2024 5th International Conference on Computer Science and Management Technology(ICCSMT 2024)
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Prof Yang Yue

SPIE Fellow

IEEE Senior Member

Xi'an Jiaotong University

Biography: Yang Yue received the B.S. and M.S. degrees in electrical engineering and optics from Nankai University, China, in 2004 and 2007, respectively. He received the Ph.D. degree in electrical engineering from the University of Southern California, USA, in 2012. He is a Professor with the School of Information and Communications Engineering, Xi'an Jiaotong University, China. Dr. Yue’s current research interest is intelligent photonics, including optical communications, optical perception, and optical chip. He has published over 260 journal papers (including Science) and conference proceedings with >10,000 citations, six edited books, two book chapters, >60 issued or pending patents, >200 invited presentations (including 1 tutorial, >30 plenary and >50 keynote talks). Dr. Yue is a Fellow of SPIE, a Senior Member of IEEE and Optica. He is an Associate Editor for IEEE Access and Frontiers in Physics, Editor Board Member for four other scientific journals, Guest Editor for >10 journal special issues. He also served as Chair or Committee Member for >100 international conferences, Reviewer for >70 prestigious journals.

Title: Machine-Learning-based Optical Performance Monitoring in Communications Channels

Abstract: In recent years, machine learning has come to the forefront as a promising technology to aid in multiparameter performance monitoring for optical communications channels. In this talk, we will introduce CNN-based techniques to effectively monitor multiple system performance parameters of optical channels using eye diagram measurements. Experimental results demonstrate this method achieves a prediction accuracy >98% when tasked with identifying the modulation format (QPSK, 8-QAM, or 16-QAM), as well as the optical signal-to-noise ratio (OSNR), roll-off factor (ROF), and timing skew for 32 GBd coherent channels. For PAM-based intensity-modulation direct detection (IMDD) channel eye-diagram-based CNN method maintain >97% identification accuracy for 432 classes under different combinations of probabilistic shaping (PS), ROF, baud rate, OSNR, and chromatic dispersion (CD) by each modulation format. Furthermore, we undertake on an extensive comparison of ResNet-18, MobileNetV3 and EfficientNetV2. Our designed VGG-based model of reduced layers, alongside the lightweight MobileNetV3, demonstrates enhanced cost-effectiveness while maintaining high accuracy.


Prof Shuwen Xu

IEEE Senior Member

Xidian University

Biography: Shuwen Xu (IEEE Senior Member) was born in Huangshan city in Anhui, China. He received the B.Eng. and Ph.D. degrees, both in electronic engineering, from Xidian University, Xi’an, China, in 2006 and 2011, respectively. He worked at the National Laboratory of Radar Signal Processing, Xidian University, after that. He worked as a visiting professor in Mcmaster University in 2017 and 2018, Canada. He is currently a professor with the National Laboratory of Radar Signal Processing, Xidian University. He is also the vice director of National Collaborative Innovation Center of Information Sensing and Understanding and the Director of radar signal processing and data processing Department. His research interests are in the fields of radar target detection, statistical Learning, and SAR image processing.

Title: Precise Modeling of Sea Clutter and Target Detection

Abstract: Most of the Earth's surface is covered by the ocean, and events that occur below, above, and above the ocean greatly affect our lives. Remote sensing and surveillance at sea are very important. Since its invention in the 1930s, radar has played a crucial role in remote sensing and surveillance. This report introduces the generation mechanism of sea clutter, the amplitude statistical model of sea clutter, the reflection coefficient of sea clutter, and the empirical model from the perspective of signal processing. Finally, it introduces various optimal detectors designed based on different sea clutter statistical models in remote sensing detection, and prospects the development of sea clutter detection and remote sensing detection technology.


Prof. Tao Lei

IEEE Senior Member

Shanxi University of Science & Technology

Biography: Tao Lei (Senior Member, IEEE) received the Ph.D. degree in information and communication engineering from Northwestern Polytechnical University, Xi’an, China, in 2011.,From 2012 to 2014, he was a Post-Doctoral Research Fellow with the School of Electronics and Information, Northwestern Polytechnical University. From 2015 to 2016, he was a Visiting Scholar with the Quantum Computation and Intelligent Systems Group, University of Technology Sydney, Sydney, NSW, Australia. He is currently a Professor with the School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an. He has authored or coauthored 80+ research articles, including the IEEE Transactions on Image Processing (TIP), IEEE Transactions on Fuzzy Systems (TFS), and IEEE Transactions on Geoscience and Remote Sensing (TGRS). His research interests include image processing, pattern recognition, and machine learning. 

