Special Sessions
 
Summary: With the fast development of remote sensor technologies, e.g. the appearance of high resolution optical sensors, SAR, LiDAR, etc., mounted on either airborne or spaceborne platforms, massive multi-source remote sensing processing techniques are developed for earth observation. By leveraging data from multiple sources, remote sensing applications become more robust, providing a holistic view of the Earth's surface and enabling better-informed decision-making across various domains. Despite the fast development, the techniques remain challenging for multi-source data processing and analyses within varying spatial and temporal resolutions. 

This workshop focuses on providing a platform for sharing knowledge and experience on recent advancements in multi-source remote sensing processing methods and applications, especially for disaster monitoring, urban planning, environmental monitoring, and military reconnaissance. The objective of this workshop is to cover various applications, advanced algorithms and models in remote sensing field. We welcome related methods and applications that include but are not limited to the following:

Feature extraction

Image restoration

Image classification

Object detection

Change detection

Multi-source sensor fusion

Various applications

Earth observation with embedded edge devices


Keywords:Remote sensing, Multi-source data, Machine learning, Deep learning

Chair: Dr. Puhong Duan, Hunan University, China


Puhong Duan received the B.S. degree from Suzhou University, Suzhou, China, in 2014, the M.S. degree from the Hefei University of Technology, Hefei, China, in 2017, and the Ph.D. degree from Hunan University, Changsha, China, in 2021. From October 2019 to October 2020, he was a visiting Ph.D. student with the Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany. He is currently an Associate Research Fellow with the College of Electrical and Information Engineering, Hunan University. His research interests include image restoration, multimodal image fusion, and classification.


Chair: Fulin Luo, Chongqing University, China

Fulin Luo received the B.E. degree in mechanical engineering and automation from Southwest Petroleum University, Chengdu, China, in 2011, and the M.E. and Ph.D. degrees in instrument science and technology from Chongqing University, Chongqing, China, in 2013 and 2016, respectively. He was an Associate Researcher and a Post-Doctoral Researcher with the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, China, from 2017 to 2021. He was a Research Fellow with Nanyang Technological University, Singapore, from 2020 to 2021. He is currently a Professor with the College of Computer Science, Chongqing University, since 2022. His research interests include remote sensing processing, computer vision, and biomedical analysis.


Chair: Qingwang Wang, Kunming University of Science and Technology, China

Qingwang Wang received the B.E. degree and Ph.D. degree in electronics and information engineering, and information and communication engineering from the Harbin Institute of Technology, Harbin, China, in 2014, and 2020, respectively. From 2020 to 2021, he worked as a senior engineer in Huawei Technology Co., Ltd. to study autonomous driving. Now he joined Kunming University of technology as a high-level talent. His research interests include machine learning and its application to remote sensing data analysis, autonomous driving and edge calculation. More specially, his studies currently focus on using kernel methods, deep learning, broad learning and graph convolutional neural networks to extracting information from RGB-T images, hyperspectral image, LiDAR data, and multispectral LiDAR point clouds.

Summary: Quantum computing  has the advantage of high efficiency compared with classical computing in a way. Quantum algorithms and quantum computing models are important research fields, having very important significant impact on current and future technological development. The aim of this workshop is to bring together the research accomplishments provided by researchers from academia and industry. Another goal is to show the latest research results in the field of quantum algorithms and quantum computing models. We encourage prospective authors to submit related  research papers on the subjects: theoretical approaches and practical case reviews.

Keywords: Models of Quantum Computation, Quantum Algorithms

Chair: Prof. Daowen Qiu, Sun Yat-Sen University, China
He has been the full professor and supervisor of PhD students of Sun Yat-Sen University since 2004. He is the associate editors of several international academic journals such as Theoretical Computer Science, Quantum Reports, Frontiers in Computer Science, Artificial Intelligence Evolution, etc. 
He has published over 200 papers in peer-reviewed academic journals. Now my main research interests are focused on the following areas: (1) Distributed quantum computing (centered on distributed quantum algorithms). (2) Quantum models of computation. (3) Quantum query algorithms. (4) Fuzzy and probabilistic as well as quantum discrete event systems.

