IEEE Transactions on Signal and Information Processing over Networks最新文献_第3页

Memory-Based Accelerated Consensus for Second-Order Multi-Agent Systems With Delay 基于内存的延迟二阶多代理系统加速共识

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-07-04 DOI: 10.1109/TSIPN.2024.3417225

Mengya Huang;Jing-Wen Yi;Li Chai

引用次数: 0

Distributed Control in Uncertain Nonlinear Multiagent Systems Under Event-Triggered Communication and General Directed Graphs 事件触发通信和一般有向图条件下不确定非线性多代理系统的分布式控制

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-07-03 DOI: 10.1109/TSIPN.2024.3422878

Gang Wang;Zongyu Zuo;Peng Li

{"title":"Distributed Control in Uncertain Nonlinear Multiagent Systems Under Event-Triggered Communication and General Directed Graphs","authors":"Gang Wang;Zongyu Zuo;Peng Li","doi":"10.1109/TSIPN.2024.3422878","DOIUrl":"10.1109/TSIPN.2024.3422878","url":null,"abstract":"Designing a consensus algorithm for multiagent systems within an event-triggered communication setting is challenging due to the discontinuous and inaccurate interaction information caused by event-triggering mechanisms. Currently, most related results require an undirected or balanced directed graph. To avoid such restrictive requirements and consider general directed graphs with a spanning tree, we first investigate the perturbed consensus problem of first-order dynamics. Then, we extend our findings to address the consensus problem of the uncertain nonlinear multiagent systems described in Lagrangian dynamics, Brunovsky dynamics, and strict-feedback dynamics under event-triggered communication. We develop three distributed consensus protocols that consider the unique characteristics of these systems and assign different reference signals accordingly. Our proposed schemes ensure that consensus errors either converge to zero or to a small adjustable neighborhood around zero without Zeno behavior while preserving signal boundedness in the closed-loop system. Finally, we conduct extensive simulations to further illustrate the efficiency of our theoretical results.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"599-609"},"PeriodicalIF":3.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141547847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fully Distributed Model-Free Flocking of Multiple Euler-Lagrange Systems 多个欧拉-拉格朗日系统的全分布式无模型成群飞行

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-06-26 DOI: 10.1109/TSIPN.2024.3419437

Mingkang Long;Yin Chen;Housheng Su

引用次数: 0

Fairness With Low Resentment in Distributed Sensor Systems to Detect Emitters 分布式传感器系统中检测发射器的低怨恨公平性

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-06-13 DOI: 10.1109/TSIPN.2024.3414146

Benedito J. B. Fonseca

{"title":"Fairness With Low Resentment in Distributed Sensor Systems to Detect Emitters","authors":"Benedito J. B. Fonseca","doi":"10.1109/TSIPN.2024.3414146","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3414146","url":null,"abstract":"Consider a single distributed sensor system to detect the occurrence of rare emitters in multiple regions, each representing a different community. Alarms are sent to a common dispatch center, which dispatches units to each alarmed community. We assume that all communities contribute equally to the cost of the system; however, the probability of detecting an emitter may vary among communities, raising the issue of fairness. We adopt in here the concept of envy-free fairness in which the goal is to equalize the worst-case probability of detection in each community. As shown in our previous work, envy-free fairness can be achieved by adjusting the probabilities of false alarm at each community. In here, we extend our results by addressing a concern that may arise from envy-free fairness: resentment. After precisely defining the concept of resentment, we show that it is possible to design an envy-free fair detection system while keeping the maximum resentment bounded by combining poorly-served communities with a high enough number of well-served communities. We also present algorithms to allocate sensors to communities to design envy-free fair systems with bounded resentment while considering different optimization goals and constraints. Our examples illustrate that our algorithms often produce close-to-optimum allocations.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"552-564"},"PeriodicalIF":3.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141448032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Tensor-Derived Large-Scale Multi-View Subspace Clustering With Faithful Semantics 具有忠实语义的张量推导大规模多视图子空间聚类技术

