ICSMD 2020 is soliciting special session proposals. Prospective organizers are invited to submit their proposals to the Special Sessions Co-Chairs, Qingbo He (qbhe@sjtu.edu.cn) and/or Jinxing Liang (j-liang@seu.edu.cn), by March 31st, 2020. The special session proposal should include the title of the special session, a short presentation and motivation of the significance of the special session topic.

The special sessions are intended to stimulate in-depth discussions in special areas relevant to the conference theme. Once received, each proposal will be carefully evaluated, and the accepted special sessions will be announced on the conference website.

The organizers are welcome to promote their special sessions through various venues, and will coordinate the review process for their session papers. The conference proceedings will include all papers from the special sessions.

Download: Call for Special Session

Special Session 01: Advanced sensing and intelligent computation for medical signals

Session Organizer: Chengyu Liu, Southeast University, China; Email: chengyu@seu.edu.cn

Recently, studies on the measurement and processing of medical signals, especially for the applications of wearable signal monitoring and artificial intelligence (AI)-based data analysis, have been gaining a significant role in the field of healthcare, and is looking to be a big and promising market in the technology industry. Advances in materials, physics, sensors, machine learning technologies have significantly promoted the value of measurement and processing of medical signals, hence providing opportunities for the development of new healthcare techniques. These could be expedient for the management of chronic illnesses, such as cardiovascular disease, sleep disorder, emotional problem, cognitive impairment and functional decline, as well as for the out-hospital applications for special populations, such as the aged, pregnant woman, athlete, astronaut, etc. The mainstream in research of sensing and computation for medical signals is moving towards more sophisticated methodologies based on advanced sensors, clinical “big data” and AI algorithms.

The purpose of this special session is to collect high-quality research papers addressing recent technology advances in signal sensing and computing for medical signals, as well as the implementation of these technologies for healthcare applications. Original, high quality contributions are welcomed, with special emphasis on, but not limited to, the following research topics.

• Advanced sensing techniques for medical signals, especially for wearable techniques
• Telemedicine, mobile healthcare and human sensor network techniques
• Flexible electrode, fabric electrode and dry electrode, working principle and performance
• Low power, and energy-efficient hardware for healthcare application devices
• Signal quality assessment and control for dynamic data
• Real-time human vital signs monitoring for the chronic illnesses and other illnesses
• Intelligent health monitoring systems (including service robots) combining methods for health monitoring and disease diagnosis
• High-performance computing and big data processing
• Compressed sensing, multi-modal signal processing, machine learning and AI for wearable ECG, EEG, blood pressure, pulse, respiration, sleep and motion signals
• Database and the corresponding annotations & analysis toolbox

Special Session 02: Fault prognosis and life prediction

Session Organizer(s):

• Yi Qin, Chongqing University, Email: qy_808@cqu.edu.cn
• Chuan Li, Chongqing Technology and Business University, E-mail: chuanli@ctbu.edu.cn

Fault prognosis and life prediction technique plays a critical role in operation safety and predictive maintenance of important equipment, such as aero-engine, wind turbine and high speed rail. In recent years, with the rapid development of machine learning, fault prognosis/life prediction has got more attentions. Various approaches have been explored, including model-based, data-driven and hybrid methods, which have been successfully applied to predict the remaining useful life (RUL) of typical mechanical and electrical equipment. However, accurate and robust fault/RUL prediction for the complex system is still a great challenge.

This special session is intended to facilitate the development of fault prognosis and life prediction theory for a variety of mechanical and electrical systems with focuses on the following topics (but not limit to):

• RUL prediction based on deep neural networks
• Fault prognosis based on machine learning algorithms
• Health indicator design by multi-source information fusion
• RUL prediction based on random process or physical model
• Hybrid fault prognosis for complex systems
• Other advances in fault prognosis and life prediction

Special Session 03：Advances in Smart Wireless Sensing and Health Monitoring

Session Organizer(s):  Yuyong Xiong and Dong Wang, School of Mechanical Engineering, Shanghai Jiao Tong University, China    Emails: yy.xiong@sjtu.edu.cn(Y.Y. Xiong), dongwang4-c@sjtu.edu.cn (D. Wang)

