检索结果-内蒙古大学图书馆

Journal of Modern Power Systems and Clean Energy 2021年第3期9卷 477-484页

作者： Ming Ni Manli Li Jun’e Li Yingjun Wu Qi Wang the NARI Group Corporation(State Grid Electric Power Research Institute) NARI Technology Co.Ltd.and the State Key Laboratory of Smart Grid Protection and ControlNanjing 211106China the School of Cyber Science and Engineering Wuhan Universityand the Key Laboratory of Aerospace Information Security and Trusted Computing of the Ministry of EducationWuhan 430072China the College of Energy and Electrical Engineering Hohai UniversityNanjing 211100China the School of Electrical Engineering Southeast UniversityNanjing 210096China

Due to the tight coupling between the cyber and physical sides of a cyber-physical power system(CPPS),the safe and reliable operation of CPPSs is being increasingly impacted by cyber *** situation poses a challenge to traditional security defense systems,which considers the threat from only one side,i.e.,cyber or *** cope with cyberattacks,this paper reaches beyond the traditional one-side security defense systems and proposes the concept of cyber-physical coordinated situation awareness and active defense to improve the ability of *** example of a regional frequency control system is used to show the validness and potential of this ***,the research framework is presented for studying and implementing this ***,key technologies for cyber-physical coordinated situation awareness and active defense against cyber-attacks are introduced.

关键词： Cyber-physical power system(CPPS) cyber security cyber-attack situation awareness active defense

Reinforcement Learning-Based Sensitive Semantic Location Privacy Protection for VANETs

学校读者我要写书评

暂无评论

China Communications 2021年第6期18卷 244-260页

作者： Minghui Min Weihang Wang Liang Xiao Yilin Xiao Zhu Han Department of Information and Communication Engineering Xiamen UniversityXiamen 361005China School of Information and Control Engineering ChinaUniversity of Mining and TechnologyXuzhou 221116China The Affiliated Hospital of China University of Mining and Technology Xuzhou 221116China Department of Electrical and Computer Engineering University of HoustonHouston TX 77004USA Beijing Key Laboratory of Mobile Computing and Pervasive Device No.6 Kexueyuan South RoadBeijing 100190China

Location-based services(LBS)in vehicular ad hoc networks(VANETs)must protect users’privacy and address the threat of the exposure of sensitive locations during LBS *** release not only their geographical but also semantic information of the visited places(e.g.,hospital).This sensitive information enables the inference attacker to exploit the users’preferences and life *** this paper we propose a reinforcement learning(RL)based sensitive semantic location privacy protection *** scheme uses the idea of differential privacy to randomize the released vehicle locations and adaptively selects the perturbation policy based on the sensitivity of the semantic location and the attack *** scheme enables a vehicle to optimize the perturbation policy in terms of the privacy and the quality of service(QoS)loss without being aware of the current inference attack model in a dynamic privacy protection *** solve the location protection problem with high-dimensional and continuous-valued perturbation policy variables,a deep deterministic policy gradientbased semantic location perturbation scheme(DSLP)is *** actor part is used to generate continuous privacy budget and perturbation angle,and the critic part is used to estimate the performance of the *** demonstrate the DSLP-based scheme outperforms the benchmark schemes,which increases the privacy,reduces the QoS loss,and increases the utility of the vehicle.

关键词： semantic location sensitivity locationbased services VANET differential privacy reinforcement learning

A Chan–Vese Model Based on the Markov Chain for Unsupervised Medical Image Segmentation

学校读者我要写书评

暂无评论

Tsinghua Science and technology 2021年第6期26卷 833-844页

作者： Quanwei Huang Yuezhi Zhou Linmi Tao Weikang Yu Yaoxue Zhang Li Huo Zuoxiang He Key Laboratory of Pervasive Computing(Ministry of Education)and the Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China School of Electronic and Information Engineering Beihang UniversityBeijing 100191China Department of Nuclear Medicine Peking Union Medical College HospitalBeijing 100730China School of Clinical Medicine Tsinghua Universityand also with Beijing Tsinghua Changgung HospitalBeijing 100084China

