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Real-world Cyber Security Demonstration for Networked Electric Drives

IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Yang, He Yang, Bowen Coshatt, Stephen Li, Qi Hu, Kun Hammond, Bryan Cooper Ye, Jin Parasuraman, Ramviyas Song, Wenzhan The Intelligent Power Electronics and Electric Machine Laboratory University of Georgia AthensGA30602 United States The Power Systems Engineering Center National Renewable Energy Laboratory GoldenCO80401 United States The Center for Cyber-Physical Systems University of Georgia AthensGA30602 United States Eaton ClevelandOH44122 United States Sino-German College of Intelligent Manufacturing Shenzhen Technology University Guangdong Shenzhen518118 China Research Lab the School of Computing University of Georgia AthensGA30602 United States

In this paper, we present the design and implementation of a cyber-physical security testbed for networked electric drive systems, aimed at conducting real-world security demonstrations. To our knowledge, this is one of the first security testbeds for networked electric drives, seamlessly integrating the domains of power electronics and computer science, and cybersecurity. By doing so, the testbed offers a comprehensive platform to explore and understand the intricate and often complex interactions between cyber and physical systems. The core of our testbed consists of four electric machine drives, meticulously configured to emulate small-scale but realistic information technology (IT) and operational technology (OT) networks. This setup both provides a controlled environment for simulating a wide array of cyber attacks, and mirrors potential real-world attack scenarios with a high degree of fidelity. The testbed serves as an invaluable resource for the study of cyberphysical security, offering a practical and dynamic platform for testing and validating cybersecurity measures in the context of networked electric drive systems. As a concrete example of the testbed’s capabilities, we have developed and implemented a Python-based script designed to execute step-stone attacks over a wireless local area network (WLAN). This script leverages a sequence of target IP addresses, simulating a real-world attack vector that could be exploited by adversaries. To counteract such threats, we demonstrate the efficacy of our developed cyberattack detection algorithms, which are integral to our testbed’s security framework. Furthermore, the testbed incorporates a real-time visualization system using InfluxDB and Grafana, providing a dynamic and interactive representation of networked electric drives and their associated security monitoring mechanisms. This visualization component not only enhances the testbed’s usability but also offers insightful, real-time data for researchers and

关键词： Testbeds

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ConvoyLLM: Dynamic Multi-Lane Convoy Control Using LLMs

arXiv

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arXiv 2025年

作者： Lu, Liping He, Zhican Chu, Duanfeng Wang, Rukang Peng, Saiqian Zhou, Pan School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430070 China Intelligent Transportation Systems Research Center Wuhan University of Technology Wuhan430070 China Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China

This paper proposes a novel method for multi-lane convoy formation control that uses large language models (LLMs) to tackle coordination challenges in dynamic highway environments. Each connected and autonomous vehicle in the convoy uses a knowledge-driven approach to make real-time adaptive decisions based on various scenarios. Our method enables vehicles to dynamically perform tasks, including obstacle avoidance, convoy joining/leaving, and escort formation switching, all while maintaining the overall convoy structure. We design a Interlaced formation control strategy based on locally dynamic distributed graphs, ensuring the convoy remains stable and flexible. We conduct extensive experiments in the SUMO simulation platform across multiple traffic scenarios, and the results demonstrate that the proposed method is effective, robust, and adaptable to dynamic environments. The code is available at: https://***/chuduanfeng/ConvoyLLM . Copyright © 2025, The Authors. All rights reserved.

关键词： Autonomous vehicles

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A TEMPLATE-BASED APPROACH TO THE GENERATION OF distributed APPLICATIONS USING A NETWORK OF WORKSTATIONS

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IEEE TRANSACTIONS ON PARALLEL AND distributed systems 1991年第1期2卷 52-67页

作者： SINGH, A SCHAEFFER, J GREEN, M Distributed Systems Research Laboratory Department of Computing Science University of Alberta Edmonton AB Canada

Despite rapid growth in workstation and networking technologies, the workstation environment continues to pose challenging problems to shared processing. In this paper, we present a computational model and system for the generation of distributed applications in such an environment. The well-known RPC model is modified by a novel concept known as template attachment. A computation consists of a network of sequential procedures which have been encapsulated in templates. A small selection of templates is available from which a distributed application with the desired communication behavior can be rapidly built. The system generates all the required low-level code for correct synchronization, communication, and scheduling. This results in a system that is easy to use and flexible, and can provide a programmer with the desired amount of control in using idle processing power over a network of workstations. The practical feasibility of the model has been demonstrated by implementing it for Unix1-based workstation environments.