Title: Medical Image Segmentation under Constrained Conditions

Abstract: Medical image segmentation is a key technology in the field of intelligent image analysis. At present, a large number of research results on medical image segmentation have been reported and used for smart medicine. However, the current mainstream medical image segmentation methods still face the following challenges. Firstly, accurate segmentation of medical images is difficult due to high noise and low contrast. Secondly, mainstream medical image segmentation models have a large number of parameters and slow inference speed, making it difficult to deploy on low resource devices. Finally, pixel-level annotation of medical images is very expensive and requires professional knowledge. To address these problems, our team proposes a high-precision hybrid CNN-Transformer network, an ultralight network, and a semi supervised network for medical image segmentations. Some of the research results have been applied to medical companies and large hospitals, providing technical support for the rapid development of smart healthcare in China.

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Prof Dr Abdul Rauf

Nanjing University of Information Science and Technology

Biography: Abdul Rauf currently working as a Foreign Professor at the School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing China. Meanwhile, He is engaged as Module Lead/ Instructor Reading Academy-NUIST affiliated (University of Reading, United Kingdom (UK). Abdul does research in Business Economics, Energy Economics, Environmental Economics, Stock Market Development, Tourism Economics, ICT, etc. He has published more than 45 high-impact peer-reviewed SSCI, SCI, and EI papers in renowned journals. His research projects are based on "Belt and Road Initiative economies in diverse aspects. His research interests; energy economics, sustainable economic development, ecological sustainability, information and communication technology, environmental economics, carbon neutrality, business economics, tourism economics, agricultural economics, financial markets, stock markets, cryptocurrencies, sustainable energy transition, etc.

Title: Sustainability at the Crossroads: Analyzing the Crucial Interplay of Sustainable Growth, Energy Consumption, and Environmental Challenges in the Belt and Road Initiative Economies – An Innovative Empirical Study

Abstract: The concept of modernization and globalization urges a tendency of bilateral cooperation and strategical relationships among the nations. Recently, China has taken the Belt and Road Initiative (BRI) in 2013 to articulate the slogan of "Going global strategy.” The primary objective of the current study is to explore the nexus between energy consumption, economic growth, population growth, financial development and carbon emission (CO2) for the panel of 65 BRI countries over the period of 1981 to 2016. Empirical results show that energy consumption, high-tech industry, and economic growth deteriorate environmental quality but financial development and renewable energy consumption have a favorable effect for the environment. The energy consumption is positively and significantly affecting the environmental quality for all regions except the South Asian region. The overall outcomes postulate a weak association of economic indicators with carbon emissions in the long run except for Europe, MENA, and Southeast Asian regions. This present study serves as a blueprint to experts, policymakers and BRI listed government officials suggesting that they should advise the masses and industries to shift towards renewable energy sources. Furthermore, the need to install the water treatment plants near to industrial zones is pertinent. Moreover, the environment monitoring organizations and portfolio investors should arrange awareness campaigns for green investments and renewable energy dependency to accomplish visionary BRI feat.


Senior Engineer 

Shiling Zhang

State Grid Chongqing Electric Power Company Chongqing Electric Power Research Institute

Biography: Zhang Shiling, Senior engineer, Doctor of Engineering, is currently the director of Science and Technology management, physical and chemical testing technology, Electric Power Research Institute of State Grid Chongqing Electric Power Company. Long engaged in high voltage and insulation technology, physical and chemical testing technology research and production work. He is a full-time key researcher of State Grid Provincial Laboratory of Chongqing Electric Power Company and Key Provincial and ministerial Laboratory of Chongqing, member of IEEE Sub-Committee on New Sensing and Monitoring Technology, member of Insulation Materials and Insulation Technology Professional Committee of Chinese Society of Electrotechnical Engineering, member of high voltage Sub-committee of Chinese Society of Electrical Engineering. The development of UHV dry converter transformer bushing and SF6 gas insulation through-wall bushing have been applied to the construction of UHV AC/DC engineering. Presided over the completion of GIS fault detection sensor technology and system, won the Outstanding Innovation Achievement Award of the International Innovation and Entrepreneurship Expo, and awarded the title of Outstanding Scientist by Chongqing Society of Electrical Engineering.

