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Fault-Tolerant dynamical Consensus of double-Integrator Multi-Agent Systems in the Presence of Asynchronous Self-Sensing Function Failures

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Journal of Shanghai Jiaotong university(Science) 2024年第4期29卷 613-624页

作者： WU Zhihai XIE Linbo Engineering research center of internet of things Technology Applications of the Ministry of Education School of Internet of Things EngineeringJiangnan UniversityWuxiJiangsu214122China

double-integrator multi-agent systems(MASs)might not achieve dynamical consensus,even if only partial agents suffer from self-sensing function failures(SSFFs).SSFFs might be asynchronous in real engineering *** existing fault-tolerant dynamical consensus protocol suitable for synchronous SSFFs cannot be directly used to tackle fault-tolerant dynamical consensus of double-integrator MASs with partial agents subject to asynchronous *** by these facts,this paper explores a new fault-tolerant dynamical consensus protocol suitable for asynchronous ***,multi-hop communication together with the idea of treating asynchronous SSFFs as multiple piecewise synchronous SSFFs is used for recovering the connectivity of network topology among all normal ***,a fault-tolerant dynamical consensus protocol is designed for double-integrator MASs by utilizing the history information of an agent subject to SSFF for computing its own state information at the instants when its minimum-hop normal neighbor set ***,it is theoretically proved that if the strategy of network topology connectivity recovery and the fault-tolerant dynamical consensus protocol with proper time-varying gains are used simultaneously,double-integrator MASs with all normal agents and all agents subject to SSFFs can reach dynamical ***,comparison numerical simulations are given to illustrate the effectiveness of the theoretical results.

关键词： double-integrator multi-agent systems asynchronous self-sensing function failures fault-tolerant dynamical consensus network topology connectivity recovery

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review on Security defense Technology research in Edge Computing Environment

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Chinese Journal of Electronics 2024年第1期33卷 1-18页

作者： Ke SHANG Weizhen HE Shuai ZHANG National digital Switching System Engineering and Technological r&d center

Edge computing, which achieves quick data processing by sinking data computing and storage to the network edge, has grown rapidly along with the internet of things. The new network architecture of edge computing brings new security challenges. Based on this, this paper investigates the edge computing security literature published in recent years and summarizes and analyzes research work on edge computing security from different attack surfaces. We start with the definition and architecture of edge computing. From the attack surface between device and edge server, as well as on edge servers, the research describes the security threats and defense methods of edge computing. In addition, the cause of the attack and the pros and cons of defense methods is introduced. The challenges and future research directions of edge computing are given.

关键词： Cloud computing Systems architecture Computer architecture Network architecture data processing Security Servers

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FlowGANAnomaly: Flow-Based Anomaly Network Intrusion detection with Adversarial Learning

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Chinese Journal of Electronics 2024年第1期33卷 58-71页

作者： Zeyi LI Pan WANG Zixuan WANG School of Computer Science Nanjing University of Posts and Telecommunications School of Modern Posts Nanjing University of Posts and Telecommunications School of internet of things Nanjing University of Posts and Telecommunications

In recent years, low recall rates and high dependencies on data labelling have become the biggest obstacle to developing deep anomaly detection(dAd) techniques. Inspired by the success of generative adversarial networks(GANs) in detecting anomalies in computer vision and imaging, we propose an anomaly detection model called Flow GANAnomaly for detecting anomalous traffic in network intrusion detection systems(NIdS). Unlike traditional GAN-based approaches, which are composed of a flow encoder, a convolutional encoder-decoder-encoder, a flow decoder and a convolutional encoder, the architecture of this model consists of a generator(G) and a discriminator(d).Flow GANAnomaly maps the different types of traffic feature data from separate datasets to a uniform feature space,thus can capture the normality of network traffic data more accurately in an adversarial manner to mitigate the problem of the high dependence on data labeling. Moreover, instead of simply detecting the anomalies by the output of d, we proposed a new anomaly scoring method that integrates the deviation between the output of two Gs’ convolutional encoders with the output of d as weighted scores to improve the low recall rate of anomaly detection. We conducted several experiments comparing existing machine learning algorithms and existing deep learning methods(Auto Encoder and VAE) on four public datasets(NSL-Kdd, CIC-IdS2017, CIC-ddo S2019, and UNSW-NB15). The evaluation results show that Flow GANAnomaly can significantly improve the performance of anomaly-based NIdS.