关键词： COARSE GRAIN CONCURRENCY distributed computing distributed SOFTWARE ENGINEERING NETWORK systems PARALLEL PROGRAMMING PARALLEL PROGRAMMING MODELS WORKSTATION ENVIRONMENT

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Adaptive frequency-domain enhanced deep model driven by heterogeneous networks for medical image segmentation

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Knowledge-Based systems 2025年 319卷

作者： Liu, Dong Kuang, Jin Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems Xiangnan University Chenzhou423300 China School of Computer and Artificial Intelligence Xiangnan University Chenzhou423300 China Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province Xiangnan University Chenzhou423300 China School of Geosciences Yangtze University Wuhan430100 China

Accurate medical image segmentation necessitates precise localization of global structures and local boundaries due to the high variability in lesion shapes and sizes. However, existing models are limited by conventional spatiotemporal features and single-network architectures, which restrict the simultaneous captures of semantic information and boundary details, thereby challenging generalizable medical image segmentation. To overcome these limitations, we propose a heterogeneous network-driven adaptive frequency-domain enhanced deep model(AFDSeg). First, we introduce the Frequency Domain Adaptive High-Frequency Feature Selection(FAHS) module, which adaptively extracts high-frequency features to enhance contour and detail representation while integrating spatiotemporal and frequency-domain features for improved consistency. Additionally, Prototype-Guided Low-Frequency Feature Aware(PFLA) and Local High-Frequency Salient-Feature Denoising (LHSD) modules are developed, which extract discriminative low-frequency features while suppressing local noise in high-frequency components, thereby facilitating efficient multi-scale feature fusion. Furthermore, the Multi-Level Prototype Feature Refinement(MPFR) Module is introduced to align low- and high-dimensional features during decoding and enhance semantic consistency. Finally, a heterogeneous network framework capable of accommodating multiple network architecture for medical image segmentation is proposed. Our method achieves mDice scores of 93.91%, 88.64%, 91.27%, 90.74%, and 81.38% on the Kvasir-SEG, BUSI, ISIC-2017, ACDC, and Synapse datasets, respectively, and attains 92.09%, 93.50%, and 83.92% in cross-domain experiments on three unseen datasets (Kvasir Capsule-SEG, BUS42, and M&Ms). Our approach consistently outperforms state-of-the-art methods on both benchmark and cross-domain datasets. Extensive quantitative and qualitative experiments demonstrated that AFDSeg accurately segments global structures and local detai

关键词： Semantic Segmentation

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Safe Reinforcement Learning for Social Motor Coordination in Two Dimensional Space

Safe Reinforcement Learning for Social Motor Coordination in...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Fan Yang Yuening Huang Chao Zhai Hehong Zhang China University of Geosciences Wuhan Campus School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Rice University George R. Brown School of Engineering and Computing Houston Texas United States Fuzhou University School of Computer and Data Science Fuzhou China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

This paper presents an interactive motion control method based on reinforcement learning, designed to assist children with autism who have social motor impairments through a mirror game intervention. The virtual teacher uses the Deep Deterministic Policy Gradient (DDPG) algorithm to optimize its actions, guiding the participant to follow a Lissajous trajectory. To ensure safety, a motion-correction mechanism was developed, which automatically adjusts actions when the predicted trajectory surpasses predefined safety boundaries. The reward function considers both the distance between the virtual teacher and the target trajectory, as well as the distance between the virtual teacher and the participant, with dynamic adjustments applied by the motion-correction mechanism. Experimental results demonstrate that the virtual teacher effectively guides the participant towards the target trajectory while adhering to safety constraints.