The accurate segmentation of medical images is crucial to medical care and research;however, many efficient supervised image segmentation methods require sufficient pixel level labels. Such requirement is difficult to meet in practice and even impossible in some cases, e.g., rare Pathoma images. Inspired by traditional unsupervised methods, we propose a novel Chan–Vese model based on the Markov chain for unsupervised medical image segmentation. It combines local information brought by superpixels with the global difference between the target tissue and the background. Based on the Chan–Vese model, we utilize weight maps generated by the Markov chain to model and solve the segmentation problem iteratively using the min-cut algorithm at the superpixel *** method exploits abundant boundary and local region information in segmentation and thus can handle images with intensity inhomogeneity and object sparsity. In our method, users gain the power of fine-tuning parameters to achieve satisfactory results for each segmentation. By contrast, the result from deep learning based methods is *** performance of our method is assessed by using four Computerized Tomography(CT) datasets. Experimental results show that the proposed method outperforms traditional unsupervised segmentation techniques.

关键词： medical image unsupervised segmentation Markov chain

Time-Frequency Fusion-Augmentation based Contrastive Learning for Self-Supervised Human Activity Recognition

学校读者我要写书评

暂无评论

Time-Frequency Fusion-Augmentation based Contrastive Learnin...

Smart World Congress (SWC), IEEE

作者： Changru Guo Yingwei Zhang Yiqiang Chen Weiwen Yang Qingyu Wu Zhong Wang School of Information Science and Engineering Lanzhou University Lanzhou China Institute of Computing Technology Chinese Academy of Sciences Beijing China Beijing Key Laboratory of Mobile Computing and Pervasive Device Beijing China University of Chinese Academy of Sciences Beijing China

Human activity recognition (HAR) using sensor data from wearable devices has significant applications in health assessment and accident monitoring. However, building a generalized recognition model requires large amounts of annotated time series data, which can be both costly and time-consuming. Contrastive learning has emerged as a promising approach for learning activity representations from raw sensor data without the need for human annotations. Therefore, we combine contrastive learning and HAR and propose a Time-Frequency Fusion-Augmentation based Contrastive Learning (FusionCL) method to realize self-supervised human activity recognition. FusionCL combines the time-domain augmentation method (named TNet) with the frequency-domain augmentation method (named FNet) to generate diverse augmentation samples. TNet aims to capture different temporal aspects of original sensor data with time-domain variations, such as jittering, scaling, and permutation. FNet aims to augment the original time-domain data with frequency-domain information, which is particularly useful for HAR tasks where different activities can have distinct frequency components. By augmenting the original data from both the time and frequency domains, the proposed FusionCL method can construct a more informative pre-train model, consequently enhancing the generalizability and robustness of the final HAR model. Extensive experiments on four benchmark HAR datasets show that the FusionCL method achieves state-of-the-art performance, demonstrating the effectiveness of our proposed method and the potential for constructing the unified HAR model with contrastive learning strategies.

关键词：

MADDPG-Based Power Control Strategy for Unmanned Vehicles

学校读者我要写书评

暂无评论

MADDPG-Based Power Control Strategy for Unmanned Vehicles

IEEE International Conference on Smart Internet of Things (SmartIoT)

作者： Zhenyuan Han Lu Sun Liangtian Wan Ruoheng Luo Xianpeng Wang Institute of Information Science Technology Dalian Maritime University Dalian China School of Software Dalian University of Technology Dalian China Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University School of Information and Communication Engineering Hainan University Haikou China

