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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Shibuya, Tatsukichi Inoue, Nakamasa Kawakami, Rei Sato, Ikuro Department of Computer Science Institute of Science Tokyo Tokyo Japan Department of Systems and Control Engineering Institute of Science Tokyo Tokyo Japan Denso IT Laboratory Tokyo Japan

ISBN: (纸本)9798350368741

For deploying deep neural networks on edge devices with limited resources, binary neural networks (BNNs) have attracted significant attention, due to their computational and memory efficiency. However, once a neural network is binarized, finetuning it on edge devices becomes challenging because most conventional training algorithms for BNNs are designed for use on centralized servers and require storing real-valued parameters during training. To address this limitation, this paper introduces binary stochastic flip optimization (BinSFO), a novel training algorithm for BNNs. BinSFO employs a parameter update rule based on Boolean operations, eliminating the need to store real-valued parameters and thereby reducing memory requirements and computational overhead. In experiments, we demonstrated the effectiveness and memory efficiency of BinSFO in finetuning scenarios on six image classification datasets. BinSFO performed comparably to conventional training algorithms with a 70.7% smaller memory requirement. Code is released at https://***/TatsukichiShibuya/ICASSP2025_BinSFO. © 2025 IEEE.

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Transferable Attention-distracting Adversarial Attack on Data-driven Models for Power Systems

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4985-4998页

作者： Huang, Rong Li, Yuancheng Yin, Peidong Shang, Xingyu Wang, Yuanyuan North China Electric Power University School of Control and Computer Engineering Beijing102206 China China Telecom Research Institute Beijing102209 China National Computer Network Emergency Response Technical Team Coordination Center Beijing100029 China

As the digitalization of power systems progresses, data-driven models have garnered widespread attention due to their performance advantages, leading to the emergence of numerous data-driven intelligent models for power tasks, such as attack detection and stability assessment. However, data-driven models are susceptible to adversarial attacks, even when deployed in highly secure control centers. Considering the similarity in the semantic features extracted by structurally diverse data-driven models when addressing the same downstream tasks, this paper proposes a transferable attention-distracting adversarial attack tailored for power systems. This attack first introduces an adversarial perturbation selection framework with physical constraints specific to power systems. It also offers different loss functions to distract attention and strategies to weaken the significance of features. Simulation experiments confirm that distracting the model’s attention results in more stable transferable attack effects and significantly reduces the performance of data-driven models across different task scenarios. The experimental results underscore the importance of not neglecting the security and robustness of models in security-critical scenarios like power systems, even while achieving optimal performance. © 2005-2012 IEEE.

关键词： Access control models

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Multi-scale network for single image deblurring based on ensemble learning module

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Multimedia Tools and Applications 2025年第11期84卷 9045-9064页

作者： Wu, WenBo Pan, Yun Su, Na Wang, JingTao Wu, ShaoChuan Xu, ZeKun Yu, YouJian Liu, YaPeng State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China School of Computer and Cyberspace Security Communication University of China Beijing100024 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China College of Computer and Control Engineering Northeast Forestry University Harbin150040 China School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China School of Computer Science and Technology Anhui University Hefei230601 China

In this paper, we have identified two primary issues with current multi-scale image deblurring methods. On the one hand, the blurring scale is ignored. On the other hand, the context information of images is not fully utilized, and the spatial detail information of images is lost. To better solve the above problems, on top of that, this paper proposes a multi-scale network for single image deblurring. The designed network contains two parts: 1) Ensemble Learning Model: The Weak Learning Model in this module connects multiple weak learners in series to increase network depth. Moreover, through multi-scale feature ensemble module, the output features and intermediate features of different weak learner are integrated, and finally strong learner is used to enhance the features with semantic information at different scales;2) Spatial Feature Enhancement Module: This module can preserve spatial detail information of images and compensate for the disadvantages of losing image spatial details due to continuous downsampling in the Ensemble Learning Model structure. Our proposed deblurring model has been extensively evaluated on several benchmark datasets, and the model achieves superior performance compared to the most advanced deblurring methods. In particular, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) values are 33.57 dB and 0.965 on the GoPro dataset, respectively, with PSNR and SSIM improving by 0.49 dB and 0.002 over NAFNet, and 0.65 dB and 0.004 over Restormer. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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Interference Suppression and Jitter Elimination Ability-Based Adaption Tracking Guidance for Robotic Fishes

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 126-137页

作者： Dongfang Li Jie Huang Rob Law Xin Xu Limin Zhu Edmond Q.Wu IEEE the School of Electrical Engineering and Automation Fuzhou University the Key Laboratory of System Control and Information Processing Ministry of Education the School of Electrical Engineering and Automation Fuzhou University 5G+Industrial Internet Institute Fuzhou University the University of Macau the College of Mechatronics and Automation National University of Defense Technology the School of Mechanical Engineering Shanghai Jiao Tong University the Department of Computer Science and Engineering Shanghai Jiao Tong University

This work presents an adaptive tracking guidance method for robotic fishes. The scheme enables robots to suppress external interference and eliminate motion jitter. An adaptive integral surge line-of-sight guidance rule is designed to eliminate dynamics interference and sideslip issues. Limited-time yaw and surge speed observers are reported to fit disturbance variables in the model. The approximation values can compensate for the system's control input and improve the robots' tracking ***, this work develops a terminal sliding mode controller and third-order differential processor to determine the rotational torque and reduce the robots' run jitter. Then, Lyapunov's theory proves the uniform ultimate boundedness of the proposed method. Simulation and physical experiments confirm that the technology improves the tracking error convergence speed and stability of robotic fishes.