As the first author, he has published more than 90 SCI/EI retrieval papers in domestic and foreign journals and international top academic conferences, and 18 Chinese core journals of Peking University. His innovation achievements have won 9 provincial and ministerial awards, such as the first prize of Chongqing Science and Technology Progress Award and the special first prize of China Water Conservancy and Electric Power Quality Management Association, and accepted 1 international invention patent. He has authorized 20 national invention patents and utility models, 18 software Copyrights, more than 20 international and domestic conference reports, and presided over 2 provincial and ministerial projects of basic frontier and 3 science and technology projects of the headquarters of State Grid Corporation as the project leader.

Title: Research and practice of the large-scale parallel computing and artificial intelligence algorithm in the typical equipment of new power system

Abstract: Taking the converter transformer for UHV converter valve hall as the research object, this special speech discusses the construction process of its three-dimensional digital model from the insulation structure of the transformer body. Further, focusing on the outgoing device and bushing structure of converter transformer, this paper introduces the typical structure, the actual valve hall operation environment and the heating theoretical model of high-voltage power equipment under high harmonic load from the perspective of high-voltage power equipment operation, analyzes electro-thermal coupling nonlinear electric field of transformer outgoing device, and optimizes its insulation structure by using RBF neural network and NSGA-II multi-objective optimization algorithm.

Focuses on the 3D construction of digital twin model in the outgoing area of converter transformer. Its research method can be extended to key components such as the converter body winding structure, oil paper insulation area and on the load switch. The research results of this paper can provide theoretical guidance and technical reference for the insulation structure design of the converter transformer body, especially for the structural design and operation maintenance of outlet device area, and can provide some theoretical guidance for the on-line analysis of short-term current carrying capacity and long-term aging performance of converter transformer outlet device area.

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Prof. Cheng Chin

IEEE Senior Member

Newcastle University

Biography: He is a Chair Professor of Intelligent Systems Modelling and Simulation at Newcastle University. He is a founding Director of Innovation (and Engagement), where he led numerous multi-disciplinary research initiatives with industries at Newcastle University in Singapore. He obtained 6 Economic Development Board (EDB)-Industrial Postgraduate Programme (IPP) and 2 Singapore Maritime Institute (SMI) grants from the Singapore government in intelligent systems design, simulation, and predictive analytics.

He has achieved over 150 publications, 5 authored books (e.g., IET, CRC Press, and Springer), 2 edited books, 2 Singapore Patent applications, and 3 US Patents. He also published in various AI-related conferences such as EANN/EAAAI, IJCNN, AIAI, and DCASE. He is a Fellow of the Higher Education Academy, a Fellow of IMarEST, a Senior Member of IEEE, and a Chartered Engineer with IET. He served as a Lead Guest Editor for the Journal of Advanced Transportation (Special Issue on Intelligent Autonomous Transport Systems Design and Simulation) with Wiley. In 2023, he was awarded the Best Presentation at the 8th International Conference on Computational Intelligence and Applications by IEEE. Before that, he was awarded the Outstanding Contributions in Reviewing for Future Generation Computer Systems Journal, Elsevier, and Best Paper Award in the 10th International Conference on Modelling, Identification, and Control, sponsored by IEEE.

Title: Creating Smart Solutions: Designing Intelligent System

Abstract: In today's rapidly evolving world, the demand for smart solutions has never been greater. From automating routine tasks to optimizing complex processes, intelligent systems are reshaping industries and improving lives. Designing these smart solutions requires careful integration of cutting-edge technologies and innovative ideas.  The first step in designing intelligent systems is to clearly understand the problem at hand. Intelligent systems rely heavily on data. Quality and quantity of data are essential for training and fine-tuning machine learning models. The success of intelligent systems largely depends on the technologies chosen to build them. Artificial Intelligence (AI) and Machine Learning (ML) are key components in developing intelligent systems. Training an intelligent system involves feeding it with labelled data, allowing it to learn patterns and make predictions or decisions. Many intelligent systems operate in real-time environments, requiring them to handle continuous data streams and respond quickly. Designers must consider how end-users will interact with the intelligent system. A well-designed user interface or conversational interface can significantly enhance user experience and adoption. Intelligent systems should be designed to scale as demand increases. The ability to handle large datasets, high user concurrency, and system resilience are crucial factors to consider during the design phase. The process of designing intelligent systems doesn't end with deployment. Continuous improvement and maintenance are vital to adapt to evolving user needs and data patterns. Hence, designing intelligent systems involves a multidisciplinary approach that combines data science, AI/ML, software engineering, and human-centered design principles. By understanding the problem domain, choosing the right technologies, ensuring security and scalability, and focusing on user experience, designers can create smart solutions that have a profound impact on society and drive us toward a more connected and intelligent future. A few examples of smart solutions will be given for areas in acoustics.