Summary:  In recent years, online and onsite optical metrology techniques have received extensive attention for many dimensional control research and industrial applications. Some representative techniques adapt 2D imaging measurement, stereo vision, 3D laser scanning and structured light to acquire the product dimensional information to evaluate the manufacturing error or improve the sequential manufacturing process. Compared with the traditional manual or offline sampling inspection methods, these online and onsite optical metrology techniques have remarkable advantages in regard to accuracy, efficiency and the amount of collected data, thus changing the way the inspection applications are performed and providing new possibilities for online/onsite product quality control. In this workshop, we will discuss the study, research and discovery of the challenges, trends and solutions related to online and onsite optical metrology techniques. It includes the novel des00ign of optical sensors, improved methods for measurement performance enhancement, novel mathematical methods for advanced data processing and various specific solutions for online/onsite optical metrology applications.

Keywords: Optical metrology, online/onsite inspection, measurement data processing, defect detection, system calibration, image processing, deep learning

Chair: Assoc. Prof. Xiao Yang, Northwestern Polytechnical University, China
Xiao Yang, associate professor of Northwestern Polytechnical University. He received a bachelor's degree from Shanghai Jiao Tong University in 2015 and a doctor's degree in mechanical engineering from Shanghai Jiao Tong University. From September 2020 to August 2022, he worked as a postdoctoral researcher at Shanghai Jiao Tong University. In September 2022, he joined Northwestern Polytechnical University as an associate professor. His research interests are optical precision three-dimensional measurement, online/in-situ vision detection, macro-micro cross-scale measurement theory and methods. He presided over 2 national and provincial-level projects such as National Natural Science Youth Fund and Shanghai integration of defense and civilian technologies Project, and participated in nearly 10 national and provincial-level projects. He was awarded the Best Commercial Value Award in the Digital Twin Technology Challenge of Siemens Global Colleges and Universities. For two consecutive years, he guided students to participate in the "internet plus" Innovation and Entrep reneurship Competition and won the provincial silver award.

Chair: Dr. Hui Du, Shanghai Jiao Tong University

Hui Du received the Ph.D. degree in mechanical engineering from Shanghai Jiao Tong University, shanghai, China, in 2021. After that, he worked at Percipio CO. LTD. as an advanced algorithm scientist for two years. He is now a Postdoc research fellow in School of Sensing Science and Engineering, Shanghai Jiao Tong University, Shanghai, China. Now he is focusing on 3D optical measurement based on active vision algorithms.



Summary: Multi-biometric systems utilize the information collected from multiple sensors or multiple modalities, through that the recognition accuracy can be improved compared to single biometric modality. There are plenty of new sensing techniques and information fusion strategies need to be explored. The aim of this workshop is to bring together the research accomplishments provided by researchers from academia and the industry. The other goal is to show the latest research results in the field of multi-modalities biometrics and application of AI in this field. We encourage prospective authors to submit related distinguished research papers on the subject of both theoretical approaches and practical case reviews. Please name the title of the submission email with “paper title_workshop title”.

Keywords: Biometrics, Multi-modalities, Information fusion, data collection

Chair: Dr. Peirui Bai, Shandong University of Science and Technology, China

Dr Peirui Bai (Member, IEEE) received the Ph.D. degree in Biomedical Engineering from Xi'an Jiaotong University, Xi’an, China in 2006. He was a visiting scholar with the Department of Biomedical Engineering, Tsinghua University from 2010 to 2011, and was a visiting scholar with the MIPG, University of Pennsylvania from 2015 to 2016. He is now a professor in the College of Electronic and Information Engineering, Shandong University of Science and Technology (SDUST). His research interest includes image processing and pattern recognition, video understanding and analysis, biometrics, etc. He is a member of China Society of Image and Graphics (CSIG) and Chinese Society of Biomedical Engineering (CSBME).

Summary: The automotive industry's embrace of smart connected vehicles has placed a premium on safeguarding data and communication. This research meticulously navigates the complex interactions among smart vehicles, vehicle network security, active collision avoidance systems, and V2X (Vehicle-to-Everything) communication. Integrating intelligent technologies into vehicles unlocks novel prospects for connectivity, efficiency, and safety. However, this evolution introduces cybersecurity challenges. 

The aim of this workshop is to bring together the research accomplishments provided by researchers from academia and the industry. Another goal is to present the latest research results in the field of in-vehicle cyber data security.