关键词： Adaptive integral surge line-of-sight approximation robotic fish speed observer

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Small Signal Stability-Guaranteed Moving Target Defense Against Load Redistribution Attack On IoT-based Smart Grid

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IEEE Internet of Things Journal 2025年第11期12卷 17413-17429页

作者： Wang, Bingdong Zhang, Zhenyong Wang, Mufeng Liu, Mengxiang Ye, Rui Deng, Ruilong Zhang, Xin Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China University of Sheffield Department of Automatic Control and System Engineering Sheffield United Kingdom Guizhou Power Grid Co. Ltd. Intelligent Operation Center China Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China Huzhou Institute of Industrial Control Technology Huzhou313000 China

Moving target defense (MTD) is a promising approach to defend against load redistribution attacks on the internet-of-things (IoT)-based smart grid networks by probing the distorted state estimates with the distributed flexible AC transmission system. However, existing studies mainly focus on optimizing the performance of MTD and ignore the safety effect of it on the system’s operation. In this paper, we fill this gap by deeply analyzing the effect of MTD on the small signal stability and aim to alleviate the negative impact and guarantee its defending performance simultaneously. Firstly, the stability is formally described using the eigenvalue sensitivity. The relationship between the MTD-induced perturbation (MTDper) and the stability criteria is derived. Secondly, a new indicator is proposed to measure the effectiveness of MTDper. Thirdly, a constrained optimization problem is formulated to compute the bound of MTDper for guaranteeing the small signal stability. In addition, a surprising finding is that the stability margin can be improved and enhanced by optimizing the value of MTDper without losing the MTD’s effectiveness. Finally, we evaluate the performance of MTDper and its impact on the small signal stability with extensive simulations on the IEEE 30-bus, 39-bus, and 68-bus test power systems. © 2014 IEEE.

关键词： Constrained optimization

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A New Benchmark and Algorithm for Clothes-Changing Video Person Re-Identification

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IEEE Transactions on Information Forensics and Security 2025年 20卷 1993-2005页

作者： Wang, Likai Zhang, Xiangqun Han, Ruize Wei, Yanjie Wang, Song Feng, Wei Tianjin University College of Intelligence and Computing Tianjin300350 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518107 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

Person re-identification (Re-ID) is a classical computer vision task and has significant applications for public security and information forensics. Recently, long-term Re-ID with clothes-changing has attracted increasing attention. However, existing methods mainly focus on image-based setting, where richer temporal information is overlooked. In this paper, we focus on the relatively new yet practical problem of Clothes-Changing Video-based Re-ID (CCVReID), which is less studied. First, given the dataset shortage, we build two new benchmark datasets for CCVReID problem, including a large-scale synthetic video dataset and a real-world one, both containing human sequences with various clothing changes. Moreover, we systematically study this problem by simultaneously considering the classical appearance feature and temporal feature contained in the video. We develop a dual-branch fusion framework that makes use of the information from both clothes-aware appearance feature and clothes-free gait feature. For better information fusion, a confidence-guided re-ranking strategy is proposed to adaptively balance the weight of these two categories of features. We have released the benchmark and code proposed in this work to the public at https://***/kkw98/CCVReID. © 2025 IEEE.

关键词： Reamers

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Interval prediction strategy for the remaining useful life of lithium-ion battery pack considering cell inconsistency

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Journal of Power Electronics 2025年 1-13页

作者： Pang, Xiaoqiong Li, Xiao Kim, Jonghoon Lee, Miyoung Zhao, Zhen Wen, Jie Zeng, Jianchao School of Computer Science and Technology North University of China Taiyuan China Department of Electrical Engineering Chungnam National University Daejeon Korea Republic of Institute of Network Technology Yantai China School of Electrical and Control Engineering North University of China Shanxi Taiyuan China

Accurate and robust remaining useful life (RUL) prediction of lithium-ion battery packs is critical for ensuring system operation reliability and safety. However, the inconsistency accelerates battery pack degradation, making RUL prediction challenging. Furthermore, traditional prediction methods offering specific values cannot meet varying safety and urgency requirements. Aiming at the above problems, this study proposed an RUL interval prediction scheme considering cell inconsistency for lithium-ion battery packs. First, the degradation and inconsistency information of the battery pack is captured by extracting health indicators from the measured data. Second, fuzzy information granulation is introduced to transform the time series data into fuzzy granules, providing upper and lower bounds for interval prediction modeling. Then, long short-term memory is employed for modeling, generating interval prediction results that support decision adjustment for different safety and emergency levels. Finally, the experiment results show that the proposed strategy can effectively cover the real value and achieve reliable interval prediction. It also indicates that the impact of inconsistency on the prediction results becomes more remarkable as the starting point moves back. With 60% training data, the interval prediction evaluation criterion P of the model with inconsistency information is approximately 43% higher than the model without. © The Author(s) under exclusive licence to The Korean institute of Power Electronics 2025.