关键词： Autism Filters Reinforcement learning Games Variable speed drives Motors Trajectory Safety Mirrors Motion control

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Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks

引用

IEEE Transactions on Dependable and Secure computing 2025年

作者： Dong, Xuewen Li, Jiachen Li, Shujun You, Zhichao Qu, Qiang Kholodov, Yaroslav Shen, Yulong The School of Computer Science and Technology Xidian University The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education China The Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China The School of Computing Kent Interdisciplinary Research Centre in Cyber Security University of Kent CanterburyCT2 7NF United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China The Intelligent Transportation Systems Lab Innopolis University Innopolis Russia The School of Computer Science and Technology Xidian University China

Recent studies show that graph neural networks (GNNs) are vulnerable to backdoor attacks. Existing backdoor attacks against GNNs use fixed-pattern triggers and lack reasonable trigger constraints, overlooking individual graph characteristics and rendering insufficient evasiveness. To tackle the above issues, we propose ABARC, the first Adaptive Backdoor Attack with Reasonable Constraints, applying to both graph-level and node-level tasks in GNNs. For graph-level tasks, we propose a subgraph backdoor attack independent of the graph’s topology. It dynamically selects trigger nodes for each target graph and modifies node features with constraints based on graph similarity, feature range, and feature type. For node-level tasks, our attack begins with an analysis of node features, followed by selecting and modifying trigger features, which are then constrained by node similarity, feature range, and feature type. Furthermore, an adaptive edge-pruning mechanism is designed to reduce the impact of neighbors on target nodes, ensuring a high attack success rate (ASR). Experimental results show that even with reasonable constraints for attack evasiveness, our attack achieves a high ASR while incurring a marginal clean accuracy drop (CAD). When combined with the state-of-the-art defense randomized smoothing (RS) method, our attack maintains an ASR over 94%, surpassing existing attacks by more than 7%. © 2004-2012 IEEE. All rights reserved.

关键词： Graph neural networks

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Safe Reinforcement Learning for Social Motor Coordination in Two Dimensional Space

Safe Reinforcement Learning for Social Motor Coordination in...

引用

26th International Conference on Industrial Technology, ICIT 2025

作者： Yang, Fan Huang, Yuening Zhai, Chao Zhang, Hehong China University of Geosciences School of Automation Wuhan Campus China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Rice University George R. Brown School of Engineering and Computing HoustonTX United States Fuzhou University School of Computer and Data Science Fuzhou China

ISBN: (纸本)9798331521950

关键词： Reinforcement learning

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Single-cell omics: experimental workflow, data analyses and applications

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Science China(Life Sciences) 2025年第1期68卷 5-102页

作者： Fengying Sun Haoyan Li Dongqing Sun Shaliu Fu Lei Gu Xin Shao Qinqin Wang Xin Dong Bin Duan Feiyang Xing Jun Wu Minmin Xiao Fangqing Zhao Jing-Dong J.Han Qi Liu Xiaohui Fan Chen Li Chenfei Wang Tieliu Shi Department of Clinical Laboratory the Affiliated Wuhu Hospital of East China Normal University(The Second People’s Hospital of Wuhu City)Wuhu 241000China Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration(Tongji University) Ministry of EducationOrthopaedic DepartmentTongji HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Frontier Science Center for Stem Cells School of Life Sciences and TechnologyTongji UniversityShanghai 200092China Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine Shanghai East HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Research Institute of Intelligent Computing Zhejiang LabHangzhou 311121China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai 201210China Center for Single-cell Omics School of Public HealthShanghai Jiao Tong University School of MedicineShanghai 200025China National Key Laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang UniversityJiaxing 314103China Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal UniversityShanghai 200241China Beijing Institutes of Life Science Chinese Academy of SciencesBeijing 100101China Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary StudiesCenter for Quantitative Biology(CQB)Peking UniversityBeijing 100871China Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases Women’s HospitalZhejiang University School of MedicineHangzhou 310006China Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of StatisticsEast China Normal U