Keywords: Smart Vehicles, Vehicle Network Security, Active Collision Avoidance, V2X Communication

Chair: Dr. Yujing Wu, Yanbian University, China

Yujing Wu received the M.S. and Ph.D. degrees in electronic and information engineering from Chonbuk National University, South Korea, in 2013 and 2016, respectively. She is currently working with the Department of Electronics & Communication Engineering, Yanbian University, China. Her research interests include VLSI implementation for digital signal processing and communication systems, which include the design of CAN data reduction and DisplayPort, implementation of security protocol for in-vehicle networks. She has participated and chaired various projects and researches at the National Natural Science Foundation of China and provincial and ministerial levels. Based on these projects, she published more than 18 papers in IEEE Trans and other journals and conferences.

Summary: To improve the diagnosis and treatment of diseases, many digital and automated techniques have been developed by scientists and eventually applied in medical practice. Computer plays an important role in the development and applications. This workshop is focused on computer-based approaches for the detection, characterization, discrimination, treatment selection and prognostic prediction of common diseases. 

Computers have been used in almost all aspects of the health-care system nowadays. Real-time computation is indispensable in most modern imaging techniques including computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), single photon emission computed tomography (SPECT), among others. Recent development of deep-learning (DL) approaches is based big-data and artificial intelligence (AI) techniques, which require heavy computational abilities and huge amount of storage. 

To address relevant research issues, the workshop welcome papers and presentations that discuss any part of the above-mentioned techniques. More specifically, imaging and image-processing related topics, including CT and MRI image reconstruction, PET and SPECT image acquisition, medical image processing in a broad sense, disease detection in medical image, machine-learning or DL based image or disease classification, image-based disease assessment and prognostic prediction, computer-aided detection/diagnosis and medical informatics, are all appropriate for this workshop.

Keywords: 
Computerized medical image analysis, quantitative image analysis, computer-aided diagnosis, computer-assisted diagnosis, machine learning, deep learning, pattern recognition, classification, prediction

Chair: Prof. Yahui Peng, Beijing Jiaotong University, China

Yahui Peng received the B.E. degree in Engineering Physics from Tsinghua University, Beijing, China, in 1998, the M.E. degree in Nuclear Technology and Applications from Tsinghua University, Beijing, China, in 2001, and the Ph.D. degree in Medical Physics from the University of Chicago, Chicago, IL, USA, in 2010. Currently, he is a full Professor affiliated with the School of Electronic and Information Engineering at Beijing Jiaotong University, Beijing, China. He has extensive research experience in computerized image analysis in medicine and industry. His current research interest is focused on quantitative medical image analysis, pattern recognition, diagnostic accuracy assessment, computer vision, artificial intelligence in medical and industrial applications, etc. He has published more than 50 peer-reviewed scientific journal papers and given talks in international scientific conferences or for industrial audience.


Summary: Recently, artificial intelligence technology and its related algorithms have achieved rapid development in computer vision. Moreover, artificial intelligence has produced positive effects in medical field, among which the most common ones are clinical decision support and medical data analysis. Clinical decision support tools give healthcare providers quick access to information or research relevant to their patients to help them make decisions about treatment, medication, mental health and other needs. In medical imaging, AI tools can be used to analyze CT, X-rays, MRIs and other images, detecting and segmenting lesions, segmenting human organs, classifying lesions to assist the radiologists in making the diagnosis. The aim of this workshop is to bring together the research accomplishments provided by researchers from academia and the industry. The other goal is to show the latest research results in the field of pattern recognition, intelligent medical treatment, brain-like computing and other related fields. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews. Please name the title of the submission email with “paper title_workshop title”.

Keywords: Pattern recognition, artificial intelligent algorithms, deep learning, intelligent medical treatment, brain-like computing

Chair: Dr. Jinlian Ma, Shandong University, China

Jinlian Ma, received a Ph.D. degree in Applied Mathematics from Zhejiang University. She worked as an associate professor at the School of Integrated Circuits, Shandong University. Her research interests include Biomedical microelectromechanical systems and intelligent medical treatment, Brain-computer interface and brain-like computing, Pattern recognition and its related intelligent algorithms. She participated in the National Natural Science Foundation, National Key Projects, National Key Research and Development Project, Provincial Youth Fund Project, etc. Based on these projects, she published multiple papers in international journals such as Applied Soft Computing, Computers in Biology and Medicine.
Summary: Gradient-Free Optimization in Deep Learning explores techniques that do not rely on gradients to optimize model parameters. Traditional optimization methods, like gradient descent, assume the availability of gradient information, which may be impractical in certain scenarios. Challenges such as non-differentiability in neural network architectures, high computational costs, and vanishing or exploding gradients motivate the need for gradient-free optimization. This approach encompasses various techniques like: Random Search, Bayesian Optimization and Evolutionary Algorithms Gradient-free optimization proves valuable when gradient information is limited, noisy, or challenging to obtain. It finds applications in scenarios like black-box optimization, hyperparameter tuning, and environments with limited access to model internals. The benefits include versatility, applicability to a wide range of optimization problems, and reduced computational costs, making it particularly advantageous in scenarios where computing gradients is expensive. The workshop will delve into these techniques, exploring their applications in hyperparameter tuning, neural architecture search, and other areas where traditional optimization methods fall short.