Smart sensing and monitoring are critical in various applications and they are the basis and premise of artificial intelligence. With the contactless manner, advanced wireless sensing (AWS) technology is of great interest for a wide range of fields, ranging from mechanical engineering to internet of things. The development of novel AWS technology can revolutionize monitoring solutions, and excite interesting new research and applications. A fundamental aspect of AWS is to exploit effective signal processing methods for data analysis and feature extraction combining with specific sensing approaches. This special session aims to provide a platform to report advances in the methods, systems and applications of various wireless sensing and health monitoring technology. 

Research interests include, but are not limited to: 

• Wireless sensors
• Wireless sensing systems: Architecture, Method and Interfaces
• Microwave/Terahertz sensing
• Vision/laser-based sensing
• Non-intrusive monitoring
• Signal processing algorithms
• Machine learning and big data analysis
• Feature extraction and system identification
• Vibration/acoustic measurement and analysis
• Imaging and tracking
• Condition monitoring and fault diagnosis
• Emerging sensing and monitoring applications

Special Session 04: Intelligent Sensing, Measurement & Data Analytics for Vehicles

Session Organizer:

• Zhongkui Zhu, Soochow University, China; Email: zhuzhongkui@suda.edu.cn
• Zaigang Chen, Southwest Jiaotong University, Email: zgchen@swjtu.edu.cn
• Yongbin Liu, Anhui University; Email: ybliu@ahu.edu.cn

Massive data are being collected in vehicles to monitor health conditions. The signals including vibration signals, acoustic signals, images, etc., are usually sensitive to abnormal/fault conditions. However, the meaningful information in these signals are typically weak, especially when the vehicle starts its fault at the initial stage. Moreover, environmental noises shall cause further interference to the extraction of fault information. To overcome the aforementioned difficulties, intelligent sensing, measurement & data analysis methods have been proposed with the potential to transform machine monitoring towards an automatic and smart direction. A focused session in this area will be organized as a platform to present high-quality original research on the latest developments of intelligent sensing, measurement & data analytics for vehicles. 

Potential topics include but are not limited to the following:

• Intelligent sensing and measurement for vehicles
• Fault mechanism and/or degradation analysis for critical components in vehicles
• Cross-domain transfer learning for robust condition monitoring of vehicles
• Model parameters optimization for satisfactory model learning of vehicles
• Advanced signal pre-processing approaches for vehicle vibration
• Learning approaches with massive unlabeled data or limited labeled data for vehicles

Session Organizer(s): 

• Prof. Zhigang Liu, Email: liuzg_cd@126.com  IET Fellow, IEEE Senior Member

• Dr. Alfredo Núñez,  Email: a.a.nunezvicencio@tudelft.nl  IEEE Senior Member

Special Session 06 Advanced sensors and intelligent signal processing methods for navigation

Session Organizer(s): 

• Chong Shen, North University of China, Email: shenchong@nuc.edu.cn
• Huiliang Cao, North University of China, Email: caohuiliang@nuc.edu.cn

Navigation techniques have been widely used in various areas, such as driverless vehicle, aerospace, ocean voyage, et.al. As the key technologies, sensors and related signal processing methods have been gaining a significant role in the field of navigation. Kinds of advanced navigation sensors and new concept signal processing methods have been explored, including novel inertial sensors, bionic navigation sensors, brain-like navigation methods, sensors compensation and calibration methods, which enriches the navigation methods enormously. This session is intended to facilitate the development of advanced sensors and intelligent signal processing methods for navigation application. 

