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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Jingcheng Yang Frank Kulwa Xuanwei Liu Yiqing Lu Yufa Fu Guanglin Li Yaping Huai Xin Zhang Yongcheng Li CAS Key Laboratory of Human-Machine Intelligence Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences (CAS) Shenzhen China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Shenzhen Dapeng New District Nanao People’s Hospital Shenzhen China Department of Rehabilitation Medicine Shenzhen Longhua District Central Hospital Shenzhen China Brain Cognition and Brain-Computer Intelligence Integration Group Kunming University of Science and Technology Kunming China Shandong Zhongke Advanced Technology Co. Ltd. Jinan China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The emerging neurorehabilitation technology, Brain-computer Interface (BCI), provides a novel prospect for stroke recovery. However, decoding brain activity during the movement present substantial challenges, and feature extraction is crutial to build a better decoder. In this study the CEBRA method were employed to extract features first based on electroencephalogram(EEG) data during Motor Execution(ME) and Motor lmagery (Ml) tasks for 20 participants (including 10 stroke patients). The results revealed that, in MI tasks, CEBRA-RF (Random Forests) achieved an average classification accuracy of 91.33%, with an average F1-score of 91.19%, and CEBRA-SVM (Support Vector Machine) achieved an average classification accuracy of 91.32%, with an average F1-score of 90.83%. Compared to other conventional feature extraction methods, CEBRA shows significantly higher accuracy (t-test, p<0.01). However, in ME tasks, CEBRA-RF achieved an average classification accuracy of 75.67%, with an average F1-score of 75.39%, and CEBRA-SVM achieved an average classification accuracy of 76.13%, with an average F1-score of 75.80%. Nevertheless, no significant differences were observed compared to other feature extraction methods. These findings demonstrate the potential of CEBRA in decoding patients’ brain activity. The results of this study hold promise in addressing the current challenges of low decoding accuracy in BCI systems, offering a new approach for designing BCI-assisted rehabilitation systems for stroke patients.

关键词： Support vector machines Brain Accuracy Stroke (medical condition) Feature extraction Motors Brain-computer interfaces Neurorehabilitation Decoding Paralysis

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A Design of Gas Turbine computer-Integrated Control System Based on Energy Cycle Optimization

A Design of Gas Turbine Computer-Integrated Control System B...

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IEEE International Conference on Power and Power and Renewable Energy (ICPRE)

作者： Jianqiao Xu Fucheng Zhang A.O. Stopakevych Institute of Automation and Computer Integration Odessa National Academy of Telecommunications China Scholarship Council Odessa Ukraine Institute of Automation Ukrainian National Makarov Ship University China Scholarship Council Nikolayev Ukraine Institute of Automation and Computer Integration Odessa National Academy of Telecommunications Odessa Ukraine

ISBN: (数字)9781728145747

ISBN: (纸本)9781728145754

In this study, a gas turbine computer integrated control system based on energy cycle optimization is designed to solve the problem of large energy loss caused by gas turbine during normal operation. At first, the circulation conditions of the internal energy of the gas turbine are analyzed through the thermodynamic structure of the gas turbine. Then, a ring structure is added to enhance the operability of the fuel supply system. Furthermore, a SCADA monitoring and control system for energy cycle are designed based on their unique system architecture. Finally, the hierarchical control principle of the system is explained from the perspective of system organization to facilitate future system development.

关键词： Turbines Control systems Fuels Combustion Optimization Monitoring Temperature measurement

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Corrections to “Brain-Inspired Computing: A Systematic Survey and Future Trends”

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Proceedings of the IEEE 2025年第12期112卷 1850-1850页

作者： Guoqi Li Lei Deng Huajin Tang Gang Pan Yonghong Tian Kaushik Roy Wolfgang Maass Institute of Automation Chinese Academy of Sciences Beijing China Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen China Department of Precision Instrument Tsinghua University Beijing China State Key Laboratory of Brain-Machine Intelligence College of Computer Science and Technology MOE Frontier Science Center for Brain Science and Brain-Machine Integration Zhejiang University Hangzhou China Department of Computer Science Peking University Beijing China School of Electrical and Computer Engineering Purdue University West Lafayette IN USA School of Computer Science Graz University of Technology Graz Austria

Presents corrections to the paper, (Corrections to “Brain-Inspired Computing: A Systematic Survey and Future Trends”).

