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Distributed generalized Nash equilibrium seeking:event-triggered coding-decoding-based secure communication

Science China(Information Sciences) 2024年第7期67卷 271-284页

作者： Shaofu YANG Wenying XU Wangli HE Jinde CAO School of Computer Science and Engineering Southeast University School of Mathematics Southeast University Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology

In this paper, we consider the distributed generalized Nash equilibrium(GNE) seeking problem in strongly monotone games. The transmission among players is implemented through a digital communication network with limited bandwidth. For improving communication efficiency or/and security, an event-triggered coding-decoding-based communication is first proposed, where the data(decision variable) are first mapped to a series of finite-level codewords and, only when an event condition is satisfied, then sent to the neighboring agents. Moreover, a distributed communication-efficient GNE seeking algorithm is constructed accordingly,and the overrelaxation scheme is further taken into consideration. Through primal-dual analysis, the proposed algorithm is proven to converge to a variational GNE with fixed step-sizes by recasting it as an inexact forward-backward iteration. Finally, numerical simulations illustrate the benefit of the proposed algorithms in terms of saving communication resources.

关键词： noncooperative games event-triggered communication coding-decoding quantization

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Dynamic Moving Target Defense Strategy Based on Adaptive Port Hopping in SDN

Dynamic Moving Target Defense Strategy Based on Adaptive Por...

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2023 International Conference on computer Network Security and Software engineering, CNSSE 2023

作者： Dang, Fangfang Wu, Kehe Li, Shuai Liu, Han Song, Yifan Zhang, Xiaoliang The State Grid Henan Information & Communication Company China School of Control and Computer Engineering North China Electric Power University Beijing102206 China

ISBN: (数字)9781510666528

ISBN: (纸本)9781510666511

Distributed Denial of Service (DDoS) is a huge hazard to software-defined networks (SDN). Active defense technology is one of the effective measures to ensure the security of SDN. Active defense can increase the difficulty of the attacker's attack and reduce the probability of being attacked successfully. However, the active defense method based on port hopping has the problems of fixed hopping strategy, lack of flexibility and poor security (for example, it is easy for an attacker to grasp the law of server port hopping). Aiming at these problems, we proposed a dynamic moving target defense method based on adaptive port hopping (DMTD-APH). The DMTD-APH combines the characteristics of SDN on the basis of port hopping, and improves the flexibility of active defense by designing strategies such as hopping synchronization, hopping and forwarding, and adaptive hopping. At the same time, the DMTD-APH dynamically detects the network status through the source address entropy value and data flow rate method, and performs time-adaptive or space-adaptive hopping adjustments to ports according to the detection results to build an adaptive active network defense model. The experimental results show that DMTD-APH enhances the anti-attack and service type of the network, and has stronger dynamics and security. © 2023 SPIE.

关键词： Network security

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PCB Defect Detection Method Based on Improved RetinaNet 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Xu, Yusheng Cao, Xinrong Hu, Rong Keikhosrokiani, Pantea Li, Zuoyong College of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China School of Computer Sciences Universiti Sains Malaysia Penang11800 Malaysia

ISBN: (数字)9783031200991

ISBN: (纸本)9783031200984

In the electronic industry product quality control, PCB defect detection is a crucial part, which has the characteristics of small defect size and high similarity. The existing defect detection methods are still not good enough for detecting small target defects;therefore, the algorithm in this paper proposes an improved algorithm for PCB defect detection based on the RetinaNet model. The ResNet-D residual structure and efficient channel focus module are introduced in the model backbone network to enhance its feature extraction capability and achieve the purpose of improving the detection accuracy. At the same time, the method replaces the original multi-step learning decay strategy with a cosine annealing scheduling learning strategy, which optimizes the training process of the model. Finally, the performance of the method is verified on the publicly available PCB defect dataset from the Open Laboratory of Intelligent Robotics, Peking University. The experimental results show that the algorithm improves the mAP value by 3.2% compared with the original algorithm, while the fastest detection speed reaches 36.9 FPS, which can effectively improve the defect detection performance of PCB. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Defects

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Boarding Pass Positioning with Jointly Multi-channel Segmentation and Perspective Transformation Correction 4th

Boarding Pass Positioning with Jointly Multi-channel Segmen...