Research interests include but are not limited to:

• Advanced inertial sensors: Gyroscopes and Acceloremeters
• Bionic navigation sensors: Polarization compass, Magnetic compass, Acoustic navigation, etc
• Navigation systems: INS, GNSS, Visual navigation, Celestial navigation system, UWB, etc
• New-concept navigation: Brain-like navigation, signals of opportunity navigation, etc
• Information fusion: Improved Kalman filters, and other filters
• Compensation and calibration method and algorithm for inertial sensors: Temperature drift compensation, high-G shock calibration, etc

Special Session 07 Intelligent sensing and data analytics for smart manufacturing

Session Organizer(s):

• Min Xia, Lancaster University, UK; Email: m.xia3@lancaster.ac.uk
• Siliang Lu, Anhui University, China; Email: silianglu@ahu.edu.cn
• Haidong Shao, Hunan University, China; Email: hdshao@hnu.edu.cn

Recent advances in artificial intelligence (AI) (e.g. Deep Learning) and information technologies (e.g. 5G, Industrial Internet of Things, Big Data, Cloud/Edge Computing, etc.) have resulted in a significant paradigm shift in manufacturing. The integration of advanced sensing technologies and the new generation of data analytics technologies in manufacturing is promoting the formation of a new generation of smart manufacturing, which can transform data gathered from a variety of manufacturing processes into actionable knowledge for decision making. The efforts in both academia and industry on sensing and data analytics for smart manufacturing is towards the directions of in-situ, distributed, real-time, intelligent, and autonomous, as well as the entire product life cycle including design, manufacturing, operation, and maintenance. This special session focuses on consolidating research efforts that aim at innovative sensing and data analytics with advanced AI for smart manufacturing. 

This special session encourages submissions that cover the following topics, but are not limited to:

• Innovative sensing methodologies and data-driven digital twin model optimization for manufacturing process
• Design optimization with closed-loop feedback of product life cycle data
• In-situ intelligent sensing methods for real-time condition monitoring
• In-process sensing and data analytics for the advanced manufacturing process
• Non-destructive testing and evaluation methods for quality assurance
• Predictive maintenance with multi-modal sensory data fusion
• Sparse sensing and advance machine learning for manufacturing data analytics

Special Session 08  Flexible sensing and intelligent diagnosis/prognosis for rolling bearings

Session Organizer(s): 

• Hongrui Cao, Xi’an Jiaotong University, China; Email: chr@mail.xjtu.edu.cn​
• Congyi Fu, Zhejiang Heqing Flexible Electronics Company, Ltd, China;Email: fucongyi@gfeit.com

Rolling bearings are indispensable components in many major equipments, such as aero-engine, wind turbine and high-speed trains. Bearing failures has a great influence on equipment safety and reliability. Therefore, running state sensing, fault detection, diagnosis and prognosis of rolling bearings have always been a research hotspot in mechanical engineering field. This special session is organized to focus on the recent development of sensing, diagnosis and remaining useful life (RUL) prediction for rolling bearings.

Potential topics include but are not limited to the following:

• Flexible sensor integration for bearing running data acquisition
• Virtual sensing based on physical bearing models
• Intelligent bearings with embedded sensors
• Multi-sensors information fusion for bearing condition monitoring
• Advanced signal process methods for feature extraction
• Intelligent fault diagnosis based on deep/transfer learning and big data
• Digital twin-driven RUL prediction for bearings

Special Session 09 Quartz MEMS devices and technologies

Session Organizer： Jing Ji, Xidian University, Email: jingji@xidian.edu.cn 

The most remarkable feature of quartz crystal is high temperature frequency stability. Using the quartz MEMS process, the miniaturization of Quartz devices is realized also with high Q-factor, good S/N ratio and product uniformity. Quartz crystal MEMS devices such as: Crystal Oscillator (including TCXO and VCXO), QCM (quartz crystal microbalance), high sensitivity sensors (including gyro sensors, temperature sensors, pressure sensors and accelerometer), are now widely used in various areas. Statistics indicated that in last 10 years, the sales volume of quartz crystal devices increased two times and still growing. There is also an increasing need for quartz MEMS devices technology to approach the challenge of Si MEMS and other new materials technology. This session is intended to focus on the new development and application of Quartz crystal MEMS devices and any related technologies.

Research interests include but are not limited to:

•  Design and optimization of quartz MEMS devices.