Chair: Assci. Prof. Rolla Almodfer, Henan Institute of science and Technology, China 

Rolla Almodfer is an Associate Professor at Henan Institute of Science and Technology in China. She earned her Doctorate in computer science and applications from the Department of Computer Science at Wuhan University of Technology, China. Serving as a Postdoctoral Researcher specializing in pattern recognition from 2016 to 2018. She is an accomplished author with numerous publications and holds a position as a member of the editorial team for various conferences and journals. Rolla Almodfer's current research interests encompass pattern recognition, intelligent agriculture, machine learning applications, and the development of optimization algorithms for real-time applications, with a particular focus on deep learning.




Chair: Dr. Chenping Zhao, Henan Institute of science and Technology, China 


Chenping Zhao received the B.S. degree and the M.S. degree from the School of Mathematics and Statistics, Henan University, Kaifeng, in 2003 and 2006, respectively; and the Ph.D. degree in Applied Mathematics from the School of Mathematics and Statistics, Xidian University, Xi’an, in 2018. She is currently an Associate Professor with the School of Computer Science and Technology, Henan Institute of Science and Technology, China. Her research interests include image processing. optimization algorithms and deep learning.
Summary: The session discusses the advancements in pattern recognition and machine vision utilizing intelligent algorithms. The potential of the algorithms lies in their ability to analyze and recognize patterns in data, images, and video streams. The session also discusses different types of intelligent algorithms such as neural networks and deep learning algorithms. These algorithms can be trained to recognize and classify objects, identify faces, track movement, and identify anomalies. The session concludes by stating that the advancements in pattern recognition and machine vision will enable new applications in fields such as autonomous driving, robotics, and healthcare.

Keywords: Neural networks, deep learning, robotics, healthcare

Chair: Prof. S.BalakrishnanSri Krishna College of Engineering and Technology, India

Dr.S.Balakrishnan is a Professor and Head, Department of Computer Science and Business Systems at Sri Krishna College of Engineering and Technology, Coimbatore, Tamilnadu, India. He has 21 years of experience in teaching, research and administration. He has published over 26 books, 10 Book Chapters, 30 Technical articles in CSI Communications Magazine, 27 technical Blogs, 1 article in Electronics for You (EFY) magazine, 13 articles in Open Source for You Magazine and over 150+ publications in highly cited Journals and Conferences. 

Some of his professional awards include: Certificate of Award with cash prize $250, for 1st place in the International Poster Challenger by Peeref, Kalpa Acharya Award (Best researcher Award) organized by BrainO Vision, IEEE MAS Best Researcher Award - 2022 (Age 40 and Below 50) by IEEE Madras Section, AICTE Lilavati Award 2021-22 winner with 1 Lakh Cash Prize, Best Performer in the Poster Display at IIC Regional Meet held at Sathyabama Institute of Science and Technology Chennai on 21st July 2022, Best Model/Technology Presentation Award by IEEE-Nanotechnology Council Student Chapter IIT Indore, International Data Science Writer of the Year 2022 and 2021 by Data Science Foundation UK, Yuva Mentor as a Changemaker Award, Faculty with Maximum Publishing in CSI Communications 2017-2019, International Data Science Writer of the Year 2019 by Data Science Foundation UK with cash prize €900, MTC Global Outstanding Researcher Award, Inspiring Authors of India, Deloitte Innovation Award Deloitte for Smart India Hackathon 2018.  He acted as a Mentor, Evaluator cum Jury Panel Member, Grand finale of SIH 2022 and MANTHAN, Mentor and Jury member in ASEAN-India Hackathon 2021, Primary SPOC and Evaluator for Toycathon 2021. He has delivered 75+ guest lectures/seminars in National & International levels, delivered 20+ keynote speech/invited speech and chaired 350+ sessions for various National and International Conferences. He is serving as a Reviewer and Editorial Board Member of many reputed Journals and acted as Technical Program Committee member of National conferences and International Conferences at Vietnam, China, America and Bangkok. His research interests are Artificial Intelligence, Cloud Computing and IoT.

