosis of critical components faces greater challenges under variable operating conditions. Traditional diagnosis methods may not be able to adapt to this complex and dynamically changing environment. Therefore, diagnostic knowledge interpretation, transfer and accumulation become particularly important in equipment critical component fault diagnosis. This special session aims to discuss and present the latest research progress and application cases of diagnostic knowledge interpretation, transfer and incremental learning for the diagnosis of critical components of equipment in variable operating conditions. Researchers and practitioners from academia and industry are welcome to share their experiences, challenges and solutions.
Research interests include but are not limited to:
• Multi-source data fusion, physical interpretation and knowledge transfer
• Domain adaptation and generalization methods for fault diagnosis
• Cross-domain learning for knowledge transfer and accumulation
• Incremental learning of critical components fault diagnosis model
• Adaptive fault diagnosis model for variable operating conditions
• Critical components diagnostics under variable operating conditions
• Critical components diagnostics with dynamic datasets
Session Organizers:
Assoc. Prof. Juan Xu, Hefei University of Technology
Email: xujuan@hfut.edu.cn
Prof. Weiguo Huang, Soochow University
Email: wghuang@suda.edu.cn
Download: Special Session #5.pdf
Practical mechanical systems usually operate under variable speed, load, and strong noise conditions, which complicate the vibration signal. Advanced signal decomposition algorithms are needed to decompose vibration signal into a series of sub-signal component with clear physical interpretation. The accurate fault characteristic extraction of univariate or multivariate signal is fundamental to mechanical fault diagnosis (FD) and remaining useful life (RUL) prediction. Furthermore, when the deep learning model uses the original vibration signal as input, the poor robustness and interpretability of the model have always been a bottleneck. The time-varying characteristics of the vibration signal demands high requirements on the generalization ability of the model, making it difficult to guarantee the mechanical diagnostic accuracy under unknown working conditions. It is of great significance to integrate signal decomposition algorithms into deep learning to achieve end-to-end mechanical FD and RUL prediction.
The topics of interest include, but are not limited to:
Session Organizers:
Chair：
Prof. Yu Chen, Xi’an Jiaotong University
E-mail: chenyu@xjtu.edu.cn
Co-Chair：
Prof. Shulin Liu, Institute of High Energy Physics, Chinese Academy of Science
E-mail: liusl@ihep.ac.cn
Prof. Yu Huang, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences E-mail: ssshycn@163.com
Prof. Zicai Shen, Beijing Institute of Spacecraft Environment Engineering, China Academy of Space Technology
E-mail: zicaishen@163.com
Prof. Zhengxu Huang, Guangzhou Hexin Instrument Co., Ltd.
E-mail: zx.huang@hxmass.com
Download: Special Session #6.pdf
As the demand for high-performance materials in critical fields such as aerospace, semiconductor chips, and photovoltaics continues to grow, the importance of material surface processing is also increasing. Photoelectron Spectroscopy instrument and analysis techniques, such as PYS, UPS, XPS, IPES, AES, etc., play a significant role in understanding the surface chemical properties of materials, investigating surface modifications, and developing new devices. This special session aims to bring together experts and scholars in the research and application of photoelectron spectroscopy instruments to discuss the latest progress of different photoelectron spectroscopy instruments and their applications in various fields.
Research interests include but are not limited to:
• Photoelectron spectroscopy instrument technology (PYS, XPS, UPS, IPES, AES, SEY, etc.)
• Key components of photoelectron spectroscopy instrument
• Photoelectron spectroscopy analysis technology
• Surface characteristic analysis method
• Theoretical modeling and calculation in photoelectron spectroscopy field
• Applications of photoelectron spectroscopy instrument technology
Advanced Sensing, Data Processing and Big Data Analytics for New Type Power Systems
Session Organizers:
Chair：
Associate Prof. Min Zhang, Northwest University
E-mail: dr.zhangmin@nwu.edu.cn
Co-Chair：
Associate Prof. Jiang Wu, Xi'an Polytechnic University
E-mail: wujiang@xpu.edu.cn
Associate Prof. Chi Chen, Xi’an University of Technology
E-mail: chenchi@xaut.edu.cn
Dr. Shuang Wang, Xi’an Jiaotong University
E-mail: shuang@xjtu.edu.cn
Download: Special Session #7.pdf
With the deepening of clean and low-carbon transformation of energy, large-scale development and utilization of renewable energy, distributed energy, and energy storage are developing rapidly. Digital technology is used to empower the traditional power grid, continuously improve the IntelliSense ability, interaction level, and operation efficiency of the power grid, effectively support various energy access and comprehensive utilization, and continuously improve energy efficiency. Based on the research of new sensing, monitoring, and diagnosis technology, the modern information technology and advanced communication technology, such as mobile Internet and artificial intelligence, are fully applied to realize the comprehensive condition IntelliSense, efficient information processing, convenient and flexible application for key equipment in smart grid, such as transformers, switches, cables, power transmission lines, wind turbines, solar photovoltaic systems. This session is intended to focus on the intellisense, monitoring, and diagnosis in the new type power system.
Research interests include but are not limited to:
• Intellisense technology and its application for power equipment and renewable energy conversion system
• Conditioning monitoring and diagnosis for transformer, GIS, cable, power transmission line, wind turbine, photovoltaic system, etc.
• Related research on edge computing, artificial intelligence, big data，and data processing
Session Organizers:
Prof. Qieshi Zhang, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences
E-mail: qs.zhang@siat.ac.cn
Assoc. Prof. Ziliang Ren, Dongguan University of Technology
E-mail: renzl@dgut.edu.cn
Dr. Min Xue, Xidian University
E-mail: xmgrace0825@163.com
Download: Special Session #8.pdf
Signal processing has soared in popularity across domains encompassing target detection and recognition, trajectory prediction, intention analysis and understanding, image reconstruction, and environmental surveillance. By leveraging cutting-edge signal processing technologies, we propel the precision and efficiency of data manipulation in these applications, thereby igniting technological breakthroughs across myriad disciplines. This prestigious forum aims to showcase and disseminate novel perspectives and groundbreaking advancements in radar signal processing and its vast array of applications, fostering a collaborative atmosphere that brings together experts, scholars, and engineers from across the globe.
The topics of interest include, but are not limited to:
• High-resolution imaging, noise suppression, multi-target detection and tracking technologies
• Advanced array antenna beamforming and direction of arrival estimation methods
• Environment reconstruction and intelligent sensing methods
• The application of artificial intelligence in radar signal processing
• Advanced Radar Signal Processing and its application
• Intention information understanding and analysis, such as action signal processing, emotional signal processing, respiratory signal, millimeter wave signals, biomedical signal, multi-modal signal fusion
• Quartz MEMS sensor signal detection and analysis
• Other signal processing technology and its applications
15th August 2024 31st August 2024- Manuscript Submission
1st October 2024 – Early Bird Registration
https://icsmd2024.aconf.org/