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Wu, Jiahui Li, Zuoyong Keikhosrokiani, Pantea Cai, Yuanzheng College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China School of Computer Sciences Universiti Sains Malaysia Penang Minden11800 Malaysia

ISBN: (纸本)9783031200953

To solve the problem of boarding pass positioning with image tilt and sticking, we proposed a jointly boarding pass positioning and correction algorithm using multi-channel color components. First, a local threshold segmentation is performed on red and blue components in RGB color space to determine the general area of the boarding pass. Then, edge detection is performed on the blue component and pixel value standardization statistics on the three components to obtain partial edges and internal information of the boarding pass to refine the binary image. Next, the watershed algorithm is used to achieve precise positioning of the boarding pass. Finally, the segmentation and the boarding pass are completely rectified through perspective transformation. The results of segmentation and correction of boarding passes in actual scenarios show that the proposed algorithm can complete segmentation and correction under complex conditions such as boarding pass images sticking to each other, uneven illumination, and large-angle tilt. The average time spent on the test set is 0.049s, and the segmentation and correction success rate is 95.16 %. The proposed algorithm has high application value. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Binary images

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Fed-Autoformer: A Federated Learning-Based Battery Anomaly Detection Approach for Electric Vehicle Charging with Privacy Preservation 30th

Fed-Autoformer: A Federated Learning-Based Battery Anomaly D...

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30th International Conference on Computational and Experimental engineering and Sciences, ICCES 2024

作者： Yan, Xuhan Yang, Shunbo Guo, Yiguo Fu, Yimu Wu, Sixing Li, Jianbin School of Control and Computer Engineering North China Electric Power University Beijing China Economic Technology Research Institute State Grid Shandong Electric Power Company Shandong Jinan City China

ISBN: (纸本)9783031829062

The battery management system ensures the safe operation of electric vehicles (EVs) by detecting abnormalities in the battery energy storage system. However, the extensive and widely dispersed data in EV battery data, along with the presence of users’ private details, raise a demand for effective anomaly detection and privacy protection. To tackle those two challenges, we propose Fed-Autoformer, a distributed anomaly detection architecture based on real vehicle charging data to detect battery anomalies and protect privacy. Thus, we first propose a novel anomaly detection model for EV charging data based on the Autoformer encoder architecture. In particular, it enables an autocorrelation attention mechanism to extract and consider the intrinsic correlation of battery data features (e.g., voltage, current, state of charge (SOC), etc.) for anomaly detection. In addition, we introduce a federated learning architecture to protect the privacy of battery charging data and improve the federated learning training process based on the differences between vehicle batteries in charging stations to ensure detection accuracy while protecting the privacy of EV charging data. Finally, we comprehensively analyze the feasibility and effectiveness of our proposed architecture using a real EV charging dataset. Our proposed federated learning approach for EV battery anomaly detection not only effectively protects privacy through federated learning but also improves the accuracy by 3.2% compared to most mainstream time series anomaly detection models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Charging stations

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Short Circuit and Connection Fault Diagnosis for Lithium-ion Battery Pack Based on Differential Voltage Changes and Independent Component Analysis 4

Short Circuit and Connection Fault Diagnosis for Lithium-ion...

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4th International Conference on Energy, Power and Electrical engineering, EPEE 2024

作者： Zhang, Luan Liu, Hanxiao Li, Yang Li, Liwei Shandong University School of Control Science and Engineering Jinan China Qingdao Power Supply Company Shandong Electric Power Company State Grid Qingdao China Shandong University School of Computer Science and Technology Qingdao China

ISBN: (纸本)9798331518066

In order to avoid thermal runaway as well as ensure safe and stable operation of battery system, it is vital to perform rapid and accurate diagnosis for early faults with similar characteristics. In this paper, a short circuit and connection fault diagnosis method that is applicable to traditional one-to-one voltage sensor topology is proposed based on differential voltage changes (DVCs) and independent component analysis. Voltage measurements is pretreated to obtain DVCs for extracting fault signature and reducing the detrimental effects of inconsistency, interference, etc. to a certain extent. Independent component analysis is combined with the DVC vector to establish diagnostic model and diagnose faults online. Fault detection and isolation is achieved using squared prediction error, and considering that traditional contributions cannot accurately determine fault location, an improved contribution calculation is introduced to localize the faulty cell. Experimental verifications show that the proposed method can timely detect and localize faults, meanwhile it can distinguish short circuit with connection fault. © 2024 IEEE.

关键词： Lithium-ion batteries

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Class Incremental Learning Method for Hyperspectral Images Based on Real Data Playback Mechanism and Classification Network Optimization 15th

Class Incremental Learning Method for Hyperspectral Images B...