•  Manufacture process of quartz MEMS devices.

•  Etching simulation of anisotropic material.

•  Sensors based on quartz crystal: gyro sensors, temperature sensors, pressure sensors, accelerometer, etc.

Special Session 10 Advanced Measurement and Data Processing for Aerospace

Session Organizer(s):

• Dr. Jingli Yang, Harbin Institute of Technology, Email: jinglidg@hit.edu.cn​
• Dr. Lianlei Lin, Harbin Institute of Technology, Email: linlianlei@163.com

With the development of science and technology, human has a deeper understanding and exploration of the aerospace. Nowadays, space exploration attracts more and more attentions in the front edge technology, which is appraised as one of the hi-tech industries. Due to the particularity of space exploration, the measurement and data processing techniques are vital tools for enhancing the long-term reliability of spacecraft, and making full use of limited resources. This session is intended to focus on the new development of measurement and data processing techniques for aerospace.

Research interests include but are not limited to:

•  Measurement techniques for improving quality, reliability and safety of spacecraft

•  Remotely sensed image processing and computational intelligence techniques

•  Advances in modeling and simulation of aerospace environment

•  Design of spacecraft systems and key components

•  Data intelligence for test of spacecraft and its components

•  Sensing intelligence in spacecraft engineering

Special Session 11 Advanced Sensing, Monitoring and Diagnosis in Smart Grid

Session Organizer(s)：

• Dr. Yu Chen, Xi’an Jiaotong University, E-mail: chenyu@xjtu.edu.cn
• Dr. Zhe Li, Shanghai Jiaotong University, E-mail: zhe_li@sjtu.edu.cn

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. On the basis of 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 advanced sensing, monitoring and diagnosis in smart grid.

Research interests include but are not limited to:

• Advanced sensing 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.

• Application of image processing in the conditioning monitoring and diagnosis field

• Emerging new applications with 5G mobile internet and IoT in electricity

• Related research on edge computing, artificial intelligence and big data in smart grid

Special Session 12 Intelligent Sensing and High Precision Measurement for Aerospace

Session Organizer(s):

• Dr. Guangcun Shan, Beihang University, E-mail: gcshan@buaa.edu.cn
• Dr. Zheng Qian, Beihang University, E-mail: qianzheng@buaa.edu.cn

With the development of science and technology, scientists and researchers pay more and more attention to aerospace engineering. Form the hot air balloon rising in 18^th century to Apollo aircraft carrying persons to the moon, the technology in aerospace always exhibits the cutting-edge science and study. Advanced aerospace relies on the perception accuracy and intelligence, for which intelligent sensing and high precision measurement play a vital role. This session is intended to focus on the new development of intelligent sensing and high precision measurement for aerospace.

Research interests include but are not limited to:

• Intelligent sensing technology for improving the intelligence, reliability and safety of spacecraft;

• High precision measurement in aerospace engineering;

• Advances in data processing for enhancing aerospace system;

• Remotely sensed image processing and computational intelligence techniques;

• Sensor network and its application in aerospace engineering.

Special Session 13 Intelligent Anomaly Detection, Fault Diagnosis

and Prognostics for Aero-engines

Session Organizer(s):

• Dr. Jianzhong Sun, Nanjing University of Aeronautics and Astronautics, Email: sunjianzhong@nuaa.edu.cn
• Dr. Liansheng Liu, Harbin Institute of Technology, Email: lianshengliu@hit.edu.cn
• Dr. Zhixiong Chen, Shanghai University of Engineering Science, Email: chenzhixiong1000@hotmail.com

In the military and civilian domains, the condition of aero-engines is a key problem, which determines the safety of aircrafts, and completion of flight tasks. Due to the complexity of aero-engines, it is hard to obtain the accurate assessment results of aero-engines. In addition, the environment factors have vital influence on the achieved results. To address the aforementioned issues, sensing technology, advanced modeling, and data analysis are always the important requirements for aero-engines. With the development of artificial intelligence, many potential methods can provide valuable insights for condition monitoring of complex systems. This session is intended to focus on the intelligent methods of anomaly detection, fault diagnosis and prognostics for aero-engines.