Chair: Dr. Prithiviraj Rajalingam, SRMIST, India.

Dr. R. Prithiviraj is an accomplished academician with 11 years of experience, currently serving as an Assistant Professor at SRM Institute of Science & Technology. He holds a Ph.D. in Analog VLSI from SRMIST, with a focus on the design of radiation-hardened voltage control oscillators for phase-locked loops. 

His educational journey includes an M.E. in VLSI Design from Anna University and a B.Tech in ECE from Anna University. Dr. Prithiviraj has made significant contributions to the field, evident in his 20 publications, including 3 in SCI-indexed journals, 14 in Scopus-indexed journals, and 3 in WoS-indexed journals. His research expertise extends to areas such as low-power phase and frequency detectors, radiation-tolerant VCOs, and current-starved sleep VCOs. 

Dr. Prithiviraj is an active member of professional organizations, including IEEE, IETE, and the Solar Energy Society of India. His commitment to service is evident through his involvement in NSS activities at SRMIST in 2020. 
Beyond academics, he has contributed to a patent on Smart Intrusion Discovery Systems using ML for Cyber Threats in IoT Networks.


Summary: The advent of smart cities is alleviating big-city issues, boost long-term economic growth, and enhance people’s quality of life. Thus, the emergence of intelligent communication systems and networks allowing to overcome the current impediments of the existing communication paradigms by proposing intelligent algorithms can guarantee a more efficient use of resources, sustainable developments, and green economy. Evolutionary Computation (EC), including a family of algorithms for global optimization inspired by biological evolution, has been extensively used to solve a variety of optimization problems from different research fields, which can help achieve intelligent networks in smart cities. This special workshop will focus on EC algorithms and their applications in communication networks for constructing smart cities.  This is a great opportunity for the delegates to exchange novel ideas, present latest cutting-edge researches, and establish global research collaborations in the areas of EC developments and applications for intelligent communication systems and networks.

Keywords: Smart cities, Evolutionary computation, Intelligent communication systems and networks

Chair: Dr. Khoa Nguyen, Carleton University, Canada

Dr. Khoa Nguyen worked as a Postdoctoral Fellow at Carleton University. He received M.Sc. degree in Telecommunications Engineering from the University of Sunderland, England, in 2013 and the Ph.D. degree in Electrical and Computer Engineering at the Department of Systems and Computer Engineering, Carleton University, Canada, in 2021, respectively. His main research interests include communication networks, cloud/edge computing, parked vehicle edge computing (PVEC), Internet of Vehicles (IoV), software-defined networks (SDN), network function virtualization (NFV), containerization technologies, evolutionary algorithms, and AI/ML applications.

Summary: Intelligent algorithms, including the genetic algorithms, neural networks-based algorithms, swarm intelligence-based algorithms, and evolutionary computation-based algorithms, have been a hot research topic in the past few decades. With their rapid development in recent years, they have received considerable attentions and been widely applied in various engineering fields, such as wireless communications, target localization and internet of vehicles, etc. However, due to actual limitations in some specific scenarios, the research on application of intelligent algorithms in wireless Communications and target Localization is still less inadequate, there developing suitable intelligent algorithms for detailed applications in such fields is of great significance and necessity. 

Keywords: Intelligent Algorithms, Wireless Communications, Target Localization

Chair: Assoc. Prof. Ye Tian, Ningbo University, China

Ye Tian received the B.Eng. and Ph.D. degrees from Jilin University, Changchun, China, both in information and communication engineering, in 2009 and 2014, respectively. He was selected as a young top talent by the Hebei Provincial Department of Education in 2016, and a leading and top-notch talent of Ningbo in 2022. He joined Ningbo University in March, 2021, where he is currently an Associate Professor. He has published more than 40 international journal/conference papers, including IEEE TSP, IEEE TWC, IEEE TAES, IEEE IoTJ and IEEE TVT, etc. His research interests include array signal processing, target localization, direction of arrival estimation using massive MIMO arrays. He is a member of IEEE.