关键词： Battery Pack

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Adaptive Fixed-Time Command Filtered Tracking control for Parametric Nonlinear Systems With Unknown Nonlinear control Coefficient and Unknown Disturbances

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IEEE Access 2025年 13卷 37839-37849页

作者： Zhang, Xiyu Zhou, Youjun Feng, Chun Deng, Xiongfeng Guangxi Science and Technology Normal University School of Mathematics and Computer Science Laibin546199 China Guang’an Institute of Technology School of Mechanical and Electrical Engineering Guang’an638550 China Anhui Polytechnic University Key Laboratory of Electric Drive and Control Anhui Higher Education Institute Wuhu241000 China

This paper addresses the issue of adaptive fixed-time tracking control (FTTC) for a category of parametric nonlinear systems characterized by unknown nonlinear control coefficient (UNCC) and unknown external disturbances (UEDs). To effectively handle unknown parameters and unknown disturbances, the adaptive parameter estimation method is introduced into the control law design. Meanwhile, some parameter adaptive laws are presented to estimate these unknown parameters. To eliminate repeated differentiation of virtual control laws, a class of nonlinear command filters is introduced. Furthermore, the filtering errors are compensated through the designed adaptive compensation control laws. Subsequently, the Nussbaum gain function technique is further used to deal with the UNCC in the system. Leveraging command-filtering backstepping control (BC) and fixed-time control method, an adaptive command filtered FTTC law is final presented to achieve the fixed time tracking. The theoretical analysis results indicate that the designed control law guarantees the convergence of tracking error to a small neighborhood of zero within a fixed time, while ensuring that all signals within the closed-loop system remain bounded. The proposed control law’s effectiveness is demonstrated through three simulation examples. © 2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License.

关键词： Backstepping

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Detection of Perfect Stealthy Attacks on Cyber-Physical Systems Subject to Measurement Quantizations: A Watermark-Based Strategy

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 114-125页

作者： Yu-Ang Wang Zidong Wang Lei Zou Bo Shen Hongli Dong the College of Information Science and Technology Donghua University the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education IEEE the College of Electrical Engineering and Automation Shandong University of Science and Technology the Department of Computer Science Brunel University London Uxbridge the Sanya Offshore Oil and Gas Research Institute Northeast Petroleum University the Artificial Intelligence Energy Research Institute Northeast Petroleum University Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University

In this paper, the attack detection problem is investigated for a class of closed-loop systems subjected to unknownbutbounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are quantized before transmission.A specific type of perfect stealthy attack, which meets certain rather stringent conditions, is taken into account. Such attacks could be injected by adversaries into both the sensor-toestimator and controller-to-actuator channels, with the aim of disrupting the normal data flow. For the purpose of defending against these perfect stealthy attacks, a novel scheme based on watermarks is developed. This scheme includes the injection of watermarks(applied to data prior to quantization) and the recovery of data(implemented before the data reaches the estimator).The watermark-based scheme is designed to be both timevarying and hidden from adversaries through incorporating a time-varying and bounded watermark signal. Subsequently, a watermark-based attack detection strategy is proposed which thoroughly considers the characteristics of perfect stealthy attacks,thereby ensuring that an alarm is activated upon the occurrence of such attacks. An example is provided to demonstrate the efficacy of the proposed mechanism for detecting attacks.

关键词： Attack detection cyber-physical systems (CPSs) perfect stealthy attacks watermark-based strategy

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Mobile Crowdsourcing Task Assignment Algorithm Based on ConvNeXt and GRU 18th

Mobile Crowdsourcing Task Assignment Algorithm Based on Con...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Fan, Zequn Pan, Qingxian Gao, Zhaolong Luan, Peng Wei, Kai Li, Jinru School of Computer and Control Engineering Yantai University Yantai264005 China Yantai Institute of Technology Yantai264005 China Yantaishan Hospital Shandong Province Yantai264003 China

ISBN: (纸本)9783031714634

Task assignment is a crucial aspect of mobile crowdsourcing research. The focus is on allocating appropriate perceptual tasks based on task and worker characteristics to optimize perceptual quality. Tasks arrive dynamically in a crowdsourcing system, and the platform cannot assign all tasks at the beginning. This can lead to the task allocation process falling into a local optimal solution. To address the problem, this paper proposes a two-stage prediction algorithm that utilises historical spatio-temporal data of crowdsourcing services. Firstly, the temporal data is converted into image data through Markov Transition Field. Then, the task availability is transformed into a categorisation problem. In the first stage, a ConvNeXt-based network is used to predict the task availability. In the second stage, a GRU-based network is used to predict the task duration. This paper presents the results of several experiments that confirm the effectiveness of the proposed algorithm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Spatio-temporal data

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