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-06-13 DOI: 10.1109/TSIPN.2024.3414134

Sujia Huang;Shide Du;Lele Fu;Zhihao Wu;Shiping Wang

{"title":"Tensor-Derived Large-Scale Multi-View Subspace Clustering With Faithful Semantics","authors":"Sujia Huang;Shide Du;Lele Fu;Zhihao Wu;Shiping Wang","doi":"10.1109/TSIPN.2024.3414134","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3414134","url":null,"abstract":"Multi-view subspace clustering is extensively investigated for its ability to extract essential information from multiple data. However, tensor-based methods often encounter several limitations: 1) They suffer from high computational complexity due to the construction of a global affinity matrix; 2) The sophisticated semantic information among samples remains under-explored. To address these issues, we propose a comprehensive framework called tensor-derived large-scale multi-view subspace clustering with faithful semantics, which replaces the original graph with a trustworthy anchor graph. In particular, a graph-optimization-based anchor selection strategy is designed to obtain salient points, and thus the anchor graph is computed to decrease the computational complexity of constructing the representation matrix. Subsequently, a refinement approach is designed to flexibly extract essential semantics between nodes by dividing the graph into significant components and undesired connections. These matrices preserving important information are fused into a tensor that is constrained by a nuclear norm to retain its low-rank property. Meanwhile, the undesired links should be eliminated to avoid confusing the clustering results. Finally, the spectral embedding is employed to directly guide the learning of anchors and graphs. The proposed model achieves a remarkable improvement of 3.3% and 13.1% of ACC on the NoisyMNIST and Prokaryotic datasets while reducing high computational complexity compared to other subspace-based clustering approaches.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"584-598"},"PeriodicalIF":3.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141543990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed Proximal Alternating Direction Method of Multipliers for Constrained Composite Optimization Over Directed Networks 用于有向网络上受限复合优化的分布式近端交替方向乘法

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-06-03 DOI: 10.1109/TSIPN.2024.3407660

Jing Yan;Xinli Shi;Luyao Guo;Ying Wan;Guanghui Wen

{"title":"Distributed Proximal Alternating Direction Method of Multipliers for Constrained Composite Optimization Over Directed Networks","authors":"Jing Yan;Xinli Shi;Luyao Guo;Ying Wan;Guanghui Wen","doi":"10.1109/TSIPN.2024.3407660","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3407660","url":null,"abstract":"In this article, we investigate a constrained composition optimization problem in a directed communication network. Each agent is equipped with a local objective function composed of both smooth and nonsmooth terms, as well as linear equality constraints. The optimization objective is to minimize the sum of all local functions, subject to linear equality constraints, through local computations and information exchange with neighboring agents. Based on the alternating direction method of multipliers (ADMM), a novel distributed optimization algorithm is proposed to address the composite optimization problem. We leverage the composite structure of the objective function, by introducing a linear approximation for the smooth term and a proximal mapping for the nonsmooth term, which simplifies the process of solving the ADMM subproblem. Furthermore, in contrast to the existing algorithms that eliminate the imbalance resulting from directed graphs using a column-stochastic matrix, the proposed algorithm only employs a row-stochastic matrix, thereby avoiding the need for agents to know their outdegree. Moreover, the step sizes of agents are uncoordinated and can be independent of the network topology. Furthermore, we prove that the proposed algorithm achieves a sublinear convergence rate when the local objective functions are convex. Finally, the effectiveness of the proposed algorithm is verified through numerical simulations.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"539-551"},"PeriodicalIF":3.2,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141308640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Natural Gradient Primal-Dual Method for Decentralized Learning 用于分散学习的自然梯度原始双法

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-04-26 DOI: 10.1109/TSIPN.2024.3388948