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15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Liu, Xiaodong Wang, Siqi Liu, Huanyu Li, Junbao Department of Automatic Test and Control School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China School of Computer Science and Technology Information Countermeasure Technique Institute Harbin Institute of Technology Harbin150080 China

ISBN: (纸本)9789819700677

In recent years, the hyperspectral image (HSI) classification has attracted great attention in the field of earth observation. With the expansion of application scenarios and the continuous improvement of application requirements, new classes of HSI continue to emerge. In the face of open and dynamic application scenarios, the model is required to be able to continuously learn new categories based on maintaining existing category knowledge. Class incremental learning algorithms have received extensive attention as key solutions to the problem above. In this paper, we use a regularization-based incremental learning algorithm, elastic weight consolidation (EWC) for class incremental learning for HSI classification. After data preprocessing to reduce data dimensions, we select new classes for research on class incremental learning. We combine 3D-2D convolutional structures and residual blocks as the classification network and use real data playback mechanism to improve class recognition accuracy and realize class incremental recognition of HSI data. We demonstrate the feasibility of the algorithm with extensive experiments based on three widely used hyperspectral datasets. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Learning algorithms

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Accelerating Data Transfer in Dataflow Architectures Through a Look-Ahead Acknowledgment Mechanism

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Journal of computer Science & Technology 2022年第4期37卷 942-959页

作者： Yu-Jing Feng De-Jian Li Xu Tan Xiao-Chun Ye Dong-Rui Fan Wen-Ming Li Da Wang Hao Zhang Zhi-Min Tang State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China Beijing Smartchip Microelectronics Technology Company Limited Beijing 100000China School of Computer and Control Engineering University of Chinese Academy of SciencesBeijing 100190China

The dataflow architecture,which is characterized by a lack of a redundant unified control logic,has been shown to have an advantage over the control-flow architecture as it improves the computational performance and power efficiency,especially of applications used in high-performance computing(HPC).Importantly,the high computational efficiency of systems using the dataflow architecture is achieved by allowing program kernels to be activated in a simultaneous ***,a proper acknowledgment mechanism is required to distinguish the data that logically belongs to different *** solutions include the tagged-token matching mechanism in which the data is sent before acknowledgments are received but retried after rejection,or a handshake mechanism in which the data is only sent after acknowledgments are ***,these mechanisms are characterized by both inefficient data transfer and increased area *** performance of the dataflow architecture depends on the efficiency of data *** order to optimize the efficiency of data transfer in existing dataflow architectures with a minimal increase in area and power cost,we propose a Look-Ahead Acknowledgment(LAA)*** accelerates the execution flow by speculatively acknowledging ahead without *** simulation analysis based on a handshake mechanism shows that our LAA increases the average utilization of computational units by 23.9%,with a reduction in the average execution time by 17.4%and an increase in the average power efficiency of dataflow processors by 22.4%.Crucially,our novel approach results in a relatively small increase in the area and power consumption of the on-chip logic of less than 0.9%.In conclusion,the evaluation results suggest that Look-Ahead Acknowledgment is an effective improvement for data transfer in existing dataflow architectures.

关键词： dataflow model control-flow model high-performance computing application data transfer power efficiency

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Enabling Borderless Office Security: A Comprehensive Zero-Trust Digital Grid Architecture for Flexible Resource Access and Data Protection 9

Enabling Borderless Office Security: A Comprehensive Zero-Tr...

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9th Annual International Conference on Network and Information Systems for computers, ICNISC 2023

作者： Liu, Fuyang Luo, Zhen Wang, Zhe Yin, Lu Zhang, Yunhou Ren, Jiyuan Peng, Yuhuai Dispatching and Control Center Northeast Branch State Grid Corporation of China Shenyang China School of Computer Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9798350306682

The traditional trust-based security architecture is hard to ensure data security in borderless office. To address this problem, a zero-trust digital grid security architecture for borderless office is proposed. The architecture mainly includes three aspects: user management layer, access control layer, and security control layer. This provides borderless office with more flexible resource access policies and more secure protection measures. © 2023 IEEE.

关键词： Access control

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A Trust Inference Method Employing Combinatorial Strategies

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Xiao, Ruili Tong, Xiangrong Lin, Yaguang School of Computer and Control Engineering Yantai University Yantai264005 China

Most trust inference algorithms based on Q -learning and learning automata often suffer from the problem of insufficient storage space, as well as some algorithms using the shortest trust path do not fully utilize the trust information. To solve the above problems, a trust inference algorithm (DuelingDQNTrust) based on deep reinforcement learning is proposed. It uses Dueling DQN to compute Q values to reduce the required storage space and does not limit the traversal depth. Moreover, the way to find out the trusted neighbors of a user is crucial because it affects the reliability of the trust path. So three combination strategies are proposed to find users’ trusted neighbors more accurately. One strategy is to define a priority metric by combining node importance and user similarity to find the top k neighbor users of the user as the trusted neighbor set. Another two strategies consider node importance and user similarity in a different order of priority, which first uses a metric to find the top k neighbor users of the user, and then further filters these k users on another metric to determine the trusted neighbor set of the user. The evaluation of the algorithm on the FilmTrust dataset indicates that the algorithm outperforms several existing trust inference algorithms. Copyright © 2023 Ruili Xiao and Xiangrong Tong.

关键词： Reinforcement learning

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