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular *** exploration of how the genomes orchestrate the formation and maintenance of each cell,and control the cellular phenotypes of various organismsis,is both captivating and *** the inception of the first single-cell RNA technology,technologies related to single-cell sequencing have experienced rapid advancements in recent *** technologies have expanded horizontally to include single-cell genome,epigenome,proteome,and metabolome,while vertically,they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR ***-cell omics represent a groundbreaking advancement in the biomedical field,offering profound insights into the understanding of complex diseases,including ***,we comprehensively summarize recent advances in single-cell omics technologies,with a specific focus on the methodology *** overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

关键词： single-cell sequencing genome epigenome proteomics metabolomics multimodal spatial transcriptomics CRISPR screening

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Fundus Image Quality Assessment and Enhancement: a Systematic Review

arXiv

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arXiv 2025年

作者： Li, Heng Li, Haojin Ou, Mingyang Yu, Xiangyang Zhang, Xiaoqing Niu, Ke Fu, Huazhu Liu, Jiang Research Institute of Trustworthy Autonomous Systems SUSTech Shenzhen China Department of Computer Science and Engineering SUSTech Shenzhen China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Computer School Beijing Information Science and Technology University Beijing China Singapore

As an affordable and convenient eye scan, fundus photography holds the potential for preventing vision impairment, especially in resource-limited regions. However, fundus image degradation is common under intricate imaging environments, impacting following diagnosis and treatment. Consequently, image quality assessment (IQA) and enhancement (IQE) are essential for ensuring the clinical value and reliability of fundus images. While existing reviews offer some overview of this field, a comprehensive analysis of the interplay between IQA and IQE, along with their clinical deployment challenges, is lacking. This paper addresses this gap by providing a thorough review of fundus IQA and IQE algorithms, research advancements, and practical applications. We outline the fundamentals of the fundus photography imaging system and the associated interferences, and then systematically summarize the paradigms in fundus IQA and IQE. Furthermore, we discuss the practical challenges and solutions in deploying IQA and IQE, as well as offer insights into potential future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Photography

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A multidisciplinary approach to prevent student anxiety: a Toolkit for educators

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Procedia Computer Science 2025年 256卷 852-860页

作者： Alberto Freitas Bruno Costa Diogo Martinho Fatima Pais Ivone Duarte Constantino Martins Goreti Marreiros Rute Almeida MEDCIDS Faculty of Medicine University of Porto Porto 4200-450 Portugal CINTESIS@RISE University of Porto Porto 4200-450 Portugal Centro Hospitalar Universitário São João 4200-319 Porto GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Institute of Engineering – Polytechnic of Porto Porto 4249-015 Portugal & LASI – Intelligent Systems Associate Laboratory Guimarães 4800-058 Portugal Sucessos Criativos Lda São João da Madeira 3700-121 Portugal

The project WAI aims to tackle the growth of mental health disorders among the younger population, namely the most prevalent anxiety disorders. It intends to enhance educational strategies to help schools, teachers, and other professionals dealing with student anxiety. With that purpose, a suite of resources with a focus on mental health promotion in the school environment was organized by collecting existing tools and creating new elements for completeness. Information on mental health, AI, and AI use on mental health, classroom strategies, and best practices, structured for interdisciplinary application and use by both teachers and students, were considered. These resources were integrated into a Toolkit with a recommendation feature, which allows easy location and access to the most adequate tool for the specific need or educational challenge. To maximize accessibility, resources will be translated into several languages. The Toolkit will guide the implementation of a capacitation formative program for educators that will be implemented based on its resources. The development of the project involves participatory design with contributions from educators, mental health experts, and AI technologists to ensure a comprehensive approach. The Toolkit is intended to provide a systematic framework for educators to promote sustainable mental health promotion practices and, when appropriate, to use AI in ways that are ethically sound, while fostering digital responsibility.

关键词： Mental Health Promotion Anxiety Disorders Educational Strategies AI in Education Toolkit Teacher Capacitation

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