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arXiv 2025年

作者： Zhang, Fan Wang, Zhaocheng Mao, Tianqi Jiao, Tianyu Zhuo, Yinxiao Wen, Miaowen Xiang, Wei Chen, Sheng Karagiannidis, George K. Department of Electronic Engineering Tsinghua University Beijing100084 China State Key Laboratory of CNS/ATM Beijing Institute of Technology Beijing100081 China Zhuhai519088 China School of Electronic and Information Engineering South China University of Technology Guangzhou China School of Computing Engineering and Mathematical Sciences La Trobe University MelbourneVIC3086 Australia School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Greece

The integrated sensing and communication (ISAC) has been envisioned as one representative usage scenario of sixth-generation (6G) network. However, the unprecedented characteristics of 6G, especially the doubly dispersive channel, make classical ISAC waveforms rather challenging to guarantee a desirable performance level. The recently proposed affine frequency division multiplexing (AFDM) can attain full diversity even under doubly dispersive effects, thus becoming a competitive candidate for next-generation ISAC waveforms. Relevant investigations are still at an early stage, which involve only straightforward design lacking explicit theoretical analysis. This paper provides an in-depth investigation on AFDM waveform design for ISAC applications. Specifically, the closed-form Crámer-Rao bounds of target detection for AFDM are derived, followed by a demonstration on its merits over existing counterparts. Furthermore, we formulate the ambiguity function of the pilot-assisted AFDM waveform for the first time, revealing conditions for stable sensing performance. To further enhance both the communication and sensing performance of the AFDM waveform, we propose a novel pilot design by exploiting the characteristics of AFDM signals. The proposed design is analytically validated to be capable of optimizing the ambiguity function property and channel estimation accuracy simultaneously as well as overcoming the sensing and channel estimation range limitation originated from the pilot spacing. Numerical results have verified the superiority of the proposed pilot design in terms of dual-functional performance. Copyright © 2025, The Authors. All rights reserved.

关键词： Cramer-Rao bounds

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Multimodal Joint Prediction of Traffic Spatial-Temporal Data with Graph Sparse Attention Mechanism and Bidirectional Temporal Convolutional Network

arXiv

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arXiv 2024年

作者： Zhang, Dongran Yan, Jiangnan Polat, Kemal Alhudhaif, Adi Li, Jun Guangdong Provincial Key Laboratory of Intelligent Transportation System School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen518107 China Department of Electrical and Electronics Engineering Faculty of Engineering Bolu Abant Izzet Baysal University Bolu Turkey Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj11942 Saudi Arabia

Traffic flow prediction plays a crucial role in the management and operation of urban transportation systems. While extensive research has been conducted on predictions for individual transportation modes, there is relatively limited research on joint prediction across different transportation modes. Furthermore, existing multimodal traffic joint modeling methods often lack flexibility in spatial-temporal feature extraction. To address these issues, we propose a method called Graph Sparse Attention Mechanism with Bidirectional Temporal Convolutional Network (GSABT) for multimodal traffic spatial-temporal joint prediction. First, we use a multimodal graph multiplied by self-attention weights to capture spatial local features, and then employ the Top-U sparse attention mechanism to obtain spatial global features. Second, we utilize a bidirectional temporal convolutional network to enhance the temporal feature correlation between the output and input data, and extract inter-modal and intra-modal temporal features through the share-unique module. Finally, we have designed a multimodal joint prediction framework that can be flexibly extended to both spatial and temporal dimensions. Extensive experiments conducted on three real datasets indicate that the proposed model consistently achieves state-of-the-art predictive performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Urban transportation

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Prototype Guided Personalized Federated Intrusion Detection System

Prototype Guided Personalized Federated Intrusion Detection ...

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Smart World Congress (SWC), IEEE

作者： Long Cheng Huiru Yan Hanlin Zhou Ying Wang Haichuan Tang Fang Fang State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing China Chinese Academy of Sciences Institute of Computing Technology Beijing China AI Lab of CRRC Academy Beijing China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

Preventing network attacks and protecting user privacy are consistently hot research topics in the Internet of Things (IoT) and edge computing fields. Recent advancements in Federated Learning (FL) have shown promise in addressing these challenges. FL allows various clients to collaboratively build an intrusion detection system (IDS) without sharing their private data. However, most existing methods train a single intrusion detection model for all clients and assume that the training and test data distribution are identical, leading to unsatisfactory detection accuracy and generalization abilities in practice. To overcome these challenges, this paper introduces a prototype-guided personalized FL approach named PG-FedIDS. We propose two novel mechanisms within this method. Firstly, we utilize class prototypes as auxiliary information carriers to generate personalized models for each client, rather than generating a single global model as in previous works. Secondly, we propose a prototype-guided ensemble learning strategy, which can leverage the global knowledge in prototypes to enhance detection accuracy and generalization abilities for each client. We conduct extensive experiments on two benchmark datasets with different evaluation test settings. The results demonstrate that our PG-FedIDS achieves promising detection accuracy and consistently outperforms other FL baselines.