ISBN: (数字)9798350366440

ISBN: (纸本)9798350366457

In the domain of unmanned vehicle networks, efficient spectrum utilization is crucial due to the scarcity of spectrum resources. This paper addresses the challenge of dynamic spectrum access by proposing an advanced strategy to enhance spectral efficiency in such networks. We present a novel approach that combines Multi-Agent Deep Deterministic Policy Gradient (MADDPG) with Echo State Networks (ESN) and prioritized experience replay mechanisms. Our approach focuses on optimizing the power control of unmanned vehicles to maximize overall spectral efficiency while maintaining communication quality for all vehicles. The proposed MADDPG_ESN algorithm integrates the ESN to effectively capture temporal dynamics and improve learning efficiency, and incorporates prioritized experience replay to optimize training stability and performance. This combination allows for rapid and accurate decision-making in the complex environment of unmanned vehicle communications. Our findings underscore the effectiveness of combining ESNs with prioritized experience replay in reinforcement learning for dynamic spectrum access, offering significant improvements in system performance and resource utilization.

关键词： Training Spectral efficiency Heuristic algorithms System performance Power control Dynamic spectrum access Solids Stability analysis Vehicle dynamics Autonomous vehicles

Incremental Learning for Cross-User sEMG Gesture Recognition: Overcoming User Forgetting

学校读者我要写书评

暂无评论

Incremental Learning for Cross-User sEMG Gesture Recognition...

Smart World Congress (SWC), IEEE

作者： Weiwen Yang Yingwei Zhang Yiqiang Chen Changru Guo Shuo Ma Shiyu Cheng Institute of Computing Technology Chinese Academy of Sciences Beijing China Beijing Key Laboratory of Mobile Computing and Pervasive Device Beijing China University of Chinese Academy of Sciences Beijing China School of Information Science and Engineering Lanzhou University Lanzhou China

Surface Electromyography (sEMG) based gesture recognition has gained significant attention in the fields of medical rehabilitation and entertainment. However, sEMG exhibits strong user-dependent properties among users with different physiological states, limiting the generalization and personalization of existing models. Although incremental learning methods can help the model balance the knowledge learned from old and new users, the existing incremental learning literature lacks attempts at sEMG gesture recognition. This paper fills these gaps and proposes the incremental learning-based cross-user sEMG gesture recognition framework (IncreEGR), to explore the catastrophic forgetting challenges in cross-user sEMG data. IncreEGR consists of two parts: a sampling method based on the center of features (CoF) and discrepancy-based distillation learning (DDL). CoF samples and saves representative user data and replays them during subsequent training processes to ensure that the model can review previous knowledge. DDL aligns the features extracted from the old and new models using a Gaussian kernel-based distance constraint to help the model overcome catastrophic forgetting. We conduct experiments on the benchmark sEMG gesture recognition dataset (i.e., the Ninapro dataset) to verify the effectiveness of IncreEGR. The experimental results demonstrate that our proposed IncreEGR method successfully balances the knowledge between new and old users and achieves the best recognition performance.

关键词：

A New Intelligent Reflecting Surface-Aided Electromagnetic Stealth Strategy

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Xiong, Xue Zheng, Beixiong Swindlehurst, A. Lee Tang, Jie Wu, Wen The School of Future Technology South China University of Technology Guangzhou511442 China The Frontier Research Center Peng Cheng Laboratory Shenzhen518055 China The School of Microelectronics School of Electronic and Information Engineering South China University of Technology Guangzhou511442 China The Center for Pervasive Communications and Computing University of California IrvineCA92697 United States

Electromagnetic wave absorbing material (EWAM) plays an essential role in manufacturing stealth aircraft, which can achieve the electromagnetic stealth (ES) by reducing the strength of the signal reflected back to the radar system. However, the stealth performance is limited by the coating thickness, incident wave angles, and working frequencies. To tackle these limitations, we propose a new intelligent reflecting surface (IRS)aided ES system where an IRS is deployed at the target to synergize with EWAM for effectively mitigating the echo signal and thus reducing the radar detection probability. Considering the monotonic relationship between the detection probability and the received signal-to-noise-ratio (SNR) at the radar, we formulate an optimization problem that minimizes the SNR under the reflection constraint of each IRS element, and a semi-closed-form solution is derived by using Karush-Kuhn-Tucker (KKT) conditions. Simulation results validate the superiority of the proposed IRS-aided ES system compared to various benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Electromagnetic waves