关键词： deep learning Machine learning algorithms Computational modeling Network intrusion detection Imaging Telecommunication traffic Generative adversarial networks

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A convolutional neural network to detect possible hidden data in spatial domain images

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Cybersecurity 2024年第1期7卷 37-52页

作者： Jean de La Croix Ntivuguruzwa Tohari Ahmad department of Informatics Institut Teknologi Sepuluh Nopember(ITS)Kampus ITS Keputih SukoliloSurabaya60111Indonesia African center of Excellence in the internet of things College of Science and TechnologyUniversity of Rwanda3900KigaliRwanda

Hiding secret data in digital multimedia has been essential to protect the ***,attackers with a steganalysis technique may break *** steganalysis methods have good results with conventional Machine Learning(ML)techniques;however,the introduction of Convolutional Neural Network(CNN),a deep learning paradigm,achieved better performance over the previously proposed ML-based *** the existing CNN-based approaches yield good results,they present performance issues in classification accuracy and stability in the network training *** research proposes a new method with a CNN architecture to improve the hidden data detection accuracy and the training phase stability in spatial domain *** proposed method comprises three phases:pre-processing,feature extraction,and ***,in the pre-processing phase,we use spatial rich model filters to enhance the noise within images altered by data hiding;secondly,in the feature extraction phase,we use two-dimensional depthwise separable convolutions to improve the signal-to-noise and regular convolutions to model local features;and finally,in the classification,we use multi-scale average pooling for local features aggregation and representability enhancement regardless of the input size variation,followed by three fully connected layers to form the final feature maps that we transform into class probabilities using the softmax *** results identify an improvement in the accuracy of the considered recent scheme ranging between 4.6 and 10.2%with reduced training time up to 30.81%.

关键词： Information security Spatial domain steganalysis deep learning Convolutional neural network Infrastructure

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HgaNets:Fusion of Visual data and Skeletal Heatmap for Human Gesture Action recognition

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Computers, Materials & Continua 2024年第4期79卷 1089-1103页

作者： Wuyan Liang Xiaolong Xu School of internet of things Nanjing University of Posts and TelecommunicationsNanjing210023China School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing210023China

recognition of human gesture actions is a challenging issue due to the complex patterns in both visual andskeletal features. Existing gesture action recognition (GAr) methods typically analyze visual and skeletal data,failing to meet the demands of various scenarios. Furthermore, multi-modal approaches lack the versatility toefficiently process both uniformand disparate input ***, in this paper, an attention-enhanced pseudo-3dresidual model is proposed to address the GAr problem, called HgaNets. This model comprises two independentcomponents designed formodeling visual rGB (red, green and blue) images and 3dskeletal heatmaps, *** specifically, each component consists of two main parts: 1) a multi-dimensional attention module forcapturing important spatial, temporal and feature information in human gestures;2) a spatiotemporal convolutionmodule that utilizes pseudo-3d residual convolution to characterize spatiotemporal features of gestures. Then,the output weights of the two components are fused to generate the recognition results. Finally, we conductedexperiments on four datasets to assess the efficiency of the proposed model. The results show that the accuracy onfour datasets reaches 85.40%, 91.91%, 94.70%, and 95.30%, respectively, as well as the inference time is 0.54 s andthe parameters is 2.74M. These findings highlight that the proposed model outperforms other existing approachesin terms of recognition accuracy.

关键词： Gesture action recognition multi-dimensional attention pseudo-3d skeletal heatmap

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A Least-squares-based Iterative Method with Better Convergence for PF/OPF in Integrated Transmission and distribution Networks

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CSEE Journal of Power and Energy Systems 2024年第3期10卷 953-962页

作者： Kunjie Tang Shufeng dong Yonghua Song College of Electrical Engineering Zhejiang UniversityHangzhou 310027China State Key Laboratory of internet of things for Smart City and department of Electrical and Computer Engineering University of MacaoMacao SARChina IEEE

The limitations of the conventional master-slavesplitting(MSS)method,which is commonly applied to power flow and optimal power flow in integrated transmission and distribution(I-T&d)networks,are first *** that the MSS method suffers from a slow convergence rate or even divergence under some circumstances,a least-squares-based iterative(LSI)method is *** with the MSS method,the LSI method modifies the iterative variables in each iteration by solving a least-squares problem with the information in previous iterations.A practical implementation and a parameter tuning strategy for the LSI method are ***,a LSI-PF method is proposed to solve I-T&d power flow and a LSIheterogeneous decomposition(LSI-HGd)method is proposed to solve optimal power *** experiments demonstrate that the proposed LSI-PF and LSI-HGd methods can achieve the same accuracy as the benchmark ***,these LSI methods,with appropriate settings,significantly enhance the convergence and efficiency of conventional ***,in some cases,where conventional methods diverge,these LSI methods can still converge.

关键词： Fixed-point theory integrated transmission and distribution networks least-squares-based iterative method master-slave-splitting method optimal power flow power flow

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Industry-Oriented detection Method of PCBA defects Using Semantic Segmentation Models

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1438-1446页

作者： Yang Li Xiao Wang Zhifan He Ze Wang Ke Cheng Sanchuan ding Yijing Fan Xiaotao Li Yawen Niu Shanpeng Xiao Zhenqi Hao Bin Gao Huaqiang Wu department of internet of things Technology and Application China Mobile Research InstituteBeijing 100053China Beijing Advanced Innovation center for Integrated Circuits the School of Integrated CircuitTsinghua UniversityBeijing 100084China SigmaStar Technology Limited Xiamen 361199China IEEE

Automated optical inspection(AOI)is a significant process in printed circuit board assembly(PCBA)production lines which aims to detect tiny defects in *** AOI equipment has several deficiencies including low throughput,large computation cost,high latency,and poor flexibility,which limits the efficiency of online PCBA *** this paper,a novel PCBA defect detection method based on a lightweight deep convolution neural network is *** this method,the semantic segmentation model is combined with a rule-based defect recognition algorithm to build up a defect detection *** improve the performance of the model,extensive real PCBA images are collected from production lines as *** optimization methods have been applied in the model according to production demand and enable integration in lightweight computing *** results show that the production line using our method realizes a throughput more than three times higher than traditional *** method can be integrated into a lightweight inference system and pro-mote the flexibility of *** proposed method builds up a general paradigm and excellent example for model design and optimization oriented towards industrial requirements.