关键词： Training Privacy Accuracy Federated learning Image edge detection Prototypes Intrusion detection Data models Ensemble learning Internet of Things

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Secure State Estimation against Sparse Attacks on a Time-varying Set of Sensors

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IFAC-PapersOnLine 2023年第2期56卷 270-275页

作者： Zishuo Li Muhammad Umar B. Niazi Changxin Liu Yilin Mo Karl H. Johansson Department of Automation Tsinghua University Beijing 100084 China Division of Decision and Control Systems Digital Futures School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm SE-100 44 Sweden Laboratory for Information and Decision Systems Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA

This paper studies the problem of secure state estimation of a linear time-invariant (LTI) system with bounded noise in the presence of sparse attacks on an unknown, time-varying set of sensors. At each time, the attacker has the freedom to choose an arbitrary set of no more than p sensors and manipulate their measurements without restraint. To this end, we propose a secure state estimation scheme and guarantee a bounded estimation error irrespective of the attack signals subject to 2p-sparse observability and a mild, technical assumption that the system matrix has no degenerate eigenvalues. The proposed scheme comprises a design of decentralized observers for each sensor based on the local observable subspace decomposition. At each time step, the local estimates of sensors are fused by a median operator to obtain a secure estimation, which is then followed by a local detection-and-resetting process of the decentralized observers. The estimation error is shown to be upper-bounded by a constant which is determined only by the system parameters and noise magnitudes. Moreover, we design the detector threshold to ensure that the benign sensors never trigger the detector. The efficacy of the proposed algorithm is demonstrated by its application on a benchmark example of IEEE 14-bus system. We show that our proposed scheme can effectively tolerate sparse attacks on an unknown set of sensors, ensuring a bounded estimation error and effectively detecting and resetting the attacked sensors.

关键词： Estimation and filtering Design of fault tolerant/reliable systems Estimation and fault detection

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State Estimation for Integrated Energy systems: Motivations, Advances, and Future Work

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IEEE Transactions on Power systems 2024年

作者： Zhao, Junbo Srivastava, Ankur Guo, Ye Cetenovic, Dragan Lin, Yuzhang Levi, Victor Yin, Guanxiong Huang, Manyun Zhang, Tingting Li, Zhigang Chen, Yanbo Exposito, Antonio G. Zhang, Wen Zheng, Weiye Chakrabarti, Saikat Li, Yang Sharma, Ankush Kamwa, Innocent Guo, Qinglai Terzija, Vladimir Mili, Lamine University of Connecticut Department of Electrical and Computer Engineering StorrsCT06269 United States Tsinghua University Tsinghua-Berkeley Shenzhen Institute Shenzhen518055 China The University of Manchester Department of Eee ManchesterM13 9PL United Kingdom University of Kragujevac Faculty of Technical Sciences Čačak Čačak32000 Serbia New York University Department of Electrical and Computer Engineering NY11201 United States Tsinghua University Department of Electrical Engineering Beijing100084 China Hohai University College of Energy and Electrical Engineering Nanjing210098 China Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Jinan250061 China The Chinese University of Hong Kong-Shenzhen School of Science and Engineering Shenzhen518172 China North China Electric Power University School of Electrical and Electronic Engineering Beijing102206 China University of Sevilla Spain Sevilla41092 Spain South China University of Technology School of Electrical Power Engineering Guangzhou510641 China Indian Institute of Technology Kanpur Department of Electrical Engineering Kanpur208016 India Northeast Electric Power University School of Electrical Engineering Jilin132012 China Laval University Department of Electrical and Computer Engineering QuebecQCG1V 0A6 Canada Tsinghua University State Key Laboratory of Power Systems Department of Electrical Engineering Beijing100084 China Newcastle University School of Engineering Newcastle upon TyneNE1 7RU United Kingdom Virginia Polytechnic Institute and State University Bradley Department of Electrical and Computer Engineering Falls ChurchVA22043 United States

This paper summarizes the technical activities of a three-year-long IEEE Task Force (TF) on State Estimation (SE) for Integrated Energy systems (IES). It presents the formal definition and characteristics of IES, along with the comprehensive discussion on Electric Power systems (EPS) model, and static and dynamic models associated with heating and natural gas systems. The paper also identifies the barriers of SE for IES, such as estimation modeling, observability analysis, and measurement requirements, together with addressing multi-scale dynamics. An extensive comparative analysis between Integrated Energy systems-State Estimation (IES-SE) and more established Electric Power System-State Estimation (EPS-SE) is presented. The paper also provides future research needs and directions related to IES-SE. © 1969-2012 IEEE.

关键词： Power system analysis

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Internet-of-Things technology and applications for clean energy systems

Energy Conversion and Economics

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Energy Conversion and Economics 2021年第4期2卷 183-185页

作者： Strunz, Kai Wang, Xuanyuan Guo, Qinglai Xie, Le Zhang, Song Fang, Xin Wang, Jianxiao School of Electrical Engineering and Computer Science Berlin10587 Germany State Grid Jibei Electric Power Co. LTD. Beijing China Department of Electrical Engineering Tsinghua University Beijing China Department of Electrical and Computer Engineering Texas A&M University College StationTX United States Department of Business Architecture and Technology ISO New England Inc. HolyokeMA United States Power Systems Engineering Center National Renewable Energy Laboratory GoldenCO United States School of Electrical and Electronic Engineering North China Electric Power University Beijing China

[...]future research interests regarding IESSs are summarised. A multi-stage PMU placement strategy was developed to alleviate power system vulnerability to possible false data injection attacks, where forward dynamic programming was used to distribute the capital cost of PMUs over a certain period. Three clustering methods, including linear regression, density-based spatial clustering of applications with noise (DBSCAN), and Gaussian mixture models (GMM), were combined for bad PMU data detection.