Kenta Niwa;Hiro Ishii;Hiroshi Sawada;Akinori Fujino;Noboru Harada;Rio Yokota

{"title":"Natural Gradient Primal-Dual Method for Decentralized Learning","authors":"Kenta Niwa;Hiro Ishii;Hiroshi Sawada;Akinori Fujino;Noboru Harada;Rio Yokota","doi":"10.1109/TSIPN.2024.3388948","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3388948","url":null,"abstract":"We propose the Natural Gradient Primal-Dual (NGPD) method for decentralized learning of parameters in Deep Neural Networks (DNNs). Conventional approaches, such as the primal-dual method, constrain the local parameters to be similar between connected nodes. However, since most of them follow a first-order optimization method and the loss functions of DNNs may have ill-conditioned curvatures, many local parameter updates and communication among local nodes are needed. For fast convergence, we integrate the second-order natural gradient method into the primal-dual method (NGPD). Since additional constraint minimizes the amount of output change before and after the parameter updates, robustness towards ill-conditioned curvatures is expected. We theoretically demonstrate the convergence rate for the averaged parameter (the average of the local parameters) under certain assumptions. As a practical implementation of NGPD without a significant increase in computational overheads, we introduce Kronecker Factored Approximate Curvature (K-FAC). Our experimental results confirmed that NGPD achieved the highest test accuracy through image classification tasks using DNNs.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"417-433"},"PeriodicalIF":3.2,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140807296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mixed Static-Dynamic Protocol-Based Tobit Recursive Filtering for Stochastic Nonlinear Systems Against Random False Data Injection Attacks 基于静态-动态混合协议的随机非线性系统托比特递归过滤，对抗随机虚假数据注入攻击

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-04-22 DOI: 10.1109/TSIPN.2024.3388953

Jun Hu;Shuo Yang;Raquel Caballero-Águila;Hongli Dong;Boying Wu

{"title":"Mixed Static-Dynamic Protocol-Based Tobit Recursive Filtering for Stochastic Nonlinear Systems Against Random False Data Injection Attacks","authors":"Jun Hu;Shuo Yang;Raquel Caballero-Águila;Hongli Dong;Boying Wu","doi":"10.1109/TSIPN.2024.3388953","DOIUrl":"10.1109/TSIPN.2024.3388953","url":null,"abstract":"In this paper, the Tobit recursive filtering (TRF) issue is discussed for a class of time-varying stochastic nonlinear systems (SNSs) with censored measurements and random false data injection attacks (FDIAs) under the mixed static-dynamic protocol. The censored measurements considered are depicted by the Tobit Type I model and the phenomenon of the random FDIAs involved is governed by a set of Bernoulli random variables. Additionally, in order to reduce the communication burden and improve the data utilization efficiency, the mixed static-dynamic protocol is elaborately adopted to schedule the signal transmission, which is managed by the time-triggered and event-triggered rules to further increase the flexibility of the data scheduling. The main goal of this paper is to present a new TRF approach such that, in the presence of censored measurements, mixed static-dynamic protocol and random FDIAs, a minimized upper bound of the filtering error covariance (FEC) can be obtained. Moreover, a sufficient criterion from the theoretical analysis perspective is established to guarantee the desired uniform boundedness of the filtering error in the mean-square sense (MSS). Finally, some experiments with comparisons applicable for three-wheeled Ackerman turning model are conducted to show the applicability and advantages of newly proposed TRF scheme.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"445-459"},"PeriodicalIF":3.2,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140634204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Median-Based Resilient Multi-Object Fusion With Application to LMB Densities 基于中值的弹性多目标融合技术在 LMB 密度中的应用

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-04-17 DOI: 10.1109/TSIPN.2024.3388951

Yao Zhou;Giorgio Battistelli;Luigi Chisci;Lin Gao;Gaiyou Li;Ping Wei

引用次数: 0

Causal Inference From Slowly Varying Nonstationary Processes 从缓慢变化的非平稳过程中进行因果推理

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-04-12 DOI: 10.1109/TSIPN.2024.3375594

Kang Du;Yu Xiang

引用次数: 0