Research interests include but are not limited to:

• Physical Modeling and Simulation for aero-engines

• Sensor deployment and data analysis in aero-engines

• Sensor data anomaly detection based on data-driven methods

• Hybrid methods for sub-system health assessment of aero-engines

• Intelligent fault diagnosis and prognostics methods for aero-engines

Special Session 14 THz Testing and Intelligent Sensing

Session Organizer(s):

• Liuyang Zhang, Professor, School of Mechanical Engineering, Xi’an Jiaotong University, China  Email: liuyangzhang@xjtu.edu.cn
• Shuncong Zhong, Professor, School of Mechanical Engineering and Automation, Fuzhou University, China Email: sczhong@fzu.edu.cn
• Patrick Mounaix, Professor, Université de Bordeaux, France Email: patrick.mounaix@u-bordeaux.fr

Terahertz (THz) wave occupies the electromagnetic spectrum from 100 GHz to 10 THz. Due to its special properties in science and applications, THz wave has been emerging as a future technique in non-destructive testing, remote sensing, national defense, and biomedical fields etc. THz wave with low power density can accurately detect concealed defects inside the multi-material and multi-structure with strong penetrability and high spectral resolution. Based on advanced manufacturing technology, terahertz sensing devices with abundant spectrum information can significantly improve the label-free measurement sensitivity of the targeted structure. In the era of artificial intelligence, THz testing and sensing technology has gradually evolved from concept to engineering application. The integration of artificial intelligence with THz science will bring unlimited potential to unprecedented THz products and applications. This session aims to report the new research progress of various THz testing and sensing technologies, focusing on more potential applications in the field of mechanical engineering.

Research interests include, but are not limited to the following topics:

• Nonlinear phenomena induced by THz radiation
• Terahertz sensor and detector
• Terahertz integrated metamaterials and plasmonic
• Advanced manufacturing for THz device
• Non-destructive THz testing and evaluation
• THz far-field and near-field testing instrument
• Real-time THz monitoring and network
• Remote THz sensing of gases and chemical/biological agents
• Super-resolution continuous THz imaging algorithm
• Deep learning on the THz testing and THz signal processing

Special Session 15 NDT&E and Intelligent Monitoring

Session Organizer(s):

• Yuhua Cheng, Professor, School of Automation Engineering, University of Electronic Science and Technology of China Email: yhcheng@uestc.edu.cn
• Liuyang Zhang, Professor, School of Mechanical Engineering, Xi’an Jiaotong University, China Email: liuyangzhang@xjtu.edu.cn

Non-Destructive Testing and Evaluation (NDT&E) is one of the major requirements in the proper operation and maintenance of mechanical equipment. Appropriate use of NDT&E guarantees safety is thus a subject of highest attention. The primary focus of the nondestructive testing effort is to pioneer advances in NDT&E and structural health monitoring through fundamental scientific research, technology development, and transferring these to the scientific and mechanical communities. The priorities will focus on the development of advanced inspection systems to detect critical flaws in both metallic and non-metallic materials and structures for mechanical equipment. The emphasis is on increasing the performance of inspection systems to provide defect detection and quantitative characterization of the material state. To communicate latest R&D achievements to academic and industrial applicants on the one hand and to discuss advanced and improved methods both with scientists as well as industrial researchers on the other, presentations addressing the following topics and considering any NDT&E methods will be welcome.

• The development of state-of-the-art NDT&E technologies such as eddy current, ultrasound, thermography and x-ray computed tomography;
• Novel NDT&E technologies such as terahertz, wavefield imaging and nonlinear ultrasound;
• Computational and analytic models to NDT&E problems;
• Advanced analytic methods to interpret large complex data sets;
• Data evaluation and singal processing with a scope on big data management;
• Mobile NDT&E for maintenance, repair and overhaul including robotics;
• Advanced methods of applying and interrogating sensors for structural health monitoring;
• Prognostics and Health Management;