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Efficient Model Training in Edge Networks with Hierarchical Split Learning

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IEEE Transactions on Mobile computing 2025年

作者： Zhang, Songge Wu, Wen Song, Lingyang Shen, Xuemin Peking University Shenzhen Graduate School School of Electronic and Computer Engineering Shenzhen518055 China Pengcheng Laboratory Frontier Research Center Shenzhen518055 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Xiangtan University Hunan Institute of Advanced Sensing and Information Technology Xiangtan China University of Waterloo Department of Electrical and Computer Engineering WaterlooN2L 3G1 Canada

In this paper, we propose an efficient model training scheme, named Group-based Hierarchical Split Learning (GHSL), which can accelerate the artificial intelligence (AI) training process in edge networks in a "first-sequential-then-parallel" manner. Specifically, the proposed scheme hierarchically splits an AI model into a user-side and server-side model, while dividing a number of users into multiple groups. Users in each group train user-side models with the interaction of the shared server-side model sequentially;different groups perform the above training process parallelly;the AI models of each group are aggregated into a global model. We also carry out the convergence analysis for the proposed scheme over non-independent and identically distributed data, which reveals that the convergence rate depends on user grouping. Furthermore, we propose a data-driven two-stage user grouping algorithm to minimize the overall training delay, taking user resource heterogeneity and the black-box training process into account. The proposed algorithm first utilizes the Gaussian process regression approach to determine the number of groups, and then employs the coalition game theory to determine the optimal user grouping decision. Comprehensive simulation results demonstrate that the proposed scheme can reduce training delay, user-side computational workload, and communication overhead by up to 19%, 53%, and 54%, respectively, comparing to state-of-the-art benchmarks. © 2002-2012 IEEE.

关键词： Game theory

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Position and Attitude Control of Quadrotor UAV Based on a New Sliding Mode Control Strategy

Position and Attitude Control of Quadrotor UAV Based on a Ne...

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Chinese Control Conference (CCC)

作者： Yinghua Wu Ancai Zhang Guangyuan Pan Yingxue Du Zhi Liu School of Automation and Electrical Engineering Linyi University Linyi Shandong P. R. China Key Laboratory of Complex Systems and Intelligent Computing Universities of Shandong Linyi University Linyi Shandong P. R. China

A Quadrotor unmanned aerial vehicle (UAV) is a typical underactuated nonlinear mechanical system with six degrees of freedom (DOF) and four inputs. This paper develops a new sliding mode control (SMC) strategy to solve the position and attitude control problem for this multi-DOF underactuated system. First, the dynamic motion equation model of the Quadrotor UAV is established. Second, the first and fourth inputs are designed by using the traditional SMC method and a novel Gaussian geometric function reaching law. And then, we design the second and third inputs based on a hierarchical SMC method. And the stability of sliding mode surfaces are analyzed using Lyapunov theory. The designed four sliding controllers guarantee the flight control objective of the Quadrotor UAV to be achieved. And the Gaussian reaching law used in the design of controllers can effectively reduce the chattering phenomena in the sliding mode surfaces. Numerical examples verify the effectiveness of our developed control strategy.

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Robust Fixed-Time Sliding Mode Control of Underactuated Furuta Pendulum System

Robust Fixed-Time Sliding Mode Control of Underactuated Furu...

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Data Driven Control and Learning systems (DDCLS)

作者： Zhujun Wang Xinhao Zhou Ancai Zhang Xiao Liang Guangyuan Pan School of Automation and Electrical Engineering Linyi University Linyi Shandong P. R. China Key Laboratory of Complex Systems and Intelligent Computing in Universities of Shandong Linyi University Linyi Shandong P. R. China

A Furuta pendulum is a typical underactuated mechanical system with two degrees of freedom (DOF) and only one input. We address the robust stabilization control problem for this 2-DOF underactuated system with a matched external disturbance. A fixed-time sliding mode control method is presented. First, the dynamic motion model of the system is established using the Euler-Lagrange modeling method. And then, we get the approximate linearization model of the system around the origin equilibrium point and construct a homeomorphic coordinate transformation for this model. After that, a sliding mode surface and a fixed-time robust controller are designed for the transformed system. The developed controller ensures the system's state variables to reach the sliding mode surface in a fixed time. This guarantees the fixed-time robust stabilization control objective to be achieved. Finally, two numerical examples demonstrate the validity of our presented control strategy.

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Safe Reinforcement Learning for Social Motor Coordination in Two Dimensional Space 26

Safe Reinforcement Learning for Social Motor Coordination in...

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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

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. © 2025 IEEE.

关键词： Reinforcement learning

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