关键词： Artificial intelligence Computational modeling Brain modeling Biological system modeling Surveys computer architecture Benchmark testing Bio-inspired computing Neuromorphic engineering Sensors Neural networks Software tools

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Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition

arXiv

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arXiv 2022年

作者： Xie, Kan Zhang, Zhe Li, Bo Kang, Jiawen Niyato, Dusit Xie, Shengli Wu, Yi The School of Automation Guangdong University of Technology Guangzhou510006 China The Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China The Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China The Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China The School of Data Science and Technology Heilongjiang University Harbin150080 China The Institute for Cryptology & Network Security Heilongjiang University Harbin150080 China School of Computer Science and Engineering Nanyang Technological University Singapore

With the gradual popularization of self-driving, it is becoming increasingly important for vehicles to smartly make the right driving decisions and autonomously obey traffic rules by correctly recognizing traffic signs. However, for machine learning-based traffic sign recognition on the Internet of Vehicles (IoV), a large amount of traffic sign data from distributed vehicles is needed to be gathered in a centralized server for model training, which brings serious privacy leakage risk because of traffic sign data containing lots of location privacy information. To address this issue, we first exploit privacy-preserving federated learning to perform collaborative training for accurate recognition models without sharing raw traffic sign data. Nevertheless, due to the limited computing and energy resources of most devices, it is hard for vehicles to continuously undertake complex artificial intelligence tasks. Therefore, we introduce powerful Spike Neural Networks (SNNs) into traffic sign recognition for energy-efficient and fast model training, which is the next generation of neural networks and is practical and well-fitted to IoV scenarios. Furthermore, we design a novel encoding scheme for SNNs based on neuron receptive fields to extract information from the pixel and spatial dimensions of traffic signs to achieve high-accuracy training. Numerical results indicate that the proposed federated SNN outperforms traditional federated convolutional neural networks in terms of accuracy, noise immunity, and energy efficiency as well. Copyright © 2022, The Authors. All rights reserved.

关键词： Traffic signs

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PDS-UKAN: Subdivision hopping connected to the U-KAN network for medical image segmentation

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computerized medical imaging and graphics : the official journal of the computerized Medical Imaging Society 2025年 124卷 102568页

作者： Liwei Deng Wenbo Wang Songyu Chen Xin Yang Sijuan Huang Jing Wang Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration School of Automation Harbin University of Science and Technology Harbin Heilongjiang 150080 China School of Computer Science and Technology Harbin University of Science and Technology Harbin Heilongjiang 150080 China. Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration School of Automation Harbin University of Science and Technology Harbin Heilongjiang 150080 China. State Key Laboratory of Oncology in South China Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Guangdong Provincial Clinical Research Center for Cancer Sun Yat-sen University Cancer Center Guangzhou Guangdong 510060 China Guangdong Esophageal Cancer Institute Guangzhou Guangdong 510060 China. Guangdong Esophageal Cancer Institute Guangzhou Guangdong 510060 China. Electronic address: huangsj@***. Institute for Brain Research and Rehabilitation South China Normal University Guangzhou 510631 China. Electronic address: wangjing_2022010@***.

Accurate and efficient segmentation of medical images plays a vital role in clinical tasks, such as diagnostic procedures and planning treatments. Traditional U-shaped encoder-decoder architectures, built on convolutional and transformer-based networks, have shown strong performance in medical image processing. However, the simple skip connections commonly used in these networks face limitations, such as insufficient nonlinear modeling capacity, weak global multiscale context modeling, and limited interpretability. To address these challenges, this study proposes the PDS-UKAN network, an innovative subdivision-based U-KAN architecture, designed to improve segmentation accuracy. The PDS-UKAN incorporates a PKAN module-comprising partial convolutions and Kolmogorov - Arnold network layers-into the encoder bottleneck, enhancing the network's nonlinear modeling and interpretability. Additionally, the proposed Dual-Branch Convolutional Boundary Enhancement Module (DBE) focuses on pixel-level boundary refinement, improving edge detail preservation in shallow skip connections. Meanwhile, the Skip Connection Channel Spatial Attention Module (SCCSA) mechanism is applied in the deeper skip connections to strengthen cross-dimensional interactions between channels and spatial features, mitigating the loss of spatial information due to downsampling. Extensive experiments across multiple medical imaging datasets demonstrate that PDS-UKAN consistently achieves superior performance compared to state-of-the-art (SOTA) methods.