Chair: Assoc. Prof. Yangong Zheng, Ningbo University, China

Yangong Zheng is an Associate Professor in the Faculty of Electrical Engineering and Computer Science, Ningbo University, China. He received his PhD from the School of Electronic Science and Technology of Dalian University of Technology in 2014. During 2011-2013, he worked as a visiting scholar in Chemistry Department of The Ohio State University in USA. Currently, his research interests focus on electronic nose and neural networks.
 



Summary: This workshop covers topics in pattern recognition, machine vision, digital image and video processing, medical image processing, intelligent systems and intelligent algorithms.

Keywords: Pattern Recognition, Image, Video, Intelligent Algorithms

Chair: Prof. Hongjian Shi, Beijing Normal University-Hong Kong Baptist University United International College, China 

Prof. Shi received his MASc and PhD in Electrical and Computer Engineering from The University of British Columbia and University of Louisville respectively. He also received his BSc, MSc and PhD in Mathematics from Henan Normal University, Peking University, and Simon Fraser University respectively. He was a full research professor at Southern University of Science and Technology before joining UIC. His research interests cover medical imaging, image and video processing, computer vision, pattern recognition. 

Prof. Shi also worked at Southern University of Science and Technology, University of Louisville Dental School, University of Wisconsin, Nanjing University of Science and Technology. He has published more than 50 papers, serves on conference committees, as keynote speaker, and as reviewer for various journals and conferences. 

Prof. Shi had worked at medical device industry in Canada and United States for more than 15 years and developed several innovative and popular medical imaging products in world market. He held patents in many countries including China, US, Japan, Korea, and Europe etc. His tru-pan product was rated as one of top 10 products by《DENTAL REPORT》in 2009.

Summary: People primarily use images to acquire and exchange information, so the application of image processing is inevitably involved in all aspects of human life and work. At present, image processing technology has played an important role in the fields of aerospace, public security, biomedicine, industrial engineering, and business communication. Up until now, image processing technology based on deep learning has rapidly developed and become the most successful applied intelligent technology. Pattern recognition is an important research field in image processing and includes image preprocessing, feature extraction and selection, classifier design, and classification decisions.

Keywords: Image Processing; Deep Learning; Pattern Recognition

Chair: Prof. Aili Wang, Harbin University of Science and Technology, China 

AILI WANG was born in Tianjin, China in 1979. She received the B.S., M.S. and Ph.D. degrees in information and signal processing from Harbin Institute of Technology, Harbin, China, in 2002, 2004 and 2008. She joined Harbin University of Science and Technology as an assistant in 2004 and she became a professor of the department of communication engineering in 2023. She has been a visiting professor to do search of 3D polyp reconstruction in Computer Science Lab in Chubu University, Japan in 2014.  Her research interests include image super resolution, image fusion, object tracking, software engineering and reinforcement learning and federated learning and so on.
Summary: This workshop will focus on exploring how to utilize the latest artificial intelligence, machine learning technologies, and statistical methods to enhance the accuracy and predictive power of data analysis. The workshop aims to bring together experts from academia and industry to share their latest research findings and practical experiences in intelligent data processing, pattern recognition, and future trend prediction. We will discuss the application of data analysis across various industries, including but not limited to art design, industrial design, equipment manufacturing, and market marketing, and explore how to apply intelligent data analysis to achieve development and design predictions in different sectors. The goal of this workshop is to foster interdisciplinary collaboration, drive innovation in intelligent data analysis techniques, and provide participants with a platform to exchange ideas, establish cooperative relationships, and collectively advance the frontier of this field."

Keywords: Intelligent Data Analytics, Prediction, artificial intelligence

Chair: Associate Researcher Yu Lei, Yanshan University, China

Yu Lei. Male, associate researcher, The research focuses on artistic design computing, complex system evaluation, artificial intelligence interaction, as well as research on design psychology and design strategies. 

Awarded the title of "Yanzhao Talent (Class A)" in Hebei Province, "Yanshan Scholar" at Yanshan University, third prize winner of Hebei Social Science Achievement Award, member of the National Social Science Foundation Art Program, member of the Ministry of Education Humanities and Social Sciences Program, and core expert of Hebei New Think Tank, Hebei Design Innovation and Industrial Development Research Center, and Hebei Public Policy Evaluation Center. Meanwhile, expert reviewers for high-level journals such as SCI journals: ‘Journal of Intelligent&Fuzzy Systems’, 'Scientific Programming'. 