关键词： Automated optical inspection(AOI) deep learning defect detection printed circuit board assembly(PCBA) semantic segmentation.

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A Correlation-Based Stochastic Model for Massive MIMO Channel

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China Communications 2024年第1期21卷 175-187页

作者： Yang Liu Gang Li Chengxiang Wang National Mobile Communications research Laboratory School of Information Science and EngineeringSoutheast UniversityNanjing 211189China School of internet of things Engineering Jiangnan UniversityWuxi 214122China Pervasive Communication research center Purple Mountain LaboratoriesNanjing 211111China

In this paper,the channel impulse response matrix(CIrM)can be expressed as a sum of couplings between the steering vectors at the base station(BS)and the eigenbases at the mobile station(MS).Nakagami distribution was used to describe the fading of the coupling between the steering vectors and the *** measurements were carried out to evaluate the performance of this proposed ***,the physical implications of this model were illustrated and the capacities are *** addition,the azimuthal power spectrum(APS)of several models was ***,the channel hardening effect was simulated and *** showed that the proposed model provides a better fit to the measured results than the other CBSM,i.e.,Weichselberger ***,the proposed model can provide better tradeoff between accuracy and complexity in channel *** CIrM model can be used for massive MIMO design in the future communication system design.

关键词： CBSM channel capacity channel hardening channel modeling massive MIMO

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Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing

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China Communications 2024年第8期21卷 1-17页

作者： Zhang Cui Xu Xiao Wu Qiong Fan Pingyi Fan Qiang Zhu Huiling Wang Jiangzhou School of internet of things Engineering Wuxi Institute of TechnologyWuxi 214121China School of internet of things Engineering Jiangnan UniversityWuxi 214122China department of Electronic Engineering Beijing National Research Center for Information Science and TechnologyTsinghua UniversityBeijing 100084China Qualcomm San Jose CA 95110USA School of Engineering University of KentCT27NT CanterburyU.K

In vehicle edge computing(VEC),asynchronous federated learning(AFL)is used,where the edge receives a local model and updates the global model,effectively reducing the global aggregation *** to different amounts of local data,computing capabilities and locations of the vehicles,renewing the global model with same weight is *** above factors will affect the local calculation time and upload time of the local model,and the vehicle may also be affected by Byzantine attacks,leading to the deterioration of the vehicle ***,based on deep reinforcement learning(drL),we can consider these factors comprehensively to eliminate vehicles with poor performance as much as possible and exclude vehicles that have suffered Byzantine attacks before *** the same time,when aggregating AFL,we can focus on those vehicles with better performance to improve the accuracy and safety of the *** this paper,we proposed a vehicle selection scheme based on drL in *** this scheme,vehi-received:dec.25,2023 revised:Apr.02,2024 Editor:Zhao Junhui cle’s mobility,channel conditions with temporal variations,computational resources with temporal variations,different data amount,transmission channel status of vehicles as well as Byzantine attacks were taken into *** results show that the proposed scheme effectively improves the safety and accuracy of the global model.

关键词： asynchronous federated learning byzantine attacks vehicle selection vehicular edge computing

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Simulation and forecasting of fishery weather based on statistical machine learning

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Information Processing in Agriculture 2024年第1期11卷 127-142页

作者： Xueqian Fu Chunyu Zhang Fuhao Chang Lingling Han Xiaolong Zhao Zhengjie Wang Qiaoyu Ma National Innovation center for digital Fishery China Agricultural UniversityBeijingChina Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock Ministry of Agriculture and Rural AffairsBeijingChina Beijing Engineering and Technology research center for internet of things in Agriculture BeijingChina College of Information and Electrical Engineering China Agricultural UniversityBeijingChina

As the new generation of artificial intelligence(AI)continues to evolve,weather big data and statistical machine learning(SML)technologies complement each other and are deeply integrated to significantly improve the processing and forecasting accuracy of fishery *** fishery weather services play a crucial role in fishery production,serving as a great safeguard for economic benefits and personal safety,enabling fishermen to carry out fishery production better,and contributing to the sustainable development of the fishery *** objective of this paper is to offer an understanding of the present state of research and development in SML technology for simulating and forecasting fishery ***,we analyze the current state of research and technical features of SML in weather and summarize the applications of SML in simulation and forecasting of fishery weather,which mainly include three aspects:fishery weather scenario generation,fishery weather forecasting,and fishery extreme weather *** also illustrate the main technical means and principles of SML ***,we summarize the most advanced SML fields and provide an outlook on their application value in the field of fishery weather.

关键词： Fishery weather Statistical machine learning Weather generation Weather forecasting Extreme weather warning

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