关键词： Medical image segmentation Skip Connections U-KAN U-Net

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Overview of the CCKS 2019 knowledge graph evaluation track: Entity, relation, event and QA

arXiv

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arXiv 2020年

作者： Han, Xianpei Wang, Zhichun Zhang, Jiangtao Wen, Qinghua Li, Wenqi Tang, Buzhou Wang, Qi Feng, Zhifan Zhang, Yang Lu, Yajuan Wang, Haitao Chen, Wenliang Shao, Hao Chen, Yubo Liu, Kang Zhao, Jun Wang, Taifeng Zhang, Kezun Wang, Meng Jiang, Yinlin Qi, Guilin Zou, Lei Hu, Sen Zhang, Minhao Lin, Yinnian Chinese Information Processing Laboratory Institute of Software Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence Beijing Normal University Beijing100875 China 305 Hospital of People’s Liberation Army Beijing100017 China Department of Computer Science and Technology Tsinghua University Beijing100084 China Yidu Cloud Beijing100000 China Harbin Institute of Technology Shenzhen Guangdong518055 China Baidu Beijing100193 China School of Computer Science & Technology Soochow University Suzhou Jiangsu215006 China Gowild Suzhou Jiangsu215002 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Ant Financial Hangzhou Zhejiang310000 China School of Computer Science and Engineering Southeast University Nanjing Jiangsu211189 China Key Laboratory of Computer Network Information Integration Southeast University Nanjing Jiangsu211189 China Peking University Beijing100871 China

Knowledge graph models world knowledge as concepts, entities, and the relationships between them, which has been widely used in many real-world tasks. CCKS 2019 held an evaluation track with 6 tasks and attracted more than 1,600 teams. In this paper we give an overview of the knowledge graph evaluation tract at CCKS 2019. By reviewing the task definition, successful methods, useful resources, good strategies and research challenges associated with each task in CCKS 2019, this paper can provide a helpful reference for developing knowledge graph applications and conducting future knowledge graph researches. Copyright © 2020, The Authors. All rights reserved.

关键词： Knowledge graph

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Self-Awareness for Autonomous Systems

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PROCEEDINGS OF THE IEEE 2020年第7期108卷 971-975页

作者： Dutt, Nikil Regazzoni, Carlo S. Rinner, Bernhard Yao, Xin Nikil Dutt (Fellow IEEE) received the Ph.D. degree from the University of Illinois at Urbana–Champaign Champaign IL USA in 1989.""He is currently a Distinguished Professor of computer science (CS) cognitive sciences and electrical engineering and computer sciences (EECS) with the University of California at Irvine Irvine CA USA. He is a coauthor of seven books. His research interests include embedded systems electronic design automation (EDA) computer architecture distributed systems healthcare Internet of Things (IoT) and brain-inspired architectures and computing.""Dr. Dutt is a Fellow of ACM. He was a recipient of the IFIP Silver Core Award. He has received numerous best paper awards. He serves as the Steering Committee Chair of the IEEE/ACM Embedded Systems Week (ESWEEK). He is also on the steering organizing and program committees of several premier EDA and embedded system design conferences and workshops. He has served on the Editorial Boards for the IEEE Transactions on Very Large Scale Integration (VLSI) Systems and the ACM Transactions on Embedded Computing Systems and also previously served as the Editor-in-Chief (EiC) for the ACM Transactions on Design Automation of Electronic Systems. He served on the Advisory Boards of the IEEE Embedded Systems Letters the ACM Special Interest Group on Embedded Systems the ACM Special Interest Group on Design Automationt and the ACM Transactions on Embedded Computing Systems. Carlo S. Regazzoni (Senior Member IEEE) received the M.S. and Ph.D. degrees in electronic and telecommunications engineering from the University of Genoa Genoa Italy in 1987 and 1992 respectively.""He is currently a Full Professor of cognitive telecommunications systems with the Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN) University of Genoa and a Co-Ordinator of the Joint Doctorate on Interactive and Cognitive Environments (JDICE) international Ph.D. course started initially as EU Erasmus Mundus Project and

Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In computer systems, one can derive such behavior from the concept of a rational agent with autonomy (“control over its own actions”), reactivity (“react to events from the environment”), proactivity (“act on its own initiative”), and sociality (“interact with other agents”) as fundamental properties \n[1]\n. Autonomous systems will undoubtedly pervade into our everyday lives, and we will find them in a variety of domains and applications including robotics, transportation, health care, communications, and entertainment to name a few. \nThe articles in this month’s special issue cover concepts and fundamentals, architectures and techniques, and applications and case studies in the exciting area of self-awareness in autonomous systems.