Led one National Social Science Foundation Art Science Project, two Ministry of Education Projects, one provincial-level key project, four provincial-level general projects, published two monograph, and published more than 20 search papers on CSSCI, SSCI, AHCI, SCI, and core or above. The resulting consultation report was accepted by provincial and municipal governments.
Summary: The innovative applications of service robots in the telecommunications domain hold significant importance, and this workshop focuses specifically on their practical applications in telecom network maintenance, communication equipment management, and user services. Through case studies and simulation techniques, we will thoroughly explore how to optimize the performance of service robots in the telecommunications industry. Additionally, the research will delve into the applications of advanced robotics, vision-based technology, and smart algorithms in the telecom domain, injecting innovation into the development of the telecommunications industry.

Keywords: Telecom Robotics Revolution: Designing, Simulating, and Elevating Performance

Chair: Qian Wang, China Mobile Group Design Institute Company Ltd., Hubei Branch, China 

Qian Wang, a Senior Member of IEEE, earned his B.Eng. degree from Wuhan University, China, in 2013, and an M.Sc. degree from the City University of Hong Kong, Hong Kong, in 2015. Currently, he serves as a Consulting Designer and Project Manager with the title of Engineer at China Mobile Group Design Institute Company Ltd., Hubei Branch. He has accumulated 7-8 years of experience in the telecom industry. 

Qian's expertise encompasses the wireless domain, involving network planning and optimization for 2G, 3G, 4G, and 5G, as well as the construction domain, including indoor and macro cell station deployment. He is well-versed in the wired transmission domain, covering PTN, server, and router technologies, and equally adept in the cloud network domain, covering cloud computing, cloud security, and cloud services. Furthermore, he is a Full member of the Hong Kong Computer Society (HKCS) and a Professional Engineer of the Chinese Society of Engineers (CSE). 

As the primary author, Qian holds 1 patent certificate and 2 computer software copyright registration certificates. He has published multiple articles in IEEE's SCI journals and serves as a reviewer for China Communications journal. Additionally, he was the workshop chair and reviewer for the International Conference on Networks, Communications, and Intelligent Computing (NCIC 2023). Qian has successfully managed 3 projects with investments in the tens of millions and 6 projects with million-level budgets, showcasing his expertise in project leadership.

Summary: Recently, as the response to higher operating speeds and increased mileage of railways has made railway operations and maintenance one of the most important issues for all countries, interest in building more environmentally friendly and energy-efficient railways has been growing. The long-term prospects for railway development are expected to be even more ambitious, despite the decline in maritime transport in recent years due to global pandemics and the situation in the Red Sea.

In addition to environmental issues, the social conditions surrounding railways are constantly changing, such as the expansion of urbanized areas, the complexity of living spaces, changes in living patterns, and new types of hazards. Considering these changes, for the expansion and continuous development of railway construction, it is necessary to more actively accommodate changes in social conditions through the discovery and application of new materials and technologies.

The aim of this Workshop is to present the latest technologies developed for the planning, design, construction, maintenance and rehabilitation of railway infrastructure. Through this Workshop, we hope to provide an opportunity for railway researchers to share the latest technologies related to railway infrastructure and to prepare for a more advanced decade together.

Keywords: stability and dynamics; safety, risks and uncertainty; infrastructure engineering; structural engineering and materials; mechanics, prognostics and diagnostics; health monitoring, inspection, NDT&E and signal processing; planning and project management; maintenance and rehabilitation technology; advanced substructure and track design; big data analytics and railway operations; bim and ai applications

Chair: Dr. Lei Kou, Taizhou Institute of Zhejiang University, Qilu Institute of Transportation, Shandong University, China;  LISTER Institute of Transport, Technische Universität Dresden, Germany 

Lei Kou graduated with a master's degree and a PhD from the Lister School of Transportation at the Technical University of Dresden and has long been involved in the field of intelligent inspection, monitoring and maintenance of railways. His postdoctoral position is with the School of Electrical Engineering, Zhejiang University. He was awarded the National Natural Science Foundation of China (NSFC) Outstanding International Student Award in 2023, and received a special grant from the Ministry of Education of China (MOE) for attracting talents from overseas based on the Qilu School of Transportation, Shandong University in 2023. He has published more than ten SCI papers and is a technical member of the Indian Society for Artificial Intelligence. iEEE transactions on instrumentation and measurement, Intelligent transportation systems, Proceedings of the He is a permanent reviewer for IEEE transactions on instrumentation and measurement, Intelligent transportation  systems, Proceedings of the Institution of Mechanical Engineers, and Sādhanā, among other SCI journals.