DarkSAM: Fooling Segment Anything Model to Segment Nothing

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Zhou, Ziqi Song, Yufei Li, Minghui Hu, Shengshan Wang, Xianlong Zhang, Leo Yu Yao, Dezhong Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM. Our codes are available at: https://***/CGCL-codes/DarkSAM. Copyright © 2024, The Authors. All rights reserved.

关键词： Frequency domain analysis

NumbOD: A Spatial-Frequency Fusion Attack Against Object Detectors

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Zhou, Ziqi Li, Bowen Song, Yufei Yu, Zhifei Hu, Shengshan Wan, Wei Zhang, Leo Yu Yao, Dezhong Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

With the advancement of deep learning, object detectors (ODs) with various architectures have achieved significant success in complex scenarios like autonomous driving. Previous adversarial attacks against ODs have been focused on designing customized attacks targeting their specific structures (e.g., NMS and RPN), yielding some results but simultaneously constraining their scalability. Moreover, most efforts against ODs stem from image-level attacks originally designed for classification tasks, resulting in redundant computations and disturbances in object-irrelevant areas (e.g., background). Consequently, how to design a model-agnostic efficient attack to comprehensively evaluate the vulnerabilities of ODs remains challenging and unresolved. In this paper, we propose NumbOD, a brand-new spatial-frequency fusion attack against various ODs, aimed at disrupting object detection within images. We directly leverage the features output by the OD without relying on its internal structures to craft adversarial examples. Specifically, we first design a dual-track attack target selection strategy to select high-quality bounding boxes from OD outputs for targeting. Subsequently, we employ directional perturbations to shift and compress predicted boxes and change classification results to deceive ODs. Additionally, we focus on manipulating the high-frequency components of images to confuse ODs' attention on critical objects, thereby enhancing the attack efficiency. Our extensive experiments on nine ODs and two datasets show that NumbOD achieves powerful attack performance and high stealthiness. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

SNR/CRB-Constrained Joint Beamforming and Reflection Designs for RIS-ISAC Systems

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Liu, Rang Li, Ming Liu, Qian Swindlehurst, A. Lee School of Information and Communication Engineering Dalian University of Technology Dalian116024 China Center for Pervasive Communications and Computing University of California IrvineCA92697 United States School of Computer Science and Technology Dalian University of Technology Dalian116024 China The Key Laboratory of Social Computing and Cognitive Intelligence Ministry of Education China

In this paper, we investigate the integration of integrated sensing and communication (ISAC) and reconfigurable intelligent surfaces (RIS) for providing wide-coverage and ultra-reliable communication and high-accuracy sensing functions. In particular, we consider an RIS-assisted ISAC system in which a multi-antenna base station (BS) simultaneously performs multiuser multi-input single-output (MU-MISO) communications and radar sensing with the assistance of an RIS. We focus on both target detection and parameter estimation performance in terms of the signal-to-noise ratio (SNR) and Cramér-Rao bound (CRB), respectively. Two optimization problems are formulated for maximizing the achievable sum-rate of the multi-user communications under an SNR constraint for target detection or a CRB constraint for parameter estimation, the transmit power budget, and the unit-modulus constraint of the RIS reflection coefficients. Efficient algorithms are developed to solve these two complicated non-convex problems. We then extend the proposed joint design algorithms to the scenario with imperfect self-interference cancellation. Extensive simulation results demonstrate the advantages of the proposed joint beamforming and reflection designs compared with other schemes. In addition, it is shown that more RIS reflection elements bring larger performance gains for direct-of-arrival (DoA) estimation than for target detection. © 2023, CC BY-NC-ND.

关键词： Budget control