关键词： Special issues and sections Autonomous systems Self-aware Robot sensing systems Computational modeling

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Asymptotic Solution for Coupled Heat and Mass Transfer During the Solidification of High Water Content Materials

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Transactions of Tianjin University 2010年第4期16卷 239-243页

作者：齐德瑄何凯杜如虚张义同 School of Mechanical Engineering Tianjin University Institute of Advanced Integration Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Department of Automation and Computer-Aided Engineering The Chinese University of Hong Kong

This paper focuses on obtaining an asymptotic solution for coupled heat and mass transfer problem during the solidification of high water content materials. It is found that a complicated function involved in governing equations can be approached by Taylor polynomials unlimitedly, which leads to the simplification of governing equations. The unknown functions involved in governing equations can then be approximated by Chebyshev polynomials. The coefficients of Chebyshev polynomials are determined and an asymptotic solution is obtained. With the asymptotic solution, the dehydration and freezing fronts of materials are evaluated easily, and are consistent with numerical results obtained by using an explicit finite difference method.

关键词： heat transfer mass transfer solidification asymptotic analysis

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Geometric algorithm for point projection and inversion onto Bézier surfaces

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中国高等学校学术文摘·计算机科学 2009年第4期3卷 472-476页

作者： Jinting XU Weijun LIU Jianhuang WU Hongyou BIAN Lun LI School of Automotive Engineering Daiian University of TechnologyDaiian 116024China Shenyang Institute of Automation Chinese Academy of SciencesShenyang 110016China Shenyang Institute of Automation Chinese Academy of SciencesShenyang 110016China Centre for Human Computer Interaction Shenzhen Institution of Advanced Integration TechnologyChinese Academy of SciencesShenzhen 518067China

This paper presents an accurate and efficient method for the computation of both point projection and inversion onto Bézier ***,these two problems are formulated in terms of solution of a polynomial equation with u and v variables expressed in the Bernstein ***,based on subdivision of the Bézier surface and the recursive quadtree decomposition,a novel solution method is *** computation of point projection is shown to be equivalent to the geometrically intuitive intersection of a surface with the u-v ***,by comparing the distances between the test point and the candidate points,the closest point is *** illustrate the feasibility of this method.

关键词： point projection point inversion Bézier surface

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Performance analysis of routing protocols for vehicle safety communications on the freeway

Performance analysis of routing protocols for vehicle safety...

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2009 3rd International Conference on Anti-counterfeiting, Security, and Identification in Communication, ASID 2009

作者： Niansheng, Liu Huihuan, Qian Jingyu, Yan Yangsheng, Xu School of Computer Engineering Jimei University Xiamen 361021 China Department of Mechanical and Automation Engineering Chinese University of Hong Kong Shatin N.T. Hong Kong Shenzhen Institute of Advanced Integration Technology CAS/CUHK Shenzhen 518054 China

ISBN: (纸本)9781424438839

In a freeway vehicular ad hoc network (VANET) environment, an efficient ad hoc routing protocol plays a very important role to enhance the safety of passengers. This paper presents the latest result of simulation model of three mobile ad hoc routing protocols: destination-sequenced distance-vector protocol (DSDV), dynamic source routing (DSR) and ad hoc on-demand distance vector (AODV). The performance of three protocols under constant bit rate traffic is evaluated and compared with five quality indices. The simulation results show that both the reactive routing protocols, DSR and AODV, have better performance than the proactive routing protocol DSDV. DSDV has largest packet loss rate and smallest system throughput among the three protocols. This paper also shows that the effect of varying mobility has undeniably influenced the performance of both reactive routing protocols. DSR is better in the protocol overhead than AODV. However, there is critical jitter variation in the DSR. As a whole, AODV is more appropriate than DSR for the freeway VANET.

关键词： Routing protocols

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