Chair: Associate researcher Jun Zheng, Taizhou Institute of Zhejiang University, China 

Doctor of Electrical Engineering of Zhejiang University, associate researcher, Taizhou City "500 elite plan" innovation B talent, director of Zhejiang Automation Society. He is a director of Zhejiang Automation Society, a postdoctoral fellow at the School of Electrical Engineering of Zhejiang University from 2005 to 2008, a teacher at the School of Electrical Engineering of Zhejiang University from 2009 to 2015, and a member of Taizhou Research Institute of Zhejiang University in 2017, and currently serves as the assistant to the director and director of the Science and Technology Innovation Department of the Taizhou Research Institute of Zhejiang University, the person in charge of the Institute of Electrical and Control Research, and the member of the General Party Branch, and the secretary of the First Branch. He has long been engaged in applied research in the fields of industrial automation system and its advanced control, smart grid and railway electrification engineering intelligent equipment. In recent years, he has presided over one sub-project of National Intelligent Manufacturing Special Project, two major scientific research cooperation projects of China Railway Electrification Bureau Group, one project of Zhejiang Provincial Natural Science Foundation, two projects of Hangzhou Informatisation Industry Funding, and participated in four key projects of National Key Research and Development Plan, National Natural Foundation and Provincial Natural Science Foundation. He has published 15 SCI/EI papers, participated in the preparation of China Encyclopedia of Electricity (Basic Volume) and Taizhou Institute of Zhejiang University Qilu Institute of Transportation, Shandong University

Summary: Our workshop aims to provide a collaborative environment for researchers and professionals to exchange knowledge, discuss challenges, and explore innovative applications in pattern recognition modeling technology. Through a series of sessions, including presentations, and interactive discussions, participants can gain a comprehensive understanding of the latest trends, advancements, and challenges in pattern recognition.

Keywords: Pattern Recognition; Feature Extraction; Model Training;Image Recognition;  Classifier

Chair: Prof. Qi Zhu, Nanjing University of Aeronautics and Astronautics, China

He is a Professor at Nanjing University of Aeronautics and Astronautics, specializing in artificial intelligence, medical image analysis, and AI security. He achieved his Ph.D. from Harbin Institute of Technology. With a distinguished academic career, he has authored and presented over 100 research papers in reputable journals and conferences, including IEEE TMI, IEEE TIFS, IEEE TAFFC, IEEE TIP, IEEE TSMC, Nature Communications, and MICCAI. His contributions have received significant recognition, accumulating more than 1700 citations on Google Scholar. Furthermore, he serves as the principal investigator for 3 projects funded by the National Natural Science Foundation and has led over 10 projects, including those supported by the Jiangsu Provincial Natural Science Foundation, among others.


Summary: Visual defect detection is the core of comprehensive intelligence in industrial manufacturing. It is currently a popular research field, which attracts increasing attentions from many researchers. With the rapid development of artificial intelligence, a large number of new intelligent algorithms have emerged. The emergence of intelligent algorithms provides support for the sustained development of visual defect detection. This workshop will focus on visual defect detection and intelligent algorithms, and explore their applications in intelligent manufacturing.

Keywords: Defect detection, intelligent manufacture, machine vision, intelligent detection

Chair: Prof. Jian Liu, Hunan University, China


Jian Liu received the Ph.D. degree from Hunan University. He is currently the professor of mechanical engineering with the Hunan university. He was Leading Talents in Science and Technology Innovation in Hunan Province for contributions to intelligent equipment research, and vice chairman of the Fasteners Standardization Technical Committee in Hunan Province. His current research interests include intelligent manufacturing, visual detection and image processing. He has published over 50 relevant high-level papers.





Chair: Assoc. Prof. Ning Chen, Hunan University, China


Ning Chen received the Ph.D. degree from Hunan University. He is currently the associate professor of mechanical engineering with the Hunan university. His current research interests include intelligent manufacture, machine vision, machine learning applications and so on.






Chair: Hang Zhang, Changsha University of Science and Technology China


Hang Zhang received the Ph.D. degree from Hunan University. He is currently the lecturer of Mechanical Design and Manufacturing with the Changsha University of Science and Technology university. His current research interests include visual learning,defect detection and image segmentation. He has published several papers on intelligent defect detection and developed several defect detection equipments for industrial parts.